Real-time forecasting of coastal flood inundations under regulated reservoir and storm-tide influences

Abstract

Coastal regions are vulnerable to flood risks due to the combined effects of storm surges, riverine flooding, upstream reservoir releases, and inland rainfall. Traditional models often fail to integrate these critical factors, leading to inaccuracies in flood extent forecasting. This study addresses this gap by developing a comprehensive coastal flood inundation forecasting framework for a region impacted by tropical cyclones and extreme inland rainfalls. This framework combines components for realtime reservoir inflow forecasting, reservoir outflow forecasting based on rule curves, storm-tide forecasting using hydrodynamic and machine learning models, and flood inundation forecasting with a two-dimensional hydrodynamic model. The model was field-tested in the twin Brahmani-Baitarani rivers in eastern India, with the simulated coastal flood extents being validated against the Sentinel-1 satellite imageries for different tropical cyclone events. The results demonstrate that the proposed framework could forecast the coastal inundation extents, with the fit measures ranging from 87.45 % to 39.57 % at 1- to 8-days’ lead times. This study underscores the importance of all causative factors of coastal flooding, providing a valuable tool for early warning systems and flood risk management in vulnerable coastal regions worldwide.