Present and future fire regime in Iberia
Bibliographic record
Abstract
In the last decades, Mediterranean Europe has been highly affected by wildfires. Larger wildfires and impacts occurred during and as a result of extreme fire weather, as observed in recent years. In the Iberian Peninsula, the influence of the fire weather on the fire incidence is particularly important, and the purpose of this study was to investigate in detail this relationship and its influence on the current and future fire regime. The Daily Severity Rating (DSR) and the other indices of the Canadian Forest Fire Weather Index (FWI) System were computed using the ECMWF Reanalysis v5 (ERA5) and CORDEX atmospheric datasets. The meteorological variables needed to compute the FWI indices were the air temperature, relative humidity, wind speed and daily accumulated precipitation, at 12 UTC. We defined the Number of Extreme Days (NED) using extreme values of DSR and Drought Code and related them with the Normalized Burnt Area (NBA), loaded from Portuguese and Spanish wildfire official datasets. A cluster analysis was performed on NBA, revealing four pyro-regions characterized by different intra-annual variability of NBA. The strong link between the NED and the NBA intra-annual patterns was used to project the future pyro-regions, using a climate ensemble for two future scenarios. Finally, we investigate the relationship between extreme wildfires and fire weather at a finer spatial scale in Continental Portugal, namely between extreme DSRp and large wildfires at the municipal level. We used weather data from ERA5 to compute DSR percentiles (DSRp) for an extended summer period (defined between 15th May and 31st October) and combine it with large (>100 ha) burnt areas (BA), with the purpose to identify the DSRp value responsible of a large amount of BA (80 or 90%) at the municipality level. A cluster analysis was performed using the relationship between DSRp and BA, in each municipality of Continental Portugal. Obtained clusters are distinguished by differences in land cover, revealing that higher (lower) DSRp is needed to explain the same high percentage of total BA when forest (scrublands) is the predominant affected vegetation type. Our findings include recent changes in fire regimes in the recent past, a strong relationship between NED and NBA, that explain those observed changes and can be used to anticipate future fire regimes. Projected changes in NED suggest different future pyro-regions mapping in the Iberian Peninsula. Forest or shrublands prevalence has a significant influence on the spatial variability of the relationship between the extreme DSR threshold and most of total BA at the municipality level, particularly in Portugal.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".