The process-based XBeach model has numerous empirical parameters because of insufficient understanding of hydrodynamics and sediment transport on the nearshore; hence, it is necessary to calibrate parameters to apply to various study areas and wave conditions. Therefore, the calibration process of parameters is essential for the improvement of model performance. Generally, the trial-and-error method is widely used; however, this method is passive and limited to various and comprehensive parameter ranges. In this study, the Generalized Likelihood Uncertainty Estimation (GLUE) method was used to estimate the optimal range of three parameters (gamma, facua, and gamma2) using morphological field data collected in Maengbang beach during the four typhoons that struck from September to October 2019. The model performance and optimal range of empirical parameters were evaluated using Brier Skill Score (BSS) along with the baseline profiles, sensitivity, and likelihood density analysis of BSS in the GLUE tools. Accordingly, the optimal parameter combinations were derived when facua was less than 0.15 and simulated well the shifting shape, from crescentic sand bar to alongshore uniform sand bars in the surf zone of Maengbang beach after storm impact. However, the erosion and accretion patterns nearby in the surf zone and shoreline remain challenges in the XBeach model.

Even though submerged breakwater reduces incident wave energy, it redistributes the coastal area's wave-induced current, sediment transport, and morphological change. This study examines the coastal hydrodynamics and the morphological response of a wave-dominated beach with submerged breakwaters installed through field observation and quasi-3D numerical modeling. The pre-and post-storm bathymetry, water level, and offshore wave under storm forcing were collected in Bongpo Beach on the East coast of Korea and used to analyze the coastal hydrodynamic response. Four vertically equidistant layers were used in the numerical simulation, and the wave-induced current was examined using quasi-3D numerical modeling. The shore normal incident wave (east-northeast) generated strong cross-shore and longshore currents toward the hinterland of the submerged breakwater. However, the oblique incident wave (east-southeast) induced the southeastward longshore current and the sedimentation in the northeast area of the beach. The results suggested that the incident wave direction is a significant factor in determining the current and sediment transport patterns in the presence of the submerged breakwaters. Moreover, the quasi-3D numerical modeling is more appropriate for estimating the wave transformation, current, and sediment transport pattern in the coastal area with the submerged breakwater.

Constant changes occur in coastal areas over different timescales, requiring observation and modeling. Specifically, modeling morphological changes resulting from short-term events, such as storms, is of great importance in coastal management. Parameter calibration is necessary to achieve more accurate simulations of process-based models that focus on specific locations and event characteristics. In this study, the XBeach depth-averaged model was adopted to simulate subaerial data pre- and post-storms, and overwash phenomena were observed using the data acquired through unmanned aerial vehicles. The parameters used for the model calibration included those proposed in previous studies. However, an emphasis was placed on calibrating the parameters related to sediment transport that were directly associated with overwash and deposition. Specifically, the parameters corresponding to the waveform parameters, wave skewness, and wave asymmetry were either integrated or separated to enable an adequate representation of the deposition resulting from overwash events. The performance and sensitivity of the model to changes in volume were assessed. Overall, the waveform parameters exhibit significant sensitivity to volume changes, forming the basis for calibrating the deposition effects caused by overwashing. These results are expected to assist in the more effective selection and calibration of parameters for simulating sediment deposition due to overwash events.

Beach profiles are constantly changing due to external ocean forces. Estimating these changes is crucial to understanding and addressing coastal erosion issues, such as shoreline advance and retreat. To estimate beach profile changes, obtaining long-term, high-resolution spatiotemporal beach profile data is essential. However, due to the limited availability of beach profile survey data both on land and underwater along the coast, generating continuous, high-resolution spatiotemporal beach profile data over extended periods is a critical technological challenge. Therefore, we herein developed a long short-term memory-based encoder–decoder network for effective spatiotemporal representation learning to estimate beach profile responses on temporal scales from weeks to months from coastal hydrodynamics. The proposed approach was applied to 12 transects from seven beaches located in three different littoral systems on the east coast of the Korean Peninsula, where coastal erosion problems are severe. The performance of the proposed method demonstrated improved results compared with a recent study that performed the same beach profile estimation task, with an average root mean square error of 0.50 m. Moreover, most of the results exhibited a reasonably accurate morphological shape of the estimated beach profile. However, instances where the results exceed the average error are attributed to extreme beach morphological changes caused by storm waves such as typhoons.

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.

In coastal video monitoring, the direct linear transform (DLT) method with ground control points (GCPs) is commonly used for geo-rectification. However, current practices often overlook the impact of GCP quantity, arrangement, and the geographical characteristics of beaches. To address this, we designed scenarios at Chuam Beach to evaluate how factors such as the distance from the camera to GCPs, the number of GCPs, and the height of each point affect the DLT method. Accuracy was assessed by calculating the root mean square error of the distance errors between the actual GCP coordinates and the image coordinates for each setting. This analysis aims to propose an optimal GCP placement method. Our results show that placing GCPs within 200 m of the camera ensures high accuracy with few points, whereas positioning them at strategic heights enhances shoreline extraction. However, since only fixed cameras were used in this study, factors like varying heights, orientations, and resolutions could not be considered. Based on data from a single location, we propose an optimal method for GCP placement that takes into account distance, number, and height using the DLT method.