Structural calibration of an semi-distributed hydrological model of the Liard River basin
Bibliographic record
Abstract
The development of hydrological models that produce practically useful and physically defensible results is an ongoing challenge in hydrology. This challenge is further compounded in large, spatially variable basins with sparse data, where a detailed understanding of a basin’s hydrological response may be limited. This study presents an iterative and stepwise calibration strategy for model structure and parameters for a hydrological model of the 275,000 km2 Liard River basin in northern Canada. The calibration procedure was optimized to exploit and represent available data at 29 stream gauges and included the use of multiple data sources to constrain model calibration and improve model function. A flexible modelling framework was used to allow the explicit inclusion of locally varied model structure within the calibration procedure. The final model exhibits strong performance in both calibration and validation, and represents significantly different hydrological responses in different portions of the basin well. The calibration procedure helped to identify differences in hydrological processes within the basin which have not been considered by other models of the Liard. The ability to modify model structure in order to account for different hydrological regimes in different parts of the basin is demonstrated to improve model performance locally and globally.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".