Abstract: The sensitivity analysis is a key step in model uncertainty quantification. And it can identify the dominant parameters, reduce the model uncertainty, and enhance the model optimization efficiency. In order to make quantitative global sensitivity analysis (GSA) more tractable, the Morris screening method is used to qualitatively assess a model first. Then, the response surface methodology (RSM) based on the statistical theory will be applied to construct a surrogate model, and to integrate with the variance-based Sobol' method to establishing a new method, named as the RSMSobol method. The new method is tested on the Yanduhe basin using the Xinanjiang model with daily precipitation data and hydrographs. The sensitivity analysis is conducted for four different objective functions. The results demonstrate that the new integrated qualitative and quantitative method can improve the efficiency of the sensitivity analysis, in which the Morris qualitative method can decrease the number of parameters by 50% for the next round of the quantitative analysis. The RSMSobol method can improve the computational cost.