Modeling Monthly Fluctuations in Submersion Area of a Dammed River Reservoir: A Case Study<sup>1</sup>
Bibliographic record
Abstract
Lu, Haorong, S. Samuel Li, and Jinsong Guo, 2012. Modeling Monthly Fluctuations in Submersion Area of a Dammed River Reservoir: A Case Study. Journal of the American Water Resources Association (JAWRA) 1‐13. DOI: 10.1111/jawr.12003 Abstract: Fluctuations in water submersion of the Three Gorges Reservoir in China have not been explored in spite of their important implications for shoreline erosion and other undesirable consequences. This article aims to quantify the monthly fluctuations in response to changing hydraulic parameters and regional climatic factors. Flow velocity and water levels distributed along the 609‐km long dammed river reservoir are calculated with a one‐dimensional hydrodynamics model. Evaporation of water from the surface of the reservoir is determined using mass transfer‐based methods. Calculated flow velocities and water levels compare well with field data. We show that the water surface slope decreases with rising water level at the dam, and decreases to almost zero during the winter months of water storage when the downstream water level reaches the normal pool level. The submersion area varies between 830 and 1,070 km2 over the year or over 20% of the reservoir zone will experience the annual cycle of dry land and partial or complete submersion. These fluctuations are of relevance to shoreline management and to the prevention and restoration of shoreline erosion. Evaporation is estimated to fluctuate between 1,240 and 26,110 tons of water per month per kilometer length of reservoir channel; this can possibly affect the hydrological budget of the reservoir region. The simple methodologies discussed in this article can easily be applied to other dammed river reservoirs for submersion estimates.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".