Forest fire risk under a changing climate in Croatia
Bibliographic record
Abstract
Fire represents one of the major disturbances in natural ecosystems around the planet. By burning ecosystems, fire has a significant role in shaping global biome distribution and influences biogeochemical cycles such as the carbon cycle. Particularly in the coastal part of Croatia during the summer months, wildfires often escalate to catastrophic levels, posing serious threats to human lives, infrastructure, and the natural environment.Our study aims to estimate the changes in forest fire danger in Croatia between 1981 and 2020 based on the Fire Weather Index (FWI) and seasonal severity rating (SSR) calculated using data from the National Meteorological Observation Network. To estimate the risk of forest fires in this region, the study utilizes the Canadian Forest Fire Weather Index System. The original system consists of six components that solely depend on meteorological conditions. The calculated FWI represents the potential fire intensity and is a very good indicator of fire danger. The initial equations are calibrated for Canadian boreal forests, characterized by distinct differences in vegetation and climate features when compared to forests of the Mediterranean regions. Despite these differences, researches have revealed a noteworthy correlation between the components of the FWI system and fire activity in Spain, Portugal, France, Italy, and Greece.Measurements of air temperature, wind speed, and relative humidity taken at 14h, along with daily precipitation records from 83 meteorological and 119 rain gauge stations were used in the analysis. Daily severity ratings were calculated from FWI values and averaged over the fire season, spanning from June to September, to obtain SSR. The station-based SSR were spatially interpolated using regression kriging and a 1 x 1 km horizontal grid, resulting in 40 raster maps (one for each year). Results from a trend analysis, aimed at identifying areas with the highest increase in fire risk during the period 1981-2020, indicate an overall increase in SSR across a significant portion of the country. The observed trends align well with the positive trends identified in maximum air temperature and the lengthening of dry periods.Additionally, to evaluate changes in fire weather extremes in Croatia the seasonal count of days with FWI > 30 (FWI30) and the seasonal 90th percentile of FWI (FWIp90) indices were calculated. A comparison of these indices between the periods 1981-2000 and 2001-2020 revealed an increase both in FWI30 and FWIp90 across a substantial portion of the country. These trends highlight a concerning escalation in fire risk for Croatia.Keywords: forest fire risk, fire weather index, extreme fire weather, climate change, trends
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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 | 0.000 |
| 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) | 0.001 | 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".