The importance of the air temperature variable for the snowmelt runoff modelling using the SRM
Bibliographic record
Abstract
Abstract Runoff regimes in most northern basins are controlled by the melting snow cover. A common method for evaluating runoff consists in correlating ambient air temperature and recorded hydrometric gauge values. The air temperature is the principal variable to estimate the importance of the melting of the snow cover when using a global conceptual model such as the snowmelt runoff model (SRM). The temperature, which is often only measured at one weather station, must be extrapolated to the whole basin according to some kind of lapse rate. This extrapolation often assumes that air temperature is representative for a wide region, which is often not the case. The estimation of temperature values is critical, especially for large basins where the surface processes are largely influenced by a forest cover. This project has two objectives: (1) applying a mostly high mountain SRM to the Batiscan River Basin, in the Province of Québec, an area occupied by a forest with a rolling hill topography; (2) investigate the impact of the extrapolation strategy for estimating temperature values and its importance in the runoff modelling. A statistical comparison between the different modelling attempts was performed. This allowed us to obtain a sensitivity analysis of the snow runoff modelling in relation to the extrapolation of the temperature values. Our results showed that the weather station, used to perform the runoff modelling, should be located in the most representative land cover of the study area. Otherwise, the values of a synthetic regional weather station were more reliable for the modelling. Finally, before pursuing any snowmelt modelling with the SRM, the temperature values must be evaluated based on the location of the weather station to see if they are representative of the total study area. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".