为了识别影响暴雨洪水管理模型（SWMM）的敏感性因素，给后续的模型率定和不确定性分析提供指导和参考，本文基于拉丁超立方抽样技术对各个参数进行抽样，分别以逐步回归法和偏秩相关法对SWMM模型进行全局敏感性分析，并以山东大学千佛山校区为例。结果表明：不透水区曼宁系数是对峰值时间和峰值流量最敏感的参数，并且随着不透水区曼宁系数的增大，峰值时间增大、峰值流量变小；汇水区面积比例因子是对总产流最敏感的参数，并且随着汇水区面积比例因子的增大，总产流量增大。逐步回归法和偏秩相关法都是线性分析方法，两种方法的对比使用，能够更加精确的识别SWMM模型的敏感性参数，对于精确建模和高效率定具有一定的借鉴意义。

In order to identify the sensitive factors that affect the rainstorm flood management model (SWMM) as well as to provide guidance and reference for the subsequent model rate determination and uncertainty analysis, this paper samples each parameter based on the Latin hypercube sampling technique. On the other hand, it also respectively analyzes the global sensitivity of swmm model by using the stepwise regression method and partial rank correlation method as well as takes the Qianfoshan Campus of Shandong University as an example. The results show that: Firstly, the manning coefficient in impermeable region is the most sensitive parameter to peak time and peak flow. In addition, with the increase of Manning coefficient in impermeable region, the peak time will increase and the peak flow will decrease. Secondly, the ratio factor of catchment area is the most sensitive parameter to total production and flow. Therefore, with the increase of water catchment area ratio factor, the total production discharge will increase. The stepwise regression and partial rank correlation are the linear analysis methods. We also can identify the sensitive parameters of swmm model more accurately through the comparison of the two methods, which is useful for the accurate modeling and high efficiency.