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Predicting massive floating macroalgal blooms in a regional sea

  • Fucang Zhou
  • , Zhi Chen
  • , Zaiyang Zhou
  • , Bing Cao
  • , Lili Xu
  • , Dongyan Liu
  • , Ruishan Chen
  • , Karline Soetaert
  • , Jianzhong Ge*
  • *此作品的通讯作者
  • East China Normal University
  • Ministry of Natural Resource
  • Ministry of Natural Resource
  • Shanghai Jiao Tong University
  • Royal Netherlands Institute for Sea Research - NIOZ

科研成果: 期刊稿件文章同行评审

摘要

Increasingly frequent and severe floating macroalgal blooms present significant challenges to coastal and ocean environments. Here a short-term forecast system of floating macroalgal blooms was developed to predict the physical-biogeochemical environment and macroalgal ecodynamic processes in a regional ocean. Predictions of macroalgal ecodynamic processes are influenced by oceanic conditions (hydrodynamics, temperature, and nutrients), as well as atmospheric conditions (wind). The system's effectiveness is demonstrated by successfully hindcasting the June 2021 green tide bloom event in the Yellow Sea and using real-time satellite data to make reliable and robust continuous short-term predictions for 2022 and 2023. The prediction accuracy of coverage reaches 87.5%, and the minimum transport error of the green tide center of mass is 6.09 nautical miles over an 7-day prediction duration. Supported by regional marine physics and biogeochemistry and macroalgal physiological characteristic datasets, this system may serve as a crucial cornerstone for similar floating macroalgal disaster prevention.

源语言英语
文章编号106310
期刊Environmental Modelling and Software
185
DOI
出版状态已出版 - 2月 2025
已对外发布

联合国可持续发展目标

此成果有助于实现下列可持续发展目标:

  1. 可持续发展目标 14 - 水下生物
    可持续发展目标 14 水下生物

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