Environmental controls on snow water equivalent in two sub-Arctic mountain catchments
Bibliographic record
Abstract
Spatial variability of seasonal snow depth poses a challenge when estimating snow water equivalent (SWE) from in-situ measurements in mountainous areas. Poor accessibility, complex topographic effects and localized microclimates make extrapolation of in-situ SWE measurements to a basin scale difficult. Remotely-sensed passive microwave SWE products are also inaccurate in complex terrain and/or at the forest-alpine tundra transition zone. To address these caveats, we investigated the relative importance of landscape qualities (altitude, slope, aspect, vegetation) and climate (winter temperatures, precipitation) on SWE distribution in two sub-Arctic mountainous catchments in Hemavan, Sweden, and Wolf Creek, Yukon, Canada. The two catchments are comparable, but have contrasted climate regimes. In-situ SWE measurements were made in March-April 2014 across the forest-tundra ecotone in both catchments. These were supplemented with historical snow-survey data since 2012 in Hemavan, and 1993 in Wolf Creek. Pairwise linear regressions of SWE against different landscape factors indicate that overall, altitude exerts the largest control on SWE at both Hemavan and Wolf Creek, but its effect is lesser within individual vegetation zones. In other respects, the two sites differ. SWE is inversely correlated to surface slope at forested sites in Hemavan (R^2 = 0.57, p = 0.25), but not in Wolf Creek. Slope aspect is positively correlated with SWE at forest-tundra transition sites (R^2 = 0.49, p = 0.12) in Wolf Creek, but not in Hemavan. For alpine tundra sites, slope angle strongly influences SWE in Hemavan (R^2 = 0.58, p = 0.24), but only weakly in Wolf Creek (R^2 = 0.05, p = 0.71). We discuss possible causes of these inter-catchment differences, and also evaluate the effect of inter-annual climate variations on SWE distribution at Wolf Creek using the long-term snow-survey record. Finally, we compare and discuss SWE estimates obtained by three different field measurement methods.
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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) | 0.001 | 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".