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ICFBR 2011International Conference on Fire Behaviour and RiskAlghero, Italy - October 4-6, 2011nei quali sono state misurate le seguenti grandezze: carico di combustibile vivo e morto, altezza dello strato di combustibile,copertura vegetale. Il carico di combustibile vivo e morto è stato suddiviso seguendo la classificazione standard (1h, 10h,100h) riportata nel National Fire Danger Rating System realizzato dall‘USDA. Nella seconda parte del lavoro, per ciascunsito sperimentale, è stato simulato, tramite l‘uso del fire behavior prediction system BEHAVE (Andrews, 1989), ilcomportamento potenziale del fuoco, usando le caratteristiche del combustibile misurate in campo come variabili di input. Ilcomportamento del fuoco è stato simulato utilizzando differenti scenari meteorologici rappresentativi delle condizionimeteorologiche estive maggiormente frequenti nelle aree oggetto di studio. I risultati delle simulazioni (intensità del fronte difiamma, velocità di propagazione, altezza della fiamma) sono stati analizzati mediante cluster analysis per identificare diverseclassi di tipologie di combustibile in funzione del comportamento potenziale dell‘incendio ad esse associato.Les incendies de végétation représentent une menace grave pour les forêts et les zones boisées du bassin méditerranéen.Concernant les dix dernières années, l‘Espagne, le Portugal, l‘Italie, la Grèce et la France ont enregistré une moyenne annuelled‘environ 50,000 incendies de forêt et 470,000 ha brûlés (Communauté européenne, 2009). La couverture, le type, l‘étatd‘humidité, ainsi que la biomasse et la charge de nécromasse de la végétation sont des variables critiques concernantl‘occurrence de feux de broussailles. En particulier, les caractéristiques physiques du combustible telles que la charge (massepar unité de surface), la taille (diamètre des particules) et la densité apparente (masse par unité de volume) de la biomassevivante et morte contribuent à la propagation, la puissance et la gravité du feu de broussailles. De ce fait, la disponibilité dedonnées appropriées sur le combustible à différentes échelles spatio-temporelles est nécessaire pour les applications de gestiond‘incendie, allant de la prévision du comportement de l‘incendie, à la simulation des effets de l‘incendie, et à la modélisation dela simulation de l‘écosystème. L‘un des objectifs du projet Proterina-C est d‘évaluer le risque d‘incendie dans les régionsméditerranéennes et de caractériser les paramètres de la végétation impliqués dans le processus de combustion. Dans cecontexte, les objectifs de ce travail sont : i) l‘identification et la description de différents types de combustibles principalementconcernés par la survenance d‘incendies en Sardaigne et en Corse et ii) le regroupement des types de combustibles sélectionnésen fonction de leur comportement potentiel lors de l‘incendie. Dans la première partie du travail, la série temporelle disponibledes périmètres des événements d‘incendie et les données de la carte d‘utilisation du sol ont été croisées et analysées en vue del‘identification des principaux territoires concernés par les incendies. Les sites d‘échantillonnage de terrain ont été identifiés defaçon aléatoire sur les types de végétation sélectionnés et les variables suivantes ont été collectées : charge combustible vivanteet morte, profondeur de couche du combustible, couverture végétale. La charge combustible vivante et morte a été inventoriéesuivant les classes normalisées (1 h, 10 h, 100 h) du système national du risque d‘incendie USDA. Dans la deuxième partie dutravail, le comportement potentiel d‘incendie pour chaque site expérimental a ensuite été calculé par le système de prévision ducomportement potentiel de l‘incendie BEHAVE (Andrews, 1989), en utilisant comme données les variables de combustiblecollectées. Le comportement de l‘incendie a été simulé en établissant différents scénarios climatiques représentant lesconditions météorologiques d‘été les plus fréquentes. Les résultats de la simulation (puissance du feu, vitesse de propagation,longueur de la flamme) ont été ensuite utilisés pour faire une analyse typologique pour regrouper les différents types decombustible en fonction de leur comportement potentiel au feu. Les résultats de cette analyse peuvent être utilisés pourproduire des cartes des combustibles selon leur comportement au feu, qui sont des outils importants pour la localisation etl‘évaluation des traitements du combustible, l‘évaluation du risque d‘incendie et du risque pour la planification de la gestion dusol, ainsi que pour l‘assistance aux évaluations environnementales et à la modélisation des programmes de risque d‘incendie.6
ICFBR 2011International Conference on Fire Behaviour and RiskAlghero, Italy - October 4-6, 2011PR.3 - Fire propagation modelingSantoni P.A.SPE UMR 6134 CNRS – University of Corsica, Campus Grimaldi BP 52, 20250 Corte, Francesantoni@univ-corse.frThe ability of the forest fire community in modelling and simulating forest fire spread, as well as developingmanagement approaches and techniques, has increased significantly in recent years. Modelling has become anessential tool in forest fire research and becomes a crucial instrument in the studies of wildland–urban interface fires,fire mitigation and risk mapping. Wildfires are driven by complex physical and chemical processes, operating onvastly different scales ranging from micrometers to kilometers. Their interactions depend on coupling between nonlinearphenomena such as turbulence in the lower part of the atmospheric boundary layer, topography, vegetation andfire itself (chemical reactions, radiation heat transfer and degradation of the vegetation). Different reviews of firespread models have been conducted these last ten years. Depending on the authors, wildland fire mathematicalmodels may be classified according to the nature of the equations (physical, quasi-physical, quasi-empirical andempirical) or according to the physical system modeled (surface fire models, crown fire models, spotting models,ground fire models). With regard to the first classification, the simplest models are the statistical ones, which makeno attempt to involve physical mechanisms. Empirical models are based upon the conservation of energy, but they donot distinguish the mode of heat transfer. Finally, physical models differentiate the various kinds of heat transfer inorder to predict fire behaviour. Among them, multiphase modeling and coupled fire–fuel–atmosphere modelsrepresent the most complete approach developed so far. Whatever the classification there is a general agreement onthe fact that simple models have to be used if one wants to provide real time operational tools. Converselymultidimensional numerical fluid-dynamical wildfire simulation models must be used to study the behavior ofwildfire and wildland–urban interface fires. However, these last models require computational resources that precludereal-time forecasts. The computational cost of physics-based wildland fire modeling limits the application of theapproach to modeling wildfire behaviour within a certain scale range. On another hand, quasi-empirical and empiricalmodel may be very efficient for fuel and environmental conditions comparable to those of test-fires, but the absenceof a real physical description makes them inapplicable to other situations. The dilemma is whether one wants tosimulate wildfire phenomenon accurately or quickly. The aim of this communication is twofold. We will first presentsome of the most important trends in modelling fire behaviour. Based on this review we will explain the reasons whysimple model must be used if one want to develop real time land management decision support systems. Secondly wewill compare the modelling assumption and the structural equations of three fire spread models used in decisionsupport systems for Mediterranean conditions at landscape scale. Finally the work conducted in Proterina-C projectconcerning fire propagation modeling strategy will be presented.Keywords: fire spread model, landscape scale, combustionLa competenza della comunità che si occupa di antincendio ricreando modelli e simulando la propagazione degliincendi boschivi, e sviluppando approcci e tecniche di gestione, è cresciuta enormemente negli anni recenti. Lamodellazione è diventata uno strumento essenziale nella ricerca sugli incendi boschivi e diventa un mezzo di crucialeimportanza negli studi sugli incendi nell‘interfaccia urbano-rurale, mitigazione degli incendi e mappatura del rischio.Gli incendi sono guidati da processi fisici e chimici complessi, che operano su scale molto diverse che vanno damicrometri a chilometri. Le loro interazioni dipendono dall‘azione congiunta fra i fenomeni non lineari come laturbolenza nella parte inferiore dello strato di confine atmosferico, la topografia, la vegetazione e lo stesso fuoco(reazioni chimiche, trasferimento del calore per irradiazione e deterioramento della vegetazione). Negli ultimi diecianni sono state condotte diverse analisi dei modelli di propagazione degli incendi. A seconda degli autori, i modellimatematici degli incendi in aree non coltivate possono essere classificati secondo la natura delle equazioni (fisiche,quasi-fisiche, quasi-empiriche ed empiriche) o secondo il sistema fisico ricreato (modelli di incendi di superficie,incendi di chioma, focolai secondari, incendi di suolo). Quanto alla prima classificazione, i modelli più semplici sonoquelli statistici, che non fanno alcun tentativo di coinvolgere meccanismi fisici. I modelli empirici si basano sullaconservazione dell‘energia, ma non distinguono la modalità di trasferimento del calore. Infine, i modelli fisicidifferenziano i vari tipi di trasferimento di calore al fine di prevedere il comportamento dell‘incendio. Fra essi, laSESSION 1: European Projects on Forest Fires7
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Photo Courtesy of Sardinian Forest
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