Groundwater Controls on Postfire Permafrost Thaw: Water and Energy Balance Effects
Bibliographic record
Abstract
Abstract Fire frequency and severity are increasing in high‐latitude regions, but the degree to which groundwater flow impacts the response of permafrost to fire remains poorly understood. Here we use the Anaktuvuk River Fire (Alaska, USA) as an example for simulating groundwater‐permafrost interactions following fire. We identify key thermal and hydrologic parameters controlling permafrost response to fire both with and without groundwater flow, and separate the relative influence of changes to the water and energy balances on active layer thickness. Our results show that mineral soil porosity, which influences the bulk subsurface thermal conductivity, is a key parameter controlling active layer response to fire in both the absence and presence of groundwater flow. However, including groundwater flow in models increases the perceived importance of subsurface hydrologic properties, such as the soil permeability, and decreases the perceived importance of subsurface thermal properties, such as the thermal conductivity of soil solids. Furthermore, we demonstrate that changes to the energy balance (increased soil temperature) drive increased active layer thickness following fire, while changes to the water balance (decreased groundwater recharge) lead to reduced landscape‐scale variability in active layer thickness and groundwater discharge to surface water features such as streams. These results indicate that explicit consideration of groundwater flow is critical to understanding how permafrost environments respond to fire.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".