Deposition Patterns and Sediment Reduction Strategies in a Large-Scale Water Diversion Channel: A One-Dimensional Modeling Study of the Shigu Water Source Project on the Jinsha River
Abstract
1. Introduction
- Characterize sedimentation patterns by systematically analyzing the grain size distribution of deposited sediment under multiple scenarios, thereby identifying the dominant particle size fraction accumulating in front of the pumping station.
- Quantify the individual and combined effects of dredging and channel length optimization on sediment control, providing a scenario-based assessment of their effectiveness under contrasting hydrological conditions.
- Identify and quantify the trade-off between total sediment deposition and water quality intake, thereby offering a rational basis for optimizing channel design.
2. One-Dimensional Total-Load Mathematical Model
2.1. Model Governing Equations and Numerical Solution
- Calculate the water surface profile to determine hydraulic parameters at each cross-section.
- Compute the deposition or erosion amounts for sediment of different particle size groups within each river reach.
- Update the geometric configuration of the corresponding cross-sections based on the deposition or erosion results.
2.2. Model Calibration and Validation
3. Case Study Description


4. Calculation Conditions and Scenarios
4.1. Calculation Area and Cross-Section Configuration
4.2. Selection of Typical Years for Sediment Deposition Calculation
4.3. Calculation Schemes
5. Results
5.1. Overview of Sediment Deposition Patterns
5.2. Effect of Dredging
5.3. Effect of Channel Length on Deposition
5.4. Synthesis of Key Findings
6. Discussion
6.1. Mechanisms Controlling Fine Sediment Deposition
6.2. Hydrological Influence on Dredging Effectiveness
6.3. Understanding the Channel Length–Sedimentation Trade-Off
6.4. Implications for Sediment Management in Large-Scale Water Diversion Projects
6.5. Methodological Value of Multi-Scenario Analysis
6.6. Limitations and Future Directions
7. Conclusions
- Sediment characteristics: Sediment deposited in front of the pumping station is predominantly fine (grain size smaller than 0.05 mm), accounting for over 95% of the total deposition mass under all simulated scenarios. This finding indicates that conventional desilting basins, designed primarily for coarse particle settling, are insufficient for complete sediment removal. Complementary fine-sediment control measures (e.g., coagulant-assisted sedimentation or ecological buffer zones) are therefore necessary.
- Effectiveness of dredging: Dredging improves sediment trapping efficiency, but the magnitude of its effectiveness varies by hydrological scenario. Quantitatively, under the first-stage long-channel configuration in a high-sediment year, dredging reduced the deposition thickness at the pump intake from 1.04 m to 0.90 m (a 13.5% reduction). Under the short-channel configuration, reduction was more pronounced: from 2.06 m to 1.54 m (a 25.2% reduction). The most pronounced benefits occur in high-sediment years, while the effect in medium- and low-sediment years is relatively modest. These quantitative, scenario-specific findings provide a basis for prioritizing dredging operations following high-sediment flood events.
- Trade-off in channel length: The trade-off between channel length and sedimentation is quantified in this study. Under the first-stage desilting basin in a high-sediment year, the long channel (Scheme I) increased total deposition volume by 13.2% and reduced the mean sediment concentration at the pump intake by 5.3% (from 0.38 kg/m3 to 0.36 kg/m3) compared to the short channel (Scheme III). Notably, the short channel exhibited a 71% greater deposition thickness at the pump intake (1.54 m vs. 0.90 m). These quantitative ratios—rather than the expected direction of the trade-off—represent the main contribution, as they provide a basis for optimizing channel length based on project-specific priorities.
- Methodological contribution and transferability: The integrated modeling-assessment framework developed in this study combines a one-dimensional sediment transport model with a multi-scenario design that systematically varies channel length, dredging condition, and hydrological year. This design enables the individual effects of channel length and dredging to be isolated and quantified (Table 4a,b), revealing trade-offs that single-scenario simulations cannot capture. The framework thus provides a reference for optimizing channel design and sediment management in large-scale water diversion projects.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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| Typical Year | Year | Sediment Transport at Shigu Station (104 t) | Difference from Long-Term Average |
|---|---|---|---|
| High-Sediment Year | 1989 | 4830 | +86.48% |
| Medium-Sediment Year | 1985 | 2567 | −0.89% |
| Low-Sediment Year | 1986 | 1098 | −57.62% |
| Long-term Average | 1963~2010 | 2590 |
| Scheme | Desilting Basin Stage | Diversion Channel Length | Condition |
|---|---|---|---|
| I | Datong First Stage | Long | Condition 1 |
| II | Condition 2 | ||
| III | Short | Condition 1 | |
| IV | Condition 2 | ||
| V | Datong Second Stage | Long | Condition 1 |
| VI | Condition 2 | ||
| VII | Short | Condition 1 | |
| VIII | Condition 2 |
| (a) | |||||||||
| Scheme | Channel Length | Dredging | Year | Vd (104 m3) | Cmean (kg/m3) | Cmax (kg/m3) | P0.05 (%) | S > 0.05 (%) | Td (m) |
| I | Long | Yes | High | 48.58 | 0.36 | 2.95 | 95.96% | 91.75% | 0.90 |
| Long | Yes | Med | 25.78 | 0.22 | 1.21 | 96.69% | 93.38% | 0.41 | |
| Long | Yes | Low | 17.62 | 0.13 | 1.73 | 95.92% | 91.71% | 0.28 | |
| II | Long | No | High | 46.32 | 0.37 | 2.95 | 94.94% | 89.43% | 1.04 |
| Long | No | Med | 25.28 | 0.22 | 1.21 | 96.10% | 92.12% | 0.40 | |
| Long | No | Low | 17.21 | 0.13 | 1.73 | 95.06% | 89.84% | 0.27 | |
| III | Short | Yes | High | 42.92 | 0.38 | 3.08 | 93.02% | 84.92% | 1.54 |
| Short | Yes | Med | 23.30 | 0.23 | 1.26 | 94.51% | 88.48% | 0.63 | |
| Short | Yes | Low | 15.94 | 0.13 | 1.80 | 93.74% | 86.67% | 0.45 | |
| IV | Short | No | High | 39.60 | 0.39 | 3.08 | 91.17% | 80.30% | 2.06 |
| Short | No | Med | 22.80 | 0.23 | 1.26 | 93.93% | 87.13% | 0.64 | |
| Short | No | Low | 15.58 | 0.14 | 1.80 | 93.01% | 84.97% | 0.43 | |
| (b) | |||||||||
| Scheme | Channel Length | Dredging | Year | Vd (104 m3) | Cmean (kg/m3) | Cmax (kg/m3) | P0.05 (%) | S > 0.05 (%) | Td (m) |
| V | Long | Yes | High | 36.74 | 0.41 | 3.211 | 89.41% | 75.73% | 2.56 |
| Long | Yes | Med | 20.99 | 0.24 | 1.312 | 92.25% | 83.00% | 0.95 | |
| Long | Yes | Low | 14.64 | 0.14 | 1.874 | 92.09% | 82.57% | 0.66 | |
| VI | Long | No | High | 33.10 | 0.42 | 3.211 | 87.14% | 69.51% | 3.69 |
| Long | No | Med | 20.59 | 0.24 | 1.312 | 91.81% | 81.90% | 1.04 | |
| Long | No | Low | 14.51 | 0.14 | 1.874 | 91.83% | 81.93% | 0.69 | |
| VII | Short | Yes | High | 33.71 | 0.42 | 3.325 | 87.32% | 70.09% | 3.51 |
| Short | Yes | Med | 20.23 | 0.24 | 1.358 | 91.11% | 80.21% | 1.14 | |
| Short | Yes | Low | 13.97 | 0.14 | 1.941 | 90.64% | 78.99% | 0.8 | |
| VIII | Short | No | High | 29.33 | 0.43 | 3.325 | 84.80% | 62.71% | 4.84 |
| Short | No | Med | 19.19 | 0.24 | 1.358 | 89.99% | 77.28% | 1.36 | |
| Short | No | Low | 13.70 | 0.14 | 1.941 | 90.18% | 77.80% | 0.86 | |
| (a) | ||||
| Configuration | Year | ∆Vd | ∆Cmean | ∆Td (%) |
| (%) | (%) | |||
| First-stage long channel | High | +4.9% | −2.7% | −13.5% |
| Med | +2.0% | 0% | +2.5% | |
| Low | +2.4% | 0% | +3.7% | |
| First-stage short channel | High | +8.4% | −2.6% | −25.2% |
| Med | +2.2% | 0% | −1.6% | |
| Low | +2.3% | −7.1% | +4.7% | |
| Second-stage long channel | High | +11.0% | −2.4% | −30.6% |
| Med | +1.9% | 0% | −8.7% | |
| Low | +0.9% | 0% | −4.3% | |
| Second-stage short channel | High | +14.9% | −2.3% | −27.5% |
| Med | +5.4% | 0% | −16.2% | |
| Low | +2.0% | 0% | −7.0% | |
| (b) | ||||
| Configuration | Year | Vd | Cmean | Td (%) |
| (%) | (%) | |||
| First-stage with dredging | High | +13.2% | −5.3% | −41.6% |
| Med | +10.6% | −4.3% | −34.9% | |
| Low | +10.5% | 0% | −37.8% | |
| First-stage without dredging | High | +17.0% | −5.1% | −49.5% |
| Med | +10.9% | −4.3% | −37.5% | |
| Low | +10.5% | −7.1% | −37.2% | |
| Second-stage with dredging | High | +9.0% | −2.4% | −27.1% |
| Med | +3.8% | 0% | −16.7% | |
| Low | +4.8% | 0% | −17.5% | |
| Second-stage without dredging | High | +12.8% | −2.3% | −23.7% |
| Med | +7.3% | 0% | −23.5% | |
| Low | +5.9% | 0% | −19.8% | |
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Zeng, X.; Yuan, Y.; Zhao, J. Deposition Patterns and Sediment Reduction Strategies in a Large-Scale Water Diversion Channel: A One-Dimensional Modeling Study of the Shigu Water Source Project on the Jinsha River. Water 2026, 18, 1530. https://doi.org/10.3390/w18131530
Zeng X, Yuan Y, Zhao J. Deposition Patterns and Sediment Reduction Strategies in a Large-Scale Water Diversion Channel: A One-Dimensional Modeling Study of the Shigu Water Source Project on the Jinsha River. Water. 2026; 18(13):1530. https://doi.org/10.3390/w18131530
Chicago/Turabian StyleZeng, Xin, Yuan Yuan, and Jinqiong Zhao. 2026. "Deposition Patterns and Sediment Reduction Strategies in a Large-Scale Water Diversion Channel: A One-Dimensional Modeling Study of the Shigu Water Source Project on the Jinsha River" Water 18, no. 13: 1530. https://doi.org/10.3390/w18131530
APA StyleZeng, X., Yuan, Y., & Zhao, J. (2026). Deposition Patterns and Sediment Reduction Strategies in a Large-Scale Water Diversion Channel: A One-Dimensional Modeling Study of the Shigu Water Source Project on the Jinsha River. Water, 18(13), 1530. https://doi.org/10.3390/w18131530
