Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Hydrology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Hydrology, 585, 2020 DOI: 10.1016/j.jhydrol.2020.124845
Accepted author manuscript, 1.77 MB, PDF document
Available under license: CC BY-NC-ND
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 - An evaluation of automated GPD threshold selection methods for hydrological extremes across different scales
AU - Curceac, S.
AU - Atkinson, P.M.
AU - Milne, A.
AU - Wu, L.
AU - Harris, P.
N1 - This is the author’s version of a work that was accepted for publication in Journal of Hydrology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Hydrology, 585, 2020 DOI: 10.1016/j.jhydrol.2020.124845
PY - 2020/6/1
Y1 - 2020/6/1
N2 - This study investigated core components of an extreme value methodology for the estimation of high-flow frequencies from agricultural surface water run-off. The Generalized Pareto distribution (GPD) was used to model excesses in time-series data that resulted from the ‘Peaks Over Threshold’ (POT) method. First, the performance of eight different GPD parameter estimators was evaluated through a Monte Carlo experiment. Second, building on the estimator comparison, two existing automated GPD threshold selection methods were evaluated against a proposed approach that automates the threshold stability plots. For this second experiment, methods were applied to discharge measured at a highly-instrumented agricultural research facility in the UK. By averaging fine-resolution 15-minute data to hourly, 6-hourly and daily scales, we were also able to determine the effect of scale on threshold selection, as well as the performance of each method. The results demonstrate the advantages of the proposed threshold selection method over two commonly applied methods, while at the same time providing useful insights into the effect of the choice of the scale of measurement on threshold selection. The results can be generalised to similar water monitoring schemes and are important for improved characterisations of flood events and the design of associated disaster management protocols.
AB - This study investigated core components of an extreme value methodology for the estimation of high-flow frequencies from agricultural surface water run-off. The Generalized Pareto distribution (GPD) was used to model excesses in time-series data that resulted from the ‘Peaks Over Threshold’ (POT) method. First, the performance of eight different GPD parameter estimators was evaluated through a Monte Carlo experiment. Second, building on the estimator comparison, two existing automated GPD threshold selection methods were evaluated against a proposed approach that automates the threshold stability plots. For this second experiment, methods were applied to discharge measured at a highly-instrumented agricultural research facility in the UK. By averaging fine-resolution 15-minute data to hourly, 6-hourly and daily scales, we were also able to determine the effect of scale on threshold selection, as well as the performance of each method. The results demonstrate the advantages of the proposed threshold selection method over two commonly applied methods, while at the same time providing useful insights into the effect of the choice of the scale of measurement on threshold selection. The results can be generalised to similar water monitoring schemes and are important for improved characterisations of flood events and the design of associated disaster management protocols.
KW - Flood frequency analysis
KW - Generalized pareto distribution
KW - Grassland agriculture
KW - Peaks over threshold
KW - Scale effects
KW - Threshold selection
KW - Agriculture
KW - Disaster prevention
KW - Disasters
KW - Flood control
KW - Floods
KW - Pareto principle
KW - Surface waters
KW - Generalized Pareto Distributions
KW - Frequency estimation
KW - agricultural research
KW - extreme event
KW - flood frequency
KW - frequency analysis
KW - grassland
KW - Gross Domestic Product
KW - methodology
KW - Monte Carlo analysis
KW - runoff
KW - scale effect
KW - spatial distribution
KW - threshold
KW - United Kingdom
U2 - 10.1016/j.jhydrol.2020.124845
DO - 10.1016/j.jhydrol.2020.124845
M3 - Journal article
VL - 585
JO - Journal of Hydrology
JF - Journal of Hydrology
SN - 0022-1694
M1 - 124845
ER -