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

By E. Bevacqua, Douglas Maraun, Ingrid Hobæk Haff, M. Widmann, Mathieu Vrac. 2017

329 citations recorded by the source corpus.

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.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/3ea33c038c4bae3c981bf2beb1424fde303ada07

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Citations in the corpus

Dependence between high sea-level and high river discharge increases flood hazard in global deltas and estuaries (2018, background): “…of coastal floods and rainfall or discharge (e.g. Loganathan et al 1987, Pugh 1987, Samuels and Burt 2002, Svensson and Jones 2002, 2004, van den Brink et al 2005, Hawkes 2008, Kew et al 2013, Lian et al 2013, Zheng et al 2014, Klerk et al 2015, van den Hurk et al 2015, Bevacqua et al 2017).”
Increased probability of compound long-duration dry and hot events in Europe during summer (1950–2013) (2019, background): “Such estimates require ensembles of suitable climate models that provide a larger sample of events and perhaps more creative methods to understand the changing probability and future likelihood of such rare events (Hazeleger et al 2015, Bevacqua et al 2017).”
Modeling the spatial dependence of floods using the Fisher copula (2019, methodology): “…skew-t distribution (Ghizzoni et al., 2010, 2012), max-stable models (Wang et al., 2014), and copula models such as pair-copula constructions (Bevacqua et al., 2017; Gräler, 2014; Schulte and Schu-mann, 2015), hierarchical Kendall copulas (Fischer et al., 2017), and factor copula models…”