<p>The floods hydrographs have important characteristics such as peak flow and volume. In the runoff of rivers throughout the years, flood hydrographs are presented in different ways, where these two aspects are random variables that have a specific relationship between them. This study proposes a comprehensive analysis for design hydrograph estimation using copulas in multivariate frequency analysis, aiming to enhance efficiency and establish a robust probabilistic foundation. The central idea is to maintain the natural dependence structure between these variables. This approach ensures coherent results demonstrating that joint data selection allows for an appropriate probability joint approximation using various Archimedean copulas, even with different marginal probability functions. The study compares methods for determining the copula association parameter, evaluating which provides a better fit for the study variables. Additionally, the relationship between the copula association parameter and dependence measures, such as Kendall’s Tau and Spearman’s rho, is established. Emphasis is placed on the importance of marginal probability and the behavior of the joint probability estimated with copulas for utilizing joint return periods. Kendall’s return period is presented as a beneficial alternative compared to “or” and “and”. The study introduces the estimation of joint probability isolines and the selection of values to create bivariate hydrographs, eliminating arbitrariness. An application example of the method to a real case is included, demonstrating its practical viability in various situations.</p>