modelling the effects of landscape fuel treatmentson fire growth and behaviour in a mediterranean...
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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).
IAWF 2007 10.1071/WF06101 1049-8001/07/050619
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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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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
(ha) (m min1) (kW m1) (% of the area burned)
Mean (s.e.) Max. Mean (s.e.) Max. Surface P A
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
Scenario 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
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
Scenario Area burned Rate of spread Fireline intensity Fire type
(ha) (m min1) (kW m1) (% of the area burned)
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
Scenario 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
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
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Modelling fire growth in eastern Spain Int. J. Wildland Fire 627
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)
Mean (s.e.) Max. Mean (s.e.) Max. Surface P A
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
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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
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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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Manuscript received 29 June 2006, accepted 11 April 2007