Shuffled Complex Evolution Metropolis Algorithm(SCEM-UA) is an adaptive Markov Chain Monte Carlo sampler

which can be applied to parameter optimization of nonlinear hydrologic model and uncertainty analysis The efficiency and effectiveness of SCEMUA for sampling the posterior distribution of model parameters are discussed based on the case study of the Min River catchment The results show that SCEMUA algorithm is consistent

effective and efficient in inferring the parameter posterior distribution Moreover

the results of regional sensitivity analysis using samples from SCEMUA algorithm sampler show that sensitivity and posterior distribution of parameters are highly interdependent High sensitive parameters correspond with distinct peak in posterior distribution

while low sensitive parameters correspond with flat posterior distribution which could highlight the uncertainty of model parameters.