The Impact of the Three Gorges Reservoir Operations on Hydraulic Characteristics in the Backwater Region: A Comprehensive 2D Modeling Study
Bibliographic record
Abstract
The Three Gorges Reservoir (TGR), a landmark of human engineering, has significantly altered the hydrodynamics and ecology of its surrounding environment. Our research explores the hydrodynamic and ecological changes in the TGR, focusing on their implications for reservoir-induced water quality and water resource issues. We designed a 2D hydrodynamic and water quality model and implemented 15 operational scenarios with an advanced dynamic storage capacity method for the TGR during flood season, drawdown and impoundment periods. Our simulations well reproduced and predicted water levels, discharge rates, and thermal conditions of the TGR, providing critical insights. The dynamic storage capacity method significantly improved the precision of water level simulations. This approach achieved modeling errors below 0.2 m when compared to real measurements from seven stations. We performed a detailed analysis of the sensitive, sub-sensitive, and insensitive areas during three reservoir operation periods. The drawdown period showed the most extensive impact range (468 km river channel), while the impoundment period had the least impact range (76 km river channel). Furthermore, we quantified the delay of temperature waves during these periods, observing a maximum delay of approximately 120 km and a minimum delay of less than 10 km, which underscores the variability in hydrodynamic responses under different operational scenarios. Our findings reveal the complex sensitivities of the TGR to varied operational modes, aiding in the development of eutrophication and water resources control strategies. Our modeling application provides different operational scenarios and insights for ecological management strategies in large dam systems globally, informing future water resource management and policy-making, ensuring sustainable and effective management of large reservoir systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".