The carbon footprint of a tropical reservoir: measured versus modeled values highlight the underestimated key role of downstream processes
Bibliographic record
Abstract
Reservoirs are important sources of greenhouse gases (GHGs) to the atmosphere and their number is rapidly increasing, especially in tropical regions. Accurately predicting their current and future emissions is essential but hindered by fragmented data on the subject, which often fail to include all emission pathways (surface diffusion, ebullition, degassing, and downstream emissions) and the high spatial and temporal flux variability. Here we conducted a comprehensive sampling of Batang Ai reservoir (Malaysia), and compared field-based versus modeled estimates of its annual carbon footprint for each emission pathway. We further explored the processes fuelling and regulating emissions downstream of the dam, which are important but commonly overlooked. Carbon dioxide (CO2) surface diffusion and methane (CH4) ebullition were lower than predicted, whereas moderate surface CH4 diffusion was accurately predicted. Most GHGs present in discharged water were degassed at the turbines, and the remainder were gradually emitted along the outflow river, leaving time for CH4 to be partly oxidized to CO2. Overall, the reservoir emitted 2475 gCO2eq m-2 yr-1, of which 89 % occurred downstream of the dam, mostly in the form of CH4. These emissions, largely underestimated by predictions, are mitigated by CH4 oxidation upstream and downstream of the dam, but could have been drastically reduced by slightly raising the water intake elevation depth. Degassing and downstream emissions are largely due to the accumulation of GHGs under the permanent thermocline. Studying the interplay between the processes regulating CO2 and CH4 concentrations in the reservoir deep layer highlighted the key role of physical factors on GHGs dynamics. Overall, our results show that exploring morphometry, soil type, and stratification patterns as predictors can improve modeling of reservoir GHGs emissions at local and global scales.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".