Snow accumulation patterns from 2023 Airborne Laser Scanning data in Trail Valley Creek, Western Canadian Arctic
Bibliographic record
Abstract
Trail Valley Creek, located in the Northwest Territories (NWT), approximately 45km north of Inuvik, Canada, marks the northern boundary of the tundra-taiga transition zone. This region, underlain by continuous permafrost, is experiencing rapid warming and vegetation changes, including shrub expansion. These shifts may lead to increased snow depths, which could in turn affect subsurface temperatures and potentially impact permafrost stability. Topography and vegetation are key drivers of spatial variation in snow depth, with wind redistribution leading to snow accumulation in topographic lows, leeward slopes, and densely vegetated areas. However, landscape complexity also affects snow measurement accuracy, adding variability to depth estimates. Understanding these relationships is essential but often limited by the scarcity of high-resolution, large-scale data that can capture landscape heterogeneity. In this study, I investigated snow depth patterns across different topographic features (landforms, slopes, and aspects) and vegetation types (height ranges and cover classes) within an area of 127 km². To achieve this, I used LiDAR (Light Detection and Ranging) data collected over the snow-covered surface (April 2, 2023) and the snow-free terrain (July 10, 2023) of Trail Valley Creek to create a 1-meter resolution snow depth map. I then compared the LiDAR data with two reference sources: 9569 coordinate reference points along the Inuvik-Tuktoyaktuk Highway (ITH), which intersects the area and is maintained at minimal snow depth throughout winter, and snow depth measurements from 4615 field survey points. Field surveys recorded deeper snow depths than LiDAR estimates, with an overall bias of 0.18 m. The discrepancy between LiDAR and field measurements varied significantly, with the largest biases over trees (0.30 m) and on steep east-facing slopes (0.37 m). However, LiDAR measurements closely aligned with the ITH reference points, showing a median depth deviation of just 0.017 m. The analysis showed that, with regard to topography, snow depth was highest over footslopes and valleys, with median depths of 0.38m and 0.44 m, respectively, and lowest on ridges (0.20 m). Snow depth also increased with slope steepness and was consistently greater on east-facing slopes, in response to predominant winds from the west and northwest. In terms of vegetation, snow depth increased with vegetation height, with medians ranging from 0.29m over vegetation shorter than 0.50m to 0.54m in areas where vegetation height exceeded 1.5 m. These findings align with results by vegetation class, where single and riparian shrubs exhibited the highest accumulations, with snow depth medians reaching 0.49 m.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".