Traditionally, most of the prescribed burning at Point Reyes National Seashore was implemented to manage shrub and grass fuel types. The objective of the prescribed burns were to decrease hazardous fuel loads and manage historical landscapes by maintaining the diversity of species through fire, similar to the practices of indigenous people of coastal California.

As Point Reyes is challenged with more diverse issues surrounding fuels and fire management, the desire to manage forested landscapes with moderate to high levels of fuels has become a necessity. High values at risk adjacent to National Park Service property boundaries add to the complexity of prioritizing fuel treatments. The fire hazard analysis coupled with potential fire behavior output from FlamMap (Finney, 2003) were used in conjunction with one another to locate areas of extreme fire hazard. This type of analysis was useful in locating areas of extreme fire hazard for the proposed Firtop prescribed burn unit.

A fire hazard analysis combines the effect of slope, aspect, and fuel model to derive hazard using a geographical information system (GIS). South and southwestern facing slopes have greater sun exposure, resulting in drier fuels that are more receptive to ignition during the fall. Furthermore, slopes greater than 40% can result in faster rates of spread, facilitated by the preheating of fuels closer to the adjacent fuel bed. Consequently, slope and aspect are important in determining extreme fire hazard, because they influence potential fire behavior.

FlamMap (Finney, 2003) is a fire prediction model used to predict potential fire behavior using a GIS. Data such as fuels, weather, wind, fuel moisture, canopy, slope and aspect are combined to predict potential fire behavior. Outputs such as flame length and fire intensity can be used to assess areas of greatest concern for extreme fire behavior.

In the following analysis, output from FlamMap was compared to a GIS fire hazard model to locate areas of overlapping extreme fire hazards. Both tools used in conjunction with one another can be used to perform a thorough assessment of potential fire behavior and hazard for the areas of greatest concern in both parks.

The GIS fire hazard analysis combined fuels, aspect, and slope. Slope was divided into 3 classes representing moderate to steep slopes, respectively: 0-20%, 21-40%, and >40%. Aspect was divided into 2 classes: south and southwest facing and all others. Aspect is the direction a slope faces and is described by 0 to 360 degrees. South and southwest facing slopes reside between 135 – 195 degrees. All other slopes reside between 0-134 and 196-360 degrees. Fuel models were derived from data obtained from 1,691 field plots that measured vegetation and cover attributes. Field data were combined with an existing vegetation map and designated to the 13 standard fuel models (Anderson, 1983). All data were queried and combined using ESRI’s ArcGis 8.3 spatial analyst raster calculator.

Slope, aspect, elevation, canopy cover, fuels were combined in Farsite v4.0.1 (Finney, 2002) to create the landscape file necessary for FlamMap. Wind and weather data were collected from Marin county RAWS stations that corresponded with the 1995 Vision Fire that burned over 8,800 acres at Point Reyes. These data were used to condition dead fuel moisture to realistic values for this region.

The fire hazard analysis was spatially compared to the FlamMap simulations to observe if extreme hazard corresponded with high fire intensities and flame lengths. Extreme fire intensities were defined in this experiment as greater than or equal to 1000 Btu/ft/sec. High flame lengths were defined as greater than 11 feet.

Preliminary results showed that extreme fire hazard using the GIS analysis had more hectares of area burned compared to the FlamMap output. However, the areas where FlamMap predicted extreme fire behavior did correspond with the extreme fire hazard from the GIS model. Input from fire personnel familiar with fire behavior for these areas will be important in validating both models. Once validated, these data used in conjunction with one another are useful for prioritizing WUI treatment areas and prescribed burns.