Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES), Key Laboratory of Physical Oceanography, Ministry of Education, Ocean University of China, Qingdao, China; Laoshan Laboratory, Qingdao, China; College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China.
Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES), Key Laboratory of Physical Oceanography, Ministry of Education, Ocean University of China, Qingdao, China; Laoshan Laboratory, Qingdao, China; State Environmental Protection Key Laboratory of Land and Sea Ecological Governance and Systematic Regulation, Jinan, China.
Sci Total Environ. 2024 Feb 1;910:168642. doi: 10.1016/j.scitotenv.2023.168642. Epub 2023 Nov 20.
Harmful algal blooms (HABs) pose a severe environmental issue and have significant economic and ecological consequences on coastal oceans. Predicting the occurrence of these blooms has become increasingly vital for coastal communities. To facilitate this, chlorophyll-a (Chl-a) levels have been widely used to forecast algal blooms. Although Hydro-biogeochemical (HBGC) process-based models display reasonable accuracy in predicting hydrodynamic variables and nutrients, they are not as effective in predicting Chl-a. Purely data-driven machine learning techniques also have limitations in accurately predicting Chl-a of high spatio-temporal resolutions. In this study, a coupled HBGC-Convolutional Neural Network (CNN) model was developed to predict the daily surface Chl-a distribution. The HBGC-CNN model integrates the information gathered by the HBGC model on temperature, salinity, dissolved inorganic nitrogen, dissolved organic phosphorus, and zooplankton with the remote sensing Chl-a products for the CNN model training. The results revealed that the HBGC-CNN model can effectively reproduce both daily and seasonal Chl-a variations, and interpret spatiotemporal information related to an HAB event triggered by the heavy rainfall during typhoon Lekima in 2019. Furthermore, this method can be used for data reconstruction, producing gap-free Chl-a products for historical reanalysis, especially in nearshore regions. The successful implementation of the HBGC-CNN model in predicting Chl-a highlights its potential in being incorporated into an operational forecasting system from a regional scale to a global scale, reducing the adverse impact of HAB disasters and facilitating emergency treatment.
有害藻华（HABs）对沿海海洋构成了严重的环境问题，并对其造成了重大的经济和生态影响。预测这些藻华的发生对于沿海社区变得越来越重要。为此，叶绿素-a（Chl-a）水平已被广泛用于预测藻类水华。尽管水动力生物地球化学（HBGC）基于过程的模型在预测水动力变量和营养物方面具有合理的准确性，但它们在预测 Chl-a 方面的效果并不理想。纯粹基于数据的机器学习技术在准确预测高时空分辨率的 Chl-a 方面也存在局限性。在本研究中，开发了一种耦合 HBGC-卷积神经网络（CNN）模型来预测每日的海面 Chl-a 分布。HBGC-CNN 模型将 HBGC 模型收集的有关温度、盐度、溶解无机氮、溶解有机磷和浮游动物的信息与 CNN 模型训练的遥感 Chl-a 产品相结合。结果表明，HBGC-CNN 模型可以有效地再现每日和季节性的 Chl-a 变化，并解释与 2019 年台风利奇马期间强降雨引发的藻华事件相关的时空信息。此外，该方法可用于数据重建，为历史再分析生成无间隙的 Chl-a 产品，特别是在近岸地区。HBGC-CNN 模型在预测 Chl-a 方面的成功实施突出了其在从区域尺度到全球尺度纳入操作预报系统中的潜力，从而减少了 HAB 灾害的不利影响并促进了紧急处理。
