Large-scale flood forecasting in coastal reservoir with hydrological modeling

Abstract

Coastal cities face increasing flood risks due to urban expansion and climate change. This study simulates flood hydrographs in the Netravathi River watershed using the HEC-HMS hydrological model to improve flood management in Mangalore, Karnataka, which has experienced severe floods recently. The SCS curve number (CN) method was selected for its efficacy in estimating surface runoff across diverse land use and soil types. GIS tools analyzed spatial data on soil types, drainage, and land cover changes from 1990 to 2021, enhancing runoff forecast accuracy. Model calibration optimized parameters with historical flood events, and validation used independent past flood events. Validation showed a strong correlation between observed and simulated runoff hydrographs, particularly during peak discharge periods. A high Nash–Sutcliffe Efficiency (0.89) and low Percentage Bias (0.65%) demonstrate the model’s accuracy. The coefficient of determination (0.86) confirms the model’s predictive capability. The HEC-HMS model effectively forecasts streamflows in ungauged catchments within the Netravathi sub-basin using measured rainfall data, enabling more precise planning and management of water resource developments.