Trivariate Flood Frequency Analysis Using the Gumbel–Hougaard Copula

By Lei Zhang, Vijay P. Singh. 2007

167 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6cf7e87fb5baee84dc111e1f3408d63aa85460a5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

In Statistics and Probability

Citations in the corpus

Copulas and Its Application in Hydrology and Water Resources (2018, methodology): “…resources can be summarized as: rainfall frequency analysis (Michele and Salvadori 2003; Grimaldi and Serinaldi 2006a; Kao and Govindaraju 2007; Zhang and Singh 2007a; Kuhn et al. 2007; and Keef et al. 2009), flood frequency analysis (Favre et al. 2004; Shiau et al. 2006; Zhang and Singh 2006,…”
Trivariate Frequency Analyses of Peak Discharge, Hydrograph Volume and Suspended Sediment Concentration Data Using Copulas (2014, methodology): “Zhang and Singh (2006) used one parameter Archimedean copulas for bivariate flood frequency analysis, while Zhang and Singh (2007b) used Gumbel-Hougaard copula for trivariate flood frequency analysis.”
Bivariate Flood Frequency Analysis with Historical Information Based on Copula (2013, methodology): “…(a) flexibility in choosing arbitrary marginals and structure of dependence, (b) extension to more than two variables, and (c) separate analysis of marginal distribution and dependence structure (Salvadori and De Michele 2007; Genest and Favre 2007; Zhang and Singh 2007a; Serinaldi et al. 2009).”
A framework of change‐point detection for multivariate hydrological series (2015, methodology): “…that can be applied to construct the dependence structure of multivariate hydrological series, i.e., symmetric copula [Joe, 1997; Nelsen, 2006; Zhang and Singh, 2007; Aas and Berg, 2009], asymmetric copula [Joe, 1997; Nelsen, 2006; Grimaldi and Serinaldi, 2006; Serinaldi and Grimaldi, 2007;…”
Multivariate hydrologic design methods under nonstationary conditions and application to engineering practice (2018, background): “The Gumbel–Hougaard copula, an extreme-value copula widely used in hydrology, accounts for the upper tail dependence and is well-suited to the dependence structure of a multivariate flood distribution (Salvadori et al., 2007; Zhang and Singh, 2007; Xiong et al., 2015).”
Probabilistic assessment of flood risks using trivariate copulas (2012, methodology): “Recently, copulas have been applied in the field of hydrology (Favre et al. 2004; Salvadori and De Michele 2004; Grimaldi and Serinaldi 2006; Zhang and Singh 2007; Chowdhary et al. 2011; Salvadori et al. 2011).”