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Record W4408428583 · doi:10.5194/egusphere-egu25-13596

The Global Importance of CO2 and CH4 Emissions from Ponds: A Large-Scale Data Perspective

2025· preprint· en· W4408428583 on OpenAlexaff
Jovana Radosavljevic, Ali Reza Shahvaran, Fereidoun Rezanezhad, Elodie Passeport, Stephanie Slowinski, Philippe Van Cappellen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsUniversity of TorontoUniversity of Waterloo
Fundersnot available
KeywordsPerspective (graphical)Scale (ratio)Environmental scienceEnvironmental resource managementNatural resource economicsGeographyEconomicsComputer scienceCartography

Abstract

fetched live from OpenAlex

Inland waters, including small lakes and ponds, play a major role in the global carbon cycle. While they typically act as organic carbon sinks, they also emit the greenhouse gases (GHGs) carbon dioxide (CO2) and methane (CH4) to the atmosphere. Nonetheless, small inland waters, defined as those with a surface area (SA) of less than 5 hectares (further referred as “ponds”), are often excluded from large-scale CO2 and CH4 budgets. To help overcome this gap, we reviewed available global datasets on inland waters and selected G1WBM, GLCF GIW, GSW, and OSM, because these datasets provide sufficient information on ponds to assess their global distribution. We further compiled a dataset of CO2 and CH4 emissions plus water chemistry data from 950 ponds worldwide from existing literature and databases. Next, we applied a Monte Carlo analysis to the estimated surface areas and CO2 and CH4 emission ranges of ponds. The results suggest that ponds with SA < 1 ha emit 0.25–0.42 Pg C yr-1, and those of 1–5 ha emit 0.18–0.45 Pg C yr-1, accounting for up to 14 and 17%, respectively, of the total carbon gas emissions from all-sized lakes and ponds worldwide. Our estimates thus further highlight the potentially disproportionate, yet poorly constrained, importance of ponds in global GHG budgets. In addition to water chemistry data, we also extracted global gridded hydrometeorological and socio-economic data matched to each HydroBASIN-delineated basin of the 950 lakes. Using Random Forest regression (RFR) models, we found that pond water pH and watershed urbanization were the most important predictors of the CO2 emissions, while electrical conductivity (EC), SA, and pond depth were the most important variables for the CH4 emissions. The RFR modeling revealed that ponds in urban areas typically exhibit elevated pH levels, probably due to the ubiquitous use of cement-based construction materials. High pH levels, in turn, suppress CO2 emissions by retaining dissolved inorganic carbon under the form of aqueous bicarbonate (HCO3-). The role of non-sulfate-derived EC in modulating the CH4 emissions is attributed to the effect of salinity on the mixing intensity and the associated impact on water column oxygenation. Overall, our findings confirm the significant role of ponds in global carbon cycling. At the same time, they suggest that site-specific characteristics, including land use and water chemistry, can induce considerable variability in GHG emissions from ponds.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.013
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.020
GPT teacher head0.281
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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