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Record W2052369304 · doi:10.5194/hessd-7-1167-2010

Hydrological response unit-based blowing snow modelling over an alpine ridge

2010· article· en· W2052369304 on OpenAlexaffabout
Matthew K. MacDonald, John W. Pomeroy, Alain Pietroniro

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Saskatchewan
Fundersnot available
KeywordsSnowWind speedSnowmeltEnvironmental scienceTerrainGeologyMeteorologyCrosswindRidgeAtmospheric sciencesClimatologyGeomorphologyGeography

Abstract

fetched live from OpenAlex

Abstract. Snow redistribution by wind and the resulting accumulation regimes were simulated for two winters over an alpine ridge transect located in the Canada Rocky Mountains. Simulations were performed using physically based blowing snow and snowmelt models. A hydrological response unit (HRU)-based spatial discretization was used rather than a more computationally expensive fully-distributed one. The HRUs were set up to follow an aerodynamic sequence, whereby eroded snow was transported from windswept, upwind HRUs to drift accumulating, downwind HRUs. HRUs were selected by examining snow accumulation patterns from manual snow depth measurements. Simulations were performed using two sets of wind speed forcing: (1) station observed wind speed, and (2) modelled wind speed from a widely applied empirical, terrain-based windflow model. Best results were obtained when using the site meteorological station wind speed data. The windflow model performed poorly when comparing the magnitude of modelled and observed wind speeds, though over-winter snow accumulation results obtained when using the modelled wind speeds were reasonable. However, there was a notable discrepancy (17%) between blowing snow sublimation quantities estimated when using the modelled and observed wind speeds. As a result, the end-of-winter snow accumulation was considerably underestimated (32%) when using the modelled wind speeds. That snow redistribution by wind can be adequately simulated in computationally efficient HRUs over this alpine ridge has important implications for representing snow transport in large-scale hydrology models and land surface schemes. Snow redistribution by wind was shown to significantly impact snow accumulation regimes in mountainous environments as snow accumulation was reduced to less than one-third of snowfall on windswept landscapes and nearly doubled in certain lee slope and treeline areas. Blowing snow sublimation losses were shown to be significant (approximately one-quarter of snowfall or greater).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.249
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2010
Admission routes2
Has abstractyes

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