Sensitivity analysis of the coastal ocean forecasts to bathymetry conditions

Increased frequency of extreme coastal events such as hurricanes and floods highlights the need for development of reliable monitoring and prediction tools. Field observations are costly and season-limited; therefore, models can be used to fill the observational gaps in time and space. However, the model-based predictions can be uncertain and not completely effective in reproducing the realistic conditions, especially during extreme conditions. One source of uncertainty in modeling coastal processes, in particular during a storm surge, is the bottom characteristics and bathymetric conditions, which serve as an important boundary condition to the models. Bathymetry information obtained from 1800 until now could be outdated and/or inaccurate due to the limitations of measurement technology. In addition, nearshore bathymetry could vary significantly during a storm event when strong current and wave actions affect the bottom. In this study, we investigate the sensitivity of an estuary-ocean hydrodynamic model to variability in bathymetric conditions. The modeling domain includes the Northwest Atlantic Ocean, the Gulf of Mexico, and the Caribbean Sea with a high-resolution grid over the Delaware Bay which is one of the major estuarine systems in Eastern US and was affected by Hurricane Irene in Aug 2011. Uncertainty in the nearshore bathymetry is quantified through a random perturbation process that represents errors in estimation of topobathy data based on vertical and horizontal length scales. By perturbing the bathymetry and running ensemble simulations, we show how uncertainty in topobathy data would translate into errors and uncertainty in coastal ocean model's output variables such as water surface elevation and surface currents.