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11 results for “Irrigated area”
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>
A new dataset of global irrigation areas from 2000 to 2015
<pre>We provide global irrigation maps README FOR GLOBAL IRRIGATION MAPS --------------------------------- Prediction Maps --------------- v3b_combined_*.tif: GeoTIFF files with model predictions, from 2001 to 2015. 0=not irrigated, 1=low-to-medium irrigated, 2=highly irrigated. Two of these are available in PNG format as well: 2001, 2015 Difference Between 2001 and 2015 -------------------------------- diff2001vs2015.tif: 0=no difference, 1=large decrease, 2=decrease, 3=no change, 4=increase, 5=large increase Also available in PNG format. Dark green=large decrease, green=decrease, grey=no change, orange=increase, red=large increase Assessment Map -------------- assessment_map.tif: FN=false negatives, FP=false positives, TP=true positives, mask=cropland mask</pre>
European Long-term Irrigation Area Datasets version 1.0
<p>These files accompany the manuscript titled "<strong><em>Climate-Driven Interannual Variability in Subnational Irrigation Areas Across Europe</em></strong>" in the journal <strong><em>Communications Earth & Environment</em>.</strong></p> <p>We have developed the <strong>European Long-term Irrigation Area Dataset (ELIAD)</strong>, which offers annual subnational data on total irrigated and irrigable areas for 32 European regions from 1990 to 2020. For most countries, the data is at the NUTS2 level, except for the UK, Germany, and Ireland, where it is at the NUTS1 level.</p> <p>Supplementary A: Contains the supplementary figures and tables referenced in the manuscript.<br>Supplementary B: Provides detailed information on the irrigation reference periods for EU farm structure surveys and agricultural censuses.<br>Supplementary C: Describes the methodology used to generate the ELIAD dataset.<br>Supplementary D: Includes the ELIAD dataset itself.<br>Supplementary E: Details the remote sensing products used for comparison with ELIAD in the manuscript.<br>Supplementary F: Contains the data directly used in the figures and tables presented in the manuscript.</p>
Potential command areas of irrigation dams
<p>Irrigation will play a vital role in meeting future food demand. Irrigation reservoirs currently account for 40% of global surface water used for irrigation and play a critical role in agricultural production by providing water to overcome scarcity, buffering against climatic variability, and enabling enhanced yields and multi-cropping. Yet while there has been ongoing establishment of dams for irrigation across the globe, there is little understanding of the extent to which irrigation has actually expanded within their potential command areas (PCA) (i.e., [the area that can be irrigated using water from a particular irrigation source]) and whether some irrigation dams have not yet realized the full extent of irrigated agriculture that they can potentially support. To assess this, we combine georeferenced information on recently established dams (year 2000 onward) (including dam height and reservoir capacity) with elevation data to first delineate the extent of each recent major irrigation reservoir globally (N=441) and estimate the extent of each dam’s PCA, based on pumping capacities for multiple classes of irrigation pumps.</p>
A calibrated groundwater model (Modflow-NWT) data repository in the Koga Irrigation Project area, Ethiopia
<p>The repository includes the research data pertaining to the Modflow-NWT based groundwater model developed for the Koga irrigation project area, Ethiopia. The database constitutes three archived data folders namely, 1. MainData (mostly excel files which include model forcings, data used in model calibration, citizen science data, etc.), 2. GIS (mostly geospatial files to assist readers with the spatial locations of the irrigation project structures, as well as the important data and administrative locations), 3. ModelFiles (mostly text files which include model inputs and outputs).</p> <p>The data has been used in preparation of the manuscript titled, "A numerical framework to advance agricultural water management under hydrological stress conditions in a data scarce environment", published in the Agricultural Water Management journal (<a href="http://dx.doi.org/10.1016/j.agwat.2021.106947">10.1016/j.agwat.2021.106947</a>). Readers are requested to go through this article to find more details on the data. The model simulations ranged from 1st January 2008 to 15th August 2019.</p> <p> </p>
Philoxenite 3D models: Irrigation chanel - area SQ4
<div> <div>The documentation has been created as part of the National Science Centre grant (UMO-2017/25/B/H3/01841).</div> </div>
Philoxenite 3D models: Irrigation chanel - area V5
<div> <div>The documentation has been created as part of the National Science Centre grant (UMO-2017/25/B/H3/01841).</div> </div>
Philoxenite 3D models: Irrigation chanel - area V3
<div> <div>The documentation has been created as part of the National Science Centre grant (UMO-2017/25/B/H3/01841).</div> </div>
A Spatiotemporal Dataset of Irrigated Agricultural Areas Across the Coastal Plain Region of South Carolina; USA
<p>A Spatiotemporal Dataset of Irrigated Agricultural Areas Across the Coastal Plain Region of South Carolina; USA</p>
PROTECTION OF IRRIGATED AREAS FROM WATER EROSION
Open the record for dataset details and reuse information.
Women and Children as the Focus for Control of Schistosomiasis Infections in the Irrigations Area of Burkina Faso
ClinicalTrials.gov study NCT00463528. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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