Evaluating the reliability of open-source hydrodynamic models in flood inundation mapping: an exhaustive approach over a sensitive coastal catchment

Abstract

Recently, the frequency and severity of flooding events have escalated, underscoring the urgent requirement for effective flood management strategies. Comprehensive flood inundation mapping and the precise identification of flood-prone areas are indispensable tools in this endeavor. In the current research, the Hydrologic Engineering Center's River Analysis System (HEC-RAS) version 6.3 software was employed to create inundation maps and delineate areas at risk of flooding for the Netravati-Gurupura River basins. Flood flows for various years have been simulated across the river basin, with terrain characteristics derived from the 30-m spatial resolution Shuttle Radar Topography Mission digital elevation model. The simulation of flow within the model was conducted using daily discharge data, with the model being calibrated at an optimal Manning's roughness coefficient value of n = 0.032. The calibration process was carried out by performance metrics, including the coefficient of determination, Nash-Sutcliffe efficiency, and the index of agreement, to ensure precision and reliability in the model's outputs. The outcomes of the model simulation provided depth, velocity, and water surface elevation data, from which potential flood-prone zones were identified. Model performance validation was conducted for the flood events of 2007 and 2014, revealing that the results of the statistical parameters fell within the anticipated range. This study demonstrated that flood events could be effectively simulated using the HEC-RAS two-dimensional model, enabling the identification of flood-prone areas. This research underscores the pivotal role of open-source hydrodynamic modeling in enhancing our predictive capabilities and preparedness for flood events. This approach not only optimizes resource allocation for flood defense infrastructure but also informs the strategic planning of land use, minimizing potential flood damage and bolstering environmental and societal well-being. © 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies.