Fire arrival and behavior at a given point on the landscape is dependent on the behavior and time of travel en route to that location. This means that fire growth projections should generally worsen with time and spread distance because errors will be compounded, regardless of the accuracy or resolution of temporal or spatial data. That is, unless errors to one extreme are compensated by equal errors to the opposite.
Logically, however, a fire growth simulation should be most accurate when using accurate data at high spatial and temporal resolution. An "optimum" resolution for each landscape parameter, fire behavior type, and simulation purpose, probably exists so that the pertinent variability is preserved without irrelevant detail. The sensitivity of spatial fire simulation to the resolution and qualities of different fire input parameters on the landscape, however, remains to be tested.
The present version of FARSITE is capable of using two types of weather/wind inputs; 1) weather/wind streams and 2) gridded weather/winds.
As with previous versions, FARSITE can use the simplified weather and wind input streams. Here, the open-winds you provide are assumed parallel to the terrain and spatially constant but can vary in speed and direction over time. Spatial variability in winds is accomplished only through use of multiple data streams. Wind speeds are adjusted for mid-flame height based on canopy characteristics and fuel model. The weather streams specify daily maximum and minimum temperatures and humidities and the elevation of the observations. The time of the maximum temperature is assumed to be coincident with the minimum humidity. This will probably not be accurate for the period where thunderstorms occur or during frontal passage.
Temperature and humidity observations are interpolated with a sine-curve (sensu Rothermel et al. 1986) to acquire temperatures and humidities throughout the day. A sine-exponential interpolation (Beck and Trevitt 1989) may be an improvement, but this will have to be tested. A lapse-rate (3.5F/1000ft) is used to adjust these observations to other elevations on the landscape. Daily precipitation amounts are included in the weather stream and assumed constant across the landscape. Solar radiation at the ground surface is computed using canopy coverage and terrain information; in this version of FARSITE, all other canopy characteristics (height, height to live crown base, crown bulk density, and foliar moisture content) are assumed spatially constant except where optional spatial data themes have been provided (height, height to live crown base, and crown bulk density).
Dead fuel moistures are calculated using the procedures implemented in BEHAVE (Rothermel et al. 1986). The calculations for daily fine fuel moistures (at 1400 hours) are different from the calculations for hourly fuel moistures at other times of the day. This results in an abrupt shift in the fuel moistures at 1400 hours. It is not known how critical this inconsistency is to the results of long term fire behavior spread simulations. Live fuel moistures are assumed to remain constant throughout the simulation unless manually changed. There are currently no general models for all species of live fuels that describe moisture variation either diurnally or seasonally.
The limitations to fire spread projections of the simplified weather data are not really known. Obviously, model results would be expected to suffer where strong interactions of wind and terrain are present. Furthermore, calculations that depend on fuel temperature and moisture may not be accurate where shadows are cast by topography, precipitation varies elevationally or spatially, or water availability is significantly is altered (e.g. higher fuel moistures near streams) .
As of version 3.0, you can provide weather and/or just winds in gridded formats. see Gridded Weather for an explanation of these data.
Fire spread patterns generated using Huygens' principle with an elliptical wave have been found to agree reasonably well with observed surface fire spread under relatively simple conditions (Anderson et al. 1982, French 1992). Wind changes produced fire spread shifts close to those observed for fires spreading in grass fuels with essentially no influence of topography. It is not yet confirmed how well Huygens' principle itself simulates fire growth on complex landscapes. Sanderlin and Sunderson (1975) were apparently the first to apply a "radial" perimeter expansion technique, now generally referred to as Huygens' principle, to simulating wildland fire spread. From a comparison of predictions with observed perimeters of the Potrero wildfire (September 1973) in Southern California, they concluded that their technique was acceptable for fire growth modeling in complex situations. The only other indications from complex circumstances are from some preliminary validations of FARSITE in which observed fire spread patterns were compared against surface fire spread predicted by the model (Finney 1994, Finney and Ryan 1995, Finney and Andrews 1996). These early comparisons were promising but the many potential sources of error in the observed data (fuel maps, perimeter maps, weather data etc.) preclude definite conclusions. More validations are planned and will be necessary before the "window" of applicability might be defined.
For practical purposes, the most important result of the FARSITE tests to date has been that spread rates for all fuel models tended to be over predicted by the Rothermel spread equation (Rothermel 1972). Sanderlin and Sunderson (1975) made a similar observation and ascribed the cause to problems relating wind speed to elliptical dimensions. Some problems may be a result of inaccurate data on fuel moistures, fuel descriptions (e.g. models), and weather. Also, wind reduction factors for forested areas and lee-side topographic sheltering can undoubtedly cause errors for spread rate calculations on some parts of a landscape.
However, given all input data to be accurate, the problem with over prediction may persist; the scale of time and space-averaged winds (e.g. hourly) and spatially homogenized fuels within rasters may be too coarse to reflect fine-scale variability in fire environment (temporal or spatial) that keeps fire actually spreading at variable rates. This could force the average fire spread rate over large areas and long time spans to be over predicted. The nonlinear relationship between wind speed, fire acceleration, and fire spread rate means that the average wind speed cannot be expected to predict the average spread rate (Richards 1993). Fluctuating wind directions also cause over prediction of spread in the heading direction because they reduce the eccentricity of the fire shape compared to the ellipse (see #6 below).
The simple approach to correcting the spread rates, perhaps too simplistic for complex landscapes, is to assign rate of spread adjustment factors to each fuel type (Rothermel and Rinehart 1983). These factors must be based on empirical observations of previous fires, or of phases of growth of the existing fire, in patches of homogeneous fuels. They should be based on the heading portion of the fire, given that spread in other directions is dependent on the elliptical dimensions. It would be possible however, to compute the spread rate for one fuel type in a mixture of fuels if the following were known: 1) the fractional distance occupied by fuel type, 2) the average spread rate for the fuel mixture, and 3) the individual spread rates of other fuel components of the mixture. Then the equation for the harmonic mean (Martin 1988, Fujioka 1985) can be solved for the unknown spread rate. The adjustment factors, however, may not be constant throughout the duration of a fire. Average spread rates may change if wind variations change frequency compared to the conditions used to obtain the adjustment factor. For example, adjustment factors determined from fire spread before a cold front may be not be adequate during and after the front passes because there may be more variability in wind speed or direction than before. FARSITE provides a means to apply and change fuel model-specific adjustment factors or fuel model parameters throughout the simulation.
A number of assumptions are critical to modeling fire growth using Huygens' principle. As discussed below, some of these assumptions are probably violated by current modeling methods. The degree to which a technical violation limits the practical application of a model, however, is not yet known. This is the critical question, because models will never be fully valid at all scales or for all purposes, but may be useful nonetheless if the scope of the assumptions are clearly understood by the user. The following paragraphs present a discussion of some major assumptions of the modeling method used for FARSITE. A detailed treatment of the subject was also written by Andre and Viegas (1994).
Fire spread is elliptical. This is probably not strictly true. The shapes of fires are assumed to be elliptical under uniform conditions because this is mathematically convenient (Van Wagner 1969). Fire shapes under different conditions have variously been described as ovoid (Peet 1967), as a pair of ellipses (Albini 1976, Anderson 1983) or as fan-shaped (Byram 1959). Green et al. (1983) found the ellipse fit as well as more complex shapes, given its simplicity and the absence of more definitive data. An analysis of fire shapes by Richards (1993) suggested that neither ovoid, double ellipse, or fan-shaped fires can be explained simply from variations in wind speeds or directions acting on an otherwise elliptical spread pattern. Richards' methods however, made the assumption that fire spread was independent of the shape of the fire front which may not be supported (see #2 below). Even if the assumption of elliptical fire shapes in continuous fuels is true, however, fire shapes in fuels that are not continuous at the scale relevant to mechanisms of fire propagation will not be elliptical or intuitive (Green 1983). For example, a fire may spread only in the heading direction because of wide spacing between fuel patches and would have the shape of a rectangular strip. Fire shapes resulting from discontinuous fuels will not be adequately modeled by FARSITE.
Fire spread in any direction is independent of the shape of the fire front (i.e. points along a fire front can be considered independent sources of wavelets). Recent studies suggest that this is not correct (Weber 1989, Cheney et al 1993). Radiative heat transfer ahead of a spreading fire has long been known to depend on the shape and length of the fire front (Byram 1959). Radiation from a continuous line fire decreases as the distance from it, but as the square of the distance from a point source fire. The violation of the shape independence assumption limits the extent to which Huygens' principle can be simply applied to spreading fire and distinguishes fire spread from the travel of light; portions along light waves do not interact as can portions of a fire front. For fire growth modeling, this means that the existing shape and length of the fire front along any segment should affect the nature of heat transfer and spread rate along that segment. Therefore, a broad flank of a fire that becomes a heading fire as a result of a wind direction shift should assume a different shape than predicted by a Huygens' algorithm. This will not be reflected in the current FARSITE model. The practical effects of these problems on fire growth patterns produced in a simulation are however, not yet known.
Fire acceleration is fuel dependent but independent of fire behavior. Fire acceleration is defined as the rate of increase in spread rate from the current rate to an equilibrium spread rate under constant environmental conditions. In FARSITE you can adjust the fire acceleration constants for each fuel type. The fire acceleration equations (Alexander et al. 1992) in FARSITE compute the average and ending rate of spread for a time step. These are likely to be important where the simulation uses small time-steps (<10min), where fuels and topography are very heterogeneous (spatially), and winds are variable. The incorporation of acceleration means that fire spread rates will not immediately adjust to the equilibrium spread rates when conditions change. The rate of fire acceleration is dependent on a rate factor. The default rate for all fuel types in FARSITE is subjectively set at .115 (Alexander et al. 1992) to allow acceleration to 90% of equilibrium rates after 20 minutes from a point source fire. Line source fires are known to accelerate much faster (Johansen 1987). These factors can be adjusted in FARSITE, but there are no data to guide settings for these factors. Although the equilibrium spread rate is dependent on fuel conditions, the buildup or acceleration rate has been found to be fuel independent for a variety of fuel types (excelsior, pine needles, conifer understories). A single acceleration rate may not be accurate for all fuel types (McAlpine and Wakimoto 1991), especially between very different fuel types. Fire in grass fuels is expected to accelerate more rapidly than in slash fuels, but there are few data to guide these settings. Acceleration is presumed to be independent of the fire behavior or eventual spread rate. Thus, the same time is required in a given fuel type to achieve a steady-state spread rate regardless of the environmental conditions.
Fires will instantly achieve the expected elliptical shape when burning conditions change (e.g. wind speed or slope steepness). This assumption is probably acceptable for simulations with a time step longer than a few minutes. Laboratory experiments (McAlpine 1989) suggest that shape changes occur relatively rapidly compared to the time required for buildup in spread rate or intensity.
The elliptical shapes are fuel independent; shape (not size) is only determined by the resultant wind-slope vector. This assumption is probably acceptable because 1) empirical relationships between wind speed and elliptical dimensions suggest shapes are common to a variety of fuel types over a wide range of ambient wind speeds (Alexander 1985), and 2) the empirical coefficients for wind and slope effects on fire spread rates used in the Rothermel spread equation are dependent of fuel bed characteristics (Rothermel 1972). These coefficients are the unit vectors used to obtain the resultant wind-slope vector.
Variation in windspeed and directions at a higher frequency than the wind stream resolution do not affect the elliptical fire shape. This is not correct, but the importance of its effect on fire growth patterns is not yet clear. Fluctuating wind directions decrease the length to breadth ratio of an otherwise elliptical fire (see Elliptical Dimensions in the Technical References). This has the effect of over predicting the heading spread of a fire at the expense of flanking spread. Some compensation for the over predicted heading spread will be achieved through the rate of spread adjustment factors.
The origin of an elliptical fire is located at the rear focus of the ellipse. The focus is assumed as a starting point because it provides an implicit means to calculate backing spread rates (see Elliptical Dimensions in the Technical References). Alexander (1985) reports that using the origin as the focus may under predict the backing spread. At present, FARSITE allows the user to select a constant backing spread rate calculated from the spread rate under zero slope and wind for the given fuel type (Rothermel 1983).
The spread of a continuous fire front can be approximated using a finite number of points. The adequacy of this assumption is dependent on the spatial resolution required by the user and the resolution specified for the simulation (see Modeling Fire Growth in the Technical References). It is assumed that a resolution can be specified that preserves the "important" features of fire growth but ignores irrelevant spatial detail. This is dependent on the purpose and requirements for the simulation. The same concept is implicit in maps of fire growth made by direct observation; minor variations in fire position that result from rocks or small discontinuities in fuel are ignored. The relevant resolution probably decreases as the fire gets larger.
The FARSITE model is not designed to determine if a fire will spread or not. It is also not technically designed for modeling fire spread only by smoldering or by the rolling of burning debris, even though the resulting spread rate may be approximated by judicious use of the adjustment factors and custom fuel models. The FARSITE model cannot determine where or if a fire will cross a barrier (e.g. a creek or a vertical cliff) unless the resolution of the data are fine enough to reflect the "bridges" etc. on which fire may cross.
Although FARSITE will handle up to 5,000 simultaneous fires, the fire spread patterns of neighboring fires will not necessarily be accurately represented because fire interaction with weather and fuels is not accounted for. For example, behavior resulting from "back fires" set for suppression purposes, or a prescribed fire ignition pattern that is applied to "draw" the fire together at different times, places, or stages of build-up will not be addressed by FARSITE. Extreme fire behavior, e.g. plume dominated fires, that are affected by feedback between the weather and fire behavior are not intended to be simulated by FARSITE. Users should not assume that ignition patterns used for prescribed burning will result in correct simulation of fire behavior!!
The crown fire models of Van Wagner (1977, 1993, and Alexander 1988) have been implemented in FARSITE. This approach requires information on crown fuels and the forest canopy, including:
Effective height to live crown base,
Crown bulk density
Tree height
Foliar moisture content
Although FARSITE presently requires a canopy cover theme, the above crown-fuel characteristics must be constant for areas having canopy coverage, unless the optional crown fuel themes have been provided to the .LCP file.
Van Wagner (1993) notes that the height to live crown base is a difficult parameter to measure. The height to live crown base is really an "effective" number that incorporates ladder fuels (see Fahnestock 1970) and understory fuels such as small trees that assist the transition to crown fire. Thus, height to live crown base will not be a simple measurement in multi-storied stands.
Heat required to ignite the crown is based only on fuel sizes on the 100hr time lag and smaller (<3" diameter). This limitation may underestimate the potential for crowning or torching because larger woody fuels (1000 hr+) and their contributions to radiative and convective heating of overstory fuels are ignored. Rothermel (1991, 1994) discusses the contribution of large woody fuels to the development of convection columns and consequent crown fire behavior.
The wind-slope vectoring for crown fire has not been tested and may not be realistic. FARSITE presently uses the wind-slope vector direction from the understory surface fire with midflame winds. The reason is that transition to crown fire in Van Wagner's (1977, 1993) model is dependent first on the surface fire behavior that is determined by midflame winds. The problem with later combining an open-wind vector with the surface slope effect is that the range of data used to develop the slope coefficient in the Rothermel (1972) model may not be applicable to crown fire. The slope coefficient depends on fuel bed parameters not accounted for by the canopy fuels in which the fire is then burning. This situation needs further work and testing.
The existing spotting models (Albini 1979, 1981, 1983a, 1983b, Morris 1987) were originally devised to predict the maximum distance burning embers would travel over flat and regularly undulating terrain. The maximum spotting distance is determined by the balance between particle size, burnout rate, and time or distance traveled. Smaller particles are lofted higher and transported further, but burnout sooner than larger particles. Thus, as published, Albini's equations for the maximum spotting distance cannot be implemented for complex topography because winds, terrain, and forest canopy can all vary.
At present only the model for spotting from torching trees (Albini 1979) is present in FARSITE. The purpose of the spotting capability of FARSITE is to compute the maximum distances that particles of different sizes would travel over complex landscapes. These indicate the potential distances ahead of the fire that spotting could be found, assuming winds vary only as a function of height above ground or as specified spatially by the weather/wind grid. Nevertheless, this greatly oversimplifies reality in mountainous terrain.
Depending on topography, Albini's equations may suggest the farthest spotting distances are produced by larger particles that aren't transported over deep ravines. The spotting model in FARSITE does not intend to predict the number of embers produced, or exact locations that embers will land, only the direction and distance embers might land.
Spotting is produced whenever some form of crown fire develops (torching and running crown fire). You must recognize, however, that the torching tree model of ember lofting was not intended for representing ember lofting from a running crown fire. It will likely underestimate both the ember sizes, lofting height, and ultimate spotting distances under conditions of running crown fire.
The mechanics of simulating direct and indirect tactics are described in the Attack Simulation section of the Technical References. As with fire growth simulation, the techniques used here assume that variations in the fire environment in time or space at scales finer than those specified by the model parameters will not necessarily have influence over the progress of fire suppression. Thus, fuel variations or fire characteristics that normally would affect suppression efforts cannot be simulated at finer scales than the fire growth simulation. There are no slope limitations to the operation of mechanized equipment such as dozers.
Fire suppression activities are not affected by areas of non-fuel (barren, rock, lakes etc) in terms of travel time across them or in changing decision tactics when they are encountered. If indirect line is routed across a lake, the crew will merely skip to the other side of the water when it encounters the lake (resuming suppression along the remaining part of the route) and not count the travel time required to circumnavigate the lake.
Line production rates are maintained independently when more than one crew is assigned to the same portion of a given fire. Two crews that attack the same part of an active fireline will essentially "leap frog" each other, attacking alternating segments of the active fire perimeter. For example, if a slow crew and a fast crew are working on the same portion of the perimeter, the suppression line will alternate between fast and slow segments.
Users are responsible for providing their own line production rates. The values in any example files are for demonstration only.
A direct attack will continue on the original fire front until finished or until the user reassigns it. For example, if an attack is initiated against an outward burning fire front it will always remain on the outside of the fire front even if it begins attacking a concave portion of the fire perimeter that then becomes isolated as an enclave. This keeps the direct attack faithful to its original intent, that of limiting the expansion of the fire. You can however, attack an inward burning fire front (enclave).
A parallel attack cannot be assigned to an inward-burning fire front (e.g. an enclave). Instead, the direct attack can be used to suppress inward fire fronts.
It is assumed by the simulation that a retardant pattern will always be impermeable to an approaching fire front until its user-defined effectiveness has expired. In reality, most applications of retardant are supported by ground crews to ensure fire doesn't breach the treated area. Furthermore, the effectiveness of a retardant drop is not sensitive to fire behavior, although spot fires can be initiated beyond the fire line.
The user is responsible for selecting realistic performance parameters for the aircraft, the coverage level and line length, and the effective duration. No explicit consideration is given for factors affecting retardant longevity such as air temperature, shading, or fuel type. The user can however, implicitly incorporate such elements into the setting for effective duration.