The Effect of Arctic Wildfires on Catchment Hydrology: A Paired Catchment Analysis on Permafrost Catchments in Canada
Bibliographic record
Abstract
Arctic hydrologic processes and wildfire regimes are intensifying with climate change. This study aims to determine the hydrological changes brought by wildfire in permafrost-underlain catchments in the Arctic. Previous studies indicate that wildfires accelerate permafrost thaw, and that permafrost thaw increases minimum annual flow and decreases maximum annual flow. Thus, I hypothesize that wildfires accelerate permafrost thaw signified by the intra-annual increase in minimum discharge and decrease in maximum discharge, and that the wildfires will amplify the local hydrological processes already affected by climate change. This was tested by using the paired-catchment approach via selecting two catchments of similar permafrost type, climate, and location. One of the catchments is burned by a single or several temporally close burning events covering about 50% of the catchment area, and the paired catchment is unburnt. Daily streamflow data that covered periods before and after the burning event were taken and analyzed together with precipitation, temperature, and evapotranspiration data. The selected catchments were the pair Rengleng (burned) and Old Crow (unburned), and the pair Willowlake (burned) and La Martre (unburned). The hydrographs were inconclusive as the shifts visible lacked statistical confidence. The water balance of the burned catchments did not seem to respond to the fire directly as the changes appear to be consistent in their corresponding unburned catchments, which suggests that these changes are more climate driven. The discharge time series of the hydrological seasons also did not seem to reflect accelerated permafrost thaw. It is thus assumed that climate-driven factors were more of the reason why the catchment hydrology responded throughout the time series. This could mean that using hydrological signatures in detecting wildfires accelerating permafrost thaw would need to be done more rigorously, and/or there are more underlying catchment characteristics that were not within the scope that contributed to these changes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".