Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
doi:10.5194/hess-2016-652
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
02 Jan 2017
Review status
A revision of this discussion paper was accepted for the journal Hydrology and Earth System Sciences (HESS) and is expected to appear here in due course.
Multivariate Statistical Modelling of Compound Events via Pair-Copula Constructions: Analysis of Floods in Ravenna
Emanuele Bevacqua1, Douglas Maraun1, Ingrid Hobæk Haff2, Martin Widmann3, and Mathieu Vrac4 1Wegener Center for Climate and Global Change, University of Graz, Graz, Austria
2Department of Mathematics, University of Oslo, Oslo, Norway
3School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom
4Laboratoire des Sciences du Climat et de l'Environnement, CEA Saclay, Gif-sur-Yvette, France
Abstract. Compound events are multivariate extreme events in which the individual contributing variables may not be extreme themselves, but their joint – dependent – occurrence causes an extreme impact. The conventional univariate statistical analysis cannot give accurate information regarding the multivariate nature of these events. We develop a conceptual model, implemented via pair-copula constructions, which allows for the quantification of the risk associated with compound events in present day and future climate, as well as the uncertainty estimates around such risk. The model includes meteorological predictors which provide insight into both the involved physical processes, and the temporal variability of CEs. Moreover, this model provides multivariate statistical downscaling of compound events. Downscaling of compound events is required to extend their risk assessment to the past or future climate, where climate models either do not simulate realistic values of the local variables driving the events, or do not simulate them at all. Based on the developed model, we study compound floods, i.e. joint storm surge and high river runoff, in Ravenna (Italy). To explicitly quantify the risk, we define the impact of compound floods as a function of sea and river levels. We use meteorological predictors to extend the analysis to the past, and get a more robust risk analysis. We quantify the uncertainties of the risk analysis observing that they are very large due to the shortness of the available data, though this may also be the case in other studies where they have not been estimated. Ignoring the dependence between sea and river levels would result in an underestimation of risk, in particular the expected return period of the highest compound flood observed increases from about 20 to 32 years when switching from the dependent to the independent case.

Citation: Bevacqua, E., Maraun, D., Hobæk Haff, I., Widmann, M., and Vrac, M.: Multivariate Statistical Modelling of Compound Events via Pair-Copula Constructions: Analysis of Floods in Ravenna, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-652, in review, 2017.
Emanuele Bevacqua et al.
Emanuele Bevacqua et al.
Emanuele Bevacqua et al.

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Short summary
We develop a conceptual model to quantify the risk of Compound events, i.e. extreme impacts which are driven by statistically dependent meteorological 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 Compound events, as well as into the temporal variability, and (2) allow to statistically downscale Compound events.
We develop a conceptual model to quantify the risk of Compound events, i.e. extreme impacts...
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