Home > Research > Publications & Outputs > A benchmarking exercise for environmental contours

Links

Text available via DOI:

View graph of relations

A benchmarking exercise for environmental contours

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published
  • A.F. Haselsteiner
  • R.G. Coe
  • L. Manuel
  • W. Chai
  • B. Leira
  • G. Clarindo
  • C. Guedes Soares
  • Á. Hannesdóttir
  • N. Dimitrov
  • A. Sander
  • J.-H. Ohlendorf
  • K.-D. Thoben
  • G.D. Hauteclocque
  • E. Mackay
  • C. Qiao
  • A. Myers
  • A. Rode
  • A. Hildebrandt
  • B. Schmidt
  • E. Vanem
  • A.B. Huseby
Close
Article number109504
<mark>Journal publication date</mark>15/09/2021
<mark>Journal</mark>Ocean Engineering
Volume236
Number of pages29
Publication StatusPublished
Early online date11/08/21
<mark>Original language</mark>English

Abstract

Environmental contours are used to simplify the process of design response analysis. A wide variety of contour methods exist; however, there have been a very limited number of comparisons of these methods to date. This paper is the output of an open benchmarking exercise, in which contributors developed contours based on their preferred methods and submitted them for a blind comparison study. The exercise had two components—one, focusing on the robustness of contour methods across different offshore sites and, the other, focusing on characterizing sampling uncertainty. Nine teams of researchers contributed to the benchmark. The analysis of the submitted contours highlighted significant differences between contours derived via different methods. For example, the highest wave height value along a contour varied by as much as a factor of two between some submissions while the number of metocean data points or observations that fell outside a contour deviated by an order of magnitude between the contributions (even for contours with a return period shorter than the duration of the record). These differences arose from both different joint distribution models and different contour construction methods, however, variability from joint distribution models appeared to be higher than variability from contour construction methods.