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.

Conventionally, overtopping prediction at coastal defence structures is based on empirical methods that yield biased estimates since their database is constructed for specific wave and structures characteristics, which limits their application. Knowing that overtopping is a highly non-linear phenomenon, strongly affected by local wave characteristics and bathymetry, this study proposes localized statistical analysis of overtopping using multi-modal data. The Yeondaepu breakwater in Jeju is selected as a study area given its high vulnerability to wave overtopping. Field data were obtained from a KMA operated Gueom buoy and locally installed wave gauges, complemented by CCTV-based video monitoring. To address the inherent uncertainty in overtopping predictions, the field data is integrated and analyzed to establish quantitative relationships between significant wave height, wave direction, time period and overtopping characteristics. Using synchronized measurements and vision-based detections, we performed correlation analyses to identify statistically significant drivers of overtopping and to determine threshold conditions under which overtopping initiates. The collected multi-modal data is used to train a YOLO-based detection model for identifying overtopping events. This integrated observational and statistical framework supports improved site-specific overtopping assessment under real environmental forcing.

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.