Markov Chain Monte Carlo (MCMC) methods,  which are popular for estimating parameters uncertainty of hydrologic models,  generally converge slowly,  and are easy to get stuck in a local optimized region in the parametric space during uncertainty assessment of hydrologic model parameters.  In this paper the Parallel Adaptive Metropolis (PAM) algorithm is presented to access the parameters uncertainty of hydrologic models.  The PAM algorithm provides an adaptive MCMC sampler to estimate the posterior probability distribution of parameters under Bayesian framework.  The performance of the PAM algorithm is greatly improved in the manner of parallel computing.  The PAM algorithm is applied to assess the parameter uncertainty of Xinanjiang model using hydrologic data from Shuangpai Reservoir.  The case study demonstrates that there is considerable uncertainty about the Xinanjiang model parameters.  The hydrograph prediction uncertainty ranges associated with the posterior distribution of the parameters estimates can bracket the observed flows well,  but not large,  indicating that the method is feasible.