Reservoir Operation Optimized for Hydropower Production Reduces Conflict with Traditional Water Uses in the Senegal River
Bibliographic record
Abstract
Manantali is a dam located on the Senegal River and is mainly used for hydropower production. Before the dam’s construction, the annual river flood alimented the flood recession agriculture, a practice based on natural irrigation and fertilization of the flood plain, used traditionally by the local populations downstream. Analysis of the actual reservoir operation shows that annual floods have been largely reduced for the benefit of hydropower production. Moreover, the Senegal River Basin authority is evaluating the construction of different new dams, which could reduce even further the water available for flood support, given that the current operational focus is on satisfying hydropower demand. This study investigates the effects of an optimal reservoir operation strategy that maximizes hydropower production only, analyzing the results of this strategy in terms of effects on the two main objectives, i.e., hydropower production and flood support. The problem of finding optimal reservoir operation strategy is solved by applying the stochastic dual dynamic programming method. Results show the existence of a release strategy in which both objectives improve (+9% for hydropower and +7% for flood production) with respect to the historically observed operation. This solution, however, may require the electric system to compensate for the variability in energy supply along the year.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".