气候变化领域集成服务门户(CCPortal): High resolution prediction and explanation of groundwater depletion across India

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DOI	10.1088/1748-9326/ad34e5
	High resolution prediction and explanation of groundwater depletion across India
	Alkon, Meir; Wang, Yaoping; Harrington, Matthew R.; Shi, Claudia; Kennedy, Ryan; Urpelainen, Johannes; Kopas, Jacob; He, Xiaogang
发表日期	2024
ISSN	1748-9326
起始页码	19
结束页码	4
卷号	19 期号:4
英文摘要	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.
英文关键词	groundwater; India; machine learning; agriculture; irrigation
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001198886000001
来源期刊	ENVIRONMENTAL RESEARCH LETTERS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/299188
作者单位	Fordham University; United States Department of Energy (DOE); Oak Ridge National Laboratory; Columbia University; Columbia University; University of Houston System; University of Houston; National University of Singapore
推荐引用方式 GB/T 7714	Alkon, Meir,Wang, Yaoping,Harrington, Matthew R.,et al. High resolution prediction and explanation of groundwater depletion across India[J],2024,19(4).
APA	Alkon, Meir.,Wang, Yaoping.,Harrington, Matthew R..,Shi, Claudia.,Kennedy, Ryan.,...&He, Xiaogang.(2024).High resolution prediction and explanation of groundwater depletion across India.ENVIRONMENTAL RESEARCH LETTERS,19(4).
MLA	Alkon, Meir,et al."High resolution prediction and explanation of groundwater depletion across India".ENVIRONMENTAL RESEARCH LETTERS 19.4(2024).