Compound flooding, driven by the combined effects of rainfall, river discharge, storm surge, and wind, poses an increasing challenge for flood risk management in coastal regions. Accurate simulation of such events depends on hydrodynamic models, whose reliability is strongly affected by uncertainties in external forcings and internal parameters. Addressing this issue is particularly important for reduced-physics models such as SFINCS, which are increasingly applied to large-scale and time-critical flood forecasting. This study evaluates the sensitivity of SFINCS during the Beryl compound flood event (July 2024, Houston) through a dual-level analysis of forcing and parameter uncertainties. Forcings were perturbed within physically realistic ranges to represent measurement and reanalysis errors, while nine model parameters were systematically varied following the official documentation and preliminary tests. Results reveal strong spatial variability in forcing sensitivity: discharge dominated upstream stations, whereas boundary water surface elevation controlled downstream levels, with wind and pressure exerting negligible influence under the hydro-geomorphic conditions of this event. Parameter sensitivity was consistent across stations and methods, with water-level accuracy primarily governed by the numerical stability parameters theta and alpha and by Manning’s roughness N. After fixing theta at its recommended value (1.0) rendered alpha insensitive, confirming that its apparent effect was numerical and coupled with theta. After constraining these numerical parameters, N emerged as the main physical control on WSE accuracy, while GridSize governed runtime with alpha exerting a minor secondary influence. Parameter-induced uncertainty remained greater than forcing-induced uncertainty at most stations, though localized forcing biases occasionally modulated the total response. By jointly quantifying forcing and parameter uncertainties and focusing on measurement uncertainty within high-quality datasets, this study provides a practical framework for improving compound flood prediction and risk management….Read more