Roughness estimation methods of hydrodynamic digital twin models for canal systems

A segmented estimation method of roughness based on the variation of hydraulic radius and estimation accuracy is proposed in order to realize the real-time and high-fidelity roughness estimation of hydrodynamic digital twin (DT) models. This method considers the spatial variability of the roughness value in the longitudinal direction of canals. Based on canal segmentation, two different estimation frameworks, the independent estimation method and joint estimation method, are proposed. The ensemble Kalman filter algorithm is applied to estimate the roughness of each canal segment online based on the limited observed water levels. The results show that the two estimation methods can improve the accuracy of the model by 20%—50%. In addition, the independent estimation method is suitable for a complex canal system with a small error accumulation, while the joint estimation method is suitable for simple canals with unavailable observations. The proposed method can be used for parameter estimation and variable updating of hydrodynamic DT models, and provide a reference for the construction of DT water networks.