Université Paris Est, LEESU, UMR MA 102 - Agro ParisTech, Ecole des Ponts ParisTech, Cité Descartes - 6-8 av. Blaise Pascal, 77455 Champs-sur-Marne Cedex 2, France.
Université Paris Est, LEESU, UMR MA 102 - Agro ParisTech, Ecole des Ponts ParisTech, Cité Descartes - 6-8 av. Blaise Pascal, 77455 Champs-sur-Marne Cedex 2, France.
Water Res. 2015 Jul 1;78:47-59. doi: 10.1016/j.watres.2015.03.030. Epub 2015 Apr 14.
The suitability of a commonly used accumulation and wash-off model for continuous modelling of urban runoff contamination was evaluated based on 11-month turbidity and flow-rate records from an urban street. Calibration and uncertainty analysis were performed using a Markov Chain Monte-Carlo sampling method for both suspended solids loads (discharge rates) and concentration modelling. Selected models failed at replicating suspended solids concentration over the complete monitoring period. The studied dataset indeed suggests that the accumulation process is rather unpredictable and cannot be satisfactorily represented with usual accumulation models unless short periods are considered. Regarding suspended solid loads modelling, noticeably better performance was achieved, but similar results could as well be obtained with much simpler constant concentration models. Unless providing very accurate estimates of concentrations in runoff, accounting for their temporal variability during rain events may therefore not always be necessary for pollutant loads modelling, as loads are in fact mostly explained by runoff volumes.
基于一条城市街道 11 个月的浊度和流量记录，评估了一种常用的累积和冲洗模型在城市径流污染连续模拟中的适用性。使用马尔可夫链蒙特卡罗抽样方法对悬浮固体负荷（流量率）和浓度模型进行了校准和不确定性分析。选定的模型无法复制整个监测期间的悬浮固体浓度。研究数据集确实表明，积累过程相当不可预测，除非考虑短时间，否则不能用常用的积累模型令人满意地表示。关于悬浮固体负荷建模，取得了明显更好的性能，但也可以使用更简单的恒定浓度模型获得类似的结果。除非能够非常准确地估计径流中的浓度，否则在降雨事件期间考虑其时间变异性对于污染物负荷建模可能并非总是必要的，因为实际上负荷主要由径流量解释。
