No abstract

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.