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Threshold models for river flow extremes

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>06/2012
<mark>Journal</mark>Environmetrics
Issue number4
Volume23
Number of pages11
Pages (from-to)295-305
<mark>State</mark>Published
Early online date8/03/12
<mark>Original language</mark>English

Abstract

We model extreme river flow data from five UK rivers with distinct hydrological properties. The data exhibit significant and complex nonstationarity, which we model using a nonlinear function of hydrological covariates corresponding to soil saturation, latent flow of the river and rainfall. We additionally consider season as a covariate, although the hydrological covariates explain most of the seasonal effect directly. The standard approach to modelling data of this kind is to fix a threshold and to model exceedances of this threshold using the generalised Pareto distribution. We identify a number of problems with this approach in nonstationary cases. To overcome these issues, we propose the use of a censored generalised extreme value distribution for threshold exceedances. The data analysis illustrates a number of features of model fit and in particular the stability of the model parameters and return levels to threshold choice.