Leveraging machine learning methods to quantify 50 years of dwindling groundwater in India

Abstract

Global compilations and regional studies, indicative of the unsustainable extraction and subsequent unremittingly depleting groundwater (GW) in India, either provide bulk estimates or are confined to the river basins and therefore conceal inferences from a nationwide policymaking perspective. Here, we provide the state-wise past (2000−2020) and future (2030–2050) assessment of dwindling groundwater in India utilizing in-situ groundwater levels (GWL) from 54,112 wells, remote sensing products, and hydrological simulations. By employing three machine learning methods, we show a decline in GWL of over 80% in North India with a notable shift towards the eastern state of Uttar Pradesh and a cumulative groundwater loss (169.96 ± 19.67 km3) equivalent to the water storage capacity of the world's biggest dam (Kariba Dam, Zimbabwe). Its likely contribution to sea-level rise (0.47 ± 0.06 mm) is about 64% of that from annual global glacier melt. Our results typically contrast the GW recovery paradox in South India (e.g., a declining trend of −84.48 ± 38.81 mm/a (p < 0.05) in Andhra Pradesh during 2000–2020), reveal high seasonal variability (e.g., up to ~6 m in Maharashtra), and illustrate the skewed effect of survivor bias in the traditional assessments. We infer the significant impact of underlying hydrogeology and the implementation of water-related policies and projects on the GWL dynamic and variability in the region. Projected GWL reveals a likely water scarcity situation for about 2.8 million km2 area and one billion residents of the country up to 2050. Our observation-based analysis offers insights into the state-level monthly GW dynamics, which is critical for efficient interstate resource allocation, development plans, and policy interventions with broad methodological implications for the water-scarce countries.