Does Data Availability Constrain Temperature-Index Snow Models? A Case Study in a Humid Boreal Forest
Bibliographic record
Abstract
Temperature-index (TI) models are commonly used to simulate the volume and occurrence of meltwater in snow-fed catchments. TI models have varying levels of complexity but are all based on air temperature observations. The quality and availability of data that drive these models affect their predictive ability, particularly given that they are frequently applied in remote environments. This study investigates the performance of non-calibrated TI models in simulating the subcanopy snow water equivalent (SWE) of a small watershed located in Eastern Canada, for which some distinctive observations were collected. Among three relatively simple TI algorithms, the model that performed the best was selected based on the average percent bias (Pbias of 24%) and root mean square error (RMSE of 100 mm w.e.), and was designated as the base TI model. Then, a series of supplemental tests were conducted in order to quantify the performance gain that resulted from including the following inputs/processes to the base TI model: subcanopy incoming radiation, canopy interception, snow surface temperature, sublimation, and cold content. As a final test, all the above modifications were performed simultaneously. Our results reveal that, with the exception of snow sublimation (Pbias of 5.4%) and snow surface temperature, the variables mentioned above were unable to improve TI models within our sites. It is therefore worth exploring other feasible alternatives to existing TI models in complex forested environments.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".