Preprints
https://doi.org/10.5194/nhess-2021-406
https://doi.org/10.5194/nhess-2021-406
 
04 Jan 2022
04 Jan 2022
Status: this preprint has been withdrawn by the authors.

Incorporating historical information to improve extreme sea level estimates

Leigh R. MacPherson1, Arne Arns1, Svenja Fischer2, Fernando J. Méndez3, and Jürgen Jensen4 Leigh R. MacPherson et al.
  • 1Faculty of Agricultural and Environmental Sciences, University of Rostock, Rostock, 18059, Germany
  • 2Institute of Hydrological Engineering and Water Resources Management, Ruhr-University Bochum, Bochum, 44801, Germany
  • 3Departamento de Ciencias y Técnicas del Agua y del Medio Ambiente, E.T.S.I de Caminos, Canales y Puertos, Universidad de Cantábria, Santander, 39005, Spain
  • 4Research Institute for Water and Environment, University of Siegen, Siegen, 57076, Germany

Abstract. Extreme value analysis seeks to assign probabilities to events which deviate significantly from the mean and is thus widely employed in disciplines dealing with natural hazards. In terms of extreme sea levels (ESLs), these probabilities help to define coastal flood risk which guides the design of coastal protection measures. While tide gauge and other systematic records are typically used to estimate ESLs, combining systematic data with historical information has been shown to reduce uncertainties and better represent statistical outliers. This paper introduces a new method for the incorporation of historical information in extreme value analysis which outperforms other commonly used approaches. Monte-Carlo Simulations are used to evaluate a posterior distribution of historical and systematic ESLs based on the prior distribution of systematic data. This approach is applied at the German town of Travemünde, providing larger ESL estimates compared to those determined using systematic data only. We highlight a potential to underestimate ESLs at Travemünde when historical information is disregarded, due to a period of relatively low ESL activity for the duration of the systematic record.

This preprint has been withdrawn.

Leigh R. MacPherson et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-406', Anonymous Referee #1, 26 Jan 2022
  • RC2: 'Comment on nhess-2021-406', Anonymous Referee #2, 31 Jan 2022
  • RC3: 'Comment on nhess-2021-406', Anonymous Referee #3, 07 Feb 2022
  • RC4: 'Comment on nhess-2021-406', Anonymous Referee #4, 10 Feb 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-406', Anonymous Referee #1, 26 Jan 2022
  • RC2: 'Comment on nhess-2021-406', Anonymous Referee #2, 31 Jan 2022
  • RC3: 'Comment on nhess-2021-406', Anonymous Referee #3, 07 Feb 2022
  • RC4: 'Comment on nhess-2021-406', Anonymous Referee #4, 10 Feb 2022

Leigh R. MacPherson et al.

Leigh R. MacPherson et al.

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This preprint has been withdrawn.

Short summary
Extreme sea levels represent one of the most damaging natural hazards due to their potential to cause flooding. We developed a new method which incorporates historical information with systematically recorded sea levels, leading to improved estimates of extreme sea levels with reduced uncertainties. Such information helps to improve coastal flood risk analyses, which in turn allows for more efficient planning of coastal protection measures.
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