Evaluation of Flood Forecasting Input Uncertainties in Transitional Inland‐Coastal Low‐Gradient Rivers

Flood forecasting in transitional inland‐coastal low‐gradient rivers is challenging due to complex interactions among meteorological, hydrologic, and coastal processes. This study evaluates how uncertainties in key forecast inputs affect flood prediction accuracy in such settings, using the Vermilion River (south‐central Louisiana). A calibrated 1D/2D hydrodynamic model was used to assess the effect of uncertainties in precipitation, tributary streamflow, and Gulf water‐level forecasts. A scenario‐based framework quantified individual and combined uncertainty effects during two tropical storms: Hurricane Barry in 2019 and Hurricane Laura in 2020. Results show that tributary streamflow uncertainty drives upstream flood‐stage errors, while Gulf boundary‐condition uncertainty governs downstream responses during peak flooding and also affects upstream stages during non‐peak periods. These spatially variable sensitivities reflect distinct inland and coastal controls on flood dynamics across the basin. Precipitation uncertainty mainly affects post‐peak recession in swamp‐connected reaches, where natural storage and delayed drainage shape the hydrograph. When combined, input uncertainties produced nonlinear amplification of flood‐stage errors, particularly at longer lead times. The findings improve understanding of how input uncertainties propagate through low‐gradient river systems and support more reliable flood forecasting and risk‐management strategies.