Total water levels (TWLs), including the contribution of wind waves, associated with tropical cyclones (TCs) are among the most damaging hazards faced by coastal communities. TC-induced economic losses are expected to increase because of stronger TC intensity, sea level rise, and increased populations along the coasts. TC intensity, translation speed, and distance to the coast affect the magnitude and duration of increased TWLs and wind waves. Under climate change, the proportion of high-intensity TCs is projected to increase globally, whereas the variation pattern of TC translation speed also depends on the ocean basin and latitude. There is an urgent need to improve our understanding of the linkages between TC characteristics and TWL components. In the past few years, hurricanes Matthew (2016), Dorian (2019), and Isaias (2020) propagated over the South Atlantic Bight (SAB) with similar paths but resulted in different coastal impacts. We combined in situ observations and numerical simulations with the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system to analyze the extreme TWLs under the three TCs. Model verification showed that the TWL components were well reproduced by the present model setup. Our results showed that the peak storm surge and the peak wave runup depended mainly on the TC intensity, the distance to the TC eye, and the TC heading direction. A decrease in TC translation speed primarily led to longer exceedance durations of TWLs, which may result in more severe economic losses. Wave-dependent water level components (i.e., wave setup and wave swash) were found to dominate the peak TWL within the near-TC field. Our results also showed that in specific conditions, the prestorm wave runup associated with the TC-induced swell may lead to TWLs higher than at the peak of the storm. This was the case along the SAB during Hurricane Isaias. Isaias's fast TC translation speed and the fact that its swell was not blocked by any islands were the main factors contributing to these peak TWLs ahead of the storm peak.

Hsu, C.-E.; Hsiao, S.-C., and Hsu, J.-T., 2017. Parametric analyses of wave-induced nearshore current system. To date, a few investigations have focused on the interaction and sensitivity analysis of different factors affecting the nearshore current system. This paper attempts to present detailed parametric analyses by analyzing a widely adopted experiment using a model based on shallow-water and mild-slope equations. Discussions and conclusions are then made by the numerical experiments in this paper. Various factors, including bed roughness and bed slope, are taken into account to figure out their impact on the velocity field. Moreover, estimation of effective bed roughness, turbulent diffusion effects, and surface roller are considered and discussed in this study. After a series of analyses, bottom roughness is found to be the most significant variable in the system, whereas surface roller is proved to be indispensable in such two-dimensional nearshore current models. When the bed slope is too...

Despite recent advancements in ocean–wave observations, how a tropical cyclone’s (TC’s) track, intensity, and translation speed affect the directional wave spectra evolution is poorly understood. Given the scarcity of available wave spectral observations during TCs, there are few studies about the performance of spectral wave models, such as Simulating Waves Nearshore (SWAN), under various TC scenarios. We combined the National Data Buoy Center observations and numerical model hindcasts to determine the linkages between wave spectrum evolution and TC characteristics during hurricanes Matthew 2016, Dorian 2019, and Isaias 2020. Five phases were identified in the wave spectrogram based on the normalized distance to the TC, the sea–swell separation frequency, and the peak wave frequency, indicating how the wave evolution relates to TC characteristics. The wave spectral structure and SWAN model’s performance for wave energy distribution within different phases were identified. The TC intensity and its normalized distance to a buoy were the dominant factors in the energy levels and peak wave frequencies. The TC heading direction and translation speed were more likely to impact the durations of the phases. TC translation speeds also influenced the model’s performance on swell energy. The knowledge gained in this work paves the way for improving model’s performance during severe weather events.

Total water levels (TWLs), including the contribution of wind waves, associated with tropical cyclones (TC) are among the most damaging hazards faced by coastal communities. According to the report of the Intergovernmental Panel on Climate Change (IPCC; Masson-Delmotte et al., 2021), TC–induced damages are expected to increase because of stronger TC intensity, sea level rise, and increased populations along the coasts. TC intensity, translation speed, and distance to the coast affect the magnitude and duration of increased TWLs and wind waves. Under climate change, the proportion of high–intensity TCs are projected to increase globally (IPCC; Masson-Delmotte et al., 2021), whereas the variation pattern of TC translation speed also depends on regions (Yamaguchi et al., 2020). There is an urgent need to improve our understanding of the linkages among TC characteristics and TWL components. In the past years, hurricanes Matthew (2016), Dorian (2019), and Isaias (2020) propagated over the South Atlantic Bight (SAB) with similar paths but resulted in different coastal impacts. We combined in situ observations and numerical simulations with the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system to analyze the extreme TWLs under the three TCs. Model verification showed that the TWL components were well reproduced by the present model setup. Our results showed that peak TWL depends mainly on the TC intensity, the distance to the TC eye, and the TC heading direction. A decrease of TC translation speed primarily led to longer exceedance duration of TWL, which may lead to more severe damage. Wave–dependent water level components (i.e., wave setup and wave swash) were found to dominate the peak TWL within the near–TC wave field (60 %). Our results also showed that in specific conditions, the pre–storm wave runup associated with the TC–induced swell may lead to TWLs higher than at the peak of the storm. This was the case along the SAB during Hurricane Isaias. Isaias’s fast TC translation speed and the fact that its swell was not blocked by any islands were the main factors contributing to these peak TWLs ahead of the storm peak.