Assessing Groundwater Dynamics and Potentiality in the Lower Ganga Plain, India
2022
Biraj Kanti Mondal | Satiprasad Sahoo | Rima Das | Prabuddh Kumar Mishra | Kamal Abdelrahman | Aditi Acharya | Ming-An Lee | Anuj Tiwari | Mohammed S. Fnais
The present study intended to assess groundwater storage dynamics (GWS) and identify potential zones using the Multi-Criteria Decision Making (MCDM) method and geospatial technology in the Murshidabad district of West Bengal, India. The study district is located in the Ganga&ndash:Padma&ndash:Bhagirathi rivers&rsquo: floodplain and covers approximately a 5324 km2 area, comprising 26 blocks in five sub-divisions. The study portrayed a quantitative investigation of the pre-monsoon and post-monsoon season&rsquo:s variability of GWS from 2000 to 2020, taking Landsat TM/Landsat 8 OLI/SRTM satellite data. The geo-spatio-temporal analysis of groundwater storage variability for 20 years was carried out by such remotely sensed data with the geospatial method to portray the dynamics and uncover the potential zones of GWS using various cartographic and statistical techniques. We determined nine parameters for the study, and the analytical hierarchy process (AHP) method was employed for the computation. The present estimation and assessment include the MCDM method, covering assorted parameters and the variations and aspects of GWS in the pre- and post-monsoon seasons from 2000 to 2020. The outcome illustrates that a decline in water storage has taken place in most of the blocks of Murshidabad district on average during the study period, which indicates a water stress provison in the near future. However, the micro (block)-level scenario of the spatiotemporal dynamics of GWS and the potential zonation in the Murshidabad District were investigated to form a location-specific micro-level arrangement for the sustainable management of water.
Afficher plus [+] Moins [-]Mots clés AGROVOC
Informations bibliographiques
Cette notice bibliographique a été fournie par Multidisciplinary Digital Publishing Institute
Découvrez la collection de ce fournisseur de données dans AGRIS