wildfire simulation software
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Wildfire Simulation SoftwareWildfire Simulation Software
Charles ErwinCharles Erwin
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Simple Wildfire Simulator from NOVASimple Wildfire Simulator from NOVA
http://www.pbs.org/wgbh/nova/fire/simulation.html
(requires Flash)
Wildfire Simulator is a simple computer simulation that
predicts the behavior of fire in a wildland environment. Not meant for research, only to demonstrate some basic
ideas about wildfire simulation.
Programming for this feature is derived from FARSITE.
http://www.pbs.org/wgbh/nova/fire/simulation.html
(requires Flash)
Wildfire Simulator is a simple computer simulation that
predicts the behavior of fire in a wildland environment. Not meant for research, only to demonstrate some basic
ideas about wildfire simulation.
Programming for this feature is derived from FARSITE.
http://www.pbs.org/wgbh/nova/fire/simulation.htmlhttp://www.pbs.org/wgbh/nova/fire/simulation.htmlhttp://www.pbs.org/wgbh/nova/fire/simulation.htmlhttp://www.pbs.org/wgbh/nova/fire/simulation.html -
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EMBYR: Ecological Model for BurningEMBYR: Ecological Model for Burning
the Yellowstone Regionthe Yellowstone Region
Created by William W. Hargrove andRobert H. Gardner Designed to simulate wildfires, the subsequent pattern of
vegetation, and then the next generation of burnpatterns.
While the EMBYR model parameters could be adjustedto reproduce a particular historical wildfire exactly, it ismore important to reproduce any wildfire relatively wellon average.
EMBYR can generate "Risk Maps", which areconstructed from many replications of a single simulatedfire. Cells which burned in many of the replications arecolored black, while cells which burned in only a fewsimulations are colored white, with gray levels inintermediate cases.
http://research.esd.ornl.gov/~hnwhttp://www.al.umces.edu/gardner.htmlhttp://www.al.umces.edu/gardner.htmlhttp://research.esd.ornl.gov/~hnw -
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EMBYR Fire ModelEMBYR Fire Model
The fire model, EMBYR, depicts the landscape as a grid in
which the dimension of each cell is 50 m (2500 m2).
Diffusive Spread: Fire spreads from each ignited cell to
any of eight unburned neighbors (the four adjacent cells
and four diagonal cells) as an independent stochastic eventwith probability I, where Imay range from 0 to 1.
Each cell burns for a single time step of variable length, and
the fire goes out if new sites are not ignited at each time
step.
Theoretical studies have demonstrated that ifIis less then
a critical value, fires are unlikely to propagate across the
landscapeci
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EMBYR Fire Model (cont)EMBYR Fire Model (cont)
They estimated by performing 50 simulations for each
value ofI (0.245 I 0.252 in increments of 0.001) on
a 300x300 grid.
The proportion of simulations with fires reaching the top
edge of the map after the entire bottom edge was ignited
was 38% forI = 0.250 and 60% forI = 0.251. Since is
the threshold at which 50% of the fires reach the
opposite edge of the map, these results indicated that
lies between 0.250 and 0.251.
ci
ci
ci
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EMBYR Fire Model (cont)EMBYR Fire Model (cont)
Simulating multiple fuel classes: EMBYR explicitly
simulates multiple classes of fuel by varying the
probability of fire spread as a function of fuel type.
The fuel classes considered are four successional
stages of lodgepole pine forest, nonforested regions
such as meadows, and nonflammable areas such as
rock, roads, and water.
Derived probabilities on fire spreading between
different types of fuel.
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EMBYR Fire Model (cont)EMBYR Fire Model (cont)
Variation in fuel moisture: EMBYR uses a standard fire
danger measure known as percent 1000-h time-lagged fuel
moisture.
In this measure, an assumption is made about how long
fuel of a particular diameter would take to soak to thecore, or to dry out once soaked.
Current internal moisture in fuels of that diameter is
modeled with appropriately time-lagged ambient
atmospheric humidity. Obviously, if fuels are sufficiently wet, fires do not occur.
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EMBYR Fire Model (cont)EMBYR Fire Model (cont)
Simulating the effects of wind: Three classes of wind
speeds (WS), measured at a standard height of 6.1 m
(20 ft) above the surface, are considered: WS 0, with speeds ranging from 0 to 3.1 kph (5 mph)
WS 1, moderate winds ranging from 3.1 to 21.7 kph (535 mph)
WS 2, strong wind with speeds greater than 21.7 kph
For each of the three wind speed classes, a bias value b
is used to modify the probability of spread to each
neighboring cell.
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EMBYR Fire Model (cont)EMBYR Fire Model (cont)
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EMBYR Fire Model (cont)EMBYR Fire Model (cont)
Simulating the effects of firebrands: EMBYR
simulates a second mechanism of fire spread the
production of firebrands which are carried aloft in the
rising convection column, and then drift and fall on
remote sites. The spotting effect of firebrands is simulated by
permitting each burning site to generate a fixed number
of firebrands as a function of fuel type.
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Simulation: homogeneous landscapesSimulation: homogeneous landscapes
Area burned (in cells) in a 500x500 cell homogeneous fuel class landscape with asingle fixed ignition as a function of the probability of fire spread, I, to the eight
surrounding neighbors where (a) fire is allowed to propagate by adjacent spread
only (no firebrands), and (b) fire is allowed to propagate by adjacent spread and by
firebrands. The simulation was ended before fire could reach the edge of the map.
Means and standard deviations are shown for five replications.
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Simulation: Using actual LandscapesSimulation: Using actual Landscapes
The cumulative
frequency of risk of fires
of increasing size for
four alternative weather
conditions of (from left
to right) (a)Scenario 1: moist with
strong winds; (b)
Scenario 2: dry
weather with moderate
winds; (c) Scenario 3:
very dry weather withmoderate winds; and
(d) Scenario 4: very dry
weather with strong
winds
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Examples of EMBYR In actionExamples of EMBYR In action
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FARSITE: Fire Area SimulatorFARSITE: Fire Area Simulator
Two Dimensional model of fire behaviour and growthsimulator.
A simple ellipse fit observed fire growth data as well as othershapes. Regardless of the correct shape (if a single oneexists), the eccentricity of the fire is known to increase with
increasing windspeed or slope steepness or both. Cellular Model: Simulate fire growth as a discrete process of
ignitions across a regularly spaced landscape grid.
In general, cellular models have had diminishing success inreproducing the expected twodimensional shapes and growth
patterns as environmental conditions become moreheterogeneous.
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FARSITE ModelFARSITE Model
Problems with cellular models are avoided by the vectoror wave approach to fire growth modeling (Huygensprinciple).
The fire front is propagated as a continuously expanding
fire polygon at specified timesteps. Essentially the inverse of the cellular method, the fire
polygon is defined by a series of two-dimensionalvertices (points with X,Y coordinates). The number ofvertices increases as the fire grows over time (polygon
expands). The expansion of the fire polygon isdetermined by computing the spread rate and directionfrom each vertex and multiplying by the duration of thetimestep.
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FARSITE Model (cont)FARSITE Model (cont)
The reliance on an assumed fire shape, in this case anellipse, is necessary because the spread rate of only theheading portion of a fire is predicted by the present firespread model. Fire spread in all other directions is
inferred from the forward spread rate using themathematical properties of the ellipse. There are still many problems in accurately simulating
fire with this approach, different methods, however, willprobably be of little consequence to the practical
application of a fire growth model until the greateruncertainties are resolved as to how wind, slope, andfuels affect fire shapes.
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FARSITE Model (cont)FARSITE Model (cont)
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FARSITE Model: Richards EquationsFARSITE Model: Richards Equations
Xs, Ys The orientation of the vertex on thefire front in terms of componentdifferentials.
The direction of maximum fire
spread rate.
a, b, c
The shape of an elliptical firedetermined from the conditionslocal to that vertex in terms ofdimensions.
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FARSITE Model (cont)FARSITE Model (cont)
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FARSITE: Transformations for SlopingFARSITE: Transformations for Sloping
TerrainTerrain
Richards equations were originally developed for flat
terrain.
On flat terrain, a horizontal coordinate system remains
unchanged when projected onto the ground surface. This
is not the case for sloping terrain.
This means that the inputs to equations [1] and [2] must
be transformed from the horizontal to the surface plane,
and outputs must be transformed from the surface plane
back to the horizontal plane.
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FARSITE Model (cont)FARSITE Model (cont)
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FARSITE Model (cont)FARSITE Model (cont)
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FARSITE Model (cont)FARSITE Model (cont)
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FARSITE Model (cont)FARSITE Model (cont)
Other models used include the Van Wagner crown fire
model, and Albinis spotting model.
For input, FARSITE uses GIS raster data in lieu of vector
data. For fuel moisture, BEHAVE and NFDRS equations
are used.
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FARSITEFARSITE
Raster Landscape input
layers required from theGIS for FARSITE
simulation.
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FARSITEFARSITE
Screen shot of a
FARSITE v4.00
simulation
utilizing the post-
frontal
combustionmodel.
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ReferencesReferences
http://www.pbs.org/wgbh/nova/fire/simulation.html
http://fire.org
Finney, Mark A. 1998. FARSITE: Fire Area Simulator-model
development and evaluation. Res. Pap. RMRS-RP-4, Ogden, UT:
U.S. Department of Agriculture, Forest Service, Rocky MountainResearch Station
http://research.esd.ornl.gov/~hnw/embyr/
Hargrove, W.W., R.H. Gardner, M.G. Turner, W.H. Romme, and
D.G. Despain. 2000. Simulating fire patterns in heterogeneous
landscapes. Ecological Modelling 135(2-3):243-263
http://www.pbs.org/wgbh/nova/fire/simulation.htmlhttp://fire.org/http://research.esd.ornl.gov/~hnw/embyr/http://research.esd.ornl.gov/~hnw/embyr/http://fire.org/http://fire.org/http://www.pbs.org/wgbh/nova/fire/simulation.html