Water leakage typically infiltrates through bolt holes and segment joints, posing a threat to the stability of tunnel structures. In this paper, we develop a copula-based Bayesian network (CBN) method for dependency modeling of complex systems to conduct risk analysis and management of water leakage in operational tunnels. Thirteen potential factors are used to construct the water leakage risk system, and the permeate water volume is selected as the observation index of water leakage risk. An application to a realistic cross-river tunnel of Wuhan Rail Transit Line 3 is implemented to verify the effectiveness of the developed model. Through correlation analysis, the bolt failure rate and seal breakage and aging rate are diagnosed as critical factors with the greatest impact on the water leakage risk. Forward and backward reasoning enables the model to be dynamically updated to provide decision support for water leakage risk management. This research contributes to the following: (a) expressing the dependence of the factors affecting water leakage; (b) solving the problem of modeling uncertainty and information fusion; and (c) updating the model to realize the safety control of tunnel water leakage.