Enhancing Flood Risk Assessment Using Multi-Sensor Remote Sensing Data and Hydraulic Modeling

Rivers have historically been the primary water source for all life forms; however, their fluctuating flow patterns pose significant risks, particularly in coastal and flood-prone regions. This study evaluates the applicability of the Hydrologic Engineering Centers River Analysis System (HEC-RAS) for simulating water surface profiles and quantifying flood magnitudes across multiple flood events with varying return periods. The study employs the Kolmogorov-Smirnov test to identify the most suitable probability distribution for flood frequency analysis at the Trimmu gauging station, where the Log Pearson Type III (LP3) distribution was the best fit. The estimated flood discharges for 5-, 10-, 50-, 100-, and 150-year return periods were computed using the Log-Normal (LN), Gumbel, and LP3 distributions. The discharge values (in m³/s) for LN were 4887, 5467, 6521, 6907, and 7179, for Gumbel 4968, 5417, 6405, 6822, and 7066, and LP3 4906, 5480, 6344, 6755, and 7155, respectively. The analysis revealed that a 50-year return period flood with a discharge of 5299 m³/s at Trimmu would result in an inundation area 500% larger than that caused by natural surges. This study integrates HEC-RAS simulations with flood frequency analysis to provide a detailed assessment of floodplain extents, water velocity dynamics, and high-risk zones. The results offer critical insights into flood management, enabling policymakers, engineers, and disaster management authorities to enhance flood preparedness and mitigation strategies. These findings can support the development of early warning systems, land-use planning, and infrastructure resilience in flood-prone regions. Overall, this study highlights the efficiency of HEC-RAS modeling in predicting flood hazards and provides a framework for floodplain mapping and risk assessment in riverine environments. Future research should incorporate climate change projections and real-time hydrological data to improve flood forecasting accuracy and resilience planning.