Ranking the sensitivity of climate variables and FWI sub-indices to global wildfire burned area
Bibliographic record
Abstract
Wildfires are integral parts of ecosystems but at the same time they consist a threat for manmade and natural environments. Variability in the area burned by wildfires has been largely attributed to weather and climate drivers, hence fire danger indices such as the Canadian Fire Weather Index (FWI) uses solely climate variables. The FWI uses four climate variables (precipitation, temperature, wind and relative humidity), to estimate two sub-indices, one for the wildfire initial spread danger - the initial spread index, and one that accounts for the longer-term drought effects on the fire danger - the buildup index, from which the FWI is finally assessed. Here, we establish correlations between the individual climate variables, FWI and its subindices, with observed GFED monthly burned area, for each one of the 14 GFED pyrographic region, at a global scale. The correlations are established on aggregated by the size of burned area data, to reduce the effect of other smaller scale climate effects, as well as other socioeconomic factors such as fire suppression activities, etc. The established correlations are then used to estimate the relative sensitivity of the area burned, to each climate variable and FWI component. The analysis is repeated for different burned area land use types, i.e. forest areas, non-forest areas as well as their combination. Our results indicate the relative importance of the four climate variables, as well as the two sub-indices of FWI index, for each GFED region. The results highlight the significance of temperature and relative humidity to the variability of area burned, in many regions, globally. This work contributes to a better understanding of the climate drivers of global wildfire activity. This work is supported by CLIMPACT - National Research Network on Climate Change and its Impacts project, financed by the Public Investment Program of Greece and supervised by General Secretariat for Research and Technology (GSRT); and by the Leverhulme Centre for Wildfires, Environment, and Society through the Leverhulme Trust, grant number RC-2018-023.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".