Impoundment Impact of the Three Gorge Reservoir on the Hydrological Regime in the Lower Han River, China
Bibliographic record
Abstract
Reservoir regulation has been playing an increasingly important role in water resources development and its influence on the hydrological processes of downstream tributaries has attracted much attention. The lower Han River is selected as a case study to examine the hydrological and hydraulic influence of the upstream flow regulation of the Three Gorges Reservoir (TGR) in the middle Yangtze River, China. Based on a hydrodynamic model and the observed data, the hydrological processes in the lower Han River were simulated and their changes were analyzed under the impoundment influences of the TGR. The results indicated that there were obviously hydrological changes in the lower Han River after the TGR operation. The decreased stage downstream the TGR during the impounding periods of the TGR resulted in an increase in the stage difference, current speed, hydraulic gradient and the discharge ratio. In addition, the stage difference between the two rivers was decreased during the periods of water compensation from the TGR, which led to the outflow congestion in the lower Han River. The hydrological changes in the lower Han River were the response to the flow regulation of the TGR and the inflow of the two rivers. The variation in the rating curve in the lower Han River mainly resulted from the stage difference between the two rivers during the dispatching periods of the TGR. These results help to explain the hydrological variability under the impounding influence of the TGR for the lower Han River and they can be extended to other river tributaries downstream to the reservoirs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".