Modelling the effects of landscape fuel treatmentson ﬁre growth and behaviour in a Mediterranean landscape (eastern Spain

8/3/2019 Modelling the effects of landscape fuel treatmentson fire growth and behaviour in a Mediterranean landscape (east

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CSIRO PUBLISHING

www.publish.csiro.au/journals/ijwf International Journal of Wildland Fire, 2007, 16, 619632

Modelling the effects of landscape fuel treatmentson fire growth and behaviour in a Mediterraneanlandscape (eastern Spain)

Beatriz DuguyA,C, Jos Antonio AllozaA, Achim RderB, Ramn VallejoA

and Francisco PastorA

ACentro de Estudios Ambientales del Mediterrneo (CEAM), Charles Darwin 14,

E-46980 Paterna, Valencia, Spain.BUniversity of Trier, Remote Sensing Department, Campus II, D-54286 Trier, Germany.CCorresponding author. Email: [email protected]

Abstract. The number of large fires increased in the 1970s in the Valencia region (eastern Spain), as in most northernMediterranean countries, owing to the fuel accumulation that affected large areas as a consequence of an intensive landabandonment.TheAyora site (Valencia province)was affectedby a large fire in July 1979. We parameterised the fire growth

model FARSITE for the 1979 fire conditions using remote sensing-derived fuel cartography. We simulated different fuel

scenarios to study the interactions between fuel spatial distribution and fire characteristics (area burned, rate of spread

and fireline intensity). We then tested the effectiveness of several firebreak networks on fire spread control. Simulations

showed that fire propagation and behaviour were greatly influenced by fuel spatial distribution. The fragmentation of large

dense shrubland areas through the introduction of wooded patches strongly reduced fire size, generally slowing fire and

limiting fireline intensity. Both the introduction of forest corridors connecting woodlands and the promotion of complex

shapes for wooded patches decreased the area burned. Firebreak networks were always very effective in reducing fire size

and their effect was enhanced in appropriate fuel-altered scenarios. Most firebreak alternatives, however, did not reduce

either rate of fire spread or fireline intensity.

Additional keywords: FARSITE, fire modelling, firebreak network, fuel spatial distribution, landscape diversity,

resilience to fire, spatial technologies.

Introduction

In most northern Mediterranean countries, a strong rural exo-

dus affected large areas throughout the 20th century, resulting

in intensive land abandonment and undergrazing. In the Valen-

cia region (eastern Spain), large cultivated areas reverted to

semi-natural vegetation (shrublands, woodlands) after the 1950s

and reforestation actions, fundamentally based on conifers, were

extensively implemented (Vallejo and Alloza 1998). The result-

ingfuel accumulationover large areas causeda dramatic increase

in the number of large fires in the 1970s, leading to very highfire frequencies in some locations, up to one fire every 4 or 5

years (Duguy 2003). Because of the increasing fuel loads, the

risk of very intense fires causing large damage to the affected

ecosystems has also increased.

Fire, thereby, has become a major environmental concern

for the local forest administration. Several fire management

plans have been launched in the last three decades, but an

integrated approach considering also the promotion of land-

scape diversity and resilience to fire has not yet been suc-

cessfully implemented. Indeed, the design of new strategies

for Mediterranean silviculture, integrating development, con-

servation and restoration objectives, incorporating fire haz-

ard considerations and considering the multifunctional role

of forests and shrublands, in agreement with recent social

demands, is still a challenge (Vlez 1990; Corona and Zeide

1999).

The objective of fuel treatments for fire hazard reduction is

to reduce fuel loads or change the spatial arrangement of fuels

(i.e. the landscape structure), so that when a wildfire ignites

in a treated landscape, it spreads more slowly, burns with less

intensity and smaller severity (effects of fire on the ecosystem),

and is less costly to suppress. An optimal design of landscape-

level fuel treatments requires, therefore, a further understandingof the functional relationships between landscape structure and

associated ecological processes, such as fire. Landscape-scale

fire patterns are considered to result from complex interactions

among topography, weather and vegetation (fuel type, mois-

ture content and spatial distribution) (Turner and Romme 1994;

Hargrove et al. 2000). It is generally accepted that greater land-

scape heterogeneity retards fire propagation (Minnich 1983;

Knight 1987), although landscape pattern may have little influ-

ence on fire growth and behaviour when weather conditions

are extreme, that is very dry and windy (Turner et al. 1994;

Hargrove et al. 2000). No universal correlation has been found

yet between fire propagation rate and landscape heterogeneity

(Morvan et al. 1995).

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620 Int. J. Wildland Fire B. Duguy et al.

The use of spatial technologies, such as remote sensing and

geographical information systems (GIS), has greatly contributed

to increase our knowledge of the relationships between fire and

landscape-scale heterogeneity of fuels (Minnich 1983; Turner

and Romme 1994; Turner et al. 1994; Lloret et al. 2002). Inrecent years, the combination of spatial technologies with fire

modelling has improved the fundamental understanding of fire

behaviour (Hargrove et al. 2000; Andrews and Queen 2001).

Spatially explicit fire growth models, in particular, are a power-

ful tool for simulating spatial characteristics of fire spread and

behaviour and are playing an increasing supporting role in the

assessment of landscapes and the evaluation of fuel manage-

ment options in relation to fire control (Andrews and Queen

2001; Gollberg et al. 2001).

A spatially explicit fire model, such as FARSITE (Finney

1994, 1998), has been widely calibrated in the USA, proving

to be efficient for producing spatial maps of fire growth and

intensity (Finney and Ryan 1995; Finney 1998), but also for test-

ing the effectiveness of silvicultural and fuel treatment options(Van Wagtendonk 1996; Stephens 1998; Finney 2001; Stratton

2004). In some northernMediterranean regions,the local admin-

istration is using FARSITE as a tool for improving wildland

fire analysis and prospecting consequences of fuel management

options on fire growth (Molina and Castellnou 2002). More val-

idations of the model in Mediterranean conditions and for real

fires are still needed, though (Arca et al. 2007).

With these matters in mind, the main objectives of the present

study were: (1) to parameterise the FARSITE model for the fuel

and weather conditions of a real fire; (2) to explore the effect

of fuel spatial distribution on fire spread and behaviour; and

(3) to test the effectiveness of different firebreak alternatives for

controlling fire propagation and moderating fire behaviour.

Study area

The Ayora study site is located 60 km south-west of the

city of Valencia (eastern Spain) and is defined by a frame

corresponding to 392015.13N/11033W (ULX/ULY) and

384953.19N/0323.53W (LRX/LRY) (Rderetal. in press;

Fig. 1). In July 1979, it was partly affected by a very large

fire (31 700 ha), which had important repercussions at socio-

economic and environmental levels.

In most of the study area, the potential vegetation is a Quer-

cus ilex forest (Bupleuro rigidi-Quercetum rotundifoliae, Rivas

Martnez 1987). The 1979 fire mainly burned planted mixed-

conifer stands (Pinus halepensis Miller andPinus pinaster Ait.),though. The site is currently covered by dense shrublands of

Rosmarino-Ericion Br.-Bl. 1931, generally dominated by the

resprouter grass Brachypodium retusum (Pers.) Beauv. and the

shrubs Ulex parviflorus Pourr., Rosmarinus officinalis L. and

Quercus coccifera L. Sometimes a sparse Pinus halepensis

tree layer is present. In some locations, small stands of Pinus

halepensis andPinus pinaster remain.

The study site pertains mainly to thedry meso-Mediterranean

bioclimatic stage (Rivas Martnez 1987): along a west-east

gradient, the mean annual temperature varies between 13 and

18C and the mean annual precipitation varies between 350

and 700 mm. The soil map (GVA 1997) indicates that the most

common soils are Chromic Luvisol, Rendsic Leptosol (both are

N

745 746

768 769

793 794

Fig. 1. Ayora study area. The perimeter of the 1979 fire is shown by a

dotted line. The outline of the six sheets of the National Topographic Map

(1 : 50 000) representing the area is shown by a dashed line.

shallow soils developed over limestone),Calcaric Regosol (mod-

erately deep soils developed over marls) and Calcaric Phaeozem(FAO-UNESCO 2003).

From many points of view, this area can be considered very

representative of the large marginal lands affected by wildfires

in the northern Mediterranean basin.

Methods

We first parameterised the FARSITE model for the fuel and

weather conditions of the 1979 fire and then simulated a set of

alternative fuel scenarios, maintaining the same high fire hazard

conditions.

Five raster data themes are required to run the model:

elevation, slope, aspect, fuel models and canopy cover. The

topographical layers were obtained in the GIS ArcGIS after

the Digital Elevation Model. They were maintained for all the

simulations. The spatial resolution was 30 by 30 m.

We combined remote sensing and extensive field work to

obtain spatially accurate fuel data. A vegetation map charac-

terising the situation before the 1979 fire was derived from a

Landsat Multispectral Scanner (MSS) image, which formed part

of an extensive time series of Landsat data. Spectral Mixture

Analysis was used for a pixel-wise characterisation of fractions

of photosynthetic active vegetation, lithological background and

shade based on spectral reference surface types (endmembers)

(Smith et al. 1990). The results were validated for a recent date

with field data, before the endmember model was applied to

older dates. Subsequently, major vegetation types were mapped

by combining the individual fractions in a rule-based classifi-cation approach (Rder et al. 2005). The classification model

was calibrated using the most updated digital vegetation map,

that is the Spanish Forest Map (MAPA 1993), and then applied

to other dates. The accuracy of the 1979 vegetation map used

in the present study was checked with aerial photographs from

1977. This map was reclassified in ArcGis into a fuel model

map assigning to each vegetation type one of the 13 standard

fire behaviour fuel models (FM hereafter) described by Ander-

son (1982) (Table 1). We used the photographical identification

keyof the Spanish ForestAdministration (MAPA-ICONA 1990),

which assigns one of these 13 fuel models to each of the main

vegetation structural types found in eastern Spain. Following

the same process, we reclassified the 1993 vegetation map of


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Modelling fire growth in eastern Spain Int. J. Wildland Fire 621

Table 1. Reclassification of the existing vegetation types into Andersons standard fuel models

Land use/vegetation type Fuel model Original description of fuel model

(dry fuel load) (Anderson 1982)

Urban area 0A Water body 98A

Bare soil (recently burned area) 1 (12 t ha1) Short grass (herbaceous fuels,

very little shrub)

Crop 2 (510 t ha1) Grass with open shrub (sometimes

with timber overstory)

Medium-density shrubland (1-m height) 5 (58 t ha1) Young short green shrubs with little

dominated by (B) or no dead woody material

Dense shrubland (2-m height) 4 (2535 t ha1) Mature flammable tall shrubs with abundant

dominated by (B), sometimes dead material (nearly continuous

with young pines secondary overstorey)

Open pine forest with dense 7 (1015 t ha1) Flammable dense shrub layer under

shrub layer conifer stand

Dense pine forest (Pinus halepensis, 8 (1012 t ha1) Closed short-needle conifer or hardwood stands

Pinus pinaster), with very little with light surface fuel loadings

or no shrub layer

ANon-fuel areas were assigned an arbitrary value in ArcGIS.BUlex parviflorus, Rosmarinus officinalis, Quercus coccifera andBrachypodium retusum.

the area (MAPA 1993) into a second fuel model map, named the

reference scenario hereafter.

Crown fires, embers from torching trees and spot fire growth

were all enabled during the simulations. FARSITE distinguishes

three fire types: surface, passive crown or active crown. Some

form of crown fire occurs when the surface fireline intensity

meets or exceeds an intensity threshold that is critical to involv-

ing the overlying crown fuels. The crown involvement may be

limited to torching trees (passive crown fire) or become an

active crown fire (Van Wagner 1977). Crown fuel variability

was assumed to be small across the area and constant values

were estimated for the required crown fuel parameters on the

basis of available quantitative data (MAPA 1993; Burriel et al.

20002004) and local forest managers knowledge: stand height

(7 m), height-to-live crown base (1.8 m) and crown bulk density

(0.18 kg m3).

The weather information was introduced with a combination

of files. Temperature, precipitation and humidity data were indi-

cated in a standard FARSITE weather file (.WTR). Wind-related

data (wind speed, wind direction and cloud cover) were intro-

duced through a file (.ATM) associated to a set of gridded files(2-km resolution), which were obtained with the RegionalAtmo-

spheric Modelling System(RAMS), a mesoscale meteorological

model (Pielke et al. 1992).

The parameterisation process was evaluated in terms of the

degree of spatial coincidence between the real and the simulated

1979 fire, considering first the former and then the latter as the

reference image. The simulation process was repeated several

times and, for each run, we calculated both the percentage of the

simulated burned area that really burned in the 1979 fire and the

percentage of the 1979 real fire that burned during the simula-

tion, aiming to maximise both variables. We progressively tuned

the parameterisation simulations to the real 1979 fire perimeter

testing different adjustment files, that is, changing the rate of

fire spread for the existing fuel models without affecting other

fire behaviour outputs (Stratton 2004).

Once the FARSITE model was calibrated, the reference sce-

nario and a set of derived scenarios were tested and reshaped

in an iterative process. At each step, the information provided

by the previous simulations about the interactions between fire

behaviour and fuel spatial configuration determined the main

guidelines to be followed in further steps for modifying the land-

scape in relation to the objectives of minimising fire propagation

risk, promoting landscape diversity and favouring the extension

of mature ecosystems (Montero de Burgos and Alcanda 1993).

In this sense, the fragmentation of large areas of highly fire-

prone fuel models through the introduction of patches of less

fire-prone vegetation types was a major landscape-level fuel

management strategy that we tested. We introduced two types

of patch shapes: large strips with simple perimeters, resulting

in the Strip-type scenarios, and irregular patches with more

convoluted perimeters, resulting in the Patch-type scenarios.

In some cases, narrow forest corridors were also introduced for

interconnecting these patches, leading to the Stripcor- and the

Patcor-type scenarios, respectively.All the simulations had the same duration, starting on 17 July

at 0830 hours and ending on 21 July at 2400 hours. In all cases,

we used the same fire ignition points, which were located after

the 1979 fire reports. In the parameterisation simulations, we

also started a second fire on 19 July and several induced fires on

21 July, following the indications found in these fire reports.

A set of firebreak network alternatives was also simulated.

The term firebreak that we use in the current study includes

both thefirelineand thefirebreakterms, as describedin Green

(1977), and describes a line from which all vegetation has been

removed down to mineral soil. The current firebreak network of

the area (Fig. 2c), which did not exist at the time of the 1979

fire, includes 1st, 2nd and 3rd order firebreaks (FB hereafter),


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Low density: 1st order Medium density: 1st + 2ndorder

High density: 1st + 2nd + 3rdorder

(c)(b)(a)

Fig. 2. Firebreak network density (the types of firebreaks present in each network are indicated).

which limit areas from 2000 to 6000 ha, from 500 to 1500 ha

and from 100 to 300 ha, respectively, depending on the typeof the potentially affected ecosystems (Velasco 2000). It is a

mixed-width network, with the FB width ranging from 1 to 70 m.

In the present study, we could only test homogeneous FB

networks because our version of FARSITE did not allow attri-

bution of different widths to different parts of a given network.

We therefore tested three network densities (high, medium, low)

and three FB widths for each density: 30, 50 and 80 m. The

high-density network corresponds to the complete current FB

network (Fig. 2c), the medium-density network includes only

the 1st and 2nd order FBs (Fig. 2b) and the low-density net-

work includes only the 1st order FBs (Fig. 2a). Although the

high-density networks with the largest FB widths (80 or 50 m)

are rather unrealistic alternatives, we also tested them as these

simulations provided interesting information about the effects

of major FB network design characteristics on fire growth and

behaviour.

We selected three FARSITE outputs for comparing the sim-

ulated scenarios: the area burned (ha), the rate of fire spread in

mmin1 (ROS hereafter) and the fireline intensity in kW m1

(FLI hereafter). Results are presented as means with their stan-

dard errors. The latter variable, which describes the energy

release per unit length of flame front, has been described as

the best fire behaviour descriptor for correlations with above-

ground fire effects (Andrews and Rothermel 1982). Moreover,

simple approaches linking fireline intensity to firefighter effec-

tiveness and safety have been described (Stubbs 2005). Finally,

we analysed whether crown fires had occurred during thesimulations.

The correlation among all variables was explored using

the Spearman coefficient () for non-parametric data, because

homogeneity of variances could not be attained in most cases.

Results

Parameterisation of the FARSITE model

A good spatial coincidence between the real and the simulated

1979 fire was only obtained after increasing the ROS adjustment

factor from 1.0 to 1.5 for FM4 and FM5. Previous FARSITE

calibrations carried out for Mediterranean landscapes in north-

easternSpain also showed the need to increase the ROS factor up

to the value of 1.5 for plant communities classified as FM4, FM5

or FM6 in order to tune the fire growth during the simulations towhat is observed for real fires (M. Castellnou, Catalan Agency

for Forest Management Actions-GRAF, pers. comm.).

The use of the FARSITE model was finally considered trust-

worthy in our site: 67.9% of the area burned by the simulated

fire was really burned in 1979 and 92.4% of the 1979 real fire

was also burned during the simulation.

Effects of fuel spatial configuration on fire spreadand behaviour

The reference scenario (Fig. 3a) was characterised by large

interconnected areas of shrub-type fuel models as described in

Anderson (1982): FM4, FM5 and FM7. The total area covered

by these three fuel models represented 44.2% of the reference

landscape (14.4, 16.3 and 13.5, respectively) but reached 73.2%

of the area burned during the reference simulation (35.8, 22.2

and 15.2%, respectively) (Fig. 3b).

The grass-type fuel models as described in Anderson (1982),

that is FM1 and FM2, represented 54.3% of the reference land-

scape (15.4 and 38.9%, respectively), but only 26.4% of the

area burned during the reference simulation (12.9 and 13.5%,

respectively).

The reference simulation showed that after the fire ignited in

an FM1 area, it spread fast across both this fuel model and the

adjacent large FM4 patch, but was effectively stopped by large

cropped areas (FM2) (Fig. 3b).

Analysing the behaviour of fire in each fuel model, we

observed that the largest mean and maximum values for ROSand FLI were reached in the FM4 area (Table 2; Fig. 3 c,d).

Mean ROS was almost three times larger for FM4 (3.1 m min1)

than for FM5 (1.2m min1). Mean FLI in FM4 (547.2kW m1)

was almost 22 times larger and 23 times larger than in FM5

(25.1 kWm1) or FM7 (23.6 kW m1), respectively.

According to the fire behaviour characteristics chart

(Rothermel 1983) and the adjective ratings for fire behaviour

(Stubbs 2005), fire behaviour during the reference simula-

tion could be described as globally Very Active (mean FLI

between 606.2 and 1299 kW m1) and Extreme (max. FLI

>1299kWm1) in some locations. It was generally Active

(mean FLI between 259.8 and 606.2 kW m1) in the FM4 areas

and Extreme in some locations (Table 2; Fig. 4d). In the FM5


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0

(a)

(c) (d)

(b)

05

510

1015

1520

2028

0500

5001000

10005000

500010 000

10 00013556

1

2

4

5

7

8

98

Fig. 3. (a) Reference landscape; the 1979 fire perimeter and the initial ignition zone are indicated. Legend

numbers correspond to fuel models. (b) Reference simulation (stippled area) over the reference landscape; spatial

distribution of (c) the rate of spread (m min1); and (d) the fireline intensity (kW m1).

Table 2. Results for the whole reference simulation (1st row) and by fuel model

s.e., standard error; FM, fuel model; P, passive crown fire; A, active crown fire

Area burned Rate of spread Fireline intensity Fire type

(ha) (m min1) (kW m1) (% of the area burned)

Mean (s.e.) Max. Mean (s.e.) Max. Surface P A

Total 40 733.6 1.8 (2.4) 28 632.4 (111.4) 13 556 67.2 32.8 0.0

FM 1 5271.3 2.3 22 6.6 7455 99.1 0.9 0.0

FM 2 5503.1 1.8 18 26.0 8186 99.7 0.3 0.0

FM 4 14 582.3 3.1 28 547.2 13 556 12.8 87.2 0.0

FM 5 9025.2 1.2 17 25.1 8343 96.0 4.0 0.0

FM 7 6187.4 1.5 21 23.6 10 010 96.4 0.6 0.0

and FM7 areas, fire behaviour was generally Low (mean FLI


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0

(a) (b)

12457898

012457898

Fig. 4. Simulations (stippled areas) with (a) scenario A and (b) scenario C from Table 3. Legend numbers

correspond to fuel models. The 1979 fire perimeter is indicated. In some cases, fuel models are indicated on the

image.

Table 3. Results for the conversion simulationsScenarios are the result of conversion from fuel model (x to y) as indicated in parentheses. s.e., standard error; P, passive crown fire;

A, active crown fire

Scenario Area burned Rate of spread Fireline intensity Fire type



A (4 to 5) 9159.3 1.2 (1.8) 18 47.2 (54.2) 791 100.0 0.0 0.0

B (4 to 7) 18 110.5 1.4 (1.8) 19 109.9 (124.7) 1057 96.4 3.6 0.0

C (4 to 8) 5686.7 1.7 (2.1) 20 35.4 (36.9) 448 100.0 0.0 0.0

D (4 to 10) 19 599.0 1.2 (1.6) 18 241.5 (311.5) 3015 80.7 19.3 0.0

Reference 40 733.6 1.8 (2.4) 28 632.4 (111.4) 13 556 67.2 32.8 0.0

Scenario C, obtained through the conversion of FM4 into

FM8, minimised the area burned as well as the mean and

maximum intensities (Table 3; Fig. 4b). In relation to the refer-

ence simulation, the burned area decreased 86%, the mean FLI

dropped from 632.4 (111.4) to 35.4 (36.9) kW m1, values in

parentheses being standard errors hereafter. The maximum FLI

was reduced from 13 556 to 448 kW m1. Fire behaviour rating

could be, therefore, changed from Very Active and sometimes

Extreme to Low and sometimes Active. The mean rate of fire

spread, however, was only slightly reduced (from 1.8 (2.4) to 1.7

(2.1) m min1).

Scenario A, obtained through the conversion of FM4 into

FM5, was the second-best scenario for minimising fire size andreducing the mean and maximum fireline intensity (Table 3;

Fig. 4a). Mean ROSdropped from 1.8 (2.4) to1.2 (1.8) m min1.

Fire could be rated as Low and sometimes Very Active.

Although being very different, scenarios A and C were both

very successful forreducingfire size andintensity andboth ledto

rather similar fire spread patterns (Fig. 4). No form of crown fire

occurred during either of these two simulations, whereas passive

crown fires were observed in the remaining scenarios of Table 3.

In relation to our objectives of minimising fire spread and

promoting mature ecosystems, the previous results led us to test

the fragmentation of the highly fire-prone largest FM4 patch

through theintroduction of dense wooded areas (FM8) in various

spatial configurations. We simulated nine scenarios (Table 4).

Fire size always decreased in relation to the reference simula-

tion, although there was a high variability among scenarios. The

area burned ranged from 11 712 ha in Stripcor2 to 22 316 ha in

Patch2.

No clear pattern appeared as being the most suitable for min-

imising fire spread, as the three scenarios leading to the smallest

fires (


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Table 4. Simulation results for the scenarios derived from the introduction of FM8 patches in the reference

landscape and for the reference simulation

Stripx, FM8 strips; Patchx, irregular FM8 patches; Stripcorx, FM8 strips connected by FM8 corridors; Patcorx, irregular

FM8 patches connected by FM8 corridor s. s.e., standard er ror; P, passive crown fire; A, active crown fire




Strip1 18 049.2 1.6 (2.3) 23 616.5 (1081) 11 262 67.6 31.1 1.3

Strip2 13 144.1 1.4 (2.0) 19 542.5 (917) 8981 68.9 29.2 1.9

Patch1 16 090.4 1.5 (1.9) 23 458.7 (825.4) 10 354 71.4 27.9 0.8

Patch2 22 316.0 1.5 (2.3) 26 528.5 (1051.9) 11 438 71.1 28.7 0.3

Patch3 12 398.4 1.4 (2.0) 20 544.2 (941.4) 9327 72.0 28.0 0.0

Stripcor1 17 587.5 1.7 (2.4) 23 643 (1106.9) 9529 67.0 33.0 0.0

Stripcor2 11 712.0 1.3 (1.8) 20 496.8 (878.9) 10 402 66.0 34.0 0.0

Patcor1 13 335.6 1.5 (2.0) 22 490.2 (878.6) 10 696 68.8 31.2 0.0

Patcor2 12 190.1 1.3 (1.8) 21 483.8 (835.4) 8248 67.4 32.6 0.0

Reference 40 733.6 1.8 (2.4) 28 632.4 (111.4) 13 556 67.2 32.8 0.0

than for the area burned. Mean ROS ranged from 1.3 (1.8) to 1.7

(2.4) m min1 and the larger decrease in relation to the refer-

ence simulation was 27.8%. The mean fireline intensity ranged

from 458.7 (825.4) to 643 (1106.9) kW m1 and we observed

decreases from 2.5 to 27.5% (for Strip1 and Patch1 respec-

tively) in relation to the reference fire. Most fires were globally

Active and Extreme in some locations, although in Strip1

and Stripcor1 fires were Very Active and Extreme in some

locations.

In most scenarios, weobserved a surface fire in more than two

thirds of the area burned (Table 4). The area affected bya passive

crown fire ranged from 27.9 to 33% of the area burned (it was

32.8% for the reference simulation) and was mostly observed on

FM4 areas. Active crown fires never represented more than 2%

of the area burned.

No pattern appeared as being the most efficient for limit-

ing either the ROS, or the FLI. For both variables, however,

the largest mean values were obtained in scenarios Strip1 and

Stripcor1, which were also characterised by some of the small-

est initial presences of FM8: 3.2 and 4.1%, respectively. This

latter variable was negatively correlated with the area burned

(=0.923, P< 0.01), with the mean ROS (=0.794,

P< 0.01) and with the mean FLI (=0.622, P< 0.05). No

significant correlation was found either between ROS and FLI,or between any of them and the area burned.

The introduction of narrow FM8 corridors between FM8

patches always resulted in a reduction of the area burned, but did

not always lead to more moderate burning conditions (Table 4);

e.g. comparisons between Strip2 and Stripcor2 and between

Patch1 and Patcor1.

We also simulated the fragmentation of the FM4 matrix with

wooded patches of differentsuccessional stages(FM7 and FM8).

The introduction of FM7 patches scattered throughout a FM8

matrix (Table 5) was generally more effective for reducing fire

size than the introduction of scattered FM8 patches in a FM7

matrix (Table 6). The FM8 matrix acted as an effective barrier

against fire propagation,whereasthe FM7matrix didnot (Fig.5).

The scenarios in Table 5 led, therefore, to larger decreases

of fire size in relation to the reference simulation (from 46

to 69.7%), Patcor17 and Patcor27 being the most effi-

cient scenarios. Both scenarios were obtained after introducing

large irregular FM8 patches connected by FM8 corridors in

the FM4 matrix and smaller FM7 patches within these FM8

areas. Scenario Patcor27 was characterised by the largest

initial presence of FM8 (6.5%) and the smallest value for the

FM7 area : FM8 area ratio (2.4) (Table 5b, Fig. 5). In scenario

Patcor17, these two variables reached values of 3.9% and 3.8,

respectively, but the total perimeter length of the FM8 patches

reached the second largest value among all scenarios and the

mean value for the ratio perimeter : area among FM8 patches

was the largest among all scenarios (Table 5b).

Considering all scenarios in Table 5, we found a significant

positive correlation between the FM7 area : FM8 arearatio and

the area burned (= 0.829, P< 0.05).

The comparisons between Strip27 and Stripcor27, on

the one hand, and between Patch17 and Patcor17, on

the other hand, showed again that introducing FM8 corridors

between the FM8 patches reduced fire size, but generally did

not result in more moderate fires (Table 5a).

The comparison of each scenario in Table 5 with the corre-

sponding scenario inTable 4 showed that theintroduction of bothFM7 patches and FM8 patches could be sometimes more effec-

tive than the sole introduction of the latter. In 50% of the cases,

fire size was smaller in the Table 5 scenario and in 66.7% of

the cases, fire was less intense. In Patcor17, for instance, we

observed a smaller, slower and less intense fire than in Patcor1.

Comparing each scenario in Table 6 with the corresponding

scenario in Table 5 (e.g. Strip28 in 7 with Strip27), we

observed that fire size was always larger in the former, whereas

mean ROS generally remained very similar, and mean FLI was

smaller in the former in 67% of the cases. Considering all sce-

narios in Table 5 and Table 6, the FM7 area : FM8 area ratio

was significantly correlated with the area burned (= 0.727,

P


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Table 5. Simulation results for scenarios derived from the introduction of scattered FM7 patches throughout an

FM8 matrix

Strip27, Strip2 fromTable4 with FM7 patches; Stripcor27, Stripcor2 from Table 4 with FM7 patches; Patchx7,Patchx

from Table 4 with FM7 patches; Patcorx7, Patcorx from Table 4 with FM7 patches. s.e., standard error; P, passive crown

fire; A, active crown fire

(a) Simulation results



Mean Max. Mean Max. Surface P A

Strip27 17 306.9 1.4 (2.2) 21 493.3 (953.9) 10 543 68.6 31.3 0.0

Stripcor27 16 354.4 1.4 (2.1) 24 489.8 (928.9) 11 621 68.4 31.6 0.0

Patch17 15 990.2 1.5 (1.9) 22 463.7 (841.2) 10 811 71.0 29.0 0.0

Patch27 21 982.8 1.5 (2.4) 31 491.6 (1039.3) 14 854 72.2 27.8 0.0

Patcor17 12 946 1.4 (1.8) 20 464.1 (835) 8834 69.2 30.8 0.0

Patcor27 12 347 1.4 (1.9) 19 488.5 (875.9) 9352 68.0 32.0 0.0

(b) Characteristics of the landscape before simulation

Scenario Area burned Presence FM7 area : FM8 area FM8 total Mean FM8

(ha) FM7 FM8 perimeter (m) perimeter :area

Strip27 17 306.9 15.2 3.6 4.22 201 000 0.0089

Stripcor27 16 354.4 14.9 4.0 3.70 238 200 0.0023

Patch17 15 990.2 14.2 3.6 3.96 189 720 0.0021

Patch27 21 982.8 14.8 2.8 5.27 233 640 0.0031

Patcor17 12 946 15.0 3.9 3.80 238 860 0.0219

Patcor27 12 347 15.6 6.5 2.41 368 640 0.0032

Table 6. Simulation results for scenarios derived from the introduction of scattered FM8 patches throughout an FM7

matrix

All scenarios were derived from the corresponding scenario in Table 5 after replacing the FM8 matrix by an FM7 matrix and

introducing FM8 patches (instead of FM7 patches) in this matrix. s .e., standard error; P, passive crown fire; A, active crown fire




Strip28 in 7 33 336.8 2.0 (3.2) 25 638.6 (1346.4) 11 454 72.0 28.0 0.0

Stripcor28 in 7 26 775.8 1.4 (1.9) 26 395 (760.6) 12 108 74.9 25.1 0.0

Patch18 in 7 20 088.5 1.5 (2.3) 34 481.9 (1005.1) 16 434 72.6 27.4 0.0

Patch28 in 7 26 358.9 1.5 (2.2) 26 454.2 (927.9) 12 903 73.6 26.4 0.0

Patcor18 in 7 23 148.8 1.4 (1.8) 20 345.4 (661.9) 9905 73.3 26.7 0.0

Patcor28 in 7 21 367.8 1.7 (2.3) 38 470.3 (954.6) 18 375 72.5 27.5 0.0

ROS (= 0.754, P< 0.01) and the maximum FLI (= 0.713,P< 0.01).

For both sets of scenarios (Tables 5 and 6), surface fires

occurred in almost three quarters of the area burned, as happened

for scenarios in Table 4, but when we compared a scenario in

Table 6 with the corresponding one in Table 5, the occurrence of

passive crown fires was always smaller in the former. No active

crown fire was observed.

Considering all scenarios in Table 4, Table 5 and Table 6,

the FM7 area : FM8 area ratio was also significantly corre-

lated with the area burned (= 0.858, P< 0.01), the mean

ROS (= 0.521, P


9/14


0

1

2

4

5

7

8

98

0

1

2

4

5

7

8

98

0

1

2

4

5

7

8

98

0

1

2

4

5

7

8

98

Fig. 5. Simulations (stippled areas) with scenarios Stripcor27 (upper left) and Patcor27 (lower left) from Table 5

and scenarios Stripcor28 in 7 (upper right) and Patcor28 in 7 (lower right) from Table 6. Legend numbers correspond

to fuel models. In some cases, fuel models are indicated on the image.

Table 7. Simulation results for the reference landscape combined with different firebreak (FB) networks

P, passive crown fire; A, active crown fire; NO FB: no firebreaks. For a given FB network density, each alternative is

named as: FB+ FB-width in m

FB network Area Rate of spread Fireline intensity Fire type

burned (ha) (m min1) (kW m1) (% of the area burned)


High density

FB80a 5879.3 2.0 (2.2) 23 552.2 (974.8) 8949 69.5 30.5 0.0

FB50a 5920.7 1.9 (2.1) 19 534.9 (960.2) 8885 67.2 32.8 0.0

FB30a 13 070.1 1.9 (2.2) 23 725.9 (1098.5) 11 511 67.5 32.5 0.0

Medium density

FB80b 9633.5 2.4 (3.2) 23 950 (1553.7) 10 670 64.0 36.0 0.0FB50b 14 179.7 2.3 (3.0) 19 906.2 (1457.9) 9215 62.5 37.5 0.0

FB30b 19 571.9 2.1 (2.4) 20 793.1 (1157.6) 9850 62.0 38.0 0.0

Low density

FB80c 13 925.2 2.3 (2.8) 25 920.5 (1339.0) 9978 49.4 50.6 0.0

FB50c 16 151.7 2.1 (2.7) 23 858.3 (1312.2) 11 135 54.0 46.0 0.0

FB30c 21 068.6 1.9 (2.4) 22 771.7 (1179.9) 10 334 54.0 46.0 0.0

NO FB 40 733.6 1.8 (2.4) 28 632.4 (111.4) 13 556 67.2 32.8 0.0

considered rather unrealistic and not even suitable, because they

would cause high ecological and visual impacts on the land-

scape. FB30a appears to be a good alternative, allowing a strong

reduction of fire size, while limiting the increase of mean ROS

and mean FLI (Table 7). It is interesting to note that FB30a

led to a smaller, slower and less intense fire than medium-

or low-density networks with wider firebreaks, such as FB50b

or FB80c.


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Table 8. Area burned (ha) for the reference landscape and for several fuel-altered scenarios from

Tables 4 and 5 combined with a firebreak (FB) network

NO FB, no firebreaks. For a given FB network density, each alternative is named as: FB + FB-width in m

FB network Reference Strip2 Stripcor2 Patcor2 Patcor17 Patcor27

High density

FB80a 5879.3 5334.5 5353.5 5422.7 4410.0 5431.1

FB50a 5920.7 5360.5 5368.0 5467.9 4694.4 5565.3

FB30a 13 070.1 6129.3 6539.9 5466.1 4602.2 5470.9

Medium density

FB80b 9633.5 7639.4 7633.0 6755.3 8017.5 7744.1

FB50b 14 179.7 8169.4 7652.8 6913.8 8010.5 8726.9

FB30b 19 571.9 8243.5 9203.7 9284.9 10204.0 9717.0

Low density

FB80c 13 925.2 7576.9 7813.8 7710.5 8063.1 7762.2

FB50c 16 151.7 7608.0 7728.2 9292.0 8121.7 9813.1

FB30c 21 068.6 9903.0 9242.0 9973.7 11053.7 8710.1

NO FB 40 733.6 13 144.1 11 712 12 190.1 12 946 12 347

The introduction of an FB network always reduced the max-

imum values of ROS and FLI in relation to the reference

simulation (Table 7), but never reduced the mean ROS and only

reduced the mean FLI in the case of FB80a and FB50a (12.7 and

15.4%, respectively). For most simulations, the fire behaviour

was rated as for the reference simulation, that is Very Active

and sometimes Extreme.

The occurrence of passive crown fires tended to decrease as

the FB network density increased (Table 7). The percentage of

area burned affected by this type of fire ranged from 30.5 (in the

case of FB80a) to 50.6% (in the case of FB80c).

The same FB networks were tested in combination with a set

of scenarios from Tables 4 and 5, which had proved to minimise

fire propagation (Table 8). For a given combination of FB net-

work and fuel-altered scenario, the area burned alwaysdecreased

both in relation to the same FB network combined with the refer-

ence landscape (column Reference in Table 8) and to the same

fuel-altered scenario tested without any firebreak network (row

NO FB in Table 8).

Only in the case of FB80a, FB50a and FB80b, the combi-

nation of an FB network with the reference landscape led to a

smaller fire than an appropriate fuel-altered scenario testedalone

(Table 8). Coupling any of the remaining FB networks with one

of the fuel-altered scenarios resulted in a strong reduction of the

area burned; up to 58.7% in the case of FB30c combined with

Patcor27 (8710.1 ha) in relation to the combination with thereference scenario (21 068.6 ha).

As for burning conditions, fires were generally slower and

less intense when the FB network was combined with a fuel-

altered scenario than with the reference one (Table 9). The

largest reductions of the mean ROS were observed for the com-

binations (FB80c+Patcor17) and (FB80b+Patcor27): from

2.3 to 1.3 m min1 and from 2.4 to 1.4m min1, respectively

(Table 9). The largest reductions of the mean FLI were found

for the same two combinations: from 920.5 to 416.1 kW m1

and from 950 to 460 kWm1, respectively. Indeed, for most

FB networks, the less intense fire (mean FLI < 500kWm1)

was observed for the combination with Patcor27 or with

Patcor17.

We observed that most fuel-altered scenarios applied alone

(row NO FB in Table 9) were more effective in reducing both

mean ROS and mean FLI than any FB network alternative tested

alone (column Reference in Table 9).

Thereplacementof thereference landscapeby oneof thefuel-

altered scenarios changed the fire behaviour rating from Very

Active and Extreme in some locations to Active and Extreme

in some locations for all FB networks, except FB80a and FB50a

(which already led to the latter rating when combined with the

reference scenario).

DiscussionEffects of fuel spatial configuration on fire spreadand behaviour

The results obtained show that fuel spatial distribution was a

key parameter influencing fire propagation and behaviour across

the studied landscape, which is in agreement with other studies

(Minnich 1983; Turner and Romme 1994).

In particular, we observed that large areas of heavy surface

fuel types, classifiedas FM4-type shrublands, favouredthe quick

spread of intense fires, as could be expected (Anderson 1982). In

our study area, these areas mostly corresponded to dense mature

shrublands dominated by the grass Brachypodium retusum and

seeder shrubs, such as Ulex parviflorus and Rosmarinus offic-

inalis, or the resprouter shrub Quercus coccifera. These plantcommunities were characterised by a very flammable foliage

and a nearly continuous secondary overstorey favouring fast-

spreading fires. The abundant dead woody material in the stands

contributed significantly to the fire intensity. High surface FLIs

combined with the presence of a sparse tree layer of Pinus

halepensis in some locationsled to theinitiation of passivecrown

fires. The rather small constant value that was attributed to the

height-to-live crown base in the model inputs probably favoured

the occurrence of such fires.

Fire behaviour (fire intensity, in particular) depends, among

other factors, on the characteristics of the vegetation (Debano

et al. 1998). Large fuel loads with an important presence of

dead fuels tend to promote high-intensity fires, specially under


11/14


Table 9. Mean rate of spread (ROS, in mmin1) and fireline intensity (FLI, in kW m1) for the reference landscape and for

several fuel-altered scenarios from Tables 4 and 5 combined with a firebreak (FB) network

NO FB, no firebreaks. For a given FB network density, each alternative is named as: FB+FB-width in m

FB network Reference Strip2 Stripcor2 Patcor2 Patcor17 Patcor27ROS FLI ROS FLI ROS FLI ROS FLI ROS FLI ROS FLI

High density

FB80a 2.0 552.2 1.9 582.3 1.9 574.6 1.7 473.7 1.8 499.3 1.7 471.4

FB50a 1.9 534.9 1.8 569.5 1.8 584.4 1.6 453.9 1.8 521.4 1.4 388.9

FB30a 1.9 725.9 1.8 579.0 1.9 671.9 1.6 429.6 1.6 465.7 1.6 437

Medium density

FB80b 2.4 950 1.6 573.2 1.6 572.2 1.6 562.5 1.5 461.1 1.4 460.0

FB50b 2.3 906.2 1.5 564.3 1.5 557.2 1.5 507.4 1.5 439.3 1.6 585.6

FB30b 2.1 793.1 1.5 555.5 1.4 548.8 1.5 535.4 1.6 549.7 1.3 457.6

Low density

FB80c 2.3 920.5 1.6 572.8 1.4 512.3 1.5 502.2 1.3 416.1 1.4 485.1

FB50c 2.1 858.3 1.6 574.6 1.5 565.3 1.5 543.7 1.3 413 1.5 519.8

FB30c 1.9 771.7 1.5 543.3 1.3 506.6 1.2 432.8 1.5 503 1.3 464.2

NO FB 1.8 632.4 1.4 542.5 1.3 496.8 1.3 483.8 1.4 464.1 1.4 488.5

extremely dry and windy conditions, resulting in very low fuel

moistures (Turneret al. 1994). This type of fire generally causes

heavy damages to the aboveground vegetation as well as large

nutrient losses in the whole system. If recurrent, such events may

even result in persistent structural changes in the ecosystems

(Moreno and Oechel 1994).

The landscape-level fuel management actions are, therefore,

often focussed on reducing surface fuel loads so that the size

of potential fires may be reduced, burning conditions become

more moderate and potential fire-caused damages per unit area

are minimised (Agee et al. 2000). In this sense, we simu-

lated an extensive hazardous fuel removal, replacing the FM4

matrix by an FM5 matrix. This treatment was very effective

for strongly reducing fire size and intensity. The replacement

of the FM4 matrix by an FM8 matrix was, however, the most

effective landscape-level fuel alteration for minimising these

two variables. Fuel model 8, which corresponds primarily to

closed-canopy stands with light surface fuel loadings, has been

previously described to favour slow burning ground fires with

moderate intensities (Anderson 1982).

These results showed that two very different fuel scenarios

may be equally successful in relation to fire size control. Con-

sidering other management objectives, such as the promotion ofbiodiversity, the extension of FM8 appears to be more suitable,

though. In any case, the total disappearance of FM4 areas that

we simulated with such scenarios is quite unrealistic and rather

unsuitable. The resulting coarse-grained landscapes as described

in Forman (1995) might limit site diversity and enhance fire

hazard. In such landscapes, characterised by the dominance of

large patches, the dispersion of multihabitat species would be

rather costly as considerable distance exists between different

fuel types (Forman 1995). It is generally considered that a cer-

tain degree of heterogeneity and fragmentation, associating the

word fragmentation with the vegetation structural diversity

(Agee et al. 2000), provides for a wider range of environ-

mental resources and conditions, and, thereby, favours a higher

biodiversity in the landscape, while making it quite resistant to

the propagation of fire. It has been proposed that in fragmented

landscapes, disturbances require a higherboundary-crossing fre-

quency and a more convoluted route and, therefore, spread less

easily (Turner and Romme 1994; Forman 1995).

FARSITE simulations confirmed that the creation of a more

fine-grained landscape through the fragmentation of a fire-

prone matrix with woodlands in different successional stages,

the introduction of narrow forest corridors between wooded

patches and the promotion of more convoluted perimeters for

patches can be very effective for reducing fire size and, in most

cases, burning conditions. The landscape structure that would

result from the combination of these treatments would probably

facilitate tree colonisation and, thus, enhance the extension of

woodlands on the medium-to-long term, in the absence of fire.

Results also showed that landscapes with similar degrees

of overall fragmentation might lead to different fire propaga-

tion patterns, depending on the precise spatial arrangement of

fuel model types. In our case, the relative area covered by each

woodland successional stage (FM7 and FM8), the precise spatial

configuration of these fuel models relative to each other and the

shape of patches appeared to be crucial parameters in relation

to fire growth and behaviour. Results suggest, for instance, thatabove a critical threshold for the FM7 area : FM8 arearatio, fire

propagation can be strongly enhanced. An identification of such

ratios and of their crtitical values in relation to fire behaviour

seems to be crucial for better predicting the risk of serious fire

events (very large and very intense). It has to be kept in mind

that this risk is also a function of changing weather conditions

(Hargrove et al. 2000).

An appropriate fuel spatial configuration for reducing fire

size did not always lead to more moderate fire behaviours. The

parameters describing fire behaviour (ROS, FLI) dont seem to

be, indeed, as strongly altered through fuel management actions

as fire size can be. Neither FARSITE nor any single model

considers, however, the necessary combination of all factors


12/14


for accurately simulating fire behaviour in heterogeneous land-

scapes (Hargrove et al. 2000). In the case of FLI, in particular,

it is known that large fires usually produce a complex spa-

tial mosaic of intensities (Albini and Anderson 1982; Turner

et al. 1994), resulting in a heterogeneous pattern of burn sever-ities that will affect subsequent vegetation recovery (Moreno

and Oechel 1994; Turner and Romme 1994). During a fire, at

each location, the various surface fuel categories interact with

one another and with other factors (topography, weather, micro-

climatic changes. . .) to determine site-level fire intensity (Agee

et al. 2000). This variable is characterised, therefore, by a high

degree of spatial variability in natural systems, which makes fire

behaviour extremely difficult to predict. A further understand-

ing of the impacts of different fuel treatments on potential fire

behaviour is, therefore, currently constrained by fire behaviour

model assumptions and uncertainties.

Our simulations allow us, however, to propose potential tar-

get landscapes selected among the most effective tested fuel

scenarios: Patcor17 and Patcor27. It is interesting to notethat these twoscenarios fit into the aggregate-with-outliersspa-

tial model (Forman and Collinge 1996), which is expected to

promote landscape biodiversity and resilience to large severe

disturbances (Turner 1989; Forman 1995).

The effectiveness of firebreak networks

The introduction of a firebreak network wasalways very effective

for reducing fire size, although changes in the network density

or the FB width led to quite different results. Fire spread was

better controlled by a dense network with medium-width FBs

than by a less dense network with wider FBs.

On the contrary, our results showed that burning conditions

were generally not more moderate after the compartmentalisa-

tion of fires by FBs. In most cases, fires were even faster and

more intense. Damage per unit of area burned outside the FBs

themselves were, therefore, not reduced, which is in agreement

with other studies (Agee et al. 2000).

The combination of an FB network with appropriate

landscape-level fuel treatments always enhanced the efficacy of

both individual strategies for reducing fire size, while generally

improving the effectiveness of FB networks in limiting ROS and

FLI. Most fuel-altered scenarios tested alone led to slower and

less intense fires than any FB network alternative tested on the

reference landscape.These results confirm that the effectiveness

of an FB network depends not only on its design characteristics

but also on the behaviour of fires approaching it, as highlightedby other authors (Agee et al. 2000). Such behaviour is strongly

determined by fuel spatial pattern in the adjacent areas.

Our results indicate that coupling an appropriate fuel spa-

tial configuration with a soft FB network (moderate network

density and FB width) allows a strong reduction in fire size,

while limiting FLI. This combined strategy would minimise the

negative impacts (ecological, visual, economical) that may be

associated with the creation and maintenance of high-density

FB networks. Moreover, appropriate landscape-level fuel treat-

ments aiming to favour the extension of late-successional plant

communities (e.g. the introduction of woody resprouters, the

plantation of small canopy stands) are expected to promote a

higher biodiversity and to confer to ecosystems and to the whole

landscape a larger resistance and resilience towards fire. It is

important, thus, to understand the tradeoffs of implementing any

of these individual strategies, or a combination of strategies, on

a landscape level.

Conclusions

The results obtained indicate that fuel spatial distribution

strongly determines fire propagation patterns and burning con-

ditions. Large interconnected areas of heavy surface fuels favour

fast and intense fires.

In the studied landscape, the fragmentation of such areas,

mostly FM4-type shrublands, through the introduction of both

dense and open woodlands was an effective way to strongly

reduce fire size and limit FLI, while promoting a higher bio-

diversityand landscape resilience towards fires. The relative area

occupied by the various woodland successional stages, the pre-

cise spatial arrangement of these patches and their shape were

all key factors influencing fire spread and behaviour. Both theincrease of connectivity between woodlands and the promotion

of complex patch shapes among them contributed to reduce the

propagation of fire.

Most FB networks proved to be very effective for control-

ling fire size, but not for strongly reducing fire behaviour. The

efficacy of FB networks was always enhanced when combined

with an appropriate fuel scenario. Coupling low-impact FB

networks (moderate density and FB widths) with appropriate

landscape-levelfuel treatments seems to be, indeed, a good strat-

egy for limiting the occurrence of large, high-intensity fires,

while avoiding the negative impacts of very dense FB networks.

Further research is needed, though, to understand the tradeoffs

of such combined approaches.

Although uncertainties remain in relation to the simulation of

fire behaviour, the FARSITE model appears to be a good tool for

prospecting the consequences of fuel management actions on fire

spread and behaviour in Mediterranean landscapes, for design-

ing target landscape structures and, therefore, for supporting the

design of sustainable landscape management strategies.

Acknowledgements

The present work was carried out within the scope of the project Geo-

matics in the Assessment and Sustainable Management of Mediterranean

Rangelands-GEORANGE (EVK2-CT200000091). CEAM is funded by

the Valencia Government (Generalitat Valenciana) and the Fundacin Ban-

caixa. We thank Jorge Surez (Conselleria Territori i Habitatge, Valen-

cia Government) for providing us with valuable information about the

FARSITE-required crown fuel parameters. We are also grateful to Mark

A. Finney, developer of the FARSITE model, for his kindness in discussing

with us someaspects of the model.Wethank several members of theCatalan

Agencyfor Forest Management Actions-GRAF for sharing with us informa-

tion about their calibrations of FARSITE in several areas of Catalonia.

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Documents

Modelling the effects of landscape fuel treatmentson ﬁre growth and behaviour in a Mediterranean landscape (eastern Spain