Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annu...Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annuallyinthe urban area of Beijing, the capital of China. Based on a selforganizing map(SOM) artificial neural network(ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product(GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANNis suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors,producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. The points that were assigned risk grades of IV or Vwere located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.展开更多
The pollutants in urban storm runoff, which lead to an non-point source contamination of water environment around cities, are of great concems. The distributions of typical contaminants and the variations of their spe...The pollutants in urban storm runoff, which lead to an non-point source contamination of water environment around cities, are of great concems. The distributions of typical contaminants and the variations of their species in short term storm runoff from different land surfaces in Xiamen City were investigated. The concentrations of various contaminants, including organic matter, nutrients （i.e., N and P） and heavy metals, were significantly higher in parking lot and road runoff than those in roof and lawn runoff. The early runoff samples from traffic road and parking lot contained much high total nitrogen （TN 6-19 mg/L） and total phosphorus （TP 1-3 mg/L）. A large proportion （around 60%） of TN existed as total dissolved nitrogen （TDN） species in most runoff. The percentage of TDN and the percentage of total dissolved phosphorus remained relatively stable during the rain events and did not decrease as dramatically as TN and TP. In addition, only parking lot and road runoff were contaminated by heavy metals, and both Pb （25-120 μg/L） and Zn （0.1-1.2 mg/L） were major heavy metals contaminating both runoff. Soluble Pb and Zn were predominantly existed as labile complex species （50%-99%）, which may be adsorbed onto the surfaces of suspended particles and could be easily released out when pH decreased. This would have the great impact to the environment.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
The rapid urbanization and industrialization involve an unsustainable use of natural systems,leading to various problems in cities.The urban hydrological system experiences fluctuating amount of surface runoff water w...The rapid urbanization and industrialization involve an unsustainable use of natural systems,leading to various problems in cities.The urban hydrological system experiences fluctuating amount of surface runoff water when it rains heavily.It has been suggested that green roofs significantly mitigate storm water runoff generation even in tropical climate.Green roofs have become popular due to its proven benefits by mitigating urban heat island effects and protecting biodiversity.The annual rainfall and runoff relationship for green roofs is determined by the depth of the substrate.Water retention capacity mostly depends on substrate's physical conditions such as dry or wetness.Generally 6 mm to 12 mm rainfall is required for dry substrate to initiate runoff whereas response of wet conditions is mostly straight.Besides,there are some other factors affecting runoff dynamics such as type of a green roof and its slope,age of green roof,type of vegetation,soil moisture characteristics,weather.The review indicates that there is not much research in green roofs performance over storm water runoff;hence there is a need for further research.This paper reviews and addresses the role of green roofs in urban storm water management.展开更多
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
基金supported by the National Key R&D Program of China (GrantN o.2016YFC0401407)National Natural Science Foundation of China (Grant Nos. 51479003 and 51279006)
文摘Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annuallyinthe urban area of Beijing, the capital of China. Based on a selforganizing map(SOM) artificial neural network(ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product(GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANNis suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors,producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. The points that were assigned risk grades of IV or Vwere located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.
基金supported by the Knowledge Innovation Program of Chinese Academy of Sciences for Young Scientists in Frontier Research (No.07l4161b10)the National Natural Science Foundation of China (No.20807033)
文摘The pollutants in urban storm runoff, which lead to an non-point source contamination of water environment around cities, are of great concems. The distributions of typical contaminants and the variations of their species in short term storm runoff from different land surfaces in Xiamen City were investigated. The concentrations of various contaminants, including organic matter, nutrients （i.e., N and P） and heavy metals, were significantly higher in parking lot and road runoff than those in roof and lawn runoff. The early runoff samples from traffic road and parking lot contained much high total nitrogen （TN 6-19 mg/L） and total phosphorus （TP 1-3 mg/L）. A large proportion （around 60%） of TN existed as total dissolved nitrogen （TDN） species in most runoff. The percentage of TDN and the percentage of total dissolved phosphorus remained relatively stable during the rain events and did not decrease as dramatically as TN and TP. In addition, only parking lot and road runoff were contaminated by heavy metals, and both Pb （25-120 μg/L） and Zn （0.1-1.2 mg/L） were major heavy metals contaminating both runoff. Soluble Pb and Zn were predominantly existed as labile complex species （50%-99%）, which may be adsorbed onto the surfaces of suspended particles and could be easily released out when pH decreased. This would have the great impact to the environment.
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.
文摘The rapid urbanization and industrialization involve an unsustainable use of natural systems,leading to various problems in cities.The urban hydrological system experiences fluctuating amount of surface runoff water when it rains heavily.It has been suggested that green roofs significantly mitigate storm water runoff generation even in tropical climate.Green roofs have become popular due to its proven benefits by mitigating urban heat island effects and protecting biodiversity.The annual rainfall and runoff relationship for green roofs is determined by the depth of the substrate.Water retention capacity mostly depends on substrate's physical conditions such as dry or wetness.Generally 6 mm to 12 mm rainfall is required for dry substrate to initiate runoff whereas response of wet conditions is mostly straight.Besides,there are some other factors affecting runoff dynamics such as type of a green roof and its slope,age of green roof,type of vegetation,soil moisture characteristics,weather.The review indicates that there is not much research in green roofs performance over storm water runoff;hence there is a need for further research.This paper reviews and addresses the role of green roofs in urban storm water management.
