Some hydrological applications of small sample estimators of Generalized Pareto and Extreme Value distributions

Michele, Carlo De; Salvadori, Gianfausto

doi:10.1016/j.jhydrol.2004.06.015

Cited by 32 publications

(15 citation statements)

References 25 publications

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“…For instance, De Michele and Salvadori (2005) pointed out that alternative estimators are possible considering only an ad hoc sub-sample of the observations. Tessier et al (1996) and Pandey et al (1998), estimated the exponent of the power law fall-off as the slope of a straight line through a linear regression on the ultimate part of a log-log probability plot, choosing subjectively the number of extreme observations to include in the analysis.…”

Section: Parametric and Semi-parametric Estimatorsmentioning

confidence: 99%

The flood probability distribution tail: how heavy is it?

Bernardara¹,

Schertzer

Sauquet³

et al. 2007

Stoch Environ Res Risk Assess

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International audienceThis paper empirically investigates the asymptotic behaviour of the flood probability distribution and more precisely the possible occurrence of heavy tail distributions, generally predicted by multiplicative cascades. Since heavy tails considerably increase the frequency of extremes, they have many practical and societal consequences. A French database of 173 daily discharge time series is analyzed. These series correspond to various climatic and hydrological conditions, drainage areas ranging from 10 to 10(5) km(2), and are from 22 to 95 years long. The peaks-over-threshold method has been used with a set of semi-parametric estimators (Hill and Generalized Hill estimators), and parametric estimators (maximum likelihood and L-moments). We discuss the respective interest of the estimators and compare their respective estimates of the shape parameter of the probability distribution of the peaks. We emphasize the influence of the selected number of the highest observations that are used in the estimation procedure and in this respect the particular interest of the semi-parametric estimators. Nevertheless, the various estimators agree on the prevalence of heavy tails and we point out some links between their presence and hydrological and climatic conditions

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Section: Parametric and Semi-parametric Estimatorsmentioning

confidence: 99%

The flood probability distribution tail: how heavy is it?

Bernardara¹,

Schertzer

Sauquet³

et al. 2007

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

show abstract

“…There have been many applications on Generalized Extreme Value in hydrology and other related fields (Gelder and Neykov, 1998;Huang et al, 2008;Michele and Salvadori, 2005;Morrison and Smith, 2002;Phien and Fang, 1989). Methods on estimating parameters for generalized extreme value distribution have also been studied by many researchers (e.g., Eduardo and Jery, 2000;Hirose, 1996;Otten and Montfort, 1980).…”

Section: Review Of Frequency Analysis On Extreme Water Levels In Coasmentioning

confidence: 99%

Frequency Analysis of Extreme Water Levels Affected by Sea-Level Rise in East and Southeast Coasts of China

Chen

Huang

2014

Journal of Coastal Research

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“…A number of similar regression equation types, between rainfall intensities and durations for given recurrence intervals, were examined by many researchers in many cases (e.g. Koutsoyiannis et al, 1998;DeMichele and Salvadori, 2005;Masimin and Harun, 2006). The power-law one as Eq.…”

Section: Empirical Idf Formulamentioning

confidence: 99%

Developing an empirical formulae to estimate rainfall intensity in Riyadh region

Al-Hassoun

2011

Journal of King Saud University - Engineering Sciences

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Trend and stationarity analysis conducted for the city center of Eskisehir province, Turkey. The Intensity-Duration-Frequency (IDF) curves and formulas for the estimation of the design rainfall intensity have been developed. Through the application of four different frequency methods, the IDF has been developed for a record of 64 years. The functions employed are; Normal, Log-Normal, Gumbel, and Log-Pearson Type III. A formula for each function developed by determining the parameters with the help of a non-linear regression method. The most appropriate fit of probability distribution determined by goodness of fit test i.e. Chi-Square. The results of the study revealed no evidence of significant trend or nonstationarity in any duration. With an exception to the Normal function which exceeded the critical region in several durations, all the functions found to show high fitting. LPT III identified as the best fit. The coefficient of correlation for the developed formulas found high in all the functions except for LPT III. The study endorsed that the estimation of rainfall intensity for any storm duration and return period can be done for Eskisehir using LPT III and Gumbel formulas as they are evaluated as best fitting.

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Some hydrological applications of small sample estimators of Generalized Pareto and Extreme Value distributions

Cited by 32 publications

References 25 publications

The flood probability distribution tail: how heavy is it?

The flood probability distribution tail: how heavy is it?

Frequency Analysis of Extreme Water Levels Affected by Sea-Level Rise in East and Southeast Coasts of China

Developing an empirical formulae to estimate rainfall intensity in Riyadh region

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