On-site development of wave energy resources is a promising way to overcome power-shortage problems on islands. It is necessary to select suitable islands to deploy wave power plants, which are influenced by multiple factors related to resources, technology, economy, society, and environment. This study develops a two-stage decision framework to identify feasible islands and determine priority order based on geographic information systems (GIS) and multicriteria decision-making (MCDM). In the exclusion stage, unfeasible marine areas are excluded based on exclusion criteria and feasible island alternatives are identified. In the evaluation stage, alternatives are evaluated by evaluation criteria using the combined weighting method and the technique for order of preference by similarity to ideal solution (TOPSIS)-grey relation analysis (GRA) method. As the combined weighting method is based on the fuzzy group decision-making (GDM)-analytic hierarchy process (AHP) and the entropy method, it can effectively reduce subjective deviation. The proposed framework is applied in Shandong Province. It identifies 13 inhabited islands feasible for constructing wave power plants, among which Daguan, South Changshan, and Xiaoguan are the optimal ones. Sensitivity analysis is performed to verify the feasibility of the proposed framework. The results show that it is effective and could provide reference for practical engineering.

The study of extreme waves is important for the protection of coastal and ocean structures. In this work, a 22-year (1990–2011) wave hindcast in the Yellow Sea is employed to perform the assessment of extreme significant wave heights in this area. To extract the independent sample from this database, the fixed window method is used, which takes the peak significant wave height within five d. With the selected samples, directional declustering is studied to extract the homogenous sample. The results show that most of the independent samples (especially large samples) are observed in the North. In this direction, the peak over threshold (POT) method is used to extract the extreme sample from the homogenous sample, and then the generalized Pareto distribution model is used to extrapolate the extreme significant wave height. In addition to this combination, the annual maxima method with the Gumbel model is also used for estimating extreme values. The comparisons show that the return significant wave heights of the first combination are reliable, resulting from a flexible sampling window in the POT method. With this conclusion, the extreme significant wave height is extrapolated from the Yellow Sea, which can be used to protect the structure in the main directional bin.

Ocean surface waves exposed to extreme weather conditions, such as tropical cyclones (TCs), are critical environmental forces acting on the coastal and offshore structures. Therefore, it is necessary to investigate the applicability of the third-generation wave model WAVEWATCH III for simulated waves in the China Seas, where TCs are frequent. This study compares different packages of wind input and wave dissipation (ST1, ST2, ST3, ST4, and ST6) during five TCs with significant wave heights (SWHs) and wave spectra in the Yellow Sea, East China Sea, and South China Sea. The results show that ST6 performs best in the simulation of the SWHs, presenting the only positive deviation, with a normalized mean bias of 1.76%. ST2 systematically underestimates the large SWHs due to insufficient wind input from the spectra. However, the suitability of the packages varies in different sea conditions. ST6 is preferred in extreme wave simulations during TCs, ST4 performs best under the weak influence of TCs, and ST2 has the best performance in calm sea conditions.