This paper describes an integrated statistical-hydrodynamic method to estimate tropical cyclone-induced coastal flood inundation hazard, applied to a coastal megacity-Shanghai, China. We identify three “worst-case” scenarios (extracted from over 5000 synthetic storms) that generate unprecedentedly high flood levels in Shanghai. Nevertheless, we find that the mainland Shanghai is relatively safe from coastal flooding under the current climate, thanks to its high-standard seawall protection. However, the city is expected to be increasingly at risk due to future sea level rise, with inundation two times and 20 times more likely to occur by mid-and late-21 st century, respectively, and inundation depth and area to greatly increase (e.g., 60-1360% increase in the inundation area for the “worst cases” by 2100). The low-lying and poorly-protected area (e.g. Chongming island) is likely to be moderately affected by flood events with long return periods at the current state but could be largely inundated in future sea-level-rise situations.

Tropical cyclones (TCs) are one of the greatest threats to coastal communities along the US Atlantic and Gulf coasts due to their extreme winds, rainfall and storm surge. Analyzing historical TC climatology and modeling TC hazards can provide valuable insight to planners and decision makers. However, detailed TC size information is typically only available from 1988 onward, preventing accurate wind, rainfall, and storm surge modeling for TCs occurring earlier in the historical record. To overcome temporally limited TC size data, we develop a database of size estimates that are based on reanalysis data and a physics-based model. Specifically, we utilize ERA5 reanalysis data to estimate the TC outer size, and a physics-based TC wind model to estimate the radius of maximum wind. We evaluate our TC size estimates using two high-resolution wind datasets as well as Best Track information for a wide variety of TCs. Using the estimated size information plus the TC track and intensity, we reconstruct historical storm tides from 1950-2020 using a basin-scale hydrodynamic model and show that our reconstructions agree well with observed peak water levels. Finally, we demonstrate that incorporating an expanded set of historical modeled storm tides beginning in 1950 can enhance our understanding of US coastal hazard. Our newly developed database of TC sizes and associated storm tides can aid in understanding North Atlantic TC climatology and modeling TC wind, storm surge, and rainfall hazard along the US Atlantic and Gulf coasts.

Future coastal flood hazard at many locations will be impacted by both tropical cyclone (TC) change and relative sea-level rise (SLR). Despite sea level and TC activity being influenced by common thermodynamic and dynamic climate variables, their future changes are generally considered independently. Here, we investigate correlations between SLR and TC change derived from simulations of 26 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. We first explore correlations between SLR and TC activity by inference from two large‑scale factors known to modulate TC activity: potential intensity (PI) and vertical wind shear. Under the high emissions SSP5-8.5, SLR is strongly correlated with PI change (positively) and vertical wind shear change (negatively) over much of the western North Atlantic and North West Pacific. To explore the impact of the joint changes on flood hazard, we then conduct climatologyhydrodynamic modeling with New York City (NYC) as an example. Coastal flood hazard at NYC correlates strongly with global mean surface air temperature (GSAT), due to joint increases in both sea level and TC storm surges, the later driven by stronger and more slowly moving TCs. If positive correlations between SLR and TC changes are ignored in estimating flood hazard, the average projected change to the historical 100 year storm tide event is under-estimated by 0.09 m (7%) and the range across CMIP6 models is underestimated by 0.17 m (11 %). Our results suggest that flood hazard assessments that neglect the joint influence of these factors and that do not reflect the full distribution of GSAT changes will not accurately represent future flood hazard.

Compound flooding, characterized by the co-occurrence of multiple flood mechanisms, is a major threat to coastlines across the globe. Tropical cyclones (TCs) are responsible for many compound floods due to their storm surge and intense rainfall. Previous efforts to quantify compound flood hazard have typically adopted statistical approaches that may be unable to fully capture spatio-temporal dynamics between rainfall-runoff and storm surge, which ultimately impact total water levels. In contrast, we pose a physics driven approach that utilizes a large set of realistic TC events and a simplified physical rainfall model and simulates each event within a hydrodynamic model framework. We apply our approach to investigate TC flooding in the Cape Fear River, NC. We find TC approach angle, forward speed, and intensity are relevant for compound flood potential, but rainfall rate and time lag between centroid of rainfall and peak storm tide are the strongest predictors of compounding magnitude. Neglecting rainfall underestimates 100-yr flood depths across 28% of the floodplain, and taking the max of each hazard modeled separately still underestimates 16% of the floodplain. We find the main stem of the river is surge-dominated, upstream portions of small streams and pluvial areas are rainfall-dominated, but midstream portions of streams are compounding zones, and areas close to the coastline are surge-dominated for lower return periods but compounding zones for high return periods (100-yrs). Our method links joint rainfall-surge occurrence to actual flood impacts and demonstrates how compound flooding is distributed across coastal catchments.