Accounting for Seasonality in Extreme Sea Level Estimation
Accounting for Seasonality in Extreme Sea Level Estimation
复制标题
极端海平面估算中的季节性因素
DOI：
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发表时间：
2021
影响因子：
1.8
通讯作者：
D. Sifnioti
中科院分区：
文献类型：
--
作者：
Ellie D'Arcy;J. Tawn;A. Joly;D. Sifnioti
<p>风暴潮对沿海社区构成越来越大的风险。这些事件，加上涨潮，可能导致沿海洪水。为了减少风暴潮的影响，准确估计沿海洪水风险是必要的。具体而言，需要估计海平面（静水）的回归水平，这是年回归概率<em>p</em>的水平。这一估计数被用作确定海岸防御高度的输入，例如海堤。回报水平估计需要基于极值理论的统计分析，因为我们需要知道比以前观察到的更极端的事件的频率。</p><p>大型风暴潮具有季节性，通常在冬季最严重，夏季最不极端。这种季节性模式与潮汐不同，潮汐的季节性是由天文学驱动的，导致在春分和秋分时出现潮汐高峰。因此，静水水位的这两个组成部分的最坏水平可能在一年中的不同时间达到峰值，因此将它们视为独立变量的统计方法可能会高估回报水平。</p><p>我们专注于斜波：在一个潮汐周期内观测到的和预测的高水位之间的差异。威廉姆斯等人（2016年）表明，潮汐和斜浪是独立的，取决于一年中的时间。Batstone等人（2013年）利用这一特性推导出用于英国沿海防洪的估计值。他们使用广义帕累托分布的斜浪涌尾部，但没有考虑到单独的季节性潮汐和斜浪涌。</p><p>这项工作的目的是模拟如何分布的偏斜浪涌的变化超过一年，我们联合收割机结合我们的结果与已知的季节性潮汐，以获得估计的静水水位返回水平。我们将我们的结果与Batstone et al.（2013）在英国海岸线上的几个地点的方法进行了比较。</p><p>参考文献：</p><p>Batstone，C.，Lawless，M.，Tawn，J.，Horsburgh，K.，Blackman，D.，McMillan，A.，沃思，D.，Laeger，S. Hunt，T. 2013.英国最佳实践方法，用于沿着复杂地形海岸线的极端海平面分析。160;《海洋工程》，71，第28 -39页。</p><p>威廉姆斯，J.，Horsburgh，K.J.，威廉姆斯，J.A.和普罗克特，RN，2016.潮汐和倾斜浪涌独立性：洪水风险的新见解。160;Geophysical Research Letters，43（12），pp.6410-6417。</p>
<p>Storm surges pose an increasing risk to coastline communities. These events, combined with high tide, can result in coastal flooding. To reduce the impact of storm surges, an accurate estimate of coastal flood risk is necessary. Specifically, estimates are required for the return level of sea levels (still water), which is the level with annual exceedance probability <em>p</em>. This estimate is used as an input to determine the height for a coastal defence, such as a sea wall. The return level estimation requires statistical analysis based on extreme value theory, as we need to know about the frequency of events that are more extreme than those previously observed.</p><p>Large storm surges exhibit seasonality, they are typically at their worst in the winter and least extreme in the summer. This seasonal pattern differs from that of the tide, whose seasonality is driven astronomically, resulting in tidal peaks at the spring and autumn equinoxes. Hence, the worst levels of these two components of still water level are likely to peak at different times in the year, and so statistical methods that treat them as independent variables are likely to over-estimate return levels.</p><p>We focus on the skew surge: the difference between the observed and predicted high water within a tidal cycle. Williams et al. (2016) show that tide and skew surge are independent conditional on the time of year. Batstone et al. (2013) used this property to derive estimates used for UK coastal flood defences. They used generalised Pareto distributions for the skew surge tail but did not account for the separate seasonality of tide and skew surge.</p><p>This work aims to model how the distribution of skew surges changes over a year and we combine our results with the known seasonality of tides to derive estimates of still water level return levels. We compare our results with the Batstone et al. (2013) approach at a few locations on the UK coastline.</p><p>References:</p><p>Batstone, C., Lawless, M., Tawn, J., Horsburgh, K., Blackman, D., McMillan, A., Worth, D., Laeger, S. and Hunt, T., 2013. A UK best-practice approach for extreme sea-level analysis along complex topographic coastlines. Ocean Engineering, 71, pp.28-39.</p><p>Williams, J., Horsburgh, K.J., Williams, J.A. and Proctor, R.N., 2016. Tide and skew surge independence: New insights for flood risk. Geophysical Research Letters, 43(12), pp.6410-6417.</p>
影响因子：
3.4
作者：
D'Arcy E
通讯作者：
D'Arcy E