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<p>Changing land use significantly affects water distribution at various scales, making groundwater an essential resource, especially where surface water is limited. This study focuses on sustainable groundwater management in response to rapid land use/land cover change (LULCC), examining how shifts such as increased built-up areas and reduced vegetation impact groundwater retention. Using the Piyali sub-basin as a case study, the research analyzes groundwater levels in relation to demographic, climatic, and environmental factors via Geographical Information System (GIS). The Global Land Data Assimilation System-2 (GLDAS-2) Catchment Land Surface Model (CLSM) assesses seasonal groundwater storage (GWS), surface soil moisture (SSM) and terrestrial water storage (TWS) changes from 2000 to 2014. Additionally, the study uses Kendall’s tau and Pearson correlation methods to analyze relationships between critical variables influencing groundwater and water storage. Groundwater storage has exhibited fluctuations ranging from 734.88 to 753.86&#xa0;mm during the pre-monsoon period, while certain areas with significant groundwater extraction have experienced a reduction in groundwater levels to below 10–13 mbgl. It has been noted the western part of the basin (Joynagar-I CD block) has higher groundwater storage (maximum 1064.58&#xa0;mm in 2016 during post-monsoon and 753.86&#xa0;mm in 2003 during pre-monsoon) and positive groundwater level (5.98–6.29 mbgl) because of a higher amount of forest cover and soil moisture compared to other regions. Within the basin regime, the mean depth of the piezometric level ranged from 2.33 to 4.60 mbgl during the post-monsoon period and from 3.10 to 4.79 mbgl during the pre-monsoon period. Vegetation cover and groundwater level (GWL) show a strong positive correlation (t = 0.60, r = + 0.8028). GWS and GWL are also positively correlated under specific climate conditions (t = 0.80, r = + 0.875). These results can inform improved, sustainable groundwater management based on public awareness.</p>

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Impact assessment of LULC changes on groundwater resources: Study in piyali river Sub-basin in Sundarban, West Bengal, India

  • 期刊论文

  • 发表日期：

    2026年3月18日

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  • Suvendu Halder,
  • Satiprasad Sahoo,
  • Tumpa Hazra,
  • Anupam Debsarkar,
  • Ajit Govind

摘要

Changing land use significantly affects water distribution at various scales, making groundwater an essential resource, especially where surface water is limited. This study focuses on sustainable groundwater management in response to rapid land use/land cover change (LULCC), examining how shifts such as increased built-up areas and reduced vegetation impact groundwater retention. Using the Piyali sub-basin as a case study, the research analyzes groundwater levels in relation to demographic, climatic, and environmental factors via Geographical Information System (GIS). The Global Land Data Assimilation System-2 (GLDAS-2) Catchment Land Surface Model (CLSM) assesses seasonal groundwater storage (GWS), surface soil moisture (SSM) and terrestrial water storage (TWS) changes from 2000 to 2014. Additionally, the study uses Kendall’s tau and Pearson correlation methods to analyze relationships between critical variables influencing groundwater and water storage. Groundwater storage has exhibited fluctuations ranging from 734.88 to 753.86 mm during the pre-monsoon period, while certain areas with significant groundwater extraction have experienced a reduction in groundwater levels to below 10–13 mbgl. It has been noted the western part of the basin (Joynagar-I CD block) has higher groundwater storage (maximum 1064.58 mm in 2016 during post-monsoon and 753.86 mm in 2003 during pre-monsoon) and positive groundwater level (5.98–6.29 mbgl) because of a higher amount of forest cover and soil moisture compared to other regions. Within the basin regime, the mean depth of the piezometric level ranged from 2.33 to 4.60 mbgl during the post-monsoon period and from 3.10 to 4.79 mbgl during the pre-monsoon period. Vegetation cover and groundwater level (GWL) show a strong positive correlation (t = 0.60, r = + 0.8028). GWS and GWL are also positively correlated under specific climate conditions (t = 0.80, r = + 0.875). These results can inform improved, sustainable groundwater management based on public awareness.