Unprecedent green macroalgae bloom: mechanism and implication to disaster prediction and prevention
Green macroalgae bloom (GMB), with the dominant species of Ulva prolifera, has regularly occurred since 2007 along the China coast. Although disaster prevention and control achieved favorable results in 2020, the satellite-observed GMB annual maximum coverage (AMC) rebounded sharply in 2021 to an un...
| Główni autorzy: | , , , , , , , , , , , , , , , , , |
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| Format: | Artykuł |
| Język: | English |
| Wydane: |
Taylor & Francis Group
2023-12-01
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| Seria: | International Journal of Digital Earth |
| Hasła przedmiotowe: | |
| Dostęp online: | http://dx.doi.org/10.1080/17538947.2023.2257658 |
| _version_ | 1827774474174332928 |
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| author | Mengmeng Cao Xuyan Li Tingwei Cui Xinliang Pan Yan Li Yanlong Chen Ning Wang Yanfang Xiao Xingai Song Yuzhu Xu Runa A Bing Mu Song Qing Rongjie Liu Wenjing Zhao Yuhai Bao Jie Zhang Lan Wei |
| author_facet | Mengmeng Cao Xuyan Li Tingwei Cui Xinliang Pan Yan Li Yanlong Chen Ning Wang Yanfang Xiao Xingai Song Yuzhu Xu Runa A Bing Mu Song Qing Rongjie Liu Wenjing Zhao Yuhai Bao Jie Zhang Lan Wei |
| author_sort | Mengmeng Cao |
| collection | DOAJ |
| description | Green macroalgae bloom (GMB), with the dominant species of Ulva prolifera, has regularly occurred since 2007 along the China coast. Although disaster prevention and control achieved favorable results in 2020, the satellite-observed GMB annual maximum coverage (AMC) rebounded sharply in 2021 to an unprecedented level. The reasons for this rebound and the significant interannual variability over past 15 years are still open questions. Here, by using long-term time-series (2007–2022) optical and Synthetic Aperture Radar satellite observations (1000+ scenes), meteorological data and water quality statistics, the mechanism analysis was performed by exploring effects from natural factors and human activities. Two key determinants for AMC are successfully identified from numerous potential factors which are the macroalgae distribution in a key area (the Subei Shoal) during a critical period (from April to May 20) and the nutrient availability. Furthermore, by using these two parameters, a novel model for AMC prediction (R2 = 0.87, p < 0.01) is proposed and independently validated, which can reasonably explain the significant interannual variability (2014–2021) and agree well with the latest observation in 2022 (percentage difference 12%). Finally, suggestions are proposed for future disaster prevention and alleviation. This work may aid future bloom prediction and management measure optimization. |
| first_indexed | 2024-03-11T13:37:56Z |
| format | Article |
| id | doaj.art-f462f83f3416462f8b7e5260dd0e9af4 |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T13:37:56Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-f462f83f3416462f8b7e5260dd0e9af42023-11-02T14:47:05ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552023-12-011613772379310.1080/17538947.2023.22576582257658Unprecedent green macroalgae bloom: mechanism and implication to disaster prediction and preventionMengmeng Cao0Xuyan Li1Tingwei Cui2Xinliang Pan3Yan Li4Yanlong Chen5Ning Wang6Yanfang Xiao7Xingai Song8Yuzhu Xu9Runa A10Bing Mu11Song Qing12Rongjie Liu13Wenjing Zhao14Yuhai Bao15Jie Zhang16Lan Wei17Inner Mongolia Normal UniversitySun Yat-Sen University & Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)Sun Yat-Sen University & Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)Sun Yat-Sen University & Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)First Institute of OceanographyNational Marine Environmental Monitoring CenterNorth China Sea Marine Forecasting Center of State Oceanic AdministrationFirst Institute of OceanographyNational Satellite Ocean Application ServiceNational Satellite Ocean Application ServiceInner Mongolia Normal UniversityOcean University of ChinaInner Mongolia Normal UniversityFirst Institute of OceanographySouth China Institute of Environmental ScienceInner Mongolia Normal UniversityFirst Institute of OceanographySun Yat-Sen University & Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)Green macroalgae bloom (GMB), with the dominant species of Ulva prolifera, has regularly occurred since 2007 along the China coast. Although disaster prevention and control achieved favorable results in 2020, the satellite-observed GMB annual maximum coverage (AMC) rebounded sharply in 2021 to an unprecedented level. The reasons for this rebound and the significant interannual variability over past 15 years are still open questions. Here, by using long-term time-series (2007–2022) optical and Synthetic Aperture Radar satellite observations (1000+ scenes), meteorological data and water quality statistics, the mechanism analysis was performed by exploring effects from natural factors and human activities. Two key determinants for AMC are successfully identified from numerous potential factors which are the macroalgae distribution in a key area (the Subei Shoal) during a critical period (from April to May 20) and the nutrient availability. Furthermore, by using these two parameters, a novel model for AMC prediction (R2 = 0.87, p < 0.01) is proposed and independently validated, which can reasonably explain the significant interannual variability (2014–2021) and agree well with the latest observation in 2022 (percentage difference 12%). Finally, suggestions are proposed for future disaster prevention and alleviation. This work may aid future bloom prediction and management measure optimization.http://dx.doi.org/10.1080/17538947.2023.2257658green macroalgaeannual maximum coverage (amc)the yellow seaporphyra maricultureextreme weatherwater quality |
| spellingShingle | Mengmeng Cao Xuyan Li Tingwei Cui Xinliang Pan Yan Li Yanlong Chen Ning Wang Yanfang Xiao Xingai Song Yuzhu Xu Runa A Bing Mu Song Qing Rongjie Liu Wenjing Zhao Yuhai Bao Jie Zhang Lan Wei Unprecedent green macroalgae bloom: mechanism and implication to disaster prediction and prevention International Journal of Digital Earth green macroalgae annual maximum coverage (amc) the yellow sea porphyra mariculture extreme weather water quality |
| title | Unprecedent green macroalgae bloom: mechanism and implication to disaster prediction and prevention |
| title_full | Unprecedent green macroalgae bloom: mechanism and implication to disaster prediction and prevention |
| title_fullStr | Unprecedent green macroalgae bloom: mechanism and implication to disaster prediction and prevention |
| title_full_unstemmed | Unprecedent green macroalgae bloom: mechanism and implication to disaster prediction and prevention |
| title_short | Unprecedent green macroalgae bloom: mechanism and implication to disaster prediction and prevention |
| title_sort | unprecedent green macroalgae bloom mechanism and implication to disaster prediction and prevention |
| topic | green macroalgae annual maximum coverage (amc) the yellow sea porphyra mariculture extreme weather water quality |
| url | http://dx.doi.org/10.1080/17538947.2023.2257658 |
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