Using Copulas in Hydrology: Benefits, Cautions, and Issues

By Debbie J. Dupuis. 2007

127 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/a1948c49bc65a3c4e2be979411a130210328ddd5

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In Finance

1. Shenglian Guo
2. Lu Chen
3. Luis A. Escobar
4. H. Chowdhary
5. Mojca Šraj
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In Global and Planetary Change

1. Shenglian Guo
2. Lu Chen
3. Mojca Šraj
4. Nejc Bezak
5. Vijay P. Singh
6. Songbai Song
7. M. Brilly
8. A. Daneshkhah

Citations in the corpus

• Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data (2014, methodology): “This is especially advantageous in cases, where the dependence structure between the variates is too complex to be modeled by a multivariate Gaussian distribution, as it is often the case for hydrometeorological variables (Salvadori and Michele, 2007; Dupuis, 2007).”
• Probabilistic modeling of flood characterizations with parametric and minimum information pair-copula model (2016, methodology): “Dupuis (2007) used 5 copulas (Normal, Student-t, Frank, Clayton, Gumbel, and associated Clayton) and warned about ignoring the tail dependence characteristics of flood data.”
• Frequency analysis of droughts using the Plackett copula and parameter estimation by genetic algorithm (2010, background): “ Dupuis (2007) discussed bivariate modeling of extreme tails of correlated hydrological random variables.”
• Bivariate Flood Frequency Analysis with Historical Information Based on Copula (2013, background): “Many hydrological frequency problems, such as design flood hydrograph that includes flood peak and flood volumes, should be solved by the multivariate distributions (Dupuis 2007; Xiao et al. 2008, 2009).”
• Meta-elliptical copulas for drought frequency analysis of periodic hydrologic data (2010, background): “In recent years considerable attention has been focused on the use of copulas for multivariate hydrologic analyses (De Michele and Salvadori 2003; Shiau 2003; Favre et al. 2004; Salvadori and De Michele 2004, 2006, 2007; De Michele et al. 2005 ;B ardossy 2006 ;B a´rdossy and Li 2008; Grimaldi and Se”
• Maximum entropy-Gumbel-Hougaard copula method for simulation of monthly streamflow in Xiangxi river, China (2015, methodology): “The methods of inference functions for margins (IFM), CG and maximum likelihood are used to estimate parameters of MEGHC (Shewchuk 1994; Dupuis 2007).”
• Copula-based assimilation of radar and gauge information to derive bias-corrected precipitation fields (2012, methodology): “Alternatively, a copula approach can be used to describe the complex spatio-temporal dependence structure and assess for non-linear behaviour (e.g. Genest and Favre , 2007; Dupuis, 2007).”
• Identification of suitable copulas for bivariate frequency analysis of flood peak and flood volume data (2011, methodology): “Along with citing benefits of copulas, Dupuis (2007) also cautioned against ignoring the tail dependence characteristics by illustrating the consequences of misspecification of copulas by employing a simulation technique involving six copula families — Normal, Student-t, Frank, Clayton, Gumbel, and…”
• Copulas and Its Application in Hydrology and Water Resources (2018, methodology): “Then bivariate distributions with non-normal margins have been proposed, such as bivariate exponential (Favre et al. 2002), bivariate gamma (Yue et al. 2001), and bivariate extreme value distributions (Adamson et al. 1999). Favre et al. (2004) summarized the drawbacks of these types of distributions”
• Bivariate frequency analysis of floods using a diffusion based kernel density estimator (2013, background): “However, they are not simple and consistent [Dupuis, 2007].”
• Bivariate flood frequency analysis using the copula function: a case study of the Litija station on the Sava River (2015, methodology): “…iversity of Ljubljana, Jamova 2, SI-1000 Ljubljana, Slovenia. ail: mojca.sraj@fgg.uni-lj.si pyright © 2014 John Wiley & Sons, Ltd. Salvadori (2003), and in the next few years, several other papers were also published, e.g. Favre et al. (2004); Salvadori and De Michele (2004) and Dupuis (2007).”