1. Crown Fire Activity (Index: 0= nome,1= surface fire,2= passive crown fire or 3=active crown fire) Spread vectors (m/min) Fire Perimeter (m) Heat per area(kj/m 2 ) Flame lenght (m) FARSITE & ArcGIS FLamMap FlamMap & ArcGIS FlamMap & ArcGIS Database Acknownlegment References M ODELING FIRE BEHAVIOR TO ASSIST FOREST MANAGEMENT IN P ORTUGUESE LANDSCAPES Botequim, B 1., Borges, J. G. 1, Calvo A. 1, Marques S. 1, Silva, A. 1 1 Centro de Estudos Florestais, Instituto Superior de Agronomia, Technical University of Lisbon, Portugal bbotequim@isa.utl.pt Vale do Sousa (Vsousa) Mixed Forest Vale do Sousa (Vsousa) Mixed Forest Globland (Glob) Eucaliptus Globulus Globland (Glob) Eucaliptus Globulus Mata Nacional de Leiria (MNL) Pinus Pinaster Mata Nacional de Leiria (MNL) Pinus Pinaster This research was supported by Project PTDC/AGR-CFL/64146/2006 “Decision support tools for integrating fire and forest management planning” funded by the Portuguese Science Foundation (FCT) and The authors would like to thank FCT for funding the PhD of Brigite Botequim (SFRH-BD-44830-2008). I. Aims Figure 1. Portugal map with the spatial distribution of the three case-studies II. Material and Methods Figure 4. Forest canopy characteristics: stand height (4.a), crwn base height (4.b) and crown bulk density (4.c) based on the inventory plots and used as crown fuel data in Farsite and FlamMap systems. Reduced: 75th percentiles, i.e. higher values occur in 25% of the day in period June until September; Control : 90th percentiles and Critical: 99th percentiles. The fuel moistures were calculated using the model from Rothermel (1983). wilfires Probability models with explanatory biometric variables available for each specie V. Sousa Globland MNL Elevation Slope AspectFuel Model Figure 5. Fire –ignition point dispersed by the landscape based on the probability of occurrence of fire. Figure 3. Landscape files from the three case studie representing the required themes of topographic factor (elevation, slope and aspect) and surface fuel model used to compute fire behavior and simulate surface fire spread. Figure 2. Methodologie applied to modeling fire expected behavior and provided information to assess the effectiveness of methods for integating stande-level fuel treatment schedule and landscape-level management planning. INPUT FIRE SIMULATORS Landscape data and forest canopy characteristics  Guidelines to support Forest Management IV. Conclusion Rate of Spread (m/min) A comprehensive review integrating wildfire modeling processes with specific wildfire simulation exercises provides a unique opportunity to examine how alternative landscape management can potentially change fire spread. Therefore, we simulated fire spread and behavior in different managed landscapes by developing multiple scenarios. The overall objective was to isolate and examine scenarios according to three important fire spread factors: landscape structure, weather, and fire - ignition location (Fig. 2). For that purpose, fire modeling was conducted by FARSITE and FlamMap systems in three Portuguese Forest areas (Fig. 1). The Systems simulators have provided capabilities both for consistent representation of fire behavior and for spatial validation of fire prediction in the three study areas. Clearly, the knowledge that results from this study will help forest managers to identify the high-risk areas and to develop management priorities in managing fuels in their landscape. Thus, it will be instrumental for innovative and effective integration of forest and fire management planning activities and will be valuable to address the most important forest catastrophic event in Portugal. FARSITE and FlamMap system have produced specific elements of each fire. These Maps were evaluated to identify stand characteristics and spatial pattern metrics of fire prone areas. Furthermore, fire behavior characteristics in each pixel on the landscape computed with FlamMap were combined with initial landscape information to develop a database with all the possible scenarios combination. Fire behavior calculations provided information to compare the spatial distribution of forest stands in current landscapes and also to identify hazardous fuel and corresponding stand biometric features to support fire prevention in each study area. We considered three case-studies (Fig. 1): Mata Nacional de Leiria (MNL), a maritime pine (P. pinaster Ait.) public forest in the Centre (extended ≈ 10 881 ha); Vale de Sousa, a diverse forested landscape (Q. suber, Q. robur, Q. faginea, Fagus silvatica, P.pinaster, P. pinea, E. globulus) with multiple non-industrial private forest owners (NIPF) in the North (extending ≈ 12 308 ha). The third case study - Globland‘ area (Glob) - consists of a group of pulp mills‘ properties where eucalypt (E. globulus) is predominant (extend ≈ 11882 ha). This allowed us to make comparisons between different topographic and fuel structure patterns on different landscapes (Fig. 3). A data set encompassing 2504 inventories plots, was used to determine the crown structural characteristics required to run crown fire activities and detect significant differences in fire-landscape interactions. The estimation of canopy parameters were made using specific models developed to Portuguese species (Figure 4). Specifically, (1) we simulated fire spread in Portugal on three landscapes, each with a different structure and fuel model; (2) we examined how weather (wind speed´s of 8km/h, 12km/h and 18km/h) affects fire spread on all three landscapes – we applied also three climate scenarios labeled reduced, control and critical (gathered along the summers of 2002, 2003 and 2004) to examine weather influences; (3) and we explored spatial variation among fires ignited in different parts of the landscape. Fire ignition locations are based on the application of risk model using biometric variables from each inventories plot (Garcia et al. submitted) (Figure 5). The fire simulation systems were run to assess the resistance to fire of current landscape mosaics according to different canopy fuel structure and meteorological scenarios. Fireline intensity (Kw/h) 4.a) 4.b) 4.c) For demonstrated purposes we considered Vale de sousa landscape: OUTPUT FIRE SIMULATORS III. Results Cruz, M. (2005) Guia fotográfico para identificação de combustíveis florestais - Região centro. Centro de Estudos de Incêndios Florestais – associação para o desenvolvimento da Aerodinâmica Industrial, Coimbra, 39pp. Garcia-Gonzalo, J., Botequim, B., Zubizarreta-Gerendiain A., Ricardo A., Borges J. G.,Marques S., Oliveira M. M., Tomé, M. and Pereira, J.M.C., Modelling wildfire risk in pure and mixed forest stands in Portugal, (submitted) Fernandes, P., Gonçalves, H., Loureiro, C., Fernandes., M., Costa., T., Cruz., M., Botelho., (2009)Modelos de combustível florestal para Portugal. Congresso Florestal Nacional, Açores. Rothermel, R.C. (1983). How to predict the spread and intensity of forest and range fires. Genral Tecnhical Report INT-143. USDa Forest service, Intermountain forest and Range Experiment Station, Ogden. 161 pp.