CSIRO PUBLISHING
Corrigendum
International Journal of Wildland Fire 2013, 22, 869–870
http://dx.doi.org/10.1071/WF12008_CO
Assessing the effect of foliar moisture on the spread rate
of crown fires
Martin E. Alexander and Miguel G. Cruz
International Journal of Wildland Fire 22, pp. 415–427.
doi.org/10.1071/WF12008
The relative effect of live fuel moisture content on rate of fire
spread in the Rothermel (1972) model as presented in Fig. 1 of
Alexander and Cruz (2013) was found to be in error. We
acknowledge François Pimont (INRA Unité de Recherches
Forestières Méditerranéennes) and Chad Hoffman (Colorado
State University) for bringing this discrepancy to our attention.
(b)
9
1.8
8
1.6
n
Va
7
r(
ni
bi
ne
Al
ag
W
1.4
6
n
Wag
Van
6)
)
89
99
(1
19
5
1.2
er (1
4
Lindenmut
989
h and Dav
1.0
is (1973)
Scha
af et
)
Relative effect of fuel moisture content on rate of fire spread
(a) 2.0
The original Rothermel formulation uses fuel moisture
content in fractional form. We inadvertedly used percentage
moisture. This causes a small error that is not detectable in a
graphical form for high fuel moisture contents (Fig. 1a) but
becomes quite pronounced at much lower values (Fig. 1b). The
live fuel moisture content (Mlive) and the live fuel moisture of
3
al. (2
erm
0.8
Ro
007)
Roth
2
el (1
972)
the
rm
el
Al
bin
(19
i(
72
1
0.6
)
80
90
100
110
120
96
)
Lindenmuth and Davis (1973)
Schaaf et al. (2007)
0
70
19
20
40
60
80
100
120
140
160
Foliar or live fuel moisture content (%)
Fig. 1. Relative effects of foliar moisture content (FMC) on crown fire rate of spread in a conifer forests based on the theoretical functions of
Van Wagner (1989), Albini (1996) and Schaaf et al. (2007) and of live fuel moisture content on rate of fire spread in shrubland fuel complexes
based on the empirical function of Lindenmuth and Davis (1973) and the theoretical function of Rothermel (1972) as discussed in the text for two
situations: (a) normal range in fuel moisture conditions involving healthy, live foliage and (b) extreme range in fuel moisture conditions
encompassing both healthy, live as well as desiccated or dead foliage. The horizontal lines of unity (i.e. 1.0 and 1) represent a neutral effect.
The kinks in the Albini (1996) function are a reflection of the fact that the numerical simulations presented in Albini (2000) were undertaken for
fixed FMC values.
870
Int. J. Wildland Fire
M. E. Alexander and M. G. Cruz
extinction (MXlive) in Eqn 9 and the dead fuel moisture content
(Mdead) in Eqn 10 are thus expressed as a percentage.
The corrected live fuel moisture effects associated with the
Rothermel (1972) rate of fire spread model are depicted in the
figure above. The Rothermel (1972) live fuel moisture function
is similar to the Lidenmuth and Davis (1973) and Schaaf et al.
(2007) functions at live moisture contents above 50% (Fig. 1b)
and is somewhat intermediary at lower moisture contents
compared to the Van Wagner (1989) and Albini (1996)
functions.
Extrapolation of the Rothermel (1972) live fuel moisture
function to a low fuel moisture content level of 7% yields a
relative effect of ,2.5 when comparing to a reference live fuel
moisture content of 75% (i.e. at a fuel moisture content of 7%, the
rate of fire spread is 2.5 times faster than if the live fuel moisture
was 75%). This extrapolation is purely illustrative as the model
should technically not use such low fuel moisture values for live
fuels. Below a moisture content of 30%, fuels should be considered as ‘dead’ as opposed to ‘live’ and therefore be treated
differently within the model (as per Rothermel and Philpot 1973).
www.publish.csiro.au/journals/ijwf
CSIRO PUBLISHING
Review
International Journal of Wildland Fire 2013, 22, 415–427
http://dx.doi.org/10.1071/WF12008
Assessing the effect of foliar moisture on the spread rate
of crown fires
Martin E. Alexander A and Miguel G. Cruz B,C
A
University of Alberta, Department of Renewable Resources and Alberta School
of Forest Science and Management, Edmonton, AB, T6G 2H1, Canada.
B
Bushfire Dynamics and Applications Team, CSIRO Ecosystem Sciences and
Climate Adaptation Flagship, GPO Box 1700, Canberra, ACT 2601, Australia.
C
Corresponding author. Email: miguel.cruz@csiro.au
Abstract. This paper constitutes a digest and critique of the currently available information pertaining to the influence of
live fuel or foliar moisture content (FMC) on the spread rate of crown fires in conifer forests and shrublands. We review
and discuss the findings from laboratory experiments and field-based fire behaviour studies. Laboratory experimentation
with single needles or leaves and small conifer trees has shown an unequivocal effect of FMC on flammability metrics.
A much less discernible effect of FMC on crown fire rate of spread was found in the existing set of experimental crown fires
carried out in conifer forests and similarly with the far more robust database of experimental fires conducted in shrubland
fuel complexes. The high convective and radiant heat fluxes associated with these fires and the lack of appropriate
experimental design may have served to mask any effect of FMC or live fuel moisture on the resulting spread rate. Four
theoretical functions and one empirical function used to adjust rate of fire spread for the effect of foliar or live fuel moisture
were also concurrently examined for their validity over a wide range of FMC conditions with varying outcomes and
relevancy. None of these model functions was found suitable for use with respect to dead canopy foliage.
Additional keywords: conifer forest, crowning, dead foliage, fire behaviour, flammability, foliar moisture content, fuel
moisture, heat transfer, live fuel moisture, shrubland.
Received 12 January 2012, accepted 6 October 2012, published online 23 November 2012
Introduction
The effect of the moisture content of fine dead fuels such as
needle litter on the spread rate of surface fires in forests has been
well substantiated in the field and the laboratory many times
over the years (e.g. Curry and Fons 1938; Anderson and
Rothermel 1965). However, to our knowledge, no field study
has been carried out to specifically examine the effect of the
moisture content of live fuels on the propagation of highintensity fires, namely crown fires in conifer forests and
shrubland fuel complexes (i.e. fires propagating through the
combustion of canopy fuels).
Given that live foliage is the main fuel involved in the canopy
fuel layer of crown fires, an understanding of the effect of live
fuel moisture on the spread rate and intensity of this type of fire
is necessary for proper model development and application.
Although the effect of foliar moisture content (FMC, % ovendry weight basis) on crown fire propagation has not been
empirically substantiated as yet (Van Wagner 1998; Cruz
et al. 2005), several models have incorporated an explicit
function based on (1) heat transfer considerations (Albini
1996; Butler et al. 2004b) or (2) a damping term based on
physical logic and related considerations (Van Wagner 1974;
Schaaf et al. 2007).
Journal compilation Ó IAWF 2013
The existing models for predicting crown fire rate of spread
(ROS) that incorporate a FMC are based on forests with green
or healthy needle foliage. In Canada, for example, FMC is
expected to vary from 85 to 120% on a seasonal basis (Forestry
Canada Fire Danger Group 1992). On the basis of a review of
several studies carried out on North American conifers, Keyes
(2006) indicated that FMC values ranged from 73 to 480%
depending on species, foliage age and season. However, he
considered a FMC of 90 or 100% a ‘prudently conservative’
low default value in assessing or predicting crown fire
behaviour. This is supported by the comment of Chandler
et al. (1983, p. 35) that ‘A general rule of thumb with regard to
living foliage moisture is that crown fire potential in conifers is
high whenever needle moisture content drops below 100
percent of dry weight’.
An important departure from normal FMC conditions are the
very low levels (e.g. down to ,7%) associated with forests
subjected to insect attacks such as the mountain pine beetle
(Dendroctonus ponderosae) (W. G. Page, Utah State University,
pers. comm., 2012). Low FMC levels can also be induced by late
spring frost kill, most notably in species like gambel oak
(Quercus gambelii) (Wilson et al. 1976), or by heat desiccation
following surface burning (Pearce 2007). The implications of
www.publish.csiro.au/journals/ijwf
416
Int. J. Wildland Fire
applying the aforementioned models to appraising fire behaviour potential in such fuel complexes are unknown.
The purpose of this paper is to ascertain the relative magnitude of a foliar moisture effect on the horizontal spread rate of
crowning conifer forest and shrubland fires based on a critical
examination of the existing and pertinent literature. This
includes (i) laboratory experiments from the needle to small
tree scales, (ii) experimental fire field studies and (iii) model
functions of theoretical and empirical origin. Both live and dead
canopy foliage are considered.
Laboratory evidence for a foliar moisture effect
on crown fire rate of spread
Chandler et al. (1983, pp. 33–34) observed that ‘There has been
little formal research on the relationship between foliage
moisture and fire behaviour, primarily because such experiments involve the deliberate initiation of crown fires in living
fuels. To explore the full range of variables would be both
expensive and dangerous’. As a result, much of our current
understanding of the effect of live fuel moisture on fire behaviour comes from laboratory scale experiments looking at
vegetation flammability metrics (White and Zipperer 2010).
These laboratory studies, involving foliage samples and small
‘Christmas trees’, have demonstrated that foliar moisture has a
profound effect on various aspects of flammability. Although
these studies do not specifically look to examine the direct
effects of FMC on horizontal crown fire ROS and thus it is
questionable whether the results are directly transferable to the
field, they nevertheless provide insight, although there are
limitations.
Needle and foliage scale laboratory experiments
Several studies have attempted to characterise the relative
flammability of fuel particles and its relationship to live fuel
moisture through bench scale experiments in which fuel samples
are subjected to a fixed heat flux and in turn metrics such as time
to ignition, flame size and duration are quantified (e.g. Dickinson
and Kirkpatrick 1985; Mak 1988; Dimitrakopoulos and
Papaioannou 2001); see White and Zipperer (2010) for an
excellent review of bench scale flammability test types. In most
of these experiments the moisture content of live fuels has been
found to have a significant effect on time to ignition and flame
characteristics (e.g. Bunting et al. 1983; Xanthopoulos and
Wakimoto 1993; Dimitrakopoulos and Papaioannou 2001;
Etlinger and Beall 2004; Pausas et al. 2012). Nonetheless, the
extrapolation of these results to real-world situations is considered questionable given that such laboratory flammability
studies seldom succeed in realistically replicating the heat
transfer mechanisms and combustion processes driving wildland fire propagation (Fernandes and Cruz 2012). Weise et al.
(2005a) used a cone calorimeter (Babrauskas 1984) with a
radiosity of 25 kW m2 (out of a possible 100 kW m2)
impinging a constant radiant flux onto the fuel samples of
,18.25 kW m2 (emissivity view factor ¼ 0.73 as per
Babrauskas 2003). Pausas et al. (2012) submitted gorse (Ulex
parviflorus) samples to a radiative flux of 4.6 kW m2 from a
quartz epiradiator. These radiative heat flux values are much
lower than observed in moderate-intensity shrubland fires where
M. E. Alexander and M. G. Cruz
peak radiative heat fluxes between 36 and 150 kW m2 have
been measured (Silvani and Morandini 2009; Cruz et al. 2011).
Peak radiative heat fluxes of 250–300 kW m2 have been
measured in crown fires in conifer forests (Butler et al. 2004a;
Frankman et al. 2012).
Convective heat transfer is also not well represented in the
laboratory experiments carried out to date; in cone calorimeter
based experiments (Dimitrakopoulos and Papaioannou 2001;
Weise et al. 2005a), convection heat transfer is negligible
(Babrauskas 1984). In epiradiator-based experiments where
the heat source is positioned below the fuel particle, convection
can act as a cooling mechanism. In the experimental setup of
Pausas et al. (2012), convection acted as a cooling mechanism
after the fuel particle temperature surpassed ,1508C. Conversely,
the Xanthopoulos and Wakimoto (1993) experiments were
based solely on convective heat transfer from a non-forced hot
air column with temperatures varying between 400 and 6408C.
Convective heat fluxes were not calculated in these experiments
but average effective heat transfer coefficients given in Xanthopoulos (1990) suggest values between 7.3 and 11.5 kW m2.
These values are much lower than the measurements of 40 and
61 kW m2 made by Silvani and Morandini (2009) in moderateintensity, wind-driven shrubland fires. Similarly, convective
heat fluxes peaking at 150 kW m2 were measured in highintensity crown fires (Butler 2010).
Measured peak convective and radiative heat fluxes in freeburning fires coincide with the arrival of the ignition interface to
the measuring sensors (Butler et al. 2004a; Silvani and
Morandini 2009; Butler 2010), implying that unburned fuel
particles are subjected to these very high heat fluxes immediately
before and at the time of ignition (Butler 2010). Heating rates
determine the rate, pathways and efficiency of pyrolysis of
woody and non-woody plant materials (Shafizadeh 1968;
Sussott 1980). As such, the low heating rates (up to two orders
of magnitude lower) obtained in some laboratory experiments
might limit the extrapolation of these data to free-spreading
outdoor fires. Weise et al. (2005a) noticed that differences in
time to ignition for different species and fuel moisture contents
tended to decrease when cone calorimeter heat fluxes were set
higher than 25 kW m2, implying that at heat fluxes representative of high-intensity fires the effect of moisture would be
small. White and Zipperer (2010) also note that experiments
with large heat sources will fail to detect differences between
distinct fuel samples.
In order to explore the hypothesis that combustion behaviour
of live vegetation is largely controlled by heat and mass transfer,
Fletcher et al. (2007) conducted experiments on a flat flame
burner (Engstrom et al. 2004) aimed at replicating the conditions
of a flame front where leaf samples were exposed to heat fluxes
ranging from 80 to 140 kW m2. Under these more realistic
heating rates Fletcher et al. (2007) did not find an effect of fuel
moisture content on time to ignition. Experiments by Pickett
et al. (2010) with the same experimental apparatus showed that
in green leaf samples moisture remains in the leaf after ignition
occurs. This suggests that the release of pyrolysates from the leaf
surface and their subsequent gas phase combustion do not
require the removal of moisture located in the internal tissues
of the leaf. This is in contrast to the commonly held modelling
assumption that all moisture is evaporated from the fuel particle
Foliar moisture effect on crown fire spread
before ignition occurs (Van Wagner 1977; de Mestre et al. 1989;
Mell et al. 2009). These results suggest that the effect of
moisture content might be made inconsequential under the high
convective and radiative heat fluxes characteristic of highintensity crown fires.
Laboratory experiments with small conifer trees
Several studies have been conducted in the laboratory using
small, individual conifer trees that have quite dramatically
shown an influence of FMC on various aspects of forest fuel
flammability. This work has been conducted in a variety of
settings by different organisations over the years, initially by
forest fire researchers in Canada investigating Christmas trees as
a fire hazard (Van Wagner 1962), then the Christmas tree
industry itself (see summary by Koelling 1998), followed by
structural or urban fire researchers (e.g. Madrzykowski 2008).
More recently this type of experimental test fire approach has
been used in connection with the investigation of fire dynamics
related to the wildland–urban interface (Baker 2011).
Van Wagner (1967a) carried out flammability tests with
small specimens (1.5 m tall) of three conifer tree species: balsam
fir (Abies balsamea), Scots pine (Pinus sylvestris), and white
spruce (Picea glauca). The two measures of flammability
selected for analysis included the proportion of crown fuel
consumed and the weight of foliage consumed per ignition. It
was determined that Scots pine and balsam fir could be ignited
with a single match at respective FMC thresholds of ,65 and
50%. At a FMC of less than 20% all three species were found to
be very flammable and would ‘burn with great violence’;
similarly, Damant and Nurbakhsh (1994) demonstrated that
the potential for very rapid fire development was possible with
‘dry’ trees using a single paper match. It was found that trees at
high moisture content levels (i.e. greater than 100%) could not
be ignited with a point source of flame (e.g. a single match).
However, ignition would occur if flame was applied in the form
of a ring around the base of each tree stem using crushed
newspaper as a fuel for producing the heat source. As Van
Wagner (1967a) was to point out, ‘Crowning forest fires, of
course, bear witness to this [same] limitation’.
Van Wagner (1967b) conducted a further laboratory experiment to demonstrate that the flammability of a single conifer
species with FMC levels more typical of living trees found under
field conditions. Several similar 1.5 m-tall white spruce trees
were locally harvested, brought inside, and treated to obtain a
FMC range of 68 to 124% by placing the butts of some in water
and allowing others to dry for varying periods. The trees were
burned inside a flame or smoke hood by igniting balls of crushed
newspaper placed around the base of each stem. The maximum
thermal radiation emitted from the flaming trees (primarily a
measure of flame size) was in turn measured with a total
hemispheric radiometer. Van Wagner (1967b) concluded that
FMC within the range observed had ‘a marked effect on the
flammability of single trees’ as indicated by the ,30-fold
increase in thermal radiation.
Quintilio (1977) similarily preconditioned 1 m-tall lodgepole
pine (Pinus contorta) trees to FMC values between 70 and 120%
in order to examine the possible increase in crown foliage
flammability associated with the ‘spring dip’ phenomenon or
seasonal minimum observed in FMC in the spring (Van Wagner
Int. J. Wildland Fire
417
1974). Trees were anchored to a weight loss recording platform
in order to examine the effect of FMC on needle weight loss
during active flaming combustion. Burning of the sample trees
was accomplished by igniting 5.0 g of air-dry excelsior that had
been placed around the base of each stem. Quintilio (1977)
found that this measure of flammability varied considerably
over the range in FMC examined. Sample trees with low FMC
lost weight at four times the rate of those with high FMC.
Several Christmas tree experimental fire studies have been
undertaken by the Fire Research Division of the US National
Institute of Standards and Technology (NIST). Several tree
species were investigated including Scots pine, Fraser fir
(A. fraseri) and Douglas-fir (Pseudotsuga menziesii). Tree
heights ranged from ,1 to 4 m. The tests were conducted over
a wide range in FMC (i.e. 2 to 154%). This work clearly
demonstrated the dependence of heat of combustion (Babrauskas
2006) and the heat release rate/tree mass ratio (Baker and
Woycheese 2007; Babrauskas 2008) on FMC for this specific
experimental setup.
The most recent work undertaken by NIST is reported by
Mell et al. (2009). They conducted a series of burning experiments using Douglas-fir trees ,2 and 5 m in height with the aim
of gathering data to validate the predictive capacity of a physicsbased numerical model. These experiments attempted to replicate a low-intensity surface fire under a tree. The authors pointed
out that the relationship between FMC and the experiment’s
burning characteristics was largely dependent on the ignition
source. At the scale of these experiments, the burning behaviour
was found to depend significantly on FMC.
The ignition source strength and FMC were the two main
controls of combustion behaviour in the small, single conifer
tree experiments. Nonetheless, the results regarding the FMC
effect might not be directly transferable to assessing horizontal
rate of fire spread in real-world crown fire situations. As
discussed in the previous subsection on laboratory experiments,
the effect of the much higher heat fluxes released by high
intensity, free-burning crown fires results in distinct rates of
pyrolysis and combustion dynamics.
Field evidence for a foliar moisture effect
on crown fire rate of spread
As pointed out above, certain aspects of wildland fuel flammability can be demonstrated in the laboratory at the scale of a
single shrub leaf or small individual conifer tree. However, of
greater interest is the specific effect of FMC or live fuel moisture
on the spread rate of a fire crowning in a conifer forest stand
comprised of much taller trees or a shrubfield.
Observations from outdoor experimental fires
in conifer forests
In addition to his studies of FMC as a factor in crown fire
ignition, Van Wagner (1974) concluded, on the basis of physical
logic, that FMC should also affect the ROS of crown fires. As
empirical proof for a FMC effect on crown fire ROS, he
presented data from two 0.19-ha sized (30.5 61 m) experimental fires, both carried out in a red pine (P. resinosa) plantation fuel complex under similar but not identical weather
conditions (Van Wagner 1968). This information, represented in
418
Int. J. Wildland Fire
M. E. Alexander and M. G. Cruz
Table 1. Summary of the burning conditions and associated fire
behaviour characteristics for three experimental crown fires carried
out in red pine plantation plots as described by Van Wagner (1964,
1968, 1977)
Adapted from Alexander (1998)
Fire environment descriptor
Experimental fires
R1
C4
C6
Date of burning
8 June 1962 14 July 1966 31 May 1967
Ambient air temperature (8C)
24.4
22.8
18.9
Relative humidity (%)
26
32
25
15
23
19
10-m open wind speed (km h1)
Litter moisture content (%)
10
12
12
Duff moisture content (%)
54
24
66
Foliar moisture content (%)
100
135
95
Forest floor consumption
2.20
1.91
1.32
(kg m2)
Rate of fire spread (m min1)
10.8
16.8
27.4
Fireline intensity (kW m1)
7300
21 100
22 500
Flame length (m)
15.0
22.0
–
Flame height (m)
14.8
19.8
30.5
Flame depth (m)
8.0
14.0
–
Flame front residence time (s)
45
50
–
Table 1 in the form of experimental fires C6 and C4, indicates a
40% difference in FMC (i.e. 95 v. 135%). As Van Wagner
(1974) notes, the difference in spread rate (27.4 v. 16.8 m min1)
is ‘difficult to explain in any other way’.
This simple comparison by Van Wagner (1974) has been
cited by others (e.g. Chandler et al. 1983) as a way of justifying a
FMC effect on crown fire ROS. Closer examination of the
burning conditions associated with these two experimental fires
as summarised in Table 1 seems to support the notion of a
significant effect of FMC on the spread of the two fires.
However, as shown in Table 1, another experimental fire (R1)
carried out in the same fuel complex at a FMC of 100% and
exhibiting a spread rate of 10.8 m min1 (Van Wagner 1964)
does not fall in line with the other two experimental fire
observations, as would be expected from the prevailing fuel
and weather conditions (i.e. whereas the FMC is 5% higher for
fire R1 compared with fire C6 and the 10-m open winds 4 km h1
less, the litter moisture content is 2.0% less). Admittedly,
the apparent discrepancies in the spread rates of the three
experimental fires presented in Table 1 could simply reflect
the natural variability in crown fire propagation (Taylor et al.
2004). Furthermore, the fairly small size of the plots in these
experimental fires may preclude a representative estimate of the
steady-state spread rate.
Van Wagner (1989, 1998) admitted that he could not find any
secondary statistical effect that could be attributed to the FMC in
the crown fire dataset used in the development of the Canadian
Forest Fire Behaviour Prediction (FBP) System (Forestry
Canada Fire Danger Group 1992). Similarly, Cruz et al.
(2005) through a reanalysis of the FBP database and other data
from Australian exotic pine plantations found that FMC was not
a significant predictor of crown fire ROS in conifer forest stands.
In this dataset, FMC was not significantly correlated with the
ROS of either active or passive crown fires, at least within the
range of the dataset (75 to 135% for n ¼ 37; Table 2).
Stocks et al. (2004) documented 10 experimental crown fires
carried out in a jack pine (P. banksiana) – black spruce (Picea
mariana) stand in Canada’s Northwest Territories at fairly low
FMC levels (i.e. generally less than 90%). An analysis of
variance was carried out modelling ROS against wind speed
and FMC to examine whether the variability in FMC might
explain any of the remaining variance in fire spread rate once the
modelled effects of wind speed had been included. They found
that FMC was not significant but did acknowledge that the FMC
varied with a narrow range (,15%; Table 2).
It is possible that the effect on the phenomena under study is
limited, and hence it is uncertain whether the lack of correlation
between FMC and crown fire ROS is the result of the effect
being masked by the high heat fluxes involved, a reflection of
the empirical nature of the database, or both. The general
difficulty of finding a distinct FMC effect in the experimental
dataset is not unique. One faces the same general problem in
conducting outdoor experimental fires in order to isolate a
particular effect, in this case FMC. In contrast to the luxury
often afforded by indoor laboratory fires, it is virtually impossible to hold everything constant while varying one parameter
(Van Wagner 1971).
Observations from outdoor experimental fires
in shrublands
Most spreading shrubland fires can be technically considered
crown fires, as the main fire-carrying fuel layer has a well
developed vertical dimension and live fuels are typically the
bulk of the fuel consumed in flaming combustion (Rothermel
1972). Several field-based experimental burning programs
carried out over the last 40 years in an effort to quantify the
effect of environment variables on rate of fire spread in shrubland fuel complexes. To our knowledge not a single published
scientific study has reported a statistically significant correlation between the moisture content in live shrubland fuels and
rate of fire spread (Table 2). Other than the Lindenmuth and
Davis (1973) and Vega et al. (1996) studies, none of the statistically based models developed from these datasets explicitly
included live fuel moisture as a variable in their models. The
model of Vega et al. (1996) indicated that rate of fire spread
increased with an increase in moisture, illustrating one of the
drawbacks of relying entirely on statistical analysis. It is worth
noting that the shrubland studies listed in Table 2 deal only with
rate of fire spread. The process of sustained fire propagation in
shrubland fuels such as dealt with by Weise et al. (2005b) for
example is not considered here.
One of the confounding problems in elucidating a live fuel
moisture effect in shrublands is the degree of variation in the
live : dead ratio even within a given species (Buck 1951; Paysen
and Cohen 1990). This is in contrast to conifer forests where the
amount of dead material is generally low, unless the stand has
been killed as a result of insect attack or disease in which case all
of the foliage is viewed as dead.
Comparison of foliar moisture functions
for adjusting crown fire rate of spread
In order to gauge the relative magnitude of a FMC effect on
crown fire ROS, a comparison was undertaken of five related
12
32
14
68
9
133
44
29
25
17
18
26
28
Calluna vulgaris (UK)
Quercus turbinella (Arizona, US)
Leucadendron laureolum (South Africa)
Gymnoschoenus sphaerocephalus (Tasmania, Australia)
Eucalyptus tetragona (Western Australia, Australia)
Heath and mallee (New Zealand and Australia)
Erica umbellata, Chamaespartium tridentatum (Portugal)
Ulex sp., Erica sp., Chamaespartium tridentatum (Portugal)
Quercus coccifera, Arbutus andrachnea (Turkey)
Quercus coccifera (Turkey)
Arbutus andrachnea, Pistacia lentiscus (Turkey)
Calluna vulgaris, Vaccinium myrtillus (UK)
Eucalyptus calicogona, E. diversifolia
(South Australia, Australia)
Eucalyptus calicogona, E. diversifolia, E. tetragona
(South Australia and Western Australia, Australia)
37
10
13
24
Number of fires
Jack pine–black spruce (Northwest Territories, Canada)
Pinus spp. and Picea spp. (Canada)
Pinus spp. and Picea spp. (Canada)
Dominant species and principal location
1.7–4.5
0.5
0.9
1.8
0.3
2
0.2–4.8
0.5
0.7
0.5
1.3
2.3
0.3
1.3–4.5
10.1
4.1–19
2.9–14
Nominal fuel
height (m)
51–93
10–60
71–142
58–147
23–132
68–90
68–235C
66–112
72–113
28–51
69–109
60–164
55–97
51–93
79–89
75–118
78–135
Range in FMC
(%)
1.2–55
1.1–18
up to 14
2.4–53
0.6–55
7.7–40.5
0.6–60.0
0.7–14.1
0.7–20.0
0.8–6.6
0.6–8.4
0.4–7.4
0.5–12.6
1.2–55
15–70
3.4–15.4
7.5–51.4
Range in ROS
(m min1)
0.13 (0.61)
A
0.08 (.0.05)B
0.30 (0.29)
0.06 (0.69)
0.17 (0.66)
D (.0.05)B
0.26 (.0.05)B
D (.0.05)B
0.36 (0.075)
0.42 (0.09)
0.36 (0.138)
E
0.17 (0.39)
0.04 (0.99)
0.120 (.0.05)
0.350 (.0.05)
Correlation
coefficient
(P)
A
The correlation coefficient and its significance were not given although the author did note that contrary to expectation there was a positive relationship between rate of fire spread and live fuel moisture
content.
B
Exact P value not given.
C
W. R. Anderson, University of New South Wales, pers. comm., 2012.
D
Correlation coefficient not given.
E
The correlation coefficient and its significance were not given although the authors did mention that live fuel moisture did not play a major role in determining the resulting behaviour of the experimental fires.
Cruz et al. (2012)
Conifer forests
Stocks et al. (2004)
Cruz et al. (2005) passive crown fires
Cruz et al. (2005) active crown fires
Shrublands
Thomas (1970)
Lindenmuth and Davis (1973)
van Wilgen et al. (1985)
Marsden-Smedley and Catchpole (1995)
McCaw et al. (1995)
Catchpole et al. (1998)
Fernandes et al. (2000)
Fernandes (2001)
Bilgili and Saglam (2003)
Saglam et al. (2007)
Saglam et al. (2008)
Davies et al. (2009)
Cruz et al. (2010)
Reference
Table 2. Summary of field studies involving crown fire behaviour in conifer forests and shrubland fire behaviour with respect to the statistical significance (P) between live fuel or foliar moisture
content (FMC) and rate of spread (ROS)
Foliar moisture effect on crown fire spread
Int. J. Wildland Fire
419
420
Int. J. Wildland Fire
M. E. Alexander and M. G. Cruz
functions, both theoretical and empirically based. Functions
based on a weighted or composite live and dead fuel moisture
content value such as that of Trabaud (1979) were not considered. The five selected functions are briefly reviewed here in the
interest of completeness.
Van Wagner’s foliar moisture effect
Van Wagner (1974) acknowledged that there is essentially no
specific crown fire theory in the literature that accounts for the
seasonal variation in FMC on the ROS of crown fires (and this
remains the case). He therefore devised an approach based on a
commonly used principle formulated by Thomas et al. (1964)
that the forward ROS R should be (i) proportional to the horizontal heat flux E through the crown layer, (ii) inversely proportional to the foliar ignition energy h and (iii) inversely
proportional to the fuel bulk density d. This can be expressed in
simple mathematical terms as follows:
R / E=hd
ð1Þ
In considering the first effect, it was assumed that E was mainly
a function of radiation. From Boltzmann’s fourth-power law of
thermal radiation we have the following relationship:
E ¼ cðT =1000Þ4
ð2Þ
where c is assumed to be equal to 1000 and T is absolute
temperature (K). Van Wagner (1974) considered a baseline
crown fire flame radiometric temperature of 1500 K assuming
oven-dry fuel and an air supply twice the minimum stoichiometric requirement (Van Wagner 1963). Considering the effect
of moisture in reducing flame temperature he proposed (Van
Wagner 1974):
T ¼ 1500 2:75 FMC
ð3Þ
Combining Eqns 2 and 3 gives us the following expression for
the effect of FMC on the flame’s radiation intensity:
E ¼ 1000ð1:5 0:00275 FMCÞ4
ð4Þ
As for the second effect, the energy h required to preheat fuel to
ignition can be stated as a simple function of FMC (Van Wagner
1977, 1989):
h ¼ 460 þ 25:9 FMC
ð5Þ
The specific details of the derivation of Eqn 5 are given in Van
Wagner (1967b, 1968).
The third effect was omitted by Van Wagner (1989) on the
grounds of simplification, thereby leaving the first two effects to
be used in developing a factor to account for the FMC effect on
crown fire ROS. Based on Eqns 4 and 5, we can now express the
foliar moisture effect (FME) function as follows:
FME ¼ 1000ð1:5 0:00275 FMCÞ4 =ð460 þ 25:9 FMCÞ ð6Þ
In practice, the FME is used in a relative sense, being normalised
against some standard FMC value, termed the FMEo:
CFS ¼ FME=FMEo
ð7Þ
where CFS is the relative crown fire spread factor. This value is
in turn used to adjust a predicted crown fire ROS for a given
FMC relative to FMEo. CFS equals 1.0 at the reference FMC
value used in Eqn 7 to derive FMEo.
In the C-6 (conifer plantation) fuel type of the FBP System,
FMEo was deemed to be 0.778 given a reference FMC of 97%,
reflecting the average value in the crown fire dataset used in the
development of the FBP System (Van Wagner 1989). Thus, given a
range in FMC of 85 to 120%, the CFS would in turn vary from 1.24
to 0.68. In a later work, Van Wagner (1993) assumed a FMEo of
1.365 based on an average FMC of 67% for the set of experimental
fires carried out in immature jack pine by Stocks (1987b).
Van Wagner’s (1989) FME function was also an option in the
first version of the NEXUS fire behaviour modelling system
(Scott and Reinhardt 2001), although this effect in assessing
crown fire behaviour potential has to our knowledge not been
used in any simulation study published to date, other than in the
sensitivity analysis undertaken by Scott (1998). Scott and
Reinhardt (2001) suggested using an ‘overall average FMC
among all species during the fire season in the Northern Rocky
Mountains’ and suggested that a FMC of 100% might be such a
value (Scott 1998). The FME function was removed from later
versions of NEXUS based on the reasoning that the wildfires
used in the formulation of the Rothermel (1991) crown fire ROS
model occurred under dry summertime conditions and inherently
were characterised by low, but unknown, FMC values (J. H. Scott,
Pyrologix Wildland Fire Science, pers. comm., 2011). It is worth
noting that Scott and Reinhardt (2001) inadvertently omitted the
1000 constant in the numerator of Eqn 6 when writing Eqn 7 of
their review of the FME computation. As a result, they state that
FMEo equals 0.0007383 for a FMC of 100%, when in fact it
should have been 0.7383 in order to be compatible with the
works of Van Wagner (1989, 1993, 1998).
Albini (1996) physics-based crown fire rate
of spread model
Albini (1996) extended his two-dimensional physics based
model (Albini 1985, 1986; Albini and Stocks 1986) to describe
the ROS of a crown fire and the shape of its ignition interface. In
his numerical model, Albini (1996) assumes radiative heating
from the fuel bed and free flame above it as the dominant heat
transfer mechanism. He further assumes that fuel heating is
caused by a gain of internal energy in fuel particles due to an
increase in temperature. The relationship between the net heat
absorbed and the fuel temperature is discontinuous due to the
fuel moisture content. As such, the effect of FMC is incorporated
into a three step heating model that takes into account the latent
(L) and specific heat of the water (cw) present in the fuel. The rise
in fuel temperature (dT) as a function of net heat absorbed (dQ)
is given by (Albini 1985; de Mestre et al. 1989):
8
>
< ðcp þ M cw ÞdT ; T o373K
LdM;
T ¼ 373K
ð8Þ
dQ ¼
>
:
373K4T 4Tig
cp dT;
Foliar moisture effect on crown fire spread
Int. J. Wildland Fire
where cp is the specific heat of dry fuel and M is an intrinsic fuel
layer moisture content weighted from fuel load, moisture and
consumption (see Albini 1996 for computational details).
Albini’s (1996) model closure required the estimation of two
semi-empirical parameters that describe the radiation intensity
from the burning zone and the free flame. Measurements of
flame radiosity in experimental crown fires (Butler et al. 2004a;
Stocks et al. 2004) and further refinements (Call and Albini
1997) led to formulation of Albini’s (1996) model as a fully
predictive model as presented in Butler et al. (2004b). As Butler
et al. (2004b) note, the Albini (1996) function presented in fig. 3
of their paper ‘clearly indicates that fire spread rate decreases
monotonically with increasing live fuel moisture content. This
behaviour matches observed behaviour in the qualitative sense;
unfortunately, the paucity of detailed field data prevents a
quantitative comparison between predicted and measured
response to live fuel moisture content’.
For the purposes of examining the FME function in the
Albini (1996) model, we relied on data plotted in Albini (2000).
A FMC of 100% was set as a reference level on the basis of
previous precedent set by Albini and Stocks (1986) and subsequently used by Butler et al. (2004b).
Lindenmuth and Davis’ (1973) Arizona oak chaparral
rate of fire spread model
Lindenmuth and Davis (1973) developed a statistical or
empirically based model for predicting the head fire ROS in oak
chaparral (predominately Quercus turbinella) shrublands in
Arizona. The model required six inputs, including FMC, and
was developed with data from 32 outdoor experimental fires
conducted over several seasons; the highest observed spread rate
was 14 m min1. The model accounted for 81% of the variation
in the observed rate of fire spread. The FMC for the experimental fires averaged 84.4% and ranged from 71.4 to 142.4%.
The relative effect of FMC on fire spread rate in the Arizona oak
chaparral fuel type was deduced by taking the ratio of predicted
rate of fire spread for a given FMC level to the predicted ROS
based on the average effects of the other input variables – air
temperature, relative humidity, net solar radiation, 6.1-m open
wind speed and foliar phosphorus content.
Rothermel (1972) surface fire model
In the Rothermel (1972) surface fire ROS model that is
imbedded within the BehavePlus fire modelling system
(Andrews et al. 2008), live fuel moisture has a damping effect on
the rate of fire spread. This effect is calculated through the
moisture damping coefficient (ZMlive) taking into account the
live fuel moisture content (Mlive) and the live fuel moisture of
extinction (MXlive):
ZMlive
Mlive
Mlive 2
þ 5:11 ¼ 1 2:59 MXlive
MXlive
3
Mlive
3:52 MXlive
and dead fuel moisture content (Mdead) (Fosberg and Schroeder
1971):
MXlive ¼ 2:9 ð9Þ
The moisture of extinction of live fuels is a dynamic quantity
formulated as a function of the ratio of live to dead fuel load (a)
1a
ð1 3:33 Mdead Þ 0:226
a
ð10Þ
Implicit in this relationship is the assumption that the energy
required for the ignition of live fuels comes from dead fuels
(Jolly 2007). As such, fuel beds made up exclusively of live fuels
do not carry fire, a state not consistent with empirical evidence
(Cohen and Bradshaw 1986). For fire behaviour fuel model 4 –
chaparral (Anderson 1982), the moisture of extinction of live
fuels varies between 52 and 373% for respective dead fuel
moisture content between 20 and 3%. The moisture damping
coefficient is used to calculate the energy released from the
combustion of live fuels (Rothermel 1972); this latter quantity is
then incorporated in a heat balance formulation to determine the
rate of fire spread.
Catchpole and Catchpole (1991) refined Rothermel’s (1972)
early work by extending Wilson’s (1990) exponential moisture
damping term and probability of extinction function (instead of
moisture of extinction) to fuel complexes with a mixture of live
and dead fine fuel particles. Their formulation improved the
performance of Rothermel’s (1972) model when tested
against the rate of fire spread data from van Wilgen et al.
(1985) for South African fynbos shrublands. A reformulation of
the Rothermel (1972) model by Sandberg et al. (2007) did not
alter the original moisture damping coefficient depicted in
Eqns 9 and 10.
We selected a reference level of 75% for the purposes of
assessing the relative effect of live fuel moisture in a chaparral
fuel complex using the BehavePlus fire modelling system. This
value was selected on the basis of seasonal trends presented in
various sources (e.g. Rothermel and Philpot 1973; Weise et al.
1998, 2005b). Chandler et al. (1983, p. 35) have noted that ‘In
Mediterranean shrub communities fires burn intensely when
foliage moisture drops below 75 percent’. Dennison and Moritz
(2009) found, for example, that a live fuel moisture level of 79%
represented a critical threshold for large fire growth in chaparral.
Fuel Characteristic Classification System crown fire
rate of spread model
Schaaf et al. (2007) proposed a crown fire ROS model designed
to be used within the Fuel Characteristic Classification System
(Sandberg et al. 2007). Schaaf et al. (2007) proposed a foliar
moisture damping term (ZFMC) that approximates the effect
incorporated in Van Wagner’s (1977) heat of ignition relationship represented by Eqn 5:
ZFMC
421
FMCref
¼
maxðFMCref ; FMCÞ
0:61
ð11Þ
where FMCref is a FMC reference minimum at maximum foliar
flammability, set at 75%. Schaaf et al. (2007) state that ZFMC is a
constant equal to unity below an FMC of 75%. Thus, the Schaaf
et al. (2007) function explicitly indicates only a decrease in the
effect of FMC on crown fire ROS, never an increase. The
application of Eqn 11 follows the general modelling approach
422
Int. J. Wildland Fire
10
(a)
(b)
9
1.8
8
1.6
n
Va
7
6)
99
(1
ni
)
bi
89
Al
19
r(
ne
ag
W
1.4
6
5
1.2
Lindenm
uth and
1.0
4
Davis (1
973)
af et
al. (2
007)
Rot
her
mel
(19
72
Scha
0.8
3
2
)
989
r (1
gne
Wa
)
Van
972
l (1
me
her
Rot
Relative effect of fuel moisture content on rate of fire spread
2.0
M. E. Alexander and M. G. Cruz
Al
bin
i(
)
1
0.6
Lindenmuth and Davis (1973)
Schaaf et al. (2007)
0
70
80
90
100
19
96
)
110
120
20
40
60
80
100
120
140
160
Foliar or live fuel moisture content (%)
Fig. 1. Relative effects of foliar moisture content on crown fire rate of spread in a conifer forests based on the
theoretical functions of Van Wagner (1989), Albini (1996) and Schaaf et al. (2007) and of live fuel moisture content
on rate of fire spread in shrubland fuel complexes based on the empirical function of Lindenmuth and Davis (1973)
and the theoretical function of Rothermel (1972) as discussed in the text for two situations: (a) normal range in fuel
moisture conditions involving healthy, live foliage and (b) extreme range in fuel moisture conditions encompassing
both healthy, live as well as desiccated or dead foliage. The horizontal lines of unity (i.e. 1.0 and 1) represent a neutral
effect. The kinks in the Albini (1996) function are a reflection of the fact that the numerical simulations presented in
Albini (2000) were undertaken for fixed FMC values.
of Rothermel (1972) as described in the previous section. The
damping coefficient is multiplied by the energy released by the
combustion of the crown fuel layer to estimate the reaction
intensity, which is then used to determine the crown fire ROS.
Our analysis of the Schaaf et al. (2007) damping function
assumes that an active crown fire dominates the propagation
process.
Findings from comparison of existing functions
for live canopy foliage
The FME function that is currently applied to predicting crown
fire ROS in the C-6 (conifer plantation) fuel type of the FBP
System (Forestry Canada Fire Danger Group 1992) as developed by Van Wagner (1989) is presented in Fig. 1a. The CFS is
shown to vary from a value of 1.65 at a FMC of 70% to 0.68 for
a FMC of 120%. Within this range, the Albini (1996) function is
very similar to that of Van Wagner (1989). This is somewhat
surprising in that although both functions have a similar
theoretical basis their implementation in the models is quite
different. In the Schaaf et al. (2007) function, FMC has a smaller
effect than that proposed by Van Wagner (1989) and Albini
(1996). The Schaaf et al. (2007) formulation has the lowest
effect on rate of fire spread when we only consider the crown fire
models. The effect of lower FMC values on ROS increases in
all of the models except that of Schaaf et al. (2007) who chose
to assume that regardless of the decrease in FMC below a
75% level, the relative effect remains a constant 1.0, although no
supporting justification was given for this assumption.
Considering the shrubland models, the live fuel moisture
function in the Lindenmuth and Davis (1973) rate of fire spread
model for Arizona oak chaparral exhibits a rather shallow effect
over a normal range of use (Fig. 1a). This is in stark contrast to
the damping function exhibited by the Rothermel (1972) surface
fire spread model when applied to the chaparral fuel model
(Anderson 1982). Lindenmuth and Davis (1973) did not provide
information on the significance of their live fuel moisture
parameter. In their study, live fuel moisture was not correlated
with ROS. It is therefore quite possible that they elected to
include this parameter in their model on theoretical grounds
even if its effect in the dataset was not statistically significant.
The Schaaf et al. (2007) formulation is seen to have a slope
similar to that of Rothermel (1972) but they differ in their
relative magnitude. Note again that the Schaaf et al. (2007)
function results in a CFS ¼ 1.0 for FMC values less than 75%
(Fig. 1a).
In our comparison between distinct FMC functions embedded in fire behaviour models, we did not address any of the
physics-based models that have been used to simulate wildland
fire dynamics, such as FIRETEC (Linn et al. 2002) or WFDS
(Mell et al. 2009). Attempting to elucidate the effect of FMC on
rate of fire spread in a model such as FIRETEC, for example, is
a complex task because of the coupling between heat transfer,
Foliar moisture effect on crown fire spread
Extension of foliar moisture effect functions
to dead canopy foliage
A broader range in FMC or live fuel moisture levels is portrayed
in Fig. 1b compared with Fig. 1a. This enables one to readily
gauge the consequences of applying any of the existing foliar
moisture fire spread rate functions to dead or desiccated foliage.
One can clearly see that the Lindenmuth and Davis (1973) and
Schaaf et al. (2007) functions are fairly insensitive at very low
FMC levels.
Conversely, the extrapolation of the Van Wagner (1989) and
Rothermel (1972) functions to comparable levels suggest CFS
values of ,8.0 at a FMC of 7% (Fig. 1b); the trend in the Albini
(1996) curve implies an even higher CFS. This suggests for
example that crown fires in stands retaining dead foliage should
spread approximately eight times (or 800%) faster than those in
healthy, green or live stands. Intuitively, this does not make
sense as it would imply crown fire rates of spread of up to
800 m min1, which have never approached this value
(Alexander and Cruz 2006). Nevertheless, Van Wagner (1989)
FME function has been suggested for possible application to
mountain pine beetle-attacked lodgepole pine stands (Moran
and Cochrane 2012).
Is there any information available to suggest what the
relative effect on crown fire ROS should approximately be at
a FMC of 7%? The only published field study of fire behaviour
423
20
M-3
(100% dead fir)
C-3
100
15
80
10
60
Type of fire
Surface
Passive crown
Active crown
40
20
M-3/C-3 ratio
120
Rate of fire spread (m min⫺1)
combustion and vapourisation embedded within the model.
FIRETEC estimates the average fuel moisture content for each
control volume taking into account the moisture contents of the
individual live and dead fuels weighted according to their
respective loads. The fuel moisture effect is accounted for
through the water vapourisation rate. FIRETEC uses a probability density function for the fraction of initial moisture that has
been evaporated or is being evaporated as a function of solid fuel
temperature (Marino et al. 2012). The probability density function allows for a continuous process where moisture evaporation
starts before the average fuel temperature reaches the boiling
point of water and at high fuel moisture levels, a significant
amount of moisture is still known to exist in the grid (fuel) cell
when combustion occurs. The simultaneous occurrence of evaporation and combustion in a grid cell result from the respective
probability density functions overlap at fairly low temperatures
(400 K). There is no correspondence between these modelling
results and the processes observed by Pickett et al. (2010) in
green leaf samples where moisture was found to remain in the
leaf following ignition (J.-L. Dupuy, INRA Unité de Recherches
Forestières Méditerranéennes, pers. comm., 2012).
Marino et al. (2012) used FIRETEC to conduct several
simulation experiments to explore the effect of the weighted
fuel moisture content on rate of fire spread in shrubland fuels.
These authors found the average moisture content effect to
follow an exponential decay function as observed in numerous
empirical studies. Nonetheless, their results are not directly
comparable with these empirical observations nor the foliar
moisture effect functions as described in this paper. This is
because the effect of moisture in FIRETEC is dynamic and
varies with fuel complex characteristics (e.g. proportion of dead
v. live fuels) and burning conditions, which in turn determine
heat fluxes and combustion outcomes.
Int. J. Wildland Fire
5
M-3/C-3 ratio
0
0
0
10
20
30
40
50
60
70
Initial spread index (ISI)
Fig. 2. Rate of spread on level to gently undulating terrain by type of fire as
a function of the Initial Spread Index component of the Canadian Forest Fire
Weather Index System (Van Wagner 1987), representing the effects of wind
speed and fine dead fuel moisture, for two Canadian Forest Fire Behaviour
Prediction System fuel types (Forestry Canada Fire Danger Group 1992):
C-3 (mature jack or lodgepole pine) representing a healthy, live conifer
forest stand and M-3 (dead balsam fir mixedwood – leafless) with 100%
dead fir representing a dead conifer forest stand. The M-3 : C-3 ratio curve
represents the relative degree of difference between the two rate of fire
spread curves.
in a standing dead conifer forest fuel complex for which ‘hard’
empirical data exist is that of Stocks (1987a) undertaken in
spruce budworm (Choristoneura fumiferana)-killed balsam fir
stands where an increase in arboreal lichens compensated for the
needle defoliation. The study by Stocks (1987a) served as the
basis for the dead balsam fir mixedwood – leafless fuel type
(M-3) in the FBP System. The M-3 fuel type allows for variable
quantities of dead balsam fir. Unfortunately, there is no true live,
healthy balsam fir fuel type that presently exists in the FBP
System. However, comparisons of active crown fire ROS in the
M-3 fuel type with 100% dead fir to the mature jack or lodgepole
pine fuel type (C-3) in the FBP System indicates that the
difference would be something of the order of 3.6 times at the
onset of the active crowning in the latter type and gradually
decreasing to ,2.5 times (Fig. 2). Rate of spread observations
gleaned from wild and prescribed fires in the ‘red’ stage of
mountain pine beetle-attacked lodgepole pine forest stands in
British Columbia suggests a difference of ,2.7 times (D. D. B.
Perrakis, British Columbia Wildfire Management Branch, pers.
comm., 2012).
Conclusions
Quantifying the effect of FMC or live fuel moisture on crown
fire ROS in conifer forests and shrublands has so far proven to be
a difficult endeavour. Laboratory-based studies where all variables but live moisture content are held constant suggest an
effect of this variable on flammability metrics. Nonetheless, the
fairly low heat fluxes of these experiments may limit the
424
Int. J. Wildland Fire
extrapolation of these results to represent the dynamics characteristic of high-intensity crown fire propagation.
Although counterintuitive, examination of the available
empirical evidence on FMC or live fuel moisture in relation to
crown fire rate of spread in conifer forests and shrublands
suggests, based on a 0.05 P value threshold, that there is no
statistically significant relationship between these variables.
This assertion applies within a FMC range of ,75–140% for
conifer forest stands and a live fuel moisture range of
,60–140% for shrublands. It is possible that a small effect
exists but the difficulty of controlling the environmental conditions in outdoor field experiments has so far precluded the
quantification of this effect. It is worth noting that this analysis
considers the ROS of an advancing crown fire and does not
address the conditions necessary for crown fire ignition.
Five foliar or live moisture effect functions for adjusting
crown fire ROS were examined. The empirical function of
Lindenmuth and Davis (1973) and theoretical function of
Rothermel (1972) for shrublands as well as the Schaaf et al.
(2007) theoretical function for conifer forests indicate a distinct
damping effect on rate of fire spread within a normal range of
conditions. On the other hand, the two theoretical functions
developed by Van Wagner (1989) and Albini (1996) both show
strong effects below FMC reference levels.
None of the functions examined were admittedly ever
intended to be applied to dry, dead canopy foliage and it is quite
clear they would not be appropriate for such use. Accounting for
the effects of critically low FMC levels on fire behaviour in
certain fuel types, such as mountain pine beetle attacked stands,
is a very real issue and need (Jenkins et al. 2012). Recent attempts
to assess the effect of low FMC levels on crown fire ROS based
entirely on conducting experimental fires in natural forest stands
have met with mixed success (Schroeder and Mooney 2012).
Like most problems in applied wildland fire behaviour
research, the most effective solution is a judicious mixture of
physical theory, simulation modelling and experimentation
(Van Wagner 1971) supplemented by experienced judgment
and wildfire case study knowledge (Alexander 2007). Simulations that could be produced by the new generation of physicsbased fire behaviour models such as FIRETEC and WFDS once
they have been adequately evaluated (Alexander and Cruz,
in press) is an encouraging possibility for helping to resolve
the issue. Field experiments will need to be well designed from a
statistical standpoint and undoubtedly novel in nature in order to
avoid previous pitfalls when it comes to analysing the data for a
FMC effect. For example, conducting experimental fires in
coastal areas would allow one to take advantage of the uniformity in weather conditions associated with sea breezes (Stull
1988). Past efforts will no doubt provide relevant insights in this
regard involving both artificial setups (e.g. Xanthopoulos 1990)
as well as conventional experimental burning (Stocks 1987a,
1987b; Stocks et al. 2004).
Acknowledgements
This article is a contribution of Joint Fire Science Program projects JFSP 09S-03–1 and JFSP 11–1-4–16. The comments of W. R. Anderson,
C. M. Hoffman, P. J. Murphy, R. M. Nelson Jr, W. G. Page, D. H. Schroeder,
A. J. Simeoni, N. M. Vaillant and D. D. Wade on an earlier draft of this paper
are duly appreciated. The comments of the Associate Editor and four
M. E. Alexander and M. G. Cruz
anonymous reviewers, as well as the production staff at IJWF, greatly
strengthened the paper.
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