Institutional Repository of Key Laboratory of Ocean Circulation and Wave Studies, Institute of Oceanology, Chinese Academy of Sciences
| 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
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| 2018-09-01 | |
| Source Publication | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
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| ISSN | 0303-2434 |
| Volume | 71Pages:121-131 |
| Corresponding Author | Liu, Yahao(yhliu@qdio.ac.cn) |
| Abstract | 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. |
| Keyword | Green tides The Yellow Sea Drift path Artificial neural network Numerical model |
| DOI | 10.1016/j.jag.2018.05.001 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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 ; 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 ; 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 Research Area | Remote Sensing |
| WOS Subject | Remote Sensing |
| WOS ID | WOS:000441116900011 |
| Publisher | ELSEVIER SCIENCE BV |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.qdio.ac.cn/handle/337002/159683 |
| Collection | 海洋环流与波动重点实验室 |
| Corresponding Author | Liu, Yahao |
| Affiliation | 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 |
| First Author Affilication | Key Laboratory of Ocean Circulation and Wave Studies, Institute of Oceanology, Chinese Academy of Sciences |
| Corresponding Author Affilication | Key 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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| An early forecasting（6280KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View | |
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