Illustration of the added value of using a multi-site calibration and correction approach to reconstruct natural inflows and inter-catchment transfer flow: a case study
Bibliographic record
Abstract
This case study investigates the benefits of spatializing the calibration of the CEQUEAU hydrological model for two catchments located in the province of Quebec, Canada. The hydrological model is also used to correct the discharge curves of a spillway at the entrance of an artificial channel used to transfer water from one catchment to another. No real stream flow measurements are available for the catchments under study, and natural inflows are reconstituted through a water balance equation, which includes the water transferred from one catchment to the other. Although the application is specific to the Lake-St-Jean region in Quebec, Canada, this study could serve other users and water managers with similar issues regarding parameters and inflow uncertainty. The current calibration of the CEQUEAU hydrological model is based exclusively on stream flows recorded at the outlet of the downstream sub-catchments of each catchment. There exist, however, intermediate stream flow measurements at the outlet of the upstream sub-catchment for each catchment. It is demonstrated that using both stream flow records (upstream and downstream sub-catchments) to calibrate the hydrological model parameters leads to improved simulations. The recalculation of the discharge curves for the Bonnard channel through hydrological modelling also reveals that it is inexact for large stream flows and a simple but efficient correction method is proposed. Altogether, this case study shows that a careful revision of the modelling practices for those catchments can lead to more accurate estimation of inflows to reservoirs, which could reduce predictive uncertainty for future inflows.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".