International Journal of Environment and Climate Change

Water is a vital natural resource, and effective water resource management requires a comprehensive understanding of the hydrological behavior of a watershed. Surface runoff is a fundamental hydrological parameter for planning and implementing sustainable water management strategies. The present study focuses on the estimation of surface runoff in the Kal River basin using the Soil Conservation Service Curve Number (SCS-CN) method integrated with geospatial techniques. Satellite imagery, digital elevation model (DEM), hydrological soil data, and long-term precipitation records were utilized for land use/land cover classification, drainage network extraction, basin delineation, and runoff estimation. The Kal River basin, located in the Raigad district of the Konkan region, Maharashtra, is a major tributary of the Savitri River and covers a total drainage area of 272.63 km². The elongated shape of the basin indicates prolonged flow duration during rainfall events. Runoff estimation using the SCS-CN method was carried out by integrating land use/land cover information with hydrological soil groups and antecedent moisture conditions. Despite receiving high annual rainfall, a significant proportion of precipitation is lost as surface runoff due to undulating topography and dominant overland flow processes. Surface runoff depth was estimated for a 33-year period (1990–2022), yielding an average annual runoff depth of 1485.18 mm from an average annual rainfall of 3389.06 mm, indicating that approximately 43.83% of total rainfall contributes to surface runoff. The established rainfall–runoff depth relationship over the study period demonstrates a strong and consistent dependence of runoff generation on precipitation variability. Validation of the estimated runoff against observed data showed good agreement, with a coefficient of determination (R² = 0.73) and a correlation coefficient (r = 0.73). The study highlights that the integration of GIS and the SCS-CN method is an effective and reliable approach for surface runoff estimation and provides valuable insights for watershed management, water conservation, and sustainable planning.