David Bazzett, Ruo-Qian Wang, Lucas Marxen

Regional flood mapping poses computational and spatial heterogeneity challenges, exacerbated by climate change-induced uncertainties. This study focuses on creating a state-wide flood mapping solution with enhanced accuracy and computational speed to support regional flooding hazard analysis and the assessment of climate change, using New Jersey as a case study. The Height Above Nearest Drainage (HAND) framework was employed for large-scale flood mapping. The model was validated against high water marks (HWMs) collected after Hurricane Irene. Based on the National Water Model (NWM), synthetic rating curves in HAND were calibrated by tuning Manning’s roughness, aligning the predicted and observed flood depths. The roughness values were generalized across the state from the validated water basins to the ungauged ones, using a multivariate regression with the hydrologic and geographic information. To map the future climate-change-induced flooding, a correlation between NOAA historical precipitation totals and NWM flow data from 2010-2020 was established to link precipitation and runoff. This study also invented a novel method for correcting catchment discontinuities, inherent in the HAND model, based on a computer vision scheme, the Sobel filter. The modeling results show that average and worst-case storm events have the potential to increase 10-50% in the state, where mountain areas and major river banks would be exposed to this impact more significantly.