Predicting coastal erosion trends using non-stationary statistics and process-based models
Predicting coastal erosion trends using non-stationary statistics and process-based models
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DOI：
10.1016/j.coastaleng.2012.06.004
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发表时间：
2012-12
影响因子：
4.4
通讯作者：
S. Corbella;D. Stretch
中科院分区：
文献类型：
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作者：
S. Corbella;D. Stretch
风暴和水位受季节变化的影响，但也可能具有十年或更长时间的趋势，在估计沿海地区的风险时需要包括这些趋势。我们提出了一个非平稳的多元广义极值模型，用于波高、波周期、风暴持续时间和水位，该模型使用阿基米德copulas构建。统计模型被应用于南非的一个案例研究，以测试年代际趋势对海滩侵蚀的影响。侵蚀使用三种基于过程的模型- SBEACH， XBEACH和时间卷积模型进行估计。XBEACH模型提供了最佳的校准结果，并用于模拟未来海滩侵蚀的潜在长期趋势。基于25年、50年和100年回归周期的5个海滩剖面的模拟侵蚀结果，估计侵蚀速率可能会增加0.20%/年/次风暴，因此应该是长期规划的一个重要因素。
Storms and water levels are subject to seasonal variations but may also have decadal or longer trends that need to be included when estimating risks in the coastal zone. We propose a non-stationary multivariate generalised extreme value model for wave height, wave period, storm duration and water levels that is constructed using Archimedean copulas. The statistical model was applied to a South African case study to test the impacts of decadal trends on beach erosion. Erosion was estimated using three process-based models — SBEACH, XBEACH, and the Time Convolution model. The XBEACH model provided the best calibration results and was used to simulate potential future long-term trends in beach erosion. Based on the simulated erosion results of 5 beach profiles for storms with 25, 50 and 100year return periods, it is estimated that the erosion rate could increase by 0.20%/year/storm and should therefore be a significant factor in long-term planning.