Implications of mountain shading on calculating energy for snowmelt using unstructured triangular meshes
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».