Wan Hee Cho

Wave overtopping is one of the most complex coastal hazards to characterize in field conditions due to its high non-linearity and the interaction between unsteady hydrodynamics and wave–structure processes. To get insights into the underlying occurrence and persistence of overtopping, this study proposes an integration of numerical and data-driven models. Multi-month field observations made at a breakwater are used to investigate the hydro-meteorological parameters causing overtopping initiation and persistence. High-frequency video-derived overtopping detections are combined with coupled ADCIRC–UnSWAN (ADvanced CIRCulation–Unstructured Simulating WAves Nearshore) hindcasts to construct near-structure hydro-meteorological conditions. The results reveal a clear dynamical asymmetry showing that overtopping initiation corresponds to exceedance of crest elevation at individual wave-scale associated with elevated wave height, water level, wave steepness, and wind characteristics, whereas overtopping persistence depends on short-term temporal effects associated with wave energy, direction, and sustained water levels. Gradient-boosted decision trees, temporal convolutional networks, and Transformer models are employed, demonstrating that persistence cannot be inferred from instantaneous sea-states alone, indicating a separation of timescales between triggering and sustained overtopping dynamics. These findings provide field-scale evidence of distinct hydrodynamic regimes governing overtopping processes, highlighting the importance of temporal characteristics for understanding overtopping dynamics and developing predictive coastal hazard frameworks.

Coastal regions are projected to experience a continuous increase in storm surge heights due to climate change–induced mean sea level rise and the intensification of typhoons. These changes substantially exacerbate the risk of coastal inundation in low-lying areas, necessitating a reassessment of existing design standards and disaster mitigation frameworks. To proactively respond to the evolving coastal inundation environment, it is essential to move beyond deterministic design approaches based solely on historical maxima and instead adopt probabilistic analyses of Extreme Sea Levels (ESLs). This study was conducted as part of a project in Korea aimed at developing storm surge–induced coastal inundation prediction maps. A total of approximately 4,100 ESL datasets for each return period, derived through computation and analysis for return periods of 50, 100, 150, and 200 years over a 13-year period (2011–2024), were used to construct spatial distribution maps of ESL heights along the entire Korean coast. To minimize inconsistencies arising from temporal and regional differences in reference sea levels, all ESLs were standardized to a common datum based on the Approximate Highest High Water (AHHW) referenced to the mean sea level at Incheon. For coastal areas where inundation prediction maps were not available, ESLs were estimated using frequency analysis based on the Gumbel distribution. To evaluate the reliability of the constructed ESL distribution maps, the estimated ESLs were compared with ESLs derived from observed tide gauge records, as well as results from extreme value analyses based on the Empirical Simulation Technique (EST) and the Annual Maximum Series (AMS) approach. The comparisons showed similar magnitudes and spatial distribution patterns across regions and return periods, indicating overall consistency in the estimated ESL characteristics. The nationwide coastal ESL distribution maps developed in this study are expected to serve as fundamental baseline data for coastal municipalities in the era of climate crisis, supporting the establishment of comprehensive natural disaster mitigation plans, coastal inundation risk assessments, designation of coastal inundation hazard zones, and the design of coastal and harbor structures.

The risk of wave overtopping is amplifying under sea-level rise and increased frequency of extreme coastal events. Conventional empirical and physical methods for estimating overtopping characteristics are limited by site-specific assumptions, which underscores the need for robust and efficient approaches. This study develops a multiscale numerical modeling framework that couples the regional ADCIRC–UnSWAN (Advanced CIRCulation and Unstructured Simulating WAves Near-shore) model with DualSPHysics (SPH) model to simulate overtopping responses under varying sea states. ADCIRC-UnSWAN provides regional-scale hydrodynamic and wave forcing, which is nested into localized SPH model to resolve wave-structure interactions. The proposed framework accurately reproduces overtopping responses including water thickness and velocity while leveraging GPU acceleration for computational efficiency. The model outputs are further analyzed to classify overtopping hazard levels and perform probabilistic pedestrian risk as sessments that account for uncertainties in wave characteristics and human vulnerability. The results supports the development of early warning systems and provide a foundation for dynamic hazard level updates in real or near-real time, contributing to improved coastal risk governance under uncertainties.