Spatial Tools for Wildland Fire Management Planning M A. Finney USDA Forest Service, Fire Sciences Laboratory, Missoula MT, USA Abstract Much of wildland fire planning is inherently spatial, requiring calculation, display, and analysis of fire behavior across large landscapes. Two tools have been developed that facilitate these efforts. FARSITE is a fire growth simulation program that computes fire movement and behavior across landscapes when weather conditions can vary in time and space. FlamMap produces fire behavior calculations for entire landscapes, allowing display and comparison of fire behavior across space. FARSITE is often used in fuel treatment planning, helping to evaluate the possible performance of fuel treatment units impacted by hypothetical wildlfires. FlamMap is often used to develop an objective measure of fuel hazard as a component of a fire risk assessment. Introduction Analysis of fire conditions at the landscape-level requires specialized tools. For example, fire conditions may involve mapping of fire hazard or modeling the growth and behavior of large wildland fires. Tools capable of performing these calculations must incorporate fire behavior models with features for data handling and display of output products. The appropriate tool depends on whether the user needs to compare fire conditions at a single point in time or visualize changes in fire conditions over time. Fire behavior calculations for fewer than 2 dimensions are handled by BEHAVE (Andrews 1986) and the new BehavePlus program (http://fire.org). Spatial Data Static Temporal Data Variable 0 and 1-Dimensional BehavePlus BehavePlus 2-Dimensional FlamMap FARSITE The FARSITE program (Finney 1998) is a fire growth simulation that uses spatially variable terrain and fuels. Weather is typically provided in a non-spatial fashion that depicts temporal changes in temperature, humidity, rainfall, cloud cover, and winds. Spatially gridded nearsurface winds can be used as well, but the wind fields must be generated independently from FARSITE (e.g. computational fluid dynamics). The FlamMap program calculates fire behavior values for the entire landscape, not for specific ignitions, and does not simulate fire growth. It generates maps of fire behavior or fuel moisture that are useful for ordinating fire potential across a landscape. Fire Behavior Models Fire behavior models incorporated into FARSITE and FlamMap are currently those developed for practical use in the US:
Surface fire: (Rothermel 1972). Surface fire spread and intensity is calculated for different fuel types, including grass, shrubs, litter, or dead and downed woody fuels. Fire behavior depends on wind, slope, and moisture content of the live and dead fuel components. Transition to crown fire (Van Wagner 1977). This model describes ignition of trees in the canopy in terms of surface fire intensity, crown base height, and foliar moisture content. Crown fire spread (Rothermel 1991, Van Wagner 1993). Once trees in the canopy are ignited, the crown fire may spread at a faster rate than the surface fires. Whether this happens or not depends on the crown bulk density, crown base height, and the surface fire intensity. Dead fuel moisture (Nelson 2000). This model is used to calculate the moisture content of dead woody fuels in size-classes of 1hr, 10hr, 100hr, and 1000hr according to changing weather conditions, as well as sun angle with respect to topography and forest cover. FARSITE also incorporates the following models that are not yet available in FlamMap: Fire acceleration from point source (McAlpine 1991). Under constant weather and fuel conditions, fires starting from point or line sources take time to accelerate to a steady state spread rate and intensity. The model is incorporated to adjust response of fire spread rate to changing environmental conditions. Spotting from torching trees (Albini 1979). Trees that torch or are consumed in crown fire loft embers into the ambient wind field and are then transported by wind and gravity across the landscape. If these embers do not burnout during flight and land on receptive fuel, then they may start new spot fires according to a user-defined probability. Post-frontal combustion (Albini and Reinhardt 1995). The Burnup model calculates fuel consumption and smoke production in flaming and smoldering combustion phases. These behaviors are described as post-frontal because they occur largely after the passage of the flaming front. To run the fire behavior models, both FARSITE and FlamMap require spatial data for topography (elevation, slope, aspect) and fuels (surface fuels, forest canopy cover, forest stand height, crown base height, crown bulk density). These data are often based on interpretations of satellite imagery with field sampling (Keane et al. 1998). FARSITE can also use two additional data layers for describing duff loading and coarse woody fuel loads. These data are required for computing smoke production and fuel consumption using the post-frontal combustion model (Albini and Reinhardt 1995). Data Products Hazard Maps Hazard is often defined as the fuel contribution to fire behavior (Brown and Davis 1973). This can be objectively expressed in terms of fire behavior calculated under a set of weather and moisture conditions. With FlamMap, hazard can be computed and compared Figure 1. Fireline Intensity kw m -1 0 1x10 1 5x10 1 1x10 2 5x10 2 1x10 3 5x10 3 1x10 4 5x10 4 1x10 5
across a large landscape, for example fireline intensity (Figure 1). Maps of fire spread rate, flame length, crown fire activity, for example, offer a spatial summary of fire behavior potential for a fire management planning unit. These maps can be combined with other elements of risk and value in a process of risk assessment. Fuel Moisture Maps Dead fuel moisture is a major determinant of surface fire behavior. Topography, canopy cover, solar radiation, and fuel size class are variables included in Nelson s (2000) physical model that is incorporated into FlamMap and FARSITE. Both of these programs can be used to generate maps of fuel moisture for all dead fuel size classes (Figure 2). This enables fire and fuel planners to distinguish dry and moist areas across landscapes and to visualize the diurnal fluctuations in moisture content as a function of landscape properties. The fuel moisture maps are calculated as a by-product of the fire behavior calculations. Figure 2. Fuel Moisture % 0 6 13 19 26 32 38 45 51 58 1-hr Timelag 10-hr Timelag Fire Growth and Behavior Maps The behavior and effects of fires change as those fires grow across variable landscapes and are subjected to variable weather conditions (Finney 1999). These spatial patterns of fire behaviors within the fire perimeter can be simulated using FARSITE for actual fires for operational support or for hypothetical fires for fire planning. Analysis of possible fuel treatment performance is often conducted by simulating fires with and without fuel treatment (Figure 3a). These simulations show the variability in flamelength or spread rate within the fire area as a consequence of changes to the fire environment. Figure 3a. Flame Length (m) 1 2 3 4 5 Figure 3b. Fuel Combustion (Mg ha -1 min) 0-0.5 0.5-1.0 >1.0
Post-Frontal Combustion Fuel consumption and smoke production after the edge of the flaming front passes are computed using the Burnup model (Albini and Reinhardt 1995) which is incorporated spatially into FARSITE. The output from these simulations shows the region of combustion activity behind the flaming front which can be summarized to depict total smoke emissions or energy released by the fire (Figure 3b). These calculations only estimate smoke production. Dispersion and smoke chemistry must be computed by a secondary set of computations in other software. Fuel Treatment Optimization Fuel treatment strategies can be optimized to retard fire growth on simple landscapes (Finney 2001), but applying these principles to complex landscape requires a computational algorithm. Current research into these algorithms will be incorporated into future versions of FlamMap. These algorithms apply the minimum travel time algorithms for fire growth (Finney 2002) to determine critical fire travel routes (Figure 4a). The algorithm then blocks these routes with fuel treatment units, progressing from the windward to the down-wind side of the landscape. The result is a map of fuel treatment units that efficiently alter fire growth across the landscape (Figure 4b). Figure 4. Fuel treatment optimization process is based on (a) minimum travel time pathways determined for a given ignition from bottom of landscape, and (b) then treatment units (purple) are identified that block the major pathways. Availability and Requirements Both FARSITE and FlamMap run under Microsoft Windows and are parallelized to make use of multiple processors. Computations for large landscapes and large wildland fires will benefit from using multi-processor workstations. Both programs are computationally intensive, performing best with more RAM and highest clock speed processors. Computer specifications, free program downloads, and more information, is available on FARSITE and FlamMap from http://fire.org. Literature Cited Albini, F.A. 1979. Spot fire distance from burning trees- a predictive model. USDA For. Serv. Gen. Tech. Rep. INT-56.
Albini, F.A. and E.D. Reinhardt. 1995. Modeling the ignition and burning rate of large woody natural fuels. Intl. J. Wildl. Fire. 5(2):81-92. Brown, A.A. and K.P. Davis. 1973. Forest fire: Control and Use. McGraw-Hill Book Co., New York, NY. 686 p. Finney, M.A. 2002. Fire growth using minimum travel time methods. Can. J. For. Res. 32(8):1420-1424. Finney, M.A. 2001. Design of regular landscape fuel treatment patterns for modifying fire growth and behavior. For. Sci. 47(2):219-228. Finney, M.A. 1999. Mechanistic modeling of landscape fire patterns. Chapter 8. In D.J. Mladenoff and W.L. Baker (eds) Spatial modeling of forest landscapes: approaches and applications. Cambridge University Press. Pp Finney, M.A. 1998. FARSITE: Fire Area Simulator model development and evaluation. USDA For. Serv. Res. Pap. RM-RP-4. Keane, R.E., J.L. Garner, K.M. Schmidt, D.G. Long, J.P. Menakis, and M.A. Finney. 1998. Development of input data layers for the FARSITE fire growth moidel for the Selway-Bitterroot Wilderness Complex, USA. USDA For. Serv. Gen. Tech. Rep. RMRS-GTR-3. McAlpine, R.S. and R.H. Wakimoto. 1991. The acceleration of fire from point source to equilibrium spread. For. Sci. 37(5):1314-1337. Rothermel, R.C. 1972. A mathematical model for predicting fire spread in wildland fuels. USDA For. Serv. Res. Pap. INT-115. Rothermel, R.C. 1991. Predicting behavior and size of crown fires in the northern Rocky Mountains. USDA For. Serv. Res. Pap. INT-438. Nelson, R.M. 2000. Prediction of diurnal change in 10-h fuel stick moisture content. Can. J. For. Res. 30:1071-1087. Van Wagner, C.E. 1977. Conditions for the start and spread of crownfire. Can. J. For. Res. 7:23-24. Van Wagner, C.E. 1993. Prediction of crown fire behavior in two stands of jack pine. Can. J. For. Res. 23:442-449.