Rapid modeling of compound flooding across broad coastal regions and the necessity to include rainfall driven processes: A case study of Hurricane Florence (2018)

Abstract

In this work, we show that large-scale compound flood models developed for North and South Carolina, USA, can skillfully simulate multiple drivers of coastal flooding as confirmed by measurements collected during Hurricane Florence (2018). Besides the accuracy of representing observed water levels, the importance of individual processes was investigated. We demonstrate that across the area of interest, it is necessary to include marine, pluvial, and fluvial forcing and the processes of wind stress and infiltration to correctly model water levels along the coast and further inland. This work highlights the need to include these processes in modeling coastal compound flooding. By using high-resolution topo-bathymetry that is incorporated via subgrid derived tables in the Super-Fast INundation of CoastS (SFINCS) model, we improved the skill of the model at efficiently simulating flooding across large-scale domains with locally relevant results.