Long-Term (1979–2024) Variation Trend in Wave Power in the South China Sea

Wave power (WP) is a strategic oceanic resource. Previous studies have extensively researched the long-term variations in WP in the South China Sea (SCS) for energy planning and utilization. This study extends the analysis of long-term trends to the last year based on ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5) reanalysis data from 1979 to 2024. Our results mainly indicate that the trends in WP after 2011 are significantly different from those before 2011. Before 2011, the WP in the SCS primarily showed an increasing trend, but, after 2011, it shifted to a decreasing trend. This trend has seasonal differences, manifested as being consistent with the annual trend in winter and spring while being inconsistent with the annual trend in summer and autumn. It indicates that the opposite trend in WP before and after 2011 was mainly the result of WP variations in winter and spring. To illustrate the driving factor for the WP’s variations, the contemporary long-term trend of the wind fields is systematically analyzed. Analysis results reveal that, regardless of seasonal differences or spatial distribution, the two trends are consistent in most situations, indicating that wind fields are the dominant factor for the long-term variations in WP. Meanwhile, the effects of the wind fields on the WP variations can also be modulated by environmental factors such as oceanic swell propagation and local topography. This study contributes to the knowledge of the latest trends and driving factors regarding the WP in the SCS.

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Publication Details

Journal
Journal of Marine Science and Engineering
Published
2025-03-09
DOI
https://doi.org/10.3390/jmse13030524
Citations
4
Primary Topic
Ocean Waves and Remote Sensing
Type
article
Field-Weighted Citation Impact
7.17

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article

Long-Term (1979–2024) Variation Trend in Wave Power in the South China Sea

Wuyang Chen, Yubing Tong, Junmin Li, Bo Li
4 citations
Journal of Marine Science and Engineering
Ocean Waves and Remote Sensing
7.17
article

Long-Term (1979–2024) Variation Trend in Wave Power in the South China Sea

Wuyang Chen, Yubing Tong, Junmin Li, Bo Li
article en
4 citations

Abstract

Wave power (WP) is a strategic oceanic resource. Previous studies have extensively researched the long-term variations in WP in the South China Sea (SCS) for energy planning and utilization. This study extends the analysis of long-term trends to the last year based on ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5) reanalysis data from 1979 to 2024. Our results mainly indicate that the trends in WP after 2011 are significantly different from those before 2011. Before 2011, the WP in the SCS primarily showed an increasing trend, but, after 2011, it shifted to a decreasing trend. This trend has seasonal differences, manifested as being consistent with the annual trend in winter and spring while being inconsistent with the annual trend in summer and autumn. It indicates that the opposite trend in WP before and after 2011 was mainly the result of WP variations in winter and spring. To illustrate the driving factor for the WP’s variations, the contemporary long-term trend of the wind fields is systematically analyzed. Analysis results reveal that, regardless of seasonal differences or spatial distribution, the two trends are consistent in most situations, indicating that wind fields are the dominant factor for the long-term variations in WP. Meanwhile, the effects of the wind fields on the WP variations can also be modulated by environmental factors such as oceanic swell propagation and local topography. This study contributes to the knowledge of the latest trends and driving factors regarding the WP in the SCS.

Journal of Marine Science and EngineeringVol. 13(3)
Institute of Oceanology (CN), South China Sea Institute Of Oceanology (CN), University of Chinese Academy of Sciences (CN)
Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 4%
Ocean Waves and Remote Sensing
7.17
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