In this study, a numerical simulation is performed to produce wave hindcasting data from 2007 to 2018 for the assessment of wave energy resources in the sea waters of Korea. The hindcasting data are obtained with a relatively fine spatial resolution of 1/20° covering 120–150 °E longitude and 22.4–47.6 °N latitude using the Simulating WAves Nearshore wave model (SWAN). Three different wind fields, those of the European Centre for Medium-Range Weather (ECMWF), National Centers for Environmental Prediction (NCEP), and Japan Meteorological Agency (JMA), are used for the numerical wave simulation. It is observed that the wind field dataset of JMA exhibits the best agreement with available field observation data. For this reason, the wave energy resources are evaluated based on the data hindcasted using the JMA wind field. It is found that the overall magnitudes of wave energy are larger in winter than in summer. The wave energy in August, however, is comparable to the mean wave energy during winter because of the influence of frequent high wave events caused by typhoons. The highest monthly average wave power around Yellow Sea, South Sea, East Sea, and Jeju Island are 13.3, 18.2, 13.7, and 40 kW/m, respectively.

Rainfall, tide, and wave overtopping can together cause inundation in some coastal areas. However, there have been few cases of inundation analysis considering compound flooding due to the difficulty of the interdisciplinary technique. The purpose of this study is to suggest an analysis technique of compound flooding in coastal areas that links flow simulation tools of coasts and watershed. XP-SWMM was used to analyze rainfall–runoff simulation and 2D surface inundation analysis. A coupled model of ADCIRC and SWAN (ADCSWAN) was used for analyzing expected flow, including wave spectrum, tide, and storm surge from the distant ocean, and the FLOW-3D model was used to estimate the flow and wave overtopping in the coastal area. The analysis technique was applied to Marine City located in Busan, the Republic of Korea, where rainfall and wave overtopping occurred simultaneously during typhoons, and the results were reasonable. The study results are meaningful as they can contribute to the improvement of reproducibility of real phenomena of compound flooding in coastal areas through linked application of flow simulation tools of coasts and watershed.

The size of phytoplankton (a key primary producer in marine ecosystems) is known to influence the contribution of primary productivity and the upper trophic level of the food web. Therefore, it is essential to identify the dominant sizes of phytoplankton while inferring the responses of marine ecosystems to change in the marine environment. However, there are few studies on the spatio-temporal variations in the dominant sizes of phytoplankton in the littoral sea of Korea. This study utilized a deep learning model as a classification algorithm to identify the dominance of different phytoplankton sizes. To train the deep learning model, we used field measurements of turbidity, water temperature, and phytoplankton size composition (chlorophyll-a) in the littoral sea of Korea, from 2018 to 2020. The new classification algorithm from the deep learning model yielded an accuracy of 70%, indicating an improvement compared with the existing classification algorithms. The developed classification algorithm could be substituted in satellite ocean color data. This enabled us to identify spatio-temporal variation in phytoplankton size composition in the littoral sea of Korea. We consider this to be highly effective as fundamental data for identifying the spatio-temporal variation in marine ecosystems in the littoral sea of Korea.

Accurate wave prediction in coastal waters is essential for marine safety and engineering, yet it is significantly influenced by uncertainties in wind forcing and dissipation parameterization. This study evaluates the sensitivity of the SWAN model around the Korean Peninsula using 2021 data from 138 observation stations. To address structural biases in wind fields, the Drag Coefficient Scaling Factor (CDFAC) was implemented alongside the Komen and ST6 physics packages. While the Komen scheme provided stable performance under normal conditions, the ST6 + CDFAC configuration exhibited superior physical consistency during extreme events. Notably, applying CDFAC to the ST6 package reduced the high-wave (H_s > 3 m) RMSE by approximately 32.7%, decreasing from 0.52 m to 0.35 m. Bathymetric stratified analysis further confirmed that the ST6 scheme maintains robust performance in offshore and deep-water regions (depth > 50 m), achieving a correlation of 0.94 and an RMSE of 0.20 m. This is attributed to ST6’s frequency-dependent saturation approach, which effectively decouples wind-sea and swell components in environments where whitecapping dissipation is the governing energy sink. In contrast, improvements in coastal waters (depth < 50 m) were moderated by topographical dissipation mechanisms such as bottom friction and depth-induced breaking. These findings demonstrate that integrating wind input bias correction with frequency-dependent dissipation physics is vital for reliable wave forecasting and coastal disaster mitigation.