Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
117
datasets available to search
ShareScore release 0.7.1
Dataset results
117 results for “evapotranspiration”
Bowen Ratio Evapotranspiration Data at the Sevilleta National Wildlife Refuge, New Mexico, 1996-1999
This file contains data collected from 1996-1999 at a Bowen ratio tower adjacent to the Deep Well Meteorological Station at Deep Well (Station 40). The Bowen ratio method employs a method of measuring the temperature and vapor pressure gradient over a vegetation canopy to quantify evapotranspiration from that canopy.
Catchment-scale estimates of Amazon evapotranspiration
<p>Amazon basin-mean monthly estimates of evapotranspiration estimated from catchment-balance analysis, satellites (MODIS, P-LSH and GLEAM), reanalysis (ERA5) and the CMIP5 and CMIP6 climate models. Catchment level estimates of climate variables that influence ET are also included (precipitation, radiation and leaf area index). Full details of all source datasets are provided in 'Evapotranspiration in the Amazon: spatial patterns, seasonality and recent trends in observations, reanalysis and CMIP models' in Hydrology and Earth System Sciences, <a href="https://doi.org/10.5194/hess-2020-523">https://doi.org/10.5194/hess-2020-523</a>.</p>
Projected changes in droughts and extreme droughts in Great Britain are strongly influenced by the choice of drought index: UKCP18-based bias adjusted potential evapotranspiration
<p>Potential evapotranspiration calculated from the UKCP18 RCM ensemble using the Penman-Monteith method as implemented by Robinson et al. (2017) and bias adjusted using Lange et al. (2019). This dataset was used for analysis of future drought characteristics in Reyniers et al. (2022). Details on the bias adjustment of this potential evapotranspiration dataset, as well as bias-adjusted precipitation and temperature from the same climate model ensemble, can be found in Reyniers et al. (2025).</p> <p>---</p> <p>Reyniers, N., Osborn, T. J., Addor, N., and Darch, G.: Projected changes in droughts and extreme droughts in Great Britain strongly influenced by the choice of drought index, Hydrol. Earth Syst. Sci., 27, 1151–1171, https://doi.org/10.5194/hess-27-1151-2023, 2023.</p> <p>Reyniers, N., Zha, Q., Addor, N., Osborn, T. J., Forstenhäusler, N., and He, Y.: Two sets of bias-corrected regional UK Climate Projections 2018 (UKCP18) of temperature, precipitation and potential evapotranspiration for Great Britain, Earth Syst. Sci. Data, 17, 2113–2133, https://doi.org/10.5194/essd-17-2113-2025, 2025. </p> <p>Robinson, E. L., Blyth, E. M., Clark, D. B., Finch, J., Rudd, A. C. (2017). Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data. HESS, <em>21</em>(2), 1189-1224.</p> <p>Lange, S. (2019). Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1. 0). <em>GMD,</em> <em>12</em>(7), 3055-3070.</p>
Standardized Dataset of the Ecosystem's Water Use Efficiency, Gross Primary Productivity and the Evapotranspiration Deficit Index for 1982–2017 over the Middle East
<p>This data aimed to investigate the spatial-temporal variability of Standardized Actual Evapotranspiration (sAET), Gross Primary Productivity (sGPP) and Water Use Efficiency (WUE) anomalies series, and the Standardized Evapotranspiration Deficit Index (SEDI). The Middle East (ME), was selected as a case study to monitoring drought events as one of the major natural disasters for the ecosystem. To this end, the yearly gross primary production of GLASS, GIMMS, FloxCom, and VPM datasets for the study area spanning 1982–2017 was used to develop the sGPPR data. On the other hand, the Global Land Evaporation Amsterdam Model (GLEAM-version (v3.3a)), which estimated the several components of terrestrial evaporation (annual actual and potential evaporation (AET, PET)) was used for the same period this aimed to detect the variability of the SEDI.<br> This version of the yearly GLASS-sGPPR dataset (1982–2017) is available for the ME at 0.05° spatial resolution, as the original data of the GPP-GLASS products, While, sGPPR dataset of GIMMS, FloxCom, and VPM are also at annual temporal resolution, and at 0.5 degree spatial resolution spanning 1982–2016 for GIMMS, FloxCom, and 2000-2016 for VPM (Excel wrokbook .xlsx). The SEDI data are also available at 0.25 degree spatial resolution for 1980–2018 ( Raster files (TIFF)). For more details about Standardization of the GPP and evapotranspiration deficit data see: <strong>Alsafadi, K., Al-Ansari, N., Mokhtar, A., Mohammed, S., Elbeltagi, A., Sammen, S. S., & Bi, S. (2021). An evapotranspiration deficit-based drought index to detect variability of terrestrial carbon productivity in the Middle East. <em>Environmental Research Letters</em>. <a href="http://dx.doi.org/10.1088/1748-9326/ac4765">10.1088/1748-9326/ac4765</a></strong></p>
High-resolution projections of evapotranspiration and water availability for Europe under climate change
<p>Europe-wide high-resolution (1 km) gridded data of estimates of monthly and annual potential evapotranspiration (ET0), annual actual evapotranspiration (AET0) and water availability for a climate normal period largely preceding an anthropogenic warming signal (1961-1990) and for two CMIP5 multimodel future projections (2011-2040 and 2041-2070). In the ET0 calculation, the monthly and annual heat index <em>I</em> and annual <em>α</em> parameter were estimated following the Thornthwaite method, and AET0 was calculated using the Budyko approach.</p> <p>For citations and more details, please refer to "High-resolution projections of evapotranspiration and water availability for Europe under climate change" by Ştefan Dezsi, Marcel Mândrescu, Dănuţ Petrea, Praveen Kumar Rai, Andreas Hamann, Mărgărit-Mircea Nistor, published in <em>International Journal of Climatology</em> (<a href="https://doi.org/10.1002/joc.5537">https://doi.org/10.1002/joc.5537</a>)</p>
Data set supporting journal article: Markwitz, C. and Siebicke, L.: "Low-cost eddy covariance: a case study of evapotranspiration over agroforestry in Germany", Atmos. Meas. Tech., 2019
<p>This data set contains evapotranspiration data obtained by a conventional eddy covariance set-up and a low-cost eddy covariance set-up as described in the research article: Markwitz, C. and Siebicke, L.: "Low-cost eddy covariance: a case study of evapotranspiration over agroforestry in Germany", Atmos. Meas. Tech., 2019.</p> <p>The data set contains all necessary data needed to replicate figures and analysis presented in the research article. The data sets are sorted and named according to the figure the data were used for. </p>
PML-V2 China staple crop (maize, wheat, rice) evapotranspiration, gross primary production and yield over 2003-2018
<p>This dataset provides the yearly evapotranspiration (ET), gross primary production (GPP) and yield of the three staple crops (i.e., maize, wheat and rice) of China from 2003 to 2018. ET and GPP are estimated by the Penman-Monteith-Leuning version 2 model (PML-V2 model), and crop yield is the product of GPP and harvest index. The units of ET, GPP and yield are mm year<sup>-1</sup>, g C m<sup>-2</sup> year<sup>-1</sup> and kg ha<sup>-1</sup>, respectively.</p> <p>The PML-V2 model is calibrated and validated against the observed ET and GPP at EC sites for each crop type. The PML_V2 model uses CMFD meteorological drive and MODIS leaf area index (LAI), reflectivity (Albedo), emissivity (Emissivity) as inputs, and finally obtains PML_V2 crop evapotranspiration, gross primary production datasets.</p> <p>The data format is tiff, the spatiotemporal resolution is yearly and 0.0125°, and the time span is 2003-2018. The file name is "crop_variable_year.tif". For example, a file named Maize_ETsum_2003.tif corresponds to the total ET of maize in 2003.</p>
Data for: Changes in evapotranspiration, transpiration and evaporation across natural and managed landscapes in the Amazon, Cerrado and Pantanal biomes
<p>This dataset contains measurements of evapotranspiration and other meteorological variables (net radiation, air temperature, vapor pressure deficit, etc) from nine eddy covariance towers located in different ecosystems in the Amazon (natural Amazon forest, cropland and pastureland), Cerrado (natural savannah, irrigated and rainfed croplands) and Pantanal (natural forest, pastureland) biomes. It also contains estimates of transpiration that were calculated using two different approaches, the transpiration estimation algorithm (TEA) and the underlying water use efficiency method (uWUE).</p>
Monthly and annual evapotranspiration maps in Berlin (Germany)
<p>Monthly and annual evapotranspiration (ET) maps of Berlin, Germany at a 10-m resolution are provided. This dataset is related to the manuscript "City-wide, high-resolution mapping of evapotranspiration to guide climate-resilient planning" (under review).</p> <p>The monthly and annual ET sums are provided as rasters (.tif files). The monthly ET sums are given in the files named "ETmonthly_2019_(month).tif" The annual ET sum for 2019 is named "ETannual_2019.tif". The coordinate reference system (CRS) is "+proj=longlat +datum=WGS84 +no_defs."</p> <p>For access to daily ET maps in 2019, please contact Stenka Vulova (stenka.vulova@tu-berlin.de).</p> <p>The abstract of the manuscript is given for background information on the dataset:</p> <p>"The impacts of global change, including extreme heat and water scarcity, are threatening an ever-growing urban world population. Evapotranspiration (ET) mitigates the urban heat island, reducing the effect of heat waves. It can also be used as a proxy for vegetation water use, making it a crucial tool to plan resilient green cities. To optimize the trade-off between urban greening and water security, reliable and up-to-date maps of ET for cities are urgently needed. Despite its importance, few studies have mapped urban ET accurately for an entire city in high spatial and temporal resolution. We mapped the ET of Berlin, Germany in high spatial (10-m) and temporal (hourly) resolution for the year of 2019. A novel machine learning (ML) approach combining Sentinel-2 time series, open geodata, and flux footprint modeling was applied. Two eddy flux towers with contrasting surrounding land cover provided the training and testing data. Flux footprint modeling allowed us to incorporate comprehensive land cover types in training the ML models. Open remote sensing and geodata used as model inputs included Normalized Difference Vegetation Index (NDVI) from Sentinel-2, building height, impervious surface fraction, vegetation fraction, and vegetation height. NDVI was used to indicate vegetation phenology and health, as plant transpiration contributes to the majority of terrestrial ET. Hourly reference ET (RET) was calculated and used as input to capture the temporal dynamics of the meteorological conditions. Predictions were carried out using Random Forest (RF) regression. Weighted averages extracted from hourly ET maps using flux footprints were compared to measured ET from the two flux towers. Validation showed that the approach is reliable for mapping urban ET, with a mean R2 of 0.76 and 0.56 and a mean RMSE of 0.0289 mm and 0.0171 mm at the more vegetated site and the city-center site, respectively. Lastly, the variation of ET between Local Climate Zones (LCZs) was analyzed to support urban planning. This study demonstrated the capacity to map urban ET at an unprecedented high spatial and temporal resolution with a novel methodology, which can be used to support the sustainable management of green infrastructure and water resources in an urbanizing world facing climate change."</p>
Long-term daily hydrometeorological drought indices, soil moisture, and evapotranspiration for ICOS ecosystem sites
<p>Standardized drought indices to support research at ICOS ecosystem sites. Dataset to Nature Scientific Data submission.</p> <p>"The dataset comprises four files for each of the 101 sites: "[site_name]_input" contains the observational data extracted from E-OBS, PET estimates as well as the simulated soil water storage and actual evapotranspiration from mHM; and threemore files for each of the standardized drought indices ("SSMI_[site_name]", "SPI_[site_name]", "SPEI_[site_name]"). Details on the variables, their units and their origin are given in Tab. 1. For the SPI and SPEI, the file of each site contains the estimates for various aggregation times, ranging from 5 to 730 days in steps of 5 days from 5 to 365 and steps of 10 days from 370 to 730. Each data file has a daily temporal resolution and covers the time span from 1950 to 2021."</p>
Long term mean Potential Evapotranspiration (PET) and Actual Evapotranspiration (EAT) estimates using World-Wide HYPE and different PET-formula
<p>Data of the article "<strong>Which Potential Evapotranspiration Formula to Use in Hydrological Modelling World-wide? </strong>" (Pimentel et al. 2023, <em>Water Resources Research, </em><a href="https://doi.org/10.1029/2022WR033447">https://doi.org/10.1029/2022WR033447</a>)</p>
CMIP6 derived ensemble of global vapor pressure deficit, potential evapotranspiration, and reference evapotranspiration
<p>Climate change induced trends in long-term aridity—via changes to atmospheric water demand for have the potential to impact surface water availability across the globe by altering efficiency by which precipitation is converted to runoff. Quantification of aridity requires estimates of evaporative demand, often using vapor pressure deficit, potential evapotranspiration, and/or reference evapotranspiration, but no comprehensive estimate of these climate variables exists to date from the Coupled Model Intercomparison Project 6 (CMIP6). Here we present global monthly estimates of the Penman-Monteith short grass reference evapotranspiration, its advective and radiation components, Priestley-Taylor potential evapotranspiration, and vapor pressure deficit from 16 CMIP6 general circulation models (GCM) for the historical period and four future emission scenarios ranging from low to high projected emissions. The purpose of this dataset is to offer structured and well-documented estimates of historical and future projected evaporative demand derived from the state-of-the-science CMIP6 climate models for use in hydrologic and ecological analyses. We produce a single file for all monthly values of each variable for individual GCM/emission scenario combination gridded at the given GCMs native resolution. Produced alongside all of the files are descriptions of each of the variables and generated python scripts that contain the functions used to estimate vapor pressure deficit, potential evapotranspiration, and reference evapotranspiration.</p>
Biometeorological Dataset for 'Novel algorithms for high resolution prediction of canopy evapotranspiration in grapevine'
<p>A head trained <strong><em>Vitis vinifera</em></strong> L. cv. Zinfandel vine was grafted on St. George rootstock (<em>V. rupestris</em>) then planted in a 1.1 m<sup>3</sup> plastic container filled with Yolo County, CA sourced sandy loam.<br> <br> To estimate evapotranspiration, we measured the wind speed, air temperature and relative humidity in vine canopies by mounting each vine with a suite of research grade sensors. We measured wind speed (units m ᐧ s<sup>-1</sup>) inside the vine canopy using a single needle anemometer (<em>East 30 Sensors</em>; Pullman, WA) that took instantaneous wind speed measurements every 10 seconds and recorded the average of the previous 12 instantaneous measurements for every 2-minute interval.</p> <p>We measured temperature (units <sup>o</sup>C) and relative humidity (units %) using HMP60L sensors (Campbell Scientific; Logan, UT) mounted both inside and outside of each vine canopy and recorded instantaneous measurements at each 2-minute interval. We filtered all biometeorological data using a 3-hour moving average to remove noise without causing any significant over or under-approximation of daily maxima and minima.</p> <p>We automated all data collection using two CR1000 data loggers (<em>Campbell Scientific</em>; Logan, UT), with 1 or 2 vines and associated sensors per logger, using custom CR1 programs. A single 30W solar cell and 12V lead acid battery powered the entire vine-sensor system.</p> <p>This dataset represents all sensor data from a single vine, as measured in August 2020. Columns are named accordingly and include units.</p> <p><strong>Please Note</strong>: The column named 'load_cell_kg' is not named accurately. The values given are in units of millivolts, and need to be translated from millivolts to kilograms. The 2020 calibration coefficient is 0.00330693663 millivolts per kilogram.</p>
Monthly runoff and evapotranspiration data at a 0.25-degree for the headwater of seven river basins in the Tibetan Plateau (2018-2100)
<p>This data set describes the temporal and spatial distribution of runoff and evapotranspiration for the headwater of seven river basins in the Tibetan Plateau under SSP245 and SSP585 scenarios. We utilized an LSTM-grid model trained with inputs from an observation-constrained distributed cryosphere-hydrology model (WEB-DHM), along with meteorological data from ISIMIP3b (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL) and glacier mass balance data (NSIDC) to generate the runoff and evapotranspiration data. The time range is 2018-2100 at a monthly scale, the spatial resolution is 0.25-degree, and the unit is mm/month. The data will provide better data support for the study of the major river in the Tibetan Plateau.</p> <p>For the convenience of users, this data is stored in NC (NetCDF) format, with each combination of different variables (runoff, evapotranspiration) and climate scenarios (SSP245 and SSP585) forming a separate file. The file names are in the format of “runoff(evapotranspiration)_SSP245(SSP585)_2018-2100.nc”, where “2018-2100” represents the years covered by the data. Each file contains three coordinates: lon (longitude), lat (latitude), and time, as well as a data variable (either runoff or evapotranspiration). These files can be opened with Python, MATLAB, and other tools.</p>
Monthly runoff and evapotranspiration data at a 0.25-degree for the headwater of seven river basins in the Tibetan Plateau (2018-2100)
<p>This data set describes the temporal and spatial distribution of runoff and evapotranspiration for the headwater of seven river basins in the Tibetan Plateau under SSP245 and SSP585 scenarios. We utilized an LSTM-grid model trained with inputs from an observation-constrained distributed cryosphere-hydrology model (WEB-DHM), along with meteorological data from ISIMIP3b (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL) and glacier mass balance data (NSIDC) to generate the runoff and evapotranspiration data. The time range is 2018-2100 at a monthly scale, the spatial resolution is 0.25-degree, and the unit is mm/month. The data will provide better data support for the study of the major river in the Tibetan Plateau.</p> <p>For the convenience of users, this data is stored in NC (NetCDF) format, with each combination of different variables (runoff, evapotranspiration) and climate scenarios (SSP245 and SSP585) forming a separate file. The file names are in the format of “runoff(evapotranspiration)_SSP245(SSP585)_2018-2100.nc”, where “2018-2100” represents the years covered by the data. Each file contains three coordinates: lon (longitude), lat (latitude), and time, as well as a data variable (either runoff or evapotranspiration). These files can be opened with Python, MATLAB, and other tools.</p>
Data from: Evapotranspiration is resilient in the face of land cover and climate change in a humid temperate catchment
Open the record for dataset details and reuse information.
The observed data used in paper titled "A hydrographic method to identify groundwater net recharge, barometric effect, and evapotranspiration from a complicated semidiurnal water table fluctuation"
<p>The water level and atmospheric pressure within the monitoring well located the semi-arid loess hilly-gully region on the piedmont of western Shaanxi Province, China (34°18′36″ N, 107°07′55″ E), were automatically monitored at 20-min intervals by the Levelogger and Barologger, respectively. These data were used as a case example to state a method of estimating groundwater net recharge rate, barometric efficiency and hourly-scaled groundwater evapotranspiration rate.</p>
Irrigation-induced potential evapotranspiration decrease in the Heihe River Basin, Northwest China, as simulated by the WRF model
<p>This dataset is for the plots in the article titled "Irrigation-induced potential evapotranspiration decrease in the Heihe River Basin, Northwest China, as simulated by the WRF model", which was published by Journal of Geophysical Research: Atmospheres. There are totally nine files in the ".mat" format for Matlab. The nine data files are corresponding to nine Figures in the article.</p>
Maps of reference evapotranspiration for the irrigation project in Brazil
<p>The maximum daily evapotranspiration data set for a project (ETproject) for Brazil. It has a spatial resolution of <strong>30 seconds (~ 1 km²)</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 (EPSG: 4326)</strong>.</p> <p>The objective study is to estimate and provide evapotranspiration values of monthly reference and the maximum of twelve months, for dimensioning irrigation systems throughout the Brazilian territory. With the meteorological data of two hundred and fifty-nine conventional INMET stations, the daily reference evapotranspiration (ETo) for 15 years was calculated. For each weather station, the data was grouped by month and the ETo for the irrigation project (ETproject) was determined to meet the eighty percent probability of occurrence, following the recommendations of FAO24. In parallel, monthly images of 15 years of ETo were acquired for Brazil, and the climatic variables of WorldClim. Using the ET values of the stations design, it was modeled for the rest of Brazil, using machine learning algorithms and the covariates. After modeling, the following performances were achieved: mean square error of 0.306 mm / d, mean bias error of -0.004 mm / d, mean absolute error of 0.227 mm / d, determination coefficient of 0.938 and efficiency of Nash-Sutcliffe 0.937. ETo values for irrigation projects were similar to several others reported in the literature when compared at a given point. With this research it was possible to determine the monthly and annual ETo for irrigation projects throughout the Brazilian territory.</p> <p>The article has been submitted for publication.</p>
Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (2000-2024)
<p>The data contains simulation results from 2000-2024, 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" (1975-1999) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671253</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.