wildfire simulation software

8/8/2019 Wildfire Simulation Software

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Wildfire Simulation SoftwareWildfire Simulation Software

Charles ErwinCharles Erwin


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CS 521: Computational Science 2

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.


(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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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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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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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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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: 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: 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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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://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

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