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Hurricane‐induced compound flooding is a combined result of multiple processes, including overland runoff, precipitation, and storm surge. This study presents a dynamical coupling method applied at the boundary of a processes‐based hydrological model (the hydrological modeling extension package of the Weather Research and Forecasting model) and the two‐dimensional Regional Ocean Modeling System on the platform of the Coupled‐Ocean‐Atmosphere‐Wave‐Sediment Transport Modeling System. The coupled model was adapted to the Cape Fear River Basin and adjacent coastal ocean in North Carolina, United States, which suffered severe losses due to the compound flood induced by Hurricane Florence in 2018. The model's robustness was evaluated via comparison against observed water levels in the watershed, estuary, and along the coast. With a series of sensitivity experiments, the contributions from different processes to the water level variations in the estuary were untangled and quantified. Based on the temporal evolution of wind, water flux, water level, and water‐level gradient, compound flooding in the estuary was categorized into four stages: (I) swelling, (II) local‐wind‐dominated, (III) transition, and (IV) overland‐runoff‐dominated. A nonlinear effect was identified between overland runoff and water level in the estuary, which indicated the estuary could serve as a buffer for surges from the ocean side by reducing the maximum surge height. Water budget analysis indicated that water in the estuary was flushed 10 times by overland runoff within 23 days after Florence's landfall. Plain Language Summary Compound flooding refers to a phenomenon in which two or more flooding sources occur simultaneously or subsequently within a short period of time. In this study, we present a new numerical model that combines hydrological and ocean models to represent the exchange of water levels at the land‐ocean interaction zone. To test the model's robustness, we use this model to simulate the water level changes in Cape Fear River Basin and adjacent coastal ocean in North Carolina, United States, for Hurricane Florence in 2018. The comparison between observed and simulated water level prove that the new model can better resolve the changes in water elevation during a hurricane event than the traditional method where the ocean model utilized the river model's outputs as its boundary condition. We further quantify the contributions from different processes to the water level variations in the estuary. The compound flooding in the estuary was categorized into four stages: (I) swelling, (II) local‐wind‐dominated, (III) transition and (IV) overland‐runoff‐dominated. The estuary could serve as a buffer for surges from the ocean side by reducing the maximum surge height. The water in the estuary was flushed 10 times by overland runoff within 23 days after Florence's landfall. Key Points A coupled hydrological‐ocean model was developed using hydrological modeling extension package of the Weather Research and Forecasting model (WRF‐Hydro) and two‐dimensional Regional Ocean Modeling System (ROMS 2D) through the Coupled‐Ocean‐Atmosphere‐Wave‐Sediment Transport modeling system The dynamical coupling method was applied to the interface boundary of WRF‐Hydro and ROMS 2D to realize a seamless model coupling Hurricane Florence‐induced compound flooding event was investigated by analyzing the modeled water level evolution, water budget, and nonlinear effects in the Cape Fear Estuary

Infragravity waves are key components of the hydro‐sedimentary processes in coastal areas, especially during extreme storms. Accurate modeling of coastal erosion and breaching requires consideration of the effects of infragravity waves. Here, we present InWave, a new infragravity wave driver of the Coupled Ocean‐Atmopshere‐Waves‐Sediment Transport (COAWST) modeling system. InWave computes the spatial and temporal variation of wave energy at the wave group scale and the associated incoming bound infragravity wave. Wave group‐varying forces drive free infragravity wave growth and propagation within the hydrodynamic model of the coupled modeling system, which is the Regional Ocean Modeling System (ROMS) in this work. Since ROMS is a three‐dimensional model, this coupling allows for the combined formation of undertow currents and infragravity waves. We verified the coupled InWave‐ROMS with one idealized test case, one laboratory experiment, and one field experiment. The coupled modeling system correctly reproduced the propagation of gravity wave energy with acceptable numerical dissipation. It also captured the transfer of energy from the gravity band to the infragravity band, and within the different infragravity bands in the surf zone, the measured three‐dimensional flow structure, and dune morphological evolution satisfactorily. The idealized case demonstrated that the infragravity wave variance depends on the directional resolution and horizontal grid resolution, which are known challenges with the approach taken here. The addition of InWave to COAWST enables novel investigation of nearshore hydro‐sedimentary dynamics driven by infragravity waves using the strengths of the other modeling components, namely the three‐dimensional nature of ROMS and the sediment transport routines. Plain Language Summary Infragravity waves have periods between 25 and 250 s and are the result of wind‐wave groups, or “sets.” When wind‐waves of similar periods travel together, they group, resulting in varying wave heights within the groups. This wave height variation at the group scale forces ocean surface infragravity waves. Coastal circulation, flooding, sand transport, and erosion are strongly influenced by these infragravity waves, especially during extreme storms. Therefore, it is important that we are able to model infragravity waves. We present a novel infragravity wave component of the Coupled‐Ocean‐Atmosphere‐Wave‐Sediment Transport (COAWST) modeling system: InWave. By coupling InWave and a circulation model, COAWST is now able to account for the main processes needed to predict coastal hazards due to extreme storms. This new coupled system is verified by reproducing observations from idealized numerical cases, a laboratory experiment on dune erosion, and a field experiment. Results show a good agreement with observations. Key Points InWave is a new infragravity wave driver of the Coupled Ocean‐Atmosphere‐Wave Sediment Transport (COAWST) modeling system Coupling InWave with the Regional Ocean Modeling System within COAWST enables the modeling of infragravity waves and three‐dimensional flows The coupled system is verified by reproducing laboratory and field observations, with good hydrodynamic and morphodynamic performance

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.

Hurricanes interact with the Gulf Stream in the South Atlantic Bight (SAB) through a wide variety of processes, which are crucial to understand for prediction of open-ocean and coastal hazards during storms. However, it remains unclear how waves are modified by large-scale ocean currents under storm conditions, when waves are aligned with the storm-driven circulation and tightly coupled to the overlying wind field. Hurricane Matthew (2016) impacted the U.S. Southeast coast, causing extensive coastal change due to large waves and elevated water levels. The hurricane traveled on the continental shelf parallel to the SAB coastline, with the right side of the hurricane directly over the Gulf Stream. Using the Coupled Ocean–Atmosphere–Wave–Sediment Transport modeling system, we investigate wave–current interaction between Hurricane Matthew and the Gulf Stream. The model simulates ocean currents and waves over a grid encompassing the U.S. East Coast, with varied coupling of the hydrodynamic and wave components to isolate the effect of the currents on the waves, and the effect of the Gulf Stream relative to storm-driven circulation. The Gulf Stream modifies the direction of the storm-driven currents beneath the right side of the hurricane. Waves transitioned from following currents that result in wave lengthening, through negative current gradients that result in wave steepening and dissipation. Wave–current interaction over the Gulf Stream modified maximum coastal total water levels and changed incident wave directions at the coast by up to 20°, with strong implications for the morphodynamic response and stability of the coast to the hurricane.

Hurricane Florence (2018) devastated the coastal communities of the Carolinas through heavy rainfall that resulted in massive flooding. Florence was characterized by an abrupt reduction in intensity (Saffir–Simpson category 4 to category 1) just prior to landfall and synoptic-scale interactions that stalled the storm over the Carolinas for several days. We conducted a series of numerical modeling experiments in coupled and uncoupled configurations to examine the impact of sea surface temperature (SST) and ocean waves on storm characteristics. In addition to experiments using a fully coupled atmosphere–ocean–wave model, we introduced the capability of the atmospheric model to modulate wind stress and surface fluxes by ocean waves through data from an uncoupled wave model. We examined these experiments by comparing track, intensity, strength, SST, storm structure, wave height, surface roughness, heat fluxes, and precipitation in order to determine the impacts of resolving ocean conditions with varying degrees of coupling. We found differences in the storm’s intensity and strength, with the best correlation coefficient of intensity ( r = 0.89) and strength ( r = 0.95) coming from the fully coupled simulations. Further analysis into surface roughness parameterizations added to the atmospheric model revealed differences in the spatial distribution and magnitude of the largest roughness lengths. Adding ocean and wave features to the model further modified the fluxes due to more realistic cooling beneath the storm, which in turn modified the precipitation field. Our experiments highlight significant differences in how air–sea processes impact hurricane modeling. The storm characteristics of track, intensity, strength, and precipitation at landfall are crucial to predictability and forecasting of future landfalling hurricanes.

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.

Climate change exerts physical influence on estuarine and open coastal morphology through sea level rise and changes to wave energy, precipitation, and sediment supply, resulting in flooding and erosion hazards to coastal communities. Along open coasts, the distribution of wave energy in the nearshore is of critical importance for assessing local vulnerability to climate change. Two methods are frequently used to predict wave conditions based on global climate model outputs of atmospheric circulation: dynamical downscaling and statistical downscaling. Statistical downscaling relies on empirical relationships between waves and atmospheric conditions. Statistical downscaling has been applied with success in relatively small ocean basins (e.g., Mediterranean, Atlantic) where waves are generated over a small area and arrive at the coast within a few days of generation. However, in the Pacific Basin, waves are generated over large and distant regions of both the North and South Pacific. Furthermore, waves can travel up to 3 weeks before arriving at the coast (e.g., Southern Ocean-generated waves arriving in Southern California). These challenges have resulted in statistical downscaling studies with limited success. Chapter 2 of this dissertation addresses these challenges by 1) partitioning wave spectra into families that have unique, discrete generation areas and 2) accounting for the time lag between wave generation and wave arrival at the coast. The success of this work in Southern California bodes well for the proliferation of wave climate projections in large ocean basins. To project future coastal hazards, deep-water waves predicted using the methods described above must be transformed over shelf bathymetry to the nearshore. In complex coastal regions, offshore canyons, shoals, and islands complicate the linkage of nearshore waves to deep-water waves and the atmospheric conditions that generated them. In Chapter 3 of this dissertation, a hybrid statistical-dynamical approach is taken to explore significant spatial variability in nearshore wave conditions of the Southern California Bight, a complex coastal region. It is found that variability is driven by not only static bathymetric controls, but also dynamic large-scale atmospheric patterns. Climate change effects on these atmospheric patterns will lead to new distributions of wave energy along the Southern California Bight coastline and other coastlines around the world. Along estuarine coasts, the distribution of tracers, such as salt, sediment, and pollutants, is a key factor in determining vulnerability to climate change and development. Extensive scientific effort has yielded a comprehensive understanding of sediment and salt transport in varied estuarine systems. However, tidal dynamics in shallow embayments, which are commonly found flanking deep estuarine channels, have not been described thoroughly. Chapter 4 of this dissertation examines the momentum and salt forcing associated with a shallow, estuarine embayment. This work illuminates mechanisms likely responsible for trapping of sediments in shallow bays and the supply of sediment to estuarine marshes. The suite of studies presented in this dissertation seeks to contribute to our scientific understanding of open and estuarine coastal response to climate change and to provide information that can readily be applied to coastal policy and engineering.

In the equilibrium range of the wave spectrum’s high-frequency tail, energy levels are proportional to the wind friction velocity. As a consequence of this intrinsic coupling, spectral tail energy levels can be used as proxy observations of surface stress and wind speed when direct observations are unavailable. Proxy observations from drifting wave-buoy networks can therefore augment existing remote sensing capabilities by providing long dwell observations of surface winds. Here we consider the skill of proxy wind estimates obtained from observations recorded by the globally distributed Sofar Spotter network (observations from 2021 to 2022) when compared with collocated observations derived from satellites (yielding over 20 000 collocations) and reanalysis data. We consider physics-motivated parameterizations (based on frequency −4 universal tail assumption), inverse modeling (estimate wind speed from spectral energy balance), and a data-driven approach (artificial neural network) as potential methods. Evaluation of trained/calibrated models on unseen test data reveals comparable performance across methods with generally of order 1 m s −1 root-mean-square difference with satellite observations.