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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
https://doi.org/10.5194/hess-21-2701-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 21, 2701-2723, 2017
https://doi.org/10.5194/hess-21-2701-2017
© 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 Bevacqua1, Douglas Maraun1, Ingrid Hobæk Haff2, Martin Widmann3, and Mathieu Vrac4 Emanuele Bevacqua et al.
  • 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, UK
  • 4Laboratoire des Sciences du Climat et de l'Environnement, CNRS/IPSL, Gif-sur-Yvette, France

Abstract. Compound events (CEs) are multivariate extreme events in which the individual contributing variables may not be extreme themselves, but their joint – dependent – occurrence causes an extreme impact. 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 predictors, which could represent for instance meteorological processes that provide insight into both the involved physical mechanisms and the temporal variability of compound events. Moreover, this model enables multivariate statistical downscaling of compound events. Downscaling is required to extend the compound events' 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.

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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...
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