Influence of Forcing Conditions on Total Water Level Prediction and Spatiotemporal Patterns in Delaware Bay, USA

David F Muñoz, University of Alabama, Civil and Environmental Engineering, Tuscaloosa, United States, Dongxiao Yin, Virginia Institute of Marine Science, Coastal and Ocean Processes, Gloucester Point, VA, United States, Jiannan Tian, University of Alabama, Computer Science, Tuscaloosa, United States, Roham Bakhtyar, NOAA / Office of Water Prediction / National Water Center, Tuscaloosa, AL, United States, Kyle T Mandli, Columbia University of New York, Applied Physics and Applied Mathematics, New York, NY, United States and Celso Ferreira, George Mason University, Sid and Reva Dewberry Department of Civil, Environmental and Infrastructure Engineering, Fairfax, United States
Abstract:
Accurate forecasts of total water level (i.e., a combination of tides, surge, wave and freshwater components) is imperative for stakeholders and federal agencies to adopt strategies for potential flooding hazards in a timely-manner. In that regard, the National Water Center in partnership with several federal agencies have been providing forecast services to the United States since 2017. However, the complex interaction of dynamical forcing conditions among other factors (e.g., anthropogenic activities, land cover change, etc.) reduce the National Water Model’s (NWM) ability to provide accurate Total Water Level (TWL) prediction in Coastal Transition Zones (CTZs). In this study, we use an existing inland to coastal model coupling framework (i.e., NWM, HWRF, Delft3D-FM and ADCIRC) to analyze the influence of dynamical forcing conditions (e.g., local wind, surge and river discharge) on TWL prediction in Delaware Bay, USA. In addition, we quantify the contribution of each component in TWL for Hurricanes Isabel and Sandy based on a systematic set of scenarios generated in Delft3D-FM. It is revealed that in both hurricanes, storm surge-induced water level is the main contributor to TWL followed by astronomical tides. River discharge induced-water level is rather small compared to the other components. Analyses of spatial variation of TWL as well as temporal variation of error in prediction suggest that wind forcing plays a key role in TWL prediction followed by river discharge. Moreover, our results suggest that the wind module of Delft3D-FM greatly improves the model performance at TWL peak when compared to the other forcing.
Back to: Inland-Coastal Model Coupling Using a Community-Based Approach II Posters