Split sampling technique for selecting a flood frequency analysis procedure

Gunasekara, T.A.G.; Cunnane, C.

doi:10.1016/0022-1694(92)90110-h

Cited by 16 publications

(14 citation statements)

References 5 publications

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“…A large number of comparative studies of FFA implementations have been reported in the research literature (e.g., Hosking et al, 1985;Gunasekara and Cunnane, 1992;Kroll and Stedinger, 1996;GREHYS, 1996;Ouarda et al, 2006;Meshgi and Khalili, 2009;Sankarasubramanian and Srinivasan, 1999). The comparison framework varies from one study to another, and can be based on Monte Carlo simulations, statistical tests, graphical methods and so on.…”

Section: Challenges Facing the Evaluation And Comparison Of Ffa Appromentioning

confidence: 99%

“…An alternative to Monte Carlo comparisons is to implement data-based predictive comparisons, where the estimations from all competing FFA implementations are simply compared with validation data (Gunasekara and Cunnane, 1992; Interagency Advisory Committee on Water Data, 1982). Recently, Renard et al (2013) proposed a data-based comparison framework that could be applied to any FFA implementation.…”

Section: Challenges Facing the Evaluation And Comparison Of Ffa Appromentioning

confidence: 99%

“…T (e.g., Interagency Advisory Committee on Water Data, 1982;Gunasekara and Cunnane, 1992;Garavaglia et al, 2011):…”

Section: Reliability Indicesmentioning

confidence: 99%

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A data-based comparison of flood frequency analysis methods used in France

Kochanek

Renard²,

Arnaud³

et al. 2014

Nat. Hazards Earth Syst. Sci.

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Abstract. Flood frequency analysis (FFA) aims at estimating quantiles with large return periods for an extreme discharge variable. Many FFA implementations are used in operational practice in France. These implementations range from the estimation of a pre-specified distribution to continuous simulation approaches using a rainfall simulator coupled with a rainfall-runoff model. This diversity of approaches raises questions regarding the limits of each implementation and calls for a nation-wide comparison of their predictive performances.This paper presents the results of a national comparison of the main FFA implementations used in France. More accurately, eight implementations are considered, corresponding to the local, regional and local-regional estimation of Gumbel and Generalized Extreme Value (GEV) distributions, as well as the local and regional versions of a continuous simulation approach. A data-based comparison framework is applied to these eight competitors to evaluate their predictive performances in terms of reliability and stability, using daily flow data from more than 1000 gauging stations in France.Results from this comparative exercise suggest that two implementations dominate their competitors in terms of predictive performances, namely the local version of the continuous simulation approach and the local-regional estimation of a GEV distribution. More specific conclusions include the following: (i) the Gumbel distribution is not suitable for Mediterranean catchments, since this distribution demonstrably leads to an underestimation of flood quantiles; (ii) the local estimation of a GEV distribution is not recommended, because the difficulty in estimating the shape parameter results in frequent predictive failures; (iii) all the purely regional implementations evaluated in this study displayed a quite poor reliability, suggesting that prediction in completely ungauged catchments remains a challenge.

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Section: Challenges Facing the Evaluation And Comparison Of Ffa Appromentioning

confidence: 99%

Section: Challenges Facing the Evaluation And Comparison Of Ffa Appromentioning

confidence: 99%

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A data-based comparison of flood frequency analysis methods used in France

Kochanek

Renard²,

Arnaud³

et al. 2014

Nat. Hazards Earth Syst. Sci.

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“…Beard (1974) estimated the 1000 year floods at 300 stations in USA with 14 200 station-years of data by eight different models and concluded, based on split sample experiments, that the two parameter lognormal (LN2) and the log Pearson Type III (LP3) were the best. Gunasekara and Cunnane (1992) repeated the split sample experiments of Beard (1974) with synthetic data consisting of samples of 40 events. They concluded that the GEV distribution with probability weighted moments (PWMs) estimated parameters was the best at-site method to estimate the 100 and 1000 year floods and the LP3 with regional skew yielded comparable results.…”

Section: Global Survey Of Flood Frequency Modelsmentioning

confidence: 99%

Probability distribution of flood flows in Tunisia

Abida

Ellouze

2008

Hydrol. Earth Syst. Sci.

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Abstract. L (Linear) moments are used in identifying regional flood frequency distributions for different zones Tunisia wide. 1134 site-years of annual maximum stream flow data from a total of 42 stations with an average record length of 27 years are considered. The country is divided into two homogeneous regions (northern and central/southern Tunisia) using a heterogeneity measure, based on the spread of the sample L-moments among the sites in a given region. Then, selection of the corresponding distribution is achieved through goodness-of-fit comparisons in L-moment diagrams and verified using an L moment based regional test that compares observed to theoretical values of L-skewness and L-kurtosis for various candidate distributions. The distributions used, which represent five of the most frequently used distributions in the analysis of hydrologic extreme variables are: (i) Generalized Extreme Value (GEV), (ii) Pearson Type III (P3), (iii) Generalized Logistic (GLO), (iv) Generalized Normal (GN), and (v) Generalized Pareto (GPA) distributions. Spatial trends, with respect to the best-fit flood frequency distribution, are distinguished: Northern Tunisia was shown to be represented by the GNO distribution while the GNO and GEV distributions give the best fit in central/southern Tunisia.

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“…Both these distributions have been used recently in hydrological analyses since they fulfil three important features: (a) they are specified in the domain of positive values, (b) they depend on only two parameters, and (c) they are characterized by positively skewed shape (Cunnane, 1989;Gunasekara & Cunnane, 1992;Haktanir, 1992;Haktanir & Horlacher, 1993;Singh et al, 1993;Singh, 1998). Both these distributions are obtained by applying the logarithmic transformation to popular Fisher-Tippett type I (Gumbel) and logistic probability density functions, respectively.…”

Section: Introductionmentioning

confidence: 99%

A note on the applicability of log-Gumbel and log-logistic probability distributions in hydrological analyses: I. Known pdf

Rowiński¹,

Strupczewski²,

Singh³

2002

Hydrological Sciences Journal

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Two probability density functions (pdf), popular in hydrological analyses, namely the log-Gumbel (LG) and log-logistic (LL), are discussed with respect to (a) their applicability to hydrological data and (b) the drawbacks resulting from their mathematical properties. This paper-the first in a two-part series-examines a classical problem in which the considered pdf is assumed to be the true distribution. The most significant drawback is the existence of the statistical moments of LG and LL for a very limited range of parameters. For these parameters, a very rapid increase of the skewness coefficient, as a function of the coefficient of variation, is observed (especially for the log-Gumbel distribution), which is seldom observed in the hydrological data. These probability distributions can be applied with confidence only to extreme situations. For other cases, there is an important disagreement between empirical data and theoretical distributions in their tails, which is very important for the characterization of the distribution asymmetry. The limited range of shape parameters in both distributions makes the analyses (such as the method of moments), that make use of the interpretation of moments, inconvenient. It is also shown that the often-used L-moments are not sufficient for the characterization of the location, scale and shape parameters of pdfs, particularly in the case where attention is paid to the tail part of probability distributions. The maximum likelihood method guarantees an asymptotic convergence of the estimators beyond the domain of the existence of the first two moments (or L-moments), but it is not sensitive enough to the upper tails shape.Key words probability density functions; log-Gumbel pdf; log-logistic pdf; statistical moments; L-moments; flood frequency analysis; Poland Applicabilité des distributions de probabilité log-Gumbel et log-logistique dans les analyses hydrologiques: I. Pdf connues Résumé Deux fonctions de densité de probabilité (pdf), largement utilisées dans les analyses hydrologiques, à savoir log-Gumbel (LG) et log-logistique (LL), sont considérées de deux points de vue: (a) leur applicabilité aux données hygrologiques, et (b) leurs défauts résultant des propriétés mathématiques. Dans cet article, qui est le premier d'une série, on étudie le problème classique où la pdf considérée est supposée être la vraie distribution. Le défaut le plus grand est que les moments statistiques de LG et de LL n'existent que pour une étendue très limitée de paramètres. Pour ces paramètres-là, on voit une forte augmentation du coefficient d'asymétrie en fonction de leur variation (surtout pour la distribution log-Gumbel), ce qu'on observe rarement dans les données hydrologiques. On ne peut appliquer ces modèles avec confiance que pour les situations extrêmes. Dans les autres cas, l'adéquation entre les données expérimentales et les distributions théoriques est mauvaise au niveau de la queue, ce qui est très important pour caractériser l'asymétrie de la distribution. L'étendue limitée ...

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Split sampling technique for selecting a flood frequency analysis procedure

Cited by 16 publications

References 5 publications

A data-based comparison of flood frequency analysis methods used in France

A data-based comparison of flood frequency analysis methods used in France

Probability distribution of flood flows in Tunisia

A note on the applicability of log-Gumbel and log-logistic probability distributions in hydrological analyses: I. Known pdf

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