An early forecasting method for the drift path of green tides: A case study in the Yellow Sea, China

doi:10.1016/j.jag.2018.05.001

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, Yahao 1,2; Hou, Yijun 1,2,3; Yi, Yuqi 1,2
	2018-09-01
发表期刊	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
ISSN	0303-2434
卷号	71 页码:121-131
通讯作者	Liu, Yahao([email protected])
摘要	Since 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.
关键词	Green tides The Yellow Sea Drift path Artificial neural network Numerical model
DOI	10.1016/j.jag.2018.05.001
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2016YFC1402000]###849; National Natural Science Foundation of China[41476018]###1126; National Natural Science Foundation of China[41421005]###312; National Natural Science Foundation of China[U1406401]###311; Public Science and Technology Research Funds Projects of the Ocean[201205010]###2042; CAS[XDA19060202]###2043; High-Performance Computing Center, IOCAS###2044; National Key Research and Development Program of China[2016YFC1402000]###849; National Natural Science Foundation of China[41476018]###1126; National Natural Science Foundation of China[41421005]###312; National Natural Science Foundation of China[U1406401]###311; Public Science and Technology Research Funds Projects of the Ocean[201205010]###2042; CAS[XDA19060202]###2043; High-Performance Computing Center, IOCAS###2044; National 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研究方向	Remote Sensing
WOS类目	Remote Sensing
WOS记录号	WOS:000441116900011
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：12[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/159683
专题	海洋环流与波动重点实验室
通讯作者	Liu, Yahao
作者单位	1.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
第一作者单位	海洋环流与波动重点实验室
通讯作者单位	海洋环流与波动重点实验室
推荐引用方式 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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