Projection of Forest Fire Danger due to Climate Change in the French Mediterranean Region
Bibliographic record
Abstract
Fire occurrence and behaviour in Mediterranean-type ecosystems strongly depend on the air temperature and wind conditions, the amount of fuel load and the drought conditions that drastically increase flammability, particularly during the summer period. In order to study the fire danger due to climate change for these ecosystems, the meteorologically based Fire Weather Index (FWI) can be used. The Fire Weather Index (FWI) system, which is part of the Canadian Forest Fire Danger Rating System (CFFDRS), has been validated and recognized worldwide as one of the most trusted and important indicators for meteorological fire danger mapping. A number of FWI system components (Fire Weather Index, Drought Code, Initial Spread Index and Fire Severity Rating) were estimated and analysed in the current study for the Mediterranean area of France. Daily raster-based data-sets for the fire seasons (1st May–31st October) of a historic and a future time period were created for the study area based on representative concentration pathway (RCP) 4.5 and RCP 8.5 scenarios, outputs of CNRM-SMHI and MPI-SMHI climate models. GIS spatial analyses were applied on the series of the derived daily raster maps in order to provide a number of output maps for the study area. The results portray various levels of changes in fire danger, in the near future, according to the examined indices. Number of days with high and very high FWI values were found to be doubled compared to the historical period, in particular in areas of the Provence-Alpes-Côte d’Azur (PACA) region and Corsica. The areas with high Initial Spread Index and Seasonal Spread Index values increased as well, forming compact zones of high fire danger in the southern part of the study area, while the Drought Code index did not show remarkable changes. The current study on the evolution of spatial and temporal distribution of forest fire danger due to climate change can provide important knowledge to the decision support process for prevention and management policies of forest fires both at a national and EU level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".