Atmospheric dryness removes barriers to the development of large forest fires
Bibliographic record
Abstract
Large forest fires have far-reaching impacts on the environment, human health, infrastructure and the economy. Forest fires become large when all forest types across a landscape are dry enough to burn. Mesic forests are the slowest to dry and can act as a barrier to fire growth when they are too wet to burn. Therefore, identifying the factors influencing fire occurrence in mesic forests is important for gauging fire risk across large landscapes. We quantified the key factors influencing the likelihood that an active wildfire would propagate through mesic forest. We analysed 35 large forest fires (> 2500 ha) that occurred in Victoria, Australia where mesic and drier eucalypt forests are interspersed across mountainous terrain. We used a random forest model to evaluate 15 meteorological, topographic and disturbance variables as potential predictors of fire occurrence. These variables were extracted for points within burnt and unburnt patches of mesic forest. The likelihood of an active wildfire spreading through mesic forest increased by 65 % as vapour pressure deficit (VPD, i.e., atmospheric dryness) rose from 2.5 to 7 kPa. Other variables had substantially less influence (< 20 % change in fire occurrence) and their effects were further reduced when VPD was very high (> 6.5 kPa). Mesic forests were less likely to burn in areas with lower aridity, shallower slopes, and more sheltered topographic positions. Mesic forests 13–15 years following stand-replacing disturbance had 6 % higher chance of burning than long undisturbed forests (50 years post-disturbance). Overall, we show that topography and disturbance history cannot substantially counter the effects of high VPD. Therefore, the effectiveness of mesic forest as a barrier to the development of large forest fires is weakening as the climate warms. Our analysis also identifies areas less likely to burn, even under high VPD conditions. These areas could be prioritised as wildfire refugia.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".