IOCAS-IR  > 海洋环流与波动重点实验室
An early forecasting method for the drift path of green tides: A case study in the Yellow Sea, China
Hu, Po1,2; Liu, Yahao1,2; Hou, Yijun1,2,3; Yi, Yuqi1,2
2018-09-01
Source PublicationINTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
ISSN0303-2434
Volume71Pages:121-131
Corresponding AuthorLiu, Yahao(yhliu@qdio.ac.cn)
AbstractSince 2007, green tides caused by massive blooms of Enteromorpha prolifera have occurred in the Yellow Sea during April and September every year. Generally, the macroalgae first gathered around the Jiangsu coastline and then moved northeastward toward the Shandong Peninsula, but the paths and distribution of green tides have featured obvious inter-annual variation. Here, we describe a new method to forecasting the drift path of green tides with some climate indices such as Nino3.4. This method may help policy makers to develop a strategy to prevent green tide disasters and mitigate the consequence more effectively. Initially, we ran a numerical ocean model to simulate the movement of hypothetical green tides for last 20 years. The model was driven by remote sensing data of sea surface winds, surface temperatures, and tracers representing macroalgae that were created on certain dates so that drift paths could be traced. Ocean color remote sensing data were employed to determine the drift parameters. Next, the relationship between the displacement of tracers, including directions and distances of movement during certain periods were then analyzed along with the corresponding values of a set of six climate indices. A forecasting algorithm based on an artificial neural network was then established and trained with these data. Using this algorithm, the drift path of green tides could be predicted from the values of certain climate indices of the previous year. The model assessment with satellite ocean color remote sensing images indicated the effectiveness and practicability of this method.
KeywordGreen tides The Yellow Sea Drift path Artificial neural network Numerical model
DOI10.1016/j.jag.2018.05.001
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2016YFC1402000] ; National Natural Science Foundation of China[41476018] ; National Natural Science Foundation of China[41421005] ; National Natural Science Foundation of China[U1406401] ; Public Science and Technology Research Funds Projects of the Ocean[201205010] ; CAS[XDA19060202] ; High-Performance Computing Center, IOCAS
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000441116900011
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.qdio.ac.cn/handle/337002/159683
Collection海洋环流与波动重点实验室
Corresponding AuthorLiu, Yahao
Affiliation1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China
2.Qingdao Natl Lab Marine Sci & Technol, Qingdao 266237, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100029, Peoples R China
First Author AffilicationKey Laboratory of Ocean Circulation and Wave Studies, Institute of Oceanology, Chinese Academy of Sciences
Corresponding Author AffilicationKey Laboratory of Ocean Circulation and Wave Studies, Institute of Oceanology, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Hu, Po,Liu, Yahao,Hou, Yijun,et al. An early forecasting method for the drift path of green tides: A case study in the Yellow Sea, China[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2018,71:121-131.
APA Hu, Po,Liu, Yahao,Hou, Yijun,&Yi, Yuqi.(2018).An early forecasting method for the drift path of green tides: A case study in the Yellow Sea, China.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,71,121-131.
MLA Hu, Po,et al."An early forecasting method for the drift path of green tides: A case study in the Yellow Sea, China".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 71(2018):121-131.
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