کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1724979 | 1520666 | 2016 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A hindcast method to simulate oil spill trajectories for the Bohai Sea, Northeast China
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی دریا (اقیانوس)
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: A hindcast method to simulate oil spill trajectories for the Bohai Sea, Northeast China A hindcast method to simulate oil spill trajectories for the Bohai Sea, Northeast China](/preview/png/1724979.png)
چکیده انگلیسی
The Bohai Sea in China is a high-risk area for oil-spill accidents because of the large number of oil platforms and the intensity of marine traffic. Oil spills can be detected using some type of remote sensing data such as synthetic aperture radar (SAR) images, but to determine of oil-spill origin is difficult. Oil spill hindcast models are commonly used to simulate the origin points. Surface winds, surface currents and random walk are three major parameters in the oil hindcast process. In coastal regions, the parameters are affected by offshore bathymetry, local weather, etc. In particular, random walk is more sensitive to the traits of coastal regions. However, most state-of-the-art hindcast models use an empirical and constant value for conducting random walk. In this paper, a random walk parameterization hindcast method (RWPHM) for the Bohai Sea is proposed, in which random walk is initially parameterized and combined with remote sensing data and oil-spill models. The backward-in-time method is validated using data for the Penglai 19-3 spill accident observed between June 11 and 19, 2011. The results confirm that the accuracy of random walk is significant for improving the accuracy of oil-spill hindcasting.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Ocean Engineering - Volume 124, 15 September 2016, Pages 363-370
Journal: Ocean Engineering - Volume 124, 15 September 2016, Pages 363-370
نویسندگان
Fangjie Yu, Jiaojiao Li, Songxue Cui, Yang Zhao, Quanzhou Feng, Ge Chen,