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

IF 6.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-10-09 DOI:10.1038/s41597-025-05920-x

Fengwei Hung, Davide Danilo Chiarelli, James S Famiglietti, Marc F Müller

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引用次数: 0

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.

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缩小全球60米分辨率的灌溉水源估算（2000-2015）

该数据集提供高分辨率（60米）的全球灌溉地图，以支持水资源和农业管理。它确定了2000年、2005年、2010年和2015年农田可能的灌溉状况（雨养或灌溉）和水源（地下水或地表水）。我们使用(i)高分辨率（30米）农田与附近地表水之间的空间格局，以及（ii）来自全球作物模型的灌溉用水需求，缩小了来自国家和地方统计数据（GMIA）的10公里灌溉数据集。验证使用了印度家庭农业调查（N = 8,355）和美国水井数据库（N = 1,505,371）。在美国，我们的方法在水井2公里范围内识别地下水使用情况的准确率达到85%，大大高于GMIA（25%）。在印度地下水占主导地位的地区，我们的估计结果与GMIA相当（73%对72%）。这些结果表明，我们的数据集提供了更准确和空间详细的灌溉水源表示，从而改进了农业用水分析。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.