Return level estimation from non-stationary spatial data exhibiting multidimensional covariate effects

Jonathan, P. and Randell, D. and Wu, Y. and Ewans, K. (2014) Return level estimation from non-stationary spatial data exhibiting multidimensional covariate effects. Ocean Engineering, 88. pp. 520-532. ISSN 0029-8018

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Abstract

Careful modelling of non-stationarity is critical to reliable specification of marine and coastal design criteria. We present a spline based methodology to incorporate spatial, directional, temporal and other covariate effects in extreme value models for environmental variables such as storm severity. For storm peak significant wave height events, the approach uses quantile regression to estimate a suitable extremal threshold, a Poisson process model for the rate of occurrence of threshold exceedances, and a generalised Pareto model for size of threshold exceedances. Multidimensional covariate effects are incorporated at each stage using penalised (tensor products of) B-splines to give smooth model parameter variation as a function of multiple covariates. Optimal smoothing penalties are selected using cross-validation, and model uncertainty is quantified using a bootstrap re-sampling procedure. The method is applied to estimate return values for large spatial neighbourhoods of locations, incorporating spatial and directional effects. Extensions to joint modelling of multivariate extremes, incorporating extremal spatial dependence (using max-stable processes) or more general extremal dependence (using the conditional extremes approach) are outlined. © 2014 Elsevier Ltd.

Item Type:
Journal Article
Journal or Publication Title:
Ocean Engineering
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2300/2305
Subjects:
ID Code:
133053
Deposited By:
Deposited On:
22 Apr 2019 15:20
Refereed?:
Yes
Published?:
Published
Last Modified:
18 Nov 2020 06:44