For the ill-posed environment hydraulic inverse problem, a methodical model was constructed based on Bayesian inference and two-dimensional water quality model. Markov chain Monte Carlo simulation was applied to get posterior probability distribution of the source's position, intensity and event init time. The result of case study shows that the method based on Bayesian inference with Markov chain Monte Carlo simulation is fit for inverse problem such as contamination event source identification featuring high accuracy and little error. Compared with the identification results of hybrid genetic algorithm and pattern search, the presented approach indicated high stability and robust on the same inverse problem.