Water tracer model-assessed contributions of source waters to changing circumpolar Arctic terrestrial evapotranspiration and river discharge
Notice bibliographique
Résumé
• Tracer model quantitatively separated contributions of source waters to evapotranspiration and discharge in the Arctic river basins. • Rain and snowmelt water are major sources to evapotranspiration and discharge, respectively, and discharge shows seasonally varying sources. • Rain plays a key role in driving interannual, seasonal, and regional variability of evapotranspiration and discharge. • The individual roles of rain and groundwater to evapotranspiration and discharge are increasing during 1979 to 2016. Climate change has resulted in alteration of snow cover, permafrost degradation, vegetation infilling, and precipitation apportionment across the Arctic terrestrial region, which has in turn affected the balance of hydrological processes including seasonality and interaction of snowmelt, rain, and soil water storage. Effects on Arctic river discharge have been widely observed, although relatively few studies have provided detailed assessments of the underlying causes of change, including adjustments in the timing and relative contributions of source waters (i.e., snowmelt, rainwater, soil water, permafrost thaw) and the effects of altered evapotranspiration regimes. Principal challenges have included limitations in the observational networks, which have often frustrated efforts to reliably model complex changes that are underway. This study explores a tracer-aided ecohydrological model that was used to quantitatively assess source water contributions to evapotranspiration and discharge from the circumpolar Arctic river basin, based on three meteorological forcing datasets from the period of 1979 to 2016. The model, which provides additional constraints on water partitioning using isotopic evidence, revealed that rain and snowmelt water accounted for 67% and 39% of the annual evapotranspiration and discharge averaged over the study period, respectively. Rainwater contributions to annual evapotranspiration and discharge were found to have increasing trends over the study period, whereas snowmelt water showed fairly stable or insignificant negative trends. Rainwater was determined to be a dominant source of evapotranspiration across the growing season, while discharge was sustained by seasonally-varying dominant water sources. Earlier snowmelt events also appear to have increased the proportion of snow and rain in peak discharge, with rainwater sources being linked largely to autumn storage in the previous year. We attribute higher evapotranspiration in summer to reduction in the proportion of rain and snowmelt water in summer discharge, resulting in negative interannual trend. Both soil water and permafrost thaw likely have contributed to increases in cold season discharge, although the proportion accounted for by permafrost thaw sources was mostly low. Our analysis renders a new perspective on underlying changes to source water partitioning, especially enhanced rainwater contributions, as a key driver of seasonal, interannual and regional changes in terrestrial evapotranspiration and discharge across the Arctic region.
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».