Probabilistic assessment of flood risks using trivariate copulas

By Poulomi Ganguli, M. Janga Reddy. Theoretical and Applied Climatology, 2012

132 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/0150bd40b56a5a763b0ffbe78d84df6115be95f4

How researchers see this paper

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

In Finance

1. A. Daneshkhah
2. I. Holman
3. Mojca Šraj
4. Renji Remesan
5. Omid Chatrabgoun
6. Nejc Bezak
7. M. Brilly

In Global and Planetary Change

1. Mojca Šraj
2. Nejc Bezak
3. M. Brilly
4. A. Daneshkhah
5. I. Holman
6. Renji Remesan
7. Omid Chatrabgoun

Citations in the corpus

• A continuous rainfall model based on vine copulas (2015, unknown): “Some applications can be found in the modelling of trivariate rainfall (Zhang and Singh, 2007; Kao and Govindaraju, 2008; Salvadori and De Michele, 2006; Grimaldi and Serinaldi, 2006), trivariate floods (Serinaldi and Grimaldi, 2007; Genest et al., 2007; Ganguli and Reddy, 2013) and trivariate droug”
• Probabilistic modeling of flood characterizations with parametric and minimum information pair-copula model (2016, methodology): “This copula has been chosen as the most appropriate model in analyzing multivariate flood data in several other studies (see Ganguli and Reddy, 2013 and reference therein).”
• Bivariate flood frequency analysis using the copula function: a case study of the Litija station on the Sava River (2015, methodology): “Frahm et al. (2005) introduced the upper tail dependence estimator, whichwas also used by Poulin et al. (2007) and Ganguli and Reddy (2013).”