Typhoon attacks on the Korean Peninsula have recently become more frequent, and the strength of these typhoons is also gradually increasing because of climate change. Typhoon attacks cause storm surges in coastal regions; therefore, forecasts that enable advanced preparation for these storm surges are important. Because storm surge forecasts require both accuracy and speed, this study uses an artificial neural network algorithm suitable for nonlinear modeling and rapid computation. A storm surge forecast model was created for five tidal stations on the Korea Strait (southern coast of the Korean Peninsula), and the accuracy of its forecasts was verified. The model consisted of a deep neural network and convolutional neural network that represent the two-dimensional spatial characteristics. Data from the Global Forecast System numerical weather model were used as input to represent the spatial characteristics. The verification of the forecast accuracy revealed an absolute relative error of ≤5% for the five tidal stations. Therefore, it appears that the proposed method can be used for forecasts for other locations in the Korea Strait. Furthermore, because accurate forecasts can be computed quickly, the method is expected to provide rapid information for use in the field to support advance preparation for storm surges.

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 flooding is caused by a complex interplay of various factors, including storm surges, wave overtopping, river flooding due to heavy rainfall, and inland water inundation. To predict and prepare for potential coastal flooding, coastal inundation predicton maps estimating flood depth and area under various hypothetical scenarios have been developed and utilized. However, most existing coastal inundation predicton maps have limitations in comprehensively considering the diverse factors contributing to coastal flooding. This study aims to overcome these limitations by incorporating multiple flood-inducing factors in coastal areas. Specifically, numerical modeling using ADCIRC and empirical formulas from EurOtop 2018 were applied to predict flooding caused by storm surges and wave overtopping. Additionally, 660 to 735 hypothetical typhoon scenarios were developed and applied for different coastal regions. To account for the impacts of future climate change, sea-level rise projections based on the SSP 5-8.5 climate scenario for the year 2100 were also included. The resulting coastal inundation predicton maps, which integrate multiple factors, were developed for four return periods: 50, 100, 150, and 200 years. These maps can serve as essential tools for developing disaster prevention policies and assessing coastal flood risks, contributing to minimizing flood damage in coastal regions.

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 Republic of Korea has developed a national coastal disaster risk assessment system led by the Ministry of Oceans and Fisheries (MOF), evolving from earlier vulnerability-based approaches toward a quantitative, scenario-based framework. Climate change drivers are incorporated using the IPCC AR6 framework, enabling consistent representation of future coastal hazard conditions. The current assessment derives a Coastal Disaster Risk Index (CDRI) by integrating hazard, exposure, and vulnerability components. These components are quantified using 25 indicators based on 31 observational and modeled datasets, evaluated on a 100 m spatial grid covering the entire national coastline. Indicator weighting and aggregation are determined through a combination of Analytic Hierarchy Process (AHP) analysis and expert-based Delphi surveys. The resulting CDRI is classified into five risk grades (Levels 1–5), with Level 5 representing the highest coastal disaster risk. These results show the methodological evolution of Korea’s coastal disaster risk assessment through changes in indicator composition, spatial resolution, data integration, and risk classification. The mapped risk grades demonstrate how the refined framework captures spatial variability in coastal disaster risk and enables regionally comparable interpretation. Finally, the assessment explores prospective directions for linking risk-based evaluation with ocean resilience concepts, including adaptive capacity and longer-term transformation planning. Although not yet operational, these considerations suggest how coastal disaster risk assessment may evolve beyond risk ranking toward supporting more resilient coastal management.