A Real-Time Refined Roughness Estimation Framework for the Digital Twin Model Calibration of Irrigation Canal Systems
Publication: Journal of Irrigation and Drainage Engineering
Volume 150, Issue 1
Abstract
Digital twin (DT) models can mirror irrigation canal systems and monitor the hydrodynamic processes in real-time to help create scheduling schemes. As for the DT model of the open channel, an important parameter that needs to be calibrated is Manning’s roughness coefficient (). To establish a refined and high-fidelity DT model, the spatial variability of along the longitudinal direction needs to be considered. Parameter optimization or identification method can estimate the values of in different longitudinal segments along the canals. However, the existing relevant studies overlook the hydraulic conditions and estimation accuracy in canal segmentation. Therefore, this study proposes a comprehensive segmentation scheme for roughness estimation of irrigation canal systems. Particularly, a practical real-time segmented estimation (SE) framework using the ensemble Kalman filter (EnKF) is proposed and embedded into the DT model calibration. Verified by two canal reaches and two real-world cases, our results show that, compared with the empirical equation, the SE with the EnKF improves the model prediction accuracy by 45%–60%, especially for the canal reach longer than 10 km. This study provides a generic means for DT model calibration of irrigation canals, leading to more refined and precise monitoring and prediction of hydraulic variables.
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Data Availability Statement
All data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work is funded by the National Natural Science Foundation of China (Grant nos. 51979202 and 51879199) and would also need to acknowledge the Construction and Administration Bureau of the Middle-Route of the South-to-North Water Division Project of China that supported the data collection.
Author contributions: Wangjiayi Liu: conceptualization, methodology, formal analysis, investigation, visualization, and writing (original draft). Guanghua Guan: conceptualization, software, supervision, funding acquisition, writing, review, and editing. Xin Tian: visualization, supervision, writing, review, and editing. Zijun Cao: conceptualization and validation. Xiaonan Chen: resources and data curation. Liangsheng Shi: validation and project administration.
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Information & Authors
Information
Published In
Journal of Irrigation and Drainage Engineering
Volume 150 • Issue 1 • February 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jun 26, 2023
Accepted: Sep 29, 2023
Published online: Nov 9, 2023
Published in print: Feb 1, 2024
Discussion open until: Apr 9, 2024
ASCE Technical Topics:
- Algorithms
- Calibration
- Canals
- Engineering fundamentals
- Environmental engineering
- Filters
- Filtration
- Hydraulic engineering
- Hydraulic properties
- Hydraulic roughness
- Hydraulic structures
- Hydrologic models
- Irrigation
- Irrigation engineering
- Irrigation systems
- Kalman filters
- Mathematics
- Measurement (by type)
- Models (by type)
- Parameters (statistics)
- Statistics
- Water and water resources
- Water treatment
- Waterways
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