Fire management notes / U.S. Department of Agriculture, Forest Service.

50 Pages·1999·4.8 MB·English

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Management Notes.
Management
notes
Volume 59 * No. 2 * Spring 1999
Contents
On the Cower:
Y2K—Why Me?. 4
Mike Funston, Mike Barrowcliff, and Bill Rush
Classification of Fire Simulation Systems. 5
Dorothy Albright and Bernard N. Meisner
FARSITE—A Program for Fire Growth Simulation. 13
Mark A. Finney and Patricia L. Andrews
BEHAVE Fire Modeling System: Redesign
(A) The Hell Roaring Fire, a
and Expansion. 16
crown Fire in lodgepole pine,
Patricia L. Andrews and Collin D. Bevins
Yellowstone National Park, WY,
1988. (B) Computer equipment
NEXUS: A System for Assessing Crown
in a Fire camp, Yellowstone
Fire Hazard. 20
National Park, WY August
Joe H. Scott
1988. Information systems
play a vital role in all aspects
A Wildfire Threat Rating System. 25
of wildlarid Fire management
today. Photos: Tiana Glenn, Brad Hawkes and Judi Beck
USDI Bureau of Land Manage¬
ment, National Interagency A Fire-Based Hazard/Risk Assessment. 31
Fire Center, Boise, ID, 1988. Timothy A. Burton, Deirdre M. Dether, John R. Erickson,
Joseph P. Frost, Lynette Z. Morelan, Leon F. Neuenschwander,
William R. Rush, John L. Thornton, and Cydney A. Weiland
Sharing Information Through Fire Reporting. 37
Delvin R. Bunton
New Weather Observation Transfer Format. 43
The FIRE 21 symbol (shown below and on the
Delvin R. Bunton
cover) stands for the safe and effective use of
wildland fire, now and in the 21st century. Its
shape represents the Fire triangle (oxygen, heat, Systems Help Desk Serves the Wildland
and fuel). The three outer red triangles represent
the basic functions of wildland Fire organi¬ Fire Community. 45
zations (planning, operations, and aviation Suz Rittenhouse
management), and the three critical aspects of
wildland Fire management (prevention,
suppression, and prescription). The black
interior represents land affected by Fire; the Short Features
emerging green points symbolize the growth,
restoration, and sustainability associated with
Fire-adapted ecosystems. The flame represents Don’t Get Bitten by the Millennium Bug!. 30
Fire itself as an ever-present force in nature. For
Delvin R. Bunton
more information on FIRE 21 and the science,
research, and innovative thinking behind it,
contact Mike Apicello, National Interagency Fire Business Continuity Planning for January 1, 2000 . .. 47
Center, 208-387-5460.
Delvin R. Bunton
Websites on Fire. 47
Guidelines for Contributors Inside front cover
Firefighter and public safety is
our first priority
Y2K—Why Me?
Mike Funston, Mike Barrowcliff, and Bill Rush
s the new millennium ap¬
We are working to ensure that,
proaches, many wildland fire
managers want to know what by the end of February 1999,
(if any) problems they might en¬ ali critical applications are Y2K compliant
counter with the computer appli¬
and migrated off the Data General.
cations they use after January 1,
2000. Almost everyone has heard
that many computer applications
initiate repairs, and verify the Meeting these two objectives is
were originally written to process
results. This sounds easy, but we NIST’s main focus.
dates with years that have only two
found that many of the date-code
digits. This works fine in process¬
issues were very subtle and some¬ We are heavily engaged in migra¬
ing information from a single
times required much more effort tion efforts and Y2K fixes, and will
century, but it won’t work for
to detect and repair than originally be very busy during winter 1999
information that spans the
anticipated. Moreover, this is one meeting our objectives. The
millennia.
project for which the deadline functionalities of some applica¬
Origins of the Problem (January 1, 2000) is absolutely tions are being consolidated into a
fixed and cannot be changed. single replacement application.
Some might wonder why com¬
Other applications will undergo
puter programmers were so Finding a Solution significant “facelifts.” Still others
shortsighted as to create a ticking
will change in stages as they
In the Forest Service, efforts were
time bomb. The main reason is
gradually evolve on the new
already under way to migrate our
that way back in the “Dark Ages,”
platform(s).
existing computer applications
computer programmers had to
from the Data General to the new
devise clever ways to conserve
All this is necessary to ensure that
IBM platform. Migration coincided
precious memory and disk space, a
there is no disruption to the
with our Year 2000 (Y2K) renova¬
concern that advancing computer
operation of critical applications
tion efforts, providing us with an
technology has virtually elimi¬
after January 1, 2000. We ask the
opportunity to address both issues
nated. Whatever the reason, the
indulgence of the wildland fire
simultaneously. Accordingly, we
problem is real and requires
community during this potentially
implemented a strategy to ensure
prompt attention.
difficult period of transition, and
that, by the end of February 1999,
we thank everyone for their
all critical applications were:
In October 1997, the USDA Forest
continued patience.
Service’s National Fire and Avia¬
• Certified for Y2K compliance,
tion Information Systems Team
and
(NIST) began a concerted effort to
• Migrated off the Data General.
assess the applications in question,
Mike Funston is the branch chief for the
USDA Forest Service's National Fire and
Aviation Information Systems Team
(NIST), Washington Office, Washington,
DC; and Mike Barrowcliff and Bill Rush
are section heads for NIST, National
Interagency Fire Center, Boise, ID.
Fire Management Notes
Classification of
Fire Simulation Systems*
WDorothy Albright and Bernard N. Meisner
ith the advent of powerful
Capable of accurately predicting fire behavior,
computer workstations, a
growing number of fire fire simulation systems can be valuable tools
simulation systems are emerging for today’s wildland fire managers.
for use by wildland fire planners
and managers. These systems, with
their graphical user interfaces,
and decide which ones best meet Physical-Statistical. Physical-
linkages to digital maps produced
their needs. statistical fire prediction models
by geographic information systems
combine physical theory with
(GIS’s), and colorful outputs of
Fire Prediction Models statistical correlation to generate
spatial fire patterns, have taken
formulas for fire behavior. Ex¬
wildland fire prediction beyond As components of fire simulation
amples include:
tables and graphs to three-dimen¬ systems, fire prediction models
sional displays of fire behavior simulate fire behavior (such as rate
• RothermeVs (1972) model as
across entire landscapes. Capable of spread, fire intensity, and flame
incorporated into the BEHAVE
of consistently representing fire height) using site-specific data
Fire Behavior Prediction and
such as weather, terrain, and fuel
behavior and spatially validating
Fuel Modeling System (Andrews
type and condition. A spreading
fire prediction models, today's fire
1986). Based on the principle of
simulation systems can be valuable fire releases energy from combus¬
conservation of energy,
tools for wildland fire manage¬ tion and transports some of it to
Rothermel (1972) represents the
ment. adjacent unburned fuels, heating
rate of fire spread as a function
them to the point of ignition
of fuel density, particle size, bulk
A fire simulation system combines (Albini 1985). Different fire predic¬
density, and rate of fuel con¬
an underlying fire prediction tion models use different predic¬
sumption. Because an analytical
model with a fire simulation tive methods to quantify these fire
solution to the problem of fire
technique. By categorizing the spread processes. There are four
behavior is not possible on this
various types of fire prediction different types of fire prediction
basis, Rothermel approximates a
models and simulation techniques, models: physical, physical-statisti¬
solution from laboratory
we can identify the similarities and cal, statistical, and probabilistic.
experiments.
differences among the systems.
• The Canadian Forest Fire
The resulting classification scheme Physical. Physical fire prediction
Behavior Prediction (FBP)
for fire simulation systems can models predict fire spread based on
System (Forestry Canada Fire
enable fire managers and planners the physics of combustion. They
Danger Group 1992). Based on
distinguish among three modes of
to compare the various systems
moisture physics research and
heat transfer: conduction, convec¬
heat transfer theory, the FBP
tion, and radiation. For example,
Dorothy Albright is a resource informa¬
System uses observations from
tion specialist for the USDA Forest Service, Albini (1986) models fire spread by
Fire and Aviation Management, Mather, balancing two-dimensional radia¬ 495 experimental fires and
CA; and Bernard Meisner is the assistant wildfires to relate fuel character¬
tive heating against the cooling
chief of the Scientific Services Division,
istics to features of fire behavior
U.S. Department of Commerce, National countereffect of reradiation and
Weather Service, Southern Region, Fort convection on unignited fuels. such as rate of spread and fuel
Worth, TX. consumption.
Although several physical models
have been developed, none are
*This article summarizes a more detailed treatment
of this subject by the authors. For a copy of the full currently used in fire management Statistical. Statistical fire predic¬
paper, contact Dorothy Albright, USDA Forest Service, tion models fit a set of equations to
Fire and Aviation Management, 3735 Neely Way, because they require such large
Mather, CA 95655, tel. [phone], fax [phone]. amounts of detailed data. data derived from test fires. The
Volume 59 • No 2 • Spring 1999 5
equations predict fire parameters Given sufficient resolution,
such as rate of spread, fuel con¬
fire simulation systems can describe
sumption, and fireline intensity.
fire behavior in heterogeneous fuels
Because these models are not
based on physical processes, their over varied terrain.
success in predicting fire behavior
is limited to conditions similar to
those of the test fires. For example, as a lattice of discrete boxes or nique represents the landscape as a
McArthur’s fire danger meters elements, then the spread of fire lattice of boxes or cells, each with a
(McArthur 1966; Noble et al. 1980) from one box to the next is gov¬ set of possible values (such as
describe forest and grassland fire erned by a specific set of rules or a slope, aspect, fuel type, or fuel
behavior based on more than 800 probability of occurrence. If the condition). Each cell begins in an
fires, but do not consider the landscape is shown as a continuous initial state at the time of ignition.
physical relationship between medium, the shape of the fireline The likelihood of fire spreading to
parameters. is represented by mathematical each cell in the lattice is deter¬
functions. mined by a set of rules that are the
Probabilistic. Probabilistic fire same for all cells. These rules
prediction models are based on Bond Percolation. The bond relate the future state of the cell to
contingency tables rather than percolation fire simulation tech¬ its initial state and the states of the
physical or statistical equations. In nique represents the landscape as a neighboring cells. Users can use
probabilistic models, each environ¬ lattice of square, triangular, or parameters such as fuel type and
mental variable (such as fuel type, hexagonal boxes. A fire in one box moisture, topography, and weather
fuel moisture, and windspeed) is spreads to neighboring boxes that to determine the spread of fire
assigned to one of several discrete contain ignitable fuel. Users can through the lattice. Because the
categories. The probabilities in the adjust spread probability for rules relating fire spread among
contingency tables are then used direction of spread due to factors the lattice of cells can be based on
to simulate the likely fire spread such as wind velocity, topography, physical processes, the cellular
from one location to the next. and differences in fuel types automaton technique can apply to
Because the numerical values for (MacKay and Jan 1984; Ohtsuki a wide variety of conditions.
the probabilities are not based on and Keyes 1986). If most of the
physical processes, probabilistic boxes contain unburned fuel and The attractiveness of using the
models are applicable only under the probability of propagation is bond percolation and cellular
conditions similar to those for high, then the fire spreads (perco¬ automaton techniques to simulate
which they were developed. They lates) throughout the lattice. fire spread lies in the fundamental
are usually used to simulate the simplicity of their components for
ignition and probability of spread A bond percolation technique must producing an overall fire behavior
for a sequence of hypothetical fires be “tuned” by adjusting the prob¬ that can be extremely complex
over a landscape, not for predicting abilities such that the modeled fire (Wolfram 1984). Both techniques
the rate of spread for a specific fire. spreads in a manner comparable to yield reasonable estimates of fire
that of actual fires over similar spread when its physical determi¬
Fire Simulation terrain under similar weather and nants are unknown.
Techniques fuel conditions. Because the
technique is not based on a physi¬ Elliptical Wave Propagation. The
Every fire simulation system uses,
cal process, success in simulating elliptical wave propagation fire
in addition to an underlying fire
fire spread is limited to conditions simulation technique projects the
prediction model, a fire simulation
similar to those for which the landscape as a continuous medium
technique to represent the spread
technique has been tuned. rather than as a lattice of boxes or
of fire through the landscape. Fire
cells. Fires burning in continuous
simulation techniques differ from
Cellular Automaton. Like the bond uniform fuels under constant
each other in how they represent
percolation technique, the cellular conditions of slope, wind velocity,
the landscape and the spreading
automaton fire simulation tech¬ and fuel moisture assume an
process. If the landscape is shown
6
Fire Management Notes
Fire simulation systems should be capable Most fire prediction models depend
on a quantitative description of
of meeting different requirements, including
forest fuels in terms of a set of
wildfire suppression, prescribed burning, fire
standard or custom fuel models, or
management training, and public education. on empirical data from a particular
landscape. For example:
elliptical shape (Richards 1990). Additional • Models based on Rothermel
Based on Huygens principle of Considerations (1972) such as BEHAVE require
wave propagation, Anderson et al. inputs based on the standard fire
In choosing a fire simulation
(1982) identify regularly spaced behavior fuel models (Anderson
system, fire planners and managers
points on the fire perimeter where 1982) developed to characterize
should consider other factors in
small fires spread elliptically typical surface fuels. Parameters
addition to modeling and simula¬
outward, with the size and shape of include fuel loading, surface-
tion techniques, such as intended
each ellipse determined by local area-to-volume ratio, fuel depth,
use, required inputs, associated
conditions. The fire perimeter at fuel particle density, heat con¬
outputs, and required platform and
each succeeding time step is the tent of fuel, and moisture of
software.
envelope that encompasses all of extinction (the minimum fuel
the small ellipses burned. moisture content that begins to
Intended Use. The developer of a
affect fire spread).
fire simulation system usually
For this group of ellipses, Richards • The FBP System (Forestry
describes its intended use. How¬
(1990) develops a set of differential Canada Fire Danger Group 1992)
ever, prospective users can also
equations that describe fire spread requires inputs based on 16
deduce the intended use from the
for variable fuel, weather, and discrete fuel types in 5 major fuel
underlying fire prediction model.
topographic conditions. The size groups (coniferous, deciduous,
For example:
and shape of each ellipse depends mixed wood, slash, and open).
on a small set of parameters based Parameter values derived from
• BEHAVE is designed to describe
on the FBP System (Forestry empirical data for rate of spread
an advancing flame front in
Canada Fire Danger Group 1992). are given for each fuel type.
surface fuels less than 6 feet
Although the technique does
(1.8 m) from the ground
require some numerical adjust¬ Additional required inputs depend
(Rothermel 1983);
ments to ensure that the small on the system’s fire prediction
• The FBP System is intended to
fires do not overlap or burn model. For example:
describe fire behavior for specific
previously burnt areas and that the
fuel types: and
simulated ignition points on the • Systems using the empirical
• The probabilistic models are
perimeter remain evenly spaced, a Rothermel model require data on
designed to describe potential
user can implement a finite dead fuel moisture content, live
burn patterns on a landscape
difference solution to the equa¬ fuel moisture content, slope, and
scale.
tions on a PC. wind direction and speed.
• Models based on the FBP System
Inputs. Fire simulation systems
The elliptical wave technique require additional inputs such as
require two general types of input:
requires no local tuning, assuming weather, topography, and foliar
that the fuels, weather, and topog¬ moisture content.
1. Digital maps (such as GIS’s)
raphy in the area of interest are
showing the spatial distribution
sufficiently similar to those for Outputs. The outputs generated by
of topography, fuel type and
which the underlying parameters the different fire simulation
condition, and weather; and
were recorded. However, this systems vary in complexity. All
2. Descriptive numerical
technique should not be used systems generate maps of predicted
parameters.
under conditions for which repre¬ fire perimeters over the study area.
sentative parameters are not Some offer additional output
available.
Volume 59 • No 2 • Spring 1 999
options, such as graphs and charts Sample Fire Simulation automaton. The system can also
showing fire area, intensity, spread Systems simulate fire behavior for a single
rates, and other data. Output fire under varying or constant
Table 1 compares recently de¬
resolution generally depends on conditions (Clarke 1994).
veloped fire simulation systems.
input resolution.
Each system is described below.
Input data are obtained from
Platform and Software. Most fire remote sensing, U.S. Geological
Clarke Cellular Automation Sys¬
simulation systems run on PC’s or Survey digital elevation models
tem. The fire simulation system
UNIX workstations. None require a (DEM's), and local environmental
developed by Clarke et al. (1993)
Macintosh platform and software, conditions. The input includes GIS
uses simulations of potential
but some require additional maps of fuel types and terrain.
wildfire propagation and extinction
software such as a GIS package Additional input includes tempera¬
behavior to assess fire risk. One
(e.g., pMAP or ARC/INFO), and ture, relative humidity, fuel
version of the system uses a
others might require program¬ moisture, and a table of wind
probabilistic approach to estimate
ming language compilers such as direction and speed. Other factors
fire risk based on a Monte Carlo
C or FORTRAN. are calibrated using site data such
implementation of the cellular
Table 1—Comparison of numerical fire simulation systems
Components Input
Fire simulation Prediction Simulation Platform and
system model technique Intended use CIS Additional Output software
Clarke Cellular Probabilistic Cellular To simulate land- • Vegetation • Temperature Map of fire risk UNIX workstation
Automation System automaton scape-scale fire • Elevation • Relative humidity (98-foot (30-m) with C compiler and
(Clarke et al. 1993) risk and assess¬ • Fuel moisture • Windspeed resolution) Xwindows interface
ment as well as • Wind direction
burn patterns.
DYNAFIRE Physical-statistical Cellular To simulate the • Standard fuel • Temperature Maps of: PC with MS-DOS
(Kalabokidis et al. (BEHAVE) automaton spread of low- to types • Relative humidity • Fire perimeter and pMAP
1991; Hay 1991) moderate-intensity • Elevation • Fuel moisture • Fireline intensity
surface fires. • Slope • Windspeed • Average spread
• Aspect • Wind direction rate
• Stream network
EMBYR Probabilistic Bond percolation To simulate land¬ • Vegetation clas¬ • Fuel moisture Map of final burn UNIX workstation
(Hargrove et al. scape-scale burn sified by species • Windspeed pattern (164-foot with FORTRAN
1995) patterns. and age • Wind direction (50-m) resolution) compiler
FARSITE Physical-statistical Elliptical wave To simulate the • Standard/custom • Temperature Maps of: PC with Windows 3.1
(Finney 1993) (BEHAVE) propagation spread and behavior fuel types • Relative humidity • Fire behavior and WIN32S,
of wildland fire. • Elevation • Windspeed • Fire perimeters Windows NT. or
• Slope • Wind direction (adjustable Windows 95
• Aspect • Canopy resolution)
• Canopy cover characteristics
FIREMAP Physical-statistical Cellular automaton To simulate the • Standard fuel • Temperature Maps of: UNIX workstation
(Ball and Guertin (BEHAVE) spread of low- to types • Relative humidity • Spread rate with PROMAP
1992) moderate-intensity • Elevation • Fuel moisture • Fireline intensity
surface fires. • Slope (optional) • Flame length
• Aspect • Windspeed • Heat/unit area
• Wind direction • Reaction intensity
• Fire perimeter
WILDFIRE Physical-statistical Elliptical wave To simulate the • Standard fuel • Windspeed Maps of: PC with MS-DOS
(Wallace 1993) (FBP System) propagation spread of low- to types • Wind direction • Fire perimeters
moderate-intensity • Elevation • Fire intensity
surface fires. (3.3-foot (1-m)
resolution)
8 Fire Management Notes