Numerical Assessment of the Long-Term Dredging Impacts on Channel Evolution in the Middle Huai River
Bibliographic record
Abstract
Large-scale dredging in the middle Huai River has induced complex geomorphic responses that compromise the long-term stability of river regulation infrastructure. To evaluate these impacts, a one-dimensional numerical model was employed, calibrated and validated using field measurements and physical modeling, to simulate 30-year channel evolution under both baseline and dredged scenarios. Results indicate that dredging reversed the reach-scale sediment budget from net erosion (69.80 × 104 m3) to net deposition (87.67 × 104 m3), while eliciting highly heterogeneous local responses. In the Liufangdi Reach, dredging produced a tripartite pattern: depositional amplification in the south branch of the Upper-Liufangdi Reach, an erosion-to-deposition transition in the Erdaohe Reach, and intensified erosion in the north branch of the Lower-Liufangdi Reach. The main channel accounted for over 84% of net volumetric changes, driving the observed morphological adjustments, while dredging promoted synchronization between main channel and floodplain evolution and established stable flow redistribution within branching channels. These findings indicate the importance of implementing spatially differentiated dredging strategies informed by sediment availability, offering critical guidance for reconciling flood control objectives with long-term morphological stability in engineered river systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".