Abstract
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.
Highlights
On 6 February 2015, a low-pressure system that developed over the north of Spain moved across the island of Corsica into Italy
The selected pair-copula constructions and fitted pair-copula families are shown in Appendices B1 and C
When considering a model which does not take the serial correlation of the contributing variables Y into account, we get an underestimation of the risk uncertainties
Summary
On 6 February 2015, a low-pressure system that developed over the north of Spain moved across the island of Corsica into Italy. Downscaling may be used to statistically extend the risk assessment back in time to periods where observations of the predictors but not of the contributing variables and impacts are available, or to assess potential future changes in CEs based on climate models. Based on this model, we study compound flooding in Ravenna. Wahl et al, 2015; Zheng et al, 2013; Kew et al, 2013; Svensson and Jones, 2002; Lian et al, 2013) Among these studies, Wahl et al (2015) observed an increase in the risk of compound flooding in major US cities driven by an increasing dependence between storm surges and extreme rainfall.
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