Geoscience Reference
In-Depth Information
It is a deterministic model, meaning that you can relate simulation results directly
to your inputs.
Produces outputs that are compatible with PC and Workstation graphics and GIS
software for later analysis and display.
Can simulate air and ground suppression actions.
Can be used for fire gaming, asking multiple “what-if” questions and comparing
the results.
Accepts both GRASS and ARC/INFO GIS raster data themes.
FARSITE also includes a manual rate of spread adjustment that analysts can use
to help calibrate the output of the model to better match their expert observations or
expectations. Though easily abused, this calibration capability can even be used dur-
ing a fire to improve the performance of the model based on observed fire behavior
in the hopes of improving the outputs of the model. Because of the level of expertise
required to understand the model parameters and output, FARSITE is intended to
be used by analysts familiar with wildland fire and the associated terminology and
the limitations of the available input data sets, and more importantly, the limitations
of FARSITE's outputs.
3.5.2 FlamMap, Predicative Fire Effects Mapping
FlamMap is an algorithmic extension of FARSITE (Finney 2006 ). Using the same
input layers as FARSITE, FlamMap computes potential fire behavior characteristics
such as spread rate, flame length, fireline intensity, and crown fire behavior for an
entire FARSITE landscape. Unlike FARSITE, FlamMap uses constant weather and
fuel moisture conditions for the entire landscape. The many output rasters represent
expected consequences of fire for each pixel in the entire study area regardless of
ignition points.
FlamMap is intended to model fire behavior assuming that fuel moisture, wind
speed and wind direction are held constant across the study area. This allows for
comparisons between various management scenarios. For example, FlamMap is par-
ticularly well suited to preplanning efforts and analyzing the possible ramifications
of management activities such as thinning projects designed to remove biomass to
reduce fire hazards (Stratton 2004 ). Once again, this software is also intended to be
used by skilled and trained fire analysts.
Like most computer models, FARSITE and FlamMap are limited by the avail-
ability and quality of the input data. Reasonably good digital elevation models
(DEMs) are now available for many areas and slope and aspect data sets are easily
produced from those DEMs. Unfortunately, these fire behavior models also require
fuels data, which is far more problematic. Many of the optional data layers that
improve the performance of these models are even harder to acquire. Many govern-
mental organizations have been working to gather their own vegetation and fuels
data sets, but quality varies widely. Often the less common optional FARSITE input
data layers, such as canopy bulk density, or height to base of crown, are simply
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