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Record W3169356181 · doi:10.5194/egusphere-egu21-14916

Fire weather thresholds and burnt area in Portugal

2021· article· en· W3169356181 on OpenAlexaboutno aff
Tomás Calheiros, Akli Benali, João M. N. Silva, Mário Pereira, João Pedro Nunes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental sciencePrecipitationMediterranean climateMeteorologyClimatologyWind speedLand coverPeninsulaHeat indexRelative humidityGeographyLand useEngineering

Abstract

fetched live from OpenAlex

Fire strongly depends on the weather, especially in Mediterranean climate regions with rainy winters but dry and hot summers, as in Portugal. Fire weather indices are commonly used to assess the current and/or cumulative effect of weather conditions on fuel moisture and fire behaviour. The Daily Severity Rating (DSR) is a numeric rating of the difficulty of controlling fires, based on the Canadian Fire Weather Index (FWI), developed to accurately assess the expected efforts required for fire suppression. Recently, the 90th percentile of DSR (90pDSR) was identified as a good indicator of extreme fire weather and well related to the burnt area in some regions of the Iberian Peninsula. The purposes of this work were: 1) to verify if this threshold is adequate for all continental Portugal; 2) to identify and characterize local variations of this threshold, at a higher spatial resolution; and, 3) to analyse other variables that can explain this spatial heterogeneity. We used fire data from the Portuguese Institute for the Conservation of Nature and Forests and weather data from ERA5, for the 2001 – 2019 study period. We also used the Land Use and Occupation Charter for 2018 (COS2018), provided by the Directorate-General for Territory, to assess land use and cover in Portugal. The meteorological variables to compute the DSR are air temperature, relative humidity, wind speed and daily accumulated precipitation, at 12 UTC. DSR percentiles (DSRp) were computed for summer period (between 15th May and 31st October) and combined with large (>100 ha) burnt areas (BA), with the purpose to identify which DSRp value is responsible of a large amount of BA (80 or 90%). Cluster analysis was performed using the relation between DSRp and BA, in each municipality of Continental Portugal. Results reveal that the 90pDSR is an adequate threshold for the entire territory. However, at the municipalities’ level, some important differences appear between DSRp thresholds that explain 90 and 80% of the total BA. Cluster analysis shows that these differences justified the existence of several statistically significant clusters. Generally, municipalities where large fires take place in high or very high DSRp are located in north and central coastal areas, Serra da Estrela, Serra de Montejunto and Algarve. In contrast, clusters where large fires where registered with low DSRp appear in northern and central hinterland. COS2018 data was assessed to analyse if and how the vegetation cover type influences the clusters’ distribution and affects the relationship between DSRp and total BA. Preliminary results expose a possible vegetation influence, especially between forests and shrublands.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.007
GPT teacher head0.197
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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