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+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Changelog

+
+ + + +
+ + + + +
+ + + +
+ + +

All notable changes to this project will be documented in this file.

+

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

+
+

Unreleased

+
+

Fixed

+
    +
  • Initialization of LateralSSF variables ssf and ssfmax with vertical hydraulic conductivity profile exponential_constant. Removed parameter khfrac from the computation, as it is already part of parameter kh_0.
    • +
  • Mutating BMI model control functions (update, update_until and finalize) and extended mutating BMI functions (load_state and save_state) should return nothing.
    • +
+
+
+

Changed

+
    +
  • Removed vertical concepts HBV and FLEXTopo.
    • +
  • Removed metadata macros exchange and grid_type. The macro grid_type is not required because this functionality is already part of BMI. The macro exchange is replaced by a function used by BMI. Remaining metadata macros get_units and grid_loc are only used by BMI.
    • +
  • Refactor the vertical SBM concept: divide the long struct SBM into different model components for interception, snow, glacier, (open water) runoff, soil, water demand and allocation stored in the struct LandHydrologySBM. Additionally, the atmospheric forcing and a shared vegetation parameterset are stored as separate fields in struct LandHydrologySBM (with soil model SbmSoilModel). The model component structs have model variables, parameters and boundary_conditions (if applicable), including associated functions for initializing and updating these model components. The original long update function of the SBM soil part has been split into separate functions.
    • +
+
+
+

Added

+
    +
  • Support direct output of snow and glacier melt, and add computation of snow water equivalent (SWE).
    • +
+
+
+
+

v0.x releases

+
+

v0.8.1 - 2024-08-27

+
+

Fixed

+
    +
  • Reduce allocations in update of vertical SBM concept.
    • +
+
+
+
+

v0.8.0 - 2024-08-19

+
+

Fixed

+
    +
  • Added missing BMI function get_grid_size, it is used for unstructured grids, for example to get the length of arrays returned by BMI functions get_grid_x and get_grid_y.
    • +
  • Added a check for the solution of the quadratic equation as part of the Modified Puls approach for lake outflow. Lower limit should be zero (very small negative values can occur).
    • +
  • Limit lake evaporation (added variable actevap) and lake outflow to prevent negative lake storage. The variable actevap has also been added to the reservoir module.
    • +
  • The set_states function for model type sbm with local inertial routing for river and land component.
    • +
  • Inflow to reservoir and lake locations for local inertial routing: 1) with floodplain routing, the floodplain discharge was not added to the inflow of these locations, 2) the to_river variable from overland flow and lateral subsurface flow was not added to the inflow of these locations.
    • +
  • Close netCDF NCDataset with state variables in extended BMI function save_state.
    • +
  • For the computation of Gash interception model parameter e_r multiply the precipitation input with the canopy fraction (this was only done for the potential evapotranspiration input).
    • +
  • Function BMI.update_until could throw an InexactError: Int64 caused by a not whole number, representing how many times (steps) an update of a wflow model is required. This is fixed by using divrem for the computation of the number of steps in this function. An error is thrown when the absolute remainder of divrem is larger than eps(), or when the number of steps is negative.
    • +
  • Fixed internal and external broken links in docs.
    • +
  • The internal time step of the local inertial model (stable_timestep function) can get zero when LoopVectorization is applied (@tturbo) to the for loop of these functions. This issue occured on a virtual machine, Windows 10 Enterprise, with Intel(R) Xeon(R) Gold 6144 CPU (2 processors). This has been fixed by replacing @tturbo with reduction of Polyester.jl.
    • +
  • Fixed required states of the model type sbm_gwf: added h_av for the river and land domain.
    • +
  • For the constanthead boundary of GroundwaterFlow the head should not be changed (was set to top elevation of the aquifer if head > top), and exfiltwater should be 0 for these boundary cells.
    • +
+
+
+

Changed

+
    +
  • Stop exposing scalar variables through BMI. The BMI.get_value_ptr function was not working correctly for scalar model variables (a view was applied). Only a few scalar model parameters are defined, and it is not expected that exposing these variables is required (e.g. for model coupling) while code changes for these variables (including struct fields) are required.
    • +
  • The local inertial routing (constant) river boundary condition depth at a ghost node (downstream river outlet) was set at 0.0 in the code and can now be provided through the model parameter netCDF file (riverdepth_bc). In addition, the boundary condition river length dl at a ghost node should be set through the model parameter netCDF file (riverlength_bc), providing this boundary condition through the [model] settings in the TOML file (model.riverlength_bc) has been deprecated.
    • +
  • As part of the vertical SBM concept: 1) add variable net_runoff (land runoff minus actual open water evaporation ae_openw_l) and 2) change the definition of variable runoff by removing the subtraction of ae_openw_l (total land runoff). The variable net_runoff is now input to the kinematic wave for overland flow (replaces the original runoff variable as input). The lower limit of the original runoff variable was set at zero, which may result in water balance errors. This lower limit was implemented in the Python version of wflow_sbm, to avoid a very slow kinematic wave solution for surface flow computation (no distinction between a kinematic wave for overland and river flow). When in the Python version of wflow_sbm the kinematic wave for surface flow was split into a river and land component, the lower limit was removed for river runoff (net_runoff_river), but not for land runoff.
    • +
  • Always use fractions for the computation of potential evapotranspiration (interception and transpiration) and potential evaporation (open water and soil). Replaced variable et_reftopot by crop coefficient kc (use of et_reftopot has been deprecated).
    • +
  • For improved code readability it is now discouraged to use non-ASCII characters for the names of variables, structs, functions and macros. Using the non-ASCII character for built-in operators is still allowed. This change in naming convention is now in effect and all invalid uses of non-ASCII characters have been replaced by ASCII equivalents. This change also impacts the TOML keys kv₀, θᵣ and θₛ. These keys have been replaced with the ASCII versions kv_0, theta_r and theta_s in v0.4.0 and the old keys are not supported anymore.
    • +
  • Docs: 1) improved description of different model configurations in model-setup.md, also in relation to hydromt_wflow in docs, 2) citing info related to wflow_sbm publication in Geosci. Model Dev. (from in review to published).
    • +
  • Root water uptake reduction (Feddes): 1) extend critical pressure head parameters with h3_low and h3_high, 2) all critical pressure head parameters can be set (values for h1, h2 and h3 were fixed) and 3) the root water uptake reduction coefficient \alpha can be set at 0 (default is 1) at critical pressure head h1 (through the model parameter alpha_h1).
    • +
  • For the actual transpiration computation in SBM, the potential transpiration is partitioned over the soil layers with roots according to the model parameter rootfraction (fraction of the total root length in a soil layer). Previously, for each soil layer (from top to bottom) the actual transpiration was computed, and the remaining potential transpiration was used in the next soil layer.
    • +
+
+
+

Added

+
    +
  • Total water storage as an export variable for SBM concept. This is the total water stored per grid cell in millimeters. Excluded from this variable are the floodplain, lakes and reservoirs.
    • +
  • Checks to see if all states are covered in the .toml file. If not all states are covered, an error is thrown. If there are more states specified than required, these states are ignored (with a warning in the logging), and the simulation will continue.
    • +
  • Support different vertical hydraulic conductivity profiles for the SBM concept. See also the following sections: The SBM soil water accounting scheme and Subsurface flow routing for a short description.
    • +
  • Local inertial routing to sbm_gwf model type.
    • +
  • Water demand and allocation computations for model types sbm and sbm_gwf.
    • +
+
+
+
+

v0.7.3 - 2024-01-12

+
+

Fixed

+
    +
  • Documentation: add leakage term to the wflow_sbm figure, document external input parameter ksathorfrac and fix description of adding external inflow to the kinematic wave.
    • +
  • Fixed BMI functions (e.g. BMI.get_value) that deviated from BMI specifications (BasicModelInterface.jl), including function arguments, return types and the BMI specification that arrays are always flattened (this was not the case for variables stored as 2-dimensional arrays or as vector of SVectors).
    • +
  • Bump compat for NCDatasets to 0.13, 0.14.
    • +
  • The solution for lake outflow as part of the Modified Puls Approach. The inflow and outflow variables are defined for period Δt, and not at t1 and t2 (instantaneous) as in the original mass balance equation of the Modified Puls Approach. Because of this, the terms of the quadratic equation (and solution) were fixed.
    • +
  • Use kvfrac for the computation of vertical saturated hydraulic conductivity at the bottom of the soil layer, since kvfrac is also used for the computation of vertical unsaturated flow.
    • +
+
+
+

Changed

+
    +
  • For cyclic parameters different cyclic time inputs are supported (only one common cyclic time (for example daily or monthly) was allowed).
    • +
  • BMI: 1) added grid information (type and location) and whether a variable can be exchanged to metadata Structs, 2) extend model grid functions for wflow components that store variables on edges (local inertial model) with get_grid_edge_count and get_grid_edge_nodes.
    • +
+
+
+

Added

+
    +
  • Functions for loading and saving states and getting the starttime in Unix time format. This is convenient for coupling with OpenDA (and other external) software. The set states functionality from the initialization function has been moved to a separate set_states function for each Model type, to support loading states independent of initialization.
    • +
+
+
+
+

v0.7.2 - 2023-09-27

+
+

Fixed

+
    +
  • Water balance of modified Rutter interception model. The sum of the stemflow partitioning coefficient pt and free throughfall coefficient p could get larger than 1, resulting in an overestimation of stemflow and throughfall amounts and negative net interception amounts. And the first drainage amount dd, controlled by a change over time in canopy storage capacity cmax, should not be subtracted from precipitation to compute net interception.
    • +
  • The netinterception (net interception) computed by the modified Rutter interception model was stored as interception in SBM, while this should be the interception (interception loss by evaporation) output of the modified Rutter interception model. The interception of SBM is used to compute the total actual evapotranspiration actevap.
    • +
+
+
+
+

v0.7.1 - 2023-06-30

+
+

Fixed

+
    +
  • State initialization of 1D floodplain volume. In the initialization function the wrong field name of type FloodPlainProfile was used (area instead of a).
    • +
+
+
+
+

v0.7.0 - 2023-06-12

+
+

Fixed

+
    +
  • BMI.get_time_units now gets called on the model rather than the type, like all other BMI functions, except BMI.initialize. Also it returns “s” instead of “seconds since 1970-01-01T00:00:00”, in line with the BMI specification.
    • +
  • Added the interception component to total actual evapotranspiration actevap of SBM (was defined as the sum of soil evaporation, transpiration and open water evaporation).
    • +
+
+
+

Changed

+
    +
  • The time values returned in the BMI interface are no longer in seconds since 1970, but in seconds since the model start time. This is more in line with standard BMI practices.
    • +
  • The starttime was defined one model timestep Δt ahead of the actual model time (the initial conditions timestamp (state time)). As a consequence this was also the case for the current model time. To allow for an easier interpretation of wflow time handling, either through BMI or directly, the starttime is now equal to the state time, resulting in current model times without an offset.
    • +
  • Using more than 8 threads can result in too much overhead with Threads.@threads. After performance testing, this has been changed for kinematic wave routing and the vertical SBM concept to spawning tasks with Threads@spawn for number of threads <= 8, where each task iterates over a chunk of size basesize. For more than 8 threads the low overhead threading Polyester.@batch (including the minbatch argument) is used. For local inertial routing the use of Threads.@threads has been changed to threaded loop vectorization (river and 1D floodplain local inertial momentum equation) and Polyester.@batch.
    • +
+
+
+

Added

+
    +
  • For (regulated) lakes with rating curve of type 1 (H-Q table), lake storage above the maximumstorage (based on maximum water level from the H-Q table) is spilled instantaneously (overflow) from the lake.
    • +
  • Added support to use sum as a reducer function for csv and scalar output options.
    • +
+
+
+
+

v0.6.3 - 2023-03-01

+
+

Fixed

+
    +
  • Removed error when _FillValue is present in the time dimension of the forcing netCDF file. The simulation is allowed to continue with the attribute present, given that there are no missing values in the time dimension. This is checked by the code, and an error is thrown if this is the case.
    • +
  • Column index of daily lake rating curves. This was incorrectly based on dayofyear with a maximum of 365. The column index should be based on julian day (leap days are not counted).
    • +
+
+
+

Changed

+
    +
  • NCDatasets version. Reading the time dimension of multifile netCDF file became very slow since NCDatasets v0.12.4, this issue has been solved in v0.12.11.
    • +
  • Store the time dimension of the forcing netCDF file as part of the struct NCreader instead of calling dataset["time"][:] each time step when loading forcing data.
    • +
+
+
+

Added

+
    +
  • Show total duration of simulation in the log file (info), and show the current time at execution of each timestep (debug).
    • +
  • Support for exponential decline in horizontal conductivity in the sbm_gwf concept. This can be enabled using the conductivity_profile setting (either “uniform” or “exponential”). If set to “exponential”, it exponentially reduces the kh0 (or conductivity) based on the value of gwf_f to the actual horizontal conductivity (k).
    • +
  • An optional 1D floodplain schematization for the river flow inertial model, based on provided flood volumes as a function of flood depth per river cell. See also the following sections: SBM + Local inertial river and River and floodplain routing for a short description, and the following section for associated model parameters.
    • +
+
+
+
+

v0.6.2 - 2022-09-01

+
+

Fixed

+
    +
  • Two issues related to reservoir and lake locations as part of local inertial model: 1) added as boundary points to the update of overland flow, 2) fixed check reservoir and lake location in update river flow.
    • +
  • Limit flow (qx, qy and q) in local inertial model when water is not available (set to zero).
    • +
  • In the grid output netCDFs, don’t set the _FillValue attribute, since the CF conventions don’t allow it.
    • +
  • Glacier parameter g_sifrac needs to be converted during initialization (time dependent).
    • +
  • The check that the sum of adaptive timesteps (Δt) of the local inertial model (1D and 2D) does not exceed the model timestep.
    • +
+
+
+

Changed

+
    +
  • Changed depth h for reservoir and lake locations as part of the river local inertial model from bankfull_depth to zero.
    • +
+
+
+

Added

+ +
+
+
+

v0.6.1 - 2022-04-26

+
+

Fixed

+
    +
  • Fixed an error with the log file, when writing to a folder that does not (yet) exists. Now, the folder is created prior to writing the log file.
    • +
  • Fixed a MethodError for read_dims, thrown when reading netCDF data with NCDatasets.jl 0.12.3 or higher.
    • +
+
+
+
+

v0.6.0 - 2022-04-14

+
+

Added

+
    +
  • The FLEXTopo model.
    • +
  • Set a (different) uniform value for each index of input parameters with an extra dimension. A list of values (instead of one uniform value) that should be equal to the length of the extra dimension can be provided in the TOML file. See also Modify parameters.
    • +
  • Modify input parameters with an extra dimension at multiple indices with different scale and offset values, through the TOML file. See also Modify parameters.
    • +
  • Added support for netCDF compression for gridded model output, through the option compressionlevel in the [output] section in the TOML file. The setting defaults to 0 (no compression), but all levels between 0 and 9 can be used.
    • +
+
+
+

Changed

+
    +
  • Re-organized the documentation: moved explanation of different model concepts to a model documentation section, added a user guide to explain setting up the model, added new figures to the description of wflow_sbm.
    • +
  • The unit of lateral subsurface flow ssf of LateralSSF is now m^3 d^{-1}. The unit was m^3 t^{-1}, where t is the model timestep. Other flow variables are already stored independently from t, this allows for easier interpretation and to use states independently of t.
    • +
  • Changed the reference level of water depth h and h_av of 2D overland flow (ShallowWaterLand) for cells containing a river from river bed elevation zb to cell elevation z.
    • +
+
+
+

Fixed

+
    +
  • Fixed calculation of average water depth h_av of 2D overland flow (ShallowWaterLand) with the local inertial approach. The summation of h was not correct, resulting in too low values for h_av. For river cells of 2D overland flow h_av was only updated as part of the river domain (ShallowWaterRiver), this value is now also updated as part of the land domain (ShallowWaterLand).
    • +
  • Fixed the following two flow width issues for 2D overland flow of the local inertial model: 1) The flow widths xwidth and ywidth were mapped incorrectly (reversed) to the flow calculation in x and y direction, respectively. 2) For river cells the effective flow width for overland flow was not determined correctly: the riverwidth vector supplied to the function set_effective_flowwidth! covered the complete model domain and should have covered the river domain, resulting in incorrect river widths and thus incorrect effective flow widths for river cells.
    • +
+
+
+
+

v0.5.2 - 2022-02-03

+
+

Changed

+
    +
  • Model types sbm_gwf and hbv use the same approach for the calculation of the drain width dw as model type sbm.
    • +
  • Renamed h_bankfull parameter to bankfull_depth for consistency between kinematic-wave and local-inertial river routing. When using the old name under the [input.lateral.river] section of the TOML file, it will work but it is suggested to update the name.
    • +
+
+
+

Added

+ +
+
+

Fixed

+
    +
  • Model type hbv: the surface width for overland flow was not corrected with the river width.
    • +
  • Fixed use of absolute path for path_forcing in TOML file, which gave an error in wflow v0.5.1.
    • +
  • Fixed a crash when glacier processes are simulated as part of the hbv concept (Δt was not defined).
    • +
  • When the surface flow width for overland flow is zero, the water level h of the kinematic wave should not be calculated, otherwise this results in NaN values. When the model is initialized from state files, q and h are set to zero for indices with a zero surface flow width.
    • +
  • Fixed how number of iterations its for kinematic wave river flow are calculated during initialization when using a fixed sub-timestep (specified in the TOML file). For a model timestep smaller than the fixed sub-timestep an InexactError was thrown.
    • +
  • Fixed providing a cyclic parameter when the netCDF variable is read during model initialization with ncread, this gave an error about the size of the netCDF time dimension.
    • +
  • Fixed CSV and netCDF scalar output of variables with dimension layer (SVector).
    • +
+
+
+
+

v0.5.1 - 2021-11-24

+
+

Fixed

+
    +
  • Fixed calculation of exfiltwater as part of the sbm_gwf model type. This was based directly on groundwater head above the surface level, without multiplying by the specific_yield, resulting in an overestimation of exfiltwater. This is required since the groundwater model estimates the head by dividing the volume by the specific yield or storativity of the aquifer. So, should the groundwater table rise above surface level, the head above surface level does not represent a water column one to one. (This also means the groundwater model (slightly) overestimates heads when the head rises above the surface level. However, this water is immediately removed, and the head will be set to surface level.)
    • +
+
+
+

Added

+
    +
  • Optional dir_input and dir_output keys in the TOML, which can be used to quickly change the path for all input or output files that are given as a relative path.
    • +
+
+
+
+

v0.5.0 - 2021-11-12

+
+

Changed

+
    +
  • Scaling of potential capillary rise is replaced by a common approach found in literature, based on the water table depth zi, a maximum water depth cap_hmax beyond which capillary rise ceases, and a coefficient cap_n. See also Capillary rise. Multiplying the scaling factor with the ratio of model time step and basetimestep in the original approach resulted in (much) smaller capillary fluxes at sub-daily model time steps compared to daily model time steps, and is not used in the new approach. Parameters cap_hmax and cap_n can be set through the TOML file, parameter capscale of the previous approach is not used anymore.
    • +
+
+
+

Fixed

+
    +
  • Conversion of GroundwaterFlow boundaries \(\SIb{}{m^3 d^{-1}}\) as part of model concept sbm_gwf to \(\SI{m^3 s^{-1}}\) for sub-daily model time steps. For the conversion the basetimestep (86400 s) should be used (and not the model time step).
    • +
+
+
+
+

v0.4.1 - 2021-11-04

+
+

Changed

+
    +
  • The \alpha parameter of the kinematic wave has a fixed value now and is not updated because of changes in water height (this could result in large water balance errors). See also Surface routing.
    • +
  • Cyclic input for other structs than vertical are also now supported (for example cyclic inflow to the river).
    • +
  • Moved update_forcing! and update_cyclic! functions to the run function. It is now easier to implement a custom run function with custom loading of input data (forcing and cyclic parameters).
    • +
+
+
+

Added

+
    +
  • Check if reservoirs and lakes have downstream nodes. Without downstream nodes is not supported and in that case an error message is thrown that is easier to understand than the previous one: “ArgumentError: Collection is empty, must contain exactly 1 element.”
    • +
  • For the input.path_forcing TOML setting we use Glob.jl to expand strings like "data/forcing-year-*.nc" to a set of netCDF files that are split in time.
    • +
  • External water inflow (supply/abstractions) added to the kinematic wave inflow in m3/s. It is added/removed to sf.qlat[v] before computing the new q[v] with the kinematic wave equation.
    • +
  • Fixed values for forcing parameters are supported, see also Fixed forcing values.
    • +
+
+
+

Added

+ +
+
+

Fixed

+
    +
  • River inflow for reservoirs and lakes in the kinematic wave. This inflow was based on sf.q[v] at the previous time step, and this has been fixed to the current time step.
    • +
+
+
+
+

v0.4.0 - 2021-09-02

+
+

Changed

+
    +
  • Changed length units for lateral subsurface flow component from millimeter to meter. This means that state netCDF files from previous versions can only be reused if ssf is divided by 10^9.
    • +
  • Add snow and glacier processes to wflow_sbm figure of the documentation.
    • +
+
+
+

Added

+
    +
  • Multi-threading of vertical SBM concept and lateral kinematic wave components (overland, river and subsurface flow) of wflow_sbm model SBM + Kinematic wave.
    • +
  • Improved error message for CSV Reducer.
    • +
  • The TOML keys kv₀, θᵣ and θₛ have been replaced with the ASCII versions kv_0, theta_r and theta_s, to avoid encoding issues with certain text editors. The old keys still work as well.
    • +
+
+
+

Fixed

+
    +
  • Calculation of volumetric water content of vertical SBM (soil layers and root zone).
    • +
  • Update of satwaterdepth in SBM (evaporation was only subtracted from a local variable, and not from sbm.satwaterdepth).
    • +
  • Fixed the index lowerlake_ind of NaturalLake. Linked lakes were not simulated correctly (flows between upstream and downstream lake).
    • +
  • Interpolation function interpolate_linear(x, xp, fp) for CSV tables lakes. When x was larger or smaller than xp, maximum(xp) or minimum(xp) was returned instead of maximum(fp) or minimum(fp).
    • +
  • Fixed model timestep of reservoirs (SimpleReservoir) and lakes (NaturalLake) in relation to precipitation and evapotranspiration fluxes. This was set to the fixed wflow basetimestep of 86400 s, and should be set to the actual model time step from the TOML configuration file.
    • +
  • Add flux from Drainage (GroundwaterFlow) in the sbm_gwf_model to the overland flow component instead of the river component of the kinematic wave.
    • +
  • Fixed option constanthead = false (TOML file), constant_head field of GroundwaterFlow was not defined in this case. Fixed this by initializing empty fields (Vector) for struct ConstantHead.
    • +
  • Fixed return max(0, boundary.flux[index]) to return max(0, flux) the flux should be returned when cell is dry, no negative value allowed.
    • +
  • Fixed path to initialize lake to: dirname(static_path)
    • +
  • Fixed outflow = 0, when lake level is below lake threshold. Before a negative value could enter the log function and model would fail.
    • +
  • Fixed the lake storage initialization. For continuation runs (reinit = false), this caused the lake to be reset to the initial conditions instead of the saved state.
    • +
+
+
+
+

v0.3.1 - 2021-05-19

+
+

Fixed

+
    +
  • Ignore extra dimensions in input netCDFs if they are size 1
    • +
+
+
+
+

v0.3.0 - 2021-05-10

+
+

Changed

+
    +
  • New deposition process for coarse sediment in the reservoirs with a new parameter restrapefficiency in the sediment model.
    • +
  • New variables added to the LandSediment and RiverSediment structs in order to save more output from the sediment model.
    • +
  • Added variables volume and inwater to SurfaceFlow struct, this is convenient for the coupling with the water quality model Delwaq.
    • +
  • River water level (h) and discharge (q) forced directly into the RiverSediment struct (instead of using the OverlandFlowSediment struct first).
    • +
  • Require Julia 1.6 or later.
    • +
+
+
+

Added

+
    +
  • Modify model parameters and forcing through the TOML file (see Modify parameters).
    • +
  • Run wflow_sbm (SBM + kinematic wave) in two parts (until recharge and after subsurface flow) from BMI, including the option to switch off the lateral subsurface component of wflow_sbm.
    • +
  • Support more netCDF dimension and axis order variants.
    • +
+
+
+

Fixed

+
    +
  • Corrected a bug in sediment deposition in the river (case when incoming sediment load is more than the river transport capacity).
    • +
  • Fixed update of snow and glacierstore fields of HBV and SBM concepts by the glacier_hbv module.
    • +
+
+
+
+

v0.2.0 - 2021-03-26

+
+

Changed

+
    +
  • Removed dependency of the f model parameter of wflow_sbm on the parameters \theta_{s}, \theta_{r} and M. This approach is used in Topog_SBM, but not applicable for wflow_sbm. The f parameter needs to be provided as part of the netCDF model parameter file.
    • +
  • Grid properties as cell length and elevation now stored as part of the model.land.network component and not as part of the vertical model components, as it is not used by these components. altitude (elevation) should now be provided as part of the [input] section of the TOML configuration file, and not as part of the [input.vertical] section.
    • +
  • Removed parameter \theta_{e} from SBM struct (not used in update). Parameters \theta_{s} and \theta_{r} included separately (instead of \theta_{e}) in LateralSSF struct, now directly linked to SBM parameters.
    • +
  • Improve error messages (netCDF and cyclic flow graph).
    • +
+
+
+

Added

+
    +
  • Export of netCDF scalar timeseries (separate netCDF file from gridded timeseries). This also allows for importing these timeseries by Delft-FEWS (General Adapter).
    • +
+
+
+

Fixed

+
    +
  • Model parameter Manning’s n now used during the update of the struct SurfaceFlow, to change the related \alpha parameter of the kinematic wave for channel flow.
    • +
+ + +
+
+
+ + Back to top
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+ + + + + + \ No newline at end of file diff --git a/previews/PR448/developments/guide.html b/previews/PR448/developments/guide.html new file mode 100644 index 000000000..384f1e62c --- /dev/null +++ b/previews/PR448/developments/guide.html @@ -0,0 +1,839 @@ + + + + + + + + + +Developers guide – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + +
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+

Developers guide

+
+ + + +
+ + + + +
+ + + +
+ + +
+

Contributions and reporting issues

+

We welcome reporting of issues on our GitHub page. Please provide a minimum working example so we are able to reproduce the issue. Furthermore, we welcome contributions. We follow the ColPrac guide for collaborative practices. New contributors should make sure to read that guide.

+
+
+

Style/decisions

+
    +
  • For improved code readability, we decided to avoid non-ASCII characters for naming variables, structs, functions and macros. Using the non-ASCII character for built-in operators is still allowed. This change in naming convention is now in effect and all invalid uses of non-ASCII characters have been replaced by ASCII equivalents.
    • +
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+
+ + +
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+
+

Developments

+
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+ + + + + + \ No newline at end of file diff --git a/previews/PR448/developments/julia_structs.html b/previews/PR448/developments/julia_structs.html new file mode 100644 index 000000000..092404200 --- /dev/null +++ b/previews/PR448/developments/julia_structs.html @@ -0,0 +1,912 @@ + + + + + + + + + +Julia structures – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Julia structures

+
+ + + +
+ + + + +
+ + + +
+ + +
+

Model

+

Below the composite type that represents all different aspects of a Wflow.Model, such as the network, parameters, clock, model type, configuration and input and output.

+
struct Model{N,L,V,R,W,T}
+    config::Config  # all configuration options
+    network::N      # connectivity information, directed graph
+    lateral::L      # lateral model that holds lateral state, moves along network
+    vertical::V     # vertical model that holds vertical state, independent of each other
+    clock::Clock    # to keep track of simulation time
+    reader::R       # provides the model with dynamic input
+    writer::W       # writes model output
+    type::T         # model type
+end
+

The lateral field of the struct Model can contain different lateral concepts. For each wflow model these different lateral concepts are mapped through the use of a NamedTuple. The vertical field of the struct Model always contains one vertical concept, for example the SBM vertical concept.

+

Below an example how lateral concepts are mapped for the SBM model through a NamedTuple:

+
(subsurface = ssf, land = olf, river = rf)
+

The subsurface part is mapped to the lateral subsurface flow kinematic wave concept, the land part is mapped the overland flow kinematic wave concept and the river part is mapped to the river flow kinematic wave concept. Knowledge of this specific mapping is required to understand and correctly set input, output and state variables in the TOML configuration file, see also Config and TOML. This mapping is described in more detail for each model in the section Models. Also the struct of each mapped concept is provided, so one can check the internal variables in the code. These structs are defined as a parametric composite type, with type parameters between curly braces after the struct name. See also the next paragraph Vertical and lateral models for a more detailed description.

+
+
+

Vertical and lateral models

+

The different model concepts used in wflow are defined as parametric composite types. For example the vertical SBM concept is defined as follows: struct SBM{T,N,M}. T, N and M between curly braces after the struct name refer to type parameters, for more information about type parameters you can check out Type parameters. Since these parameters can be of any type, it is possible to declare an unlimited number of composite types. The type parameters are used to set the type of struct fields, below an example with a part of the SBM struct:

+
@get_units @with_kw struct SBM{T,N,M}
+    # Model time step [s]
+    dt::T | "s"
+    # Maximum number of soil layers
+    maxlayers::Int | "-"
+    # number of cells
+    n::Int | "-"
+    # Number of soil layers
+    nlayers::Vector{Int} | "-"
+    # Number of unsaturated soil layers
+    n_unsatlayers::Vector{Int} | "-"
+    # Fraction of river [-]
+    riverfrac::Vector{T} | "-"
+    # Saturated water content (porosity) [mm mm⁻¹]
+    theta_s::Vector{T} | "mm mm-1"
+    # Residual water content [mm mm⁻¹]
+    theta_r::Vector{T} | "mm mm-1"
+    # Vertical hydraulic conductivity [mm Δt⁻¹] at soil surface
+    kv_0::Vector{T} | "mm dt-1"
+    # Muliplication factor [-] applied to kv_z (vertical flow)
+    kvfrac::Vector{SVector{N,T}} | "-"
+

The type parameter T is used in wflow as a subtype of AbstractFloat, allowing to store fields with a certain floating point precision (e.g. Float64 or Float32) in a flexible way. N refers to the maximum number of soil layers of the SBM soil column, and M refers to the maximum number of soil layers + 1. See also part of the following instance of SBM:

+
sbm = SBM{Float,maxlayers,maxlayers + 1}(
+    dt = tosecond(dt),
+    maxlayers = maxlayers,
+    n = n,
+

For the other model concepts, we refer to the code to check these type parameters.

+ + +
+ + Back to top
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+ + + + + + \ No newline at end of file diff --git a/previews/PR448/examples/index.html b/previews/PR448/examples/index.html new file mode 100644 index 000000000..6f9e1d3c6 --- /dev/null +++ b/previews/PR448/examples/index.html @@ -0,0 +1,781 @@ + + + + + + + + + +To be added in the future – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
+
+

To be added in the future

+
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+ +
+ + + + + + \ No newline at end of file diff --git a/previews/PR448/getting_started/building_a_model.html b/previews/PR448/getting_started/building_a_model.html new file mode 100644 index 000000000..0928ada55 --- /dev/null +++ b/previews/PR448/getting_started/building_a_model.html @@ -0,0 +1,896 @@ + + + + + + + + + +Building a model from scratch – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Building a model from scratch

+
+ + + +
+ + + + +
+ + + +
+ + +
+

HydroMT-wflow

+

hydroMT is a Python package, developed by Deltares, to build and analyze hydro models. It provides a generic model api with attributes to access the model schematization, (dynamic) forcing data, results and states.

+

The wflow plugin hydroMT-wflow of hydroMT can be used to build and analyze the following model configurations:

+ +

To learn more about the wflow plugin of this Python package, we refer to the hydroMT-wflow documentation.

+

To inspect or modify (for example in QGIS) the netCDF static data of these wflow models it is convenient to export the maps to a raster format. This can be done as part of the hydroMT-wflow plugin, see also the following example. It is also possible to create again the netCDF static data file based on the modified raster map stack.

+
+
+

Data requirements

+

The actual data requirements depend on the application of the Model and the Model type. Both forcing and static data should be provided in netCDF format, with the same grid definition for forcing and static data. The only exception is storage and rating curves for lakes, that should be provided in CSV format, see also Additional settings for waterbodies.

+
    +
  • Forcing data: +
      +
    • Precipitation
      • +
    • Potential evapotranspiration
      • +
    • Temperature (optional, only needed for snow and glacier modelling)
      • +
    • +
+

The requirements for static data (including model parameters) depend on the Model type. The following data is required for all Model types, but not directly part of a Model component:

+
    +
  • flow direction data (D8)
    • +
  • river map (location of the river)
    • +
  • sub-catchment map (model domain)
    • +
+

For the flow direction (D8) data, the PCRaster ldd convention is used, see also PCRaster ldd. An approach to generate ldd data is to make use of the Python package pyflwdir:

+ +

see also Eilander et al. (2021) for more information. Pyflwdir is also used by the hydroMT Python package described in the next paragraph. Another approach to generate ldd data is to make use of PCRaster functionality, see for example lddcreate.

+

Optionally, but also not directly part of a model component are gauge locations, that are used to extract gridded data from certain locations.

+

The different supported model configurations are described in the section Model configurations. Wflow_sbm models have the vertical concept SBM in common and input parameters for this component are described in the SBM model parameters table. For wflow_sbm models there are two ways to include subsurface flow:

+
    +
  1. The kinematic wave approach (see section Subsurface flow routing) as part of the sbm model type. Parameters that are part of this component are described in the Lateral subsurface flow section of Model parameters. Input parameters for this component are derived from the SBM vertical concept and the land slope. One external parameter (ksathorfrac) is used to calculate the horizontal hydraulic conductivity at the soil surface kh_0.
    2. +
  2. Groundwater flow (see section Groundwater flow component) as part of the sbm_gwf model type. For the unconfined aquifer the input parameters are described in the section Unconfined aquifer of Model parameters. The bottom (bottom) of the groundwater layer is derived from from the soilthickness [mm] parameter of SBM and the provided surface elevation altitude [m] as part of the static input. The area parameter is derived from the model grid. Parameters that are part of the boundary conditions of the unconfined aquifer are listed under Constant Head and Boundary conditions of the Model parameters section.
    3. +
+

Most hydrological model configurations make use of the kinematic wave surface routing (river flow, overland flow or both) and input data required for the river and overland flow components is described in Surface flow. There is also the option to use the local inertial model as part of the wflow_sbm models (model types sbm and sbm_gwf):

+ +

Input parameters for this approach are described in River flow (local inertial), including the optional 1D floodplain schematization, and Overland flow (local inertial) of the Model parameters section.

+

Reservoirs or lakes can be part of the kinematic wave or local inertial model for river flow and input parameters are described in Reservoirs and Lakes.

+

The wflow_sediment model configuration consists of the vertical Soil Erosion concept and the input parameters for this concept are described in the Sediment section of the Model parameters. The parameters of the lateral Sediment Flux in overland flow concept are described in the Overland flow section of the Model parameters. Parameters of this component are not directly set by data from static input. The input parameters of the lateral concept River Sediment Model are listed in River flow of the Model parameters section.

+

The Model parameters section lists all the parameters per Model component and these Tables can also be used to check which parameters can be part of the output, see also Output netCDF section and Output CSV section.

+

Example models can be found in the Example models section.

+
+
+

References

+
    +
  • Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H. and Pavelsky, T. M.: MERIT Hydro: A high‐resolution global hydrography map based on latest topography datasets, Water Resour. Res., 2019WR024873, doi:10.1029/2019WR024873, 2019.
    • +
  • Eilander, D., van Verseveld, W., Yamazaki, D., Weerts, A., Winsemius, H. C., and Ward, P. J.: A hydrography upscaling method for scale-invariant parametrization of distributed hydrological models, Hydrol. Earth Syst. Sci., 25, 5287–5313, https://doi.org/10.5194/hess-25-5287-2021, 2021.
    • +
+ + +
+ + Back to top
+ +
+ + + + + + \ No newline at end of file diff --git a/previews/PR448/getting_started/download_example_models.html b/previews/PR448/getting_started/download_example_models.html new file mode 100644 index 000000000..9487c10b0 --- /dev/null +++ b/previews/PR448/getting_started/download_example_models.html @@ -0,0 +1,949 @@ + + + + + + + + + +Download example models – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Download example models

+
+ + + +
+ + + + +
+ + + +
+ + +

For each wflow Model a test model is available that can help to understand the data requirements and the usage of each Model. The TOML configuration file per available model are listed in the Table below:

+ + + + + + + + + + + + + + + + + + + + + +
modelTOML configuration file
wflow_sbm + kinematic wavesbm_config.toml
wflow_sbm + groundwater flowsbm_gwf_config.toml
wflow_sedimentsediment_config.toml
+

The associated Model files (input static, forcing and state files) can easily be downloaded and for this we share the following Julia code (per Model) that downloads the required files to your current working directory. For running these test model see also Usage and Command Line Interface.

+
+

wflow_sbm + kinematic wave

+
# urls to TOML and netCDF of the Moselle example model
+toml_url = "https://raw.githubusercontent.com/Deltares/Wflow.jl/master/test/sbm_config.toml"
+staticmaps = "https://github.com/visr/wflow-artifacts/releases/download/v0.2.9/staticmaps-moselle.nc"
+forcing = "https://github.com/visr/wflow-artifacts/releases/download/v0.2.6/forcing-moselle.nc"
+instates = "https://github.com/visr/wflow-artifacts/releases/download/v0.2.6/instates-moselle.nc"
+
+# create a "data" directory in the current directory
+datadir = joinpath(@__DIR__, "data")
+mkpath(datadir)
+toml_path = joinpath(@__DIR__, "sbm_config.toml")
+
+# download resources to current and data dirs
+download(staticmaps, joinpath(datadir, "staticmaps-moselle.nc"))
+download(forcing, joinpath(datadir, "forcing-moselle.nc"))
+download(instates, joinpath(datadir, "instates-moselle.nc"))
+download(toml_url, toml_path)
+
+
+

wflow_sbm + groundwater flow

+
# urls to TOML and netCDF of the Moselle example model
+toml_url = "https://raw.githubusercontent.com/Deltares/Wflow.jl/master/test/sbm_gwf_config.toml"
+staticmaps = "https://github.com/visr/wflow-artifacts/releases/download/v0.2.3/staticmaps-sbm-groundwater.nc"
+forcing = "https://github.com/visr/wflow-artifacts/releases/download/v0.2.1/forcing-sbm-groundwater.nc"
+
+# create a "data" directory in the current directory
+datadir = joinpath(@__DIR__, "data")
+mkpath(datadir)
+toml_path = joinpath(@__DIR__, "sbm_gwf_config.toml")
+
+# download resources to current and data dirs
+download(staticmaps, joinpath(datadir, "staticmaps-sbm-groundwater.nc"))
+download(forcing, joinpath(datadir, "forcing-sbm-groundwater.nc"))
+download(toml_url, toml_path)
+
+
+

wflow_sediment

+
# urls to TOML and netCDF of the Moselle example model
+toml_url = "https://raw.githubusercontent.com/Deltares/Wflow.jl/master/test/sediment_config.toml"
+staticmaps = "https://github.com/visr/wflow-artifacts/releases/download/v0.2.3/staticmaps-moselle-sed.nc"
+forcing = "https://github.com/visr/wflow-artifacts/releases/download/v0.2.3/forcing-moselle-sed.nc"
+instates = "https://github.com/visr/wflow-artifacts/releases/download/v0.2.0/instates-moselle-sed.nc"
+
+# create a "data" directory in the current directory
+datadir = joinpath(@__DIR__, "data")
+mkpath(datadir)
+toml_path = joinpath(@__DIR__, "sediment_config.toml")
+
+# download resources to current and data dirs
+download(staticmaps, joinpath(datadir, "staticmaps-moselle-sed.nc"))
+download(forcing, joinpath(datadir, "forcing-moselle-sed.nc"))
+download(instates, joinpath(datadir, "instates-moselle-sed.nc"))
+download(toml_url, toml_path)
+ + +
+ + Back to top
+ +
+ + + + + + \ No newline at end of file diff --git a/previews/PR448/getting_started/index.html b/previews/PR448/getting_started/index.html new file mode 100644 index 000000000..c766e85cd --- /dev/null +++ b/previews/PR448/getting_started/index.html @@ -0,0 +1,919 @@ + + + + + + + + + +Getting started – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Getting started

+
+ + + +
+ + + + +
+ + + +
+ + +
+ + + + +
+ + + + Back to top
+ +
+ + + + + + \ No newline at end of file diff --git a/previews/PR448/getting_started/install.html b/previews/PR448/getting_started/install.html new file mode 100644 index 000000000..f2f99c672 --- /dev/null +++ b/previews/PR448/getting_started/install.html @@ -0,0 +1,970 @@ + + + + + + + + + +Installing wflow – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Installing wflow

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Wflow can be used in two different ways, depending on the required use of the code:

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Below we describe how to install both versions of wflow.

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Installing as Julia package

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Wflow is a Julia package that can be installed in several ways. Below, we show how to install wflow from Julia’s package repository and how to install the latest version from GitHub.

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First, download and install the current stable release of Julia. If you have any issues installing Julia, please see platform specific instructions for further instructions.

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If you are new to Julia, it might be a good idea to check out the Getting Started section of the Julia Manual. You can also find additional learning resources at julialang.org/learning.

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Install from Julia’s package repository

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To access Julia’s package manager, press ] in the Julia REPL. To get back to the Julia REPL, press backspace or ^C.

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If you haven’t used Julia in a while, it’s a good idea to run up to update your packages.

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pkg> up
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To access Julia’s package manager and install wflow, use:

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pkg> add Wflow
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This process can take a while, especially on the first run, as compatible dependencies are automatically resolved and installed from the Pkg General registry.

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Install from GitHub

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You can also install wflow from the master branch on the repository as follows:

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pkg> add Wflow#master
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This command tracks the master branch and updates to the latest commit on that branch when you run update, or simply up, in the Pkg REPL. The add installs wflow in your home directory under .julia/packages/Wflow. Note that packages installed under packages by add should not be changed in the directory, as the change could disrupt Pkg’s automatic dependency handling.

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If you want to modify any files in the repository, you need to do a development install. This can be done using:

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pkg> dev Wflow
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This will clone the git repository, place it under your home directory in .julia/dev/Wflow, and add the wflow package to your project environment. To receive updates, you’ll need to pull the latest changes manually using git pull.

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Check installation of wflow

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Finally, go back to the Julia REPL and try to load wflow:

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julia> using Wflow
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The first time you do this, it may take longer as any new or changed packages need to be precompiled to enable faster loading on subsequent uses. No error messages should appear, which indicates that you have successfully installed wflow.

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Before concluding this section, we recommend a few tools that can make using and developing Julia code easier.

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There is a section on editors and IDEs for Julia on https://julialang.org/, scroll down to see it. We use and recommend Microsoft’s free and open source Visual Studio Code. When combined with the Julia extension it provides a powerful and interactive development experience.

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If you plan to modify the code of wflow, we recommend installing the Revise.jl package. This package allows you to modify code and use the changes without restarting Julia. Install it with add Revise from the Pkg REPL. Then create a file called .julia/config/startup.jl, and put using Revise there. This will load Revise every time you start a Julia session.

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Installing the compiled executable

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Binaries of wflow_cli can be downloaded from our website download.deltares.nl, and are currently available for Windows. Download and install the .msi file. After installation, you will see two folders in the installation directory. Only the bin/wflow_cli is used. The artifacts folder contains binary dependencies such as netCDF.

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artifacts\
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To verify whether the installation was completed successfully, run wflow_cli with no arguments in the command line. This will display the following message:

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Usage: wflow_cli 'path/to/config.toml'
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The old version of wflow, based on Python and PCRaster libraries is also available to download from our website download.deltares.nl. We recommend installing the Julia version, as this documentation is written to support this version.

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+ + + + + + \ No newline at end of file diff --git a/previews/PR448/getting_started/running_wflow.html b/previews/PR448/getting_started/running_wflow.html new file mode 100644 index 000000000..9721a8af8 --- /dev/null +++ b/previews/PR448/getting_started/running_wflow.html @@ -0,0 +1,902 @@ + + + + + + + + + +Running a simulation – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Running a simulation

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Using Julia

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Once you installed Julia and Wflow.jl, a simulation can be started from the command line as follows:

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julia -e 'using Wflow; Wflow.run()' path/to/config.toml
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Furthermore, it is possible to write a Julia script to run a simulation. Example data to explore how this works can be found here.

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using Wflow
+Wflow.run(toml_path)
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Julia can also be used to modify settings after reading the settings file. In the example below, we show how to adjust the end date of the simulation.

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using Dates
+config = Wflow.Config(toml_path)
+config.endtime = DateTime("2000-01-03T00:00:00")
+Wflow.run(config)
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Using the command line interface

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If you don’t need the extra features of using wflow as a library, but just want to run simulations, the command line interface makes it easier to do so. It consists of a single executable, wflow_cli that accepts a single argument, the path to a TOML configuration file.

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Binaries of wflow_cli can be downloaded from our website download.deltares.nl, and are currently available for Windows.

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After installing you can see three folders in the installation directory. It is only the bin/wflow_cli that is directly used. All three folders need to stay together however. The share folder contains TOML files with more information about the build.

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bin\wflow_cli
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Simply running wflow_cli with no arguments will give the following message:

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Usage: wflow_cli 'path/to/config.toml'
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When starting a run, you will see basic run information on the screen, as well as a progress bar, that gives an estimate of how much time is needed to finish the simulation:

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┌ Info: Run information
+│   model_type = "sbm"
+│   starttime = CFTime.DateTimeStandard(2000-01-01T00:00:00)
+│   dt = 86400 seconds
+│   endtime = CFTime.DateTimeStandard(2000-12-31T00:00:00)
+└   nthreads() = 4
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+Progress: 100%|██████████████████████████████████████████████████| Time: 0:00:27
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+ + + + + + \ No newline at end of file diff --git a/previews/PR448/home/case_studies.html b/previews/PR448/home/case_studies.html new file mode 100644 index 000000000..28b41111d --- /dev/null +++ b/previews/PR448/home/case_studies.html @@ -0,0 +1,852 @@ + + + + + + + + + +Case studies – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Case studies

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Wflow models for the Meuse and Rhine

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Reliable hydrological models for the Rhine and the Meuse river basins are necessary for short-term forecasting of river flows and long-term predictions for strategic water management planning. In collaboration with Rijkswaterstaat, Deltares is developing a new line of models for the Rhine and the Meuse basins. The models will be used for forecasting and to estimate the impact of climate change on water resources and extreme streamflow. In the model development, we aim to improve hydrological predictions by including relevant processes in the model schematization. The modularity of the wflow framework is ideal for this as we can easily evaluate the combination of different vertical and lateral model components. For example, the local inertial routing for river and overland flow enables us to consider retention of water in the floodplains, which is likely to improve extreme streamflow predictions.

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Operational flood forecasting in Australia

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In Australia, there was a need for high-resolution, fast and accurate rainfall-runoff models to provide boundary conditions for a fast and detailed flood inundation model (SFINCS). The domain of the flood model covers the entire North and East Coast of Australia. Although many gauging stations are available to provide real-time information, many rivers are not covered. For these locations, wflow_sbm models are used to provide this real-time information. Additionally, these models are used to provide projections for potential future scenarios. Using the HydroMT library, all wflow_sbm models were automatically built. The high level of flexibility in spatial and temporal resolution, combined with the physics-based nature of the concept, makes Wflow_sbm particularly suitable for ungauged basins. Furthermore, the model is detailed and computationally efficient enough for coupling with the fast flood inundation model SFINCS.

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The results of this proof of concept are very promising. Technically, we were able to quickly set up the wflow_sbm models, couple them to the flood inundation models (SFINCS), and run the models operationally under the Delft-FEWS platform. Model validation was conducted for two basins by comparing the results of Wflow_sbm against observations and the results of calibrated URBS models. This validation demonstrated that the uncalibrated Wflow_sbm model results were already quite satisfactory, especially given the complex nature of these basins, which include several small and large reservoirs. We could also show the potential for further calibration by adjusting the KsatHorFrac parameter.

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De Kleermaeker, S., Leijnse, T., Morales, Y., Druery, C., & Maguire, S. (2022). Developing a real-time data and modelling framework for operational flood inundation forecasting in Australia. In Hydrology & Water Resources Symposium 2022 (HWRS 2022): The Past, the Present, the Future. Engineers Australia. https://search.informit.org/doi/10.3316/informit.916755150845355

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Simulating plastic transport in Thailand

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For the Pollution Control Board of the Government of Thailand and the World Bank, we supported a material flow analysis of plastics in Thailand using wflow. Plastic pollution is a growing global issue. Plastic waste enters rivers and is transported to the ocean where it persists and threatens the health of the ocean, seas and coasts. The initial movement of plastic waste is in many cases triggered by runoff from (heavy) rainfall and transported by water flow towards small streams and rivers. Therefore there is strong relation to rainfall-runoff processes, which can be modeled using high-resolution rainfall-runoff models.

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In this study we applied the wflow_sbm model in combination with a fate-and-transport and water quality model (DelWaq) to simulate the movement of plastics through five large river basins and on three island and coastal zones (Krabi, Phuket, and Ko Samui; see screenshot of the model below) in Thailand. Together with our partners Panya Consultants and HII, we were able to identify hotspots of plastic pollution, estimate how much plastic waste would end up in the Gulf of Thailand and recommend priority areas for reducing plastic waste reaching the sea.

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The wflow_sbm models for the five large basins were calibrated. The presence of large dams and reservoirs complicated calibration, but with the input for the dam operation, the model performance for these basins could be largely improved. The figure below shows the calibrated model results for the Chao Phraya, just upstream of Bangkok. The input from the hydrological wflow_sbm model was used as input for the fate and transport model to assess the amount of plastic transported to the ocean.

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Link to World Bank report: https://www.worldbank.org/en/country/thailand/publication/plastic-waste-material-flow-analysis-for-thailand

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+ + + + + + \ No newline at end of file diff --git a/previews/PR448/home/publications.html b/previews/PR448/home/publications.html new file mode 100644 index 000000000..cfb1f963d --- /dev/null +++ b/previews/PR448/home/publications.html @@ -0,0 +1,914 @@ + + + + + + + + + +Publications – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Publications

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Citing wflow

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For publications, please cite the following paper introducing Wflow.jl and describing the wflow_sbm concept, together with some case studies:

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van Verseveld, W. J., Weerts, A. H., Visser, M., Buitink, J., Imhoff, R. O., Boisgontier, H., Bouaziz, L., Eilander, D., Hegnauer, M., ten Velden, C., and Russell, B., 2024. Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications. Geosci. Model Dev., 17, 3199–3234. https://doi.org/10.5194/gmd-17-3199-2024.

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To cite a specific software version please use the DOI provided in the Zenodo badge DOI, that points to the latest release. The DOIs of previous versions are also available at Zenodo. If you use a snapshot of the development (without a DOI) please cite as follows:

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van Verseveld, Willem, Visser, Martijn, Buitink, Joost, Bouaziz, Laurène, Boisgontier, Hélène, Bootsma, Huite, Weerts, Albrecht, Baptista, Carlos Fernando, Pronk, Maarten, Eilander, Dirk, Hartgring, Sebastian, Dalmijn, Brendan, Hofer, Julian, Hegnauer, Mark, & Mendoza, Raul, (YEAR). Deltares/Wflow.jl: unstable-master. https://github.com/Deltares/Wflow.jl, obtained: DATE_OF_DOWNLOAD.

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Publications using wflow

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Peer reviewed journal papers

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Aerts, J. P. M., Hut, R. W., van de Giesen, N. C., Drost, N., van Verseveld, W. J., Weerts, A. H., and Hazenberg, P., 2022. Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model. Hydrol. Earth Syst. Sci., 26, 4407–4430. https://doi.org/10.5194/hess-26-4407-2022.

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de Boer-Euser, T., Bouaziz, L., De Niel, J., Brauer, C., Dewals, B., Drogue, G., Fenicia, F., Grelier, B., Nossent, J., Pereira, F., Savenije, H., Thirel, G., Willems, P., 2017. Looking beyond general metrics for model comparison – lessons from an international model intercomparison study. Hydrol. Earth Syst. Sci. 21, 423–440. https://doi:10.5194/hess-21-423-2017.

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Bouaziz, L.J.E., Fenicia, F., Thirel, G., de Boer-Euser, T., Buitink, J., Brauer, C.C., De Niel, J., Dewals, B.J., Drogue, G., Grelier, B., Melsen, L. A., Moustakas, S., Nossent, J., Pereira, F., Sprokkereef, E., Stam, J., Weerts, A.H., Willems, P., Savenije, H.H.G., Hrachowitz, M., 2021. Behind the scenes of streamflow model performance. Hydrol. Earth Syst. Sci., 25, 1069–1095. https://doi.org/10.5194/hess-25-1069-2021.

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Bouaziz, L. J. E., Aalbers, E. E., Weerts, A. H., Hegnauer, M., Buiteveld, H., Lammersen, R., Stam, J., Sprokkereef, E., Savenije, H. H. G., and Hrachowitz, M., 2022. Ecosystem adaptation to climate change: the sensitivity of hydrological predictions to time-dynamic model parameters, Hydrol. Earth Syst. Sci., 26, 1295–1318. https://doi.org/10.5194/hess-26-1295-2022.

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Casson, D. R., Werner, M., Weerts, A., and Solomatine, D., 2018. Global re-analysis datasets to improve hydrological assessment and snow water equivalent estimation in a sub-Arctic watershed. Hydrol. Earth Syst. Sci., 22, 4685–4697. https://doi.org/10.5194/hess-22-4685-2018.

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Droppers, B., Rakovec, O., Avila, L., Azimi, S., Cortés-Torres N., De León Pérez, D., Imhoff, R., Francés, F., Kollet, S., Rigon, R., Weerts, A. & Samaniego, L, 2024. Multi-model hydrological reference dataset over continental Europe and an African basin. Sci Data, 11, 1009. https://doi.org/10.1038/s41597-024-03825-9.

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Droppers, B., Rakovec, O., Avila, L., Azimi, S., Cortés-Torres N., De León Pérez, D., Imhoff, R., Francés, F., Kollet, S., Rigon, R., Weerts, A. & Samaniego, L, 2024. Multi-model hydrological reference dataset over continental Europe and an African basin. Sci Data, 11, 1009. https://doi.org/10.1038/s41597-024-03825-9.

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Emerton, R.E., Stephens, E.M., Pappenberger, F., Pagano, T.C., Weerts, A.H., Wood, A.W., Salamon, P., Brown, J.D., Hjerdt, N., Donnelly, C., Baugh, C.A., Cloke, H.L., 2016. Continental and global scale flood forecasting systems. WIREs Water 3, 391–418. https://doi.org/10.1002/wat2.1137.

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Gebremicael, T.G., Mohamed, Y.A., Van der Zaag, P., 2019. Attributing the hydrological impact of different land use types and their long-term dynamics through combining parsimonious hydrological modelling, alteration analysis and PLSR analysis. Science of The Total Environment, 660, 1155-1167, https://doi.org/10.1016/jscitotenv.2019.01.085.

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Giardino, A., Schrijvershof, R., Nederhoff, C.M., de Vroeg, H., Brière, C., Tonnon, P.-K., Caires, S., Walstra, D.J., Sosa, J., van Verseveld, W., Schellekens, J., Sloff, C.J., 2018. A quantitative assessment of human interventions and climate change on the West African sediment budget, Ocean & Coastal Management, 156, 249-265. https://doi.org/10.1016/j.ocecoaman.2017.11.008.

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Hally, A., Caumont, O., Garrote, L., Richard, E., Weerts, A., Delogu, F., Fiori, E., Rebora, N., Parodi, A., Mihalović, A., Ivković, M., Dekić, L., van Verseveld, W., Nuissier, O., Ducrocq, V., D’Agostino, D., Galizia, A., Danovaro, E., Clematis, A., 2015. Hydrometeorological multi-model ensemble simulations of the 4 November 2011 flash flood event in Genoa, Italy, in the framework of the DRIHM project. Nat. Hazards Earth Syst. Sci. 15, 537–555. https://doi:10.5194/nhess-15-537-2015.

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Hassaballah, K., Mohamed, Y., Uhlenbrook, S., and Biro, K., 2017. Analysis of streamflow response to land use and land cover changes using satellite data and hydrological modelling: case study of Dinder and Rahad tributaries of the Blue Nile (Ethiopia–Sudan), Hydrol. Earth Syst. Sci., 21, 5217–5242. https://doi.org/10.5194/hess-21-5217-2017.

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Imhoff, R.O., Buitink, J., van Verseveld, W.J., Weerts, A.H., 2024. A fast high resolution distributed hydrological model for forecasting, climate scenarios and digital twin applications using wflow_sbm. Environmental Modelling & Software, 179, 106099. https://doi.org/10.1016/j.envsoft.2024.106099

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Imhoff, R.O, van Verseveld, W.J., van Osnabrugge, B., Weerts, A.H., 2020. Scaling Point-Scale (Pedo)transfer Functions to Seamless Large-Domain Parameter Estimates for High-Resolution Distributed Hydrologic Modeling: An Example for the Rhine River. Water Resources Research, 56, e2019WR026807. https://doi.org/10.1029/2019WR026807.

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Imhoff, R.O., van Verseveld, W., van Osnabrugge, B., Weerts, A.H., 2020. Ruimtelijk schaalbare hydrologische modelparameters uit open-source omgevingsdata: een voorbeeld voor de Rijn. Stromingen: vakblad voor hydrologen, 26(3), 19-36 https://edepot.wur.nl/540682.

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Jeuken, A., Bouaziz, L., Corzo, G., Alfonso, L., 2016. Analyzing Needs for Climate Change Adaptation in the Magdalena River Basin in Colombia, in: Filho, W.L., Musa, H., Cavan, G., O’Hare, P., Seixas, J. (Eds.), Climate Change Adaptation, Resilience and Hazards, Climate Change Management. Springer International Publishing, pp. 329–344 https://doi.org/10.1007/978-3-319-39880-8.

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López López, P., Wanders, N., Schellekens, J., Renzullo, L.J., Sutanudjaja, E.H., Bierkens, M.F.P., 2016. Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations. Hydrol. Earth Syst. Sci., 20, 3059–3076. https://doi.org/10.5194/hess-20-3059-2016.

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Pranoto, B., Soekarno, H., Hartulistiyoso, E., Nur Aidi, M., Sutrisno, D., Pohan, D., Radhika, Sutejo, B., Heru Kuncoro, A., Nahib, I., 2024. Integrating Flood Early Warning System (FEWS) for Optimizing Small Hydropower Sites: A West Java Case Study. EVERGREEN Joint Journal of Novel Carbon Resource Sciences & Green Asia Strategy, 11, 3, 2691-2699. https://www.tj.kyushu-u.ac.jp/evergreen/contents/EG2024-11_3_content/pdf/p2691-2699.pdf

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Rakovec, O., Weerts, A.H., Sumihar, J., Uijlenhoet, R., 2015. Operational aspects of asynchronous filtering for flood forecasting. Hydrol. Earth Syst. Sci., 19, 2911–2924, https://doi.org/10.5194/hess-19-2911-2015.

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Ratri, D.N., A.H. Weerts, R. Muharsyah, K. Whan, A. Klein Tank, E. Aldrian, M. H. Hariadi, Calibration of ECMWF SEAS5 based streamflow forecast in Seasonal hydrological forecasting for Citarum river basin, West Java, Indonesia, Journal of Hydrology: Regional Studies,45,101305, https://doi.org/10.1016/j.ejrh.2022.101305.

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Rusli, S.R., A.H. Weerts, A. Taufiq, V. Bense, 2021. Estimating water balance components and their uncertainty bounds in highly groundwater-dependent and data-scarce area: An example for the Upper Citarum basin, J. Hydrol. Regional Studies, https://doi.org/10.1016/j.ejrh.2021.100911.

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Rusli, S.R., V.F. Bense, A. Taufiq, A.H. Weerts,2023. Quantifying basin-scale changes in groundwater storage using GRACE and one-way coupled hydrological and groundwater flow model in the data-scarce Bandung groundwater Basin, Indonesia, Groundwater for Sustainable Development,22, 100953, https://doi.org/10.1016/j.gsd.2023.100953.

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Rusli, S.R., A.H. Weerts, S.M.T. Mustafa, D.E. Irawan, A. Taufiq, V.F. Bense, 2023. Quantifying aquifer interaction using numerical groundwater flow model evaluated by environmental water tracer data: Application to the data-scarce area of the Bandung groundwater basin, West Java, Indonesia, Journal of Hydrology: Regional Studies, 50, https://doi.org/10.1016/j.ejrh.2023.101585.

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Schaller, N., Sillmann, J., Müller, M., Haarsma, R., Hazeleger, W., Hegdahl, T.J., Kelder, T., van den Oord, G., Weerts, A., Whan, K., 2020. The role of spatial and temporal model resolution in a flood event storyline approach in western Norway. Weather and Climate Extremes, doi: https://doi.org/10.1016/j.wace.2020.100259.

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Seizarwati, W. and M. Syahidah, 2021. Rainfall-Runoff Simulation for Water Availability Estimation in Small Island Using Distributed Hydrological Model wflow. IOP Conf. Ser.: Earth Environ. Sci., 930,012050, doi:10.1088/1755-1315/930/1/012050. https://iopscience.iop.org/article/10.1088/1755-1315/930/1/012050/pdf

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Sperna Weiland, F.C., R.D. Visser, P. Greve, B. Bisselink, L. Brunner and A.H. Weerts, 2021. Estimating Regionalized Hydrological Impacts of Climate Change Over Europe by Performance-Based Weighting of CORDEX Projections, Frontiers of Water, https://doi.org/10.3389/frwa.2021.713537.

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Tangdamrongsub, N., Steele-Dunne, S.C., Gunter, B.C., Ditmar, P.G., Weerts, A.H., 2015. Data assimilation of GRACE terrestrial water storage estimates into a regional hydrological model of the Rhine River basin. Hydrol. Earth Syst. Sci. 19, 2079–2100. https://doi:10.5194/hess-19-2079-2015.

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van der Laan, E., P. Hazenberg, A.H. Weerts, 2024. Simulation of long-term storage dynamics of headwater reservoirs across the globe using public cloud computing infrastructure. Science of The Total Environment, 172678, https://doi.org/10.1016/j.scitotenv.2024.172678.

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Pranoto, B., Soekarno, H., Hartulistiyoso, E., Nur Aidi, M., Sutrisno, D., Pohan, D., Radhika, Sutejo, B., Heru Kuncoro, A., Nahib, I., 2024. Integrating Flood Early Warning System (FEWS) for Optimizing Small Hydropower Sites: A West Java Case Study. EVERGREEN Joint Journal of Novel Carbon Resource Sciences & Green Asia Strategy, 11, 3, 2691-2699. https://www.tj.kyushu-u.ac.jp/evergreen/contents/EG2024-11_3_content/pdf/p2691-2699.pdf

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van Osnabrugge, B., Weerts, A.H., Uijlenhoet, R., 2017. genRE: A method to extend gridded precipitation climatology data sets in near real-time for hydrological forecasting purposes. Water Resources Research, 53. https://doi.org/10.1002/2017WR021201.

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van Osnabrugge, B., Uijlenhoet, R., Weerts, A., 2019. Contribution of potential evaporation forecasts to 10-day streamflow forecast skill for the Rhine River, Hydrol. Earth Syst. Sci., 23, 1453–1467, https://doi.org/10.5194/hess-23-1453-2019.

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van der Vat, M., Boderie, P., Bons, K.A., Hegnauer, M., Hendriksen, G., van Oorschot, M., Ottow, B., Roelofsen, F., Sankhua, R.N., Sinha, S.K., Warren, A., Young, W., 2019. Participatory Modelling of Surface and Groundwater to Support Strategic Planning in the Ganga Basin in India. Water, 11, 2443. https://doi.org/10.3390/w11122443.

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Wannasin, C., Brauer, C. C., Uijlenhoet, R., van Verseveld, W. J., Weerts, A. H., 2021. Daily flow simulation in Thailand Part I: Testing a distributed hydrological model with seamless parameter maps based on global data. Journal of Hydrology: Regional Studies, 34, 1-19. https://doi.org/10.1016/j.ejrh.2021.100794.

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Wannasin, C., Brauer, C. C., Uijlenhoet, R., van Verseveld, W. J., Weerts, A. H., 2021. Daily flow simulation in Thailand Part II: Unraveling effects of reservoir operation. Journal of Hydrology: RegionalStudies, 34, 1-17. https://doi.org/10.1016/j.ejrh.2021.100792.

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Wang, X., Zhang, J., Babovic, V., 2016. Improving real-time forecasting of water quality indicators with combination of process-based models and data assimilation technique. Ecological Indicators 66, 428–439. https://doi:10.1016/j.ecolind.2016.02.016.

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PhD, MSc, BSc Theses & Internship reports

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Abdelnour, A., 2022. Bias Correction of Climate Simulations to Assess Climate Change Impacts on Low Flows in the Rhine River, MSc thesis, Delft Universitry of technology, https://repository.tudelft.nl/islandora/object/uuid:50489399-cbd2-467f-9b82-98e5f3e371e9.

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Ali, M.A., 2023. Machine learning for predicting spatially variable lateral hydraulic conductivity: a step towards efficient hydrological model calibration and global applicability, Intersnhip report, Deltares.

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Arnal, L., 2014. An intercomparison of flood forecasting models for the Meuse River basin, MSc Thesis, Vrije Universiteit, Amsterdam, https://hal.inrae.fr/hal-02600749.

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Alkemade, G., 2019. Routing and calibration of distributed hydrological models, MSc Thesis, Vrije Universiteit, Amsterdam, Faculty of Science, Hydrology.

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Azadeh Karami Fard, 2015. Modeling runoff of an Ethiopian catchment with WFLOW, MSc. Thesis, Vrije Universiteit, Amsterdam.

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Benschop, J., 2023. The application of hybrid lateral routing in hydrological simulations of the Rhine catchment, MSc Thesis, Hydrology and Environmental Hydraulics Group, Wageningen University.

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Beusen, B., 2021. The effect of rooting depth on discharge and evapotranspiration in (semi-)arid areas, MSc Thesis, Hydrology and Quantitative Water Management Group, Wageningen University.

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Beusen, B., 2021. Plastic transport and the effect of climate change in the Rhine, Internship report, Deltares.

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Bouaziz, L. J. E., 2021. Internal processes in hydrological models: A glance at the Meuse basin from space. Delft University of Technology, Delft, the Netherlands, Doctoral dissertation, https://doi.org/10.4233/uuid:09d84cc1-27e2-4327-a8c7-207a75952061.

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Hartgring, S., 2023. On Forecasting the Rur River: Using hindcasts and forecasts of the 2021 flood event to improve understanding of flood forecasting in the Rur catchment. Delft University of Technology, Delft, the Netherlands, MSc thesis, http://resolver.tudelft.nl/uuid:2909d997-a983-490a-a588-87119998543a.

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Jaime, D.E.V, 2021. Ensemble hydrological forecasts to derive extreme return periods: Case Study of the Overijsselse Vecht River using the wflow_sbm model, MSc thesis, Unesco IHE, Delft.

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López López, P., 2018. Application of Global Hydrological Datasets for River Basin Modelling Utrecht University, Utrecht, the Netherlands, pp. 1-214, Doctoral dissertation, http://dspace.library.uu.nl/handle/1874/364148.

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Maat, W.H., 2015. Simulating discharges and forecasting floods using a conceptual rainfall-runoff model for the Bolivian Mamoré basin, MSc Thesis, University of Twente, Enschede. https://essay.utwente.nl/67046/.

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van Osnabrugge, B., 2020. Interpolate, simulate, assimilate: operational aspects of improving hydrological forecasts in the Rhine basin. Wageningen University, Doctoral dissertation, https://doi.org/10.18174/513157.

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Rohrmueller, I., 2019. BENCHMARKING THE NEW WFLOW DISTRIBUTED HYDROLOGICAL MODEL, MSc Thesis, School of Engineering - Newcastle University.

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Rusli, S.R., 2024. Deepening the quantitative understanding of groundwater systems in data-scarce areas: application in the Bandung Groundwater Basin, Indonesia. Wageningen University, Doctoral dissertation, https://doi.org/10.18174/640983.

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Tretjakova, D., 2015. Investigating the effect of using fully-distributed model and data assimilation on the performance of hydrological forecasting in the Karasu catchment, Turkey, MSc thesis, Wageningen University.

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van der Gaast, R.H., 2024. Evaluating the transferability of data-driven pedo-transfer functions for the wflow_sbm parameter KsatHorFrac in central and Western Europe. Universiteit Twente, Enschede, The Netherlands, https://essay.utwente.nl/103634/1/vandergaast_MA_ET.pdf.

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Verbrugge, M., 2019. Reservoir Operation Optimization, a case study in the Chao Phraya Basin, BSc thesis, Hydrology and Quantitative Water Management Group, Wageningen University.

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Verbrugge, M., 2023. Bias-correcting meteorological forcing to improve seasonal discharge forecasting of the Rhine, Internship report, Deltares.

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Viguures, P., 2020. Modelling of flash floods in current and future climate with high resolution convection permitting regional climate models in the European Alps, MSc Thesis, Wageningen University.

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Visser, B., 2020. Impact of climate change on local water resources of European catchments, Intersnhip report, Deltares.

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Wannasin, C., 2023. Modelling and forecasting daily streamflow with reservoir operation in the upper Chao Phraya River basin, Thailand. Wageningen University, Doctoral dissertation, https://doi.org/10.18174/584572.

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Reports

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World Bank. 2021. Plastic Waste Discharges from Rivers and Coastlines in Indonesia. Marine Plastics Series;. World Bank, Washington, DC. © World Bank, https://openknowledge.worldbank.org/handle/10986/35607, License: CC BY 3.0 IGO.

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World Meteorological Organization (WMO), 2023. State of Global Water Resources report 2022, WMO-No. 1333, https://library.wmo.int/idurl/4/68473.

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World Meteorological Organization (WMO), 2024. State of Global Water Resources report 2023, WMO-No. 1362, https://library.wmo.int/idurl/4/69033.

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Wflow.jl

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Build Status Coverage Dev Stable DOI ColPrac: Contributor’s Guide on Collaborative Practices for Community Packages

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About wflow

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Wflow is Deltares’ solution for modelling hydrological processes, allowing users to account for precipitation, interception, snow accumulation and melt, evapotranspiration, soil water, surface water, groundwater recharge, and water demand and allocation in a fully distributed environment. Successfully applied worldwide for analyzing flood hazards, drought, climate change impacts and land use changes, wflow is growing to be a leader in hydrology solutions. Wflow is conceived as a framework, within which multiple distributed model concepts are available, which maximizes the use of open earth observation data, making it the hydrological model of choice for data scarce environments. Based on gridded topography, soil, land use and climate data, wflow calculates all hydrological fluxes at any given grid cell in the model at a given time step.

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Wflow was born out of the creation of Deltares in 2008, when a strategic review identified the need for a distributed hydrological model to allow the simulation of flows at the catchment scale. With the intention being to encourage greater scientific collaboration. For this reason:

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  • Wflow is free and open source software.
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  • Wflow is easily coupled with other models and software applications.
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  • Contribution to the wflow code development is encouraged.
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From 2021 the wflow code is distributed under the MIT License. Wflow is also available as a compiled executable under the Deltares terms and conditions. The wflow computational engine is built in the Julia language, a high-performance computing language. Wflow does not include a graphical user interface and is designed for maximum user flexibility. Prior to 2021, wflow was developed in Python on top of the PCRaster Python extension. The Python version is still available, but not actively developed.

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Quick overview

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About

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As opposed to the user guide, which describes the steps needed to build and apply a model in the software, this section explains the different model concepts that are available within the modelling framework of wflow. Descriptions are given regarding the model concepts with links to the original scientific papers which explain the concepts in more detail. The model parameters which influence the processes are also shown, using inline code blocks. An overview of all model parameters is also provided for easy reference, including their short names, long descriptions and their units (see parameters vertical concepts and parameters lateral concepts).

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Division between vertical and lateral

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In the documentation we talk of vertical and lateral concepts. These are components in the model that describe the vertical movement of water in each model grid cell and the lateral movement of water across grid cells.

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Groundwater flow

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Single layer groundwater flow requires the four following components, and each is described in more detail below:

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Aquifer types

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Groundwater flow can occur either in a confined or unconfined aquifer. Confined aquifers are overlain by a poorly permeable confining layer (e.g. clay). No air can get in to fill the pore space so that the aquifer always remains fully saturated. For a confined aquifer, water will always flow along the complete height \(\SIb{H}{m}\) over the aquifer and transmissivity \(\SIb{kH}{m^2 d^{-1}}\) is a constant (\(\SIb{k}{m d^{-1}}\) is the horizontal hydraulic conductivity). Specific storage is the amount of water an aquifer releases per unit change in hydraulic head, per unit volume of aquifer, as the aquifer and the groundwater itself is compressed. Its value is much smaller than specific yield, between \(10^{-5}\) (stiff) and \(10^{-2}\) (weak).

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The upper boundary of an unconfined aquifer is the water table (the phreatic surface). Specific yield (or drainable porosity) represents the volumetric fraction the aquifer will yield when all water drains and the pore volume is filled by air instead. Specific yield will vary roughly between \(0.05\) (clay) and \(0.45\) (peat) (Johnson, 1967).

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Groundwater flow is solved forward in time and central in space. The vertically averaged governing equation for an inhomogeneous and isotropic aquifer in one dimension can be written as:

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\[ + S \frac{\partial \phi}{\partial t} = \frac{\partial}{\partial x} \left(kH \frac{\phi}{\delta x}\right) + Q +\]

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where \(\SIb{S}{m m^{-1}}\) is storativity (or specific yield), \(\SIb{\phi}{m}\) is hydraulic head, \(t\) is time, \(\SIb{k}{m t^{-1}}\) is horizontal hydraulic conductivity, \(\SIb{H}{m}\) is the (saturated) aquifer height: groundwater level - aquifer bottom elevation and \(\SIb{Q}{m +t^{-1}}\) represents fluxes from boundary conditions (e.g. recharge or abstraction), see also Aquifer boundary conditions.

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The simplest finite difference formulation is forward in time, central in space, and can be written as:

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\[ + S_i \frac{\phi_{i}^{t+1} - \phi_i^{t}}{\Delta t} = -C_{i-1} (\phi_{i-1} - \phi_i) - C_i (\phi_{i+1} - \phi_i) + Q_i +\]

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where \(i\) is the cell index, \(t\) is time, \(\Delta t\) is the step size, \(C_{i-1}\) is the the intercell conductance between cell \(i-1\) and \(i\) and \(C_i\) is the intercell conductance between cell \(i\) and \(i+1\). The connection data between cells is stored as part of the Connectivity struct, see also Connectivity for more information.

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Conductance \(C\) is defined as:

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\[ +C = \frac{kH w}{l} +\]

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where \(\SIb{w}{m}\) is the width of the cell to cell connection, and \(\SIb{l}{m}\) is the length of the cell to cell connection. \(k\) and \(H\) may both vary in space; intercell conductance is therefore an average using the properties of two cells. For the calculation of the intercell conductance \(C\) the harmonic mean is used (see also Goode and Appel, 1992), here between cell index \(i\) and cell index \(i+1\), in the \(x\) direction:

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\[ + C_i = w \frac{k_iH_i\cdot k_{i+1}H_{i+1}}{k_iH_i \cdot l_{i+1} + k_{i+1}H_{i+1} \cdot l_i} +\]

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where \(\SIb{H}{m}\) is the aquifer top - aquifer bottom, and \(k\), \(l_i\) is the length in cell \(i\) (\(0.5 \Delta x_i\)), \(l_{i+1}\) is the length in cell \(i+1\) (\(0.5 \Delta x_{i+1}\)) and \(w\) as previously defined. For an unconfined aquifer the intercell conductance is scaled by using the “upstream saturated fraction” as the MODFLOW documentation calls it. In this approach, the saturated thickness of a cell-to-cell is approximated using the cell with the highest head. This results in a consistent overestimation of the saturated thickness, but it avoids complexities related with cell drying and rewetting, such as having to define a “wetting threshold” or a “wetting factor”. See also the documentation for MODFLOW-NWT (Niswonger et al., 1) or MODFLOW6 (Langevin et al., 2017) for more background information. For more background on drying and rewetting, see for example McDonald et al. (1991).

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For the finite difference formulation, there is only one unknown, \(\phi_i^{t+1}\). Reshuffling terms:

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\[ +\phi_i^{t+1} = \phi_i^t + (C_{i-1} (\phi_i - \phi_{i-1}) + C_i (\phi_{i+1} - \phi_i) + Q_i) \frac{Δt}{S_i} +\]

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This can be generalized to two dimensions, for both regular and irregular cell connectivity. Finally, a stable time step size can be computed given the forward-in-time, central in space scheme, based on the following criterion from Chu and Willis (1984):

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\[ + \frac{\Delta t k H}{\Delta x \Delta y S} \le \frac{1}{4} +\]

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where \(\SIb{\Delta t}{d}\) is the stable time step size, \(\SIb{\Delta x}{m}\) is the cell length in the \(x\) direction and \(\SIb{\Delta y}{m}\) is the cell length in the \(y\) direction, \(\SIb{k}{m^2 d^{-1}}\) is the horizontal hydraulic conductivity and \(\SIb{H}{m}\) is the saturated thickness of the aquifer. For each cell \(\frac{\Delta x \Delta y S}{k H}\) is calculated, the minimum of these values is determined, and multiplied by \(\frac{1}{4}\), to get the stable time step size.

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For more details about the finite difference formulation and the stable time step size criterion we refer to the paper of Chu and Willis (1984).

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Boundary conditions can be classified into three categories:

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Neumann and Robin conditions are implemented by adding to or subtracting from a net (lumped) cell flux. Dirichlet conditions are special cased, since they cannot (easily) be implemented via the flux, but the head is set directly instead.

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Connectivity

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The connectivity between cells is defined as follows.

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Wflow.Connectivity
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Constant head

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Dirichlet boundary conditions can be specified through the field constanthead (type ConstantHead) of the GroundwaterFlow struct.

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@get_units struct ConstantHead{T}
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For the model SBM + Groundwater flow this boundary condition is optional, and if used should be specified in the TOML file as follows (see also sbm_gwf_config.toml):

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Aquifer boundary conditions

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River

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The flux between river and aquifer is calculated using Darcy’s law following the approach in MODFLOW:

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\[ + \subtext{Q}{riv} = + \begin{align*} + \begin{cases} + C_i \min \left\{\subtext{h}{riv} - \subtext{B}{riv}, \subtext{h}{riv} - \phi\right\} &\text{ if }\quad \subtext{h}{riv} > \phi \\ + C_e (\subtext{h}{riv} - \phi) &\text{ if }\quad \subtext{h}{riv} \le \phi + \end{cases} + \end{align*} +\]

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where \(\SIb{\subtext{Q}{riv}}{L^3 T^{-1}}\) is the exchange flux from river to aquifer, \(\SIb{C_i}{L^2 T^{-1}}\) is the river bed infiltration conductance, \(\SIb{C_e}{L^2 T^{-1}}\) is the river bed exfiltration conductance, \(\SIb{\subtext{B}{riv}}{L}\) the bottom of the river bed, \(\SIb{\subtext{h}{riv}}{L}\) is the river stage and \(\SIb{\phi}{L}\) is the hydraulic head in the river cell.

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The Table in the Groundwater flow river boundary condition section of the Model parameters provides the parameters of the struct River. Parameters that can be set directly from the static input data (netCDF) are marked in this Table.

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The exchange flux (river to aquifer) \(\subtext{Q}{riv}\) is an output variable (field flux of the River struct), and is used to update the total flux in a river cell. For the model SBM + Groundwater flow, the water level \(\SIb{h}{m}\) of the river kinematic wave in combination with the river bottom is used to update the stage field of the River struct each time step.

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Drainage

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The flux from drains to the aquifer is calculated as follows:

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\[ +\subtext{Q}{drain} = \subtext{C}{drain} \min(0, \subtext{h}{drain} - \phi) +\]

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where \(\SIb{\subtext{Q}{drain}}{L^3 T^{-1}}\) is the exchange flux from drains to aquifer, \(\SIb{\subtext{C}{drain}}{L^2 T^{-1}}\) is the drain conductance, \(\SIb{\subtext{h}{drain}}{L}\) is the drain elevation and \(\SIb{\phi}{L}\) is the hydraulic head in the cell with drainage.

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The table in the Groundwater flow drainage boundary condition section of the Model parameters provides the parameters of the struct Drainage. Parameters that can be set directly from the static input data (netCDF) are marked in this Table.

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The exchange flux (drains to aquifer) \(\subtext{Q}{drain}\) is an output variable (field flux of struct Drainage), and is used to update the total flux in a cell with drains. For the model SBM + Groundwater flow this boundary condition is optional, and if used should be specified in the TOML file as follows (see also sbm_gwf_config.toml):

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[model]
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Recharge

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The recharge flux \(Q_r\) to the aquifer is calculated as follows:

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\[ +Q_r = R \, A +\]

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with \(\SIb{}{L T^{-1}}\) the recharge rate and \(\SIb{A}{L^2}\) the area of the aquifer cell.

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The table in the Groundwater flow recharge boundary condition section of the Model parameters section provides the parameters of the struct Recharge. Parameters that can be set directly from the static input data (netCDF) are marked in this Table.

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The recharge flux \(Q_r\) is an output variable (field flux of struct Recharge), and is used to update the total flux in a cell where recharge occurs. For the model SBM + Groundwater flow, the recharge rate from the vertical SBM concept recharge [mm] is used to update the rate field of the Recharge struct each time step. The rate field is multiplied by the area field of the aquifer.

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Head boundary

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This boundary is a fixed head with time (not affected by the model stresses over time) outside of the model domain, and is generally used to avoid an unnecessary extension of the model domain to the location of the fixed boundary (for example a large lake). The flux from the boundary \(\SIb{Q_{hb}}{L^3 T^{-1}}\) is calculated as follows:

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\[ +Q_{hb} = C_{hb} (\phi_{hb} - \phi) +\]

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with \(\SIb{C_{hb}}{L^2 T^{-1}}\) the conductance of the head boundary, \(\SIb{\phi_{hb}}{L}\) the head of the head boundary and \(\phi\) the head of the aquifer cell.

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The table in the Groundwater flow head boundary condition section of the Model parameters provides the parameters of the struct HeadBoundary.

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The head boundary flux \(Q_{hb}\) is an output variable (field flux of struct HeadBoundary), and is used to update the total flux in a cell where this type of boundary occurs. The parameter Head \(\phi_{hb}\) can be specified as a fixed or time dependent value.

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+
+Note +
+
+
+

This boundary is not (yet) part of the model SBM + Groundwater flow.

+
+
+
+
+

Well boundary

+

A volumetric well rate \(\SIb{}{L^3 T^{-1}}\) can be specified as a boundary condition.

+

The Table in the well boundary condition section of the Model parameters provides the parameters of the struct Well.

+

The volumetric well rate \(\subtext{Q}{well}\) can be can be specified as a fixed or time dependent value. If a cell is dry, the actual well flux flux is set to zero (see also the last note on this page).

+
+
+
+ +
+
+Note +
+
+
+

This boundary is not (yet) part of the model SBM + Groundwater flow.

+
+
+
+
+
+ +
+
+Note +
+
+
+

For an unconfined aquifer the boundary fluxes are checked, in case of a dry aquifer cell a negative flux is not allowed.

+
+
+
+
+
+

References

+
    +
  • Chu, W. S., & Willis, R. (1984). An explicit finite difference model for unconfined aquifers. Groundwater, 22(6), 728-734.
    • +
  • Goode, D. J., & Appel, C. A. (1992). Finite-Difference Interblock Transmissivity for Unconfined Aquifers and for Aquifers having Smoothly Varying Transmissivity Water-resources investigations report, 92, 4124.
    • +
  • Johnson, A. I. (1967), Specific yield: compilation of specific yields for various materials, Water Supply Paper 1662-D, Washington, D.C.: U.S. Government Printing Office, p. 74, doi:10.3133/wsp1662D.
    • +
  • Langevin, C.D., Hughes, J.D., Banta, E.R., Niswonger, R.G., Panday, Sorab, and Provost, A.M., 2017, Documentation for the MODFLOW 6 Groundwater Flow Model: U.S. Geological Survey Techniques and Methods, book 6, chap. A55, 197 p., https://doi.org/10.3133/tm6A55.
    • +
  • McDonald, M.G., Harbaugh, A.W., Orr, B.R., and Ackerman, D.J., 1991, A method of converting no-flow cells to variable-head cells for the U.S. Geological Survey modular finite-difference groundwater flow model: U.S. Geological Survey Open-File Report 91-536, 99 p.
    • +
  • Niswonger, R.G., Panday, Sorab, and Ibaraki, Motomu, 2011, MODFLOW-NWT, A Newton formulation for MODFLOW-2005: U.S. Geological Survey Techniques and Methods 6-A37, 44 p.
    • +
+ + +
+ + Back to top
+ +
+ + + + + + \ No newline at end of file diff --git a/previews/PR448/model_docs/lateral/kinwave.html b/previews/PR448/model_docs/lateral/kinwave.html new file mode 100644 index 000000000..4b8ecd514 --- /dev/null +++ b/previews/PR448/model_docs/lateral/kinwave.html @@ -0,0 +1,1104 @@ + + + + + + + + + +Kinematic wave – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
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+ +
+ + +
+ + + +
+ +
+
+

Kinematic wave

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+ + + +
+ + + + +
+ + + +
+ + +
+

Surface routing

+

The main flow routing scheme available in Wflow.jl is the kinematic wave approach for channel and overland flow, assuming that water flow is mostly controlled by topography. The kinematic wave equations are (Chow, 1988):

+

\[ +\begin{gathered} + \dfrac{\partial Q}{\partial x} + \dfrac{\partial A}{\partial t} = q,\\ + A = \alpha Q^{\beta}. +\end{gathered} +\]

+

These equations can then be combined as a function of streamflow only:

+

\[ + \dfrac{\partial Q}{\partial x} + \alpha \beta Q^{\beta - 1} \dfrac{\partial Q}{\partial t} = q. +\]

+

Here \(\SIb{Q}{m^3 s^{-1}}\) is the surface runoff in the kinematic wave, \(\SIb{x}{m}\) is the length of the runoff pathway, \(\SIb{A}{m}\) is the cross-section area of the runoff pathway, \(\SIb{t}{s}\) is the integration timestep and \(\alpha\) and \(\beta\) are unitless coefficients.

+

These equations are solved with a nonlinear scheme using Newton’s method and can also be iterated depending on the model space and time resolution. By default, the iterations are performed until a stable solution is reached (\(\epsilon < 10^{-12}\)). For larger models, the number of iterations can also be fixed for to a specific sub-timestep (in seconds) for both overland and channel flows to improve simulation time. To enable (fixed or not) iterations of the kinematic wave the following lines can be inserted in the TOML file of the model:

+
[model]
+# Enable iterations of the kinematic wave
+kin_wave_iteration = true
+# Fixed sub-timestep for iterations of channel flow (river cells)
+kw_river_tstep = 900
+# Fixed sub-timestep for iterations of overland flow (land cells)
+kw_land_tstep = 3600
+

The \(\alpha\) parameter of the kinematic wave is fixed. To estimate the wetted perimeter for the calculation of the \(\alpha\) parameter a bankfull river depth map (default value is \(\SI{1.0}{m}\)) for the river can be provided as follows:

+
[input.lateral.river]
+bankfull_depth = "wflow_riverdepth"
+

The wetted perimeter of the river is based on half bankfull river depth. For the land part the wetted perimeter is based on the flow width.

+

Simplified reservoir and lake models can be included as part of the river kinematic wave network.

+
+
+

Inflow

+

External water (supply/abstraction) inflow \(\SIb{}{m^3 s^{-1}}\) can be added to the kinematic wave for surface water routing, as a cyclic parameter or as part of forcing (see also Input section).

+
+
+

Abstractions

+

Abstractions from the river through the variable abstraction \(\SIb{}{m^3 s^{-1}}\) are possible when water demand and allocation is computed. The variable abstraction is set from the water demand and allocation module each time step. The abstraction is divided by the length of the runoff pathway and subtracted from the lateral inflow of the kinematic wave routing scheme for river flow.

+
+
+

Subsurface flow routing

+

In the SBM model the kinematic wave approach is used to route subsurface flow laterally. Different vertical hydraulic conductivity depth profiles are possible as part of the vertical SBM concept, and these profiles (after unit conversion) are also used to compute lateral subsurface flow. The following profiles (see SBM for a detailed description) are available: - exponential (default) - exponential_constant - layered - layered_exponential

+

For the profiles exponential and exponential_constant, the saturated store S is drained laterally by saturated downslope subsurface flow for a slope with width \SIb{w}{m} according to:

+

\[ + Q = K_0\tan(\beta)w\begin{cases} + \frac{1}{f}\left(e^{-fz_i}-e^{-f\subtext{z}{exp}}\right) + + e^{-f\subtext{z}{exp}}(z_t-\subtext{z}{exp}) & \text{if $z_i < \subtext{z}{exp}$}\\ + \\ + e^{-f\subtext{z}{exp}}(z_t - z_i) & \text{if $z_i \ge \subtext{z}{exp}$}, + \end{cases} +\]

+

where \(\beta\) is element slope angle, \(\SIb{Q}{m^3 d^{-1}}\) is subsurface flow, \(\SIb{K_0}{m +d^{-1}}\) is the saturated hydraulic conductivity at the soil surface, \(\SIb{z_i}{m}\) is the water table depth, \(\SIb{z_{t}}{m}\) is the total soil depth, \(\SIb{f}{m^{-1}}\) is a scaling parameter that controls the decrease of \(K_0\) with depth and \(\SIb{\subtext{z}{exp}}{m}\) is the depth from soil surface for which the exponential decline of \(K_0\) is valid. For the exponential profile, \(\subtext{z}{exp}\) is equal to \(z_t\).

+

Combining with the following continuity equation: \[ + (\theta_s-\theta_r)w\frac{\partial h}{\partial t} = -\frac{\partial Q}{\partial x} + wr +\]

+

where \(\SIb{h}{m}\) is the water table height, \(\SIb{x}{m}\) is the distance downslope, and \(\SIb{r}{m d^{-1}}\) is the net input rate to the saturated store. Substituting for \(h +(\frac{\partial Q}{\partial h})\), gives:

+

\[ + \frac{\partial Q}{\partial t} = -c\frac{\partial Q}{\partial x} + cwr +\]

+

where celerity \(c\) is calculated as follows: \[ + c = \frac{K_0 \tan(\beta)}{\theta_s-\theta_r}\begin{cases} + e^{-fz_i} + + e^{-f\subtext{z}{exp}} & \text{if $z_i < \subtext{z}{exp}$}\\ + \\ + e^{-f\subtext{z}{exp}} & \text{if $z_i \ge \subtext{z}{exp}$}. + \end{cases} +\]

+

For the layered and layered_exponential profiles the equivalent horizontal hydraulic conductivity \(\SIb{K_h}{m d^{-1}}\) is calculated for water table height \(\SIb{h = z_t-z_i}{m}\) and lateral subsurface flow is calculated as follows:

+

\[ + Q = K_h h \tan(\beta) w, +\]

+

and celerity \(c\) is given by:

+

\[ + c = \frac{K_h \tan(\beta)}{\theta_s-\theta_r}. +\]

+

The kinematic wave equation for lateral subsurface flow is solved iteratively using Newton’s method.

+
+
+
+ +
+
+Note +
+
+
+

For the lateral subsurface flow kinematic wave the model timestep is not adjusted. For certain model timestep and model grid size combinations this may result in loss of accuracy.

+
+
+
+
+

Multi-Threading

+

The kinematic wave calculations for surface - and subsurface flow routing can be executed in parallel using multiple threads. In the model section of the TOML file, a minimum stream order can be provided to define subbasins for the river (default is 6) and land domain (default is 5). Subbasins are created at all confluences where each branch has a minimal stream order. Based on the subbasins a directed acyclic graph is created that controls the order of execution and which subbasins can run in parallel.

+
[model]
+min_streamorder_river = 5 # minimum stream order to delineate subbasins for river domain, default is 6
+min_streamorder_land = 4  # minimum stream order to delineate subbasins for land domain, default is 5
+
+
+

Subcatchment flow

+

Normally the the kinematic wave is continuous throughout the model. By using the pits entry in the model and input sections of the TOML file all flow is at the subcatchment only (upstream of the pit locations, defined by the netCDF variable wflow_pits in the example below) and no flow is transferred from one subcatchment to another. This can be convenient when connecting the result of the model to a water allocation model such as Ribasim.

+
[input]
+# these are not directly part of the model
+pits = "wflow_pits"
+
+[model]
+pits = true
+
+
+

Limitations

+

The kinematic wave approach for channel, overland and lateral subsurface flow, assumes that the topography controls water flow mostly. This assumption holds for steep terrain, but in less steep terrain the hydraulic gradient is likely not equal to the surface slope (subsurface flow), or pressure differences and inertial momentum cannot be neglected (channel and overland flow). In addition, while the kinematic wave equations are solved with a nonlinear scheme using Newton’s method (Chow, 1988), other model equations are solved through a simple explicit scheme. In summary the following limitations apply:

+
    +
  • Channel flow, and to a lesser degree overland flow, may be unrealistic in terrain that is not steep, and where pressure forces and inertial momentum are important.
    • +
  • The lateral movement of subsurface flow may be very wrong in terrain that is not steep.
    • +
+
+
+

External inflows

+

External inflows, for example water supply or abstractions, can be added to the kinematic wave via the inflow variable. For this, the user can supply a 2D map of the inflow, as a cyclic parameter or as part of forcing (see also Input section). These inflows are added or abstracted from the upstream inflow qin before running the kinematic wave to solve the impact on resulting q. In case of a negative inflow (abstractions), a minimum of zero is applied to the upstream flow qin.

+
+
+

References

+
    +
  • Chow, V., Maidment, D. and Mays, L., 1988, Applied Hydrology. McGraw-Hill Book Company, New York.
    • +
+ + +
+ + Back to top
+ +
+ + + + + + \ No newline at end of file diff --git a/previews/PR448/model_docs/lateral/local-inertial.html b/previews/PR448/model_docs/lateral/local-inertial.html new file mode 100644 index 000000000..ee5c8518a --- /dev/null +++ b/previews/PR448/model_docs/lateral/local-inertial.html @@ -0,0 +1,1059 @@ + + + + + + + + + +Local inertial – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + +
+ +
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+ + + +
+ +
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+

Local inertial

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+ + + +
+ + + + +
+ + + +
+ + +
+

River and floodplain routing

+

The local inertial approximation of shallow water flow neglects only the convective acceleration term in the Saint-Venant momentum conservation equation. The numerical solution of the local inertial approximation on a staggered grid is as follows (Bates et al., 2010):

+

\[ +Q_{t+\Delta t} = \frac{Q_t - g A_t \Delta t S_t}{(1+g\Delta t n^2 |Q_t| / (R_t^{4/3} A_t))} +\]

+

where \(\SIb{Q_{t+\Delta t}}{m^3 s^{-1}}\) is the river flow at time step \(t+\Delta t\), \(\SIb{g}{m s^{-2}}\) is acceleration due to gravity, \(\SIb{A_t}{m^2}\) is the cross sectional flow area at the previous time step, \(\SIb{R_t}{m}\) is the hydraulic radius at the previous time step, \(\SIb{Q_t}{m^3 s^{-1}}\) is the river flow at the previous time step, \(S_t\) is the water surface slope at the previous time step and \(\SIb{n}{m^{-\frac{1}{3}} s}\) is the Manning’s roughness coefficient.

+

The momentum equation is applied to each link between two river grid cells, while the continuity equation over \(\Delta t\) is applied to each river cell:

+

\[ +h^{t+\Delta t} = h^t + \Delta t \frac{\subtext{Q^{t+\Delta t}}{src} - \subtext{Q^{t+\Delta t}}{dst}}{A} +\]

+

where \(\SIb{h^{t+\Delta t}}{m}\) is the water depthat time step \(t+\Delta t\), \(\SIb{h^t}{m}\) is the water depth at the previous time step, \(\SIb{A}{m^2}\) is the river area and \(\SIb{\subtext{Q}{src}}{m^3 s^{-1}}\) and \(\SIb{\subtext{Q}{dst}}{m^3 s^{-1}}\) represent river flow at the upstream and downstream link of the river cell, respectively.

+

The model time step \(\Delta t\) for the local inertial model is estimated based on the Courant-Friedrichs-Lewy condition (Bates et al., 2010):

+

\[ +\Delta t = \alpha \min_i\left(\frac{\Delta x_i}{\sqrt{gh_i}}\right) +\]

+

where \(\sqrt{gh_i}\) is the wave celerity for river cell \(i\) , \(\SIb{\Delta x_i}{m}\) is the river length for river cell \(i\) and \(\alpha\) is a coefficient (typically between \(0.2\) and \(0.7\)) to enhance the stability of the simulation.

+

In the TOML file the following properties related to the local inertial model can be provided for the sbm and sbm_gwf model types:

+
[model]
+river_routing = "local-inertial"  # default is "kinematic-wave"
+inertial_flow_alpha = 0.5         # alpha coefficient for model stability (default = 0.7)
+froude_limit = true               # default is true, limit flow to subcritical-critical according to Froude number
+h_thresh = 0.1                    # water depth [m] threshold for calculating flow between cells (default = 1e-03)
+floodplain_1d = true              # include 1D floodplain schematization (default = false)
+

Two optional constant boundary conditions riverlength_bc and riverdepth_bc can be provided at a river outlet node (or multiple river outlet nodes) through the model parameter netCDF file, as follows:

+
[input.lateral.river]
+riverlength_bc = "riverlength_bc"   # optional river length [m], default = 1e04
+riverdepth_bc = "riverdepth_bc"     # optional river depth [m], default = 0.0
+

These boundary conditions are copied to a ghost node (downstream of the river outlet node) in the code.

+

The optional 1D floodplain schematization is based on provided flood volumes as a function of flood depth (per flood depth interval) for each river cell. Wflow calculates from these flood volumes a rectangular floodplain profile for each flood depth interval. Routing is done separately for the river channel and floodplain.

+

The momentum equation is most stable for low slope environments, and to keep the simulation stable for (partly) steep environments the froude_limit option is set to true by default. This setting limits flow conditions to subcritical-critical conditions based on the Froude number (\(\le 1\)), similar to Coulthard et al. (2013) in the CAESAR-LISFLOOD model and Adams et al. (2017) in the Landlab v1.0 OverlandFlow component. The froude number \(\mathrm{Fr}\) on a link is calculated as follows:

+

\[ + \mathrm{Fr} = \frac{u}{\sqrt{gh_f}} +\]

+

where \(\sqrt{gh_f}\) is the wave celerity on a link and \(u\) is the water velocity on a link. If the water velocity from the local inertial model is causing the Froude number to be greater than \(1.0\) , the water velocity (and flow) is reduced in order to maintain a Froude number of \(1.0\).

+

The downstream boundary condition basically simulates a zero water depth boundary condition at a set distance, as follows. For the downstream boundary condition (ghost point) the river width, river bed elevation and Manning’s roughness coefficient are copied from the upstream river cell. The river length \(\SIb{}{m}\) of the boundary cell can be set through the TOML file with riverlength_bc, and has a default value of \(\SI{10}{km}\). The water depth at the boundary cell is fixed at \(\SI{0.0}{m}\).

+

Simplified reservoir and lake models can be included as part of the local inertial model for river flow (1D) and river and overland flow combined (see next section). Reservoir and lake models are included as a boundary point with zero water depth for both river and overland flow. For river flow the reservoir or lake model replaces the local inertial model at the reservoir or lake location, and \(Q\) is set by the outflow from the reservoir or lake. Overland flow at a reservoir or lake location is not allowed to or from the downstream river grid cell.

+
+
+

Overland flow (2D)

+

For the simulation of 2D overland flow on a staggered grid the numerical scheme proposed by de Almeida et al. (2012) is adopted. The explicit solution for the estimation of water discharge between two cells in the x-direction is of the following form (following the notation of Almeida et al. (2012)):

+

\[ +\begin{split} +Q_{i-1/2}^{n+1} = \frac{\left[ \theta Q_{i-1/2}^{n} +\frac{(1-\theta)}{2}(Q_{(i-3/2)}^{n} + +Q_{(i+1/2)}^{n})\right]- g h_f \frac{\Delta t}{\Delta x} (\eta^n_i - \eta^n_{i-1}) \Delta y}{1+g\Delta t n^2 |Q_{i-1/2}^{n}|/(h_f^{7/3} \Delta y)} +\end{split} +\]

+

where subscripts \(i\) and \(n\) refer to space and time indices, respectively. Subscript \(i-\frac{1}{2}\) is to the link between node \(i\) and \(i-1\), subscript \(i+\frac{1}{2}\) is the link between node \(i\) and node \(i+1\), and subscript \(i-\frac{3}{2}\) is the link between node \(i-1\) and node \(i-2\). \(\SIb{Q}{m^3 s^{-1}}\) is the water discharge, \(\SIb{\eta}{m}\) is the water surface elevation, \(\SIb{h_f}{m}\) is the water depth between cells, \(\SIb{n}{m^{-\frac{1}{3}} s}\) is the Manning’s roughness coefficient, \(\SIb{g}{m s^{-2}}\) is acceleration due to gravity, \(\SIb{\Delta t}{s}\) is the adaptive model time step, \(\SIb{\Delta +x}{m}\) is the distance between two cells and \(\SIb{\Delta y}{m}\) is the flow width. Below the staggered grid and variables of the numerical solution in the x-direction, based on Almeida et al. (2012):

+

+

The overland flow local inertial approach is used in combination with the local inertial river routing. This is a similar to the modelling approach of Neal et al. (2012), where the hydraulic model LISFLOOD-FP was extended with a subgrid channel model. For the subgrid channel, Neal et al. (2012) make use of a D4 (four direction) scheme, while here a D8 (eight direction) scheme is used, in combination with the D4 scheme for 2D overland flow.

+

In the TOML file the following properties related to the local inertial model with 1D river routing and 2D overland flow can be provided for the sbm model type:

+
[model]
+land_routing = "local-inertial"  # default is kinematic-wave
+river_routing = "local-inertial" # default is kinematic-wave
+inertial_flow_alpha = 0.5        # alpha coefficient for model stability (default = 0.7)
+froude_limit = true              # default is true, limit flow to subcritical-critical according to Froude number
+h_thresh = 0.1                   # water depth [m] threshold for calculating flow between cells (default = 1e-03)
+

The properties inertial_flow_alpha, froude_limit and h_thresh apply to 1D river routing as well as 2D overland flow. The properties inertial_flow_alpha and froude_limit, and the adaptive model time step \(\Delta t\) are explained in more detail in the River and floodplain routing section of the local inertial model.

+
+
+

Inflow

+

External water (supply/abstraction) inflow \(\SIb{}{m^3 s^{-1}}\) can be added to the local inertial model for river flow (1D) and river and overland flow combined (1D-2D), as a cyclic parameter or as part of forcing (see also Input section).

+
+
+

Abstractions

+

Abstractions from the river through the variable abstraction \(\SIb{}{m^3 s^{-1}}\) are possible when water demand and allocation is computed. The variable abstraction is set from the water demand and allocation module each time step. Abstractions are subtracted as part of the continuity equation of the local inertial model.

+
+
+

Multi-Threading

+

The local inertial model for river flow (1D) and river and overland flow combined (1D-2D) can be executed in parallel using multiple threads.

+
+
+

References

+
    +
  • Adams, J. M., Gasparini, N. M., Hobley, D. E. J., Tucker, G. E., Hutton, E. W. H., Nudurupati, S. S., and Istanbulluoglu, E., 2017, The Landlab v1.0 OverlandFlow component: a Python tool for computing shallow-water flow across watersheds, Geosci. Model Dev., 10, 1645–1663, https://doi.org/10.5194/gmd-10-1645-2017.
    • +
  • de Almeida, G. A. M., P. Bates, J. E. Freer, and M. Souvignet, 2012, Improving the stability of a simple formulation of the shallow water equations for 2-D flood modeling, Water Resour. Res., 48, W05528, https://doi.org/10.1029/2011WR011570.
    • +
  • Bates, P. D., M. S. Horritt, and T. J. Fewtrell, 2010, A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling, J. Hydrol., 387, 33–45, https://doi.org/10.1016/j.jhydrol.2010.03.027.
    • +
  • Coulthard, T. J., Neal, J. C., Bates, P. D., Ramirez, J., de Almeida, G. A. M., and Hancock, G. R., 2013, Integrating the LISFLOOD-FP 2- D hydrodynamic model with the CAESAR model: implications for modelling landscape evolution, Earth Surf. Proc. Land., 38, 1897–1906, https://doi.org/10.1002/esp.3478.
    • +
  • Neal, J., G. Schumann, and P. Bates (2012), A subgrid channel model for simulating river hydraulics and floodplaininundation over large and data sparse areas, Water Resour.Res., 48, W11506, https://doi.org/10.1029/2012WR012514.
    • +
+ + +
+ + Back to top
+ +
+ + + + + + \ No newline at end of file diff --git a/previews/PR448/model_docs/lateral/sediment_flux.html b/previews/PR448/model_docs/lateral/sediment_flux.html new file mode 100644 index 000000000..aab9e789c --- /dev/null +++ b/previews/PR448/model_docs/lateral/sediment_flux.html @@ -0,0 +1,1347 @@ + + + + + + + + + +Sediment flux – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
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+

Sediment flux

+
+ + + +
+ + + + +
+ + + +
+ + +

Both the inland and river sediment model take into account sediment flux or transport of sediment in water, either in overland flow or in the stream flow. These two transport are distinguished in two different structures.

+
+

Inland Sediment Model

+
+

Sediment Flux in overland flow

+

Once the amount of soil detached by both rainfall and overland flow has been estimated, it has then to be routed and delivered to the river network. Inland routing in sediment models is usually done by comparing the amount of detached sediment with the transport capacity of the flow, which is the maximum amount of sediment that the flow can carry downslope. There are several existing formulas available in the literature. For a wide range of slopes and for overland flow, the Govers equation (1990) seems the most appropriate choice (Hessel et al, 2007). However, as the wflow_sediment model was developed to be linked to water quality issues, the Yalin transport equation was chosen as it can handle particle differentiation (Govers equation can still be used if wflow_sediment is used to only model inland processes with no particle differentiation). For land cells, wflow_sediment assumes that erosion can mobilize 5 classes of sediment:

+
    +
  • Clay (mean diameter of \(\SI{2}{\mu m}\))
    • +
  • Silt (mean diameter of \(\SI{10}{\mu m}\))
    • +
  • Sand (mean diameter of \(\SI{200}{\mu m}\))
    • +
  • Small aggregates (mean diameter of \(\SI{30}{\mu m}\))
    • +
  • Large aggregates (mean diameter of \(\SI{50}{\mu m}\)).
    • +
+$$ +\[\begin{gathered} + \mathrm{PSA} = \mathrm{SAN} (1-\mathrm{CLA})^{2.4} \\ + \mathrm{PSI} = 0.13\mathrm{SIL}\\ + \mathrm{PCL} = 0.20\mathrm{CLA} \\ + + \mathrm{SAG} = + \begin{align*} + \begin{cases} + 2.0\mathrm{CLA} &\text{ if }\quad \mathrm{CLA} < 0.25 \\ + 0.28(\mathrm{CLA}-0.25)+0.5 &\text{ if }\quad 0.25 \leq \mathrm{CLA} \leq 0.5 \\ + 0.57 &\text{ if }\quad \mathrm{CLA} > 0.5 + \end{cases} + \end{align*} \\ + + \mathrm{LAG} = 1 - \mathrm{PSA} - \mathrm{PSI} - \mathrm{PCL} - \mathrm{SAG} +\end{gathered}\] +

$$

+

where \(\mathrm{CLA}\), \(\mathrm{SIL}\) and \(\mathrm{SAN}\) are the primary clay, silt, sand fractions of the topsoil and \(\mathrm{PCL}\), \(\mathrm{PSI}\), \(\mathrm{PSA}\), \(\mathrm{SAG}\) and \(\mathrm{LAG}\) are the clay, silt, sand, small and large aggregates fractions of the detached sediment respectively. The transport capacity of the flow using Yalin’s equation with particle differentiation, developed by Foster (1982), is: \[ + \mathrm{TC}_i = (P_e)_i (S_g)_i \, \rho_w \, g \, d_i V_* +\] where \(\mathrm{TC}_i\) is the transport capacity of the flow for the particle class \(i\), \((P_e)_i\) is the effective number of particles of class \(i\), \(\SIb{(S_g)_i}{kg m^{-3}}\) is the specific gravity for the particle class \(i\), \(\SIb{\rho_w}{kg m^{-3}}\) is the mass density of the fluid, \(\SIb{g}{m s^{-2}}\) is the acceleration due to gravity, \(\SIb{d_i}{m}\) is the diameter of the particle of class \(i\) and \(V_* = \SIb{(g R S)^{0.5}}{m s^{-1}}\) is the shear velocity of the flow with \(S\) the slope gradient and \(\SIb{R}{m}\) the hydraulic radius of the flow. The detached sediment are then routed down slope until the river network using the accucapacityflux, accupacitystate functions depending on the transport capacity from Yalin.

+

The choice of transport capacity method for the overland flow is set up in the model section of the TOML:

+
[model]
+landtransportmethod = "yalinpart" # Overland flow transport capacity method: ["yalinpart", "govers", "yalin"]
+

Note that the “govers” and “yalin” equations can only assess total transport capacity of the flow and can therefore not be used in combination with the river part of the sediment model.

+
+
+
+

River Sediment Model

+

Sediment dynamics in rivers can be described by the same three processes on land: erosion, deposition and transport. The difference is that channel flow is much higher, deeper and permanent compared to overland flow. In channels, erosion is the direct removal of sediments from the river bed or bank (lateral erosion). Sediments are transported in the river either by rolling, sliding and silting (bed load transport) or via turbulent flow in the higher water column (suspended load transport). The type of transport is determined by the river bed shear stress. As sediment particles have a higher density than water, they can also be deposited on the river bed according to their settling velocity compared to the flow velocity. In addition to regular deposition in the river, lakes, reservoirs and floodplains represents additional major sediment settling pools.

+

Complete models of sediment dynamics based on hydrology and not on hydraulics or hydrodynamics are much rarer than for soil loss and inland dynamics. The simpler models such as the SWAT default sediment river model uses again the transport capacity of the flow to determine if there is erosion or deposition (Neitsch et al., 2011). A more physics-based approach (Partheniades, 1965) to determine river erosion is used by Liu et al. (2018) and in the new SWAT’s approach developed by Narasimhan et al. (2017). For wflow_sediment, the new physics-based model of SWAT was chosen for transport and erosion as it enables the use of parameter estimation for erosion of bed and bank of the channel and separates the suspended from the bed loads.

+
+
+

+
Overview of the different processes for a river cell in wflow_sediment.
+
+
+

Running the river model is an option of the wflow_sediment model and is enabled using the TOML file. By default it is false:

+
[model]
+runrivermodel = true
+
+

Sediment inputs in a river cell

+

The first part of the river model assesses how much detached sediment are in the river cell at the beginning of the timestep \(t\). Sources of detached sediment are sediments coming from land erosion, estimated with the soil loss part of wflow_sediment model, the sediment coming from upstream river cells and the detached sediment that were left in the cell at the end of the previous timestep (t-1): \[ + (\subtext{\mathrm{sed}}{in})_t = (\subtext{\mathrm{sed}}{land})_t + \mathrm{upstream}\left[(\subtext{\text{sed}}{out})_{t-1}\right] + (\subtext{\text{sed}}{riv})_{t-1} +\]

+
+
+

River transport and erosion

+

Once the amount of sediment inputs at the beginning of the timestep is known, the model then estimates transport, and river erosion if there is a deficit of sediments. Transport in the river system is estimated via a transport capacity formula. There are several transport capacity formulas available in wflow_sediment, some requiring calibration and some not. Choosing a transport capacity equation depends on the river characteristics (some equation are more suited for narrow or wider rivers), and on the reliability of the required river parameters (such as slope, width or mean particle diameter of the river channel). Several river transport capacity are available and the choice is set up in the model section of the TOML:

+
[model]
+rivtransportmethod = "bagnold" # River flow transport capacity method: ["bagnold", "engelund", "yang", "kodatie", "molinas"]
+

Simplified Bagnold

+

Originally more valid for intermediate to large rivers, this simplified version of the Bagnold equation relates sediment transport to flow velocity with two simple calibration parameters (Neitsch et al, 2011): \[ +C_{\max} = \subtext{c}{sp} \left( \dfrac{\mathrm{prf} Q}{h W} \right)^{\subtext{\mathrm{sp}}{exp}} +\]

+

where \(\SIb{C_{\max}}{kg L^{-1}}\) (or \(\SIb{}{ton m^{-1}}\)) is the sediment concentration, \(\SIb{Q}{m^3 s^{-1}}\) is the surface runoff in the river cell, \(\SIb{h}{m}\) is the river water level, \(\SIb{W}{m}\) is the river width and \(\subtext{c}{sp}\), \(\mathrm{prf}\) and \(\subtext{\mathrm{sp}}{exp}\) are calibration parameters. The \(\mathrm{prf}\) coefficient is usually used to deduce the peak velocity of the flow, but for simplification in wflow_sediment, the equation was simplified to only get two parameters to calibrate: \(\subtext{\mathrm{sp}}{exp}\) and \(\subtext{c}{Bagnold} = \subtext{c}{sp} \, +\mathrm{prf}^{\subtext{\mathrm{sp}}{exp}}\). The coefficient \(\subtext{\mathrm{sp}}{exp}\) usually varies between \(1\) and \(2\) while \(\mathrm{prf}\) and \(\subtext{c}{sp}\) have a wider range of variation. The table below summarizes ranges and values of the three Bagnold coefficients used by other studies:

+ + +++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Range of the simplified Bagnold coefficients (and calibrated value)
StudyRiver\(\mathrm{prf}\) range\(\subtext{c}{sp}\) range\(\subtext{\mathrm{sp}}{exp}\) range
Vigiak 2015Danube0.5-2 (/)0.0001-0.01 (0.003-0.006)1-2 (1.4)
Vigiak 2017Danube/0.0001-0.01 (0.0015)1-2 (1.4)
Abbaspour 2007Thur (CH)0.2-0.25 (/)0.001-0.002 (/)0.35-1.47 (/)
Oeurng 2011Save (FR)0-2 (0.58)0.0001-0.01 (0.01)1-2 (2)
+

Engelund and Hansen This transport capacity is not present in SWAT but used in many models such as Delft3D-WAQ, Engelund and Hansen calculates the total sediment load as (Engelund and Hansen, 1967): \[ + C_w = 0.05 \left( \dfrac{\rho_{s}}{\rho_{s} - \rho} \right) \left( \dfrac{u S}{\sqrt{\left( \dfrac{\rho_{s}}{\rho_{s} - \rho} \right) g D_{50}}} \right) \theta^{1/2} +\] where \(C_w\) is the sediment concentration by weight, \(\SIb{\rho}{g m^{-3}}\) and \(\SIb{\rho_{s}}{g m^{-3}}\) are the fluid and sediment density (here respectively equal to \(\SI{1000}{g m^{-3}}\) and \(\SI{2650}{g +m^{-3}}\)), \(\SIb{u}{m s^{-1}}\) is the water mean velocity, \(S\) is the river slope, \(g\) is the acceleration due to gravity, \(\SIb{D_{50}}{m}\) is the river mean diameter and \(\theta\) is the Shields parameter.

+

Kodatie Kodatie (1999) developed the power relationships from Posada (1995) using field data and linear optimization so that they would be applicable for a wider range of riverbed sediment size. The resulting equation, for a rectangular channel, is (Neitsch et al, 2011): \[ + C_{\max} = \left( \dfrac{a u^{b} h^{c} S^{d}}{\subtext{V}{in}} \right) W +\] where \(\SIb{\subtext{V}{in}}{m^3}\) in the volume of water entering the river cell during the timestep and \(a\), \(b\), \(c\) and \(d\) are coefficients depending on the riverbed sediment size. Values of these coefficients are summarized in the table below.

+ + +++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Range of the simplified Bagnold coefficients (and calibrated value)
River sediment diameterabcd
\(D_{50} \leq \SI{0.05}{mm}\)281.42.6220.1820
\(\SI{0.05}{mm} < D_{50} \leq \SI{0.25}{mm}\)2 829.63.6460.4060.412
\(\SI{0.25}{mm} < D_{50} \leq \SI{2.0}{mm}\)2 123.43.3000.4680.613
\(D_{50} > \SI{2.0}{mm}\)431 884.81.0001.0002.000
+

Yang Yang (1996) developed a set of two equations giving transport of sediments for sand-bed or gravel-bed rivers. The sand equation (\(D_{50} < \SI{2.0}{mm}\)) is: \[ + \log\left(C_{ppm}\right) = 5.435 - 0.286\log\left(\frac{\omega_{s,50}D_{50}}{\nu}\right)-0.457\log\left(\frac{u_*}{\omega_{s,50}}\right) \\ + +\left(1.799-0.409\log\left(\frac{\omega_{s,50}D_{50}}{\nu}\right)-0.314\log\left(\frac{u_*}{\omega_{s,50}}\right)\right)\log\left(\frac{uS}{\omega_{s,50}}-\frac{u_{cr}S}{\omega_{s,50}}\right) +\] And the gravel equation (\(\SI{2.0}{mm} \leq D_{50} < \SI{10.0}{mm}\)) is: \[ + \log\left(C_{ppm}\right) = 6.681 - 0.633\log\left(\frac{\omega_{s,50}D_{50}}{\nu}\right)-4.816\log\left(\frac{u_*}{\omega_{s,50}}\right) \\ + +\left(2.784-0.305\log\left(\frac{\omega_{s,50}D_{50}}{\nu}\right)-0.282\log\left(\frac{u_*}{\omega_{s,50}}\right)\right)\log\left(\frac{uS}{\omega_{s,50}}-\frac{u_{cr}S}{\omega_{s,50}}\right) +\] where \(C_{ppm}\) is sediment concentration in parts per million by weight, \(\SIb{\omega_{s,50}}{m s^{-1}}\) is the settling velocity of a particle with the median riverbed diameter estimated with Stokes, \(\SIb{\nu}{m^2 s^{-1}}\) is the kinematic viscosity of the fluid, \(\SIb{u_*}{m s^{-1}}\) is the shear velocity where \(u_* = \sqrt{gR_{H}S}\) with \(R_{H}\) the hydraulic radius of the river and \(\SIb{u_{cr}}{m s^{-1}}\) is the critical velocity (equation can be found in Hessel, 2007).

+

Molinas and Wu The Molinas and Wu (2001) transport equation was developed for large sand-bed rivers based on the universal stream power \(\psi\). The corresponding equation is (Neitsch et al, 2011): \[ + C_{w} = \dfrac{1430 (0.86+\sqrt{\psi}) \psi^{1.5}}{0.016+\psi} 10^{-6} +\] where \(\psi\) is the universal stream power given by: \[ + \psi = \dfrac{\psi^{3}}{\left(\dfrac{\rho_{s}}{\rho}-1\right) g h \omega_{s,50} \left[ \log_{10}\left(\dfrac{h}{D_{50}}\right)\right]^{2}} +\]

+

Once the maximum concentration \(C_{\max}\) is established with one of the above transport formula, the model then determines if there is erosion of the river bed and bank. In order to do that, the difference \(sed_{ex}\) between the maximum amount of sediment estimated with transport (\(\mathrm{sed}_{\max} = C_{\max} \subtext{V}{in}\)) and the sediment inputs to the river cell (\(\subtext{\mathrm{sed}}{in}\) calculated above) is calculated. If too much sediment is coming in and \(\subtext{\mathrm{sed}}{ex}\) is negative, then there is no river bed and bank erosion. And if the river has not reach its maximum transport capacity, then erosion of the river happens.

+

First, the sediments stored in the cell from deposition in previous timesteps \(\subtext{\mathrm{sed}}{stor}\) are eroded from clay to gravel. If this amount is not enough to cover \(\subtext{\mathrm{sed}}{ex}\), then erosion of the local river bed and bank material starts.

+

Instead of just setting river erosion amount to just cover the remaining difference \(\subtext{\mathrm{sed}}{exeff}\) between \(\subtext{\mathrm{sed}}{ex}\) and \(\subtext{\mathrm{sed}}{stor}\), actual erosion potential is adjusted using river characteristics and is separated between the bed and bank of the river using the physics-based approach of Knight (1984).

+

The bed and bank of the river are supposed to only be able to erode a maximum amount of their material E_{R,\mathrm{bed}} for the bed and E_{R,\mathrm{bank}} for the river bank. For a rectangular channel, assuming it is meandering and thus only one bank is prone to erosion, they are calculated from the equations (Neitsch et al, 2011): \[ +\begin{gathered} + E_{R,\mathrm{bed}} = k_{d,\mathrm{bed}} \left( \tau_{e,\mathrm{bed}} - \tau_{cr,\mathrm{bed}} \right) 10^{-6} L W \rho_{b, \mathrm{bed}} \Delta t \\ + E_{R,\mathrm{bank}} = k_{d,\mathrm{bank}} \left( \tau_{e,\mathrm{bank}} - \tau_{cr,\mathrm{bank}} \right) 10^{-6} L h \rho_{b, \mathrm{bank}} \Delta t +\end{gathered} +\] where \(\SIb{E_R}{ton}\) is the potential bed/bank erosion rates, \(\SIb{k_d}{cm^3 N^{-1}, +s^{-1}}\) is the erodibility of the bed/bank material, \(\SIb{\tau_e}{N m^{-2}}\) is the effective shear stress from the flow on the bed/bank, \(\SIb{\tau_{cr}}{N m^{-2}}\) is the critical shear stress for erosion to happen, \(\SIb{L}{m}\), \(\SIb{W}{m}\) and \(\SIb{h}{m}\) are the channel length, width and water height, \(\SIb{\rho_{b}}{g cm^{-3}}\) is the bulk density of the bed/bank of the river and \(\SIb{\Delta t}{s}\) is the model timestep.

+

In wflow_sediment, the erodibility of the bed/bank are approximated using the formula from Hanson and Simon (2001): \[ + k_d=0.2 \tau_{cr}^{-0.5} +\] Normally erodibilities are evaluated using jet test in the field and there are several reviews and some adjustments possible to this equation (Simon et al, 2011). However, to avoid too heavy calibration and for the scale considered, this equation is supposed to be efficient enough. The critical shear stress \(\tau_{cr}\) is evaluated differently for the bed and bank. For the bed, the most common formula from Shields initiation of movement is used. For the bank, a more recent approach from Julian and Torres (2006) is used : \[ + \tau_{cr,\mathrm{bank}} = (0.1+0.1779 SC+0.0028 SC^{2}-2.34 10^{-5} SC^{3}) C_{ch} +\] where \(SC\) is the percent clay and silt content of the river bank and \(C_{ch}\) is a coefficient taking into account the positive impact of vegetation on erosion reduction. This coefficient is then dependent on the land use and classical values are shown in the table below. These values where then adapted for use with the GlobCover land use map. Percent of clay and silt (along with sand and gravel) for the channel is estimated from the river median particle diameter assuming the same values as SWAT shown in the table below. Median particle diameter is here estimated depending on the Strahler river order. The higher the order, the smaller the diameter is. As the median diameter is only used in wflow_sediment for the estimation of the river bed/bank sediment composition, this supposition should be enough. Actual refined data or calibration may however be needed if the median diameter is also required for the transport formula. In a similar way, the bulk densities of river bed and bank are also just assumed to be of respectively \(\SI{1.5}{g cm^{-3}}\) and \(\SI{1.4}{g cm^{-3}}\).

+ + + + + + + + + + + + + + + + + + + + + + + + + + +
Classical values of the channel cover vegetation coefficient (Julian and Torres, 2006)
Bank vegetation\(C_{ch}\)
None1.00
Grassy1.97
Sparse trees5.40
Dense trees19.20
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Composition of the river bed/bank depending on the median diameter \(\SIb{d_{50}}{\mu m}\) (Neitsch et al, 2011)
Sediment Fraction\(\leq\) 55 to 5050 to 2000\(>\) 2000
Sand0.150.150.650.15
Silt0.150.650.150.15
Clay0.650.150.150.05
Gravel0.050.050.050.65
+

Then, the repartition of the flow shear stress is refined into the effective shear stress and the bed and bank of the river using the equations developed by Knight (1984) for a rectangular channel: \[ +\begin{gathered} + \tau_{e,\mathrm{bed}} = \rho g R_{H} S \left(1 - \dfrac{SF_{\mathrm{bank}}}{100}\right) \left(1+\dfrac{2h}{W}\right) \\ + \tau_{e,\mathrm{bank}} = \rho g R_{H} S \left( SF_{\mathrm{bank}}\right) \left(1+\dfrac{W}{2h}\right) +\end{gathered} +\] where \(\rho g\) is the fluid specific weight (\(\SI{9800}{N m^{-3}}\) for water), \(\SIb{R_H}{m}\) is the hydraulic radius of the channel, \(\SIb{h}{m}\) and \(\SIb{W}{m}\) are the water level and river width. \(SF_{\mathrm{bank}}\) is the proportion of shear stress acting on the bank (%) and is estimated from (Knight, 1984): \[ + \mathrm{SF}_{\mathrm{bank}} = \exp \left( -3.230 \log_{10}\left(\dfrac{W}{h}+3\right)+6.146 \right) +\] Finally the relative erosion potential of the bank and bed of the river is calculated by: \[ +\begin{gathered} + \mathrm{RTE}_{\mathrm{bed}} = \dfrac{E_{R,\mathrm{bed}}}{E_{R,\mathrm{bed}}+E_{R,\mathrm{bank}}} \\ + \mathrm{RTE}_{\mathrm{bank}} = 1 - RTE_{\mathrm{bed}} +\end{gathered} +\] And the final actual eroded amount for the bed and bank is the maximum between \(\mathrm{RTE} +\subtext{\mathrm{sed}}{exeff}\) and the erosion potential \(E_R\). Total eroded amount of sediment \(\subtext{\mathrm{sed}}{erod}\) is then the sum of the eroded sediment coming from the storage of previously deposited sediment and the river bed/bank erosion.

+
+
+

River deposition

+

As sediments have a higher density than water, moving sediments in water can be deposited in the river bed. The deposition process depends on the mass of the sediment, but also on flow characteristics such as velocity. In wflow_sediment, as in SWAT, deposition is modelled with Einstein’s equation (Neitsch et al, 2011): \[ + \subtext{P}{dep}=\left(1-\dfrac{1}{e^{x}}\right)100 +\] where \(\subtext{P}{dep}\) is the percentage of sediments that is deposited on the river bed and x is a parameter calculated with: \[ + x = \dfrac{1.055 L \omega_{s}}{u h} +\] where \(\SIb{L}{m}\) and \(\SIb{h}{m}\) are channel length and water height, \(\SIb{\omega_s}{m +s^{-1}}\) is the particle settling velocity calculated with Stokes’ formula and \(\SIb{u}{m +s^{-1}}\) is the mean flow velocity. The calculated percentage is then subtracted from the amount of sediment input and eroded river sediment for each particle size class (\(\subtext{\mathrm{sed}}{dep} = \subtext{P}{dep}/100 (\subtext{\mathrm{sed}}{in} + +\subtext{\mathrm{sed}}{erod})\)). Resulting deposited sediment are then stored in the river bed and can be re-mobilized in future time steps by erosion.

+
+
+

Mass balance and sediment concentration

+

Finally after estimating inputs, deposition and erosion with the transport capacity of the flow, the amount of sediment actually leaving the river cell to go downstream is estimated using: \[ + \subtext{\mathrm{sed}}{out} = (\subtext{\mathrm{sed}}{in} + \subtext{\mathrm{sed}}{erod} - \subtext{\mathrm{sed}}{dep}) \dfrac{\subtext{V}{out}}{V} +\]

+

where \(\SIb{\subtext{\mathrm{sed}}{out}}{ton}\) is the amount of sediment leaving the river cell (tons), \(\SIb{\subtext{\mathrm{sed}}{in}}{ton}\) is the amount of sediment coming into the river cell (storage from previous timestep, land erosion and sediment flux from upstream river cells), \(\SIb{\subtext{\mathrm{sed}}{erod}}{ton}\) is the amount of sediment coming from river erosion, \(\SIb{\subtext{\mathrm{sed}}{dep}}{ton}\) is the amount of deposited sediments, \(\SIb{\subtext{V}{out}}{m^3}\) is the volume of water leaving the river cell (surface runoff \(Q\) times timestep \(\Delta t\)) and \(\SIb{V}{m^3}\) is the total volume of water in the river cell (\(\subtext{V}{out}\) plus storage \(h W L\)).

+

A mass balance is then used to calculate the amount of sediment remaining in the cell at the end of the timestep \((\subtext{\mathrm{sed}}{riv})_t\): \[ + (\subtext{\mathrm{sed}}{riv})_t = (\subtext{\mathrm{sed}}{riv})_{t-1} + (\subtext{\mathrm{sed}}{land})_t + \mathrm{upstream}\left[(\subtext{\mathrm{sed}}{out})_{t-1}\right] + (\subtext{\mathrm{sed}}{erod})_t - (\subtext{\mathrm{sed}}{dep})_t - (\subtext{\mathrm{sed}}{out})_t +\]

+
+
+

Lake and reservoir modelling

+

Apart from land and river, the hydrologic wflow_sbm model also handles lakes and reservoirs modelling. In wflow_sbm, lakes and large reservoirs are modelled using a 1D bucket model at the cell corresponding to the outlet. For the other cells belonging to the lake/reservoir which are not the outlet, processes such as precipitation and evaporation are filtered out and shifted to the outlet cell. wflow_sediment handles the lakes and reservoirs in the same way. If a cell belongs to a lake/reservoir and is not the outlet then the model assumes that no erosion/deposition of sediments is happening and the sediments are only all transported to the lake/reservoir outlet. Once the sediments reach the outlet, then sediments are deposited in the lake/reservoir according to Camp’s model (1945) (Verstraeten et al, 2000): \[ + \mathrm{TE} = \dfrac{\omega_s}{u_{cr,\mathrm{res}}} = \dfrac{\subtext{A}{res}}{\subtext{Q}{out,res}} \omega_s +\]

+

where \(\mathrm{TE}\) is the trapping efficiency of the lake/reservoir (or the fraction of particles trapped), \(\SIb{\omega_{s}}{m s^{-1}}\) is the particle velocity from Stokes, \(\SIb{\subtext{u}{cr,res}}{m s^{-1}}\) is the reservoir’s critical settling velocity which is equal to the reservoir’s outflow \(\SIb{\subtext{Q}{out,res}}{m^3 s^{-1}}\) divided by the reservoir’s surface area \(\SIb{\subtext{A}{res}}{m^2}\).

+

For reservoirs, coarse sediment particles from the bed load are also assumed to be trapped by the dam structure. This adding trapping is taken into account with a reservoir trapping efficiency coefficient for large particles (between \(0\) and \(1\)). Depending on the type of the dam, all bed load particles are trapped (restrapefficiency = 1.0, for example for a gravity dam) or only partly (for example for run-of-the-river dams).

+

Lake and reservoir modelling is enabled in the model section of the TOML and require the extra following input arguments:

+
[model]
+doreservoir = true
+dolake = false
+
+[input.vertical]
+# Reservoir
+resareas = "wflow_reservoirareas"
+# Lake
+lakeareas = "wflow_lakeareas"
+
+[input.lateral.river]
+# Reservoir
+resarea = "ResSimpleArea"
+restrapefficiency = "ResTrapEff"
+resareas = "wflow_reservoirareas"
+reslocs = "wflow_reservoirlocs"
+# Lake
+lakearea = "LakeArea"
+lakeareas = "wflow_lakeareas"
+lakelocs = "wflow_lakelocs"
+

Note that in the inland part, lake and reservoir coverage are used to filter erosion and transport in overland flow.

+
+
+
+

References

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  • K.C. Abbaspour, J. Yang, I. Maximov, R. Siber, K. Bogner, J. Mieleitner, J. Zobrist, and R.Srinivasan. Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of Hydrology, 333(2-4):413-430, 2007. 10.1016/j.jhydrol.2006.09.014
    • +
  • P. Borrelli, M. Märker, P. Panagos, and B. Schütt. Modeling soil erosion and river sediment yield for an intermountain drainage basin of the Central Apennines, Italy. Catena, 114:45-58, +
      +
    1. 10.1016/j.catena.2013.10.007
      2. +
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  • F. Engelund and E. Hansen. A monograph on sediment transport in alluvial streams. Technical University of Denmark 0stervoldgade 10, Copenhagen K., 1967.
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  • G. Govers. Empirical relationships for the transport capacity of overland flow. IAHS Publication, (January 1990):45-63 ST, 1990.
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  • G.J Hanson and A Simon. Erodibility of cohesive streambeds in the loess area of the midwestern USA. Hydrological Processes, 15(May 1999):23-38, 2001.
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  • R Hessel and V Jetten. Suitability of transport equations in modelling soil erosion for a small Loess Plateau catchment. Engineering Geology, 91(1):56-71, 2007. 10.1016/j.enggeo.2006.12.013
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  • J.P Julian, and R. Torres. Hydraulic erosion of cohesive riverbanks. Geomorphology, 76:193-206, 2006. 10.1016/j.geomorph.2005.11.003
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  • D.W. Knight, J.D. Demetriou, and M.E. Hamed. Boundary Shear in Smooth Rectangular Channels. J. Hydraul. Eng., 110(4):405-422, 1984. 10.1061/(ASCE)0733-9429(1987)113:1(120)
    • +
  • S.L Neitsch, J.G Arnold, J.R Kiniry, and J.R Williams. SWAT Theoretical Documentation Version +
      +
    1. Texas Water Resources Institute, pages 1-647, 2011. 10.1016/j.scitotenv.2015.11.063
      2. +
    • +
  • C. Oeurng, S. Sauvage, and J.M. Sanchez-Perez. Assessment of hydrology, sediment and particulate organic carbon yield in a large agricultural catchment using the SWAT model. Journal of Hydrology, 401:145-153, 2011. 10.1016/j.hydrol.2011.02.017
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  • A. Simon, N. Pollen-Bankhead, and R.E Thomas. Development and application of a deterministic bank stability and toe erosion model for stream restoration. Geophysical Monograph Series, 194:453-474, 2011. 10.1029/2010GM001006
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  • G. Verstraeten and J. Poesen. Estimating trap efficiency of small reservoirs and ponds: methods and implications for the assessment of sediment yield. Progress in Physical Geography, 24(2):219-251, 2000. 10.1177/030913330002400204
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  • O. Vigiak, A. Malago, F. Bouraoui, M. Vanmaercke, and J. Poesen. Adapting SWAT hillslope erosion model to predict sediment concentrations and yields in large Basins. Science of the Total Environment, 538:855-875, 2015. 10.1016/j.scitotenv.2015.08.095
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  • O. Vigiak, A. Malago, F. Bouraoui, M. Vanmaercke, F. Obreja, J. Poesen, H. Habersack, J. Feher, and S. Groselj. Modelling sediment fluxes in the Danube River Basin with SWAT. Science of the Total Environment, 2017. 10.1016/j.scitotenv.2017.04.236
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Reservoirs and Lakes

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Simplified reservoirs and lakes models can be included as part of the river network.

+
+

Reservoirs

+

Simple reservoirs can be included within the river routing by supplying the following reservoir parameters:

+
    +
  • locs - Outlet of the reservoirs in which each reservoir has a unique id
    • +
  • area - Surface area of the reservoirs \(\SIb{}{m^2}\)
    • +
  • areas - Reservoir coverage
    • +
  • targetfullfrac - Target fraction full (of max storage) for the reservoir: number between 0 and 1
    • +
  • targetminfrac - Target minimum full fraction (of max storage). Number between 0 and 1
    • +
  • maxvolume - Maximum reservoir storage (above which water is spilled) \(\SIb{}{m^3}\)
    • +
  • demand - Minimum (environmental) flow requirement downstream of the reservoir \(\SIb{}{m^3 s^{-1}}\)
    • +
  • maxrelease - Maximum \(Q\) that can be released if below spillway \(\SIb{}{m^3 s^{-1}}\)
    • +
+

By default the reservoirs are not included in the model. To include them put the following lines in the TOML file of the model:

+
[model]
+reservoirs = true
+

Finally there is a mapping required between external and internal parameter names in the TOML file, with below an example:

+
[input]
+
+[input.lateral.river.reservoir]
+area = "ResSimpleArea"
+areas = "wflow_reservoirareas"
+demand = "ResDemand"
+locs = "wflow_reservoirlocs"
+maxrelease = "ResMaxRelease"
+maxvolume = "ResMaxVolume"
+targetfullfrac = "ResTargetFullFrac"
+targetminfrac = "ResTargetMinFrac"
+
+

Lakes (unregulated and regulated)

+

Lakes are modelled using a mass balance approach:

+

\[ + \dfrac{S(t + \Delta t)}{\Delta t} = \dfrac{S(t)}{\Delta t} + \subtext{Q}{in} + \dfrac{(P-E) A}{\Delta t} - \subtext{Q}{out} +\]

+

where \(\SIb{S}{m^3}\) is lake storage, \(\SIb{\Delta t}{s}\) is the model timestep, \(\SIb{\subtext{Q}{in}}{m^3 s^{-1}}\) is the sum of inflows (river, overland and lateral subsurface flow), \(\SIb{\subtext{Q}{out}}{m^3 s^{-1}}\) is the lake outflow at the outlet, \(\SIb{P}{m}\) is precipitation, \(\SIb{E}{m}\) is lake evaporation and \(\SIb{A}{m^2}\) is the lake surface area.

+
+
+

+
Lake schematization.
+
+
+

Most of the variables in this equation are already known or coming from previous timestep, apart from \(S(t+ \Delta t)\) and \(\subtext{Q}{out}\) which can both be linked to the water level \(H\) in the lake using a storage curve \(S = f(H)\) and a rating curve \(Q = f(H)\). In wflow, several options are available to select storage and rating curves, and in most cases, the mass balance is then solved by linearization and iteration or using the Modified Puls Approach from Maniak (Burek et al., 2013). Storage curves in wflow can either:

+
    +
  • Come from the interpolation of field data linking volume and lake height,
    • +
  • Be computed from the simple relationship \(S = A H\).
    • +
+

Rating curves in wflow can either:

+
    +
  • Come from the interpolation of field data linking lake outflow and water height, also appropriate for regulated lakes/ dams,
    • +
  • Be computed from a rating curve of the form \(\subtext{Q}{out} = \alpha (H-H_0)^\beta\), where \(H_{0}\) is the minimum water level under which the outflow is zero. Usual values for \(\beta\) are \(\frac{3}{2}\) for a rectangular weir or \(2\) for a parabolic weir (Bos, 1989).
    • +
+
+
+

Modified Puls Approach

+

The Modified Puls Approach is a resolution method of the lake balance that uses an explicit relationship between storage and outflow. Storage is assumed to be equal to \(A H\) and the rating curve for a parabolic weir (\(\beta = 2\)):

+

\[ + S = A H = A (h + H_{0}) = A \sqrt{\frac{Q}{\alpha}} + A H_0 +\]

+

Inserting this equation in the mass balance gives:

+

\[ + \dfrac{A}{\Delta t} \sqrt{\frac{Q}{\alpha}} + Q = \dfrac{S(t)}{\Delta t} + \subtext{Q}{in} + + A\dfrac{P-E}{\Delta t} - \dfrac{A H_0}{\Delta t} = \mathrm{SI} - \dfrac{A H_0}{\Delta t} +\]

+

The solution for \(Q\) is then:

+

\[ + Q = + \begin{cases} + \begin{align*} + \frac{1}{4}\left(-\mathrm{LF} + \sqrt{\mathrm{LF}^{2} + 4 \left(\mathrm{SI} - \dfrac{A H_0}{\Delta t} \right)} + \right)^2 &\text{ if }\quad \mathrm{SI} > \dfrac{A H_0}{\Delta t} \\ + 0 &\text{ if }\quad \mathrm{SI} \leq \dfrac{A H_0}{\Delta t} + \end{align*} + \end{cases} +\]

+

where

+

\[ +\mathrm{LF} = \dfrac{A}{\Delta t \sqrt{\alpha}}. +\]

+
+
+

Lake parameters

+

Lakes can be included within the kinematic wave river routing in wflow, by supplying the following parameters:

+
    +
  • area - Surface area of the lakes [m\(^2\)]
    • +
  • areas - Coverage of the lakes
    • +
  • locs - Outlet of the lakes in which each lake has a unique id
    • +
  • linkedlakelocs - Outlet of linked (downstream) lakes (unique id)
    • +
  • waterlevel - Lake water level [m], used to reinitiate lake model
    • +
  • threshold - Water level threshold \(H_{0}\) under which outflow is zero [m]
    • +
  • storfunc - Type of lake storage curve ; 1 for \(S = AH\) (default) and 2 for \(S = f(H)\) from lake data and interpolation
    • +
  • outflowfunc - Type of lake rating curve ; 1 for \(Q = f(H)\) from lake data and interpolation, 2 for general \(Q = b(H - H_{0})^{e}\) and 3 in the case of Puls Approach \(Q = b(H - H_{0})^{2}\) (default)
    • +
  • b - Rating curve coefficient
    • +
  • e - Rating curve exponent
    • +
+

By default, the lakes are not included in the model. To include them, put the following line in the TOML file of the model:

+
[model]
+lakes = true
+

There is also a mapping required between external and internal parameter names in the TOML file, with below an example:

+
[input]
+
+[input.lateral.river.lake]
+area = "lake_area"
+areas = "wflow_lakeareas"
+b = "lake_b"
+e = "lake_e"
+locs = "wflow_lakelocs"
+outflowfunc = "lake_outflowfunc"
+storfunc  = "lake_storfunc"
+threshold  = "lake_threshold"
+waterlevel = "lake_waterlevel"
+
+
+

Additional settings

+

Storage and rating curves from field measurement can be supplied to wflow via CSV files supplied in the same folder of the TOML file. Naming of the files uses the ID of the lakes where data are available and is of the form lake_sh_1.csv and lake_hq_1.csv for respectively the storage and rating curves of lake with ID 1.

+

The storage curve is stored in a CSV file with lake level \(\SIb{}{m}\) in the first column H and corresponding lake storage \(\SIb{}{m^3}\) in the second column S:

+
H,  S
+392.21, 0
+393.21, 430202000
+393.71, 649959000
+394.21, 869719000
+

The rating curve uses level and discharge data depending on the Julian day of the year (JDOY), and can be also used for regulated lakes/ dams. The first line contains H for the first column. The other lines contain the water level and the corresponding discharges for the different JDOY (1-365), see also the example below, that shows part of a CSV file (first 4 Julian days). The volume above the maximum water level of the rating curve is assumed to flow instantaneously out of the lake (overflow).

+
H
+394,    43,     43,     43,     43
+394.01, 44.838, 44.838, 44.838, 44.838
+394.02, 46.671, 46.671, 46.671, 46.671
+394.03, 48.509, 48.509, 48.509, 48.509
+394.04, 50.347, 50.347, 50.347, 50.347
+394.05, 52.179, 52.179, 52.179, 52.179
+

Linked lakes: In some cases, lakes can be linked and return flow can be allowed from the downstream to the upstream lake. The linked lakes are defined in the linkedlakelocs parameter that represent the downstream lake location ID, at the grid cell of the upstream lake location.

+
+
+
+ +
+
+Note +
+
+
+

In every file, level units are meters [m] above lake bottom and not meters above sea level [m asl]. Especially with storage/rating curves coming from data, please be careful and convert units if needed.

+
+
+
+
+
+

References

+
    +
  • Bos M.G., 1989. Discharge measurement structures. Third revised edition, International Institute for Land Reclamation and Improvement ILRI, Wageningen, The Netherlands.
    • +
  • Burek P., Van der Knijf J.M., Ad de Roo, 2013. LISFLOOD – Distributed Water Balance and flood Simulation Model – Revised User Manual. DOI: http://dx.doi.org/10.2788/24719.
    • +
+ + +
+ + Back to top
+ +
+ + + + + + \ No newline at end of file diff --git a/previews/PR448/model_docs/model_configurations.html b/previews/PR448/model_docs/model_configurations.html new file mode 100644 index 000000000..d47a5cf96 --- /dev/null +++ b/previews/PR448/model_docs/model_configurations.html @@ -0,0 +1,1103 @@ + + + + + + + + + +Model configurations – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Model configurations

+
+ + + +
+ + + + +
+ + + +
+ + +

There are several model configurations supported by wflow. These model configurations require slightly different input requirements, yet the general structure is similar for each model. A wflow model configuration consists of a vertical SBM concept in combination with lateral concepts that control how water is routed for example over the land or river domain. For the wflow_sbm model different model configurations are possible. The following model configurations are supported in wflow:

+ +

Below, some explanation will be given on how to prepare a basic wflow_sbm model. Example data for other model configurations is provided in the section with sample data.

+
+

wflow_sbm

+

Wflow_sbm represents hydrological models derived from the CQflow model (Köhler et al., 2006) that have the SBM vertical concept in common, but can have different lateral concepts that control how water is routed for example over the land or river domain. The soil part of SBM is largely based on the Topog_SBM model but has had considerable changes over time. Topog_SBM is specifically designed to simulate fast runoff processes in small catchments while the wflow_sbm model can be applied more widely. The main differences are for the vertical concept SBM of wflow_sbm:

+
    +
  • The unsaturated zone can be split-up in different layers
    • +
  • The addition of evapotranspiration losses
    • +
  • The addition of a capillary rise
    • +
  • The addition of water demand and allocation
    • +
+

The water demand and allocation computations are supported by the wflow_sbm model configurations:

+
    +
  • SBM + Kinematic wave
    • +
  • SBM + Groundwater flow
    • +
  • SBM + Local inertial river
    • +
  • SBM + Local inertial river (1D) and land (2D)
    • +
+

The vertical SBM concept is explained in more detail in the following section SBM vertical concept.

+

Topog_SBM uses an element network based on contour lines and trajectories for water routing. Wflow_sbm models differ in how the lateral components river, land, and subsurface are solved. Below the different wflow_sbm model configurations are described.

+
+

SBM + Kinematic wave

+

For the lateral components of this wflow_sbm model water is routed over a D8 network, and the kinematic wave approach is used for river, overland and lateral subsurface flow. This is described in more detail in the section Kinematic wave.

+

An overview of the different processes and fluxes in the wflow_sbm model with the kinematic wave approach for river, overland and lateral subsurface flow:

+
+
+

+
Conceptual overview of the wflow_sbm model
+
+
+

Below the mapping for this wflow_sbm model (type sbm) to the vertical SBM concept (instance of struct SBM) and the different lateral concepts is presented. For an explanation about the type parameters between curly braces after the struct name see the section on the model parameters.

+
vertical => struct SBM{T,N,M}
+lateral.subsurface => struct LateralSSF{T}
+lateral.land => struct SurfaceFlow{T,R,L}
+lateral.river => struct SurfaceFlow{T,R,L}
+lateral.river.lake => struct NaturalLake{T} # optional
+lateral.river.reservoir => struct SimpleReservoir{T} # optional
+
+
+

SBM + Groundwater flow

+

For river and overland flow the kinematic wave approach over a D8 network is used for this wflow_sbm model. For the subsurface domain, an unconfined aquifer with groundwater flow in four directions (adjacent cells) is used. This is described in more detail in the section Groundwater flow.

+
[model]
+type = "sbm_gwf"
+
+[input.lateral.subsurface]
+ksathorfrac = "KsatHorFrac"
+conductivity = "conductivity"
+specific_yield = "specific_yield"
+exfiltration_conductance = "exfilt_cond"
+infiltration_conductance = "infilt_cond"
+river_bottom = "river_bottom"
+conductivity_profile = "exponential"
+gwf_f.value = 3.0
+

Below the mapping for this wflow_sbm model (type sbm_gwf) to the vertical SBM concept (instance of struct SBM) and the different lateral concepts. For an explanation about the type parameters between curly braces after the struct name see the section on model parameters.

+
vertical => struct SBM{T,N,M}
+lateral.subsurface.flow => struct GroundwaterFlow{A, B}
+lateral.subsurface.recharge => struct Recharge{T} <: AquiferBoundaryCondition
+lateral.subsurface.river => struct River{T} <: AquiferBoundaryCondition
+lateral.subsurface.drain => struct Drainage{T} <: AquiferBoundaryCondition # optional
+lateral.land => struct SurfaceFlow{T,R,L}
+lateral.river => struct SurfaceFlow{T,R,L}
+lateral.river.lake => struct NaturalLake{T} # optional
+lateral.river.reservoir => struct SimpleReservoir{T} # optional
+
+
+

SBM + Local inertial river

+

By default the model types sbm and sbm_gwf use the kinematic wave approach for river flow. There is also the option to use the local inertial model for river flow with an optional 1D floodplain schematization (routing is done separately for the river channel and floodplain), by providing the following in the TOML file:

+
[model]
+river_routing = "local-inertial"    # optional, default is "kinematic-wave"
+floodplain_1d = true                # optional, default is false
+

Only the mapping for the river component changes, as shown below. For an explanation about the type parameters between curly braces after the struct name see the section on the model parameters.

+
lateral.river => struct ShallowWaterRiver{T,R,L}
+
+
+

SBM + Local inertial river (1D) and land (2D)

+

By default the model types sbm and sbm_gwf use the kinematic wave approach for river and overland flow. There is also the option to use the local inertial model for 1D river and 2D overland flow, by providing the following in the TOML file:

+
[model]
+river_routing = "local-inertial"
+land_routing = "local-inertial"
+

The mapping for the river and land component changes, as shown below. For an explanation about the type parameters between curly braces after the struct name see the section on the model parameters.

+
lateral.river => struct ShallowWaterRiver{T,R,L}
+lateral.land => struct ShallowWaterLand{T}
+

The local inertial approach is described in more detail in the section Local inertial model.

+
+
+
+

wflow_sediment

+

The processes and fate of many particles and pollutants impacting water quality at the catchment level are intricately linked to the processes governing sediment dynamics. Both nutrients such as phosphorus, carbon or other pollutants such as metals are influenced by sediment properties in processes such as mobilization, flocculation or deposition. To better assert and model water quality in inland systems, a better comprehension and modelling of sediment sources and fate in the river is needed at a spatial and time scale relevant to such issues.

+

The wflow_sediment model was developed to answer such issues. It is a distributed physics-based model, based on the distributed hydrologic wflow_sbm model. It is able to simulate both land and in-stream processes, and relies on available global datasets, parameter estimation and small calibration effort.

+

In order to model the exports of terrestrial sediment to the coast through the Land Ocean Aquatic Continuum or LOAC (inland waters network such as streams, lakes…), two different modelling parts were considered. The first part, called the inland sediment model, is the modelling and estimation of soil loss and sediment yield to the river system by land erosion, separated into vertical Soil Erosion processes and lateral Sediment Flux in overland flow. The second part, called the River Sediment Model is the transport and processes of the sediment in the river system. The two parts together constitute the wflow_sediment model.

+

Overview of the concepts of the wflow_sediment model: wflow_sediment

+
+

Configuration

+

As sediment generation and transport processes are linked to the hydrology and water flows, the inputs to the wflow_sediment model come directly from a hydrological model. The required dynamic inputs to run wflow_sediment are:

+
    +
  • Precipitation (can also come from the hydrological forcing data),
    • +
  • Land runoff (overland flow) from the kinematic wave,
    • +
  • River runoff from the kinematic wave,
    • +
  • Land water level in the kinematic wave,
    • +
  • River water level in the kinematic wave,
    • +
  • Rainfall interception by the vegetation.
    • +
+

These inputs can be obtained from wflow_sbm or from other sources.

+

Model outputs can be saved for both the inland and the instream part of the model. Some examples are listed below.

+
[output.vertical]
+# Soil splash erosion [ton]
+sedspl = "sedspl"
+# Soil erosion by overland flow [ton]
+sedov = "sedov"
+# Total soil loss [ton]
+soilloss = "soilloss"
+# Total transport capacity of overland flow [ton]
+TCsed = "TCsed"
+# Transport capacity per particle class (clay) [ton]
+TCclay = "TCclay"
+
+[output.lateral.land]
+# Total (or per particle class) sediment flux in overland flow [ton]
+olsed = "olsed"
+olclay = "olclay"
+# Total (or per particle class) sediment yield to the river [ton]
+inlandsed = "inlandsed"
+inlandclay = "inlandclay"
+
+[output.lateral.river]
+# Total sediment concentration in the river (suspended + bed load) [kg/m3]
+Sedconc = "Sedconc"
+# Suspended load [kg/m3]
+SSconc = "SSconc"
+# Bed load [kg/m3]
+Bedconc = "Bedconc"
+
+
+
+

References

+
    +
  • Köhler, L., Mulligan, M., Schellekens, J., Schmid, S., Tobón, C., 2006, Hydrological impacts of converting tropical montane cloud forest to pasture, with initial reference to northern Costa Rica. Final Technical Report DFID‐FRP Project No. R799.
    • +
+ + +
+ + Back to top
+ +
+ + + + + + \ No newline at end of file diff --git a/previews/PR448/model_docs/parameters_lateral.html b/previews/PR448/model_docs/parameters_lateral.html new file mode 100644 index 000000000..bbb579d49 --- /dev/null +++ b/previews/PR448/model_docs/parameters_lateral.html @@ -0,0 +1,3492 @@ + + + + + + + + + +Lateral concepts – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Lateral concepts

+
+ + + +
+ + + + +
+ + + +
+ + +
+

Kinematic wave

+
+

Surface flow

+

The Table below shows the parameters (fields) of struct SurfaceFlowRiver used for river flow, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.river] to map the internal model parameter to the external netCDF variable. The input parameter slope (listed under [input.lateral.river]) is not equal to the internal model parameter sl, and is listed in the Table below between parentheses.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
betaconstant in Manning’s equation--
sl (slope)slopem m\(^{-1}\)-
nManning’s roughnesss m\(^{-\frac{1}{3}}\)0.036
dllengthm-
qdischargem\(^3\) s\(^{-1}\)-
qininflow from upstream cellsm\(^3\) s\(^{-1}\)-
q_avaverage dischargem\(^3\) s\(^{-1}\)-
qlatlateral inflow per unit lengthm\(^2\) s\(^{-1}\)-
inwaterlateral inflowm\(^3\) s\(^{-1}\)-
inflowexternal inflow (abstraction/supply/demand)m\(^3\) s\(^{-1}\)0.0
inflow_wbinflow waterbody (lake or reservoir model) from land partm\(^3\) s\(^{-1}\)0.0
abstractionabstraction (computed as part of water demand and allocation)m\(^3\) s\(^{-1}\)0.0
volumekinematic wave volumem\(^3\)-
hwater levelm-
h_avaverage water levelm-
bankfull_depthbankfull river depthm1.0
dtmodel time steps-
itsnumber of fixed iterations--
widthwidthm-
alpha_powused in the power part of \(\alpha\)--
alpha_termterm used in computation of \(\alpha\)--
alphaconstant in momentum equation \(A = \alpha Q^{\beta}\)s\(^{\frac{3}{5}}\) m\(^{\frac{1}{5}}\)-
celcelerity of kinematic wavem s\(^{-1}\)-
reservoir_indexmap cell to 0 (no reservoir) or i (pick reservoir i in reservoir field)--
lake_indexmap cell to 0 (no lake) or i (pick lake i in lake field)--
reservoiran array of reservoir models SimpleReservoir--
lakean array of lake models Lake--
allocationwater allocation of type AllocationRiver--
kinwave_itboolean for kinematic wave iterations-false
+

The Table below shows the parameters (fields) of struct SurfaceFlowLand used for overland flow, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.land] to map the internal model parameter to the external netCDF variable. The input parameter slope (listed under [input.lateral.land]) is not equal to the internal model parameter sl, and is listed in the Table below between parentheses.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
betaconstant in Manning’s equation--
sl (slope)slopem m\(^{-1}\)-
nManning’s roughnesss m\(^{-\frac{1}{3}}\)0.072
dllengthm-
qdischargem\(^3\) s\(^{-1}\)-
qininflow from upstream cellsm\(^3\) s\(^{-1}\)-
q_avaverage dischargem\(^3\) s\(^{-1}\)-
qlatlateral inflow per unit lengthm\(^2\) s\(^{-1}\)-
inwaterlateral inflowm\(^3\) s\(^{-1}\)-
volumekinematic wave volumem\(^3\)-
hwater levelm-
h_avaverage water levelm-
dtmodel time steps-
itsnumber of fixed iterations--
widthwidthm-
alpha_powused in the power part of \(\alpha\)--
alpha_termterm used in computation of \(\alpha\)--
alphaconstant in momentum equation \(A = \alpha Q^{\beta}\)s\(^{\frac{3}{5}}\) m\(^{\frac{1}{5}}\)-
celcelerity of kinematic wavem s\(^{-1}\)-
to_riverpart of overland flow that flows to the riverm\(^3\) s\(^{-1}\)-
kinwave_itboolean for kinematic wave iterations-false
+
+
+

Lateral subsurface flow

+

The Table below shows the parameters (fields) of struct LateralSSF, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF). The soil related parameters f, soilthickness, z_exp, theta_s and theta_r are derived from the vertical SBM concept (including unit conversion for f, z_exp and soilthickness), and can be listed in the TOML configuration file under [input.vertical], to map the internal model parameter to the external netCDF variable. The internal slope model parameter slope is set through the TOML file as follows:

+
[input.lateral.land]
+slope = "Slope"
+

The parameter kh_0 is computed by multiplying the vertical hydraulic conductivity at the soil surface kv_0 (including unit conversion) of the vertical SBM concept with the internal parameter khfrac [-] (default value of 1.0). The internal model parameter khfrac is set through the TOML file as follows:

+
[input.lateral.subsurface]
+ksathorfrac = "KsatHorFrac"
+

The khfrac parameter compensates for anisotropy, small scale kv_0 measurements (soil core) that do not represent larger scale hydraulic conductivity, and smaller flow length scales (hillslope) in reality, not represented by the model resolution.

+

For the vertical SBM concept different vertical hydraulic conductivity depth profiles are possible, and these also determine which LateralSSF parameters are used including the input requirements for the computation of lateral subsurface flow. For the exponential profile the model parameters kh_0 and f are used. For the exponential_constant profile kh_0 and f are used, and z_exp is required as part of [input.vertical]. For the layered profile, SBM model parameter kv is used, and for the layered_exponential profile kv is used and z_exp is required as part of [input.vertical].

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
kh_0horizontal hydraulic conductivity at soil surfacem d\(^{-1}\)3.0
fa scaling parameter (controls exponential decline of kh_0)m\(^{-1}\)1.0
khhorizontal hydraulic conductivitym d\(^{-1}\)-
khfrac (ksathorfrac)a muliplication factor applied to vertical hydraulic conductivity kv-100.0
soilthicknesssoil thicknessm2.0
theta_ssaturated water content (porosity)-0.6
theta_rresidual water content-0.01
dtmodel time stepd-
slopeslopem m\(^{-1}\)-
dldrain lengthm-
dwdrain widthm-
zipseudo-water table depth (top of the saturated zone)m-
z_expdepth from soil surface for which exponential decline of kh_0 is validm-
exfiltwaterexfiltration (groundwater above surface level, saturated excess conditions)m Δt⁻¹-
rechargenet recharge to saturated storem\(^2\) Δt⁻¹-
ssfsubsurface flowm\(^3\) d\({-1}\)-
ssfininflow from upstream cellsm\(^3\) d\({-1}\)-
ssfmaxmaximum subsurface flowm\(^2\) d\({-1}\)-
to_riverpart of subsurface flow that flows to the riverm\(^3\) d\({-1}\)-
volumesubsurface water volumem\(^3\)-
+
+
+
+

Local inertial

+
+

River flow

+

The Table below shows the parameters (fields) of struct ShallowWaterRiver, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.river], to map the internal model parameter to the external netCDF variable. The parameter river bed elevation zb is based on the bankfull elevation and depth input data:

+
[input.lateral.river]
+bankfull_elevation = "RiverZ"
+bankfull_depth = "RiverDepth"
+

When floodplain routing (parameter floodplain) is included as part of local inertial river flow, parameter q_av represents the total average discharge of the river channel and floodplain routing, and parameter q_channel_av represents average river channel discharge. Otherwise parameters q_av and q_channel_av represent both average river channel discharge (are equal).

+

The input parameter n (listed under [input.lateral.river]) is not equal to the internal model parameter mannings_n, and is listed in the Table below between parentheses.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
mannings_n (n)Manning’s roughnesss m\(^{-\frac{1}{3}}\)0.036
widthriver widthm-
zbriver bed elevationm-
lengthriver lengthm-
nnumber of cells--
nenumber of edges/links--
active_nactive nodes--
active_eactive edges--
gacceleration due to gravitym s\(^{-2}\)-
alphastability coefficient (Bates et al., 2010)-0.7
h_threshdepth threshold for calculating flowm0.001
dtmodel time steps-
qriver discharge (subgrid channel)m\(^3\) s\(^{-1}\)-
q_avaverage river channel (+ floodplain) dischargem\(^3\) s\(^{-1}\)-
q_channel_avaverage river channel dischargem\(^3\) s\(^{-1}\)-
zb_maxmaximum channel bed elevationm-
mannings_n_sqManning’s roughness squared at edge/link(s m\(^{-\frac{1}{3}}\))\(^2\)-
hwater depthm-
zs_maxmaximum water elevationm-
zs_srcwater elevation of source node of edgem-
zs_dstwater elevation of downstream node of edgem-
hfwater depth at edge/linkm-
h_avaverage water depthm-
dlriver lengthm-
dl_at_linkriver length at edge/linkm-
widthriver widthm-
width_at_linkriver width at edge/linkm-
aflow area at edge/linkm\(^2\)-
rhydraulic radius at edge/linkm-
volumeriver volumem\(^3\)-
errorerror volumem\(^3\)-
inwaterlateral inflowm\(^3\) s\(^{-1}\)-
inflowexternal inflow (abstraction/supply/demand)m\(^3\) s\(^{-1}\)0.0
abstractionabstraction (computed as part of water demand and allocation)m\(^3\) s\(^{-1}\)0.0
inflow_wbinflow waterbody (lake or reservoir model) from land partm\(^3\) s\(^{-1}\)0.0
bankfull_volumebankfull volumem\(^3\)-
bankfull_depthbankfull depthm-
froude_limitif true a check is performed if froude number > 1.0 (algorithm is modified)--
reservoir_indexriver cell index with a reservoir--
lake_indexriver cell index with a lake--
waterbodywater body cells (reservoir or lake)--
reservoiran array of reservoir models SimpleReservoir--
lakean array of lake models Lake--
allocationoptional water allocation of type AllocationRiver--
floodplainoptional 1D floodplain routing FloodPlain--
+
+
+

1D floodplain

+

The Table below shows the parameters (fields) of struct FloodPlain (part of struct ShallowWaterRiver), including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.river.floodplain], to map the internal model parameter to the external netCDF variable. The input parameter n (listed under [input.lateral.river.floodplain]) is not equal to the internal model parameter mannings_n, and is listed in the Table below between parentheses.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
profileFloodplain profile FloodPlainProfile
mannings_n (n)Manning’s roughness for the floodplains m\(^{-\frac{1}{3}}\)0.072
mannings_n_sqManning’s roughness squared at edge/link(s m\(^{-\frac{1}{3}}\))\(^2\)-
volumeflood volumem\(^3\)-
hflood depthm-
h_avaverage flood depthm-
errorerror volumem\(^3\)
aflow area at edge/linkm\(^2\)-
rhydraulic radius at edge/linkm-
hfflood depth at edge/linkm-
zb_maxmaximum bankfull elevation at edgem-
q0discharge at previous time stepm\(^3\) s\(^{-1}\)-
qdischargem\(^3\) s\(^{-1}\)-
q_avaverage dischargem\(^3\) s\(^{-1}\)-
hf_indexindex with hf above depth threshold--
+

The floodplain profile FloodPlainProfile contains the following parameters:

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
depth (flood_depth)flood depthsm-
volumecumulative flood volume (per flood depth)m\(^3\)-
widthcumulative floodplain width (per flood depth)m-
acumulative floodplain flow area (per flood depth)m\(^2\)-
pcumulative floodplain wetted perimeter (per flood depth)m-
+

The floodplain volumes (per flood depth interval) can be set as follows through the TOML file:

+
[input.lateral.river.floodplain]
+volume = "floodplain_volume"
+

The input parameter flood_depth (dimension of floodplain volume) is not equal to the internal model parameter depth, and is listed in the Table below between parentheses.

+
+
+

Overland flow

+

The Table below shows the parameters (fields) of struct ShallowWaterLand, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.land], to map the internal model parameter to the external netCDF variable.

+

The mannings roughness (for the computation of mannings_n_sq) should be provided as follows in the TOML file:

+
[input.lateral.land]
+n = "n_land" # mannings roughness
+

The input parameter elevation (listed under [input.lateral.land]) is not equal to the internal model parameter z, and is listed in the Table below between parentheses.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
nnumber of cells--
xlcell length x directionm-
ylcell length y directionm-
xwidtheffective flow width x direction (floodplain)m-
ywidtheffective flow width y direction (floodplain)m-
gacceleration due to gravitym s\(^{-2}\)-
thetaweighting factor (de Almeida et al., 2012)-0.8
alphastability coefficient (Bates et al., 2010)-0.7
h_threshdepth threshold for calculating flowm0.001
dtmodel time steps-
qy0flow in y direction at previous time stepm\(^3\) s\(^{-1}\)-
qx0flow in x direction at previous time stepm\(^3\) s\(^{-1}\)-
qxflow in x directionm\(^3\) s\(^{-1}\)-
qyflow in y directionm\(^3\) s\(^{-1}\)-
zx_maxmaximum cell elevation (x direction)m-
zy_maxmaximum cell elevation (y direction)m-
mannings_n_sqManning’s roughness squareds m\(^{-\frac{1}{3}}\)based on 0.072
volumetotal volume of cell (including river volume for river cells)m\(^3\)-
errorerror volumem\(^3\)-
runoffrunoff from hydrological modelm\(^3\) s\(^{-1}\)-
hwater depth of cellm-
z (elevation)elevation of cellm-
froude_limitif true a check is performed if froude number > 1.0 (algorithm is modified)--
rivercellsriver cells--
h_avaverage water depthm-
+
+
+
+

Water bodies

+
+

Reservoirs

+

The Table below shows the parameters (fields) of struct SimpleReservoir, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.river.reservoir], to map the internal model parameter to the external netCDF variable.

+

Two parameters reservoir coverage areas and the outlet of reservoirs (unique id) locs that are not part of the SimpleReservoir struct are also required, and can be set as follows through the TOML file:

+
[input.lateral.river.reservoir]
+areas = "wflow_reservoirareas"
+locs = "wflow_reservoirlocs"
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
parameterdescriptionunitdefault
areaaream^2-
demandminimum (environmental) flow requirement downstream of the reservoirm^3 s^{-1}-
maxreleasemaximum amount that can be released if below spillwaym^3 s^{-1}-
maxvolumemaximum storage (above which water is spilled)m^3-
targetfullfractarget fraction full (of max storage)--
targetminfractarget minimum full fraction (of max storage)--
demandreleaseminimum (environmental) flow released from reservoirm^3 s^{-1}-
dtmodel time steps-
volumevolumem^3-
inflowtotal inflow into reservoirm^3-
outflowoutflow of reservoirm^3 s^{-1}-
totaloutflowtotal outflow of reservoirm^3-
percfullfraction full (of max storage)--
precipitationaverage precipitation for reservoir areamm Δt⁻¹-
evaporationaverage potential evaporation for reservoir areamm Δt⁻¹-
actevapaverage actual evaporation for lake areamm Δt⁻¹-
+
+
+

Lakes

+

The Table below shows the parameters (fields) of struct Lake, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.river.lake], to map the internal model parameter to the external netCDF variable.

+

Two parameters lake coverage areas and the outlet of lakes (unique id) locs that are not part of the Lake struct are also required, and can be set as follows through the TOML file:

+
[input.lateral.river.lake]
+areas = "wflow_lakeareas"
+locs = "wflow_lakelocs"
+

The input parameter linkedlakelocs (listed under [input.lateral.river.lake]) is not equal to the internal model parameter lowerlake_ind, and is listed in the Table below between parentheses.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
parameterdescriptionunitdefault
areaaream^2-
bRating curve coefficient--
eRating curve exponent--
outflowfunctype of lake rating curve--
storfunctype of lake storage curve--
thresholdwater level threshold H_0 below that level outflow is zerom-
waterlevelwaterlevel H of lakem-
lowerlake_ind (linkedlakelocs)Index of lower lake (linked lakes)-0
shdata for storage curve--
hqdata rating curve--
dtmodel time steps-
inflowtotal inflow to the lakem^3-
storagestorage lakem^3-
maxstoragemaximum storage lake with rating curve type 1m^3-
outflowoutflow lakem^3 s^{-1}-
totaloutflowtotal outflow lakem^3-
precipitationaverage precipitation for lake areamm Δt⁻¹-
evaporationaverage potential evaporation for lake areamm Δt⁻¹-
actevapaverage actual evaporation for lake areamm Δt⁻¹-
+
+
+
+

Water allocation river

+

The Table below shows the parameters (fields) of struct AllocationRiver, used when water demand and allocation is computed (optional), including a description of these parameters, the unit, and default value if applicable.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
parameterdescriptionunitdefault
act_surfacewater_abstactual surface water abstractionmm Δt⁻¹-
act_surfacewater_abst_volactual surface water abstractionm\(^3\) Δt⁻¹-
available_surfacewateravailable surface waterm\(^3\)-
nonirri_returnflowreturn flow from non-irrigation (industry, domestic and livestock)mm Δt⁻¹-
+
+
+

Groundwater flow

+
+

Confined aquifer

+

The Table below shows the parameters (fields) of struct ConfinedAquifer, including a description of these parameters, the unit, and default value if applicable. Struct ConfinedAquifer is not (yet) part of a wflow model.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
khorizontal conductivitym d\(^{-1}\)s-
storativitystorativitym m\(^{-1}\)-
specific_storagespecific storagem\(^{-1}\)-
toptop groundwater layersm-
bottombottom groundwater layersm-
areacell aream\(^2\)-
headgroundwater headm-
conductanceconductancem\(^2\) d\(^{-1}\)-
+
+
+

Unconfined aquifer

+

The Table below shows the parameters (fields) of struct UnconfinedAquifer, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.subsurface], to map the internal model parameter to the external netCDF variable. For some input parameters the parameter listed under [input.lateral.subsurface] is not equal to the internal model parameter, these are listed in the Table below between parentheses after the internal model parameter. The top parameter is provided by the external parameter altitude as part of the static input data and set as follows through the TOML file:

+
[input]
+# these are not directly part of the model
+altitude = "wflow_dem"
+

The input parameter conductivity (listed under [input.lateral.subsurface]) is not equal to the internal model parameter kh_0, and is listed in the Table below between parentheses.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
kh_0 (conductivity)horizontal conductivitym d\(^{-1}\)s-
specific_yieldspecific yieldm m\(^{-1}\)-
top (altitude)top groundwater layerm-
bottombottom groundwater layerm-
areacell aream\(^2\)-
headgroundwater headm-
conductanceconductancem\(^2\) d\(^{-1}\)-
ffactor controlling the reduction of reference horizontal conductivity-3.0
+
+
+

Constant Head

+

The Table below shows the parameters (fields) of struct ConstantHead, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.subsurface], to map the internal model parameter to the external netCDF variable. The input parameter constant_head (listed under [input.lateral.subsurface]) is not equal to the internal model parameter head, and is listed in the Table below between parentheses.

+ + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
head (constant_head)groundwater headm-
indexconstant head cell index--
+
+
+

Boundary conditions

+
+

River

+

The Table below shows the parameters (fields) of struct River, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.subsurface], to map the internal model parameter to the external netCDF variable. The input parameter river_bottom (listed under [input.lateral.subsurface]) is not equal to the internal model parameter bottom, and is listed in the Table below between parentheses.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
stageriver stagem-
infiltration_conductanceriver bed infiltration conductancem\(^2\) day\(^{-1}\) m\(^2\) day\(^{-1}\)-
exfiltration_conductanceriver bed exfiltration conductancem\(^2\) day\(^{-1}\)-
bottom (river_bottom)river bottom elevationm-
indexriver cell index--
fluxexchange flux (river to aquifer)m\(^3\) d\(^{-1}\)-
+
+
+

Drainage

+

The Table below shows the parameters (fields) of struct Drainage, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.subsurface], to map the internal model parameter to the external netCDF variable. For some input parameters the parameter listed under [input.lateral.subsurface] is not equal to the internal model parameter, these are listed in the Table below between parentheses after the internal model parameter.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
elevation (drain_elevation)drain elevationm-
conductance (drain_conductance)drain conductancem\(^2\) day\(^{-1}\)-
index (drain)drain cell index--
fluxexchange flux (drains to aquifer)m\(^3\) day\(^{-1}\)-
+
+
+

Recharge

+

The Table below shows the parameters (fields) of struct Recharge, including a description of these parameters, the unit, and default value if applicable.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
raterecharge ratem\(^3\) day\(^{-1}\)-
indexrecharge cell index--
fluxrecharge fluxm\(^3\) day\(^{-1}\)-
+
+
+

Head boundary

+

The Table below shows the parameters (fields) of struct HeadBoundary, including a description of these parameters, the unit, and default value if applicable.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
headheadm-
conductanceconductance of the head boundarym\(^2\) day\(^{-1}\)-
indexhead boundary cell index--
fluxconductance of the head boundarym\(^3\) day\(^{-1}\)-
+
+
+

Well boundary

+

The Table below shows the parameters (fields) of struct Well, including a description of these parameters, the unit, and default value if applicable.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
volumetric_ratevolumetric well ratem\(^3\) d\(^{-1}\)-
indexwell index--
fluxactual well fluxm\(^3\) day\(^{-1}\)-
+
+
+
+
+

Sediment

+
+

Overland flow

+

The Table below shows the parameters (fields) of struct OverlandFlowSediment, including a description of these parameters, the unit, and default value if applicable.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
nnumber of cells--
rivcellriver cells--
soillosstotal eroded soilton Δt\(^{-1}\)-
erosclayeroded soil for particle class clayton Δt\(^{-1}\)-
erossilteroded soil for particle class siltton Δt\(^{-1}\)-
erossanderoded soil for particle class sandton Δt\(^{-1}\)-
erossaggeroded soil for particle class small aggregateston Δt\(^{-1}\)-
eroslaggeroded soil for particle class large aggregateston Δt\(^{-1}\)-
TCsedtotal transport capacity of overland flowton Δt\(^{-1}\)-
TCclaytransport capacity of overland flow for particle class clayton Δt\(^{-1}\)-
TCsilttransport capacity of overland flow for particle class siltton Δt\(^{-1}\)-
TCsandtransport capacity of overland flow for particle class sandton Δt\(^{-1}\)-
TCsaggtransport capacity of overland flow for particle class small aggregateston Δt\(^{-1}\)-
TClaggtransport capacity of overland flow for particle class large aggregateston Δt\(^{-1}\)-
inlandsedsediment reaching the river with overland flowton Δt\(^{-1}\)-
inlandclaysediment with particle class clay reaching the river with overland flowton Δt\(^{-1}\)-
inlandsiltsediment with particle class silt reaching the river with overland flowton Δt\(^{-1}\)-
inlandsandsediment with particle class sand reaching the river with overland flowton Δt\(^{-1}\)-
inlandsaggsediment with particle class small aggregates reaching the river with overland flowton Δt\(^{-1}\)-
inlandlaggsediment with particle class large aggregates reaching the river with overland flowton Δt\(^{-1}\)-
+
+
+

River flow

+

The Table below shows external parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.river]. These external parameters are not part of struct RiverSediment, but used to calculate parameters of struct RiverSediment.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
reslocsreservoir location (outlet)--
resareasreservoir coverage--
resareareservoir area-m\(^2\)
restrapeffreservoir trapping efficiency coefficient--
lakelocslake location (outlet)--
lakeareaslake coverage--
lakearealake area-m\(^2\)
+

The Table below shows the parameters (fields) of struct RiverSediment, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static and forcing input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.river], to map the internal model parameter to the external netCDF variable. For some input parameters the parameter listed under [input.lateral.river] is not equal to the internal model parameter, these are listed in the Table below between parentheses after the internal model parameter. For example, internal model parameter sl is mapped as follows in the TOML file to the external netCDF variable RiverSlope:

+
[input.vertical]
+slope = "RiverSlope"
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
dl (length)river lengthm-
widthriver widthm-
sl (slope)river slope--
rhos (rhosed)density of sedimentkg m\(^{-3}1\)2650.0
dmclaymedian diameter particle size class claymm2.0
dmsiltmedian diameter particle size class siltmm10.0
dmsandmedian diameter particle size class sandmm200.0
dmsaggmedian diameter particle size class small aggregatesmm30.0
dmlaggmedian diameter particle size class large aggregatesmm500.0
dmgravmedian diameter particle size class gravelmm2000.0
fclayrivfraction of particle class clay--
fsiltrivfraction of particle class silt--
fsandrivfraction of particle class sand--
fsaggrivfraction of particle class small aggregates--
flaggrivfraction of particle class large aggregates--
fgravrivfraction of particle class gravel--
d50 (d50riv)river sediment median diametermm-
d50engelundriver mean diametermm-
cbagnoldBagnold c coefficient--
ebagnoldBagnold exponent--
nnumber of cells--
dtmodel time steps-
akKodatie coefficient a--
bkKodatie coefficient b--
ckKodatie coefficient c--
dkKodatie coefficient d--
kdbankbank erodibiltym\(^3\) N\(^{-1}\) s\(^{-1}\)-
kdbedbed erodibilitym\(^3\) N\(^{-1}\) s\(^{-1}\)-
TCrbankcritical bed bank shear stressm\(^3\) N\(^{-2}\)-
TCrbedcritical bed shear stressm\(^3\) N\(^{-2}\)-
h_rivriver water levelm-
q_rivriver dischargem\(^3\) s\(^{-1}\)-
inlandclaysediment input with particle class clay from land erosiont Δt\(^{-1}\)-
inlandsiltsediment input with particle class silt from land erosiont Δt\(^{-1}\)-
inlandsandsediment input with particle class sand from land erosiont Δt\(^{-1}\)-
inlandsaggsediment input with particle class small aggregates from land erosiont Δt\(^{-1}\)-
inlandlaggsediment input with particle class large aggregates from land erosiont Δt\(^{-1}\)-
inlandsedsediment input from land erosiont Δt\(^{-1}\)-
sedloadsediment left in the cellt-
clayloadsediment with particle class clay left in the cellt-
siltloadsediment with particle class silt left in the cellt-
sandloadsediment with particle class sand left in the cellt-
saggloadsediment with particle class small aggregates left in the cellt-
laggloadsediment with particle class large aggregates in the cellt-
gravloadsediment with particle class gravel left in the cellt-
sedstoresediment stored on the river bed after depositiont Δt\(^{-1}\)-
claystoresediment with particle class clay stored on the river bed after depositiont Δt\(^{-1}\)-
siltstoresediment with particle class silt stored on the river bed after depositiont Δt\(^{-1}\)-
sandstoresediment with particle class sand stored on the river bed after depositiont Δt\(^{-1}\)-
saggstoresediment with particle class small aggregates stored on the river bed after depositiont Δt\(^{-1}\)-
laggstoresediment with particle class large aggregates stored on the river bed after depositiont Δt\(^{-1}\)-
gravstoresediment with particle class gravel stored on the river bed after depositiont Δt\(^{-1}\)-
outsedsediment fluxt Δt\(^{-1}\)-
outclaysediment with particle class clay fluxt Δt\(^{-1}\)-
outsiltsediment with particle class siltt Δt\(^{-1}\)-
outsandsediment with particle class sandt Δt\(^{-1}\)-
outsaggsediment with particle class small aggregatest Δt\(^{-1}\)-
outlaggsediment with particle class large aggregatest Δt\(^{-1}\)-
outgravsediment with particle class gravelt Δt\(^{-1}\)-
Sedconctotal sediment concentration (SSconc + Bedconc)g m\(^{-3}\)-
SSconcsuspended load concentrationg m\(^{-3}\)-
Bedconcbed load concentrationg m\(^{-3}\)-
maxsedriver transport capacityt Δt\(^{-1}\)-
erodsedtotal eroded sedimentt Δt\(^{-1}\)-
erodsedbankeroded bank sedimentt Δt\(^{-1}\)-
erodsedbederoded bed sedimentt Δt\(^{-1}\)-
depseddeposited sedimentt Δt\(^{-1}\)-
insedsediment input fluxt Δt\(^{-1}\)-
wbcoverwaterbody coverage--
wblocswaterbody locations--
wbareawaterbody aream\(^2\)-
wbtrapwaterbody trapping efficiency coefficient--
+ + +
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+ + Back to top
+ +
+ + + + + + \ No newline at end of file diff --git a/previews/PR448/model_docs/parameters_vertical.html b/previews/PR448/model_docs/parameters_vertical.html new file mode 100644 index 000000000..b2947e692 --- /dev/null +++ b/previews/PR448/model_docs/parameters_vertical.html @@ -0,0 +1,2378 @@ + + + + + + + + + +Vertical concepts – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + +
+ +
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+

Vertical concepts

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+ + +
+

SBM

+

The Table below shows the parameters (fields) of struct SBM, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static and forcing input data (netCDF), and can be listed in the TOML configuration file under [input.vertical], to map the internal model parameter to the external netCDF variable. For some input parameters the parameter listed under [input.vertical] is not equal to the internal model parameter, these are listed in the Table below between parentheses after the internal model parameter. For example, internal model parameter sl is mapped as follows in the TOML file to the external netCDF variable Sl:

+
[input.vertical]
+specific_leaf = "Sl"
+

Different vertical hydraulic conductivity depth profiles: exponential (default), exponential_constant, layered and layered_exponential can be provided through the TOML file. Below an example for the exponential_constant profile:

+
[input.vertical]
+ksat_profile = "exponential_constant"
+

For the exponential profile the input parameters kv_0 and f are used. For the exponential_constant profile kv_0 and f are used, and z_exp is required as input. For the layered profile, input parameter kv is used, and for the layered_exponential profile kv is used and z_layered is required as input.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
cfmaxdegree-day factormm ᵒC\(^{-1}\) Δt\(^{-1}\)3.75653 mm ᵒC\(^{-1}\) day\(^{-1}\)
ttthreshold temperature for snowfallᵒC0.0
ttithreshold temperature interval lengthᵒC1.0
ttmthreshold temperature for snowmeltᵒC0.0
whcwater holding capacity as fraction of current snow pack-0.1
w_soilsoil temperature smooth factor-0.1125
cf_soilcontrols soil infiltration reduction factor when soil is frozen-0.038
g_ttthreshold temperature for snowfall above glacierᵒC0.0
g_cfmaxDegree-day factor for glaciermm ᵒC\(^{-1}\) Δt\(^{-1}\)3.0 mm ᵒC\(^{-1}\) day\(^{-1}\)
g_sifracfraction of the snowpack on top of the glacier converted into iceΔt\(^{-1}\)0.001 day\(^{-1}\)
glacierfracfraction covered by a glacier-0.0
glacierstorewater within the glaciermm5500.0
theta_ssaturated water content (porosity)-0.6
theta_rresidual water content-0.01
kv_0Vertical hydraulic conductivity at soil surfacemm Δt\(^{-1}\)3000.0 mm day\(^{-1}\)
kvVertical hydraulic conductivity per soil layermm Δt\(^{-1}\)1000.0 mm day\(^{-1}\)
fscaling parameter (controls exponential decline of kv_0)mm\(^{-1}\)0.001
z_expDepth from soil surface for which exponential decline of kv_0 is validmm-
z_layeredDepth from soil surface for which layered profile (of layered_exponential) is validmm-
hbair entry pressure of soil (Brooks-Corey)cm-10.0
soilthicknesssoil thicknessmm2000.0
infiltcappathinfiltration capacity of the compacted areasmm Δt\(^{-1}\)10.0 mm day\(^{-1}\)
infiltcapsoilsoil infiltration capacitymm Δt\(^{-1}\)100.0 mm day\(^{-1}\)
maxleakagemaximum leakage from saturated zonemm Δt\(^{-1}\)0.0 mm day\(^{-1}\)
cBrooks-Corey power coefficient for each soil layer-10.0
kvfracmultiplication factor applied to kv_z (vertical flow)-1.0
waterfracfraction of open water (excluding rivers)-0.0
pathfracfraction of compacted area-0.01
rootingdepthrooting depthmm750.0
rootfractionfraction of the root length density in each soil layer--
h1soil water pressure head h1 of the root water uptake reduction function (Feddes)cm0.0 cm
h2soil water pressure head h2 of the root water uptake reduction function (Feddes)cm-100.0 cm
h3_highsoil water pressure head h3_high of the root water uptake reduction function (Feddes)cm-400.0 cm
h3_lowsoil water pressure head h3_low of the root water uptake reduction function (Feddes)cm-1000.0 cm
h4soil water pressure head h4 of the root water uptake reduction function (Feddes)cm-15849.0 cm
alpha_h1root water uptake reduction at soil water pressure head h1 (0.0 or 1.0)-1.0
rootdistparcontrols how roots are linked to water table--500.0
cap_hmaxwater depth beyond which capillary flux ceasesmm2000.0
cap_ncoefficient controlling capillary rise-2.0
kccrop coefficient Kc-1.0
sl (specific_leaf)specific leaf storagemm-
swood (storage_wood)storage woody part of vegetationmm-
kextextinction coefficient (to calculate canopy gap fraction)--
cmaxmaximum canopy storagemm1.0
e_r (eoverr)Gash interception model parameter-0.1
canopygapfractioncanopy gap fraction-0.1
dtmodel time steps-
maxlayersmaximum number of soil layers--
nnumber of grid cells--
nlayersnumber of soil layers--
n_unsatlayersnumber of unsaturated soil layers--
nlayers_kvnumber of soil layers with vertical hydraulic conductivity value kv--
riverfracfraction of river--
act_thicklthickness of soil layersmm-
sumlayerscumulative sum of soil layers thickness, starting at soil surfacemm-
stemflowstemflowmm Δt\(^{-1}\)-
throughfallthroughfallmm Δt\(^{-1}\)-
ustorelayerdepthamount of water in the unsaturated store, per layermm-
satwaterdepthsaturated storemm-
zipseudo-water table depth (top of the saturated zone)mm-
soilwatercapacitysoilwater capacitymm-
canopystoragecanopy storagemm-
precipitationprecipitationmm Δt\(^{-1}\)-
temperaturetemperatureᵒC-
potential_evaporationpotential reference evapotranspirationmm Δt\(^{-1}\)-
pottransinterception subtracted from potential evapotranspirationmm Δt\(^{-1}\)-
transpirationtranspirationmm Δt\(^{-1}\)-
ae_ustoreactual evaporation from unsaturated storemm Δt\(^{-1}\)-
interceptioninterception loss by evaporationmm Δt\(^{-1}\)-
soilevaptotal soil evaporation from unsaturated and saturated storemm Δt\(^{-1}\)-
soilevapsatsoil evaporation from saturated storemm Δt\(^{-1}\)-
actcapfluxactual capillary risemm Δt\(^{-1}\)-
actevapsatactual transpiration from saturated storemm Δt\(^{-1}\)-
actevaptotal actual evapotranspirationmm Δt\(^{-1}\)-
runoff_riverrunoff from river based on riverfracmm Δt\(^{-1}\)-
runoff_landrunoff from land based on waterfracmm Δt\(^{-1}\)-
ae_openw_lactual evaporation from open water (land)mm Δt\(^{-1}\)-
ae_openw_ractual evaporation from rivermm Δt\(^{-1}\)-
net_runoff_rivernet runoff from river (runoff_river - ae_openw_r)mm Δt\(^{-1}\)-
avail_forinfiltwater available for infiltrationmm Δt\(^{-1}\)-
actinfiltactual infiltration into the unsaturated zonemm Δt\(^{-1}\)-
actinfiltsoilactual infiltration into non-compacted fractionmm Δt\(^{-1}\)-
actinfiltpathactual infiltration into compacted fractionmm Δt\(^{-1}\)-
infiltsoilpathinfiltration into the unsaturated zonemm Δt\(^{-1}\)-
infiltexcessinfiltration excess watermm Δt\(^{-1}\)-
excesswaterwater that cannot infiltrate due to saturated soil (saturation excess)mm Δt\(^{-1}\)-
exfiltsatwaterwater exfiltrating during saturation excess conditionsmm Δt\(^{-1}\)-
exfiltustorewater exfiltrating from unsaturated store because of change in water tablemm Δt\(^{-1}\)-
excesswatersoilexcess water for non-compacted fractionmm Δt\(^{-1}\)-
excesswaterpathexcess water for compacted fractionmm Δt\(^{-1}\)-
runofftotal surface runoff from infiltration and saturation excessmm Δt\(^{-1}\)-
net_runoffnet surface runoff (runoff - ae_openw_l)mm Δt\(^{-1}\)-
vwcvolumetric water content per soil layer (including theta_r and saturated zone)--
vwc_percvolumetric water content per soil layer (including theta_r and saturated zone)%-
rootstoreroot water storage in unsaturated and saturated zone (excluding theta_r)mm-
vwc_rootvolumetric water content in root zone (including theta_r and saturated zone)--
vwc_percrootvolumetric water content in root zone (including theta_r and saturated zone)%-
ustoredepthtotal amount of available water in the unsaturated zonemm-
transferdownward flux from unsaturated to saturated zonemm Δt\(^{-1}\)-
rechargenet recharge to saturated zonemm Δt\(^{-1}\)-
actleakageactual leakage from saturated storemm Δt\(^{-1}\)-
snowsnow storagemm-
snowwaterliquid water content in the snow packmm-
rainfallplusmeltsnowmelt + precipitation as rainfallmm Δt\(^{-1}\)-
tsoiltop soil temperatureᵒC-
leaf_area_indexleaf area indexm\(^2\) m\(^{-2}\)-
waterlevel_landwater level landmm-
waterlevel_riverwater level rivermm-
total_storagetotal water storage (excluding floodplains, lakes and reservoirs)mm-
paddyoptional paddy (rice) fields of type Paddy (water demand and irrigation)--
nonpaddyoptional non-paddy fields of type NonPaddy (water demand and irrigation)--
domesticoptional domestic water demand of type NonIrrigationDemand--
livestockoptional livestock water demand of type NonIrrigationDemand--
industryoptional industry water demand of type NonIrrigationDemand--
allocationoptional water allocation of type AllocationLand--
+
+
+

Water demand and allocation

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+

Paddy

+

The Table below shows the parameters (fields) of struct Paddy, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static and forcing input data (netCDF), and can be listed in the TOML configuration file under [input.vertical.paddy], to map the internal model parameter to the external netCDF variable.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
parameterdescriptionunitdefault
demand_grossirrigation gross demandmm Δt\(^{-1}\)-
irrigation_efficiencyirrigation efficiency--
maximum_irrigation_ratemaximum irrigation ratemm Δt\(^{-1}\)25.0 mm day\(^{-1}\)
irrigation_areasirrigation areas--
irrigation_triggerirrigation on or off (boolean)--
h_minminimum required water depth in the irrigated paddy fieldsmm20.0
h_optoptimal water depth in the irrigated paddy fieldsmm50.0
h_maxwater depth when paddy field starts spilling water (overflow)mm80.0
hactual water depth in paddy fieldmm-
+
+
+

Non-paddy

+

The Table below shows the parameters (fields) of struct NonPaddy, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static and forcing input data (netCDF), and can be listed in the TOML configuration file under [input.vertical.nonpaddy], to map the internal model parameter to the external netCDF variable.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
parameterdescriptionunitdefault
demand_grossirrigation gross demandmm Δt\(^{-1}\)-
irrigation_efficiencyirrigation efficiency--
maximum_irrigation_ratemaximum irrigation ratemm Δt\(^{-1}\)25.0 mm day\(^{-1}\)
irrigation_areasirrigation areas--
irrigation_triggerirrigation on or off (boolean)--
+
+
+

Non-irrigation (industry, domestic and livestock)

+

The Table below shows the parameters (fields) of struct NonIrrigationDemand, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static and forcing input data (netCDF). These parameters can be listed for the sectors industry, domestic and livestock, in the TOML configuration file under [input.vertical.industry], [input.vertical.domestic] and [input.vertical.livestock], to map the internal model parameter to the external netCDF variable.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
parameterdescriptionunitdefault
demand_grossgross industry water demandmm Δt\(^{-1}\)0.0
demand_netnet industry water demandmm Δt\(^{-1}\)0.0
returnflow_fractionreturn flow fraction--
returnflowreturn flowmm Δt\(^{-1}\)-
+
+
+

Water allocation land

+

The Table below shows the parameters (fields) of struct AllocationLand, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static and forcing input data (netCDF), and can be listed in the TOML configuration file under [input.vertical.allocation], to map the internal model parameter to the external netCDF variable.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
parameterdescriptionunitdefault
irri_demand_grossirrigation gross demandmm Δt\(^{-1}\)-
nonirri_demand_grossnon-irrigation gross demandmm Δt\(^{-1}\)-
total_gross_demandtotal gross demandmm Δt\(^{-1}\)-
frac_sw_usedfraction surface water used-1.0
areasallocation areas-1
surfacewater_demanddemand from surface watermm Δt\(^{-1}\)-
surfacewater_allocallocation from surface watermm Δt\(^{-1}\)-
act_groundwater_abstactual groundwater abstractionmm Δt\(^{-1}\)-
act_groundwater_abst_volactual groundwater abstractionm\(^3\) Δt\(^{-1}\)-
available_groundwateravailable groundwaterm\(^3\)-
groundwater_demandgroundwater_demandmm Δt\(^{-1}\)-
groundwater_allocallocation from groundwatermm Δt\(^{-1}\)-
irri_allocallocated water for irrigationmm Δt\(^{-1}\)-
nonirri_allocallocated water for non-irrigationmm Δt\(^{-1}\)-
total_alloctotal allocated watermm Δt\(^{-1}\)-
nonirri_returnflowreturn flow from non-irrigationmm Δt\(^{-1}\)-
+
+
+
+

Sediment

+

The Table below shows external parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.vertical]. These external parameters are not part of struct LandSediment, but used to calculate parameters of struct LandSediment.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
pclaypercentage clay%0.1
psiltpercentage silt%0.1
resareasreservoir coverage--
lakeareaslake coverage--
+

The Table below shows the parameters (fields) of struct LandSediment, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static and forcing input data (netCDF), and can be listed in the TOML configuration file under [input.vertical], to map the internal model parameter to the external netCDF variable. For some input parameters the parameter listed under [input.vertical] is not equal to the internal model parameter, these are listed in the Table below between parentheses after the internal model parameter. For example, internal model parameter sl is mapped as follows in the TOML file to the external netCDF variable Sl:

+
[input.vertical]
+specific_leaf = "Sl"
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionUnitDefault
canopyheightcanopy heightm3.0
eroskcoefficient for EUROSEM rainfall erosion-0.6
erossplexponent for EUROSEM rainfall erosion-2.0
erosovcoefficient for ANSWERS overland flow erosion-0.9
pathfracfraction of impervious area per grid cell-0.01
slopeland slope-0.01
usleCUSLE crop management factor-0.01
usleKUSLE soil erodibility factor-0.1
sl (specific_leaf)specific leaf storagemm-
swood (storage_wood)storage woody part of vegetationmm-
kextextinction coefficient (to calculate canopy gap fraction)--
cmaxmaximum canopy storagemm1.0
canopygapfractioncanopy gap fraction-0.1
dmclaymedian diameter particle size class clayµm2.0
dmsiltmedian diameter particle size class siltµm10.0
dmsandmedian diameter particle size class sandµm200.0
dmsaggmedian diameter particle size class small aggregatesµm30.0
dmlaggmedian diameter particle size class large aggregatesµm500.0
rhos (rhosed)density of sedimentkg m\(^{-3}1\)2650.0
nnumber of cells--
yllength of cells in y directionm-
xllength of cells in x directionm-
riverfracfraction of river--
wbcoverwaterbody coverage--
h_landdepth of overland flowm-
interceptioncanopy interceptionmm Δt\(^{-1}\)-
precipitationprecipitationmm Δt\(^{-1}\)-
q_landoverland flowm\(^3\) s\(^{-1}\)-
sedsplsediment eroded by rainfallton Δt\(^{-1}\)-
sedovsediment eroded by overland flowton Δt\(^{-1}\)-
soillosstotal eroded soilton Δt\(^{-1}\)-
erosclayeroded soil for particle class clayton Δt\(^{-1}\)-
erossilteroded soil for particle class siltton Δt\(^{-1}\)-
erossanderoded soil for particle class sandton Δt\(^{-1}\)-
erossaggeroded soil for particle class small aggregateston Δt\(^{-1}\)-
eroslaggeroded soil for particle class large aggregateston Δt\(^{-1}\)-
leaf_area_indexleaf area indexm\(^2\) m\(^{-2}\)-
dldrain lengthm-
dwflow widthm-
cGoversGovers transport capacity coefficient--
nGoversGovers transport capacity coefficient--
D50median particle diameter of the topsoilmm-
fclayfraction of particle class clay--
fsiltfraction of particle class silt--
fsandfraction of particle class sand--
fsaggfraction of particle class small aggregates--
flaggfraction of particle class large aggregates--
rivcellriver cells--
TCsedtotal transport capacity of overland flowton Δt\(^{-1}\)-
TCclaytransport capacity of overland flow for particle class clayton Δt\(^{-1}\)-
TCsilttransport capacity of overland flow for particle class siltton Δt\(^{-1}\)-
TCsandtransport capacity of overland flow for particle class sandton Δt\(^{-1}\)-
TCsaggtransport capacity of overland flow for particle class small aggregateston Δt\(^{-1}\)-
TClaggtransport capacity of overland flow for particle class large aggregateston Δt\(^{-1}\)-
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+ + Back to top
+ +
+ + + + + + \ No newline at end of file diff --git a/previews/PR448/model_docs/vertical/sbm.html b/previews/PR448/model_docs/vertical/sbm.html new file mode 100644 index 000000000..cd6afb9d5 --- /dev/null +++ b/previews/PR448/model_docs/vertical/sbm.html @@ -0,0 +1,1482 @@ + + + + + + + + + +SBM – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

SBM

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+

Introduction

+

The SBM vertical concept has its roots in the Topog_SBM model but has had considerable changes over time. The main differences are:

+
    +
  • The unsaturated zone can be split-up in different layers
    • +
  • The addition of evapotranspiration losses
    • +
  • The addition of a capillary rise
    • +
+

The sections below describe the working of the SBM vertical concept in more detail.

+
+
+

Precipitation

+

The division between solid and liquid precipitation (snowfall and rainfall, respectively) is performed based on the air temperature. If the temperature is below a threshold temperature (tt), precipitation will fall as snow. An interval parameter (tti) defines the range over which precipitation is partly falling as snow, and partly as rain. Snowfall is added to the snowpack, where it is subject to melting and refreezing (see the section on snow and glaciers). The amount of rainfall is subject to interception, and ultimately becomes available for evaporation and/or soil processes.

+
+
+

+
Division between snow and precipitation based on the threshold temperature
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Rainfall interception

+

Two different interception models are available: the analytical Gash model, and the modified Rutter model. The simulation timestep defines which interception model is used, where daily (or larger) timesteps use the Gash model, and timesteps smaller than daily use the modified Rutter model.

+
+

The analytical (Gash) model (Gash, 1979)

+

The analytical model of rainfall interception is based on Rutter’s numerical model. Simplifications allow the model to be applied on a daily basis, although a storm-based approach will yield better results in situations with more than one storm per day. The amount of water needed to completely saturate the canopy is defined as:

+

\[ + P'=\frac{-\overline{R}S}{\overline{E}_{w}}\log\left[1-\frac{\overline{E}_{w}}{\overline{R}}(1-p-p_{t})^{-1}\right] +\]

+

where \(\overline{R}\) is the average precipitation intensity on a saturated canopy and \(\overline{E}_{w}\) the average evaporation from the wet canopy and with the vegetation parameters \(S\), \(p\) and \(p_t\) as defined previously. The model uses a series of expressions to calculate the interception loss during different phases of a storm. An analytical integration of the total evaporation and rainfall under saturated canopy conditions is performed for each storm to determine average values of \(\overline{E}_{w}\) and \(\overline{R}\). The total evaporation from the canopy (the total interception loss) is calculated as the sum of the components listed in the table below. Interception losses from the stems are calculated for days with \(P\geq S_{t}/p_{t}\). \(p_t\) and \(S_t\) are small and neglected.

+ + ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Formulation of the components of interception loss according to Gash:
ComponentsInterception loss
For \(m\) small storms (\(P_{g}<{P'}_{g}\))\((1-p-p_{t})\sum_{j=1}^{m}P_{g,j}\)
Wetting up the canopy in \(n\) large storms (\(P_{g}\geq{P'}_{g}\))\(n(1-p-p_{t}){P'}_{g}-nS\)
Evaporation from saturated canopy during rainfall\(\overline{E}/\overline{R}\sum_{j=1}^{n}(P_{g,j}-{P'}_{g})\)
Evaporation after rainfall ceases for \(n\) large storms\(nS\)
Evaporation from trunks in \(q\) storms that fill the trunk storage\(qS_{t}\)
Evaporation from trunks in \(m+n-q\) storms that do not fill the trunk storage\(p_{t}\sum_{j=1}^{m+n-q}P_{g,j}\)
+

In applying the analytical model, saturated conditions are assumed to occur when the hourly rainfall exceeds a certain threshold. Often a threshold of 0.5 mm/hr is used. \(\overline{R}\) is calculated for all hours when the rainfall exceeds the threshold to give an estimate of the mean rainfall rate onto a saturated canopy.

+

Gash (1979) has shown that in a regression of interception loss on rainfall (on a storm basis) the regression coefficient should equal to \(\overline{E}_w/\overline{R}\). Assuming that neither \(\overline{E}_w\) nor \(\overline{R}\) vary considerably in time, \(\overline{E}_w\) can be estimated in this way from \(\overline{R}\) in the absence of above-canopy climatic observations. Values derived in this way generally tend to be (much) higher than those calculated with the penman-monteith equation.

+
+
+

The modified rutter model

+

For sub daily timesteps the interception is calculated using a simplification of the Rutter model. The simplified model is solved explicitly and does not take drainage from the canopy into account. The amount of stemflow is taken as a fraction (0.1 * canopygapfraction) of the precipitation. Throughfall equals to the amount of water that cannot be stored by the canopy, plus the rainfall that is not captured by the canopy. Water can evaporate from the canopy storage, taken as the minimum between potential evaporation and the current storage. The “left-over” potential evaporation (if any) is returned as output.

+
Wflow.rainfall_interception_modrut
+
+
+

Interception parameters from LAI

+

The SBM concept can determine the interception parameters from leaf area index (LAI) climatology. In order to switch this on you must define this cyclic parameter in the TOML file, the parameter is read from path_static, as follows:

+
[input]
+path_forcing = "data/forcing-moselle.nc"
+path_static = "data/staticmaps-moselle.nc"
+
+cyclic = ["vertical.leaf_area_index"]
+

Furthermore, these additional parameters are required: + Specific leaf storage (sl \SIb{}{mm}) + Storage woody part of vegetation (swood \SIb{}{mm}) + Extinction coefficient (kext \SIb{}{-})

+

Here it is assumed that cmax \SIb{}{mm} (leaves) (canopy storage capacity for the leaves only) relates linearly with LAI (c.f. Van Dijk and Bruijnzeel 2001). This is done via the sl. sl can be determined through a lookup table with land cover based on literature (Pitman 1989, Lui 1998). Next the cmax (leaves) is determined using:

+

\[ + c_{\max}(\mathrm{leaves}) = \mathrm{sl} \cdot \mathrm{LAI} +\]

+

To get to total storage (cmax) the woody part of the vegetation also needs to be added. As for sl, the storage of the woody part swood can also be related to land cover (lookup table).

+

The canopy gap fraction is determined using the extinction coefficient kext (van Dijk and Bruijnzeel 2001):

+

\[ + \mathrm{canopygapfraction} = \exp(-\subtext{k}{ext} \cdot \mathrm{LAI}) +\]

+

The extinction coefficient kext can be related to land cover.

+
+
+
+

Evaporation

+

The wflow_sbm model assumes the input to be potential reference evapotranspiration. A crop coefficient (kc, set to 1 by default) is used to convert the potential evapotranspiration rate of a reference crop fully covering the soil to the potential evapotranspiration rate of vegetation (natural and agricultural) fully covering the soil. The crop coefficient kc of wflow_sbm is used for a surface completely covered by vegetation, and does not include the effect of growing stages of vegetation and soil cover. These effects are handled separately through the use of the canopy gap fraction.

+

It is assumed that the potential evaporation rate of intercepted water by vegetation is equal to the potential evapotranspiration rate of vegetation (fully covering the soil) multiplied by the canopy fraction. The potential evapotranspiration rate left over after interception is available for transpiration. For potential open water evaporation (river and water bodies) the potential reference evapotranspiration rate is used (multipled by the river fraction riverfrac, and open water fraction waterfrac). Also for potential soil evaporation the potential reference evapotranspiration rate is used, multiplied by the canopy gap fraction corrected by the sum of total water fraction (riverfrac and waterfrac) and the fraction covered by a glacier (glacierfrac).

+
+

Bare soil evaporation

+

If there is only one soil layer present in the wflow_sbm model, the bare soil evaporation is scaled according to the wetness of the soil layer. The fraction of bare soil is assumed to be equal to the fraction not covered by the canopy (canopygapfraction) corrected by the total water fraction. When the soil is fully saturated, evaporation is set to equal the potential reference evaporation. When the soil is not fully saturated, actual evaporation decreases linearly with decreasing soil moisture values, as indicated by the figure below.

+
+
+

+
Evaporation reduction as function of available soil moisture
+
+
+ +

When more soil layers are present, soil evaporation is only provided from the upper soil layer, and soil evaporation is split in evaporation from the unsaturated store and evaporation from the saturated store. Water is first evaporated from the unsaturated store. The remaining potential soil evaporation can be used for evaporation from the saturated store, but only when the water table is present in the upper soil layer. Both the evaporation from the unsaturated store and the evaporation from the saturated store are limited by the minimum of the remaining potential soil evaporation and the available water in the unsaturated/saturated zone of the upper soil layer. Also for multiple soil layers, the evaporation (both unsaturated and saturated) decreases linearly with decreasing water availability.

+
+
+

Transpiration

+

The maximum possible root water extraction rate for each soil layer is determined by partitioning the potential transpiration rate T_p based on the fraction of the total root length (rootfraction \SIb{}{-}) in each soil layer. A root water uptake reduction model is used to calculate a reduction coefficient as a function of soil water pressure, that may reduce the maximum possible root water extraction rate. The root water uptake reduction model is based on the concept proposed by Feddes et al. (1978). This concept defines a reduction coefficient \SIb{\alpha}{-} as a function of soil water pressure (\SIb{h}{cm}). Four different levels of h are defined: h1, h2, h3 and h4. h1 represents anoxic moisture conditions, h2 represents field capacity, h3 represents the point of critical soil moisture content (onset of drought stress), and h4 represents the wilting point. The value of h3 is a function of the potential transpiration rate, between \SIb{1}{mm d^{-1}} and \SIb{5}{mm d^{-1}}. If T_p \le \SIb{1}{mm d^{-1}}, h3 is set equal to h3_low (input model parameter). If T_p \ge \SIb{5}{mm d^{-1}}, h3 is set equal to h3_high (input model parameter). For T_p values between \SIb{1}{mm d^{-1}} and \SIb{5}{mm d^{-1}}, the value of h3 is linearly related to T_p (between h3_low and h3_high). Besides model parameters h3_high and h3_low, the critical pressure heads h1, h2 and h4 can be defined as input to the model.

+

The current soil water pressure is determined following the concept defined by Brooks and Corey (1964):

+

\[ + \frac{\theta-\theta_r}{\theta_s-\theta_r} = \min\left\{1, \left(\frac{h_b}{h}\right)^\lambda\right\} +\]

+

where \(\SIb{h}{cm}\) is the pressure head, \(\SIb{h_b}{cm}\) is the air entry pressure head, and \(\theta\), \(\theta_s\), \(\theta_r\) and \(\lambda\) as previously defined.

+

Whenever the current soil water pressure drops below h4, the root water uptake is set to zero. The root water uptake is at ideal conditions whenever the soil water pressure is above h3, with a linear transition between h3 and h4. The assumption that very wet conditions do not affect root water uptake too much is probably generally applicable to natural vegetation. For crops this assumption is not valid and in this case root water uptake above h1 should be set to zero (oxygen deficit) and between h1 and h2 root water uptake is limited. This is possible by setting the input model parameter alpha_h1 at 0 (default is 1).

+
+
+

+
Root water uptake reduction coefficient as a function of soil water pressure
+
+
+ +

The maximum allowed root water extraction from each soil layer in the unsaturated zone is determined based on the fraction of each soil layer in the unsaturated zone that is above the rooting depth (availcap) and the unsaturated storage usld, see conceptual figure below. This is implemented using the following code (i refers to the index of the vector that contains all active cells within the spatial model domain and k refers to the soil layer (from top to bottom) in the unsaturated zone):

+
  # availcap is fraction of soil layer containing roots
+  # if `ust` is `true`, the whole unsaturated store is available for transpiration
+  if ust
+      availcap = usld[k] * 0.99
+  else
+      availcap =
+          min(1.0, max(0.0, (sbm.rootingdepth[i] - sbm.sumlayers[i][k]) / usl[k]))
+  end
+  maxextr = usld[k] * availcap
+
+
+

+
Conceptual overview of how maxextr depends on rooting depth and water table depth
+
+
+ +
+
+
+ +
+
+Note +
+
+
+

When whole_ust_available is set to true in the TOML file, almost the complete unsaturated storage (99%) is available for transpiration, independent of the rootingdepth.

+
[model]
+whole_ust_available = true
+
+
+

The computation of transpiration from the saturated store depends on the water table depth, rooting depth, the reduction coefficient \(\alpha\), the fraction of wet roots and the rootfraction below the water table. The fraction of wet roots is determined using a sigmoid fuction (see figure below). The parameter rootdistpar defines the sharpness of the transition between fully wet and fully dry roots. If the water table depth is equal to or lower than the rooting depth, the remaining potential transpiration is used based on the potential transpiration and actual transpiration in the unsaturated zone. The remaining potential transpiration is multiplied by the wet roots fraction and the reduction coefficient (and limited by the available water in saturated zone) to get the transpiration from the saturated part of the soil. If the water table depth intersects the rooting depth, the potential transpiration is multiplied by the remaining rootfraction (below the water table), wet roots fraction and the reduction coefficient (and limited by the available water in saturated zone) to get the transpiration from the saturated part of the soil. This is implemented using the following code (i refers to the index of the vector that contains all active cells within the spatial model domain):

+
# transpiration from saturated store
+wetroots = scurve(sbm.zi[i], sbm.rootingdepth[i], Float(1.0), sbm.rootdistpar[i])
+alpha = rwu_reduction_feddes(
+    Float(0.0),
+    sbm.h1[i],
+    sbm.h2[i],
+    sbm.h3[i],
+    sbm.h4[i],
+    sbm.alpha_h1[i],
+)
+# include remaining root fraction if rooting depth is below water table zi
+if sbm.zi[i] >= sbm.rootingdepth[i]
+    f_roots = wetroots
+    restevap = sbm.pottrans[i] - actevapustore
+else
+    f_roots = wetroots * (1.0 - rootfraction_unsat)
+    restevap = sbm.pottrans[i]
+end
+actevapsat = min(restevap * f_roots * alpha, satwaterdepth)
+satwaterdepth = satwaterdepth - actevapsat
+
+
+

+
Amount of wet roots and the effect of the rootdistpar parameter
+
+
+ +
+
+
+

Snow and glaciers

+

The snow and glacier model is described in Snow and glaciers. Both options can be enabled by specifying the following in the TOML file:

+
[model]
+snow = true
+glacier = true
+
+
+

Soil processes

+
+

The SBM soil water accounting scheme

+

A detailed description of the Topog_SBM model has been given by Vertessy (1999). Briefly: the soil is considered as a bucket with a certain depth (\(\SIb{z_t}{mm}\)), divided into a saturated store (\(\SIb{S}{mm}\)) and an unsaturated store (\(\SIb{U}{mm}\)). The top of the \(S\) store forms a pseudo-water table at depth \(\SIb{z_{i}}{mm}\) such that the value of \(S\) at any time is given by:

+

\[ + S=(z_t-z_i)(\theta_s-\theta_r) +\]

+

where \(\SIb{\theta_{s}}{-}\) and \(\SIb{\theta_{r}}{-}\) are the saturated and residual soil water contents, respectively.

+

The unsaturated store \(U\) is subdivided into storage (\(\SIb{U_s}{mm}\)) and deficit (\(\SIb{U_d}{m}\)):

+

\[ + U_d=(\theta_s-\theta_r)z_i-U\\ + U_s=U-U_d +\]

+

The saturation deficit (\(\SIb{S_d}{mm}\)) for the soil profile as a whole is defined as:

+

\[ + S_d=(\theta_s-\theta_r)z_t-S +\]

+

All infiltrating water that enters the \(U\) store first. The unsaturated layer can be split-up in different layers, by providing the thickness \(\SIb{}{mm}\) of the layers in the TOML file. The following example specifies three layers (from top to bottom) of 100, 300 and 800 mm:

+
[model]
+thicknesslayers = [100, 300, 800]
+

The code checks for each grid cell the specified layers against the soilthickness \(\SIb{}{mm}\), and adds or removes (partly) layer(s) based on the soilthickness.

+

Assuming a unit head gradient, the transfer of water (\(\SIb{\mathrm{st}}{mm t^{-1}}\)) from a \(\SIb{U}{mm}\) store layer is controlled by the saturated hydraulic conductivity \(\SIb{\subtext{K}{sat}}{mm t^{-1}}\) at depth \(\SIb{z}{mm}\) (bottom layer) or \(\SIb{z_i}{mm}\), the effective saturation degree of the layer, and a Brooks-Corey power coefficient (parameter \(c\)) based on the pore size distribution index \(\lambda\) (Brooks and Corey, 1964):

+

\[ +\begin{gathered} + \mathrm{st}=\subtext{K}{sat}\left(\frac{\theta-\theta_r}{\theta_s-\theta_r}\right)^c\\ + c=\frac{2+3\lambda}{\lambda} +\end{gathered} +\]

+

Here \(\SIb{}{mm t^{-1}}\) denotes milimeter per time step.

+

When the unsaturated layer is not split-up into different layers, it is possible to use the original Topog\_SBM vertical transfer formulation, by specifying in the TOML file:

+
[model]
+transfermethod = true
+

The transfer of water from the \(\SIb{U}{mm}\) store to the \(\SIb{S}{mm}\) store (\(\SIb{st}{mm +t^{-1}}\)) is in that case controlled by the saturated hydraulic conductivity \(\SIb{\subtext{K}{sat}}{mm s^{-1}}\) at depth \(\SIb{z_i}{mm}\) and the ratio between \(\SIb{U}{mm}\) and \(\SIb{S_d}{mm}\):

+

\[ + \mathrm{st}=\subtext{K}{sat}\frac{U_s}{S_d} +\]

+

Four different saturated hydraulic conductivity depth profiles (ksat_profile) are available and a ksat_profile can be specified in the TOML file as follows:

+
[input.vertical]
+ksat_profile = "exponential_constant" # optional, one of ("exponential", "exponential_constant", "layered", "layered_exponential"), default is "exponential"
+

Soil measurements are often available for about the upper \(\SI{1.5-2}{m}\) of the soil column to estimate the saturated hydraulic conductivity, while these measurements are often lacking for soil depths beyond \(\SI{1.5-2}{m}\). These different profiles allow to extent the saturated hydraulic conductivity profile based on measurements (either an exponential fit or hydraulic conductivity value per soil layer) with an exponential or constant profile. By default, with ksat_profile “exponential”, the saturated hydraulic conductivity \(\SIb{\subtext{K}{sat}}{mm +t^{-1}}\) declines with soil depth \(\SIb{z}{mm}\) in the model according to:

+

\[ + \subtext{K}{sat} = K_0 e^{-fz}, +\]

+

where \(\SIb{K_0}{mm s^{-1}}\) is the saturated hydraulic conductivity at the soil surface and \(\SIb{f}{mm^{-1}}\) is a scaling parameter.

+

The plot below shows the relation between soil depth \(z\) and saturated hydraulic conductivity \(\subtext{K}{sat}\) for different values of \(f\).

+
+
+Code +
using Printf
+using CairoMakie
+
+let
+    fig = Figure(resolution=(800, 400))
+    ax = Axis(fig[1, 1], xlabel=L"K_\mathrm{sat}\;[\mathrm{mm/day}]", ylabel=L"-z\;[\mathrm{mm}]")
+
+    z = 0:5.0:1000
+    ksat = 100.0
+    f = 0.6 ./ collect(50:150.0:800)
+
+    for fi in f
+        lines!(ax, ksat .* exp.(-fi .* z), -z, label=@sprintf("%.2e", fi))
+    end
+
+    Legend(fig[1, 2], ax, L"f")
+    fig
+end
+
+
+
┌ Warning: Found `resolution` in the theme when creating a `Scene`. The `resolution` keyword for `Scene`s and `Figure`s has been deprecated. Use `Figure(; size = ...` or `Scene(; size = ...)` instead, which better reflects that this is a unitless size and not a pixel resolution. The key could also come from `set_theme!` calls or related theming functions.
+└ @ Makie ~/.julia/packages/Makie/XQfuO/src/scenes.jl:229
+
+
+
+
+

+
+
+
+
+

With ksat_profile “exponential_constant”, \(\subtext{K}{sat}\) declines exponentially with soil depth \(\SIb{z}{mm}\) until \(\SIb{\subtext{z}{mm}}{mm}\) below the soil surface, and stays constant at and beyond soil depth \(\subtext{z}{exp}\):

+

\[ + \subtext{K}{sat} = \begin{cases} + K_0e^{-fz} & \text{if $z < \subtext{z}{exp}$}\\ + K_0e^{-f\subtext{z}{exp}} & \text{if $z \ge \subtext{z}{exp}$}. + \end{cases} +\]

+

It is also possible to provide a \(\subtext{K}{sat}\) value per soil layer by specifying ksat_profile “layered”, these \(\subtext{K}{sat}\) values are used directly to compute the vertical transfer of water between soil layers and to the saturated store \(S\). Finally, with the ksat_profile “layered_exponential” a \(\subtext{K}{sat}\) value per soil layer is used until depth \(\subtext{z}{layered}\) below the soil surface, and beyond \(\subtext{z}{layered}\) an exponential decline of \(\subtext{K}{sat}\) (of the soil layer with bottom \(\subtext{z}{layered}\)) controlled by \(f\) occurs. The different available ksat_profle options are schematized in the figure below where the blue line represents the \(\subtext{K}{sat}\) value.

+
+
+

+
Overview of available ksat_profile options, for a soil column with five layers
+
+
+
+
+

Infiltration

+

The water available for infiltration is taken as the rainfall including meltwater. Infiltration is determined separately for the compacted and non-compacted areas, as these have different infiltration capacities. Naturally, only the water that can be stored in the soil can infiltrate. If not all water can infiltrate, this is added as excess water to the runoff routing scheme.

+

The infiltrating water is split in two parts, the part that falls on compacted areas and the part that falls on non-compacted areas. The maximum amount of water that can infiltrate in these areas is calculated by taking the minimum of the maximum infiltration rate (infiltcapsoil \(\SIb{}{mm t^{-1}}\) for non-compacted areas and infiltcappath \(\SIb{}{mm +t^{-1}}\) for compacted areas) and the amount of water available for infiltration avail_forinfilt \(\SIb{}{mm t^{-1}}\). The water that can actually infiltrate infiltsoilpath \(\SIb{}{mm t^{-1}}\) is calculated by taking the minimum of the total maximum infiltration rate (compacted and non-compacted areas) and the remaining storage capacity.

+

Infiltration excess occurs when the infiltration capacity is smaller then the throughfall and stemflow rate. This amount of water (infiltexcess \(\SIb{}{mm t^{-1}}\)) becomes overland flow (infiltration excess overland flow). Saturation excess occurs when the (upper) soil becomes saturated and water cannot infiltrate anymore. This amount of water excesswater \(\SIb{}{mm +t^{-1}}\) becomes overland flow (saturation excess overland flow).

+
+

Infiltration in frozen soils

+

If snow processes are modelled, the infiltration capacity is reduced when the soil is frozen (or near freezing point). A infiltration correction factor is defined as a S-curve with the shape as defined below. A parameter (cf_soil) defines the base factor of infiltration when the soil is frozen. The soil temperature is calculated based on the soil temperature on the previous timestep, and the temperature difference between air and soil temperature weighted with a factor (w_soil, which defaults to 0.1125).

+

The near surface soil temperature is modelled using a simple equation (Wigmosta et al., 2009):

+

\[ +T_s^t = T_s^{t-1} + w (T_a - T_s^{t-1}) +\] where \(\SIb{T_s^{t}}{\degree C}\) is the near-surface soil temperature at time \(t\), \(\SIb{T_a}{\degree C}\) is air temperature and \(\SIb{w}{-}\) is a weighting coefficient determined through calibration (default is 0.1125 for daily timesteps).

+

A reduction factor (cf_soil \(\SIb{}{-}\), default is 0.038) is applied to the maximum infiltration rate (infiltcapsoil and infiltcappath), when the following model settings are specified in the TOML file:

+
[model]
+soilinfreduction = true
+snow = true
+

If soilinfreduction is set to false, water is allowed to infiltrate the soil, even if the soil is frozen.

+

A S-curve (see plot below) is used to make a smooth transition (a c-factor (\(c\)) of 8.0 is used):

+

\[ +\begin{gathered} + b = \frac{1.0}{1.0 - \subtext{\mathrm{cf}}{soil}}\\ + \mathrm{soilinfredu} = \frac{1.0}{b + \exp(-c (T_s - a))} + \subtext{\mathrm{cf}}{soil}\\ + a = 0.0\\ + c = 8.0 +\end{gathered} +\]

+
+
+

+
Infiltration correction factor as a function of soil temperature
+
+
+ +
+
+
+

Capillary rise

+

The actual capillary rise actcapflux \(\SIb{}{mm t^{-1}}\) is determined using the following approach: first the saturated hydraulic conductivity ksat \(\SIb{}{mm t^{-1}}\) is determined at the water table \(z_i\); next a potential capillary rise maxcapflux \(\SIb{}{mm t^{-1}}\) is determined from the minimum of ksat, actual transpiration actevapustore \(\SIb{}{mm t^{-1}}\) taken from the \(U\) store, available water in the \(S\) store (satwaterdepth \(\SIb{}{mm}\)) and the deficit of the \(U\) store (ustorecapacity \(\SIb{}{mm}\)), as shown by the following code block:

+
maxcapflux = max(0.0, min(ksat, actevapustore, ustorecapacity, satwaterdepth))
+

Then the potential rise maxcapflux is scaled using the water table depth zi, a maximum water depth cap_hmax \(\SIb{}{mm}\) beyond which capillary rise ceases and a coefficient cap_n \(\SIb{}{-}\), as follows in the code block below (i refers to the index of the vector that contains all active cells within the spatial model domain):

+
if sbm.zi[i] > rootingdepth
+    capflux =
+        maxcapflux * pow(
+            1.0 - min(sbm.zi[i], sbm.cap_hmax[i]) / (sbm.cap_hmax[i]),
+            sbm.cap_n[i],
+        )
+else
+    capflux = 0.0
+end
+

If the roots reach the water table (rootingdepth \(\ge\) sbm.zi), capflux is set to zero.

+

Finally, the capillary rise capflux is limited by the unsaturated store deficit (one or multiple layers), calculated as follows in the code block below (i refers to the index of the vector that contains all active cells within the spatial model domain, and k refers to the layer position):

+
usl[k] * (sbm.theta_s[i] - sbm.theta_r[i]) - usld[k]
+

where usl \(\SIb{}{mm}\) is the unsaturated layer thickness, usld is the ustorelayerdepth \(\SIb{}{mm}\) (amount of water in the unsaturated layer), and \(\theta_s\) and \(\theta_r\) as previously defined.

+

The calculation of the actual capillary rise actcapflux is as follows in the code block below (i refers to the index of the vector that contains all active cells within the spatial model domain, and k refers to the layer position):

+
actcapflux = 0.0
+netcapflux = capflux
+for k = n_usl:-1:1
+    toadd =
+        min(netcapflux, max(usl[k] * (sbm.theta_s[i] - sbm.theta_r[i]) - usld[k], 0.0))
+    usld = setindex(usld, usld[k] + toadd, k)
+    netcapflux = netcapflux - toadd
+    actcapflux = actcapflux + toadd
+end
+

In case of multiple unsaturated layers (n_usl \(>\) 1), the calculation of the actual capillary rise starts at the lowest unsaturated layer while keeping track of the remaining capillary rise netcapflux \(\SIb{}{mm t^{-1}}\).

+
+
+

Leakage

+

If the maxleakage \(\SIb{}{mm day^{-1}}\) input model parameter is set > 0, water is lost from the saturated zone and runs out of the model.

+
+
+
+

Open water

+

Part of the water available for infiltration is diverted to the open water, based on the fractions of river and lakes of each grid cell. The amount of evaporation from open water is assumed to be equal to potential evaporation (if sufficient water is available).

+
+
+

Non-irrigation

+

Non-irrigation water demand and allocation computations are supported for the sectors domestic, industry and livestock. These computations can be enabled by specifying the following in the TOML file:

+
[model.water_demand]
+domestic = true
+industry = true
+livestock = true
+

For these non-irrigation sectors the gross demand (\(d_\mathrm{gross}\) \(\SIb{}{mm t^{-1}}\)) and net demand (\(d_\mathrm{net}\) \(\SIb{}{mm t^{-1}}\)) are provided to the model (input through cyclic or forcing data). Gross demand represents the total demand and hence the total abstraction from surface water or groundwater when sufficient water is available. Net demand represents water consumption. The portion of total abstracted water that is not consumed is returned as surface water. The return flow fraction (\(f_\mathrm{return}\) [-]) is calculated as follows:

+

\[ + \subtext{f}{return} = 1.0 - \frac{\subtext{d}{net}}{\subtext{d}{gross}}, +\] and used to calculate the return flow rate (water abstracted from surface water or groundwater but not consumed). For grid cells containing a river the return flow is directly returned to the river routing component, otherwise the return flow is returned to the overland flow routing component.

+
+
+

Non-paddy irrigation

+

Non-paddy (other crops than flooded rice) water demand and allocation computations are supported. These computations can be enabled by specifying the following in the TOML file:

+
[model.water_demand]
+nonpaddy = true
+

Irrigation is applied during the growing season (when input parameter irrigation_trigger \(\SIb{}{-}\) is true (or on)) and when water depletion exceeds the readily available water:

+

\[ + (\subtext{U}{field} - \subtext{U}{a}) \ge (\subtext{U}{field} - \subtext{U}{h3}) +\] where \(\SIb{\subtext{U}{field}}{mm}\) is the unsaturated store in the root zone at field capacity (defined at a soil water pressure head of \(\SI{-100}{cm}\)), \(\SIb{\subtext{U}{a}}{mm}\) is the actual unsaturated store in the root zone and \(\SIb{\subtext{U}{h3}}{mm}\) is the unsaturated store in the root zone at the critical soil water pressure head h3, below this pressure head reduction of root water uptake starts due to drought stress. The net irrigation demand \(\SIb{}{mm t^{-1}}\) is the irrigation rate that brings the root zone back to field capacity, limited by the soil infiltration capacity \(\SIb{}{mm t^{-1}}\), assuming that farmers do not apply an irrigation rate higher than the soil infiltration capacity. To account for limited irrigation efficiency the net irrigation demand is divided by the irrigation efficiency for non-paddy crops (irrigation_efficiency \(\SIb{}{-}\), default is \(1.0\)), resulting in gross irrigation demand \(\SIb{}{mm t^{-1}}\). Finally, the gross irrigation demand is limited by the maximum irrigation rate (maximum_irrigation_rate \(\SIb{}{mm t^{-1}}\), default is \(\SI{25}{mm\;day-1}\)). If the maximum irrigation rate is applied, irrigation continues at subsequent time steps until field capacity is reached. Irrigation is added to the SBM variable avail_forinfilt \(\SIb{}{mm t^{-1}}\), the amount of water available for infiltration.

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Paddy irrigation

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Paddy (flooded rice) water demand and allocation computations are supported. These computations can be enabled by specifying the following in the TOML file:

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[model.water_demand]
+paddy = true
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Irrigation is applied during the growing season (when input parameter irrigation_trigger \(\SIb{}{-}\) is true (or on)) and when the paddy water depth h \(\SIb{}{mm}\) reaches below the minimum water depth h_min \(\SIb{}{mm}\) (see also the figure below). The net irrigation demand [mm t\(^{-1}\)] is the irrigation rate required to reach the optimal paddy water depth h_opt \(\SIb{}{mm}\), an approach similar to Xie and Cui (2011). To account for limited irrigation efficiency the net irrigation demand is divided by the irrigation efficiency for paddy fields (irrigation_efficiency \(\SIb{}{-}\), default is 1.0), resulting in gross irrigation demand \(\SIb{}{mm t^{-1}}\). Finally, the gross irrigation demand is limited by the maximum irrigation rate (maximum_irrigation_rate \(\SIb{}{mm t^{-1}}\), default is \(\SIb{25}{mm +d^{-1}}\)). If the maximum irrigation rate is applied, irrigation continues at subsequent time steps until the optimal paddy water depth h_opt is reached. Irrigation is added to the SBM variable avail_forinfilt \(\SIb{}{mm t^{-1}}\), the amount of water available for infiltration. When the paddy water depth h exceeds h_max \(\SIb{}{mm}\) runoff occurs, and this amount is added to the runoff routing scheme for overland flow. The figure below shows a typical vertical soil profile of a puddled rice soil with a muddy layer of about 15 cm (in this case represented by two soil layers of 5 cm and 10 cm thickness), a plow soil layer of 5 cm with relative low permeability (vertical hydraulic conductivity \(k_v\) of about \(\SI{5}{mm d^{-1}}\)), and a non-puddled soil below the plow soil layer. The low vertical hydraulic conductivity of the plow soil layer can be realized by making use of the parameter kvfrac \(\SIb{}{-}\), a multiplication factor applied to the vertical hydraulic conductivity at soil depth \(\SIb{z}{mm}\).

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paddy_profile
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Schematic diagram of a paddy field with water balance components and soil profile

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Water withdrawal and allocation

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For the water withdrawal the total gross demand is computed (sum over the irrigation and non-irrigation water demand sectors), in case sufficient water is available the water withdrawal is equal to the total gross demand. In case of insufficient water availability, the water withdrawal is scaled down to the available water, and allocation is then proportional to the gross demand per sector (industry, domestic, livestock and irrigation). Water can be abstracted from the following sources:

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The model parameter frac_sw_used (fraction surface water used, default is 1.0) determines how much water is supplied by available surface water and groundwater.

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Local

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First, surface water abstraction (excluding reservoir and lake locations) is computed to satisfy local (same grid cell) water demand. The available surface water volume is limited by a fixed scaling factor of \(0.8\) to prevent rivers from completely drying out. It is assumed that the water demand cannot be satisfied completely from local surface water and groundwater. The next step is to satisfy the remaining water demand for allocation areas \(\SIb{}{-}\), described in the next sub-section.

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Allocation areas

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For allocation areas the water demand \(\SIb{\subtext{V}{sw, demand}}{m^3}\) and availability \(\SIb{\subtext{V}{sw, availabilty}}{m^3}\) are summed (including reservoir and lake locations limited by a fixed scaling factor of \(0.98\)), and the total surface water abstraction is then:

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\[ + \subtext{V}{sw, abstraction} = \min (\subtext{V}{sw, demand}, \subtext{V}{sw, availabilty}) +\] The fraction of available surface water that can be abstracted \(\SIb{\subtext{f}{sw,abstraction}}{-}\) at the allocation area level is then:

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\[ + \subtext{f}{sw, abstraction} = \frac{\subtext{V}{sw, abstraction}}{\subtext{V}{sw, available}} +\] This fraction is applied to the remaining available surface water of each river cell (including lake and reservoir locations) to compute surface water abstraction at each river cell and to update the local surface water abstraction.

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The fraction of water demand that can be satisfied by available surface water \(\SIb{\subtext{f}{sw, allocation}}{-}\) at the allocation area level is then:

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\[ + \subtext{f}{sw, allocation} = \frac{\subtext{V}{sw, abstraction}}{\subtext{V}{sw, demand}} +\] This fraction is applied to the remaining surface water demand of each land cell to compute the allocated surface water to each land cell.

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Then groundwater abstraction is computed to satisfy the remaining local water demand, where groundwater abstraction is limited by a fixed scaling factor of \(0.75\) applied to the groundwater volume. Finally, for allocation areas the water demand \(\SIb{\subtext{V}{gw,demand}}{m^3}\) and availability \(\SIb{\subtext{V}{gw, availabilty}}{m^3}\) are summed, and the total groundwater abstraction is then:

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\[ + \subtext{V}{gw, abstraction} = \min(\subtext{V}{gw, demand}, \subtext{V}{gw, availabilty}) +\] The fraction of available groundwater that can be abstracted at allocation area level \(\SIb{\subtext{f}{gw, abstraction}}{-}\) at the allocation area level is then:

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\[ + \subtext{f}{gw, abstraction} = \frac{\subtext{V}{gw, abstraction}}{\subtext{V}{gw, available}} +\] This fraction is applied to the remaining available groundwater of each land cell to compute groundwater abstraction and to update the local groundwater abstraction.

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The fraction of water demand that can be satisfied by available groundwater \(\SIb{\subtext{f}{gw,allocation}}{-}\) at the allocation area level is then:

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\[ + \subtext{f}{gw, allocation} = \frac{\subtext{V}{gw, abstraction}}{\subtext{V}{gw, demand}} +\] This fraction is applied to the remaining groundwater demand of each land cell to compute the allocated groundwater to each land cell.

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Abstractions

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Groundwater abstraction is implemented by subtracting this amount from the recharge variable of the lateral subsurface flow component (kinematic wave) or the recharge rate of the groundwater flow module. Surface water abstraction \(\SIb{}{m^3 s^{-1}}\) is divided by the flow length dl \(\SIb{}{m}\) and subtracted from the lateral inflow of kinematic wave routing scheme for river flow. For the local inertial routing scheme (river and optional floodplain routing), the surface water abstraction \(\SIb{}{m^3 s^{-1}}\) is subtracted as part of the continuity equation of the local inertial model. For reservoir and lake locations surface water is abstracted (act_surfacewater_abst_vol \(\SIb{}{m^3 s^{-1}}\)) from the reservoir volume \(\SIb{}{m^3}\) and lake storage \(\SIb{}{m^3}\) respectively, with a subsequent update of the lake waterlevel \(\SIb{}{m}\).

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References

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  • Brooks, R. H., and Corey, A. T., 1964, Hydraulic properties of porous media, Hydrology Papers 3, Colorado State University, Fort Collins, 27 p.
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  • Feddes, R.A., Kowalik, P.J. and Zaradny, H., 1978, Simulation of field water use and crop yield, Pudoc, Wageningen, Simulation Monographs.
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  • Gash, J. H. C., 1979, An analytical model of rainfall interception by forests, Q. J. Roy. Meteor. Soc., 105, 43–55, doi:1026 10.1002/qj.497105443041027.
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  • Liu, S., 1998, Estimation of rainfall storage capacity in the canopies of cypress wetlands and slash pine uplands in North-Central Florida, J. Hydr., 207, 32–41, doi: 10.1016/S0022-1694(98)00115-2.
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  • Pitman, J., 1989, Rainfall interception by bracken in open habitats—relations between leaf area, canopy storage and drainage rate, J. Hydr. 105, 317–334, doi: 10.1016/0022-1694(89)90111-X.
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  • Van Dijk, A. I. J. M., and Bruijnzeel, L. A., 2001, Modelling rainfall interception by vegetation of variable density using an adapted analytical model, Part 2, Model validation for a tropical upland mixed cropping system, J. Hydr., 247, 239–262.
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  • Vertessy, R., and Elsenbeer, H., 1999, Distributed modeling of storm flow generation in an amazonian rain forest catchment: effects of model parameterization, Water Resour. Res., 35, 2173–2187. doi: 10.1029/1999WR9000511257.
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  • Wigmosta, M. S., Lane, L. J., Tagestad, J. D., and Coleman A. M., 2009, Hydrologic and erosion models to assess land use and management practices affecting soil erosion, J. Hydrol. Eng., 14, 27-41.
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  • Xie, X. and Cui, Y., 2011, Development and test of SWAT for modeling hydrological processes in irrigation districts with paddy rice, J. Hydrol., 396, pp. 61-71.
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Sediment

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Over the land, soil erosion, also called soil loss, is closely linked to the water cycle. The main processes governing sediment generation are splash erosion from rain droplets, and sheet and rill erosion from the shear stress caused by overland flow. The intensity of soil erosion by rain or flow depends on the land and soil characteristics such as slope, land use or soil type. Once soil is eroded, the detached particles can be transported downslope by overland flow. Along the transport pathways, soil particles can also be deposited due to a low flow velocity, a change of topography in depressions, footslopes or valley bottoms, and/or can be filtered and stopped by a change in vegetation such as field boundaries.

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The inland part of the sediment gathers these different processes, separated in a vertical structure for the soil loss and lateral structure for the transport in overland flow.

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Overview of the different processes for a land cell in wflow_sediment.
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Soil Erosion

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The first process to consider in sediment dynamics is the generation of sediments by land erosion. The main processes behind soil loss are rainfall erosion and overland flow erosion. In order to model such processes at a fine time and space scale, physics-based models such as ANSWERS and EUROSEM were chosen here.

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The choice of rainfall erosion method is set up in the model section of the TOML:

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[model]
+rainerosmethod = "answers" # Rainfall erosion equation: ["answers", "eurosem"]
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Rainfall erosion

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In wflow_sediment, rainfall erosion can both be modelled using EUROSEM or ANSWERS equation. The main difference between the models is that EUROSEM uses a more physics-based approach based on the kinetic energy of the rain drops impacting the soil (Morgan et al, 1998), while ANSWERS is more empirical and uses parameters from the USLE model (Beasley et al, 1991).

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In EUROSEM, rainfall erosion is modelled according to rainfall intensity and its kinetic energy when it reaches the soil according to equations developed by Brandt (1990). As the intensity of the rain kinetic energy depends on the length of the fall, rainfall intercepted by vegetation will then be reduced compared to direct throughfall. The kinetic energy of direct throughfall is estimated by (Morgan et al, 1998): \[ + \subtext{\mathrm{KE}}{direct} = 8.95 + 8.44\,\log_{10}(R_i) +\]

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where \(\SIb{\subtext{\mathrm{KE}}{direct}}{J m^{-2} mm^{-1}}\) is the kinetic energy of direct throughfall and \(\SIb{R_i}{mm h^{-1}}\) is rainfall intensity. If the rainfall is intercepted by vegetation and falls as leaf drainage, its kinetic energy is then reduced according to (Brandt, 1990): \[ + \subtext{\mathrm{KE}}{leaf} = 15.8\,\sqrt{H_p} - 5.87 +\] where \(\SIb{\subtext{\mathrm{KE}}{leaf}}{J m^{-2} mm^{-1}}\) is kinetic energy of leaf drainage and \(\SIb{H_p}{m}\) is the effective canopy height (half of plant height). Canopy height can be derived from the global map from Simard & al. (2011) or by user input depending on the land use.

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Kinetic energies from both direct throughfall and leaf drainage are then multiplied by the respective depths of direct throughfall and leaf drainage (mm) and added to get the total rainfall kinetic energy \(\mathrm{KE}\). The soil detached by rainfall \(\SIb{D_R}{g m^{-2}}\) is then: \[ + D_R = k\,\mathrm{KE}\,e^{-\varphi h} +\] where \(\SIb{k}{g J^{-1}}\) is an index of the detachability of the soil, \(\SIb{\mathrm{KE}}{J +m^{-2}}\) is the total rainfall kinetic energy, \(\SIb{h}{m}\) is the surface runoff depth on the soil and \(\varphi\) is an exponent varying between \(0.9\) and \(3.1\) used to reduce rainfall impact if the soil is already covered by water. As a simplification, Torri (1987) has shown that a value of \(2.0\) for \(\varphi\) is representative enough for a wide range of soil conditions. The detachability of the soil \(k\) depends on the soil texture (proportion of clay, silt and sand content) and corresponding values are defined in EUROSEM user guide (Morgan et al, 1998). As a simplification, in wflow_sediment, the mean value of the detachability shown in the table below are used. Soil texture can for example be derived from the topsoil clay and silt content from SoilGrids (Hengl et al, 2017).

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Mean detachability of soil depending on its texture (Morgan et al, 1998).
Texture (USDA system)Mean detachability \(\SIb{k}{g J^{-1}}\)
Clay2.0
Clay Loam1.7
Silt1.2
Silt Loam1.5
Loam2.0
Sandy Loam2.6
Loamy Sand3.0
Fine Sand3.5
Sand1.9
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Rainfall erosion is handled differently in ANSWERS. There, the impacts of vegetation and soil properties are handled through the USLE coefficients in the equation (Beasley et al, 1991): \[ + D_R = 0.108 \, \subtext{C}{USLE} \, \subtext{K}{USLE} \, A_i \, R_i^2 +\] where \(\SIb{D_R}{kg min^{-1}}\) is the soil detachment by rainfall, \(\subtext{C}{USLE}\) is the soil cover-management factor from the USLE equation, \(\subtext{K}{USLE}\) is the soil erodibility factor from the USLE equation, \(\SIb{A_i}{m^2}\) is the area of the cell and \(\SIb{R_i}{mm\;min^{-1}}\) is the rainfall intensity. There are several methods available to estimate the \(C\) and \(K\) factors from the USLE. They can come from user input maps, for example maps resulting from Panagos & al.’s recent studies for Europe (Panagos et al, 2015) (Ballabio et al, 2016). To get an estimate of the \(C\) factor globally, the other method is to estimate \(C\) values for the different land use type in from global land cover maps (e.g. GlobCover). An example is given for the global land cover map GlobCover, summed up in the table below, the values come from a literature study including Panagos et al.’s review (2015), Gericke & al. (2015), Mansoor & al. (2013), Chadli et al. (2016), de Vente et al. (2009), Borrelli et al. (2014), Yang et al. (2003) and Bosco et al. (2015).

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The other methods to estimate the USLE \(K\) factor are to use either topsoil composition or topsoil geometric mean diameter. \(K\) estimation from topsoil composition is estimated with the equation developed in the EPIC model (Williams et al, 1983): \[ +\begin{gathered} + \subtext{K}{USLE} = \left[ 0.2 + 0.3\exp\left(-0.0256\;\mathrm{SAN}\frac{(1-\mathrm{SIL})}{100}\right) \right] + \left(\frac{\mathrm{SIL}}{\mathrm{CLA}+\mathrm{SIL}}\right)^{0.3} \\ + \left(1-\frac{0.25\;\mathrm{OC}}{\mathrm{OC}+e^{3.72-2.95\;\mathrm{OC}}}\right)\left(1-\frac{0.75\;\mathrm{SN}}{\mathrm{SN}+e^{-5.51+22.9\;\mathrm{SN}}}\right) +\end{gathered} +\] where \(\SIb{\mathrm{CLA}}{\%}\), \(\SIb{\mathrm{SIL}}{\%}\), \(\SIb{\mathrm{SAN}}{\%}\) are respectively the clay, silt and sand fractions of the topsoil, \(\SIb{OC}{\%}\) is the topsoil organic carbon content and \(\mathrm{SN} = 1-\mathrm{SAN}/100\). These soil parameters can be derived for example from the SoilGrids dataset. The \(K\) factor can also be estimated from the soil mean geometric diameter using the formulation from the RUSLE guide by Renard & al. (1997): \[ + \subtext{K}{USLE} = 0.0034 + 0.0405\exp\left(-\dfrac{1}{2}\left(\dfrac{\log_{10}(D_g)+1.659}{0.7101}\right)^2\right) +\] where \(D_g\) is the soil geometric mean diameter (mm) estimated from topsoil clay, silt, sand fraction.

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Estimation of USLE C factor per Globcover land use type
GlobCover ValueGlobcover label\(\subtext{C}{USLE}\)
11Post-flooding or irrigated croplands (or aquatic)0.2
14Rainfed croplands0.35
20Mosaic cropland (50-70%) vegetation (grassland/shrubland/forest) (20-50%)0.27
30Mosaic vegetation (grassland/shrubland/forest) (50-70%) / cropland (20-50%)0.25
40Closed to open (>15%) broadleaved evergreen or semi-deciduous forest (>5m)0.0065
50Closed (>40%) broadleaved deciduous forest (>5m)0.001
60Open (15-40%) broadleaved deciduous forest/woodland (>5m)0.01
70Closed (>40%) needleleaved evergreen forest (>5m)0.001
90Open (15-40%) needleleaved deciduous or evergreen forest (>5m)0.01
100Closed to open (>15%) mixed broadleaved and needleleaved forest (>5m)0.02
110Mosaic forest or shrubland (50-70%) / grassland (20-50%)0.015
120Mosaic grassland (50-70%) / forest or shrubland (20-50%)0.03
130Closed to open (>15%) (broadleaved or needleleaved, evergreen or deciduous) shrubland (<5m)0.035
140Closed to open (>15%) herbaceous vegetation (grassland, savannas or lichens/mosses)0.05
150Sparse (<15%) vegetation0.35
160Closed to open (>15%) broadleaved forest regularly flooded (semi-permanently or temporarily) - Fresh or brackish water0.001
170Closed (>40%) broadleaved forest or shrubland permanently flooded - Saline or brackish water0.0005
180Closed to open (>15%) grassland or woody vegetation on regularly flooded or waterlogged soil - Fresh, brackish or saline water0.04
190Artificial surfaces and associated areas (Urban areas >50%)0.0
200Bare areas0.0
210Water bodies0.0
220Permanent snow and ice0.0
230No data (burnt areas, clouds,…)0.0
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Overland flow erosion

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Overland flow (or surface runoff) erosion is induced by the strength of the shear stress of the surface water on the soil. As in rainfall erosion, the effect of the flow shear stress can be reduced by the soil vegetation or by the soil properties. In wflow_sediment, soil detachment by overland flow is modelled as in ANSWERS with (Beasley et al, 1991): \[ + D_G = 0.90 \, \subtext{C}{USLE} \, \subtext{K}{USLE} \, A_i \, S \, q +\] where \(\SIb{D_F}{kg\;min^{-1}}\) is soil detachment by flow, \(\subtext{C}{USLE}\) and \(\subtext{K}{USLE}\) are the USLE cover and soil erodibility factors, \(\SIb{A_i}{m^2}\) is the cell area, \(S\) is the slope gradient and \(\SIb{q}{m^2 min^{-1}}\) is the overland flow rate per unit width. The USLE \(C\) and \(K\) factors can be estimated with the same methods as for rainfall erosion and here the slope gradient is obtained from the sinus rather than the tangent of the slope angle.

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Delivery to the river system

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Once soil is detached, it can be transported by overland flow and reach the river system. This process is described in Sediment Flux in overland flow.

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References

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  • D.B Beasley and L.F Huggins. ANSWERS - Users Manual. Technical report, EPA, 1991.
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  • P. Borrelli, M. Märker, P. Panagos, and B. Schütt. Modeling soil erosion and river sediment yield for an intermountain drainage basin of the Central Apennines, Italy. Catena, 114:45-58, +
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    1. 10.1016/j.catena.2013.10.007
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  • C. Bosco, D. De Rigo, O. Dewitte, J. Poesen, and P. Panagos. Modelling soil erosion at European scale: Towards harmonization and reproducibility. Natural Hazards and Earth System Sciences, 15(2):225-245, 2015. 10.5194/nhess-15-225-2015
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  • C.J Brandt. Simulation of the size distribution and erosivity of raindrops and throughfall drops. Earth Surface Processes and Landforms, 15(8):687-698, dec 1990.
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  • K. Chadli. Estimation of soil loss using RUSLE model for Sebou watershed (Morocco). Modeling Earth Systems and Environment, 2(2):51, 2016. 10.1007/s40808-016-0105-y
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  • G R Foster. Modeling the erosion process. Hydrologic modeling of small watersheds, pages 295-380, 1982.
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  • A. Gericke. Soil loss estimation and empirical relationships for sediment delivery ratios of European river catchments. International Journal of River Basin Management, 2015. 10.1080/15715124.2014.1003302
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  • L.D.K. Mansoor, M.D. Matlock, E.C. Cummings, and L.L. Nalley. Quantifying and mapping multiple ecosystem services change in West Africa. Agriculture, Ecosystems and Environment, 165:6-18, 2013. 10.1016/j.agee.2012.12.001
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  • Q Morgan, J.N Smith, R.E Govers, G Poesen, J.W.A Auerswald, K Chisci, G Torri, D Styczen, and M E Folly. The European soil erosion model (EUROSEM): documentation and user guide. Technical report, 1998.
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  • S.L Neitsch, J.G Arnold, J.R Kiniry, and J.R Williams. SWAT Theoretical Documentation Version +
      +
    1. Texas Water Resources Institute, pages 1-647, 2011. 10.1016/j.scitotenv.2015.11.063
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  • P. Panagos, P. Borrelli, K. Meusburger, C. Alewell, E. Lugato, and L. Montanarella. Estimating the soil erosion cover-management factor at the European scale. Land Use Policy, 48:38-50, 2015. 10.1016/j.landusepol.2015.05.021
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  • K Renard, Gr Foster, Ga Weesies, Dk McCool, and Dc Yoder. Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). Washington, 1997.
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  • D. Torri, M. Sfalanga, and M. Del Sette. Splash detachment: Runoff depth and soil cohesion. Catena, 14(1-3):149-155, 1987. 10.1016/S0341-8162(87)80013-9
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  • J. de Vente, J. Poesen, G. Govers, and C. Boix-Fayos. The implications of data selection for regional erosion and sediment yield modelling. Earth Surface Processes and Landforms, 34(15):1994-2007, 2009. 10.1002/esp.1884
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  • G. Verstraeten and J. Poesen. Estimating trap efficiency of small reservoirs and ponds: methods and implications for the assessment of sediment yield. Progress in Physical Geography, 24(2):219-251, 2000. 10.1177/030913330002400204
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  • O. Vigiak, A. Malago, F. Bouraoui, M. Vanmaercke, and J. Poesen. Adapting SWAT hillslope erosion model to predict sediment concentrations and yields in large Basins. Science of the Total Environment, 538:855-875, 2015. 10.1016/j.scitotenv.2015.08.095
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  • J.R. Williams, K.G. Renard, and P.T. Dyke. EPIC A new method for assessing erosion’s effect on soil productivity. Journal of Soil and Water Conservation, 38(5):381-383, 1983.
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  • D. Yang, S. Kanae, T. Oki, T. Koike, and K. Musiake. Global potential soil erosion with reference to land use and climate changes. Hydrological Processes, 17(14):2913-2928, 2003. 10.1002/hyp.1441
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+ + + + + + \ No newline at end of file diff --git a/previews/PR448/model_docs/vertical/shared_processes.html b/previews/PR448/model_docs/vertical/shared_processes.html new file mode 100644 index 000000000..b753ab59e --- /dev/null +++ b/previews/PR448/model_docs/vertical/shared_processes.html @@ -0,0 +1,1109 @@ + + + + + + + + + +Shared processes – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Shared processes

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Snow and glaciers

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Snow modelling

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If the air temperature, \(T_a\), is below a user-defined threshold tt \(\SIb{}{\degree C}\) precipitation occurs as snowfall, whereas it occurs as rainfall if \(T_a ≥ \mathrm{tt}\). A another parameter tti defines how precipitation can occur partly as rain or snowfall (see the figure below). If precipitation occurs as snowfall, it is added to the dry snow component within the snow pack. Otherwise it ends up in the free water reservoir, which represents the liquid water content of the snow pack. Between the two components of the snow pack, interactions take place, either through snow melt (if temperatures are above a threshold tt) or through snow refreezing (if temperatures are below threshold tt).

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The respective rates of snow melt and refreezing are:

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\[ +\begin{align*} + Q_m &=& \subtext{\mathrm{cf}}{max}(T_a−\mathrm{tt})\, &&T_a > \mathrm{tt} \\ + Q_r &=& \subtext{\mathrm{cf}}{max} \, \mathrm{cf}_r(\mathrm{tt}−T_a) &&T_a < \mathrm{tt} +\end{align*} +\]

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where \(Q_m\) is the rate of snow melt, \(Q_r\) is the rate of snow refreezing, and \(\SIb{\subtext{\mathrm{cf}}{max}}{mm\;(\degree C)^{-1} day^{-1}}\) and \(\mathrm{cf}_r\) are user defined model parameters (the melting factor and the refreezing factor respectively).

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The fraction of liquid water in the snow pack is at most equal to a user defined fraction, whc, of the water equivalent of the dry snow content. If the liquid water concentration exceeds whc, either through snow melt or incoming rainfall, the surplus water (rainfall) becomes available for infiltration into the soil:

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snowwater = snowwater - refreezing  # free water content in snow
+maxsnowwater = snow * whc  # max water in the snow
+snowwater = snowwater + snowmelt + rainfall  # add all water and potentially supersaturate the snowpack
+rainfall = max(snowwater - maxsnowwater, 0.0)  # rain + surplus snowwater
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Snowmelt and refreezing rates as a function of temperature (for set melting and refreezing factors)
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Glacier modelling

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Glacier processes can be modelled if the snow model is enabled. Glacier modelling is very similar to snow modelling and considers two main processes: glacier build-up from snow turning into firn/ice (using the HBV-light model) and glacier melt (using a temperature degree-day model).

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The definition of glacier boundaries and initial volume is defined by two parameters. The parameter glacierfrac gives the fraction of each grid cell covered by a glacier as a number between zero and one. The state parameter glacierstore gives the amount of water (in mm w.e.) within the glaciers at each grid cell. Because the glacier store (glacierstore) cannot be initialized by running thFe model for a couple of years, a default initial state should be supplied by adding this parameter to the input static file. The required glacier data can be prepared from available glacier datasets.

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First, a fixed fraction of the snowpack on top of the glacier is converted into ice for each timestep and added to the glacierstore using the HBV-light model (Seibert et al., 2018). This fraction g_sifrac typically ranges from \(0.001\) to \(0.006\).

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Then, when the snowpack on top of the glacier is almost all melted (snow cover \(< +\SI{10}{mm}\)), glacier melt is enabled and estimated with a degree-day model. If the air temperature, \(T_a\), is below a certain threshold g_tt (\(\SIb{}{\degree C}\)) precipitation occurs as snowfall, whereas it occurs as rainfall if \(T_a ≥\) g_tt.

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With this the rate of glacier melt in mm is estimated as:

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\[ +Q_m = \subtext{g}{cfmax}(T_a − \subtext{g}{tt})\, ; \, T_a > \subtext{g}{tt} +\]

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where \(Q_m\) is the rate of glacier melt and \(\SIb{\subtext{g}{cfmax}}{mm (\degree +C)^{-1}day^{-1}}\) is the melting factor. Parameter g_tt can be taken as equal to the snow tt parameter. Values of the melting factor g_cfmax normally varies from one glacier to another and some values are reported in the literature. g_cfmax can also be estimated by multiplying snow cfmax by a factor between 1 and 2, to take into account the higher albedo of ice compared to snow.

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Rainfall interception

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Both the Gash and Rutter models are available to estimate rainfall interception by the vegetation. The selection of an interception model depends on the simulation timestep.

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The analytical (Gash) model

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The analytical model of rainfall interception is based on Rutter’s numerical model. The simplifications that introduced allow the model to be applied on a daily basis, although a storm-based approach will yield better results in situations with more than one storm per day. The amount of water needed to completely saturate the canopy is defined as:

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\[ +P'=\frac{-\overline{R}S}{\overline{E}_w}\log\left[1-\frac{\overline{E}_w}{\overline{R}}(1-p-p_t)^{-1}\right] +\]

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where \(\overline{R}\) is the average precipitation intensity on a saturated canopy and \(\overline{E}_{w}\) the average evaporation from the wet canopy and with the vegetation parameters \(S\), \(p\) and \(p_t\) as defined previously. The model uses a series of expressions to calculate the interception loss during different phases of a storm. An analytical integration of the total evaporation and rainfall under saturated canopy conditions is then done for each storm to determine average values of \(\overline{E}_{w}\) and \(\overline{R}\). The total evaporation from the canopy (the total interception loss) is calculated as the sum of the components listed in the table below. Interception losses from the stems are calculated for days with \(P\geq S_{t}/p_{t}\). \(p_t\) and \(S_t\) are small and neglected.

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Formulation of the components of interception loss according to Gash:
ComponentsInterception loss
For \(m\) small storms (\(P_g<{P'}_g\))\((1-p-p_t)\sum_{j=1}^m P_{g,j}\)
Wetting up the canopy in \(n\) large storms (\(P_g\geq{P'}_g\))\(n(1-p-p_{t}){P'}_g-nS\)
Evaporation from saturated canopy during rainfall\(\overline{E}/\overline{R}\sum_{j=1}^n(P_{g,j}-{P'}_g)\)
Evaporation after rainfall ceases for \(n\) large storms\(nS\)
Evaporation from trunks in \(q\) storms that fill the trunk storage\(qS_t\)
Evaporation from trunks in \(m+n-q\) storms that do not fill the trunk storage\(p_t\sum_{j=1}^{m+n-q}P_{g,j}\)
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In applying the analytical model, saturated conditions are assumed to occur when the hourly rainfall exceeds a certain threshold. Often a threshold of 0.5 mm/hr is used. \(\overline{R}\) is calculated for all hours when the rainfall exceeds the threshold to give an estimate of the mean rainfall rate onto a saturated canopy.

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Gash (1979) has shown that in a regression of interception loss on rainfall (on a storm basis) the regression coefficient should equal to \(\overline{E}_w/\overline{R}\). Assuming that neither \(\overline{E}_w\) nor \(\overline{R}\) vary considerably in time, \(\overline{E}_w\) can be estimated in this way from \(\overline{R}\) in the absence of above-canopy climatic observations. Values derived in this way generally tend to be (much) higher than those calculated with the penman-monteith equation.

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The modified rutter model

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For sub daily timesteps the interception is calculated using a simplification of the Rutter model. The simplified model is solved explicitly and does not take drainage from the canopy into account.

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Wflow.rainfall_interception_modrut
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Interception parameters from LAI

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The SBM concept can determine the interception parameters from leaf area index (LAI) climatology. In order to switch this on you must define this cyclic parameter in the TOML file, the parameter is read from path_static, as follows:

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[input]
+path_forcing = "data/forcing-moselle.nc"
+path_static = "data/staticmaps-moselle.nc"
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+cyclic = ["vertical.leaf_area_index"]
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Furthermore these additional parameters are required: + Specific leaf storage (sl \(\SIb{}{mm}\)) + Storage woody part of vegetation (swood \(\SIb{}{mm}\)) + Extinction coefficient (kext \(\SIb{}{-}\))

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Here it is assumed that cmax \(\SIb{}{mm}\) (leaves) (canopy storage capacity for the leaves only) relates linearly with LAI (c.f. Van Dijk and Bruijnzeel 2001). This done via the sl. sl can be determined through a lookup table with land cover based on literature (Pitman 1989, Lui 1998). Next the cmax (leaves) is determined using:

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\[ + \mathrm{cmax}(\mathrm{leaves}) = \mathrm{sl} \cdot \mathrm{LAI} +\]

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To get to total storage (cmax) the woody part of the vegetation also needs to be added. As for sl, the storage of the woody part swood can also be related to land cover (lookup table).

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The canopy gap fraction is determined using the extinction coefficient kext (van Dijk and Bruijnzeel 2001):

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\[ + \mathrm{canopygapfraction} = \exp(-\subtext{k}{ext} \cdot \mathrm{LAI}) +\]

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The extinction coefficient kext can be related to land cover.

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References

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  • Seibert, J., Vis, M. J. P., Kohn, I., Weiler, M., and Stahl, K., 2018, Technical note: Representing glacier geometry changes in a semi-distributed hydrological model, Hydrol. Earth Syst. Sci., 22, 2211–2224, https://doi.org/10.5194/hess-22-2211-2018.
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Note that more pages are available in the sidebar.\n\n\n\n\n \n \n Required files\n \n \n \n \n Explanation .toml file\n \n \n \n \n Frequently asked questions\n \n \n\n\n\n\n\n\n Back to top", + "crumbs": [ + "User guide" + ] + }, + { + "objectID": "getting_started/index.html", + "href": "getting_started/index.html", + "title": "Getting started", + "section": "", + "text": "Installing wflow\n \n \n \n \n Download example models\n \n \n \n \n Building a model\n \n \n\n\n\n\n\n\n Back to top", + "crumbs": [ + "Getting started" + ] + }, + { + "objectID": "model_docs/vertical/sbm.html", + "href": "model_docs/vertical/sbm.html", + "title": "SBM", + "section": "", + "text": "The SBM vertical concept has its roots in the Topog_SBM model but has had considerable changes over time. The main differences are:\n\nThe unsaturated zone can be split-up in different layers\nThe addition of evapotranspiration losses\nThe addition of a capillary rise\n\nThe sections below describe the working of the SBM vertical concept in more detail.", + "crumbs": [ + "Model documentation", + "Vertical processes", + "SBM" + ] + }, + { + "objectID": "model_docs/vertical/sbm.html#introduction", + "href": "model_docs/vertical/sbm.html#introduction", + "title": "SBM", + "section": "", + "text": "The SBM vertical concept has its roots in the Topog_SBM model but has had considerable changes over time. The main differences are:\n\nThe unsaturated zone can be split-up in different layers\nThe addition of evapotranspiration losses\nThe addition of a capillary rise\n\nThe sections below describe the working of the SBM vertical concept in more detail.", + "crumbs": [ + "Model documentation", + "Vertical processes", + "SBM" + ] + }, + { + "objectID": "model_docs/vertical/sbm.html#precipitation", + "href": "model_docs/vertical/sbm.html#precipitation", + "title": "SBM", + "section": "Precipitation", + "text": "Precipitation\nThe division between solid and liquid precipitation (snowfall and rainfall, respectively) is performed based on the air temperature. If the temperature is below a threshold temperature (tt), precipitation will fall as snow. An interval parameter (tti) defines the range over which precipitation is partly falling as snow, and partly as rain. Snowfall is added to the snowpack, where it is subject to melting and refreezing (see the section on snow and glaciers). The amount of rainfall is subject to interception, and ultimately becomes available for evaporation and/or soil processes.\n\n\n\nDivision between snow and precipitation based on the threshold temperature", + "crumbs": [ + "Model documentation", + "Vertical processes", + "SBM" + ] + }, + { + "objectID": "model_docs/vertical/sbm.html#rainfall-interception", + "href": "model_docs/vertical/sbm.html#rainfall-interception", + "title": "SBM", + "section": "Rainfall interception", + "text": "Rainfall interception\nTwo different interception models are available: the analytical Gash model, and the modified Rutter model. The simulation timestep defines which interception model is used, where daily (or larger) timesteps use the Gash model, and timesteps smaller than daily use the modified Rutter model.\n\nThe analytical (Gash) model (Gash, 1979)\nThe analytical model of rainfall interception is based on Rutter’s numerical model. Simplifications allow the model to be applied on a daily basis, although a storm-based approach will yield better results in situations with more than one storm per day. The amount of water needed to completely saturate the canopy is defined as:\n\\[\n P'=\\frac{-\\overline{R}S}{\\overline{E}_{w}}\\log\\left[1-\\frac{\\overline{E}_{w}}{\\overline{R}}(1-p-p_{t})^{-1}\\right]\n\\]\nwhere \\(\\overline{R}\\) is the average precipitation intensity on a saturated canopy and \\(\\overline{E}_{w}\\) the average evaporation from the wet canopy and with the vegetation parameters \\(S\\), \\(p\\) and \\(p_t\\) as defined previously. The model uses a series of expressions to calculate the interception loss during different phases of a storm. An analytical integration of the total evaporation and rainfall under saturated canopy conditions is performed for each storm to determine average values of \\(\\overline{E}_{w}\\) and \\(\\overline{R}\\). The total evaporation from the canopy (the total interception loss) is calculated as the sum of the components listed in the table below. Interception losses from the stems are calculated for days with \\(P\\geq S_{t}/p_{t}\\). \\(p_t\\) and \\(S_t\\) are small and neglected.\n\nFormulation of the components of interception loss according to Gash:\n\n\n\n\n\n\nComponents\nInterception loss\n\n\n\n\nFor \\(m\\) small storms (\\(P_{g}<{P'}_{g}\\))\n\\((1-p-p_{t})\\sum_{j=1}^{m}P_{g,j}\\)\n\n\nWetting up the canopy in \\(n\\) large storms (\\(P_{g}\\geq{P'}_{g}\\))\n\\(n(1-p-p_{t}){P'}_{g}-nS\\)\n\n\nEvaporation from saturated canopy during rainfall\n\\(\\overline{E}/\\overline{R}\\sum_{j=1}^{n}(P_{g,j}-{P'}_{g})\\)\n\n\nEvaporation after rainfall ceases for \\(n\\) large storms\n\\(nS\\)\n\n\nEvaporation from trunks in \\(q\\) storms that fill the trunk storage\n\\(qS_{t}\\)\n\n\nEvaporation from trunks in \\(m+n-q\\) storms that do not fill the trunk storage\n\\(p_{t}\\sum_{j=1}^{m+n-q}P_{g,j}\\)\n\n\n\nIn applying the analytical model, saturated conditions are assumed to occur when the hourly rainfall exceeds a certain threshold. Often a threshold of 0.5 mm/hr is used. \\(\\overline{R}\\) is calculated for all hours when the rainfall exceeds the threshold to give an estimate of the mean rainfall rate onto a saturated canopy.\nGash (1979) has shown that in a regression of interception loss on rainfall (on a storm basis) the regression coefficient should equal to \\(\\overline{E}_w/\\overline{R}\\). Assuming that neither \\(\\overline{E}_w\\) nor \\(\\overline{R}\\) vary considerably in time, \\(\\overline{E}_w\\) can be estimated in this way from \\(\\overline{R}\\) in the absence of above-canopy climatic observations. Values derived in this way generally tend to be (much) higher than those calculated with the penman-monteith equation.\n\n\nThe modified rutter model\nFor sub daily timesteps the interception is calculated using a simplification of the Rutter model. The simplified model is solved explicitly and does not take drainage from the canopy into account. The amount of stemflow is taken as a fraction (0.1 * canopygapfraction) of the precipitation. Throughfall equals to the amount of water that cannot be stored by the canopy, plus the rainfall that is not captured by the canopy. Water can evaporate from the canopy storage, taken as the minimum between potential evaporation and the current storage. The “left-over” potential evaporation (if any) is returned as output.\nWflow.rainfall_interception_modrut\n\n\nInterception parameters from LAI\nThe SBM concept can determine the interception parameters from leaf area index (LAI) climatology. In order to switch this on you must define this cyclic parameter in the TOML file, the parameter is read from path_static, as follows:\n[input]\npath_forcing = \"data/forcing-moselle.nc\"\npath_static = \"data/staticmaps-moselle.nc\"\n\ncyclic = [\"vertical.leaf_area_index\"]\nFurthermore, these additional parameters are required: + Specific leaf storage (sl \\SIb{}{mm}) + Storage woody part of vegetation (swood \\SIb{}{mm}) + Extinction coefficient (kext \\SIb{}{-})\nHere it is assumed that cmax \\SIb{}{mm} (leaves) (canopy storage capacity for the leaves only) relates linearly with LAI (c.f. Van Dijk and Bruijnzeel 2001). This is done via the sl. sl can be determined through a lookup table with land cover based on literature (Pitman 1989, Lui 1998). Next the cmax (leaves) is determined using:\n\\[\n c_{\\max}(\\mathrm{leaves}) = \\mathrm{sl} \\cdot \\mathrm{LAI}\n\\]\nTo get to total storage (cmax) the woody part of the vegetation also needs to be added. As for sl, the storage of the woody part swood can also be related to land cover (lookup table).\nThe canopy gap fraction is determined using the extinction coefficient kext (van Dijk and Bruijnzeel 2001):\n\\[\n \\mathrm{canopygapfraction} = \\exp(-\\subtext{k}{ext} \\cdot \\mathrm{LAI})\n\\]\nThe extinction coefficient kext can be related to land cover.", + "crumbs": [ + "Model documentation", + "Vertical processes", + "SBM" + ] + }, + { + "objectID": "model_docs/vertical/sbm.html#evaporation", + "href": "model_docs/vertical/sbm.html#evaporation", + "title": "SBM", + "section": "Evaporation", + "text": "Evaporation\nThe wflow_sbm model assumes the input to be potential reference evapotranspiration. A crop coefficient (kc, set to 1 by default) is used to convert the potential evapotranspiration rate of a reference crop fully covering the soil to the potential evapotranspiration rate of vegetation (natural and agricultural) fully covering the soil. The crop coefficient kc of wflow_sbm is used for a surface completely covered by vegetation, and does not include the effect of growing stages of vegetation and soil cover. These effects are handled separately through the use of the canopy gap fraction.\nIt is assumed that the potential evaporation rate of intercepted water by vegetation is equal to the potential evapotranspiration rate of vegetation (fully covering the soil) multiplied by the canopy fraction. The potential evapotranspiration rate left over after interception is available for transpiration. For potential open water evaporation (river and water bodies) the potential reference evapotranspiration rate is used (multipled by the river fraction riverfrac, and open water fraction waterfrac). Also for potential soil evaporation the potential reference evapotranspiration rate is used, multiplied by the canopy gap fraction corrected by the sum of total water fraction (riverfrac and waterfrac) and the fraction covered by a glacier (glacierfrac).\n\nBare soil evaporation\nIf there is only one soil layer present in the wflow_sbm model, the bare soil evaporation is scaled according to the wetness of the soil layer. The fraction of bare soil is assumed to be equal to the fraction not covered by the canopy (canopygapfraction) corrected by the total water fraction. When the soil is fully saturated, evaporation is set to equal the potential reference evaporation. When the soil is not fully saturated, actual evaporation decreases linearly with decreasing soil moisture values, as indicated by the figure below.\n\n\n\nEvaporation reduction as function of available soil moisture\n\n\n\nWhen more soil layers are present, soil evaporation is only provided from the upper soil layer, and soil evaporation is split in evaporation from the unsaturated store and evaporation from the saturated store. Water is first evaporated from the unsaturated store. The remaining potential soil evaporation can be used for evaporation from the saturated store, but only when the water table is present in the upper soil layer. Both the evaporation from the unsaturated store and the evaporation from the saturated store are limited by the minimum of the remaining potential soil evaporation and the available water in the unsaturated/saturated zone of the upper soil layer. Also for multiple soil layers, the evaporation (both unsaturated and saturated) decreases linearly with decreasing water availability.\n\n\nTranspiration\nThe maximum possible root water extraction rate for each soil layer is determined by partitioning the potential transpiration rate T_p based on the fraction of the total root length (rootfraction \\SIb{}{-}) in each soil layer. A root water uptake reduction model is used to calculate a reduction coefficient as a function of soil water pressure, that may reduce the maximum possible root water extraction rate. The root water uptake reduction model is based on the concept proposed by Feddes et al. (1978). This concept defines a reduction coefficient \\SIb{\\alpha}{-} as a function of soil water pressure (\\SIb{h}{cm}). Four different levels of h are defined: h1, h2, h3 and h4. h1 represents anoxic moisture conditions, h2 represents field capacity, h3 represents the point of critical soil moisture content (onset of drought stress), and h4 represents the wilting point. The value of h3 is a function of the potential transpiration rate, between \\SIb{1}{mm d^{-1}} and \\SIb{5}{mm d^{-1}}. If T_p \\le \\SIb{1}{mm d^{-1}}, h3 is set equal to h3_low (input model parameter). If T_p \\ge \\SIb{5}{mm d^{-1}}, h3 is set equal to h3_high (input model parameter). For T_p values between \\SIb{1}{mm d^{-1}} and \\SIb{5}{mm d^{-1}}, the value of h3 is linearly related to T_p (between h3_low and h3_high). Besides model parameters h3_high and h3_low, the critical pressure heads h1, h2 and h4 can be defined as input to the model.\nThe current soil water pressure is determined following the concept defined by Brooks and Corey (1964):\n\\[\n \\frac{\\theta-\\theta_r}{\\theta_s-\\theta_r} = \\min\\left\\{1, \\left(\\frac{h_b}{h}\\right)^\\lambda\\right\\}\n\\]\nwhere \\(\\SIb{h}{cm}\\) is the pressure head, \\(\\SIb{h_b}{cm}\\) is the air entry pressure head, and \\(\\theta\\), \\(\\theta_s\\), \\(\\theta_r\\) and \\(\\lambda\\) as previously defined.\nWhenever the current soil water pressure drops below h4, the root water uptake is set to zero. The root water uptake is at ideal conditions whenever the soil water pressure is above h3, with a linear transition between h3 and h4. The assumption that very wet conditions do not affect root water uptake too much is probably generally applicable to natural vegetation. For crops this assumption is not valid and in this case root water uptake above h1 should be set to zero (oxygen deficit) and between h1 and h2 root water uptake is limited. This is possible by setting the input model parameter alpha_h1 at 0 (default is 1).\n\n\n\nRoot water uptake reduction coefficient as a function of soil water pressure\n\n\n\nThe maximum allowed root water extraction from each soil layer in the unsaturated zone is determined based on the fraction of each soil layer in the unsaturated zone that is above the rooting depth (availcap) and the unsaturated storage usld, see conceptual figure below. This is implemented using the following code (i refers to the index of the vector that contains all active cells within the spatial model domain and k refers to the soil layer (from top to bottom) in the unsaturated zone):\n # availcap is fraction of soil layer containing roots\n # if `ust` is `true`, the whole unsaturated store is available for transpiration\n if ust\n availcap = usld[k] * 0.99\n else\n availcap =\n min(1.0, max(0.0, (sbm.rootingdepth[i] - sbm.sumlayers[i][k]) / usl[k]))\n end\n maxextr = usld[k] * availcap\n\n\n\nConceptual overview of how maxextr depends on rooting depth and water table depth\n\n\n\n\n\n\n\n\n\nNote\n\n\n\nWhen whole_ust_available is set to true in the TOML file, almost the complete unsaturated storage (99%) is available for transpiration, independent of the rootingdepth.\n[model]\nwhole_ust_available = true\n\n\nThe computation of transpiration from the saturated store depends on the water table depth, rooting depth, the reduction coefficient \\(\\alpha\\), the fraction of wet roots and the rootfraction below the water table. The fraction of wet roots is determined using a sigmoid fuction (see figure below). The parameter rootdistpar defines the sharpness of the transition between fully wet and fully dry roots. If the water table depth is equal to or lower than the rooting depth, the remaining potential transpiration is used based on the potential transpiration and actual transpiration in the unsaturated zone. The remaining potential transpiration is multiplied by the wet roots fraction and the reduction coefficient (and limited by the available water in saturated zone) to get the transpiration from the saturated part of the soil. If the water table depth intersects the rooting depth, the potential transpiration is multiplied by the remaining rootfraction (below the water table), wet roots fraction and the reduction coefficient (and limited by the available water in saturated zone) to get the transpiration from the saturated part of the soil. This is implemented using the following code (i refers to the index of the vector that contains all active cells within the spatial model domain):\n# transpiration from saturated store\nwetroots = scurve(sbm.zi[i], sbm.rootingdepth[i], Float(1.0), sbm.rootdistpar[i])\nalpha = rwu_reduction_feddes(\n Float(0.0),\n sbm.h1[i],\n sbm.h2[i],\n sbm.h3[i],\n sbm.h4[i],\n sbm.alpha_h1[i],\n)\n# include remaining root fraction if rooting depth is below water table zi\nif sbm.zi[i] >= sbm.rootingdepth[i]\n f_roots = wetroots\n restevap = sbm.pottrans[i] - actevapustore\nelse\n f_roots = wetroots * (1.0 - rootfraction_unsat)\n restevap = sbm.pottrans[i]\nend\nactevapsat = min(restevap * f_roots * alpha, satwaterdepth)\nsatwaterdepth = satwaterdepth - actevapsat\n\n\n\nAmount of wet roots and the effect of the rootdistpar parameter", + "crumbs": [ + "Model documentation", + "Vertical processes", + "SBM" + ] + }, + { + "objectID": "model_docs/vertical/sbm.html#snow-and-glaciers", + "href": "model_docs/vertical/sbm.html#snow-and-glaciers", + "title": "SBM", + "section": "Snow and glaciers", + "text": "Snow and glaciers\nThe snow and glacier model is described in Snow and glaciers. Both options can be enabled by specifying the following in the TOML file:\n[model]\nsnow = true\nglacier = true", + "crumbs": [ + "Model documentation", + "Vertical processes", + "SBM" + ] + }, + { + "objectID": "model_docs/vertical/sbm.html#soil-processes", + "href": "model_docs/vertical/sbm.html#soil-processes", + "title": "SBM", + "section": "Soil processes", + "text": "Soil processes\n\nThe SBM soil water accounting scheme\nA detailed description of the Topog_SBM model has been given by Vertessy (1999). Briefly: the soil is considered as a bucket with a certain depth (\\(\\SIb{z_t}{mm}\\)), divided into a saturated store (\\(\\SIb{S}{mm}\\)) and an unsaturated store (\\(\\SIb{U}{mm}\\)). The top of the \\(S\\) store forms a pseudo-water table at depth \\(\\SIb{z_{i}}{mm}\\) such that the value of \\(S\\) at any time is given by:\n\\[\n S=(z_t-z_i)(\\theta_s-\\theta_r)\n\\]\nwhere \\(\\SIb{\\theta_{s}}{-}\\) and \\(\\SIb{\\theta_{r}}{-}\\) are the saturated and residual soil water contents, respectively.\nThe unsaturated store \\(U\\) is subdivided into storage (\\(\\SIb{U_s}{mm}\\)) and deficit (\\(\\SIb{U_d}{m}\\)):\n\\[\n U_d=(\\theta_s-\\theta_r)z_i-U\\\\\n U_s=U-U_d\n\\]\nThe saturation deficit (\\(\\SIb{S_d}{mm}\\)) for the soil profile as a whole is defined as:\n\\[\n S_d=(\\theta_s-\\theta_r)z_t-S\n\\]\nAll infiltrating water that enters the \\(U\\) store first. The unsaturated layer can be split-up in different layers, by providing the thickness \\(\\SIb{}{mm}\\) of the layers in the TOML file. The following example specifies three layers (from top to bottom) of 100, 300 and 800 mm:\n[model]\nthicknesslayers = [100, 300, 800]\nThe code checks for each grid cell the specified layers against the soilthickness \\(\\SIb{}{mm}\\), and adds or removes (partly) layer(s) based on the soilthickness.\nAssuming a unit head gradient, the transfer of water (\\(\\SIb{\\mathrm{st}}{mm t^{-1}}\\)) from a \\(\\SIb{U}{mm}\\) store layer is controlled by the saturated hydraulic conductivity \\(\\SIb{\\subtext{K}{sat}}{mm t^{-1}}\\) at depth \\(\\SIb{z}{mm}\\) (bottom layer) or \\(\\SIb{z_i}{mm}\\), the effective saturation degree of the layer, and a Brooks-Corey power coefficient (parameter \\(c\\)) based on the pore size distribution index \\(\\lambda\\) (Brooks and Corey, 1964):\n\\[\n\\begin{gathered}\n \\mathrm{st}=\\subtext{K}{sat}\\left(\\frac{\\theta-\\theta_r}{\\theta_s-\\theta_r}\\right)^c\\\\\n c=\\frac{2+3\\lambda}{\\lambda}\n\\end{gathered}\n\\]\nHere \\(\\SIb{}{mm t^{-1}}\\) denotes milimeter per time step.\nWhen the unsaturated layer is not split-up into different layers, it is possible to use the original Topog\\_SBM vertical transfer formulation, by specifying in the TOML file:\n[model]\ntransfermethod = true\nThe transfer of water from the \\(\\SIb{U}{mm}\\) store to the \\(\\SIb{S}{mm}\\) store (\\(\\SIb{st}{mm\nt^{-1}}\\)) is in that case controlled by the saturated hydraulic conductivity \\(\\SIb{\\subtext{K}{sat}}{mm s^{-1}}\\) at depth \\(\\SIb{z_i}{mm}\\) and the ratio between \\(\\SIb{U}{mm}\\) and \\(\\SIb{S_d}{mm}\\):\n\\[\n \\mathrm{st}=\\subtext{K}{sat}\\frac{U_s}{S_d}\n\\]\nFour different saturated hydraulic conductivity depth profiles (ksat_profile) are available and a ksat_profile can be specified in the TOML file as follows:\n[input.vertical]\nksat_profile = \"exponential_constant\" # optional, one of (\"exponential\", \"exponential_constant\", \"layered\", \"layered_exponential\"), default is \"exponential\"\nSoil measurements are often available for about the upper \\(\\SI{1.5-2}{m}\\) of the soil column to estimate the saturated hydraulic conductivity, while these measurements are often lacking for soil depths beyond \\(\\SI{1.5-2}{m}\\). These different profiles allow to extent the saturated hydraulic conductivity profile based on measurements (either an exponential fit or hydraulic conductivity value per soil layer) with an exponential or constant profile. By default, with ksat_profile “exponential”, the saturated hydraulic conductivity \\(\\SIb{\\subtext{K}{sat}}{mm\nt^{-1}}\\) declines with soil depth \\(\\SIb{z}{mm}\\) in the model according to:\n\\[\n \\subtext{K}{sat} = K_0 e^{-fz},\n\\]\nwhere \\(\\SIb{K_0}{mm s^{-1}}\\) is the saturated hydraulic conductivity at the soil surface and \\(\\SIb{f}{mm^{-1}}\\) is a scaling parameter.\nThe plot below shows the relation between soil depth \\(z\\) and saturated hydraulic conductivity \\(\\subtext{K}{sat}\\) for different values of \\(f\\).\n\n\nCode\nusing Printf\nusing CairoMakie\n\nlet\n fig = Figure(resolution=(800, 400))\n ax = Axis(fig[1, 1], xlabel=L\"K_\\mathrm{sat}\\;[\\mathrm{mm/day}]\", ylabel=L\"-z\\;[\\mathrm{mm}]\")\n\n z = 0:5.0:1000\n ksat = 100.0\n f = 0.6 ./ collect(50:150.0:800)\n\n for fi in f\n lines!(ax, ksat .* exp.(-fi .* z), -z, label=@sprintf(\"%.2e\", fi))\n end\n\n Legend(fig[1, 2], ax, L\"f\")\n fig\nend\n\n\n┌ Warning: Found `resolution` in the theme when creating a `Scene`. The `resolution` keyword for `Scene`s and `Figure`s has been deprecated. Use `Figure(; size = ...` or `Scene(; size = ...)` instead, which better reflects that this is a unitless size and not a pixel resolution. The key could also come from `set_theme!` calls or related theming functions.\n└ @ Makie ~/.julia/packages/Makie/XQfuO/src/scenes.jl:229\n\n\n\n\n\n\n\n\n\nWith ksat_profile “exponential_constant”, \\(\\subtext{K}{sat}\\) declines exponentially with soil depth \\(\\SIb{z}{mm}\\) until \\(\\SIb{\\subtext{z}{mm}}{mm}\\) below the soil surface, and stays constant at and beyond soil depth \\(\\subtext{z}{exp}\\):\n\\[\n \\subtext{K}{sat} = \\begin{cases}\n K_0e^{-fz} & \\text{if $z < \\subtext{z}{exp}$}\\\\\n K_0e^{-f\\subtext{z}{exp}} & \\text{if $z \\ge \\subtext{z}{exp}$}.\n \\end{cases}\n\\]\nIt is also possible to provide a \\(\\subtext{K}{sat}\\) value per soil layer by specifying ksat_profile “layered”, these \\(\\subtext{K}{sat}\\) values are used directly to compute the vertical transfer of water between soil layers and to the saturated store \\(S\\). Finally, with the ksat_profile “layered_exponential” a \\(\\subtext{K}{sat}\\) value per soil layer is used until depth \\(\\subtext{z}{layered}\\) below the soil surface, and beyond \\(\\subtext{z}{layered}\\) an exponential decline of \\(\\subtext{K}{sat}\\) (of the soil layer with bottom \\(\\subtext{z}{layered}\\)) controlled by \\(f\\) occurs. The different available ksat_profle options are schematized in the figure below where the blue line represents the \\(\\subtext{K}{sat}\\) value.\n\n\n\nOverview of available ksat_profile options, for a soil column with five layers\n\n\n\n\nInfiltration\nThe water available for infiltration is taken as the rainfall including meltwater. Infiltration is determined separately for the compacted and non-compacted areas, as these have different infiltration capacities. Naturally, only the water that can be stored in the soil can infiltrate. If not all water can infiltrate, this is added as excess water to the runoff routing scheme.\nThe infiltrating water is split in two parts, the part that falls on compacted areas and the part that falls on non-compacted areas. The maximum amount of water that can infiltrate in these areas is calculated by taking the minimum of the maximum infiltration rate (infiltcapsoil \\(\\SIb{}{mm t^{-1}}\\) for non-compacted areas and infiltcappath \\(\\SIb{}{mm\nt^{-1}}\\) for compacted areas) and the amount of water available for infiltration avail_forinfilt \\(\\SIb{}{mm t^{-1}}\\). The water that can actually infiltrate infiltsoilpath \\(\\SIb{}{mm t^{-1}}\\) is calculated by taking the minimum of the total maximum infiltration rate (compacted and non-compacted areas) and the remaining storage capacity.\nInfiltration excess occurs when the infiltration capacity is smaller then the throughfall and stemflow rate. This amount of water (infiltexcess \\(\\SIb{}{mm t^{-1}}\\)) becomes overland flow (infiltration excess overland flow). Saturation excess occurs when the (upper) soil becomes saturated and water cannot infiltrate anymore. This amount of water excesswater \\(\\SIb{}{mm\nt^{-1}}\\) becomes overland flow (saturation excess overland flow).\n\nInfiltration in frozen soils\nIf snow processes are modelled, the infiltration capacity is reduced when the soil is frozen (or near freezing point). A infiltration correction factor is defined as a S-curve with the shape as defined below. A parameter (cf_soil) defines the base factor of infiltration when the soil is frozen. The soil temperature is calculated based on the soil temperature on the previous timestep, and the temperature difference between air and soil temperature weighted with a factor (w_soil, which defaults to 0.1125).\nThe near surface soil temperature is modelled using a simple equation (Wigmosta et al., 2009):\n\\[\nT_s^t = T_s^{t-1} + w (T_a - T_s^{t-1})\n\\] where \\(\\SIb{T_s^{t}}{\\degree C}\\) is the near-surface soil temperature at time \\(t\\), \\(\\SIb{T_a}{\\degree C}\\) is air temperature and \\(\\SIb{w}{-}\\) is a weighting coefficient determined through calibration (default is 0.1125 for daily timesteps).\nA reduction factor (cf_soil \\(\\SIb{}{-}\\), default is 0.038) is applied to the maximum infiltration rate (infiltcapsoil and infiltcappath), when the following model settings are specified in the TOML file:\n[model]\nsoilinfreduction = true\nsnow = true\nIf soilinfreduction is set to false, water is allowed to infiltrate the soil, even if the soil is frozen.\nA S-curve (see plot below) is used to make a smooth transition (a c-factor (\\(c\\)) of 8.0 is used):\n\\[\n\\begin{gathered}\n b = \\frac{1.0}{1.0 - \\subtext{\\mathrm{cf}}{soil}}\\\\\n \\mathrm{soilinfredu} = \\frac{1.0}{b + \\exp(-c (T_s - a))} + \\subtext{\\mathrm{cf}}{soil}\\\\\n a = 0.0\\\\\n c = 8.0\n\\end{gathered}\n\\]\n\n\n\nInfiltration correction factor as a function of soil temperature\n\n\n\n\n\n\nCapillary rise\nThe actual capillary rise actcapflux \\(\\SIb{}{mm t^{-1}}\\) is determined using the following approach: first the saturated hydraulic conductivity ksat \\(\\SIb{}{mm t^{-1}}\\) is determined at the water table \\(z_i\\); next a potential capillary rise maxcapflux \\(\\SIb{}{mm t^{-1}}\\) is determined from the minimum of ksat, actual transpiration actevapustore \\(\\SIb{}{mm t^{-1}}\\) taken from the \\(U\\) store, available water in the \\(S\\) store (satwaterdepth \\(\\SIb{}{mm}\\)) and the deficit of the \\(U\\) store (ustorecapacity \\(\\SIb{}{mm}\\)), as shown by the following code block:\nmaxcapflux = max(0.0, min(ksat, actevapustore, ustorecapacity, satwaterdepth))\nThen the potential rise maxcapflux is scaled using the water table depth zi, a maximum water depth cap_hmax \\(\\SIb{}{mm}\\) beyond which capillary rise ceases and a coefficient cap_n \\(\\SIb{}{-}\\), as follows in the code block below (i refers to the index of the vector that contains all active cells within the spatial model domain):\nif sbm.zi[i] > rootingdepth\n capflux =\n maxcapflux * pow(\n 1.0 - min(sbm.zi[i], sbm.cap_hmax[i]) / (sbm.cap_hmax[i]),\n sbm.cap_n[i],\n )\nelse\n capflux = 0.0\nend\nIf the roots reach the water table (rootingdepth \\(\\ge\\) sbm.zi), capflux is set to zero.\nFinally, the capillary rise capflux is limited by the unsaturated store deficit (one or multiple layers), calculated as follows in the code block below (i refers to the index of the vector that contains all active cells within the spatial model domain, and k refers to the layer position):\nusl[k] * (sbm.theta_s[i] - sbm.theta_r[i]) - usld[k]\nwhere usl \\(\\SIb{}{mm}\\) is the unsaturated layer thickness, usld is the ustorelayerdepth \\(\\SIb{}{mm}\\) (amount of water in the unsaturated layer), and \\(\\theta_s\\) and \\(\\theta_r\\) as previously defined.\nThe calculation of the actual capillary rise actcapflux is as follows in the code block below (i refers to the index of the vector that contains all active cells within the spatial model domain, and k refers to the layer position):\nactcapflux = 0.0\nnetcapflux = capflux\nfor k = n_usl:-1:1\n toadd =\n min(netcapflux, max(usl[k] * (sbm.theta_s[i] - sbm.theta_r[i]) - usld[k], 0.0))\n usld = setindex(usld, usld[k] + toadd, k)\n netcapflux = netcapflux - toadd\n actcapflux = actcapflux + toadd\nend\nIn case of multiple unsaturated layers (n_usl \\(>\\) 1), the calculation of the actual capillary rise starts at the lowest unsaturated layer while keeping track of the remaining capillary rise netcapflux \\(\\SIb{}{mm t^{-1}}\\).\n\n\nLeakage\nIf the maxleakage \\(\\SIb{}{mm day^{-1}}\\) input model parameter is set > 0, water is lost from the saturated zone and runs out of the model.", + "crumbs": [ + "Model documentation", + "Vertical processes", + "SBM" + ] + }, + { + "objectID": "model_docs/vertical/sbm.html#open-water", + "href": "model_docs/vertical/sbm.html#open-water", + "title": "SBM", + "section": "Open water", + "text": "Open water\nPart of the water available for infiltration is diverted to the open water, based on the fractions of river and lakes of each grid cell. The amount of evaporation from open water is assumed to be equal to potential evaporation (if sufficient water is available).", + "crumbs": [ + "Model documentation", + "Vertical processes", + "SBM" + ] + }, + { + "objectID": "model_docs/vertical/sbm.html#non-irrigation", + "href": "model_docs/vertical/sbm.html#non-irrigation", + "title": "SBM", + "section": "Non-irrigation", + "text": "Non-irrigation\nNon-irrigation water demand and allocation computations are supported for the sectors domestic, industry and livestock. These computations can be enabled by specifying the following in the TOML file:\n[model.water_demand]\ndomestic = true\nindustry = true\nlivestock = true\nFor these non-irrigation sectors the gross demand (\\(d_\\mathrm{gross}\\) \\(\\SIb{}{mm t^{-1}}\\)) and net demand (\\(d_\\mathrm{net}\\) \\(\\SIb{}{mm t^{-1}}\\)) are provided to the model (input through cyclic or forcing data). Gross demand represents the total demand and hence the total abstraction from surface water or groundwater when sufficient water is available. Net demand represents water consumption. The portion of total abstracted water that is not consumed is returned as surface water. The return flow fraction (\\(f_\\mathrm{return}\\) [-]) is calculated as follows:\n\\[\n \\subtext{f}{return} = 1.0 - \\frac{\\subtext{d}{net}}{\\subtext{d}{gross}},\n\\] and used to calculate the return flow rate (water abstracted from surface water or groundwater but not consumed). For grid cells containing a river the return flow is directly returned to the river routing component, otherwise the return flow is returned to the overland flow routing component.", + "crumbs": [ + "Model documentation", + "Vertical processes", + "SBM" + ] + }, + { + "objectID": "model_docs/vertical/sbm.html#non-paddy-irrigation", + "href": "model_docs/vertical/sbm.html#non-paddy-irrigation", + "title": "SBM", + "section": "Non-paddy irrigation", + "text": "Non-paddy irrigation\nNon-paddy (other crops than flooded rice) water demand and allocation computations are supported. These computations can be enabled by specifying the following in the TOML file:\n[model.water_demand]\nnonpaddy = true\nIrrigation is applied during the growing season (when input parameter irrigation_trigger \\(\\SIb{}{-}\\) is true (or on)) and when water depletion exceeds the readily available water:\n\\[\n (\\subtext{U}{field} - \\subtext{U}{a}) \\ge (\\subtext{U}{field} - \\subtext{U}{h3})\n\\] where \\(\\SIb{\\subtext{U}{field}}{mm}\\) is the unsaturated store in the root zone at field capacity (defined at a soil water pressure head of \\(\\SI{-100}{cm}\\)), \\(\\SIb{\\subtext{U}{a}}{mm}\\) is the actual unsaturated store in the root zone and \\(\\SIb{\\subtext{U}{h3}}{mm}\\) is the unsaturated store in the root zone at the critical soil water pressure head h3, below this pressure head reduction of root water uptake starts due to drought stress. The net irrigation demand \\(\\SIb{}{mm t^{-1}}\\) is the irrigation rate that brings the root zone back to field capacity, limited by the soil infiltration capacity \\(\\SIb{}{mm t^{-1}}\\), assuming that farmers do not apply an irrigation rate higher than the soil infiltration capacity. To account for limited irrigation efficiency the net irrigation demand is divided by the irrigation efficiency for non-paddy crops (irrigation_efficiency \\(\\SIb{}{-}\\), default is \\(1.0\\)), resulting in gross irrigation demand \\(\\SIb{}{mm t^{-1}}\\). Finally, the gross irrigation demand is limited by the maximum irrigation rate (maximum_irrigation_rate \\(\\SIb{}{mm t^{-1}}\\), default is \\(\\SI{25}{mm\\;day-1}\\)). If the maximum irrigation rate is applied, irrigation continues at subsequent time steps until field capacity is reached. Irrigation is added to the SBM variable avail_forinfilt \\(\\SIb{}{mm t^{-1}}\\), the amount of water available for infiltration.", + "crumbs": [ + "Model documentation", + "Vertical processes", + "SBM" + ] + }, + { + "objectID": "model_docs/vertical/sbm.html#paddy-irrigation", + "href": "model_docs/vertical/sbm.html#paddy-irrigation", + "title": "SBM", + "section": "Paddy irrigation", + "text": "Paddy irrigation\nPaddy (flooded rice) water demand and allocation computations are supported. These computations can be enabled by specifying the following in the TOML file:\n[model.water_demand]\npaddy = true\nIrrigation is applied during the growing season (when input parameter irrigation_trigger \\(\\SIb{}{-}\\) is true (or on)) and when the paddy water depth h \\(\\SIb{}{mm}\\) reaches below the minimum water depth h_min \\(\\SIb{}{mm}\\) (see also the figure below). The net irrigation demand [mm t\\(^{-1}\\)] is the irrigation rate required to reach the optimal paddy water depth h_opt \\(\\SIb{}{mm}\\), an approach similar to Xie and Cui (2011). To account for limited irrigation efficiency the net irrigation demand is divided by the irrigation efficiency for paddy fields (irrigation_efficiency \\(\\SIb{}{-}\\), default is 1.0), resulting in gross irrigation demand \\(\\SIb{}{mm t^{-1}}\\). Finally, the gross irrigation demand is limited by the maximum irrigation rate (maximum_irrigation_rate \\(\\SIb{}{mm t^{-1}}\\), default is \\(\\SIb{25}{mm\nd^{-1}}\\)). If the maximum irrigation rate is applied, irrigation continues at subsequent time steps until the optimal paddy water depth h_opt is reached. Irrigation is added to the SBM variable avail_forinfilt \\(\\SIb{}{mm t^{-1}}\\), the amount of water available for infiltration. When the paddy water depth h exceeds h_max \\(\\SIb{}{mm}\\) runoff occurs, and this amount is added to the runoff routing scheme for overland flow. The figure below shows a typical vertical soil profile of a puddled rice soil with a muddy layer of about 15 cm (in this case represented by two soil layers of 5 cm and 10 cm thickness), a plow soil layer of 5 cm with relative low permeability (vertical hydraulic conductivity \\(k_v\\) of about \\(\\SI{5}{mm d^{-1}}\\)), and a non-puddled soil below the plow soil layer. The low vertical hydraulic conductivity of the plow soil layer can be realized by making use of the parameter kvfrac \\(\\SIb{}{-}\\), a multiplication factor applied to the vertical hydraulic conductivity at soil depth \\(\\SIb{z}{mm}\\).\n\n\n\npaddy_profile\n\n\nSchematic diagram of a paddy field with water balance components and soil profile", + "crumbs": [ + "Model documentation", + "Vertical processes", + "SBM" + ] + }, + { + "objectID": "model_docs/vertical/sbm.html#water-withdrawal-and-allocation", + "href": "model_docs/vertical/sbm.html#water-withdrawal-and-allocation", + "title": "SBM", + "section": "Water withdrawal and allocation", + "text": "Water withdrawal and allocation\nFor the water withdrawal the total gross demand is computed (sum over the irrigation and non-irrigation water demand sectors), in case sufficient water is available the water withdrawal is equal to the total gross demand. In case of insufficient water availability, the water withdrawal is scaled down to the available water, and allocation is then proportional to the gross demand per sector (industry, domestic, livestock and irrigation). Water can be abstracted from the following sources:\n\nsurface water from rivers (max 80% of total available water)\nreservoirs and lakes (max 98% of total available water)\ngroundwater (max 75% of total available water)\n\nThe model parameter frac_sw_used (fraction surface water used, default is 1.0) determines how much water is supplied by available surface water and groundwater.\n\nLocal\nFirst, surface water abstraction (excluding reservoir and lake locations) is computed to satisfy local (same grid cell) water demand. The available surface water volume is limited by a fixed scaling factor of \\(0.8\\) to prevent rivers from completely drying out. It is assumed that the water demand cannot be satisfied completely from local surface water and groundwater. The next step is to satisfy the remaining water demand for allocation areas \\(\\SIb{}{-}\\), described in the next sub-section.\n\n\nAllocation areas\nFor allocation areas the water demand \\(\\SIb{\\subtext{V}{sw, demand}}{m^3}\\) and availability \\(\\SIb{\\subtext{V}{sw, availabilty}}{m^3}\\) are summed (including reservoir and lake locations limited by a fixed scaling factor of \\(0.98\\)), and the total surface water abstraction is then:\n\\[\n \\subtext{V}{sw, abstraction} = \\min (\\subtext{V}{sw, demand}, \\subtext{V}{sw, availabilty})\n\\] The fraction of available surface water that can be abstracted \\(\\SIb{\\subtext{f}{sw,abstraction}}{-}\\) at the allocation area level is then:\n\\[\n \\subtext{f}{sw, abstraction} = \\frac{\\subtext{V}{sw, abstraction}}{\\subtext{V}{sw, available}}\n\\] This fraction is applied to the remaining available surface water of each river cell (including lake and reservoir locations) to compute surface water abstraction at each river cell and to update the local surface water abstraction.\nThe fraction of water demand that can be satisfied by available surface water \\(\\SIb{\\subtext{f}{sw, allocation}}{-}\\) at the allocation area level is then:\n\\[\n \\subtext{f}{sw, allocation} = \\frac{\\subtext{V}{sw, abstraction}}{\\subtext{V}{sw, demand}}\n\\] This fraction is applied to the remaining surface water demand of each land cell to compute the allocated surface water to each land cell.\nThen groundwater abstraction is computed to satisfy the remaining local water demand, where groundwater abstraction is limited by a fixed scaling factor of \\(0.75\\) applied to the groundwater volume. Finally, for allocation areas the water demand \\(\\SIb{\\subtext{V}{gw,demand}}{m^3}\\) and availability \\(\\SIb{\\subtext{V}{gw, availabilty}}{m^3}\\) are summed, and the total groundwater abstraction is then:\n\\[\n \\subtext{V}{gw, abstraction} = \\min(\\subtext{V}{gw, demand}, \\subtext{V}{gw, availabilty})\n\\] The fraction of available groundwater that can be abstracted at allocation area level \\(\\SIb{\\subtext{f}{gw, abstraction}}{-}\\) at the allocation area level is then:\n\\[\n \\subtext{f}{gw, abstraction} = \\frac{\\subtext{V}{gw, abstraction}}{\\subtext{V}{gw, available}}\n\\] This fraction is applied to the remaining available groundwater of each land cell to compute groundwater abstraction and to update the local groundwater abstraction.\nThe fraction of water demand that can be satisfied by available groundwater \\(\\SIb{\\subtext{f}{gw,allocation}}{-}\\) at the allocation area level is then:\n\\[\n \\subtext{f}{gw, allocation} = \\frac{\\subtext{V}{gw, abstraction}}{\\subtext{V}{gw, demand}}\n\\] This fraction is applied to the remaining groundwater demand of each land cell to compute the allocated groundwater to each land cell.\n\n\nAbstractions\nGroundwater abstraction is implemented by subtracting this amount from the recharge variable of the lateral subsurface flow component (kinematic wave) or the recharge rate of the groundwater flow module. Surface water abstraction \\(\\SIb{}{m^3 s^{-1}}\\) is divided by the flow length dl \\(\\SIb{}{m}\\) and subtracted from the lateral inflow of kinematic wave routing scheme for river flow. For the local inertial routing scheme (river and optional floodplain routing), the surface water abstraction \\(\\SIb{}{m^3 s^{-1}}\\) is subtracted as part of the continuity equation of the local inertial model. For reservoir and lake locations surface water is abstracted (act_surfacewater_abst_vol \\(\\SIb{}{m^3 s^{-1}}\\)) from the reservoir volume \\(\\SIb{}{m^3}\\) and lake storage \\(\\SIb{}{m^3}\\) respectively, with a subsequent update of the lake waterlevel \\(\\SIb{}{m}\\).", + "crumbs": [ + "Model documentation", + "Vertical processes", + "SBM" + ] + }, + { + "objectID": "model_docs/vertical/sbm.html#references", + "href": "model_docs/vertical/sbm.html#references", + "title": "SBM", + "section": "References", + "text": "References\n\nBrooks, R. H., and Corey, A. T., 1964, Hydraulic properties of porous media, Hydrology Papers 3, Colorado State University, Fort Collins, 27 p.\nFeddes, R.A., Kowalik, P.J. and Zaradny, H., 1978, Simulation of field water use and crop yield, Pudoc, Wageningen, Simulation Monographs.\nGash, J. H. C., 1979, An analytical model of rainfall interception by forests, Q. J. Roy. Meteor. Soc., 105, 43–55, doi:1026 10.1002/qj.497105443041027.\nLiu, S., 1998, Estimation of rainfall storage capacity in the canopies of cypress wetlands and slash pine uplands in North-Central Florida, J. Hydr., 207, 32–41, doi: 10.1016/S0022-1694(98)00115-2.\nPitman, J., 1989, Rainfall interception by bracken in open habitats—relations between leaf area, canopy storage and drainage rate, J. Hydr. 105, 317–334, doi: 10.1016/0022-1694(89)90111-X.\nVan Dijk, A. I. J. M., and Bruijnzeel, L. A., 2001, Modelling rainfall interception by vegetation of variable density using an adapted analytical model, Part 2, Model validation for a tropical upland mixed cropping system, J. Hydr., 247, 239–262.\nVertessy, R., and Elsenbeer, H., 1999, Distributed modeling of storm flow generation in an amazonian rain forest catchment: effects of model parameterization, Water Resour. Res., 35, 2173–2187. doi: 10.1029/1999WR9000511257.\nWigmosta, M. S., Lane, L. J., Tagestad, J. D., and Coleman A. M., 2009, Hydrologic and erosion models to assess land use and management practices affecting soil erosion, J. Hydrol. Eng., 14, 27-41.\nXie, X. and Cui, Y., 2011, Development and test of SWAT for modeling hydrological processes in irrigation districts with paddy rice, J. Hydrol., 396, pp. 61-71.", + "crumbs": [ + "Model documentation", + "Vertical processes", + "SBM" + ] + }, + { + "objectID": "user_guide/zmq_server.html", + "href": "user_guide/zmq_server.html", + "title": "Run wflow as a ZMQ Server", + "section": "", + "text": "It is possible to run wflow as a ZMQ Server, for example for the coupling to the OpenDA software for data-assimilation. The code for the wflow ZMQ Server is not part of the Wflow.jl package, and is located here.\n\n\n\n Back to top", + "crumbs": [ + "User guide", + "Integrations", + "Run wflow as a ZMQ Server" + ] + }, + { + "objectID": "user_guide/toml_file.html", + "href": "user_guide/toml_file.html", + "title": "Simulation settings", + "section": "", + "text": "All model configuration and settings are passed through the .toml file, which contains all relevant settings, information about the model configuration, simulation period, input files, and parameters. The settings are provided in a TOML file. The settings file is structured in several sections, which are explained below. The file paths provided in this file are relative to the location of the TOML file, or to dir_input and dir_output if they are specified.", + "crumbs": [ + "User guide", + "Simulation settings" + ] + }, + { + "objectID": "user_guide/toml_file.html#general-time-info", + "href": "user_guide/toml_file.html#general-time-info", + "title": "Simulation settings", + "section": "General time info", + "text": "General time info\nTime information is optional. When omitted, wflow will perform computations for each timestamp in the forcing netCDF file, except for the first forcing timestamp, which is considered equal to the initial conditions of the wflow model (state time). If you wish to calculate a subset of this time range, or a different timestep, you can specify a starttime, endtime and timestepsecs. The starttime is defined as the model state time. In the TOML file settings below, the starttime is 2000-01-01T00:00:00 (state time) and the first update (and output) of the wflow model is at 2000-01-02T00:00:00. The time_units optional information is used by the writer of the model, for model output in netCDF format. The calendar option allows you to calculate in one of the different CF conventions calendars provided by the CFTime.jl package, such as \"360_day\". This is useful if you want to calculate climate scenarios which are sometimes provided in these alternative calendars.\ncalendar = \"standard\" # optional, this is the default value\nstarttime = 2000-01-01T00:00:00 # optional, default from forcing netCDF\nendtime = 2000-02-01T00:00:00 # optional, default from forcing netCDF\ntime_units = \"days since 1900-01-01 00:00:00\" # optional, this is the default value\ntimestepsecs = 86400 # optional, default from forcing netCDF\ndir_input = \"data/input\" # optional, default is the path of the TOML\ndir_output = \"data/output\" # optional, default is the path of the TOML", + "crumbs": [ + "User guide", + "Simulation settings" + ] + }, + { + "objectID": "user_guide/toml_file.html#logging", + "href": "user_guide/toml_file.html#logging", + "title": "Simulation settings", + "section": "Logging", + "text": "Logging\nWflow prints logging messages at various levels such as debug, info, and error. These messages are sent to both the terminal and a log file. Note that logging to a file is only part of the Wflow.run(tomlpath::AbstractString) method. If you want to debug an issue, it can be helpful to set loglevel = \"debug\" in the TOML. To avoid flooding the screen, debug messages are only sent to the log file. The following settings will affect the logging:\nsilent = false # optional, default is \"false\"\nloglevel = \"debug\" # optional, default is \"info\"\npath_log = \"log.txt\" # optional, default is \"log.txt\"\nfews_run = false # optional, default value is false\nsilent avoids logging to the terminal, and only writes to the log file. loglevel controls which levels are filtered out; for instance, the default setting \"info\" does not print any debug-level messages. Note that for finer control, you can also pass an integer log level. For details, see Julia’s Logging documentation. path_log sets the desired output path for the log file. For information on fews_run, see Run from Delft-FEWS.", + "crumbs": [ + "User guide", + "Simulation settings" + ] + }, + { + "objectID": "user_guide/toml_file.html#model-section", + "href": "user_guide/toml_file.html#model-section", + "title": "Simulation settings", + "section": "Model section", + "text": "Model section\nModel-specific settings can be included in the model section of the TOML file.\n[model]\ntype = \"sbm\" # one of (\"sbm\" or \"sbm_gwf)\nmasswasting = false # include lateral snow transport in the model, default is false\nsnow = false # include snow modelling, default is false\nreinit = true # cold (reinit = true) or warm state (reinit = false), default is true\nreservoirs = false # include reservoir modelling, default is false\nkin_wave_iteration = false # enable kinematic wave iterations in the model, default is false\nthicknesslayers = [100, 300, 800] # specific SBM setting: for each soil layer, a thickness [mm] is specified\nmin_streamorder_river = 5 # minimum stream order to delineate subbasins for river domain, default is 6 (for multi-threading computing purposes)\nmin_streamorder_land = 4 # minimum stream order to delineate subbasins for land domain, default is 5 (for multi-threading computing purposes)", + "crumbs": [ + "User guide", + "Simulation settings" + ] + }, + { + "objectID": "user_guide/toml_file.html#state-options", + "href": "user_guide/toml_file.html#state-options", + "title": "Simulation settings", + "section": "State options", + "text": "State options\nThe state section in the TOML file provides information about the location of input and output states of the model. This section is mostly relevant if the model needs to start with a “warm” state (i.e. based on the results of a previous simulation). The example below shows how to save the output states of the current simulation, so it can be used to initialize another model in the future. Details on the settings required to start a model with a warm state can be found in the additional model options. If it is not required to store the outstates of the current simulation, the entire state section can be removed.\n[state]\npath_input = \"instates-moselle.nc\"\npath_output = \"outstates-moselle.nc\"\n\n[state.vertical]\nsatwaterdepth = \"satwaterdepth\"\nsnow = \"snow\"\ntsoil = \"tsoil\"\nustorelayerdepth = \"ustorelayerdepth\"\nsnowwater = \"snowwater\"\ncanopystorage = \"canopystorage\"\n\n[state.lateral.river]\nq = \"q_river\"\nh = \"h_river\"\nh_av = \"h_av_river\"\n\n[state.lateral.river.reservoir]\nvolume = \"volume_reservoir\"\n\n[state.lateral.subsurface]\nssf = \"ssf\"\n\n[state.lateral.land]\nq = \"q_land\"\nh = \"h_land\"\nh_av = \"h_av_land\"", + "crumbs": [ + "User guide", + "Simulation settings" + ] + }, + { + "objectID": "user_guide/toml_file.html#input-section", + "href": "user_guide/toml_file.html#input-section", + "title": "Simulation settings", + "section": "Input section", + "text": "Input section\nThe input section of the TOML file contains information about the input forcing and model parameters files (in netCDF format). Forcing is applied to the vertical component of the model, and needs to be mapped to the external netCDF variable name. forcing lists the internal model forcing parameters, and these are mapped to the external netCDF variables listed under the section [input.vertical]. It is possible to provide cyclic parameters to the model (minimum time step of 1 day). In the example below, the internal vertical.leaf_area_index model parameter is mapped to the external netCDF variable “LAI” variable. Cyclic time inputs of parameters can be different (e.g., daily or monthly). The time dimension name of these cylic input parameters in the model parameter netCDF file should start with “time”. If a model parameter is not mapped, a default value will be used, if available.\n[input]\n# use \"forcing-year-*.nc\" if forcing files are split in time\npath_forcing = \"forcing-moselle.nc\" # Location of the forcing data\npath_static = \"staticmaps-moselle.nc\" # Location of the static data\n\n# these are not directly part of the model\ngauges = \"wflow_gauges\"\nldd = \"wflow_ldd\"\nriver_location = \"wflow_river\"\nsubcatchment = \"wflow_subcatch\"\n\n# specify the internal IDs of the parameters which vary over time\n# the external name mapping needs to be below together with the other mappings\nforcing = [\n\"vertical.precipitation\",\n\"vertical.temperature\",\n\"vertical.potential_evaporation\",\n]\n\ncyclic = [\"vertical.leaf_area_index\"]\n\n[input.vertical] # Map internal model variable/parameter names to variable names in the netCDF files\naltitude = \"wflow_dem\"\nc = \"c\"\ncf_soil = \"cf_soil\"\ncfmax = \"Cfmax\"\ne_r = \"EoverR\"\ninfiltcappath = \"InfiltCapPath\"\ninfiltcapsoil = \"InfiltCapSoil\"\nkext = \"Kext\"\nkv_0 = \"KsatVer\"\nleaf_area_index = \"LAI\" # Cyclic variable\nm = \"M\"\nmaxleakage = \"MaxLeakage\"\npathfrac = \"PathFrac\"\npotential_evaporation = \"PET\" # Forcing variable\nprecipitation = \"P\" # Forcing variable\nrootdistpar = \"rootdistpar\"\nrootingdepth = \"RootingDepth\"\nsoilminthickness = \"SoilMinThickness\"\nsoilthickness = \"SoilThickness\"\nspecific_leaf = \"Sl\"\nstorage_wood = \"Swood\"\ntemperature = \"TEMP\" # Forcing variable\ntt = \"TT\"\ntti = \"TTI\"\nttm = \"TTM\"\nw_soil = \"wflow_soil\"\nwater_holding_capacity = \"WHC\"\nwaterfrac = \"WaterFrac\"\ntheta_r = \"thetaR\"\ntheta_s = \"thetaS\"\n\n[input.lateral.river]\nlength = \"wflow_riverlength\"\nn = \"N_River\"\nslope = \"RiverSlope\"\nwidth = \"wflow_riverwidth\"\n\n[input.lateral.subsurface]\nksathorfrac = \"KsatHorFrac\"\n\n[input.lateral.land]\nn = \"N\"\nslope = \"Slope\"", + "crumbs": [ + "User guide", + "Simulation settings" + ] + }, + { + "objectID": "user_guide/toml_file.html#output-netcdf-section", + "href": "user_guide/toml_file.html#output-netcdf-section", + "title": "Simulation settings", + "section": "Output netCDF section", + "text": "Output netCDF section\n\nGrid data\nThis optional section of the TOML file specifies the output netCDF file for writing gridded model output. It includes a mapping between internal model parameter components and external netCDF variables.\nTo limit the size of the resulting netCDF file, file compression can be enabled. Compression increases computational time but can significantly reduce the size of the netCDF file. Set the compressionlevel variable to a value between 0 and 9. A setting of 0 means no compression, while values between 1 and 9 indicate increasing levels of compression (1: least compression, minimal run-time impact, 9: highest compression, maximum run-time impact). If file size is a concern, we recommend using a value of 1, as higher compression levels generally have a limited effect on file size.\n[output]\npath = \"output_moselle.nc\" # Location of the output file\ncompressionlevel = 1 # Compression level (default 0)\n\n[output.vertical] # Mapping of names between internal model components and external netCDF variables\nsatwaterdepth = \"satwaterdepth\"\nsnow = \"snow\"\ntsoil = \"tsoil\"\nustorelayerdepth = \"ustorelayerdepth\"\nsnowwater = \"snowwater\"\ncanopystorage = \"canopystorage\"\n\n[output.lateral.river]\nq = \"q_river\"\nh = \"h_river\"\n\n[output.lateral.river.reservoir]\nvolume = \"volume_reservoir\"\n\n[output.lateral.subsurface]\nssf = \"ssf\"\n\n[output.lateral.land]\nq = \"q_land\"\nh = \"h_land\"\n\n\nScalar data\nIn addition to gridded data, scalar data can also be written to a netCDF file. Below is an example that shows how to write scalar data to the file “output_scalar_moselle.nc”. For each netCDF variable, a name (external variable name) and a parameter (internal model parameter) are required. A reducer can be specified to apply to the model output. See more details in the Output CSV section section. If a map is provided to extract data for specific locations (e.g. gauges) or areas (e.g. subcatchment), the netCDF location names are extracted from these maps. For a specific location (grid cell) a location is required. For layered model parameters and variables that have an extra layer dimension and are part of the vertical sbm concept, an internal layer index can be specified (an example is provided below). Similarly, for model parameters and variables that have an extra dimension classes and are part of the vertical FLEXTopo concept, the class name can be specified. If multiple layers or classes are desired, this can be specified in separate [[netcdf.variable]] entries. Note that the additional dimension should be specified when wflow is integrated with Delft-FEWS, for netCDF scalar data an extra dimension is not allowed by the importNetcdfActivity of the Delft-FEWS General Adapter. In the section Output CSV section, similar functionality is available for CSV. For integration with Delft-FEWS, it is recommended to write scalar data to netCDF format, as the General Adapter of Delft-FEWS can directly ingest data from netCDF files. For more information, see Run from Delft-FEWS.\n[netcdf]\npath = \"output_scalar_moselle.nc\" # Location of the results\n\n[[netcdf.variable]] # Extract the values of lateral.river.q using the gauges map, and assign it with the name 'Q' as a variable in the netCDF file\nname = \"Q\"\nmap = \"gauges\"\nparameter = \"lateral.river.q\"\n\n[[netcdf.variable]] # Using coordinates to extract temperature\ncoordinate.x = 6.255\ncoordinate.y = 50.012\nname = \"vwc_layer2_bycoord\"\nlocation = \"vwc_bycoord\"\nparameter = \"vertical.vwc\"\nlayer = 2\n\n[[netcdf.variable]] # Using indices to extract temperature\nlocation = \"temp_byindex\"\nname = \"temp_index\"\nindex.x = 100\nindex.y = 264\nparameter = \"vertical.temperature\"", + "crumbs": [ + "User guide", + "Simulation settings" + ] + }, + { + "objectID": "user_guide/toml_file.html#output-csv-section", + "href": "user_guide/toml_file.html#output-csv-section", + "title": "Simulation settings", + "section": "Output CSV section", + "text": "Output CSV section\nModel output can also be written to a CSV file. Below is an example that writes model output to the file “output_moselle.csv”. For each CSV column, a header and parameter (internal model parameter) are required. A reducer can be specified to apply to the model output, with the following available reducers:\n\nmaximum\nminimum\nmean\nmedian\nsum\nfirst\nlast\nonly\n\nwith only as the default. To extract data for a specific location (grid cell), the index of the vector, the coordinates coordinate.x and coordinate.y, or the x and y indices of the 2D array (index.x and index.y) can be provided. Additionally, a map can be provided to extract data for certain locations (e.g. gauges) or areas (e.g. subcatchment). In this case, a single entry can lead to multiple columns in the CSV file, which will be of the form header_id, e.g. Q_20, for a gauge with integer ID 20. For layered model parameters and variables that have an extra dimension layer and are part of the vertical sbm concept an internal layer index (see also example below) should be specified. For model parameters and variables that have an extra dimension classes and are part of the vertical FLEXTopo concept, it is possible to specify the class name. If multiple layers or classes are desired, this can be specified in separate [[csv.column]] entries.\nThe double brackets in [[csv.column]] follow TOML syntax, indicating that it is part of a list. You can specify as many entries as you want.\n[csv]\npath = \"output_moselle.csv\"\n\n[[csv.column]]\nheader = \"Q\"\nparameter = \"lateral.river.q\"\nreducer = \"maximum\"\n\n[[csv.column]]\nheader = \"volume\"\nindex = 1\nparameter = \"lateral.river.reservoir.volume\"\n\n[[csv.column]]\ncoordinate.x = 6.255\ncoordinate.y = 50.012\nheader = \"temp_bycoord\"\nparameter = \"vertical.temperature\"\n\n[[csv.column]]\ncoordinate.x = 6.255\ncoordinate.y = 50.012\nheader = \"vwc_layer2_bycoord\"\nparameter = \"vertical.vwc\"\nlayer = 2\n\n[[csv.column]]\nheader = \"temp_byindex\"\nindex.x = 100\nindex.y = 264\nparameter = \"vertical.temperature\"\n\n[[csv.column]]\nheader = \"Q\"\nmap = \"gauges\"\nparameter = \"lateral.river.q\"\n\n[[csv.column]]\nheader = \"recharge\"\nmap = \"subcatchment\"\nparameter = \"vertical.recharge\"\nreducer = \"mean\"", + "crumbs": [ + "User guide", + "Simulation settings" + ] + }, + { + "objectID": "user_guide/toml_file.html#modify-parameters", + "href": "user_guide/toml_file.html#modify-parameters", + "title": "Simulation settings", + "section": "Modify parameters", + "text": "Modify parameters\nIt is possible to modify model parameters and forcing through the TOML file. Two options to modify input parameters are available:\n\nSet an input parameter (static) to a uniform value.\nModify an input parameter (cyclic and static) or forcing variable using a scale factor and offset.\n\nFor example, to set the input parameter cfmax to an uniform value of 2.5:\n[input.vertical]\nwater_holding_capacity = \"WHC\"\nwaterfrac = \"WaterFrac\"\ncfmax.value = 2.5\nFor input parameters with an extra dimension (e.g. layer or classes), one uniform value can be provided or a list of values that matches the length of the additional dimension. For example, a list of values can be provided for input parameter c as follows:\n[input.vertical]\nwater_holding_capacity = \"WHC\"\nwaterfrac = \"WaterFrac\"\nc.value = [10.5, 11.25, 9.5, 7.0]\nTo change the forcing variable precipitation with a scale factor of 1.5 and an offset of 0.5:\n[input.vertical.precipitation]\nnetcdf.variable.name = \"P\"\nscale = 1.5\noffset = 0.5\nFor input parameters with an extra dimension, it is also possible to modify multiple indices simultaneously with different scale and offset values. In the example below, the external netCDF variable c is modified at layer index 1 and 2, with a scale factor of 2.0 and 1.5 respectively, and an offset of 0.0 for both indices:\n[input.vertical.c]\nnetcdf.variable.name = \"c\"\nscale = [2.0, 1.5]\noffset = [0.0, 0.0]\nlayer = [1, 2]", + "crumbs": [ + "User guide", + "Simulation settings" + ] + }, + { + "objectID": "user_guide/toml_file.html#fixed-forcing-values", + "href": "user_guide/toml_file.html#fixed-forcing-values", + "title": "Simulation settings", + "section": "Fixed forcing values", + "text": "Fixed forcing values\nIt is possible to set fixed values for forcing parameters through the TOML file. For example, to set temperature to a fixed value of 10 \\degreeC, the complete forcing list is required:\nforcing = [\n \"vertical.precipitation\",\n \"vertical.temperature\",\n \"vertical.potential_evaporation\",\n]\n\n[input.vertical.temperature]\nvalue = 10\nNote that the mapping to the external netCDF variable listed under the [input.vertical] section needs to be removed or commented out:\n[input.vertical]\npotential_evaporation = \"PET\" # forcing\n# temperature = \"TEMP\" # forcing\nprecipitation = \"P\" # forcing", + "crumbs": [ + "User guide", + "Simulation settings" + ] + }, + { + "objectID": "user_guide/multi_threading.html", + "href": "user_guide/multi_threading.html", + "title": "Multithreading", + "section": "", + "text": "Wflow supports multi-threading execution of the wflow_sbm model that uses the kinematic wave approach for river, overland and lateral subsurface flow. Both the vertical SBM concept and the kinematic wave components of this model can run on multiple threads. The optional local inertial model for river flow and the optional local inertial model for river (1D) and land (2D), both part of wflow_sbm, can also run on multiple threads. The threading functionality for the kinematic wave may also be useful for the wflow_sbm model SBM + Groundwater flow. The multi-threading functionality in wflow is considered experimental, see also the following issue, where an error was not thrown running code multi-threaded. Because of this we advise to start with running a wflow model single-threaded (for example during the testing phase of setting up an new wflow model).\nFor information on how to start Julia with multiple threads we refer to How to start Julia with multiple threads.\nAdditionally, when running Julia + wflow via the command line (note that this is different from the wflow_cli), it is possible to define the number of threads via the -t flag. An example where we start Julia with three threads:\njulia -t 3 -e 'using Wflow; Wflow.run()' path/to/config.toml", + "crumbs": [ + "User guide", + "Supported options", + "Multithreading" + ] + }, + { + "objectID": "user_guide/multi_threading.html#using-wflow-in-julia", + "href": "user_guide/multi_threading.html#using-wflow-in-julia", + "title": "Multithreading", + "section": "", + "text": "Wflow supports multi-threading execution of the wflow_sbm model that uses the kinematic wave approach for river, overland and lateral subsurface flow. Both the vertical SBM concept and the kinematic wave components of this model can run on multiple threads. The optional local inertial model for river flow and the optional local inertial model for river (1D) and land (2D), both part of wflow_sbm, can also run on multiple threads. The threading functionality for the kinematic wave may also be useful for the wflow_sbm model SBM + Groundwater flow. The multi-threading functionality in wflow is considered experimental, see also the following issue, where an error was not thrown running code multi-threaded. Because of this we advise to start with running a wflow model single-threaded (for example during the testing phase of setting up an new wflow model).\nFor information on how to start Julia with multiple threads we refer to How to start Julia with multiple threads.\nAdditionally, when running Julia + wflow via the command line (note that this is different from the wflow_cli), it is possible to define the number of threads via the -t flag. An example where we start Julia with three threads:\njulia -t 3 -e 'using Wflow; Wflow.run()' path/to/config.toml", + "crumbs": [ + "User guide", + "Supported options", + "Multithreading" + ] + }, + { + "objectID": "user_guide/multi_threading.html#using-the-command-line-interface", + "href": "user_guide/multi_threading.html#using-the-command-line-interface", + "title": "Multithreading", + "section": "Using the command line interface", + "text": "Using the command line interface\nAs explained above, we need to start julia with multiple threads to make use of this speedup. For wflow_cli, the only way to do this is by setting the JULIA_NUM_THREADS environment variable, as explained in these julia docs.\nWhen a model run starts, among the run information the number of threads that are used is printed, so nthreads() = 4 means 4 threads are used, because JULIA_NUM_THREADS has been set to 4.", + "crumbs": [ + "User guide", + "Supported options", + "Multithreading" + ] + }, + { + "objectID": "user_guide/fews.html", + "href": "user_guide/fews.html", + "title": "Run from Delft-FEWS", + "section": "", + "text": "Wflow integrates easily as part of an operational system by linking to the Delft-FEWS platform. Delft-FEWS integrates data and models, and is for example used in many active flood forecasting systems around the world.\nThis can be done without a model adapter that provides the interface between Delft-FEWS and an external model (or module). This is possible because time information in the TOML configuration file is optional and Delft-FEWS can import and export netCDF files. When time information is left out from the TOML configuration file, the starttime, endtime and timestepsecs (timestep) of the run is extracted from the netCDF forcing file by wflow.\nTo indicate that a wflow model runs from Delft-FEWS, the following setting needs to be specified in the main section of the TOML configuration file:\nfews_run = true # optional, default value is false\nThis ensures that wflow offsets the time handling, to meet the expectations of Delft-FEWS.\nIt also uses a different format for the log file such that each log message takes up only one line. That meets the General Adapter logFile expectations, which then can get parsed with these Delft-FEWS log parsing settings:\n<logFile>\n <file>log.txt</file>\n <errorLinePattern >* [Error] *</errorLinePattern >\n <warningLinePattern>* [Warn] *</warningLinePattern>\n <infoLinePattern>* [Info] *</infoLinePattern>\n <debugLinePattern >* [Debug] *</debugLinePattern >\n</logFile>\n\n\n\n Back to top", + "crumbs": [ + "User guide", + "Integrations", + "Run from Delft-FEWS" + ] + }, + { + "objectID": "user_guide/bmi.html", + "href": "user_guide/bmi.html", + "title": "Basic Model Interface (BMI)", + "section": "", + "text": "The Community Surface Dynamics Modeling System (CSMDS) has developed the Basic Model Interface (BMI). BMI consists of a set of standard control and query functions that can be added by a developer to the model code and makes a model both easier to learn and easier to couple with other software elements.\nFor more information see also: http://csdms.colorado.edu/wiki/BMI_Description\nCSDMS provides specifications for the languages C, C++, Fortran and Python. Wflow, written in the Julia programming language, makes use of the following Julia specification, based on BMI 2.0 version.\nFor the BMI implementation of wflow all grids are defined as unstructured grids, including the special cases scalar and points. While the input (forcing and model parameters) is structured (uniform rectilinear), internally wflow works with one dimensional arrays based on the active grid cells of the 2D model domain.", + "crumbs": [ + "User guide", + "Integrations", + "Basic Model Interface (BMI)" + ] + }, + { + "objectID": "user_guide/bmi.html#introduction", + "href": "user_guide/bmi.html#introduction", + "title": "Basic Model Interface (BMI)", + "section": "", + "text": "The Community Surface Dynamics Modeling System (CSMDS) has developed the Basic Model Interface (BMI). BMI consists of a set of standard control and query functions that can be added by a developer to the model code and makes a model both easier to learn and easier to couple with other software elements.\nFor more information see also: http://csdms.colorado.edu/wiki/BMI_Description\nCSDMS provides specifications for the languages C, C++, Fortran and Python. Wflow, written in the Julia programming language, makes use of the following Julia specification, based on BMI 2.0 version.\nFor the BMI implementation of wflow all grids are defined as unstructured grids, including the special cases scalar and points. While the input (forcing and model parameters) is structured (uniform rectilinear), internally wflow works with one dimensional arrays based on the active grid cells of the 2D model domain.", + "crumbs": [ + "User guide", + "Integrations", + "Basic Model Interface (BMI)" + ] + }, + { + "objectID": "user_guide/bmi.html#configuration", + "href": "user_guide/bmi.html#configuration", + "title": "Basic Model Interface (BMI)", + "section": "Configuration", + "text": "Configuration\nThe variables that wflow can exchange through BMI are based on the different model components and these components should be listed under the API section of the TOML configuration file of the model type. Below an example of this API section, that lists the vertical component and different lateral components:\n[API]\ncomponents = [\n \"vertical\",\n \"lateral.subsurface\",\n \"lateral.land\",\n \"lateral.river\",\n \"lateral.river.reservoir\"\n]\nSee also:\nWflow.BMI.initialize\nWflow.BMI.get_input_var_names\nVariables with a third dimension, for example layer as part of the vertical SBM concept, are exposed as two-dimensional grids through the wflow BMI implementation. For these variables the index of this third dimension is required, by adding [k] to the variable name (k refers to the index of the third dimension). For example, the variable vertical.vwc[1] refers to the first soil layer of the vertical SBM concept.", + "crumbs": [ + "User guide", + "Integrations", + "Basic Model Interface (BMI)" + ] + }, + { + "objectID": "user_guide/bmi.html#couple-to-a-groundwater-model", + "href": "user_guide/bmi.html#couple-to-a-groundwater-model", + "title": "Basic Model Interface (BMI)", + "section": "Couple to a groundwater model", + "text": "Couple to a groundwater model\nFor the coupling of wflow_sbm (SBM + kinematic wave) with a groundwater model (e.g. MODFLOW) it is possible to run: - wflow_sbm in parts from the BMI, and - to switch off the lateral subsurface flow component of wflow_sbm.\nThe lateral subsurface component of wflow_sbm is not initialized by wflow when the [input.lateral.subsurface] part of the TOML file is not included. Then from the BMI it is possible to run first the recharge part of SBM:\nmodel = BMI.update(model, run=\"sbm_until_recharge\")\nand to exchange recharge and for example river waterlevels to the groundwater model. After the groundwater model update, and the exchange of groundwater head and for example drain and river flux to wflow_sbm, the SBM part that mainly determines exfiltration of water from the unsaturated store, and the kinematic wave for river - and overland flow can be run as follows:\nmodel = BMI.update(model, run=\"sbm_after_subsurfaceflow\")\nSee also:\nWflow.BMI.update", + "crumbs": [ + "User guide", + "Integrations", + "Basic Model Interface (BMI)" + ] + }, + { + "objectID": "model_docs/vertical/sediment.html", + "href": "model_docs/vertical/sediment.html", + "title": "Sediment", + "section": "", + "text": "Over the land, soil erosion, also called soil loss, is closely linked to the water cycle. The main processes governing sediment generation are splash erosion from rain droplets, and sheet and rill erosion from the shear stress caused by overland flow. The intensity of soil erosion by rain or flow depends on the land and soil characteristics such as slope, land use or soil type. Once soil is eroded, the detached particles can be transported downslope by overland flow. Along the transport pathways, soil particles can also be deposited due to a low flow velocity, a change of topography in depressions, footslopes or valley bottoms, and/or can be filtered and stopped by a change in vegetation such as field boundaries.\nThe inland part of the sediment gathers these different processes, separated in a vertical structure for the soil loss and lateral structure for the transport in overland flow.", + "crumbs": [ + "Model documentation", + "Vertical processes", + "Sediment" + ] + }, + { + "objectID": "model_docs/vertical/sediment.html#soil-erosion", + "href": "model_docs/vertical/sediment.html#soil-erosion", + "title": "Sediment", + "section": "Soil Erosion", + "text": "Soil Erosion\nThe first process to consider in sediment dynamics is the generation of sediments by land erosion. The main processes behind soil loss are rainfall erosion and overland flow erosion. In order to model such processes at a fine time and space scale, physics-based models such as ANSWERS and EUROSEM were chosen here.\nThe choice of rainfall erosion method is set up in the model section of the TOML:\n[model]\nrainerosmethod = \"answers\" # Rainfall erosion equation: [\"answers\", \"eurosem\"]\n\nRainfall erosion\nIn wflow_sediment, rainfall erosion can both be modelled using EUROSEM or ANSWERS equation. The main difference between the models is that EUROSEM uses a more physics-based approach based on the kinetic energy of the rain drops impacting the soil (Morgan et al, 1998), while ANSWERS is more empirical and uses parameters from the USLE model (Beasley et al, 1991).\nIn EUROSEM, rainfall erosion is modelled according to rainfall intensity and its kinetic energy when it reaches the soil according to equations developed by Brandt (1990). As the intensity of the rain kinetic energy depends on the length of the fall, rainfall intercepted by vegetation will then be reduced compared to direct throughfall. The kinetic energy of direct throughfall is estimated by (Morgan et al, 1998): \\[\n \\subtext{\\mathrm{KE}}{direct} = 8.95 + 8.44\\,\\log_{10}(R_i)\n\\]\nwhere \\(\\SIb{\\subtext{\\mathrm{KE}}{direct}}{J m^{-2} mm^{-1}}\\) is the kinetic energy of direct throughfall and \\(\\SIb{R_i}{mm h^{-1}}\\) is rainfall intensity. If the rainfall is intercepted by vegetation and falls as leaf drainage, its kinetic energy is then reduced according to (Brandt, 1990): \\[\n \\subtext{\\mathrm{KE}}{leaf} = 15.8\\,\\sqrt{H_p} - 5.87\n\\] where \\(\\SIb{\\subtext{\\mathrm{KE}}{leaf}}{J m^{-2} mm^{-1}}\\) is kinetic energy of leaf drainage and \\(\\SIb{H_p}{m}\\) is the effective canopy height (half of plant height). Canopy height can be derived from the global map from Simard & al. (2011) or by user input depending on the land use.\nKinetic energies from both direct throughfall and leaf drainage are then multiplied by the respective depths of direct throughfall and leaf drainage (mm) and added to get the total rainfall kinetic energy \\(\\mathrm{KE}\\). The soil detached by rainfall \\(\\SIb{D_R}{g m^{-2}}\\) is then: \\[\n D_R = k\\,\\mathrm{KE}\\,e^{-\\varphi h}\n\\] where \\(\\SIb{k}{g J^{-1}}\\) is an index of the detachability of the soil, \\(\\SIb{\\mathrm{KE}}{J\nm^{-2}}\\) is the total rainfall kinetic energy, \\(\\SIb{h}{m}\\) is the surface runoff depth on the soil and \\(\\varphi\\) is an exponent varying between \\(0.9\\) and \\(3.1\\) used to reduce rainfall impact if the soil is already covered by water. As a simplification, Torri (1987) has shown that a value of \\(2.0\\) for \\(\\varphi\\) is representative enough for a wide range of soil conditions. The detachability of the soil \\(k\\) depends on the soil texture (proportion of clay, silt and sand content) and corresponding values are defined in EUROSEM user guide (Morgan et al, 1998). As a simplification, in wflow_sediment, the mean value of the detachability shown in the table below are used. Soil texture can for example be derived from the topsoil clay and silt content from SoilGrids (Hengl et al, 2017).\n\nMean detachability of soil depending on its texture (Morgan et al, 1998).\n\n\nTexture (USDA system)\nMean detachability \\(\\SIb{k}{g J^{-1}}\\)\n\n\n\n\nClay\n2.0\n\n\nClay Loam\n1.7\n\n\nSilt\n1.2\n\n\nSilt Loam\n1.5\n\n\nLoam\n2.0\n\n\nSandy Loam\n2.6\n\n\nLoamy Sand\n3.0\n\n\nFine Sand\n3.5\n\n\nSand\n1.9\n\n\n\nRainfall erosion is handled differently in ANSWERS. There, the impacts of vegetation and soil properties are handled through the USLE coefficients in the equation (Beasley et al, 1991): \\[\n D_R = 0.108 \\, \\subtext{C}{USLE} \\, \\subtext{K}{USLE} \\, A_i \\, R_i^2\n\\] where \\(\\SIb{D_R}{kg min^{-1}}\\) is the soil detachment by rainfall, \\(\\subtext{C}{USLE}\\) is the soil cover-management factor from the USLE equation, \\(\\subtext{K}{USLE}\\) is the soil erodibility factor from the USLE equation, \\(\\SIb{A_i}{m^2}\\) is the area of the cell and \\(\\SIb{R_i}{mm\\;min^{-1}}\\) is the rainfall intensity. There are several methods available to estimate the \\(C\\) and \\(K\\) factors from the USLE. They can come from user input maps, for example maps resulting from Panagos & al.’s recent studies for Europe (Panagos et al, 2015) (Ballabio et al, 2016). To get an estimate of the \\(C\\) factor globally, the other method is to estimate \\(C\\) values for the different land use type in from global land cover maps (e.g. GlobCover). An example is given for the global land cover map GlobCover, summed up in the table below, the values come from a literature study including Panagos et al.’s review (2015), Gericke & al. (2015), Mansoor & al. (2013), Chadli et al. (2016), de Vente et al. (2009), Borrelli et al. (2014), Yang et al. (2003) and Bosco et al. (2015).\nThe other methods to estimate the USLE \\(K\\) factor are to use either topsoil composition or topsoil geometric mean diameter. \\(K\\) estimation from topsoil composition is estimated with the equation developed in the EPIC model (Williams et al, 1983): \\[\n\\begin{gathered}\n \\subtext{K}{USLE} = \\left[ 0.2 + 0.3\\exp\\left(-0.0256\\;\\mathrm{SAN}\\frac{(1-\\mathrm{SIL})}{100}\\right) \\right]\n \\left(\\frac{\\mathrm{SIL}}{\\mathrm{CLA}+\\mathrm{SIL}}\\right)^{0.3} \\\\\n \\left(1-\\frac{0.25\\;\\mathrm{OC}}{\\mathrm{OC}+e^{3.72-2.95\\;\\mathrm{OC}}}\\right)\\left(1-\\frac{0.75\\;\\mathrm{SN}}{\\mathrm{SN}+e^{-5.51+22.9\\;\\mathrm{SN}}}\\right)\n\\end{gathered}\n\\] where \\(\\SIb{\\mathrm{CLA}}{\\%}\\), \\(\\SIb{\\mathrm{SIL}}{\\%}\\), \\(\\SIb{\\mathrm{SAN}}{\\%}\\) are respectively the clay, silt and sand fractions of the topsoil, \\(\\SIb{OC}{\\%}\\) is the topsoil organic carbon content and \\(\\mathrm{SN} = 1-\\mathrm{SAN}/100\\). These soil parameters can be derived for example from the SoilGrids dataset. The \\(K\\) factor can also be estimated from the soil mean geometric diameter using the formulation from the RUSLE guide by Renard & al. (1997): \\[\n \\subtext{K}{USLE} = 0.0034 + 0.0405\\exp\\left(-\\dfrac{1}{2}\\left(\\dfrac{\\log_{10}(D_g)+1.659}{0.7101}\\right)^2\\right)\n\\] where \\(D_g\\) is the soil geometric mean diameter (mm) estimated from topsoil clay, silt, sand fraction.\n\nEstimation of USLE C factor per Globcover land use type\n\n\n\n\n\n\n\nGlobCover Value\nGlobcover label\n\\(\\subtext{C}{USLE}\\)\n\n\n\n\n11\nPost-flooding or irrigated croplands (or aquatic)\n0.2\n\n\n14\nRainfed croplands\n0.35\n\n\n20\nMosaic cropland (50-70%) vegetation (grassland/shrubland/forest) (20-50%)\n0.27\n\n\n30\nMosaic vegetation (grassland/shrubland/forest) (50-70%) / cropland (20-50%)\n0.25\n\n\n40\nClosed to open (>15%) broadleaved evergreen or semi-deciduous forest (>5m)\n0.0065\n\n\n50\nClosed (>40%) broadleaved deciduous forest (>5m)\n0.001\n\n\n60\nOpen (15-40%) broadleaved deciduous forest/woodland (>5m)\n0.01\n\n\n70\nClosed (>40%) needleleaved evergreen forest (>5m)\n0.001\n\n\n90\nOpen (15-40%) needleleaved deciduous or evergreen forest (>5m)\n0.01\n\n\n100\nClosed to open (>15%) mixed broadleaved and needleleaved forest (>5m)\n0.02\n\n\n110\nMosaic forest or shrubland (50-70%) / grassland (20-50%)\n0.015\n\n\n120\nMosaic grassland (50-70%) / forest or shrubland (20-50%)\n0.03\n\n\n130\nClosed to open (>15%) (broadleaved or needleleaved, evergreen or deciduous) shrubland (<5m)\n0.035\n\n\n140\nClosed to open (>15%) herbaceous vegetation (grassland, savannas or lichens/mosses)\n0.05\n\n\n150\nSparse (<15%) vegetation\n0.35\n\n\n160\nClosed to open (>15%) broadleaved forest regularly flooded (semi-permanently or temporarily) - Fresh or brackish water\n0.001\n\n\n170\nClosed (>40%) broadleaved forest or shrubland permanently flooded - Saline or brackish water\n0.0005\n\n\n180\nClosed to open (>15%) grassland or woody vegetation on regularly flooded or waterlogged soil - Fresh, brackish or saline water\n0.04\n\n\n190\nArtificial surfaces and associated areas (Urban areas >50%)\n0.0\n\n\n200\nBare areas\n0.0\n\n\n210\nWater bodies\n0.0\n\n\n220\nPermanent snow and ice\n0.0\n\n\n230\nNo data (burnt areas, clouds,…)\n0.0\n\n\n\n\n\nOverland flow erosion\nOverland flow (or surface runoff) erosion is induced by the strength of the shear stress of the surface water on the soil. As in rainfall erosion, the effect of the flow shear stress can be reduced by the soil vegetation or by the soil properties. In wflow_sediment, soil detachment by overland flow is modelled as in ANSWERS with (Beasley et al, 1991): \\[\n D_G = 0.90 \\, \\subtext{C}{USLE} \\, \\subtext{K}{USLE} \\, A_i \\, S \\, q\n\\] where \\(\\SIb{D_F}{kg\\;min^{-1}}\\) is soil detachment by flow, \\(\\subtext{C}{USLE}\\) and \\(\\subtext{K}{USLE}\\) are the USLE cover and soil erodibility factors, \\(\\SIb{A_i}{m^2}\\) is the cell area, \\(S\\) is the slope gradient and \\(\\SIb{q}{m^2 min^{-1}}\\) is the overland flow rate per unit width. The USLE \\(C\\) and \\(K\\) factors can be estimated with the same methods as for rainfall erosion and here the slope gradient is obtained from the sinus rather than the tangent of the slope angle.", + "crumbs": [ + "Model documentation", + "Vertical processes", + "Sediment" + ] + }, + { + "objectID": "model_docs/vertical/sediment.html#delivery-to-the-river-system", + "href": "model_docs/vertical/sediment.html#delivery-to-the-river-system", + "title": "Sediment", + "section": "Delivery to the river system", + "text": "Delivery to the river system\nOnce soil is detached, it can be transported by overland flow and reach the river system. This process is described in Sediment Flux in overland flow.", + "crumbs": [ + "Model documentation", + "Vertical processes", + "Sediment" + ] + }, + { + "objectID": "model_docs/vertical/sediment.html#references", + "href": "model_docs/vertical/sediment.html#references", + "title": "Sediment", + "section": "References", + "text": "References\n\nD.B Beasley and L.F Huggins. ANSWERS - Users Manual. Technical report, EPA, 1991.\nP. Borrelli, M. Märker, P. Panagos, and B. Schütt. Modeling soil erosion and river sediment yield for an intermountain drainage basin of the Central Apennines, Italy. Catena, 114:45-58,\n\n10.1016/j.catena.2013.10.007\n\nC. Bosco, D. De Rigo, O. Dewitte, J. Poesen, and P. Panagos. Modelling soil erosion at European scale: Towards harmonization and reproducibility. Natural Hazards and Earth System Sciences, 15(2):225-245, 2015. 10.5194/nhess-15-225-2015\nC.J Brandt. Simulation of the size distribution and erosivity of raindrops and throughfall drops. Earth Surface Processes and Landforms, 15(8):687-698, dec 1990.\nK. Chadli. Estimation of soil loss using RUSLE model for Sebou watershed (Morocco). Modeling Earth Systems and Environment, 2(2):51, 2016. 10.1007/s40808-016-0105-y\nG R Foster. Modeling the erosion process. Hydrologic modeling of small watersheds, pages 295-380, 1982.\nA. Gericke. Soil loss estimation and empirical relationships for sediment delivery ratios of European river catchments. International Journal of River Basin Management, 2015. 10.1080/15715124.2014.1003302\nL.D.K. Mansoor, M.D. Matlock, E.C. Cummings, and L.L. Nalley. Quantifying and mapping multiple ecosystem services change in West Africa. Agriculture, Ecosystems and Environment, 165:6-18, 2013. 10.1016/j.agee.2012.12.001\nQ Morgan, J.N Smith, R.E Govers, G Poesen, J.W.A Auerswald, K Chisci, G Torri, D Styczen, and M E Folly. The European soil erosion model (EUROSEM): documentation and user guide. Technical report, 1998.\nS.L Neitsch, J.G Arnold, J.R Kiniry, and J.R Williams. SWAT Theoretical Documentation Version\n\nTexas Water Resources Institute, pages 1-647, 2011. 10.1016/j.scitotenv.2015.11.063\n\nP. Panagos, P. Borrelli, K. Meusburger, C. Alewell, E. Lugato, and L. Montanarella. Estimating the soil erosion cover-management factor at the European scale. Land Use Policy, 48:38-50, 2015. 10.1016/j.landusepol.2015.05.021\nK Renard, Gr Foster, Ga Weesies, Dk McCool, and Dc Yoder. Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). Washington, 1997.\nD. Torri, M. Sfalanga, and M. Del Sette. Splash detachment: Runoff depth and soil cohesion. Catena, 14(1-3):149-155, 1987. 10.1016/S0341-8162(87)80013-9\nJ. de Vente, J. Poesen, G. Govers, and C. Boix-Fayos. The implications of data selection for regional erosion and sediment yield modelling. Earth Surface Processes and Landforms, 34(15):1994-2007, 2009. 10.1002/esp.1884\nG. Verstraeten and J. Poesen. Estimating trap efficiency of small reservoirs and ponds: methods and implications for the assessment of sediment yield. Progress in Physical Geography, 24(2):219-251, 2000. 10.1177/030913330002400204\nO. Vigiak, A. Malago, F. Bouraoui, M. Vanmaercke, and J. Poesen. Adapting SWAT hillslope erosion model to predict sediment concentrations and yields in large Basins. Science of the Total Environment, 538:855-875, 2015. 10.1016/j.scitotenv.2015.08.095\nJ.R. Williams, K.G. Renard, and P.T. Dyke. EPIC A new method for assessing erosion’s effect on soil productivity. Journal of Soil and Water Conservation, 38(5):381-383, 1983.\nD. Yang, S. Kanae, T. Oki, T. Koike, and K. Musiake. Global potential soil erosion with reference to land use and climate changes. Hydrological Processes, 17(14):2913-2928, 2003. 10.1002/hyp.1441", + "crumbs": [ + "Model documentation", + "Vertical processes", + "Sediment" + ] + }, + { + "objectID": "model_docs/parameters_lateral.html", + "href": "model_docs/parameters_lateral.html", + "title": "Lateral concepts", + "section": "", + "text": "The Table below shows the parameters (fields) of struct SurfaceFlowRiver used for river flow, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.river] to map the internal model parameter to the external netCDF variable. The input parameter slope (listed under [input.lateral.river]) is not equal to the internal model parameter sl, and is listed in the Table below between parentheses.\n\n\n\n\n\n\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\nbeta\nconstant in Manning’s equation\n-\n-\n\n\nsl (slope)\nslope\nm m\\(^{-1}\\)\n-\n\n\nn\nManning’s roughness\ns m\\(^{-\\frac{1}{3}}\\)\n0.036\n\n\ndl\nlength\nm\n-\n\n\nq\ndischarge\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nqin\ninflow from upstream cells\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nq_av\naverage discharge\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nqlat\nlateral inflow per unit length\nm\\(^2\\) s\\(^{-1}\\)\n-\n\n\ninwater\nlateral inflow\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\ninflow\nexternal inflow (abstraction/supply/demand)\nm\\(^3\\) s\\(^{-1}\\)\n0.0\n\n\ninflow_wb\ninflow waterbody (lake or reservoir model) from land part\nm\\(^3\\) s\\(^{-1}\\)\n0.0\n\n\nabstraction\nabstraction (computed as part of water demand and allocation)\nm\\(^3\\) s\\(^{-1}\\)\n0.0\n\n\nvolume\nkinematic wave volume\nm\\(^3\\)\n-\n\n\nh\nwater level\nm\n-\n\n\nh_av\naverage water level\nm\n-\n\n\nbankfull_depth\nbankfull river depth\nm\n1.0\n\n\ndt\nmodel time step\ns\n-\n\n\nits\nnumber of fixed iterations\n-\n-\n\n\nwidth\nwidth\nm\n-\n\n\nalpha_pow\nused in the power part of \\(\\alpha\\)\n-\n-\n\n\nalpha_term\nterm used in computation of \\(\\alpha\\)\n-\n-\n\n\nalpha\nconstant in momentum equation \\(A = \\alpha Q^{\\beta}\\)\ns\\(^{\\frac{3}{5}}\\) m\\(^{\\frac{1}{5}}\\)\n-\n\n\ncel\ncelerity of kinematic wave\nm s\\(^{-1}\\)\n-\n\n\nreservoir_index\nmap cell to 0 (no reservoir) or i (pick reservoir i in reservoir field)\n-\n-\n\n\nlake_index\nmap cell to 0 (no lake) or i (pick lake i in lake field)\n-\n-\n\n\nreservoir\nan array of reservoir models SimpleReservoir\n-\n-\n\n\nlake\nan array of lake models Lake\n-\n-\n\n\nallocation\nwater allocation of type AllocationRiver\n-\n-\n\n\nkinwave_it\nboolean for kinematic wave iterations\n-\nfalse\n\n\n\nThe Table below shows the parameters (fields) of struct SurfaceFlowLand used for overland flow, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.land] to map the internal model parameter to the external netCDF variable. The input parameter slope (listed under [input.lateral.land]) is not equal to the internal model parameter sl, and is listed in the Table below between parentheses.\n\n\n\n\n\n\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\nbeta\nconstant in Manning’s equation\n-\n-\n\n\nsl (slope)\nslope\nm m\\(^{-1}\\)\n-\n\n\nn\nManning’s roughness\ns m\\(^{-\\frac{1}{3}}\\)\n0.072\n\n\ndl\nlength\nm\n-\n\n\nq\ndischarge\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nqin\ninflow from upstream cells\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nq_av\naverage discharge\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nqlat\nlateral inflow per unit length\nm\\(^2\\) s\\(^{-1}\\)\n-\n\n\ninwater\nlateral inflow\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nvolume\nkinematic wave volume\nm\\(^3\\)\n-\n\n\nh\nwater level\nm\n-\n\n\nh_av\naverage water level\nm\n-\n\n\ndt\nmodel time step\ns\n-\n\n\nits\nnumber of fixed iterations\n-\n-\n\n\nwidth\nwidth\nm\n-\n\n\nalpha_pow\nused in the power part of \\(\\alpha\\)\n-\n-\n\n\nalpha_term\nterm used in computation of \\(\\alpha\\)\n-\n-\n\n\nalpha\nconstant in momentum equation \\(A = \\alpha Q^{\\beta}\\)\ns\\(^{\\frac{3}{5}}\\) m\\(^{\\frac{1}{5}}\\)\n-\n\n\ncel\ncelerity of kinematic wave\nm s\\(^{-1}\\)\n-\n\n\nto_river\npart of overland flow that flows to the river\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nkinwave_it\nboolean for kinematic wave iterations\n-\nfalse\n\n\n\n\n\n\nThe Table below shows the parameters (fields) of struct LateralSSF, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF). The soil related parameters f, soilthickness, z_exp, theta_s and theta_r are derived from the vertical SBM concept (including unit conversion for f, z_exp and soilthickness), and can be listed in the TOML configuration file under [input.vertical], to map the internal model parameter to the external netCDF variable. The internal slope model parameter slope is set through the TOML file as follows:\n[input.lateral.land]\nslope = \"Slope\"\nThe parameter kh_0 is computed by multiplying the vertical hydraulic conductivity at the soil surface kv_0 (including unit conversion) of the vertical SBM concept with the internal parameter khfrac [-] (default value of 1.0). The internal model parameter khfrac is set through the TOML file as follows:\n[input.lateral.subsurface]\nksathorfrac = \"KsatHorFrac\"\nThe khfrac parameter compensates for anisotropy, small scale kv_0 measurements (soil core) that do not represent larger scale hydraulic conductivity, and smaller flow length scales (hillslope) in reality, not represented by the model resolution.\nFor the vertical SBM concept different vertical hydraulic conductivity depth profiles are possible, and these also determine which LateralSSF parameters are used including the input requirements for the computation of lateral subsurface flow. For the exponential profile the model parameters kh_0 and f are used. For the exponential_constant profile kh_0 and f are used, and z_exp is required as part of [input.vertical]. For the layered profile, SBM model parameter kv is used, and for the layered_exponential profile kv is used and z_exp is required as part of [input.vertical].\n\n\n\n\n\n\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\nkh_0\nhorizontal hydraulic conductivity at soil surface\nm d\\(^{-1}\\)\n3.0\n\n\nf\na scaling parameter (controls exponential decline of kh_0)\nm\\(^{-1}\\)\n1.0\n\n\nkh\nhorizontal hydraulic conductivity\nm d\\(^{-1}\\)\n-\n\n\nkhfrac (ksathorfrac)\na muliplication factor applied to vertical hydraulic conductivity kv\n-\n100.0\n\n\nsoilthickness\nsoil thickness\nm\n2.0\n\n\ntheta_s\nsaturated water content (porosity)\n-\n0.6\n\n\ntheta_r\nresidual water content\n-\n0.01\n\n\ndt\nmodel time step\nd\n-\n\n\nslope\nslope\nm m\\(^{-1}\\)\n-\n\n\ndl\ndrain length\nm\n-\n\n\ndw\ndrain width\nm\n-\n\n\nzi\npseudo-water table depth (top of the saturated zone)\nm\n-\n\n\nz_exp\ndepth from soil surface for which exponential decline of kh_0 is valid\nm\n-\n\n\nexfiltwater\nexfiltration (groundwater above surface level, saturated excess conditions)\nm Δt⁻¹\n-\n\n\nrecharge\nnet recharge to saturated store\nm\\(^2\\) Δt⁻¹\n-\n\n\nssf\nsubsurface flow\nm\\(^3\\) d\\({-1}\\)\n-\n\n\nssfin\ninflow from upstream cells\nm\\(^3\\) d\\({-1}\\)\n-\n\n\nssfmax\nmaximum subsurface flow\nm\\(^2\\) d\\({-1}\\)\n-\n\n\nto_river\npart of subsurface flow that flows to the river\nm\\(^3\\) d\\({-1}\\)\n-\n\n\nvolume\nsubsurface water volume\nm\\(^3\\)\n-", + "crumbs": [ + "Model documentation", + "Parameters", + "Lateral concepts" + ] + }, + { + "objectID": "model_docs/parameters_lateral.html#kinematic-wave", + "href": "model_docs/parameters_lateral.html#kinematic-wave", + "title": "Lateral concepts", + "section": "", + "text": "The Table below shows the parameters (fields) of struct SurfaceFlowRiver used for river flow, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.river] to map the internal model parameter to the external netCDF variable. The input parameter slope (listed under [input.lateral.river]) is not equal to the internal model parameter sl, and is listed in the Table below between parentheses.\n\n\n\n\n\n\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\nbeta\nconstant in Manning’s equation\n-\n-\n\n\nsl (slope)\nslope\nm m\\(^{-1}\\)\n-\n\n\nn\nManning’s roughness\ns m\\(^{-\\frac{1}{3}}\\)\n0.036\n\n\ndl\nlength\nm\n-\n\n\nq\ndischarge\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nqin\ninflow from upstream cells\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nq_av\naverage discharge\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nqlat\nlateral inflow per unit length\nm\\(^2\\) s\\(^{-1}\\)\n-\n\n\ninwater\nlateral inflow\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\ninflow\nexternal inflow (abstraction/supply/demand)\nm\\(^3\\) s\\(^{-1}\\)\n0.0\n\n\ninflow_wb\ninflow waterbody (lake or reservoir model) from land part\nm\\(^3\\) s\\(^{-1}\\)\n0.0\n\n\nabstraction\nabstraction (computed as part of water demand and allocation)\nm\\(^3\\) s\\(^{-1}\\)\n0.0\n\n\nvolume\nkinematic wave volume\nm\\(^3\\)\n-\n\n\nh\nwater level\nm\n-\n\n\nh_av\naverage water level\nm\n-\n\n\nbankfull_depth\nbankfull river depth\nm\n1.0\n\n\ndt\nmodel time step\ns\n-\n\n\nits\nnumber of fixed iterations\n-\n-\n\n\nwidth\nwidth\nm\n-\n\n\nalpha_pow\nused in the power part of \\(\\alpha\\)\n-\n-\n\n\nalpha_term\nterm used in computation of \\(\\alpha\\)\n-\n-\n\n\nalpha\nconstant in momentum equation \\(A = \\alpha Q^{\\beta}\\)\ns\\(^{\\frac{3}{5}}\\) m\\(^{\\frac{1}{5}}\\)\n-\n\n\ncel\ncelerity of kinematic wave\nm s\\(^{-1}\\)\n-\n\n\nreservoir_index\nmap cell to 0 (no reservoir) or i (pick reservoir i in reservoir field)\n-\n-\n\n\nlake_index\nmap cell to 0 (no lake) or i (pick lake i in lake field)\n-\n-\n\n\nreservoir\nan array of reservoir models SimpleReservoir\n-\n-\n\n\nlake\nan array of lake models Lake\n-\n-\n\n\nallocation\nwater allocation of type AllocationRiver\n-\n-\n\n\nkinwave_it\nboolean for kinematic wave iterations\n-\nfalse\n\n\n\nThe Table below shows the parameters (fields) of struct SurfaceFlowLand used for overland flow, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.land] to map the internal model parameter to the external netCDF variable. The input parameter slope (listed under [input.lateral.land]) is not equal to the internal model parameter sl, and is listed in the Table below between parentheses.\n\n\n\n\n\n\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\nbeta\nconstant in Manning’s equation\n-\n-\n\n\nsl (slope)\nslope\nm m\\(^{-1}\\)\n-\n\n\nn\nManning’s roughness\ns m\\(^{-\\frac{1}{3}}\\)\n0.072\n\n\ndl\nlength\nm\n-\n\n\nq\ndischarge\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nqin\ninflow from upstream cells\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nq_av\naverage discharge\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nqlat\nlateral inflow per unit length\nm\\(^2\\) s\\(^{-1}\\)\n-\n\n\ninwater\nlateral inflow\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nvolume\nkinematic wave volume\nm\\(^3\\)\n-\n\n\nh\nwater level\nm\n-\n\n\nh_av\naverage water level\nm\n-\n\n\ndt\nmodel time step\ns\n-\n\n\nits\nnumber of fixed iterations\n-\n-\n\n\nwidth\nwidth\nm\n-\n\n\nalpha_pow\nused in the power part of \\(\\alpha\\)\n-\n-\n\n\nalpha_term\nterm used in computation of \\(\\alpha\\)\n-\n-\n\n\nalpha\nconstant in momentum equation \\(A = \\alpha Q^{\\beta}\\)\ns\\(^{\\frac{3}{5}}\\) m\\(^{\\frac{1}{5}}\\)\n-\n\n\ncel\ncelerity of kinematic wave\nm s\\(^{-1}\\)\n-\n\n\nto_river\npart of overland flow that flows to the river\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nkinwave_it\nboolean for kinematic wave iterations\n-\nfalse\n\n\n\n\n\n\nThe Table below shows the parameters (fields) of struct LateralSSF, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF). The soil related parameters f, soilthickness, z_exp, theta_s and theta_r are derived from the vertical SBM concept (including unit conversion for f, z_exp and soilthickness), and can be listed in the TOML configuration file under [input.vertical], to map the internal model parameter to the external netCDF variable. The internal slope model parameter slope is set through the TOML file as follows:\n[input.lateral.land]\nslope = \"Slope\"\nThe parameter kh_0 is computed by multiplying the vertical hydraulic conductivity at the soil surface kv_0 (including unit conversion) of the vertical SBM concept with the internal parameter khfrac [-] (default value of 1.0). The internal model parameter khfrac is set through the TOML file as follows:\n[input.lateral.subsurface]\nksathorfrac = \"KsatHorFrac\"\nThe khfrac parameter compensates for anisotropy, small scale kv_0 measurements (soil core) that do not represent larger scale hydraulic conductivity, and smaller flow length scales (hillslope) in reality, not represented by the model resolution.\nFor the vertical SBM concept different vertical hydraulic conductivity depth profiles are possible, and these also determine which LateralSSF parameters are used including the input requirements for the computation of lateral subsurface flow. For the exponential profile the model parameters kh_0 and f are used. For the exponential_constant profile kh_0 and f are used, and z_exp is required as part of [input.vertical]. For the layered profile, SBM model parameter kv is used, and for the layered_exponential profile kv is used and z_exp is required as part of [input.vertical].\n\n\n\n\n\n\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\nkh_0\nhorizontal hydraulic conductivity at soil surface\nm d\\(^{-1}\\)\n3.0\n\n\nf\na scaling parameter (controls exponential decline of kh_0)\nm\\(^{-1}\\)\n1.0\n\n\nkh\nhorizontal hydraulic conductivity\nm d\\(^{-1}\\)\n-\n\n\nkhfrac (ksathorfrac)\na muliplication factor applied to vertical hydraulic conductivity kv\n-\n100.0\n\n\nsoilthickness\nsoil thickness\nm\n2.0\n\n\ntheta_s\nsaturated water content (porosity)\n-\n0.6\n\n\ntheta_r\nresidual water content\n-\n0.01\n\n\ndt\nmodel time step\nd\n-\n\n\nslope\nslope\nm m\\(^{-1}\\)\n-\n\n\ndl\ndrain length\nm\n-\n\n\ndw\ndrain width\nm\n-\n\n\nzi\npseudo-water table depth (top of the saturated zone)\nm\n-\n\n\nz_exp\ndepth from soil surface for which exponential decline of kh_0 is valid\nm\n-\n\n\nexfiltwater\nexfiltration (groundwater above surface level, saturated excess conditions)\nm Δt⁻¹\n-\n\n\nrecharge\nnet recharge to saturated store\nm\\(^2\\) Δt⁻¹\n-\n\n\nssf\nsubsurface flow\nm\\(^3\\) d\\({-1}\\)\n-\n\n\nssfin\ninflow from upstream cells\nm\\(^3\\) d\\({-1}\\)\n-\n\n\nssfmax\nmaximum subsurface flow\nm\\(^2\\) d\\({-1}\\)\n-\n\n\nto_river\npart of subsurface flow that flows to the river\nm\\(^3\\) d\\({-1}\\)\n-\n\n\nvolume\nsubsurface water volume\nm\\(^3\\)\n-", + "crumbs": [ + "Model documentation", + "Parameters", + "Lateral concepts" + ] + }, + { + "objectID": "model_docs/parameters_lateral.html#local-inertial", + "href": "model_docs/parameters_lateral.html#local-inertial", + "title": "Lateral concepts", + "section": "Local inertial", + "text": "Local inertial\n\nRiver flow\nThe Table below shows the parameters (fields) of struct ShallowWaterRiver, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.river], to map the internal model parameter to the external netCDF variable. The parameter river bed elevation zb is based on the bankfull elevation and depth input data:\n[input.lateral.river]\nbankfull_elevation = \"RiverZ\"\nbankfull_depth = \"RiverDepth\"\nWhen floodplain routing (parameter floodplain) is included as part of local inertial river flow, parameter q_av represents the total average discharge of the river channel and floodplain routing, and parameter q_channel_av represents average river channel discharge. Otherwise parameters q_av and q_channel_av represent both average river channel discharge (are equal).\nThe input parameter n (listed under [input.lateral.river]) is not equal to the internal model parameter mannings_n, and is listed in the Table below between parentheses.\n\n\n\n\n\n\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\nmannings_n (n)\nManning’s roughness\ns m\\(^{-\\frac{1}{3}}\\)\n0.036\n\n\nwidth\nriver width\nm\n-\n\n\nzb\nriver bed elevation\nm\n-\n\n\nlength\nriver length\nm\n-\n\n\nn\nnumber of cells\n-\n-\n\n\nne\nnumber of edges/links\n-\n-\n\n\nactive_n\nactive nodes\n-\n-\n\n\nactive_e\nactive edges\n-\n-\n\n\ng\nacceleration due to gravity\nm s\\(^{-2}\\)\n-\n\n\nalpha\nstability coefficient (Bates et al., 2010)\n-\n0.7\n\n\nh_thresh\ndepth threshold for calculating flow\nm\n0.001\n\n\ndt\nmodel time step\ns\n-\n\n\nq\nriver discharge (subgrid channel)\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nq_av\naverage river channel (+ floodplain) discharge\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nq_channel_av\naverage river channel discharge\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nzb_max\nmaximum channel bed elevation\nm\n-\n\n\nmannings_n_sq\nManning’s roughness squared at edge/link\n(s m\\(^{-\\frac{1}{3}}\\))\\(^2\\)\n-\n\n\nh\nwater depth\nm\n-\n\n\nzs_max\nmaximum water elevation\nm\n-\n\n\nzs_src\nwater elevation of source node of edge\nm\n-\n\n\nzs_dst\nwater elevation of downstream node of edge\nm\n-\n\n\nhf\nwater depth at edge/link\nm\n-\n\n\nh_av\naverage water depth\nm\n-\n\n\ndl\nriver length\nm\n-\n\n\ndl_at_link\nriver length at edge/link\nm\n-\n\n\nwidth\nriver width\nm\n-\n\n\nwidth_at_link\nriver width at edge/link\nm\n-\n\n\na\nflow area at edge/link\nm\\(^2\\)\n-\n\n\nr\nhydraulic radius at edge/link\nm\n-\n\n\nvolume\nriver volume\nm\\(^3\\)\n-\n\n\nerror\nerror volume\nm\\(^3\\)\n-\n\n\ninwater\nlateral inflow\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\ninflow\nexternal inflow (abstraction/supply/demand)\nm\\(^3\\) s\\(^{-1}\\)\n0.0\n\n\nabstraction\nabstraction (computed as part of water demand and allocation)\nm\\(^3\\) s\\(^{-1}\\)\n0.0\n\n\ninflow_wb\ninflow waterbody (lake or reservoir model) from land part\nm\\(^3\\) s\\(^{-1}\\)\n0.0\n\n\nbankfull_volume\nbankfull volume\nm\\(^3\\)\n-\n\n\nbankfull_depth\nbankfull depth\nm\n-\n\n\nfroude_limit\nif true a check is performed if froude number > 1.0 (algorithm is modified)\n-\n-\n\n\nreservoir_index\nriver cell index with a reservoir\n-\n-\n\n\nlake_index\nriver cell index with a lake\n-\n-\n\n\nwaterbody\nwater body cells (reservoir or lake)\n-\n-\n\n\nreservoir\nan array of reservoir models SimpleReservoir\n-\n-\n\n\nlake\nan array of lake models Lake\n-\n-\n\n\nallocation\noptional water allocation of type AllocationRiver\n-\n-\n\n\nfloodplain\noptional 1D floodplain routing FloodPlain\n-\n-\n\n\n\n\n\n1D floodplain\nThe Table below shows the parameters (fields) of struct FloodPlain (part of struct ShallowWaterRiver), including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.river.floodplain], to map the internal model parameter to the external netCDF variable. The input parameter n (listed under [input.lateral.river.floodplain]) is not equal to the internal model parameter mannings_n, and is listed in the Table below between parentheses.\n\n\n\n\n\n\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\nprofile\nFloodplain profile FloodPlainProfile\n\n\n\n\nmannings_n (n)\nManning’s roughness for the floodplain\ns m\\(^{-\\frac{1}{3}}\\)\n0.072\n\n\nmannings_n_sq\nManning’s roughness squared at edge/link\n(s m\\(^{-\\frac{1}{3}}\\))\\(^2\\)\n-\n\n\nvolume\nflood volume\nm\\(^3\\)\n-\n\n\nh\nflood depth\nm\n-\n\n\nh_av\naverage flood depth\nm\n-\n\n\nerror\n\nerror volume\nm\\(^3\\)\n\n\na\nflow area at edge/link\nm\\(^2\\)\n-\n\n\nr\nhydraulic radius at edge/link\nm\n-\n\n\nhf\nflood depth at edge/link\nm\n-\n\n\nzb_max\nmaximum bankfull elevation at edge\nm\n-\n\n\nq0\ndischarge at previous time step\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nq\ndischarge\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nq_av\naverage discharge\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nhf_index\nindex with hf above depth threshold\n-\n-\n\n\n\nThe floodplain profile FloodPlainProfile contains the following parameters:\n\n\n\n\n\n\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\ndepth (flood_depth)\nflood depths\nm\n-\n\n\nvolume\ncumulative flood volume (per flood depth)\nm\\(^3\\)\n-\n\n\nwidth\ncumulative floodplain width (per flood depth)\nm\n-\n\n\na\ncumulative floodplain flow area (per flood depth)\nm\\(^2\\)\n-\n\n\np\ncumulative floodplain wetted perimeter (per flood depth)\nm\n-\n\n\n\nThe floodplain volumes (per flood depth interval) can be set as follows through the TOML file:\n[input.lateral.river.floodplain]\nvolume = \"floodplain_volume\"\nThe input parameter flood_depth (dimension of floodplain volume) is not equal to the internal model parameter depth, and is listed in the Table below between parentheses.\n\n\nOverland flow\nThe Table below shows the parameters (fields) of struct ShallowWaterLand, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.land], to map the internal model parameter to the external netCDF variable.\nThe mannings roughness (for the computation of mannings_n_sq) should be provided as follows in the TOML file:\n[input.lateral.land]\nn = \"n_land\" # mannings roughness\nThe input parameter elevation (listed under [input.lateral.land]) is not equal to the internal model parameter z, and is listed in the Table below between parentheses.\n\n\n\n\n\n\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\nn\nnumber of cells\n-\n-\n\n\nxl\ncell length x direction\nm\n-\n\n\nyl\ncell length y direction\nm\n-\n\n\nxwidth\neffective flow width x direction (floodplain)\nm\n-\n\n\nywidth\neffective flow width y direction (floodplain)\nm\n-\n\n\ng\nacceleration due to gravity\nm s\\(^{-2}\\)\n-\n\n\ntheta\nweighting factor (de Almeida et al., 2012)\n-\n0.8\n\n\nalpha\nstability coefficient (Bates et al., 2010)\n-\n0.7\n\n\nh_thresh\ndepth threshold for calculating flow\nm\n0.001\n\n\ndt\nmodel time step\ns\n-\n\n\nqy0\nflow in y direction at previous time step\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nqx0\nflow in x direction at previous time step\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nqx\nflow in x direction\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nqy\nflow in y direction\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nzx_max\nmaximum cell elevation (x direction)\nm\n-\n\n\nzy_max\nmaximum cell elevation (y direction)\nm\n-\n\n\nmannings_n_sq\nManning’s roughness squared\ns m\\(^{-\\frac{1}{3}}\\)\nbased on 0.072\n\n\nvolume\ntotal volume of cell (including river volume for river cells)\nm\\(^3\\)\n-\n\n\nerror\nerror volume\nm\\(^3\\)\n-\n\n\nrunoff\nrunoff from hydrological model\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nh\nwater depth of cell\nm\n-\n\n\nz (elevation)\nelevation of cell\nm\n-\n\n\nfroude_limit\nif true a check is performed if froude number > 1.0 (algorithm is modified)\n-\n-\n\n\nrivercells\nriver cells\n-\n-\n\n\nh_av\naverage water depth\nm\n-", + "crumbs": [ + "Model documentation", + "Parameters", + "Lateral concepts" + ] + }, + { + "objectID": "model_docs/parameters_lateral.html#water-bodies", + "href": "model_docs/parameters_lateral.html#water-bodies", + "title": "Lateral concepts", + "section": "Water bodies", + "text": "Water bodies\n\nReservoirs\nThe Table below shows the parameters (fields) of struct SimpleReservoir, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.river.reservoir], to map the internal model parameter to the external netCDF variable.\nTwo parameters reservoir coverage areas and the outlet of reservoirs (unique id) locs that are not part of the SimpleReservoir struct are also required, and can be set as follows through the TOML file:\n[input.lateral.river.reservoir]\nareas = \"wflow_reservoirareas\"\nlocs = \"wflow_reservoirlocs\"\n\n\n\n\n\n\n\n\n\nparameter\ndescription\nunit\ndefault\n\n\n\n\narea\narea\nm^2\n-\n\n\ndemand\nminimum (environmental) flow requirement downstream of the reservoir\nm^3 s^{-1}\n-\n\n\nmaxrelease\nmaximum amount that can be released if below spillway\nm^3 s^{-1}\n-\n\n\nmaxvolume\nmaximum storage (above which water is spilled)\nm^3\n-\n\n\ntargetfullfrac\ntarget fraction full (of max storage)\n-\n-\n\n\ntargetminfrac\ntarget minimum full fraction (of max storage)\n-\n-\n\n\ndemandrelease\nminimum (environmental) flow released from reservoir\nm^3 s^{-1}\n-\n\n\ndt\nmodel time step\ns\n-\n\n\nvolume\nvolume\nm^3\n-\n\n\ninflow\ntotal inflow into reservoir\nm^3\n-\n\n\noutflow\noutflow of reservoir\nm^3 s^{-1}\n-\n\n\ntotaloutflow\ntotal outflow of reservoir\nm^3\n-\n\n\npercfull\nfraction full (of max storage)\n-\n-\n\n\nprecipitation\naverage precipitation for reservoir area\nmm Δt⁻¹\n-\n\n\nevaporation\naverage potential evaporation for reservoir area\nmm Δt⁻¹\n-\n\n\nactevap\naverage actual evaporation for lake area\nmm Δt⁻¹\n-\n\n\n\n\n\nLakes\nThe Table below shows the parameters (fields) of struct Lake, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.river.lake], to map the internal model parameter to the external netCDF variable.\nTwo parameters lake coverage areas and the outlet of lakes (unique id) locs that are not part of the Lake struct are also required, and can be set as follows through the TOML file:\n[input.lateral.river.lake]\nareas = \"wflow_lakeareas\"\nlocs = \"wflow_lakelocs\"\nThe input parameter linkedlakelocs (listed under [input.lateral.river.lake]) is not equal to the internal model parameter lowerlake_ind, and is listed in the Table below between parentheses.\n\n\n\n\n\n\n\n\n\nparameter\ndescription\nunit\ndefault\n\n\n\n\narea\narea\nm^2\n-\n\n\nb\nRating curve coefficient\n-\n-\n\n\ne\nRating curve exponent\n-\n-\n\n\noutflowfunc\ntype of lake rating curve\n-\n-\n\n\nstorfunc\ntype of lake storage curve\n-\n-\n\n\nthreshold\nwater level threshold H_0 below that level outflow is zero\nm\n-\n\n\nwaterlevel\nwaterlevel H of lake\nm\n-\n\n\nlowerlake_ind (linkedlakelocs)\nIndex of lower lake (linked lakes)\n-\n0\n\n\nsh\ndata for storage curve\n-\n-\n\n\nhq\ndata rating curve\n-\n-\n\n\ndt\nmodel time step\ns\n-\n\n\ninflow\ntotal inflow to the lake\nm^3\n-\n\n\nstorage\nstorage lake\nm^3\n-\n\n\nmaxstorage\nmaximum storage lake with rating curve type 1\nm^3\n-\n\n\noutflow\noutflow lake\nm^3 s^{-1}\n-\n\n\ntotaloutflow\ntotal outflow lake\nm^3\n-\n\n\nprecipitation\naverage precipitation for lake area\nmm Δt⁻¹\n-\n\n\nevaporation\naverage potential evaporation for lake area\nmm Δt⁻¹\n-\n\n\nactevap\naverage actual evaporation for lake area\nmm Δt⁻¹\n-", + "crumbs": [ + "Model documentation", + "Parameters", + "Lateral concepts" + ] + }, + { + "objectID": "model_docs/parameters_lateral.html#water-allocation-river", + "href": "model_docs/parameters_lateral.html#water-allocation-river", + "title": "Lateral concepts", + "section": "Water allocation river", + "text": "Water allocation river\nThe Table below shows the parameters (fields) of struct AllocationRiver, used when water demand and allocation is computed (optional), including a description of these parameters, the unit, and default value if applicable.\n\n\n\n\n\n\n\n\n\nparameter\ndescription\nunit\ndefault\n\n\n\n\nact_surfacewater_abst\nactual surface water abstraction\nmm Δt⁻¹\n-\n\n\nact_surfacewater_abst_vol\nactual surface water abstraction\nm\\(^3\\) Δt⁻¹\n-\n\n\navailable_surfacewater\navailable surface water\nm\\(^3\\)\n-\n\n\nnonirri_returnflow\nreturn flow from non-irrigation (industry, domestic and livestock)\nmm Δt⁻¹\n-", + "crumbs": [ + "Model documentation", + "Parameters", + "Lateral concepts" + ] + }, + { + "objectID": "model_docs/parameters_lateral.html#groundwater-flow", + "href": "model_docs/parameters_lateral.html#groundwater-flow", + "title": "Lateral concepts", + "section": "Groundwater flow", + "text": "Groundwater flow\n\nConfined aquifer\nThe Table below shows the parameters (fields) of struct ConfinedAquifer, including a description of these parameters, the unit, and default value if applicable. Struct ConfinedAquifer is not (yet) part of a wflow model.\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\nk\nhorizontal conductivity\nm d\\(^{-1}\\)s\n-\n\n\nstorativity\nstorativity\nm m\\(^{-1}\\)\n-\n\n\nspecific_storage\nspecific storage\nm\\(^{-1}\\)\n-\n\n\ntop\ntop groundwater layers\nm\n-\n\n\nbottom\nbottom groundwater layers\nm\n-\n\n\narea\ncell area\nm\\(^2\\)\n-\n\n\nhead\ngroundwater head\nm\n-\n\n\nconductance\nconductance\nm\\(^2\\) d\\(^{-1}\\)\n-\n\n\n\n\n\nUnconfined aquifer\nThe Table below shows the parameters (fields) of struct UnconfinedAquifer, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.subsurface], to map the internal model parameter to the external netCDF variable. For some input parameters the parameter listed under [input.lateral.subsurface] is not equal to the internal model parameter, these are listed in the Table below between parentheses after the internal model parameter. The top parameter is provided by the external parameter altitude as part of the static input data and set as follows through the TOML file:\n[input]\n# these are not directly part of the model\naltitude = \"wflow_dem\"\nThe input parameter conductivity (listed under [input.lateral.subsurface]) is not equal to the internal model parameter kh_0, and is listed in the Table below between parentheses.\n\n\n\n\n\n\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\nkh_0 (conductivity)\nhorizontal conductivity\nm d\\(^{-1}\\)s\n-\n\n\nspecific_yield\nspecific yield\nm m\\(^{-1}\\)\n-\n\n\ntop (altitude)\ntop groundwater layer\nm\n-\n\n\nbottom\nbottom groundwater layer\nm\n-\n\n\narea\ncell area\nm\\(^2\\)\n-\n\n\nhead\ngroundwater head\nm\n-\n\n\nconductance\nconductance\nm\\(^2\\) d\\(^{-1}\\)\n-\n\n\nf\nfactor controlling the reduction of reference horizontal conductivity\n-\n3.0\n\n\n\n\n\nConstant Head\nThe Table below shows the parameters (fields) of struct ConstantHead, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.subsurface], to map the internal model parameter to the external netCDF variable. The input parameter constant_head (listed under [input.lateral.subsurface]) is not equal to the internal model parameter head, and is listed in the Table below between parentheses.\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\nhead (constant_head)\ngroundwater head\nm\n-\n\n\nindex\nconstant head cell index\n-\n-\n\n\n\n\n\nBoundary conditions\n\nRiver\nThe Table below shows the parameters (fields) of struct River, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.subsurface], to map the internal model parameter to the external netCDF variable. The input parameter river_bottom (listed under [input.lateral.subsurface]) is not equal to the internal model parameter bottom, and is listed in the Table below between parentheses.\n\n\n\n\n\n\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\nstage\nriver stage\nm\n-\n\n\ninfiltration_conductance\nriver bed infiltration conductance\nm\\(^2\\) day\\(^{-1}\\) m\\(^2\\) day\\(^{-1}\\)\n-\n\n\nexfiltration_conductance\nriver bed exfiltration conductance\nm\\(^2\\) day\\(^{-1}\\)\n-\n\n\nbottom (river_bottom)\nriver bottom elevation\nm\n-\n\n\nindex\nriver cell index\n-\n-\n\n\nflux\nexchange flux (river to aquifer)\nm\\(^3\\) d\\(^{-1}\\)\n-\n\n\n\n\n\nDrainage\nThe Table below shows the parameters (fields) of struct Drainage, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.subsurface], to map the internal model parameter to the external netCDF variable. For some input parameters the parameter listed under [input.lateral.subsurface] is not equal to the internal model parameter, these are listed in the Table below between parentheses after the internal model parameter.\n\n\n\n\n\n\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\nelevation (drain_elevation)\ndrain elevation\nm\n-\n\n\nconductance (drain_conductance)\ndrain conductance\nm\\(^2\\) day\\(^{-1}\\)\n-\n\n\nindex (drain)\ndrain cell index\n-\n-\n\n\nflux\nexchange flux (drains to aquifer)\nm\\(^3\\) day\\(^{-1}\\)\n-\n\n\n\n\n\nRecharge\nThe Table below shows the parameters (fields) of struct Recharge, including a description of these parameters, the unit, and default value if applicable.\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\nrate\nrecharge rate\nm\\(^3\\) day\\(^{-1}\\)\n-\n\n\nindex\nrecharge cell index\n-\n-\n\n\nflux\nrecharge flux\nm\\(^3\\) day\\(^{-1}\\)\n-\n\n\n\n\n\nHead boundary\nThe Table below shows the parameters (fields) of struct HeadBoundary, including a description of these parameters, the unit, and default value if applicable.\n\n\n\n\n\n\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\nhead\nhead\nm\n-\n\n\nconductance\nconductance of the head boundary\nm\\(^2\\) day\\(^{-1}\\)\n-\n\n\nindex\nhead boundary cell index\n-\n-\n\n\nflux\nconductance of the head boundary\nm\\(^3\\) day\\(^{-1}\\)\n-\n\n\n\n\n\nWell boundary\nThe Table below shows the parameters (fields) of struct Well, including a description of these parameters, the unit, and default value if applicable.\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\nvolumetric_rate\nvolumetric well rate\nm\\(^3\\) d\\(^{-1}\\)\n-\n\n\nindex\nwell index\n-\n-\n\n\nflux\nactual well flux\nm\\(^3\\) day\\(^{-1}\\)\n-", + "crumbs": [ + "Model documentation", + "Parameters", + "Lateral concepts" + ] + }, + { + "objectID": "model_docs/parameters_lateral.html#sediment", + "href": "model_docs/parameters_lateral.html#sediment", + "title": "Lateral concepts", + "section": "Sediment", + "text": "Sediment\n\nOverland flow\nThe Table below shows the parameters (fields) of struct OverlandFlowSediment, including a description of these parameters, the unit, and default value if applicable.\n\n\n\n\n\n\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\nn\nnumber of cells\n-\n-\n\n\nrivcell\nriver cells\n-\n-\n\n\nsoilloss\ntotal eroded soil\nton Δt\\(^{-1}\\)\n-\n\n\nerosclay\neroded soil for particle class clay\nton Δt\\(^{-1}\\)\n-\n\n\nerossilt\neroded soil for particle class silt\nton Δt\\(^{-1}\\)\n-\n\n\nerossand\neroded soil for particle class sand\nton Δt\\(^{-1}\\)\n-\n\n\nerossagg\neroded soil for particle class small aggregates\nton Δt\\(^{-1}\\)\n-\n\n\neroslagg\neroded soil for particle class large aggregates\nton Δt\\(^{-1}\\)\n-\n\n\nTCsed\ntotal transport capacity of overland flow\nton Δt\\(^{-1}\\)\n-\n\n\nTCclay\ntransport capacity of overland flow for particle class clay\nton Δt\\(^{-1}\\)\n-\n\n\nTCsilt\ntransport capacity of overland flow for particle class silt\nton Δt\\(^{-1}\\)\n-\n\n\nTCsand\ntransport capacity of overland flow for particle class sand\nton Δt\\(^{-1}\\)\n-\n\n\nTCsagg\ntransport capacity of overland flow for particle class small aggregates\nton Δt\\(^{-1}\\)\n-\n\n\nTClagg\ntransport capacity of overland flow for particle class large aggregates\nton Δt\\(^{-1}\\)\n-\n\n\ninlandsed\nsediment reaching the river with overland flow\nton Δt\\(^{-1}\\)\n-\n\n\ninlandclay\nsediment with particle class clay reaching the river with overland flow\nton Δt\\(^{-1}\\)\n-\n\n\ninlandsilt\nsediment with particle class silt reaching the river with overland flow\nton Δt\\(^{-1}\\)\n-\n\n\ninlandsand\nsediment with particle class sand reaching the river with overland flow\nton Δt\\(^{-1}\\)\n-\n\n\ninlandsagg\nsediment with particle class small aggregates reaching the river with overland flow\nton Δt\\(^{-1}\\)\n-\n\n\ninlandlagg\nsediment with particle class large aggregates reaching the river with overland flow\nton Δt\\(^{-1}\\)\n-\n\n\n\n\n\nRiver flow\nThe Table below shows external parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.river]. These external parameters are not part of struct RiverSediment, but used to calculate parameters of struct RiverSediment.\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\nreslocs\nreservoir location (outlet)\n-\n-\n\n\nresareas\nreservoir coverage\n-\n-\n\n\nresarea\nreservoir area\n-\nm\\(^2\\)\n\n\nrestrapeff\nreservoir trapping efficiency coefficient\n-\n-\n\n\nlakelocs\nlake location (outlet)\n-\n-\n\n\nlakeareas\nlake coverage\n-\n-\n\n\nlakearea\nlake area\n-\nm\\(^2\\)\n\n\n\nThe Table below shows the parameters (fields) of struct RiverSediment, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static and forcing input data (netCDF), and can be listed in the TOML configuration file under [input.lateral.river], to map the internal model parameter to the external netCDF variable. For some input parameters the parameter listed under [input.lateral.river] is not equal to the internal model parameter, these are listed in the Table below between parentheses after the internal model parameter. For example, internal model parameter sl is mapped as follows in the TOML file to the external netCDF variable RiverSlope:\n[input.vertical]\nslope = \"RiverSlope\"\n\n\n\n\n\n\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\ndl (length)\nriver length\nm\n-\n\n\nwidth\nriver width\nm\n-\n\n\nsl (slope)\nriver slope\n-\n-\n\n\nrhos (rhosed)\ndensity of sediment\nkg m\\(^{-3}1\\)\n2650.0\n\n\ndmclay\nmedian diameter particle size class clay\nmm\n2.0\n\n\ndmsilt\nmedian diameter particle size class silt\nmm\n10.0\n\n\ndmsand\nmedian diameter particle size class sand\nmm\n200.0\n\n\ndmsagg\nmedian diameter particle size class small aggregates\nmm\n30.0\n\n\ndmlagg\nmedian diameter particle size class large aggregates\nmm\n500.0\n\n\ndmgrav\nmedian diameter particle size class gravel\nmm\n2000.0\n\n\nfclayriv\nfraction of particle class clay\n-\n-\n\n\nfsiltriv\nfraction of particle class silt\n-\n-\n\n\nfsandriv\nfraction of particle class sand\n-\n-\n\n\nfsaggriv\nfraction of particle class small aggregates\n-\n-\n\n\nflaggriv\nfraction of particle class large aggregates\n-\n-\n\n\nfgravriv\nfraction of particle class gravel\n-\n-\n\n\nd50 (d50riv)\nriver sediment median diameter\nmm\n-\n\n\nd50engelund\nriver mean diameter\nmm\n-\n\n\ncbagnold\nBagnold c coefficient\n-\n-\n\n\nebagnold\nBagnold exponent\n-\n-\n\n\nn\nnumber of cells\n-\n-\n\n\ndt\nmodel time step\ns\n-\n\n\nak\nKodatie coefficient a\n-\n-\n\n\nbk\nKodatie coefficient b\n-\n-\n\n\nck\nKodatie coefficient c\n-\n-\n\n\ndk\nKodatie coefficient d\n-\n-\n\n\nkdbank\nbank erodibilty\nm\\(^3\\) N\\(^{-1}\\) s\\(^{-1}\\)\n-\n\n\nkdbed\nbed erodibility\nm\\(^3\\) N\\(^{-1}\\) s\\(^{-1}\\)\n-\n\n\nTCrbank\ncritical bed bank shear stress\nm\\(^3\\) N\\(^{-2}\\)\n-\n\n\nTCrbed\ncritical bed shear stress\nm\\(^3\\) N\\(^{-2}\\)\n-\n\n\nh_riv\nriver water level\nm\n-\n\n\nq_riv\nriver discharge\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\ninlandclay\nsediment input with particle class clay from land erosion\nt Δt\\(^{-1}\\)\n-\n\n\ninlandsilt\nsediment input with particle class silt from land erosion\nt Δt\\(^{-1}\\)\n-\n\n\ninlandsand\nsediment input with particle class sand from land erosion\nt Δt\\(^{-1}\\)\n-\n\n\ninlandsagg\nsediment input with particle class small aggregates from land erosion\nt Δt\\(^{-1}\\)\n-\n\n\ninlandlagg\nsediment input with particle class large aggregates from land erosion\nt Δt\\(^{-1}\\)\n-\n\n\ninlandsed\nsediment input from land erosion\nt Δt\\(^{-1}\\)\n-\n\n\nsedload\nsediment left in the cell\nt\n-\n\n\nclayload\nsediment with particle class clay left in the cell\nt\n-\n\n\nsiltload\nsediment with particle class silt left in the cell\nt\n-\n\n\nsandload\nsediment with particle class sand left in the cell\nt\n-\n\n\nsaggload\nsediment with particle class small aggregates left in the cell\nt\n-\n\n\nlaggload\nsediment with particle class large aggregates in the cell\nt\n-\n\n\ngravload\nsediment with particle class gravel left in the cell\nt\n-\n\n\nsedstore\nsediment stored on the river bed after deposition\nt Δt\\(^{-1}\\)\n-\n\n\nclaystore\nsediment with particle class clay stored on the river bed after deposition\nt Δt\\(^{-1}\\)\n-\n\n\nsiltstore\nsediment with particle class silt stored on the river bed after deposition\nt Δt\\(^{-1}\\)\n-\n\n\nsandstore\nsediment with particle class sand stored on the river bed after deposition\nt Δt\\(^{-1}\\)\n-\n\n\nsaggstore\nsediment with particle class small aggregates stored on the river bed after deposition\nt Δt\\(^{-1}\\)\n-\n\n\nlaggstore\nsediment with particle class large aggregates stored on the river bed after deposition\nt Δt\\(^{-1}\\)\n-\n\n\ngravstore\nsediment with particle class gravel stored on the river bed after deposition\nt Δt\\(^{-1}\\)\n-\n\n\noutsed\nsediment flux\nt Δt\\(^{-1}\\)\n-\n\n\noutclay\nsediment with particle class clay flux\nt Δt\\(^{-1}\\)\n-\n\n\noutsilt\nsediment with particle class silt\nt Δt\\(^{-1}\\)\n-\n\n\noutsand\nsediment with particle class sand\nt Δt\\(^{-1}\\)\n-\n\n\noutsagg\nsediment with particle class small aggregates\nt Δt\\(^{-1}\\)\n-\n\n\noutlagg\nsediment with particle class large aggregates\nt Δt\\(^{-1}\\)\n-\n\n\noutgrav\nsediment with particle class gravel\nt Δt\\(^{-1}\\)\n-\n\n\nSedconc\ntotal sediment concentration (SSconc + Bedconc)\ng m\\(^{-3}\\)\n-\n\n\nSSconc\nsuspended load concentration\ng m\\(^{-3}\\)\n-\n\n\nBedconc\nbed load concentration\ng m\\(^{-3}\\)\n-\n\n\nmaxsed\nriver transport capacity\nt Δt\\(^{-1}\\)\n-\n\n\nerodsed\ntotal eroded sediment\nt Δt\\(^{-1}\\)\n-\n\n\nerodsedbank\neroded bank sediment\nt Δt\\(^{-1}\\)\n-\n\n\nerodsedbed\neroded bed sediment\nt Δt\\(^{-1}\\)\n-\n\n\ndepsed\ndeposited sediment\nt Δt\\(^{-1}\\)\n-\n\n\ninsed\nsediment input flux\nt Δt\\(^{-1}\\)\n-\n\n\nwbcover\nwaterbody coverage\n-\n-\n\n\nwblocs\nwaterbody locations\n-\n-\n\n\nwbarea\nwaterbody area\nm\\(^2\\)\n-\n\n\nwbtrap\nwaterbody trapping efficiency coefficient\n-\n-", + "crumbs": [ + "Model documentation", + "Parameters", + "Lateral concepts" + ] + }, + { + "objectID": "model_docs/lateral/waterbodies.html", + "href": "model_docs/lateral/waterbodies.html", + "title": "Reservoirs and Lakes", + "section": "", + "text": "Simplified reservoirs and lakes models can be included as part of the river network.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Reservoirs and Lakes" + ] + }, + { + "objectID": "model_docs/lateral/waterbodies.html#reservoirs", + "href": "model_docs/lateral/waterbodies.html#reservoirs", + "title": "Reservoirs and Lakes", + "section": "Reservoirs", + "text": "Reservoirs\nSimple reservoirs can be included within the river routing by supplying the following reservoir parameters:\n\nlocs - Outlet of the reservoirs in which each reservoir has a unique id\narea - Surface area of the reservoirs \\(\\SIb{}{m^2}\\)\nareas - Reservoir coverage\ntargetfullfrac - Target fraction full (of max storage) for the reservoir: number between 0 and 1\ntargetminfrac - Target minimum full fraction (of max storage). Number between 0 and 1\nmaxvolume - Maximum reservoir storage (above which water is spilled) \\(\\SIb{}{m^3}\\)\ndemand - Minimum (environmental) flow requirement downstream of the reservoir \\(\\SIb{}{m^3 s^{-1}}\\)\nmaxrelease - Maximum \\(Q\\) that can be released if below spillway \\(\\SIb{}{m^3 s^{-1}}\\)\n\nBy default the reservoirs are not included in the model. To include them put the following lines in the TOML file of the model:\n[model]\nreservoirs = true\nFinally there is a mapping required between external and internal parameter names in the TOML file, with below an example:\n[input]\n\n[input.lateral.river.reservoir]\narea = \"ResSimpleArea\"\nareas = \"wflow_reservoirareas\"\ndemand = \"ResDemand\"\nlocs = \"wflow_reservoirlocs\"\nmaxrelease = \"ResMaxRelease\"\nmaxvolume = \"ResMaxVolume\"\ntargetfullfrac = \"ResTargetFullFrac\"\ntargetminfrac = \"ResTargetMinFrac\"\n\nLakes (unregulated and regulated)\nLakes are modelled using a mass balance approach:\n\\[\n \\dfrac{S(t + \\Delta t)}{\\Delta t} = \\dfrac{S(t)}{\\Delta t} + \\subtext{Q}{in} + \\dfrac{(P-E) A}{\\Delta t} - \\subtext{Q}{out}\n\\]\nwhere \\(\\SIb{S}{m^3}\\) is lake storage, \\(\\SIb{\\Delta t}{s}\\) is the model timestep, \\(\\SIb{\\subtext{Q}{in}}{m^3 s^{-1}}\\) is the sum of inflows (river, overland and lateral subsurface flow), \\(\\SIb{\\subtext{Q}{out}}{m^3 s^{-1}}\\) is the lake outflow at the outlet, \\(\\SIb{P}{m}\\) is precipitation, \\(\\SIb{E}{m}\\) is lake evaporation and \\(\\SIb{A}{m^2}\\) is the lake surface area.\n\n\n\nLake schematization.\n\n\nMost of the variables in this equation are already known or coming from previous timestep, apart from \\(S(t+ \\Delta t)\\) and \\(\\subtext{Q}{out}\\) which can both be linked to the water level \\(H\\) in the lake using a storage curve \\(S = f(H)\\) and a rating curve \\(Q = f(H)\\). In wflow, several options are available to select storage and rating curves, and in most cases, the mass balance is then solved by linearization and iteration or using the Modified Puls Approach from Maniak (Burek et al., 2013). Storage curves in wflow can either:\n\nCome from the interpolation of field data linking volume and lake height,\nBe computed from the simple relationship \\(S = A H\\).\n\nRating curves in wflow can either:\n\nCome from the interpolation of field data linking lake outflow and water height, also appropriate for regulated lakes/ dams,\nBe computed from a rating curve of the form \\(\\subtext{Q}{out} = \\alpha (H-H_0)^\\beta\\), where \\(H_{0}\\) is the minimum water level under which the outflow is zero. Usual values for \\(\\beta\\) are \\(\\frac{3}{2}\\) for a rectangular weir or \\(2\\) for a parabolic weir (Bos, 1989).\n\n\n\nModified Puls Approach\nThe Modified Puls Approach is a resolution method of the lake balance that uses an explicit relationship between storage and outflow. Storage is assumed to be equal to \\(A H\\) and the rating curve for a parabolic weir (\\(\\beta = 2\\)):\n\\[\n S = A H = A (h + H_{0}) = A \\sqrt{\\frac{Q}{\\alpha}} + A H_0\n\\]\nInserting this equation in the mass balance gives:\n\\[\n \\dfrac{A}{\\Delta t} \\sqrt{\\frac{Q}{\\alpha}} + Q = \\dfrac{S(t)}{\\Delta t} + \\subtext{Q}{in} +\n A\\dfrac{P-E}{\\Delta t} - \\dfrac{A H_0}{\\Delta t} = \\mathrm{SI} - \\dfrac{A H_0}{\\Delta t}\n\\]\nThe solution for \\(Q\\) is then:\n\\[\n Q =\n \\begin{cases}\n \\begin{align*}\n \\frac{1}{4}\\left(-\\mathrm{LF} + \\sqrt{\\mathrm{LF}^{2} + 4 \\left(\\mathrm{SI} - \\dfrac{A H_0}{\\Delta t} \\right)}\n \\right)^2 &\\text{ if }\\quad \\mathrm{SI} > \\dfrac{A H_0}{\\Delta t} \\\\\n 0 &\\text{ if }\\quad \\mathrm{SI} \\leq \\dfrac{A H_0}{\\Delta t}\n \\end{align*}\n \\end{cases}\n\\]\nwhere\n\\[\n\\mathrm{LF} = \\dfrac{A}{\\Delta t \\sqrt{\\alpha}}.\n\\]\n\n\nLake parameters\nLakes can be included within the kinematic wave river routing in wflow, by supplying the following parameters:\n\narea - Surface area of the lakes [m\\(^2\\)]\nareas - Coverage of the lakes\nlocs - Outlet of the lakes in which each lake has a unique id\nlinkedlakelocs - Outlet of linked (downstream) lakes (unique id)\nwaterlevel - Lake water level [m], used to reinitiate lake model\nthreshold - Water level threshold \\(H_{0}\\) under which outflow is zero [m]\nstorfunc - Type of lake storage curve ; 1 for \\(S = AH\\) (default) and 2 for \\(S = f(H)\\) from lake data and interpolation\noutflowfunc - Type of lake rating curve ; 1 for \\(Q = f(H)\\) from lake data and interpolation, 2 for general \\(Q = b(H - H_{0})^{e}\\) and 3 in the case of Puls Approach \\(Q = b(H - H_{0})^{2}\\) (default)\nb - Rating curve coefficient\ne - Rating curve exponent\n\nBy default, the lakes are not included in the model. To include them, put the following line in the TOML file of the model:\n[model]\nlakes = true\nThere is also a mapping required between external and internal parameter names in the TOML file, with below an example:\n[input]\n\n[input.lateral.river.lake]\narea = \"lake_area\"\nareas = \"wflow_lakeareas\"\nb = \"lake_b\"\ne = \"lake_e\"\nlocs = \"wflow_lakelocs\"\noutflowfunc = \"lake_outflowfunc\"\nstorfunc = \"lake_storfunc\"\nthreshold = \"lake_threshold\"\nwaterlevel = \"lake_waterlevel\"\n\n\nAdditional settings\nStorage and rating curves from field measurement can be supplied to wflow via CSV files supplied in the same folder of the TOML file. Naming of the files uses the ID of the lakes where data are available and is of the form lake_sh_1.csv and lake_hq_1.csv for respectively the storage and rating curves of lake with ID 1.\nThe storage curve is stored in a CSV file with lake level \\(\\SIb{}{m}\\) in the first column H and corresponding lake storage \\(\\SIb{}{m^3}\\) in the second column S:\nH, S\n392.21, 0\n393.21, 430202000\n393.71, 649959000\n394.21, 869719000\nThe rating curve uses level and discharge data depending on the Julian day of the year (JDOY), and can be also used for regulated lakes/ dams. The first line contains H for the first column. The other lines contain the water level and the corresponding discharges for the different JDOY (1-365), see also the example below, that shows part of a CSV file (first 4 Julian days). The volume above the maximum water level of the rating curve is assumed to flow instantaneously out of the lake (overflow).\nH\n394, 43, 43, 43, 43\n394.01, 44.838, 44.838, 44.838, 44.838\n394.02, 46.671, 46.671, 46.671, 46.671\n394.03, 48.509, 48.509, 48.509, 48.509\n394.04, 50.347, 50.347, 50.347, 50.347\n394.05, 52.179, 52.179, 52.179, 52.179\nLinked lakes: In some cases, lakes can be linked and return flow can be allowed from the downstream to the upstream lake. The linked lakes are defined in the linkedlakelocs parameter that represent the downstream lake location ID, at the grid cell of the upstream lake location.\n\n\n\n\n\n\nNote\n\n\n\nIn every file, level units are meters [m] above lake bottom and not meters above sea level [m asl]. Especially with storage/rating curves coming from data, please be careful and convert units if needed.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Reservoirs and Lakes" + ] + }, + { + "objectID": "model_docs/lateral/waterbodies.html#references", + "href": "model_docs/lateral/waterbodies.html#references", + "title": "Reservoirs and Lakes", + "section": "References", + "text": "References\n\nBos M.G., 1989. Discharge measurement structures. Third revised edition, International Institute for Land Reclamation and Improvement ILRI, Wageningen, The Netherlands.\nBurek P., Van der Knijf J.M., Ad de Roo, 2013. LISFLOOD – Distributed Water Balance and flood Simulation Model – Revised User Manual. DOI: http://dx.doi.org/10.2788/24719.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Reservoirs and Lakes" + ] + }, + { + "objectID": "model_docs/lateral/local-inertial.html", + "href": "model_docs/lateral/local-inertial.html", + "title": "Local inertial", + "section": "", + "text": "The local inertial approximation of shallow water flow neglects only the convective acceleration term in the Saint-Venant momentum conservation equation. The numerical solution of the local inertial approximation on a staggered grid is as follows (Bates et al., 2010):\n\\[\nQ_{t+\\Delta t} = \\frac{Q_t - g A_t \\Delta t S_t}{(1+g\\Delta t n^2 |Q_t| / (R_t^{4/3} A_t))}\n\\]\nwhere \\(\\SIb{Q_{t+\\Delta t}}{m^3 s^{-1}}\\) is the river flow at time step \\(t+\\Delta t\\), \\(\\SIb{g}{m s^{-2}}\\) is acceleration due to gravity, \\(\\SIb{A_t}{m^2}\\) is the cross sectional flow area at the previous time step, \\(\\SIb{R_t}{m}\\) is the hydraulic radius at the previous time step, \\(\\SIb{Q_t}{m^3 s^{-1}}\\) is the river flow at the previous time step, \\(S_t\\) is the water surface slope at the previous time step and \\(\\SIb{n}{m^{-\\frac{1}{3}} s}\\) is the Manning’s roughness coefficient.\nThe momentum equation is applied to each link between two river grid cells, while the continuity equation over \\(\\Delta t\\) is applied to each river cell:\n\\[\nh^{t+\\Delta t} = h^t + \\Delta t \\frac{\\subtext{Q^{t+\\Delta t}}{src} - \\subtext{Q^{t+\\Delta t}}{dst}}{A}\n\\]\nwhere \\(\\SIb{h^{t+\\Delta t}}{m}\\) is the water depthat time step \\(t+\\Delta t\\), \\(\\SIb{h^t}{m}\\) is the water depth at the previous time step, \\(\\SIb{A}{m^2}\\) is the river area and \\(\\SIb{\\subtext{Q}{src}}{m^3 s^{-1}}\\) and \\(\\SIb{\\subtext{Q}{dst}}{m^3 s^{-1}}\\) represent river flow at the upstream and downstream link of the river cell, respectively.\nThe model time step \\(\\Delta t\\) for the local inertial model is estimated based on the Courant-Friedrichs-Lewy condition (Bates et al., 2010):\n\\[\n\\Delta t = \\alpha \\min_i\\left(\\frac{\\Delta x_i}{\\sqrt{gh_i}}\\right)\n\\]\nwhere \\(\\sqrt{gh_i}\\) is the wave celerity for river cell \\(i\\) , \\(\\SIb{\\Delta x_i}{m}\\) is the river length for river cell \\(i\\) and \\(\\alpha\\) is a coefficient (typically between \\(0.2\\) and \\(0.7\\)) to enhance the stability of the simulation.\nIn the TOML file the following properties related to the local inertial model can be provided for the sbm and sbm_gwf model types:\n[model]\nriver_routing = \"local-inertial\" # default is \"kinematic-wave\"\ninertial_flow_alpha = 0.5 # alpha coefficient for model stability (default = 0.7)\nfroude_limit = true # default is true, limit flow to subcritical-critical according to Froude number\nh_thresh = 0.1 # water depth [m] threshold for calculating flow between cells (default = 1e-03)\nfloodplain_1d = true # include 1D floodplain schematization (default = false)\nTwo optional constant boundary conditions riverlength_bc and riverdepth_bc can be provided at a river outlet node (or multiple river outlet nodes) through the model parameter netCDF file, as follows:\n[input.lateral.river]\nriverlength_bc = \"riverlength_bc\" # optional river length [m], default = 1e04\nriverdepth_bc = \"riverdepth_bc\" # optional river depth [m], default = 0.0\nThese boundary conditions are copied to a ghost node (downstream of the river outlet node) in the code.\nThe optional 1D floodplain schematization is based on provided flood volumes as a function of flood depth (per flood depth interval) for each river cell. Wflow calculates from these flood volumes a rectangular floodplain profile for each flood depth interval. Routing is done separately for the river channel and floodplain.\nThe momentum equation is most stable for low slope environments, and to keep the simulation stable for (partly) steep environments the froude_limit option is set to true by default. This setting limits flow conditions to subcritical-critical conditions based on the Froude number (\\(\\le 1\\)), similar to Coulthard et al. (2013) in the CAESAR-LISFLOOD model and Adams et al. (2017) in the Landlab v1.0 OverlandFlow component. The froude number \\(\\mathrm{Fr}\\) on a link is calculated as follows:\n\\[\n \\mathrm{Fr} = \\frac{u}{\\sqrt{gh_f}}\n\\]\nwhere \\(\\sqrt{gh_f}\\) is the wave celerity on a link and \\(u\\) is the water velocity on a link. If the water velocity from the local inertial model is causing the Froude number to be greater than \\(1.0\\) , the water velocity (and flow) is reduced in order to maintain a Froude number of \\(1.0\\).\nThe downstream boundary condition basically simulates a zero water depth boundary condition at a set distance, as follows. For the downstream boundary condition (ghost point) the river width, river bed elevation and Manning’s roughness coefficient are copied from the upstream river cell. The river length \\(\\SIb{}{m}\\) of the boundary cell can be set through the TOML file with riverlength_bc, and has a default value of \\(\\SI{10}{km}\\). The water depth at the boundary cell is fixed at \\(\\SI{0.0}{m}\\).\nSimplified reservoir and lake models can be included as part of the local inertial model for river flow (1D) and river and overland flow combined (see next section). Reservoir and lake models are included as a boundary point with zero water depth for both river and overland flow. For river flow the reservoir or lake model replaces the local inertial model at the reservoir or lake location, and \\(Q\\) is set by the outflow from the reservoir or lake. Overland flow at a reservoir or lake location is not allowed to or from the downstream river grid cell.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Local inertial" + ] + }, + { + "objectID": "model_docs/lateral/local-inertial.html#river-and-floodplain-routing", + "href": "model_docs/lateral/local-inertial.html#river-and-floodplain-routing", + "title": "Local inertial", + "section": "", + "text": "The local inertial approximation of shallow water flow neglects only the convective acceleration term in the Saint-Venant momentum conservation equation. The numerical solution of the local inertial approximation on a staggered grid is as follows (Bates et al., 2010):\n\\[\nQ_{t+\\Delta t} = \\frac{Q_t - g A_t \\Delta t S_t}{(1+g\\Delta t n^2 |Q_t| / (R_t^{4/3} A_t))}\n\\]\nwhere \\(\\SIb{Q_{t+\\Delta t}}{m^3 s^{-1}}\\) is the river flow at time step \\(t+\\Delta t\\), \\(\\SIb{g}{m s^{-2}}\\) is acceleration due to gravity, \\(\\SIb{A_t}{m^2}\\) is the cross sectional flow area at the previous time step, \\(\\SIb{R_t}{m}\\) is the hydraulic radius at the previous time step, \\(\\SIb{Q_t}{m^3 s^{-1}}\\) is the river flow at the previous time step, \\(S_t\\) is the water surface slope at the previous time step and \\(\\SIb{n}{m^{-\\frac{1}{3}} s}\\) is the Manning’s roughness coefficient.\nThe momentum equation is applied to each link between two river grid cells, while the continuity equation over \\(\\Delta t\\) is applied to each river cell:\n\\[\nh^{t+\\Delta t} = h^t + \\Delta t \\frac{\\subtext{Q^{t+\\Delta t}}{src} - \\subtext{Q^{t+\\Delta t}}{dst}}{A}\n\\]\nwhere \\(\\SIb{h^{t+\\Delta t}}{m}\\) is the water depthat time step \\(t+\\Delta t\\), \\(\\SIb{h^t}{m}\\) is the water depth at the previous time step, \\(\\SIb{A}{m^2}\\) is the river area and \\(\\SIb{\\subtext{Q}{src}}{m^3 s^{-1}}\\) and \\(\\SIb{\\subtext{Q}{dst}}{m^3 s^{-1}}\\) represent river flow at the upstream and downstream link of the river cell, respectively.\nThe model time step \\(\\Delta t\\) for the local inertial model is estimated based on the Courant-Friedrichs-Lewy condition (Bates et al., 2010):\n\\[\n\\Delta t = \\alpha \\min_i\\left(\\frac{\\Delta x_i}{\\sqrt{gh_i}}\\right)\n\\]\nwhere \\(\\sqrt{gh_i}\\) is the wave celerity for river cell \\(i\\) , \\(\\SIb{\\Delta x_i}{m}\\) is the river length for river cell \\(i\\) and \\(\\alpha\\) is a coefficient (typically between \\(0.2\\) and \\(0.7\\)) to enhance the stability of the simulation.\nIn the TOML file the following properties related to the local inertial model can be provided for the sbm and sbm_gwf model types:\n[model]\nriver_routing = \"local-inertial\" # default is \"kinematic-wave\"\ninertial_flow_alpha = 0.5 # alpha coefficient for model stability (default = 0.7)\nfroude_limit = true # default is true, limit flow to subcritical-critical according to Froude number\nh_thresh = 0.1 # water depth [m] threshold for calculating flow between cells (default = 1e-03)\nfloodplain_1d = true # include 1D floodplain schematization (default = false)\nTwo optional constant boundary conditions riverlength_bc and riverdepth_bc can be provided at a river outlet node (or multiple river outlet nodes) through the model parameter netCDF file, as follows:\n[input.lateral.river]\nriverlength_bc = \"riverlength_bc\" # optional river length [m], default = 1e04\nriverdepth_bc = \"riverdepth_bc\" # optional river depth [m], default = 0.0\nThese boundary conditions are copied to a ghost node (downstream of the river outlet node) in the code.\nThe optional 1D floodplain schematization is based on provided flood volumes as a function of flood depth (per flood depth interval) for each river cell. Wflow calculates from these flood volumes a rectangular floodplain profile for each flood depth interval. Routing is done separately for the river channel and floodplain.\nThe momentum equation is most stable for low slope environments, and to keep the simulation stable for (partly) steep environments the froude_limit option is set to true by default. This setting limits flow conditions to subcritical-critical conditions based on the Froude number (\\(\\le 1\\)), similar to Coulthard et al. (2013) in the CAESAR-LISFLOOD model and Adams et al. (2017) in the Landlab v1.0 OverlandFlow component. The froude number \\(\\mathrm{Fr}\\) on a link is calculated as follows:\n\\[\n \\mathrm{Fr} = \\frac{u}{\\sqrt{gh_f}}\n\\]\nwhere \\(\\sqrt{gh_f}\\) is the wave celerity on a link and \\(u\\) is the water velocity on a link. If the water velocity from the local inertial model is causing the Froude number to be greater than \\(1.0\\) , the water velocity (and flow) is reduced in order to maintain a Froude number of \\(1.0\\).\nThe downstream boundary condition basically simulates a zero water depth boundary condition at a set distance, as follows. For the downstream boundary condition (ghost point) the river width, river bed elevation and Manning’s roughness coefficient are copied from the upstream river cell. The river length \\(\\SIb{}{m}\\) of the boundary cell can be set through the TOML file with riverlength_bc, and has a default value of \\(\\SI{10}{km}\\). The water depth at the boundary cell is fixed at \\(\\SI{0.0}{m}\\).\nSimplified reservoir and lake models can be included as part of the local inertial model for river flow (1D) and river and overland flow combined (see next section). Reservoir and lake models are included as a boundary point with zero water depth for both river and overland flow. For river flow the reservoir or lake model replaces the local inertial model at the reservoir or lake location, and \\(Q\\) is set by the outflow from the reservoir or lake. Overland flow at a reservoir or lake location is not allowed to or from the downstream river grid cell.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Local inertial" + ] + }, + { + "objectID": "model_docs/lateral/local-inertial.html#overland-flow-2d", + "href": "model_docs/lateral/local-inertial.html#overland-flow-2d", + "title": "Local inertial", + "section": "Overland flow (2D)", + "text": "Overland flow (2D)\nFor the simulation of 2D overland flow on a staggered grid the numerical scheme proposed by de Almeida et al. (2012) is adopted. The explicit solution for the estimation of water discharge between two cells in the x-direction is of the following form (following the notation of Almeida et al. (2012)):\n\\[\n\\begin{split}\nQ_{i-1/2}^{n+1} = \\frac{\\left[ \\theta Q_{i-1/2}^{n} +\\frac{(1-\\theta)}{2}(Q_{(i-3/2)}^{n} +\nQ_{(i+1/2)}^{n})\\right]- g h_f \\frac{\\Delta t}{\\Delta x} (\\eta^n_i - \\eta^n_{i-1}) \\Delta y}{1+g\\Delta t n^2 |Q_{i-1/2}^{n}|/(h_f^{7/3} \\Delta y)}\n\\end{split}\n\\]\nwhere subscripts \\(i\\) and \\(n\\) refer to space and time indices, respectively. Subscript \\(i-\\frac{1}{2}\\) is to the link between node \\(i\\) and \\(i-1\\), subscript \\(i+\\frac{1}{2}\\) is the link between node \\(i\\) and node \\(i+1\\), and subscript \\(i-\\frac{3}{2}\\) is the link between node \\(i-1\\) and node \\(i-2\\). \\(\\SIb{Q}{m^3 s^{-1}}\\) is the water discharge, \\(\\SIb{\\eta}{m}\\) is the water surface elevation, \\(\\SIb{h_f}{m}\\) is the water depth between cells, \\(\\SIb{n}{m^{-\\frac{1}{3}} s}\\) is the Manning’s roughness coefficient, \\(\\SIb{g}{m s^{-2}}\\) is acceleration due to gravity, \\(\\SIb{\\Delta t}{s}\\) is the adaptive model time step, \\(\\SIb{\\Delta\nx}{m}\\) is the distance between two cells and \\(\\SIb{\\Delta y}{m}\\) is the flow width. Below the staggered grid and variables of the numerical solution in the x-direction, based on Almeida et al. (2012):\n\nThe overland flow local inertial approach is used in combination with the local inertial river routing. This is a similar to the modelling approach of Neal et al. (2012), where the hydraulic model LISFLOOD-FP was extended with a subgrid channel model. For the subgrid channel, Neal et al. (2012) make use of a D4 (four direction) scheme, while here a D8 (eight direction) scheme is used, in combination with the D4 scheme for 2D overland flow.\nIn the TOML file the following properties related to the local inertial model with 1D river routing and 2D overland flow can be provided for the sbm model type:\n[model]\nland_routing = \"local-inertial\" # default is kinematic-wave\nriver_routing = \"local-inertial\" # default is kinematic-wave\ninertial_flow_alpha = 0.5 # alpha coefficient for model stability (default = 0.7)\nfroude_limit = true # default is true, limit flow to subcritical-critical according to Froude number\nh_thresh = 0.1 # water depth [m] threshold for calculating flow between cells (default = 1e-03)\nThe properties inertial_flow_alpha, froude_limit and h_thresh apply to 1D river routing as well as 2D overland flow. The properties inertial_flow_alpha and froude_limit, and the adaptive model time step \\(\\Delta t\\) are explained in more detail in the River and floodplain routing section of the local inertial model.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Local inertial" + ] + }, + { + "objectID": "model_docs/lateral/local-inertial.html#inflow", + "href": "model_docs/lateral/local-inertial.html#inflow", + "title": "Local inertial", + "section": "Inflow", + "text": "Inflow\nExternal water (supply/abstraction) inflow \\(\\SIb{}{m^3 s^{-1}}\\) can be added to the local inertial model for river flow (1D) and river and overland flow combined (1D-2D), as a cyclic parameter or as part of forcing (see also Input section).", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Local inertial" + ] + }, + { + "objectID": "model_docs/lateral/local-inertial.html#abstractions", + "href": "model_docs/lateral/local-inertial.html#abstractions", + "title": "Local inertial", + "section": "Abstractions", + "text": "Abstractions\nAbstractions from the river through the variable abstraction \\(\\SIb{}{m^3 s^{-1}}\\) are possible when water demand and allocation is computed. The variable abstraction is set from the water demand and allocation module each time step. Abstractions are subtracted as part of the continuity equation of the local inertial model.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Local inertial" + ] + }, + { + "objectID": "model_docs/lateral/local-inertial.html#multi-threading", + "href": "model_docs/lateral/local-inertial.html#multi-threading", + "title": "Local inertial", + "section": "Multi-Threading", + "text": "Multi-Threading\nThe local inertial model for river flow (1D) and river and overland flow combined (1D-2D) can be executed in parallel using multiple threads.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Local inertial" + ] + }, + { + "objectID": "model_docs/lateral/local-inertial.html#references", + "href": "model_docs/lateral/local-inertial.html#references", + "title": "Local inertial", + "section": "References", + "text": "References\n\nAdams, J. M., Gasparini, N. M., Hobley, D. E. J., Tucker, G. E., Hutton, E. W. H., Nudurupati, S. S., and Istanbulluoglu, E., 2017, The Landlab v1.0 OverlandFlow component: a Python tool for computing shallow-water flow across watersheds, Geosci. Model Dev., 10, 1645–1663, https://doi.org/10.5194/gmd-10-1645-2017.\nde Almeida, G. A. M., P. Bates, J. E. Freer, and M. Souvignet, 2012, Improving the stability of a simple formulation of the shallow water equations for 2-D flood modeling, Water Resour. Res., 48, W05528, https://doi.org/10.1029/2011WR011570.\nBates, P. D., M. S. Horritt, and T. J. Fewtrell, 2010, A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling, J. Hydrol., 387, 33–45, https://doi.org/10.1016/j.jhydrol.2010.03.027.\nCoulthard, T. J., Neal, J. C., Bates, P. D., Ramirez, J., de Almeida, G. A. M., and Hancock, G. R., 2013, Integrating the LISFLOOD-FP 2- D hydrodynamic model with the CAESAR model: implications for modelling landscape evolution, Earth Surf. Proc. Land., 38, 1897–1906, https://doi.org/10.1002/esp.3478.\nNeal, J., G. Schumann, and P. Bates (2012), A subgrid channel model for simulating river hydraulics and floodplaininundation over large and data sparse areas, Water Resour.Res., 48, W11506, https://doi.org/10.1029/2012WR012514.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Local inertial" + ] + }, + { + "objectID": "model_docs/lateral/gwf.html", + "href": "model_docs/lateral/gwf.html", + "title": "Groundwater flow", + "section": "", + "text": "Single layer groundwater flow requires the four following components, and each is described in more detail below:", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Groundwater flow" + ] + }, + { + "objectID": "model_docs/lateral/gwf.html#aquifer-types", + "href": "model_docs/lateral/gwf.html#aquifer-types", + "title": "Groundwater flow", + "section": "Aquifer types", + "text": "Aquifer types\nGroundwater flow can occur either in a confined or unconfined aquifer. Confined aquifers are overlain by a poorly permeable confining layer (e.g. clay). No air can get in to fill the pore space so that the aquifer always remains fully saturated. For a confined aquifer, water will always flow along the complete height \\(\\SIb{H}{m}\\) over the aquifer and transmissivity \\(\\SIb{kH}{m^2 d^{-1}}\\) is a constant (\\(\\SIb{k}{m d^{-1}}\\) is the horizontal hydraulic conductivity). Specific storage is the amount of water an aquifer releases per unit change in hydraulic head, per unit volume of aquifer, as the aquifer and the groundwater itself is compressed. Its value is much smaller than specific yield, between \\(10^{-5}\\) (stiff) and \\(10^{-2}\\) (weak).\nThe upper boundary of an unconfined aquifer is the water table (the phreatic surface). Specific yield (or drainable porosity) represents the volumetric fraction the aquifer will yield when all water drains and the pore volume is filled by air instead. Specific yield will vary roughly between \\(0.05\\) (clay) and \\(0.45\\) (peat) (Johnson, 1967).\nGroundwater flow is solved forward in time and central in space. The vertically averaged governing equation for an inhomogeneous and isotropic aquifer in one dimension can be written as:\n\\[\n S \\frac{\\partial \\phi}{\\partial t} = \\frac{\\partial}{\\partial x} \\left(kH \\frac{\\phi}{\\delta x}\\right) + Q\n\\]\nwhere \\(\\SIb{S}{m m^{-1}}\\) is storativity (or specific yield), \\(\\SIb{\\phi}{m}\\) is hydraulic head, \\(t\\) is time, \\(\\SIb{k}{m t^{-1}}\\) is horizontal hydraulic conductivity, \\(\\SIb{H}{m}\\) is the (saturated) aquifer height: groundwater level - aquifer bottom elevation and \\(\\SIb{Q}{m\nt^{-1}}\\) represents fluxes from boundary conditions (e.g. recharge or abstraction), see also Aquifer boundary conditions.\nThe simplest finite difference formulation is forward in time, central in space, and can be written as:\n\\[\n S_i \\frac{\\phi_{i}^{t+1} - \\phi_i^{t}}{\\Delta t} = -C_{i-1} (\\phi_{i-1} - \\phi_i) - C_i (\\phi_{i+1} - \\phi_i) + Q_i\n\\]\nwhere \\(i\\) is the cell index, \\(t\\) is time, \\(\\Delta t\\) is the step size, \\(C_{i-1}\\) is the the intercell conductance between cell \\(i-1\\) and \\(i\\) and \\(C_i\\) is the intercell conductance between cell \\(i\\) and \\(i+1\\). The connection data between cells is stored as part of the Connectivity struct, see also Connectivity for more information.\nConductance \\(C\\) is defined as:\n\\[\nC = \\frac{kH w}{l}\n\\]\nwhere \\(\\SIb{w}{m}\\) is the width of the cell to cell connection, and \\(\\SIb{l}{m}\\) is the length of the cell to cell connection. \\(k\\) and \\(H\\) may both vary in space; intercell conductance is therefore an average using the properties of two cells. For the calculation of the intercell conductance \\(C\\) the harmonic mean is used (see also Goode and Appel, 1992), here between cell index \\(i\\) and cell index \\(i+1\\), in the \\(x\\) direction:\n\\[\n C_i = w \\frac{k_iH_i\\cdot k_{i+1}H_{i+1}}{k_iH_i \\cdot l_{i+1} + k_{i+1}H_{i+1} \\cdot l_i}\n\\]\nwhere \\(\\SIb{H}{m}\\) is the aquifer top - aquifer bottom, and \\(k\\), \\(l_i\\) is the length in cell \\(i\\) (\\(0.5 \\Delta x_i\\)), \\(l_{i+1}\\) is the length in cell \\(i+1\\) (\\(0.5 \\Delta x_{i+1}\\)) and \\(w\\) as previously defined. For an unconfined aquifer the intercell conductance is scaled by using the “upstream saturated fraction” as the MODFLOW documentation calls it. In this approach, the saturated thickness of a cell-to-cell is approximated using the cell with the highest head. This results in a consistent overestimation of the saturated thickness, but it avoids complexities related with cell drying and rewetting, such as having to define a “wetting threshold” or a “wetting factor”. See also the documentation for MODFLOW-NWT (Niswonger et al., 1) or MODFLOW6 (Langevin et al., 2017) for more background information. For more background on drying and rewetting, see for example McDonald et al. (1991).\nFor the finite difference formulation, there is only one unknown, \\(\\phi_i^{t+1}\\). Reshuffling terms:\n\\[\n\\phi_i^{t+1} = \\phi_i^t + (C_{i-1} (\\phi_i - \\phi_{i-1}) + C_i (\\phi_{i+1} - \\phi_i) + Q_i) \\frac{Δt}{S_i}\n\\]\nThis can be generalized to two dimensions, for both regular and irregular cell connectivity. Finally, a stable time step size can be computed given the forward-in-time, central in space scheme, based on the following criterion from Chu and Willis (1984):\n\\[\n \\frac{\\Delta t k H}{\\Delta x \\Delta y S} \\le \\frac{1}{4}\n\\]\nwhere \\(\\SIb{\\Delta t}{d}\\) is the stable time step size, \\(\\SIb{\\Delta x}{m}\\) is the cell length in the \\(x\\) direction and \\(\\SIb{\\Delta y}{m}\\) is the cell length in the \\(y\\) direction, \\(\\SIb{k}{m^2 d^{-1}}\\) is the horizontal hydraulic conductivity and \\(\\SIb{H}{m}\\) is the saturated thickness of the aquifer. For each cell \\(\\frac{\\Delta x \\Delta y S}{k H}\\) is calculated, the minimum of these values is determined, and multiplied by \\(\\frac{1}{4}\\), to get the stable time step size.\nFor more details about the finite difference formulation and the stable time step size criterion we refer to the paper of Chu and Willis (1984).\nBoundary conditions can be classified into three categories:\n\nspecified head (Dirichlet)\nspecified flux (Neumann)\nhead-dependent flux (Robin)\n\nNeumann and Robin conditions are implemented by adding to or subtracting from a net (lumped) cell flux. Dirichlet conditions are special cased, since they cannot (easily) be implemented via the flux, but the head is set directly instead.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Groundwater flow" + ] + }, + { + "objectID": "model_docs/lateral/gwf.html#connectivity", + "href": "model_docs/lateral/gwf.html#connectivity", + "title": "Groundwater flow", + "section": "Connectivity", + "text": "Connectivity\nThe connectivity between cells is defined as follows.\nWflow.Connectivity", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Groundwater flow" + ] + }, + { + "objectID": "model_docs/lateral/gwf.html#constant-head", + "href": "model_docs/lateral/gwf.html#constant-head", + "title": "Groundwater flow", + "section": "Constant head", + "text": "Constant head\nDirichlet boundary conditions can be specified through the field constanthead (type ConstantHead) of the GroundwaterFlow struct.\n@get_units struct ConstantHead{T}\n head::Vector{T} | \"m\"\n index::Vector{Int} | \"-\"\nend\nFor the model SBM + Groundwater flow this boundary condition is optional, and if used should be specified in the TOML file as follows (see also sbm_gwf_config.toml):\n[model]\nconstanthead = true", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Groundwater flow" + ] + }, + { + "objectID": "model_docs/lateral/gwf.html#aquifer-boundary-conditions", + "href": "model_docs/lateral/gwf.html#aquifer-boundary-conditions", + "title": "Groundwater flow", + "section": "Aquifer boundary conditions", + "text": "Aquifer boundary conditions\n\nRiver\nThe flux between river and aquifer is calculated using Darcy’s law following the approach in MODFLOW:\n\\[\n \\subtext{Q}{riv} =\n \\begin{align*}\n \\begin{cases}\n C_i \\min \\left\\{\\subtext{h}{riv} - \\subtext{B}{riv}, \\subtext{h}{riv} - \\phi\\right\\} &\\text{ if }\\quad \\subtext{h}{riv} > \\phi \\\\\n C_e (\\subtext{h}{riv} - \\phi) &\\text{ if }\\quad \\subtext{h}{riv} \\le \\phi\n \\end{cases}\n \\end{align*}\n\\]\nwhere \\(\\SIb{\\subtext{Q}{riv}}{L^3 T^{-1}}\\) is the exchange flux from river to aquifer, \\(\\SIb{C_i}{L^2 T^{-1}}\\) is the river bed infiltration conductance, \\(\\SIb{C_e}{L^2 T^{-1}}\\) is the river bed exfiltration conductance, \\(\\SIb{\\subtext{B}{riv}}{L}\\) the bottom of the river bed, \\(\\SIb{\\subtext{h}{riv}}{L}\\) is the river stage and \\(\\SIb{\\phi}{L}\\) is the hydraulic head in the river cell.\nThe Table in the Groundwater flow river boundary condition section of the Model parameters provides the parameters of the struct River. Parameters that can be set directly from the static input data (netCDF) are marked in this Table.\nThe exchange flux (river to aquifer) \\(\\subtext{Q}{riv}\\) is an output variable (field flux of the River struct), and is used to update the total flux in a river cell. For the model SBM + Groundwater flow, the water level \\(\\SIb{h}{m}\\) of the river kinematic wave in combination with the river bottom is used to update the stage field of the River struct each time step.\n\n\nDrainage\nThe flux from drains to the aquifer is calculated as follows:\n\\[\n\\subtext{Q}{drain} = \\subtext{C}{drain} \\min(0, \\subtext{h}{drain} - \\phi)\n\\]\nwhere \\(\\SIb{\\subtext{Q}{drain}}{L^3 T^{-1}}\\) is the exchange flux from drains to aquifer, \\(\\SIb{\\subtext{C}{drain}}{L^2 T^{-1}}\\) is the drain conductance, \\(\\SIb{\\subtext{h}{drain}}{L}\\) is the drain elevation and \\(\\SIb{\\phi}{L}\\) is the hydraulic head in the cell with drainage.\nThe table in the Groundwater flow drainage boundary condition section of the Model parameters provides the parameters of the struct Drainage. Parameters that can be set directly from the static input data (netCDF) are marked in this Table.\nThe exchange flux (drains to aquifer) \\(\\subtext{Q}{drain}\\) is an output variable (field flux of struct Drainage), and is used to update the total flux in a cell with drains. For the model SBM + Groundwater flow this boundary condition is optional, and if used should be specified in the TOML file as follows (see also sbm_gwf_config.toml):\n[model]\ndrains = true\n\n\nRecharge\nThe recharge flux \\(Q_r\\) to the aquifer is calculated as follows:\n\\[\nQ_r = R \\, A\n\\]\nwith \\(\\SIb{}{L T^{-1}}\\) the recharge rate and \\(\\SIb{A}{L^2}\\) the area of the aquifer cell.\nThe table in the Groundwater flow recharge boundary condition section of the Model parameters section provides the parameters of the struct Recharge. Parameters that can be set directly from the static input data (netCDF) are marked in this Table.\nThe recharge flux \\(Q_r\\) is an output variable (field flux of struct Recharge), and is used to update the total flux in a cell where recharge occurs. For the model SBM + Groundwater flow, the recharge rate from the vertical SBM concept recharge [mm] is used to update the rate field of the Recharge struct each time step. The rate field is multiplied by the area field of the aquifer.\n\n\nHead boundary\nThis boundary is a fixed head with time (not affected by the model stresses over time) outside of the model domain, and is generally used to avoid an unnecessary extension of the model domain to the location of the fixed boundary (for example a large lake). The flux from the boundary \\(\\SIb{Q_{hb}}{L^3 T^{-1}}\\) is calculated as follows:\n\\[\nQ_{hb} = C_{hb} (\\phi_{hb} - \\phi)\n\\]\nwith \\(\\SIb{C_{hb}}{L^2 T^{-1}}\\) the conductance of the head boundary, \\(\\SIb{\\phi_{hb}}{L}\\) the head of the head boundary and \\(\\phi\\) the head of the aquifer cell.\nThe table in the Groundwater flow head boundary condition section of the Model parameters provides the parameters of the struct HeadBoundary.\nThe head boundary flux \\(Q_{hb}\\) is an output variable (field flux of struct HeadBoundary), and is used to update the total flux in a cell where this type of boundary occurs. The parameter Head \\(\\phi_{hb}\\) can be specified as a fixed or time dependent value.\n\n\n\n\n\n\nNote\n\n\n\nThis boundary is not (yet) part of the model SBM + Groundwater flow.\n\n\n\n\nWell boundary\nA volumetric well rate \\(\\SIb{}{L^3 T^{-1}}\\) can be specified as a boundary condition.\nThe Table in the well boundary condition section of the Model parameters provides the parameters of the struct Well.\nThe volumetric well rate \\(\\subtext{Q}{well}\\) can be can be specified as a fixed or time dependent value. If a cell is dry, the actual well flux flux is set to zero (see also the last note on this page).\n\n\n\n\n\n\nNote\n\n\n\nThis boundary is not (yet) part of the model SBM + Groundwater flow.\n\n\n\n\n\n\n\n\nNote\n\n\n\nFor an unconfined aquifer the boundary fluxes are checked, in case of a dry aquifer cell a negative flux is not allowed.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Groundwater flow" + ] + }, + { + "objectID": "model_docs/lateral/gwf.html#references", + "href": "model_docs/lateral/gwf.html#references", + "title": "Groundwater flow", + "section": "References", + "text": "References\n\nChu, W. S., & Willis, R. (1984). An explicit finite difference model for unconfined aquifers. Groundwater, 22(6), 728-734.\nGoode, D. J., & Appel, C. A. (1992). Finite-Difference Interblock Transmissivity for Unconfined Aquifers and for Aquifers having Smoothly Varying Transmissivity Water-resources investigations report, 92, 4124.\nJohnson, A. I. (1967), Specific yield: compilation of specific yields for various materials, Water Supply Paper 1662-D, Washington, D.C.: U.S. Government Printing Office, p. 74, doi:10.3133/wsp1662D.\nLangevin, C.D., Hughes, J.D., Banta, E.R., Niswonger, R.G., Panday, Sorab, and Provost, A.M., 2017, Documentation for the MODFLOW 6 Groundwater Flow Model: U.S. Geological Survey Techniques and Methods, book 6, chap. A55, 197 p., https://doi.org/10.3133/tm6A55.\nMcDonald, M.G., Harbaugh, A.W., Orr, B.R., and Ackerman, D.J., 1991, A method of converting no-flow cells to variable-head cells for the U.S. Geological Survey modular finite-difference groundwater flow model: U.S. Geological Survey Open-File Report 91-536, 99 p.\nNiswonger, R.G., Panday, Sorab, and Ibaraki, Motomu, 2011, MODFLOW-NWT, A Newton formulation for MODFLOW-2005: U.S. Geological Survey Techniques and Methods 6-A37, 44 p.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Groundwater flow" + ] + }, + { + "objectID": "home/publications.html", + "href": "home/publications.html", + "title": "Publications", + "section": "", + "text": "For publications, please cite the following paper introducing Wflow.jl and describing the wflow_sbm concept, together with some case studies:\nvan Verseveld, W. J., Weerts, A. H., Visser, M., Buitink, J., Imhoff, R. O., Boisgontier, H., Bouaziz, L., Eilander, D., Hegnauer, M., ten Velden, C., and Russell, B., 2024. Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications. Geosci. Model Dev., 17, 3199–3234. https://doi.org/10.5194/gmd-17-3199-2024.\nTo cite a specific software version please use the DOI provided in the Zenodo badge , that points to the latest release. The DOIs of previous versions are also available at Zenodo. If you use a snapshot of the development (without a DOI) please cite as follows:\nvan Verseveld, Willem, Visser, Martijn, Buitink, Joost, Bouaziz, Laurène, Boisgontier, Hélène, Bootsma, Huite, Weerts, Albrecht, Baptista, Carlos Fernando, Pronk, Maarten, Eilander, Dirk, Hartgring, Sebastian, Dalmijn, Brendan, Hofer, Julian, Hegnauer, Mark, & Mendoza, Raul, (YEAR). Deltares/Wflow.jl: unstable-master. https://github.com/Deltares/Wflow.jl, obtained: DATE_OF_DOWNLOAD.", + "crumbs": [ + "Home", + "Publications" + ] + }, + { + "objectID": "home/publications.html#citing-wflow", + "href": "home/publications.html#citing-wflow", + "title": "Publications", + "section": "", + "text": "For publications, please cite the following paper introducing Wflow.jl and describing the wflow_sbm concept, together with some case studies:\nvan Verseveld, W. J., Weerts, A. H., Visser, M., Buitink, J., Imhoff, R. O., Boisgontier, H., Bouaziz, L., Eilander, D., Hegnauer, M., ten Velden, C., and Russell, B., 2024. Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications. Geosci. Model Dev., 17, 3199–3234. https://doi.org/10.5194/gmd-17-3199-2024.\nTo cite a specific software version please use the DOI provided in the Zenodo badge , that points to the latest release. The DOIs of previous versions are also available at Zenodo. If you use a snapshot of the development (without a DOI) please cite as follows:\nvan Verseveld, Willem, Visser, Martijn, Buitink, Joost, Bouaziz, Laurène, Boisgontier, Hélène, Bootsma, Huite, Weerts, Albrecht, Baptista, Carlos Fernando, Pronk, Maarten, Eilander, Dirk, Hartgring, Sebastian, Dalmijn, Brendan, Hofer, Julian, Hegnauer, Mark, & Mendoza, Raul, (YEAR). Deltares/Wflow.jl: unstable-master. https://github.com/Deltares/Wflow.jl, obtained: DATE_OF_DOWNLOAD.", + "crumbs": [ + "Home", + "Publications" + ] + }, + { + "objectID": "home/publications.html#publications-using-wflow", + "href": "home/publications.html#publications-using-wflow", + "title": "Publications", + "section": "Publications using wflow", + "text": "Publications using wflow\n\nPeer reviewed journal papers\nAerts, J. P. M., Hut, R. W., van de Giesen, N. C., Drost, N., van Verseveld, W. J., Weerts, A. H., and Hazenberg, P., 2022. Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model. Hydrol. Earth Syst. Sci., 26, 4407–4430. https://doi.org/10.5194/hess-26-4407-2022.\nde Boer-Euser, T., Bouaziz, L., De Niel, J., Brauer, C., Dewals, B., Drogue, G., Fenicia, F., Grelier, B., Nossent, J., Pereira, F., Savenije, H., Thirel, G., Willems, P., 2017. Looking beyond general metrics for model comparison – lessons from an international model intercomparison study. Hydrol. Earth Syst. Sci. 21, 423–440. https://doi:10.5194/hess-21-423-2017.\nBouaziz, L.J.E., Fenicia, F., Thirel, G., de Boer-Euser, T., Buitink, J., Brauer, C.C., De Niel, J., Dewals, B.J., Drogue, G., Grelier, B., Melsen, L. A., Moustakas, S., Nossent, J., Pereira, F., Sprokkereef, E., Stam, J., Weerts, A.H., Willems, P., Savenije, H.H.G., Hrachowitz, M., 2021. Behind the scenes of streamflow model performance. Hydrol. Earth Syst. Sci., 25, 1069–1095. https://doi.org/10.5194/hess-25-1069-2021.\nBouaziz, L. J. E., Aalbers, E. E., Weerts, A. H., Hegnauer, M., Buiteveld, H., Lammersen, R., Stam, J., Sprokkereef, E., Savenije, H. H. G., and Hrachowitz, M., 2022. Ecosystem adaptation to climate change: the sensitivity of hydrological predictions to time-dynamic model parameters, Hydrol. Earth Syst. Sci., 26, 1295–1318. https://doi.org/10.5194/hess-26-1295-2022.\nCasson, D. R., Werner, M., Weerts, A., and Solomatine, D., 2018. Global re-analysis datasets to improve hydrological assessment and snow water equivalent estimation in a sub-Arctic watershed. Hydrol. Earth Syst. Sci., 22, 4685–4697. https://doi.org/10.5194/hess-22-4685-2018.\nDroppers, B., Rakovec, O., Avila, L., Azimi, S., Cortés-Torres N., De León Pérez, D., Imhoff, R., Francés, F., Kollet, S., Rigon, R., Weerts, A. & Samaniego, L, 2024. Multi-model hydrological reference dataset over continental Europe and an African basin. Sci Data, 11, 1009. https://doi.org/10.1038/s41597-024-03825-9.\nDroppers, B., Rakovec, O., Avila, L., Azimi, S., Cortés-Torres N., De León Pérez, D., Imhoff, R., Francés, F., Kollet, S., Rigon, R., Weerts, A. & Samaniego, L, 2024. Multi-model hydrological reference dataset over continental Europe and an African basin. Sci Data, 11, 1009. https://doi.org/10.1038/s41597-024-03825-9.\nEmerton, R.E., Stephens, E.M., Pappenberger, F., Pagano, T.C., Weerts, A.H., Wood, A.W., Salamon, P., Brown, J.D., Hjerdt, N., Donnelly, C., Baugh, C.A., Cloke, H.L., 2016. Continental and global scale flood forecasting systems. WIREs Water 3, 391–418. https://doi.org/10.1002/wat2.1137.\nGebremicael, T.G., Mohamed, Y.A., Van der Zaag, P., 2019. Attributing the hydrological impact of different land use types and their long-term dynamics through combining parsimonious hydrological modelling, alteration analysis and PLSR analysis. Science of The Total Environment, 660, 1155-1167, https://doi.org/10.1016/jscitotenv.2019.01.085.\nGiardino, A., Schrijvershof, R., Nederhoff, C.M., de Vroeg, H., Brière, C., Tonnon, P.-K., Caires, S., Walstra, D.J., Sosa, J., van Verseveld, W., Schellekens, J., Sloff, C.J., 2018. A quantitative assessment of human interventions and climate change on the West African sediment budget, Ocean & Coastal Management, 156, 249-265. https://doi.org/10.1016/j.ocecoaman.2017.11.008.\nHally, A., Caumont, O., Garrote, L., Richard, E., Weerts, A., Delogu, F., Fiori, E., Rebora, N., Parodi, A., Mihalović, A., Ivković, M., Dekić, L., van Verseveld, W., Nuissier, O., Ducrocq, V., D’Agostino, D., Galizia, A., Danovaro, E., Clematis, A., 2015. Hydrometeorological multi-model ensemble simulations of the 4 November 2011 flash flood event in Genoa, Italy, in the framework of the DRIHM project. Nat. Hazards Earth Syst. Sci. 15, 537–555. https://doi:10.5194/nhess-15-537-2015.\nHassaballah, K., Mohamed, Y., Uhlenbrook, S., and Biro, K., 2017. Analysis of streamflow response to land use and land cover changes using satellite data and hydrological modelling: case study of Dinder and Rahad tributaries of the Blue Nile (Ethiopia–Sudan), Hydrol. Earth Syst. Sci., 21, 5217–5242. https://doi.org/10.5194/hess-21-5217-2017.\nImhoff, R.O., Buitink, J., van Verseveld, W.J., Weerts, A.H., 2024. A fast high resolution distributed hydrological model for forecasting, climate scenarios and digital twin applications using wflow_sbm. Environmental Modelling & Software, 179, 106099. https://doi.org/10.1016/j.envsoft.2024.106099\nImhoff, R.O, van Verseveld, W.J., van Osnabrugge, B., Weerts, A.H., 2020. Scaling Point-Scale (Pedo)transfer Functions to Seamless Large-Domain Parameter Estimates for High-Resolution Distributed Hydrologic Modeling: An Example for the Rhine River. Water Resources Research, 56, e2019WR026807. https://doi.org/10.1029/2019WR026807.\nImhoff, R.O., van Verseveld, W., van Osnabrugge, B., Weerts, A.H., 2020. Ruimtelijk schaalbare hydrologische modelparameters uit open-source omgevingsdata: een voorbeeld voor de Rijn. Stromingen: vakblad voor hydrologen, 26(3), 19-36 https://edepot.wur.nl/540682.\nJeuken, A., Bouaziz, L., Corzo, G., Alfonso, L., 2016. Analyzing Needs for Climate Change Adaptation in the Magdalena River Basin in Colombia, in: Filho, W.L., Musa, H., Cavan, G., O’Hare, P., Seixas, J. (Eds.), Climate Change Adaptation, Resilience and Hazards, Climate Change Management. Springer International Publishing, pp. 329–344 https://doi.org/10.1007/978-3-319-39880-8.\nLópez López, P., Wanders, N., Schellekens, J., Renzullo, L.J., Sutanudjaja, E.H., Bierkens, M.F.P., 2016. Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations. Hydrol. Earth Syst. Sci., 20, 3059–3076. https://doi.org/10.5194/hess-20-3059-2016.\nPranoto, B., Soekarno, H., Hartulistiyoso, E., Nur Aidi, M., Sutrisno, D., Pohan, D., Radhika, Sutejo, B., Heru Kuncoro, A., Nahib, I., 2024. Integrating Flood Early Warning System (FEWS) for Optimizing Small Hydropower Sites: A West Java Case Study. EVERGREEN Joint Journal of Novel Carbon Resource Sciences & Green Asia Strategy, 11, 3, 2691-2699. https://www.tj.kyushu-u.ac.jp/evergreen/contents/EG2024-11_3_content/pdf/p2691-2699.pdf\nRakovec, O., Weerts, A.H., Sumihar, J., Uijlenhoet, R., 2015. Operational aspects of asynchronous filtering for flood forecasting. Hydrol. Earth Syst. Sci., 19, 2911–2924, https://doi.org/10.5194/hess-19-2911-2015.\nRatri, D.N., A.H. Weerts, R. Muharsyah, K. Whan, A. Klein Tank, E. Aldrian, M. H. Hariadi, Calibration of ECMWF SEAS5 based streamflow forecast in Seasonal hydrological forecasting for Citarum river basin, West Java, Indonesia, Journal of Hydrology: Regional Studies,45,101305, https://doi.org/10.1016/j.ejrh.2022.101305.\nRusli, S.R., A.H. Weerts, A. Taufiq, V. Bense, 2021. Estimating water balance components and their uncertainty bounds in highly groundwater-dependent and data-scarce area: An example for the Upper Citarum basin, J. Hydrol. Regional Studies, https://doi.org/10.1016/j.ejrh.2021.100911.\nRusli, S.R., V.F. Bense, A. Taufiq, A.H. Weerts,2023. Quantifying basin-scale changes in groundwater storage using GRACE and one-way coupled hydrological and groundwater flow model in the data-scarce Bandung groundwater Basin, Indonesia, Groundwater for Sustainable Development,22, 100953, https://doi.org/10.1016/j.gsd.2023.100953.\nRusli, S.R., A.H. Weerts, S.M.T. Mustafa, D.E. Irawan, A. Taufiq, V.F. Bense, 2023. Quantifying aquifer interaction using numerical groundwater flow model evaluated by environmental water tracer data: Application to the data-scarce area of the Bandung groundwater basin, West Java, Indonesia, Journal of Hydrology: Regional Studies, 50, https://doi.org/10.1016/j.ejrh.2023.101585.\nSchaller, N., Sillmann, J., Müller, M., Haarsma, R., Hazeleger, W., Hegdahl, T.J., Kelder, T., van den Oord, G., Weerts, A., Whan, K., 2020. The role of spatial and temporal model resolution in a flood event storyline approach in western Norway. Weather and Climate Extremes, doi: https://doi.org/10.1016/j.wace.2020.100259.\nSeizarwati, W. and M. Syahidah, 2021. Rainfall-Runoff Simulation for Water Availability Estimation in Small Island Using Distributed Hydrological Model wflow. IOP Conf. Ser.: Earth Environ. Sci., 930,012050, doi:10.1088/1755-1315/930/1/012050. https://iopscience.iop.org/article/10.1088/1755-1315/930/1/012050/pdf\nSperna Weiland, F.C., R.D. Visser, P. Greve, B. Bisselink, L. Brunner and A.H. Weerts, 2021. Estimating Regionalized Hydrological Impacts of Climate Change Over Europe by Performance-Based Weighting of CORDEX Projections, Frontiers of Water, https://doi.org/10.3389/frwa.2021.713537.\nTangdamrongsub, N., Steele-Dunne, S.C., Gunter, B.C., Ditmar, P.G., Weerts, A.H., 2015. Data assimilation of GRACE terrestrial water storage estimates into a regional hydrological model of the Rhine River basin. Hydrol. Earth Syst. Sci. 19, 2079–2100. https://doi:10.5194/hess-19-2079-2015.\nvan der Laan, E., P. Hazenberg, A.H. Weerts, 2024. Simulation of long-term storage dynamics of headwater reservoirs across the globe using public cloud computing infrastructure. Science of The Total Environment, 172678, https://doi.org/10.1016/j.scitotenv.2024.172678.\nPranoto, B., Soekarno, H., Hartulistiyoso, E., Nur Aidi, M., Sutrisno, D., Pohan, D., Radhika, Sutejo, B., Heru Kuncoro, A., Nahib, I., 2024. Integrating Flood Early Warning System (FEWS) for Optimizing Small Hydropower Sites: A West Java Case Study. EVERGREEN Joint Journal of Novel Carbon Resource Sciences & Green Asia Strategy, 11, 3, 2691-2699. https://www.tj.kyushu-u.ac.jp/evergreen/contents/EG2024-11_3_content/pdf/p2691-2699.pdf\nvan Osnabrugge, B., Weerts, A.H., Uijlenhoet, R., 2017. genRE: A method to extend gridded precipitation climatology data sets in near real-time for hydrological forecasting purposes. Water Resources Research, 53. https://doi.org/10.1002/2017WR021201.\nvan Osnabrugge, B., Uijlenhoet, R., Weerts, A., 2019. Contribution of potential evaporation forecasts to 10-day streamflow forecast skill for the Rhine River, Hydrol. Earth Syst. Sci., 23, 1453–1467, https://doi.org/10.5194/hess-23-1453-2019.\nvan der Vat, M., Boderie, P., Bons, K.A., Hegnauer, M., Hendriksen, G., van Oorschot, M., Ottow, B., Roelofsen, F., Sankhua, R.N., Sinha, S.K., Warren, A., Young, W., 2019. Participatory Modelling of Surface and Groundwater to Support Strategic Planning in the Ganga Basin in India. Water, 11, 2443. https://doi.org/10.3390/w11122443.\nWannasin, C., Brauer, C. C., Uijlenhoet, R., van Verseveld, W. J., Weerts, A. H., 2021. Daily flow simulation in Thailand Part I: Testing a distributed hydrological model with seamless parameter maps based on global data. Journal of Hydrology: Regional Studies, 34, 1-19. https://doi.org/10.1016/j.ejrh.2021.100794.\nWannasin, C., Brauer, C. C., Uijlenhoet, R., van Verseveld, W. J., Weerts, A. H., 2021. Daily flow simulation in Thailand Part II: Unraveling effects of reservoir operation. Journal of Hydrology: RegionalStudies, 34, 1-17. https://doi.org/10.1016/j.ejrh.2021.100792.\nWang, X., Zhang, J., Babovic, V., 2016. Improving real-time forecasting of water quality indicators with combination of process-based models and data assimilation technique. Ecological Indicators 66, 428–439. https://doi:10.1016/j.ecolind.2016.02.016.\n\n\nPhD, MSc, BSc Theses & Internship reports\nAbdelnour, A., 2022. Bias Correction of Climate Simulations to Assess Climate Change Impacts on Low Flows in the Rhine River, MSc thesis, Delft Universitry of technology, https://repository.tudelft.nl/islandora/object/uuid:50489399-cbd2-467f-9b82-98e5f3e371e9.\nAli, M.A., 2023. Machine learning for predicting spatially variable lateral hydraulic conductivity: a step towards efficient hydrological model calibration and global applicability, Intersnhip report, Deltares.\nArnal, L., 2014. An intercomparison of flood forecasting models for the Meuse River basin, MSc Thesis, Vrije Universiteit, Amsterdam, https://hal.inrae.fr/hal-02600749.\nAlkemade, G., 2019. Routing and calibration of distributed hydrological models, MSc Thesis, Vrije Universiteit, Amsterdam, Faculty of Science, Hydrology.\nAzadeh Karami Fard, 2015. Modeling runoff of an Ethiopian catchment with WFLOW, MSc. Thesis, Vrije Universiteit, Amsterdam.\nBenschop, J., 2023. The application of hybrid lateral routing in hydrological simulations of the Rhine catchment, MSc Thesis, Hydrology and Environmental Hydraulics Group, Wageningen University.\nBeusen, B., 2021. The effect of rooting depth on discharge and evapotranspiration in (semi-)arid areas, MSc Thesis, Hydrology and Quantitative Water Management Group, Wageningen University.\nBeusen, B., 2021. Plastic transport and the effect of climate change in the Rhine, Internship report, Deltares.\nBouaziz, L. J. E., 2021. Internal processes in hydrological models: A glance at the Meuse basin from space. Delft University of Technology, Delft, the Netherlands, Doctoral dissertation, https://doi.org/10.4233/uuid:09d84cc1-27e2-4327-a8c7-207a75952061.\nHartgring, S., 2023. On Forecasting the Rur River: Using hindcasts and forecasts of the 2021 flood event to improve understanding of flood forecasting in the Rur catchment. Delft University of Technology, Delft, the Netherlands, MSc thesis, http://resolver.tudelft.nl/uuid:2909d997-a983-490a-a588-87119998543a.\nJaime, D.E.V, 2021. Ensemble hydrological forecasts to derive extreme return periods: Case Study of the Overijsselse Vecht River using the wflow_sbm model, MSc thesis, Unesco IHE, Delft.\nLópez López, P., 2018. Application of Global Hydrological Datasets for River Basin Modelling Utrecht University, Utrecht, the Netherlands, pp. 1-214, Doctoral dissertation, http://dspace.library.uu.nl/handle/1874/364148.\nMaat, W.H., 2015. Simulating discharges and forecasting floods using a conceptual rainfall-runoff model for the Bolivian Mamoré basin, MSc Thesis, University of Twente, Enschede. https://essay.utwente.nl/67046/.\nvan Osnabrugge, B., 2020. Interpolate, simulate, assimilate: operational aspects of improving hydrological forecasts in the Rhine basin. Wageningen University, Doctoral dissertation, https://doi.org/10.18174/513157.\nRohrmueller, I., 2019. BENCHMARKING THE NEW WFLOW DISTRIBUTED HYDROLOGICAL MODEL, MSc Thesis, School of Engineering - Newcastle University.\nRusli, S.R., 2024. Deepening the quantitative understanding of groundwater systems in data-scarce areas: application in the Bandung Groundwater Basin, Indonesia. Wageningen University, Doctoral dissertation, https://doi.org/10.18174/640983.\nTretjakova, D., 2015. Investigating the effect of using fully-distributed model and data assimilation on the performance of hydrological forecasting in the Karasu catchment, Turkey, MSc thesis, Wageningen University.\nvan der Gaast, R.H., 2024. Evaluating the transferability of data-driven pedo-transfer functions for the wflow_sbm parameter KsatHorFrac in central and Western Europe. Universiteit Twente, Enschede, The Netherlands, https://essay.utwente.nl/103634/1/vandergaast_MA_ET.pdf.\nVerbrugge, M., 2019. Reservoir Operation Optimization, a case study in the Chao Phraya Basin, BSc thesis, Hydrology and Quantitative Water Management Group, Wageningen University.\nVerbrugge, M., 2023. Bias-correcting meteorological forcing to improve seasonal discharge forecasting of the Rhine, Internship report, Deltares.\nViguures, P., 2020. Modelling of flash floods in current and future climate with high resolution convection permitting regional climate models in the European Alps, MSc Thesis, Wageningen University.\nVisser, B., 2020. Impact of climate change on local water resources of European catchments, Intersnhip report, Deltares.\nWannasin, C., 2023. Modelling and forecasting daily streamflow with reservoir operation in the upper Chao Phraya River basin, Thailand. Wageningen University, Doctoral dissertation, https://doi.org/10.18174/584572.\n\n\nReports\nWorld Bank. 2021. Plastic Waste Discharges from Rivers and Coastlines in Indonesia. Marine Plastics Series;. World Bank, Washington, DC. © World Bank, https://openknowledge.worldbank.org/handle/10986/35607, License: CC BY 3.0 IGO.\nWorld Meteorological Organization (WMO), 2023. State of Global Water Resources report 2022, WMO-No. 1333, https://library.wmo.int/idurl/4/68473.\nWorld Meteorological Organization (WMO), 2024. State of Global Water Resources report 2023, WMO-No. 1362, https://library.wmo.int/idurl/4/69033.", + "crumbs": [ + "Home", + "Publications" + ] + }, + { + "objectID": "getting_started/running_wflow.html", + "href": "getting_started/running_wflow.html", + "title": "Running a simulation", + "section": "", + "text": "Once you installed Julia and Wflow.jl, a simulation can be started from the command line as follows:\njulia -e 'using Wflow; Wflow.run()' path/to/config.toml\nFurthermore, it is possible to write a Julia script to run a simulation. Example data to explore how this works can be found here.\nusing Wflow\nWflow.run(toml_path)\nJulia can also be used to modify settings after reading the settings file. In the example below, we show how to adjust the end date of the simulation.\nusing Dates\nconfig = Wflow.Config(toml_path)\nconfig.endtime = DateTime(\"2000-01-03T00:00:00\")\nWflow.run(config)", + "crumbs": [ + "Getting started", + "Running a simulation" + ] + }, + { + "objectID": "getting_started/running_wflow.html#using-julia", + "href": "getting_started/running_wflow.html#using-julia", + "title": "Running a simulation", + "section": "", + "text": "Once you installed Julia and Wflow.jl, a simulation can be started from the command line as follows:\njulia -e 'using Wflow; Wflow.run()' path/to/config.toml\nFurthermore, it is possible to write a Julia script to run a simulation. Example data to explore how this works can be found here.\nusing Wflow\nWflow.run(toml_path)\nJulia can also be used to modify settings after reading the settings file. In the example below, we show how to adjust the end date of the simulation.\nusing Dates\nconfig = Wflow.Config(toml_path)\nconfig.endtime = DateTime(\"2000-01-03T00:00:00\")\nWflow.run(config)", + "crumbs": [ + "Getting started", + "Running a simulation" + ] + }, + { + "objectID": "getting_started/running_wflow.html#using-the-command-line-interface", + "href": "getting_started/running_wflow.html#using-the-command-line-interface", + "title": "Running a simulation", + "section": "Using the command line interface", + "text": "Using the command line interface\nIf you don’t need the extra features of using wflow as a library, but just want to run simulations, the command line interface makes it easier to do so. It consists of a single executable, wflow_cli that accepts a single argument, the path to a TOML configuration file.\nBinaries of wflow_cli can be downloaded from our website download.deltares.nl, and are currently available for Windows.\nAfter installing you can see three folders in the installation directory. It is only the bin/wflow_cli that is directly used. All three folders need to stay together however. The share folder contains TOML files with more information about the build.\nbin\\wflow_cli\nlib\nshare\nSimply running wflow_cli with no arguments will give the following message:\nUsage: wflow_cli 'path/to/config.toml'\nWhen starting a run, you will see basic run information on the screen, as well as a progress bar, that gives an estimate of how much time is needed to finish the simulation:\n┌ Info: Run information\n│ model_type = \"sbm\"\n│ starttime = CFTime.DateTimeStandard(2000-01-01T00:00:00)\n│ dt = 86400 seconds\n│ endtime = CFTime.DateTimeStandard(2000-12-31T00:00:00)\n└ nthreads() = 4\n\nProgress: 100%|██████████████████████████████████████████████████| Time: 0:00:27", + "crumbs": [ + "Getting started", + "Running a simulation" + ] + }, + { + "objectID": "getting_started/download_example_models.html", + "href": "getting_started/download_example_models.html", + "title": "Download example models", + "section": "", + "text": "For each wflow Model a test model is available that can help to understand the data requirements and the usage of each Model. The TOML configuration file per available model are listed in the Table below:\nThe associated Model files (input static, forcing and state files) can easily be downloaded and for this we share the following Julia code (per Model) that downloads the required files to your current working directory. For running these test model see also Usage and Command Line Interface.", + "crumbs": [ + "Getting started", + "Download example models" + ] + }, + { + "objectID": "getting_started/download_example_models.html#wflow_sbm-kinematic-wave", + "href": "getting_started/download_example_models.html#wflow_sbm-kinematic-wave", + "title": "Download example models", + "section": "wflow_sbm + kinematic wave", + "text": "wflow_sbm + kinematic wave\n# urls to TOML and netCDF of the Moselle example model\ntoml_url = \"https://raw.githubusercontent.com/Deltares/Wflow.jl/master/test/sbm_config.toml\"\nstaticmaps = \"https://github.com/visr/wflow-artifacts/releases/download/v0.2.9/staticmaps-moselle.nc\"\nforcing = \"https://github.com/visr/wflow-artifacts/releases/download/v0.2.6/forcing-moselle.nc\"\ninstates = \"https://github.com/visr/wflow-artifacts/releases/download/v0.2.6/instates-moselle.nc\"\n\n# create a \"data\" directory in the current directory\ndatadir = joinpath(@__DIR__, \"data\")\nmkpath(datadir)\ntoml_path = joinpath(@__DIR__, \"sbm_config.toml\")\n\n# download resources to current and data dirs\ndownload(staticmaps, joinpath(datadir, \"staticmaps-moselle.nc\"))\ndownload(forcing, joinpath(datadir, \"forcing-moselle.nc\"))\ndownload(instates, joinpath(datadir, \"instates-moselle.nc\"))\ndownload(toml_url, toml_path)", + "crumbs": [ + "Getting started", + "Download example models" + ] + }, + { + "objectID": "getting_started/download_example_models.html#wflow_sbm-groundwater-flow", + "href": "getting_started/download_example_models.html#wflow_sbm-groundwater-flow", + "title": "Download example models", + "section": "wflow_sbm + groundwater flow", + "text": "wflow_sbm + groundwater flow\n# urls to TOML and netCDF of the Moselle example model\ntoml_url = \"https://raw.githubusercontent.com/Deltares/Wflow.jl/master/test/sbm_gwf_config.toml\"\nstaticmaps = \"https://github.com/visr/wflow-artifacts/releases/download/v0.2.3/staticmaps-sbm-groundwater.nc\"\nforcing = \"https://github.com/visr/wflow-artifacts/releases/download/v0.2.1/forcing-sbm-groundwater.nc\"\n\n# create a \"data\" directory in the current directory\ndatadir = joinpath(@__DIR__, \"data\")\nmkpath(datadir)\ntoml_path = joinpath(@__DIR__, \"sbm_gwf_config.toml\")\n\n# download resources to current and data dirs\ndownload(staticmaps, joinpath(datadir, \"staticmaps-sbm-groundwater.nc\"))\ndownload(forcing, joinpath(datadir, \"forcing-sbm-groundwater.nc\"))\ndownload(toml_url, toml_path)", + "crumbs": [ + "Getting started", + "Download example models" + ] + }, + { + "objectID": "getting_started/download_example_models.html#wflow_sediment", + "href": "getting_started/download_example_models.html#wflow_sediment", + "title": "Download example models", + "section": "wflow_sediment", + "text": "wflow_sediment\n# urls to TOML and netCDF of the Moselle example model\ntoml_url = \"https://raw.githubusercontent.com/Deltares/Wflow.jl/master/test/sediment_config.toml\"\nstaticmaps = \"https://github.com/visr/wflow-artifacts/releases/download/v0.2.3/staticmaps-moselle-sed.nc\"\nforcing = \"https://github.com/visr/wflow-artifacts/releases/download/v0.2.3/forcing-moselle-sed.nc\"\ninstates = \"https://github.com/visr/wflow-artifacts/releases/download/v0.2.0/instates-moselle-sed.nc\"\n\n# create a \"data\" directory in the current directory\ndatadir = joinpath(@__DIR__, \"data\")\nmkpath(datadir)\ntoml_path = joinpath(@__DIR__, \"sediment_config.toml\")\n\n# download resources to current and data dirs\ndownload(staticmaps, joinpath(datadir, \"staticmaps-moselle-sed.nc\"))\ndownload(forcing, joinpath(datadir, \"forcing-moselle-sed.nc\"))\ndownload(instates, joinpath(datadir, \"instates-moselle-sed.nc\"))\ndownload(toml_url, toml_path)", + "crumbs": [ + "Getting started", + "Download example models" + ] + }, + { + "objectID": "examples/index.html", + "href": "examples/index.html", + "title": "To be added in the future", + "section": "", + "text": "Back to top" + }, + { + "objectID": "developments/guide.html", + "href": "developments/guide.html", + "title": "Developers guide", + "section": "", + "text": "We welcome reporting of issues on our GitHub page. Please provide a minimum working example so we are able to reproduce the issue. Furthermore, we welcome contributions. We follow the ColPrac guide for collaborative practices. New contributors should make sure to read that guide.", + "crumbs": [ + "Developments", + "Developers guide" + ] + }, + { + "objectID": "developments/guide.html#contributions-and-reporting-issues", + "href": "developments/guide.html#contributions-and-reporting-issues", + "title": "Developers guide", + "section": "", + "text": "We welcome reporting of issues on our GitHub page. Please provide a minimum working example so we are able to reproduce the issue. Furthermore, we welcome contributions. We follow the ColPrac guide for collaborative practices. New contributors should make sure to read that guide.", + "crumbs": [ + "Developments", + "Developers guide" + ] + }, + { + "objectID": "developments/guide.html#styledecisions", + "href": "developments/guide.html#styledecisions", + "title": "Developers guide", + "section": "Style/decisions", + "text": "Style/decisions\n\nFor improved code readability, we decided to avoid non-ASCII characters for naming variables, structs, functions and macros. Using the non-ASCII character for built-in operators is still allowed. This change in naming convention is now in effect and all invalid uses of non-ASCII characters have been replaced by ASCII equivalents.", + "crumbs": [ + "Developments", + "Developers guide" + ] + }, + { + "objectID": "changelog.html", + "href": "changelog.html", + "title": "Changelog", + "section": "", + "text": "All notable changes to this project will be documented in this file.\nThe format is based on Keep a Changelog, and this project adheres to Semantic Versioning.", + "crumbs": [ + "Home", + "Changelog" + ] + }, + { + "objectID": "changelog.html#unreleased", + "href": "changelog.html#unreleased", + "title": "Changelog", + "section": "Unreleased", + "text": "Unreleased\n\nFixed\n\nInitialization of LateralSSF variables ssf and ssfmax with vertical hydraulic conductivity profile exponential_constant. Removed parameter khfrac from the computation, as it is already part of parameter kh_0.\nMutating BMI model control functions (update, update_until and finalize) and extended mutating BMI functions (load_state and save_state) should return nothing.\n\n\n\nChanged\n\nRemoved vertical concepts HBV and FLEXTopo.\nRemoved metadata macros exchange and grid_type. The macro grid_type is not required because this functionality is already part of BMI. The macro exchange is replaced by a function used by BMI. Remaining metadata macros get_units and grid_loc are only used by BMI.\nRefactor the vertical SBM concept: divide the long struct SBM into different model components for interception, snow, glacier, (open water) runoff, soil, water demand and allocation stored in the struct LandHydrologySBM. Additionally, the atmospheric forcing and a shared vegetation parameterset are stored as separate fields in struct LandHydrologySBM (with soil model SbmSoilModel). The model component structs have model variables, parameters and boundary_conditions (if applicable), including associated functions for initializing and updating these model components. The original long update function of the SBM soil part has been split into separate functions.\n\n\n\nAdded\n\nSupport direct output of snow and glacier melt, and add computation of snow water equivalent (SWE).", + "crumbs": [ + "Home", + "Changelog" + ] + }, + { + "objectID": "changelog.html#v0.x-releases", + "href": "changelog.html#v0.x-releases", + "title": "Changelog", + "section": "v0.x releases", + "text": "v0.x releases\n\nv0.8.1 - 2024-08-27\n\nFixed\n\nReduce allocations in update of vertical SBM concept.\n\n\n\n\nv0.8.0 - 2024-08-19\n\nFixed\n\nAdded missing BMI function get_grid_size, it is used for unstructured grids, for example to get the length of arrays returned by BMI functions get_grid_x and get_grid_y.\nAdded a check for the solution of the quadratic equation as part of the Modified Puls approach for lake outflow. Lower limit should be zero (very small negative values can occur).\nLimit lake evaporation (added variable actevap) and lake outflow to prevent negative lake storage. The variable actevap has also been added to the reservoir module.\nThe set_states function for model type sbm with local inertial routing for river and land component.\nInflow to reservoir and lake locations for local inertial routing: 1) with floodplain routing, the floodplain discharge was not added to the inflow of these locations, 2) the to_river variable from overland flow and lateral subsurface flow was not added to the inflow of these locations.\nClose netCDF NCDataset with state variables in extended BMI function save_state.\nFor the computation of Gash interception model parameter e_r multiply the precipitation input with the canopy fraction (this was only done for the potential evapotranspiration input).\nFunction BMI.update_until could throw an InexactError: Int64 caused by a not whole number, representing how many times (steps) an update of a wflow model is required. This is fixed by using divrem for the computation of the number of steps in this function. An error is thrown when the absolute remainder of divrem is larger than eps(), or when the number of steps is negative.\nFixed internal and external broken links in docs.\nThe internal time step of the local inertial model (stable_timestep function) can get zero when LoopVectorization is applied (@tturbo) to the for loop of these functions. This issue occured on a virtual machine, Windows 10 Enterprise, with Intel(R) Xeon(R) Gold 6144 CPU (2 processors). This has been fixed by replacing @tturbo with reduction of Polyester.jl.\nFixed required states of the model type sbm_gwf: added h_av for the river and land domain.\nFor the constanthead boundary of GroundwaterFlow the head should not be changed (was set to top elevation of the aquifer if head > top), and exfiltwater should be 0 for these boundary cells.\n\n\n\nChanged\n\nStop exposing scalar variables through BMI. The BMI.get_value_ptr function was not working correctly for scalar model variables (a view was applied). Only a few scalar model parameters are defined, and it is not expected that exposing these variables is required (e.g. for model coupling) while code changes for these variables (including struct fields) are required.\nThe local inertial routing (constant) river boundary condition depth at a ghost node (downstream river outlet) was set at 0.0 in the code and can now be provided through the model parameter netCDF file (riverdepth_bc). In addition, the boundary condition river length dl at a ghost node should be set through the model parameter netCDF file (riverlength_bc), providing this boundary condition through the [model] settings in the TOML file (model.riverlength_bc) has been deprecated.\nAs part of the vertical SBM concept: 1) add variable net_runoff (land runoff minus actual open water evaporation ae_openw_l) and 2) change the definition of variable runoff by removing the subtraction of ae_openw_l (total land runoff). The variable net_runoff is now input to the kinematic wave for overland flow (replaces the original runoff variable as input). The lower limit of the original runoff variable was set at zero, which may result in water balance errors. This lower limit was implemented in the Python version of wflow_sbm, to avoid a very slow kinematic wave solution for surface flow computation (no distinction between a kinematic wave for overland and river flow). When in the Python version of wflow_sbm the kinematic wave for surface flow was split into a river and land component, the lower limit was removed for river runoff (net_runoff_river), but not for land runoff.\nAlways use fractions for the computation of potential evapotranspiration (interception and transpiration) and potential evaporation (open water and soil). Replaced variable et_reftopot by crop coefficient kc (use of et_reftopot has been deprecated).\nFor improved code readability it is now discouraged to use non-ASCII characters for the names of variables, structs, functions and macros. Using the non-ASCII character for built-in operators is still allowed. This change in naming convention is now in effect and all invalid uses of non-ASCII characters have been replaced by ASCII equivalents. This change also impacts the TOML keys kv₀, θᵣ and θₛ. These keys have been replaced with the ASCII versions kv_0, theta_r and theta_s in v0.4.0 and the old keys are not supported anymore.\nDocs: 1) improved description of different model configurations in model-setup.md, also in relation to hydromt_wflow in docs, 2) citing info related to wflow_sbm publication in Geosci. Model Dev. (from in review to published).\nRoot water uptake reduction (Feddes): 1) extend critical pressure head parameters with h3_low and h3_high, 2) all critical pressure head parameters can be set (values for h1, h2 and h3 were fixed) and 3) the root water uptake reduction coefficient \\alpha can be set at 0 (default is 1) at critical pressure head h1 (through the model parameter alpha_h1).\nFor the actual transpiration computation in SBM, the potential transpiration is partitioned over the soil layers with roots according to the model parameter rootfraction (fraction of the total root length in a soil layer). Previously, for each soil layer (from top to bottom) the actual transpiration was computed, and the remaining potential transpiration was used in the next soil layer.\n\n\n\nAdded\n\nTotal water storage as an export variable for SBM concept. This is the total water stored per grid cell in millimeters. Excluded from this variable are the floodplain, lakes and reservoirs.\nChecks to see if all states are covered in the .toml file. If not all states are covered, an error is thrown. If there are more states specified than required, these states are ignored (with a warning in the logging), and the simulation will continue.\nSupport different vertical hydraulic conductivity profiles for the SBM concept. See also the following sections: The SBM soil water accounting scheme and Subsurface flow routing for a short description.\nLocal inertial routing to sbm_gwf model type.\nWater demand and allocation computations for model types sbm and sbm_gwf.\n\n\n\n\nv0.7.3 - 2024-01-12\n\nFixed\n\nDocumentation: add leakage term to the wflow_sbm figure, document external input parameter ksathorfrac and fix description of adding external inflow to the kinematic wave.\nFixed BMI functions (e.g. BMI.get_value) that deviated from BMI specifications (BasicModelInterface.jl), including function arguments, return types and the BMI specification that arrays are always flattened (this was not the case for variables stored as 2-dimensional arrays or as vector of SVectors).\nBump compat for NCDatasets to 0.13, 0.14.\nThe solution for lake outflow as part of the Modified Puls Approach. The inflow and outflow variables are defined for period Δt, and not at t1 and t2 (instantaneous) as in the original mass balance equation of the Modified Puls Approach. Because of this, the terms of the quadratic equation (and solution) were fixed.\nUse kvfrac for the computation of vertical saturated hydraulic conductivity at the bottom of the soil layer, since kvfrac is also used for the computation of vertical unsaturated flow.\n\n\n\nChanged\n\nFor cyclic parameters different cyclic time inputs are supported (only one common cyclic time (for example daily or monthly) was allowed).\nBMI: 1) added grid information (type and location) and whether a variable can be exchanged to metadata Structs, 2) extend model grid functions for wflow components that store variables on edges (local inertial model) with get_grid_edge_count and get_grid_edge_nodes.\n\n\n\nAdded\n\nFunctions for loading and saving states and getting the starttime in Unix time format. This is convenient for coupling with OpenDA (and other external) software. The set states functionality from the initialization function has been moved to a separate set_states function for each Model type, to support loading states independent of initialization.\n\n\n\n\nv0.7.2 - 2023-09-27\n\nFixed\n\nWater balance of modified Rutter interception model. The sum of the stemflow partitioning coefficient pt and free throughfall coefficient p could get larger than 1, resulting in an overestimation of stemflow and throughfall amounts and negative net interception amounts. And the first drainage amount dd, controlled by a change over time in canopy storage capacity cmax, should not be subtracted from precipitation to compute net interception.\nThe netinterception (net interception) computed by the modified Rutter interception model was stored as interception in SBM, while this should be the interception (interception loss by evaporation) output of the modified Rutter interception model. The interception of SBM is used to compute the total actual evapotranspiration actevap.\n\n\n\n\nv0.7.1 - 2023-06-30\n\nFixed\n\nState initialization of 1D floodplain volume. In the initialization function the wrong field name of type FloodPlainProfile was used (area instead of a).\n\n\n\n\nv0.7.0 - 2023-06-12\n\nFixed\n\nBMI.get_time_units now gets called on the model rather than the type, like all other BMI functions, except BMI.initialize. Also it returns “s” instead of “seconds since 1970-01-01T00:00:00”, in line with the BMI specification.\nAdded the interception component to total actual evapotranspiration actevap of SBM (was defined as the sum of soil evaporation, transpiration and open water evaporation).\n\n\n\nChanged\n\nThe time values returned in the BMI interface are no longer in seconds since 1970, but in seconds since the model start time. This is more in line with standard BMI practices.\nThe starttime was defined one model timestep Δt ahead of the actual model time (the initial conditions timestamp (state time)). As a consequence this was also the case for the current model time. To allow for an easier interpretation of wflow time handling, either through BMI or directly, the starttime is now equal to the state time, resulting in current model times without an offset.\nUsing more than 8 threads can result in too much overhead with Threads.@threads. After performance testing, this has been changed for kinematic wave routing and the vertical SBM concept to spawning tasks with Threads@spawn for number of threads <= 8, where each task iterates over a chunk of size basesize. For more than 8 threads the low overhead threading Polyester.@batch (including the minbatch argument) is used. For local inertial routing the use of Threads.@threads has been changed to threaded loop vectorization (river and 1D floodplain local inertial momentum equation) and Polyester.@batch.\n\n\n\nAdded\n\nFor (regulated) lakes with rating curve of type 1 (H-Q table), lake storage above the maximumstorage (based on maximum water level from the H-Q table) is spilled instantaneously (overflow) from the lake.\nAdded support to use sum as a reducer function for csv and scalar output options.\n\n\n\n\nv0.6.3 - 2023-03-01\n\nFixed\n\nRemoved error when _FillValue is present in the time dimension of the forcing netCDF file. The simulation is allowed to continue with the attribute present, given that there are no missing values in the time dimension. This is checked by the code, and an error is thrown if this is the case.\nColumn index of daily lake rating curves. This was incorrectly based on dayofyear with a maximum of 365. The column index should be based on julian day (leap days are not counted).\n\n\n\nChanged\n\nNCDatasets version. Reading the time dimension of multifile netCDF file became very slow since NCDatasets v0.12.4, this issue has been solved in v0.12.11.\nStore the time dimension of the forcing netCDF file as part of the struct NCreader instead of calling dataset[\"time\"][:] each time step when loading forcing data.\n\n\n\nAdded\n\nShow total duration of simulation in the log file (info), and show the current time at execution of each timestep (debug).\nSupport for exponential decline in horizontal conductivity in the sbm_gwf concept. This can be enabled using the conductivity_profile setting (either “uniform” or “exponential”). If set to “exponential”, it exponentially reduces the kh0 (or conductivity) based on the value of gwf_f to the actual horizontal conductivity (k).\nAn optional 1D floodplain schematization for the river flow inertial model, based on provided flood volumes as a function of flood depth per river cell. See also the following sections: SBM + Local inertial river and River and floodplain routing for a short description, and the following section for associated model parameters.\n\n\n\n\nv0.6.2 - 2022-09-01\n\nFixed\n\nTwo issues related to reservoir and lake locations as part of local inertial model: 1) added as boundary points to the update of overland flow, 2) fixed check reservoir and lake location in update river flow.\nLimit flow (qx, qy and q) in local inertial model when water is not available (set to zero).\nIn the grid output netCDFs, don’t set the _FillValue attribute, since the CF conventions don’t allow it.\nGlacier parameter g_sifrac needs to be converted during initialization (time dependent).\nThe check that the sum of adaptive timesteps (Δt) of the local inertial model (1D and 2D) does not exceed the model timestep.\n\n\n\nChanged\n\nChanged depth h for reservoir and lake locations as part of the river local inertial model from bankfull_depth to zero.\n\n\n\nAdded\n\nExternal inflow to the SBM + Local inertial river (1D) and land (2D) model configuration.\n\n\n\n\nv0.6.1 - 2022-04-26\n\nFixed\n\nFixed an error with the log file, when writing to a folder that does not (yet) exists. Now, the folder is created prior to writing the log file.\nFixed a MethodError for read_dims, thrown when reading netCDF data with NCDatasets.jl 0.12.3 or higher.\n\n\n\n\nv0.6.0 - 2022-04-14\n\nAdded\n\nThe FLEXTopo model.\nSet a (different) uniform value for each index of input parameters with an extra dimension. A list of values (instead of one uniform value) that should be equal to the length of the extra dimension can be provided in the TOML file. See also Modify parameters.\nModify input parameters with an extra dimension at multiple indices with different scale and offset values, through the TOML file. See also Modify parameters.\nAdded support for netCDF compression for gridded model output, through the option compressionlevel in the [output] section in the TOML file. The setting defaults to 0 (no compression), but all levels between 0 and 9 can be used.\n\n\n\nChanged\n\nRe-organized the documentation: moved explanation of different model concepts to a model documentation section, added a user guide to explain setting up the model, added new figures to the description of wflow_sbm.\nThe unit of lateral subsurface flow ssf of LateralSSF is now m^3 d^{-1}. The unit was m^3 t^{-1}, where t is the model timestep. Other flow variables are already stored independently from t, this allows for easier interpretation and to use states independently of t.\nChanged the reference level of water depth h and h_av of 2D overland flow (ShallowWaterLand) for cells containing a river from river bed elevation zb to cell elevation z.\n\n\n\nFixed\n\nFixed calculation of average water depth h_av of 2D overland flow (ShallowWaterLand) with the local inertial approach. The summation of h was not correct, resulting in too low values for h_av. For river cells of 2D overland flow h_av was only updated as part of the river domain (ShallowWaterRiver), this value is now also updated as part of the land domain (ShallowWaterLand).\nFixed the following two flow width issues for 2D overland flow of the local inertial model: 1) The flow widths xwidth and ywidth were mapped incorrectly (reversed) to the flow calculation in x and y direction, respectively. 2) For river cells the effective flow width for overland flow was not determined correctly: the riverwidth vector supplied to the function set_effective_flowwidth! covered the complete model domain and should have covered the river domain, resulting in incorrect river widths and thus incorrect effective flow widths for river cells.\n\n\n\n\nv0.5.2 - 2022-02-03\n\nChanged\n\nModel types sbm_gwf and hbv use the same approach for the calculation of the drain width dw as model type sbm.\nRenamed h_bankfull parameter to bankfull_depth for consistency between kinematic-wave and local-inertial river routing. When using the old name under the [input.lateral.river] section of the TOML file, it will work but it is suggested to update the name.\n\n\n\nAdded\n\nAdditional log messages and log file as output, see also Logging.\nOption to use the local inertial model for river flow as part of the sbm model type. See also SBM + Local inertial river.\nOption to use the local inertial model for 1D river flow combined with 2D overland flow as part of the sbm model type. See also SBM + Local inertial river (1D) and land (2D).\n\n\n\nFixed\n\nModel type hbv: the surface width for overland flow was not corrected with the river width.\nFixed use of absolute path for path_forcing in TOML file, which gave an error in wflow v0.5.1.\nFixed a crash when glacier processes are simulated as part of the hbv concept (Δt was not defined).\nWhen the surface flow width for overland flow is zero, the water level h of the kinematic wave should not be calculated, otherwise this results in NaN values. When the model is initialized from state files, q and h are set to zero for indices with a zero surface flow width.\nFixed how number of iterations its for kinematic wave river flow are calculated during initialization when using a fixed sub-timestep (specified in the TOML file). For a model timestep smaller than the fixed sub-timestep an InexactError was thrown.\nFixed providing a cyclic parameter when the netCDF variable is read during model initialization with ncread, this gave an error about the size of the netCDF time dimension.\nFixed CSV and netCDF scalar output of variables with dimension layer (SVector).\n\n\n\n\nv0.5.1 - 2021-11-24\n\nFixed\n\nFixed calculation of exfiltwater as part of the sbm_gwf model type. This was based directly on groundwater head above the surface level, without multiplying by the specific_yield, resulting in an overestimation of exfiltwater. This is required since the groundwater model estimates the head by dividing the volume by the specific yield or storativity of the aquifer. So, should the groundwater table rise above surface level, the head above surface level does not represent a water column one to one. (This also means the groundwater model (slightly) overestimates heads when the head rises above the surface level. However, this water is immediately removed, and the head will be set to surface level.)\n\n\n\nAdded\n\nOptional dir_input and dir_output keys in the TOML, which can be used to quickly change the path for all input or output files that are given as a relative path.\n\n\n\n\nv0.5.0 - 2021-11-12\n\nChanged\n\nScaling of potential capillary rise is replaced by a common approach found in literature, based on the water table depth zi, a maximum water depth cap_hmax beyond which capillary rise ceases, and a coefficient cap_n. See also Capillary rise. Multiplying the scaling factor with the ratio of model time step and basetimestep in the original approach resulted in (much) smaller capillary fluxes at sub-daily model time steps compared to daily model time steps, and is not used in the new approach. Parameters cap_hmax and cap_n can be set through the TOML file, parameter capscale of the previous approach is not used anymore.\n\n\n\nFixed\n\nConversion of GroundwaterFlow boundaries \\(\\SIb{}{m^3 d^{-1}}\\) as part of model concept sbm_gwf to \\(\\SI{m^3 s^{-1}}\\) for sub-daily model time steps. For the conversion the basetimestep (86400 s) should be used (and not the model time step).\n\n\n\n\nv0.4.1 - 2021-11-04\n\nChanged\n\nThe \\alpha parameter of the kinematic wave has a fixed value now and is not updated because of changes in water height (this could result in large water balance errors). See also Surface routing.\nCyclic input for other structs than vertical are also now supported (for example cyclic inflow to the river).\nMoved update_forcing! and update_cyclic! functions to the run function. It is now easier to implement a custom run function with custom loading of input data (forcing and cyclic parameters).\n\n\n\nAdded\n\nCheck if reservoirs and lakes have downstream nodes. Without downstream nodes is not supported and in that case an error message is thrown that is easier to understand than the previous one: “ArgumentError: Collection is empty, must contain exactly 1 element.”\nFor the input.path_forcing TOML setting we use Glob.jl to expand strings like \"data/forcing-year-*.nc\" to a set of netCDF files that are split in time.\nExternal water inflow (supply/abstractions) added to the kinematic wave inflow in m3/s. It is added/removed to sf.qlat[v] before computing the new q[v] with the kinematic wave equation.\nFixed values for forcing parameters are supported, see also Fixed forcing values.\n\n\n\nAdded\n\nOption to use the local inertial model for river flow as part of the SBM + Kinematic wave. See also SBM + Local inertial river.\n\n\n\nFixed\n\nRiver inflow for reservoirs and lakes in the kinematic wave. This inflow was based on sf.q[v] at the previous time step, and this has been fixed to the current time step.\n\n\n\n\nv0.4.0 - 2021-09-02\n\nChanged\n\nChanged length units for lateral subsurface flow component from millimeter to meter. This means that state netCDF files from previous versions can only be reused if ssf is divided by 10^9.\nAdd snow and glacier processes to wflow_sbm figure of the documentation.\n\n\n\nAdded\n\nMulti-threading of vertical SBM concept and lateral kinematic wave components (overland, river and subsurface flow) of wflow_sbm model SBM + Kinematic wave.\nImproved error message for CSV Reducer.\nThe TOML keys kv₀, θᵣ and θₛ have been replaced with the ASCII versions kv_0, theta_r and theta_s, to avoid encoding issues with certain text editors. The old keys still work as well.\n\n\n\nFixed\n\nCalculation of volumetric water content of vertical SBM (soil layers and root zone).\nUpdate of satwaterdepth in SBM (evaporation was only subtracted from a local variable, and not from sbm.satwaterdepth).\nFixed the index lowerlake_ind of NaturalLake. Linked lakes were not simulated correctly (flows between upstream and downstream lake).\nInterpolation function interpolate_linear(x, xp, fp) for CSV tables lakes. When x was larger or smaller than xp, maximum(xp) or minimum(xp) was returned instead of maximum(fp) or minimum(fp).\nFixed model timestep of reservoirs (SimpleReservoir) and lakes (NaturalLake) in relation to precipitation and evapotranspiration fluxes. This was set to the fixed wflow basetimestep of 86400 s, and should be set to the actual model time step from the TOML configuration file.\nAdd flux from Drainage (GroundwaterFlow) in the sbm_gwf_model to the overland flow component instead of the river component of the kinematic wave.\nFixed option constanthead = false (TOML file), constant_head field of GroundwaterFlow was not defined in this case. Fixed this by initializing empty fields (Vector) for struct ConstantHead.\nFixed return max(0, boundary.flux[index]) to return max(0, flux) the flux should be returned when cell is dry, no negative value allowed.\nFixed path to initialize lake to: dirname(static_path)\nFixed outflow = 0, when lake level is below lake threshold. Before a negative value could enter the log function and model would fail.\nFixed the lake storage initialization. For continuation runs (reinit = false), this caused the lake to be reset to the initial conditions instead of the saved state.\n\n\n\n\nv0.3.1 - 2021-05-19\n\nFixed\n\nIgnore extra dimensions in input netCDFs if they are size 1\n\n\n\n\nv0.3.0 - 2021-05-10\n\nChanged\n\nNew deposition process for coarse sediment in the reservoirs with a new parameter restrapefficiency in the sediment model.\nNew variables added to the LandSediment and RiverSediment structs in order to save more output from the sediment model.\nAdded variables volume and inwater to SurfaceFlow struct, this is convenient for the coupling with the water quality model Delwaq.\nRiver water level (h) and discharge (q) forced directly into the RiverSediment struct (instead of using the OverlandFlowSediment struct first).\nRequire Julia 1.6 or later.\n\n\n\nAdded\n\nModify model parameters and forcing through the TOML file (see Modify parameters).\nRun wflow_sbm (SBM + kinematic wave) in two parts (until recharge and after subsurface flow) from BMI, including the option to switch off the lateral subsurface component of wflow_sbm.\nSupport more netCDF dimension and axis order variants.\n\n\n\nFixed\n\nCorrected a bug in sediment deposition in the river (case when incoming sediment load is more than the river transport capacity).\nFixed update of snow and glacierstore fields of HBV and SBM concepts by the glacier_hbv module.\n\n\n\n\nv0.2.0 - 2021-03-26\n\nChanged\n\nRemoved dependency of the f model parameter of wflow_sbm on the parameters \\theta_{s}, \\theta_{r} and M. This approach is used in Topog_SBM, but not applicable for wflow_sbm. The f parameter needs to be provided as part of the netCDF model parameter file.\nGrid properties as cell length and elevation now stored as part of the model.land.network component and not as part of the vertical model components, as it is not used by these components. altitude (elevation) should now be provided as part of the [input] section of the TOML configuration file, and not as part of the [input.vertical] section.\nRemoved parameter \\theta_{e} from SBM struct (not used in update). Parameters \\theta_{s} and \\theta_{r} included separately (instead of \\theta_{e}) in LateralSSF struct, now directly linked to SBM parameters.\nImprove error messages (netCDF and cyclic flow graph).\n\n\n\nAdded\n\nExport of netCDF scalar timeseries (separate netCDF file from gridded timeseries). This also allows for importing these timeseries by Delft-FEWS (General Adapter).\n\n\n\nFixed\n\nModel parameter Manning’s n now used during the update of the struct SurfaceFlow, to change the related \\alpha parameter of the kinematic wave for channel flow.", + "crumbs": [ + "Home", + "Changelog" + ] + }, + { + "objectID": "developments/julia_structs.html", + "href": "developments/julia_structs.html", + "title": "Julia structures", + "section": "", + "text": "Below the composite type that represents all different aspects of a Wflow.Model, such as the network, parameters, clock, model type, configuration and input and output.\nstruct Model{N,L,V,R,W,T}\n config::Config # all configuration options\n network::N # connectivity information, directed graph\n lateral::L # lateral model that holds lateral state, moves along network\n vertical::V # vertical model that holds vertical state, independent of each other\n clock::Clock # to keep track of simulation time\n reader::R # provides the model with dynamic input\n writer::W # writes model output\n type::T # model type\nend\nThe lateral field of the struct Model can contain different lateral concepts. For each wflow model these different lateral concepts are mapped through the use of a NamedTuple. The vertical field of the struct Model always contains one vertical concept, for example the SBM vertical concept.\nBelow an example how lateral concepts are mapped for the SBM model through a NamedTuple:\n(subsurface = ssf, land = olf, river = rf)\nThe subsurface part is mapped to the lateral subsurface flow kinematic wave concept, the land part is mapped the overland flow kinematic wave concept and the river part is mapped to the river flow kinematic wave concept. Knowledge of this specific mapping is required to understand and correctly set input, output and state variables in the TOML configuration file, see also Config and TOML. This mapping is described in more detail for each model in the section Models. Also the struct of each mapped concept is provided, so one can check the internal variables in the code. These structs are defined as a parametric composite type, with type parameters between curly braces after the struct name. See also the next paragraph Vertical and lateral models for a more detailed description.", + "crumbs": [ + "Developments", + "Julia structures" + ] + }, + { + "objectID": "developments/julia_structs.html#model", + "href": "developments/julia_structs.html#model", + "title": "Julia structures", + "section": "", + "text": "Below the composite type that represents all different aspects of a Wflow.Model, such as the network, parameters, clock, model type, configuration and input and output.\nstruct Model{N,L,V,R,W,T}\n config::Config # all configuration options\n network::N # connectivity information, directed graph\n lateral::L # lateral model that holds lateral state, moves along network\n vertical::V # vertical model that holds vertical state, independent of each other\n clock::Clock # to keep track of simulation time\n reader::R # provides the model with dynamic input\n writer::W # writes model output\n type::T # model type\nend\nThe lateral field of the struct Model can contain different lateral concepts. For each wflow model these different lateral concepts are mapped through the use of a NamedTuple. The vertical field of the struct Model always contains one vertical concept, for example the SBM vertical concept.\nBelow an example how lateral concepts are mapped for the SBM model through a NamedTuple:\n(subsurface = ssf, land = olf, river = rf)\nThe subsurface part is mapped to the lateral subsurface flow kinematic wave concept, the land part is mapped the overland flow kinematic wave concept and the river part is mapped to the river flow kinematic wave concept. Knowledge of this specific mapping is required to understand and correctly set input, output and state variables in the TOML configuration file, see also Config and TOML. This mapping is described in more detail for each model in the section Models. Also the struct of each mapped concept is provided, so one can check the internal variables in the code. These structs are defined as a parametric composite type, with type parameters between curly braces after the struct name. See also the next paragraph Vertical and lateral models for a more detailed description.", + "crumbs": [ + "Developments", + "Julia structures" + ] + }, + { + "objectID": "developments/julia_structs.html#vertical-and-lateral-models", + "href": "developments/julia_structs.html#vertical-and-lateral-models", + "title": "Julia structures", + "section": "Vertical and lateral models", + "text": "Vertical and lateral models\nThe different model concepts used in wflow are defined as parametric composite types. For example the vertical SBM concept is defined as follows: struct SBM{T,N,M}. T, N and M between curly braces after the struct name refer to type parameters, for more information about type parameters you can check out Type parameters. Since these parameters can be of any type, it is possible to declare an unlimited number of composite types. The type parameters are used to set the type of struct fields, below an example with a part of the SBM struct:\n@get_units @with_kw struct SBM{T,N,M}\n # Model time step [s]\n dt::T | \"s\"\n # Maximum number of soil layers\n maxlayers::Int | \"-\"\n # number of cells\n n::Int | \"-\"\n # Number of soil layers\n nlayers::Vector{Int} | \"-\"\n # Number of unsaturated soil layers\n n_unsatlayers::Vector{Int} | \"-\"\n # Fraction of river [-]\n riverfrac::Vector{T} | \"-\"\n # Saturated water content (porosity) [mm mm⁻¹]\n theta_s::Vector{T} | \"mm mm-1\"\n # Residual water content [mm mm⁻¹]\n theta_r::Vector{T} | \"mm mm-1\"\n # Vertical hydraulic conductivity [mm Δt⁻¹] at soil surface\n kv_0::Vector{T} | \"mm dt-1\"\n # Muliplication factor [-] applied to kv_z (vertical flow)\n kvfrac::Vector{SVector{N,T}} | \"-\"\nThe type parameter T is used in wflow as a subtype of AbstractFloat, allowing to store fields with a certain floating point precision (e.g. Float64 or Float32) in a flexible way. N refers to the maximum number of soil layers of the SBM soil column, and M refers to the maximum number of soil layers + 1. See also part of the following instance of SBM:\nsbm = SBM{Float,maxlayers,maxlayers + 1}(\n dt = tosecond(dt),\n maxlayers = maxlayers,\n n = n,\nFor the other model concepts, we refer to the code to check these type parameters.", + "crumbs": [ + "Developments", + "Julia structures" + ] + }, + { + "objectID": "getting_started/building_a_model.html", + "href": "getting_started/building_a_model.html", + "title": "Building a model from scratch", + "section": "", + "text": "hydroMT is a Python package, developed by Deltares, to build and analyze hydro models. It provides a generic model api with attributes to access the model schematization, (dynamic) forcing data, results and states.\nThe wflow plugin hydroMT-wflow of hydroMT can be used to build and analyze the following model configurations:\n\nwflow_sbm + kinematic wave routing\nwflow_sbm + local inertial river and floodplain\nwflow_sbm + local inertial river (1D) and land (2D)\nwflow_sediment\n\nTo learn more about the wflow plugin of this Python package, we refer to the hydroMT-wflow documentation.\nTo inspect or modify (for example in QGIS) the netCDF static data of these wflow models it is convenient to export the maps to a raster format. This can be done as part of the hydroMT-wflow plugin, see also the following example. It is also possible to create again the netCDF static data file based on the modified raster map stack.", + "crumbs": [ + "Getting started", + "Building a model from scratch" + ] + }, + { + "objectID": "getting_started/building_a_model.html#hydromt-wflow", + "href": "getting_started/building_a_model.html#hydromt-wflow", + "title": "Building a model from scratch", + "section": "", + "text": "hydroMT is a Python package, developed by Deltares, to build and analyze hydro models. It provides a generic model api with attributes to access the model schematization, (dynamic) forcing data, results and states.\nThe wflow plugin hydroMT-wflow of hydroMT can be used to build and analyze the following model configurations:\n\nwflow_sbm + kinematic wave routing\nwflow_sbm + local inertial river and floodplain\nwflow_sbm + local inertial river (1D) and land (2D)\nwflow_sediment\n\nTo learn more about the wflow plugin of this Python package, we refer to the hydroMT-wflow documentation.\nTo inspect or modify (for example in QGIS) the netCDF static data of these wflow models it is convenient to export the maps to a raster format. This can be done as part of the hydroMT-wflow plugin, see also the following example. It is also possible to create again the netCDF static data file based on the modified raster map stack.", + "crumbs": [ + "Getting started", + "Building a model from scratch" + ] + }, + { + "objectID": "getting_started/building_a_model.html#data-requirements", + "href": "getting_started/building_a_model.html#data-requirements", + "title": "Building a model from scratch", + "section": "Data requirements", + "text": "Data requirements\nThe actual data requirements depend on the application of the Model and the Model type. Both forcing and static data should be provided in netCDF format, with the same grid definition for forcing and static data. The only exception is storage and rating curves for lakes, that should be provided in CSV format, see also Additional settings for waterbodies.\n\nForcing data:\n\nPrecipitation\nPotential evapotranspiration\nTemperature (optional, only needed for snow and glacier modelling)\n\n\nThe requirements for static data (including model parameters) depend on the Model type. The following data is required for all Model types, but not directly part of a Model component:\n\nflow direction data (D8)\nriver map (location of the river)\nsub-catchment map (model domain)\n\nFor the flow direction (D8) data, the PCRaster ldd convention is used, see also PCRaster ldd. An approach to generate ldd data is to make use of the Python package pyflwdir:\n\nto upscale existing flow direction data as the 3 arcsec MERIT Hydro data (Yamazaki et al., 2019)\nor to derive flow directions from elevation data,\n\nsee also Eilander et al. (2021) for more information. Pyflwdir is also used by the hydroMT Python package described in the next paragraph. Another approach to generate ldd data is to make use of PCRaster functionality, see for example lddcreate.\nOptionally, but also not directly part of a model component are gauge locations, that are used to extract gridded data from certain locations.\nThe different supported model configurations are described in the section Model configurations. Wflow_sbm models have the vertical concept SBM in common and input parameters for this component are described in the SBM model parameters table. For wflow_sbm models there are two ways to include subsurface flow:\n\nThe kinematic wave approach (see section Subsurface flow routing) as part of the sbm model type. Parameters that are part of this component are described in the Lateral subsurface flow section of Model parameters. Input parameters for this component are derived from the SBM vertical concept and the land slope. One external parameter (ksathorfrac) is used to calculate the horizontal hydraulic conductivity at the soil surface kh_0.\nGroundwater flow (see section Groundwater flow component) as part of the sbm_gwf model type. For the unconfined aquifer the input parameters are described in the section Unconfined aquifer of Model parameters. The bottom (bottom) of the groundwater layer is derived from from the soilthickness [mm] parameter of SBM and the provided surface elevation altitude [m] as part of the static input. The area parameter is derived from the model grid. Parameters that are part of the boundary conditions of the unconfined aquifer are listed under Constant Head and Boundary conditions of the Model parameters section.\n\nMost hydrological model configurations make use of the kinematic wave surface routing (river flow, overland flow or both) and input data required for the river and overland flow components is described in Surface flow. There is also the option to use the local inertial model as part of the wflow_sbm models (model types sbm and sbm_gwf):\n\nfor river flow, see also the Local inertial river and floodplain model.\nfor 1D river flow and 2D overland flow combined, see also the Local inertial river (1D) and land (2D) model.\n\nInput parameters for this approach are described in River flow (local inertial), including the optional 1D floodplain schematization, and Overland flow (local inertial) of the Model parameters section.\nReservoirs or lakes can be part of the kinematic wave or local inertial model for river flow and input parameters are described in Reservoirs and Lakes.\nThe wflow_sediment model configuration consists of the vertical Soil Erosion concept and the input parameters for this concept are described in the Sediment section of the Model parameters. The parameters of the lateral Sediment Flux in overland flow concept are described in the Overland flow section of the Model parameters. Parameters of this component are not directly set by data from static input. The input parameters of the lateral concept River Sediment Model are listed in River flow of the Model parameters section.\nThe Model parameters section lists all the parameters per Model component and these Tables can also be used to check which parameters can be part of the output, see also Output netCDF section and Output CSV section.\nExample models can be found in the Example models section.", + "crumbs": [ + "Getting started", + "Building a model from scratch" + ] + }, + { + "objectID": "getting_started/building_a_model.html#references", + "href": "getting_started/building_a_model.html#references", + "title": "Building a model from scratch", + "section": "References", + "text": "References\n\nYamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H. and Pavelsky, T. M.: MERIT Hydro: A high‐resolution global hydrography map based on latest topography datasets, Water Resour. Res., 2019WR024873, doi:10.1029/2019WR024873, 2019.\nEilander, D., van Verseveld, W., Yamazaki, D., Weerts, A., Winsemius, H. C., and Ward, P. J.: A hydrography upscaling method for scale-invariant parametrization of distributed hydrological models, Hydrol. Earth Syst. Sci., 25, 5287–5313, https://doi.org/10.5194/hess-25-5287-2021, 2021.", + "crumbs": [ + "Getting started", + "Building a model from scratch" + ] + }, + { + "objectID": "getting_started/install.html", + "href": "getting_started/install.html", + "title": "Installing wflow", + "section": "", + "text": "Wflow can be used in two different ways, depending on the required use of the code:\nBelow we describe how to install both versions of wflow.", + "crumbs": [ + "Getting started", + "Installing wflow" + ] + }, + { + "objectID": "getting_started/install.html#installing-as-julia-package", + "href": "getting_started/install.html#installing-as-julia-package", + "title": "Installing wflow", + "section": "Installing as Julia package", + "text": "Installing as Julia package\nWflow is a Julia package that can be installed in several ways. Below, we show how to install wflow from Julia’s package repository and how to install the latest version from GitHub.\nFirst, download and install the current stable release of Julia. If you have any issues installing Julia, please see platform specific instructions for further instructions.\nIf you are new to Julia, it might be a good idea to check out the Getting Started section of the Julia Manual. You can also find additional learning resources at julialang.org/learning.\n\nInstall from Julia’s package repository\nTo access Julia’s package manager, press ] in the Julia REPL. To get back to the Julia REPL, press backspace or ^C.\n\n\n\n\n\n\nTip\n\n\n\nIf you haven’t used Julia in a while, it’s a good idea to run up to update your packages.\npkg> up\n\n\nTo access Julia’s package manager and install wflow, use:\npkg> add Wflow\nThis process can take a while, especially on the first run, as compatible dependencies are automatically resolved and installed from the Pkg General registry.\n\n\nInstall from GitHub\nYou can also install wflow from the master branch on the repository as follows:\npkg> add Wflow#master\nThis command tracks the master branch and updates to the latest commit on that branch when you run update, or simply up, in the Pkg REPL. The add installs wflow in your home directory under .julia/packages/Wflow. Note that packages installed under packages by add should not be changed in the directory, as the change could disrupt Pkg’s automatic dependency handling.\nIf you want to modify any files in the repository, you need to do a development install. This can be done using:\npkg> dev Wflow\nThis will clone the git repository, place it under your home directory in .julia/dev/Wflow, and add the wflow package to your project environment. To receive updates, you’ll need to pull the latest changes manually using git pull.\n\n\nCheck installation of wflow\nFinally, go back to the Julia REPL and try to load wflow:\njulia> using Wflow\nThe first time you do this, it may take longer as any new or changed packages need to be precompiled to enable faster loading on subsequent uses. No error messages should appear, which indicates that you have successfully installed wflow.\nBefore concluding this section, we recommend a few tools that can make using and developing Julia code easier.\n\n\n\n\n\n\nTip\n\n\n\nThere is a section on editors and IDEs for Julia on https://julialang.org/, scroll down to see it. We use and recommend Microsoft’s free and open source Visual Studio Code. When combined with the Julia extension it provides a powerful and interactive development experience.\n\n\n\n\n\n\n\n\nTip\n\n\n\nIf you plan to modify the code of wflow, we recommend installing the Revise.jl package. This package allows you to modify code and use the changes without restarting Julia. Install it with add Revise from the Pkg REPL. Then create a file called .julia/config/startup.jl, and put using Revise there. This will load Revise every time you start a Julia session.", + "crumbs": [ + "Getting started", + "Installing wflow" + ] + }, + { + "objectID": "getting_started/install.html#installing-the-compiled-executable", + "href": "getting_started/install.html#installing-the-compiled-executable", + "title": "Installing wflow", + "section": "Installing the compiled executable", + "text": "Installing the compiled executable\nBinaries of wflow_cli can be downloaded from our website download.deltares.nl, and are currently available for Windows. Download and install the .msi file. After installation, you will see two folders in the installation directory. Only the bin/wflow_cli is used. The artifacts folder contains binary dependencies such as netCDF.\nartifacts\\\nbin\\wflow_cli\nTo verify whether the installation was completed successfully, run wflow_cli with no arguments in the command line. This will display the following message:\nUsage: wflow_cli 'path/to/config.toml'\n\n\n\n\n\n\nNote\n\n\n\nThe old version of wflow, based on Python and PCRaster libraries is also available to download from our website download.deltares.nl. We recommend installing the Julia version, as this documentation is written to support this version.", + "crumbs": [ + "Getting started", + "Installing wflow" + ] + }, + { + "objectID": "home/case_studies.html", + "href": "home/case_studies.html", + "title": "Case studies", + "section": "", + "text": "Reliable hydrological models for the Rhine and the Meuse river basins are necessary for short-term forecasting of river flows and long-term predictions for strategic water management planning. In collaboration with Rijkswaterstaat, Deltares is developing a new line of models for the Rhine and the Meuse basins. The models will be used for forecasting and to estimate the impact of climate change on water resources and extreme streamflow. In the model development, we aim to improve hydrological predictions by including relevant processes in the model schematization. The modularity of the wflow framework is ideal for this as we can easily evaluate the combination of different vertical and lateral model components. For example, the local inertial routing for river and overland flow enables us to consider retention of water in the floodplains, which is likely to improve extreme streamflow predictions.", + "crumbs": [ + "Home", + "Case studies" + ] + }, + { + "objectID": "home/case_studies.html#wflow-models-for-the-meuse-and-rhine", + "href": "home/case_studies.html#wflow-models-for-the-meuse-and-rhine", + "title": "Case studies", + "section": "", + "text": "Reliable hydrological models for the Rhine and the Meuse river basins are necessary for short-term forecasting of river flows and long-term predictions for strategic water management planning. In collaboration with Rijkswaterstaat, Deltares is developing a new line of models for the Rhine and the Meuse basins. The models will be used for forecasting and to estimate the impact of climate change on water resources and extreme streamflow. In the model development, we aim to improve hydrological predictions by including relevant processes in the model schematization. The modularity of the wflow framework is ideal for this as we can easily evaluate the combination of different vertical and lateral model components. For example, the local inertial routing for river and overland flow enables us to consider retention of water in the floodplains, which is likely to improve extreme streamflow predictions.", + "crumbs": [ + "Home", + "Case studies" + ] + }, + { + "objectID": "home/case_studies.html#operational-flood-forecasting-in-australia", + "href": "home/case_studies.html#operational-flood-forecasting-in-australia", + "title": "Case studies", + "section": "Operational flood forecasting in Australia", + "text": "Operational flood forecasting in Australia\nIn Australia, there was a need for high-resolution, fast and accurate rainfall-runoff models to provide boundary conditions for a fast and detailed flood inundation model (SFINCS). The domain of the flood model covers the entire North and East Coast of Australia. Although many gauging stations are available to provide real-time information, many rivers are not covered. For these locations, wflow_sbm models are used to provide this real-time information. Additionally, these models are used to provide projections for potential future scenarios. Using the HydroMT library, all wflow_sbm models were automatically built. The high level of flexibility in spatial and temporal resolution, combined with the physics-based nature of the concept, makes Wflow_sbm particularly suitable for ungauged basins. Furthermore, the model is detailed and computationally efficient enough for coupling with the fast flood inundation model SFINCS.\n\nThe results of this proof of concept are very promising. Technically, we were able to quickly set up the wflow_sbm models, couple them to the flood inundation models (SFINCS), and run the models operationally under the Delft-FEWS platform. Model validation was conducted for two basins by comparing the results of Wflow_sbm against observations and the results of calibrated URBS models. This validation demonstrated that the uncalibrated Wflow_sbm model results were already quite satisfactory, especially given the complex nature of these basins, which include several small and large reservoirs. We could also show the potential for further calibration by adjusting the KsatHorFrac parameter.\nDe Kleermaeker, S., Leijnse, T., Morales, Y., Druery, C., & Maguire, S. (2022). Developing a real-time data and modelling framework for operational flood inundation forecasting in Australia. In Hydrology & Water Resources Symposium 2022 (HWRS 2022): The Past, the Present, the Future. Engineers Australia. https://search.informit.org/doi/10.3316/informit.916755150845355", + "crumbs": [ + "Home", + "Case studies" + ] + }, + { + "objectID": "home/case_studies.html#simulating-plastic-transport-in-thailand", + "href": "home/case_studies.html#simulating-plastic-transport-in-thailand", + "title": "Case studies", + "section": "Simulating plastic transport in Thailand", + "text": "Simulating plastic transport in Thailand\nFor the Pollution Control Board of the Government of Thailand and the World Bank, we supported a material flow analysis of plastics in Thailand using wflow. Plastic pollution is a growing global issue. Plastic waste enters rivers and is transported to the ocean where it persists and threatens the health of the ocean, seas and coasts. The initial movement of plastic waste is in many cases triggered by runoff from (heavy) rainfall and transported by water flow towards small streams and rivers. Therefore there is strong relation to rainfall-runoff processes, which can be modeled using high-resolution rainfall-runoff models.\nIn this study we applied the wflow_sbm model in combination with a fate-and-transport and water quality model (DelWaq) to simulate the movement of plastics through five large river basins and on three island and coastal zones (Krabi, Phuket, and Ko Samui; see screenshot of the model below) in Thailand. Together with our partners Panya Consultants and HII, we were able to identify hotspots of plastic pollution, estimate how much plastic waste would end up in the Gulf of Thailand and recommend priority areas for reducing plastic waste reaching the sea.\n\nThe wflow_sbm models for the five large basins were calibrated. The presence of large dams and reservoirs complicated calibration, but with the input for the dam operation, the model performance for these basins could be largely improved. The figure below shows the calibrated model results for the Chao Phraya, just upstream of Bangkok. The input from the hydrological wflow_sbm model was used as input for the fate and transport model to assess the amount of plastic transported to the ocean.\n\nLink to World Bank report: https://www.worldbank.org/en/country/thailand/publication/plastic-waste-material-flow-analysis-for-thailand", + "crumbs": [ + "Home", + "Case studies" + ] + }, + { + "objectID": "model_docs/index.html", + "href": "model_docs/index.html", + "title": "About", + "section": "", + "text": "As opposed to the user guide, which describes the steps needed to build and apply a model in the software, this section explains the different model concepts that are available within the modelling framework of wflow. Descriptions are given regarding the model concepts with links to the original scientific papers which explain the concepts in more detail. The model parameters which influence the processes are also shown, using inline code blocks. An overview of all model parameters is also provided for easy reference, including their short names, long descriptions and their units (see parameters vertical concepts and parameters lateral concepts).", + "crumbs": [ + "Model documentation", + "About" + ] + }, + { + "objectID": "model_docs/index.html#division-between-vertical-and-lateral", + "href": "model_docs/index.html#division-between-vertical-and-lateral", + "title": "About", + "section": "Division between vertical and lateral", + "text": "Division between vertical and lateral\nIn the documentation we talk of vertical and lateral concepts. These are components in the model that describe the vertical movement of water in each model grid cell and the lateral movement of water across grid cells.", + "crumbs": [ + "Model documentation", + "About" + ] + }, + { + "objectID": "model_docs/lateral/kinwave.html", + "href": "model_docs/lateral/kinwave.html", + "title": "Kinematic wave", + "section": "", + "text": "The main flow routing scheme available in Wflow.jl is the kinematic wave approach for channel and overland flow, assuming that water flow is mostly controlled by topography. The kinematic wave equations are (Chow, 1988):\n\\[\n\\begin{gathered}\n \\dfrac{\\partial Q}{\\partial x} + \\dfrac{\\partial A}{\\partial t} = q,\\\\\n A = \\alpha Q^{\\beta}.\n\\end{gathered}\n\\]\nThese equations can then be combined as a function of streamflow only:\n\\[\n \\dfrac{\\partial Q}{\\partial x} + \\alpha \\beta Q^{\\beta - 1} \\dfrac{\\partial Q}{\\partial t} = q.\n\\]\nHere \\(\\SIb{Q}{m^3 s^{-1}}\\) is the surface runoff in the kinematic wave, \\(\\SIb{x}{m}\\) is the length of the runoff pathway, \\(\\SIb{A}{m}\\) is the cross-section area of the runoff pathway, \\(\\SIb{t}{s}\\) is the integration timestep and \\(\\alpha\\) and \\(\\beta\\) are unitless coefficients.\nThese equations are solved with a nonlinear scheme using Newton’s method and can also be iterated depending on the model space and time resolution. By default, the iterations are performed until a stable solution is reached (\\(\\epsilon < 10^{-12}\\)). For larger models, the number of iterations can also be fixed for to a specific sub-timestep (in seconds) for both overland and channel flows to improve simulation time. To enable (fixed or not) iterations of the kinematic wave the following lines can be inserted in the TOML file of the model:\n[model]\n# Enable iterations of the kinematic wave\nkin_wave_iteration = true\n# Fixed sub-timestep for iterations of channel flow (river cells)\nkw_river_tstep = 900\n# Fixed sub-timestep for iterations of overland flow (land cells)\nkw_land_tstep = 3600\nThe \\(\\alpha\\) parameter of the kinematic wave is fixed. To estimate the wetted perimeter for the calculation of the \\(\\alpha\\) parameter a bankfull river depth map (default value is \\(\\SI{1.0}{m}\\)) for the river can be provided as follows:\n[input.lateral.river]\nbankfull_depth = \"wflow_riverdepth\"\nThe wetted perimeter of the river is based on half bankfull river depth. For the land part the wetted perimeter is based on the flow width.\nSimplified reservoir and lake models can be included as part of the river kinematic wave network.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Kinematic wave" + ] + }, + { + "objectID": "model_docs/lateral/kinwave.html#surface-routing", + "href": "model_docs/lateral/kinwave.html#surface-routing", + "title": "Kinematic wave", + "section": "", + "text": "The main flow routing scheme available in Wflow.jl is the kinematic wave approach for channel and overland flow, assuming that water flow is mostly controlled by topography. The kinematic wave equations are (Chow, 1988):\n\\[\n\\begin{gathered}\n \\dfrac{\\partial Q}{\\partial x} + \\dfrac{\\partial A}{\\partial t} = q,\\\\\n A = \\alpha Q^{\\beta}.\n\\end{gathered}\n\\]\nThese equations can then be combined as a function of streamflow only:\n\\[\n \\dfrac{\\partial Q}{\\partial x} + \\alpha \\beta Q^{\\beta - 1} \\dfrac{\\partial Q}{\\partial t} = q.\n\\]\nHere \\(\\SIb{Q}{m^3 s^{-1}}\\) is the surface runoff in the kinematic wave, \\(\\SIb{x}{m}\\) is the length of the runoff pathway, \\(\\SIb{A}{m}\\) is the cross-section area of the runoff pathway, \\(\\SIb{t}{s}\\) is the integration timestep and \\(\\alpha\\) and \\(\\beta\\) are unitless coefficients.\nThese equations are solved with a nonlinear scheme using Newton’s method and can also be iterated depending on the model space and time resolution. By default, the iterations are performed until a stable solution is reached (\\(\\epsilon < 10^{-12}\\)). For larger models, the number of iterations can also be fixed for to a specific sub-timestep (in seconds) for both overland and channel flows to improve simulation time. To enable (fixed or not) iterations of the kinematic wave the following lines can be inserted in the TOML file of the model:\n[model]\n# Enable iterations of the kinematic wave\nkin_wave_iteration = true\n# Fixed sub-timestep for iterations of channel flow (river cells)\nkw_river_tstep = 900\n# Fixed sub-timestep for iterations of overland flow (land cells)\nkw_land_tstep = 3600\nThe \\(\\alpha\\) parameter of the kinematic wave is fixed. To estimate the wetted perimeter for the calculation of the \\(\\alpha\\) parameter a bankfull river depth map (default value is \\(\\SI{1.0}{m}\\)) for the river can be provided as follows:\n[input.lateral.river]\nbankfull_depth = \"wflow_riverdepth\"\nThe wetted perimeter of the river is based on half bankfull river depth. For the land part the wetted perimeter is based on the flow width.\nSimplified reservoir and lake models can be included as part of the river kinematic wave network.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Kinematic wave" + ] + }, + { + "objectID": "model_docs/lateral/kinwave.html#inflow", + "href": "model_docs/lateral/kinwave.html#inflow", + "title": "Kinematic wave", + "section": "Inflow", + "text": "Inflow\nExternal water (supply/abstraction) inflow \\(\\SIb{}{m^3 s^{-1}}\\) can be added to the kinematic wave for surface water routing, as a cyclic parameter or as part of forcing (see also Input section).", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Kinematic wave" + ] + }, + { + "objectID": "model_docs/lateral/kinwave.html#abstractions", + "href": "model_docs/lateral/kinwave.html#abstractions", + "title": "Kinematic wave", + "section": "Abstractions", + "text": "Abstractions\nAbstractions from the river through the variable abstraction \\(\\SIb{}{m^3 s^{-1}}\\) are possible when water demand and allocation is computed. The variable abstraction is set from the water demand and allocation module each time step. The abstraction is divided by the length of the runoff pathway and subtracted from the lateral inflow of the kinematic wave routing scheme for river flow.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Kinematic wave" + ] + }, + { + "objectID": "model_docs/lateral/kinwave.html#subsurface-flow-routing", + "href": "model_docs/lateral/kinwave.html#subsurface-flow-routing", + "title": "Kinematic wave", + "section": "Subsurface flow routing", + "text": "Subsurface flow routing\nIn the SBM model the kinematic wave approach is used to route subsurface flow laterally. Different vertical hydraulic conductivity depth profiles are possible as part of the vertical SBM concept, and these profiles (after unit conversion) are also used to compute lateral subsurface flow. The following profiles (see SBM for a detailed description) are available: - exponential (default) - exponential_constant - layered - layered_exponential\nFor the profiles exponential and exponential_constant, the saturated store S is drained laterally by saturated downslope subsurface flow for a slope with width \\SIb{w}{m} according to:\n\\[\n Q = K_0\\tan(\\beta)w\\begin{cases}\n \\frac{1}{f}\\left(e^{-fz_i}-e^{-f\\subtext{z}{exp}}\\right) +\n e^{-f\\subtext{z}{exp}}(z_t-\\subtext{z}{exp}) & \\text{if $z_i < \\subtext{z}{exp}$}\\\\\n \\\\\n e^{-f\\subtext{z}{exp}}(z_t - z_i) & \\text{if $z_i \\ge \\subtext{z}{exp}$},\n \\end{cases}\n\\]\nwhere \\(\\beta\\) is element slope angle, \\(\\SIb{Q}{m^3 d^{-1}}\\) is subsurface flow, \\(\\SIb{K_0}{m\nd^{-1}}\\) is the saturated hydraulic conductivity at the soil surface, \\(\\SIb{z_i}{m}\\) is the water table depth, \\(\\SIb{z_{t}}{m}\\) is the total soil depth, \\(\\SIb{f}{m^{-1}}\\) is a scaling parameter that controls the decrease of \\(K_0\\) with depth and \\(\\SIb{\\subtext{z}{exp}}{m}\\) is the depth from soil surface for which the exponential decline of \\(K_0\\) is valid. For the exponential profile, \\(\\subtext{z}{exp}\\) is equal to \\(z_t\\).\nCombining with the following continuity equation: \\[\n (\\theta_s-\\theta_r)w\\frac{\\partial h}{\\partial t} = -\\frac{\\partial Q}{\\partial x} + wr\n\\]\nwhere \\(\\SIb{h}{m}\\) is the water table height, \\(\\SIb{x}{m}\\) is the distance downslope, and \\(\\SIb{r}{m d^{-1}}\\) is the net input rate to the saturated store. Substituting for \\(h\n(\\frac{\\partial Q}{\\partial h})\\), gives:\n\\[\n \\frac{\\partial Q}{\\partial t} = -c\\frac{\\partial Q}{\\partial x} + cwr\n\\]\nwhere celerity \\(c\\) is calculated as follows: \\[\n c = \\frac{K_0 \\tan(\\beta)}{\\theta_s-\\theta_r}\\begin{cases}\n e^{-fz_i}\n + e^{-f\\subtext{z}{exp}} & \\text{if $z_i < \\subtext{z}{exp}$}\\\\\n \\\\\n e^{-f\\subtext{z}{exp}} & \\text{if $z_i \\ge \\subtext{z}{exp}$}.\n \\end{cases}\n\\]\nFor the layered and layered_exponential profiles the equivalent horizontal hydraulic conductivity \\(\\SIb{K_h}{m d^{-1}}\\) is calculated for water table height \\(\\SIb{h = z_t-z_i}{m}\\) and lateral subsurface flow is calculated as follows:\n\\[\n Q = K_h h \\tan(\\beta) w,\n\\]\nand celerity \\(c\\) is given by:\n\\[\n c = \\frac{K_h \\tan(\\beta)}{\\theta_s-\\theta_r}.\n\\]\nThe kinematic wave equation for lateral subsurface flow is solved iteratively using Newton’s method.\n\n\n\n\n\n\nNote\n\n\n\nFor the lateral subsurface flow kinematic wave the model timestep is not adjusted. For certain model timestep and model grid size combinations this may result in loss of accuracy.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Kinematic wave" + ] + }, + { + "objectID": "model_docs/lateral/kinwave.html#multi-threading", + "href": "model_docs/lateral/kinwave.html#multi-threading", + "title": "Kinematic wave", + "section": "Multi-Threading", + "text": "Multi-Threading\nThe kinematic wave calculations for surface - and subsurface flow routing can be executed in parallel using multiple threads. In the model section of the TOML file, a minimum stream order can be provided to define subbasins for the river (default is 6) and land domain (default is 5). Subbasins are created at all confluences where each branch has a minimal stream order. Based on the subbasins a directed acyclic graph is created that controls the order of execution and which subbasins can run in parallel.\n[model]\nmin_streamorder_river = 5 # minimum stream order to delineate subbasins for river domain, default is 6\nmin_streamorder_land = 4 # minimum stream order to delineate subbasins for land domain, default is 5", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Kinematic wave" + ] + }, + { + "objectID": "model_docs/lateral/kinwave.html#subcatchment-flow", + "href": "model_docs/lateral/kinwave.html#subcatchment-flow", + "title": "Kinematic wave", + "section": "Subcatchment flow", + "text": "Subcatchment flow\nNormally the the kinematic wave is continuous throughout the model. By using the pits entry in the model and input sections of the TOML file all flow is at the subcatchment only (upstream of the pit locations, defined by the netCDF variable wflow_pits in the example below) and no flow is transferred from one subcatchment to another. This can be convenient when connecting the result of the model to a water allocation model such as Ribasim.\n[input]\n# these are not directly part of the model\npits = \"wflow_pits\"\n\n[model]\npits = true", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Kinematic wave" + ] + }, + { + "objectID": "model_docs/lateral/kinwave.html#limitations", + "href": "model_docs/lateral/kinwave.html#limitations", + "title": "Kinematic wave", + "section": "Limitations", + "text": "Limitations\nThe kinematic wave approach for channel, overland and lateral subsurface flow, assumes that the topography controls water flow mostly. This assumption holds for steep terrain, but in less steep terrain the hydraulic gradient is likely not equal to the surface slope (subsurface flow), or pressure differences and inertial momentum cannot be neglected (channel and overland flow). In addition, while the kinematic wave equations are solved with a nonlinear scheme using Newton’s method (Chow, 1988), other model equations are solved through a simple explicit scheme. In summary the following limitations apply:\n\nChannel flow, and to a lesser degree overland flow, may be unrealistic in terrain that is not steep, and where pressure forces and inertial momentum are important.\nThe lateral movement of subsurface flow may be very wrong in terrain that is not steep.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Kinematic wave" + ] + }, + { + "objectID": "model_docs/lateral/kinwave.html#external-inflows", + "href": "model_docs/lateral/kinwave.html#external-inflows", + "title": "Kinematic wave", + "section": "External inflows", + "text": "External inflows\nExternal inflows, for example water supply or abstractions, can be added to the kinematic wave via the inflow variable. For this, the user can supply a 2D map of the inflow, as a cyclic parameter or as part of forcing (see also Input section). These inflows are added or abstracted from the upstream inflow qin before running the kinematic wave to solve the impact on resulting q. In case of a negative inflow (abstractions), a minimum of zero is applied to the upstream flow qin.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Kinematic wave" + ] + }, + { + "objectID": "model_docs/lateral/kinwave.html#references", + "href": "model_docs/lateral/kinwave.html#references", + "title": "Kinematic wave", + "section": "References", + "text": "References\n\nChow, V., Maidment, D. and Mays, L., 1988, Applied Hydrology. McGraw-Hill Book Company, New York.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Kinematic wave" + ] + }, + { + "objectID": "model_docs/lateral/sediment_flux.html", + "href": "model_docs/lateral/sediment_flux.html", + "title": "Sediment flux", + "section": "", + "text": "Both the inland and river sediment model take into account sediment flux or transport of sediment in water, either in overland flow or in the stream flow. These two transport are distinguished in two different structures.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Sediment flux" + ] + }, + { + "objectID": "model_docs/lateral/sediment_flux.html#inland-sediment-model", + "href": "model_docs/lateral/sediment_flux.html#inland-sediment-model", + "title": "Sediment flux", + "section": "Inland Sediment Model", + "text": "Inland Sediment Model\n\nSediment Flux in overland flow\nOnce the amount of soil detached by both rainfall and overland flow has been estimated, it has then to be routed and delivered to the river network. Inland routing in sediment models is usually done by comparing the amount of detached sediment with the transport capacity of the flow, which is the maximum amount of sediment that the flow can carry downslope. There are several existing formulas available in the literature. For a wide range of slopes and for overland flow, the Govers equation (1990) seems the most appropriate choice (Hessel et al, 2007). However, as the wflow_sediment model was developed to be linked to water quality issues, the Yalin transport equation was chosen as it can handle particle differentiation (Govers equation can still be used if wflow_sediment is used to only model inland processes with no particle differentiation). For land cells, wflow_sediment assumes that erosion can mobilize 5 classes of sediment:\n\nClay (mean diameter of \\(\\SI{2}{\\mu m}\\))\nSilt (mean diameter of \\(\\SI{10}{\\mu m}\\))\nSand (mean diameter of \\(\\SI{200}{\\mu m}\\))\nSmall aggregates (mean diameter of \\(\\SI{30}{\\mu m}\\))\nLarge aggregates (mean diameter of \\(\\SI{50}{\\mu m}\\)).\n\n$$\n\\[\\begin{gathered}\n \\mathrm{PSA} = \\mathrm{SAN} (1-\\mathrm{CLA})^{2.4} \\\\\n \\mathrm{PSI} = 0.13\\mathrm{SIL}\\\\\n \\mathrm{PCL} = 0.20\\mathrm{CLA} \\\\\n\n \\mathrm{SAG} =\n \\begin{align*}\n \\begin{cases}\n 2.0\\mathrm{CLA} &\\text{ if }\\quad \\mathrm{CLA} < 0.25 \\\\\n 0.28(\\mathrm{CLA}-0.25)+0.5 &\\text{ if }\\quad 0.25 \\leq \\mathrm{CLA} \\leq 0.5 \\\\\n 0.57 &\\text{ if }\\quad \\mathrm{CLA} > 0.5\n \\end{cases}\n \\end{align*} \\\\\n\n \\mathrm{LAG} = 1 - \\mathrm{PSA} - \\mathrm{PSI} - \\mathrm{PCL} - \\mathrm{SAG}\n\\end{gathered}\\]\n$$\nwhere \\(\\mathrm{CLA}\\), \\(\\mathrm{SIL}\\) and \\(\\mathrm{SAN}\\) are the primary clay, silt, sand fractions of the topsoil and \\(\\mathrm{PCL}\\), \\(\\mathrm{PSI}\\), \\(\\mathrm{PSA}\\), \\(\\mathrm{SAG}\\) and \\(\\mathrm{LAG}\\) are the clay, silt, sand, small and large aggregates fractions of the detached sediment respectively. The transport capacity of the flow using Yalin’s equation with particle differentiation, developed by Foster (1982), is: \\[\n \\mathrm{TC}_i = (P_e)_i (S_g)_i \\, \\rho_w \\, g \\, d_i V_*\n\\] where \\(\\mathrm{TC}_i\\) is the transport capacity of the flow for the particle class \\(i\\), \\((P_e)_i\\) is the effective number of particles of class \\(i\\), \\(\\SIb{(S_g)_i}{kg m^{-3}}\\) is the specific gravity for the particle class \\(i\\), \\(\\SIb{\\rho_w}{kg m^{-3}}\\) is the mass density of the fluid, \\(\\SIb{g}{m s^{-2}}\\) is the acceleration due to gravity, \\(\\SIb{d_i}{m}\\) is the diameter of the particle of class \\(i\\) and \\(V_* = \\SIb{(g R S)^{0.5}}{m s^{-1}}\\) is the shear velocity of the flow with \\(S\\) the slope gradient and \\(\\SIb{R}{m}\\) the hydraulic radius of the flow. The detached sediment are then routed down slope until the river network using the accucapacityflux, accupacitystate functions depending on the transport capacity from Yalin.\nThe choice of transport capacity method for the overland flow is set up in the model section of the TOML:\n[model]\nlandtransportmethod = \"yalinpart\" # Overland flow transport capacity method: [\"yalinpart\", \"govers\", \"yalin\"]\nNote that the “govers” and “yalin” equations can only assess total transport capacity of the flow and can therefore not be used in combination with the river part of the sediment model.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Sediment flux" + ] + }, + { + "objectID": "model_docs/lateral/sediment_flux.html#river-sediment-model", + "href": "model_docs/lateral/sediment_flux.html#river-sediment-model", + "title": "Sediment flux", + "section": "River Sediment Model", + "text": "River Sediment Model\nSediment dynamics in rivers can be described by the same three processes on land: erosion, deposition and transport. The difference is that channel flow is much higher, deeper and permanent compared to overland flow. In channels, erosion is the direct removal of sediments from the river bed or bank (lateral erosion). Sediments are transported in the river either by rolling, sliding and silting (bed load transport) or via turbulent flow in the higher water column (suspended load transport). The type of transport is determined by the river bed shear stress. As sediment particles have a higher density than water, they can also be deposited on the river bed according to their settling velocity compared to the flow velocity. In addition to regular deposition in the river, lakes, reservoirs and floodplains represents additional major sediment settling pools.\nComplete models of sediment dynamics based on hydrology and not on hydraulics or hydrodynamics are much rarer than for soil loss and inland dynamics. The simpler models such as the SWAT default sediment river model uses again the transport capacity of the flow to determine if there is erosion or deposition (Neitsch et al., 2011). A more physics-based approach (Partheniades, 1965) to determine river erosion is used by Liu et al. (2018) and in the new SWAT’s approach developed by Narasimhan et al. (2017). For wflow_sediment, the new physics-based model of SWAT was chosen for transport and erosion as it enables the use of parameter estimation for erosion of bed and bank of the channel and separates the suspended from the bed loads.\n\n\n\nOverview of the different processes for a river cell in wflow_sediment.\n\n\nRunning the river model is an option of the wflow_sediment model and is enabled using the TOML file. By default it is false:\n[model]\nrunrivermodel = true\n\nSediment inputs in a river cell\nThe first part of the river model assesses how much detached sediment are in the river cell at the beginning of the timestep \\(t\\). Sources of detached sediment are sediments coming from land erosion, estimated with the soil loss part of wflow_sediment model, the sediment coming from upstream river cells and the detached sediment that were left in the cell at the end of the previous timestep (t-1): \\[\n (\\subtext{\\mathrm{sed}}{in})_t = (\\subtext{\\mathrm{sed}}{land})_t + \\mathrm{upstream}\\left[(\\subtext{\\text{sed}}{out})_{t-1}\\right] + (\\subtext{\\text{sed}}{riv})_{t-1}\n\\]\n\n\nRiver transport and erosion\nOnce the amount of sediment inputs at the beginning of the timestep is known, the model then estimates transport, and river erosion if there is a deficit of sediments. Transport in the river system is estimated via a transport capacity formula. There are several transport capacity formulas available in wflow_sediment, some requiring calibration and some not. Choosing a transport capacity equation depends on the river characteristics (some equation are more suited for narrow or wider rivers), and on the reliability of the required river parameters (such as slope, width or mean particle diameter of the river channel). Several river transport capacity are available and the choice is set up in the model section of the TOML:\n[model]\nrivtransportmethod = \"bagnold\" # River flow transport capacity method: [\"bagnold\", \"engelund\", \"yang\", \"kodatie\", \"molinas\"]\nSimplified Bagnold\nOriginally more valid for intermediate to large rivers, this simplified version of the Bagnold equation relates sediment transport to flow velocity with two simple calibration parameters (Neitsch et al, 2011): \\[\nC_{\\max} = \\subtext{c}{sp} \\left( \\dfrac{\\mathrm{prf} Q}{h W} \\right)^{\\subtext{\\mathrm{sp}}{exp}}\n\\]\nwhere \\(\\SIb{C_{\\max}}{kg L^{-1}}\\) (or \\(\\SIb{}{ton m^{-1}}\\)) is the sediment concentration, \\(\\SIb{Q}{m^3 s^{-1}}\\) is the surface runoff in the river cell, \\(\\SIb{h}{m}\\) is the river water level, \\(\\SIb{W}{m}\\) is the river width and \\(\\subtext{c}{sp}\\), \\(\\mathrm{prf}\\) and \\(\\subtext{\\mathrm{sp}}{exp}\\) are calibration parameters. The \\(\\mathrm{prf}\\) coefficient is usually used to deduce the peak velocity of the flow, but for simplification in wflow_sediment, the equation was simplified to only get two parameters to calibrate: \\(\\subtext{\\mathrm{sp}}{exp}\\) and \\(\\subtext{c}{Bagnold} = \\subtext{c}{sp} \\,\n\\mathrm{prf}^{\\subtext{\\mathrm{sp}}{exp}}\\). The coefficient \\(\\subtext{\\mathrm{sp}}{exp}\\) usually varies between \\(1\\) and \\(2\\) while \\(\\mathrm{prf}\\) and \\(\\subtext{c}{sp}\\) have a wider range of variation. The table below summarizes ranges and values of the three Bagnold coefficients used by other studies:\n\nRange of the simplified Bagnold coefficients (and calibrated value)\n\n\n\n\n\n\n\n\n\nStudy\nRiver\n\\(\\mathrm{prf}\\) range\n\\(\\subtext{c}{sp}\\) range\n\\(\\subtext{\\mathrm{sp}}{exp}\\) range\n\n\n\n\nVigiak 2015\nDanube\n0.5-2 (/)\n0.0001-0.01 (0.003-0.006)\n1-2 (1.4)\n\n\nVigiak 2017\nDanube\n/\n0.0001-0.01 (0.0015)\n1-2 (1.4)\n\n\nAbbaspour 2007\nThur (CH)\n0.2-0.25 (/)\n0.001-0.002 (/)\n0.35-1.47 (/)\n\n\nOeurng 2011\nSave (FR)\n0-2 (0.58)\n0.0001-0.01 (0.01)\n1-2 (2)\n\n\n\nEngelund and Hansen This transport capacity is not present in SWAT but used in many models such as Delft3D-WAQ, Engelund and Hansen calculates the total sediment load as (Engelund and Hansen, 1967): \\[\n C_w = 0.05 \\left( \\dfrac{\\rho_{s}}{\\rho_{s} - \\rho} \\right) \\left( \\dfrac{u S}{\\sqrt{\\left( \\dfrac{\\rho_{s}}{\\rho_{s} - \\rho} \\right) g D_{50}}} \\right) \\theta^{1/2}\n\\] where \\(C_w\\) is the sediment concentration by weight, \\(\\SIb{\\rho}{g m^{-3}}\\) and \\(\\SIb{\\rho_{s}}{g m^{-3}}\\) are the fluid and sediment density (here respectively equal to \\(\\SI{1000}{g m^{-3}}\\) and \\(\\SI{2650}{g\nm^{-3}}\\)), \\(\\SIb{u}{m s^{-1}}\\) is the water mean velocity, \\(S\\) is the river slope, \\(g\\) is the acceleration due to gravity, \\(\\SIb{D_{50}}{m}\\) is the river mean diameter and \\(\\theta\\) is the Shields parameter.\nKodatie Kodatie (1999) developed the power relationships from Posada (1995) using field data and linear optimization so that they would be applicable for a wider range of riverbed sediment size. The resulting equation, for a rectangular channel, is (Neitsch et al, 2011): \\[\n C_{\\max} = \\left( \\dfrac{a u^{b} h^{c} S^{d}}{\\subtext{V}{in}} \\right) W\n\\] where \\(\\SIb{\\subtext{V}{in}}{m^3}\\) in the volume of water entering the river cell during the timestep and \\(a\\), \\(b\\), \\(c\\) and \\(d\\) are coefficients depending on the riverbed sediment size. Values of these coefficients are summarized in the table below.\n\nRange of the simplified Bagnold coefficients (and calibrated value)\n\n\n\n\n\n\n\n\n\nRiver sediment diameter\na\nb\nc\nd\n\n\n\n\n\\(D_{50} \\leq \\SI{0.05}{mm}\\)\n281.4\n2.622\n0.182\n0\n\n\n\\(\\SI{0.05}{mm} < D_{50} \\leq \\SI{0.25}{mm}\\)\n2 829.6\n3.646\n0.406\n0.412\n\n\n\\(\\SI{0.25}{mm} < D_{50} \\leq \\SI{2.0}{mm}\\)\n2 123.4\n3.300\n0.468\n0.613\n\n\n\\(D_{50} > \\SI{2.0}{mm}\\)\n431 884.8\n1.000\n1.000\n2.000\n\n\n\nYang Yang (1996) developed a set of two equations giving transport of sediments for sand-bed or gravel-bed rivers. The sand equation (\\(D_{50} < \\SI{2.0}{mm}\\)) is: \\[\n \\log\\left(C_{ppm}\\right) = 5.435 - 0.286\\log\\left(\\frac{\\omega_{s,50}D_{50}}{\\nu}\\right)-0.457\\log\\left(\\frac{u_*}{\\omega_{s,50}}\\right) \\\\\n +\\left(1.799-0.409\\log\\left(\\frac{\\omega_{s,50}D_{50}}{\\nu}\\right)-0.314\\log\\left(\\frac{u_*}{\\omega_{s,50}}\\right)\\right)\\log\\left(\\frac{uS}{\\omega_{s,50}}-\\frac{u_{cr}S}{\\omega_{s,50}}\\right)\n\\] And the gravel equation (\\(\\SI{2.0}{mm} \\leq D_{50} < \\SI{10.0}{mm}\\)) is: \\[\n \\log\\left(C_{ppm}\\right) = 6.681 - 0.633\\log\\left(\\frac{\\omega_{s,50}D_{50}}{\\nu}\\right)-4.816\\log\\left(\\frac{u_*}{\\omega_{s,50}}\\right) \\\\\n +\\left(2.784-0.305\\log\\left(\\frac{\\omega_{s,50}D_{50}}{\\nu}\\right)-0.282\\log\\left(\\frac{u_*}{\\omega_{s,50}}\\right)\\right)\\log\\left(\\frac{uS}{\\omega_{s,50}}-\\frac{u_{cr}S}{\\omega_{s,50}}\\right)\n\\] where \\(C_{ppm}\\) is sediment concentration in parts per million by weight, \\(\\SIb{\\omega_{s,50}}{m s^{-1}}\\) is the settling velocity of a particle with the median riverbed diameter estimated with Stokes, \\(\\SIb{\\nu}{m^2 s^{-1}}\\) is the kinematic viscosity of the fluid, \\(\\SIb{u_*}{m s^{-1}}\\) is the shear velocity where \\(u_* = \\sqrt{gR_{H}S}\\) with \\(R_{H}\\) the hydraulic radius of the river and \\(\\SIb{u_{cr}}{m s^{-1}}\\) is the critical velocity (equation can be found in Hessel, 2007).\nMolinas and Wu The Molinas and Wu (2001) transport equation was developed for large sand-bed rivers based on the universal stream power \\(\\psi\\). The corresponding equation is (Neitsch et al, 2011): \\[\n C_{w} = \\dfrac{1430 (0.86+\\sqrt{\\psi}) \\psi^{1.5}}{0.016+\\psi} 10^{-6}\n\\] where \\(\\psi\\) is the universal stream power given by: \\[\n \\psi = \\dfrac{\\psi^{3}}{\\left(\\dfrac{\\rho_{s}}{\\rho}-1\\right) g h \\omega_{s,50} \\left[ \\log_{10}\\left(\\dfrac{h}{D_{50}}\\right)\\right]^{2}}\n\\]\nOnce the maximum concentration \\(C_{\\max}\\) is established with one of the above transport formula, the model then determines if there is erosion of the river bed and bank. In order to do that, the difference \\(sed_{ex}\\) between the maximum amount of sediment estimated with transport (\\(\\mathrm{sed}_{\\max} = C_{\\max} \\subtext{V}{in}\\)) and the sediment inputs to the river cell (\\(\\subtext{\\mathrm{sed}}{in}\\) calculated above) is calculated. If too much sediment is coming in and \\(\\subtext{\\mathrm{sed}}{ex}\\) is negative, then there is no river bed and bank erosion. And if the river has not reach its maximum transport capacity, then erosion of the river happens.\nFirst, the sediments stored in the cell from deposition in previous timesteps \\(\\subtext{\\mathrm{sed}}{stor}\\) are eroded from clay to gravel. If this amount is not enough to cover \\(\\subtext{\\mathrm{sed}}{ex}\\), then erosion of the local river bed and bank material starts.\nInstead of just setting river erosion amount to just cover the remaining difference \\(\\subtext{\\mathrm{sed}}{exeff}\\) between \\(\\subtext{\\mathrm{sed}}{ex}\\) and \\(\\subtext{\\mathrm{sed}}{stor}\\), actual erosion potential is adjusted using river characteristics and is separated between the bed and bank of the river using the physics-based approach of Knight (1984).\nThe bed and bank of the river are supposed to only be able to erode a maximum amount of their material E_{R,\\mathrm{bed}} for the bed and E_{R,\\mathrm{bank}} for the river bank. For a rectangular channel, assuming it is meandering and thus only one bank is prone to erosion, they are calculated from the equations (Neitsch et al, 2011): \\[\n\\begin{gathered}\n E_{R,\\mathrm{bed}} = k_{d,\\mathrm{bed}} \\left( \\tau_{e,\\mathrm{bed}} - \\tau_{cr,\\mathrm{bed}} \\right) 10^{-6} L W \\rho_{b, \\mathrm{bed}} \\Delta t \\\\\n E_{R,\\mathrm{bank}} = k_{d,\\mathrm{bank}} \\left( \\tau_{e,\\mathrm{bank}} - \\tau_{cr,\\mathrm{bank}} \\right) 10^{-6} L h \\rho_{b, \\mathrm{bank}} \\Delta t\n\\end{gathered}\n\\] where \\(\\SIb{E_R}{ton}\\) is the potential bed/bank erosion rates, \\(\\SIb{k_d}{cm^3 N^{-1},\ns^{-1}}\\) is the erodibility of the bed/bank material, \\(\\SIb{\\tau_e}{N m^{-2}}\\) is the effective shear stress from the flow on the bed/bank, \\(\\SIb{\\tau_{cr}}{N m^{-2}}\\) is the critical shear stress for erosion to happen, \\(\\SIb{L}{m}\\), \\(\\SIb{W}{m}\\) and \\(\\SIb{h}{m}\\) are the channel length, width and water height, \\(\\SIb{\\rho_{b}}{g cm^{-3}}\\) is the bulk density of the bed/bank of the river and \\(\\SIb{\\Delta t}{s}\\) is the model timestep.\nIn wflow_sediment, the erodibility of the bed/bank are approximated using the formula from Hanson and Simon (2001): \\[\n k_d=0.2 \\tau_{cr}^{-0.5}\n\\] Normally erodibilities are evaluated using jet test in the field and there are several reviews and some adjustments possible to this equation (Simon et al, 2011). However, to avoid too heavy calibration and for the scale considered, this equation is supposed to be efficient enough. The critical shear stress \\(\\tau_{cr}\\) is evaluated differently for the bed and bank. For the bed, the most common formula from Shields initiation of movement is used. For the bank, a more recent approach from Julian and Torres (2006) is used : \\[\n \\tau_{cr,\\mathrm{bank}} = (0.1+0.1779 SC+0.0028 SC^{2}-2.34 10^{-5} SC^{3}) C_{ch}\n\\] where \\(SC\\) is the percent clay and silt content of the river bank and \\(C_{ch}\\) is a coefficient taking into account the positive impact of vegetation on erosion reduction. This coefficient is then dependent on the land use and classical values are shown in the table below. These values where then adapted for use with the GlobCover land use map. Percent of clay and silt (along with sand and gravel) for the channel is estimated from the river median particle diameter assuming the same values as SWAT shown in the table below. Median particle diameter is here estimated depending on the Strahler river order. The higher the order, the smaller the diameter is. As the median diameter is only used in wflow_sediment for the estimation of the river bed/bank sediment composition, this supposition should be enough. Actual refined data or calibration may however be needed if the median diameter is also required for the transport formula. In a similar way, the bulk densities of river bed and bank are also just assumed to be of respectively \\(\\SI{1.5}{g cm^{-3}}\\) and \\(\\SI{1.4}{g cm^{-3}}\\).\n\nClassical values of the channel cover vegetation coefficient (Julian and Torres, 2006)\n\n\nBank vegetation\n\\(C_{ch}\\)\n\n\n\n\nNone\n1.00\n\n\nGrassy\n1.97\n\n\nSparse trees\n5.40\n\n\nDense trees\n19.20\n\n\n\n\nComposition of the river bed/bank depending on the median diameter \\(\\SIb{d_{50}}{\\mu m}\\) (Neitsch et al, 2011)\n\n\nSediment Fraction\n\\(\\leq\\) 5\n5 to 50\n50 to 2000\n\\(>\\) 2000\n\n\n\n\nSand\n0.15\n0.15\n0.65\n0.15\n\n\nSilt\n0.15\n0.65\n0.15\n0.15\n\n\nClay\n0.65\n0.15\n0.15\n0.05\n\n\nGravel\n0.05\n0.05\n0.05\n0.65\n\n\n\nThen, the repartition of the flow shear stress is refined into the effective shear stress and the bed and bank of the river using the equations developed by Knight (1984) for a rectangular channel: \\[\n\\begin{gathered}\n \\tau_{e,\\mathrm{bed}} = \\rho g R_{H} S \\left(1 - \\dfrac{SF_{\\mathrm{bank}}}{100}\\right) \\left(1+\\dfrac{2h}{W}\\right) \\\\\n \\tau_{e,\\mathrm{bank}} = \\rho g R_{H} S \\left( SF_{\\mathrm{bank}}\\right) \\left(1+\\dfrac{W}{2h}\\right)\n\\end{gathered}\n\\] where \\(\\rho g\\) is the fluid specific weight (\\(\\SI{9800}{N m^{-3}}\\) for water), \\(\\SIb{R_H}{m}\\) is the hydraulic radius of the channel, \\(\\SIb{h}{m}\\) and \\(\\SIb{W}{m}\\) are the water level and river width. \\(SF_{\\mathrm{bank}}\\) is the proportion of shear stress acting on the bank (%) and is estimated from (Knight, 1984): \\[\n \\mathrm{SF}_{\\mathrm{bank}} = \\exp \\left( -3.230 \\log_{10}\\left(\\dfrac{W}{h}+3\\right)+6.146 \\right)\n\\] Finally the relative erosion potential of the bank and bed of the river is calculated by: \\[\n\\begin{gathered}\n \\mathrm{RTE}_{\\mathrm{bed}} = \\dfrac{E_{R,\\mathrm{bed}}}{E_{R,\\mathrm{bed}}+E_{R,\\mathrm{bank}}} \\\\\n \\mathrm{RTE}_{\\mathrm{bank}} = 1 - RTE_{\\mathrm{bed}}\n\\end{gathered}\n\\] And the final actual eroded amount for the bed and bank is the maximum between \\(\\mathrm{RTE}\n\\subtext{\\mathrm{sed}}{exeff}\\) and the erosion potential \\(E_R\\). Total eroded amount of sediment \\(\\subtext{\\mathrm{sed}}{erod}\\) is then the sum of the eroded sediment coming from the storage of previously deposited sediment and the river bed/bank erosion.\n\n\nRiver deposition\nAs sediments have a higher density than water, moving sediments in water can be deposited in the river bed. The deposition process depends on the mass of the sediment, but also on flow characteristics such as velocity. In wflow_sediment, as in SWAT, deposition is modelled with Einstein’s equation (Neitsch et al, 2011): \\[\n \\subtext{P}{dep}=\\left(1-\\dfrac{1}{e^{x}}\\right)100\n\\] where \\(\\subtext{P}{dep}\\) is the percentage of sediments that is deposited on the river bed and x is a parameter calculated with: \\[\n x = \\dfrac{1.055 L \\omega_{s}}{u h}\n\\] where \\(\\SIb{L}{m}\\) and \\(\\SIb{h}{m}\\) are channel length and water height, \\(\\SIb{\\omega_s}{m\ns^{-1}}\\) is the particle settling velocity calculated with Stokes’ formula and \\(\\SIb{u}{m\ns^{-1}}\\) is the mean flow velocity. The calculated percentage is then subtracted from the amount of sediment input and eroded river sediment for each particle size class (\\(\\subtext{\\mathrm{sed}}{dep} = \\subtext{P}{dep}/100 (\\subtext{\\mathrm{sed}}{in} +\n\\subtext{\\mathrm{sed}}{erod})\\)). Resulting deposited sediment are then stored in the river bed and can be re-mobilized in future time steps by erosion.\n\n\nMass balance and sediment concentration\nFinally after estimating inputs, deposition and erosion with the transport capacity of the flow, the amount of sediment actually leaving the river cell to go downstream is estimated using: \\[\n \\subtext{\\mathrm{sed}}{out} = (\\subtext{\\mathrm{sed}}{in} + \\subtext{\\mathrm{sed}}{erod} - \\subtext{\\mathrm{sed}}{dep}) \\dfrac{\\subtext{V}{out}}{V}\n\\]\nwhere \\(\\SIb{\\subtext{\\mathrm{sed}}{out}}{ton}\\) is the amount of sediment leaving the river cell (tons), \\(\\SIb{\\subtext{\\mathrm{sed}}{in}}{ton}\\) is the amount of sediment coming into the river cell (storage from previous timestep, land erosion and sediment flux from upstream river cells), \\(\\SIb{\\subtext{\\mathrm{sed}}{erod}}{ton}\\) is the amount of sediment coming from river erosion, \\(\\SIb{\\subtext{\\mathrm{sed}}{dep}}{ton}\\) is the amount of deposited sediments, \\(\\SIb{\\subtext{V}{out}}{m^3}\\) is the volume of water leaving the river cell (surface runoff \\(Q\\) times timestep \\(\\Delta t\\)) and \\(\\SIb{V}{m^3}\\) is the total volume of water in the river cell (\\(\\subtext{V}{out}\\) plus storage \\(h W L\\)).\nA mass balance is then used to calculate the amount of sediment remaining in the cell at the end of the timestep \\((\\subtext{\\mathrm{sed}}{riv})_t\\): \\[\n (\\subtext{\\mathrm{sed}}{riv})_t = (\\subtext{\\mathrm{sed}}{riv})_{t-1} + (\\subtext{\\mathrm{sed}}{land})_t + \\mathrm{upstream}\\left[(\\subtext{\\mathrm{sed}}{out})_{t-1}\\right] + (\\subtext{\\mathrm{sed}}{erod})_t - (\\subtext{\\mathrm{sed}}{dep})_t - (\\subtext{\\mathrm{sed}}{out})_t\n\\]\n\n\nLake and reservoir modelling\nApart from land and river, the hydrologic wflow_sbm model also handles lakes and reservoirs modelling. In wflow_sbm, lakes and large reservoirs are modelled using a 1D bucket model at the cell corresponding to the outlet. For the other cells belonging to the lake/reservoir which are not the outlet, processes such as precipitation and evaporation are filtered out and shifted to the outlet cell. wflow_sediment handles the lakes and reservoirs in the same way. If a cell belongs to a lake/reservoir and is not the outlet then the model assumes that no erosion/deposition of sediments is happening and the sediments are only all transported to the lake/reservoir outlet. Once the sediments reach the outlet, then sediments are deposited in the lake/reservoir according to Camp’s model (1945) (Verstraeten et al, 2000): \\[\n \\mathrm{TE} = \\dfrac{\\omega_s}{u_{cr,\\mathrm{res}}} = \\dfrac{\\subtext{A}{res}}{\\subtext{Q}{out,res}} \\omega_s\n\\]\nwhere \\(\\mathrm{TE}\\) is the trapping efficiency of the lake/reservoir (or the fraction of particles trapped), \\(\\SIb{\\omega_{s}}{m s^{-1}}\\) is the particle velocity from Stokes, \\(\\SIb{\\subtext{u}{cr,res}}{m s^{-1}}\\) is the reservoir’s critical settling velocity which is equal to the reservoir’s outflow \\(\\SIb{\\subtext{Q}{out,res}}{m^3 s^{-1}}\\) divided by the reservoir’s surface area \\(\\SIb{\\subtext{A}{res}}{m^2}\\).\nFor reservoirs, coarse sediment particles from the bed load are also assumed to be trapped by the dam structure. This adding trapping is taken into account with a reservoir trapping efficiency coefficient for large particles (between \\(0\\) and \\(1\\)). Depending on the type of the dam, all bed load particles are trapped (restrapefficiency = 1.0, for example for a gravity dam) or only partly (for example for run-of-the-river dams).\nLake and reservoir modelling is enabled in the model section of the TOML and require the extra following input arguments:\n[model]\ndoreservoir = true\ndolake = false\n\n[input.vertical]\n# Reservoir\nresareas = \"wflow_reservoirareas\"\n# Lake\nlakeareas = \"wflow_lakeareas\"\n\n[input.lateral.river]\n# Reservoir\nresarea = \"ResSimpleArea\"\nrestrapefficiency = \"ResTrapEff\"\nresareas = \"wflow_reservoirareas\"\nreslocs = \"wflow_reservoirlocs\"\n# Lake\nlakearea = \"LakeArea\"\nlakeareas = \"wflow_lakeareas\"\nlakelocs = \"wflow_lakelocs\"\nNote that in the inland part, lake and reservoir coverage are used to filter erosion and transport in overland flow.", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Sediment flux" + ] + }, + { + "objectID": "model_docs/lateral/sediment_flux.html#references", + "href": "model_docs/lateral/sediment_flux.html#references", + "title": "Sediment flux", + "section": "References", + "text": "References\n\nK.C. Abbaspour, J. Yang, I. Maximov, R. Siber, K. Bogner, J. Mieleitner, J. Zobrist, and R.Srinivasan. Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of Hydrology, 333(2-4):413-430, 2007. 10.1016/j.jhydrol.2006.09.014\nP. Borrelli, M. Märker, P. Panagos, and B. Schütt. Modeling soil erosion and river sediment yield for an intermountain drainage basin of the Central Apennines, Italy. Catena, 114:45-58,\n\n10.1016/j.catena.2013.10.007\n\nF. Engelund and E. Hansen. A monograph on sediment transport in alluvial streams. Technical University of Denmark 0stervoldgade 10, Copenhagen K., 1967.\nG. Govers. Empirical relationships for the transport capacity of overland flow. IAHS Publication, (January 1990):45-63 ST, 1990.\nG.J Hanson and A Simon. Erodibility of cohesive streambeds in the loess area of the midwestern USA. Hydrological Processes, 15(May 1999):23-38, 2001.\nR Hessel and V Jetten. Suitability of transport equations in modelling soil erosion for a small Loess Plateau catchment. Engineering Geology, 91(1):56-71, 2007. 10.1016/j.enggeo.2006.12.013\nJ.P Julian, and R. Torres. Hydraulic erosion of cohesive riverbanks. Geomorphology, 76:193-206, 2006. 10.1016/j.geomorph.2005.11.003\nD.W. Knight, J.D. Demetriou, and M.E. Hamed. Boundary Shear in Smooth Rectangular Channels. J. Hydraul. Eng., 110(4):405-422, 1984. 10.1061/(ASCE)0733-9429(1987)113:1(120)\nS.L Neitsch, J.G Arnold, J.R Kiniry, and J.R Williams. SWAT Theoretical Documentation Version\n\nTexas Water Resources Institute, pages 1-647, 2011. 10.1016/j.scitotenv.2015.11.063\n\nC. Oeurng, S. Sauvage, and J.M. Sanchez-Perez. Assessment of hydrology, sediment and particulate organic carbon yield in a large agricultural catchment using the SWAT model. Journal of Hydrology, 401:145-153, 2011. 10.1016/j.hydrol.2011.02.017\nA. Simon, N. Pollen-Bankhead, and R.E Thomas. Development and application of a deterministic bank stability and toe erosion model for stream restoration. Geophysical Monograph Series, 194:453-474, 2011. 10.1029/2010GM001006\nG. Verstraeten and J. Poesen. Estimating trap efficiency of small reservoirs and ponds: methods and implications for the assessment of sediment yield. Progress in Physical Geography, 24(2):219-251, 2000. 10.1177/030913330002400204\nO. Vigiak, A. Malago, F. Bouraoui, M. Vanmaercke, and J. Poesen. Adapting SWAT hillslope erosion model to predict sediment concentrations and yields in large Basins. Science of the Total Environment, 538:855-875, 2015. 10.1016/j.scitotenv.2015.08.095\nO. Vigiak, A. Malago, F. Bouraoui, M. Vanmaercke, F. Obreja, J. Poesen, H. Habersack, J. Feher, and S. Groselj. Modelling sediment fluxes in the Danube River Basin with SWAT. Science of the Total Environment, 2017. 10.1016/j.scitotenv.2017.04.236", + "crumbs": [ + "Model documentation", + "Lateral processes", + "Sediment flux" + ] + }, + { + "objectID": "model_docs/model_configurations.html", + "href": "model_docs/model_configurations.html", + "title": "Model configurations", + "section": "", + "text": "There are several model configurations supported by wflow. These model configurations require slightly different input requirements, yet the general structure is similar for each model. A wflow model configuration consists of a vertical SBM concept in combination with lateral concepts that control how water is routed for example over the land or river domain. For the wflow_sbm model different model configurations are possible. The following model configurations are supported in wflow:\nBelow, some explanation will be given on how to prepare a basic wflow_sbm model. Example data for other model configurations is provided in the section with sample data.", + "crumbs": [ + "Model documentation", + "Model configurations" + ] + }, + { + "objectID": "model_docs/model_configurations.html#wflow_sbm", + "href": "model_docs/model_configurations.html#wflow_sbm", + "title": "Model configurations", + "section": "wflow_sbm", + "text": "wflow_sbm\nWflow_sbm represents hydrological models derived from the CQflow model (Köhler et al., 2006) that have the SBM vertical concept in common, but can have different lateral concepts that control how water is routed for example over the land or river domain. The soil part of SBM is largely based on the Topog_SBM model but has had considerable changes over time. Topog_SBM is specifically designed to simulate fast runoff processes in small catchments while the wflow_sbm model can be applied more widely. The main differences are for the vertical concept SBM of wflow_sbm:\n\nThe unsaturated zone can be split-up in different layers\nThe addition of evapotranspiration losses\nThe addition of a capillary rise\nThe addition of water demand and allocation\n\nThe water demand and allocation computations are supported by the wflow_sbm model configurations:\n\nSBM + Kinematic wave\nSBM + Groundwater flow\nSBM + Local inertial river\nSBM + Local inertial river (1D) and land (2D)\n\nThe vertical SBM concept is explained in more detail in the following section SBM vertical concept.\nTopog_SBM uses an element network based on contour lines and trajectories for water routing. Wflow_sbm models differ in how the lateral components river, land, and subsurface are solved. Below the different wflow_sbm model configurations are described.\n\nSBM + Kinematic wave\nFor the lateral components of this wflow_sbm model water is routed over a D8 network, and the kinematic wave approach is used for river, overland and lateral subsurface flow. This is described in more detail in the section Kinematic wave.\nAn overview of the different processes and fluxes in the wflow_sbm model with the kinematic wave approach for river, overland and lateral subsurface flow:\n\n\n\nConceptual overview of the wflow_sbm model\n\n\nBelow the mapping for this wflow_sbm model (type sbm) to the vertical SBM concept (instance of struct SBM) and the different lateral concepts is presented. For an explanation about the type parameters between curly braces after the struct name see the section on the model parameters.\nvertical => struct SBM{T,N,M}\nlateral.subsurface => struct LateralSSF{T}\nlateral.land => struct SurfaceFlow{T,R,L}\nlateral.river => struct SurfaceFlow{T,R,L}\nlateral.river.lake => struct NaturalLake{T} # optional\nlateral.river.reservoir => struct SimpleReservoir{T} # optional\n\n\nSBM + Groundwater flow\nFor river and overland flow the kinematic wave approach over a D8 network is used for this wflow_sbm model. For the subsurface domain, an unconfined aquifer with groundwater flow in four directions (adjacent cells) is used. This is described in more detail in the section Groundwater flow.\n[model]\ntype = \"sbm_gwf\"\n\n[input.lateral.subsurface]\nksathorfrac = \"KsatHorFrac\"\nconductivity = \"conductivity\"\nspecific_yield = \"specific_yield\"\nexfiltration_conductance = \"exfilt_cond\"\ninfiltration_conductance = \"infilt_cond\"\nriver_bottom = \"river_bottom\"\nconductivity_profile = \"exponential\"\ngwf_f.value = 3.0\nBelow the mapping for this wflow_sbm model (type sbm_gwf) to the vertical SBM concept (instance of struct SBM) and the different lateral concepts. For an explanation about the type parameters between curly braces after the struct name see the section on model parameters.\nvertical => struct SBM{T,N,M}\nlateral.subsurface.flow => struct GroundwaterFlow{A, B}\nlateral.subsurface.recharge => struct Recharge{T} <: AquiferBoundaryCondition\nlateral.subsurface.river => struct River{T} <: AquiferBoundaryCondition\nlateral.subsurface.drain => struct Drainage{T} <: AquiferBoundaryCondition # optional\nlateral.land => struct SurfaceFlow{T,R,L}\nlateral.river => struct SurfaceFlow{T,R,L}\nlateral.river.lake => struct NaturalLake{T} # optional\nlateral.river.reservoir => struct SimpleReservoir{T} # optional\n\n\nSBM + Local inertial river\nBy default the model types sbm and sbm_gwf use the kinematic wave approach for river flow. There is also the option to use the local inertial model for river flow with an optional 1D floodplain schematization (routing is done separately for the river channel and floodplain), by providing the following in the TOML file:\n[model]\nriver_routing = \"local-inertial\" # optional, default is \"kinematic-wave\"\nfloodplain_1d = true # optional, default is false\nOnly the mapping for the river component changes, as shown below. For an explanation about the type parameters between curly braces after the struct name see the section on the model parameters.\nlateral.river => struct ShallowWaterRiver{T,R,L}\n\n\nSBM + Local inertial river (1D) and land (2D)\nBy default the model types sbm and sbm_gwf use the kinematic wave approach for river and overland flow. There is also the option to use the local inertial model for 1D river and 2D overland flow, by providing the following in the TOML file:\n[model]\nriver_routing = \"local-inertial\"\nland_routing = \"local-inertial\"\nThe mapping for the river and land component changes, as shown below. For an explanation about the type parameters between curly braces after the struct name see the section on the model parameters.\nlateral.river => struct ShallowWaterRiver{T,R,L}\nlateral.land => struct ShallowWaterLand{T}\nThe local inertial approach is described in more detail in the section Local inertial model.", + "crumbs": [ + "Model documentation", + "Model configurations" + ] + }, + { + "objectID": "model_docs/model_configurations.html#wflow_sediment", + "href": "model_docs/model_configurations.html#wflow_sediment", + "title": "Model configurations", + "section": "wflow_sediment", + "text": "wflow_sediment\nThe processes and fate of many particles and pollutants impacting water quality at the catchment level are intricately linked to the processes governing sediment dynamics. Both nutrients such as phosphorus, carbon or other pollutants such as metals are influenced by sediment properties in processes such as mobilization, flocculation or deposition. To better assert and model water quality in inland systems, a better comprehension and modelling of sediment sources and fate in the river is needed at a spatial and time scale relevant to such issues.\nThe wflow_sediment model was developed to answer such issues. It is a distributed physics-based model, based on the distributed hydrologic wflow_sbm model. It is able to simulate both land and in-stream processes, and relies on available global datasets, parameter estimation and small calibration effort.\nIn order to model the exports of terrestrial sediment to the coast through the Land Ocean Aquatic Continuum or LOAC (inland waters network such as streams, lakes…), two different modelling parts were considered. The first part, called the inland sediment model, is the modelling and estimation of soil loss and sediment yield to the river system by land erosion, separated into vertical Soil Erosion processes and lateral Sediment Flux in overland flow. The second part, called the River Sediment Model is the transport and processes of the sediment in the river system. The two parts together constitute the wflow_sediment model.\nOverview of the concepts of the wflow_sediment model: \n\nConfiguration\nAs sediment generation and transport processes are linked to the hydrology and water flows, the inputs to the wflow_sediment model come directly from a hydrological model. The required dynamic inputs to run wflow_sediment are:\n\nPrecipitation (can also come from the hydrological forcing data),\nLand runoff (overland flow) from the kinematic wave,\nRiver runoff from the kinematic wave,\nLand water level in the kinematic wave,\nRiver water level in the kinematic wave,\nRainfall interception by the vegetation.\n\nThese inputs can be obtained from wflow_sbm or from other sources.\nModel outputs can be saved for both the inland and the instream part of the model. Some examples are listed below.\n[output.vertical]\n# Soil splash erosion [ton]\nsedspl = \"sedspl\"\n# Soil erosion by overland flow [ton]\nsedov = \"sedov\"\n# Total soil loss [ton]\nsoilloss = \"soilloss\"\n# Total transport capacity of overland flow [ton]\nTCsed = \"TCsed\"\n# Transport capacity per particle class (clay) [ton]\nTCclay = \"TCclay\"\n\n[output.lateral.land]\n# Total (or per particle class) sediment flux in overland flow [ton]\nolsed = \"olsed\"\nolclay = \"olclay\"\n# Total (or per particle class) sediment yield to the river [ton]\ninlandsed = \"inlandsed\"\ninlandclay = \"inlandclay\"\n\n[output.lateral.river]\n# Total sediment concentration in the river (suspended + bed load) [kg/m3]\nSedconc = \"Sedconc\"\n# Suspended load [kg/m3]\nSSconc = \"SSconc\"\n# Bed load [kg/m3]\nBedconc = \"Bedconc\"", + "crumbs": [ + "Model documentation", + "Model configurations" + ] + }, + { + "objectID": "model_docs/model_configurations.html#references", + "href": "model_docs/model_configurations.html#references", + "title": "Model configurations", + "section": "References", + "text": "References\n\nKöhler, L., Mulligan, M., Schellekens, J., Schmid, S., Tobón, C., 2006, Hydrological impacts of converting tropical montane cloud forest to pasture, with initial reference to northern Costa Rica. Final Technical Report DFID‐FRP Project No. R799.", + "crumbs": [ + "Model documentation", + "Model configurations" + ] + }, + { + "objectID": "model_docs/parameters_vertical.html", + "href": "model_docs/parameters_vertical.html", + "title": "Vertical concepts", + "section": "", + "text": "The Table below shows the parameters (fields) of struct SBM, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static and forcing input data (netCDF), and can be listed in the TOML configuration file under [input.vertical], to map the internal model parameter to the external netCDF variable. For some input parameters the parameter listed under [input.vertical] is not equal to the internal model parameter, these are listed in the Table below between parentheses after the internal model parameter. For example, internal model parameter sl is mapped as follows in the TOML file to the external netCDF variable Sl:\n[input.vertical]\nspecific_leaf = \"Sl\"\nDifferent vertical hydraulic conductivity depth profiles: exponential (default), exponential_constant, layered and layered_exponential can be provided through the TOML file. Below an example for the exponential_constant profile:\n[input.vertical]\nksat_profile = \"exponential_constant\"\nFor the exponential profile the input parameters kv_0 and f are used. For the exponential_constant profile kv_0 and f are used, and z_exp is required as input. For the layered profile, input parameter kv is used, and for the layered_exponential profile kv is used and z_layered is required as input.\n\n\n\n\n\n\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\ncfmax\ndegree-day factor\nmm ᵒC\\(^{-1}\\) Δt\\(^{-1}\\)\n3.75653 mm ᵒC\\(^{-1}\\) day\\(^{-1}\\)\n\n\ntt\nthreshold temperature for snowfall\nᵒC\n0.0\n\n\ntti\nthreshold temperature interval length\nᵒC\n1.0\n\n\nttm\nthreshold temperature for snowmelt\nᵒC\n0.0\n\n\nwhc\nwater holding capacity as fraction of current snow pack\n-\n0.1\n\n\nw_soil\nsoil temperature smooth factor\n-\n0.1125\n\n\ncf_soil\ncontrols soil infiltration reduction factor when soil is frozen\n-\n0.038\n\n\ng_tt\nthreshold temperature for snowfall above glacier\nᵒC\n0.0\n\n\ng_cfmax\nDegree-day factor for glacier\nmm ᵒC\\(^{-1}\\) Δt\\(^{-1}\\)\n3.0 mm ᵒC\\(^{-1}\\) day\\(^{-1}\\)\n\n\ng_sifrac\nfraction of the snowpack on top of the glacier converted into ice\nΔt\\(^{-1}\\)\n0.001 day\\(^{-1}\\)\n\n\nglacierfrac\nfraction covered by a glacier\n-\n0.0\n\n\nglacierstore\nwater within the glacier\nmm\n5500.0\n\n\ntheta_s\nsaturated water content (porosity)\n-\n0.6\n\n\ntheta_r\nresidual water content\n-\n0.01\n\n\nkv_0\nVertical hydraulic conductivity at soil surface\nmm Δt\\(^{-1}\\)\n3000.0 mm day\\(^{-1}\\)\n\n\nkv\nVertical hydraulic conductivity per soil layer\nmm Δt\\(^{-1}\\)\n1000.0 mm day\\(^{-1}\\)\n\n\nf\nscaling parameter (controls exponential decline of kv_0)\nmm\\(^{-1}\\)\n0.001\n\n\nz_exp\nDepth from soil surface for which exponential decline of kv_0 is valid\nmm\n-\n\n\nz_layered\nDepth from soil surface for which layered profile (of layered_exponential) is valid\nmm\n-\n\n\nhb\nair entry pressure of soil (Brooks-Corey)\ncm\n-10.0\n\n\nsoilthickness\nsoil thickness\nmm\n2000.0\n\n\ninfiltcappath\ninfiltration capacity of the compacted areas\nmm Δt\\(^{-1}\\)\n10.0 mm day\\(^{-1}\\)\n\n\ninfiltcapsoil\nsoil infiltration capacity\nmm Δt\\(^{-1}\\)\n100.0 mm day\\(^{-1}\\)\n\n\nmaxleakage\nmaximum leakage from saturated zone\nmm Δt\\(^{-1}\\)\n0.0 mm day\\(^{-1}\\)\n\n\nc\nBrooks-Corey power coefficient for each soil layer\n-\n10.0\n\n\nkvfrac\nmultiplication factor applied to kv_z (vertical flow)\n-\n1.0\n\n\nwaterfrac\nfraction of open water (excluding rivers)\n-\n0.0\n\n\npathfrac\nfraction of compacted area\n-\n0.01\n\n\nrootingdepth\nrooting depth\nmm\n750.0\n\n\nrootfraction\nfraction of the root length density in each soil layer\n-\n-\n\n\nh1\nsoil water pressure head h1 of the root water uptake reduction function (Feddes)\ncm\n0.0 cm\n\n\nh2\nsoil water pressure head h2 of the root water uptake reduction function (Feddes)\ncm\n-100.0 cm\n\n\nh3_high\nsoil water pressure head h3_high of the root water uptake reduction function (Feddes)\ncm\n-400.0 cm\n\n\nh3_low\nsoil water pressure head h3_low of the root water uptake reduction function (Feddes)\ncm\n-1000.0 cm\n\n\nh4\nsoil water pressure head h4 of the root water uptake reduction function (Feddes)\ncm\n-15849.0 cm\n\n\nalpha_h1\nroot water uptake reduction at soil water pressure head h1 (0.0 or 1.0)\n-\n1.0\n\n\nrootdistpar\ncontrols how roots are linked to water table\n-\n-500.0\n\n\ncap_hmax\nwater depth beyond which capillary flux ceases\nmm\n2000.0\n\n\ncap_n\ncoefficient controlling capillary rise\n-\n2.0\n\n\nkc\ncrop coefficient Kc\n-\n1.0\n\n\nsl (specific_leaf)\nspecific leaf storage\nmm\n-\n\n\nswood (storage_wood)\nstorage woody part of vegetation\nmm\n-\n\n\nkext\nextinction coefficient (to calculate canopy gap fraction)\n-\n-\n\n\ncmax\nmaximum canopy storage\nmm\n1.0\n\n\ne_r (eoverr)\nGash interception model parameter\n-\n0.1\n\n\ncanopygapfraction\ncanopy gap fraction\n-\n0.1\n\n\ndt\nmodel time step\ns\n-\n\n\nmaxlayers\nmaximum number of soil layers\n-\n-\n\n\nn\nnumber of grid cells\n-\n-\n\n\nnlayers\nnumber of soil layers\n-\n-\n\n\nn_unsatlayers\nnumber of unsaturated soil layers\n-\n-\n\n\nnlayers_kv\nnumber of soil layers with vertical hydraulic conductivity value kv\n-\n-\n\n\nriverfrac\nfraction of river\n-\n-\n\n\nact_thickl\nthickness of soil layers\nmm\n-\n\n\nsumlayers\ncumulative sum of soil layers thickness, starting at soil surface\nmm\n-\n\n\nstemflow\nstemflow\nmm Δt\\(^{-1}\\)\n-\n\n\nthroughfall\nthroughfall\nmm Δt\\(^{-1}\\)\n-\n\n\nustorelayerdepth\namount of water in the unsaturated store, per layer\nmm\n-\n\n\nsatwaterdepth\nsaturated store\nmm\n-\n\n\nzi\npseudo-water table depth (top of the saturated zone)\nmm\n-\n\n\nsoilwatercapacity\nsoilwater capacity\nmm\n-\n\n\ncanopystorage\ncanopy storage\nmm\n-\n\n\nprecipitation\nprecipitation\nmm Δt\\(^{-1}\\)\n-\n\n\ntemperature\ntemperature\nᵒC\n-\n\n\npotential_evaporation\npotential reference evapotranspiration\nmm Δt\\(^{-1}\\)\n-\n\n\npottrans\ninterception subtracted from potential evapotranspiration\nmm Δt\\(^{-1}\\)\n-\n\n\ntranspiration\ntranspiration\nmm Δt\\(^{-1}\\)\n-\n\n\nae_ustore\nactual evaporation from unsaturated store\nmm Δt\\(^{-1}\\)\n-\n\n\ninterception\ninterception loss by evaporation\nmm Δt\\(^{-1}\\)\n-\n\n\nsoilevap\ntotal soil evaporation from unsaturated and saturated store\nmm Δt\\(^{-1}\\)\n-\n\n\nsoilevapsat\nsoil evaporation from saturated store\nmm Δt\\(^{-1}\\)\n-\n\n\nactcapflux\nactual capillary rise\nmm Δt\\(^{-1}\\)\n-\n\n\nactevapsat\nactual transpiration from saturated store\nmm Δt\\(^{-1}\\)\n-\n\n\nactevap\ntotal actual evapotranspiration\nmm Δt\\(^{-1}\\)\n-\n\n\nrunoff_river\nrunoff from river based on riverfrac\nmm Δt\\(^{-1}\\)\n-\n\n\nrunoff_land\nrunoff from land based on waterfrac\nmm Δt\\(^{-1}\\)\n-\n\n\nae_openw_l\nactual evaporation from open water (land)\nmm Δt\\(^{-1}\\)\n-\n\n\nae_openw_r\nactual evaporation from river\nmm Δt\\(^{-1}\\)\n-\n\n\nnet_runoff_river\nnet runoff from river (runoff_river - ae_openw_r)\nmm Δt\\(^{-1}\\)\n-\n\n\navail_forinfilt\nwater available for infiltration\nmm Δt\\(^{-1}\\)\n-\n\n\nactinfilt\nactual infiltration into the unsaturated zone\nmm Δt\\(^{-1}\\)\n-\n\n\nactinfiltsoil\nactual infiltration into non-compacted fraction\nmm Δt\\(^{-1}\\)\n-\n\n\nactinfiltpath\nactual infiltration into compacted fraction\nmm Δt\\(^{-1}\\)\n-\n\n\ninfiltsoilpath\ninfiltration into the unsaturated zone\nmm Δt\\(^{-1}\\)\n-\n\n\ninfiltexcess\ninfiltration excess water\nmm Δt\\(^{-1}\\)\n-\n\n\nexcesswater\nwater that cannot infiltrate due to saturated soil (saturation excess)\nmm Δt\\(^{-1}\\)\n-\n\n\nexfiltsatwater\nwater exfiltrating during saturation excess conditions\nmm Δt\\(^{-1}\\)\n-\n\n\nexfiltustore\nwater exfiltrating from unsaturated store because of change in water table\nmm Δt\\(^{-1}\\)\n-\n\n\nexcesswatersoil\nexcess water for non-compacted fraction\nmm Δt\\(^{-1}\\)\n-\n\n\nexcesswaterpath\nexcess water for compacted fraction\nmm Δt\\(^{-1}\\)\n-\n\n\nrunoff\ntotal surface runoff from infiltration and saturation excess\nmm Δt\\(^{-1}\\)\n-\n\n\nnet_runoff\nnet surface runoff (runoff - ae_openw_l)\nmm Δt\\(^{-1}\\)\n-\n\n\nvwc\nvolumetric water content per soil layer (including theta_r and saturated zone)\n-\n-\n\n\nvwc_perc\nvolumetric water content per soil layer (including theta_r and saturated zone)\n%\n-\n\n\nrootstore\nroot water storage in unsaturated and saturated zone (excluding theta_r)\nmm\n-\n\n\nvwc_root\nvolumetric water content in root zone (including theta_r and saturated zone)\n-\n-\n\n\nvwc_percroot\nvolumetric water content in root zone (including theta_r and saturated zone)\n%\n-\n\n\nustoredepth\ntotal amount of available water in the unsaturated zone\nmm\n-\n\n\ntransfer\ndownward flux from unsaturated to saturated zone\nmm Δt\\(^{-1}\\)\n-\n\n\nrecharge\nnet recharge to saturated zone\nmm Δt\\(^{-1}\\)\n-\n\n\nactleakage\nactual leakage from saturated store\nmm Δt\\(^{-1}\\)\n-\n\n\nsnow\nsnow storage\nmm\n-\n\n\nsnowwater\nliquid water content in the snow pack\nmm\n-\n\n\nrainfallplusmelt\nsnowmelt + precipitation as rainfall\nmm Δt\\(^{-1}\\)\n-\n\n\ntsoil\ntop soil temperature\nᵒC\n-\n\n\nleaf_area_index\nleaf area index\nm\\(^2\\) m\\(^{-2}\\)\n-\n\n\nwaterlevel_land\nwater level land\nmm\n-\n\n\nwaterlevel_river\nwater level river\nmm\n-\n\n\ntotal_storage\ntotal water storage (excluding floodplains, lakes and reservoirs)\nmm\n-\n\n\npaddy\noptional paddy (rice) fields of type Paddy (water demand and irrigation)\n-\n-\n\n\nnonpaddy\noptional non-paddy fields of type NonPaddy (water demand and irrigation)\n-\n-\n\n\ndomestic\noptional domestic water demand of type NonIrrigationDemand\n-\n-\n\n\nlivestock\noptional livestock water demand of type NonIrrigationDemand\n-\n-\n\n\nindustry\noptional industry water demand of type NonIrrigationDemand\n-\n-\n\n\nallocation\noptional water allocation of type AllocationLand\n-\n-", + "crumbs": [ + "Model documentation", + "Parameters", + "Vertical concepts" + ] + }, + { + "objectID": "model_docs/parameters_vertical.html#sbm", + "href": "model_docs/parameters_vertical.html#sbm", + "title": "Vertical concepts", + "section": "", + "text": "The Table below shows the parameters (fields) of struct SBM, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static and forcing input data (netCDF), and can be listed in the TOML configuration file under [input.vertical], to map the internal model parameter to the external netCDF variable. For some input parameters the parameter listed under [input.vertical] is not equal to the internal model parameter, these are listed in the Table below between parentheses after the internal model parameter. For example, internal model parameter sl is mapped as follows in the TOML file to the external netCDF variable Sl:\n[input.vertical]\nspecific_leaf = \"Sl\"\nDifferent vertical hydraulic conductivity depth profiles: exponential (default), exponential_constant, layered and layered_exponential can be provided through the TOML file. Below an example for the exponential_constant profile:\n[input.vertical]\nksat_profile = \"exponential_constant\"\nFor the exponential profile the input parameters kv_0 and f are used. For the exponential_constant profile kv_0 and f are used, and z_exp is required as input. For the layered profile, input parameter kv is used, and for the layered_exponential profile kv is used and z_layered is required as input.\n\n\n\n\n\n\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\ncfmax\ndegree-day factor\nmm ᵒC\\(^{-1}\\) Δt\\(^{-1}\\)\n3.75653 mm ᵒC\\(^{-1}\\) day\\(^{-1}\\)\n\n\ntt\nthreshold temperature for snowfall\nᵒC\n0.0\n\n\ntti\nthreshold temperature interval length\nᵒC\n1.0\n\n\nttm\nthreshold temperature for snowmelt\nᵒC\n0.0\n\n\nwhc\nwater holding capacity as fraction of current snow pack\n-\n0.1\n\n\nw_soil\nsoil temperature smooth factor\n-\n0.1125\n\n\ncf_soil\ncontrols soil infiltration reduction factor when soil is frozen\n-\n0.038\n\n\ng_tt\nthreshold temperature for snowfall above glacier\nᵒC\n0.0\n\n\ng_cfmax\nDegree-day factor for glacier\nmm ᵒC\\(^{-1}\\) Δt\\(^{-1}\\)\n3.0 mm ᵒC\\(^{-1}\\) day\\(^{-1}\\)\n\n\ng_sifrac\nfraction of the snowpack on top of the glacier converted into ice\nΔt\\(^{-1}\\)\n0.001 day\\(^{-1}\\)\n\n\nglacierfrac\nfraction covered by a glacier\n-\n0.0\n\n\nglacierstore\nwater within the glacier\nmm\n5500.0\n\n\ntheta_s\nsaturated water content (porosity)\n-\n0.6\n\n\ntheta_r\nresidual water content\n-\n0.01\n\n\nkv_0\nVertical hydraulic conductivity at soil surface\nmm Δt\\(^{-1}\\)\n3000.0 mm day\\(^{-1}\\)\n\n\nkv\nVertical hydraulic conductivity per soil layer\nmm Δt\\(^{-1}\\)\n1000.0 mm day\\(^{-1}\\)\n\n\nf\nscaling parameter (controls exponential decline of kv_0)\nmm\\(^{-1}\\)\n0.001\n\n\nz_exp\nDepth from soil surface for which exponential decline of kv_0 is valid\nmm\n-\n\n\nz_layered\nDepth from soil surface for which layered profile (of layered_exponential) is valid\nmm\n-\n\n\nhb\nair entry pressure of soil (Brooks-Corey)\ncm\n-10.0\n\n\nsoilthickness\nsoil thickness\nmm\n2000.0\n\n\ninfiltcappath\ninfiltration capacity of the compacted areas\nmm Δt\\(^{-1}\\)\n10.0 mm day\\(^{-1}\\)\n\n\ninfiltcapsoil\nsoil infiltration capacity\nmm Δt\\(^{-1}\\)\n100.0 mm day\\(^{-1}\\)\n\n\nmaxleakage\nmaximum leakage from saturated zone\nmm Δt\\(^{-1}\\)\n0.0 mm day\\(^{-1}\\)\n\n\nc\nBrooks-Corey power coefficient for each soil layer\n-\n10.0\n\n\nkvfrac\nmultiplication factor applied to kv_z (vertical flow)\n-\n1.0\n\n\nwaterfrac\nfraction of open water (excluding rivers)\n-\n0.0\n\n\npathfrac\nfraction of compacted area\n-\n0.01\n\n\nrootingdepth\nrooting depth\nmm\n750.0\n\n\nrootfraction\nfraction of the root length density in each soil layer\n-\n-\n\n\nh1\nsoil water pressure head h1 of the root water uptake reduction function (Feddes)\ncm\n0.0 cm\n\n\nh2\nsoil water pressure head h2 of the root water uptake reduction function (Feddes)\ncm\n-100.0 cm\n\n\nh3_high\nsoil water pressure head h3_high of the root water uptake reduction function (Feddes)\ncm\n-400.0 cm\n\n\nh3_low\nsoil water pressure head h3_low of the root water uptake reduction function (Feddes)\ncm\n-1000.0 cm\n\n\nh4\nsoil water pressure head h4 of the root water uptake reduction function (Feddes)\ncm\n-15849.0 cm\n\n\nalpha_h1\nroot water uptake reduction at soil water pressure head h1 (0.0 or 1.0)\n-\n1.0\n\n\nrootdistpar\ncontrols how roots are linked to water table\n-\n-500.0\n\n\ncap_hmax\nwater depth beyond which capillary flux ceases\nmm\n2000.0\n\n\ncap_n\ncoefficient controlling capillary rise\n-\n2.0\n\n\nkc\ncrop coefficient Kc\n-\n1.0\n\n\nsl (specific_leaf)\nspecific leaf storage\nmm\n-\n\n\nswood (storage_wood)\nstorage woody part of vegetation\nmm\n-\n\n\nkext\nextinction coefficient (to calculate canopy gap fraction)\n-\n-\n\n\ncmax\nmaximum canopy storage\nmm\n1.0\n\n\ne_r (eoverr)\nGash interception model parameter\n-\n0.1\n\n\ncanopygapfraction\ncanopy gap fraction\n-\n0.1\n\n\ndt\nmodel time step\ns\n-\n\n\nmaxlayers\nmaximum number of soil layers\n-\n-\n\n\nn\nnumber of grid cells\n-\n-\n\n\nnlayers\nnumber of soil layers\n-\n-\n\n\nn_unsatlayers\nnumber of unsaturated soil layers\n-\n-\n\n\nnlayers_kv\nnumber of soil layers with vertical hydraulic conductivity value kv\n-\n-\n\n\nriverfrac\nfraction of river\n-\n-\n\n\nact_thickl\nthickness of soil layers\nmm\n-\n\n\nsumlayers\ncumulative sum of soil layers thickness, starting at soil surface\nmm\n-\n\n\nstemflow\nstemflow\nmm Δt\\(^{-1}\\)\n-\n\n\nthroughfall\nthroughfall\nmm Δt\\(^{-1}\\)\n-\n\n\nustorelayerdepth\namount of water in the unsaturated store, per layer\nmm\n-\n\n\nsatwaterdepth\nsaturated store\nmm\n-\n\n\nzi\npseudo-water table depth (top of the saturated zone)\nmm\n-\n\n\nsoilwatercapacity\nsoilwater capacity\nmm\n-\n\n\ncanopystorage\ncanopy storage\nmm\n-\n\n\nprecipitation\nprecipitation\nmm Δt\\(^{-1}\\)\n-\n\n\ntemperature\ntemperature\nᵒC\n-\n\n\npotential_evaporation\npotential reference evapotranspiration\nmm Δt\\(^{-1}\\)\n-\n\n\npottrans\ninterception subtracted from potential evapotranspiration\nmm Δt\\(^{-1}\\)\n-\n\n\ntranspiration\ntranspiration\nmm Δt\\(^{-1}\\)\n-\n\n\nae_ustore\nactual evaporation from unsaturated store\nmm Δt\\(^{-1}\\)\n-\n\n\ninterception\ninterception loss by evaporation\nmm Δt\\(^{-1}\\)\n-\n\n\nsoilevap\ntotal soil evaporation from unsaturated and saturated store\nmm Δt\\(^{-1}\\)\n-\n\n\nsoilevapsat\nsoil evaporation from saturated store\nmm Δt\\(^{-1}\\)\n-\n\n\nactcapflux\nactual capillary rise\nmm Δt\\(^{-1}\\)\n-\n\n\nactevapsat\nactual transpiration from saturated store\nmm Δt\\(^{-1}\\)\n-\n\n\nactevap\ntotal actual evapotranspiration\nmm Δt\\(^{-1}\\)\n-\n\n\nrunoff_river\nrunoff from river based on riverfrac\nmm Δt\\(^{-1}\\)\n-\n\n\nrunoff_land\nrunoff from land based on waterfrac\nmm Δt\\(^{-1}\\)\n-\n\n\nae_openw_l\nactual evaporation from open water (land)\nmm Δt\\(^{-1}\\)\n-\n\n\nae_openw_r\nactual evaporation from river\nmm Δt\\(^{-1}\\)\n-\n\n\nnet_runoff_river\nnet runoff from river (runoff_river - ae_openw_r)\nmm Δt\\(^{-1}\\)\n-\n\n\navail_forinfilt\nwater available for infiltration\nmm Δt\\(^{-1}\\)\n-\n\n\nactinfilt\nactual infiltration into the unsaturated zone\nmm Δt\\(^{-1}\\)\n-\n\n\nactinfiltsoil\nactual infiltration into non-compacted fraction\nmm Δt\\(^{-1}\\)\n-\n\n\nactinfiltpath\nactual infiltration into compacted fraction\nmm Δt\\(^{-1}\\)\n-\n\n\ninfiltsoilpath\ninfiltration into the unsaturated zone\nmm Δt\\(^{-1}\\)\n-\n\n\ninfiltexcess\ninfiltration excess water\nmm Δt\\(^{-1}\\)\n-\n\n\nexcesswater\nwater that cannot infiltrate due to saturated soil (saturation excess)\nmm Δt\\(^{-1}\\)\n-\n\n\nexfiltsatwater\nwater exfiltrating during saturation excess conditions\nmm Δt\\(^{-1}\\)\n-\n\n\nexfiltustore\nwater exfiltrating from unsaturated store because of change in water table\nmm Δt\\(^{-1}\\)\n-\n\n\nexcesswatersoil\nexcess water for non-compacted fraction\nmm Δt\\(^{-1}\\)\n-\n\n\nexcesswaterpath\nexcess water for compacted fraction\nmm Δt\\(^{-1}\\)\n-\n\n\nrunoff\ntotal surface runoff from infiltration and saturation excess\nmm Δt\\(^{-1}\\)\n-\n\n\nnet_runoff\nnet surface runoff (runoff - ae_openw_l)\nmm Δt\\(^{-1}\\)\n-\n\n\nvwc\nvolumetric water content per soil layer (including theta_r and saturated zone)\n-\n-\n\n\nvwc_perc\nvolumetric water content per soil layer (including theta_r and saturated zone)\n%\n-\n\n\nrootstore\nroot water storage in unsaturated and saturated zone (excluding theta_r)\nmm\n-\n\n\nvwc_root\nvolumetric water content in root zone (including theta_r and saturated zone)\n-\n-\n\n\nvwc_percroot\nvolumetric water content in root zone (including theta_r and saturated zone)\n%\n-\n\n\nustoredepth\ntotal amount of available water in the unsaturated zone\nmm\n-\n\n\ntransfer\ndownward flux from unsaturated to saturated zone\nmm Δt\\(^{-1}\\)\n-\n\n\nrecharge\nnet recharge to saturated zone\nmm Δt\\(^{-1}\\)\n-\n\n\nactleakage\nactual leakage from saturated store\nmm Δt\\(^{-1}\\)\n-\n\n\nsnow\nsnow storage\nmm\n-\n\n\nsnowwater\nliquid water content in the snow pack\nmm\n-\n\n\nrainfallplusmelt\nsnowmelt + precipitation as rainfall\nmm Δt\\(^{-1}\\)\n-\n\n\ntsoil\ntop soil temperature\nᵒC\n-\n\n\nleaf_area_index\nleaf area index\nm\\(^2\\) m\\(^{-2}\\)\n-\n\n\nwaterlevel_land\nwater level land\nmm\n-\n\n\nwaterlevel_river\nwater level river\nmm\n-\n\n\ntotal_storage\ntotal water storage (excluding floodplains, lakes and reservoirs)\nmm\n-\n\n\npaddy\noptional paddy (rice) fields of type Paddy (water demand and irrigation)\n-\n-\n\n\nnonpaddy\noptional non-paddy fields of type NonPaddy (water demand and irrigation)\n-\n-\n\n\ndomestic\noptional domestic water demand of type NonIrrigationDemand\n-\n-\n\n\nlivestock\noptional livestock water demand of type NonIrrigationDemand\n-\n-\n\n\nindustry\noptional industry water demand of type NonIrrigationDemand\n-\n-\n\n\nallocation\noptional water allocation of type AllocationLand\n-\n-", + "crumbs": [ + "Model documentation", + "Parameters", + "Vertical concepts" + ] + }, + { + "objectID": "model_docs/parameters_vertical.html#water-demand-and-allocation", + "href": "model_docs/parameters_vertical.html#water-demand-and-allocation", + "title": "Vertical concepts", + "section": "Water demand and allocation", + "text": "Water demand and allocation\n\nPaddy\nThe Table below shows the parameters (fields) of struct Paddy, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static and forcing input data (netCDF), and can be listed in the TOML configuration file under [input.vertical.paddy], to map the internal model parameter to the external netCDF variable.\n\n\n\n\n\n\n\n\n\nparameter\ndescription\nunit\ndefault\n\n\n\n\ndemand_gross\nirrigation gross demand\nmm Δt\\(^{-1}\\)\n-\n\n\nirrigation_efficiency\nirrigation efficiency\n-\n-\n\n\nmaximum_irrigation_rate\nmaximum irrigation rate\nmm Δt\\(^{-1}\\)\n25.0 mm day\\(^{-1}\\)\n\n\nirrigation_areas\nirrigation areas\n-\n-\n\n\nirrigation_trigger\nirrigation on or off (boolean)\n-\n-\n\n\nh_min\nminimum required water depth in the irrigated paddy fields\nmm\n20.0\n\n\nh_opt\noptimal water depth in the irrigated paddy fields\nmm\n50.0\n\n\nh_max\nwater depth when paddy field starts spilling water (overflow)\nmm\n80.0\n\n\nh\nactual water depth in paddy field\nmm\n-\n\n\n\n\n\nNon-paddy\nThe Table below shows the parameters (fields) of struct NonPaddy, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static and forcing input data (netCDF), and can be listed in the TOML configuration file under [input.vertical.nonpaddy], to map the internal model parameter to the external netCDF variable.\n\n\n\n\n\n\n\n\n\nparameter\ndescription\nunit\ndefault\n\n\n\n\ndemand_gross\nirrigation gross demand\nmm Δt\\(^{-1}\\)\n-\n\n\nirrigation_efficiency\nirrigation efficiency\n-\n-\n\n\nmaximum_irrigation_rate\nmaximum irrigation rate\nmm Δt\\(^{-1}\\)\n25.0 mm day\\(^{-1}\\)\n\n\nirrigation_areas\nirrigation areas\n-\n-\n\n\nirrigation_trigger\nirrigation on or off (boolean)\n-\n-\n\n\n\n\n\nNon-irrigation (industry, domestic and livestock)\nThe Table below shows the parameters (fields) of struct NonIrrigationDemand, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static and forcing input data (netCDF). These parameters can be listed for the sectors industry, domestic and livestock, in the TOML configuration file under [input.vertical.industry], [input.vertical.domestic] and [input.vertical.livestock], to map the internal model parameter to the external netCDF variable.\n\n\n\n\n\n\n\n\n\nparameter\ndescription\nunit\ndefault\n\n\n\n\ndemand_gross\ngross industry water demand\nmm Δt\\(^{-1}\\)\n0.0\n\n\ndemand_net\nnet industry water demand\nmm Δt\\(^{-1}\\)\n0.0\n\n\nreturnflow_fraction\nreturn flow fraction\n-\n-\n\n\nreturnflow\nreturn flow\nmm Δt\\(^{-1}\\)\n-\n\n\n\n\n\nWater allocation land\nThe Table below shows the parameters (fields) of struct AllocationLand, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static and forcing input data (netCDF), and can be listed in the TOML configuration file under [input.vertical.allocation], to map the internal model parameter to the external netCDF variable.\n\n\n\n\n\n\n\n\n\nparameter\ndescription\nunit\ndefault\n\n\n\n\nirri_demand_gross\nirrigation gross demand\nmm Δt\\(^{-1}\\)\n-\n\n\nnonirri_demand_gross\nnon-irrigation gross demand\nmm Δt\\(^{-1}\\)\n-\n\n\ntotal_gross_demand\ntotal gross demand\nmm Δt\\(^{-1}\\)\n-\n\n\nfrac_sw_used\nfraction surface water used\n-\n1.0\n\n\nareas\nallocation areas\n-\n1\n\n\nsurfacewater_demand\ndemand from surface water\nmm Δt\\(^{-1}\\)\n-\n\n\nsurfacewater_alloc\nallocation from surface water\nmm Δt\\(^{-1}\\)\n-\n\n\nact_groundwater_abst\nactual groundwater abstraction\nmm Δt\\(^{-1}\\)\n-\n\n\nact_groundwater_abst_vol\nactual groundwater abstraction\nm\\(^3\\) Δt\\(^{-1}\\)\n-\n\n\navailable_groundwater\navailable groundwater\nm\\(^3\\)\n-\n\n\ngroundwater_demand\ngroundwater_demand\nmm Δt\\(^{-1}\\)\n-\n\n\ngroundwater_alloc\nallocation from groundwater\nmm Δt\\(^{-1}\\)\n-\n\n\nirri_alloc\nallocated water for irrigation\nmm Δt\\(^{-1}\\)\n-\n\n\nnonirri_alloc\nallocated water for non-irrigation\nmm Δt\\(^{-1}\\)\n-\n\n\ntotal_alloc\ntotal allocated water\nmm Δt\\(^{-1}\\)\n-\n\n\nnonirri_returnflow\nreturn flow from non-irrigation\nmm Δt\\(^{-1}\\)\n-", + "crumbs": [ + "Model documentation", + "Parameters", + "Vertical concepts" + ] + }, + { + "objectID": "model_docs/parameters_vertical.html#sediment", + "href": "model_docs/parameters_vertical.html#sediment", + "title": "Vertical concepts", + "section": "Sediment", + "text": "Sediment\nThe Table below shows external parameters that can be set through static input data (netCDF), and can be listed in the TOML configuration file under [input.vertical]. These external parameters are not part of struct LandSediment, but used to calculate parameters of struct LandSediment.\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\npclay\npercentage clay\n%\n0.1\n\n\npsilt\npercentage silt\n%\n0.1\n\n\nresareas\nreservoir coverage\n-\n-\n\n\nlakeareas\nlake coverage\n-\n-\n\n\n\nThe Table below shows the parameters (fields) of struct LandSediment, including a description of these parameters, the unit, and default value if applicable. The parameters in bold represent model parameters that can be set through static and forcing input data (netCDF), and can be listed in the TOML configuration file under [input.vertical], to map the internal model parameter to the external netCDF variable. For some input parameters the parameter listed under [input.vertical] is not equal to the internal model parameter, these are listed in the Table below between parentheses after the internal model parameter. For example, internal model parameter sl is mapped as follows in the TOML file to the external netCDF variable Sl:\n[input.vertical]\nspecific_leaf = \"Sl\"\n\n\n\n\n\n\n\n\n\nParameter\nDescription\nUnit\nDefault\n\n\n\n\ncanopyheight\ncanopy height\nm\n3.0\n\n\nerosk\ncoefficient for EUROSEM rainfall erosion\n-\n0.6\n\n\nerosspl\nexponent for EUROSEM rainfall erosion\n-\n2.0\n\n\nerosov\ncoefficient for ANSWERS overland flow erosion\n-\n0.9\n\n\npathfrac\nfraction of impervious area per grid cell\n-\n0.01\n\n\nslope\nland slope\n-\n0.01\n\n\nusleC\nUSLE crop management factor\n-\n0.01\n\n\nusleK\nUSLE soil erodibility factor\n-\n0.1\n\n\nsl (specific_leaf)\nspecific leaf storage\nmm\n-\n\n\nswood (storage_wood)\nstorage woody part of vegetation\nmm\n-\n\n\nkext\nextinction coefficient (to calculate canopy gap fraction)\n-\n-\n\n\ncmax\nmaximum canopy storage\nmm\n1.0\n\n\ncanopygapfraction\ncanopy gap fraction\n-\n0.1\n\n\ndmclay\nmedian diameter particle size class clay\nµm\n2.0\n\n\ndmsilt\nmedian diameter particle size class silt\nµm\n10.0\n\n\ndmsand\nmedian diameter particle size class sand\nµm\n200.0\n\n\ndmsagg\nmedian diameter particle size class small aggregates\nµm\n30.0\n\n\ndmlagg\nmedian diameter particle size class large aggregates\nµm\n500.0\n\n\nrhos (rhosed)\ndensity of sediment\nkg m\\(^{-3}1\\)\n2650.0\n\n\nn\nnumber of cells\n-\n-\n\n\nyl\nlength of cells in y direction\nm\n-\n\n\nxl\nlength of cells in x direction\nm\n-\n\n\nriverfrac\nfraction of river\n-\n-\n\n\nwbcover\nwaterbody coverage\n-\n-\n\n\nh_land\ndepth of overland flow\nm\n-\n\n\ninterception\ncanopy interception\nmm Δt\\(^{-1}\\)\n-\n\n\nprecipitation\nprecipitation\nmm Δt\\(^{-1}\\)\n-\n\n\nq_land\noverland flow\nm\\(^3\\) s\\(^{-1}\\)\n-\n\n\nsedspl\nsediment eroded by rainfall\nton Δt\\(^{-1}\\)\n-\n\n\nsedov\nsediment eroded by overland flow\nton Δt\\(^{-1}\\)\n-\n\n\nsoilloss\ntotal eroded soil\nton Δt\\(^{-1}\\)\n-\n\n\nerosclay\neroded soil for particle class clay\nton Δt\\(^{-1}\\)\n-\n\n\nerossilt\neroded soil for particle class silt\nton Δt\\(^{-1}\\)\n-\n\n\nerossand\neroded soil for particle class sand\nton Δt\\(^{-1}\\)\n-\n\n\nerossagg\neroded soil for particle class small aggregates\nton Δt\\(^{-1}\\)\n-\n\n\neroslagg\neroded soil for particle class large aggregates\nton Δt\\(^{-1}\\)\n-\n\n\nleaf_area_index\nleaf area index\nm\\(^2\\) m\\(^{-2}\\)\n-\n\n\ndl\ndrain length\nm\n-\n\n\ndw\nflow width\nm\n-\n\n\ncGovers\nGovers transport capacity coefficient\n-\n-\n\n\nnGovers\nGovers transport capacity coefficient\n-\n-\n\n\nD50\nmedian particle diameter of the topsoil\nmm\n-\n\n\nfclay\nfraction of particle class clay\n-\n-\n\n\nfsilt\nfraction of particle class silt\n-\n-\n\n\nfsand\nfraction of particle class sand\n-\n-\n\n\nfsagg\nfraction of particle class small aggregates\n-\n-\n\n\nflagg\nfraction of particle class large aggregates\n-\n-\n\n\nrivcell\nriver cells\n-\n-\n\n\nTCsed\ntotal transport capacity of overland flow\nton Δt\\(^{-1}\\)\n-\n\n\nTCclay\ntransport capacity of overland flow for particle class clay\nton Δt\\(^{-1}\\)\n-\n\n\nTCsilt\ntransport capacity of overland flow for particle class silt\nton Δt\\(^{-1}\\)\n-\n\n\nTCsand\ntransport capacity of overland flow for particle class sand\nton Δt\\(^{-1}\\)\n-\n\n\nTCsagg\ntransport capacity of overland flow for particle class small aggregates\nton Δt\\(^{-1}\\)\n-\n\n\nTClagg\ntransport capacity of overland flow for particle class large aggregates\nton Δt\\(^{-1}\\)\n-", + "crumbs": [ + "Model documentation", + "Parameters", + "Vertical concepts" + ] + }, + { + "objectID": "model_docs/vertical/shared_processes.html", + "href": "model_docs/vertical/shared_processes.html", + "title": "Shared processes", + "section": "", + "text": "If the air temperature, \\(T_a\\), is below a user-defined threshold tt \\(\\SIb{}{\\degree C}\\) precipitation occurs as snowfall, whereas it occurs as rainfall if \\(T_a ≥ \\mathrm{tt}\\). A another parameter tti defines how precipitation can occur partly as rain or snowfall (see the figure below). If precipitation occurs as snowfall, it is added to the dry snow component within the snow pack. Otherwise it ends up in the free water reservoir, which represents the liquid water content of the snow pack. Between the two components of the snow pack, interactions take place, either through snow melt (if temperatures are above a threshold tt) or through snow refreezing (if temperatures are below threshold tt).\nThe respective rates of snow melt and refreezing are:\n\\[\n\\begin{align*}\n Q_m &=& \\subtext{\\mathrm{cf}}{max}(T_a−\\mathrm{tt})\\, &&T_a > \\mathrm{tt} \\\\\n Q_r &=& \\subtext{\\mathrm{cf}}{max} \\, \\mathrm{cf}_r(\\mathrm{tt}−T_a) &&T_a < \\mathrm{tt}\n\\end{align*}\n\\]\nwhere \\(Q_m\\) is the rate of snow melt, \\(Q_r\\) is the rate of snow refreezing, and \\(\\SIb{\\subtext{\\mathrm{cf}}{max}}{mm\\;(\\degree C)^{-1} day^{-1}}\\) and \\(\\mathrm{cf}_r\\) are user defined model parameters (the melting factor and the refreezing factor respectively).\nThe fraction of liquid water in the snow pack is at most equal to a user defined fraction, whc, of the water equivalent of the dry snow content. If the liquid water concentration exceeds whc, either through snow melt or incoming rainfall, the surplus water (rainfall) becomes available for infiltration into the soil:\nsnowwater = snowwater - refreezing # free water content in snow\nmaxsnowwater = snow * whc # max water in the snow\nsnowwater = snowwater + snowmelt + rainfall # add all water and potentially supersaturate the snowpack\nrainfall = max(snowwater - maxsnowwater, 0.0) # rain + surplus snowwater\n\n\n\nSnowmelt and refreezing rates as a function of temperature (for set melting and refreezing factors)\n\n\n\n\n\n\nGlacier processes can be modelled if the snow model is enabled. Glacier modelling is very similar to snow modelling and considers two main processes: glacier build-up from snow turning into firn/ice (using the HBV-light model) and glacier melt (using a temperature degree-day model).\nThe definition of glacier boundaries and initial volume is defined by two parameters. The parameter glacierfrac gives the fraction of each grid cell covered by a glacier as a number between zero and one. The state parameter glacierstore gives the amount of water (in mm w.e.) within the glaciers at each grid cell. Because the glacier store (glacierstore) cannot be initialized by running thFe model for a couple of years, a default initial state should be supplied by adding this parameter to the input static file. The required glacier data can be prepared from available glacier datasets.\nFirst, a fixed fraction of the snowpack on top of the glacier is converted into ice for each timestep and added to the glacierstore using the HBV-light model (Seibert et al., 2018). This fraction g_sifrac typically ranges from \\(0.001\\) to \\(0.006\\).\nThen, when the snowpack on top of the glacier is almost all melted (snow cover \\(<\n\\SI{10}{mm}\\)), glacier melt is enabled and estimated with a degree-day model. If the air temperature, \\(T_a\\), is below a certain threshold g_tt (\\(\\SIb{}{\\degree C}\\)) precipitation occurs as snowfall, whereas it occurs as rainfall if \\(T_a ≥\\) g_tt.\nWith this the rate of glacier melt in mm is estimated as:\n\\[\nQ_m = \\subtext{g}{cfmax}(T_a − \\subtext{g}{tt})\\, ; \\, T_a > \\subtext{g}{tt}\n\\]\nwhere \\(Q_m\\) is the rate of glacier melt and \\(\\SIb{\\subtext{g}{cfmax}}{mm (\\degree\nC)^{-1}day^{-1}}\\) is the melting factor. Parameter g_tt can be taken as equal to the snow tt parameter. Values of the melting factor g_cfmax normally varies from one glacier to another and some values are reported in the literature. g_cfmax can also be estimated by multiplying snow cfmax by a factor between 1 and 2, to take into account the higher albedo of ice compared to snow.", + "crumbs": [ + "Model documentation", + "Vertical processes", + "Shared processes" + ] + }, + { + "objectID": "model_docs/vertical/shared_processes.html#snow-and-glaciers", + "href": "model_docs/vertical/shared_processes.html#snow-and-glaciers", + "title": "Shared processes", + "section": "", + "text": "If the air temperature, \\(T_a\\), is below a user-defined threshold tt \\(\\SIb{}{\\degree C}\\) precipitation occurs as snowfall, whereas it occurs as rainfall if \\(T_a ≥ \\mathrm{tt}\\). A another parameter tti defines how precipitation can occur partly as rain or snowfall (see the figure below). If precipitation occurs as snowfall, it is added to the dry snow component within the snow pack. Otherwise it ends up in the free water reservoir, which represents the liquid water content of the snow pack. Between the two components of the snow pack, interactions take place, either through snow melt (if temperatures are above a threshold tt) or through snow refreezing (if temperatures are below threshold tt).\nThe respective rates of snow melt and refreezing are:\n\\[\n\\begin{align*}\n Q_m &=& \\subtext{\\mathrm{cf}}{max}(T_a−\\mathrm{tt})\\, &&T_a > \\mathrm{tt} \\\\\n Q_r &=& \\subtext{\\mathrm{cf}}{max} \\, \\mathrm{cf}_r(\\mathrm{tt}−T_a) &&T_a < \\mathrm{tt}\n\\end{align*}\n\\]\nwhere \\(Q_m\\) is the rate of snow melt, \\(Q_r\\) is the rate of snow refreezing, and \\(\\SIb{\\subtext{\\mathrm{cf}}{max}}{mm\\;(\\degree C)^{-1} day^{-1}}\\) and \\(\\mathrm{cf}_r\\) are user defined model parameters (the melting factor and the refreezing factor respectively).\nThe fraction of liquid water in the snow pack is at most equal to a user defined fraction, whc, of the water equivalent of the dry snow content. If the liquid water concentration exceeds whc, either through snow melt or incoming rainfall, the surplus water (rainfall) becomes available for infiltration into the soil:\nsnowwater = snowwater - refreezing # free water content in snow\nmaxsnowwater = snow * whc # max water in the snow\nsnowwater = snowwater + snowmelt + rainfall # add all water and potentially supersaturate the snowpack\nrainfall = max(snowwater - maxsnowwater, 0.0) # rain + surplus snowwater\n\n\n\nSnowmelt and refreezing rates as a function of temperature (for set melting and refreezing factors)\n\n\n\n\n\n\nGlacier processes can be modelled if the snow model is enabled. Glacier modelling is very similar to snow modelling and considers two main processes: glacier build-up from snow turning into firn/ice (using the HBV-light model) and glacier melt (using a temperature degree-day model).\nThe definition of glacier boundaries and initial volume is defined by two parameters. The parameter glacierfrac gives the fraction of each grid cell covered by a glacier as a number between zero and one. The state parameter glacierstore gives the amount of water (in mm w.e.) within the glaciers at each grid cell. Because the glacier store (glacierstore) cannot be initialized by running thFe model for a couple of years, a default initial state should be supplied by adding this parameter to the input static file. The required glacier data can be prepared from available glacier datasets.\nFirst, a fixed fraction of the snowpack on top of the glacier is converted into ice for each timestep and added to the glacierstore using the HBV-light model (Seibert et al., 2018). This fraction g_sifrac typically ranges from \\(0.001\\) to \\(0.006\\).\nThen, when the snowpack on top of the glacier is almost all melted (snow cover \\(<\n\\SI{10}{mm}\\)), glacier melt is enabled and estimated with a degree-day model. If the air temperature, \\(T_a\\), is below a certain threshold g_tt (\\(\\SIb{}{\\degree C}\\)) precipitation occurs as snowfall, whereas it occurs as rainfall if \\(T_a ≥\\) g_tt.\nWith this the rate of glacier melt in mm is estimated as:\n\\[\nQ_m = \\subtext{g}{cfmax}(T_a − \\subtext{g}{tt})\\, ; \\, T_a > \\subtext{g}{tt}\n\\]\nwhere \\(Q_m\\) is the rate of glacier melt and \\(\\SIb{\\subtext{g}{cfmax}}{mm (\\degree\nC)^{-1}day^{-1}}\\) is the melting factor. Parameter g_tt can be taken as equal to the snow tt parameter. Values of the melting factor g_cfmax normally varies from one glacier to another and some values are reported in the literature. g_cfmax can also be estimated by multiplying snow cfmax by a factor between 1 and 2, to take into account the higher albedo of ice compared to snow.", + "crumbs": [ + "Model documentation", + "Vertical processes", + "Shared processes" + ] + }, + { + "objectID": "model_docs/vertical/shared_processes.html#rainfall-interception", + "href": "model_docs/vertical/shared_processes.html#rainfall-interception", + "title": "Shared processes", + "section": "Rainfall interception", + "text": "Rainfall interception\nBoth the Gash and Rutter models are available to estimate rainfall interception by the vegetation. The selection of an interception model depends on the simulation timestep.\n\nThe analytical (Gash) model\nThe analytical model of rainfall interception is based on Rutter’s numerical model. The simplifications that introduced allow the model to be applied on a daily basis, although a storm-based approach will yield better results in situations with more than one storm per day. The amount of water needed to completely saturate the canopy is defined as:\n\\[\nP'=\\frac{-\\overline{R}S}{\\overline{E}_w}\\log\\left[1-\\frac{\\overline{E}_w}{\\overline{R}}(1-p-p_t)^{-1}\\right]\n\\]\nwhere \\(\\overline{R}\\) is the average precipitation intensity on a saturated canopy and \\(\\overline{E}_{w}\\) the average evaporation from the wet canopy and with the vegetation parameters \\(S\\), \\(p\\) and \\(p_t\\) as defined previously. The model uses a series of expressions to calculate the interception loss during different phases of a storm. An analytical integration of the total evaporation and rainfall under saturated canopy conditions is then done for each storm to determine average values of \\(\\overline{E}_{w}\\) and \\(\\overline{R}\\). The total evaporation from the canopy (the total interception loss) is calculated as the sum of the components listed in the table below. Interception losses from the stems are calculated for days with \\(P\\geq S_{t}/p_{t}\\). \\(p_t\\) and \\(S_t\\) are small and neglected.\n\nFormulation of the components of interception loss according to Gash:\n\n\n\n\n\n\nComponents\nInterception loss\n\n\n\n\nFor \\(m\\) small storms (\\(P_g<{P'}_g\\))\n\\((1-p-p_t)\\sum_{j=1}^m P_{g,j}\\)\n\n\nWetting up the canopy in \\(n\\) large storms (\\(P_g\\geq{P'}_g\\))\n\\(n(1-p-p_{t}){P'}_g-nS\\)\n\n\nEvaporation from saturated canopy during rainfall\n\\(\\overline{E}/\\overline{R}\\sum_{j=1}^n(P_{g,j}-{P'}_g)\\)\n\n\nEvaporation after rainfall ceases for \\(n\\) large storms\n\\(nS\\)\n\n\nEvaporation from trunks in \\(q\\) storms that fill the trunk storage\n\\(qS_t\\)\n\n\nEvaporation from trunks in \\(m+n-q\\) storms that do not fill the trunk storage\n\\(p_t\\sum_{j=1}^{m+n-q}P_{g,j}\\)\n\n\n\nIn applying the analytical model, saturated conditions are assumed to occur when the hourly rainfall exceeds a certain threshold. Often a threshold of 0.5 mm/hr is used. \\(\\overline{R}\\) is calculated for all hours when the rainfall exceeds the threshold to give an estimate of the mean rainfall rate onto a saturated canopy.\nGash (1979) has shown that in a regression of interception loss on rainfall (on a storm basis) the regression coefficient should equal to \\(\\overline{E}_w/\\overline{R}\\). Assuming that neither \\(\\overline{E}_w\\) nor \\(\\overline{R}\\) vary considerably in time, \\(\\overline{E}_w\\) can be estimated in this way from \\(\\overline{R}\\) in the absence of above-canopy climatic observations. Values derived in this way generally tend to be (much) higher than those calculated with the penman-monteith equation.\n\n\nThe modified rutter model\nFor sub daily timesteps the interception is calculated using a simplification of the Rutter model. The simplified model is solved explicitly and does not take drainage from the canopy into account.\nWflow.rainfall_interception_modrut\n\n\nInterception parameters from LAI\nThe SBM concept can determine the interception parameters from leaf area index (LAI) climatology. In order to switch this on you must define this cyclic parameter in the TOML file, the parameter is read from path_static, as follows:\n[input]\npath_forcing = \"data/forcing-moselle.nc\"\npath_static = \"data/staticmaps-moselle.nc\"\n\ncyclic = [\"vertical.leaf_area_index\"]\nFurthermore these additional parameters are required: + Specific leaf storage (sl \\(\\SIb{}{mm}\\)) + Storage woody part of vegetation (swood \\(\\SIb{}{mm}\\)) + Extinction coefficient (kext \\(\\SIb{}{-}\\))\nHere it is assumed that cmax \\(\\SIb{}{mm}\\) (leaves) (canopy storage capacity for the leaves only) relates linearly with LAI (c.f. Van Dijk and Bruijnzeel 2001). This done via the sl. sl can be determined through a lookup table with land cover based on literature (Pitman 1989, Lui 1998). Next the cmax (leaves) is determined using:\n\\[\n \\mathrm{cmax}(\\mathrm{leaves}) = \\mathrm{sl} \\cdot \\mathrm{LAI}\n\\]\nTo get to total storage (cmax) the woody part of the vegetation also needs to be added. As for sl, the storage of the woody part swood can also be related to land cover (lookup table).\nThe canopy gap fraction is determined using the extinction coefficient kext (van Dijk and Bruijnzeel 2001):\n\\[\n \\mathrm{canopygapfraction} = \\exp(-\\subtext{k}{ext} \\cdot \\mathrm{LAI})\n\\]\nThe extinction coefficient kext can be related to land cover.", + "crumbs": [ + "Model documentation", + "Vertical processes", + "Shared processes" + ] + }, + { + "objectID": "model_docs/vertical/shared_processes.html#references", + "href": "model_docs/vertical/shared_processes.html#references", + "title": "Shared processes", + "section": "References", + "text": "References\n\nSeibert, J., Vis, M. J. P., Kohn, I., Weiler, M., and Stahl, K., 2018, Technical note: Representing glacier geometry changes in a semi-distributed hydrological model, Hydrol. Earth Syst. Sci., 22, 2211–2224, https://doi.org/10.5194/hess-22-2211-2018.", + "crumbs": [ + "Model documentation", + "Vertical processes", + "Shared processes" + ] + }, + { + "objectID": "user_guide/faq.html", + "href": "user_guide/faq.html", + "title": "Frequently asked questions", + "section": "", + "text": "How do I easily modify input parameters?\nSee this section on how to adjust maps, and this section on how to directly pass uniform values. Note that both options work for any parameter.\n\n\nHow do I start wflow with initial conditions from a previous run?\nSee here\n\n\nHow do I add external inflows and/or abstractions?\nlateral.river.inflow: positive for inflows, negative for abstraction. If parameter is time varying, add it to the correct section, see below.\n\n\nHow do I add time-varying parameters?\nEither through cyclic (add parameter to cyclic list in the toml), or to the forcing section.\n\n\nHow do I add different output?\nSee here for csv output, here for scalar netcdf data, and here for gridded output.\n\n\n\n\n Back to top", + "crumbs": [ + "User guide", + "Frequently asked questions" + ] + }, + { + "objectID": "user_guide/model_config.html", + "href": "user_guide/model_config.html", + "title": "Different model configurations", + "section": "", + "text": "[model]\nsnow = true\nmasswasting = true\nglacier = true\n\n[input.vertical]\ntt = \"TT\"\ntti = \"TTI\"\nttm = \"TTM\"\nwater_holding_capacity = \"WHC\"\nglacierstore = \"wflow_glacierstore\"\nglacierfrac = \"wflow_glacierfrac\"\ng_cfmax = \"G_Cfmax\"\ng_tt = \"G_TT\"\ng_sifrac = \"G_SIfrac\"\n\n[state.vertical]\nsnow = \"snow\"\ntsoil = \"tsoil\"\nsnowwater = \"snowwater\"\nglacierstorage = \"glacierstorage\"", + "crumbs": [ + "User guide", + "Supported options", + "Different model configurations" + ] + }, + { + "objectID": "user_guide/model_config.html#enabling-snow-and-glacier-processes", + "href": "user_guide/model_config.html#enabling-snow-and-glacier-processes", + "title": "Different model configurations", + "section": "", + "text": "[model]\nsnow = true\nmasswasting = true\nglacier = true\n\n[input.vertical]\ntt = \"TT\"\ntti = \"TTI\"\nttm = \"TTM\"\nwater_holding_capacity = \"WHC\"\nglacierstore = \"wflow_glacierstore\"\nglacierfrac = \"wflow_glacierfrac\"\ng_cfmax = \"G_Cfmax\"\ng_tt = \"G_TT\"\ng_sifrac = \"G_SIfrac\"\n\n[state.vertical]\nsnow = \"snow\"\ntsoil = \"tsoil\"\nsnowwater = \"snowwater\"\nglacierstorage = \"glacierstorage\"", + "crumbs": [ + "User guide", + "Supported options", + "Different model configurations" + ] + }, + { + "objectID": "user_guide/model_config.html#enabling-reservoirs", + "href": "user_guide/model_config.html#enabling-reservoirs", + "title": "Different model configurations", + "section": "Enabling reservoirs", + "text": "Enabling reservoirs\n[model]\nreservoirs = true\n\n[input.lateral.river.reservoir]\narea = \"ResSimpleArea\"\nareas = \"wflow_reservoirareas\"\ndemand = \"ResDemand\"\nlocs = \"wflow_reservoirlocs\"\nmaxrelease = \"ResMaxRelease\"\nmaxvolume = \"ResMaxVolume\"\ntargetfullfrac = \"ResTargetFullFrac\"\ntargetminfrac = \"ResTargetMinFrac\"\n\n[state.lateral.river.reservoir]\nvolume = \"volume_reservoir\"", + "crumbs": [ + "User guide", + "Supported options", + "Different model configurations" + ] + }, + { + "objectID": "user_guide/model_config.html#enabling-lakes", + "href": "user_guide/model_config.html#enabling-lakes", + "title": "Different model configurations", + "section": "Enabling lakes", + "text": "Enabling lakes\n[model]\nlakes = true\n\n[input.lateral.river.lake]\narea = \"lake_area\"\nareas = \"wflow_lakeareas\"\nb = \"lake_b\"\ne = \"lake_e\"\nlocs = \"wflow_lakelocs\"\noutflowfunc = \"lake_outflowfunc\"\nstorfunc = \"lake_storfunc\"\nthreshold = \"lake_threshold\"\nwaterlevel = \"lake_waterlevel\"\n\n[state.lateral.river.lake]\nwaterlevel = \"waterlevel_lake\"", + "crumbs": [ + "User guide", + "Supported options", + "Different model configurations" + ] + }, + { + "objectID": "user_guide/model_config.html#enabling-floodplain-routing", + "href": "user_guide/model_config.html#enabling-floodplain-routing", + "title": "Different model configurations", + "section": "Enabling floodplain routing", + "text": "Enabling floodplain routing\nAs part of the local inertial model for river flow.\n\n1D floodplains\n[model]\nfloodplain_1d = true\n\n[input.lateral.river.floodplain]\nvolume = \"floodplain_volume\"\nn = \"floodplain_n\"\n\n[state.lateral.river.floodplain]\nq = \"q_floodplain\"\nh = \"h_floodplain\"\n\n\n2D floodplains\n[model]\nland_routing = \"local-inertial\"\n\n[input.lateral.river]\nbankfull_elevation = \"hydrodem_avg_D4\"\n\n[input.lateral.land]\nelevation = \"hydrodem_avg_D4\"\n\n[state.lateral.land]\nqx = \"qx_land\"\nqy = \"qy_land\"", + "crumbs": [ + "User guide", + "Supported options", + "Different model configurations" + ] + }, + { + "objectID": "user_guide/model_config.html#enabling-water-demand-and-allocation", + "href": "user_guide/model_config.html#enabling-water-demand-and-allocation", + "title": "Different model configurations", + "section": "Enabling water demand and allocation", + "text": "Enabling water demand and allocation\nThe model types sbm and sbm_gwf support water demand and allocation computations, in combination with the kinematic wave and local inertial runoff routing scheme for river and overland flow.\n# example of water demand and allocation input parameters as cyclic data\n[input]\ncyclic = [\"vertical.domestic.demand_gross\", \"vertical.domestic.demand_net\",\n\"vertical.industry.demand_gross\", \"vertical.industry.demand_net\",\n\"vertical.livestock.demand_gross\", \"vertical.livestock.demand_net\",\n\"vertical.paddy.irrigation_trigger\", \"vertical.nonpaddy.irrigation_trigger\",]\n\n[model.water_demand]\ndomestic = true # optional, default is \"false\"\nindustry = true # optional, default is \"false\"\nlivestock = true # optional, default is \"false\"\npaddy = true # optional, default is \"false\"\nnonpaddy = true # optional, default is \"false\"\n\n[input.vertical.allocation]\nareas = \"allocation_areas\"\nfrac_sw_used = \"SurfaceWaterFrac\"\n\n[input.vertical.domestic]\ndemand_gross = \"dom_gross\"\ndemand_net = \"dom_net\"\n\n[input.vertical.industry]\ndemand_gross = \"ind_gross\"\ndemand_net = \"ind_net\"\n\n[input.vertical.livestock]\ndemand_gross = \"lsk_gross\"\ndemand_net = \"lsk_net\"\n\n[input.vertical.paddy]\nirrigation_areas = \"paddy_irrigation_areas\"\nirrigation_trigger = \"irrigation_trigger\"\n\n[input.vertical.nonpaddy]\nirrigation_areas = \"nonpaddy_irrigation_areas\"\nirrigation_trigger = \"irrigation_trigger\"\n\n# required if paddy is set to \"true\"\n[state.vertical.paddy]\nh = \"h_paddy\"", + "crumbs": [ + "User guide", + "Supported options", + "Different model configurations" + ] + }, + { + "objectID": "user_guide/output.html", + "href": "user_guide/output.html", + "title": "Handling model output", + "section": "", + "text": "After running the model example from the previous step 4, the model results can be found in data/output_moselle_simple.csv.\nIf required, it is also possible to output netCDF files as output, by modifying the TOML file. An example is shown below:\n# Spatial output\n[output]\npath = \"data/output.nc\"\n\n[output.lateral.river]\nq_av = \"q_river\"\n\n[output.lateral.land]\nq = \"q_land\"\nh = \"h_land\"\n\n# Scalar output (mapped to the specified map)\n[netcdf]\npath = \"data/output_scalar.nc\"\n\n[[netcdf.variable]]\nname = \"Q\"\nmap = \"gauges\"\nparameter = \"lateral.river.q_av\"\n\n[[netcdf.variable]]\nname = \"prec\"\nmap = \"subcatchment\"\nparameter = \"vertical.precipitation\"\nreducer = \"mean\"\nUsing your own preferred programming language, the model output files can be easily read and visualized.\n\n\n\n Back to top", + "crumbs": [ + "User guide", + "Handling model output" + ] + }, + { + "objectID": "user_guide/warm_states.html", + "href": "user_guide/warm_states.html", + "title": "Starting with “warm” states", + "section": "", + "text": "The state section in the TOML file provides information on the input file if the model is initialized with a warm state (path_input) and to what file the states are written at the end of the model run (path_output). Please note that the model setting reinit needs to be set to false in order to initialize the model with states from the file located at path_input. A mapping between external state names and internal model states is required. This information is specified for each model component, the vertical model and lateral model components. In the example below the vertical component represents the SBM concept, and for the lateral components there is a river (including optional reservoir, lake and floodplain components), land and subsurface domain. The internal model states are listed on the left side, and the external state names are listed on the right side. Note that path_input is only required when reinit is set to false. path_output is optional, an output state file is only written when it is defined. If neither is set, the entire state section can be left out.\n[model]\nreinit = false # cold (reinit = true) or warm state (reinit = false), default is true\n\n[state]\npath_input = \"data/instates-moselle.nc\" # Location of the file with the input states\npath_output = \"data/outstates-moselle.nc\" # Output location of the states after the model run\n\n[state.vertical]\nsatwaterdepth = \"satwaterdepth\"\nsnow = \"snow\"\ntsoil = \"tsoil\"\nustorelayerdepth = \"ustorelayerdepth\"\ncanopystorage = \"canopystorage\"\nsnowwater = \"snowwater\"\nglacierstore =\"glacierstore\"\n\n[state.lateral.river]\nq = \"q_river\"\nh = \"h_river\"\nh_av = \"h_av_river\"\n\n[state.lateral.river.floodplain]\nq = \"q_floodplain\"\nh = \"h_floodplain\"\n\n[state.lateral.river.reservoir]\nvolume = \"volume_reservoir\"\n\n[state.lateral.river.lake]\nwaterlevel = \"waterlevel_lake\"\n\n[state.lateral.subsurface]\nssf = \"ssf\"\n\n[state.lateral.land]\nq = \"q_land\"\nh = \"h_land\"\nh_av = \"h_av_land\"\n\n\n\n Back to top", + "crumbs": [ + "User guide", + "Supported options", + "Starting with \"warm\" states" + ] + }, + { + "objectID": "user_guide/required_files.html", + "href": "user_guide/required_files.html", + "title": "Required files", + "section": "", + "text": "To run wflow, several files are required. These include a settings file and input data. The input data is typically separated into static maps and forcing data, and both are provided in netCDF files, except for lake storage and rating curves that are supplied via CSV files. Below is a brief overview of the different files:", + "crumbs": [ + "User guide", + "Required files" + ] + }, + { + "objectID": "user_guide/required_files.html#the-configuration-file-settings.toml", + "href": "user_guide/required_files.html#the-configuration-file-settings.toml", + "title": "Required files", + "section": "The configuration file (settings.toml)", + "text": "The configuration file (settings.toml)\nThe configuration file contains all relevant settings for running wflow, such as the simulation period, the model settings, the mapping between input files and (internal) model parameters. More details and explanations can be found here. An example configuration file is presented below.\n\n\nClick to show example .toml file\n\n\n# This is a TOML configuration file for Wflow.\n# Relative file paths are interpreted as being relative to this TOML file.\n# Wflow documentation https://deltares.github.io/Wflow.jl/dev/\n# TOML documentation: https://github.com/toml-lang/toml\n\nendtime = 2000-01-10T00:00:00\ndir_input = \"data/input\"\ndir_output = \"data/output\"\nloglevel = \"info\"\n\n[input]\npath_forcing = \"forcing-moselle.nc\"\npath_static = \"staticmaps-moselle.nc\"\n\n# these are not directly part of the model\nldd = \"wflow_ldd\"\nriver_location = \"wflow_river\"\nsubcatchment = \"wflow_subcatch\"\n\n# specify the internal IDs of the parameters which vary over time\n# the external name mapping needs to be below together with the other mappings\nforcing = [\n \"vertical.atmospheric_forcing.precipitation\",\n \"vertical.atmospheric_forcing.temperature\",\n \"vertical.atmospheric_forcing.potential_evaporation\",\n]\n\ncyclic = [\"vertical.vegetation_parameter_set.leaf_area_index\"]\n\n[input.vertical.vegetation_parameter_set]\nleaf_area_index = \"LAI\"\nkext = \"Kext\"\nstorage_specific_leaf = \"Sl\"\nstorage_wood = \"Swood\"\nrootingdepth = \"RootingDepth\"\n\n[input.vertical.interception.parameters]\ne_r = \"EoverR\"\n\n[input.vertical.atmospheric_forcing]\npotential_evaporation = \"pet\"\nprecipitation = \"precip\"\ntemperature = \"temp\"\n\n[input.vertical.runoff.parameters]\nwaterfrac = \"WaterFrac\"\n\n[input.vertical.soil.parameters]\nc = \"c\"\ncf_soil = \"cf_soil\"\nf = \"f\"\ninfiltcappath = \"InfiltCapPath\"\ninfiltcapsoil = \"InfiltCapSoil\"\ntheta_r = \"thetaR\"\ntheta_s = \"thetaS\"\nmaxleakage = \"MaxLeakage\"\npathfrac = \"PathFrac\"\nrootdistpar = \"rootdistpar\"\nsoilthickness = \"SoilThickness\"\nkv_0 = \"KsatVer\"\n\n[input.vertical.snow.parameters]\ntt = \"TT\"\ntti = \"TTI\"\nttm = \"TTM\"\ncfmax = \"Cfmax\"\n\n[input.lateral.river]\nlength = \"wflow_riverlength\"\nn = \"N_River\"\nslope = \"RiverSlope\"\nwidth = \"wflow_riverwidth\"\n\n[input.lateral.river.reservoir]\narea = \"ResSimpleArea\"\nareas = \"wflow_reservoirareas\"\ndemand = \"ResDemand\"\nlocs = \"wflow_reservoirlocs\"\nmaxrelease = \"ResMaxRelease\"\nmaxvolume = \"ResMaxVolume\"\ntargetfullfrac = \"ResTargetFullFrac\"\ntargetminfrac = \"ResTargetMinFrac\"\n\n[input.lateral.subsurface]\nksathorfrac = \"KsatHorFrac\"\n\n[input.lateral.land]\nn = \"N\"\nslope = \"Slope\"\n\n[model]\nthicknesslayers = [100, 300, 800]\ntype = \"sbm\"\n\n[csv]\npath = \"output_moselle_simple.csv\"\n\n[[csv.column]]\ncoordinate.x = 7.378\ncoordinate.y = 50.204\nheader = \"Q\"\nparameter = \"lateral.river.q\"\n\n[[csv.column]]\nheader = \"recharge\"\nparameter = \"vertical.soil.variables.recharge\"\nreducer = \"mean\"", + "crumbs": [ + "User guide", + "Required files" + ] + }, + { + "objectID": "user_guide/required_files.html#the-static-input-data-staticmaps.nc", + "href": "user_guide/required_files.html#the-static-input-data-staticmaps.nc", + "title": "Required files", + "section": "The static input data (staticmaps.nc)", + "text": "The static input data (staticmaps.nc)\nThe list below contains a brief overview of several essential static maps required to run wflow. These NC variables names refer to the example data of the wflow_sbm + kinematic wave model (see here). Example data for the other model configurations can be found here.\n\n\n\nDescription\nNC variable name\nunit\n\n\n\n\nFlow direction (1-9)\nwflow_ldd\n-\n\n\nMap indicating the river cells (0-1)\nwflow_river\n-\n\n\nThe length of the river\nwflow_riverlength\nm\n\n\nThe width of the river\nwflow_riverwidth\nm\n\n\nMask of the basin\nwflow_subcatch\n-\n\n\nLand slope\nSlope\nm m\\(^{-1}\\)\n\n\nRiver slope\nRiverSlope\nm m\\(^{-1}\\)\n\n\n\nAs mentioned before, the model parameters can also be defined as spatial maps. They can be included in the same netCDF file, as long as their variable names are correctly mapped in the TOML settings file. See the section on example models on how to use this functionality.\n\n\n\n\n\n\nImportant\n\n\n\nWhen using cyclic data, three different options are supported: - 12 (monthly) - 365 (daily, where Feb. 29 is not present, so the value for Feb. 28 is taken. Dec. 31 is represented by DOY 365) - 366 (where Feb. 29 represents DOY 60, and Dec. 31 DOY 366)\nIn contrast to the right-labelling of the forcing data (see below), the DOY/month of the current time step is used. For example: to simulate 2023-06-14 00:00:00 (with a daily time step), the DOY value at position 6 (when 12 values are provided), 165 (when 365 values are provided) or 166 (when 366 values are provided).", + "crumbs": [ + "User guide", + "Required files" + ] + }, + { + "objectID": "user_guide/required_files.html#the-forcing-data-forcing.nc", + "href": "user_guide/required_files.html#the-forcing-data-forcing.nc", + "title": "Required files", + "section": "The forcing data (forcing.nc)", + "text": "The forcing data (forcing.nc)\nThe forcing data typically contains the meteorological boundary conditions. This data is provided as a single netCDF file, with several variables containing the forcing data for precipitation, temperature and potential evaporation. The code snippet below shows the contents of the example file (downloaded here), and displaying the content with NCDatasets in Julia. As can be seen, each forcing variable (precip, pet and temp) consists of a three-dimensional dataset (x, y, and time), and each timestep consists of a two-dimensional map with values at each grid cell. Only values within the basin are required.\n\n\n\n\n\n\nImportant\n\n\n\nWflow expects the forcing to be “right-labelled”. This means that e.g. daily precipitation at 2023-06-15 00:00:00 is the accumulated total precipitation between 2023-06-14 00:00:00 and 2023-06-15 00:00:00.\n\n\n\n\n\n\n\n\nNote\n\n\n\nFor optimal computational performance, it is recommended to have chunks in the time dimension. This way, only part of the forcing file needs to be read and kept in memory. We recommend using a chunk size in the time dimension with size 1. Using larger chunks can largely degrade computational performance.\n\n\n\n\nClick to show example forcing.nc file\n\nGroup: /\n\nDimensions\n time = 366\n y = 313\n x = 291\n\nVariables\n time (366)\n Datatype: Int64\n Dimensions: time\n Attributes:\n units = days since 2000-01-02 00:00:00\n calendar = proleptic_gregorian\n\n y (313)\n Datatype: Float64\n Dimensions: y\n Attributes:\n _FillValue = NaN\n\n x (291)\n Datatype: Float64\n Dimensions: x\n Attributes:\n _FillValue = NaN\n\n spatial_ref\n Attributes:\n crs_wkt = GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]\n x_dim = x\n y_dim = y\n dim0 = time\n\n precip (291 × 313 × 366)\n Datatype: Float32\n Dimensions: x × y × time\n Attributes:\n _FillValue = NaN\n unit = mm\n precip_fn = era5\n coordinates = idx_out spatial_ref mask\n\n idx_out (291 × 313)\n Datatype: Int32\n Dimensions: x × y\n\n mask (291 × 313)\n Datatype: UInt8\n Dimensions: x × y\n\n pet (291 × 313 × 366)\n Datatype: Float32\n Dimensions: x × y × time\n Attributes:\n _FillValue = NaN\n unit = mm\n pet_fn = era5\n pet_method = debruin\n coordinates = idx_out spatial_ref mask\n\n temp (291 × 313 × 366)\n Datatype: Float32\n Dimensions: x × y × time\n Attributes:\n _FillValue = NaN\n unit = degree C.\n temp_fn = era5\n temp_correction = True\n coordinates = idx_out spatial_ref mask\n\nGlobal attributes\n unit = mm\n precip_fn = era5", + "crumbs": [ + "User guide", + "Required files" + ] + }, + { + "objectID": "developments/index.html", + "href": "developments/index.html", + "title": "Developments", + "section": "", + "text": "Changelog\n \n \n \n \n Developers guide\n \n \n \n \n Julia structures\n \n \n\n\n\n\n\n\n Back to top", + "crumbs": [ + "Developments" + ] + }, + { + "objectID": "index.html#quick-overview", + "href": "index.html#quick-overview", + "title": "Wflow.jl", + "section": "Quick overview", + "text": "Quick overview\n\n\n\n\n \n \n Installation guide\n \n \n \n \n How do I use Wflow.jl?\n \n \n \n \n Examples\n \n \n \n \n Frequently asked questions\n \n \n \n \n Model documentation\n \n \n \n \n Changelog", + "crumbs": [ + "Home", + "Wflow.jl" + ] + } +] \ No newline at end of file diff --git a/previews/PR448/site_libs/bootstrap/bootstrap-dark.min.css b/previews/PR448/site_libs/bootstrap/bootstrap-dark.min.css new file mode 100644 index 000000000..65646723f --- /dev/null +++ b/previews/PR448/site_libs/bootstrap/bootstrap-dark.min.css @@ -0,0 +1,12 @@ +@import"https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@300;400;700&display=swap";.navbar-nav .dropdown-menu{background-color:#373a3c}.navbar-nav .dropdown-menu a{color:#0ebbf0}.nav-tabs 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.nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-sm .navbar-nav-scroll{overflow:visible}.navbar-expand-sm .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-sm .navbar-toggler{display:none}.navbar-expand-sm .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-sm .offcanvas .offcanvas-header{display:none}.navbar-expand-sm .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 768px){.navbar-expand-md{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-md .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1200px){.navbar-expand-xl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xl .navbar-nav-scroll{overflow:visible}.navbar-expand-xl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xl .navbar-toggler{display:none}.navbar-expand-xl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #cbcccc;--bs-navbar-hover-color: rgba(14, 187, 240, 0.8);--bs-navbar-disabled-color: rgba(203, 204, 204, 0.75);--bs-navbar-active-color: #0EBBF0;--bs-navbar-brand-color: #cbcccc;--bs-navbar-brand-hover-color: #0EBBF0;--bs-navbar-toggler-border-color: rgba(203, 204, 204, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23cbcccc' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23cbcccc' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #212529;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #ffffff;--bs-accordion-bg: #212529;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #ffffff;--bs-accordion-btn-bg: #212529;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23ffffff'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2310335b'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #93c0f1;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #10335b;--bs-accordion-active-bg: #d4e6f9}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(255, 255, 255, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(255, 255, 255, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #0EBBF0;--bs-pagination-bg: #212529;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #0b96c0;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #0b96c0;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(255, 255, 255, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #ffffff;--bs-list-group-bg: #212529;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(255, 255, 255, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #ffffff;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(255, 255, 255, 0.75);--bs-list-group-disabled-bg: #212529;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(33, 37, 41, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(255, 255, 255, 0.75);--bs-toast-header-bg: rgba(33, 37, 41, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #212529;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static 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auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(1px*-1)}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart 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0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #343a40;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23343a40'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2310335b'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #93c0f1;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #10335b;--bs-accordion-active-bg: #d4e6f9}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #080c80;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #060a66;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #060a66;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:"Source Sans Pro",-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow{bottom:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-top-color:var(--bs-tooltip-bg)}.bs-tooltip-end 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W{static get NAME(){return"button"}toggle(){this._element.setAttribute("aria-pressed",this._element.classList.toggle("active"))}static jQueryInterface(t){return this.each((function(){const e=Y.getOrCreateInstance(this);"toggle"===t&&e[t]()}))}}N.on(document,"click.bs.button.data-api",X,(t=>{t.preventDefault();const e=t.target.closest(X);Y.getOrCreateInstance(e).toggle()})),m(Y);const U=".bs.swipe",G=`touchstart${U}`,J=`touchmove${U}`,Z=`touchend${U}`,tt=`pointerdown${U}`,et=`pointerup${U}`,it={endCallback:null,leftCallback:null,rightCallback:null},nt={endCallback:"(function|null)",leftCallback:"(function|null)",rightCallback:"(function|null)"};class st extends H{constructor(t,e){super(),this._element=t,t&&st.isSupported()&&(this._config=this._getConfig(e),this._deltaX=0,this._supportPointerEvents=Boolean(window.PointerEvent),this._initEvents())}static get Default(){return it}static get DefaultType(){return nt}static get 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t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&N.on(this._element,ft,(t=>this._keydown(t))),"hover"===this._config.pause&&(N.on(this._element,pt,(()=>this.pause())),N.on(this._element,mt,(()=>this._maybeEnableCycle()))),this._config.touch&&st.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of z.find(".carousel-item img",this._element))N.on(t,gt,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(ct)),rightCallback:()=>this._slide(this._directionToOrder(ht)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new st(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=Tt[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=z.findOne(wt,this._indicatorsElement);e.classList.remove(yt),e.removeAttribute("aria-current");const i=z.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(yt),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===at,s=e||b(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>N.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(dt).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const 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t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get 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000000000..343d2f80c --- /dev/null +++ b/previews/PR448/site_libs/quarto-html/quarto-syntax-highlighting-dark.css @@ -0,0 +1,189 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-al-color: #f07178; + --quarto-hl-an-color: #d4d0ab; + --quarto-hl-at-color: #00e0e0; + --quarto-hl-bn-color: #d4d0ab; + --quarto-hl-bu-color: #abe338; + --quarto-hl-ch-color: #abe338; + --quarto-hl-co-color: #f8f8f2; + --quarto-hl-cv-color: #ffd700; + --quarto-hl-cn-color: #ffd700; + --quarto-hl-cf-color: #ffa07a; + --quarto-hl-dt-color: #ffa07a; + --quarto-hl-dv-color: #d4d0ab; + --quarto-hl-do-color: #f8f8f2; + --quarto-hl-er-color: #f07178; + --quarto-hl-ex-color: #00e0e0; + --quarto-hl-fl-color: #d4d0ab; + --quarto-hl-fu-color: #ffa07a; + --quarto-hl-im-color: #abe338; + --quarto-hl-in-color: #d4d0ab; + --quarto-hl-kw-color: #ffa07a; + --quarto-hl-op-color: #ffa07a; + --quarto-hl-ot-color: #00e0e0; + --quarto-hl-pp-color: #dcc6e0; + --quarto-hl-re-color: #00e0e0; + --quarto-hl-sc-color: #abe338; + --quarto-hl-ss-color: #abe338; + --quarto-hl-st-color: #abe338; + --quarto-hl-va-color: #00e0e0; + --quarto-hl-vs-color: #abe338; + --quarto-hl-wa-color: #dcc6e0; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +code span.al { + background-color: #2a0f15; + font-weight: bold; + color: #f07178; +} + +code span.an { + color: #d4d0ab; +} + +code span.at { + color: #00e0e0; +} + +code span.bn { + color: #d4d0ab; +} + +code span.bu { + color: #abe338; +} + +code span.ch { + color: #abe338; +} + +code span.co { + font-style: italic; + color: #f8f8f2; +} + +code span.cv { + color: #ffd700; +} + +code span.cn { + color: #ffd700; +} + +code span.cf { + font-weight: bold; + color: #ffa07a; +} + +code span.dt { + color: #ffa07a; +} + +code span.dv { + color: #d4d0ab; +} + +code span.do { + color: #f8f8f2; +} + +code span.er { + color: #f07178; + text-decoration: underline; +} + +code span.ex { + font-weight: bold; + color: #00e0e0; +} + +code span.fl { + color: #d4d0ab; +} + +code span.fu { + color: #ffa07a; +} + +code span.im { + color: #abe338; +} + +code span.in { + color: #d4d0ab; +} + +code span.kw { + font-weight: bold; + color: #ffa07a; +} + +pre > code.sourceCode > span { + color: #f8f8f2; +} + +code span { + color: #f8f8f2; +} + +code.sourceCode > span { + color: #f8f8f2; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #f8f8f2; +} + +code span.op { + color: #ffa07a; +} + +code span.ot { + color: #00e0e0; +} + +code span.pp { + color: #dcc6e0; +} + +code span.re { + background-color: #f8f8f2; + color: #00e0e0; +} + +code span.sc { + color: #abe338; +} + +code span.ss { + color: #abe338; +} + +code span.st { + color: #abe338; +} + +code span.va { + color: #00e0e0; +} + +code span.vs { + color: #abe338; +} + +code span.wa { + color: #dcc6e0; +} + +.prevent-inlining { + content: " code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id="${anchor}"]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + // This is the no-scroll case where last section should be the active one + sectionIndex = 0; + } else { + // This finds the last section visible on screen that should be made active + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + child.style.pointerEvents = "none"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.pointerEvents = null; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + child.style.pointerEvents = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + // toc-expand: false + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/previews/PR448/site_libs/quarto-html/tippy.css b/previews/PR448/site_libs/quarto-html/tippy.css new file mode 100644 index 000000000..e6ae635cb --- /dev/null +++ b/previews/PR448/site_libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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b/previews/PR448/site_libs/quarto-nav/quarto-nav.js new file mode 100644 index 000000000..38cc43057 --- /dev/null +++ b/previews/PR448/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,325 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +const announceDismiss = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + annEl.remove(); + + const annId = annEl.getAttribute("data-announcement-id"); + window.localStorage.setItem(`quarto-announce-${annId}`, "true"); + } +}; + +const announceRegister = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + const annId = annEl.getAttribute("data-announcement-id"); + const isDismissed = + window.localStorage.getItem(`quarto-announce-${annId}`) || false; + if (isDismissed) { + announceDismiss(); + return; + } else { + annEl.classList.remove("hidden"); + } + + const actionEl = annEl.querySelector(".quarto-announcement-action"); + if (actionEl) { + actionEl.addEventListener("click", function (e) { + e.preventDefault(); + // Hide the bar immediately + announceDismiss(); + }); + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + announceRegister(); + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function dashboardOffset() { + const dashboardNavEl = window.document.getElementById( + "quarto-dashboard-header" + ); + if (dashboardNavEl !== null) { + return dashboardNavEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset() + dashboardOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver(() => { + setTimeout(updateDocumentOffsetWithoutAnimation, 0); + }); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].dataset.originalHref = links[i].href; + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item, a.quarto-navigation-tool, a.quarto-navbar-tools, a.quarto-navbar-tools-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git 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this._bitapSearch.searchIn(e)}}],[{key:"type",get:function(){return"fuzzy"}},{key:"multiRegex",get:function(){return/^"(.*)"$/}},{key:"singleRegex",get:function(){return/^(.*)$/}}]),n}(z),H=function(e){a(n,e);var t=l(n);function n(e){return r(this,n),t.call(this,e)}return o(n,[{key:"search",value:function(e){for(var t,n=0,r=[],i=this.pattern.length;(t=e.indexOf(this.pattern,n))>-1;)n=t+i,r.push([t,n-1]);var o=!!r.length;return{isMatch:o,score:o?0:1,indices:r}}}],[{key:"type",get:function(){return"include"}},{key:"multiRegex",get:function(){return/^'"(.*)"$/}},{key:"singleRegex",get:function(){return/^'(.*)$/}}]),n}(z),Q=[K,H,B,J,V,U,q,G],X=Q.length,Y=/ +(?=(?:[^\"]*\"[^\"]*\")*[^\"]*$)/;function Z(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return e.split("|").map((function(e){for(var n=e.trim().split(Y).filter((function(e){return e&&!!e.trim()})),r=[],i=0,o=n.length;i1&&void 0!==arguments[1]?arguments[1]:{},i=n.isCaseSensitive,o=void 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0===r?I.includeMatches:r,o=n.includeScore,c=void 0===o?I.includeScore:o,a=[];return i&&a.push(de),c&&a.push(ve),e.map((function(e){var n=e.idx,r={item:t[n],refIndex:n};return a.length&&a.forEach((function(t){t(e,r)})),r}))}var ye=function(){function e(n){var i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},o=arguments.length>2?arguments[2]:void 0;r(this,e),this.options=t(t({},I),i),this.options.useExtendedSearch,this._keyStore=new S(this.options.keys),this.setCollection(n,o)}return o(e,[{key:"setCollection",value:function(e,t){if(this._docs=e,t&&!(t instanceof $))throw new Error("Incorrect 'index' type");this._myIndex=t||F(this.options.keys,this._docs,{getFn:this.options.getFn,fieldNormWeight:this.options.fieldNormWeight})}},{key:"add",value:function(e){k(e)&&(this._docs.push(e),this._myIndex.add(e))}},{key:"remove",value:function(){for(var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:function(){return!1},t=[],n=0,r=this._docs.length;n1&&void 0!==arguments[1]?arguments[1]:{},n=t.limit,r=void 0===n?-1:n,i=this.options,o=i.includeMatches,c=i.includeScore,a=i.shouldSort,s=i.sortFn,u=i.ignoreFieldNorm,h=g(e)?g(this._docs[0])?this._searchStringList(e):this._searchObjectList(e):this._searchLogical(e);return fe(h,{ignoreFieldNorm:u}),a&&h.sort(s),y(r)&&r>-1&&(h=h.slice(0,r)),ge(h,this._docs,{includeMatches:o,includeScore:c})}},{key:"_searchStringList",value:function(e){var t=re(e,this.options),n=this._myIndex.records,r=[];return n.forEach((function(e){var n=e.v,i=e.i,o=e.n;if(k(n)){var c=t.searchIn(n),a=c.isMatch,s=c.score,u=c.indices;a&&r.push({item:n,idx:i,matches:[{score:s,value:n,norm:o,indices:u}]})}})),r}},{key:"_searchLogical",value:function(e){var t=this,n=function(e,t){var n=(arguments.length>2&&void 0!==arguments[2]?arguments[2]:{}).auto,r=void 0===n||n,i=function e(n){var i=Object.keys(n),o=ue(n);if(!o&&i.length>1&&!se(n))return e(le(n));if(he(n)){var c=o?n[ce]:i[0],a=o?n[ae]:n[c];if(!g(a))throw new Error(x(c));var s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=le(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t(); \ No newline at end of file diff --git a/previews/PR448/site_libs/quarto-search/quarto-search.js b/previews/PR448/site_libs/quarto-search/quarto-search.js new file mode 100644 index 000000000..d788a9581 --- /dev/null +++ b/previews/PR448/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1290 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + placeholder: language["search-text-placeholder"], + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // If this is a file URL, note that + + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + document.addEventListener("keyup", (event) => { + const { key } = event; + const kbds = quartoSearchOptions["keyboard-shortcut"]; + const focusedEl = document.activeElement; + + const isFormElFocused = [ + "input", + "select", + "textarea", + "button", + "option", + ].find((tag) => { + return focusedEl.tagName.toLowerCase() === tag; + }); + + if ( + kbds && + kbds.includes(key) && + !isFormElFocused && + !document.activeElement.isContentEditable + ) { + event.preventDefault(); + window.quartoOpenSearch(); + } + }); + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = throttle(() => { + // Only do this if we're not detached + // Bug #7117 + // This will happen when the keyboard is shown on ios (resulting in a scroll) + // which then closed the search UI + if (!window.matchMedia(detachedMediaQuery).matches) { + setIsOpen(false); + } + }, 50); + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.flatMap((event) => { + // This API limits the number of items per event to 20 + const chunkSize = 20; + const itemChunks = []; + const eventItems = event.items; + for (let i = 0; i < eventItems.length; i += chunkSize) { + itemChunks.push(eventItems.slice(i, i + chunkSize)); + } + // Split the items into multiple events that can be sent + const events = itemChunks.map((items) => { + return { + ...event, + items, + }; + }); + return events; + }); + + for (const event of events) { + insights.viewedObjectIDs(event); + } + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +var shownWarning = false; + +// fuse index options +const kFuseIndexOptions = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, +}; + +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + if (window.location.protocol === "file:" && !shownWarning) { + window.alert( + "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server." + ); + shownWarning = true; + return; + } + const fuse = new window.Fuse([], kFuseIndexOptions); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href, + item.crumbs, + quartoSearchOptions + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard( + createElement, + icon, + title, + section, + text, + href, + crumbs, + quartoSearchOptions +) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContents = [iconEl, titleEl]; + const showParent = quartoSearchOptions["show-item-context"]; + if (crumbs && showParent) { + let crumbsOut = undefined; + const crumbClz = ["search-result-crumbs"]; + if (showParent === "root") { + crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined; + } else if (showParent === "parent") { + crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined; + } else { + crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined; + crumbClz.push("search-result-crumbs-wrap"); + } + + const crumbEl = createElement( + "p", + { class: crumbClz.join(" ") }, + crumbsOut + ); + titleContents.push(crumbEl); + } + + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + titleContents + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text", "crumbs"].forEach( + (keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + } + ); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +let subSearchTerm = undefined; +let subSearchFuse = undefined; +const kFuseMaxWait = 125; + +async function fuseSearch(query, fuse, fuseOptions) { + let index = fuse; + // Fuse.js using the Bitap algorithm for text matching which runs in + // O(nm) time (no matter the structure of the text). In our case this + // means that long search terms mixed with large index gets very slow + // + // This injects a subIndex that will be used once the terms get long enough + // Usually making this subindex is cheap since there will typically be + // a subset of results matching the existing query + if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) { + // Use the existing subSearchFuse + index = subSearchFuse; + } else if (subSearchFuse !== undefined) { + // The term changed, discard the existing fuse + subSearchFuse = undefined; + subSearchTerm = undefined; + } + + // Search using the active fuse + const then = performance.now(); + const resultsRaw = await index.search(query, fuseOptions); + const now = performance.now(); + + const results = resultsRaw.map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + crumbs: result.item.crumbs, + }; + }); + + // If we don't have a subfuse and the query is long enough, go ahead + // and create a subfuse to use for subsequent queries + if ( + now - then > kFuseMaxWait && + subSearchFuse === undefined && + resultsRaw.length < fuseOptions.limit + ) { + subSearchTerm = query; + subSearchFuse = new window.Fuse([], kFuseIndexOptions); + resultsRaw.forEach((rr) => { + subSearchFuse.add(rr.item); + }); + } + return results; +} diff --git a/previews/PR448/user_guide/bmi.html b/previews/PR448/user_guide/bmi.html new file mode 100644 index 000000000..c9d2a6ba6 --- /dev/null +++ b/previews/PR448/user_guide/bmi.html @@ -0,0 +1,961 @@ + + + + + + + + + +Basic Model Interface (BMI) – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Basic Model Interface (BMI)

+
+ + + +
+ + + + +
+ + + +
+ + +
+

Introduction

+

The Community Surface Dynamics Modeling System (CSMDS) has developed the Basic Model Interface (BMI). BMI consists of a set of standard control and query functions that can be added by a developer to the model code and makes a model both easier to learn and easier to couple with other software elements.

+

For more information see also: http://csdms.colorado.edu/wiki/BMI_Description

+

CSDMS provides specifications for the languages C, C++, Fortran and Python. Wflow, written in the Julia programming language, makes use of the following Julia specification, based on BMI 2.0 version.

+

For the BMI implementation of wflow all grids are defined as unstructured grids, including the special cases scalar and points. While the input (forcing and model parameters) is structured (uniform rectilinear), internally wflow works with one dimensional arrays based on the active grid cells of the 2D model domain.

+
+
+

Configuration

+

The variables that wflow can exchange through BMI are based on the different model components and these components should be listed under the API section of the TOML configuration file of the model type. Below an example of this API section, that lists the vertical component and different lateral components:

+
[API]
+components = [
+  "vertical",
+  "lateral.subsurface",
+  "lateral.land",
+  "lateral.river",
+  "lateral.river.reservoir"
+]
+

See also:

+
Wflow.BMI.initialize
+Wflow.BMI.get_input_var_names
+

Variables with a third dimension, for example layer as part of the vertical SBM concept, are exposed as two-dimensional grids through the wflow BMI implementation. For these variables the index of this third dimension is required, by adding [k] to the variable name (k refers to the index of the third dimension). For example, the variable vertical.vwc[1] refers to the first soil layer of the vertical SBM concept.

+
+
+

Couple to a groundwater model

+

For the coupling of wflow_sbm (SBM + kinematic wave) with a groundwater model (e.g. MODFLOW) it is possible to run: - wflow_sbm in parts from the BMI, and - to switch off the lateral subsurface flow component of wflow_sbm.

+

The lateral subsurface component of wflow_sbm is not initialized by wflow when the [input.lateral.subsurface] part of the TOML file is not included. Then from the BMI it is possible to run first the recharge part of SBM:

+
model = BMI.update(model, run="sbm_until_recharge")
+

and to exchange recharge and for example river waterlevels to the groundwater model. After the groundwater model update, and the exchange of groundwater head and for example drain and river flux to wflow_sbm, the SBM part that mainly determines exfiltration of water from the unsaturated store, and the kinematic wave for river - and overland flow can be run as follows:

+
model = BMI.update(model, run="sbm_after_subsurfaceflow")
+

See also:

+
Wflow.BMI.update
+ + +
+ + Back to top
+ +
+
+
+
+

Made possible by: Deltares Deltares

+
+
+   +
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/previews/PR448/user_guide/faq.html b/previews/PR448/user_guide/faq.html new file mode 100644 index 000000000..d00d348b3 --- /dev/null +++ b/previews/PR448/user_guide/faq.html @@ -0,0 +1,916 @@ + + + + + + + + + +Frequently asked questions – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Frequently asked questions

+
+ + + +
+ + + + +
+ + + +
+ + +
+

How do I easily modify input parameters?

+

See this section on how to adjust maps, and this section on how to directly pass uniform values. Note that both options work for any parameter.

+
+
+

How do I start wflow with initial conditions from a previous run?

+

See here

+
+
+

How do I add external inflows and/or abstractions?

+

lateral.river.inflow: positive for inflows, negative for abstraction. If parameter is time varying, add it to the correct section, see below.

+
+
+

How do I add time-varying parameters?

+

Either through cyclic (add parameter to cyclic list in the toml), or to the forcing section.

+
+
+

How do I add different output?

+

See here for csv output, here for scalar netcdf data, and here for gridded output.

+ + +
+ + Back to top
+ +
+
+
+
+

Made possible by: Deltares Deltares

+
+
+   +
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+   +
+
+
+ + + + + \ No newline at end of file diff --git a/previews/PR448/user_guide/fews.html b/previews/PR448/user_guide/fews.html new file mode 100644 index 000000000..59a0e5d02 --- /dev/null +++ b/previews/PR448/user_guide/fews.html @@ -0,0 +1,933 @@ + + + + + + + + + +Run from Delft-FEWS – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Run from Delft-FEWS

+
+ + + +
+ + + + +
+ + + +
+ + +

Wflow integrates easily as part of an operational system by linking to the Delft-FEWS platform. Delft-FEWS integrates data and models, and is for example used in many active flood forecasting systems around the world.

+

This can be done without a model adapter that provides the interface between Delft-FEWS and an external model (or module). This is possible because time information in the TOML configuration file is optional and Delft-FEWS can import and export netCDF files. When time information is left out from the TOML configuration file, the starttime, endtime and timestepsecs (timestep) of the run is extracted from the netCDF forcing file by wflow.

+

To indicate that a wflow model runs from Delft-FEWS, the following setting needs to be specified in the main section of the TOML configuration file:

+
fews_run = true  # optional, default value is false
+

This ensures that wflow offsets the time handling, to meet the expectations of Delft-FEWS.

+

It also uses a different format for the log file such that each log message takes up only one line. That meets the General Adapter logFile expectations, which then can get parsed with these Delft-FEWS log parsing settings:

+
<logFile>
+    <file>log.txt</file>
+    <errorLinePattern >* [Error] *</errorLinePattern >
+    <warningLinePattern>* [Warn] *</warningLinePattern>
+    <infoLinePattern>* [Info] *</infoLinePattern>
+    <debugLinePattern >* [Debug] *</debugLinePattern >
+</logFile>
+ + + + Back to top
+ +
+
+
+
+

Made possible by: Deltares Deltares

+
+
+   +
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/previews/PR448/user_guide/index.html b/previews/PR448/user_guide/index.html new file mode 100644 index 000000000..bbe73203b --- /dev/null +++ b/previews/PR448/user_guide/index.html @@ -0,0 +1,978 @@ + + + + + + + + + +User guide – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

User guide

+
+ + + +
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+ + + +
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Use the buttons below to quickly navigate to the most visited pages. Note that more pages are available in the sidebar.

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Made possible by: Deltares Deltares

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+
+ + + + + \ No newline at end of file diff --git a/previews/PR448/user_guide/model_config.html b/previews/PR448/user_guide/model_config.html new file mode 100644 index 000000000..124c2cb5f --- /dev/null +++ b/previews/PR448/user_guide/model_config.html @@ -0,0 +1,1072 @@ + + + + + + + + + +Different model configurations – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Different model configurations

+
+ + + +
+ + + + +
+ + + +
+ + +
+

Enabling snow and glacier processes

+
[model]
+snow = true
+masswasting = true
+glacier = true
+
+[input.vertical]
+tt = "TT"
+tti = "TTI"
+ttm = "TTM"
+water_holding_capacity = "WHC"
+glacierstore = "wflow_glacierstore"
+glacierfrac = "wflow_glacierfrac"
+g_cfmax = "G_Cfmax"
+g_tt = "G_TT"
+g_sifrac = "G_SIfrac"
+
+[state.vertical]
+snow = "snow"
+tsoil = "tsoil"
+snowwater = "snowwater"
+glacierstorage = "glacierstorage"
+
+
+

Enabling reservoirs

+
[model]
+reservoirs = true
+
+[input.lateral.river.reservoir]
+area = "ResSimpleArea"
+areas = "wflow_reservoirareas"
+demand = "ResDemand"
+locs = "wflow_reservoirlocs"
+maxrelease = "ResMaxRelease"
+maxvolume = "ResMaxVolume"
+targetfullfrac = "ResTargetFullFrac"
+targetminfrac = "ResTargetMinFrac"
+
+[state.lateral.river.reservoir]
+volume = "volume_reservoir"
+
+
+

Enabling lakes

+
[model]
+lakes = true
+
+[input.lateral.river.lake]
+area = "lake_area"
+areas = "wflow_lakeareas"
+b = "lake_b"
+e = "lake_e"
+locs = "wflow_lakelocs"
+outflowfunc = "lake_outflowfunc"
+storfunc  = "lake_storfunc"
+threshold  = "lake_threshold"
+waterlevel = "lake_waterlevel"
+
+[state.lateral.river.lake]
+waterlevel = "waterlevel_lake"
+
+
+

Enabling floodplain routing

+

As part of the local inertial model for river flow.

+
+

1D floodplains

+
[model]
+floodplain_1d = true
+
+[input.lateral.river.floodplain]
+volume = "floodplain_volume"
+n = "floodplain_n"
+
+[state.lateral.river.floodplain]
+q = "q_floodplain"
+h = "h_floodplain"
+
+
+

2D floodplains

+
[model]
+land_routing = "local-inertial"
+
+[input.lateral.river]
+bankfull_elevation = "hydrodem_avg_D4"
+
+[input.lateral.land]
+elevation = "hydrodem_avg_D4"
+
+[state.lateral.land]
+qx = "qx_land"
+qy = "qy_land"
+
+
+
+

Enabling water demand and allocation

+

The model types sbm and sbm_gwf support water demand and allocation computations, in combination with the kinematic wave and local inertial runoff routing scheme for river and overland flow.

+
# example of water demand and allocation input parameters as cyclic data
+[input]
+cyclic = ["vertical.domestic.demand_gross", "vertical.domestic.demand_net",
+"vertical.industry.demand_gross", "vertical.industry.demand_net",
+"vertical.livestock.demand_gross", "vertical.livestock.demand_net",
+"vertical.paddy.irrigation_trigger", "vertical.nonpaddy.irrigation_trigger",]
+
+[model.water_demand]
+domestic = true     # optional, default is "false"
+industry = true     # optional, default is "false"
+livestock = true    # optional, default is "false"
+paddy = true        # optional, default is "false"
+nonpaddy = true     # optional, default is "false"
+
+[input.vertical.allocation]
+areas = "allocation_areas"
+frac_sw_used = "SurfaceWaterFrac"
+
+[input.vertical.domestic]
+demand_gross = "dom_gross"
+demand_net = "dom_net"
+
+[input.vertical.industry]
+demand_gross = "ind_gross"
+demand_net = "ind_net"
+
+[input.vertical.livestock]
+demand_gross = "lsk_gross"
+demand_net = "lsk_net"
+
+[input.vertical.paddy]
+irrigation_areas = "paddy_irrigation_areas"
+irrigation_trigger = "irrigation_trigger"
+
+[input.vertical.nonpaddy]
+irrigation_areas = "nonpaddy_irrigation_areas"
+irrigation_trigger = "irrigation_trigger"
+
+# required if paddy is set to "true"
+[state.vertical.paddy]
+h = "h_paddy"
+ + +
+ + Back to top
+ +
+
+
+
+

Made possible by: Deltares Deltares

+
+
+   +
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/previews/PR448/user_guide/multi_threading.html b/previews/PR448/user_guide/multi_threading.html new file mode 100644 index 000000000..f6029fd3e --- /dev/null +++ b/previews/PR448/user_guide/multi_threading.html @@ -0,0 +1,939 @@ + + + + + + + + + +Multithreading – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Multithreading

+
+ + + +
+ + + + +
+ + + +
+ + +
+

Using wflow in Julia

+

Wflow supports multi-threading execution of the wflow_sbm model that uses the kinematic wave approach for river, overland and lateral subsurface flow. Both the vertical SBM concept and the kinematic wave components of this model can run on multiple threads. The optional local inertial model for river flow and the optional local inertial model for river (1D) and land (2D), both part of wflow_sbm, can also run on multiple threads. The threading functionality for the kinematic wave may also be useful for the wflow_sbm model SBM + Groundwater flow. The multi-threading functionality in wflow is considered experimental, see also the following issue, where an error was not thrown running code multi-threaded. Because of this we advise to start with running a wflow model single-threaded (for example during the testing phase of setting up an new wflow model).

+

For information on how to start Julia with multiple threads we refer to How to start Julia with multiple threads.

+

Additionally, when running Julia + wflow via the command line (note that this is different from the wflow_cli), it is possible to define the number of threads via the -t flag. An example where we start Julia with three threads:

+
julia -t 3 -e 'using Wflow; Wflow.run()' path/to/config.toml
+
+
+

Using the command line interface

+

As explained above, we need to start julia with multiple threads to make use of this speedup. For wflow_cli, the only way to do this is by setting the JULIA_NUM_THREADS environment variable, as explained in these julia docs.

+

When a model run starts, among the run information the number of threads that are used is printed, so nthreads() = 4 means 4 threads are used, because JULIA_NUM_THREADS has been set to 4.

+ + +
+ + Back to top
+ +
+
+
+
+

Made possible by: Deltares Deltares

+
+
+   +
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/previews/PR448/user_guide/output.html b/previews/PR448/user_guide/output.html new file mode 100644 index 000000000..0a6c86089 --- /dev/null +++ b/previews/PR448/user_guide/output.html @@ -0,0 +1,948 @@ + + + + + + + + + +Handling model output – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Handling model output

+
+ + + +
+ + + + +
+ + + +
+ + +

After running the model example from the previous step 4, the model results can be found in data/output_moselle_simple.csv.

+

If required, it is also possible to output netCDF files as output, by modifying the TOML file. An example is shown below:

+
# Spatial output
+[output]
+path = "data/output.nc"
+
+[output.lateral.river]
+q_av = "q_river"
+
+[output.lateral.land]
+q = "q_land"
+h = "h_land"
+
+# Scalar output (mapped to the specified map)
+[netcdf]
+path = "data/output_scalar.nc"
+
+[[netcdf.variable]]
+name = "Q"
+map = "gauges"
+parameter = "lateral.river.q_av"
+
+[[netcdf.variable]]
+name = "prec"
+map = "subcatchment"
+parameter = "vertical.precipitation"
+reducer = "mean"
+

Using your own preferred programming language, the model output files can be easily read and visualized.

+ + + + Back to top
+ +
+
+
+
+

Made possible by: Deltares Deltares

+
+
+   +
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/previews/PR448/user_guide/required_files.html b/previews/PR448/user_guide/required_files.html new file mode 100644 index 000000000..7535c2256 --- /dev/null +++ b/previews/PR448/user_guide/required_files.html @@ -0,0 +1,1255 @@ + + + + + + + + + +Required files – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Required files

+
+ + + +
+ + + + +
+ + + +
+ + +

To run wflow, several files are required. These include a settings file and input data. The input data is typically separated into static maps and forcing data, and both are provided in netCDF files, except for lake storage and rating curves that are supplied via CSV files. Below is a brief overview of the different files:

+
    +
  • The settings.toml file contains information on the simulation period, links to the input files (and their names in the netCDF files), and connect the correct variable names in the netCDF files to the variables and parameters of wflow.
    • +
  • The staticmaps.nc file contains spatial information such as elevation, gauge locations, land use, and drainage direction, etc. This file can also contain maps with parameter values.
    • +
  • The forcing.nc file contains time series data for precipitation, temperature and potential evaporation (as a 3D array).
    • +
+
+

The configuration file (settings.toml)

+

The configuration file contains all relevant settings for running wflow, such as the simulation period, the model settings, the mapping between input files and (internal) model parameters. More details and explanations can be found here. An example configuration file is presented below.

+
+ +Click to show example .toml file + +

+# This is a TOML configuration file for Wflow.
+# Relative file paths are interpreted as being relative to this TOML file.
+# Wflow documentation https://deltares.github.io/Wflow.jl/dev/
+# TOML documentation: https://github.com/toml-lang/toml
+
+endtime = 2000-01-10T00:00:00
+dir_input = "data/input"
+dir_output = "data/output"
+loglevel = "info"
+
+[input]
+path_forcing = "forcing-moselle.nc"
+path_static = "staticmaps-moselle.nc"
+
+# these are not directly part of the model
+ldd = "wflow_ldd"
+river_location = "wflow_river"
+subcatchment = "wflow_subcatch"
+
+# specify the internal IDs of the parameters which vary over time
+# the external name mapping needs to be below together with the other mappings
+forcing = [
+  "vertical.atmospheric_forcing.precipitation",
+  "vertical.atmospheric_forcing.temperature",
+  "vertical.atmospheric_forcing.potential_evaporation",
+]
+
+cyclic = ["vertical.vegetation_parameter_set.leaf_area_index"]
+
+[input.vertical.vegetation_parameter_set]
+leaf_area_index = "LAI"
+kext = "Kext"
+storage_specific_leaf = "Sl"
+storage_wood = "Swood"
+rootingdepth = "RootingDepth"
+
+[input.vertical.interception.parameters]
+e_r = "EoverR"
+
+[input.vertical.atmospheric_forcing]
+potential_evaporation = "pet"
+precipitation = "precip"
+temperature = "temp"
+
+[input.vertical.runoff.parameters]
+waterfrac = "WaterFrac"
+
+[input.vertical.soil.parameters]
+c = "c"
+cf_soil = "cf_soil"
+f = "f"
+infiltcappath = "InfiltCapPath"
+infiltcapsoil = "InfiltCapSoil"
+theta_r = "thetaR"
+theta_s = "thetaS"
+maxleakage = "MaxLeakage"
+pathfrac = "PathFrac"
+rootdistpar = "rootdistpar"
+soilthickness = "SoilThickness"
+kv_0 = "KsatVer"
+
+[input.vertical.snow.parameters]
+tt = "TT"
+tti = "TTI"
+ttm = "TTM"
+cfmax = "Cfmax"
+
+[input.lateral.river]
+length = "wflow_riverlength"
+n = "N_River"
+slope = "RiverSlope"
+width = "wflow_riverwidth"
+
+[input.lateral.river.reservoir]
+area = "ResSimpleArea"
+areas = "wflow_reservoirareas"
+demand = "ResDemand"
+locs = "wflow_reservoirlocs"
+maxrelease = "ResMaxRelease"
+maxvolume = "ResMaxVolume"
+targetfullfrac = "ResTargetFullFrac"
+targetminfrac = "ResTargetMinFrac"
+
+[input.lateral.subsurface]
+ksathorfrac = "KsatHorFrac"
+
+[input.lateral.land]
+n = "N"
+slope = "Slope"
+
+[model]
+thicknesslayers = [100, 300, 800]
+type = "sbm"
+
+[csv]
+path = "output_moselle_simple.csv"
+
+[[csv.column]]
+coordinate.x = 7.378
+coordinate.y = 50.204
+header = "Q"
+parameter = "lateral.river.q"
+
+[[csv.column]]
+header = "recharge"
+parameter = "vertical.soil.variables.recharge"
+reducer = "mean"
+
+
+
+
+

The static input data (staticmaps.nc)

+

The list below contains a brief overview of several essential static maps required to run wflow. These NC variables names refer to the example data of the wflow_sbm + kinematic wave model (see here). Example data for the other model configurations can be found here.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
DescriptionNC variable nameunit
Flow direction (1-9)wflow_ldd-
Map indicating the river cells (0-1)wflow_river-
The length of the riverwflow_riverlengthm
The width of the riverwflow_riverwidthm
Mask of the basinwflow_subcatch-
Land slopeSlopem m\(^{-1}\)
River slopeRiverSlopem m\(^{-1}\)
+

As mentioned before, the model parameters can also be defined as spatial maps. They can be included in the same netCDF file, as long as their variable names are correctly mapped in the TOML settings file. See the section on example models on how to use this functionality.

+
+
+
+ +
+
+Important +
+
+
+

When using cyclic data, three different options are supported: - 12 (monthly) - 365 (daily, where Feb. 29 is not present, so the value for Feb. 28 is taken. Dec. 31 is represented by DOY 365) - 366 (where Feb. 29 represents DOY 60, and Dec. 31 DOY 366)

+

In contrast to the right-labelling of the forcing data (see below), the DOY/month of the current time step is used. For example: to simulate 2023-06-14 00:00:00 (with a daily time step), the DOY value at position 6 (when 12 values are provided), 165 (when 365 values are provided) or 166 (when 366 values are provided).

+
+
+
+
+

The forcing data (forcing.nc)

+

The forcing data typically contains the meteorological boundary conditions. This data is provided as a single netCDF file, with several variables containing the forcing data for precipitation, temperature and potential evaporation. The code snippet below shows the contents of the example file (downloaded here), and displaying the content with NCDatasets in Julia. As can be seen, each forcing variable (precip, pet and temp) consists of a three-dimensional dataset (x, y, and time), and each timestep consists of a two-dimensional map with values at each grid cell. Only values within the basin are required.

+
+
+
+ +
+
+Important +
+
+
+

Wflow expects the forcing to be “right-labelled”. This means that e.g. daily precipitation at 2023-06-15 00:00:00 is the accumulated total precipitation between 2023-06-14 00:00:00 and 2023-06-15 00:00:00.

+
+
+
+
+
+ +
+
+Note +
+
+
+

For optimal computational performance, it is recommended to have chunks in the time dimension. This way, only part of the forcing file needs to be read and kept in memory. We recommend using a chunk size in the time dimension with size 1. Using larger chunks can largely degrade computational performance.

+
+
+
+ +Click to show example forcing.nc file + +
Group: /
+
+Dimensions
+  time = 366
+  y = 313
+  x = 291
+
+Variables
+  time   (366)
+    Datatype:    Int64
+    Dimensions:  time
+    Attributes:
+    units                = days since 2000-01-02 00:00:00
+    calendar             = proleptic_gregorian
+
+  y   (313)
+    Datatype:    Float64
+    Dimensions:  y
+    Attributes:
+    _FillValue           = NaN
+
+  x   (291)
+    Datatype:    Float64
+    Dimensions:  x
+    Attributes:
+    _FillValue           = NaN
+
+  spatial_ref
+    Attributes:
+    crs_wkt              = GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]
+    x_dim                = x
+    y_dim                = y
+    dim0                 = time
+
+  precip   (291 × 313 × 366)
+    Datatype:    Float32
+    Dimensions:  x × y × time
+    Attributes:
+    _FillValue           = NaN
+    unit                 = mm
+    precip_fn            = era5
+    coordinates          = idx_out spatial_ref mask
+
+  idx_out   (291 × 313)
+    Datatype:    Int32
+    Dimensions:  x × y
+
+  mask   (291 × 313)
+    Datatype:    UInt8
+    Dimensions:  x × y
+
+  pet   (291 × 313 × 366)
+    Datatype:    Float32
+    Dimensions:  x × y × time
+    Attributes:
+    _FillValue           = NaN
+    unit                 = mm
+    pet_fn               = era5
+    pet_method           = debruin
+    coordinates          = idx_out spatial_ref mask
+
+  temp   (291 × 313 × 366)
+    Datatype:    Float32
+    Dimensions:  x × y × time
+    Attributes:
+    _FillValue           = NaN
+    unit                 = degree C.
+    temp_fn              = era5
+    temp_correction      = True
+    coordinates          = idx_out spatial_ref mask
+
+Global attributes
+  unit                 = mm
+  precip_fn            = era5
+
+ + +
+ + Back to top
+ +
+
+
+
+

Made possible by: Deltares Deltares

+
+
+   +
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/previews/PR448/user_guide/toml_file.html b/previews/PR448/user_guide/toml_file.html new file mode 100644 index 000000000..1f7fac917 --- /dev/null +++ b/previews/PR448/user_guide/toml_file.html @@ -0,0 +1,1236 @@ + + + + + + + + + +Simulation settings – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Simulation settings

+
+ + + +
+ + + + +
+ + + +
+ + +

All model configuration and settings are passed through the .toml file, which contains all relevant settings, information about the model configuration, simulation period, input files, and parameters. The settings are provided in a TOML file. The settings file is structured in several sections, which are explained below. The file paths provided in this file are relative to the location of the TOML file, or to dir_input and dir_output if they are specified.

+
+

General time info

+

Time information is optional. When omitted, wflow will perform computations for each timestamp in the forcing netCDF file, except for the first forcing timestamp, which is considered equal to the initial conditions of the wflow model (state time). If you wish to calculate a subset of this time range, or a different timestep, you can specify a starttime, endtime and timestepsecs. The starttime is defined as the model state time. In the TOML file settings below, the starttime is 2000-01-01T00:00:00 (state time) and the first update (and output) of the wflow model is at 2000-01-02T00:00:00. The time_units optional information is used by the writer of the model, for model output in netCDF format. The calendar option allows you to calculate in one of the different CF conventions calendars provided by the CFTime.jl package, such as "360_day". This is useful if you want to calculate climate scenarios which are sometimes provided in these alternative calendars.

+
calendar = "standard"                           # optional, this is the default value
+starttime = 2000-01-01T00:00:00                 # optional, default from forcing netCDF
+endtime = 2000-02-01T00:00:00                   # optional, default from forcing netCDF
+time_units = "days since 1900-01-01 00:00:00"   # optional, this is the default value
+timestepsecs = 86400                            # optional, default from forcing netCDF
+dir_input = "data/input"                        # optional, default is the path of the TOML
+dir_output = "data/output"                      # optional, default is the path of the TOML
+
+
+

Logging

+

Wflow prints logging messages at various levels such as debug, info, and error. These messages are sent to both the terminal and a log file. Note that logging to a file is only part of the Wflow.run(tomlpath::AbstractString) method. If you want to debug an issue, it can be helpful to set loglevel = "debug" in the TOML. To avoid flooding the screen, debug messages are only sent to the log file. The following settings will affect the logging:

+
silent = false          # optional, default is "false"
+loglevel = "debug"      # optional, default is "info"
+path_log = "log.txt"    # optional, default is "log.txt"
+fews_run = false        # optional, default value is false
+

silent avoids logging to the terminal, and only writes to the log file. loglevel controls which levels are filtered out; for instance, the default setting "info" does not print any debug-level messages. Note that for finer control, you can also pass an integer log level. For details, see Julia’s Logging documentation. path_log sets the desired output path for the log file. For information on fews_run, see Run from Delft-FEWS.

+
+
+

Model section

+

Model-specific settings can be included in the model section of the TOML file.

+
[model]
+type = "sbm"                        # one of ("sbm" or "sbm_gwf)
+masswasting = false                 # include lateral snow transport in the model, default is false
+snow = false                        # include snow modelling, default is false
+reinit = true                       # cold (reinit = true) or warm state (reinit = false), default is true
+reservoirs = false                  # include reservoir modelling, default is false
+kin_wave_iteration = false          # enable kinematic wave iterations in the model, default is false
+thicknesslayers = [100, 300, 800]   # specific SBM setting: for each soil layer, a thickness [mm] is specified
+min_streamorder_river = 5           # minimum stream order to delineate subbasins for river domain, default is 6 (for multi-threading computing purposes)
+min_streamorder_land = 4            # minimum stream order to delineate subbasins for land domain, default is 5 (for multi-threading computing purposes)
+
+
+

State options

+

The state section in the TOML file provides information about the location of input and output states of the model. This section is mostly relevant if the model needs to start with a “warm” state (i.e. based on the results of a previous simulation). The example below shows how to save the output states of the current simulation, so it can be used to initialize another model in the future. Details on the settings required to start a model with a warm state can be found in the additional model options. If it is not required to store the outstates of the current simulation, the entire state section can be removed.

+
[state]
+path_input = "instates-moselle.nc"
+path_output = "outstates-moselle.nc"
+
+[state.vertical]
+satwaterdepth = "satwaterdepth"
+snow = "snow"
+tsoil = "tsoil"
+ustorelayerdepth = "ustorelayerdepth"
+snowwater = "snowwater"
+canopystorage = "canopystorage"
+
+[state.lateral.river]
+q = "q_river"
+h = "h_river"
+h_av = "h_av_river"
+
+[state.lateral.river.reservoir]
+volume = "volume_reservoir"
+
+[state.lateral.subsurface]
+ssf = "ssf"
+
+[state.lateral.land]
+q = "q_land"
+h = "h_land"
+h_av = "h_av_land"
+
+
+

Input section

+

The input section of the TOML file contains information about the input forcing and model parameters files (in netCDF format). Forcing is applied to the vertical component of the model, and needs to be mapped to the external netCDF variable name. forcing lists the internal model forcing parameters, and these are mapped to the external netCDF variables listed under the section [input.vertical]. It is possible to provide cyclic parameters to the model (minimum time step of 1 day). In the example below, the internal vertical.leaf_area_index model parameter is mapped to the external netCDF variable “LAI” variable. Cyclic time inputs of parameters can be different (e.g., daily or monthly). The time dimension name of these cylic input parameters in the model parameter netCDF file should start with “time”. If a model parameter is not mapped, a default value will be used, if available.

+
[input]
+# use "forcing-year-*.nc" if forcing files are split in time
+path_forcing = "forcing-moselle.nc"    # Location of the forcing data
+path_static = "staticmaps-moselle.nc"  # Location of the static data
+
+# these are not directly part of the model
+gauges = "wflow_gauges"
+ldd = "wflow_ldd"
+river_location = "wflow_river"
+subcatchment = "wflow_subcatch"
+
+# specify the internal IDs of the parameters which vary over time
+# the external name mapping needs to be below together with the other mappings
+forcing = [
+"vertical.precipitation",
+"vertical.temperature",
+"vertical.potential_evaporation",
+]
+
+cyclic = ["vertical.leaf_area_index"]
+
+[input.vertical]    # Map internal model variable/parameter names to variable names in the netCDF files
+altitude = "wflow_dem"
+c = "c"
+cf_soil = "cf_soil"
+cfmax = "Cfmax"
+e_r = "EoverR"
+infiltcappath = "InfiltCapPath"
+infiltcapsoil = "InfiltCapSoil"
+kext = "Kext"
+kv_0 = "KsatVer"
+leaf_area_index = "LAI"             # Cyclic variable
+m = "M"
+maxleakage = "MaxLeakage"
+pathfrac = "PathFrac"
+potential_evaporation = "PET"       # Forcing variable
+precipitation = "P"                 # Forcing variable
+rootdistpar = "rootdistpar"
+rootingdepth = "RootingDepth"
+soilminthickness = "SoilMinThickness"
+soilthickness = "SoilThickness"
+specific_leaf = "Sl"
+storage_wood = "Swood"
+temperature = "TEMP"                # Forcing variable
+tt = "TT"
+tti = "TTI"
+ttm = "TTM"
+w_soil = "wflow_soil"
+water_holding_capacity = "WHC"
+waterfrac = "WaterFrac"
+theta_r = "thetaR"
+theta_s = "thetaS"
+
+[input.lateral.river]
+length = "wflow_riverlength"
+n = "N_River"
+slope = "RiverSlope"
+width = "wflow_riverwidth"
+
+[input.lateral.subsurface]
+ksathorfrac = "KsatHorFrac"
+
+[input.lateral.land]
+n = "N"
+slope = "Slope"
+
+
+

Output netCDF section

+
+

Grid data

+

This optional section of the TOML file specifies the output netCDF file for writing gridded model output. It includes a mapping between internal model parameter components and external netCDF variables.

+

To limit the size of the resulting netCDF file, file compression can be enabled. Compression increases computational time but can significantly reduce the size of the netCDF file. Set the compressionlevel variable to a value between 0 and 9. A setting of 0 means no compression, while values between 1 and 9 indicate increasing levels of compression (1: least compression, minimal run-time impact, 9: highest compression, maximum run-time impact). If file size is a concern, we recommend using a value of 1, as higher compression levels generally have a limited effect on file size.

+
[output]
+path = "output_moselle.nc"         # Location of the output file
+compressionlevel = 1               # Compression level (default 0)
+
+[output.vertical]   # Mapping of names between internal model components and external netCDF variables
+satwaterdepth = "satwaterdepth"
+snow = "snow"
+tsoil = "tsoil"
+ustorelayerdepth = "ustorelayerdepth"
+snowwater = "snowwater"
+canopystorage = "canopystorage"
+
+[output.lateral.river]
+q = "q_river"
+h = "h_river"
+
+[output.lateral.river.reservoir]
+volume = "volume_reservoir"
+
+[output.lateral.subsurface]
+ssf = "ssf"
+
+[output.lateral.land]
+q = "q_land"
+h = "h_land"
+
+
+

Scalar data

+

In addition to gridded data, scalar data can also be written to a netCDF file. Below is an example that shows how to write scalar data to the file “output_scalar_moselle.nc”. For each netCDF variable, a name (external variable name) and a parameter (internal model parameter) are required. A reducer can be specified to apply to the model output. See more details in the Output CSV section section. If a map is provided to extract data for specific locations (e.g. gauges) or areas (e.g. subcatchment), the netCDF location names are extracted from these maps. For a specific location (grid cell) a location is required. For layered model parameters and variables that have an extra layer dimension and are part of the vertical sbm concept, an internal layer index can be specified (an example is provided below). Similarly, for model parameters and variables that have an extra dimension classes and are part of the vertical FLEXTopo concept, the class name can be specified. If multiple layers or classes are desired, this can be specified in separate [[netcdf.variable]] entries. Note that the additional dimension should be specified when wflow is integrated with Delft-FEWS, for netCDF scalar data an extra dimension is not allowed by the importNetcdfActivity of the Delft-FEWS General Adapter. In the section Output CSV section, similar functionality is available for CSV. For integration with Delft-FEWS, it is recommended to write scalar data to netCDF format, as the General Adapter of Delft-FEWS can directly ingest data from netCDF files. For more information, see Run from Delft-FEWS.

+
[netcdf]
+path = "output_scalar_moselle.nc"  # Location of the results
+
+[[netcdf.variable]] # Extract the values of lateral.river.q using the gauges map, and assign it with the name 'Q' as a variable in the netCDF file
+name = "Q"
+map = "gauges"
+parameter = "lateral.river.q"
+
+[[netcdf.variable]] # Using coordinates to extract temperature
+coordinate.x = 6.255
+coordinate.y = 50.012
+name = "vwc_layer2_bycoord"
+location = "vwc_bycoord"
+parameter = "vertical.vwc"
+layer = 2
+
+[[netcdf.variable]] # Using indices to extract temperature
+location = "temp_byindex"
+name = "temp_index"
+index.x = 100
+index.y = 264
+parameter = "vertical.temperature"
+
+
+
+

Output CSV section

+

Model output can also be written to a CSV file. Below is an example that writes model output to the file “output_moselle.csv”. For each CSV column, a header and parameter (internal model parameter) are required. A reducer can be specified to apply to the model output, with the following available reducers:

+
    +
  • maximum
    • +
  • minimum
    • +
  • mean
    • +
  • median
    • +
  • sum
    • +
  • first
    • +
  • last
    • +
  • only
    • +
+

with only as the default. To extract data for a specific location (grid cell), the index of the vector, the coordinates coordinate.x and coordinate.y, or the x and y indices of the 2D array (index.x and index.y) can be provided. Additionally, a map can be provided to extract data for certain locations (e.g. gauges) or areas (e.g. subcatchment). In this case, a single entry can lead to multiple columns in the CSV file, which will be of the form header_id, e.g. Q_20, for a gauge with integer ID 20. For layered model parameters and variables that have an extra dimension layer and are part of the vertical sbm concept an internal layer index (see also example below) should be specified. For model parameters and variables that have an extra dimension classes and are part of the vertical FLEXTopo concept, it is possible to specify the class name. If multiple layers or classes are desired, this can be specified in separate [[csv.column]] entries.

+

The double brackets in [[csv.column]] follow TOML syntax, indicating that it is part of a list. You can specify as many entries as you want.

+
[csv]
+path = "output_moselle.csv"
+
+[[csv.column]]
+header = "Q"
+parameter = "lateral.river.q"
+reducer = "maximum"
+
+[[csv.column]]
+header = "volume"
+index = 1
+parameter = "lateral.river.reservoir.volume"
+
+[[csv.column]]
+coordinate.x = 6.255
+coordinate.y = 50.012
+header = "temp_bycoord"
+parameter = "vertical.temperature"
+
+[[csv.column]]
+coordinate.x = 6.255
+coordinate.y = 50.012
+header = "vwc_layer2_bycoord"
+parameter = "vertical.vwc"
+layer = 2
+
+[[csv.column]]
+header = "temp_byindex"
+index.x = 100
+index.y = 264
+parameter = "vertical.temperature"
+
+[[csv.column]]
+header = "Q"
+map = "gauges"
+parameter = "lateral.river.q"
+
+[[csv.column]]
+header = "recharge"
+map = "subcatchment"
+parameter = "vertical.recharge"
+reducer = "mean"
+
+
+

Modify parameters

+

It is possible to modify model parameters and forcing through the TOML file. Two options to modify input parameters are available:

+
    +
  • Set an input parameter (static) to a uniform value.
    • +
  • Modify an input parameter (cyclic and static) or forcing variable using a scale factor and offset.
    • +
+

For example, to set the input parameter cfmax to an uniform value of 2.5:

+
[input.vertical]
+water_holding_capacity = "WHC"
+waterfrac = "WaterFrac"
+cfmax.value = 2.5
+

For input parameters with an extra dimension (e.g. layer or classes), one uniform value can be provided or a list of values that matches the length of the additional dimension. For example, a list of values can be provided for input parameter c as follows:

+
[input.vertical]
+water_holding_capacity = "WHC"
+waterfrac = "WaterFrac"
+c.value = [10.5, 11.25, 9.5, 7.0]
+

To change the forcing variable precipitation with a scale factor of 1.5 and an offset of 0.5:

+
[input.vertical.precipitation]
+netcdf.variable.name = "P"
+scale = 1.5
+offset = 0.5
+

For input parameters with an extra dimension, it is also possible to modify multiple indices simultaneously with different scale and offset values. In the example below, the external netCDF variable c is modified at layer index 1 and 2, with a scale factor of 2.0 and 1.5 respectively, and an offset of 0.0 for both indices:

+
[input.vertical.c]
+netcdf.variable.name = "c"
+scale = [2.0, 1.5]
+offset = [0.0, 0.0]
+layer = [1, 2]
+
+
+

Fixed forcing values

+

It is possible to set fixed values for forcing parameters through the TOML file. For example, to set temperature to a fixed value of 10 \degreeC, the complete forcing list is required:

+
forcing = [
+  "vertical.precipitation",
+  "vertical.temperature",
+  "vertical.potential_evaporation",
+]
+
+[input.vertical.temperature]
+value = 10
+

Note that the mapping to the external netCDF variable listed under the [input.vertical] section needs to be removed or commented out:

+
[input.vertical]
+potential_evaporation = "PET" # forcing
+# temperature = "TEMP" # forcing
+precipitation = "P" # forcing
+ + +
+ + Back to top
+ +
+
+
+
+

Made possible by: Deltares Deltares

+
+
+   +
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/previews/PR448/user_guide/warm_states.html b/previews/PR448/user_guide/warm_states.html new file mode 100644 index 000000000..eb63cc07f --- /dev/null +++ b/previews/PR448/user_guide/warm_states.html @@ -0,0 +1,959 @@ + + + + + + + + + +Starting with “warm” states – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Starting with “warm” states

+
+ + + +
+ + + + +
+ + + +
+ + +

The state section in the TOML file provides information on the input file if the model is initialized with a warm state (path_input) and to what file the states are written at the end of the model run (path_output). Please note that the model setting reinit needs to be set to false in order to initialize the model with states from the file located at path_input. A mapping between external state names and internal model states is required. This information is specified for each model component, the vertical model and lateral model components. In the example below the vertical component represents the SBM concept, and for the lateral components there is a river (including optional reservoir, lake and floodplain components), land and subsurface domain. The internal model states are listed on the left side, and the external state names are listed on the right side. Note that path_input is only required when reinit is set to false. path_output is optional, an output state file is only written when it is defined. If neither is set, the entire state section can be left out.

+
[model]
+reinit = false # cold (reinit = true) or warm state (reinit = false), default is true
+
+[state]
+path_input = "data/instates-moselle.nc"     # Location of the file with the input states
+path_output = "data/outstates-moselle.nc"   # Output location of the states after the model run
+
+[state.vertical]
+satwaterdepth = "satwaterdepth"
+snow = "snow"
+tsoil = "tsoil"
+ustorelayerdepth = "ustorelayerdepth"
+canopystorage = "canopystorage"
+snowwater = "snowwater"
+glacierstore ="glacierstore"
+
+[state.lateral.river]
+q = "q_river"
+h = "h_river"
+h_av = "h_av_river"
+
+[state.lateral.river.floodplain]
+q = "q_floodplain"
+h = "h_floodplain"
+
+[state.lateral.river.reservoir]
+volume = "volume_reservoir"
+
+[state.lateral.river.lake]
+waterlevel = "waterlevel_lake"
+
+[state.lateral.subsurface]
+ssf = "ssf"
+
+[state.lateral.land]
+q = "q_land"
+h = "h_land"
+h_av = "h_av_land"
+ + + + Back to top
+ +
+
+
+
+

Made possible by: Deltares Deltares

+
+
+   +
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/previews/PR448/user_guide/zmq_server.html b/previews/PR448/user_guide/zmq_server.html new file mode 100644 index 000000000..42f302ade --- /dev/null +++ b/previews/PR448/user_guide/zmq_server.html @@ -0,0 +1,887 @@ + + + + + + + + + +Run wflow as a ZMQ Server – Wflow.jl + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Run wflow as a ZMQ Server

+
+ + + +
+ + + + +
+ + + +
+ + +

It is possible to run wflow as a ZMQ Server, for example for the coupling to the OpenDA software for data-assimilation. The code for the wflow ZMQ Server is not part of the Wflow.jl package, and is located here.

+ + + + Back to top
+ +
+ + + + + + \ No newline at end of file