Spatial Relationships between Trees and Snow in a Cold Regions Montane Forest
Bibliographic record
Abstract
Vegetation structure is one of the primary factors that drives spatial variation of snow accumulation in forests due to interactions between falling snow, intercepted snow, and the forest canopy. These processes result in spatially heterogeneous snowpacks and snowpack energy fluxes, driving areal snow cover depletion rates during melt periods with repercussions for stand- and basin-scale ablation rates and snowmelt runoff quantities and timings. While spatial variation of forest snowpack has been documented at scales from individual tree branches to forest stands, the underlying processes are not fully understood. Understanding these relationships is critical to understanding the combined effects of climate and vegetation changes on streamflow and ecology in basins with seasonal snowpacks. To better understand these processes, this study examined the spatial relationships between branch-scale canopy structure and subcanopy snow accumulation over two accumulation events in February of 2019, at an instrumented montane forest site in Marmot Creek Research Basin on the eastern slope of the Canadian Rockies. Repeated UAV lidar surveys were paired with manual snow surveys to produce estimates of snowpack snow water equivalent (SWE) and change in snowpack (ΔSWE) over each event at high spatial resolutions. Lidar observations of the forest canopy were combined with contemporary hemispherical photography to produce a diverse set of canopy metrics, including light transmittance metrics from a novel voxel ray sampling method. Results showed that over 75% of the spatial variance in subcanopy ΔSWE for each event was found within 2.0 m of horizontal distance, indicating that the spatial scale of canopy effects on snow interception and redistribution were primarily found at the scale of tree branches in this forest. Significant vertical asymmetry was seen in the relationships between snow accumulation and surrounding vegetation which was explained by prevailing wind directions. A descriptive Gaussian snowfall model that was consistent with the tight coupling observed between near-overhead canopy characteristics and snow accumulation explained more of the spatial variation in observed ΔSWE than any canopy metric considered and performed better than two other forest snow accumulation models based on larger scale canopy characteristics found in the literature. These findings emphasize the importance of representing branch-scale forest heterogeneity in models of snow accumulation and suggest that representation of vertical asymmetry in parametrizations of snow-vegetation relationships may yield more physically realistic models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".