Probability-Based Rule Curves for Multi-Purpose Reservoir System in the Seine River Basin, France
Bibliographic record
Abstract
Multiple reservoir operation is of paramount importance due to tradeoffs in water supply and their cost functions. Understanding this complexity is important for optimizing water supply and increasing synergies gained from the joint operation. Therefore, this study aimed to develop a conceptual framework for addressing the effects of climate change on water security under the operating rules of the multiple reservoir system in northern France. A dynamic programming approach (DP) was employed to find the cost–benefit analysis that best fit with the objectives of reservoir operation, while the space rule was applied to balance the available space in each reservoir of a parallel system. A finite-horizon optimal regulation was then adopted for determining daily reservoir storage based on probability-based rule curves. The results indicated that the predicted inflow during the drawdown–refill cycle period to the Marne and Pannecière reservoirs would be the largest and lowest, respectively. The proposed upper rule curves during high-flow conditions suggested that the release from Aube reservoir should be postponed from July to August until September. At 50- and 100-year return periods, quite a high release rate from Seine and Marne reservoirs was observed during the dry season. A decrease in future water supply from Pannecière reservoir was found during summer, while the withdrawal in November could cause excessive water in the Seine tributary and Paris City. Under low-flow conditions in all return periods, the proposed lower rule curves recommended that the reservoir storage should go below the current operating rule, with a clear difference in July (the largest in Marne and the smallest in Pannecière) and almost no difference in November. Moreover, the web-based support system IRMaRA was developed for revising operating rules of four main reservoirs located in the Seine River Basin. The novelty of this modeling framework would contribute to the practice of deriving optimal operating rules for a multi-reservoir system by the probability-based rule curve method. Based on the evaluation of the effects of applying the estimated reservoir storage capacity under different return periods, both less overflow and water shortage represented by different levels of quantity and severity can be expected compared to the existing target storage at specified control points. Finally, the obtained finding revealed that the application of dynamic programming for reservoir optimization would help in developing a robust operating policy for tackling the effects of climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".