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FIREMAP: Simulation of Fire
Behavior - A GIS Supported
System 1
MariQ J. Vasconcelos, D. Phillip Guertin,
Malcolm J. Zwolinski2
Forest management and planning
is a multiobjective task. Fire will impact the potential resource outputs
and is one of the many factors that
should be addressed, depending on
context and goals. From a planning
viewpoint, we must be able to predict the consequences of site-specific
management actions on the fire characteristics (areas burned and intensity) for the area of concern.
From the literature, fire behavior
prediction and modeling are limited
to homogeneous cover types (Rothermel1972,1983), where the other driving variables (weather and topography) are assumed to be uniform over
the same area. These models are,
therefore, unsuitable for dealing with
the different spatial combinations of
vegetation which a management alternative might consider and cannot
be readily applied to patchy enviroments. The FIREMAP system attempts to address the problem of
spatial/ temporal variability of the
driving variables which has not yet
been adequatelly considered in fire
modeling.
The FIREMAP System
The objective of FIREMAP is to
simulate the consequences of hypothesized changes in vegetation
'Poster paper presented at the conference. Effects of Rre in Management of
Southwestern Natural Resources (Tucson.
AZ. November 14-17. 1988).
'Division of Forest-Watershed Resources.
School of Renewable Natural Resources.
University of Arizona.
composition and density on the fire
characteristics (area burned and fire
intensity) in well known ecosystems.
This system estimates fire characteristics taking spatial and temporal
variability into account and simulating the spread of fire in discrete time
steps.
FIREMAP is mainly applicable as
a prescriptive tool but it can also be
used for predicting fire behavior in
"on site" situations when time effects
have to be analyzed. Mapped outputs can also provide a basis for better communication.
Model Development Tools
The development of thi~ simulation tool consists of integrating a fire
behavior modeling system- DIRECT,
a module from the BEHAVE system
(Andrews 1986), with a Geographic
Information System-MAP (Tomlin
1985).
DIRECT uses Rothermel's fire
spread model (1972) to predict fire
characteristics for a given continuous
and relatively homogeneous area.
Andrews (1980) reports favorable
statistical comparisons between
model predicted and observed rates
of fire spread when burning conditions are uniform. To deal with spatial nonuniformity of fuels, weather
and topography the field must first
be partitioned into homogeneous
units. This partitioning allows the
use of the spread model within each
unit (Fujioka 1985).
217
MAP is a raster based GIS designed to run on IBM compatible
microcomputers. It has a grid cell
data structure in which map information is stored as numeric values in
arrays, each cell representing an uniform parcel of land located within
the overall rectangular grid. MAP
provides for storage, processing and
display of cartographic data allowing
for input in the form of grid· cells,
digitized points, lines or polygons.
The processing capabilities consist of
spatial data base management, spatial statistics and cartographic modeling that use sequential processing
of mathematical operations plus
maps and a common database to
store intermediate results (Berry and
Reed 1987). The simulation of fire
spread is based on the "distance
function" spread. This distance accumulating process can be limited to
upward or downward directions
over a 3-dimensional surface (Tomlin
1985).
FIREMAP Structure and Interface
There are three main sections in
FIREMAP. The first section generates
the input overlays required to run
the fire model which are based on
meteorological data, time of the day,
month of the year, an altitude overlay, a vegetation overlay, and a
stream channels overlay, by following the framework described in Rothermel (1983). The second section consists of a program, written in FOR-
TRAN77, that reads the maps as arrays, runs the fire model and creates
the output overlays that store the
values describing fire characteristics
for each cell in the data base (fig.l).
The third section consists of the
actual simulation of the fire spread
for the given conditions. A source (or
sources) of ignition, is selected. It is
assumed that the wind is consistently
blowing from the same direction,
and an overlay with a constant inclination in the direction of the wind is
created (WIND).
The spread operation previously
described, is used over this surface,
through an overlay, FRICTION, that
has assigned to each grid cell the
number of time units it takes the cell
to be consumed by a fire, with the
given conditions. This calculation is
based on the rate of spread (feet/
min) and size of each cell (feet). The
value of friction assigned to stream
channels is the result of a calibration
done for this particular situation. Using spread operation in the direction
of the wind, fire spreads preferentially through the path of least resistance, or the cells taking the least
time to be consumed by the same
fire, up to the point where the predefined simulation time is reached.
The other output overlays (heat
per unit area, fireline intensity, effective windspeed, flame length, reaction intensity and direction of maximum spread) give useful mapped
information about the characteristics
of the fire in each of the grid cell
units, for the time interval on which
the weather conditions utilized apply. Because the fire model predicts
characteristics of the fire in the flaming front, they are valid only when
the fire is still burning on that cell.
Three-dimensional displays of the
areas burning can be included.
Mountain area, on the Fort Apache
Indian Reservation in east-central
Arizona. It corresponds to an area 9
square miles in size with elevations
ranging from 5800 to 7000 feet, on
steep slopes. The vegetative cover
consists of three types: ponderosa
pine stands (Pinus ponderosa) with
variable crown and understory densities, pine-Douglas fir stands (Pinus
ponderosa-Pseudotsuga menziesii), and
pinyon-juniper. The vegetation management alternatives considered are
no intervention and harvesting by
selective cutting (fig. 2).
To analyze the influence of one
variable in the system, all other variables have to remain constant. Here
spatial arrangement of vegetation,
under harvest or non-harvest conditions is the driving variable under
consideration; therefore, weather
conditions and source of the fire are
the same for the two simulations.
The weather data utilized were
taken at Ivins Canyon on June 11,
1988 at 3:00p.m.: Dry bulb temperature- 82° F; Relative humidity -12%;
Windspeed at 6ft- 12 m.p.h.; Wind
direction - South. Simulations are
performed for a period of three
hours, whe:re each time step is one
hour long. Another example is given
Results
The final result of the simulation is
shown on figure 3. Figure 4 shows
how a change in the wind direction
(accounted for between time steps)
can affect the area and shape of the
burned area, illustrating the utilization of FIREMAP for a known
weather situation. Figure 5 displays
the expected flame lengths for the no
intervention alternative during the
three hours of simulation.
Conclusions
The results of the FIREMAP application described here indicate that
the approach followed works reasonably well. The integration of a fire
model like BEHAVE with a GIS may
be an efficient way of accounting for
spatial variability when attempting
to predict fire behavior.
FIREMAP, in the same way as
BEHAVE, is a direct implementation
FUELS
SLOPE
WIND SPEED
WIND DIRECTION
MOISTURE
FIRE MODEL
RATE OF SPREAD
FLAME LENGTH
HEAT/UNIT AREA
Application of Firemap
FIRELINE INTENSITY
Problem Description
REACTION INTENSITY
The area considered in this example is located in the Spotted
on how a change in wind direction
from south to west, at the end of the
second hour would affect the area
burned.
Figure 1.-Using GIS as a data base.
218
COUER TYPES
Ponde~osa
Pin•
Pinuon-Ju~ipep
II
II
k:::·J Pine-Douglas
Ji~
[ ] Ha~u@sted A~eas
Figure 2.-Vegetation: left - no Intervention, right - harvested.
AIEAt
SOU¥-Ct
Art~r on~ hou~
BURt~D
II II
II []
Arter t1111o llours
Afte~ th~ee ~ou~s
Figure 3.-Simulation results.
219
of Rothermel's fire spread model,
and the predictions it makes are subject to the limitations and assumptions of the same model. However
there are significant differences between this system and the BEHAVE
system.
The capabilities of FIREMAP for
spatial/temporal simulation of fire
behavior make it a useful tool that
goes beyond the simple display of
spatially summarized, rapidly available information. FIREMAP simulates the actual spread of fire predicting its varying intensity and showing
areas burned for chosen time intervals. It can be used for "on site fighting," thanks to MAP capabilities for
quick information update (like a
change in fuel types due to clear cutting) predicting the extent and intensity of a fire for a certain period of
time.
In order to choose a fire management program it is necessary to consider not only ranges of fire behavior
under various management alternatives, but also to assess the relative
probability of occurrence of certain
fire events. FIREMAP does not consider this latter aspect, a point that
should not be overlooked in the decision-making process. It might be
more practical to direct more attention on planning for those less probable ignitions that are likely to escape
and cause extensive damage when
they do occur (Salazar and Bradshaw
1986).
Future work with FIREMAP
should include validation and sensitivity analysis, and fine tuning of the
module presently running. The prediction capabilities can be greatly increased by addition of other modules, either from BEHAVE or new
ones. For example, a module to simulate spotting, or a module to compute scorch height can be easily integrated.
The use of a more sophisticated
GIS with flexible command language
and built-in modelling modules, real
number processing capabilities, and
larger memory, will also help in
AREAS BURNED
(wind
c~ange
f~cM
ar tel' one
FLAME LENGTH
S to W>
J'ou~
h~u~s
aft•~ t~~@@ hou~s
art.P tMo
••
•
II
~
•
15ij5D
Figure 4.-Area burned with a wind change.
overcoming some of the present
limitations of FIREMAP.
Aknowledgements
Information for this study was
provided by the USDI Bureau of Indian Affairs.
Uterature Cited
Andrews, P. L. 1980. Testing the fire
behavior model Proceedings, Sixth
Conference on Fire and Forest Me-
<£eet)
Q
4
4
8
8
11
> 11
Figure 5.-Expected flame lengths.
teorology [Seatle, Wash., Apr. 2224,1980]: Soc. Am. For., Washington, D.C. 70-77
Andrews, P. L. 1986. BEHAVE: Fire
behavior prediction and fuel modeling system - BURN subsystem,
part 1. USDA Forest Service, General Technical Report, INT-194,
Intermountain Forest and Range
Experiment Station. 130 p.
-Berry, J.K. and Reed, K.L. 1987. Computer assisted map analysis: a set
of primitive operators for a flexible approach. Paper presented at
the 1987 ASPRS-ACSM Conven-
220
tion Baltimore, Maryland, March
29-April3, 1987. 7 p.
Fujioka, F. M. 1985. Estimating
wildland fire rate of spread in a
spatially nonuniform environment. Forest Science, 31 (1): 21-29
Rothermel, R.C. 1972. A mathematical model for predicting fire
spread in wildland fuels. USDA
Forest Service, Research Paper
INT-115, Intermountain Forest
and Range Experiment Station.
40 p.
Rothermel, R. C. 1983. How to predict the spread and intensity of
forest and range fires. USDA Forest Service, General Technical Report, INT-143,Intermountain Forest and Range Experiment Station.
61 p.
Salazar, L.A. and Bradshaw, L. S.
1986. Display and interpretation of
fire behavior probabilities for
long-term planning. Environmental Management 10 (3): 393402
Tomlin, C. Dana 1986 The IBM personal computer version of the
Map Analysis Package. GSD /IBM
AdS Project, Report No. LCGSA85-16 Laboratory for Computer
Graphics and Spatial Analysis.
Graduate School of Design, Harvard University. 60 p.
221