The Devil Is in the Tail Dependence: An Assessment of Multivariate Copula?Based Frameworks and Dependence Concepts for Coastal Compound Flood Dynamics

A hurricane event can often produce both intense rainfall and a storm tide that cause major compound flooding threat to coastlines. This paper examined applications of multivariate copula-based time series models using data observed during Hurricane Irma (2017) along the coastlines Florida, Georgia, South Carolina, United States. Multivariate were developed bivariate copulas wherein modeled LOWESS-based autoregressive moving average (ARMA). n samples then synthesized Monte Carlo approach in which empirical copula parametric estimate obtained approximate two-sided p-values Rosenblatt probability integral transform method. Analysis suggested proper selection underlying ARMA model was crucial aspect for modeling Archimedean, Elliptical, Extreme Value all offered consistent flexibility terms dependence modeling. As backdrop flood probabilities, this research also outlined theoretical applied frameworks calculation non-exceedance probabilities multidimensional environment classical isofrequency assumptions “AND” (a joint probability) Survival Kendall definitions. Random realizations from combined with definitions ultimately showed there periods temporal yet cyclical high intensities lasted 1–2 hr. Lastly, discussion is presented on broader application proposed methodology within field engineering design risk management may serve as catalyst continued flooding.