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• 研究报告 • 上一篇    下一篇

SWMM模型参数局部灵敏度分析

李春林1,2,胡远满1,刘淼1**,徐岩岩1,2,孙凤云1,2,陈探1,2   

  1. 1森林与土壤生态国家重点实验室, 中国科学院沈阳应用生态研究所, 沈阳 110016; 2中国科学院大学, 北京 100049)
  • 出版日期:2014-04-10 发布日期:2014-04-10

Local sensitivity analysis of parameters in Storm Water Management Model.

LI Chun-lin1,2, HU Yuan-man1, LIU Miao1**, XU Yan-yan1,2, SUN Feng-yun1,2, CHEN Tan1,2   

  1. (1State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China; 2University of Chinese Academy of Sciences, Beijing 100049, China)
  • Online:2014-04-10 Published:2014-04-10

摘要: 利用Morris筛选法,分析了3场不同雨强降雨条件下,城市降雨径流模型SWMM的水文水力模块和水质模块的参数灵敏度。结果表明:对SWMM模型径流总量和径流峰值最敏感的参数依次是不透水面洼蓄量(destore-imperv)、管道曼宁系数(conduit roughness)和汇流区宽度(width-K),最大灵敏度分别为-0.329、-0.144、0.133和-0.294、0.171和0.143;对污染物总量敏感性较高的是路面和屋面的最大积累量(max buildup)、冲刷系数(coefficient)和冲刷指数(exponent)参数。雨强对于SWMM模型水文水力参数中的下渗参数的敏感性有较大影响,而对水质参数的敏感性影响较小;研究区的土地利用状况对参数敏感性也有较大的影响。水质参数总体的稳定性要高于水文水力参数。

关键词: 改进TOPSIS法, 灰色GM(1, 1), 预警, 旅游生态安全, 张家界

Abstract: Sensitivity analysis is a crucial procedure for parameter identification. The parameter sensitivity of hydrological and contaminated modules in Storm Water Management Model (SWMM) was evaluated with three different intensities of rainfall based on the Morris screening method. The results showed that the descending order of parameter sensitivity to total runoff volume and peak flow was destoreimperv, conduit roughness and width-K, with sensitivity indices of -0.329, -0.144, 0.133 and -0.294, 0.171, 0.143, respectively. Max buildup, coefficient and exponent of road and roof parameters had higher sensitivities to water quality modeling. Rainfall intensity had a great influence on the sensitivities of infiltration parameters, but a weak influence on the sensitivities of water quality parameters. Land use had a great influence on the
parameter sensitivity. The parameters of water quality were more stable than those of hydrological module in SWMM.

Key words: improved TOPSIS method, Zhangjiajie, early warning, grey GM (1,1) model, tourism ecological security