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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 6
Hydrol. Earth Syst. Sci., 21, 2701–2723, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 21, 2701–2723, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 08 Jun 2017

Research article | 08 Jun 2017

Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy)

Emanuele Bevacqua et al.

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Cited articles

Aas, K., Czado, C., Frigessi, A., and Bakken, H.: Pair-copula constructions of multiple dependence, Insurance: Mathematics and Economics, 44, 182–198,, 2009.
Acar, E. F., Genest, C., and Nešlehová, J.: Beyond simplified pair-copula constructions, J. Multivariate Anal., 110, 74–90,, 2012.
Aghakouchak, A., Cheng, L., Mazdiyasni, O., and Farahmand, A.: Global warming and changes in risk of concurrent climate extremes: Insights from the 2014 California drought, Geophys. Res. Lett., 41, 8847–8852,, 2014.
Arpa Emilia-Romagna: Servizio IdroMeteoClima, Unità Radarmeteorologia, Radarpluviometria, Nowcasting e Reti non convenzionali, Area Centro Funzionale e Sala Operativa Previsioni, Unità gestione Rete idrometeorologica RIRER,Area Modellistica Meteo: Rapporto dell'evento meteorologico del 5 e 6 febbraio 2015, Bologna, Italy, 2015.
Bedford, T. and Cooke, R. M.: Monte Carlo simulation of vine dependent random variables for applications in uncertainty analysis, Proceedings of the European Conference on Safety and Reliability 2001, Turin, Italy, 2001a.
Publications Copernicus
Short summary
We develop a conceptual model to quantify the risk of compound events (CEs), i.e. extreme impacts to society which are driven by statistically dependent climatic variables. Based on this model we study compound floods, i.e. joint storm surge and high river level, in Ravenna (Italy). The model includes meteorological predictors which (1) provide insight into the physical processes underlying CEs, as well as into the temporal variability, and (2) allow us to statistically downscale CEs.
We develop a conceptual model to quantify the risk of compound events (CEs), i.e. extreme...