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Probabilistic reanalysis of storm surge extremes in Europe.
Proceedings of the National Academy of Sciences of the United States of America ( IF 11.1 ) Pub Date : 2020-01-13 , DOI: 10.1073/pnas.1913049117
Francisco M Calafat 1 , Marta Marcos 2, 3
Affiliation  

Extreme sea levels are a significant threat to life, property, and the environment. These threats are managed by coastal planers through the implementation of risk mitigation strategies. Central to such strategies is knowledge of extreme event probabilities. Typically, these probabilities are estimated by fitting a suitable distribution to the observed extreme data. Estimates, however, are often uncertain due to the small number of extreme events in the tide gauge record and are only available at gauged locations. This restricts our ability to implement cost-effective mitigation. A remarkable fact about sea-level extremes is the existence of spatial dependences, yet the vast majority of studies to date have analyzed extremes on a site-by-site basis. Here we demonstrate that spatial dependences can be exploited to address the limitations posed by the spatiotemporal sparseness of the observational record. We achieve this by pooling all of the tide gauge data together through a Bayesian hierarchical model that describes how the distribution of surge extremes varies in time and space. Our approach has two highly desirable advantages: 1) it enables sharing of information across data sites, with a consequent drastic reduction in estimation uncertainty; 2) it permits interpolation of both the extreme values and the extreme distribution parameters at any arbitrary ungauged location. Using our model, we produce an observation-based probabilistic reanalysis of surge extremes covering the entire Atlantic and North Sea coasts of Europe for the period 1960-2013.

中文翻译:

对欧洲极端风暴潮的概率重新分析。

极端的海平面严重威胁生命,财产和环境。这些威胁由沿海规划人员通过实施风险缓解策略来进行管理。此类策略的核心是对极端事件概率的了解。通常,通过将合适的分布拟合到观察到的极端数据来估计这些概率。但是,由于潮汐仪记录中的极少数事件,估计值通常是不确定的,并且仅在测量位置可用。这限制了我们实施具有成本效益的缓解措施的能力。关于海平面极端的一个显着事实是空间依赖性的存在,但是迄今为止,绝大多数研究都是逐点分析极端情况的。在这里,我们证明可以利用空间依赖性来解决观测记录的时空稀疏性带来的局限性。我们通过贝叶斯分层模型将所有潮汐仪数据汇总在一起,从而描述了这一点,该模型描述了潮汐极限的分布如何随时间和空间变化。我们的方法具有两个非常可取的优点:1)它使跨数据站点的信息共享成为可能,从而大大减少了估计的不确定性。2)它允许在任意非测量位置上插值极值和极限分布参数。使用我们的模型,我们对1960-2013年期间覆盖欧洲整个大西洋和北海沿岸的激增极端进行了基于观测的概率重新分析。
更新日期:2020-01-29
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