FUEL LOADING CALCULATIONS Following Brown's
(1974) methods, fuel loadings (t/ha) were
calculated for each size and decay category.
Brown's quadratric mean diameters for subalpine
fir were used for 1-, 10-, and 100-hr TL fuels;
they are 0.079, 1.96, and 20.13 cm2 respectively.
Each transect interval was corrected for slope.
Wood densities were determined by decay class for
subalpine fir, whitebark pine, and Alaska
yellow-cedar. For each species, 15-24 wood
samples representative of decay categories 1-3
were collected. Wood density was estimated by
determining the oven-dry weight and dividing it by
the sample volume. An average density for each
species by decay class was determined. Decay
categories 4 and 5 were estimated by subtracting
from the category 3 results, 0.1 and 0.25,
respectively. Although the wood density estimated
in Table 5 are high compared with other tree
species (see USDA Forest Service, 1974), the
narrow growth rings exhibited by these high
elevation species creates a very dense and heavy
wood, Using a water displacement method to
estimate wood density produced similar results.
LIVE FUEL MOISTURE Current year foliage, older
(1+ yr) foliage, and fine twigs (< 0.5 cm) were
sampled in 1986 for Abies lasiocarpa at OLYM near
Hurricane Ridge. Three trees were repeatedly
sampled during August-September. All samples were
approximately 30 g wet weight, were collected
between 1400 and 1800 hours, and sampled only
during dry weather to avoid condensed moisture on
the sample. For each tree, two replicates of
current year and older foliage, and one sample of
twigs, were collected in poly bottles and tightly
sealed. For 1987, only current year foliage was
collected, and two new locations were added: MORA,
near Sunrise, and Crater Lake National Park (CRI~)
Sampling was conducted over a longer time period
of the summer and for two species; Abies
lasiocarpa at OLYM and MORA, and Abies magnifica
shastensis at CRLA. All samples were returned to
the laboratory and oven-dried at 80 C for 48 to 72
hrs. All values are expressed as percent by dry
weight. Association of foliar moisture with easily
obtained fire weather indices was attempted,
because foliar moisture is time-consuming to
measure. The National Fire-Danger Rating System
(NFDRS), based on daily fire weather during the
dry season, has two indices that might be
associated with live fuel moisture: woody fuel
moisture and 1000-hr timelag fuel moisture
(moisture content of logs between 7.62 and 20.32
cm diameter). The 1000-hr timelag fuel moisture
was chosen because it responds to wetting and
drying cycles similar to live fuels (Burgan 1979)
and because the woody fuel moisture in NDFRS is a
linear function of the 1000-hr timelag fuel
moisture. Values for 1000-hr timelag fuel
moisture from nearby stations were recorded for
each sample date. AGE AND REGIONAL COMPARISONS
The effects of age and region on fuel loadings
were evaluated by analysis of variance. Since the
study sites were not randomly selected and the
sample size is very small, three per cell in each
of 6 cells, analysis of variance was used for
interpretive purposes only and not for hypothesis
testing. Hence, no inferences were drawn to a
larger population of fire-regenerated subalpine
forests. Detrended correspondence analysis
(DCA), an multivariate ordination technique (see
Gauch 1982), was employed to examine the patterns
of fuel composition among the 23 study areas. The
Cornell University programs for DCA developed by
Hill (1979) were used. A data matrix consisting
of study sites and fuel loadings was created.
Fuel loadings for the different fuel and decay TL
categories replaced the more commonly used species
portion of the stand by species matrix, hence each
study site was represented by a suite of fuel
loadings. Because the large fuels dominate the
fuel weight of every study site, they conceal the
patterns of the other categories; therefore, all
fuel categories were standardized based on a ratio
of the fuel loading for a site to the maximum fuel
loading for that category. Stand DCA scores for
the community of fuels were graphed for the first
two axes and interpretation of environmental
gradients influencing the community patterns were
investigated. FIRE BEHAVIOR Field methods of
Burgan and Rothermel (1984), Brown (1974), and
Agee and Pickford (1985) were followed for
building new fuel models in fire behavior program
BEHAVE (Burgan and Rothermel 1984). Fuel
reconnaissance plots (Agee and Pickford 1985) were
taken at 13 different sites. Additional shrub,
herb/grass, and litter measurements were recorded
within a 40 cm radius plot systematically placed
along the 4 fine fuel transect lines for each "T"
transect interval. Percent cover estimates were
recorded in cover classes: 0- trace, trace-1,
1-5, >5-25, >25-50, >50-75, >75-95, and >95
percent. Litter depth was the mean depth for the
40 cm plot and included duff, although the duff
layer was rarely perceptible or measurable. The
results of the regional comparisons showed that
fuels were relatively similar between regions but
not for stand age. As a result, the regional
information for each of the young and mature
stands was pooled, from which two fuel models were
built. Once the fuel models were built, several
important parameters were varied incrementally,
namely, depth and percent cover of litter, shrubs
and grasses, to evaluate and provide insight to
their overall effect on building fuel models. No
attempt was made, however, to vary surface area to
volume ratios which could have a pronounced effect
on the rate of spread for sites with loosely
packed fuels (e.g. young subalpine sites) (Burgan
and Rothermel 1984). Surface fire behavior is
predicted by the model when specific environmental
data is combined with a fuel model. We used the
following environmental values: 1) mid-flame wind
speed of 12 mph; 2) slope of 50 percent; 3) dead
1-, 10-, and 100-hr moistures at 3, 4, and 5
percent, respectively; and 4) live herbaceous and
woody moistures of 90 and 100 percent,
respectively. Fire behavior variables predicted
by the model are rate of spread (ft/min), flame
length (ft), Reaction IntensitY (Ir) (Btu/ft2
/min), and fireline intensity (Btu/ft/sec). For
each run of the fire behavior model all
environmental and fuel loading variables are held
constant except one. Hence, there is seemingly
almost an infinite number of fuel and
environmental combinations one could input into
the model. Because of this, we developed two
strategies to streamline the results. First, all
variables in Table 6 and the environmental values
listed above were held constant in order to
predict several fire behavior variables
simultaneously (e.g. fireline intensity and rate
of spread). This was done for the young and mature
fuel models. Then, additional runs varying wind
at speeds of 6 and 18 mph were examined.
Secondly, we examined certain fuel and
environmental variables as continuous variables.
To accomplish this, we predicted one fire behavior
parameter, rate of spread (y) while 1) varying one
of fifteen fuel or environmental parameters (x)
and 2) holding everything else constant. The end
result was a comparison of fire behavior (rate of
spread) between young and mature subalpine forests
under varying fuel and environmental conditions.
Also, fire behavior for stands with high
within-site variation could be evaluated from
these results.