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117 results for “evapotranspiration”
Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1975-1999)
<p>The data contains simulation results from 1975-1999, 25 years total.</p> <p>You can access the remaining part of the dataset via Qingchen Xu and Lu Li (2025) using the following reference:</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1950-1974) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671245</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (2000-2024) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671254</p>
Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1950-1974)
<p>The data contains simulation results from 1950-1974, 25 years total.</p> <p>You can access the remaining part of the dataset via Qingchen Xu and Lu Li (2025) using the following reference:<br>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1975-1999) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671253</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (2000-2024) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671254</p>
Dataset for the SFmodel, applied in Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches
<p>Dataset used for the SFmodel, applied in the work "Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches".</p> <p>For units and nomenclature of the variables refer to Units_and_Nomenclature_for_SFmodel_in_Evapotranspiration_dynamics_and_partitioning_in_a_grassed_vineyard.pdf. </p>
Dataset for Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches
<p>Data sets of the work "Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches".</p> <p>You will find all data files needed for this work, organised by the figures of the paper. For the codes, refer to Flavio Bastos Campos. (2024). flaviobastoscampos/ET_dynamics_and_partitioning_vineyard: v2024.1 (v2024.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.10864169" target="_blank" rel="nofollow noopener">https://doi.org/10.5281/zenodo.10864169</a>. </p>
ALEXI evapotranspiration product (v10E) at 36 km SMAP grid for CONUS
<p><span>The ALEXI surface energy balance model is designed to estimate E for continental to global scale applications (Anderson et al., 2007, 2011). It maintains a physically realistic representation of land-atmosphere exchange over a wide range of landscapes, while being relatively independent of ancillary meteorological or plant functional information. Importantly for the present analysis, it does not require any knowledge of soil moisture conditions. The main diagnostic inputs to the ALEXI model are land surface temperature, net radiation (and its components), leaf area index (to inform partitioning of radiation between soil and canopy), windspeed, and the early morning lapse rate profile. The internal model structure solves the energy balance for the soil and canopy separately. ALEXI uses an atmospheric boundary layer model to translate the change in temperature over the morning hours into a self-consistent boundary layer temperature and to constrain the sensible heat flux. </span></p> <p><span>The product shared here is regridded from the 4 km CONUS ALEXI E daily product (v10E) for the 2015-2022 period to the coarser 36 km grid of the SMAP data. <span>In addition, the corresponding downwelling short wave radiation (R<sub>sw</sub>), obtained from the Climate Forecast System Reanalysis (Saha et al. 2014) is provided. <br></span></span></p>
Analysis of global evapotranspiration datasets reveal hotspots of low and high agreement
<p><span>Evapotranspiration (ET) is a key component of the water cycle and varies widely across the globe. With increasing efforts to create multi-year global gridded products using a multitude of approaches, there is a need to understand where datasets agree and disagree to pinpoint where our collective understanding may fall short. We processed and homogenized fourteen gridded datasets from 2000-2019 to find hotspots of low and high dataset agreement world-wide as well as over different biomes, land cover, IPCC reference regions, elevation classes and ET intensity classes. The use of standardized quartile ranges and distribution testing established two complementary angles of collective agreement. We identified hotspots with low agreement, such as cold dry regions, as well as regions with high agreement. In regions with high agreement, we identified product-specific outliers. These regions can be of high interest for developers and inform users on product selection. </span></p>
Challenges and limitations of applying the flux variance similarity (FVS) method to partition evapotranspiration in a montane cloud forest
<p>Dataset</p> <table> <tbody> <tr> <td>Name</td> <td>Description</td> </tr> <tr> <td>FVS_ori.zip</td> <td>the output from FVS method</td> </tr> <tr> <td>ModFVS.zip</td> <td>the output from ModFVS method</td> </tr> <tr> <td>CLM.zip</td> <td>the output from CLM </td> </tr> <tr> <td>Chilan_30min_sap_velocity_20200601_20211120_QC.csv</td> <td>the sap flow data in Chi-Lan</td> </tr> <tr> <td>*_clim.csv</td> <td>the observation data in Chi-Lan and Lien-Hua-Chih</td> </tr> </tbody> </table> <p> </p> <p>Codes for Analysis</p> <table> <tbody> <tr> <td>Name</td> <td>Description</td> </tr> <tr> <td>*.ipynb</td> <td>the python code used for analyzing output</td> </tr> <tr> <td>*_FVS_process.py</td> <td>the python code used for process ModFVS method</td> </tr> </tbody> </table> <p> </p> <p>ModFVS method (fluxpart-0.2.10+rhtest-py3-none-any.whl)</p> <ul> <li>use "pip install fluxpart-0.2.10+rhtest-py3-none-any.whl" to install the package</li> <li> <p>To specify a maximum allowable relative humidity when calculating WUE, set a value for "max_rh" in "wue_options". For example, to set the max RH to 95%, you would change your example code to this:</p> <p>wue_options = {"meas_ht": 23.7,"canopy_ht":10, "ppath": "C3","ci_mod":ci_mod, "max_rh":95}</p> </li> <li> <p>Note that this code is a fork of (https://github.com/usda-ars-ussl/fluxpart)</p> </li> </ul> <p> </p> <p> </p>
Parameter variability across different timescales in the energy balance-based model and its effect on evapotranspiration estimation
<p>Our dataset is for the manuscript "Parameter variability across different timescales in the energy balance-based model and its effect on evapotranspiration estimation". It includes the instantaneous and daily <em>z<sub>0m</sub></em>, <em>z<sub>0h</sub></em>, <em>g<sub>s</sub></em>, and <em>EBR</em>, which are derived from FLUXNET2015 dataset. The training and test datasets for building the data-driven parameter models are also uploaded.</p>
Hybrid deep learning framework for evaluating field evapotranspiration considering the impact of soil salinity
<p>Entitled “A novel hybrid deep learning framework for evaluating field evapotranspiration considering the impact of soil salinity” for possible publication in Water Resources Research.</p> <p>Data:</p> <p>The Salinized Farmland Flux Station sites are located in the typical irrigated agricultural area of the arid continental monsoon region in northwest China.In this study, we used data from four flux tower located in saline farmland: two for maize (MZ1, MZ2) and two for sunflower (SF1, SF2).</p> <p>For each site, we collected the following variables at half-hourly temporal resolution: (i) latent heat (<em>LE</em>, W m<sup>-2</sup>) fluxes, serving as a direct measure representing the energy component of <em>ET</em> (mm h<sup>-1</sup>), (ii) net radiation (<em>R<sub>n</sub></em>, W m<sup>-2</sup>), (iii) ground heat flux (<em>G</em>, W m<sup>-2</sup>), (iv) solar irradiance (<em>R<sub>s</sub></em>, W m<sup>-2</sup>), (v) air temperature (<em>T</em><sub>a</sub>, °C), (vi) vapor pressure deficit (<em>VPD</em>, KP<sub>a</sub>), (vii) wind speed (<em>U<sub>s</sub></em>, m s<sup>-1</sup>), (viii) relative humidity (<em>RH,</em> %), and (ix) atmospheric carbon dioxide concentration (<em>C<sub>a</sub></em>, mg m<sup>-3</sup>). During the crop growth period, field in-situ measurements of soil and vegetation data from these farmlands are conducted approximately every 10 days. </p> <p>Code:</p> <p>All the codes were executed in Python. The provided hybrid deep learning model code can be run on Jupyter Notebook.</p>
Construction of an evapotranspiration model and analysis of spatiotemporal variation in Xilin River Basin, China
<p>The publication for this dataset will be published in plos one journal, and can be accessed here:https://doi.org/10.1371/journal.pone.0256981. Please cite this when using the dataset.</p>
CHclim25 - potential evapotranspiration (etp)
<p>Potential evapotranspiration is calculated according to the Turc method from <a href="https://www.wsl.ch/staff/niklaus.zimmermann/programs/aml1_7.html">solar radiation</a>, and CHclim25 average temperature (Tave) and CHclim25 relative sunshine duration (Srel) . Monthly and yearly current average (1981-2010) and future average (2020-2049, 2045-2074, and 2070-2099)<strong> </strong>layers can be downloaded from separate zip files. </p> <p>Future layers are based on the transient daily time series of gridded climate scenarios of temperature at 0.02°D (~2.2 km) provided by the <a href="https://www.nccs.admin.ch/nccs/en/home/climate-change-and-impacts/swiss-climate-change-scenarios/ch2018---climate-scenarios-for-switzerland.html">CH2018 initiative</a>. We calculated future climatic layers for 4 GCMs (HADGEM, ECEARTH, MPIESM, and IPSL), 3 time slices (2020-2049, 2045-2074, and 2070-2099) and 3 representative concentration pathways (RCP 2.6, 4.5 and 8.5)</p> <p>The layer files are stored in compressed GeoTIFF format with the “deflate” algorithm with option “predictor2” from the GDAL. This format has a high compression ratio but allows direct import in most GIS softwares. All the maps are projected in the Swiss coordinate system CH 1903+ LV95 (epsg:2056) with a resolution of 25x25m using the extent of the digital height model DHM25 of the Swiss office for topography (swisstopo).</p>
Long-term evapotranspiration rates for rainfed corn vs. perennial bioenergy crops in a mesic landscape
Open the record for dataset details and reuse information.
Evapotranspiration data from eddy-covariance flux-tower measurements and Landsat imagery in California’s Sierra Nevada from 1985 to 2019
Open the record for dataset details and reuse information.
Riparian Evapotranspiration (ET) Study (SEON) from the Middle Rio Grande River Bosque, New Mexico (1999-2011): Energy Balance Data
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 & 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. These data are energy balance data collected as part of this study.
Riparian Evapotranspiration (ET) Study (SEON) from the Middle Rio Grande River Bosque, New Mexico (1999-2011 ): Soil Thermal Flux, Temperature and Moisture Data
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, 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. This data set includes the soil temperature and moisture data collected during this study.
Riparian Evapotranspiration (ET) Study (SEON) from the Middle Rio Grande River Bosque, New Mexico (1999-2011): Micrometeorological Data
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, 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. These are the meteorological data collected as part of this study.
Evapotranspiration data of the TERENO sites Graswang and Fendt for 2013 and 2014 measured by eddy-covariance and lysimeters
<p>Further details on this data set can be found in the following papers:</p> <p>Mauder, M., Genzel, S., Fu, J., Kiese, R., Soltani, M., Steinbrecher, R., Kunstmann, H., Zeeman, M., Banerjee, T., Roo, F. De, De Roo, F., Kunstmann, H. and Zeeman, M.: Evaluation of energy balance closure adjustment methods by independent evapotranspiration estimates from lysimeters and hydrological simulations, Hydrol. Process., 32(October), 39–50, doi:10.1002/hyp.11397, 2018.</p> <p>Widmoser, P. and Michel, D.: Partial energy balance closure of eddy covariance evaporation measurements using concurrent lysimeter observations over grassland, Hydrol. Earth Syst. Sci. Discuss., (July), doi:10.5194/hess-2020-299, 2020.</p>
Transpiration from subarctic deciduous woodlands: environmental controls and contribution to ecosystem evapotranspiration
<p><strong>Data from the paper: </strong></p> <p>Sabater, AM, Ward, HC, Hill, TC, et al. Transpiration from subarctic deciduous woodlands: Environmental controls and contribution to ecosystem evapotranspiration. <em>Ecohydrology</em>. 2020; 13:e2190. <span><span><span><u><span><span>https://doi.org/10.1002/eco.2190</span></span></u></span></span></span></p> <p><br><br></p>
OpenET model data for assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications
<h2>Overview</h2> <p>This dataset includes daily and monthly evapotranspiration (ET) data from the remote sensing models that comprise the [OpenET](https://openetdata.org/) ensemble as described in Melton et al., 2022 (https://doi.org/10.1111/1752-1688.12956); these data were extracted at specific locations within the contiguous United States that coincide with *in situ* measurement stations, including eddy covaraiance, Bowen-ratio, and lysimeter stations. Model ET data where extracted at each site in this dataset using flux footprints as described in Volk et al., (2023) (https://doi.org/10.1016/j.agrformet.2023.109307). These model data alongside the corresponding *in situ* ET data (https://doi.org/10.1016/j.dib.2023.109274) were subsequently used in the manuscript for the OpenET Phase II Intercomparison and Accuracy Assessment (https://doi.org/10.1038/s44221-023-00181-7). </p> <h3><br>Description of the data and file structure</h3> <p>The dataset is in a compressed (zipped) archive titled "OpenET_PhaseII_model_ET_dataset", so first it needs to be downloaded and extracted. The dataset is comprised of just three files. The first file is a Microsoft Excel file "Station_metadata.xlsx" that contains information about the *in situ* ET measurement stations where the OpenET model data was extracted. This file contains information such as site ID's, coordinates, land cover information, and site principal investigator (PI) contact information. Again, the corresponding *in situ* ET data are not included in this dataset. The other two files are tab-delimited text files containing timeseries the OpenET model data themselves, namely the daily ET [mm/day] and monthly ET [mm/month] as extracted for each model and the ensemble value as used in the OpenET Phase II Intercomparison and Accuracy Assessment. </p> <h3><br>Access information and code/software</h3> <p>OpenET data that was used here was produced using operational methods that are implemented on the Google Earth Engine platform. Monthly OpenET model data can be retrieved through Google Earth Data Catalog (e.g., https://developers.google.com/earth-engine/datasets/catalog/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0) or through the [online data explorer](https://openetdata.org/) or using the [OpenET API](https://openetdata.org/api-info/).</p>
PML_V2 global evapotranspiration and gross primary production (2000.02-2023.12)
<h2>Summary</h2> <p>This data is an <strong>8-day 5km (0.05°)</strong> data aggregated from the latest <strong>8day 500m PML-V2 global evapotranspiration and gross primary production data</strong> in Google Earth Engine, available since 2000.2.26 to 2023 (latest and will update annually).</p> <p><strong>Notes</strong></p> <ul> <li> <p>8-day means an average of the variable for the 8 days (xx d-1).</p> </li> <li> <p>Land evapotranspiration (ET) can be computed as a sum of Ec, Ei, and Es, while in water, Penman evapotranspiration denotes actual evaporation (ET_water).</p> </li> <li> <p>In a 5km resolution, please do not add ET_water to land ET as they represent a different coverage of area within the 5km pixel. Please see the coverage ratio file for each variable.</p> </li> </ul> <table> <tbody> <tr> <th>BandName</th> <th>Units</th> <th>Scale</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>GPP</td> <td>gC m-2 d-1</td> <td>0.01</td> <td>Gross primary product</td> </tr> <tr> <td>Ec</td> <td>mm d-1</td> <td>0.01</td> <td>Vegetation transpiration</td> </tr> <tr> <td>Es</td> <td>mm d-1</td> <td>0.01</td> <td>Soil evaporation</td> </tr> <tr> <td>Ei</td> <td>mm d-1</td> <td>0.01</td> <td>Interception from vegetation canopy</td> </tr> <tr> <td>ET_water</td> <td>mm d-1</td> <td>0.01</td> <td>Water body, snow and ice evaporation. Penman <br>evapotranspiration is regarded as actual evaporation for them.</td> </tr> </tbody> </table> <h2>Changes</h2> <p>Here, this PML-V2 dataset denotes <strong>the latest update</strong> that follows the original implementation of Zhang et al., 2019, <strong>except with </strong>Terra LAI for longer temporal coverage and annual updates.</p> <ul> <li> <p>Temporal coverage lengthened to 2000.2-2023.12</p> </li> <li> <p>Using MODIS Terra LAI (MOD15A2H) with original wWhd smoother processing as in Kong et al., 2019</p> </li> <li> <p>Recalibrated with the new MODIS Terra LAI</p> </li> <li> <p>Other climatic forcing and MODIS input remain the same</p> </li> </ul> <h2>Google Earth Engine</h2> <p>Original 500m 8-day data in GEE</p> <p>https://developers.google.com/earth-engine/datasets/catalog/CAS_IGSNRR_PML_V2_v018</p> <h2>Methods</h2> <p>Penman-Monteith-Leuning Evapotranspiration V2 (PML_V2) products include evapotranspiration (ET), its three components, and gross primary product (GPP) at 500m and 8-day resolution during 2000-2017 and with spatial range from -60°S to 90°N. The major advantages of the PML_V2 products are:</p> <ol> <li> <p>coupled estimates of transpiration and GPP via canopy conductance (Gan et al., 2018; Zhang et al., 2019)</p> </li> <li> <p>partitioning ET into three components: transpiration from vegetation, direct evaporation from the soil and vaporization of intercepted rainfall from vegetation (Zhang et al., 2016).</p> </li> </ol> <p>The PML_V2 products perform well against observations at 95 flux sites across globe, and are similar to or noticeably better than major state-of-the-art ET and GPP products widely used by water and ecology science communities (Zhang et al., 2019).</p> <h2>References</h2> <ul> <li> <p>Zhang, Y., Kong, D., Gan, R., Chiew, F.H.S., McVicar, T.R., Zhang, Q., and Yang, Y., 2019. Coupled estimation of 500m and 8-day resolution global evapotranspiration and gross primary production in 2002-2017. Remote Sens. Environ. 222, 165-182, <a href="https://doi.org/10.1016/j.rse.2018.12.031">doi:10.1016/j.rse.2018.12.031</a></p> </li> <li> <p>Gan, R., Zhang, Y.Q., Shi, H., Yang, Y.T., Eamus, D., Cheng, L., Chiew, F.H.S., Yu, Q., 2018. Use of satellite leaf area index estimating evapotranspiration and gross assimilation for Australian ecosystems. Ecohydrology, <a href="https://doi.org/10.1002/eco.1974">doi:10.1002/eco.1974</a></p> </li> <li> <p>Zhang, Y., Peña-Arancibia, J.L., McVicar, T.R., Chiew, F.H.S., Vaze, J., Liu, C., Lu, X., Zheng, H., Wang, Y., Liu, Y.Y., Miralles, D.G., Pan, M., 2016. Multi-decadal trends in global terrestrial evapotranspiration and its components. Sci. Rep. 6, 19124. <a href="https://doi.org/10.1038/srep19124">doi:10.1038/srep19124</a></p> </li> </ul>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.