Estimation of the Uncertainty of Aquifer Simulation-Optimization Model Using the Monte Carlo Markov Chain Algorithm

Uncertainty analysis is an inseparable step in the process of hydrological modeling. Quantitative assessment of the uncertainty in the simulation model outputs and its parameters, thereby increasing confidence in the results of modeling and understanding of the sources of uncertainty. Due to the increasing use of groundwater management model and predict the behavior of the aquifer, this research is seeking to analyze the uncertainty in quantitate-qualitative aquifer simulation and its effect on optimization results. Using SWAT hydrologic model, the amount of recharge is specified and entered into MODFLOW groundwater flow model and MT3DMS transmission model. In this research, the DREAM(zs) algorithm (based on Monte Carlo Markov chain algorithms) was used to examine the uncertainty of MODFLOW model parameters. Then by linking the model with MOPSO, the optimum head and salinity are obtained in the aquifer. The results show that the accuracy of the inputs of the model leads to the desirability of the results in relation to the intended purpose of reducing the water table.