The Migration of the Erosion Center Downstream of the Three Gorges Dam, China, and the Role Played by Underlying Gravel Layer
Bibliographic record
Abstract
Abstract Rivers disrupted by sediment cutoff often experience degradation, but the migration of the erosion center, defined as the location with the greatest degradation rates, has not been thoroughly understood. This paper focuses on the streamwise migration of the erosion center along the ∼400‐km‐long Yichang to Chenglingji reach (YCR) downstream of the Three Gorges Dam (TGD), China. We analyzed channel morphological adjustment based on water, sediment and channel geometry data collected during 2002–2020. Based on the location and time for the occurrence of relatively large channel degradation, a clustering algorithm was used to identify the location of the erosion center. Characteristics and morphodynamic controls of the erosion centers were studied based on the migration of incisional and coarsening waves simulated by a one‐dimensional morphodynamic model for nonuniform sediment. Results show that the erosion center migrated downstream along the Yichang‐Zhicheng reach with gravel‐sand bed during 2002–2012, the migration rate was rapid after the dam closure then decreased with time. After ∼2012, large cascade dams started to operate along the upper Yangtze River, sediment load further decreased and degradation accelerated at the YCR. Correspondingly, the erosion center migrated to the sand‐bedded upper Jingjiang reach with faster rates. The erosion center migrated for a total of over 200 km with an average rate of ∼14 km/yr during 2002–2020. The underlying gravel layer was exposed due to degradation, which enhanced bed coarsening and resulted in the propagation of the erosion center downstream of the TGD.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".