Hydrological models are essential for simulating watershed processes, yet uncertainties in parameters often lead to discrepancies between simulations and observations. This study introduced an integrated framework coupling the Soil and Water Assessment Tool (SWATSoil and Water Assessment Tool (SWAT)) with Variational Bayesian Monte CarloVariational Bayesian Monte Carlo (VBMC) and Factorial AnalysisFactorial analysis (FA) to address parameter uncertainty in hydrological modeling. The framework not only obtains the parameter posterior distributions at a low computational cost, but also quantifies the independence and interaction of the parameter factors. An application of the framework is made to the Wuding River basin in the Jiziwan region of the Yellow River to analyze the effects of parameter uncertainty. Results indicate: (i) SWATSoil and Water Assessment Tool (SWAT) model can accurately simulate runoff processes in the study area, with NSE values of 0.67 (calibration) and 0.63 (validation); (ii) posterior distributions showed cn2 with low uncertainty versus latq_co with higher uncertainty; (iii) factorial analysisFactorial analysis identified cn2 as the primary driver of increased mean and peak runoff, moderated by interactions with awc and ovn, respectively. This framework enhances computational efficiency and parameter identifiability, offering a rigorous approach to uncertainty characterization and improved hydrological predictions in complex scenarios.