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Distributions of return values for ocean wave characteristics in the South China Sea using directional-seasonal extreme value analysis

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Distributions of return values for ocean wave characteristics in the South China Sea using directional-seasonal extreme value analysis. / Randell, D.; Feld, G.; Ewans, K. et al.
In: Environmetrics, Vol. 26, No. 6, 2015, p. 442-450.

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Randell, D. ; Feld, G. ; Ewans, K. et al. / Distributions of return values for ocean wave characteristics in the South China Sea using directional-seasonal extreme value analysis. In: Environmetrics. 2015 ; Vol. 26, No. 6. pp. 442-450.

Bibtex

@article{0eebaf68ca684dd4842a157ca04afcda,
title = "Distributions of return values for ocean wave characteristics in the South China Sea using directional-seasonal extreme value analysis",
abstract = "Estimation of ocean environmental return values is critical to the safety and reliability of marine and coastal structures. For ocean waves and storm severity, return values are typically estimated by extreme value analysis of time series of measured or hindcast sea state significant wave height HS. For a single location, this analysis is complicated by the serial dependence of HS in time and its non-stationarity with respect to multiple covariates, particularly direction and season. Here, we report a non-stationary extreme value analysis of storm peak significant wave height HSsp, assumed temporally independent given covariates, incorporating directional and seasonal effects using a spline-based methodology incorporating an ensemble of models for different extreme value thresholds. Quantile regression is used to estimate suitable thresholds. For each threshold, a Poisson process is used to estimate the rate of occurrence of threshold exceedances, and a generalised Pareto model characterises the magnitude of threshold exceedances. Covariate effects are incorporated at each stage using penalised tensor products of B-splines to give smooth model parameter variation as a function of covariates. Optimal smoothing penalties are selected using cross-validation, and uncertainty is quantified using bias-corrected and accelerated bootstrap resampling. We use the model to estimate environmental return values for a location in the Makassar Strait, in the South China Sea. Return values distributions for HSsp are estimated by simulation under the threshold ensemble model. Return values for HS are then estimated by simulating intra-storm trajectories of HS consistent with the characteristics of the simulated storm peak events using a matching procedure. Return values for maximum individual crest elevation C are estimated by marginalisation using a pre-specified conditional distribution for C given HS and other sea state parameters. Model validation is performed by comparing confidence intervals for cumulative distribution functions of HSsp and HS for the period of the data with empirical sample-based estimates. {\textcopyright} 2015 John Wiley & Sons, Ltd.",
keywords = "Bootstrap, Covariate, Cross-validation, Extreme, Generalised Pareto, Non-stationarity, Ocean wave, Penalised likelihood, Poisson process, Quantile regression, Return value, Spline, Storm severity, bootstrapping, coastal structure, covariance analysis, hindcasting, least squares method, maximum likelihood analysis, model validation, numerical model, ocean wave, Poisson ratio, reliability analysis, safety, storm, time series, uncertainty analysis, valuation, Indonesia, Makassar Strait, Pacific Ocean, South China Sea",
author = "D. Randell and G. Feld and K. Ewans and P. Jonathan",
year = "2015",
doi = "10.1002/env.2350",
language = "English",
volume = "26",
pages = "442--450",
journal = "Environmetrics",
issn = "1180-4009",
publisher = "John Wiley and Sons Ltd",
number = "6",

}

RIS

TY - JOUR

T1 - Distributions of return values for ocean wave characteristics in the South China Sea using directional-seasonal extreme value analysis

AU - Randell, D.

AU - Feld, G.

AU - Ewans, K.

AU - Jonathan, P.

PY - 2015

Y1 - 2015

N2 - Estimation of ocean environmental return values is critical to the safety and reliability of marine and coastal structures. For ocean waves and storm severity, return values are typically estimated by extreme value analysis of time series of measured or hindcast sea state significant wave height HS. For a single location, this analysis is complicated by the serial dependence of HS in time and its non-stationarity with respect to multiple covariates, particularly direction and season. Here, we report a non-stationary extreme value analysis of storm peak significant wave height HSsp, assumed temporally independent given covariates, incorporating directional and seasonal effects using a spline-based methodology incorporating an ensemble of models for different extreme value thresholds. Quantile regression is used to estimate suitable thresholds. For each threshold, a Poisson process is used to estimate the rate of occurrence of threshold exceedances, and a generalised Pareto model characterises the magnitude of threshold exceedances. Covariate effects are incorporated at each stage using penalised tensor products of B-splines to give smooth model parameter variation as a function of covariates. Optimal smoothing penalties are selected using cross-validation, and uncertainty is quantified using bias-corrected and accelerated bootstrap resampling. We use the model to estimate environmental return values for a location in the Makassar Strait, in the South China Sea. Return values distributions for HSsp are estimated by simulation under the threshold ensemble model. Return values for HS are then estimated by simulating intra-storm trajectories of HS consistent with the characteristics of the simulated storm peak events using a matching procedure. Return values for maximum individual crest elevation C are estimated by marginalisation using a pre-specified conditional distribution for C given HS and other sea state parameters. Model validation is performed by comparing confidence intervals for cumulative distribution functions of HSsp and HS for the period of the data with empirical sample-based estimates. © 2015 John Wiley & Sons, Ltd.

AB - Estimation of ocean environmental return values is critical to the safety and reliability of marine and coastal structures. For ocean waves and storm severity, return values are typically estimated by extreme value analysis of time series of measured or hindcast sea state significant wave height HS. For a single location, this analysis is complicated by the serial dependence of HS in time and its non-stationarity with respect to multiple covariates, particularly direction and season. Here, we report a non-stationary extreme value analysis of storm peak significant wave height HSsp, assumed temporally independent given covariates, incorporating directional and seasonal effects using a spline-based methodology incorporating an ensemble of models for different extreme value thresholds. Quantile regression is used to estimate suitable thresholds. For each threshold, a Poisson process is used to estimate the rate of occurrence of threshold exceedances, and a generalised Pareto model characterises the magnitude of threshold exceedances. Covariate effects are incorporated at each stage using penalised tensor products of B-splines to give smooth model parameter variation as a function of covariates. Optimal smoothing penalties are selected using cross-validation, and uncertainty is quantified using bias-corrected and accelerated bootstrap resampling. We use the model to estimate environmental return values for a location in the Makassar Strait, in the South China Sea. Return values distributions for HSsp are estimated by simulation under the threshold ensemble model. Return values for HS are then estimated by simulating intra-storm trajectories of HS consistent with the characteristics of the simulated storm peak events using a matching procedure. Return values for maximum individual crest elevation C are estimated by marginalisation using a pre-specified conditional distribution for C given HS and other sea state parameters. Model validation is performed by comparing confidence intervals for cumulative distribution functions of HSsp and HS for the period of the data with empirical sample-based estimates. © 2015 John Wiley & Sons, Ltd.

KW - Bootstrap

KW - Covariate

KW - Cross-validation

KW - Extreme

KW - Generalised Pareto

KW - Non-stationarity

KW - Ocean wave

KW - Penalised likelihood

KW - Poisson process

KW - Quantile regression

KW - Return value

KW - Spline

KW - Storm severity

KW - bootstrapping

KW - coastal structure

KW - covariance analysis

KW - hindcasting

KW - least squares method

KW - maximum likelihood analysis

KW - model validation

KW - numerical model

KW - ocean wave

KW - Poisson ratio

KW - reliability analysis

KW - safety

KW - storm

KW - time series

KW - uncertainty analysis

KW - valuation

KW - Indonesia

KW - Makassar Strait

KW - Pacific Ocean

KW - South China Sea

U2 - 10.1002/env.2350

DO - 10.1002/env.2350

M3 - Journal article

VL - 26

SP - 442

EP - 450

JO - Environmetrics

JF - Environmetrics

SN - 1180-4009

IS - 6

ER -