Kleidorfer M, Möderl M, Fach S, Rauch W
Unit of Environmental Engineering, Faculty of Civil Engineering, University of Innsbruck, A6020, Innsbruck, Austria.
Water Sci Technol. 2009;59(8):1523-30. doi: 10.2166/wst.2009.154.
To simulate hydrological models of combined sewer systems an accurate calibration is indispensable. In addition to all sources of uncertainties in data collection due to the measurement methods itself, it is a key question which data has to be collected to calibrate a hydrological model, how long measurement campaigns should last and where that data has to be collected in a spatial distributed system as it is neither possible nor sensible to measure the complete system characteristics. In this paper we address this question by means of stochastic modelling. Using Monte Carlo Simulation different calibration strategies (selection of measurement sites, selection of rainfall-events) and different calibration parameters (overflow volume, number of overflows) are tested, in order to evaluate the influence on predicting the total overflow volume of the entire system. This methodology is applied in a case study with the aim to calculate the combined sewer overflow (CSO) efficiency. It can be shown that a distributed hydrological model can be calibrated sufficiently when calibration is done on 30% of all existing CSOs based on long-term observation. Event based calibration is limited possible to a limited extend when calibration events are selected carefully as wrong selection of calibration events can result in a complete failure of the calibration exercise.
为了模拟合流制排水系统的水文模型，精确校准必不可少。除了由于测量方法本身导致的数据收集过程中所有不确定性来源之外，校准水文模型需要收集哪些数据、测量活动应持续多长时间以及在空间分布式系统中应在何处收集数据，这些都是关键问题，因为测量整个系统的特征既不可能也不明智。在本文中，我们通过随机建模来解决这个问题。使用蒙特卡罗模拟测试不同的校准策略（测量站点的选择、降雨事件的选择）和不同的校准参数（溢流量、溢流次数），以评估对预测整个系统总溢流量的影响。这种方法应用于一个案例研究，旨在计算合流制排水溢流（CSO）效率。结果表明，基于长期观测，当对所有现有CSO的30%进行校准时，分布式水文模型可以得到充分校准。当仔细选择校准事件时，基于事件的校准在有限程度上是可行的，因为错误选择校准事件可能导致校准工作完全失败。
