Brezonik Patrick L, Stadelmann Teresa H
Department of Civil Engineering, Water Resources Center, University of Minnesota, Minneapolis 55455, USA.
Water Res. 2002 Apr;36(7):1743-57. doi: 10.1016/s0043-1354(01)00375-x.
Urban nonpoint source pollution is a significant contributor to water quality degradation. Watershed planners need to be able to estimate nonpoint source loads to lakes and streams if they are to plan effective management strategies. To meet this need for the twin cities metropolitan area, a large database of urban and suburban runoff data was compiled. Stormwater runoff loads and concentrations of 10 common constituents (six N and P forms, TSS, VSS, COD, Pb) were characterized, and effects of season and land use were analyzed. Relationships between runoff variables and storm and watershed characteristics were examined. The best regression equation to predict runoff volume for rain events was based on rainfall amount, drainage area, and percent impervious area (R2 = 0.78). Median event-mean concentrations (EMCs) tended to be higher in snowmelt runoff than in rainfall runoff, and significant seasonal differences were found in yields (kg/ha) and EMCs for most constituents. Simple correlations between explanatory variables and stormwater loads and EMCs were weak. Rainfall amount and intensity and drainage area were the most important variables in multiple linear regression models to predict event loads, but uncertainty was high in models developed with the pooled data set. The most accurate models for EMCs generally were found when sites were grouped according to common land use and size.
城市面源污染是水质恶化的一个重要因素。流域规划者若要制定有效的管理策略，就需要能够估算流入湖泊和溪流的面源污染负荷。为满足双城都市区的这一需求，编制了一个包含城市和郊区径流数据的大型数据库。对雨水径流负荷以及10种常见成分（六种氮和磷形态、总悬浮物、挥发性悬浮物、化学需氧量、铅）的浓度进行了表征，并分析了季节和土地利用的影响。研究了径流变量与暴雨及流域特征之间的关系。预测降雨事件径流量的最佳回归方程基于降雨量、排水面积和不透水面积百分比（R2 = 0.78）。融雪径流的事件平均浓度中位数往往高于降雨径流，并且大多数成分的产量（千克/公顷）和事件平均浓度存在显著季节差异。解释变量与雨水负荷和事件平均浓度之间的简单线性关系较弱。降雨量和强度以及排水面积是预测事件负荷的多元线性回归模型中最重要的变量，但使用汇总数据集建立的模型不确定性较高。通常发现，根据常见土地利用和规模对站点进行分组时，事件平均浓度的模型最为准确。
