Sensitivity Analysis of Hydraulic Roughness and Boundary Conditions in 2D HEC-RAS Models for Urbanizing Basins
DOI:
https://doi.org/10.32628/IJSRCE26103Keywords:
HEC-RAS 2D, hydraulic roughness, sensitivity analysis, urban flooding, boundary conditions, shallow water equations, flood risk modelingAbstract
Rapid urbanization alters watershed hydrodynamics by increasing impervious surface coverage, reducing infiltration capacity, and modifying natural flow pathways, thereby intensifying flood risks in developing urban basins. Two-dimensional (2D) hydraulic models, particularly HEC-RAS 2D, provide advanced capabilities for simulating spatially distributed flood processes; however, model outputs remain highly sensitive to hydraulic parameterization and boundary condition assumptions. This study presents a structured sensitivity analysis of hydraulic roughness and boundary conditions in a 2D HEC-RAS framework applied to an urbanizing watershed. Spatially distributed Manning’s roughness coefficients were assigned based on land-use classification, while multiple upstream discharge and downstream normal-depth scenarios were evaluated through a scenario matrix. Sensitivity was quantified using local and normalized sensitivity indices to assess parameter influence on maximum water depth, velocity magnitude, and flood extent area. Results indicate that hydraulic roughness is the dominant control on inundation depth and spatial flooding patterns, whereas upstream discharge primarily governs velocity response and downstream boundary conditions regulate backwater effects. Interaction analysis reveals nonlinear coupling between resistance and boundary forcing, demonstrating that parameter effects are not independent. The findings highlight key sources of uncertainty in urban flood modeling and provide engineering guidance for calibration prioritization and improved flood risk assessment. Recommendations include incorporation of time-varying roughness, coupling with rainfall–runoff models, and integration with real-time forecasting systems to enhance predictive reliability in rapidly urbanizing environments.
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