Toward a combined Bayesian frameworks to quantify parameter uncertainty in a large mountainous catchment with high spatial variability.

Although hydrological models play an essential role in managing water resources, quantifying different sources of uncertainties is a challenging task. In this study, the application of two parameter uncertainty quantification methods and their performances for predicting runoff was investigated. Sequential Uncertainty Fitting version 2 (SUFI-2) and DiffeRential Evolution Adaptive Metropolis (DREAM-ZS) algorithms were employed to explore the output uncertainty of Soil and Water Assessment Tool (SWAT) at a multisite flow gauging station.