Data assimilation for flow forecasting in urban drainage systems by updating a hydrodynamic model of Damhusåen Catchment, Copenhagen

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.

KEYWORDS:

• Filtering algorithm
• auto-regressive model
• data assimilation
• 1D hydrodynamic model
• updating

Acknowledgements

The research was funded by the Asian Institute of Technology (AIT), Thailand and the Danish Hydraulic Institute (DHI), Denmark.

Disclosure statement

No potential conflict of interest was reported by the author(s).