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Testing of the Storm Water Management Model Low Impact Development Modules

机译：雨水管理模型低影响开发模块的测试

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Stormwater infrastructure designers and operators rely heavily on the United States Environmental Protection Agency's Storm Water Management Model (SWMM) to simulate stormwater and wastewater infrastructure performance. Since its inception in the late 1970s, improvements and extensions have been tested and evaluated rigorously to verify the accuracy of the model. As a continuation of this progress, the main objective of this study was to quantify how accurately SWMM simulates the hydrologic activity of low impact development (LID) storm control measures. Model performance was evaluated by quantitatively comparing empirical data to model results using a multievent, multiobjective calibration method. The calibration methodology utilized the PEST software, a Parameter ESTimation tool, to determine unmeasured hydrologic parameters for SWMM's LID modules. The calibrated LID modules' Nash-Sutcliffe efficiencies averaged 0.81; average percent bias (PBIAS) -9%; average ratio of root mean square error to standard deviation of measured values 0.485; average index of agreement 0.94; and the average volume error, simulated vs. observed, was +9%. SWMM accurately predicted the timing of peak flows, but usually underestimated their magnitudes by 10%. The average volume reduction, measured outflow volume divided by inflow volume, was 48%. We had more difficulty in calibrating one study, an infiltration trench, which identified a significant limitation of the current version of the SWMM LID module; it cannot simulate lateral exfiltration of water out of the storage layers of a LID storm control measure. This limitation is especially severe for a deep LIDs, such as infiltration trenches. Nevertheless, SWMM satisfactorily simulated the hydrologic performance of eight of the nine LID practices.
机译：雨水基础设施的设计者和运营商严重依赖美国环境保护局的雨水管理模型（SWMM）来模拟雨水和废水基础设施的性能。自1970年代末创建以来，已经对改进和扩展进行了严格的测试和评估，以验证模型的准确性。作为这一进展的延续，本研究的主要目的是量化SWMM如何精确地模拟低影响发展（LID）暴雨控制措施的水文活动。通过使用多事件，多目标校准方法将经验数据与模型结果进行定量比较来评估模型性能。校准方法使用PEST软件（一种参数估算工具）来确定SWMML LID模块的未测水文参数。校准后的LID模块的Nash-Sutcliffe效率平均为0.81;平均偏差百分比（PBIAS）-9％;均方根误差与测量值标准偏差的平均比为0.485;协议平均指数0.94;模拟与观察到的平均体积误差为+ 9％。 SWMM可以准确预测峰值流量的时间，但通常会将其幅度低估10％。测得的流出量除以流入量的平均体积减少率为48％。我们在校准一项研究（渗透沟槽）方面遇到了更多困难，该研究确定了当前版本的SWMML LID模块的显着局限;它无法模拟水从LID暴雨控制措施的存储层中的横向渗出。对于较深的LID（例如渗透沟），此限制尤其严重。尽管如此，SWMM仍令人满意地模拟了9种LID做法中的8种的水文性能。

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