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<title>10. Data — Community Water Model</title>
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Community Water Model
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<section id="data">
<h1><a class="toc-backref" href="#id58" role="doc-backlink"><span class="section-number">10. </span>Data</a><a class="headerlink" href="#data" title="Permalink to this heading"></a></h1>
<nav class="contents" id="contents">
<p class="topic-title">Contents</p>
<ul class="simple">
<li><p><a class="reference internal" href="#data" id="id58">Data</a></p>
<ul>
<li><p><a class="reference internal" href="#data-requirements" id="id59">Data requirements</a></p></li>
<li><p><a class="reference internal" href="#data-format" id="id60">Data format</a></p></li>
<li><p><a class="reference internal" href="#data-storage-structure" id="id61">Data storage structure</a></p></li>
<li><p><a class="reference internal" href="#static-data" id="id62">Static data</a></p>
<ul>
<li><p><a class="reference internal" href="#mask-map" id="id63">Mask map</a></p></li>
<li><p><a class="reference internal" href="#landsurface" id="id64">Landsurface</a></p></li>
<li><p><a class="reference internal" href="#river-drainage-maps" id="id65">River drainage maps</a></p></li>
<li><p><a class="reference internal" href="#river-channel-maps" id="id66">River channel maps</a></p></li>
<li><p><a class="reference internal" href="#soil-and-soil-hydraulic-properties" id="id67">Soil and soil hydraulic properties</a></p></li>
<li><p><a class="reference internal" href="#groundwater" id="id68">Groundwater</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#temporal-data-for-each-year" id="id69">Temporal data for each year</a></p>
<ul>
<li><p><a class="reference internal" href="#crop-coefficient" id="id70">Crop coefficient</a></p></li>
<li><p><a class="reference internal" href="#land-cover" id="id71">Land cover</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#continous-temporal-data" id="id72">Continous temporal data</a></p>
<ul>
<li><p><a class="reference internal" href="#meteorological-data" id="id73">Meteorological data</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#references" id="id74">References</a></p></li>
</ul>
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<section id="data-requirements">
<h2><a class="toc-backref" href="#id59" role="doc-backlink">Data requirements</a><a class="headerlink" href="#data-requirements" title="Permalink to this heading"></a></h2>
</section>
<section id="data-format">
<h2><a class="toc-backref" href="#id60" role="doc-backlink">Data format</a><a class="headerlink" href="#data-format" title="Permalink to this heading"></a></h2>
<p>In general data format is netCDF (version3 or version4)</p>
<p>For the mask map (to define the area of calculation) or the stations (to define the time series outputs) in can be either
netCDF, Geotiff or PCRaster maps</p>
</section>
<section id="data-storage-structure">
<h2><a class="toc-backref" href="#id61" role="doc-backlink">Data storage structure</a><a class="headerlink" href="#data-storage-structure" title="Permalink to this heading"></a></h2>
<div class="highlight-rest notranslate"><div class="highlight"><pre><span></span>project
├── README.txt
│
├── areamaps
│ └── maskmap, stationmap
│
├── landcover
│ ├──forest
│ │ ├── cropCoefficientForest_10days
│ │ ├── interceptcapForest10days
│ │ ├── maxRootdepth, minSoilDepthFrac
│ │ └── rootFraction1, rootFraction2
│ │
│ ├── grassland (same var as forest)
│ │
│ ├── irrNonPaddy (same var as forest)
│ │
│ └── irrPaddy (same var as forest)
│
├───landsurface
│ ├── fractionlandcover, global_clone
│ │
│ ├── albedo
│ │ └── albedo
│ │
│ ├── topo
│ │ └── dz_Rel_hydro1k, elvstd , tanslope
│ │
│ └── waterDemand
│ └── domesticWaterDemand, industryWaterDemand, irrigationArea, efficiency
│
├── soil
│ ├── alpha, forest_alpha, lamdba, forest_lambda, ksat, forest_ksat, thetas, forest_thetas, thetar, forest_thetar
│ └── cropgrp
│
├── groundwater
│ └── kSatAquifer, recessionCoeff, specificYield
│
└── routing
├── ldd, catchment, cellarea
│
├── kinematic
│ └── chanbnkf, chanbw, changrad, chanleng, chanman
│
└── lakereservoirs
├── lakeResArea, lakeResDis,lakeResID, lakeResType, lakeResVolRes, lakeResYear,
└── smallLakesRes, smalllakesresArea, smalllakesresDis, smallwatershedarea
</pre></div>
</div>
</section>
<section id="static-data">
<h2><a class="toc-backref" href="#id62" role="doc-backlink">Static data</a><a class="headerlink" href="#static-data" title="Permalink to this heading"></a></h2>
<section id="mask-map">
<h3><a class="toc-backref" href="#id63" role="doc-backlink">Mask map</a><a class="headerlink" href="#mask-map" title="Permalink to this heading"></a></h3>
<ul class="simple">
<li><p>mask map or coordinates to model only regions or catchments (value in mask = 1)</p></li>
<li><p>maps or coordinates for station to print time series</p></li>
</ul>
<a class="reference internal image-reference" href="_images/mask_rhine.jpg"><img alt="_images/mask_rhine.jpg" src="_images/mask_rhine.jpg" style="width: 300px;" /></a>
<p>Figure 1: Mask map for the Rhine basin at 5’ showing in addition 6 stations</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Make sure any cell defined in the mask map has a value (not NaN!) in the following map. A missing value in a cell will lead to a missing value in the result maps from the process this map is linked to.</p>
<p>The routing process will carry this missing value downstream!</p>
</div>
</section>
<section id="landsurface">
<h3><a class="toc-backref" href="#id64" role="doc-backlink">Landsurface</a><a class="headerlink" href="#landsurface" title="Permalink to this heading"></a></h3>
<section id="digital-elevation-model-and-river-channel-network">
<h4>Digital elevation model and river channel network<a class="headerlink" href="#digital-elevation-model-and-river-channel-network" title="Permalink to this heading"></a></h4>
<p>The model uses a digital elevation model and its derivate (e.g. standards deviation, slope) as variables for the snow processes and for the routing of surface runoff. The Shuttle Radar Topography Mission - SRTM (Jarvis et al., 2008) <a class="footnote-reference brackets" href="#id30" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> is used for latitudes <= 60 deg North and DEM Hydro1k (US Geological Survey Center for Earth Resources Observation and Science) <a class="footnote-reference brackets" href="#id31" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a> is used for latitudes > 60 deg North</p>
<a class="reference internal image-reference" href="_images/dem.jpg"><img alt="_images/dem.jpg" src="_images/dem.jpg" style="width: 600px;" /></a>
<p>Figure 1: Digital elevation based on SRTM for 30’ and 5’</p>
<a class="reference internal image-reference" href="_images/Standard_deviation_elevation_5min.jpg"><img alt="_images/Standard_deviation_elevation_5min.jpg" src="_images/Standard_deviation_elevation_5min.jpg" style="width: 300px;" /></a>
<p>Figure 2: Standard deviation of elevation based on SRTM and 5’</p>
</section>
</section>
<section id="river-drainage-maps">
<h3><a class="toc-backref" href="#id65" role="doc-backlink">River drainage maps</a><a class="headerlink" href="#river-drainage-maps" title="Permalink to this heading"></a></h3>
<p>The river drainage map or local drain direction (LDD) is the essential component to connect the grid cells in order to express the flow direction from one cell to another and forming a river network from the springs to the mouth.</p>
<p>The approach to find the flow direction is in theory quite simple:
There are eight valid output directions relating to the eight adjacent cells into which flow could travel. This approach is commonly referred to as an eight-direction (D8) flow model. The direction from each cell to its steepest downslope neighbour is chosen as flow direction. If the flow direction for each cell is given, a raster of accumulated flow into each cell can be calculated. Figure 4 shows the steps from DEM to flow direction to flow accumulation. Flow direction is shown in PC-Raster coding of the direction (ArcGIS uses another coding).</p>
<p>CWatM uses a local drainage direction map which defines the dominant flow direction in one of the eight neighboring grid cells (D8 flow model). This forms a river network from the springs to the mouth of a basin. To be compliant with the ISIMIP framework the 0.5° drainage direction map (DDM30) of (Döll and Lehner, 2002) <a class="footnote-reference brackets" href="#id32" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a> is used. For higher resolution e.g. 5’ different sources of river network maps are available e.g. HydroSheds (Lehner et al., 2008) <a class="footnote-reference brackets" href="#id33" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>4<span class="fn-bracket">]</span></a> – DRT (Wu et al., 2011) <a class="footnote-reference brackets" href="#id34" id="id5" role="doc-noteref"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></a> and CaMa-Flood (Yamazaki et al., 2009) <a class="footnote-reference brackets" href="#id35" id="id6" role="doc-noteref"><span class="fn-bracket">[</span>6<span class="fn-bracket">]</span></a>. These approaches uses the same hydrological sound digital elevation model but differ in the upscaling methods. Zhao et al. (2017) <a class="footnote-reference brackets" href="#id36" id="id7" role="doc-noteref"><span class="fn-bracket">[</span>7<span class="fn-bracket">]</span></a> shows the importance of routing schemes and river networks in peak discharge simulation.
For CWatM the DDM30 is used for 0.5° and DRT is used for 5’.</p>
<a class="reference internal image-reference" href="_images/elevation_flowaccu.jpg"><img alt="_images/elevation_flowaccu.jpg" src="_images/elevation_flowaccu.jpg" style="width: 600px;" /></a>
<p>Figure 3: From elevation to flow accumulation</p>
<a class="reference internal image-reference" href="_images/ldd.jpg"><img alt="_images/ldd.jpg" src="_images/ldd.jpg" style="width: 600px;" /></a>
<p>Figure 4: River network for the Rhine basin</p>
</section>
<section id="river-channel-maps">
<h3><a class="toc-backref" href="#id66" role="doc-backlink">River channel maps</a><a class="headerlink" href="#river-channel-maps" title="Permalink to this heading"></a></h3>
<p>Channel maps are describing the geometry like the length, slope, width and depth of the main channel inside a grid cell.
Data used to get the geometry are mainly taken from elevation model and channel network.</p>
<section id="methodology">
<h4>Methodology<a class="headerlink" href="#methodology" title="Permalink to this heading"></a></h4>
<p>Flow through the channel is simulated using the kinematic wave equations. The basic equations used are the equations of continuity and momentum.
The continuity equation is:</p>
<p><span class="math">{\frac{\delta Q}{\delta x}} + {\frac{\delta A }{\delta t}} = q</span></p>
<div class="line-block">
<div class="line">where:</div>
<div class="line">Q: channel discharge [m3 s-1],</div>
<div class="line">A: cross-sectional area of the flow [m2]</div>
<div class="line">q: amount of lateral inflow per unit flow length [m2 s-1].</div>
</div>
<p>The momentum equation can also be expressed as (Chow et al., 1988):</p>
<p><span class="math">{A = \alpha Q^\beta}</span></p>
<p>The coefficients α and β are calculated by putting in Manning’s equation</p>
<p><span class="math">Q = A v = \frac{AR^{2/3} \sqrt{So}}{n} = \frac{A^{5/3} \sqrt{So}}{n P^{2/3}}</span></p>
<div class="line-block">
<div class="line">where:</div>
<div class="line">v: velocity [m/s]</div>
<div class="line">n: Manning’s roughness coefficient</div>
<div class="line">P: wetted perimeter of a cross-section of the surface flow [m]</div>
<div class="line">R: hydraulic Radius R=A/P</div>
</div>
<p>Solving this for α and β gives:</p>
<p><span class="math">\alpha = (\frac{nP^{2/3}}{\sqrt{So}})^\beta</span> and <span class="math">\beta = 0.6</span></p>
<div class="line-block">
<div class="line">To calculate α CWatM uses static maps of:</div>
<div class="line">P: wetted perimeter approximated in CWatM: P = channel width + 2 * channel bankful depth</div>
<div class="line">n: Manning’s coefficient</div>
<div class="line">S0: gradient (slope) of the water surface: S0 = Δelevation/channel length</div>
</div>
</section>
<section id="channel-length">
<h4>Channel length<a class="headerlink" href="#channel-length" title="Permalink to this heading"></a></h4>
<p>The network upscaling method of Wu et al. (2011) <a class="footnote-reference brackets" href="#id34" id="id8" role="doc-noteref"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></a> is tracing the finer river network inside the coarser resolution.
Channel length of 5’ is traced from original SRTM channel length with the diagonal path taken to be √2 ∙ straight path.</p>
</section>
<section id="channel-gradient">
<h4>Channel gradient<a class="headerlink" href="#channel-gradient" title="Permalink to this heading"></a></h4>
<p>Channel gradient (or channel slope) is the average gradient of the main river inside a cell.</p>
<p>The approach taken here is to take the elevation from where the fine resolution channel enters the coarser grid cell and the elevation where it leaves the grid cell. Channel gradient is then calculated as:</p>
<p>Channel gradient = (elevation[in] –elevation[out]) / channel length.</p>
<a class="reference internal image-reference" href="_images/Channel_gradient.png"><img alt="_images/Channel_gradient.png" src="_images/Channel_gradient.png" style="width: 600px;" /></a>
<p>Figure x: Channel gradient at 5 in % or tan(α)’</p>
</section>
<section id="mannings-roughness">
<h4>Manning’s roughness<a class="headerlink" href="#mannings-roughness" title="Permalink to this heading"></a></h4>
<p>Manning’s roughness coefficient (n) is one of the calibration parameter in CWatM. But on subbasin level an estimation of the spatial distribution of n is needed. n normally range between 0.025 (low land rivers) and 0.075 (mountainous rivers with a lot of vegetation, gravels). A low n = smooth surface results in a faster travel time and higher peaks. A high n = rough surface results in slower travel time and lower peaks. Inspection of the riverbed will reveal characteristics related to roughness. A treatment of the use of Manning’s coefficients is in McCuen (1998) <a class="footnote-reference brackets" href="#id37" id="id9" role="doc-noteref"><span class="fn-bracket">[</span>8<span class="fn-bracket">]</span></a>. Below is a first-approximation of Manning’s coefficients for some widely observed beds:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">n</span> <span class="o">=</span> <span class="mf">0.04</span> <span class="o">-</span> <span class="mf">0.05</span> <span class="n">Mountain</span> <span class="n">streams</span>
<span class="n">n</span> <span class="o">=</span> <span class="mf">0.035</span> <span class="n">Winding</span><span class="p">,</span> <span class="n">weedy</span> <span class="n">streams</span>
<span class="n">n</span> <span class="o">=</span> <span class="mf">0.028</span> <span class="o">-</span> <span class="mf">0.035</span> <span class="n">Major</span> <span class="n">streams</span> <span class="k">with</span> <span class="n">widths</span> <span class="o">></span> <span class="mi">30</span><span class="n">m</span> <span class="n">at</span> <span class="n">flood</span> <span class="n">stage</span>
<span class="n">n</span> <span class="o">=</span> <span class="mf">0.015</span> <span class="n">Clean</span><span class="p">,</span> <span class="n">earthen</span> <span class="n">channels</span>
</pre></div>
</div>
<p>For the base map of Manning a regression function is used with 0.025 as the minimum value for flatland rivers with large upstream areas. A maximum of 0.015 is added for flatland rivers and small upstream areas (upstream area dependent) and another maximum of 0.030 is added if in mountainous areas (elevation dependent):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Manning</span> <span class="o">=</span><span class="mf">0.025</span> <span class="o">+</span> <span class="mf">0.015</span> <span class="o">*</span> <span class="nb">min</span><span class="p">(</span><span class="mi">50</span><span class="o">/</span><span class="n">upstream</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.030</span><span class="o">*</span><span class="nb">min</span><span class="p">(</span><span class="n">DEM</span><span class="o">/</span><span class="mi">2000</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
<span class="n">Where</span><span class="p">:</span>
<span class="n">upstream</span><span class="p">:</span> <span class="n">upstream</span> <span class="n">catchment</span> <span class="n">area</span> <span class="p">[</span><span class="n">km</span><span class="p">]</span>
<span class="n">DEM</span><span class="p">:</span> <span class="n">elevation</span> <span class="kn">from</span> <span class="nn">Digital</span> <span class="n">elevation</span> <span class="n">model</span> <span class="p">[</span><span class="n">m</span><span class="p">]</span>
</pre></div>
</div>
<a class="reference internal image-reference" href="_images/Mannings_roughness_5min.png"><img alt="_images/Mannings_roughness_5min.png" src="_images/Mannings_roughness_5min.png" style="width: 600px;" /></a>
<p>Figure x: Manning’s roughness coefficient for 5’</p>
</section>
<section id="channel-bottom-width">
<h4>Channel Bottom Width<a class="headerlink" href="#channel-bottom-width" title="Permalink to this heading"></a></h4>
<p>The channel bottom width is calculated in two steps with the first step using a simply regression between channel width and upstream area and the second uses a better correlated one between average discharge and channel width.
First the channel bottom width is calculated by a simply regression between upstream catchment area and width:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>Channel width=upstreamArea ×0.0032
</pre></div>
</div>
<p>This first map is used to run CWatM to get an estimate on average discharge.</p>
<p>In the second step a regression formula from Pistocchi et al. 2006 <a class="footnote-reference brackets" href="#id38" id="id10" role="doc-noteref"><span class="fn-bracket">[</span>9<span class="fn-bracket">]</span></a> is used to calculate the channel bottom width with average discharge as regressor, because discharge seems to be better correlated to width than upstream area. This is quite obvious if you look at small alpine catchment with high precipitation and therefore high discharge and on the other side at big, almost semiarid catchments on the Iberian peninsula with low average discharge:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Channel</span> <span class="n">width</span><span class="o">=</span><span class="n">average</span> <span class="n">Q</span> <span class="o">^</span> <span class="mf">0.539</span>
</pre></div>
</div>
<a class="reference internal image-reference" href="_images/Channel_width_5min.png"><img alt="_images/Channel_width_5min.png" src="_images/Channel_width_5min.png" style="width: 600px;" /></a>
<p>Figure 6: Channel width at 5’</p>
</section>
<section id="channel-bankful-depth">
<h4>Channel bankful depth<a class="headerlink" href="#channel-bankful-depth" title="Permalink to this heading"></a></h4>
<p>Instead of deriving channel hydraulic properties from a non linear correlation with the upstream area we are using the Manning’s equation to get a better estimate. But for the first estimate (same as for channel bottom width) we use a correlation with upstream area:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Channel</span> <span class="n">bankful</span> <span class="n">depth</span> <span class="o">=</span> <span class="mf">0.27</span> <span class="n">upstreamArea</span><span class="o">^</span><span class="mf">0.33</span>
</pre></div>
</div>
<p>In the second step we use the Manning’s equation. We adopt a rectangular cross section and we assume depth is small compared to width. So the perimeter is assumed to be:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">P</span> <span class="o">=</span> <span class="mf">1.01</span> <span class="o">*</span> <span class="n">channel</span> <span class="n">bottom</span> <span class="n">width</span>
</pre></div>
</div>
<p>Discharge for bankful discharge is assumed to be two times the average discharge (Qavg)</p>
<p><span class="math">Q = 2 * Qavg</span></p>
<p><span class="math">Q = \frac{A^{5/3} \sqrt{So}}{n P^{2/3}} \approx \frac{Wh^{5/3} \sqrt{So}}{n (1.01W)^{2/3}}</span></p>
<div class="line-block">
<div class="line">Where:</div>
<div class="line">W: Channel width</div>
<div class="line">h: bankful depth</div>
<div class="line">Q: bankful discharge ~ 2 * average discharge</div>
</div>
<p>As we now know all the other variables we can solve this equation for bankful depth with some assumption:</p>
<p>This leads to the equation:</p>
<p><span class="math">Channel bankful depth (h)= 1.004 N^{3/5} Q^{3/5} W^{-3/5} So^{-3/10}</span></p>
<div class="line-block">
<div class="line">Where:</div>
<div class="line">W: Channel width</div>
<div class="line">Q: bankful discharge ~ 2 * average discharge</div>
</div>
</section>
</section>
<section id="soil-and-soil-hydraulic-properties">
<h3><a class="toc-backref" href="#id67" role="doc-backlink">Soil and soil hydraulic properties</a><a class="headerlink" href="#soil-and-soil-hydraulic-properties" title="Permalink to this heading"></a></h3>
<p>Modeling of unsaturated flow and transport processes can be done with the 1D Richard equation, which requires a high spatial and temporal distribution of the soil hydraulic properties</p>
<p><span class="math">\frac{\delta \Theta}{\delta t} = \frac{\delta}{\delta z}[K(\Theta(\frac{\delta h(\Theta)}{\delta z}-1)]-S(\Theta)</span> (1D Richard equation)</p>
<div class="line-block">
<div class="line">Where:</div>
<div class="line">θ: soil volumetric moisture content [L3/L3]</div>
<div class="line">t: time [T]</div>
<div class="line">h: soil water pressure head [L]</div>
<div class="line">K(θ): unsaturated hydraulic conductivity [L/T]</div>
<div class="line">z: vertical coordinate</div>
<div class="line">S: source sink term [T-1]</div>
</div>
<p>With the simplification the 1D Richard equation e.g. flow of soil moisture is entirely gravitu-driven and matrix potential gradient is zero this implies a flow tha tis always in downward direction at a rate that equals the conductivity of the soil. The relationship can now be described with the model of Mualem (1976) <a class="footnote-reference brackets" href="#id39" id="id11" role="doc-noteref"><span class="fn-bracket">[</span>10<span class="fn-bracket">]</span></a> and with the van Genuchten model (1980) <a class="footnote-reference brackets" href="#id40" id="id12" role="doc-noteref"><span class="fn-bracket">[</span>11<span class="fn-bracket">]</span></a> equation. Please find a full description of the modeled soil processes in <a class="reference external" href="https://gmd.copernicus.org/articles/13/3267/2020">Burek et al. 2020</a></p>
<p><span class="math">K(\Theta) = K_s(\frac{\Theta - \Theta_r}{\Theta_s - \Theta_r})^{0.5} \lbrace 1-[1-(\frac{\Theta - \Theta_r}{\Theta_s - \Theta_r})^{1/m}]^{m} \rbrace^{2}</span> (Van Genuchten equation)</p>
<div class="line-block">
<div class="line">Where:</div>
<div class="line">Ks: saturated conductivity of the soil [cm/d-1]</div>
<div class="line">K(θ): unsaturated conductivity</div>
<div class="line"><span class="math">\Theta</span> <span class="math">\Theta_s</span> <span class="math">\Theta_r</span> : actual, maximum and residual amounts of moisture in the soil [mm]</div>
<div class="line">m: is calculated from the pore-size index <span class="math">\lambda</span> : <span class="math">m = \frac{\lambda}{\lambda + 1}</span></div>
</div>
<p>The soil hydraulic parameter <span class="math">\Theta_s</span> <span class="math">\Theta_r</span> <span class="math">\lambda</span> and <span class="math">K_s</span> are needed to simulated soil water transport for the van Genuchten model.</p>
<div class="line-block">
<div class="line">The infiltration capacity of the soil is using the Xinanjiang (also known as VIC/ARNO) model (Todini, 1996) <a class="footnote-reference brackets" href="#id41" id="id13" role="doc-noteref"><span class="fn-bracket">[</span>12<span class="fn-bracket">]</span></a></div>
<div class="line">The soil hydraulic parameter <span class="math">\alpha</span> (inverse of air entry suction) is needed for calculating infiltration capacity</div>
</div>
<section id="harmonized-world-soil-database">
<h4>Harmonized World Soil Database<a class="headerlink" href="#harmonized-world-soil-database" title="Permalink to this heading"></a></h4>
<p>The Harmonized World Soil Database 1.2 (HWSD) FAO et al. (2012) <a class="footnote-reference brackets" href="#id42" id="id14" role="doc-noteref"><span class="fn-bracket">[</span>13<span class="fn-bracket">]</span></a> - Version 1.2 7 March, 2012 was developed by the Land Use Change and Agriculture Program of IIASA (LUC) and the Food and Agriculture Organization of the United Nations (FAO). The HWSD is a 30 arc-second raster database with over 16000 different soil mapping units that combines existing regional and national updates of soil information worldwide – the European Soil Database (ESDB), the 1:1 million soil map of China, various regional SOTER databases (SOTWIS Database), and the Soil Map of the World – with the information contained within the 1:5000000 scale FAO-UNESCO Soil Map of the World. The resulting raster database is linked to harmonized soil property data.</p>
<a class="reference internal image-reference" href="_images/HWSD_index.jpg"><img alt="_images/HWSD_index.jpg" src="_images/HWSD_index.jpg" style="width: 600px;" /></a>
<p>Figure x: Harmonized World Soil Database Index, FAO et al. (2012)</p>
<p>From the HWSD the standard soil properties like texture, porosity, soil minerals (% of sand, clay), organic mater and bulk density are used.
For example Bulk density second soil layer 5-30 cm depth:</p>
<a class="reference internal image-reference" href="_images/Bulk_Density1.png"><img alt="_images/Bulk_Density1.png" src="_images/Bulk_Density1.png" style="width: 600px;" /></a>
<p>Figure x: Bulk density second soil layer 5-30 cm at 5’</p>
</section>
<section id="pedotransfer-function-rosetta3">
<h4>Pedotransfer function Rosetta3<a class="headerlink" href="#pedotransfer-function-rosetta3" title="Permalink to this heading"></a></h4>
<p>Soil parameters required by CWatM are obtained from soil properties by using a pedotransfer function.</p>
<p>A pedotransfer is used from Zhang and Schaap 2016 <a class="footnote-reference brackets" href="#id43" id="id15" role="doc-noteref"><span class="fn-bracket">[</span>14<span class="fn-bracket">]</span></a> to transfer the standard soil properties (soil texture, porosity, organic mater and bulk density) to the van Genuchten model parameters: <span class="math">\Theta_s</span> (maximal amount of moisture) <span class="math">\Theta_r</span> (residual amount of moisture) <span class="math">\lambda</span> (pore-size index) <span class="math">K_s</span> (saturated conductivity of the soil) and <span class="math">\alpha</span> (inverse of air entry suction)</p>
<p>Rosetta3 code is available at: <a class="reference external" href="http://www.cals.arizona.edu/research/rosettav3.html">http://www.cals.arizona.edu/research/rosettav3.html</a></p>
<p>For example θs and Ks:</p>
<a class="reference internal image-reference" href="_images/soil_theta.jpg"><img alt="_images/soil_theta.jpg" src="_images/soil_theta.jpg" style="width: 600px;" /></a>
<p>Figure x: Soil volumetric moisture content (θs) [%] second soil layer 5-30 cm at 5’</p>
<a class="reference internal image-reference" href="_images/soil_ks.jpg"><img alt="_images/soil_ks.jpg" src="_images/soil_ks.jpg" style="width: 600px;" /></a>
<p>Figure x: Saturated hydraulic conductivity (Ks) [cm/day] second soil layer 5-30 cm at 5’</p>
</section>
</section>
<section id="groundwater">
<h3><a class="toc-backref" href="#id68" role="doc-backlink">Groundwater</a><a class="headerlink" href="#groundwater" title="Permalink to this heading"></a></h3>
<p>For groundwater modeling maps of the recession constant of the hydraulic conductivity and the storage coefficient are needed.
Gleeson et al., (2011) <a class="footnote-reference brackets" href="#id44" id="id16" role="doc-noteref"><span class="fn-bracket">[</span>15<span class="fn-bracket">]</span></a> and Gleeson et al. (2014) <a class="footnote-reference brackets" href="#id45" id="id17" role="doc-noteref"><span class="fn-bracket">[</span>16<span class="fn-bracket">]</span></a> can provide data for this.</p>
<div class="line-block">
<div class="line">Global RecessionConstant GLIM: [1/day] based on drainage theory (linear reservoir)</div>
<div class="line">Global SatHydraulicConductivity: Mean permeability of consolidated and unconsolidated geologic units below the soil [log10 m2]</div>
<div class="line">Global StorageCoefficient [m/m]: specific yields or storage coefficients</div>
</div>
<div class="line-block">
<div class="line">Data:</div>
<div class="line">GLHYMPS—Global Hydrogeology Maps of permeability and porosity (Gleeson et al., 2014)</div>
<div class="line"><a class="reference external" href="http://crustalpermeability.weebly.com/data-sources.html">http://crustalpermeability.weebly.com/data-sources.html</a></div>
<div class="line"><a class="reference external" href="http://spatial.cuahsi.org/gleesont01/">http://spatial.cuahsi.org/gleesont01/</a></div>
</div>
<a class="reference internal image-reference" href="_images/Recession_Constant.png"><img alt="_images/Recession_Constant.png" src="_images/Recession_Constant.png" style="width: 600px;" /></a>
<p>Figure x: Recession constant GLIM: [1/day] at 5’</p>
<section id="lakes-and-reservoirs">
<h4>Lakes and Reservoirs<a class="headerlink" href="#lakes-and-reservoirs" title="Permalink to this heading"></a></h4>
<p>The HydroLakes database <a class="reference external" href="http://www.hydrosheds.org/page/hydrolakes">http://www.hydrosheds.org/page/hydrolakes</a> (Lehner et al. (2011) <a class="footnote-reference brackets" href="#id46" id="id18" role="doc-noteref"><span class="fn-bracket">[</span>17<span class="fn-bracket">]</span></a>; Messager et al. (2016) <a class="footnote-reference brackets" href="#id47" id="id19" role="doc-noteref"><span class="fn-bracket">[</span>18<span class="fn-bracket">]</span></a>, provides 1.4 million global lakes and reservoirs with a surface area of at least 10ha. CWatM differentiate between big lakes and reservoirs which are connected inside the river network and smaller lakes and reservoirs which are part of a single grid cell and part of the runoff concentration within a grid cell. Therefore the HydroLakes database is separated into “big” lakes and reservoirs with an area ≥ 100 km2 or a upstream area ≥ 5000 km2 and “small” lakes which represents the non-big lakes. All lakes and reservoirs are combined at grid cell level but big lakes can have the expansion of several grid cells. Lakes bigger than 10000 km2 are shifted according to the ISIMIP protocol.
Lake and reservoir (LR) data are specified by an id for each LR, type of LR (1 for lake, 2 for reservoir), area of LR, year of constraction of reservoir and average discharge at the outlet of LR.</p>
</section>
</section>
</section>
<section id="temporal-data-for-each-year">
<h2><a class="toc-backref" href="#id69" role="doc-backlink">Temporal data for each year</a><a class="headerlink" href="#temporal-data-for-each-year" title="Permalink to this heading"></a></h2>
<section id="crop-coefficient">
<h3><a class="toc-backref" href="#id70" role="doc-backlink">Crop coefficient</a><a class="headerlink" href="#crop-coefficient" title="Permalink to this heading"></a></h3>
<p>Based on:
MIRCA2000—Global data set of monthly irrigated and rainfed crop areas around the year 2000. <a class="reference external" href="http://www.uni-frankfurt.de/45218023/MIRCA">http://www.uni-frankfurt.de/45218023/MIRCA</a> (Portmann et al., 2010) <a class="footnote-reference brackets" href="#id48" id="id20" role="doc-noteref"><span class="fn-bracket">[</span>19<span class="fn-bracket">]</span></a></p>
</section>
<section id="land-cover">
<h3><a class="toc-backref" href="#id71" role="doc-backlink">Land cover</a><a class="headerlink" href="#land-cover" title="Permalink to this heading"></a></h3>
<p>Land cover is used to calculate fraction of water, forest, irrigated area, rice irrigated area, sealed (impermeable area) and the remaining fraction for each cell. For each fraction the soil module runs separately. The total runoff of each cell is calculated by weighting the cell according to the different fractions.</p>
<p>Source: <a class="reference external" href="https://lta.cr.usgs.gov/GLCC">https://lta.cr.usgs.gov/GLCC</a> (US Geological Survey Center for Earth Resources Observation and Science)</p>
<section id="forest">
<h4>Forest<a class="headerlink" href="#forest" title="Permalink to this heading"></a></h4>
<p>Forest land cover is used from from Hansen et al. (2013) <a class="footnote-reference brackets" href="#id49" id="id21" role="doc-noteref"><span class="fn-bracket">[</span>20<span class="fn-bracket">]</span></a></p>
<a class="reference internal image-reference" href="_images/Tree_cover_5min.jpg"><img alt="_images/Tree_cover_5min.jpg" src="_images/Tree_cover_5min.jpg" style="width: 600px;" /></a>
<p>Figure x: Tree cover in 2010 at 5’</p>
</section>
<section id="sealed">
<h4>Sealed<a class="headerlink" href="#sealed" title="Permalink to this heading"></a></h4>
<p>Urban area or impervious surface area (ISA) based on.</p>
<div class="line-block">
<div class="line">Based on 1km version of Elvidge et al. (2007) <a class="footnote-reference brackets" href="#id50" id="id22" role="doc-noteref"><span class="fn-bracket">[</span>21<span class="fn-bracket">]</span></a></div>
<div class="line"><a class="reference external" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3841857/">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3841857/</a></div>
<div class="line"><a class="reference external" href="ftp://ftp.ngdc.noaa.gov/DMSP/">ftp://ftp.ngdc.noaa.gov/DMSP/</a></div>
</div>
<p>Future projection based on:</p>
<p>Transient, future land use pattern generated by the LU model MAgPIE (Popp et al. 2014 <a class="footnote-reference brackets" href="#id51" id="id23" role="doc-noteref"><span class="fn-bracket">[</span>22<span class="fn-bracket">]</span></a>; Stevanovic et al. 2016 <a class="footnote-reference brackets" href="#id52" id="id24" role="doc-noteref"><span class="fn-bracket">[</span>23<span class="fn-bracket">]</span></a>), assuming population growth and economic as in SSP2 and climate change scenario RCP6.0</p>
<a class="reference internal image-reference" href="_images/Sealed_5min.png"><img alt="_images/Sealed_5min.png" src="_images/Sealed_5min.png" style="width: 600px;" /></a>
<p>Figure x: Sealed area in 2010 at 5’</p>
</section>
<section id="albedo">
<h4>Albedo<a class="headerlink" href="#albedo" title="Permalink to this heading"></a></h4>
<p>Global Albedo dataset from Muller et al., (2012) <a class="footnote-reference brackets" href="#id53" id="id25" role="doc-noteref"><span class="fn-bracket">[</span>24<span class="fn-bracket">]</span></a></p>
</section>
</section>
</section>
<section id="continous-temporal-data">
<h2><a class="toc-backref" href="#id72" role="doc-backlink">Continous temporal data</a><a class="headerlink" href="#continous-temporal-data" title="Permalink to this heading"></a></h2>
<section id="meteorological-data">
<h3><a class="toc-backref" href="#id73" role="doc-backlink">Meteorological data</a><a class="headerlink" href="#meteorological-data" title="Permalink to this heading"></a></h3>
<ul class="simple">
<li><p>max, min, avg temperature [K]</p></li>
<li><p>humidity (relative[%] or specific[%])</p></li>
<li><p>surface pressure [Pa]</p></li>
<li><p>radiation (short wave and long wave downwards) [W m-2]</p></li>
<li><p>windspeed [m/s]</p></li>
</ul>
<p>If potential evaporation is already calculated in a prerun or from external source</p>
<ul class="simple">
<li><p>Precipitation [Kg m-2 s-1] or [m] or [mm] (can be adjusted by a conversion factor in the settings file)</p></li>
<li><p>Temperature (avg) [K]</p></li>
<li><p>Potential evaporation [Kg m-2 s-1] or [m] or [mm] (can be adjusted by a conversion factor in the settings file)</p></li>
</ul>
<p>From observation: (see ISI-MIP 2a)</p>
<ul class="simple">
<li><p>WFDEI.GPCC (Weedon et al. 2014) <a class="footnote-reference brackets" href="#id54" id="id26" role="doc-noteref"><span class="fn-bracket">[</span>25<span class="fn-bracket">]</span></a> WFD—Watch forcing data set: 0.5 3/6 hourly meteorological forcing from ECMRWF reanalysis (ERA40) bias-corrected and extrapolated by CRU TS and GPCP (rainfall) and corrections for under catch</p></li>
<li><p>PGMFD v.2 - Princeton (Sheffield et al. 2006) <a class="footnote-reference brackets" href="#id55" id="id27" role="doc-noteref"><span class="fn-bracket">[</span>26<span class="fn-bracket">]</span></a></p></li>
<li><p>GSWP3 (Kim et al.) <a class="footnote-reference brackets" href="#id56" id="id28" role="doc-noteref"><span class="fn-bracket">[</span>27<span class="fn-bracket">]</span></a></p></li>
<li><p>MSWEP (Beck et al. 2017) <a class="footnote-reference brackets" href="#id57" id="id29" role="doc-noteref"><span class="fn-bracket">[</span>28<span class="fn-bracket">]</span></a></p></li>
</ul>
<p>From Global Circulation models GCMs (see ISI-Mip 2b)</p>
<ul class="simple">
<li><p>HadGem2-ES (Met Office Hadley Centre, UK)</p></li>
<li><p>IPSL-CM5A-LR (Institut Pierre-Simon Laplace, France)</p></li>
<li><p>GFDL-ESM2M (NOAA, USA)</p></li>
<li><p>MIROC-ESM-CHEM (JAMSTEC, AORI, University of Tokyo, NIES, Japan)</p></li>
<li><p>NorESM1-M (Norwegian Climate Centre, Norway)</p></li>
</ul>
</section>
</section>
<section id="references">
<h2><a class="toc-backref" href="#id74" role="doc-backlink">References</a><a class="headerlink" href="#references" title="Permalink to this heading"></a></h2>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id30" role="note">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
<p>Jarvis, A., H. I. Reuter, A. Nelson and E. Guevara (2008). Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m Database (<a class="reference external" href="http://srtm.csi.cgiar.org">http://srtm.csi.cgiar.org</a>).</p>
</aside>
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<p>US Geological Survey Center for Earth Resources Observation and Science Hydro1k. U. E. Land Processes Distributed Active Archive Center (LP DAAC), Sioux Falls, SD.</p>
</aside>
<aside class="footnote brackets" id="id32" role="note">
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<p>Mualem, Y. (1976). A New Model for Predicting the Hydraulic Conductivity of Unsaturated Porous Medial. Water Resources Research, Vol. 12, 513-522</p>
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<p>Van Genuchten, M. T. (1980). A Closed Form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils. Soil Science Society of America Journal</p>
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<p>Todini, E. (1996). The ARNO rainfall—runoff model. Journal of Hydrology, 175(1), 339-382</p>
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<p>FAO, IIASA, ISRIC, ISSCAS, & JRC. (2012). Harmonized World Soil Database (version 1.2). <a class="reference external" href="http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/">http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/</a></p>
</aside>
<aside class="footnote brackets" id="id43" role="note">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id15">14</a><span class="fn-bracket">]</span></span>
<p>Zhang, Y., Schaap, M.,(2017): Weighted recalibration of the Rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (Rosetta3),Journal of Hydrology,Volume 547,Pages 39-53,ISSN 0022-1694,https://doi.org/10.1016/j.jhydrol.2017.01.004. (<a class="reference external" href="http://www.sciencedirect.com/science/article/pii/S0022169417300057">http://www.sciencedirect.com/science/article/pii/S0022169417300057</a>)</p>
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<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id16">15</a><span class="fn-bracket">]</span></span>
<p>Gleeson, T., L. Smith, N. Moosdorf, J. Hartmann, H. H. Dürr, A. H. Manning, L. P. H. van Beek, and A. M. Jellinek (2011), Mapping permeability over the surface of the Earth, Geophys. Res. Lett., 38, L02401, doi:10.1029/2010GL045565.</p>
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<p>Gleeson, T., N. Moosdorf, J. Hartmann and L. P. H. Van Beek (2014). “A glimpse beneath earth’s surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity.” Geophysical Research Letters 41(11): 3891-3898.</p>
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<p>Lehner, B., C. R. Liermann, C. Revenga, C. Vörösmarty, B. Fekete, P. Crouzet, P. Döll, M. Endejan, K. Frenken, J. Magome, C. Nilsson, J. C. Robertson, R. Rödel, N. Sindorf and D. Wisser (2011). “High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management.” Frontiers in Ecology and the Environment 9(9): 494-502.</p>
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<p>Messager, M. L., B. Lehner, G. Grill, I. Nedeva and O. Schmitt (2016). “Estimating the volume and age of water stored in global lakes using a geo-statistical approach.” 7: 13603.</p>
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<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id20">19</a><span class="fn-bracket">]</span></span>
<p>Portmann, F. T., S. Siebert and P. Döll (2010). “MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling.” Global Biogeochemical Cycles 24(1): n/a-n/a.</p>
</aside>
<aside class="footnote brackets" id="id49" role="note">
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<p>Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from: <a class="reference external" href="http://earthenginepartners.appspot.com/science-2013-global-forest">http://earthenginepartners.appspot.com/science-2013-global-forest</a>.</p>
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<p>Elvidge, C. D., Tuttle, B. T., Sutton, P. C., Baugh, K. E., Howard, A. T., Milesi, C., Bhaduri, B., Nemani, R. (2007). Global Distribution and Density of Constructed Impervious Surfaces. Sensors (Basel, Switzerland), 7(9), 1962-1979. doi:10.3390/s7091962</p>
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<p>Popp, A., Humpenöder, F., Weindl, I., Bodirsky, B. L., Bonsch, M., Lotze-Campen, H., Müller, C., Biewald, A., Rolinski, S., Stevanovic, M., & Dietrich, J. P. (2014). Land-use protection for climate change mitigation. Nature Climate Change, 4, 1095</p>
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<p>Stevanović, M., Popp, A., Lotze-Campen, H., Dietrich, J. P., Müller, C., Bonsch, M., Schmitz, C., Bodirsky, B. L., Humpenöder, F., and Weindl, I.(2016): The impact of high-end climate change on agricultural welfare, Science Advances, 2, 2016. <a class="reference external" href="http://advances.sciencemag.org/content/2/8/e1501452">http://advances.sciencemag.org/content/2/8/e1501452</a></p>
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<aside class="footnote brackets" id="id53" role="note">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id25">24</a><span class="fn-bracket">]</span></span>
<p>Muller, P. J., P. Lewis, J. Fischer, P. North and U. Framer (2012). The ESA GlobAlbedo Project for mapping the Earth’s land surface albedo for 15 Years from European Sensors., paper presented at IEEE Geoscience and Remote Sensing Symposium (IGARSS) IEEE Geoscience and Remote Sensing Symposium (IGARSS) 2012. Munich, Germany. <a class="reference external" href="http://www.globalbedo.org">http://www.globalbedo.org</a></p>
</aside>
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<p>Weedon, G. P., G. Balsamo, N. Bellouin, S. Gomes, M. J. Best and P. Viterbo (2014). “The WFDEI meteorological forcing data set: WATCH Forcing data methodology applied to ERA-Interim reanalysis data.” Water Resources Research 50(9): 7505-7514.</p>
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<p>Sheffield, J., G. Goteti and E. F. Wood (2006). “Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling.” Journal of Climate 19(13): 3088-3111.</p>
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<p>Kim, H., S. Watanabe, E.-C. Chang, K. Yoshimura, Y. Hirabayashi, J. Famiglietti and T. Oki “Century long observation constrained global dynamic downscaling and hydrologic implication [in preparation].”</p>
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<aside class="footnote brackets" id="id57" role="note">
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<p>Beck, H. E., A. I. J. M. Van Dijk, V. Levizzani, J. Schellekens, D. G. Miralles, B. Martens and A. De Roo (2017). “MSWEP: 3-hourly 0.25° global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data.” Hydrology and Earth System Sciences 21(1): 589-615.</p>
</aside>
</aside>
<ul class="simple">
<li><p>Döll, P. and S. Siebert (2002). “Global modeling of irrigation water requirements.” Water Resources Research 38(4): 81-811.</p></li>
<li><p>Siebert, S., P. Döll, J. Hoogeveen, J. M. Faures, K. Frenken and S. Feick (2005). “Development and validation of the global map of irrigation areas.” Hydrology and Earth System Sciences 9(5): 535-547.</p></li>
</ul>
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