Accurate parameterization of water quality routines within stormwater models can improve simulation, prediction of system behavior, and lead to more informed decision making. This work recognizes that factors controlling runoff concentrations and treatment in stormwater best management practices (BMPs) vary with region, assumes region is a proxy for these factors, and seeks to improve tool parameterization by (1) identifying a regionalization scheme that best explains variability in national datasets of runoff water quality and BMP performance, (2) generating region‐specific model parameter values, and (3) demonstrating how model output varies when using national vs. regional parameters. Of the four regionalization schemes tested, the National Climatic Data Center’s Regions best explained variance in pollutant runoff concentrations from the National Stormwater Quality Database, accounting for more data variability (1.1%) than the watershed’s land use (0.4%). For BMP performance extracted from the International Stormwater BMP Database, the United States (U.S.) Environmental Protection Agency’s Rain Zones explained the most variance (1.1%), which is one‐fifth of the variance explained by BMP type (5.0%). These results were used to generate regional parameter lookup tables for stormwater quality modeling. Test cases from the 100 most populous cites in the U.S. showed national parameters predicted BMP effluent concentrations that were 15% lower than the regional parameters for five pollutants.

中文翻译：

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城市雨水质量模型默认参数的区域化

在雨水模型中对水质例行程序进行准确的参数化可以改善仿真，系统行为的预测并导致更明智的决策。这项工作认识到控制雨水最佳管理实践（BMP）中径流浓度和处理的因素随地区而异，假定地区是这些因素的代名词，并通过（1）找出能最好地解释变异性的区域化方案来寻求改进工具参数化。径流水质和BMP性能的国家数据集，（2）生成特定于区域的模型参数值，以及（3）展示使用国家参数与区域参数时模型输出如何变化。在所测试的四个分区计划中，国家气候数据中心的地区可以最好地解释污染物径流的变化来自国家雨水质量数据库的浓度数据，其数据变异性（1.1％）比流域的土地利用（0.4％）多。对于从国际雨水BMP数据库中提取的BMP性能，美国环境保护署的雨区解释了最大的差异（1.1％），是BMP类型解释的差异（5.0％）的五分之一。这些结果用于生成区域参数查找表，以进行雨水质量建模。来自美国100个人口最多的城市的测试案例显示，国家参数预测的BMP废水浓度比五种污染物的区域参数低15％。