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.

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.