Ensemble data assimilation for open channel water level forecasting under flow disturbances: A synthetic data study

Abstract

Accurate water level forecasts are essential for effective river management. Data assimilation (DA), involving real-time model correction through observational data, serves as a pivotal approach to enhance the predictive precision of model-based state forecasting. Nevertheless, prevailing DA methodologies encounter constraints in swiftly and accurately forecasting water levels amidst substantial uncertainties stemming from flow disturbances. To surmount this challenge, an innovative joint ensemble Kalman filter (EnKF) framework that facilitates concurrent estimation of flow disturbances and water levels is proposed. By incorporating the flow disturbance state variable into the EnKF framework for water level assimilation, a joint EnKF framework that effectively estimates flow disturbances at known disturbance points through assimilation techniques is established. Additionally, for scenarios where the precise location of the flow disturbance point remains unknown, the introduction of a fictitious lateral outflow in proximity to the observation point allows estimation of the flow disturbance process of this fictitious outflow. Synthetic case study results demonstrate that the incorporation of flow disturbance assimilation yields superior water level estimations compared to scenarios disregarding flow disturbances, regardless of the concordance between estimated and actual disturbance flow values. Furthermore, even when the location of the disturbance point is known, utilization of a fictitious lateral outflow and and subsequent flow correction exhibits heightened robustness and stability in comparison to direct correction of the disturbance point's flow. Therefore, in practical applications, utilizing a fictitious lateral outflow and flow correction within the joint EnKF framework emerges as a more suitable approach, irrespective of the knowledge of disturbance location. The proposed method constitutes a substantial contribution to the field of water level forecasting in river systems under conditions of uncertain flow disturbances.