Modelling fire frequency and area burned across phytoclimatic regions in Spain using reanalysis data and the Canadian Fire Weather Index System
Bibliographic record
Abstract
Abstract. We develop fire occurrence and burned area models in peninsular Spain, an area of high variability in climate and fuel types, for the period 1990–2008. We based the analysis on a phytoclimatic classification aiming to the stratification of the territory into homogeneous units in terms of climatic and fuel type characteristics, allowing to test model performance under different climatic and fuel conditions. We used generalized linear models (GLM) and multivariate adaptive regression splines (MARS) as modelling algorithms and temperature, relative humidity, precipitation and wind speed, taken from the ERA-Interim reanalysis, as well as the components of the Canadian Forest Fire Weather Index (FWI) System as predictors. We also computed the standardized precipitation-evapotranspiration index (SPEI) as an additional predictor for the models of burned area. We found two contrasting fire regimes in terms of area burned and number of fires: one characterized by a bimodal annual pattern, characterizing the Nemoral and Oro-boreal phytoclimatic types, and another one exhibiting an unimodal annual cycle, with the fire season concentrated in the summer months in the Mediterranean and Arid regions. The fire occurrence models attained good skill in most of the phytoclimatic zones considered, yielding in some zones notably high correlation coefficients between the observed and modelled inter–annual fire frequencies. Total area burned also exhibited a high dependence on the meteorological drivers, although their ability to reproduce the observed annual burned area time series was poor in most cases. We identified temperature and some FWI system components as the most important explanatory variables, and also SPEI in some of the burned area models, highlighting the adequacy of the FWI system for fire modelling applications and leaving the door opened to the development a more complex modelling framework based on these predictors. Furthermore, we demonstrate the potential usefulness of ERA-Interim reanalysis data for the reconstruction of historical fire-climate relationships at the scale of analysis. Fire frequency predictions may provide a preferable basis for past fire history reconstruction, long-term monitoring and the assessment of future climate impacts on fire regimes across regions, posing several advantages over burned area as response variable.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".