Environmental Controls on Snow Cover Thickness and Water Equivalent in Two Sub-Arctic Mountain Catchments
Bibliographic record
Abstract
The spatial variability of snow cover characteristics (depth, density, and snow water equivalent [SWE]) has paramount importance for the management of water resources in mountain environments. Passive microwave (PM) inference of SWE from space-borne instrumentation is increasingly used but the reliability of this technique remains limited in mountainous areas. Complex topography and the transition between forest and alpine tundra vegetation zones create large spatial heterogeneities in the snowpack in such environments. A better understanding of the factors that control these heterogeneities is therefore needed to improve and extend the use of PM-derived SWE estimation to mountain settings. In this study, two seasonally snow-covered sub-Arctic mountain catchments at comparable latitudes, one in Hemavan, northern Sweden and the other in Wolf Creek, Yukon, Canada, were investigated to evaluate the relative influence of climate vs. landscape factors on the variability of snow cover characteristics. Field measurements of snowpack stratigraphy and SWE were performed at the approximate time of late winter snow depth maximum using various in situ methodologies. Regression analysis was then employed to identify possible relationships between snow depth, density and SWE, and landscape properties (altitude, slope angle and aspect) at both sites, both within and between different vegetation zones. Snow depth, density and SWE were found to be greatest in the alpine tundra zone of both catchments, and were largest in Hemavan, probably on account of the relatively warmer and wetter winter climate of northern Sweden compared to that of the Yukon. Elevation was the only quantifiable landscape property found to show a positive and significant relationship with SWE in both catchments. Notable differences in the spatial variability of snowpack properties were also found between the two study sites. The local variability of snow depth was greatest in the forest-alpine transition zone at Hemavan, but greatest in the alpine zone at Wolf Creek. Differences in the vegetation cover type between the two catchments (coniferous vs. deciduous in the forest zone) is suspected to exert an important influence on spatial patterns of snow depth, density and SWE, likely because of differences in the efficiency of snow interception. Further investigations of how different vegetation characteristics (e.g. leaf area index) influence snowpack properties over the course of the winter are recommended in order to improve and extend the use of PM-based SWE retrievals in high-latitude mountain 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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".