Rainfall-Runoff Estimation for Groundwater Enhancement Through Remote Sensing and GIS-Based Geostatistical Model

Groundwater decline in any region is a significant issue that has been rising daily. Rainfall and runoff assessment of the area gives groundwater management and sustainability ideas since the arid regions rely mainly on rainfall for groundwater recharge. Therefore, rainfall-runoff is one of the necessary studies to implement groundwater recharge. This study primarily focuses on rainfall-runoff estimation with the help of Geographic Information System (GIS) techniques. Three input variables, topographic, remote sensing, and Antecedent Moisture Condition (AMC), were adopted to define this Soil Conservation Service—Curve Number (SCS-CN) model. Texture-based soil categorization was used to create the hydrologic soil group (HSG) map. The dominance of D-type HSG was categorised as a high runoff region. The 10 years (2006–2015) of rainfall information were used to generate the rainfall spatial map based on the Theissen-Polygon method. The rainfall-runoff data shows that the higher rainfall (1477.5 mm) and runoff (470.8 mm) during 2007. The Land Use and Land Cover (LULC) map was created through the Indian Remote Sensing Satellite P6's Linear Imaging Self-Scanning Sensor-IIi (IRS-P6 LISS-III) image. In dry, normal, and wet circumstances, the corresponding curve numbers (CN) values were CN 1 = 62.3, CN 2 = 79.03, and CN 3 = 89.8. In addition, the mean surface runoff was calculated as 330.2 mm with an average runoff volume of 164.13 mm3, which was 13.91% of the overall average rainfall. The final results (rainfall and runoff) also strongly correlated (r = 0.857). Thus, this study can be a basis for many researchers in various water resource management studies. © 2025 Elsevier B.V., All rights reserved.