Peatland Heterogeneity Impacts on Regional Carbon Flux and its Radiative Effect within a Boreal Landscape
Bibliographic record
Abstract
Peatlands, with high spatial variability in ecotypes and microforms, constitute a significant part of the boreal landscape and play an important role in the global carbon (C) cycle. However, the effects of this peatland heterogeneity within the boreal landscape are rarely quantified. Here, we use field-based measurements, high-resolution land cover classification, and biogeochemical and atmospheric models to estimate the atmosphere-ecosystem C fluxes and corresponding radiative effect (RE) for a boreal landscape (Kaamanen) in northern Finland. Our result shows that the Kaamanen catchment currently functioned as a sink of carbon dioxide (CO<sub>2</sub>) and a source of methane (CH<sub>4</sub>). Peatlands (26% of the area) contributed 22% of the total CO<sub>2</sub> uptake and 89% of CH<sub>4</sub> emissions; forests (61%) accounted for 78% of CO<sub>2</sub> uptake and offset 6% of CH<sub>4</sub> emissions; water bodies (13%) offset 7% of CO<sub>2</sub> uptake and contributed 11% of CH<sub>4</sub> emissions. The heterogeneity of peatlands accounted for 11%, 88%, and 75% of the area-weighted variability (deviation from the area-weighted mean among different land cover types (LCTs) within the catchment) in CO<sub>2</sub> flux, CH<sub>4</sub> flux, and the combined RE of CO<sub>2</sub> and CH<sub>4</sub> exchanges over the 25-yr time horizon, respectively. Aggregating peatland LCTs or misclassifying them as non-peatland LCTs can significantly (<em>p</em> < 0.05) bias the regional CH<sub>4</sub> exchange and RE estimates, while differentiating between drier non-inundated and wetter inundated peatlands can effectively reduce the bias. Current land cover products lack such details in peatland heterogeneity, which would be needed to better constrain boreal C budgets and global C-climate feedbacks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".