Increasing studies have highlighted that the remarkable inherent uncertainty in design flood hydrograph (DFH) can potentially undermine flood management decisions. In order to quantitatively trace the propagation of DFH uncertainty in reservoir flood control system, we propose a novel methodological framework including three corn parts. First, a copula-based DFH estimation model integrating Bayes’ theorem is presented to estimate DFH under model parameter uncertainty. Second, we perform an optimal reservoir operation model for flood control (OROM_FC) with uncertain DFH as input variable to derive reservoir flood control operations (i.e. output variable). Third, an information theory-based model is designed to trace the DFH uncertainty propagation in reservoir flood control system. A reservoir flood control system in the Han River basin in China is selected as case study. Related results indicate that uncertainty in reservoir flood control operations reduces in comparison with the remarkable uncertainty in DFH due to the performance of the OROM_FC. Specifically, uncertainty in reservoir flood control operations in periods close to peak flow is much smaller compared with that in other periods. The phenomenon further highlights the importance of reservoir flood control operations during periods prior to peak flow. Additionally, we find that uncertainty in flood peak of DFH is the dominant factor affecting reservoir flood control operations compared with that in flood volume of DFH. Moreover, we explore the impact of reservoir flood control capacity on reservoir flood operations in the context of DFH uncertainty. An interesting linear expression is found and fitted for identifying design flood events inducing reservoir overtopping under specific reservoir flood control capacity.