Hydrologic model calibration approaches for highly regulated river basin: A comprehensive assessment
Bibliographic record
Abstract
Adequate calibration of hydrologic models is crucial for their effective applications in water resources management, especially in regulated river basins where human interventions add complexity. This study investigated various calibration strategies, including single-site, multi-site, single-variable, multi-variable, single-objective, and multi-objective approaches using semi-distributed RAVEN models in the highly regulated Montreal River basin, Ontario, Canada. The study employed Dynamically Dimensioned Search (DDS) and Pareto Archive Dynamically Dimensioned Search (PADDS) optimization algorithms for automatic calibration. Results highlighted that multi-site-multi-variable calibration, incorporating reservoir inflow and outflow from regulated stations alongside snow-water equivalent (SWE) data, significantly improved simulation performance. Multi-objective calibration, optimized through PADDS, produced multiple sets of model parameters delivering simulation quality equivalent to weighted single-objective calibration. Notably, the ensembles generated by the multi-objective approach helped address parameter uncertainty to a certain extent. Additionally, the use of water level as an operational constraint during calibration, yielded satisfactory results, whereas overriding reservoir outflow led to poorer water level prediction performance, suggesting caution in using it for calibrating regulated reservoirs. These findings provide some guidance for effective hydrological model calibration in highly regulated river basins. This new insight can notably enhance water resource management and decision-making processes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".