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117 results for “evapotranspiration”
AET01 Konza prairie grass reference evapotranspiration
Estimated evapotranspiration from a hypothetical short grass with a height of 0.12 m, a surface resistance of 70 s m-1, and an albedo of 0.23 (no water stress). Dataset contains daily total estimated evapotranspiration.
SM2RAIN test dataset with ASCAT and SMAP satellite soil moisture (plus ERA5 evapotranspiration)
<p>Are you looking for a research contest?</p> <p>Here [SM_RAIN_EVAP_1009points.nc] you can find a 5-year dataset at 1009 points in Italy, the United States, India and Australia of co-located in space and time:</p> <ol> <li>satellite soil moisture (from ASCAT, Wagner et al., 2013, doi:10.1127/0941-2948/2013/0399)</li> <li>evapotranspiration (from ERA5 reanalysis by ECMWF: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview)</li> <li>ground-based rainfall.</li> </ol> <p>and a ~3-year dataset at the same points including soil moisture from SMAP (April-2015 --> December 2017) [SM_SMAP_ASCAT_ETERA5_Pobs_1009opints.nc]</p> <p>The dataset can be used for testing multiple approaches for rainfall estimation from soil moisture, as done in <a href="https://www.linkedin.com/feed/hashtag/?keywords=%23SM2RAIN">#SM2RAIN</a> algorithm (<a href="http://hydrology.irpi.cnr.it/research/sm2rain/">http://hydrology.irpi.cnr.it/research/sm2rain/</a>).</p> <p>The global dataset we have developed is available here: <a href="https://zenodo.org/record/3635932">https://zenodo.org/record/3635932</a></p> <p>The NetCDF file contains all the data, and the figures (PNG files) represent an example of the results we have obtained in the paper and of the new dataset including SMAP.</p> <p><strong>Reference</strong><br> Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., Wagner, W. (2019). SM2RAIN-ASCAT (2007-2018): global daily satellite rainfall from ASCAT soil moisture. <em>Earth System Science Data</em>, 11, 1583–1601, doi:10.5194/essd-11-1583-2019. <a href="https://doi.org/10.5194/essd-11-1583-2019">https://doi.org/10.5194/essd-11-1583-2019</a>.</p> <p>For clarifications and support contact me at <a href="mailto:luca.brocca@irpi.cnr.it?subject=SM2RAIN%20test%20dataset">luca.brocca@irpi.cnr.it</a> </p>
Monthly time series of rainfall, potential evapotranspiration and streamflow for 201 catchments in South-East Australia
<p>The data set contains data for 201 catchments located in South-Eastern Australia. The data was extracted from the datasets collated by Lerat, Thyer et al. (2020) including rainfall and potential-evapotranspiration data obtained from the Bureau of Meteorology Australian Water Outlook website (Frost, Ramchurn et al. 2016) and streamflow data obtained from the Bureau of Meteorology Water Data Online website (Bureau of Meteorology 2019). The data was collected over the period from 1980 to 2018, split into the two sub-periods 1980-1999 (Period 1) and 1999-2018 (Period 2).</p><p> </p><p>Bureau of Meteorology. (2019). "Water Data Online." from <a href="http://www.bom.gov.au/waterdata">http://www.bom.gov.au/waterdata</a>.</p><p>Frost, A. J., A. Ramchurn and A. Smith (2016). "The bureau's operational AWRA landscape (AWRA-L) Model." Bureau of Meteorology Technical Report.</p><p>Lerat, J., M. Thyer, D. McInerney, D. Kavetski, F. Woldemeskel, C. Pickett-Heaps, D. Shin and P. Feikema (2020). "A robust approach for calibrating a daily rainfall-runoff model to monthly streamflow data." Journal of Hydrology<strong>591</strong>: 125129.</p>
Ensemble monthly evapotranspiration over italy 1991-2020
<p>Six open access actual ET datasets are merged using an expert-based multiple collocation (MC) approach, with the aim of reconstructing a spatiotemporal consistent monthly dataset for the climatological period 1991-2020 over Italy at a spatial resolution of 1-km.</p> <p>The merged products include: three water balance datasets (BIG BANG, LSA SAF, and LISFLOOD), two residual surface energy balance models (SSEBop, and ALEXI) and the MODIS standard product.</p> <p>More details can be found in Cammalleri et al. (2023, under review)</p>
Saddle catchment Distributed Hydrology Soil Vegetation Model Simulation (DHSVM) precipitation and transpiration variable outputs (precipitation, total, potential and actual evapotranspiration), 2 meter, 2000-2019.
The Saddle Catchment of the Niwot Ridge LTER is a densely observed, high elevation site that is ideal for hydrological model simulation and calibration. The files produced are the result of a calibration of the Distributed Hydrology Soil Vegetation model (DHSVM) using observationally based states and forcings. Input state files of vegetation, soil properties, shading, and elevation were generated using ground and satellite observations, which, in the case of coarse-resolution or point scale observations, were then interpolated to match the high resolution of the model (2-meter grid cells). Temporally continuous meteorological forcings at the hourly time-step were used to force the model to produce an hourly simulation of the surface and subsurface hydrology within the Saddle catchment. DHSVM was calibrated to effectively reproduce the annual cycle (r^2) and total volume (percent bias) of observed runoff using observations of streamflow at the outflow pour point of the Saddle Catchment from 2001-2019. Calibrated parameters include the lateral conductivity of soil types, exponential decrease of soil conductivity, snow roughness, the snow melting temperature threshold, and the vertical conductivity of the soils. The resulting simulation generated spatially distributed time series of the snow water equivalent, snow melt, precipitation, total evapotranspiration, potential evapotranspiration, and a time-series of the total runoff generated at the outflow pour-point of the Saddle catchment. This data package contains the spatially distributed time series of precipitation, total evapotranspiration and actual evapotranspiration Outputs of snow water equivalent, snow melt, and runoff, as well as the model configuration file, as well as model inputs are archived separately on the Environmental Data Initiative.
Monthly reference evapotranspiration for Brazil
<p>The set of monthly ETo data referred to as monthly reference evapotranspiration for Brazil. It has a <strong>spatial resolution</strong> of <strong>30 seconds (~ 1 km²)</strong> and a <strong>temporal resolution of 1 month</strong>. The data set grid is in <strong>GeoTIFF format</strong>, and corresponds perfectly to WorldClim. It uses the <strong>geographic coordinate</strong> reference system, with <strong>WGS84 projection</strong> <strong>(EPSG: 4326)</strong>. The files are named as YEAR MONTH.</p> <p>Reference evapotranspiration (ETo) is a fundamental parameter for hydrological studies and irrigation management. The Penman-Monteith method is the standard for estimating ETo and requires several meteorological elements. Free remote sensing products with evapotranspiration information are rare. The objective of this study was to estimate the monthly ETo from the potential evapotranspiration (PET) made available by the MOD16 product. The monthly ETo estimated by the Penman-Monteith method was considered the standard. For this, data were acquired from the 265 meteorological station of the National Institute of Meteorology (INMET), throughout Brazil, in the period from 2000 to 2014 (15 years). Using machine learning algorithms, MOD16 images and WorldClim information as covariates, the ETo. All machine learning models were effective in improving the performance of the metrics evaluated. Cubist was the model that presented the best metrics for r² (0.91), NSE (0.90) and nRMSE (8.54%) and should be the preferred one for ETo prediction. The use of monthly ETo is recommended, which opens up possibilities for its use in numerous other studies.</p> <p>The article: <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0245834">https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0245834</a></p>
Data set supporting journal article: Markwitz, C., Knohl, A. and Siebicke, L.: "Evapotranspiration over agroforestry sites in Germany", Biogeosciences, 2020
<p>This data set contains all necessary data needed to replicate figures and analysis presented in the research article: Markwitz, C., Knohl, A. and Siebicke, L.: "Evapotranspiration over agroforestry sites in Germany", Biogeosciences, 2020.</p> <p>In detail, this data set contains 1) meteorological data and half-hourly evapotranspiration rates obtained by a conventional eddy covariance set-up, a low-cost eddy covariance set-up and an energy balance eddy covariance set-up for measurement campaigns of approximately four weeks duration (*_Fluxes_Campaigns_*); 2) raw data to recalculate flux footprints for the campaigns of approximately four weeks duration (*_Campaign_Footprints_*) and for the whole year (*_Annual_Footprints_*); 3) half-hourly evapotranspiration rates obtained by a low-cost eddy covariance set-up and an energy balance eddy covariance set-up gap-filled and corrected for energy balance closure (*_Fluxes_Annual_*). The data were collected at five agroforestry systems and five monoculture agriculture systems without trees across Northern Germany. </p>
Characterising evapotranspiration signatures for improved behavioural insights
<p>The dataset provides actual evapotranspiration (AET) data extracted at three temporal scales from eddy covariance flux towers and two remotely sensed AET products, namely MOD16A2GFv06.1 and CMRSET, across 17 Fluxnet sites in Australia. The study that utilized this dataset is currently under review in the journal of <em>Hydrology and Earth System Sciences</em>, and the preprint is available at https://doi.org/10.5194/hess-2024-373</p>
Assessing evapotranspiration realism in rainfall-runoff models using evapotranspiration signatures
<p> </p> <p>This dataset contains simulated actual evapotranspiration (AET) data derived from five conceptual hydrological models and input data applied to 14 catchments in Australia. The data spans the period from 1980 to 2022. The five models included in this dataset are:</p> <ul> <li>SIMHYD</li> <li>IHACRES</li> <li>VIC</li> <li>SACRAMENTO</li> <li>GR4J</li> </ul> <p>These models were implemented in version 2.1 of the MaRRMoT framework.</p> <p>Here, the models were calibrated using two different approaches:</p> <ol> <li>Calibration based on discharge data only. (Folder: ModelCalQ_Data)</li> <li>Calibration using a composite objective function that incorporates both discharge and AET data. (Folder: ModelCalQnAET_Data)</li> </ol> <p>Example script is also included in each model folder, such as ‘<em>Run_Simhyd_MaRRMoT_Cal_Spartan.m’</em>, to facilitate the running of the models and understanding of the calibration process.</p> <p> </p>
WaterGAP2.2d model derived Potential evapotranspiration and Renewable water resources variables with standard and modified PET calculation methods
<p>This data set is produced as a part of the ''Improving the quantification of climate change hazards by hydrological models: A simple ensemble approach for considering the uncertain effect of vegetation response to climate change on potential evapotranspiration" journal publication (in preparation). WaterGAP2.2d global hydrological model with two different settings; 1) with standard PET method Priestley-Taylor (PT) and 2) with modified approach (PT-MA) (please refer to the publication for more details on the method) used to derive the data set. The bias-adjusted GCM-derived (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5) climate data under RCP2.6 and RCP8.5 emission scenarios were used as the input. The model-derived potential evapotranspiration and the renewable water resources variables are available from 1981 to 2099 on the monthly scale for each land grid cell (spatial resolution: 0.5 degrees x 0.5 degrees). The data files are in the netCDF format (.nc4). </p>
Monthly Standardized Precipitation Evapotranspiration Index (SPEI) for Australia at 0.05 degree from 1982 to 2014
<p>This monthly SPEI dataset in 1-48 scale is calculated using R's <a href="https://cran.r-project.org/web/packages/SPEI/index.html">SPEI </a>package in 'kernel -- rectangular', 'distribute -- log-Logistic' and 'fit -- ub-pwm' mode, with <a href="http://www.csiro.au/awap/">AWAP'</a>s monthly rainfall and <a href="http://www.bom.gov.au/water/landscape/">ALWB</a>'s potential evapotranspiration.</p>
SCOPE Climate: Penman-Monteith reference evapotranspiration
<p>SCOPE Climate (Spatially COherent Probabilistic Extended Climate dataset) is a 25-member ensemble of 142-year high-resolution reconstructions of precipitation, temperature and Penmann-Monteith reference evapotranspiration over France, from 1 January 1871 to 29 December 2012. SCOPE Climate results from the statical downscaling of the global extended reanalysis 20CR V2 with the SCOPE method (Caillouet et al., 2016, 2017). SCOPE Climate provides an ensemble of 25 equally-plausible spatially-coherent gridded multivariate time series. Data are available at a daily time step on a 8 km grid over France as 25 files in NetCDF format. Reconstructed values cover grid cells located only within metropolitan France national borders (including Corsica). The SCOPE Climate dataset is fully described by Caillouet et al. (2019).</p> <p>This dataset provides reconstructions of Penman-Monteith reference evapotranspiration as a 25-member ensemble of gridded time series. Reconstructed evapotranspiration values from member 1 should be used with reconstructed precipitation/temperature values from member 1 (and so on). The corresponding precipitation dataset can be found at http://dx.doi.org/10.5281/zenodo.1299760, and the temperature dataset at http://dx.doi.org/10.5281/zenodo.1299712.</p>
Dataset for the manuscript "Are remote sensing evapotranspiration models reliable 2 across South American ecoregions?" published in WRR
<p><strong>Metadata of ‘<em>Are remote sensing evapotranspiration models reliable across South American ecoregions?</em>’ </strong></p> <p>This document describes the file formatting and data used to run and evaluate the evapotranspiration models in this study. Because forcing data varies among models, each input file contains a different set of meteorological data placed within a folder named after the corresponding model.</p> <p> </p> <p><strong>File format and time stamps</strong></p> <p>Data files are CSV formatted with timestamps in the first column of the file. The following timestamps are used:</p> <ul> <li>GLEAM: Year (YYYY); Day of Year (DDD)</li> <li>PT-JPL: Year (YYYY); Month (MM); Day (DD)</li> <li>PM-MOD: Year (YYYY); Month (MM); Day (DD)</li> <li>PM-VI: Date (MM/DD/YYYY)</li> </ul> <p> </p> <p><strong>Missing data</strong></p> <p>Missing data are reported using ‘NaN’ as a replacement flag. Data for all days in a leap year are reported. </p> <p> </p> <p><strong>Data format</strong></p> <p>The column headers Name, Description and Units are adopted used in the data files to describe the following variables::</p> <ul> <li>ETo, Penman-Monteith FAO-56 reference evapotranspiration (mm day<sup>-1</sup>);</li> <li>ETobs, Observed evapotranspiration (mm day<sup>-1</sup>);</li> <li>Rn, Surface Net Radiation (w m<sup>-2</sup>);</li> <li>Rg, Daylight shortwave Incoming Radiation (w m<sup>-2</sup>);</li> <li>Rgs_out, Shortwave Radiation - outgoing (w m<sup>-2</sup>);</li> <li>G, Soil heat flux (w m<sup>-2</sup>);</li> <li>P, Rainfall (mm day<sup>-1</sup>);</li> <li>T, Surface Air Temperature (ºC);</li> <li>Tmax, Maximum Temperature (ºC);</li> <li>Tmin, Minimum Temperature (ºC);</li> <li>Tday, Daytime Temperature (ºC);</li> <li>TminDay, Daytime Minimum Temperature (ºC);</li> <li>TminNight, Nighttime Minimum Temperature (ºC);</li> <li>Patm, Atmospheric Air Pressure (Pa);</li> <li>ea, Actual Vapor Pressure (kPa);</li> <li>es, Saturation Vapor Pressure (kPa);</li> <li>VPD, Vapor Pressure Deficit (kPa);</li> <li>eaDay, Daytime Actual Vapor Pressure (kPa);</li> <li>eaNight, Nighttime Actual Vapor Pressure (kPa);</li> <li>RH, Air Relative Humidity;</li> <li>RHDayTime, Daytime Air Relative Humidity;</li> <li>RHNightTime, Nighttime Air Relative Humidity;</li> <li>LAI, Leaf Area Index (m² m<sup>-</sup>²);</li> <li>SWC, Soil Water Content (mm m<sup>-1</sup>).</li> </ul> <p> </p> <p><strong>Forcing data per model</strong></p> <p>Each model requires a different set of forcing data, as follows:</p> <ul> <li>GLEAM: Rn, P, T, Rgs_out;</li> <li>PT-JPL: Tmax, Rn, RH (or e<sub>a</sub>);</li> <li>PM-MOD: Rg, Tday, TminDay, TminNight, RHDayTime, RHNighttime, eaDay, eaNight;</li> <li>PM-VI: ETo.</li> </ul> <p> </p> <p><strong>Tower sites (IDs) and co-authors/PIs:</strong></p> <ul> <li>SDF: J. P. Quezada and M. Galleguillos;</li> <li>TF1 and TF2: L. Kutzbach and D. Holl;</li> <li>GRO and SLU: G. Posse;</li> <li>BAL and MCC: M. Gassman and C. Perez;</li> <li>PDG, EUC and USR: O. Cabral;</li> <li>FM and SIN: J.S. Nogueira and T. Range;</li> <li>CAA: M. Moura;</li> <li>CST: A. C. D. Antonino;</li> <li>SJO: E. S. Souza and J. R. S. Lima;</li> <li>ESEC: B. Bezerra.</li> </ul>
Spatio-temporal water surplus and evapotranspiration in the catchment area of the Vögelsberg landslide (Tyrol, Austria)
<p>Multi-temporal maps of daily water surplus and evapotranspiration in the catchment area of the Vögelsberg landslide (Tyrol, Austria) based on the SVAT model LWF-Brook90 from 01/01/2008 to 31/12/2019. The spatio-temporal results represent the hydrological forcing of acceleration phases of the deep-seated landslide (see also Pfeiffer et al. 2021, <a href="https://doi.org/10.1002/esp.5129">https://doi.org/10.1002/esp.5129</a>). To investigate the feasibility of a modified land cover as nature-based solutions to reduce the landslide's activity, three land cover scenarios were considered (under current climatic conditions):</p> <p>- Current land cover conditions classified based on air-borne laser scanning data</p> <p>- Forest scenario: catchment area completely covered by forests (hypothetical scenario)</p> <p>- Pole timber scenario: open land above agricultural areas is replaced by areas of pole timber (considered realistic)</p> <p>Three land cover classes (open land, pole timber, mature forest), 11 soil types and 5 vertical meteorological domains were distinguished. Maps were produced with a spatial resolution of 10m (Projection: Austria GK West, EPSG: 31254). The maps are provided as raster stacks in tif-format with each layer representing one day.</p> <p>For further details see OPERANDUM deliverables D4.5 and D4.6.</p>
Indonesia, monthly Standardized Precipitation-Evapotranspiration Index (SPEI) blend 1960 - 2021
<p>IDN_CLI_SPEI_blend_0p042_1961_2021 is currently the only comprehensive high resolution Indonesia gridded historical dataset of SPEI blend and available for public.</p> <p>The SPEI - https://spei.csic.es/ is an extension of the widely used SPI. The SPEI is designed to take into account both precipitation and potential evapotranspiration (PET) in determining drought. Thus, unlike the SPI, the SPEI captures the main impact of increased temperatures on water demand.</p> <p>The IDN_CLI_SPEI_blend_0p042_1960_2021 is derived using precipitation and potential evapotranspiration from TerraClimate data - https://www.climatologylab.org/terraclimate.html, it has 0.042 degree gridded resolution, a monthly and available from 1958 to 2021. The calibration period is January 1961 to December 2020. The starting date of the dataset is 1960 in order to provide common information across the different SPEI time-scales.</p> <p>The SPEI blend integrate several SPEI scales into a single product, combine 3-, 6-, 9-, 12- and 24-month SPEI to estimate the overall dry/wet condition. </p> <p>The SPEI processed using climate_indices, an open source Python library providing reference implementations of commonly used climate indices. https://pypi.org/project/climate-indices/</p>
Post-processed data and graphical tools for a CONUS-wide eddy flux evapotranspiration dataset
<p><strong>Post-processed data and graphical tools for a CONUS-wide eddy flux evapotranspiration dataset</strong><br> </p> <p>We curated a dataset of post-processed <em>in situ</em> evapotranspiration (ET) measurements, primarily from eddy covariance flux towers, from stations located within the contiguous United States. The dataset includes daily and monthly aggregated ET, energy balance metrics, and micrometeorological data that were post-processed from 148 flux towers, 4 weighing lysimters, and 8 Bowen Ration stations. Original data was retrieved from the <a href="https://ameriflux.lbl.gov/">AmeriFlux</a> network and other networks and partners. The dataset is oriented towards ET and includes both ET that has been corrected for energy balance closure error as well as the uncorrected values. Energy balance components (latent and sensble heat flux, soil heat flux, and net radiation) were subject to limited gap-filling and latent energy (ET) was subject to additional visual quality control. Other meteorological measurements such as air temperature, precipitation, humidity, etc. are included for most stations depending on availability, and some additional variables were calculated. Interactive graphics of most post-processed data are also included. The dataset has many potential uses including evaluation of regional hydrologic and atmospheric models, energy balance analysis, and more.</p> <p><br><strong>Description of the Data and file structure</strong></p> <p>The dataset is in a compressed (zipped) archive titled "flux_ET_dataset", so first it needs to be downloaded and extracted. Once extracted there are four major components within: </p> <p>1. A collection of time series files with daily aggregated data (one for each station), these are in the directory named "daily_data_files" and are in CSV format.<br>2. A similar collection of time series files for monthly aggregated data in "monthly_data_files". <br>3. Interactive graphic files (HTML format) for each station which are in the "graphical_files" directory. <br>4. Two additional tables in the root directory, including a metadata file named "station_metadata.xlsx" with site information such as site ID, coordinates, land cover type, principal investigator information, etc. The other table named "variable_explanation.xlsx" lists all variables that were post-processed in the flux dataset and gives a short description of each as well as their units. </p> <p>Each data and plot file starts with the station's ID or site ID which are listed in the station_metadata.xlsx file. </p> <p>Here is a visual of the file structure:</p> <blockquote> <p><br>flux_ET_dataset<br>│ README.md<br>│ variable_explanation.xlsx<br>│ station_metadata.xlsx<br>│<br>└───daily_data_files<br>│ │ [site ID]_daily_data.csv<br>│ │ ...<br>└───monthly_data_files<br>│ │ [site ID]_monthly_data.csv<br>│ │ ...<br>└───graphical_files<br>│ │ [site ID]_plots.html<br>│ │ ...<br>```</p> </blockquote> <p>The variable names in the daily and monthly data files as well as the graphics all follow the same naming scheme which are defined in the variable_explanation.xlsx file. For example, LE stands for latent energy flux and is in units of W/m<sup>2</sup>. </p> <p><br><strong>Sharing/access Information</strong></p> <p>Currently, this repository is the only location where the data are hosted. Original data, prior to post-processing, were retrieved from multiple providers listed below:</p> <p>* AmeriFlux network (https://ameriflux.lbl.gov/) </p> <p>* California State University, Monterey Bay, Seaside, CA, USA </p> <p>* Desert Research Institute, Reno, NV, USA </p> <p>* gridMET, Northwest Knowledge Network at the University of Idaho (https://thredds.northwestknowledge.net/) </p> <p>* United States Geological Survey Nevada Water Science Center, Carson City, NV, USA </p> <p>* Delta-Flux network, Arkansas, Louisiana, MS, USA </p> <p>* United States Department of Agriculture Agricultural Research Service (USDS-ARS): </p> <p> * Sustainable Water Management Research Unit, Stoneville, MS, USA </p> <p> * US Salinity Laboratory, Agricultural Water Efficiency and Salinity Research Unit, Riverside, CA, USA </p> <p> * Conservation & Production Research Laboratory, Bushland, TX, USA </p> <p> * US Arid-Land Agricultural Research Center, Maricopa, AZ, USA </p> <p> * Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA </p> <p>Further contact information for each station as well as DOI's for original AmeriFlux data are included in the "station_metadata.xlsx" file. </p> <p><br><strong>Code/Software</strong></p> <p>All files that comprise this dataset were generated using the "flux-data-qaqc" open-source Python package version 0.1.6. The package is hosted on <a href="https://github.com/Open-ET/flux-data-qaqc">GitHub</a> and <a href="https://pypi.org/project/fluxdataqaqc/">PyPI</a>, it also has <a href="https://flux-data-qaqc.readthedocs.io/en/latest/">online documentation</a> including an in depth user tutorial. </p>
Structural constraints in current stomatal conductance models preclude accurate estimation of evapotranspiration and its partitions
<p>This archive includes the scripts and related input data to produce results for the paper entitled - "Structural constraints in current stomatal conductance models preclude accurate estimation of evapotranspiration and its partitions". Following is the description of files/folders:</p> <p>1. Input_Data: This folder contains all the required input data including FluxNet data, soil properties, quality controlled training-validation data, and metadata & other supporting information of the sites. </p> <p>2. Model_EMP: This folder contains all the scripts for empirical model of stomatal conductance. (Note: Scripts have been written in MATLAB"). No need to change anything except the MATLAB executive path in two files "run_all_tasks_to_optimize_params.sh" and "prediction.sh". Read "ReadMe.txt" file in the folder "Model_EMP" for more instructions on running the model. </p> <p>3. Model_ML: This folder contains all the scripts for pure machine learning model of stomatal conductance. It contains four sub-folders: 1. Model_Config_1 (Model with configuration-1); 2. Model_Config_2_TEA (Model with Configuration-2 & TEA-based T estimates); 3. Model_Config_2_uWUE (Model with Configuration-2 & uWUE-based T estimates); 4. Model_Config_2_Yu22 (Model with Configuration-2 & Yu22-based T estimates). Further instructions have been given in each jupyter notebooks. Briefly, in folder "Model_Config_1", the notebook "train_ML_config_1.ipynb" trains the model parameters and notebook "Predictions_ML_config_1" is used to do predictions. Similar instructions apply for other subfolders. (Note: Scripts have been written in Python Language"). All the scripts are fully functional as long as all the required modules are installed.</p> <p>4. Model_PH_exp: This folder contains all the scripts for plant hydraulics model with explicit representation. All the scripts are self explanatory and further instructions are provided in the scripts as needed. (Note: Scripts have been written in Python Language"). All the scripts are fully functional as long as all the required modules are installed.</p> <p>5. Model_PN_imp: This folder contains all the scripts for plant hydraulics model with implicit representation. Instructions given for "Model_ML" are applicable here. (Note: Scripts have been written in Python Language"). All the scripts are fully functional as long as all the required modules are installed.<br> </p> <p>Versions: Tensorflow 2.11.0, MATLAB_R2022a, Python 3.10.9</p>
An Isotopic Approach to Partition Evapotranspiration in a Mixed Deciduous Forest at the University of Michigan Biological Station, Pellston, MI (2017)
Transpiration (T) is perhaps the largest fluxes of water from the land surface to the atmosphere and is susceptible to changes in climate, land use and vegetation structure. However, predictions of future transpiration fluxes vary widely and are poorly constrained. Stable water isotopes can help expand our understanding of land–atmosphere water fluxes but are limited by a lack of observations and a poor understanding of how the isotopic composition of transpired vapour (δT) varies. Here, we present isotopic data of water vapour, terrestrial water and plant water from a deciduous forest to understand how vegetation affects water budgets and land–atmosphere water fluxes. We measured subdiurnal variations of δ18OT from three tree species and used water isotopes to partition T from evapotranspiration (ET) to quantify the role of vegetation in the local water cycle. We find that δ18OT deviated from isotopic steady‐state during the day but find no species‐specific patterns. The ratio of T to ET varied from 53% to 61% and was generally invariant during the day, indicating that diurnal evaporation and transpiration fluxes respond to similar atmospheric and micrometeorological conditions at this site. Finally, we compared the isotope‐inferred ratio of T to ET with results from another ET partitioning approach that uses eddy covariance and sap flux data. We find broad midday agreement between these two partitioning techniques, in particular, the absence of a diurnal cycle, which should encourage future ecohydrological isotope studies. Isotope‐inferred estimates of transpiration can inform land surface models and improve our understanding of land–atmosphere water fluxes.
Riparian Evapotranspiration (ET) Study (SEON) along the Middle Rio Grande Bosque, New Mexico (1999-2011 )
This study originated with the objective of parameterizing riparian evapotranspiration (ET) in the water budget of the Middle Rio Grande. We hypothesized that flooding and invasions of non-native species would strongly impact ecosystem water use. Our objectives were to measure and compare water use of native (Rio Grande cottonwood, Populus deltoides ssp. wizleni) and non-native (saltcedar, Tamarix chinensis and Russian olive, Eleagnus angustifolia) vegetation and to evaluate how water use is affected by climatic variability resulting in high river flows and flooding as well as drought conditions and deep water tables. Eddy covariance flux towers to measure ET and shallow wells to monitor water tables were instrumented in 1999. Active sites in their second decade of monitoring include a xeroriparian, non-flooding salt cedar woodland within Sevilleta National Wildlife Refuge (NWR) and a dense, monotypic salt cedar stand at Bosque del Apache NWR, which is subject to flood pulses associated with high river flows.
Riparian Evapotranspiration (ET) Study (SEON) from the Middle Rio Grande River Bosque, New Mexico (1999-2011 ): Vapor Pressure Deficit (VPD) Data
We hypothesized that flooding and invasions of non-native species would strongly impact ecosystem water use. Our objectives were to measure and compare water use of native (Rio Grande cottonwood, Populus deltoides ssp. wizleni) and non-native (saltcedar, Tamarix chinensis, Russian olive, Eleagnus angustifolia) vegetation and to evaluate how water use is affected by climatic variability resulting in high river flows and flooding as well as drought conditions and deep water tables. Eddy covariance flux towers to measure ET and shallow wells to monitor water tables were instrumented in 1999. Active sites in their second decade of monitoring include a xeroriparian, non-flooding salt cedar woodland within Sevilleta National Wildlife Refuge and a dense, monotypic salt cedar stand at Bosque del Apache NWR, which is subject to flood pulses associated with high river flows.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.