为实现吴忠市暴雨洪水管理模型(storm water management model,SWMM)的高效率定,分别采用修正的莫里斯(Morris)法和互信息法,分析了洪峰流量和径流系数的模拟结果对SWMM的7个相关产流参数的局部和全局敏感性.2种方法均识别出不透水面-...为实现吴忠市暴雨洪水管理模型(storm water management model,SWMM)的高效率定,分别采用修正的莫里斯(Morris)法和互信息法,分析了洪峰流量和径流系数的模拟结果对SWMM的7个相关产流参数的局部和全局敏感性.2种方法均识别出不透水面-曼宁系数(IMP-N)和不透水区洼地蓄水深度(IMP-DS)为SWMM的主要敏感参数.洪峰流量对IMP-N和IMP-DS最敏感,径流系数对IMP-DS最敏感;参数的敏感性随降雨强度的增大先增大后减小,洪峰流量对IMP-DS和IMP-N的敏感性分别在3和10 a的降雨重现期达到最大值,径流系数对IMP-DS和IMP-N的敏感性分别在2和3 a的降雨重现期达最大值;敏感参数间的协同作用随降雨强度增大而减弱.结果表明,吴忠市中心城区的易涝区应优先考虑增加地表粗糙度与洼地蓄水深度.本成果可为以高不透水率为特征的其他城市密集建成区的削峰减排提供参考.展开更多
Stormwater runoff has been identified as a source of pollution for the environment, especially for receiving waters. In order to quantify and manage the impacts of stormwater runoff on the environment, predictive mode...Stormwater runoff has been identified as a source of pollution for the environment, especially for receiving waters. In order to quantify and manage the impacts of stormwater runoff on the environment, predictive models and mathematical models have been developed. Predictive tools such as regression models have been widely used to predict stormwater discharge characteristics. Storm event characteristics, such as antecedent dry days （ADD）, have been related to response variables, such as pollutant loads and concentrations. However it has been a controversial issue among many studies to consider ADD as an important variable in predicting stormwater discharge characteristics. In this study, we examined the accuracy of general linear regression models in predicting discharge characteristics of roadway runoff. A total of 17 storm events were monitored in two highway segments, located in Gwangju, Korea. Data from the monitoring were used to calibrate United States Environmental Protection Agency＇s Storm Water Management Model （SWMM）. The calibrated SWMM was simulated for 55 storm events, and the results of total suspended solid （TSS） discharge loads and event mean concentrations （EMC） were extracted. From these data, linear regression models were developed. R2 and p-values of the regression of ADD for both TSS loads and EMCs were investigated. Results showed that pollutant loads were better predicted than pollutant EMC in the multiple regression models. Regression may not provide the true effect of site-specific characteristics, due to uncertainty in the data.展开更多
基金supported by the Korea Ministry of Environment, as "The Eco-innovation Project" (No. 413111-003)
文摘Stormwater runoff has been identified as a source of pollution for the environment, especially for receiving waters. In order to quantify and manage the impacts of stormwater runoff on the environment, predictive models and mathematical models have been developed. Predictive tools such as regression models have been widely used to predict stormwater discharge characteristics. Storm event characteristics, such as antecedent dry days （ADD）, have been related to response variables, such as pollutant loads and concentrations. However it has been a controversial issue among many studies to consider ADD as an important variable in predicting stormwater discharge characteristics. In this study, we examined the accuracy of general linear regression models in predicting discharge characteristics of roadway runoff. A total of 17 storm events were monitored in two highway segments, located in Gwangju, Korea. Data from the monitoring were used to calibrate United States Environmental Protection Agency＇s Storm Water Management Model （SWMM）. The calibrated SWMM was simulated for 55 storm events, and the results of total suspended solid （TSS） discharge loads and event mean concentrations （EMC） were extracted. From these data, linear regression models were developed. R2 and p-values of the regression of ADD for both TSS loads and EMCs were investigated. Results showed that pollutant loads were better predicted than pollutant EMC in the multiple regression models. Regression may not provide the true effect of site-specific characteristics, due to uncertainty in the data.
