Abstract: The emerging and popularity of distributed hydrological models promote the development of hydrology sciences meanwhile massive model parameters are introduced, emphasizing the necessity of screening out sensitive parameters. Sensitivity analysis（SA) helps recognize sensitive parameters, reduce the number of model parameters to be analyzed and facilitates parametric optimization and uncertainty estimation. In this study the Xin’anjiang model was applied to Gaotan catchment located in the upstream of Han River basin and Nash-Sutcliffe efficiency（DC), water balance coefficient（WB) and their combination served as objective functions to evaluate the accuracy of daily streamflow simulation. A qualitative SA approach, Morris method was first used to screen out sensitive parameters and reduce the dimensionality of model parameters. The Sobol method was then adopted to quantify degrees of sensitivity of sensitive parameters quantitatively. Both methods are implemented for three different objective functions. The result showed that among 13 selected model parameters, runoff generation parameter KC, runoff separation parameters SM and KI/ KG, runoff routing parameters CI,CG and CS are identified as sensitive parameters, among which KC is most sensitive. Furthermore, the selection of objective functions to be evaluated has significant impact on the determination of sensitive parameters: water balance coefficient is most sensitive to KC while Nash-Sutcliffe efficiency more sensitive to other sensitive parameters, especially CS. Further investigation into sensitive parameters revealed that some parameters like SM have higher interaction sensitivities compared to first-order sensitivities, indicating that the interactions between model parameters should be regarded as an unnegligible factor of parameters sensitivity. The Morris method facilitates the reduction of dimensionalities of model parameters while the Sobol method precisely characterizes the features of parametric sensitivities and provides more information on the sensitivities. The integration of both methods is demonstrated to enhance the reliability and accuracy of sensitivity analysis.