济南市南部山区崮山流域地处我国北方土石山地丘陵区，以水力侵蚀为主的土壤侵蚀现象严重且生态环境脆弱，因此需要运用高精度降雨产流估算模型进行产流估算，从而为该地区其水土保持监测和预报提供技术支撑与数据基础。根据SCS-CN模型原理和崮山流域内五个雨量站、一个水文站近10年的实测降雨、径流资料, 借助Arc GIS平台利用优度拟合统计分析法及Nash-Sutcliffe效率系数验证法对模型参数初损率（λ）和径流曲线数（CN）进行了优化检验。结果表明：参数优化后的模型精确度较高（实测值与计算值分析结果为：回归直线K=0.9058、R2=0.8127，NSE=0.7969）可以更好的适用于崮山流域降雨产流估算；对2019年崮山流域29次侵蚀性降雨进行降雨产流估算，并累加计算得流域年径流量为0.53×108 m3，年径流深处于34.15 mm ~ 371.52 mm之间，年均径流深为134.52 mm，汛期降雨产生的径流量占年径流量的90.27%。

Gushan watershed in the southern mountainous area of Jinan is located in the hilly region of northern China. The soil erosion phenomenon dominated by hydraulic erosion is serious and the ecological environment is fragile. Therefore, it is necessary to use a high-precision rainfall runoff estimation model for runoff estimation, so as to provide technical support and data basis for the monitoring and forecasting of soil and water conservation in this area. According to the SCS-CN model principle and the measured rainfall and runoff data of five rainfall stations and one hydrological station in Gushan watershed in recent 10 years, the initial loss rate (λ) and runoff curve number (CN) of the model parameters are optimized by using the goodness-of-fit statistical analysis method and Nash-Sutcliffe efficiency coefficient verification method with the help of Arc GIS. The results show that: the optimized model has high accuracy (the analysis results of measured and calculated values are: regression line K=0.9058, R2=0.8127, NSE=0.7969), which can be better applied to the rainfall runoff estimation in Gushan watershed; The rainfall runoff generation of 29 erosive rainfalls in the Gushan watershed in 2019 is estimated, and the cumulative calculations show that the annual runoff in the watershed is 0.53×108 m3, the annual runoff depth is between 34.15 mm and 371.52 mm, and the average annual runoff depth is 134.52 mm. The runoff generated by rainfall during the flood season accounts for 90.27% of the annual runoff.

TV213.9

山东水土保持学会重点领域创新项目“济南市南部山区植被变化的水文响应规律研究”(sdsbxh-2019-01)；山东水土保持学会重点领域创新项目“高分卫星数据在小流域植被资源监测中的应用”(sdsbxh-2018-04)。