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Joint modelling of extreme ocean environments incorporating covariate effects

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Joint modelling of extreme ocean environments incorporating covariate effects. / Jonathan, P.; Ewans, K.; Randell, D.
In: Coastal Engineering, Vol. 79, 2013, p. 22-31.

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Jonathan P, Ewans K, Randell D. Joint modelling of extreme ocean environments incorporating covariate effects. Coastal Engineering. 2013;79:22-31. doi: 10.1016/j.coastaleng.2013.04.005

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Jonathan, P. ; Ewans, K. ; Randell, D. / Joint modelling of extreme ocean environments incorporating covariate effects. In: Coastal Engineering. 2013 ; Vol. 79. pp. 22-31.

Bibtex

@article{0bfe0eef728c49d3907c13288bffc735,
title = "Joint modelling of extreme ocean environments incorporating covariate effects",
abstract = "Characterising the joint distribution of extremes of ocean environmental variables such as significant wave height (HS) and spectral peak period (TP) is important for understanding extreme ocean environments and in the design and assessment of marine and coastal structures. Many applications of multivariate extreme value analysis adopt models that assume a particular form of extremal dependence between variables without justification. Models are also typically restricted to joint regions in which all variables are extreme, but regions where only a subset of variables is extreme can be equally important for design. The conditional extremes model of Heffernan and Tawn (2004) provides one approach to overcoming these difficulties.Here, we extend the conditional extremes model to incorporate covariate effects in all of threshold selection, marginal and dependence modelling. Quantile regression is used to select appropriate covariate-dependent extreme value thresholds. Marginal and dependence modelling of extremes is performed within a penalised likelihood framework, using a Fourier parameterisation of marginal and dependence model parameters, with cross-validation to estimate suitable model parameter roughness, and bootstrapping to estimate parameter uncertainty with respect to covariate.We illustrate the approach in application to joint modelling of storm peak HS and TP at a Northern North Sea location with storm direction as covariate. We evaluate the impact of incorporating directional effects on estimates for return values, including those of a structure variable, similar to the structural response of a floating structure. We believe the approach offers the ocean engineer a straightforward procedure, based on sound statistics, to incorporate covariate effects in estimation of joint extreme environmental conditions. {\textcopyright} 2013 Elsevier B.V..",
keywords = "Conditional extremes, Covariates, Joint extremes, Offshore design, Environmental conditions, Environmental variables, Multivariate extremes, Parameter uncertainty, Significant wave height, Estimation, Oceanography, Offshore structures, Storms, Uncertainty analysis, bootstrapping, ocean wave, offshore engineering, offshore structure, parameterization, regression analysis, wave height, Atlantic Ocean, North Sea",
author = "P. Jonathan and K. Ewans and D. Randell",
year = "2013",
doi = "10.1016/j.coastaleng.2013.04.005",
language = "English",
volume = "79",
pages = "22--31",
journal = "Coastal Engineering",
issn = "0378-3839",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Joint modelling of extreme ocean environments incorporating covariate effects

AU - Jonathan, P.

AU - Ewans, K.

AU - Randell, D.

PY - 2013

Y1 - 2013

N2 - Characterising the joint distribution of extremes of ocean environmental variables such as significant wave height (HS) and spectral peak period (TP) is important for understanding extreme ocean environments and in the design and assessment of marine and coastal structures. Many applications of multivariate extreme value analysis adopt models that assume a particular form of extremal dependence between variables without justification. Models are also typically restricted to joint regions in which all variables are extreme, but regions where only a subset of variables is extreme can be equally important for design. The conditional extremes model of Heffernan and Tawn (2004) provides one approach to overcoming these difficulties.Here, we extend the conditional extremes model to incorporate covariate effects in all of threshold selection, marginal and dependence modelling. Quantile regression is used to select appropriate covariate-dependent extreme value thresholds. Marginal and dependence modelling of extremes is performed within a penalised likelihood framework, using a Fourier parameterisation of marginal and dependence model parameters, with cross-validation to estimate suitable model parameter roughness, and bootstrapping to estimate parameter uncertainty with respect to covariate.We illustrate the approach in application to joint modelling of storm peak HS and TP at a Northern North Sea location with storm direction as covariate. We evaluate the impact of incorporating directional effects on estimates for return values, including those of a structure variable, similar to the structural response of a floating structure. We believe the approach offers the ocean engineer a straightforward procedure, based on sound statistics, to incorporate covariate effects in estimation of joint extreme environmental conditions. © 2013 Elsevier B.V..

AB - Characterising the joint distribution of extremes of ocean environmental variables such as significant wave height (HS) and spectral peak period (TP) is important for understanding extreme ocean environments and in the design and assessment of marine and coastal structures. Many applications of multivariate extreme value analysis adopt models that assume a particular form of extremal dependence between variables without justification. Models are also typically restricted to joint regions in which all variables are extreme, but regions where only a subset of variables is extreme can be equally important for design. The conditional extremes model of Heffernan and Tawn (2004) provides one approach to overcoming these difficulties.Here, we extend the conditional extremes model to incorporate covariate effects in all of threshold selection, marginal and dependence modelling. Quantile regression is used to select appropriate covariate-dependent extreme value thresholds. Marginal and dependence modelling of extremes is performed within a penalised likelihood framework, using a Fourier parameterisation of marginal and dependence model parameters, with cross-validation to estimate suitable model parameter roughness, and bootstrapping to estimate parameter uncertainty with respect to covariate.We illustrate the approach in application to joint modelling of storm peak HS and TP at a Northern North Sea location with storm direction as covariate. We evaluate the impact of incorporating directional effects on estimates for return values, including those of a structure variable, similar to the structural response of a floating structure. We believe the approach offers the ocean engineer a straightforward procedure, based on sound statistics, to incorporate covariate effects in estimation of joint extreme environmental conditions. © 2013 Elsevier B.V..

KW - Conditional extremes

KW - Covariates

KW - Joint extremes

KW - Offshore design

KW - Environmental conditions

KW - Environmental variables

KW - Multivariate extremes

KW - Parameter uncertainty

KW - Significant wave height

KW - Estimation

KW - Oceanography

KW - Offshore structures

KW - Storms

KW - Uncertainty analysis

KW - bootstrapping

KW - ocean wave

KW - offshore engineering

KW - offshore structure

KW - parameterization

KW - regression analysis

KW - wave height

KW - Atlantic Ocean

KW - North Sea

U2 - 10.1016/j.coastaleng.2013.04.005

DO - 10.1016/j.coastaleng.2013.04.005

M3 - Journal article

VL - 79

SP - 22

EP - 31

JO - Coastal Engineering

JF - Coastal Engineering

SN - 0378-3839

ER -