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In this study, we design a statistical method to couple observations with a physics-based tropical cyclone (TC) rainfall model (TCR) and engineered-synthetic storms for assessing TC rainfall hazard. We first propose a bias-correction method to minimize the errors induced by TCR via matching the probability distribution of TCR-simulated historical TC rainfall with gauge observations. Then we assign occurrence probabilities to engineered-synthetic storms to reflect local climatology, through a resampling method that matches the probability distribution of a newly proposed storm parameter named rainfall potential (POT) in the synthetic dataset with that in the observation. POT is constructed to include several important storm parameters for TC rainfall such as TC intensity, duration, and distance and environmental humidity near landfall, and it is shown to be correlated with TCR-simulated rainfall. The proposed method has a satisfactory performance in reproducing the rainfall hazard curve in various locations in the continental United States; it is an improvement over the traditional joint probability method (JPM) for TC rainfall hazard assessment.

 

In this study, we investigate both the changes of tropical cyclone (TC) rainfall hazard in the United States under climate change and the relative importance of the factors that cause the changes. We find that under the SSP5 8.5 scenario, the 100‐year TC rainfall level can increase by up to 320% along the U.S. coastline by the end of this century. The influence of TC rainfall‐producing ability increase is more significant than the influence of TC frequency increase on the increase of the 100‐year TC rainfall level (up to 180% vs. 60% increase). Among the different physical drivers for the increase in storm rainfall‐producing ability, the increase of TC intensity is the leading factor, followed by changes in TC duration and atmospheric temperature. The projected increase of TC rainfall hazard is robust against the uncertainty in the TC frequency projection.

 

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 physics‐based 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 the centroid of rainfall and peak storm tide are the strongest predictors of compounding magnitude. Neglecting rainfall underestimates 100‐year flood depths across 28% of the floodplain, and taking the maximum 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 years). Our method links joint rainfall‐surge occurrence to actual flood impacts and demonstrates how compound flooding is distributed across coastal catchments.

 

Abstract Tropical cyclone (TC) rainfall hazard assessment is subject to the bias in TC climatology estimation from climate simulations or synthetic downscaling. In this study, we investigate the uncertainty in TC rainfall hazard assessment induced by this bias using both rain gauge and radar observations and synthetic-storm-model-coupled TC rainfall simulations. We identify the storm’s maximum intensity, impact duration, and minimal distance to the site to be the three most important storm parameters for TC rainfall hazard, and the relationship between the important storm parameters and TC rainfall can be well captured by a physics-based TC rainfall model. The uncertainty in the synthetic rainfall hazard induced by the bias in TC climatology can be largely explained by the bias in the important storm parameters simulated by the synthetic storm model. Correcting the distribution of the most biased parameter may significantly improve rainfall hazard estimation. Bias correction based on the joint distribution of the important parameters may render more accurate rainfall hazard estimations; however, the general technical difficulties in resampling from high dimensional joint probability distributions prevent more accurate estimations in some cases. The results of the study also support future investigation of the impact of climate change on TC rainfall hazards through the lens of future changes in the identified important storm parameters. 

Abstract Tropical cyclones (TCs) are drivers of extreme rainfall and surge, but the current and future TC rainfall–surge joint hazard has not been well quantified. Using a physics-based approach to simulate TC rainfall and storm tides, we show drastic increases in the joint hazard from historical to projected future (SSP5–8.5) conditions. The frequency of joint extreme events (exceeding both hazards’ historical 100-year levels) may increase by 7–36-fold in the southern US and 30–195-fold in the Northeast by 2100. This increase in joint hazard is induced by sea-level rise and TC climatology change; the relative contribution of TC climatology change is higher than that of sea-level rise for 96% of the coast, largely due to rainfall increases. Increasing storm intensity and decreasing translation speed are the main TC change factors that cause higher rainfall and storm tides and up to 25% increase in their dependence. 

Abstract Sea level rise (SLR) and tropical cyclone (TC) climatology change could impact future flood hazards in Jamaica Bay—an urbanized back-barrier bay in New York—yet their compound impacts are not well understood. This study estimates the compound effects of SLR and TC climatology change on flood hazards in Jamaica Bay from a historical period in the late twentieth century (1980–2000) to future periods in the mid- and late-twenty-first century (2030–2050 and 2080–2100, under RCP8.5 greenhouse gas concentration scenario). Flood return periods are estimated based on probabilistic projections of SLR and peak storm tides simulated by a hydrodynamic model for large numbers of synthetic TCs. We find a substantial increase in the future flood hazards, e.g., the historical 100-year flood level would become a 9- and 1-year flood level in the mid- and late-twenty-first century and the 500-year flood level would become a 143- and 4-year flood level. These increases are mainly induced by SLR. However, TC climatology change would considerably contribute to the future increase in low-probability, high-consequence flood levels (with a return period greater than 100 year), likely due to an increase in the probability of occurrence of slow-moving but intense TCs by the end of twenty-first century. We further conduct high-resolution coastal flood simulations for a series of SLR and TC scenarios. Due to the SLR projected with a 5% exceedance probability, 125- and 1300-year flood events in the late-twentieth century would become 74- and 515-year flood events, respectively, in the late-twenty-first century, and the spatial extent of flooding over coastal floodplains of Jamaica Bay would increase by nearly 10 and 4 times, respectively. In addition, SLR leads to larger surface waves induced by TCs in the bay, suggesting a potential increase in hazards associated with wave runup, erosion, and damage to coastal infrastructure.