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Wildfire Simulation Software

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Presentation on theme: "Wildfire Simulation Software"— Presentation transcript:

1 Wildfire Simulation Software
Charles Erwin

2 Simple Wildfire Simulator from NOVA
(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. CS 521: Computational Science

3 EMBYR: “Ecological Model for Burning the Yellowstone Region”
Created by William W. Hargrove and Robert H. Gardner Designed to simulate wildfires, the subsequent pattern of vegetation, and then the next generation of burn patterns. While the EMBYR model parameters could be adjusted to reproduce a particular historical wildfire exactly, it is more important to reproduce any wildfire relatively well on average. EMBYR can generate "Risk Maps", which are constructed from many replications of a single simulated fire. Cells which burned in many of the replications are colored black, while cells which burned in only a few simulations are colored white, with gray levels in intermediate cases. CS 521: Computational Science

4 CS 521: Computational Science
EMBYR 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 event with probability I, where I may 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 if I is less then a critical value, fires are unlikely to propagate across the landscape CS 521: Computational Science

5 EMBYR Fire Model (cont)
They estimated by performing 50 simulations for each value of I ( I 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% for I = and 60% for I = Since is the threshold at which 50% of the fires reach the opposite edge of the map, these results indicated that lies between and CS 521: Computational Science

6 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. CS 521: Computational Science

7 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 the core, 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. CS 521: Computational Science

8 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 (5–35 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. CS 521: Computational Science

9 EMBYR Fire Model (cont)
CS 521: Computational Science

10 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. CS 521: Computational Science

11 Simulation: homogeneous landscapes
Area burned (in cells) in a 500x500 cell homogeneous fuel class landscape with a single 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. CS 521: Computational Science

12 Simulation: Using actual Landscapes
CS 521: Computational Science

13 Simulation: 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 with ‘moderate’ winds; and (d) Scenario 4: ‘very dry’ weather with ‘strong’ winds CS 521: Computational Science

14 Examples of EMBYR In action
CS 521: Computational Science

15 FARSITE: “Fire Area Simulator”
Two Dimensional model of fire behaviour and growth simulator. A simple ellipse fit observed fire growth data as well as other shapes. Regardless of the correct shape (if a single one exists), 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 in reproducing the expected twodimensional shapes and growth patterns as environmental conditions become more heterogeneous. CS 521: Computational Science

16 CS 521: Computational Science
FARSITE Model Problems with cellular models are avoided by the vector or wave approach to fire growth modeling (Huygens’ principle). 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-dimensional vertices (points with X,Y coordinates). The number of vertices increases as the fire grows over time (polygon expands). The expansion of the fire polygon is determined by computing the spread rate and direction from each vertex and multiplying by the duration of the timestep. CS 521: Computational Science

17 CS 521: Computational Science
FARSITE Model (cont) The reliance on an assumed fire shape, in this case an ellipse, is necessary because the spread rate of only the heading portion of a fire is predicted by the present fire spread model. Fire spread in all other directions is inferred from the forward spread rate using the mathematical properties of the ellipse. There are still many problems in accurately simulating fire with this approach, different methods, however, will probably be of little consequence to the practical application of a fire growth model until the greater uncertainties are resolved as to how wind, slope, and fuels affect fire shapes. CS 521: Computational Science

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FARSITE Model (cont) CS 521: Computational Science

19 FARSITE Model: Richards’ Equations
Xs, Ys The orientation of the vertex on the fire front in terms of component differentials. The direction of maximum fire spread rate. a, b, c The shape of an elliptical fire determined from the conditions local to that vertex in terms of dimensions. CS 521: Computational Science

20 CS 521: Computational Science
FARSITE Model (cont) CS 521: Computational Science

21 FARSITE: Transformations for Sloping Terrain
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. CS 521: Computational Science

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FARSITE Model (cont) CS 521: Computational Science

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FARSITE Model (cont) CS 521: Computational Science

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FARSITE Model (cont) CS 521: Computational Science

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FARSITE Model (cont) Other models used include the Van Wagner crown fire model, and Albini’s spotting model. For input, FARSITE uses GIS raster data in lieu of vector data. For fuel moisture, BEHAVE and NFDRS equations are used. CS 521: Computational Science

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FARSITE Raster Landscape input layers required from the GIS for FARSITE simulation. CS 521: Computational Science

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FARSITE Animation of a FARSITE v4.0x simulation in a 3D window. CS 521: Computational Science

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FARSITE Screen shot of a FARSITE v4.00 simulation utilizing the post-frontal combustion model. CS 521: Computational Science

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References Finney, Mark A FARSITE: Fire Area Simulator-model development and evaluation. Res. Pap. RMRS-RP-4, Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station Hargrove, W.W., R.H. Gardner, M.G. Turner, W.H. Romme, and D.G. Despain Simulating fire patterns in heterogeneous landscapes. Ecological Modelling 135(2-3): CS 521: Computational Science

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