Probability distribution of flood flows in Tunisia

Abida, Habib; Ellouze, Manel

doi:10.5194/hessd-4-957-2007

Cited by 12 publications

(15 citation statements)

References 25 publications

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“…It is common because it inevitably happens with a given frequency in all natural drainage systems (Hussain & Pasha, ), and devastating for being often associated with severe economical and human losses (Hussain & Pasha, ; Rahman et al, ). Reliable estimates of flood magnitude and frequency are essential in the design of hydraulic structures such as dams, spillways, irrigation ditches, bridges, culverts, and urban drainage systems (Abida & Ellouze, ). In such cases, events associated with return periods (RPs) greater than the existing observed frequency, for instance 1,000–10,000 years and 10,000 years for spillways and dams, respectively, are the most relevant (Aydoğan, Kankal, & Onsoy, ).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, many at‐site frequency analyses of hydrological variables have been conducted (Beskow, Caldeira, Mello, Faria, & Guedes, ; Cassalho et al, ; Rahman et al, ). However, current research effort has been given worldwide to regional flood frequency analysis (RFFA) rather than at‐site FFA (Abida & Ellouze, ). This characteristic can be mainly attributed to the lack of sufficiently long recorded streamflow periods, thereby limiting statistical robustness of at‐site FFA (Aydoğan et al, ; Rahman et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…The development of the L‐moments method is probably the most significant contribution to the field of statistical hydrology in the last century (Abida & Ellouze, ). Many authors have verified its greater performance for a wide range of hydrologic applications (Beskow et al, ; Kar et al, ; Rahman et al, ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Regional flood frequency analysis using L‐moments for geographically defined regions: An assessment in Brazil

Cassalho

Beskow

Mello

et al. 2018

J Flood Risk Management

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The devastating effects of floods, combined with scarce data sets, have stimulated the development of hydrological regionalisation techniques. The present study proposed an evaluation of the L-moments based index-flood procedure, coupled with watershed grouping based on geographical convenience for regionalisation of maximum streamflows. A pioneer analysis for South America, addressing the geographical classification method adopted by National Water Agency of Brazil (ANA), was conducted considering over 100 watersheds in southern Brazil. Nonstationary and discordant maximum annual streamflow (MAS) series were removed with the aid of the Mann-Kendall test and discordancy measure, whereas the heterogeneity measure was used to check regional homogeneity. The best regional distribution was identified by the Z DIST goodness-of-fit measure. Finally, multiple nonlinear regressions, considering morphological and meteorological watershed characteristics, were performed to obtain better index-flood estimates. It was concluded that: (a) the proposed methodological strategy provided satisfactory estimation of design floods; (b) area, mean slope, stream gradient, and flow length, were the most satisfactory explanatory variables; (c) the fitted equations stand out as a state-of-art alternative for the scarce hydrological monitoring in southern Brazil; and (d) the hydrological boundaries defined by ANA might not be the most adequate approach from a regional point of view.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Regional flood frequency analysis using L‐moments for geographically defined regions: An assessment in Brazil

Cassalho

Beskow

Mello

et al. 2018

J Flood Risk Management

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“…In Taiwan the Log-Pearson type III distribution (LPE3) was also suggested for rainfall frequency analysis (TW-WRA 2000). Flood flow in Northern Tunisia was shown to be best represented by the GEV distribution (Abida and Elouze 2007). To our knowledge, acceptance regions for L-moments based GOF tests for three-parameter distributions such as PE3 and GEV have not been established.…”

Section: Introductionmentioning

confidence: 99%

Establishing acceptance regions for L-moments based goodness-of-fit tests for the Pearson type III distribution

Liou

et al. 2011

Stoch Environ Res Risk Assess

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Goodness-of-fit tests based on the L-momentratio diagram for selection of appropriate distributions for hydrological variables have had many applications in recent years. For such applications, sample-size-dependent acceptance regions need to be established in order to take into account the uncertainties induced by sample L-skewness and L-kurtosis. Acceptance regions of two-parameter distributions such as the normal and Gumbel distributions have been developed. However, many hydrological variables are better characterized by three-parameter distributions such as the Pearson type III and generalized extreme value distributions. Establishing acceptance regions for these three-parameter distributions is more complicated since their L-momentratio diagrams plot as curves, instead of unique points for two-parameter distributions. Through stochastic simulation we established sample-size-dependent 95% acceptance regions for the Pearson type III distribution. The proposed approach involves two key elements-the conditional distribution of population L-skewness given a sample L-skewness and the conditional distribution of sample L-kurtosis given a sample L-skewness. The established 95% acceptance regions of the Pearson type III distribution were further validated through two types of validity check, and were found to be applicable for goodness-of-fit tests for random samples of any sample size between 20 and 300 and coefficient of skewness not exceeding 3.0.

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“…The same methodology had also been exercised by Hosking and Wallis (), Parida et al . (), Fowler and Kilsby (), Yue and Wang (), Abida and Ellouze (), Saf (), Seckin et al . (), Ahmad et al .…”

Section: Introductionmentioning

confidence: 99%

Estimation of wind speed using regional frequency analysis based on linear‐moments

Fawad

Ahmad

Nadeem

et al. 2018

Intl Journal of Climatology

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The quantiles of annual maximum wind speed (AMWS) can be estimated for different meteorological stations of interest by using at-site frequency analysis and extreme value theory. These estimates are of immense importance for the codification of wind speed. However, the historical data of wind speed at the number of meteorological stations are sometimes unavailable and often insufficient due to the shorter length, especially in developing countries like Pakistan. The scarcity of the data increases the uncertainty of the quantiles estimates regarding policy implications. To cope with the problem, an approach of Regional Frequency Analysis (RFA) is opted here. In this study, RFA of AMWS using linear-moments (Lmoments) is carried out by considering wind speed data of nine meteorological stations of province Punjab, Pakistan. No station is found to be discordant. A single homogenous region is constituted from these nine stations using a subjective approach based on their geographical locations. Heterogeneity measures justify that these nine stations of Punjab form a single homogeneous region. Regional quantiles estimates are found through the most appropriate probability distribution among generalized normal (GNO), generalized logistic (GLO), Pearson Type 3 (P3), generalized Pareto (GPA), Weibull (WEI), log Pearson Type 3 (LP3) and generalized extreme value (GEV) distributions. Z-statistic and L-moment ratio diagram suggest that GLO and GNO distributions are better choices than others. Robustness of both distributions is evaluated through relative bias (RB) and relative root mean square error (RRMSE). Findings indicate that overall, GLO distribution is better than GNO. Further, we also find at-site quantiles from dimensionless quantities (regional quantiles) using the sample mean and median as scaling factors. Quantiles' estimates calculated from this study can be used in codified structural designs for policy implications. K E Y W O R D S linear-moments, Monte Carlo simulation, quantile estimates, wind speed

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Probability distribution of flood flows in Tunisia

Cited by 12 publications

References 25 publications

Regional flood frequency analysis using L‐moments for geographically defined regions: An assessment in Brazil

Regional flood frequency analysis using L‐moments for geographically defined regions: An assessment in Brazil

Establishing acceptance regions for L-moments based goodness-of-fit tests for the Pearson type III distribution

Estimation of wind speed using regional frequency analysis based on linear‐moments

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