Notice bibliographique
Résumé
Abstract. Permafrost thaw profoundly changes landscapes in the Arctic-boreal region, affecting ecosystem composition, structure, function and services and their hydrological controls. The water balance provides insights into water movement and distribution within a specific area and thus helps understand how different components of the hydrological cycle interact with each other. However, the water balance of small- (<101 km²) and meso-scale basins (101–103 km²) in thawing landscapes remains poorly understood. Here, we conducted an observational study in three small-scale basins (0.1–0.3 km²) of a thawing boreal peatland complex. The three small-scale basins were situated in the Scotty Creek basin headwater portion, a meso-scale low-relief basin (drainage area estimates from 130 to 202 km²) near the southern permafrost limit in the Taiga Plains ecozone in western Canada. By measuring water losses (discharge, evapotranspiration [ET]), inputs (rainfall [R], snow water equivalent [SWE]) and storage change (ΔS), and calculating runoff (Q), we (1) aimed at quantifying growing season (May–September, 2014–2016) headwater small-scale basins water balances, i.e., sub-basins. After (2) comparing monthly sub-basin- and corresponding basin water losses through ET and Q, we aimed at (3) assessing the long-term (1996–2022) annual basin water balance using publicly available observations of discharge (and thus calculated Q), R and SWE in combination with simulated ET. (1) Growing season water balance residuals (RES) for the sub-basins ranged from -81 to +122 mm. The monthly growing season water balance for the sub-basin for which all the water balance components throughout the three-year study period were recorded exhibited large positive RES for May (+117 to +176 mm) since it included late-winter SWE routinely estimated in late March right before snowmelt. In contrast, lower monthly RES were obtained from June to September (-41 to 0 mm). For two sub-basins, we provide two different drainage area estimates highlighting the challenge of automated terrain analysis using digital elevation models in low-relief landscapes. Drainage areas were similar for one sub-basin but exhibited a fivefold difference for the other. This discrepancy was attributed to the high degree of landscape heterogeneity and resulting hydrological connectivity with implications for Q calculations and RES. (2) The spring freshet contributed 41 to 100 % (sub-basins) and 50 to 79 % (basin) of the April–September Q. Spring freshet peaks were comparable, except for the driest year (2014), when basin Q was more than ten times lower than in the sub-basins. At both scales ET was the dominating water loss, more than twice Q. (3) Over the long-term (1996–2022), the increase of basin runoff ratio (ratio of runoff to precipitation) from 1996 to 2012 (0.1 to 0.5) has been attributed to the increasing connectivity of wetlands to the drainage network caused by permafrost thaw. However, the smaller average and more variable runoff ratio from 2013 to 2022 may be due to wetland drying and/or changes in precipitation patterns. Long-term hydrological monitoring is crucial to identify and understand potential threshold effects (e.g., hydrological connectivity) and ecohydrological feedbacks affecting local (e.g., subsistence activities), regional (e.g., weather) and global ecosystem services (e.g., carbon storage) provided by thawing boreal peatland complexes.
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 | 0,002 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,021 | 0,021 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,170 | 0,127 |
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 ».