Mourad M, Bertrand-Krajewski J L, Chebbo G
URGC Hydrologie Urbaine, INSA de Lyon, 34 avenue des Arts, 69621 Villeurbanne, France.
Water Sci Technol. 2005;52(5):61-8.
Stormwater quality modelling is a useful tool in sewer systems management. Available models range from simple to detailed complex ones. The models need local data to be calibrated. In practice, calibration data are rather lacking. Only few measured events are commonly used. In this paper, the effect of the number and the variability of calibration data on models of various levels of complexity are investigated. The study is carried out on "Le Marais" catchment for suspended solids where 40 reliable measured events and good knowledge of the sewer system are available. The method used is based on resampling subsets of measured events among the 40 available ones. Three types of models were calibrated using subsets of events of different sizes and characteristics resampled among the 40 available ones. For each calibration, the model was validated against the remaining events to stand upon the quality of the model. It was found that the models are quite sensitive to calibration data, a problem neglected in practical studies. The use of more complex models does not necessarily improve modelling results since more problems and error sources are to be expected. The findings are specific to "Le Marais" catchment and the models used.
雨水水质建模是下水道系统管理中的一种有用工具。现有的模型从简单到详细复杂的都有。这些模型需要本地数据进行校准。在实践中，校准数据相当匮乏。通常只使用少数实测事件。本文研究了校准数据的数量和变异性对不同复杂程度模型的影响。该研究在“勒马赖”流域针对悬浮固体进行，那里有40个可靠的实测事件且对下水道系统有充分了解。所采用的方法基于从40个可用实测事件中对其子集进行重采样。使用从40个可用事件中重采样得到的不同大小和特征的事件子集，对三种类型的模型进行了校准。对于每次校准，都根据剩余事件对模型进行验证，以评估模型的质量。结果发现，模型对校准数据相当敏感，这是实际研究中被忽视的一个问题。使用更复杂的模型不一定能改善建模结果，因为预期会出现更多问题和误差源。这些发现特定于“勒马赖”流域和所使用的模型。
