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Threshold models for river flow extremes

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Threshold models for river flow extremes. / Grigg, Olivia Ann Jane; Tawn, Jonathan Angus.
In: Environmetrics, Vol. 23, No. 4, 06.2012, p. 295-305.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Grigg, OAJ & Tawn, JA 2012, 'Threshold models for river flow extremes', Environmetrics, vol. 23, no. 4, pp. 295-305. https://doi.org/10.1002/env.2138

APA

Grigg, O. A. J., & Tawn, J. A. (2012). Threshold models for river flow extremes. Environmetrics, 23(4), 295-305. https://doi.org/10.1002/env.2138

Vancouver

Grigg OAJ, Tawn JA. Threshold models for river flow extremes. Environmetrics. 2012 Jun;23(4):295-305. Epub 2012 Mar 8. doi: 10.1002/env.2138

Author

Grigg, Olivia Ann Jane ; Tawn, Jonathan Angus. / Threshold models for river flow extremes. In: Environmetrics. 2012 ; Vol. 23, No. 4. pp. 295-305.

Bibtex

@article{fa11f0169ea74992aedf6542ce11e41a,
title = "Threshold models for river flow extremes",
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. ",
keywords = "censored likelihood, covariates, generalised extreme value, generalised Pareto, hydrology",
author = "Grigg, {Olivia Ann Jane} and Tawn, {Jonathan Angus}",
year = "2012",
month = jun,
doi = "10.1002/env.2138",
language = "English",
volume = "23",
pages = "295--305",
journal = "Environmetrics",
issn = "1180-4009",
publisher = "John Wiley and Sons Ltd",
number = "4",

}

RIS

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 -