Downscaled global 60-meter resolution estimates of irrigation water sources (2000–2015)

This dataset provides high-resolution (60 m) global irrigation maps to support water resource and agricultural management. It identifies the likely irrigation status (rainfed or irrigated) and water source (groundwater or surface water) of croplands for 2000, 2005, 2010, and 2015. We downscaled a 10-km irrigation dataset derived from national and subnational statistics (GMIA) using (i) spatial patterns between high-resolution (30 m) cropland and nearby surface water, and (ii) irrigation water requirements from a global crop model. Validation used household agriculture surveys in India (N = 8,355) and a U.S. well database (N = 1,505,371). In the U.S., our method achieved 85% accuracy in distinguishing groundwater use within 2 km of wells - substantially higher than GMIA (25%). In India's groundwater-dominated regions, our estimates performed comparably to GMIA (73% vs. 72%). These results suggest our dataset offers a more accurate and spatially detailed representation of irrigation water sources, enabling improved analysis of agricultural water use.

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Publication Details

Journal
Scientific Data
Published
2025-10-09
DOI
https://doi.org/10.1038/s41597-025-05920-x
Primary Topic
Water resources management and optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

Downscaled global 60-meter resolution estimates of irrigation water sources (2000–2015)

Davide Danilo Chiarelli, J. S. Famiglietti, Fengwei Hung, Marc F. Müller
Scientific Data
Water resources management and optimization
article

Downscaled global 60-meter resolution estimates of irrigation water sources (2000–2015)

Davide Danilo Chiarelli, J. S. Famiglietti, Fengwei Hung, Marc F. Müller
article en

Abstract

This dataset provides high-resolution (60 m) global irrigation maps to support water resource and agricultural management. It identifies the likely irrigation status (rainfed or irrigated) and water source (groundwater or surface water) of croplands for 2000, 2005, 2010, and 2015. We downscaled a 10-km irrigation dataset derived from national and subnational statistics (GMIA) using (i) spatial patterns between high-resolution (30 m) cropland and nearby surface water, and (ii) irrigation water requirements from a global crop model. Validation used household agriculture surveys in India (N = 8,355) and a U.S. well database (N = 1,505,371). In the U.S., our method achieved 85% accuracy in distinguishing groundwater use within 2 km of wells - substantially higher than GMIA (25%). In India's groundwater-dominated regions, our estimates performed comparably to GMIA (73% vs. 72%). These results suggest our dataset offers a more accurate and spatially detailed representation of irrigation water sources, enabling improved analysis of agricultural water use.

Scientific DataVol. 12(1)
University of Notre Dame (US), Arizona State University (US), Swiss Federal Institute of Aquatic Science and Technology (CH), Politecnico di Milano (IT)
National Science Foundation
Openalex Percentile: Top 65%
Water resources management and optimization
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Downscaled global 60-meter resolution estimates of irrigation water sources (2000–2015) — Davide Danilo Chiarelli, J. S. Famiglietti, et al. · Scientific Data (2025) | TGRS Research Map | TGRS