Publication detail

Comparison of precipitation extremes estimation using parametric and nonparametric methods

HOLEŠOVSKÝ, J. FUSEK, M. BLACHUT, V. MICHÁLEK, J.

Original Title

Comparison of precipitation extremes estimation using parametric and nonparametric methods

English Title

Comparison of precipitation extremes estimation using parametric and nonparametric methods

Type

journal article in Web of Science

Language

en

Original Abstract

Due to recent occurrence of extreme hydrological events in Central Europe, there is an increasing interest in more accurate prediction of return levels of such events. The precipitation records from 6 ombrographic stations operated by the Czech Hydrometeorological Institute were analyzed in order to estimate the intensity-duration-frequency. Although the longest rainfall series consists of more than 40 years of measurements, data set contains also records from newly established stations with only short-time series available. Impact of the series length on the estimation quality is part of this study. Parametric and nonparametric approaches to drawing samples are assumed. In the first case, we consider a threshold model and we estimate the unknown parameters using maximum likelihood and probability weighted moments methods. In the latter case, k largest order statistics are considered and the bootstrap methodology is applied as a resampling technique together with the moment estimator of extreme value index.

English abstract

Due to recent occurrence of extreme hydrological events in Central Europe, there is an increasing interest in more accurate prediction of return levels of such events. The precipitation records from 6 ombrographic stations operated by the Czech Hydrometeorological Institute were analyzed in order to estimate the intensity-duration-frequency. Although the longest rainfall series consists of more than 40 years of measurements, data set contains also records from newly established stations with only short-time series available. Impact of the series length on the estimation quality is part of this study. Parametric and nonparametric approaches to drawing samples are assumed. In the first case, we consider a threshold model and we estimate the unknown parameters using maximum likelihood and probability weighted moments methods. In the latter case, k largest order statistics are considered and the bootstrap methodology is applied as a resampling technique together with the moment estimator of extreme value index.

Keywords

partial duration series; maximum likelihood; probability weighted moments; bootstrap; intensity-duration-frequency curves; moment estimator

Released

02.10.2016

Pages from

2376

Pages to

2386

Pages count

11

URL

Documents

BibTex


@article{BUT124860,
  author="Jan {Holešovský} and Michal {Fusek} and Vít {Blachut} and Jaroslav {Michálek}",
  title="Comparison of precipitation extremes estimation using parametric and nonparametric methods",
  annote="Due to recent occurrence of extreme hydrological events in Central Europe, there is an increasing interest in more accurate prediction of return levels of such events. The precipitation records from 6 ombrographic stations operated by the Czech Hydrometeorological Institute were analyzed in order to estimate the intensity-duration-frequency. Although the longest rainfall series consists of more than 40 years of measurements, data set contains also records from newly established stations with only short-time series available. Impact of the series length on the estimation quality is part of this study. Parametric and nonparametric approaches to drawing samples are assumed. In the first case, we consider a threshold model and we estimate the unknown parameters using maximum likelihood and probability weighted moments methods. In the latter case, k largest order statistics are considered and the bootstrap methodology is applied as a resampling technique together with the moment estimator of extreme value index.",
  chapter="124860",
  doi="10.1080/02626667.2015.1111517",
  howpublished="print",
  number="13",
  volume="61",
  year="2016",
  month="october",
  pages="2376--2386",
  type="journal article in Web of Science"
}