Mapping fire probability and severity in a Mediterranean area using different weather and fuel scenarios Fig. 1 – Location of the study area (La Maddalena National Park) RESULTS FMS (Fig. 4). Significant differences in burned areas were observed moving from moderate (Fig. 4a,d) to severe (+15%, Fig. 4b,e) and to extreme (+26%, Fig. 4c,f) scenarios. A remarkable increase of the average ROS and FLI was observed for both severe (+66% and +93%) and extreme (+177% and +248%) scenarios. FLS (Fig. 5). The increment of DF and the reduction of LF (Fig. 5c,f) lead to a slight growth of the burned areas (+4%) with respect to the reference scenario (Fig. 5b,e). Moreover, the average values of ROS and FLI grow of 17% and 38% respectively. On the other hand, the reduction of DF and the increment of LF (Fig. 5a,d) cause a valuable reduction of the burned areas (-22%). This reduction is also important for average ROS and FLI (-48% and -69% respectively). WIS (Fig. 6). A low sensitivity to wind speed variations was observed: due to the properties of the elliptical shape growth, a little reduction of the burned areas is showed by the highest wind speed scenario (-3%, Fig. 6c,f), instead of the most moderate wind speed scenario (+1%, Fig. 6a,d). Considering the average ROS and FLI, differences among scenarios still remain small, with slightly higher values (+2%) for WI = 30 km h -1 , and slightly lower values (-2%) for WI = 20 km h -1 . STUDY AREA  Location: La Maddalena National Park (5100 ha), North East Sardinia (Italy) (Fig. 1)  Climate: sub-arid Mediterranean climate (water deficit from May to September)  Main Vegetation: Mediterranean shrubland vegetation (80%), plant height 1-3 m (Fig. 2)  Topography: the study area is flat (elevation range: 0-204 m a.s.l.) (Fig. 3)  Fire Ignitions: 1000 random ignition points (75% randomly distributed across a buffer area of 10 meters round the roads and the urban areas) CONCLUSIONS The analysis of the information on fire behaviour provided by FlamMap can be useful to identify the areas with high level of danger, and therefore, high potential risk due to human activities. The future fire regimes determined by global change will result in fires with higher spread rate and fireline intensity, due to the predicted increase of severe weather conditions frequency. The fire probability maps can be used as components of decision support systems for fire danger and risk assessment. Fig. 2 – La Maddalena land use map Fig. 3 – La Maddalena elevation map FUEL MODELS  Custom fuel models for shrubland vegetation  Standard fuel models for the other vegetation types (model 1 and 2 of Anderson, TL2 and TL3 of Scott&Burgan) FLAMMAP SIMULATIONS In recent years several authors proposed to use fire simulators as an accurate methodology to derive fire probability maps in function of defined weather conditions (Farris et al. 2000, Stratton et al. 2004, Finney 2005). Given a large number of simulations across a landscape, areas that have most probability to burn can be quantified and different scenarios can be tested (Clarke et al. 1994). FlamMap simulator (USDA Forest Service, Missoula; Finney, 2006) was used to predict the potential fire behaviour over the study area for given weather, wind, fuel load and moisture scenarios. All simulations lasted sixty minutes, with output perimeter resolution of 30 m. Historical weather data were acquired to analyze the study area: several recurrent weather scenarios (associated with high fire occurrence and burned areas) were defined to perform the simulations, and specific wind field maps were realized by using a computational fluid dynamic mass-consistent model (NUATMOS, Ross et al., 1988). Different Fuel Moisture Scenarios Moderate Scenario Extreme Scenario a) Effects on the Fire Rate of Spread (ROS) Moderate Scenario Extreme Scenario b) Effects on the Fireline Intensity (FLI) Different Live Fuel (LF) and Dead Fuel (DF) Load Scenarios LF Reduction; DF Increment a) Effects on the Fire Rate of Spread (ROS) b) Effects on the Fireline Intensity (FLI) Different Wind Intensity Scenarios Wind Speed a) Effects on the Fire Rate of Spread (ROS) b) Effects on the Fireline Intensity (FLI) LF Reduction; DF Increment European Geosciences Union – General Assembly 2009 – Vienna, 19-24 April 2009 REFERENCE SCENARIO SCENARIO DESCRIPTION FUEL MOISTURE SCENARIOS (FMS, Fig. 4). Effects of fuel dryness on fire behaviour. In the extreme scenario, the live fuel (LF) moisture for shrubland vegetation was set at 70% and the dead fuel (DF) moisture was set at 8%. FUEL LOAD SCENARIOS (FLS, Fig. 5). Effects of an increment of LF and a reduction of DF (both of 10%), and the inverse situation, on fire behaviour. WIND INTENSITY SCENARIOS (WIS, Fig. 6). Effects of different wind speed (20, 25, 30 km h -1 ) on fire behaviour. Wind Speed Fuel Models FM1 FM2 TU1 TU2 CM GARR CM MAQ - DF Load (Mg ha -1 ) (1, 10, 100 hr) 1.66 7.84 5.82 8.96 14.0 18.0 - LF Load (Mg ha -1 ) (Herb. & Woody) 0 1.12 2.46 0.45 10.0 13.0 - Fuelbed Depth (cm) 33 33 66 100 100 170 - Moisture of Ext. (%) 11 14 20 30 25 25 - DF & LF Heat Cont. (kJ kg -1 ) 18620 18620 18620 18620 18620 18620 - Dead 1-hr (%) 14 14 14 14 14 14 - Dead 10-hr (%) 14 14 14 14 14 14 - Dead 100-hr (%) 15 15 15 15 15 15 - Live Herbaceous (%) 0 0 0 0 0 0 - Live Woody (%) 0 120 125 125 115 125 - Max/Min Temp. (°C) 28/16 - Max/Min RH (%) 70/40 - Wind Int./Dir. (kmh/°) 25/300 Fig. 4 Fig. 5 Fig. 6 (a) (b) (c) (d) (e) (f) (a) (b) (c) (d) (e) (f) (a) (b) (c) (d) (e) (f) OBJECTIVES  TO EVALUATE THE CAPABILITIES OF FLAMMAP FOR ESTIMATING FIRE SEVERITY WITH DIFFERENT FUEL, WEATHER AND WIND SCENARIOS  TO PROVIDE SPATIAL INFORMATION ON THE EFFECT OF CLIMATE SCENARIOS ON FIRE PROBABILITY AND SEVERITY INTRODUCTION In the Mediterranean Basin the forest fires are mainly ignited by arson or by human negligence. The peaks of fire season coincide with extreme weather conditions (mainly strong winds, hot temperatures, low atmospheric water vapour content). Many studies reported that the predicted impact of climate changes in the Mediterranean Basin will cause greater weather variability and will increase the frequency of extreme weather conditions, as drier and hotter summers, heat waves, etc. At long- term scale, climate changes can affect the fuel load and the dead/live fuel ratio, and therefore the vegetation flammability. At short-time scale, the increase of extreme weather events can directly affect the fuel water status and it can increase the large fire occurrence. In this context, detecting the areas characterized by high probability of both large fire occurrence and relevant fire severity can represent an important component of the fire management strategies. B. Arca 1-3 *, M. Salis 2-3 *, V. Bacciu 2-3 , P. Duce 1-3 , G. Pellizzaro 1-3 , A. Ventura 1-3 , D. Spano 2-3 1 National Research Council of Italy, Institute of Biometeorology (IBIMET), Sassari, Italy *(b.arca@ibimet.cnr.it); 2 Department of Economics and Woody Plant Ecosystems (DESA), University of Sassari, Italy *(miksalis@uniss.it); 3 Euro-Mediterranean Centre for Climate Change, IAFENT Division, Sassari, Italy