EGU2020-4888
https://doi.org/10.5194/egusphere-egu2020-4888
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Probabilistic reanalysis of storm surge extremes in Europe

Francisco Mir Calafat1 and Marta Marcos2
Francisco Mir Calafat and Marta Marcos
  • 1National Oceanography Centre, Liverpool, United Kingdom (francisco.calafat@noc.ac.uk)
  • 2IMEDEA (CSIC-UIB), Esporles, Spain

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.

How to cite: Mir Calafat, F. and Marcos, M.: Probabilistic reanalysis of storm surge extremes in Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4888, https://doi.org/10.5194/egusphere-egu2020-4888, 2020

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Presentation version 3 – uploaded on 01 May 2020
Added clarification comment
  • CC1: How to apply in Denmark?, Aslak Grinsted, 04 May 2020


    Great work and definitely a step forward. I am wondering how you would apply a similar approach to Denmark. In Denmark we have 3 different kinds of extreme surges: West-coast surges (those are included in your study), North-coast surges, and Baltic surges from the east. They correspond to very different storm forcings. How would you attack that problem. 

    would you separate into 3 different regions based on knowledge and experience, and do apply your method to each region individually, or can the method figure this out by itself? 

    • AC1: Reply to CC1, Francisco Mir Calafat, 04 May 2020

      Hi Aslak,

      That depends. The current version of the model assumes that, once bathymetric effects are accounted for, the spatial scales over which changes occur are similar over the entire domain. In other words, the model is based on stationary isotropic covariance functions. If the three regions you mentioned are affected by storms with very different spatial extent (lengths scales), then you could try to incoporate this into the model by using more flexible covariance functions, though this would come at the cost of increased computational complexity. An easier solution would be, as you mentioned, to model each region separately. In any case, I would suggest doing an exploratory analysis of the surge data in the three regions and then set up your extremes model based on what such analysis tells you.

       

Presentation version 2 – uploaded on 01 May 2020 , no comments
Added a few clarification comments
Presentation version 1 – uploaded on 01 May 2020 , no comments