MODELING FIRE BEHAVIOR TO ASSIST FOREST MANAGEMENT IN
PORTUGUESE LANDSCAPES
Botequim, B1., Borges, J. G. 1, Calvo A. 1 , Marques S. 1, Silva, A.1
1
Centro de Estudos Florestais, Instituto Superior de Agronomia, Technical University of Lisbon, Portugal
[email protected]
I. Aims
Vale do Sousa
(Vsousa)
Mixed Forest
A comprehensive review integrating wildfire modeling processes with specific wildfire simulation exercises provides a unique opportunity to examine
how alternative landscape management can potentially change fire spread. Therefore, we simulated fire spread and behavior in different managed
Mata Nacional de Leiria
(MNL)
Pinus Pinaster
Globland (Glob)
Eucaliptus Globulus
landscapes by developing multiple scenarios. The overall objective was to isolate and examine scenarios according to three important fire spread
factors: landscape structure, weather, and fire - ignition location (Fig. 2). For that purpose, fire modeling was conducted by FARSITE and FlamMap
systems in three Portuguese Forest areas (Fig. 1).
Figure 1. Portugal map with the spatial distribution of the three case-studies
Fuel Moisture contents
II. Material and Methods
Climate
values
Study
area
We considered three case-studies (Fig. 1): Mata Nacional de Leiria (MNL), a maritime pine (P.
pinaster Ait.) public forest in the Centre (extended ≈ 10 881 ha); Vale de Sousa, a diverse forested
MNL
Glob
V.Sousa
landscape (Q. suber, Q. robur, Q. faginea, Fagus silvatica, P.pinaster , P. pinea, E. globulus) with multiple
non-industrial private forest owners (NIPF) in the North (extending ≈ 12 308 ha). The third case
study - Globland‘ area (Glob) - consists of a group of pulp mills‘ properties where eucalypt (E.
globulus) is predominant (extend ≈ 11882 ha). This allowed us to make comparisons between
different topographic and fuel structure patterns on different landscapes (Fig. 3). A data set
Climate
Scenarios
Reduced
Control
Critical
Reduced
Control
Critical
Reduced
Control
Critical
Wheater
Station
Monte Real
(2002 - 2004)
Marianos
(2002 - 2004)
Barragem
C. Burgães
(2004 -2005)
T (° C)
27.8
30.6
35.9
35.8
37.8
40.1
30.4
32.6
36.4
H (%)
45.4
36.1
24.6
22.5
20.0
17.2
30.9
26.4
20.7
Dead Fuels
Live Fuels
1h 10h 100h LiveH LiveW
7
8
11
70
95
5
6
9
70
95
3
4
7
70
95
4
5
8
70
95
3
4
7
70
95
2
3
6
70
95
4
5
8
70
95
3
4
7
70
95
2
3
6
70
95
LiveF
100
100
100
100
100
100
100
100
100
Reduced
Surface Fire
Climate
Scenarios
Control
FlamMap
Critical
Reduced: 75th percentiles, i.e. higher values occur in 25% of the day in period June until September;
Control : 90th percentiles and Critical: 99th percentiles. The fuel moistures were calculated using the
model from Rothermel (1983) .
Spread &
Fire
Behavior
Topography
FarSite
Mata Nacional de Leiria
Globland
Vale do Sousa
Management area
10881.0055 ha
11882.13874 ha 20763.058 ha
Forest area
10881.0055 ha
11882.13874 ha 12308.41 ha
Resolution
Resolution
Resolution
DTM
25 x 25 m
25 x 25 m
90 x 90 m
min
max
min
max
min
max
Elevation (m)
4
142
0
196
37
541
Slope (º)
0
35
0
35.9
0
37.4
More freq
More freq
More freq
Aspect
Nw
Sw
Sw
Canopy
Characteristics
Crown Fire
Forest
Cover
Ignition Point
Location
Fuel Model
wilfires Probability models with explanatory
biometric variables available for each specie
encompassing 2504 inventories plots, was used to determine the crown structural characteristics
Fuel Model
required to run crown fire activities and detect significant differences in fire-landscape interactions.
Elevation
Slope
Aspect
Fuel Model
INPUT FIRE SIMULATORS
Description
Case Study
PPIN-03
P. pinaster plantations without understorey
MNL
PPIN-04
P. pinaster plantations with understorey
MNL
PPIN-05
Mature P. pinaster plantations
MNL
EUC-01
Young E. globulus plantations
Glob
EUC-02
E. globulus plantations without understorey
Glob
EUC-03
E. globulus plantations with understorey
Glob
F-PIN
P. pinaster litter
MNL
M-ESC
Broadleaf evergreen (or evergreen
Reference
Initial Input
Fire Simulators
Cruz (2005)
Evaluation Matrix
VSousa
Information
For
Forest Manager
hardwood) litter and understorey
MNL
Landscape
data and forest
canopy
characteristics
M-EUC
M-H
E. globulus litter and understorey
Herbaceous understorey
M-PIN
P. pinaster litter and understorey
V-MAa
Tall Erica sp., Ulex sp. and
Glob, VSousa
VSousa
MNL , VSousa
Fernandes,
VSousa
et al. (2009)
Pterospartum tridentatum shrubland
V-MH
V-MMa
Young shrubs and grassland
VSousa
Tall Q. coccifera, Cistus ladanifer
VSousa
GUIDELINES
Figure 2. Methodologie applied to modeling fire expected behavior and provided
information to assess the effectiveness of methods for integating stande-level fuel
treatment schedule and landscape-level management planning.
Globland
and Cytisus striatus shrubland
4.c)
4.b)
4.a)
Figure 4. Forest canopy characteristics: stand height (4.a), crwn base height (4.b) and crown bulk density
(4.c) based on the inventory plots and used as crown fuel data in Farsite and FlamMap systems.
V. Sousa
Intermediate Results
Figure 5. Fire –ignition point dispersed by
the landscape based on the probability of
occurrence of fire.
The estimation of canopy parameters were made using specific models developed to Portuguese species (Figure 4).
Specifically, (1) we simulated fire spread in Portugal on three landscapes, each with a different structure and fuel model;
(2) we examined how weather (wind speed´s of 8km/h, 12km/h and 18km/h) affects fire spread on all three landscapes
– we applied also three climate scenarios labeled reduced, control and critical (gathered along the summers of 2002,
Figure 3. Landscape files from the three case studie representing the required
themes of topographic factor (elevation, slope and aspect) and surface fuel model
used to compute fire behavior and simulate surface fire spread.
2003 and 2004) to examine weather influences; (3) and we explored spatial variation among fires ignited in different
parts of the landscape. Fire ignition locations are based on the application of risk model using biometric variables from
each inventories plot (Garcia et al. submitted) (Figure 5). The fire simulation systems were run to assess the resistance
to fire of current landscape mosaics according to different canopy fuel structure and meteorological scenarios.
III. Results
For demonstrated purposes we considered
Vale de sousa landscape:
• Fire Perimeter (m)
• Heat per area(kj/m2)
• Flame lenght (m)
• Crown Fire Activity (Index: 0= nome,1=
surface fire,2= passive crown fire or 3=active crown fire)
• Spread vectors (m/min)
FARSITE
OUTPUT FIRE SIMULATORS
& ArcGIS
FARSITE and FlamMap system have produced
FLamMap
specific elements of each fire. These Maps were
evaluated to identify stand characteristics and
Database
spatial pattern metrics of fire prone areas.
Furthermore, fire behavior characteristics in
each pixel on the landscape computed with
FlamMap were combined with initial landscape
FlamMap
FlamMap
& ArcGIS
& ArcGIS
information to develop a database with all the
possible scenarios combination.
• Rate of Spread (m/min)
• Fireline intensity (Kw/h)
Guidelines to support Forest Management

Fire behavior calculations provided information to compare the spatial distribution of forest
stands in current landscapes and also to identify hazardous fuel and corresponding stand
biometric features to support fire prevention in each study area.
IV. Conclusion
The Systems simulators have provided capabilities both for consistent representation of fire behavior and for spatial validation of fire prediction in the three study areas. Clearly, the
knowledge that results from this study will help forest managers to identify the high-risk areas and to develop management priorities in managing fuels in their landscape. Thus, it will be
instrumental for innovative and effective integration of forest and fire management planning activities and will be valuable to address the most important forest catastrophic event in
Portugal.
Acknownlegment
This research was supported by Project PTDC/AGR-CFL/64146/2006 “Decision support tools for
integrating fire and forest management planning” funded by the Portuguese Science Foundation (FCT) and
The authors would like to thank FCT for funding the PhD of Brigite Botequim (SFRH-BD-44830-2008).
References
Cruz, M. (2005) Guia fotográfico para identificação de combustíveis florestais - Região centro . Centro de Estudos de Incêndios
Florestais – associação para o desenvolvimento da Aerodinâmica Industrial , Coimbra, 39pp.
Garcia-Gonzalo, J., Botequim, B., Zubizarreta-Gerendiain A., Ricardo A., Borges J. G.,Marques S., Oliveira M. M. , Tomé, M. and
Pereira, J.M.C., Modelling wildfire risk in pure and mixed forest stands in Portugal, (submitted)
Fernandes, P., Gonçalves, H., Loureiro, C., Fernandes., M., Costa., T., Cruz., M., Botelho., (2009)Modelos de combustível florestal
para Portugal. Congresso Florestal Nacional ,Açores.
Rothermel, R.C. (1983). How to predict the spread and intensity of forest and range fires. Genral Tecnhical Report INT-143.
USDa Forest service, Intermountain forest and Range Experiment Station, Ogden. 161 pp.