Compound Flood Hazards from Tropical Cyclones in the Northeastern United States

Compound flooding, the concurrence of multiple flooding mechanisms such as storm surge, heavy rainfall, and riverine flooding, poses a significant threat to coastal communities. Quantifying compound flood hazards in coastal regions is essential for effective adaptation planning, both for current climate conditions and future scenarios influenced by climate change. However, this task is filled with challenges due to the limitations of existing modeling tools. Reanalysis datasets and global circulation models (GCMs) typically have a coarse resolution, which makes it difficult to accurately resolve tropical cyclones (TCs). Although high-resolution GCMs and regional downscale climate models offer better precision in resolving TCs, they are still computationally expensive, making them impractical for widespread flood hazard assessments. A different approach is to use large sets of synthetic TCs created from reanalysis data or climate models to run hydrodynamic models and evaluate flood hazard. In this study, we use the Princeton Environmental-Dependent Probabilistic Tropical Cyclone (PepC) model, which generates synthetic TCs based on reanalysis datasets and global circulation models (GCMs). This model has three key parts: a Poisson model for TC formation, an analog-wind track model, and a Markov model for TC intensity. These components rely on environmental factors like potential intensity, wind patterns, vertical wind shear, humidity, and ocean cooling, all of which vary with climate. We apply PepC to generate large numbers of synthetic TCs that could impact the Northeast US and adjust for any biases in their frequency, intensity, and track characteristics, which can significantly affect storm surge in the Northeast US as demonstrated by Hurricane Sandy. We then use physics-based wind and rainfall models to generate the spatial-temporal wind, rainfall, and pressure fields for each of the synthetic TCs. The wind and pressure fields are used to drive the advanced hydrodynamic model ADCIRC to simulate storm surges from these synthetic TCs. To overcome the computational challenge of high-resolution inundation modeling, we apply our recently developed numerical solver called CoaSToRM (Coastal Subgrid Topography Research Model) with subgrid corrections (utilizing information at smaller scales to “correct” water levels and current velocities averaged over the model scale) to compute the compound flooding induced by storm surge, rainfall, riverine flooding, and wind-driven processes. Given the large synthetic samples, the probabilities of extreme flooding events under the historical and future climates are quantified for the Northeast region.