Abstract
<p>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 the tide gauge data together through a Bayesian hierarchical model that describes how the distribution of surge extremes varies in time and space. Our new 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 the first, to our knowledge, observation-based probabilistic reanalysis of surge extremes covering the entire Atlantic and North Sea coasts of Europe for the period 1960-2013.</p>
Highlights
Estimation of these probabilities, is challenging due to the small sample of extreme events in the historical sea-level record. We address this challenge by exploiting spatial dependences in the extreme data through a spatiotemporal probabilistic model
This study focuses on describing the Bayesian hierarchical model, assessing its performance through a number of evaluation metrics, and presenting the reanalysis
We present the probability distribution of surge extremes as estimated by the Bayesian hierarchical model from the real tide gauge data
Summary
Maxima method, which divides the observations into consecutive nonoverlapping blocks (or periods), typically years, and selects the maximum value in each block; and the peaks-over-threshold method, which considers all of the values above a certain threshold. There is abundant observational evidence that the sea-level distribution is changing with time, both its mean values [7] and its tail behavior [8–11], which violates the assumption of stationarity on which classical EVT is predicated Taking this nonstationarity into account is essential to ensure that EVT remains applicable, and that risk mitigation strategies select a level of protection that matches the real risk of extremes. Our approach leads to estimates of event probabilities with high accuracy and precision, allows for estimation at ungauged locations, and involves a comprehensive treatment of uncertainties These three properties make the reanalysis presented here a valuable tool to support both planning decisions in relation to coastal flooding and current efforts to understand the link between extreme events and climate change
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