Spatial Relationships between Trees and Snow in a Cold Regions Montane Forest
Notice bibliographique
Résumé
Vegetation structure is one of the primary factors that drives spatial variation of snow accumulation in forests due to interactions between falling snow, intercepted snow, and the forest canopy. These processes result in spatially heterogeneous snowpacks and snowpack energy fluxes, driving areal snow cover depletion rates during melt periods with repercussions for stand- and basin-scale ablation rates and snowmelt runoff quantities and timings. While spatial variation of forest snowpack has been documented at scales from individual tree branches to forest stands, the underlying processes are not fully understood. Understanding these relationships is critical to understanding the combined effects of climate and vegetation changes on streamflow and ecology in basins with seasonal snowpacks. To better understand these processes, this study examined the spatial relationships between branch-scale canopy structure and subcanopy snow accumulation over two accumulation events in February of 2019, at an instrumented montane forest site in Marmot Creek Research Basin on the eastern slope of the Canadian Rockies. Repeated UAV lidar surveys were paired with manual snow surveys to produce estimates of snowpack snow water equivalent (SWE) and change in snowpack (ΔSWE) over each event at high spatial resolutions. Lidar observations of the forest canopy were combined with contemporary hemispherical photography to produce a diverse set of canopy metrics, including light transmittance metrics from a novel voxel ray sampling method. Results showed that over 75% of the spatial variance in subcanopy ΔSWE for each event was found within 2.0 m of horizontal distance, indicating that the spatial scale of canopy effects on snow interception and redistribution were primarily found at the scale of tree branches in this forest. Significant vertical asymmetry was seen in the relationships between snow accumulation and surrounding vegetation which was explained by prevailing wind directions. A descriptive Gaussian snowfall model that was consistent with the tight coupling observed between near-overhead canopy characteristics and snow accumulation explained more of the spatial variation in observed ΔSWE than any canopy metric considered and performed better than two other forest snow accumulation models based on larger scale canopy characteristics found in the literature. These findings emphasize the importance of representing branch-scale forest heterogeneity in models of snow accumulation and suggest that representation of vertical asymmetry in parametrizations of snow-vegetation relationships may yield more physically realistic models.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 tête enseignante, 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 ».