Abstract. Hydrological multivariate analysis has been widely studied using copula-based modelling, in which marginal distribution inference is one of the key issues. The main object of this study is to discuss the applicability of the principle of maximum entropy (POME) in marginal distribution inference, thus to develop a POME-copula framework to analyse the dependence of hydrological variables. Marginal distributions are derived with the POME approach before bivariate copulas constructed with corresponding parameters estimated by the dependence of the derived margins. The proposed POME-copula has been employed in hydrological dependence analyses, with the annual maximum streamflow and water level collected from the Yangtze River, and the monthly streamflow from the Yellow River. Results show that the POME-copula method performs well in capturing dependence patterns of various hydrological variables.