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