%0 Journal Article
%T On the Use of Knowledge-Informed Machine Learning and Multisource Data for Spatially Explicit Estimation of Irrigation Water Withdrawal
%A Zhang, Ling
%A Ma, Hui
%A Hu, Yingyi
%A Wang, Yixiao
%A Ma, Qimin
%A Zhao, Yanbo
%J Earth’s Future
%D 2025
%V 13
%N 12
%F https://doi.org/10.1029/2025EF006704
%O e2025EF006704 2025EF006704
%X Abstract Irrigation plays a crucial role in the earth system, yet our understanding of irrigation water withdrawal (IWW) remains limited due to the scarcity of spatially explicit data. While process-based models and remote sensing can bridge this data gap, their estimates often fail to capture real IWW and are associated with large uncertainties. Here, we present a knowledge-informed, explainable machine learning framework that combines random forest (RF) with Shapley additive explanations to generate spatially explicit IWW estimates across China. Our framework incorporates irrigation domain knowledge, state-of-the-art irrigated cropland maps, and various socioeconomic, hydroclimatic, and auxiliary factors. RF shows reasonable performance in spatial and temporal cross-validation, achieving a coefficient of determination exceeding 0.85 and a root mean square error below 0.45 km3/year when evaluated against held-out prefecture-level data. The predictions of IWW depth are primarily driven by geographic and knowledge-based predictors, most of which exhibit nonlinear and non-monotonic impacts on model outputs. By integrating the RF model with a temporal downscaling approach, we develop a new gridded IWW product for China (named CIWW1km), which provides monthly IWW depth and volume at 1 km resolution from 2000 to 2020. CIWW1km aligns closely with prefecture-level IWW reports and explains over 85% of the variance in independent IWW observations (i.e., data excluded from training) across over 150 basins and counties. It highlights a rapid increase in IWW in China’s arid zone, driven by irrigated area expansions. CIWW1km outperforms existing products and is well-suited for hydrological and climate studies, and water-food nexus analyses.
%K irrigation withdrawal, knowledge-informed machine learning, explainable machine learning, remote sensing and reanalysis, feature selection, China
%R https://doi.org/10.1029/2025EF006704
%U https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2025EF006704
%U https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2025EF006704
%U https://doi.org/https://doi.org/10.1029/2025EF006704
%P e2025EF006704