DOI: 10.5281/zenodo.14177959
title: Projected Global Area Equipped for Irrigation Datasets during 2020-2100 under SSP scenarios

description: 1. Background

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 Projected Global Area Equipped for Irrigation Datasets (PGAEID), which provide spatially explicit estimates of Area Equipped for Irrigation (AEI) from 2020 to 2100 under three Shared Socioeconomic Pathway (SSP) scenarios: SSP1 (sustainable development), SSP2 (intermediate development), and SSP3 (regional rivalry), SSP4 (unequal development), and SSP5 (fossil-fueled development).

2. Methodology

2.1 Ensemble Machine Learning (EML) Framework



Algorithms: Integrated six machine learning models:



Multiple Linear Regression (MLR)

Decision Trees (DT)

Autoregressive Integrated Moving Average (ARIMA)

Multi-Layer Perceptron (MLP)

Radial Basis Function (RBF)

Random Forests (RF)



Training Data: Historical national irrigation records (FAO AQUASTAT, 1961–2015).

Validation Metrics:



Nash-Sutcliffe Efficiency (NSE): 0.98

Kling-Gupta Efficiency (KGE): 0.97

Mean Absolute Percentage Error (MAPE): 1.7%




2.2 Spatial Downscaling



Baseline: FAO 2005 irrigation data combined with GMIA2005 gridded agricultural intensity maps.

Dynamic Projection: Annual change rates applied to 5′ × 5′ grids under SSP-specific socioeconomic drivers.


3. Dataset Overview

3.1 Key Features



Temporal Coverage: 2020–2100 (10-year intervals).

Spatial Resolution: 5-arcminute (≈10 km at the equator).

Scenarios: SSP1, SSP2, SSP3, SSP4, SSP5.

Variables: area equipped for irrigation (103 ha/year).


3.2 Dataset Structure

The dataset is provided as a compressed archive (PGAEID_Ver3.0.rar), containing:

1.Global_Area_Equipped_for_Irrigation_GeoTiff/



Subfolders:



SSP1

SSP2

SSP3

SSP4

SSP5



File Format: GeoTIFF (45 files total).

Naming Convention:AEI_[SSP]_[Year].tif



Example: AEI_SSP1_2020.tif




2. National & Regional_AEI/



Shapefiles: National/regional area equipped for irrigation for 26 prediction units (2020–2100).

Excel File: Global Area Equipped for Irrigation (2020-2100).xlsx.


3. Technical Annex.docx



Detailed methodology, validation, and workflow documentation.


4. Applications

This dataset supports:



Earth System Simulation: Supporting irrigation parameterization in global climate and hydrological models.

Water Resource Management: Assisting decision-makers in sustainable irrigation planning.

Climate Change Adaptation: Providing insights into how irrigation practices evolve under different socioeconomic pathways.

Environmental Conservation: Assessing the impact of irrigation on regional ecosystems.


Note:

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:



The country/region-based data (26 units) is recommended for national-scale analyses or policy evaluations requiring administrative boundaries.

The 5-arcminute gridded data is preferable for spatially explicit modeling or subnational assessments.


Both datasets maintain equivalent quality and methodological rigor; the choice depends on the desired spatial granularity and application context.