Snow accumulation patterns from 2023 Airborne Laser Scanning data in Trail Valley Creek, Western Canadian Arctic
Bibliographic record
Abstract
Snow cover plays a pivotal role in the Arctic's climate, hydrology, and ecology, making the understanding of its deposition and accumulation dynamics crucial. Snow depth and its duration can directly influence soil temperature: the insulating properties of snow increase with greater snow depth, which prevents soil temperatures from declining in winter. Trail Valley Creek, NWT, Canada, is located at the northern boundary of the tundra-taiga transition zone, approximately 45 km north of Inuvik, and is underlain by continuous permafrost. The region’s rapid warming points to a trend of vegetation changes such as shrub expansion northwards into the tundra.Topography and vegetation cover are the main drivers of spatial variation of snow depth across different landscapes, while wind significantly influences snow redistribution. This reallocation causes snow to accumulate preferably in terrain features such as valleys and leeward sides of ridges, and taller vegetation, as their height and intricate structure can favour snow trapping. Understanding the relationships among snow distribution, topography features, and vegetation types is vital, though it is often limited by the scarcity of high-resolution data with broad spatial cover.To investigate the spatial snow distribution in Trail Valley Creek, we analyzed how snow depth varies according to different topography classes and slope aspects, as well as the region’s different vegetation classes and heights. For this purpose, we explored records from Aerial Laser Scanning (ALS) collected during both winter and summer of 2023, covering an area of over 170 km2. We generated a high-resolution Digital Elevation Model (DEM) from the winter snow-covered surface (2023-04-02), a Digital Terrain Model (DTM) from the summer snow-free terrain (2023-07-10), and by combining both, created a 1-m resolution snow depth map of the area. Additionally, we used 3129 Magnaprobe ground-based snow depth measurements for validation (2023-03-26 to 2023-03-29). For the topography analysis, we classified the slope aspects, and subdivided the terrain into 10 geomorphological classes using the geomorphons approach. This method calculates terrain forms, such as plateaus, slopes, ridges and valleys, and their associated geometry using a machine vision approach. To analyze the role of vegetation cover, we used a 13-class map that categorizes land-cover features and vegetation types, such as graminoids, shrubs and trees, and vegetation height rasters, derived from the ALS summer data.Snow is the main driver of the hydrological system in Trail Valley Creek, and the outcomes of this study will provide insights in the important interplay between vegetation, snow depth and terrain characteristics in a permafrost landscape.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".