Calibration of the Fire Danger Classes and Trend analysis over the Mediterranean basin, based on the Canadian Forest Fire Weather Index System and the Fire Released Energy from SEVIRI/MSG. 
Notice bibliographique
Résumé
Fire danger rating systems (FDRS) are widely used for many purposes from planning for daily deployment of fire suppression resources to the evaluation of fire management strategies. FDRS can also be incorporated in different types of models to assess the short and long-term effects of specific fire regimes and fire management policies. The Canadian Forest Fire Weather Index System (CFFWIS) is one of the most known FDRS’s, being extensively used for a fire early warning in several regions around the world, namely in Europe. The CFFWIS includes a set of 6 indices, based on meteorological data, which is used to predict fire weather danger and fire behavior over regions under study. To obtain a reliable assessment of the fire danger based on the CFFWIS it is crucial to determine the threshold values for each class of the CFFWIS sub-indices over different regions. One of the simplest methods to define the classes is to use percentiles based on historical data, but this method lacks information regarding wildfire history and its relation to CFFWIS sub-indices. The proposed method is based on Fire Radiative Energy (FRE) released by fires, computed from Fire Radiative Power (FRP) product, that is generated, and disseminated in near real-time by EUMETSAT Land Surface Analysis Satellite Applications Facility. Since FRP estimates the radiative power emitted by a fire, it can be linked to fuel burned amounts and used as a proxy of fire intensity. By integrating FRP measures over a fire’s lifetime, an estimate of the total FRE released can be obtained for each event. In this work, daily FRE was derived for the 2010-2021 period, over the Mediterranean region countries. Thresholds values of each defined danger class for the FWI, FFMC, and ISI indices were obtained considering the FRE percentiles computed for different regions of the Mediterranean basin and discussed based on the different fire regimes for the region. A trend analysis of the CFFWIS sub-indices was performed to assess the fire danger behavior and the extreme fire weather over the different Mediterranean regions. The regions where the extreme fire weather conditions have become more prevalent were identified considering the spatial correlations, and applying field significance testing allows the identification of the regions with significant trends. Since fire regimes in Southern Mediterranean countries have been changing over the last two decades, mostly due to climate-driven factors changes and to the load and structure of fuels, the observed trend towards warmer and drier conditions are expected to continue in the next years, possibly leading to an increased risk of large fires. In this context, the knowledge of fire danger trends and variability is a key factor for fire managing activities, planning and preparedness, and resources allocation. Acknowledgments: This study was performed within the framework of the LSA-SAF, co-funded by EUMETSAT and was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under project FIRECAST (PCIF/GRF/0204/2017) and by the 2021 FirEUrisk project funded by European Union’s Horizon 2020 research and innovation programme under the Grant Agreement no. 101003890).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».