Krämer S, Grum M, Verworn H R, Redder A
Inst. of Water Resources Management, University of Hannover, Appelstr. 9a, 30167 Hannover, Germany.
Water Sci Technol. 2005;52(5):1-8.
In urban drainage the estimation of runoff with the help of models is a complex task. This is in part due to the fact that rainfall, the most important input to urban drainage modelling, is highly uncertain. Added to the uncertainty of rainfall is the complexity of performing accurate flow measurements. In terms of deterministic modelling techniques these are needed for calibration and evaluation of the applied model. Therefore, the uncertainties of rainfall and flow measurements have a severe impact on the model parameters and results. To overcome these problems a new methodology has been developed which is based on simple rain plane and runoff models that are incorporated into a stochastic state space model approach. The state estimation is done by using the extended Kalman filter in combination with a maximum likelihood criterion and an off-line optimization routine. This paper presents the results of this new methodology with respect to the combined consideration of uncertainties in distributed rainfall derived from radar data and uncertainties in measured flows in an urban catchment within the Emscher river basin, Germany.
在城市排水中，借助模型估算径流是一项复杂的任务。部分原因在于，降雨作为城市排水建模最重要的输入，具有高度不确定性。除了降雨的不确定性，进行精确流量测量也很复杂。就确定性建模技术而言，这些测量对于所应用模型的校准和评估是必要的。因此，降雨和流量测量的不确定性对模型参数和结果有严重影响。为克服这些问题，已开发出一种新方法，该方法基于简单的雨面和径流模型，并融入随机状态空间模型方法。状态估计通过使用扩展卡尔曼滤波器结合最大似然准则和离线优化程序来完成。本文展示了这种新方法在综合考虑源自雷达数据的分布式降雨不确定性以及德国埃姆斯河流域一个城市集水区实测流量不确定性方面的结果。
