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The NRCS-CN method, integrated with GIS and remote sensing, can be used for estimating curve numbers (CN) and surface runoff in geohydrological systems. The study area is divided into 63 sub-basins, and the land use land cover (LULC)-hydrologic soil group (HSG) complex is identified for each sub-basin. The CN values for three antecedent soil moisture (AMC) conditions are calculated and corrected for surface slope variations. The surface runoff depth is determined using the rainfall data for 16 years (2005–2020). The average runoff depth and mean annual precipitation ranges from 444.50 to 1960.55 mm and 936.99 to 3520.55 mm, respectively. For all sub-basins, strong correlations between runoff depth and rainfall (R2 ≥ 0.8) as well as between simulated runoff and measured runoff (R2 ≥ 0.8) are observed. The Nash–Sutcliffe model efficiency coefficient (NSE) values suggest that the model's efficiency is good to satisfactory.

The NRCS-CN method, integrated with GIS and remote sensing, can be used for estimating curve numbers (CN) and surface runoff in geohydrological systems. The study area is divided into 63 sub-basins, and the land use land cover (LULC)-hydrologic soil group (HSG) complex is identified for each sub-basin. The CN values for three antecedent soil moisture (AMC) conditions are calculated and corrected for surface slope variations. The surface runoff depth is determined using the rainfall data for 16 years (2005-2020). The average runoff depth and mean annual precipitation ranges from 444.50 to 1960.55 mm and 936.99 to 3520.55 mm, respectively. For all sub-basins, strong correlations between runoff depth and rainfall (R² ≥ 0.8) as well as between simulated runoff and measured runoff (R² ≥ 0.8) are observed. The Nash-Sutcliffe model efficiency coefficient (NSE) values suggest that the model's efficiency is good to satisfactory. [ABSTRACT FROM AUTHOR]