High resolution prediction and explanation of groundwater depletion across India

Bibliographic Data

ID15550668
AuthorsMeir Alkon (0000-0002-7615-2276, Fordham University), Yaoping Wang (0000-0002-5162-1910, Oak Ridge National Laboratory, corresponding author), Matthew R Harrington (Columbia University), Claudia Shi (Columbia University), Ryan Kennedy (0000-0003-1881-6887, University of Houston), Johannes Urpelainen (Johns Hopkins University), Jacob Kopas (Earthjustice), Xiaogang He (0000-0001-7428-0269, National University of Singapore)
Year2024
Volume19
Issue4
Pages044072-044072
Publication date2024-03-18
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ad34e5
OpenAlexW4392900540
LanguageEN
References cited35

Food production in much of the world relies on groundwater resources. In many regions, groundwater levels are declining due to a combination of anthropogenic extraction, localized meteorological and geological characteristics, and climate change. Groundwater in India is characteristic of this global trend, with an agricultural sector that is highly dependent on groundwater and increasingly threatened by extraction far in excess of recharge. The complexity of inputs makes groundwater depletion highly heterogeneous across space and time. However, modeling this heterogeneity has thus far proven difficult. Using two ensemble tree-based regression models, we predict district level seasonal groundwater dynamics to an accuracy of R 2 = 0.4–0.6 and Pearson correlations between 0.6 and 0.8. Further using two high-resolution feature importance methods, we demonstrate that atmospheric humidity, groundwater groundwater-based irrigation, and crop cultivation are the most important predictors of seasonal groundwater dynamics at the district level in India. We further demonstrate a shift in the predictors of groundwater depletion over 1998–2014 that is robustly found between the two feature importance methods, namely increasing importance of deep-well irrigation in Central and Eastern India. These areas coincide with districts where groundwater depletion is most severe. Further analysis shows decreases in crop yields per unit of irrigation over those regions, suggesting decreasing marginal returns for largely increasing quantities of groundwater irrigation used. This analysis demonstrates the public policy value of machine learning models for providing high spatiotemporal accuracy in predicting groundwater depletion, while also highlighting how anthropogenic activity impacts groundwater in India, with consequent implications for productivity and well-being

Climatology · Geotechnical engineering · Groundwater · Hydrology (agriculture · Resolution (logic · Water resource management · Computer Science · Environmental Science · Groundwater and Watershed Analysis · Hydrological Forecasting Using AI · Hydrology and Watershed Management Studies · Geology

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