Supplemental Data for "Spatially Resolved Measurements in Tropical Reservoirs Reveal Elevated Methane Ebullition at River Inflows and at High Productivity"
Notice bibliographique
Résumé
An increasing number of rivers are being dammed, particularly in the tropics, and reservoir water surfaces can be a substantial anthropogenic source of greenhouse gases. On average, 80% of the CO2-equivalent emission of reservoirs globally has been attributed to CH4, which is predominantly emitted via ebullition. Since ebullition is highly variable across space and time, both measuring and upscaling to an entire reservoir is challenging, and estimates of reservoir CH4 emission are therefore not well constrained. We measured CH4 ebullition at high spatial resolution with an echosounder and bubble traps in two reservoirs of different use (water storage and hydropower), size and productivity in the tropical Brazilian Atlantic Rainforest biome. Based on the spatially most well-resolved whole-reservoir ebullition measurements in the tropics so far, we found that mean CH4 ebullition was twice as high in river inflow areas than in other parts of the reservoirs, and more than four times higher in the eutrophic reservoir compared to the oligotrophic one. Using different upscaling approaches rendered similar whole-reservoir CH4 ebullition estimates, suggesting that highly spatially resolved measurements may be more important for constraining reservoir-wide CH4 estimates than choice of upscaling approach. The minimum sampling effort was high (>250 and >1700 30-m segments of hydroacoustic survey to reach within 50% or 80% accuracy, respectively). This suggests that traditional manual bubble trap measurements should be abandoned in favour of highly resolved measurements in order to get spatially representative estimates of CH4 ebullition, which accounted for 60 and 99% of total C emission in the two studied reservoirs. Dam construction is currently booming, especially in the tropics, both for production of renewable hydropower and for water supply to a growing population. However, reservoirs can emit large amounts of the greenhouse gases carbon dioxide and methane to the atmosphere. The most climate-relevant emission from reservoirs typically stems from methane bubbles that form in the reservoir sediment and rise to the water surface, and it is challenging to quantify this sporadic bubbling across an entire reservoir. We measured methane bubbling in two reservoirs in Brazil, using a method that allows for a very high spatial coverage. We found a two times higher methane bubble emission from areas in which rivers are entering the reservoirs as compared to areas further away from river inflows. Also, methane bubble emission was four times higher in the nutrient-rich reservoir than in the nutrient-poor reservoir. We found that the minimum number of sampling sites required for a representative whole-reservoir methane bubble emission estimate was high, calling for the use of spatially highly resolved methods. The dataset was originally published in DiVA and moved to SND in 2024.
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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,515 | 0,096 |
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 ».