Quantifying the Effect of Catchment Snow Cover on Stream Temperature Dynamics in a Mountainous Region
Bibliographic record
Abstract
ABSTRACT Stream temperature is an important water quality parameter, particularly as it influences thermal habitat suitability for a range of species. Empirical models are commonly used for estimating stream temperature at locations where no or limited data exist and for making projections of stream temperature response to future climate scenarios. Previous research has shown that snow dynamics strongly influence stream temperature in mountainous regions. The objective of this study is to evaluate the use of catchment‐scale fractional snow cover () as a predictor in temporal stream temperature models. The study focused on 26 catchments in the southern Coast Mountains of British Columbia, where stream temperature has been monitored for at least 3 years for the months of April through October between 2016 and 2020. Daily mean air temperature series were estimated for each location using the ECMWF Reanalysis v5 (ERA5) daily surface product. Daily time series of fractional snow cover in the catchment areas were extracted from the MODIS snow cover product. Mean fractional snow cover for June of each year was used as a predictor in models for the following July to October to represent the thermal memory associated with catchment snow cover. Statistical modelling indicated strong support for including in May, June, September and October. For May and June, was significant for catchments larger than about 10 km 2 , but there did not appear to be an area threshold for significance for September and October. Mean June snow cover as an indicator of antecedent snow conditions was a significant predictor for some locations for July and August. Further research should explore the utility of for sites with longer periods of record, and should also explore the use of alternative snow indices, such as snow cover predicted by a hydrological model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".