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117 results for “evapotranspiration”
Long time series (2001-2018) of daily evapotranspiration in China generated based on SEBAL: Part 2
<p>The dataset named SEBAL evapotranspiration in China (SEBAL ET) characterized the daily evapotranspiration (in millimeter) of vegetation in China from 2001 to 2018, the spatial resolution is 1 km × 1km and the temporal resolution is 1 day with the coordinate system of GCS_WGS_1984. The products were generated using Surface Energy Balance Algorithm of Land (SEBAL) and multi-sources remote sensing data, including MOD43A1 daily surface albedo, MOD11A1 daily surface temperature and MOD13 vegetation indices (obtained from NASA: https://ladsweb.modaps.eosdis.nasa.gov/search/), the meteorological data obtained from GMAO (https://gmao.gsfc.nasa.gov/research/highlights/2013-2015.php), the input variables were all aggregated of resampled to 1 km × 1km. The products were evaluated using the eight flux towers observation data for point validation and water balance method for regional validation and showed R value of 0.79 and 0.98, respectively, which indicated the products have a great performance. SEBAL ET can be used for several geoscience studies, especially for global change, water resources mangement and agricultural drought monitoring, etc.</p>
Long time series (2001-2018) of daily evapotranspiration in China generated based on SEBAL: Part 1
<p>The dataset named SEBAL evapotranspiration in China (SEBAL ET) characterized the daily evapotranspiration (in millimeter) of vegetation in China from 2001 to 2018, the spatial resolution is 1 km × 1km and the temporal resolution is 1 day with the coordinate system of GCS_WGS_1984. The products were generated using Surface Energy Balance Algorithm of Land (SEBAL) and multi-sources remote sensing data, including MOD43A1 daily surface albedo, MOD11A1 daily surface temperature and MOD13 vegetation indices (obtained from NASA: https://ladsweb.modaps.eosdis.nasa.gov/search/), the meteorological data obtained from GMAO (https://gmao.gsfc.nasa.gov/research/highlights/2013-2015.php), the input variables were all aggregated of resampled to 1 km × 1km. The products were evaluated using the eight flux towers observation data for point validation and water balance method for regional validation and showed R value of 0.79 and 0.98, respectively, which indicated the products have a great performance. SEBAL ET can be used for several geoscience studies, especially for global change, water resources mangement and agricultural drought monitoring, etc.</p> <p> </p>
Datasets for "Reconciling global terrestrial evapotranspiration estimates from multi-product intercomparison and evaluation"
<p>Datasets for "Reconciling global terrestrial evapotranspiration estimates from multi-product intercomparison and evaluation"</p>
Model simulations for " Potential impacts of LUCC and climate change on evapotranspiration and gross primary productivity in the Haihe River Basin, China"
<p>Experiment_1.rar, Experiment_2.rar, and Experiment_3.rar are the model simulations from experiment 1, experiment 2, and experiment 3, respectively. All the simulations are original from the CLM5 model in netcdf format.</p> <p>More details on these data can be found in the paper "Potential impacts of LUCC and climate change on evapotranspiration and gross primary productivity in the Haihe River Basin, China". </p>
Evapotranspiration components, soil water content and net primary productivity in a black locust plantation
<p>ET components, i.e. transpiration (T), soil evaporation(E) and canopy interception (I), soil water and net primary productivity (NPP), water use efficiency (WUE) were estimated in a black locust plantation during a drier (2015) and a wetter (2016) growing season (i.e., June to September).</p>
An Excel spreadsheet including 7-year eddy covariance-based evapotranspiration and auxilary data at Yunxiao mangrove flux tower
<p>An Excel spreadsheet including 7-year eddy covariance-based evapotranspiration and auxilary data at Yunxiao mangrove flux tower required to reproduce key results in the main text of a manuscript (each figure corresponds to a single sheet). Contact Xudong Zhu at Xiamen University (xdzhu@xmu.edu.cn) if you have any question.</p>
The evapotranspiration in the Haihe River basin, China
<p>Evapotranspiration with 500 m spatial resolution in the Haihe River Basin (HRB) from 2000 to 2020.</p>
A benchmark dataset for global evapotranspiration estimation based on FLUXNET2015 from 2000 to 2022 (V1.0)
<p>Our released data mainly contains four types of data:</p> <p>(1) Half-hourly or hourly gap-filled LE data: The data are well gap-filled LE data using the novel bias-corrected RF algorithm. In the filenames, “HH” or “HR” indicate half-hourly or hourly scale data, respectively. The time information in the data files includes a pair of timestamps consistent with those in FLUXNET2015. The data are recorded at local time. The start time is “2000-02-18, 00:00:00”, and the end time is the same as the observation time at each site. For the quality control flags (QC), a value of 0 indicates observed data, while 1 indicates gap-filled data.</p> <p>(2) Prolonged daily LE data: This dataset provides the prolonged daily LE data using the novel bias-corrected RF algorithm. The seamless data covers the period from February 18, 2000, to December 31, 2022. For the prolonged part, the quality flag is set to 2. The rest data is consistent with the aggregated daily LE data.</p> <p>(3) Aggregated daily, monthly and yearly LE data: The hourly dataset is aggregated from the gap-filled half-hourly data to a daily scale. The start time is “2000-02-18”, and the end time is the same as the observation time at each site. Data quality control flags are also provided, with the values representing the percentage of hourly observations for each day. The monthly and yearly LE data are aggregated from the prolonged daily LE data. Quality control flags represent the proportion of days with more than 90% of hourly observations in a given month or a given year. No distinction is made between prolonged data and data with complete missing observations within a day. The start time for the monthly data is March 2000, and that for the yearly data is 2001.</p> <p>All files are formatted as csv files. NDVI and debiased reference variables from ERA5-Land are also provided.</p> <p>For more details of our data, please refer to a companion research article submitted to ESSD. <span>Li, W., Yao, Z., Qu, Y., Yang, H., Song, Y., Song, L., Wu, L., and Cui, Y.: A benchmark dataset for global evapotranspiration estimation based on FLUXNET2015 from 2000-2022, Earth Syst. Sci. Data, under review, 2024.</span> </p>
Derived Optimal Linear Combination Evapotranspiration (DOLCE)
<p>The Derived Optimal Linear Combination Evapotranspiration (DOLCE) dataset consists of monthly Evapotranspiration (ET) and their associated uncertainty on a global scale. DOLCE is derived at 0.5 spatial resolution and monthly temporal resolution over the period 2000-2009.</p> <p>DOLCE is derived by weighting six existent global ET products based on their ability to match site-level data from 159 globally distributed flux tower sites.</p> <p>The six evapotranspiration products are: GLEAM V2A, GLEAM V2B, GLEAM V3A, MOD16, MPIBGC and PML .</p> <p>DOLCE is a mosaic of three tiers, we derive each tier from a different weighting ensemble and subset of FLUXNET data. The dominant part (i.e. tier1) involves six ET products and data from 138 flux towers, tier2 involves 5 products and 151 flux towers, while tier3 involved 2 products and 159 sites.</p> <p>This dataset was produced by Dr Sanaa Hobeichi as part of her PhD thesis and of the Centre of Excellence for Climate System Science "The role of land surface forcing and feedbacks for regional climate" research program.</p>
Supplementary data for "Recent increase in global land evapotranspiration is overestimated"
<p>Supplementary data for "Recent increase in global land evapotranspiration is overestimated"</p>
Genova - Rate of evapotranspiration
<p>Calculated rate of evapotranspiration based on weather station measurements in Genova.</p>
Data for "Energy Surplus and Atmosphere – Land-Surface "Tug of War" Induced by Climate Change Control Future Evapotranspiration"
<p>USGS gauges used in manuscript "<strong>Energy Surplus and An Atmosphere-Land-Surface “Tug of War” Control Future Evapotranspiration"</strong>. USGS_CTL15_Gage.mat contains the USGS gauge ID, and one can use retrieve_daily_streamflow.m to download the corresponding streamflow time series. </p>
Data from: Evapotranspiration response to multiyear dry periods in the semiarid western United States
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 ): CO2 Concentration and Flux 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 data are CO2 concentration at canopy and CO2 flux from canopy collected as part of this study.
Gridded runoff and evapotranspiration dataset for seven major river basins of the Tibetan Plateau during 1998-2017
<p>This data set describes the spatiotemporal distribution of runoff and evapotranspiration for the headwater of seven river basins (the Yellow, Yangtze, Mekong, Salween, Brahmaputra, Ganges, and Indus) in the Tibetan Plateau. This was achieved using an observation-constrained distributed cryosphere-hydrology model, known as the WEB-DHM. The time range is 1998-2017 at a monthly scale, the spatial resolution is 5km×5km, and the unit is mm/month. </p><p>For the convenience of users, this data is stored in TIF format, with each combination of different watersheds forming a separate file. Each file contains two variable: either runoff or evapotranspiration. These files can be opened with ArcGIS, Python, R, and other tools.</p>
Evapotranspiration frequently increases during droughts
<p>Datasets and code necessary to reproduce main results in the paper。</p>
Characterising the Chilean megadrought using satellite data of precipitation and evapotranspiration (supplementary material)
<p>This is the supplementary material accompanying the article submitted to Remote Sensing on Environment on February 26th, 2019.</p>
Linking evapotranspiration, boundary-layer processes and atmospheric moisture using isotope tracer modeling and data
Open the record for dataset details and reuse information.
MODIS/Aqua Net Evapotranspiration Gap-Filled 8-Day L4 Global 500m SIN Grid V061
The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) MYD16A2GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled 8-day composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover.The MYD16A2GF will be generated at the end of each year when the entire yearly 8-day [MYD15A2H](https://doi.org/10.5067/MODIS/MYD15A2H.061) is available. Hence, the gap-filled MYD16A2GF is the improved MYD16, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD16A2GF in near-real time because it will be generated only at the end of a given year.Provided in the MYD16A2GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A2GF granule.The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. The last acquisition period of each year is a 5 or 6-day composite period, depending on the year.Known Issues* Operational and uncertainty issues are provided under Section 3 in the User Guide.* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).Improvments/Changes from Previous Version* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.
MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V061
The MOD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. Provided in the MOD16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A2 granule.The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period, depending on the year.Known Issues* Operational and uncertainty issues are provided under Section 3 in the User Guide.* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).Improvements/Changes from Previous Versions* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.