Statistical analysis and forecasts of long-term sandbank evolution at Great Yarmouth, UK
Statistical analysis and forecasts of long-term sandbank evolution at Great Yarmouth, UK
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DOI：
10.1016/j.ecss.2008.04.016
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发表时间：
2008-09
影响因子：
2.8
通讯作者：
D. Reeve;J. Horrillo-Caraballo;V. Magar
中科院分区：
文献类型：
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作者：
D. Reeve;J. Horrillo-Caraballo;V. Magar
一个数据驱动的模式已经开发出来，以分析的长期演变的沙洲系统，并作出集合预测，在一个为期8年。该方法使用的经验正交函数（ERF）分析，（以定义空间和时间模式的变化），刀切resstrom，（以产生一个合奏的EOFs），因果自回归技术，（外推的时间特征函数），并直接统计分析的结果合奏的预测，以确定一个“预测”和相关的不确定性的组合。该方法已被应用到一个非常苛刻的网站，其中包括一个弯曲的海岸线和一组移动的近岸沙洲。该网站是在英国东海岸，包括大雅茅斯沙洲和邻近的海岸线。利用一系列1848年以来的33幅高质量的历史测量图，分析了沙洲系统的格局，并预测了沙洲系统的形态演变。预测表明，相对于持久性的假设，一个改进的技能，但遭受的位置，有传播功能的形态，没有很好地描述EOFs。
A data-driven model has been developed to analyse the long-term evolution of a sandbank system and to make ensemble predictions in a period of 8 years. The method uses a combination of empirical orthogonal function (EOF) analysis, (to define spatial and temporal patterns of variability), jack-knife resampling, (to generate an ensemble of EOFs), a causal auto-regression technique, (to extrapolate the temporal eigenfunctions), and straightforward statistical analysis of the resulting ensemble of predictions to determine a ‘forecast’ and associated uncertainty. The methodology has been applied to a very demanding site which includes a curved shoreline and a group of mobile nearshore sandbanks. The site is on the eastern coast of the UK and includes the Great Yarmouth sandbanks and neighbouring shoreline. A sequence of 33 high quality historical survey charts reaching back to 1848 have been used to analyse the patterns and to predict morphological evolution of the sandbank system. The forecasts demonstrate an improved skill relative to an assumption of persistence, but suffer in locations where there are propagating features in the morphology that are not well-described by EOFs.