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Spatiotemporal Analysis and Automated Identification of Manning’s Roughness Coefficient in Lined Canals

Li, Li, Bai, Dan, Bai, Xueli, Zhou, Wen·2025·Water
DOI 10.3390/w17233446
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Abstract

This study addresses the problem of inaccurate channel flow simulation and uneven irrigation water distribution caused by the spatiotemporal variability of Manning’s roughness coefficient. The SCE-UA optimization algorithm was applied to calibrate Manning’s roughness coefficients and quantify their spatiotemporal variation patterns using 1728 sets of measured water level–discharge data collected in the Yellow River Irrigation District. Results show that accounting for spatiotemporal variability reduces the mean absolute percentage error (MAPE) for water level simulation by nearly 8%. Based on these findings, an automatic roughness update system specifically designed for grassroots water distribution stations has been developed, which can integrate water demand and gate control to achieve efficient water allocation in canals in the future.

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Crossref · 10.3390/w17233446

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Figures

Figure 1 Study Area and Field Photographs.
Figure 1 Study Area and Field Photographs.CC-BY · From the PDF
Figure 2 Flowchart of SCE-UA-based calibration for channel roughness.
Figure 2 Flowchart of SCE-UA-based calibration for channel roughness.CC-BY · From the PDF
Figure 3 Channel Segmentation Schematic Diagram.
Figure 3 Channel Segmentation Schematic Diagram.CC-BY · From the PDF
Figure 4 Calibration Process and Results of Manning's Roughness Coefficient for March 2025.
Figure 4 Calibration Process and Results of Manning's Roughness Coefficient for March 2025.CC-BY · From the PDF
Figure 5 Calibration Process and Results of Manning's Roughness Coefficient for August 2024.
Figure 5 Calibration Process and Results of Manning's Roughness Coefficient for August 2024.CC-BY · From the PDF
Figure 6 Comparison of Cross-Seasonal Water Depth Simulations. ( a ) Using the Manning's roughness calibrated in March 2025 (Mar_n0) and August 2024 (Aug_n0) to fit the water depth data of the same irrigation season as Mar_n0; ( b ) Using Mar_n0 (calibrated with the March 2025 dataset) and Aug_n0 (calibrated with the August 2024 dataset) to fit the water depth data of the same irrigation season as Aug_n0.
Figure 6 Comparison of Cross-Seasonal Water Depth Simulations. ( a ) Using the Manning's roughness calibrated in March 2025 (Mar_n0) and August 2024 (Aug_n0) to fit the water depth data of the same irrigation season as Mar_n0; ( b ) Using Mar_n0 (calibrated with the March 2025 dataset) and Aug_n0 (calibrated with the August 2024 dataset) to fit the water depth data of the same irrigation season as Aug_n0.CC-BY · From the PDF
Figure 7 Heatmap of Parameter Sensitivity.
Figure 7 Heatmap of Parameter Sensitivity.CC-BY · From the PDF
Figure 8 Flowchart of the Automatic Calibration System.
Figure 8 Flowchart of the Automatic Calibration System.CC-BY · From the PDF

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Cited by

Metadata

Authors
Li, L., Bai, D., Bai, X., & Zhou, W.
Affiliations
State Key Laboratory of Eco-Hydraulics in Northwest Arid Region, Xi’an University of Technology, Xi’an 710048, China
Venue
Water
Publisher
MDPI AG
Published
2025-12-04
Volume
17
Issue
23
Pages
3446
ISSN
2073-4441
DOI
10.3390/w17233446
Cited by
3
References
0
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
National Natural Science Foundation of China (41571222, 51909208)

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