Modeling the spatial and temporal variability in surface water CO2 and CH4 concentrations in a newly created complex of boreal hydroelectric reservoirs
Bibliographic record
Abstract
Hydroelectric reservoirs emit carbon dioxide (CO 2 ) and methane (CH 4 ) to the atmosphere, yet there is still much uncertainty concerning the magnitude and drivers of these greenhouse gas (GHG) emissions. This uncertainty is particularly large over the initial years after flooding and in complex, cascade reservoir systems where studies are rare. We assessed the spatial and temporal patterns of CO 2 and CH 4 concentrations in the newly created La Romaine complex, which is composed of three consecutive reservoirs (RO1, RO2, RO3) along the La Romaine River. Dissolved CO 2 and CH 4 concentrations were intensively measured over three seasons for four years. Results show elevated CH 4 and especially CO 2 concentrations in surface waters of all three reservoirs upon flooding, with strong seasonality and high spatial heterogeneity within reservoirs. There was a strong seasonal decoupling of surface water CO 2 and CH 4 concentrations. Contrary to expectations, surface water CO 2 and CH 4 concentrations were relatively stable over the initial years of flooding, with exception of the decrease in CO 2 concentrations in the shallower RO1 reservoir. Further, individual reservoir characteristics, notably reservoir morphometry and pre-flood land cover , together with climatic factors were the main drivers of CO 2 and CH 4 concentrations, and the reservoir position in the cascade played a minor role. Models differed for CO 2 and CH 4 , and also between reservoirs highlighting the need to capture these specificities in reservoir functioning. We establish a modeling framework to effectively fill the spatial and temporal gaps that inevitably exist in the sampling coverage of large and heterogeneous reservoirs, which combined with appropriately modeled gas transfer velocities, will serve as a platform to derive robust estimates of diffusive fluxes. This modeling framework can be transposed to other reservoirs, and will contribute to more accurate and representative estimates of diffusive carbon emissions from hydroelectric reservoirs.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".