Implementación de índices para la evaluación del riesgo de incendios forestales en la cabecera del júcar y análisis de las repercusiones del cambio climático en los incendios
Notice bibliographique
Résumé
[EN] For many years, forest fires have been one of the main environmental problems in the province of Cuenca, in the summertime. It is vital that appropriate measures be taken to prevent these fires happening, if the frequent occurrence of serious disasters is to be avoided. To this end, forest managers have a series of aids to help them prevent forest fires, such as fire danger indices. Two fire danger indices have been used in this study: the Forest Fire Weather Index (FWI) developed by the Canadian Forest Service, and the Keetch-Byram Drought Index (KBDI) used by the US Department of Agriculture’s Forest Service, both of which were designed to be used in real time, based on daily weather updates from a given area. To begin with, daily readings were taken at the Júcar’s headwaters, with statistics dating from 1994 to January 2014, with both indices showing an increased risk of fire in the summer months. Readings were then taken for the same period of time, but on a monthly basis. At this point in the study, the indices were modified so that these readings tallied with those taken on a daily basis, thus validating these modifications and providing a model of modification for both indices, on a daily to monthly scale. Once the indices had been modified, there then followed a breakdown of the monthly historical trend data for the period 1940-2012, with findings showing variations between the two indices, making the Canadian FWI the more reliable index, given that it was consistent with forest-fire statistics recorded by the Spanish Ministry of Agriculture, Food and the Environment (MAGRAMA) since 1968. Finally, two general circulation models – the HadGEM2-ES and the MPI-ESM-MR – were used to model climate change scenarios RCP 2.6, 4.5, 6.0, and 8.5, yielding new sets of data, both for temperatures and rainfall during a given period (e.g. short-term, medium-term, long-term). This data formed the basis for calculating projected indices in the future. As a result of applying these indices, it was established that, in the long term (2070-2090), and in the worst-case scenario (RCP 8.5), both indices show values that register as EXTREME, in summer. In other words, the FWI went from a HIGH risk of fires, in the summer, to an EXTREME risk, whilst the variation on the KBDI was even more pronounced, going from MODERATE to EXTREME. It can thus be concluded that, in the event of climate change, the risk of increased occurrence of disasters, in the long term, is quite high
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 tête enseignante, 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 ».