"High resolution prediction and explanation of groundwater depletion across India" - by CWP alum Meir Alkon

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 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.

Meir Alkon¹, Yaoping Wang², Matthew Harrington³, Claudia Shi³, Ryan Kennedy⁴, Johannes Urpelainen⁵, Jacob Kopas⁶ and Xiaogang He⁷

^{https://iopscience.iop.org/article/10.1088/1748-9326/ad34e5}

Meir is an Assistant Professor at Fordham University and a former Harvard Environmental Fellow at the Department of Government and the Harvard University Center for the Environment. He is also an associate in research at Harvard’s Fairbank Center and a non-resident fellow at the Global China Initiative at Boston University’s Global Development Policy Center. Meir received his PhD jointly from Princeton’s Department of Politics and School of Public and International Affairs. His research bridges political economy and interdisciplinary approaches to public policy, analyzing the behavioral and institutional foundations of environmental and economic governance.

Photo Credit: https://pixabay.com/users/ronymichaud-647623/