Abstract
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.
Highlights
Extreme sea levels are a significant threat to life, property, and the environment
There is abundant observational evidence that the sea-level distribution is changing with time, both its mean values [7] and its tail behavior [8,9,10,11], which violates the assumption of stationarity on which classical Extreme value theory (EVT) is predicated
Occurrence probabilities of extreme sea-level events are required in the design of flood protection measures. 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
Summary
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. With climate projections indicating a significant increase in the intensity and frequency of sea-level extremes by 2100 [2, 3], those numbers are bound to grow even further To manage these threats, coastal planners use measures of extreme event likelihood to estimate risk and determine appropriate levels of protection that balance expected damage with protection costs. Estimates of event probabilities, which are central to risk estimation, are often subject to large uncertainty, primarily due to the sparseness of the observational record This uncertainty can lead to a significant shortfall in the performance of risk mitigation strategies, including the premature failure of infrastructure, with disastrously expensive consequences. There are two ways of defining extremes, both widely used: the block-
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