Climatic influences on forest fire and mountain pine beetle outbreaks and resulting runoff effects in large watersheds in British Columbia, Canada
Bibliographic record
Abstract
Abstract Many studies have defined the interrelationships between climate, forest disturbance, and runoff at small scales (<100 km2), but few have translated these relationships to large watersheds (>500 km2). In this study, we explore the relationship between climate, extreme forest fire seasons, mountain pine beetle (MPB) outbreaks, and runoff in eight large watersheds within the Fraser and Peace drainage basins in British Columbia (BC), Canada from 1981–2019. Using a climate index based on precipitation and air temperature anomalies, we find extreme forest fire seasons (those that burned >5% of a watershed's area) are most likely to occur when a warm/dry summer is preceded by multiple seasons of cool/wet conditions. Using the climate suitability class (CSC) model to explore the relationship between climate and MPB outbreaks, we validate previous findings that lower‐than‐average precipitation, warm growing season temperatures, and lack of extremely cold temperatures during winter are connected to MPB outbreaks within central BC. However, the CSC model needs improvements to accurately assess MPB suitability in northern watersheds that are located outside the model's calibration region, either through weighted variables or lower degree day thresholds. Minimal runoff response occurs from these forest disturbances, with the most prominent runoff change being related to the 2014 fire season in the Osilinka and Mesilinka watersheds. The limited effects of forest disturbance on annual runoff are likely related to large watershed sizes, low percentages of disturbed area in some study watersheds and post‐MPB forest dynamics. These results provide valuable insight into the interrelationships of climate, forest disturbance and runoff in large Canadian boreal forested watersheds.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".