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Record W4409859012 · doi:10.1002/hyp.70135

Snow Interception Relationships With Meteorology and Canopy Density

2025· article· en· W4409859012 on OpenAlexafffundabout
A. Cebulski, John W. Pomeroy

Bibliographic record

VenueHydrological Processes · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaGlobal Water FuturesAlberta InnovatesCanada First Research Excellence FundCanada Research ChairsEnvironment and Climate Change CanadaCanada Foundation for InnovationUniversity of Saskatchewan
KeywordsSnowInterceptionEnvironmental scienceMeteorologyCanopyAtmospheric sciencesClimatologyGeographyGeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.226
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2025
Admission routes3
Has abstractyes

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