Assessing Groundwater Prospects through Terrain, Lithology, and Structural Controls in a GIS Environment in Kalvarayan Firka, Salem, Tamil Nadu, India

Abstract

Groundwater is very important for keeping homes and farms in the semiarid area of Kalvarayan Firka, Salem, Tamil Nadu, which covers about 197 square kilometres. A combined Remote Sensing (RS) and Geographic Information System (GIS) method was used in this study to identify groundwater potential zones throughout the study area. Satellite images and other data were used to create thematic layers showing geology, lineament density, drainage density, land use/land cover (LULC), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and Normalized Difference Drought Index. The Analytical Hierarchy Process (AHP) integrated with the Weightage Overlay Method (WOM) was used to assign suitable weights to each thematic layer based on its hydrogeological significance. The resulting map classified the study area into three groundwater potential groups: the study area was predominantly characterized by moderate groundwater potential, covering 159 km² (80.71%) of the total 197 km² area. High-potential zones occupied 27 km² (13.70%), whereas low-potential zones accounted for only 11 km² (5.58%). Low-potential zones are found where there is a lot of water and solid rock present. High-potential zones were found where there was a lot of weathered or broken rock, low slopes, and good land use. Validation using existing well-yield data shows that the predicted zones and actual field conditions are very similar. However, this study was limited by the availability of temporal groundwater data, seasonal variations, limited field validation, and the resolution of the input datasets. An in-depth study could be conducted using geophysical interpretations. The results show that RS-GIS combined with AHP is a good method for identifying groundwater prospects. This provides scientists with a way to plan for groundwater growth, artificial recharge, and long-term resource management. Future research should focus on integrating temporal groundwater fluctuation data, climate change impacts, and machine learning techniques to improve the accuracy and reliability of groundwater potential assessments.