Modeling Thermal and Biogeochemical Dynamics in Two Ponds Within Alaska's Yukon–Kuskokwim Delta: Impacts of Climatic Variability on Greenhouse Gas Fluxes
Bibliographic record
Abstract
Abstract Fluxes of carbon dioxide (CO 2 ) and methane (CH 4 ) from open water bodies are critical components of carbon‐climate feedbacks in high latitudes. Processes governing the spatial and temporal variability of these aquatic greenhouse gas (GHG) fluxes are still highly uncertain due to limited observational data sets and lack of modeling studies incorporating comprehensive thermal and biochemical processes. This research investigates how slight variations in climate propagate through the biogeochemical cycles of ponds and resulting impacts on GHG emissions. We examine the thermal and biogeochemical dynamics of two ponds in the Yukon–Kuskokwim Delta, Alaska, under varying climatic conditions to study the impacts on CO 2 , CH 4 , and oxygen (O 2 ) concentrations and fluxes. We performed multiple numerical experiments, using the LAKE process‐based model and field measurements, to analyze how these ponds respond to variations in air temperature, shortwave radiation, and snow cover. Our study demonstrates that ice cover duration and water temperature are primary climatic drivers of GHG fluxes. Climate experiments led to reductions in ice cover duration and increased water temperatures, which subsequently enhanced CH 4 and CO 2 gas emissions from two study ponds. On average, cumulative CH 4 and CO 2 emissions were 5% and 10% higher, respectively, under increases in air temperature and shortwave radiation. Additionally, we uncovered a need to incorporate groundwater influxes of dissolved gases and nutrients in order to fully represent processes governing aquatic biochemical activity. Our work highlights the importance of understanding local‐scale processes in predicting future Arctic contributions to GHG emissions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".