Examining the groundwater level in a semi-arid district of eastern India: spatiotemporal trends, determinants, and future prospects

Tarun Goswami () and Somnath Ghosal ()
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Tarun Goswami: Indian Institute of Technology (IIT) Kharagpur
Somnath Ghosal: Indian Institute of Technology (IIT) Kharagpur

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2023, vol. 25, issue 10, No 16, 10929-10953

Abstract: Abstract The present study aims to examine the spatio-temporal and seasonal fluctuation of groundwater levels in an eastern Indian semi-arid region. Also, it attempted to investigate the involvement of different significant groundwater usage factors in causing such variations and trends. Furthermore, by evaluating temporal patterns and the influence of the crucial drivers, generate meaningful predictions for raising community awareness about the future. A combination of statistical, spatial analysis, and machine learning techniques were used to fulfill the objectives. Overall, the findings revealed that the south-eastern corner (consisting of Bundwan and Barabazar Blocks), the far western region (especially Jhalda I and II Blocks), and some of the eastern Blocks are most vulnerable to groundwater scarcity. The monitoring stations in this region are typically suffering a significant declining trend in groundwater level, with a substantial amount of Sen's Slope. In contrast, the centrally positioned Blocks have lesser susceptibility. The MLR analysis and spatial thematic maps demonstrate a strong association between this spatial pattern of declining groundwater table and the groundwater drivers in consideration. Thus, it is acceptable to suggest that drivers from the human world must be included in the groundwater management planning of the Purulia district. Finally, the predictions from the time series confirm that if no interventions are implemented, the average groundwater level will decline by 2.5 m by 2030. Therefore, Purulia urgently requires a realistic plan to prevent future generations from becoming refugees in their own land.

Keywords: Groundwater depletion; Mann–Kendall trend test; Groundwater usage factors; Multiple linear regression; Neural network model (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10668-022-02512-2

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