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Spatially-explicit fire behavior models require measurements of elevation, slope,
aspect, weather and vegetation to simulate fire behavior across the landscape.
The vegetation parameters (collectively termed "fuel models") used in these
simulations vary based upon the specific requirements of the model chosen
to forecast fire-spread. In general, most fire behavior models will require
estimates of the following variables in order to generate predictions:
total above-ground biomass contributing to the forward rate-of-spread of
the fire; mean stand height; fuel particle surface-area-to-volume (SAV);
and moisture content. Acquiring these estimates in chaparral systems can
be very costly due to shrub density, the ruggedness of the terrain in which
chaparral occurs, and the complex architecture of individual shrubs. As
a result, the use of spatial fuels datasets mapped exclusively from field
inventory data is generally limited to small-scale projects with an area
on the order of 0.5 km2 or less. Spatial fuels data for areas
exceeding this size are typically derived from one of the thirteen standard
National Forest Fire Laboratory (NFFL) fuel models described in Anderson's
1982 publication, "Aids to Determining Fuel Models For Estimating Fire
Behavior". The NFFL fuel models assist in generating spatial fuels data
by providing suggested reasonable values for each fuel property based upon
the stand species composition and site condition. Existing spatial data
in a Geographic Information System (GIS), such as vegetation and fire history,
is often used to assign the most appropriate fuel model to each area
in a heuristic fashion. In many cases (rather than simply relying solely
on the thirteen NFFL models), additional, more-customized fuel models that
may or may not have been developed from field inventory are also used.
This is the approach used for mapping fuels
in the Santa Monica Mountains. |
