Comparison of snowpack structure in gaps and under the canopy in a humid boreal forest
Bibliographic record
Abstract
Abstract The boreal forest covers a significant portion of the Northern Hemisphere and is snow‐covered for over half of the year. Understanding the interactions between the forest canopy and snow is essential in hydrological, meteorological, and climate modelling. However, this is challenging because the density of a forest can range from closed canopies to open gaps. In winter 2018–2019, we assessed differences in snowpack microstructure in small forest gaps and under the canopy of a humid boreal site in eastern Canada. Our experimental approach consisted of quasi‐continuous weekly observations of stratigraphy and measurements of density profiles and temperature in a series of snow pits in both environments. High‐resolution specific surface area (SSA) profiles were measured twice, allowing for an estimation of snow permeability and hydraulic conductivity. The shallower snowpack under the canopy displayed a stronger vertical temperature gradient and less compaction than in forest gaps. This resulted in the dominance of faceted snow crystals with a small SSA. In contrast, we observed that small, rounded grains with a larger SSA than that of faceted crystals prevailed in the gaps. Due to denser snow and higher SSA, snow permeability inside gaps was found to be lower than under the canopy. Implicitly, the estimated hydraulic conductivity was also lower in gaps. Following rain‐on‐snow events, snow under the canopy displayed layers of melt‐freeze polycrystals, while in the gaps, well‐defined ice layers were formed. The combination of low snow permeability and ice layers is likely to affect liquid water transport in the gap snowpack as compared to the canopy. Although observed at relatively small scales in our study, if these differences are confirmed at a catchment scale, they are likely to impact the hydrology of forested areas during snowmelt.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".