A Multivariate Frequency Analysis Framework to Estimate the Return Period of Hurricane Events Using Event-Based Copula

This study proposed a framework to evaluate multivariate return periods of hurricanes using event-based frequency analysis techniques. The applicability of the proposed framework was demonstrated using point-based and spatial analyses on a recent catastrophic event, Hurricane Ian. Univariate, bivariate, and trivariate frequency analyses were performed by applying generalized extreme value distribution and copula on annual maximum series of flood volume, peak discharge, total rainfall depth, maximum wind speed, wave height and storm surge. As a result of point-based analyses, return periods of Hurricane Ian was investigated by using our framework; univariate return periods were estimated from 39.2 to 60.2 years, bivariate from 824.1 to 1,592.6 years, and trivariate from 332.1 to 1,722.9 years for the Daytona-St. Augustine Basin. In the Florida Bay-Florida Keys Basin, univariate return periods were calculated from 7.5 to 32.9 years, bivariate from 36.5 to 114.9 years, and trivariate from 25.0 to 214.8 years. Using the spatial analyses, we were able to generate the return period map of Hurricane Ian across Florida. Based on bivariate frequency analyses, 18% of hydrologic unit code 8 (HUC8) basins had an average return period of more than 30 years. Sources of uncertainty, due to the scarcity of analysis data, stationarity assumption and impact of other weather systems such as strong frontal passages, were also discussed. Despite these limitations, our framework and results will be valuable in disaster response and recovery. We introduced a new framework for evaluating multivariate return periods of hurricane events through event-based frequency analysesUsing this framework, we estimated the univariate, bivariate, and trivariate return periods of Hurricane IanA return period map was produced using the framework to identify the HUC8 basins affected by Hurricane Ian

Automated summary

This study proposed a framework for evaluating multivariate return periods of hurricanes and demonstrated its applicability through a case study. The framework allows for estimation of univariate, bivariate, and trivariate return periods of hurricanes, and generates return period maps. The study also discusses sources of uncertainty and highlights the importance of the framework and results in disaster response and recovery.