Automated threshold selection methods for extreme wave analysis |
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| Authors: | Paul Thompson Yuzhi Cai Dominic Reeve Julian Stander |
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| Institution: | aC-CoDE, School of Engineering, University of Plymouth, Devon, PL4 8AA, UK;bSchool of Mathematics and Statistics, University of Plymouth, Devon, PL4 8AA, UK |
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| Abstract: | The study of the extreme values of a variable such as wave height is very important in flood risk assessment and coastal design. Often values above a sufficiently large threshold can be modelled using the Generalized Pareto Distribution, the parameters of which are estimated using maximum likelihood. There are several popular empirical techniques for choosing a suitable threshold, but these require the subjective interpretation of plots by the user.In this paper we present a pragmatic automated, simple and computationally inexpensive threshold selection method based on the distribution of the difference of parameter estimates when the threshold is changed, and apply it to a published rainfall and a new wave height data set. We assess the effect of the uncertainty associated with our threshold selection technique on return level estimation by using the bootstrap procedure. We illustrate the effectiveness of our methodology by a simulation study and compare it with the approach used in the JOINSEA software. In addition, we present an extension that allows the threshold selected to depend on the value of a covariate such as the cosine of wave direction. |
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| Keywords: | Bootstrap Covariate dependent thresholds Distribution with Generalized Pareto tail Generalized Pareto Distribution GPD JOINSEA Return level confidence intervals |
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