Modélisation de la structure de dépendance hauteur—durée d'événements pluvieux par la copule de Gumbel

Gargouri-Ellouze, Emna; Chebchoub, Assia

doi:10.1623/hysj.53.4.802

Cited by 19 publications

(5 citation statements)

References 35 publications

(30 reference statements)

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“…Copulas are often a good alternative to the most commonly used univariate frequency analyses. Among other applications, copula functions have been used for modelling droughts (Kao and Govindaraju, 2010;Wong et al, 2010;Liu et al, 2011;Reddy and Ganguli, 2012a;Lee et al, 2013;Ma et al, 2013;Wong et al, 2013), rainfall analysis (Singh and Zhang, 2007;Zhang and Singh, 2007a;Gargouri-Ellouze and Chebchoub, 2008;Ghosh, 2010;Vandenberghe et al, 2010;Balistrocchi and Bacchi, 2011;Ariff et al, 2012), assessing the risk of dam overtopping (De Michele et al, 2005), hyetograph analysis (Grimaldi and Serinaldi, 2006b), flood coincidence risk analysis (Chen et al, 2012), geostatistical models (Bardossy, 2006) and, indeed, flood frequency analysis (Grimaldi and Serinaldi, 2006a;Zhang and Singh, 2006;Renard and Lang, 2007;Zhang and Singh, 2007b;Karmakar and Simonovic, 2009). The first paper on copulas in hydrology was written by De Michele and Salvadori (2003), and in the next few years, several other papers were also published, e.g.…”

Section: Introductionmentioning

confidence: 99%

Bivariate flood frequency analysis using the copula function: a case study of the Litija station on the Sava River

2014

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As an alternative to the commonly used univariate flood frequency analysis, copula frequency analysis can be used. In this study, 58 flood events at the Litija gauging station on the Sava River in Slovenia were analysed, selected based on annual maximum discharge values. Corresponding hydrograph volumes and durations were considered. Different bivariate copulas from three families were applied and compared using different statistical, graphical and upper tail dependence tests. The parameters of the copulas were estimated using the method of moments with the inversion of Kendall's tau. The Gumbel–Hougaard copula was selected as the most appropriate for the pair of peak discharge and hydrograph volume (Q‐V). The same copula was also selected for the pair hydrograph volume and duration (V‐D), and the Student‐t copula was selected for the pair of peak discharge and hydrograph duration (Q‐D). The differences among most of the applied copulas were not significant. Different primary, secondary and conditional return periods were calculated and compared, and some relationships among them were obtained. Copyright © 2014 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Bivariate flood frequency analysis using the copula function: a case study of the Litija station on the Sava River

2014

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“…It is known to be a powerful test (Stephens, ), placing more weight than other tests in the tail of the distributions, which are the main focus of interest in IDF analyses (Laio, ). It has therefore often been used in previous studies on IDF curves (Ben‐Zvi, ; Chang et al, ; Gargouri‐Ellouze & Chebchoub, ; Panthou et al, ; Van de Vyver & Demarée, ). The lower the value of the AD statistics A 2 , the better the fit (Ben‐Zvi, ).…”

Section: Methodsmentioning

confidence: 99%

Integrated Bayesian Estimation of Intensity‐Duration‐Frequency Curves: Consolidation and Extensive Testing of a Method

Boukhelifa

Meddi

Gaumé³

2018

Water Resources Research

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Intensity‐duration‐frequency (IDF) curves are one of the most common rainfall statistical models used in hydrologic design and analysis projects. The uncertainties related to the elaboration of these IDF curves have nevertheless seldom been evaluated in the past. The article will recall the existing link between the IDF formulation and some properties of the rainfall series such as simple scaling and multifractal structure. Assuming that these properties are valid, the IDF curves formulation is then the product of a dimensionless (i.e., reduced) distribution function for the annual maximum rainfall intensities/depths and a duration‐dependent scaling factor. Its parameters can be evaluated in an integrated way (i.e., based on a unique pooled sample of peak intensities over a range of durations: from 15 min to 24 hr). The use of likelihood‐based Bayesian Markov chain Monte Carlo statistical inference methods for this evaluation provides consistent uncertainties for all the parameters of the IDF relation and for the corresponding rainfall quantiles. This methodology has been tested, via a local analysis, on a large data set of 48 rain gauge records, spread over the north central part of Algeria (25,000 km2), under various climatic regimes. The integrated approach is undoubtedly consistent with estimates from annual maximum rainfall fitted to single durations. Furthermore, credibility intervals are significantly reduced. Also, this integrated approach appears to be robust: Unlike the traditional method based single durations, it generally provides rational quantile estimates, even when short observed series are available. This is a significant advantage for engineering applications.

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“…and respectively [38]. Therefore, the dependence parameter was estimated using the above conditions.…”

Section: Detecting Dependencymentioning

confidence: 99%

A Copula-based Approach for Assessing Flood Protection Overtopping Associated with a Seasonal Flood Forecast in Niamey, West Africa

Djibo

Seidou

Saley

et al. 2018

JGEESI

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Flood is one of the most important natural disasters that cause huge loss of life and properties every year around the world. Moreover, the International Federation of the Red Cross and Red Crescent Societies pointed out that floods were by far the greatest cause of homelessness. In West Africa, many countries are damaged from flooding almost every season. Thus, this study aimed to set a seasonal flood forecast model and carried out an evaluation of the level of risk associated with each seasonal forecast. HEC-RAS (Hydrologic Engineering Centers River Analysis System) was used to develop a hydro-dynamical model of Niger river on a 160km reach (80km upstream to 80kmdownstream of Niamey), then a simple risk measure was used to calculate the probability of overtopping the flood protection dykes in Niamey. Results show that the hydro-dynamical model reproduced well the rating curve over the period 2009-2014. A subsequent copula analysis demonstrated a dependency between flow on the Niger river and flow on the Sirba River, the main tributary contributing to the seasonal flood at Niamey. The Gumbel copula was found to be the best among the tested 5 copulas to represent the dependency between peak flow on the main channel of the Niger River and concomitant flow on the Sirba river. It is found that for the six dykes the probabilities of being overtopped by the flood range from very high (100%) to relatively low (16.67 %) over the 34 years of simulation.

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Modélisation de la structure de dépendance hauteur—durée d'événements pluvieux par la copule de Gumbel

Cited by 19 publications

References 35 publications

Bivariate flood frequency analysis using the copula function: a case study of the Litija station on the Sava River

Bivariate flood frequency analysis using the copula function: a case study of the Litija station on the Sava River

Integrated Bayesian Estimation of Intensity‐Duration‐Frequency Curves: Consolidation and Extensive Testing of a Method

A Copula-based Approach for Assessing Flood Protection Overtopping Associated with a Seasonal Flood Forecast in Niamey, West Africa

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