Ensemble hydrological forecasts for reservoir management of the Shipshaw River catchment using limited data
Bibliographic record
Abstract
Many hydropower companies continue to rely on expert judgment to manage the operations of their reservoirs. Decision-support systems, composed of a hydrological forecasting system and a reservoir model, can ensure that reservoir operation objectives are attained more effectively than by relying solely on expert judgment. In this study, a simple ensemble inflow forecasting system coupled with a reservoir model is developed and the proposed model-based operational water management decisions are compared with those based on expert judgment for the Shipshaw River in Quebec, Canada. Given that no natural streamflow records are available for the Shipshaw River, the HEC-HMS hydrological model is calibrated using a regionalization method based on physical similarity. The calibrated hydrological model is fed by ensemble meteorological forecasts that include 20 members, with a 10-day horizon and a 6-hour time step. The proposed decision-support system can help avoid small flooding events while potentially improving energy production by 2 to 60% for this case study. The proposed forecasting system also allows water-resource managers to anticipate events with a greater lead time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".