Optimal Forecast of Open-Channel Flow through Sequential Node Selection for Data Assimilation in a Hydrodynamic Model

Abstract

Predicting river flow is important for flood management, river erosion protection, navigability update, and so on. The traditional approach to predicting the flow using a hydrodynamic model (HM) requires manual calibration and recalibrations of uncertain parameters; it has faced inefficiency and uncertainty issues. This study aims to explore assimilating observational data of water level and velocity into the HM as a new approach to predicting open-channel flow. The background of this study is that ever advancing technologies for flow monitoring have offered or will soon offer good amounts of data in real time, making data assimilation (DA) practical. The new approach’s novelty lies in combining hydrodynamic laws (via the momentum principle) with data during HM run time without the need for calibrations and optimizing the prediction accuracy while minimizing computing costs. Its performance is validated using observational data from flume experiments and hydrometric stations in the Danube River. The results show that the adaptive proportional–integral–derivative–based DA technique is more efficient than the model-predictive-controller-based DA technique, enhancing the computation efficiency by an order of magnitude. Both techniques automate the correction of the channel-bed friction factor itself, which may also alleviate the impact of other uncertain parameters on the prediction. Both techniques have achieved low relative errors ( < 1 % ). The sequential selection method successfully locates optimal DA stations in the channel, supported by open-channel flow theories. The new approach improves the flow prediction for the entire HM channel. This study has contributed to the development of a robust framework for forecasting open-channel flow in real time.