While Xin'anjiang model is applied to simulate hydrograph, the "best"  parameter set calibrated may be not unique and uncertain because of model limitation, more parameters and limited information. Considering previously parameter optimization of Xin'anjiang model, there is only a unique "best"  parameter set to be found and it doesn't describe uncertainty of parameter. This paper presents using SCEM-UA algorithm based Markov Chain Monte Carlo (MCMC) methods for optimization and uncertainty assessment of Xin'anjiang model parameters by means of 36 historical floods data with one hour interval. The results demonstrate that SCEM-UA algorithm is well suited to infer the posterior distribution of Xin'anjiang model parameters. The results of calibration and validation indicate that it is feasible and effective for optimization and uncertainty assessment of Xin'anjiang model parameters.