So far flood forecasting and simulation in hydrologic literature suffers from lack of explicit recognition of forcing, parameters, and model structural error. However, since any model is a simplification of reality, there remains a great deal of uncertainty even after the calibration of model parameters. Hydrologic models often contain parameters that cannot be measured directly but which can only be inferred by a trial-and-error (calibration) process that adjusts the parameter values to closely match the input-output behavior of the model to the real system it represents. This work addresses calibration of spatially physically based rainfall-runoff model (AFFDEF) implemented in FORTRAN language programming and implemented to quantify parameter uncertainty and its effect on the prediction of streamflow for Abolabbas subwatershed (??? km?) located in Khuzestan Province. This research was intended to take advantage of novel Markov chain Monte Carlo (MCMC) sampler entitled DiffeRential Evolution Adaptive Metropolis (DREAM) that is especially designed to estimate the posterior probability density function of hydrologic model parameters efficiently in complex high-dimensional sampling problems. The results for calibration period showed that observational discharge values especially peak values bracketed well within %95 confidence interval. Regarding rising and recession limb as a result of initial conditions and uncertainties originating from baseflow determination procedures have caused predictions to be out confidence interval. Updating the scale and orientation of proposed distribution during sampling is the main advantage of MCMC scheme compared