Changes in surface water dynamics across northwestern Canada are influenced by wildfire and permafrost thaw
Bibliographic record
Abstract
Abstract The abundance and distribution of surface water at high latitudes is shifting rapidly in response to both climate change and permafrost thaw. In particular, the expansion and drainage of lakes and ponds is widespread but spatially variable, and more research is needed to understand factors driving these processes. In this study we used medium resolution (30 m) remote sensing data to analyse changes in lake area in permafrost-rich lowland regions across northwestern Canada. First, we used the Global Surface Water Dataset developed by the GLAD research group to map the absolute area of different land–water transitions across a 1.4 million km2 study domain. Next, we selected six regional study areas representing a range of climatic, geologic and hydrologic conditions. Within these regional study areas, we used the Landsat satellite archive to map annual trends in the area of 27 755 lakes between 1985 and 2020. We trained a random forests model to classify lakes exhibiting significant increasing or decreasing trends in area, and assessed the relative importance of climate, disturbance and environmental variables in determining the direction of change. Our analysis shows that significant increases in lake area were 5.6 times more frequent than decreases during the study period. Wildfire and ground ice abundance were the most important predictors of the direction of change. Greater ground ice content was associated with regions that experienced increases in lake area, while wildfire was associated with regions that experienced decreases in lake area. The effects of climate, including trends in mean annual temperature and total annual precipitation were smaller than disturbance and environmental factors, indicating that climate has likely had indirect effects on lake area changes over our period of study.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".