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.
References
Abbaspour, K. C., E. Rouholahnejad, S. Vaghefi, R. Srinivasan, H. Yang, and B. Kløve. 2015. “A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model.” J. Hydrol. 524 (May): 733–752. https://doi.org/10.1016/j.jhydrol.2015.03.027.
Akan, A. O., and S. Iyer. 2021. “Design of open channels.” In Chap. 5 in Open channel hydraulics. 2nd ed., 169–213. New York: Butterworth Heinemann. https://doi.org/10.1016/B978-0-12-821770-2.00002-6.
Akkuzu, E. B. U. H., B. S. Karatas, M. Avci, and S. Asik. 2008. “Evaluation of irrigation canal maintenance according to roughness and active canal capacity values.” J. Irrig. Drain. Eng. 134 (1): 60–66. https://doi.org/10.1061/(ASCE)0733-9437(2008)134:1(60).
Ardıçlıoğlu, M., and A. Kuriqi. 2019. “Calibration of channel roughness in intermittent rivers using HEC-RAS model: Case of Sarimsakli creek, Turkey.” SN Appl. Sci. 1 (Sep): 1–9. https://doi.org/10.1007/s42452-019-1141-9.
Aricò, C., C. Nasello, and T. Tucciarelli. 2009. “Using unsteady-state water level data to estimate channel roughness and discharge hydrograph.” Adv. Water Resour. 32 (8): 1223–1240. https://doi.org/10.1016/j.advwatres.2009.05.001.
Attari, M., and S. M. Hosseini. 2019. “A simple innovative method for calibration of Manning’s roughness coefficient in rivers using a similarity concept.” J. Hydrol. 575 (Jul): 810–823. https://doi.org/10.1016/j.jhydrol.2019.05.083.
Attari, M., M. Taherian, S. M. Hosseini, S. B. Niazmand, M. Jeiroodi, and A. Mohammadian. 2021. “A simple and robust method for identifying the distribution functions of Manning’s roughness coefficient along a natural river.” J. Hydrol. 595 (Apr): 125680. https://doi.org/10.1016/j.jhydrol.2020.125680.
Bao, H., and L. Zhao. 2011. “Hydraulic model with roughness coefficient updating method based on Kalman filter for channel flood forecast.” Water Sci. Eng. 4 (1): 13–23. https://doi.org/10.3882/j.issn.1674-2370.2011.01.002.
Bartos, M., and B. Kerkez. 2021. “Pipedream: An interactive digital twin model for natural and urban drainage systems.” Environ. Modell. Software 144 (Oct): 105120. https://doi.org/10.1016/j.envsoft.2021.105120.
Boulomytis, V. T. G., A. C. Zuffo, J. G. Dalfré Filho, and M. A. Imteaz. 2017. “Estimation and calibration of Manning’s roughness coefficients for ungauged watersheds on coastal floodplains.” Int. J. River Basin Manage. 15 (2): 199–206. https://doi.org/10.1080/15715124.2017.1298605.
Das, A. 2004. “Parameter estimation for flow in open-channel networks.” J. Irrig. Drain. Eng. 130 (2): 160–165. https://doi.org/10.1061/(ASCE)0733-9437(2004)130:2(160).
Dash, S. S., and K. K. Khatua. 2016. “Sinuosity dependency on stage discharge in meandering channels.” J. Irrig. Drain. Eng. 142 (9): 04016030. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001037.
Errico, A., V. Pasquino, M. Maxwald, G. B. Chirico, L. Solari, and F. Preti. 2018. “The effect of flexible vegetation on flow in drainage channels: Estimation of roughness coefficients at the real scale.” Ecol. Eng. 120 (Sep): 411–421. https://doi.org/10.1016/j.ecoleng.2018.06.018.
Evensen, G. 2003. “The ensemble Kalman filter: Theoretical formulation and practical implementation.” Ocean Dyn. 53 (4): 343–367. https://doi.org/10.1007/s10236-003-0036-9.
Fan, Y., H. Chen, Z. Gao, Y. Fan, X. Chang, M. Yang, and B. Fang. 2023. “Water distribution and scheduling model of an irrigation canal system.” Comput. Electron. Agric. 209 (Jun): 107866. https://doi.org/10.1016/j.compag.2023.107866.
García-Pintado, J., G. G. Barberá, M. Erena, and V. M. Castillo. 2009. “Calibration of structure in a distributed forecasting model for a semiarid flash flood: Dynamic surface storage and channel roughness.” J. Hydrol. 377 (1–2): 165–184. https://doi.org/10.1016/j.jhydrol.2009.08.024.
Grieves, M., and J. Vickers. 2017. “Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems.” In Transdisciplinary perspectives on complex systems, edited by J. Kahlen, S. Flumerfelt, and A. Alves, 85–113. Berlin: Springer. https://doi.org/https://doi.org/10.1007/978-3-319-38756-7_4.
Hosseiny, H. 2022. “Implementation of heuristic search algorithms in the calibration of a river hydraulic model.” Environ. Modell. Software 157 (Nov): 105537. https://doi.org/10.1016/j.envsoft.2022.105537.
Hsu, M.-H., J.-C. Fu, and W.-C. Liu. 2006. “Dynamic routing model with real-time roughness updating for flood forecasting.” J. Hydraul. Eng. 132 (6): 605–619. https://doi.org/10.1061/(ASCE)0733-9429(2006)132:6(605).
Kaghazchi, A., S. M. Hashemy Shahdany, and A. Roozbahani. 2021. “Simulation and evaluation of agricultural water distribution and delivery systems with a hybrid Bayesian network model.” Agric. Water Manage. 245 (Jun): 106578. https://doi.org/10.1016/j.agwat.2020.106578.
Lai, R.-X., H.-W. Fang, G.-J. He, X. Yu, M. Yang, and M. Wang. 2013. “Dual state-parameter optimal estimation of one-dimensional open channel model using ensemble Kalman filter.” J. Hydrodyn. 25 (4): 564–571. https://doi.org/10.1016/s1001-6058(11)60397-2.
Li, C., and L. Ren. 2011. “Estimation of unsaturated soil hydraulic parameters using the ensemble Kalman filter.” Vadose Zone J. 10 (4): 1205–1227. https://doi.org/10.2136/vzj2010.0159.
Litrico, X., and V. Fromion. 2004. “Analytical approximation of open-channel flow for controller design.” Appl. Math. Modell. 28 (7): 677–695. https://doi.org/10.1016/j.apm.2003.10.014.
Liu, K., G. Huang, Z. Jiang, X. Xu, Y. Xiong, Q. Huang, and J. Šimůnek. 2020. “A Gaussian process-based iterative ensemble Kalman filter for parameter estimation of unsaturated flow.” J. Hydrol. 589 (Oct): 125210. https://doi.org/10.1016/j.jhydrol.2020.125210.
Najafzadeh, M., and G. Oliveto. 2020. “Riprap incipient motion for overtopping flows with machine learning models.” J. Hydroinf. 22 (4): 749–767. https://doi.org/10.2166/hydro.2020.129.
Ogunsakin, R., N. Mehandjiev, and C. A. Marin. 2023. “Towards adaptive digital twins architecture.” Comput. Ind. 149 (Aug): 103920. https://doi.org/10.1016/j.compind.2023.103920.
Ou, G., S. J. Dyke, and A. Prakash. 2017. “Real time hybrid simulation with online model updating: An analysis of accuracy.” Mech. Syst. Signal Process. 84 (Feb): 223–240. https://doi.org/10.1016/j.ymssp.2016.06.015.
Papaioannou, G., L. Vasiliades, A. Loukas, and G. T. Aronica. 2017. “Probabilistic flood inundation mapping at ungauged streams due to roughness coefficient uncertainty in hydraulic modeling.” Adv. Geosci. 44 (Apr): 23–34. https://doi.org/10.5194/adgeo-44-23-2017.
Piadeh, F., K. Behzadian, and A. M. Alani. 2022. “A critical review of real-time modelling of flood forecasting in urban drainage systems.” J. Hydrol. 607 (Apr): 127476. https://doi.org/10.1016/j.jhydrol.2022.127476.
Piazzi, G., G. Thirel, C. Perrin, and O. Delaigue. 2021. “Sequential data assimilation for streamflow forecasting: Assessing the sensitivity to uncertainties and updated variables of a conceptual hydrological model at basin scale.” Water Resour. Res. 57 (Apr): 4. https://doi.org/10.1029/2020WR028390.
Pillai, U. P. A., N. Pinardi, J. Alessandri, I. Federico, S. Causio, S. Unguendoli, A. Valentini, and J. Staneva. 2022. “A digital twin modelling framework for the assessment of seagrass Nature Based Solutions against storm surges.” Sci. Total Environ. 847 (Nov): 157603. https://doi.org/10.1016/j.scitotenv.2022.157603.
Pylianidis, C., S. Osinga, and I. N. Athanasiadis. 2021. “Introducing digital twins to agriculture.” Comput. Electron. Agric. 184 (May): 105942. https://doi.org/10.1016/j.compag.2020.105942.
Shahrokhnia, M. A., and M. Javan. 2006. “Influence of roughness changes on offtaking discharge in irrigation canals.” Water Resour. Manage. 21 (3): 635–647. https://doi.org/10.1007/s11269-006-9034-2.
Wang, J., J. Zhao, T. Zhao, and H. Wang. 2022. “Partition of one-dimensional river flood routing uncertainty due to boundary conditions and riverbed roughness.” J. Hydrol. 608 (May): 127660. https://doi.org/10.1016/j.jhydrol.2022.127660.
Wuhan University. 2011. Simulation and control of canal systems: China. Wuhan, China: Wuhan Univ.
Xu, M., P. J. van Overloop, and N. C. van de Giesen. 2011. “On the study of control effectiveness and computational efficiency of reduced Saint-Venant model in model predictive control of open channel flow.” Adv. Water Resour. 34 (2): 282–290. https://doi.org/10.1016/j.advwatres.2010.11.009.
Yang, X., and T. Delsole. 2009. “Using the ensemble Kalman filter to estimate multiplicative model parameters.” Tellus A: Dyn. Meteorol. Oceanogr. 61 (5): 601–609. https://doi.org/10.1111/j.1600-0870.2009.00407.x.
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© 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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