ABSTRACT

Accurate model-based forecasts (discharge and water level) are considered significant for efficient planning and management of urban drainage systems. These model-based predictions can be improved by assimilating system measurements in physically based, distributed, 1D hydrodynamic urban drainage models. In the present research, a combined filtering and error forecast method was applied for the data assimilation to update the states of the urban drainage model. The developed data assimilation set-up in combination with the 1D hydrodynamic model was applied at the Damhusåen Catchment, Copenhagen. Discharge assimilation represented significant potential to update model forecast, and maximum volume error was reduced by 22% and 6% at two verification locations. The assimilation of water levels had a minor impact on the update of the system states. The updated forecast skill using error forecast models was enhanced up to 1–2 hours and 6–7 hours lead time at upstream assimilation and downstream verification locations, respectively.

摘要

基于模型的准确预测（流量和水位）被认为对于有效规划和管理城市排水系统非常重要。这些基于模型的预测可以通过在基于物理的，分布式的一维水力城市排水模型中吸收系统测量值来进行改进。在本研究中，将过滤和误差预测相结合的方法应用于数据同化以更新城市排水模型的状态。将开发的数据同化设置与一维流体动力学模型相结合，已在哥本哈根的Damhusåen集水区应用。排放同化代表了更新模型预测的巨大潜力，并且在两个验证位置的最大体积误差减少了22％和6％。水位的吸收对系统状态的更新影响很小。