 This paper provides an overview of the current state of the art for estimating multivariate design events. It discusses the various approaches available for constructing multivariate distribution functions, including copula models, which have been shown to be effective in capturing complex dependencies between variables. The paper also presents a synthetic case study to illustrate how different modeling choices can affect the resulting design event. Finally, it concludes by suggesting that careful consideration must be given to the choice of the approach used, depending on the specific application and the real-world problem at hand. This article was authored by B. Grayler, M. J. Vandenberg, S. Vandenberg, and others.