A Comprehensive Review of the Geospatial Data Based SCS–Curve Number Method for Runoff Estimation

Abstract

Runoff estimation plays a vital role in hydrological analysis, watershed management, flood prediction, and sustainable water resource planning. This study presents a comprehensive overview of studies conducted on runoff estimation using the Soil Conservation Service–Curve Number (SCS–CN) method integrated with Geographic Information System (GIS), Remote Sensing (RS), and hydrological modeling techniques across different regions of the world. The reviewed studies demonstrate that the SCS–CN method is widely applied due to its simplicity, reliability, and ability to incorporate watershed characteristics such as land use/land cover (LULC), hydrologic soil groups (HSG), antecedent moisture conditions (AMC), rainfall, and slope. Several researchers reported strong correlations between observed and estimated runoff values, with coefficient of determination (R²) values often exceeding 0.70, indicating satisfactory model performance. The review also highlights the importance of modified initial abstraction ratios (Ia/S), weighted curve numbers, and spatial variability in improving runoff prediction accuracy. Integration of GIS and remote sensing significantly enhanced the efficiency of thematic layer generation, watershed delineation, and spatial runoff assessment, particularly in ungauged and data-scarce basins. Findings from various studies consistently indicate that increased rainfall intensity and higher curve number values lead to greater runoff generation, while land use changes, urbanization, and soil conditions strongly influence hydrological response. Overall, the reviewed literature confirms that the GIS-based SCS–CN approach is an effective and practical tool for runoff estimation, flood assessment, groundwater recharge planning, and watershed management in diverse climatic and geographic conditions.