Accessibility navigation


Dependence of ocean wave return levels on water depth and sampling length: a focus on the South Yellow Sea

Feng, X., Li, H., Feng, X., Zhao, J. and Feng, W. (2020) Dependence of ocean wave return levels on water depth and sampling length: a focus on the South Yellow Sea. Ocean Engineering. 108295. ISSN 0029-8018

[img] Text - Accepted Version
· Restricted to Repository staff only until 29 October 2021.
· Available under License Creative Commons Attribution Non-commercial No Derivatives.

267kB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1016/j.oceaneng.2020.108295

Abstract/Summary

A deterministic Extreme Value Analysis method is of particular importance in an engineering-oriented context. In this paper, three extreme value analysis (EVA) methods, in which annual maxima, monthly maxima or the Peaks-Over-Threshold are fitted into Generalized Extreme Value distribution (GEV) function or Generalized Pareto distribution (GP) function, are used to estimate return values of significant wave height. Sensitivity of return levels on water depth and sampling length is vigorously investigated, based on a 40-year long and high-resolution wave hindcast for the South Yellow Sea (SYS). A spatially-and-temporally varied POT sampling method combined with GP function produces most conservative and confident estimates of return levels for a large return period, but produces wider confidence level than the other two EVA approaches based on GEV function for short return period. In the SYS, the return level estimates are significantly reduced with a longer sample length. However, we find that the reduction is closely related to the long-term trend in extreme wave heights, rather than due to the sampling effect. From deep to shallow waters, spatial inhomogeneity of return levels increases, which should be considered in the engineering practice.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > NCAS
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:93750
Publisher:Elsevier

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation