Bivariate flood frequency analysis. Part 2: a copula‐based approach with mixed marginal distributions

By Subhankar Karmakar, Slobodan P. Simonovic. 2009

153 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/68301038608ceaceff9e5d65f30a5d6e7b2b73ec

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In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

An uncertain journey around the tails of multivariate hydrological distributions (2013, methodology): “…procedures, providing a copula-based joint distribution and their lower dimensional marginals and conditional distributions as a final result [e.g., Yue et al., 1999; Yue, 2000, 2001a, 2001b; Grimaldi and Serinaldi, 2006; Zhang and Singh, 2007; Karmakar and Simonovic, 2009; Ghizzoni et al., 2010].”
Bivariate Flood Frequency Analysis of Upper Godavari River Flows Using Archimedean Copulas (2012, methodology): “Karmakar and Simonovic (2009) applied three Archimedean copulas namely, Ali-Mikhal-Haq, Clayton and Gumbel-Hougaard for bivariate flood frequency analysis of flood flows of Red River at Grand Forks, Dakota, and found that Gumbel-Hougaard copula as the best model for representing flood properties.”
Probabilistic modeling of flood characterizations with parametric and minimum information pair-copula model (2016, background): “Karmakar and Simonovic (2009) identified that the generalized hyperbolic copula is better at obtaining pair-wise joint distributions among flood peak flow, volume and duration.”
Comparison between the peaks-over-threshold method and the annual maximum method for flood frequency analysis (2014, methodology): “This type of AIC test was used by Karmakar and Simonovic (2008, 2009).”
Bivariate Flood Frequency Analysis with Historical Information Based on Copula (2013, background): “…of upper tail-dependence (Poulin et al. 2007) and because of good performance in many applications (De Michele et al. 2005; Zhang and Singh 2006; Poulin et al. 2007; Salvadori and De Michele 2007; Karmakar and Simonovic 2009; Giustarini et al. 2010; Chebana and Quarda 2011; Salvadori et al. 2011).”
Bivariate frequency analysis of floods using a diffusion based kernel density estimator (2013, methodology): “…been extensively used for multivariate flood frequency analysis (FFA) in hydrology [e.g., Favre et al., 2004; Grimaldi and Serinaldi, 2006; Zhang and Singh, 2007a, 2007b; Genest et al., 2007; Karmakar and Simonovic, 2008, 2009; Chebana and Ouarda, 2009; Klein et al., 2010, and references therein].”
Bivariate flood frequency analysis: Part 1. Determination of marginals by parametric and nonparametric techniques (2008, methodology): “Some investigation is performed and presented in Part 2 (Karmakar and Simonovic, 2008) to identify the pattern of mutual correlation between P, V and D for different threshold discharges.”
Identification of suitable copulas for bivariate frequency analysis of flood peak and flood volume data (2011, methodology): “…for this flood peak flow and volume data is at odds with the results reported by De Michele et al. (2005), Zhang & Singh (2006), and Genest & Favre (2007), Poulin et al. (2007) and Karmakar & Simonovic (2009), wherein GH copula had been adjudged better than other copulas, including the Clayton.”
Bivariate flood frequency analysis using the copula function: a case study of the Litija station on the Sava River (2015, background): “…functions over classical bivariate frequency analyses is that the selection of marginal distributions and multivariate dependence modelling are two separate processes, giving additional flexibility to the practitioner (Zhang and Singh, 2006; Genest and Favre, 2007; Karmakar and Simonovic, 2009).”
Modelling bivariate rainfall distribution and generating bivariate correlated rainfall data in neighbouring meteorological subdivisions using copula (2010, background): “…the mathematical relationship (Nelsen, 1999) between Kendall’s coefficient of correlation , and generating function t , which is given by (Karmakar and Simonovic, 2009): D 1 C 4 ∫ 1 0 t 0 t dt 4 where t is either u or v. Kendall’s coefficient of correlation ( ) is a well-known…”