Comparison of various flood frequency distributions using annual flood peaks data of rivers in Anatolia

Haktanır, Tefaruk

doi:10.1016/0022-1694(92)90002-d

Cited by 53 publications

(35 citation statements)

References 27 publications

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“…The main problem with parametric simulation is that the parametric distribution of most hydrological variables is unknown and that the choice of the parent distribution usually is arbitrary. Although the robustness of the conclusions to the hypothesis of a given parent distribution may be studied Kuczera, 1982;Haktanir, 1992], parametric simulation has been criticized for comparing statistical models in an artificial setting [Klemes, 1986;Potter, 1987 …”

Section: Simulation In Statistical Hydrologymentioning

confidence: 99%

Simulation, Bayes, and bootstrap in statistical hydrology

Fortin¹,

Bernier²,

Bobée³

1997

Water Resources Research

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Abstract. Statistical simulation in hydrology is discussed from a Bayesian perspective. The inherent difficulties in both parametric simulation, based on a parent distribution, and classical nonparametric simulation, based on the bootstrap, are discussed. As an alternative to these procedures, a nonparametric Bayesian simulation methodology, P61ya resampling, is introduced. It consists of simulating from a nonparametric predictive distribution obtained from the analysis of a reference sample, and it is asymptotically equivalent to the bootstrap. The method is generalized to take into account a prior hypothesis on the parametric distribution of a variable. A hybrid simulation model is then obtained that includes parametric and nonparametric simulation as particular cases. An extensive application is presented in a related paper [Fortin et al., 1997], where P61ya resampling is used to compare statistical models for flood frequency analysis. In this paper an example is used to demonstrate how P61ya resampling can help assess the influence of a distribution hypothesis on simulation results. IntroductionIn statistical hydrology, parametric and nonparametric simulation procedures have been extensively used to compare statistical models of hydrological variables. The purpose of this paper is to present an alternative, based on Bayesian analysis, to classical parametric and nonparametric simulation techniques for independent and identically distributed (i.i.d.) variables. The proposed method, which we call P61ya resampling, is similar to the bootstrap [Elton, 1979], which consists in drawing observations with replacement from a reference sample. This simulation scheme, introduced by Lo [1988], is discussed in detail for binomial variables, then for multinomial variables, and finally in a completely nonparametric setting. The methodology is then generalized to take into account prior information independent from the reference sample. The present paper is mainly theoretical; an illustrative example which compares the GEV and Gumbel distributions for at-site flood frequency analysis is included. An extensive application to at-site flood frequency analysis is presented in a related paper [Fortin et al., 1997]. Simulation in Statistical HydrologyA common problem in statistical hydrology is to approximate the unknown statistical distribution F of a (hydrological) random variable X, given prior information and observed data. The usual approach consists in selecting a parametric distribution having probability density function (p.d. In both cases, statistical distributions and methods for estimating the parameters may be compared in terms of their ability to approximate the distribution of the simulated samples. The main problem with parametric simulation is that the parametric distribution of most hydrological variables is unknown and that the choice of the parent distribution usually is arbitrary. Although the robustness of the conclusions to the hypothesis of a given parent distribution may be studied Kuczera, 1982;Haktanir,...

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Section: Simulation In Statistical Hydrologymentioning

confidence: 99%

Simulation, Bayes, and bootstrap in statistical hydrology

Fortin¹,

Bernier²,

Bobée³

1997

Water Resources Research

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“…Adamowski [8] studied probability density functions for oods in the Provinces of Ontario and Quebec, Canada. In Turkey, Haktanir [9], Sorman and Okur [10], Atiem and Harmancioglu [11], Saf [12], and Bayazit and Onoz [13] carried out Lmoments based RFFA. In recent years, Seckin et al (2011) showed that GEV and WAK distributions, whose parameters are computed by the L-moments method, are adequate in predicting quantile estimates in Turkey.…”

Section: Introductionmentioning

confidence: 99%

The estimation of flood quantiles in ungauged sites using teaching-learning based optimization and artificial bee colony algorithms

et al. 2017

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Abstract. In this study, a Regional Flood Frequency Analysis (RFFA) was applied to 33 stream gauging stations in the Eastern Black Sea Basin, Turkey. Homogeneity of the region was determined by discordancy (D i ) and heterogeneity measures (H i ) based on L-moments. Generalized extreme-value, lognormal, Pearson type III, and generalized logistic distributions were tted to the ood data of the homogeneous region. Based on the appropriate distribution for the region, ood quantiles were estimated for return periods of T = 5; 10; 25; 50; 100, and 500 years. A non-linear regression model was then developed to determine the relationship between ood discharges and meteorological and hydrological characteristics of the catchment. In order to compare regression analysis with other models, Arti cial Bee Colony algorithm (ABC) and Teaching-Learning Based Optimization (TLBO) models were developed. The equations were obtained using the ABC and TLBO algorithms to estimate ood discharges for di erent return periods. The analysis showed that the TLBO and ABC results were superior to the regression analysis. Error values indicated that the TLBO method yielded better results for the estimation of ood quantiles for di erent independent variables.

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“…Houghton (1978) is the first researcher to use this distribution for flood frequency analysis. Landwehr et al (1979a,b), Kumar and Chander (1987), Rao and Arora (1987), Haktanir (1992), Vogel et al (1993) and Haktanir and Horlacher (1993) are some of the others who employed the five-parameter Wakeby (W5) distribution for flood frequency analysis. While, in * Correspondence to: TekinÖztekin, University of Gaziosmanpasa, Faculty of Agriculture, Department of Agricultural Engineering, Tasliciftlik Kampus, 60250 Tokat, Turkey.…”

Section: Introductionmentioning

confidence: 99%

Wakeby distribution for representing annual extreme and partial duration rainfall series

Öztekin

2007

Meteorological Applications

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ABSTRACT:In this study, to measure the upper right tail estimate performances for representing annual extreme and partial duration precipitation series at 31 stations in the northeastern and southeastern United States, the results of fiveparameter Wakeby-L moments distribution were compared to those by beta-κ, and beta-P distributions. The statistics of modified Anderson-Darling and average deviation (AD) and the bootstrap resampling to extrapolate the right tail beyond the data showed that the Wakeby distribution gave mostly better results than beta-κ and beta-P , and sometimes it produced comparable results to those by beta-κ and beta-P .

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Comparison of various flood frequency distributions using annual flood peaks data of rivers in Anatolia

Cited by 53 publications

References 27 publications

Simulation, Bayes, and bootstrap in statistical hydrology

Simulation, Bayes, and bootstrap in statistical hydrology

The estimation of flood quantiles in ungauged sites using teaching-learning based optimization and artificial bee colony algorithms

Wakeby distribution for representing annual extreme and partial duration rainfall series

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