Supplemental Data for "Spatially Resolved Measurements in Tropical Reservoirs Reveal Elevated Methane Ebullition at River Inflows and at High Productivity"
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.515 | 0.096 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".