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Multiple flood-generating mechanisms can affect the communities and infrastructure systems across coastal areas. Reliable design flood estimation and risk assessment in these regions demand characterization of the interrelationships between different drivers as well as the corresponding compounding effects. In this study, we assess the compound flood risks across Canada's coasts considering eight bivariate flooding scenarios acquired from four major drivers including precipitation, streamflow, skew surge, and total water level. For each scenario, an initial dependency test based on Kendall's Tau is conducted. The individual variability of each driver, at different locations, is characterized through parametric extreme value distributions followed by developing the bivariate joint distributions using copula functions. Parameters of the copula functions and the marginal distributions are inferred based on the Bayesian approach. Further, compound flood risks and failure probabilities are analyzed considering the OR, AND, Kendall, and conditional hazard scenarios. Results suggest that most locations can be affected by compound flooding associated with at least two bivariate events. Among the studied scenarios, the concurrent occurrence of extreme precipitation and sea level is the most common across ~ 80% of the coastal locations. In addition, critical sites that can be affected by the majority of the bivariate events (six out of eight combinations) are identified at the Pacific, south of Lake Huron, Lake Erie, and Lake Ontario. Besides, the estimated failure probabilities show higher hydrologic risks associated with the bivariate analysis of flood drivers compared to the univariate estimates. [ABSTRACT FROM AUTHOR]

The paper introduces a function defined by a power series in the probabilities of multivariate records in a sequence of independent identically distributed random vectors with continuous components. In the univariate case, this function is always the same, but in the multivariate case there exists a broad variety of such functions determined by distribution copulas. The probabilistic meaning of this function and its derivatives is presented. A calculation method using the Kendall distribution function is given. The concepts of favorableness of copulas for records, record time distribution, and mean record time (without taking into account the order) are introduced. Examples are given. [ABSTRACT FROM AUTHOR]