预测区域海洋中大量漂浮的大型藻华

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Environmental Modelling & Software Pub Date : 2025-02-01 DOI:10.1016/j.envsoft.2024.106310
Fucang Zhou , Zhi Chen , Zaiyang Zhou , Bing Cao , Lili Xu , Dongyan Liu , Ruishan Chen , Karline Soetaert , Jianzhong Ge
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引用次数: 0

摘要

日益频繁和严重的浮藻华对沿海和海洋环境构成了重大挑战。为了预测区域海洋浮游藻华的物理-生物地球化学环境和生态动力学过程,建立了浮游藻华短期预报系统。大藻生态动力学过程的预测受到海洋条件(水动力学、温度和营养物)以及大气条件(风)的影响。该系统的有效性通过成功预测2021年6月黄海绿潮事件以及使用实时卫星数据对2022年和2023年进行可靠和稳健的连续短期预测来证明。覆盖预报精度达到87.5%,7 d预报周期内绿潮质心最小输运误差为6.09海里。在区域海洋物理、生物地球化学和大型藻生理特征数据集的支持下,该系统可作为类似大型藻漂浮灾害预防的重要基石。
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Predicting massive floating macroalgal blooms in a regional sea
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.
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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