Integrating Remote Sensing and GIS Derived Mapping Groundwater Potential Zone in Hard Rock Region of Vathalagundu Firka, Tamilnadu, India

Abstract

Groundwater is a vital resource for meeting domestic and agricultural water requirements in the semiarid region of Vathalagundu Firka, Nilakottai Taluk, Dindigul District, Tamil Nadu, encompassing an area of approximately 64.07 km². This study aims to delineate groundwater potential zones (GWPZs) using an integrated Remote Sensing (RS) and Geographic Information System (GIS) approach. The methodology involved the preparation of thematic layers, including lithology, lineament density, land use/land cover, geomorphology, drainage density, and rainfall, derived from satellite imagery and ancillary data. These layers were assigned weights based on their relative hydrogeological importance and integrated using a Multicriterial Weighted Overlay Index (MWOI) model. Inverse Distance Weighting (IDW) interpolation was applied to generate spatial groundwater potential maps. The resulting groundwater potential zonation classified the study area into high-, moderate-, and low-potential zones. The analysis reveals considerable spatial variation, with moderate groundwater potential zones occupying 70.23% of the area, followed by high potential zones covering 1.56% while low potential zones are limited to 28.09%. High-potential zones are mainly associated with weathered and fractured formations, gentle slopes, low drainage density, and favourable land-use conditions, whereas low-potential zones correspond to areas characterized by steep slopes, high runoff, and relatively impermeable lithological units. The GWPZ map was validated using existing well yield data and field observations, which showed strong agreement between the predicted zones and observed groundwater conditions, confirming the study’s findings. This study clearly identified poor groundwater potential zones for future water recharge domains. The study demonstrates that RS–GIS, integrated with the MWOI model, is an effective tool for assessing groundwater potential. Future research may focus on incorporating time-series groundwater level data, machine learning techniques, and climate variability analyses to improve prediction accuracy and support sustainable groundwater management and artificial recharge planning.