Investigating the seasonal influence of atmospheric rivers on runoff generation during rain-on-snow
Notice bibliographique
Résumé
Numerous studies in the western United States have shown that atmospheric rivers (AR) have been responsible for flood-prone rain-on-snow (ROS) conditions such as intense rainfall, rapid warming of the air, and important snowmelt. Intense AR events were also shown to affect snowpack depth on a seasonal scale. The documentation of such impacts is less extensive in other areas, such as the east coast of North America. Meanwhile, climate projections indicate that the intensity and frequency of extreme events associated with atmospheric rivers will increase for the region, leading to an elevated hydrological impact caused by AR. This study focuses on the Côte-Nord region in Quebec (Canada), which experiences important yearly snow accumulation (>300 mm) and where snowpack monitoring is crucial due to the high hydroelectricity production in the area. The impacts of AR are analyzed by combining hydrometeorological observations with automated snow water equivalent measurements at 42 sites for the 2012–2021 period, as well as atmospheric river intensity scales derived from reanalysis. The ROS events were separated between those accompanied by AR and those that were not, resulting in 149 AR and 58 non-AR events. The intensity of the events was represented by the generated water available for runoff (WAR), which combines net rainfall and snowmelt. A seasonal analysis revealed that early winter was characterized by a high frequency of AR-associated events (36), exhibiting the greatest yearly frequency of high-scale AR events. The median WAR for AR events during this period was 36 mm, with rainfall predominating. In instances of extreme precipitation, WAR was significantly amplified by snowmelt, resulting from the rapid warming of shallow snowpacks. In late winter, there was a more balanced distribution of non-AR (54) and AR (74) events, which were characterized by generally lower intensity scales. This resulted in lower median WAR of, respectively, 30 mm and 19 mm for AR and non-AR events. However, the contribution of snowmelt during these events closely resembled that of rainfall, due to the generally warmer temperatures and the presence of lower-scale AR. The seasonal behaviour of the ROS events suggests a precipitation phase sensibility for WAR generation and variability in energy balance components. The sensitivity to precipitation phase is expected to vary between early and late winter, due to their distinct WAR compositions. Similarly, the event energy balance is bound to differ between early and late winter due to the contrasting conditions provided by low- and high-scale AR. This study underscores the distinctions between early and late winter ROS events and the necessity of accounting for the effects of atmospheric rivers on snowpack dynamics. Additionally, the findings outline considerations for snowpack modelling to more accurately represent extreme weather events projected to increase in frequency in the future.
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 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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».