Year‐2020 Global Distribution and Pathways of Reservoir Methane and Carbon Dioxide Emissions According to the Greenhouse Gas From Reservoirs (G‐res) Model
Bibliographic record
Abstract
Abstract Collectively, reservoirs constitute a significant global source of C‐based greenhouse gases (GHGs). Yet, global estimates of reservoir carbon dioxide (CO 2 ) and methane (CH 4 ) emissions remain uncertain, varying more than four‐fold in recent analyses. Here we present results from a global application of the Greenhouse Gas from Reservoirs (G‐res) model wherein we estimate per‐area and per‐reservoir CO 2 and CH 4 fluxes, by specific flux pathway and in a spatially and temporally explicit manner, as a function of reservoir characteristics. We show: (a) CH 4 fluxes via degassing and ebullition are much larger than previously recognized and diffusive CH 4 fluxes are lower than previously estimated, while CO 2 emissions are similar to those reported in past work; (b) per‐area reservoir GHG fluxes are >29% higher than suggested by previous studies, due in large part to our novel inclusion of the degassing flux in our global estimate; (c) CO 2 flux is the dominant emissions pathway in boreal regions and CH 4 degassing and ebullition are dominant in tropical and subtropical regions, with the highest overall reservoir GHG fluxes in the tropics and subtropics; and (d) reservoir GHG fluxes are quite sensitive to input parameters that are both poorly constrained and likely to be strongly influenced by climate change in coming decades (parameters such as temperature and littoral area, where the latter may be expanded by deepening thermoclines expected to accompany warming surface waters). Together these results highlight a critical need to both better understand climate‐related drivers of GHG emission and to better quantify GHG emissions via CH 4 ebullition and degassing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".