Assessment of Regional Floods Using L-Moments Approach: The Case of The River Nile

Atiem, Isameldin A.; ̆lu, Nilgün B. Harmancio

doi:10.1007/s11269-005-9004-0

Cited by 47 publications

(36 citation statements)

References 22 publications

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“…In [9] used the method of L-moment estimators to estimate the parameters of polynomial quantile mixture. Some of literatures used L-moments such as [1,7,16,21]. In [24] suggested LH-moments method that considered a generalization of L-moments based on linear combinations of higher-order statistics for describing the larger events in data and the upper part of distributions and has lately been used in hydrology by [14].…”

Section: Introductionmentioning

confidence: 99%

Rainfall frequency analysis using LH-moments approach: A case of Kemaman Station, Malaysia

Zakaria¹,

Suleiman²,

Mohamad³

2018

IJET

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Statistical analysis of extreme events is often carried out to obtain the probability distribution of floods data and then predict the occurrence of floods for a significant return period. L-moments approach is known as the most popular approach in frequency analysis. This paper discusses comparison of the L-moments method with higher order moments (LH-moments) method. LH-moment, a generalization of Lmoment, which is proposed based on the linear combinations of higher-order statistics has been used to characterize the upper part of distributions and larger events in flood data. It is observed from a comparative study that the results of the analysis of observed data and the diagram based on the K3D-II distribution using LH-moments method is more efficient and reasonable than the L-moments method for estimating data of the upper part of the distribution events.

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Section: Introductionmentioning

confidence: 99%

Rainfall frequency analysis using LH-moments approach: A case of Kemaman Station, Malaysia

Zakaria¹,

Suleiman²,

Mohamad³

2018

IJET

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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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“…In India, L-moments based regional flood frequency analysis was carried out by Paradia et al(1998) and Kumar et al(1999Kumar et al( , 2003Kumar et al( and 2005 to developed flood frequency relationship for both gauged and ungauged catchments for different regions. In addition to this, some other recent application of regional flood frequency analysis includes Atiem and Harmancioglu (2006), Saf (2008) and Hussain and Pasha (2008).The present study has been undertaken up to estimates quantiles, based on L-moment approach for gauged and ungauged catchments of Tipura, India and to measure the accuracy of the estimated quantiles, Monte Carlo simulation procedure is used. For this various distributions viz.…”

Section: Introductionmentioning

confidence: 99%

Regional flood frequency analysis of Tripura based on L-moment

Bhuyan

Borah

2012

JANS

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Abstract:The annual maximum discharge data of six gauging sites have been considered for L-moment based regional flood frequency analysis of Tripura, India. Homogeneity of the region has been tested based on heterogeneity measure (H) using method of L-moment. Based on heterogeneity measure it has been observed that the region consist of six gauging sites is homogeneous. Different probability distributions viz. Generalized extreme value (GEV), Generalized Logistic (GLO), Generalized Pareto (GPA), Generalized Normal (GNO), Pearson Type III (PE3) and Wakebay (WAK) have been considered for this investigation. PE3, GNO and GEV have been identified as the candidate distributions based on the L-moment ratio diagram and DIST Z -statistics criteria. Regional growth curves for three candidate distributions have been developed for gauged and ungauged catchments. Monte Carlo simulations technique has also been used to estimate accuracy of the estimated regional growth curves and quantiles. From simulation study it has been observed that PE3 distribution is the robust one.

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Assessment of Regional Floods Using L-Moments Approach: The Case of The River Nile

Cited by 47 publications

References 22 publications

Rainfall frequency analysis using LH-moments approach: A case of Kemaman Station, Malaysia

Rainfall frequency analysis using LH-moments approach: A case of Kemaman Station, Malaysia

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

Regional flood frequency analysis of Tripura based on L-moment

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