Monitoring of the Groundwater Level using GRACE with GLDAS Satellite Data in Ganga Plain, India to Understand the Challenges of Groundwater, Depletion, Problems, and Strategies for Mitigation - Pawan Kumar Gautam; Sushil Chandra; Pradnita Komal Henry (Environmental Challenges, 2024)
A sub-surface hydrogeological investigation conducted in the Gangetic Plain of India would involve studying the groundwater resources, aquifer characteristics, and overall hydrogeological conditions in the region. The Gangetic Plain is a vast alluvial plain formed by the sediments deposited by the Ganges and its tributaries, making it a significant groundwater (GW) bearing region, which is surrounded by a thick stratum of unconsolidated Alluvial deposits and comprises rich aquifer systems. This study describes a procedure for estimation of the subsurface GW levels for pre-monsoon, 2021 which has been determined by JPL, RL05, temporal datasets of Gravity Recovery and Climate Experiment (GRACE), and Global Land Data Assimilation System (GLDAS). The water level observed ranged from 2.3 m to 13 m in the Bhabhar and Terai Belt, 14 to 23 m in the Central Alluvial Plain and Khadar Belt, and 24 m to 31 m in the Marginal Alluvial Plain. Two types of variation are seen: local and geological variation. The GW storage justifies the remote sensing perspective and further validates our research study that regional terristrial water storage mapping can be expeditiously and accurately cognizable by the GRACE/GLDAS mission. This method is used to estimate changes in regional GW, it is a very accurate and time-limited method rather than the traditional method of GW estimation. The use of this technology will help in knowing the discharge status of GW in the state and make policies for its over-exploitation. Drinking water can be conserved to a great extent and monitoring of GW can also be done in the time frame. And attention can be given to those districts where water is being exploited more. In view of the increasing impact of climate change on water resources, this modeling will prove vital for understanding future scenarios and planning sustainable management strategies that will aid in the development of adaptation and mitigation measures.