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.
608
datasets available to search
ShareScore release 0.7.1
Dataset results
608 results for “irrigation”
Historical and future irrigation water demand for the STARS4Water river basins
<p>Dataset contains data on historical and future irrigation water demand for seven European river basins (Danube, Drammen, Duero, East Anglia, Messara, Rhine and Seine) being case study basin in the STARS4Water, and a shapefile with river basin boundaries. The average summer net irrigation requirement [mm/year] for each combination GCM model (5 models)/time window (2 windows) was calculated within the boundaries of the project river basin hubs. The difference between the future and historical period was also calculated for each GCM. In addition, ensemble mean values for both horizons and ensemble mean differences were calculated. This dataset was prepared based on the data available in the "Net irrigation requirement under different climate scenarios using AquaCrop over Europe" repository (Busschaert et al., 2022, DOI: 10.5281/zendo.6760976).</p>
WAT01 Konza Prairie long-term irrigation transect study
In 1991, an irrigation transect experiment was established near the Konza Prairie HQ to assess the effects of supplemental water on ecological processes in tallgrass prairie. The site is burned annually in the spring. The transect spans upland, hillside and lowland topographic positions with irrigation and sampling points (12) located at 10 m intervals. Adjacent control transects are marked on both sides of the irrigation transect. Irrigation is scheduled according to estimates of actual evapotranspiration and measures of plant water status. In 1992, an additional 4 irrigation sprinklers were added to the transect (2 at each end). In 1993, a second line of sprinklers and control plots was added (#16-31). At the time of peak aboveground biomass (late August to October), six 0.1 m2 quadrats are harvested at each of the 30 sites (no #9 due to rock outcrop) for the irrigated and control/non-irrigated lines. Biomass is separated into live grass, forb and woody. As of 2006, c.dead is no longer separated from live grass. Vegetative species composition was initially measured in 1991 at each site, and continues to be measured at midseason by using a modified Daubenmire canopy coverage technique in a 10 m2 circular plot. At approximately 10 day intervals, predawn and midday plant water potentials are measured in Andropogon gerardii at each site in both irrigated and control transects. Since 1992, reproductive effort of the dominant grasses Andropogon gerardii (ANGE), Sorghastrum nutans (SONU), Schizachyrium scoparius (ANSC) has been assessed in irrigated and control transects by measuring heights (n=9) and densities (n=4) of flowering stalks. In 1993, soil moisture measurements at 15 and 30 cm depths were begun with a Time Domain Reflectometry system. Data set also includes measured natural precipitation and supplemental water added to the site. Full data used in the Broderick et al. (2022) paper, "Climate legacies determine grassland responses to future rainfall regimes", ca
InnoVine WP3: 105 phenolic compound quantification of 2014 and 2015 mature grape berries from a core-collection of 279 irrigated and non-irrigated Vitis vinifera cultivars
<p>FP7/311775 InnoVine (Innovation in vineyard): Combining innovation in vineyard management and genetic diversity for a sustainable European viticulture</p> <p>WP3: Exploiting the genetic diversity in grapevine</p> <p>105 phenolic or related compounds, from 2014 and 2015 mature grape berries from a core-collection of 279 irrigated and non-irrigated <em>Vitis vinifera</em> cultivars, were quantified by UPLC-TQ-MRM Mass Spectrometry (Lambert M<em> et al., Molecules</em> <strong>2015</strong>, <em>20</em>(5), 7890-7914; doi:10.3390/molecules20057890 & Pinasseau L <em>et al.</em>, <em>Molecules</em> <strong>2016</strong>, <em>21</em>(10), 1409; doi:10.3390/molecules21101409).</p> <p>3 parameters were added:<br> - water/drought status (delta C13)<br> - sugar content (refractive index, brix degree)<br> - weight of 100 grape berries</p> <p>All plant material was collected at the Vassal repository: French National Grapevine Germplasm Collection, INRA Domaine de Vassal, 34340 Marseillan-Plage, France (Centre de Ressources Biologiques de la Vigne (CRB-Vigne) de Vassal-Montpellier).</p>
Raw data for the submitted manuscript entitled "Mapping and Disposal of Irrigation Pipes for a Sustainable Management of Agricultural Plastic Waste", authors Ileana Blanco, Giuliano Vox, Fabiana Convertino, and Evelia Schettini
<p><span>The file regards the evaluation of plastic indexes and agricultural plastic waste quantities in Apulia region due to the use of irrigation pipes. The data is used to identify the critical areas for plastic waste production due to irrigation pipes.</span></p>
Identifying the mechanisms by which irrigation can cool urban green spaces in summer
<p>This dataset contains the measured soil moisture and microclimate data from two (2021 and 2022) urban green space irrigation experiments conducted in Burnley, Melbourne, Australia. The experiments consisted of two treatments, irrigated turf and unirrigated turf. The purpose of the experiments was to provide testing (2021) and evaluation (2022) data for an urban ecohydrological model, UT&C. </p> <p><br>After evaluating the performance of UT&C in modelling soil moisture and microclimate, UT&C was used to model the surface energy balance and evapotranspiration processes of the irrigated and unirrigated turf. This dataset also contains the modelled soil moisture, microclimate, surface energy balance and evapotranspiration data, as well as the measured background climate data at the reference climate station and the forcing data for the model.</p> <p><br>The aims of this study were to:<br>i) identify the proportional contribution of different evapotranspiration processes to irrigation cooling effect, and <br>ii) quantify the impacts of different irrigation amounts (from 2 to 30 mm/d) on the cooling effect of irrigating turfgrass in Melbourne, Australia during normal summer conditions.</p> <p>This study was published in:<br>Pui Kwan Cheung, Naika Meili, Kerry A. Nice, Stephen J. Livesley (2024). Identifying the mechanisms by which irrigation can cool urban green spaces in summer. Urban Climate. 55,101914. https://doi.org/10.1016/j.uclim.2024.101914.</p>
MIRCA-BC-USMX: Irrigated and planted fractions over the continental United States and Mexico for years 1992, 2002, and 2012
<p>The MIRCA-BC-USMX project contains a spatially explicit mean annual cycle of monthly planted and irrigated fractions at 0.0625 degree (6 km) spatial resolution over the continental United States and Mexico for years 1992, 2002, and 2012.</p> <p>These fractions were generated by (1) reconciling the MIRCA2000 Global Monthly Irrigated and Rainfed Crop Areas dataset (Portmann et al., 2010) with the cropland and pasture classes of year 2001 of the harmonized NLCD_INEGI land cover dataset (Bohn and Vivoni, 2019b); (2) bias-correcting the irrigated and planted fractions to match state-by-state total irrigated and planted areas from government records in the United States (USDA, 2016) and Mexico (SADER, 2014; SAGARPA, 2016).</p> <p>These fractions have been added to land surface parameter files for the Variable Infiltration Capacity (VIC) model (Liang et al., 1994) version 5.1 (Hamman et al., 2018), extended to include the irrigation module of Haddeland et al. (2006), available on <a href="https://github.com/tbohn/VIC/tree/feature/irrig.imperv.deep_esoil">GitHub</a>. The parameter files were taken from the MOD-LSP project, available on <a href="https://zenodo.org/record/2612560">Zenodo</a> (Bohn and Vivoni, 2019a). The VIC 5 image driver requires a "domain" file to accompany the parameter file. This domain file is also necessary for disaggregating the daily gridded meteorological forcings to hourly for input to VIC via the disaggregating tool <a href="https://github.com/UW-Hydro/MetSim">MetSim</a> (Bennett et al., 2018). We have provided a domain file compatible with the meteorological forcings of Livneh et al (2015) and the MIRCA-BC-USMX parameters, on <a href="https://zenodo.org/record/2564019">Zenodo</a> (Bohn et al., 2019a,b).</p> <p>Contents:</p> <ul> <li>Input Files <ul> <li>county_codes.csv - table mapping the numerical codes for counties with the county names used by the US Census Bureau and USDA. This was created by parsing this information from US Census tables from years 1990, 2000, and 2010 and USDA tables from years 1992, 2002, and 2012 and manually reconciling discrepancies across years. Thus the names may not match county names in the original files exactly from year to year, but rather represent my own naming convention. However these discrepancies were rare.</li> <li>mun_us.0.01_deg.asc.tgz and mun_mx.0.0.01_deg.asc.tgz - gzipped tar archives containing mun_us.0.01_deg.asc and mun_mx.0.01_deg.asc, which are ascii-format ESRI grid files created by rasterizing publicly available shapefiles of US and Mexican counties/municipios. These have 0.01 degree (1 km) spatial resolution and pixels have numerical values equal to the codes in county_codes.csv.</li> </ul> </li> <li>Output Files <ul> <li>fplant_firr_bc.$LCYEAR.nc, where $LCYEAR is one of ("s1992","2001", or "2011") - NetCDF-format files at 0.0625 degree (6 km) resolution containing 12 monthly maps each of bias-corrected "fplant" (planted area fraction) and "firr" (irrigated area fraction) for a specific historical year.The value of $LCYEAR indicates the snapshot of the NLCD_INEGI harmonized land cover classification with which fplant and firr were reconciled (so that these area fractions would not exceed the total agricultural/pastoral area given by NLCD_INEGI). Values of fplant and firr were bias corrected so that state-wide total areas matched government records from USDA (USDA, 2014) and SAGARPA (SADER, 2014; SAGARPA, 2016). For $LCYEAR = ("s1992", "2001", "2011"), the agricultural census year used in the bias correction was (1992, 2002, 2012).</li> <li>fplant_firr_bc.2011.mun_mx.nc - same as fplant_firr_bc.2011.nc, but bias-corrected at the municipio level in Mexico. County-level bias correction was not possible in the US due to lack of sufficient resolution USDA records. Similarly, municipio-level records were not available in Mexico prior to year 2003.</li> <li>params.USMX.NLCD_INEGI.$LCYEAR.$YEAR1_$YEAR2.with_irrig.nc - VIC 5 image driver-compliant input parameter files into which fplant and firr of the given $LCYEAR have been inserted. $YEAR1 and $YEAR2 indicate the first and last years of MODIS data used to estimate the annual cycle of monthly LAI, fcanopy, and albedo (independent of the values of fplant and firr).</li> <li>params.USMX.NLCD_INEGI.2011.$YEAR1_$YEAR2.with_irrig.mun_mx.nc - same as params.USMX.NLCD_INEGI.2011.$YEAR1_$YEAR2.with_irrig.nc but with fplant and firr bias-corrected at the municipio level in Mexico.</li> </ul> </li> </ul> <p>These parameters were created with scripts archived on <a href="https://github.com/tbohn/MIRCA-BC-USMX/releases/tag/v1.1">GitHub</a> (Bohn, 2019).</p>
GIS55 GIS Coverages Defining the Konza HQ Irrigation System (1982-present)
These data show the components of the irrigation system near Konza Prairie HQ. Record types 1, 2, 3 and 4 demarcate the locations of the study plots heads (GIS550), transect lines (GIS551), irrigation lines (GIS552), and irrigation line joints (GIS553). Record types 4 and 5 describe the location of the storage piles (GIS554) and the irrigation reservoir (GIS555). This data may be used in conjunction with the Irrigation Transect Studies (WATXX) data. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).
Landsat-based dataset for mapping annual center-pivot irrigated cropland in Brazil
<p>Center-pivot irrigated cropland (CPIC) is a critical component of irrigation and plays an essential role in improving water use efficiency and increasing food production. To automatically extract the spatial distribution of CPIC in Brazil based on the remote sensing technology, we constructed a training dataset that supports the semantic segmentation models.</p><p>The dataset were built with the <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/landsat-5">Landsat 5</a> , 7 and 8<a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/landsat-7"> </a> images as well as the CPIC maps from <a href="https://metadados.snirh.gov.br/geonetwork/srv/por/catalog.search#/metadata/e2d38e3f-5e62-41ad-87ab-990490841073">ANA reference</a> data. We used the Landsat images in 2005, 2010 and 2015 to build the dataset.</p><p>The samples in train_images and train_masks were used to train and valid the Convolutional Neural Network models; </p><p>The samples in valid_data were used to test the model's prediction accuracy.</p><p>Pixels with values 255 and 0 in the mask samples represent the CPIC and background categories.</p><p><strong>For technical details that used to create the dataset, please refer to </strong><i><strong>https://doi.org/</strong></i><strong>10.1016/j.isprsjprs.2023.10.007.</strong></p>
Projected Global Area Equipped for Irrigation Datasets during 2020-2100 under SSP scenarios
<h1><strong>1. Background</strong></h1> <p>Accurately predicting the global area equipped for irrigation in the future is crucial for providing essential datasets relevant to fields such as earth system simulation, agricultural water resource management, climate change adaptation, and environmental conservation. However, the predictive datasets of the area equipped for irrigation are still lacking. To address this gap, we provide the <strong>Projected Global Area Equipped for Irrigation Datasets (PGAEID)</strong>, which provide spatially explicit estimates of Area Equipped for Irrigation (AEI) from 2020 to 2100 under three Shared Socioeconomic Pathway (SSP) scenarios: <strong>SSP1</strong> (sustainable development), <strong>SSP2</strong> (intermediate development), and <strong>SSP3</strong> (regional rivalry), <strong>SSP4</strong> (unequal development), and <strong>SSP5</strong> (fossil-fueled development).</p> <h1><strong>2. Methodology</strong></h1> <h3><strong>2.1 Ensemble Machine Learning (EML) Framework</strong></h3> <ul> <li><strong>Algorithms</strong>: Integrated six machine learning models: <ul> <li>Multiple Linear Regression (MLR)</li> <li>Decision Trees (DT)</li> <li>Autoregressive Integrated Moving Average (ARIMA)</li> <li>Multi-Layer Perceptron (MLP)</li> <li>Radial Basis Function (RBF)</li> <li>Random Forests (RF)</li> </ul> </li> <li><strong>Training Data</strong>: Historical national irrigation records (FAO AQUASTAT, 1961–2015).</li> <li><strong>Validation Metrics</strong>: <ul> <li>Nash-Sutcliffe Efficiency (NSE): <strong>0.9</strong><strong>8</strong></li> <li>Kling-Gupta Efficiency (KGE): <strong>0.</strong><strong>97</strong></li> <li>Mean Absolute Percentage Error (MAPE): <strong>1</strong><strong>.</strong><strong>7%</strong></li> </ul> </li> </ul> <h3><strong>2.2 Spatial Downscaling</strong></h3> <ul> <li><strong>Baseline</strong>: FAO 2005 irrigation data combined with GMIA2005 gridded agricultural intensity maps.</li> <li><strong>Dynamic Projection</strong>: Annual change rates applied to 5′ × 5′ grids under SSP-specific socioeconomic drivers.</li> </ul> <h1><strong>3. Dataset Overview</strong></h1> <h3><strong>3.1 Key Features</strong></h3> <ul> <li><strong>Temporal Coverage</strong>: 2020–2100 (10-year intervals).</li> <li><strong>Spatial Resolution</strong>: 5-arcminute (≈10 km at the equator).</li> <li><strong>Scenarios</strong>: SSP1, SSP2, SSP3, SSP4, SSP5.</li> <li><strong>Variables</strong>: area equipped for irrigation (10<sup>3</sup> ha/year).</li> </ul> <h3><strong>3.2 Dataset Structure</strong></h3> <p>The dataset is provided as a compressed archive (PGAEID_Ver3.0.rar), containing:</p> <p>1.<strong>Global_Area_Equipped_for_Irrigation_GeoTiff</strong><strong>/</strong></p> <ul> <li><strong>Subfolders</strong>: <ul> <li>SSP1</li> <li>SSP2</li> <li>SSP3</li> <li>SSP4</li> <li>SSP5</li> </ul> </li> <li><strong>File Format</strong>: GeoTIFF (45 files total).</li> <li><strong>Naming Convention</strong>:<br>AEI_[SSP]_[Year].tif <ul> <li>Example: AEI_SSP1_2020.tif</li> </ul> </li> </ul> <p>2. <strong>National & Regional_AEI</strong><strong>/</strong></p> <ul> <li><strong>Shapefiles</strong>: National/regional area equipped for irrigation for 26 prediction units (2020–2100).</li> <li><strong>Excel File</strong>: Global Area Equipped for Irrigation (2020-2100).xlsx.</li> </ul> <p>3. <strong>Technical Annex.docx</strong></p> <ul> <li>Detailed methodology, validation, and workflow documentation.</li> </ul> <h1><strong>4. Applications</strong></h1> <p>This dataset supports:</p> <ul> <li><strong>Earth System Simulation: </strong>Supporting irrigation parameterization in global climate and hydrological models.</li> <li><strong>Water Resource Management: </strong>Assisting decision-makers in sustainable irrigation planning.</li> <li><strong>Climate Change Adaptation: </strong>Providing insights into how irrigation practices evolve under different socioeconomic pathways.</li> <li><strong>Environmental Conservation: </strong>Assessing the impact of irrigation on regional ecosystems.</li> </ul> <p><strong>Note:</strong></p> <p>Global aggregated totals of area equipped for irrigation derived from the 26 prediction units (country/regional scale) may exhibit minor discrepancies compared to sums calculated from the 5-arcminute gridded data (≈10 km resolution). Such differences stem from variations in spatial aggregation methods, file formats (vector vs. raster), and underlying data processing frameworks. Users may select the dataset best aligned with their analytical objectives:</p> <ul> <li>The <strong>country/region-based data (26 units)</strong> is recommended for national-scale analyses or policy evaluations requiring administrative boundaries.</li> <li>The <strong>5-arcminute gridded data</strong> is preferable for spatially explicit modeling or subnational assessments.</li> </ul> <p>Both datasets maintain equivalent quality and methodological rigor; the choice depends on the desired spatial granularity and application context.</p>
Global energy use and carbon emissions from irrigated agriculture
<p>This repository contains supporting data for: "<strong>Global energy use and carbon emissions from irrigated agriculture"</strong></p><p>Email: qinjingxiu17@mails.ucas.ac.cn and duanweili@ms.xjb.ac.cn</p><p>The dataset contains:</p><p>-Global energy consumption and CO2 emissions from irrigation . </p><p>-Global CO2 emissions from groundwater degassing . </p><p>-Energy consumption and CO2 emissions with different irrigation and pumping systems and irrigation water sources. </p><p>-Global energy consumption and CO2 under drip and sprinkler scenarios. </p><p>-Global energy consumption and CO2 under mix electricity scenarios. </p><p>-Energy units: Terajoule (TJ); CO2 emissions units: (Tonnes CO2)</p><p>-Files are uploaded in .tif raster data. </p>
A novel educational approach for safe endodontic syringe irrigation: a randomized controlled study
<p><span>(1) Educational video emphasizing the fundamentals of safe irrigation practices, incorporating evidence-based guidelines on appropriate plunger forces and the required time for safe irrigant delivery. </span></p> <p><span>(2) Dataset comprising the measurement data collected and processed during this study.</span></p>
Supplemental data and code for "Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff"
<p>This dataset provides all data compiled and generated for the manuscript entitled "Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff" (https://doi.org/10.1016/j.oneear.2023.08.002). This includes the boundaries for 3614 hydrological catchments, the curated data used for analysis and modelling, the developed machine learning model, shapley values and area of applicability results, and data for global extrapolation</p> <p>It also contains a markdown file ('code.html') which shows how to access and use the data, and generic sample codes used to generate these results.</p> <p> </p> <p> </p> <p> </p>
Crop-specific salinity and irrigation data for river sub-basin water scarcity analyses in the US and AU
<p>This dataset contains observed monthly and annual salinity (EC) data for surface water (river) respectively groundwater, spatially averaged over sub-basins within the Central Valley, CA and the Murray Darling basin, AU, used for salinity-inclusive water scarcity assessments. The data also includes crop-specific irrigated area, irrigation withdrawals and salinity thresholds and other parameters specified, as well as example codes for analyses related to the manuscript: Thorslund et al., <em>Salinity impacts on irrigation water-scarcity in food bowl regions of the US and Australia.</em></p>
Panel data used in the paper ""The shadow price of irrigation water in major groundwater depleting countries"
<p>The panel data are used in an econometric analysis estimating Cobb-Douglas production functions that are subsequently used to calculate the shadow price (current marginal value) or irrigation water in 11 major groundwater depleting countries.</p>
Roles of irrigation and reservoir operations in modulating terrestrial water and energy budgets in the Indian sub-continental river basins
<p>We have simulated water budget and energy budget over Indian subcontinental basins, using three scenarios from the Variable Infiltration Capacity (VIC) model by including irrigation and reservoir practices in it:</p> <p>1). No irrigation and reservoir (VIC-NATURAL)<br> 2). Free irrigation and no reservoir (VIC-FREE)<br> 3). Reservoir and restricted irrigation (VIC-MANAGED)</p> <p>Here, we have shared results in below folders.</p> <p>Fig1: Annual precipitation (P) and reservoir locations used in study.<br> Fig2: Satellite (MODIS and GLEAM) based annual evapotranspiration (ET) and VIC-MANAGED simulated annual ET.<br> Fig3: Annual land surface temperature (LST) from MODIS, AATSR and VIC-MANAGED.<br> Fig4: Mean monthly observed and simulated reservoir storage.<br> Fig5: P, ET, total runoff (TR) and LST from one grid.<br> Fig6: Annual ET change between VIC-NATURAL and VIC-MANAGED run.<br> Fig7: Same as Fig6 but for TR.<br> Fig8: Same as Fig6 but for LST.<br> Fig9: Annual ET change between VIC-FREE and VIC-MANAGED run.<br> Fig10: Annual latent heat flux and sensible heat flux change between VIC-NATURAL and VIC-MANAGED run.</p> <p>More detail is available in "Roles of irrigation and reservoir operations in modulating terrestrial water and energy budgets in the Indian sub-continental river basins" paper in JGR-Atmosphere. Or contact at harsh.lovekumar.shah@iitgn.ac.in</p> <p>Harsh Shah</p>
The first geospatial dataset of irrigated fields (2020-2024) in Vojvodina (Serbia)
<p><span>Irrigation is a cornerstone of global food security, enabling sustainable agricultural production and helping to ensure that food is available for people around the world, now and in the future. Mapping irrigated fields provides valuable information for sustainable water management, agricultural development, and environmental conservation efforts. However, the collection of high-quality training data, which is necessary for accurate irrigation mapping remains costly and labour-intensive. To address this, we created a georeferenced regional dataset consisting of location, crop type, and occurrence of the irrigation equipment which are essential information for mapping irrigated fields. Four main irrigated crops were considered: maize, soybean, sugar beet, and wheat. The dataset<span>,</span> consisting of a total of 1256 parcels<span>,</span> is created for Vojvodina, the main agricultural area in Serbia, spanning the period of five years (2020 - 2024). This study’s goal is to give accessibility to our dataset which further can be explored and used for building or fine-tuning machine learning and deep learning models for the automatic detection of irrigated fields using satellite imagery.</span></p>
High-resolution water budget estimates over the Po basin: progress towards digital replicas (OL): open loop without irrigation
<p>NASA LIS output with water budget variables at 0.7 km^2 resolution over the Po river basin (Italy) for 2015-2023. Netcdf files for 8 + 2 experiments, described in De Lannoy et al. (2024, JAMES). Because of storage limitations, this upload only contains the baseline open loop without irrigation (OL), i.e. 1 of the 8 experiments with ERA5. The other 7 experiments are on a separate zenodo link (see below).</p> <p><strong>8 experiments forced with ERA5 meteorology</strong></p> <p>po_ol_hymap_noirr: (OL) open loop simulation, no irrigation modeling <br>po_ol_hymap_irr: (OL*) open loop simulation, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p>po_da_hymap_gamma_noirr: (DAg) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, no irrigation modeling --> 10.5281/zenodo.13754454<br>po_da_hymap_gamma_irr: (DAg*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p>po_da_hymap_snd_noirr: (DAs) data assimilation of Sentinel-1 snow depth retrievals, no irrigation modeling --> 10.5281/zenodo.13754454<br>po_da_hymap_snd_irr: (DAs*) data assimilation of Sentinel-1 snow depth retrievals, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p>po_da_hymap_gamma_snd_noirr: (DAgs) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, no irrigation modeling --> 10.5281/zenodo.13754454<br>po_da_hymap_gamma_snd_irr: (DAgs*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p><strong>2 experiments forced with MERRA2 meteorology </strong></p> <p><strong>--> These are not provided on Zenodo, because we hit the maximum storage limit. Feel free to reach out to the authors and ask for these data.<br></strong></p> <p>po_ol_hymap_noirr_M2: open loop simulation, no irrigation modeling<br>po_ol_hymap_irr_M2: open loop simulation, with irrigation modeling</p>
LANID-US: Landsat-based Irrigation Dataset for the United States
<p><strong>Annual 30-m resolution irrigation maps, derivative products, and ground reference locations for the United States, 1997-2017</strong>, including: </p> <p>(1) lanidYYYY (binary): Annual irrigation layer for the year YYYY (ranging from 1997 to 2017), e.g., lanid2017 is the irrigation map for the year 2017.</p> <p>(2) irrFreq (1-21): The number of years irrigated during 1997-2017.</p> <p>(3) irrFreqPasture_West: irrFreq of pasture/hay for the western CONUS.</p> <p>(4) maxIrrExt (binary): Croplands that have been irrigated at least once during 1997-2017.</p> <p>(5) irrFreqChange (1-21): The difference of number of years irrigated between 1998-2007 and that of between 2008-2017 (i.e., irrFreq<sub>2008-2017</sub> - irrFreq<sub>1998-2007</sub>).</p> <p>(6) irrAreaGain (-1684-2333): LANID-derived irrigation gain (in hectare) from 1998-2007 to 2008-2017 at the 6km×6km scale.</p> <p>(7) formerIrr (binary): Not irrigated anytime in 2015-2017, but irrigated at least 3 times prior.</p> <p>(8) irrWholePeriod (binary): Irrigated at least once for both 1997-1999 and 2015-2017.</p> <p>(9) intermittentIrr (binary): Irrigated at least once for both 1997-1999 and 2015-2017, and irrigation frequency ≤ 18.</p> <p>(10) irrSamples_eastCONUS: Sample locations of center pivot fields in the eastern CONUS (4976 sample locations).</p> <p>(11) rainfedSamples_eastCONUS: Sample locations of stable rainfed fields in the eastern CONUS (4978 sample locations).</p> <p><strong>For technical details that used to create the dataset, please refer to <em>https://doi.org/10.1016/j.rse.2021.112445</em>; Data description details are available via <em>https://essd.copernicus.org/preprints/essd-2021-207/</em>.</strong></p>
Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space
<p>The products are the first <strong>regional-scale and high-resolution (1 and 6 km) irrigation water data sets obtained from remote sensing observations</strong>. They cover three major river basins: the Ebro river basin (North-eastern Spain), the Po valley (Northern Italy), and the Murray-Darling basin (South-eastern Australia). The data sets are an outcome of the European Space Agency (ESA) Irrigation+ project (<a href="https://esairrigationplus.org/">https://esairrigationplus.org/</a>). The irrigation amounts have been estimated through the <strong>SM-based (Soil-Moisture-based) inversion approach</strong>. The satellite-derived irrigation products referring to the European sites have a spatial resolution of 1 km, and they are retrieved by exploiting Sentinel-1 soil moisture data obtained through the RT1 (first-order Radiative Transfer) model. A spatial sampling of 6 km is instead used for the Australian pilot area, since in this case the soil moisture information comes from CYGNSS (Cyclone Global Navigation Satellite System) observations. The three irrigation products are delivered with a weekly temporal aggregation. The 1 km data sets over the two European regions cover a period ranging from January 2016 to July 2020, while the irrigation estimates over the Murray-Darling basin are available for the time span April 2017 – July 2020. Details on the data sets development and on their performance assessment can be found in:</p> <p><strong>Dari, J.</strong>, Brocca, L., Modanesi, S., Massari, C., Tarpanelli, A., Barbetta, S., Quast, R., Vreugdenhil, M., Freeman, V., Barella-Ortiz, A., Quintana-Seguí, P., Bretreger, D., Volden, E. <strong>Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space</strong>. <em>Earth System Science Data, </em>15, 1555–1575, https://doi.org/10.5194/essd-15-1555-2023, 2023.</p> <p> </p> <p><strong>Novelties in v1.1 with respect to v1.0:</strong></p> <p>v1.1 of irrigation estimates through the SM-based inversion approach are currently available for the Ebro basin and the Po valley only. The novelties with respect to the previous version are: (i) the use of high-resolution (1 km) potential evapotranspiration rates in the algorithm and (ii) temporal extension as now the data sets cover a 6-year period from January 2016 to December 2021.</p> <p><strong>Acknowledgements</strong>:</p> <p>ESA Irrigation+ project, <a href="https://esairrigationplus.org/">https://esairrigationplus.org/</a>, (contract n. 4000129870/20/I-NB).</p> <p>ESA 4DMED-Hydrology project, <a href="https://esairrigationplus.org/">https://www.4dmed-hydrology.org/</a>, (contract n. 4000136272/21/I-EF).</p>
UCSB SONGS Mitigation Monitoring: Wetland Process Study - Irrigation, Decompaction, Amendment, Planting and Seeding Experiment Vegetation Cover
These data describe estimates of the percent cover of marsh plants in experimental plots designed to evaluate the effectiveness of various soil treatments on increasing vegetation cover at the San Dieguito Wetlands (Del Mar, California). Plots established between 1.61 – 2.1 m MLLW were manipulated to test the effects of irrigation, decompaction, soil amendments, and planting versus seeding, whereas plots between 1.6 – 1.7 m MLLW tested the effects of planting versus seeding alone. Data collection was conducted from 2020 to 2022. During each survey, species of marsh plants were identified and recorded under 98 uniformly spaced points within 4.5 m2 quadrats in each plot.
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.