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Wave resource characterization at regional and nearshore scales for the U.S. Alaska coast based on a 32-year high-resolution hindcast

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  • García-Medina, Gabriel
  • Yang, Zhaoqing
  • Wu, Wei-Cheng
  • Wang, Taiping

Abstract

A wave resource characterization was performed for the southern coast of Alaska based on a 32-year hindcast covering the period from 1979 to 2010. The characterization closely followed International Electrotechnical Commission (IEC) Technical Specifications. An unstructured-grid Simulating WAves Nearshore (SWAN) model, which had an approximate spatial resolution of 300 m within 30 km from the nearest shoreline, was developed. Extensive model validation and error characterization was performed. The model was found to perform well with an average absolute percent error of 8.6% in significant wave height, averaged over 18 buoys. Statistics for the six IEC wave resource parameters were calculated and aggregated at 20 km from shore to quantify the incident wave power and its variability at a regional scale. The southern coast of the Aleutian Archipelago was found to have the most available wave energy in the region. A nearshore resource assessment was performed by evaluating resource hotspots located 1 km from shore. More than 28% of the nearshore stations analyzed had an Optimum Hotspot Identifier value of at least 5 [kW/m] at diverse water depths, thereby positioning Alaska as a promising location for wave energy development.

Suggested Citation

  • García-Medina, Gabriel & Yang, Zhaoqing & Wu, Wei-Cheng & Wang, Taiping, 2021. "Wave resource characterization at regional and nearshore scales for the U.S. Alaska coast based on a 32-year high-resolution hindcast," Renewable Energy, Elsevier, vol. 170(C), pages 595-612.
    • Handle: RePEc:eee:renene:v:170:y:2021:i:c:p:595-612
    DOI: 10.1016/j.renene.2021.02.005
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    Citations

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    Cited by:

    1. Li, Ning & García Medina, Gabriel & Yang, Zhaoqing & Cheung, Kwok Fai & Hitzl, David & Chen, Yi-Leng, 2023. "Wave climate and energy resources in the Mariana Islands from a 42-year high-resolution hindcast," Renewable Energy, Elsevier, vol. 215(C).
    2. Kilcher, Levi & García Medina, Gabriel & Yang, Zhaoqing, 2023. "A scalable wave resource assessment methodology: Application to U.S. waters," Renewable Energy, Elsevier, vol. 217(C).
    3. Delpey, Matthias & Lastiri, Ximun & Abadie, Stéphane & Roeber, Volker & Maron, Philippe & Liria, Pedro & Mader, Julien, 2021. "Characterization of the wave resource variability in the French Basque coastal area based on a high-resolution hindcast," Renewable Energy, Elsevier, vol. 178(C), pages 79-95.
    4. Rusu, Liliana, 2022. "The near future expected wave power in the coastal environment of the Iberian Peninsula," Renewable Energy, Elsevier, vol. 195(C), pages 657-669.
    5. Li, Ning & García-Medina, Gabriel & Cheung, Kwok Fai & Yang, Zhaoqing, 2021. "Wave energy resources assessment for the multi-modal sea state of Hawaii," Renewable Energy, Elsevier, vol. 174(C), pages 1036-1055.
    6. Shao, Zhuxiao & Gao, Huijun & Liang, Bingchen & Lee, Dongyoung, 2022. "Potential, trend and economic assessments of global wave power," Renewable Energy, Elsevier, vol. 195(C), pages 1087-1102.
    7. Ruth Branch & Gabriel García-Medina & Zhaoqing Yang & Taiping Wang & Fadia Ticona Rollano & Lucia Hosekova, 2021. "Modeling Sea Ice Effects for Wave Energy Resource Assessments," Energies, MDPI, vol. 14(12), pages 1-15, June.
    8. García Medina, Gabriel & Yang, Zhaoqing & Li, Ning & Cheung, Kwok Fai & Lutu-McMoore, Elinor, 2023. "Wave climate and energy resources in American Samoa from a 42-year high-resolution hindcast," Renewable Energy, Elsevier, vol. 210(C), pages 604-617.
    9. Shi, Xueli & Liang, Bingchen & Li, Shaowu & Zhao, Jianchun & Wang, Junhui & Wang, Zhenlu, 2024. "Wave energy resource classification system for the China East Adjacent Seas based on multivariate clustering," Energy, Elsevier, vol. 299(C).

    More about this item

    Keywords

    Wave energy resource; Wave climate; Alaska coast; SWAN; WW3;
    All these keywords.

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