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A high resolution application of a stormwater management model (SWMM) using genetic parameter optimization

机译:遗传参数优化在雨水管理模型(SWMM)中的高分辨率应用

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摘要

Low Impact Development (LID) tools and green infrastructure approaches have been developed and applied to mitigate the urbanization impacts on increasing runoff and pollutant washoff. The present work is the first part of a larger effort to simulate LID scenarios for a large scale urban catchment through up-scaling of high-resolution study catchments using the Stormwater Management Model (SWMM). In this study we present the setup, calibration, validation, and the results of a parameter sensitivity analysis of a high-resolution SWMM model for a highly urbanized small catchment located in Southern Finland. The homogenous subcatchments and associated narrow parameter boundaries, which are allowed by the high spatial resolution, result in insensitivity of SWMM to the fraction of impervious cover. The model optimization, using only the two identified key parameters "depression storage" and "Manning's roughness n for conduit flow", yielded good performance statistics for both calibration and validation of the model.
机译:已开发并应用了低影响开发(LID)工具和绿色基础设施方法,以减轻城市化对径流增加和污染物冲刷的影响。本工作是通过使用雨水管理模型(SWMM)扩大高分辨率研究集水区规模来模拟大型城市集水区的LID方案的更大努力的第一部分。在这项研究中,我们介绍了位于芬兰南部高度城市化的小流域的高分辨率SWMM模型的设置,校准,验证和参数敏感性分析的结果。高空间分辨率允许的均质子汇水面积和相关的狭窄参数边界,导致SWMM对不透水覆盖的部分不敏感。仅使用两个确定的关键参数“降压存储”和“管道流动的曼宁粗糙度n”进行模型优化,就可以为模型的校准和验证产生良好的性能统计数据。

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