Coastal dunes are important morphological features for both ecosystems and coastal hazard mitigation. Because understanding and predicting dune erosion phenomena is very important, various numerical models have been developed to improve the accuracy. In the present study, a process-based model (XBeachX) was tested and calibrated to improve the accuracy of the simulation of dune erosion from a storm event by adjusting the coefficients in the model and comparing it with the large-scale experimental data. The breaker slope coefficient was calibrated to predict cross-shore wave transformation more accurately. To improve the prediction of the dune erosion profile, the coefficients related to skewness and asymmetry were adjusted. Moreover, the bermslope coefficient was calibrated to improve the simulation performance of the bermslope near the dune face. Model performance was assessed based on the model-data comparisons. The calibrated XBeachX successfully predicted wave transformation and dune erosion phenomena. In addition, the results obtained from other two similar experiments on dune erosion with the same calibrated set matched well with the observed wave and profile data. However, the prediction of underwater sand bar evolution remains a challenge.

Key parameters in a process-based model depicting the morphological changes during storm events should be adjusted to simulate the hydro- and morphodynamics, leading to site-, profile-, and event-specific calibration. Although area models eliminate variability in calibrated parameters along with each profile in complex bathymetry, the amount of influence datasets with different wave conditions have on model performance is still unclear in an area model in a given parameter space. This study collected hydrodynamic and bathymetric field data over four different storm conditions (two single and two cluster storms) at Maengbang Beach, South Korea. The numerical model XBeach was adopted using four storm datasets with four key parameters to examine the influence of event-specific calibration data on subaerial storm erosion. When using clustered storm data, a relatively limited number of parameter combinations showed higher model sensitivity to different parameter sets as opposed to single storm data with the same parameter sets. Model sensitivity to different storm events was correlated with cumulative storm power and resultant erosion volume in comparison with other features in the datasets. The results are expected to guide the selection of an event-specific dataset with various morphological and hydrodynamic factors in an area model under complex bathymetry.

Coastal cities protected by artificial structures are vulnerable to wave overtopping during typhoons, where flooding can occur even when the still water level stays below the structure crest. This study develops EurOtop-based wave overtopping modules within the cross-scale SCHISM-WWM modelling framework and applies the coupled model to Marine City, Busan, South Korea. At each time step, the module extracts the local water level and wave conditions at a coastal-structure pair-node, computes the overtopping or overflow discharge from the EurOtop formulae while accounting for wave direction, roughness reduction, and crest-width transfer losses, and injects it as a landward source term for inland flood propagation. The framework is evaluated against two typhoon events. For Typhoon Hinnamnor in 2022, comparison with the CCTV-based overtopping record reproduces the timing and intensity of overtopping. For Typhoon Chaba in 2016, validation against field-surveyed inundation marks shows that the modelled maximum inundation depth matches the observed spatial pattern and the survey-point observations to within a mean absolute difference of about 12 cm. These results demonstrate the framework’s applicability to overtopping-induced compound flooding in urbanised coastal cities and, since it is fully coupled within the SCHISM-WWM codebase, its capacity to incorporate further drivers such as pluvial and fluvial flooding.

Coastal regions in Korea are densely occupied by artificial coastal structures, making them highly vulnerable to wave overtopping during extreme storm events. Even when water levels remain below the crest elevation, overtopping can induce significant coastal flooding. To address this, we developed a dynamic overtopping-flooding coupled system within the cross-scale SCHISM-WWM modeling framework. The system computes time-varying overtopping discharge at each model time step using the empirical formulations of EurOtop(2018), and transfers the resulting flow into the land domain, enabling the coupled interaction among waves, water levels, and coastal structures to be resolved in a unified modeling environment. The model was applied to Marine City, Busan, during Typhoon CHABA (1618). Simulated water levels and wave conditions were validated against tide-gauge and buoy observations, followed by comparison of inundation patterns with post-surveyed inundation marks. The results show that the system successfully reproduces the spatial characteristics of observed inundation with reasonable accuracy. The framework can be further extended to incorporate rainfall-runoff and sewer backflow processes, allowing for a fully integrated compound flooding modeling system.

Key parameters in the process-based model for morphological changes during storm events (i.e., XBeachX, Roelvink et al., 2018) should be adjusted to simulate morpho-hydrodynamics to a specific area of interest, leading to site-, profile-, and event-specific calibration. Although 2-dimensional simulations (area model) eliminate variability in calibrated parameters along with each profile in complex bathymetry, the degree to which datasets having different wave conditions influence model performance is still unclear in the area model at a given parameter space. The main objective of this study is to examine the influence of event-specific calibration data (single storm event and storm clusters) in modelling subaerial storm erosion under complex bathymetry. Using bathymetric field data over four different storm conditions, this study presented guidance for the selection of event-specific data in modeling calibration.