Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Threshold models for river flow extremes
AU - Grigg, Olivia Ann Jane
AU - Tawn, Jonathan Angus
PY - 2012/6
Y1 - 2012/6
N2 - 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.
AB - 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.
KW - censored likelihood
KW - covariates
KW - generalised extreme value
KW - generalised Pareto
KW - hydrology
U2 - 10.1002/env.2138
DO - 10.1002/env.2138
M3 - Journal article
VL - 23
SP - 295
EP - 305
JO - Environmetrics
JF - Environmetrics
SN - 1180-4009
IS - 4
ER -