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Fuel Model Selection for BEHAVE
in Midwestern Oak Savannas


The reliable prediction of fire behavior has become increasingly important as natural resource managers rely more heavily on fire as a management tool. At the same time, reliable predictions of wildfire behavior allow fire control agencies to determine what resources are needed to contain wildfires, minimize damage to natural resources, and protect property. BEHAVE, a widely distributed and accepted fire behavior predictive model, developed by the USDA Forest Service, allows planners to predict fire rate-of-spread, flame lengths, and fireline intensity (rate of heat release) using one of several generalized fuel models (Andrews 1986, Wilson 1990). The 12 generalized fuel models (Table 1) take into account 1 hr, 10 hr, 100 hr, and live fuels under a variety of scenarios. A wide variety of studies have shown that BEHAVE can be used to accurately predict fire behavior (Andrews 1980, Norum 1982, van Wilgan and Wills 1988), but may or may not be appropriate for certain conditions. In addition to developing safe and reliable fire management plans, these predictions can be used to aid managers by expanding burn windows (Andrews and Bradshaw 1990) and providing additional justification for fire management decisions (Stanton 1993).

The BEHAVE fire behavior prediction system can be downloaded from the Internet ( and run from a personal computer (Andrews 1986). When using BEHAVE, the selection of the appropriate fuel model is not always clear, especially when the existing fuel models do not seem to fit the particular set of conditions that a fire manager may face. As an example, in the Midwestern United States, an increasing emphasis has been placed on the restoration of oak savanna-woodlands (Guyette et al. 1993, McCarty 1993). Historically, it is estimated that Missouri had about 13 million ac of oak savanna-woodlands prior to European settlement (Nelson 1985). Today, the oak savanna-woodland ecosystem (hereafter referred to as savannas) occupies only about 0.02% of its historic range of 27 to 32 million acres in the Midwest (Nuzzo 1986). Prescribed fire is an important tool in oak savanna restoration and management (McCarty 1993). Valid fire behavior predictions in established oak savannas are of major interest because of the increasing number of acres being restored or maintained as oak savanna.

As resource managers' use of prescribed fire increases, the possibility of a prescribed fire escaping will increase. A recent case in point was the May 5, 2000 Cerro Grande prescribed fire in Bandelier National Monument, New Mexico. This was intended to be a 1,000 ac prescribed fire, the fire escaped, and suppression efforts failed. The Cerro Grande fire burned 47,650 ac and destroyed 260 homes in the town of Los Alamos before it was contained. One of the conclusions drawn by an interagency investigation of the Cerro Grande incident was that fuel conditions within the prescribed burn units were more complex than indicated in the original burn plan. These complex fuel conditions created an intricate prescribed fire environment that was not adequately addressed in the original burn plan (USDI NPS 2000). Another example is the July 2, 1999 Lowden Ranch prescribed fire near Redding, California. Planned as a 100 ac fire to combat the spread of an invasive weed, the fire escaped and burned 2,000 ac and 23 residences before it was contained. The review team reported that there was inadequate evaluation of fire behavior and predicted fire behavior, which included inaccurate descriptions of fuels and fuel models (USDI BLM 1999). On a regional perspective, the Mack Lake fire in Michigan, May 1980, was a prescribed fire that escaped and killed one person and destroyed 44 homes and buildings (Simard et al. 1983). The intensities of Midwestern wildfires are not comparable to western wildfires, but the potential for property damage or loss is a serious concern.

The purpose of this study was to determine which of four standardized fuel models produced the most accurate fire behavior predictions for oak savanna fuel conditions. The models used were: Fuel Model (FM) 1—short grass, FM 2—timber and grass, FM 3—tall grass, and FM 9—hardwood litter (Anderson 1982). Intuitively, we felt these models were the most appropriate for the oak savanna landscape.

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