Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Joint modelling of extreme ocean environments incorporating covariate effects. / Jonathan, P.; Ewans, K.; Randell, D.
In: Coastal Engineering, Vol. 79, 2013, p. 22-31.Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -