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Record W3080931274

Implications of mountain shading on calculating energy for snowmelt using unstructured triangular meshes

2012· article· en· W3080931274 on OpenAlexaff
Christopher B. Marsh, John W. Pomeroy, Raymond J. Spiteri

Bibliographic record

VenueUniversity Library - University of Saskatchewan (University of Saskatchewan) · 2012
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSnowmeltShadingGeologyMeteorologyHydrology (agriculture)Physical geographyComputer scienceGeomorphologyGeographyComputer graphics (images)SnowGeotechnical engineering
DOInot available

Abstract

fetched live from OpenAlex

In many parts of the world, the snowmelt energy balance is dominated by net solar shortwave radiation. This is the case in the Canadian Rocky Mountains, where clear skies dominate the winter and spring. In mountainous regions, solar irradiance at the snow surface is not only affected by solar angles, atmospheric transmittance, and the slope and aspect of immediate topography, but also by shadows from surrounding terrain. Many hydrological models do not consider such horizon-shadows. The accumulation of errors in estimating solar irradiance by neglecting horizon-shadows can lead to significant errors in calculating the timing and rate of snowmelt due to the seasonal storage of internal energy in the snowpack. A common approach to representing the landscape is through structured meshes. However, such representations introduce errors due to the rigid nature of the mesh, creating artefacts and other constraints. Unstructured triangular meshes are more efficient in their representation of the terrain by allowing for a variable resolution. These meshes do not suffer from the artefact problems of a structured mesh. This thesis demonstrates the increased accuracy of using a horizon-shading model with an unstructured mesh versus standard self-shading algorithms in Marmot Creek Research Basin (MCRB), Alberta, Canada. A systematic basin-wide over-prediction (basin mean expressed as phase change mass: 14 mm, maximum: 200 mm) in net shortwave is observed when only self-shadows are considered. The horizon-shadow model was run at a point scale at three sites throughout MCRB to investigate the effects of scale on the model results. It was found that small triangles were best suited for this topographic region and that shadow patterns were captured accurately. Large triangles were found to be too easily shaded by the model, created many disjointed regions. As well, model results were compared to measurements of mountain shadows by timelapse digital cameras. These images were orthorectified and the shadow regions extracted allowing for a quantitative comparison. It was found that the horizon-model produced results within 10 m of the measured shadows, and properly captured shadow transits. A point-scale energy balance model SNOBAL was run via The Cold Regions Hydrological Model, an HRU based hydrologic model. It was found that in the highly shaded valleys, snowpack ablation could be incorrect by approximately 4 days. Although MCRB was generally not significantly impacted by the over-estimation in irradiance in this study, insight into the horizon-shadowing process was possible as a result of the existing network of radiometers and other meteorological stations at MCRB. Because down-stream processes such as flooding depend on correct headwater snowmelt predictions, quantitative results demonstrating inaccuracies in a modelled component of the surface energy balance can help improve snowmelt modelling.

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)0.001
metaresearch head score (Gemma)0.011
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: none
Teacher disagreement score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.015
GPT teacher head0.210
Teacher spread0.195 · 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".

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Citations0
Published2012
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