Hydroclimate and landscape diversity drive highly variable greenhouse gas emissions from tropical and subtropical inland waters
Bibliographic record
Abstract
(Sub)tropical inland waters are important greenhouse gas (GHG) sources, yet limited observations have long hindered broad analyses of GHG variability across this diverse region. Here, through a meta-analysis, we have examined the rates and drivers of GHG emissions from flowing and standing (sub)tropical inland waters. We find considerable spatial variation in fluxes, largely related to differences in hydroclimate, geomorphology, land cover and human disturbance. Flowing waters emit more carbon dioxide (3,387 2,121 5,702 TgCO2 yr−1, expressing median first quartile third quartile ), methane (10.6 0.1 28.8 TgCH4 yr−1) and nitrous oxide (0.62 0.35 1.10 TgN2O yr−1) than standing waters (114 73 219 TgCO2 yr−1, 5.4 2.1 9.1 TgCH4 yr−1 and 0.03 0.02 0.05 TgN2O yr−1, respectively). (Sub)tropical inland waters release 4,238 2473 7375 TgCO2-equivalents annually, with first- to third-order streams contributing 75% of riverine emissions and lakes larger than 100 km2 contributing 59% of standing water emissions. Our results suggest emissions from (sub)tropical waters are 29–72% lower than earlier estimates, a downward revision with important implications for global GHG budgets. This meta-analysis assesses the rates and drivers of greenhouse gas emissions from flowing and standing (sub)tropical inland waters, finding that emissions are lower than previous estimates. Considerable spatial variation in fluxes arises mainly from differences in hydroclimate, geomorphology, land cover and human disturbance.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".