Sensitivity Analysis of a Coastal Ocean Forecast Model to Nearshore Bathymetric Variability

The escalating frequency of extreme coastal events, exemplified by hurricanes and floods, underscores the necessity of robust monitoring and flood prediction tools. Given the limitations of observations, numerical models offer opportunities to address these gaps, yet their predictive efficiency is prone to uncertainties. Coastal models require several inputs, including bathymetry, which is a first-order forcing and an important boundary condition. However, bathymetric information is susceptible to inaccuracy due to the constraints of underwater topography measurement technologies; therefore, it can be a significant source of uncertainty in ocean models. Moreover, nearshore bathymetry is subject to frequent variability due to its highly dynamic seafloor morphology, especially during storm events. In this study, we investigate the sensitivity of a 3D tributary-estuary-ocean hydrodynamic model and its ability to forecast flood inundation under bathymetric uncertainty, focusing on Delaware Bay—a major estuarine system in the eastern US that Hurricane Irene profoundly impacted in August 2011. Bathymetry uncertainty is quantified based on NOAA’s Category Zone of Confidence (CATZOC) and a random perturbation process that represents errors in the estimation of topobathy data based on vertical and horizontal length scales. Our results indicate that bathymetric errors can lead to model uncertainties of about 24% and 28% differences between original bathymetry and average ensemble conditions, respectively, for water level and currents predictions at locations of interest. Also, we observed standard deviations of 30 cm and 0.35 m/s for water level and currents in the perturbed conditions, which exceed acceptable error thresholds of NOAA’s operational forecast models. Additionally, simulated currents showed more sensitivity in deeper regions, while water levels were more affected nearshore. These findings highlight the importance of accounting for input data uncertainty in marine operations and flood risk assessments, and support the development of resilient coastal planning strategies.