Parameters Uncertainty Analysis in distributed single - event rainfall-runoff model with MCMC approach

f MCMC scheme rather than some others like SCEM-UA algorithm as another MCMC methoSo far flood forecasting and simulation in hydrologic literature suffer from lack of explicit recognition of forcing and parameter 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 the quantification of parameter uncertainty and its effect on the prediction of streamflow for Abolabbas subwatershed (284 km2) is located in Khuzestan Province. This paper intent to take advantage of novel Markov chain Monte Carlo (MCMC) sampler entitled DiffeRential Evolution Adaptive Metropolis (DREAM) that is especially designed to efficiently estimate the posterior probability density function of hydrologic model parameters in complex high-dimensional sampling problems which recently is developed. 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 originate from baseflow separation methods they were predicted out of the confidence interval. Updating