Coupled eco-hydrology and biogeochemistry algorithms enable the simulation of water table depth effects on boreal peatland net CO <sub>2</sub> exchange
Notice bibliographique
Résumé
Abstract. Water table depth (WTD) effects on net ecosystem CO2 exchange of boreal peatlands are largely mediated by hydrological effects on peat biogeochemistry and the ecophysiology of peatland vegetation. The lack of representation of these effects in carbon models currently limits our predictive capacity for changes in boreal peatland carbon deposits under potential future drier and warmer climates. We examined whether a process-level coupling of a prognostic WTD with (1) oxygen transport, which controls energy yields from microbial and root oxidation–reduction reactions, and (2) vascular and nonvascular plant water relations could explain mechanisms that control variations in net CO2 exchange of a boreal fen under contrasting WTD conditions, i.e., shallow vs. deep WTD. Such coupling of eco-hydrology and biogeochemistry algorithms in a process-based ecosystem model, ecosys, was tested against net ecosystem CO2 exchange measurements in a western Canadian boreal fen peatland over a period of drier-weather-driven gradual WTD drawdown. A May–October WTD drawdown of ∼ 0.25 m from 2004 to 2009 hastened oxygen transport to microbial and root surfaces, enabling greater microbial and root energy yields and peat and litter decomposition, which raised modeled ecosystem respiration (Re) by 0.26 µmol CO2 m−2 s−1 per 0.1 m of WTD drawdown. It also augmented nutrient mineralization, and hence root nutrient availability and uptake, which resulted in improved leaf nutrient (nitrogen) status that facilitated carboxylation and raised modeled vascular gross primary productivity (GPP) and plant growth. The increase in modeled vascular GPP exceeded declines in modeled nonvascular (moss) GPP due to greater shading from increased vascular plant growth and moss drying from near-surface peat desiccation, thereby causing a net increase in modeled growing season GPP by 0.39 µmol CO2 m−2 s−1 per 0.1 m of WTD drawdown. Similar increases in GPP and Re caused no significant WTD effects on modeled seasonal and interannual variations in net ecosystem productivity (NEP). These modeled trends were corroborated well by eddy covariance measured hourly net CO2 fluxes (modeled vs. measured: R2 ∼ 0.8, slopes ∼ 1 ± 0.1, intercepts ∼ 0.05 µmol m−2 s−1), hourly measured automated chamber net CO2 fluxes (modeled vs. measured: R2 ∼ 0.7, slopes ∼ 1 ± 0.1, intercepts ∼ 0.4 µmol m−2 s−1), and other biometric and laboratory measurements. Modeled drainage as an analog for WTD drawdown induced by climate-change-driven drying showed that this boreal peatland would switch from a large carbon sink (NEP ∼ 160 g C m−2 yr−1) to carbon neutrality (NEP ∼ 10 g C m−2 yr−1) should the water table deepen by a further ∼ 0.5 m. This decline in projected NEP indicated that a further WTD drawdown at this fen would eventually lead to a decline in GPP due to water limitation. Therefore, representing the effects of interactions among hydrology, biogeochemistry and plant physiological ecology on ecosystem carbon, water, and nutrient cycling in global carbon models would improve our predictive capacity for changes in boreal peatland carbon sequestration under changing climates.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| 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) | 0,000 | 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 ».