Improved treatment of non-stationary conditions and uncertainties in probabilistic models of storm wave climate

Davies, Gareth, Callaghan, David P., Gravois, Uriah, Jiang, Wenping, Hanslow, David, Nichol, Scott and Baldock, Tom (2017) Improved treatment of non-stationary conditions and uncertainties in probabilistic models of storm wave climate. Coastal Engineering, 127 1-19. doi:10.1016/j.coastaleng.2017.06.005

Author Davies, Gareth
Callaghan, David P.
Gravois, Uriah
Jiang, Wenping
Hanslow, David
Nichol, Scott
Baldock, Tom
Title Improved treatment of non-stationary conditions and uncertainties in probabilistic models of storm wave climate
Journal name Coastal Engineering   Check publisher's open access policy
ISSN 0378-3839
Publication date 2017-09-01
Sub-type Article (original research)
DOI 10.1016/j.coastaleng.2017.06.005
Open Access Status Not yet assessed
Volume 127
Start page 1
End page 19
Total pages 19
Place of publication Amsterdam, Netherlands
Publisher Elsevier BV
Language eng
Subject 2305 Environmental Engineering
2212 Ocean Engineering
Abstract A framework is presented for the probabilistic modelling of non-stationary coastal storm event sequences. Such modelling is required to integrate seasonal, climatic and long-term non-stationarities into coastal erosion hazard assessments. The framework is applied to a study site on the East Australian Coast where storm waves are found to exhibit non-stationarities related to El Niño-Southern Oscillation (ENSO) and seasonality. The impact of ENSO is most prominent for storm wave direction, long term mean sea level (MSL) and the rate of storms, while seasonal non-stationarity is more ubiquitous, affecting the latter variables as well as storm wave height, duration, period and surge. The probabilistic framework herein separates the modelling of ENSO and seasonal non-stationarity in the storm wave properties from the modelling of their marginal distributions, using copulas. The advantage of this separation is that non-stationarities can be straightforwardly modelled in all storm wave variables, irrespective of whether parametric or non-parametric techniques are used to model their marginal distributions. Storm wave direction and steepness are modelled with non-parametric distributions whereas storm wave height, duration and surge are modelled parametrically using extreme value mixture distributions. The advantage of the extreme value mixture distributions, compared with the standard extreme value distribution for peaks-over-threshold data (Generalized Pareto), is that the statistical threshold becomes a model parameter instead of being fixed, and so uncertainties in the threshold can be straightforwardly integrated into the analysis. Robust quantification of uncertainties in the model predictions is crucial to support hazard applications, and herein uncertainties are quantified using a novel mixture of parametric percentile bootstrap and Bayesian techniques. Percentile bootstrap confidence intervals are shown to non-conservatively underestimate uncertainties in the extremes (e.g. 1% annual exceedance probability wave heights), both in an idealized setting and in our application. The Bayesian approach is applied to the extreme value models to remedy this shortcoming. The modelling framework is applicable to any site where multivariate storm wave properties and timings are affected by seasonal, climatic and long-term non-stationarities, and can be used to account for such non-stationarities in coastal hazard assessments.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Civil Engineering Publications
HERDC Pre-Audit
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Mon, 11 Sep 2017, 01:00:34 EST by Web Cron on behalf of Learning and Research Services (UQ Library)