Snow Interception Relationships With Meteorology and Canopy Density
Bibliographic record
Abstract
ABSTRACT Snow accumulation models differ in their representation of snow interception and ablation processes, and their applicability across diverse climates and forest types remains uncertain. Existing parameterisations of initial snow interception before unloading include inherently coupled canopy snow accumulation and ablation processes. This leads to difficulty in diagnosing processes and adds possible errors when incorporated in models that already account for canopy snow ablation. This study evaluates the theory underpinning parameterisations of initial snow interception using high‐temporal resolution and fine‐scale measurements of throughfall for events with minimal snow ablation and redistribution in both the canopy and on the ground. Relationships between throughfall measurements, event meteorology, and a novel lidar‐based canopy density measurement were assessed in two subalpine forest plots in the Canadian Rockies. Contrary to existing theories, no association of canopy snow load or air temperature with initial interception efficiency was observed. Instead, snow‐leaf contact area emerged as the primary factor governing snow accumulation, before ablation. A wind‐driven snowfall event demonstrated that non‐vertical hydrometeor trajectories can significantly increase snow‐leaf contact area, thereby enhancing initial interception. Prediction of interception efficiency for this event was improved when adjusted for hydrometeor trajectory angle based on the wind speed at one‐third of the canopy height. Snow‐leaf contact area showed a high sensitivity to wind speed, increasing by up to 95% with a 1 m s −1 wind speed. The study proposes a new parameterisation that calculates throughfall, independent of processes that ablate snow from the canopy, as a function of snowfall, canopy cover, wind speed, and hydrometeor fall velocity. The parameterisation successfully estimated subcanopy snow accumulation for a snowfall event at two forest plots of differing canopy density and structure. By separating canopy snow ablation from snow interception processes, this new model offers potentially improved prediction of subcanopy snow accumulation when combined with canopy snow ablation parameterisations.
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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) | 0.000 | 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".