Analysing multivariate extreme conditions using environmental contours and accounting for serial dependence

Erik Vanem

Renewable Energy, 2023, vol. 202, issue C, 470-482

Abstract: Accurate statistical description of extreme environmental conditions is needed for risk assessment and management of marine structures and is a crucial input to design of any structure that need to withstand loads from environmental forces. Such descriptions are essentially multivariate extreme value problems, where the environmental loads are due to concurrent extreme combinations of several environmental variables. Typically, in coastal and ocean engineering applications, the simultaneous joint behaviour of significant wave height and wave period is of particular interest and is needed to describe the wave loads on marine structures. Environmental contours are often used to explore the extreme wave loads, and essentially consider extreme combinations of simultaneous significant wave height and wave period, and are based on a joint statistical distribution fitted to relevant metocean data. However, typical applications of environmental contours do not account for temporal dependencies in the environmental variables, and this may lead to an overestimation of extreme conditions. In this paper, an approach for partially accounting for serial dependence in the construction of environmental contours is proposed, based on simulating time-series of a primary variable which preserves both its marginal distribution and auto-correlation structure. It is shown that this gives lower estimates of extreme environmental conditions compared to conventional application of environmental contours that do not account for serial dependence. Hence, more accurate description of the extreme environment can be available for design and construction of structures exposed to environmental loads.

Keywords: Extreme environmental conditions; Environmental contours; Multivariate extremes; Serial dependence; Simulate time series; Ocean environment (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:202:y:2023:i:c:p:470-482

DOI: 10.1016/j.renene.2022.11.033

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