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Terrestrial.qmd
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# Theme: Terrestrial Ecosystems
**What:** Net ecosystem exchange(NEE) of CO~2~ and evapotranspiration in terrestrial ecosystems
**Where**: 47 NEON sites across the U.S. and Puerto Rico.
**When**: Daily forecasts for at least 30-days in the future are accepted at any time. The only requirement is that submissions are predictions of the future at the time the forecast is submitted.
**Why:** Carbon and water cycling are fundamental for climate and water regulation services provided by ecosystems
```{r echo = FALSE, message = FALSE}
library("tidyverse")
```
## Overview
The exchange of water and carbon dioxide between the atmosphere and the land is akin to earth's terrestrial ecosystems breathing rate and lung capacity. One of the best ways to monitor changes in the amount of carbon and water in an ecosystem is the eddy-covariance method. This method observes the net amount of carbon and water entering and exiting ecosystems at half-hourly timesteps, which is important because it can provide information on ecosystem processes such as photosynthesis, respiration, and transpiration, their sensitivities to ongoing climate and land use change, and greenhouse gas budgets for carbon accounting and natural climate solutions. Forecasts of carbon uptake and release along with water use can provide insights into future production of food, fiber, timber, and carbon credits. Additionally, forecasts will highlight the influence that stress and disturbance have on carbon and water cycling.
## Challenge
This forecasting challenge asks teams to forecast net ecosystem exchange of carbon dioxide (NEE) and latent heat flux of evapotranspiration (LE) across 47 NEON sites with differing climates. Forecasts can be submitted the 30-minute and/or daily time step over the next 30-days.
Teams are asked to submit their forecast of NEE and LE, along with uncertainty estimates. Any existing NEE and LE data may be used to build and improve the models used to generate forecasts. Other data can be used to generate forecasts.
## Data: Targets
The challenge uses the following NEON data products:
[DP4.00200.001](https://data.neonscience.org/data-products/DP4.00200.001){target="_blank"}: Bundled data products - eddy covariance
A file with previously released NEON data that has been processed into "targets" is provided below. The same processing will be applied to new data that are used for forecast evaluation.
### Net ecosystem exchange
**Definition**
Net ecosystem exchange (NEE) is the net movement of carbon dioxide from the atmosphere to the ecosystem. At the 30-minute time resolution it is reported as $\mu$mol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup>. At the daily time resolution it is reported as g C m<sup>-2</sup> day<sup>-1</sup>. Negative values correspond to an ecosystem absorbing CO<sup>2</sup> from the atmosphere, positive values correspond to an ecosystem emitting CO<sub>2</sub> to the atmosphere.
**Motivation**
NEE quantifies the net exchange of CO<sub>2</sub> between the ecosystem and the atmosphere over that 30-minute or daily time period. Assessing skill at predicting 1/2 hourly - sub daily measurements provides more insight into ability to capture diel processes. The diel curve contains information on how plants and soil immediately respond to variations in meteorology.
Making daily predictions will allow us to rapidly assess skill and provide information in a timeframe pertinent to inform and implement natural resource management. It also allows for models that do not produce sub-daily estimates to participate
### Latent heat flux
**Definition**
Latent heat flux is the movement of water as water vapor from the ecosystem to the atmosphere. It is reported as W m<sup>-2</sup> (equivalent to J m<sup>-2</sup> s<sup>-1</sup>). At the daily time resolution it is reported as mean W m<sup>-2</sup>. Positive values correspond to a transfer of water vapor from the ecosystem to the atmosphere.
**Motivation**
Latent heat measures the water loss from an ecosystem to the atmosphere through evapotranspiration (transpiration through plants + evaporation from surfaces).
Forecasting latent heat (evapotranspiration) can provide insights to water stress for plants and the efficiency that plants are using water relative to NEE, and to the amount of liquid water remaining in the soil for soil moisture forecasting
### Focal sites
Information on the sites can be found here:
```{r message = FALSE}
site_data <- readr::read_csv("https://raw.githubusercontent.com/eco4cast/neon4cast-targets/main/NEON_Field_Site_Metadata_20220412.csv") |>
dplyr::filter(terrestrial == 1)
```
with full site table at the end of this page.
The distribution of sites across ecosystems types is:
```{r echo = FALSE}
site_data |>
dplyr::group_by(field_dominant_nlcd_classes) |>
dplyr::count() |>
dplyr::arrange(desc(n)) |>
dplyr::rename(`Vegetation type` = field_dominant_nlcd_classes,
Count = n) |>
knitr::kable()
```
### 30-minute target data calculation
To create the data for evaluation (and training) for NEE and LE we extract NEE and LE that pass the turbulence quality control flags (`qfqm.fluxCo2.turb.qfFinl` = 0 ) provided by NEON and has flux values between -50 and 50 umol CO2 m<sup>-2</sup> s<sup>-1</sup>.
### Daily target data calculation
To evaluate the models that produce daily flux forecasts, we select only days with at least 24 of 48 half-hours that pass the quality control flags. For these days, we average the half-hours and convert carbon to daily units (gC/m2/day).
## Flux data latency
NEON data officially releases the flux data on their data portal and API in monthly data packages. Data for a given month is scheduled to be released around the 15th of the following month.
NEON is also processing flux data with only a 5 day delay (latency). Any data that has been processed but not included in a released monthly package is available on NEON s3 storage. The list of files that can be downloaded can found [here](https://s3.data.neonscience.org/neon-sae-files/ods/sae_files_unpublished/sae_file_url_unpublished.csv).
Our targets file is the combination of NEON's monthly releases and the files on the s3 bucket. As a result, flux data within 5-days of the restart of a forecast are available to inform the forecast.
The reduction of the latency from monthly to 5-days allows this theme to forecast in real-time - a major advancement for this forecasting challenge. Thank you NEON!
## Site table
```{r echo = FALSE}
site_data %>%
select(field_site_id, field_site_name, field_dominant_nlcd_classes, field_latitude, field_longitude, neon_url) %>%
rename(siteID = field_site_id,
`site name` = field_site_name,
`vegetation type` = field_dominant_nlcd_classes,
`latitude` = field_latitude,
`longtitude` = field_longitude,
`NEON site URL` = neon_url) %>%
arrange(siteID) %>%
knitr::kable()
```