Modeling methane emissions from the Alaskan Yukon River basin, 1986–2005, by coupling a large‐scale hydrological model and a process‐based methane model
Bibliographic record
Abstract
Much progress has been made in methane modeling for the Arctic. However, there is still large uncertainty in emissions estimates due to the spatial variability in water table depth resulting from complex topographic gradients, and due to variations in methane production and oxidation due to complex freezing and thawing processes. Here we extended an extant methane emission module within a biogeochemistry model, the Terrestrial Ecosystem Model (TEM), to include a large‐scale hydrology model, the variable infiltration capacity (VIC) model. The VIC model provides the required inputs, including freezing and thawing fronts, soil temperature and moisture, to the methane module. The effect of topography on the redistribution of soil moisture and water table depth was explicitly modeled using the TOPMODEL approach. The coupled modeling framework was applied to the Yukon River basin at a spatial resolution of 1 km from 1986 to 2005. The simulations show that the average annual net emissions of CH4 from the region are 4.01 Tg CH4 yr−1. El Niño phenomena usually lead to positive emission anomalies, while decreases in net CH4 emissions may be associated with strong La Niña events. Precipitation was found to be more closely related to CH4 dynamics than to soil temperature and active layer depth during the study period. This study suggests that the effects of soil freezing and thawing processes and the effects of microtopography on hydrology should be considered in the quantification of CH4 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".