Integrating aquatic and terrestrial biogeochemical model to predict effects of reservoir creation on CO<sub>2</sub> emissions
Bibliographic record
Abstract
Abstract. There is considerable debate on the role of hydroelectric reservoirs for the emission of CO2 and other greenhouse gases. To quantify CO2 emissions from a newly created reservoir that was formed by flooding the boreal landscape we developed a daily time-step reservoir model by integrating a terrestrial and an aquatic ecosystem model. We calibrated the model using the measurements of dissolved organic and inorganic carbon (C) in a ~ 600 km2 boreal hydroelectric reservoir, Eastmain-1, in northern Quebec, Canada. A major constraint we dealt with is the dearth of basic environmental data for the Boreal region so we took a parsimonious approach for required inputs. We then evaluated the model performance against observed CO2 fluxes data from an eddy covariance tower in the middle of the EM-1 reservoir for the period from 2006 to 2012 and compared internal variables such as water column respiration, chlorophyll-a concentration, and sedimentation rate to measurements from field campaigns during 2006–2008. The model predicted the seasonal and inter-annual variability of CO2 emissions reasonably well compared to the observations. Discrepancies between simulation results and observations usually occurred near ice-off dates when there was large amount of dissolved CO2 under ice-cover. We applied the model to assess the effects of reservoir creation on C dynamics over the estimated “engineering” reservoir lifetime (i.e., 100 years). We found that the reservoir acts as a net C source over its lifetime and simulated CO2 fluxes were 204 g C m−2 yr−1 in the first year after flooding, steeply declined in the first three years, and then steadily decreased to ~110 g C m−2 yr−1 with increasing reservoir age. Sensitivity analyses revealed that the amount of terrestrial organic C flooded and oxygen effects can positively enhance benthic respiration and CO2 fluxes across air–water interface, but the effects on CO2 emissions were not significant. Higher temperatures dramatically stimulate CO2 emissions by enhancing CO2 production in both the water column and the sediment, and extending the duration of the open water period over which emissions can occur. Changing wind speeds had large uncertainties on annual CO2 emissions, given that wind speeds not only affect the gas transfer rate but also the open water period by affecting the surface energy balance. The model is useful for the estimation of CO2 emissions from reservoirs to the atmosphere and could be used to assist the hydro-power industry and others interested in emissions evaluate the role of boreal reservoirs as sources of greenhouse gas emissions.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".