Subtropical coastlines are impacted by both tropical and extratropical cyclones. While both may lead to substantial damage to coastal communities, it is difficult to determine the contribution of tropical cyclones to coastal flooding relative to that of extratropical cyclones. We conduct a large-scale flood hazard and impact assessment across the subtropical Southeast Atlantic Coast of the United States, from Virginia to Florida, including different flood hazards. The physics-based hydrodynamic modeling skillfully reproduces coastal water levels based on a comprehensive validation of tides, almost two hundred historical storms, and an in-depth hindcast of Hurricane Florence. We show that yearly flood impacts are two times as likely to be driven by extratropical than tropical cyclones. On the other hand, tropical cyclones are 30 times more likely to affect people during rarer 100-year events than extratropical cyclones and contribute to more than half of the regional flood risk. With increasing sea levels, more areas will be flooded, regardless of whether flooding is driven by tropical or extratropical cyclones. Most of the absolute flood risk is contained in the greater Miami metropolitan area. However, several less populous counties have the highest relative risks. The results of this study provide critical information for understanding the source and frequency of compound flooding across the Southeast Atlantic Coast of the United States.

An unstructured hydrodynamic model is presented that is able to simulate 2D nearshore hydrodynamics on the wave group scale. A non-stationary wave driver with directional spreading, with physics similar to XBeach (Roelvink et al., 2009) is linked to an improved and extended version of the existing unstructured flow solver Delft3D–FM (Kernkamp et al., 2011; Martyr-Koller et al., 2017). The model equations are discretised on meshes consisting of triangular and rectangular elements. The model allows for coverage of the model domain with locally optimised resolution to accurately resolve the dominant processes, yet with a smaller total number of grid cells. The model also allows a larger explicit time step, compared to structured models with similar functionality. The model reliably reproduces measured datasets of water levels, sea/swell and low frequency wave heights in laboratory and field conditions, and is as such widely deployable in a variety of simple and complex coastal settings to study nearshore hydrodynamics.

The prediction of wave runup, as well as its components, time-averaged setup and the time-varying swash, is a key element of coastal storm hazard assessments, as wave runup controls the transitions between morphodynamic response types such as dune erosion and overwash, and the potential for flooding by wave overtopping. While theoretically able to simulate the dominant low-frequency swash, previous studies using the infragravity-wave–resolving model XBeach (XBSB) have shown an underestimation of the observed swash variance and wave runup, which was in part related to the absence of incident-band swash motions in the model. Here, we use an incident-band wave-resolving, non-hydrostatic version of the XBeach model (XBNH) to simulate wave runup observed during the SandyDuck '97 experiment on an intermediate–reflective sandy beach. The results show that the XBNH model describes wave runup and the individual setup and swash components well. We subsequently examine differences in wave runup prediction between the XBSB and XBNH models and find that the XBNH model is a better predictor of wave runup than XBSB for this beach, which is due to better predictions of both the incident-band and infragravity-band swash. For a range of beach states from reflective to dissipative it is shown that incident-band swash is underestimated by XBSB relative to XBNH, in particular for reflective conditions. Infragravity-band swash is shown to be lower in XBSB than XBNH for most conditions, including dissipative conditions for which the mean difference is 16% of the deep water wave height. The difference in infragravity-band swash in XBNH relative to XBSB is shown to mainly be the result of processes occurring outside the swash zone, but approximately 15% of the difference is caused by explicitly resolving incident-band wave motions within the swash zone, such as swash-swash interactions, which inherently cannot be simulated by wave-averaged models.

Intertidal bars are naturally occurring morphological features along the waterline of sandy beaches. Present quantitative knowledge on intertidal bar behavior is limited, due to the scarcity of data resources and the limitations of traditional survey techniques. To investigate and quantify the cross-shore morphologic behavior of intertidal bars, hourly terrestrial laser scans of Kijkduin beach (The Netherlands) are used and a conceptual evolution intertidal bar model is constructed. In a six-week period in January and February 2017, a pronounced intertidal bar formed at Kijkduin beach and migrated onshore during mild wave conditions and eroded again during storm conditions. The observed maximum shoreward migration was 30 m horizontally with a maximum growth of about 1 m in the vertical direction. Onshore sediment transport fluxes peaked around 2 m³ per m width per day. In the conceptual model proposed here, run-up and overwash processes are dominant for shoreward growth and migration of the bar and submersion processes are responsible for bar destruction.

Many coral reef-lined coasts are low-lying with elevations <4 m above mean sea level. Climate-change-driven sea-level rise, coral reef degradation, and changes in storm wave climate will lead to greater occurrence and impacts of wave-driven flooding. This poses a significant threat to their coastal communities. While greatly at risk, the complex hydrodynamics and bathymetry of reef-lined coasts make flood risk assessment and prediction costly and difficult. Here we use a large (>30,000) dataset of measured coral reef topobathymetric cross-shore profiles, statistics, machine learning, and numerical modeling to develop a set of representative cluster profiles (RCPs) that can be used to accurately represent the shoreline hydrodynamics of a large variety of coral reef-lined coasts around the globe. In two stages, the large dataset is reduced by clustering cross-shore profiles based on morphology and hydrodynamic response to typical wind and swell wave conditions. By representing a large variety of coral reef morphologies with a reduced number of RCPs, a computationally feasible number of numerical model simulations can be done to obtain wave runup estimates, including setup at the shoreline and swash separated into infragravity and sea-swell components, of the entire dataset. The predictive capability of the RCPs is tested against 5,000 profiles from the dataset. The wave runup is predicted with a mean error of 9.7–13.1%, depending on the number of cluster profiles used, ranging from 312 to 50. The RCPs identified here can be combined with probabilistic tools that can provide an enhanced prediction given a multivariate wave and water level climate and reef ecology state. Such a tool can be used for climate change impact assessments and studying the effectiveness of reef restoration projects, as well as for the provision of coastal flood predictions in a simplified (global) early warning system.

In predicting storm impacts on sandy coasts, possibly with structures, accurate runup and overtopping simulation is an important aspect. Recent investigations (Stockdon et al., 2014; Palmsten and Splinter, 2016) show that despite accurate predictions of the morphodynamics of dissipative sandy beaches, the XBeach model (Roelvink et al., 2009) does not correctly simulate the individual contributions of set-up, and infragravity and incident-band swash to the wave run-up. In this paper we describe an improved numerical scheme and a different way of simulating the propagation of directionally-spread short wave groups in XBeach to better predict the groupiness of the short waves and the resulting infragravity waves. The new approach is tested against field measurements from the DELILAH campaign at Duck, NC, and against video-derived runup measurements at Praia de Faro, a relatively steep sandy beach. Compared to the empirical fit by Vousdoukas et al. (2012) the XBeach model performs much better for more extreme wave conditions, which are severely underestimated by existing empirical formulations.For relatively steep beaches incident-band swash cannot be neglected and a wave-resolving simulation mode is required. Therefore in this paper we also test the non-hydrostatic, wave-resolving model within XBeach for runup and overtopping against three datasets. Results for a high-quality flume test show non-hydrostatic XBeach predicts the run-up height with good accuracy (maximum deviation 15%). A case with a very shallow foreshore typical for the Belgian coast at Wenduine was compared against detailed measurements. Overall the model shows correct behavior for this case. Finally, the model is tested against a large number (551) of physical model tests of overtopping from the CLASH database. For relatively high overtopping discharges the non-hydrostatic XBeach performs quite well, with increasing accuracy for increasing overtopping rates. However, for relatively low overtopping rates of less than 10-20 l/m/s, the model systematically underestimates measured overtopping rates.

Low frequency, high impact storm events can have large impacts on sandy coasts. The physical processes governing these impacts are complex because of the feedback between the hydrodynamics of surges and waves, sediment transport and morphological change. Predicting these coastal changes using a numerical model requires a large amount of computational time, which in the case of an operational prediction for the purpose of Early Warning is not available. For this reason morphodynamic predictions are not commonly included in Early Warning Systems (EWSs). However, omitting these physical processes in an EWS may lead to potential under or over estimation of the impact of a storm event. To solve this problem, a method has been developed to construct a probabilistic Bayesian Network (BN). This BN connects three elements: offshore hydraulic boundary conditions, characteristics of the coastal zone, and onshore hazards, such as erosion and overwash depths and velocities. The hydraulic boundary conditions are derived at a water depth of approximately 20 m from a statistical analysis of observed data using copulas, and site characteristics are obtained from measurements. This BN is trained using output data from many pre-computed process-based model simulations, which connect the three elements. Once trained, the response of the BN is instantaneous and can be used as a surrogate for a process-based model in an EWS in which the BN can be updated with an observation of the hydraulic boundary conditions to give a prediction for onshore hazards. The method was applied to Praia de Faro, Portugal, a low-lying urbanised barrier island, which is subject to frequent flooding. Using a copula-based statistical analysis, which preserves the natural variability of the observations, a synthetic dataset containing 100 events was created, based on 20 years of observations, but extended to return periods of significant wave height of up to 50 years. These events were transformed from offshore to onshore using a 2D XBeach (Roelvink et al., 2009) model. Three BN configurations were constructed, of which the best performing one was able to predict onshore hazards as computed by the model with an accuracy ranging from 81% to 88% and predict events with no significant onshore hazards with an accuracy ranging from 90% to 95%. Two examples are presented on the use of a BN in operational predictions or as an analysis tool. The added value of this method is that it can be applied to many coastal sites: (1) limited observations of offshore hydrodynamic parameters can be extended using the copula method which retains the original observations’ natural variability, (2) the transformation from offshore observations to onshore hazards can be computed with any preferred coastal model and (3) a BN can be adjusted to fit any relevant connections between offshore hydraulic boundary conditions and onshore hazards. Furthermore, a BN can be continuously updated with new information and expanded to include different morphological conditions or risk reduction measures. As such, it is a promising extension of existing EWSs and as a planning tool for coastal managers.