CSIRO PUBLISHING
www.publish.csiro.au/journals/ijwf
International Journal of Wildland Fire, 2007, 16, 161–173
Modelling the probability of sustained flaming: predictive
value of fire weather index components compared with
observations of site weather and fuel moisture conditions
Jennifer L. BeverlyA,C and B. Mike WottonB
A Canadian Forest Service, Northern Forestry Centre, 5320-122 Street, Edmonton, AB T6H 3S5, Canada.
B Canadian
Forest Service, Great Lakes Forestry Centre, 1219 Queen Street East,
Sault Ste Marie, ON P6A 2E5, Canada.
C Corresponding author. Email: [email protected]
Abstract. We investigated the likelihood that short-duration sustained flaming would develop in forest ground fuels that
had direct contact with a small and short-lived flame source. Data from 1027 small-scale experimental test fires conducted
in field trials at six sites in British Columbia and the North-West Territories between 1958 and 1961 were used to develop
logistic regression models for ten fuel categories that represent unique combinations of forest cover, ground fuel type, and
in some cases, season. Separate models were developed using two subsets of independent variables: (1) weather variables
and fuel moisture measurements taken at the site of the test fire; and (2) Canadian Fire Weather Index (FWI) system
components calculated from weather observations recorded at a nearby station. Results indicated that models developed
with FWI system components were as effective as models developed with site variables at predicting the probability of
short-duration sustained flaming in most fuel categories. FWI system components were not useful for predicting sustained
flaming in spring grass fuels and had limited usefulness for modelling the probability of sustained flaming in aspen leaf
ground fuels during summer conditions. For all other fuel categories, FWI system components were highly effective
substitutes for site variables for modelling the probability of sustained flaming.
Additional keywords: Canada, fire behaviour, fire danger, fire hazard, logistic regression, probability of ignition.
Introduction
The conditions under which sustained flaming develops in forest
ground fuels exposed to an ignition source are both varied and
complex. Sustained flaming can be considered the outcome of a
successful fire ignition and encompasses three factors identified
by Anderson (1970) as determinants of flammability: ignitability, sustainability and combustibility. Ignitability refers to the
ease of ignition. Combustibility is the post-ignition rate of burning, and sustainability refers to how well the fuel continues to
burn, or combustion stability. Forest fuel receptivity to ignition
will depend on the type of fuel exposed to the ignition source, the
fuel moisture content, the characteristics of the ignition source,
and the influence of micro-site variables such as air flow, wind
(Brown and Davis 1973) and fuel shading (Lin 1999). Fuelspecific assessments of the likelihood of sustained flaming are
important for understanding the mechanisms that lead to the
initiation and spread of forest fires and for predicting forest
susceptibility to fire in a given geographical location and time
period, which is important for both research and operational fire
management applications.
Daily, hourly, and instantaneous fluctuations in fire susceptibility can result from changes in weather variables that influence
fire behaviour and fuel moisture content. Seasonal variations
are associated with long-term weather trends and phenological changes in vegetation. Fire danger rating systems, like the
© IAWF 2007
Canadian Fire Weather Index (FWI) system (Van Wagner 1987),
were developed to provide daily and hourly ratings of fire susceptibility. Calculated for individual weather stations from a
set of weather observations, the FWI system provides nationally consistent and readily available ratings of fire susceptibility.
Although fuel moisture codes of the FWI system were developed to be representative of fuels in a mature, closed canopy
jack pine (Pinus banksiana Lamb.) or lodgepole pine (Pinus
contorta Dougl.) stand, in practice FWI system components are
not regarded as fuel-specific indicators. FWI system components are considered generalisations of fuel moisture and fire
susceptibility applicable to a wide variety of stands and are based
on fundamental relationships between weather variables, fuel
moisture conditions, and observed fire behaviour.
Statistical models have been used to investigate these fundamental relationships using either historical data of observed
wildfire events or experimental fires ignited in laboratory or
field settings. Models based on historical data have been used to
describe landscape level relationships among weather variables,
fuel or vegetation characteristics, and patterns of observed fire
events (e.g. Wotton and Martell 2005; Krawchuk et al. 2006).
Socioeconomic variables and fire management activities have
also been combined with ecological variables to model variability in fire events across large areas (e.g. Mercer and Prestemon
2005). Effectiveness of fire danger rating indices at predicting
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Int. J. Wildland Fire
fire activity across large areas has also been explored through
investigations of the weather associated with observed fire events
(e.g. Andrews et al. 2003). Landscape-level models provide
coarse-scale estimations that are limited by the accuracy of historical fire event data, which varies in completeness, and by
the quality of weather data and spatial data used to represent
landscape characteristics.
Fine-scale modelling of ignition processes can be achieved
with a small-scale experimental test fire approach, which has
been applied in numerous studies in Canada and elsewhere using
either laboratory apparatus or field ignition trials. Small-scale
experimental fires are an appealing research method because
they are inexpensive; can be conducted under controlled laboratory conditions or with minimal disruption of field site locations;
and they can be conducted under extreme fire weather conditions
that would often preclude the use of large-scale experimental
fires. Laboratory test fires (e.g. Latham and Schlieter 1989;
Frandsen 1997; Lawson et al. 1997; Pérez-Gorostiaga et al.
2002; Engstrom et al. 2004; Liodakis et al. 2005) have been
used to isolate specific fuel properties relevant to ignition by
controlling external influences (e.g. weather, climate, and location). Laboratory tests have also been used to explore interactions
among a small number of factors that influence ignition (e.g. Lin
2005). Field ignition trials (e.g. Lawson and Dalrymple 1996;
Lin 1999; Larjavaara et al. 2004; Ray et al. 2005; Tanskanen
et al. 2005) differ in that they incorporate complex micro-site
variablities among many variables that are known to be present
at actual wildfire ignition points.
In typical field ignition trials, the outcome of a fire ignition is
categorised as either a success or a failure, and logistic regression
methods are used to model the probability of a successful fire
ignition as a function of one or more independent variables.
Independent variables used to model the probability of ignition
generally fall into two groups: site variables that describe weather
and fuel moisture conditions at the time and location of the fire,
and fire weather index values that represent approximations of
these conditions.
The practical application of models based on site variables is
restricted to those situations where on-site sampling and weather
measurement is feasible, such as experimental or prescribed
burning operations. Models that predict the probability of sustained flaming in an area from readily available fire weather
index values have widespread research and operational applications. Daily FWI system values for multiple weather stations
across an area can be interpolated to provide continuous spatial
ratings of fire susceptibility, which can be associated with a fuel
map to identify FWI system component values that correspond
to a particular fuel type in a given area on a given day. The spatial representation of actual site variables relevant to fire ignition
(e.g. fuel moisture content) is currently impractical because of
the time and resources that would be associated with a large-scale
daily fuel moisture sampling program. Remote sensing technology may provide a future means of estimating spatial variation
in fuel moisture conditions of live fuels, but it currently has limited use for assessing the condition of dead fuels (see review by
Chuvieco et al. 2004). By modelling the probability of sustained
flaming with FWI system values, fast and inexpensive assessments of fuel-specific fire susceptibility can be obtained for an
area. Predictive models of the likelihood of sustained flaming
J. L. Beverly and B. M. Wotton
can be used in research applications that require fuel-specific
representations of fire processes and in daily operational planning by fire management agencies to distribute fire response
resources in a manner that reflects spatial variations in relative
fire susceptibility across their jurisdictions.
While the practical advantages of modelling with FWI system components are clear, the degree to which predictions of fire
susceptibility based on FWI system components approximate
predictions based on site variables has been largely unexplored.
We used data from field trials of small-scale test fires to investigate the likelihood that short-duration sustained flaming would
develop in forest ground fuels that had direct contact with a
small and short-lived flame source (wooden match). Models
were developed for 10 fuel categories that represent unique
combinations of forest cover, ground fuel type, and in some
cases, season. For each fuel category, we compared the predictive ability of models composed of FWI system components to
models that used site observations of weather variables and fuel
moisture content to predict the probability of sustained flaming.
Implications of results for fire research and fire management
applications are discussed.
Methods
Study sites
Four of the six study sites included in the analysis were located
near Fort Smith, North-West Territories (60◦ 00 N, 111◦ 53W)
and two were located near 100 Mile House, British Columbia
(51◦ 39 N, 121◦ 17W). Study sites were within a 4.8 and 2.9-km
radius of a weather station established at Fort Smith and 100
Mile House locations, respectively. Test fire sites were typically
square or rectangular, ranging from 232 to 3716 m2 in size, and
surrounded by a 0.5-m trench cleared to mineral soil (Macleod
1948). Detailed descriptions of the six test fire sites are provided
in Table 1.
Test fire data
Canadian federal government fire researchers initiated a smallscale test fire program in the 1930s. By 1940, program procedures had been standardised, and between 1940 and 1961,
20 643 small-scale test fires were conducted at nine field stations across Canada. The test fire program involved daily weather
documentation, systematic fuel moisture sampling, and detailed
evaluations of the outcomes of small-scale experimental test fires
conducted at field sites chosen to reflect representative fuel types
across the country (Paul 1969). Results were used to develop
early systems for rating fire susceptibility on a given day. These
early systems were instrumental in the conceptual development
of the FWI system, although test fire data collected between
1940 and 1961 were not used directly in FWI system models.
Paul (1969) noted that the small-scale test fire program
involved documentation of extensive supplementary information on fire weather, fuel moisture, and fire behaviour that was
undertaken without a specific objective or research plan. This
may explain why large portions of the database have never been
subjected to a thorough and systematic analysis. Since the release
of the FWI system in 1970, only a few studies have utilised
data collected during the small-scale test fire program. Notably,
90103
90106
FC8. Spruce, moss
FC9. Aspen, grass (summer)
FC10. Aspen, leaf (summer)
Fort Smith,
North-west
Territories
Fort Smith,
North-west
Territories
Fort Smith,
North-west
Territories
Fort Smith,
North-west
Territories
Aspen
Spruce
Mixed wood
Jack pine
Grass
Grass
Cover type
Grass, leaf
Moss
Moss, needles,
leaf
Lichen, moss,
needles
Grass
Grass
Ground fuel
Pure, 60-year old even-aged trembling aspen stand with a basal area of ∼22 m2 per hectare. During the
summer months there is dense minor vegetation cover consisting of clumps of Salix spp., Shepherdia
spp. and Rose (Rosa spp.) bushes. Underneath this shrub layer is a fairly complete cover of Epilobium,
Lathyrus, Vicia spp. and grass. Leaf cover is 0.6–1.3 cm in depth.
Very dense, 85-year old, even-aged black spruce (Picea mariana (Mill.) BSP) stand with a basal area of
∼37 m2 per hectare. A large proportion of the trees are suppressed, giving the stand an uneven-aged
appearance. Hylocomium spp. moss, 1.3–12.7 cm deep covers 100% of the ground surface.
The organic layer reaches a depth of 18 cm in some locations, overlying very fine silty sand.
Mature, uneven-aged white spruce (Picea glauca (Moench) Voss)-trembling aspen (Populus
tremuloides spp.)-jack pine stand with a basal area of 34 m2 per hectare. High bush cranberry
(Viburnum opulus L. var. americanum (Mill.) Ait.) and Shepherdia spp. shrubs cover 20–30%
of the understory. Dominant surface fuels include Hylocomium and Calliergon spp. mosses, leaves
and needles. The full organic layer varies in depth from 1.3 to 3.8 cm. Researchers noted evidence of fire
45 to 50 years before the test fires.
Dense, even-aged, 85-year old jack pine (Pinus banksiana Lamb.) stand of fire origin, located
∼80 m from the weather station with a basal area of 26 m2 per hectare. Average diameter at
breast height (DBH) is 10 cm and maximum tree height is 20 m. There is a low density of
minor vegetation. Predominant surface fuels are Hylocomium and Calliergon spp. mosses
and Cladonia spp., with scattered needles and twigs, and clumps of Linnea, Vaccinium, Arctostaphylos
spp. and grass present. Scattered Shepherdia and Salix spp. shrubs occur. Surface fuels overlie a thin,
partly decomposed layer (fermentation layer [F]) and a 1.3 cm decomposed layer (humus [H]).
The full organic layer is generally <5 cm deep over fine sand.
Open site with scattered lodgepole pine (Pinus contorta Dougl.) on an exposed south-west slope.
Open site with scattered Douglas fir (Pseudotsuga menziesii (Mirb.) Franco) on a south-west slope.
Site descriptionA
progress reports on forest fire research, Fort Smith (1961) and 100 Mile House (1957).
90105
FC6. Mixed wood, moss
FC7. Mixed wood,
needles–leaf (summer)
A Unpublished
90101
100 Mile House,
British Columbia
80110
FC3. Pine, lichen
FC4. Pine, moss
FC5. Pine, needles
100 Mile House,
British Columbia
80108
FC1. Grass (spring)
FC2. Grass (summer)
Location
Site
Fuel category
Table 1. Site descriptions by fuel category
Modelling the probability of sustained flaming
Int. J. Wildland Fire
163
164
Int. J. Wildland Fire
Lawson and Dalrymple (1996) presented models of the probability of sustained ignition that were based on a small number of
test fires conducted in British Columbia.
Data collected during the test fire program is contained in a
Canadian Forest Service database (Paul 1969; Simard 1970).1
Each test fire record in the database contains information on the
location of the fire; weather conditions recorded on site and at a
nearby weather station; a description of the fuels and a measure
of fuel moisture content; observations of test fire behaviour;
and FWI system components calculated from weather station
data. Not all test fire records are complete, and while all records
include weather observations from a nearby weather station, relatively few include observations of site weather conditions at the
time of the fire. Only 1845 test fire records contain site observations of temperature and relative humidity, and 1662 (90%) of
these test fires were conducted at seven sites located at two field
stations: Fort Smith, North-West Territories (five sites), and 100
Mile House, British Columbia (two sites).
We used data from these two field stations to investigate the
predictive ability of FWI system components in comparison with
site variables for modelling the probability of sustained flaming.
Test fire records (345) from one site in Fort Smith were dropped
from the analysis because of a lack of site descriptive data. Of
the remaining records, only those that contained a fuel moisture
measurement for the ground fuel type consumed by the fire (e.g.
grass, lichen, moss, needles, and leaf) were included in the analysis. For each of the six sites, remaining data was divided into
categories that reflected unique fuel conditions based on forest
cover, ground fuel type, and in some cases, season. Some fuel
categories were excluded from the analysis because they had
insufficient numbers of test fires. In total, 10 fuel categories and
1027 test fires were included in the analysis.
Following technological changes, the records contained in
the test fire database have undergone several format transformations and data manipulations over the past 60 years. Test fire
behaviour was originally documented with field notes written by
researchers who observed the test fire. This information, along
with weather and fuel moisture data, were transferred to punched
cards (Joly and Fraser 1961, 1962; Fraser and Joly 1965) and
eventually to magnetic tape (Simard 1970). More recently, FWI
components were added to each test fire record and initial data
cleaning was undertaken.2 We merged test fire records that had
been archived separately for each province into a single database,
converted all fields into metric, and assembled detailed descriptions of each field in the database. To ensure that data accuracy
and integrity of the test fire database had not been compromised
by these transformations and data manipulations, we compared
original test fire field notes from a random sample of 100 test
fires with records of those fires currently contained in the test fire
database.Variables particularly significant to the present analysis
were selected for comparisons and included fire ignition type,
three variables rating fire behaviour and three variables describing fuel type. An error was recorded if an entry for a variable
J. L. Beverly and B. M. Wotton
in the digital database did not match the corresponding entry
recorded in the field notes.
Site fuel moisture and weather data
Efforts were made to locate the test fire area in close proximity to the moisture sampling area, and moisture sampling sites
were chosen to approximate tree and ground cover characteristics found at the test fire site (Macleod 1948). At the Fort Smith
field sites, fuel moisture sampling was carried out twice daily to
coincide with morning and afternoon test fire schedules. These
fuel samples were collected during or immediately after each
test fire observation (unpublished report on forest fire research,
1961, Fort Smith, NWT). Fuel moisture samples at 100 Mile
House field sites were collected in the afternoons only, and field
notes indicate that moisture samples at a site were conducted at
the same time as test fires ignited at the site.
Fuel moisture samples were collected for individual ground
fuel types, including grass, moss and lichen, and for a ground
fuel category called the ‘top layer duff,’which could contain needles, leaf or a mixture of the two, depending on the duff species
code recorded at the site of the fire. Samples were collected on
site in tins, weighed, and oven-dried at a temperature of 118◦ C
overnight without their lids, before re-weighing (Paul 1969). Percent moisture content was calculated as the difference between
the wet sample and dry sample weights divided by the difference
between the dry sample weight and the container tare weight.
Most moisture-content values associated with the test fire
records are calculated from single samples. Although sampling
locations were chosen to represent conditions at the location
of the test fire, micro-site characteristics no doubt introduced
variability in fuel moisture at a fine scale. Variability in fuel
moisture samples can be expected to decrease as fuel conditions become progressively drier. The majority of test fires
(91%) occurred on days when the diurnally adjusted Fine Fuel
Moisture Code (FFMC) was ≥70. Test fires conducted on days
with a FFMC <70 were excluded from the analysis to control
for increasing variability in fuel moisture measurements taken
during moister conditions.
Existing documentation of the test fire data and field procedures associated with the test fire program (e.g. Paul 1969;
Simard 1970) make little reference to the site weather measurements taken at the location and time of the test fires. Information
on the timing of site weather measurements in relation to the timing of the test fires, as well as the instrumentation used to obtain
these measurements, were verified from annual progress reports
that documented activities at each field station and from the original test fire field notes completed by researchers at the site of
each test fire. These records indicate that a sling psychrometer
was used to record dry bulb temperature, wet bulb temperature
and relative humidity each time a test fire was conducted. In
cases where a series of consecutive test fires were conducted at
a site within a short period of time (i.e. 5–15 min), a single set of
1 A more detailed report describing the test fire procedures and experimental sites is being prepared: J. L. Beverly and B. M. Wotton: The Canadian small-scale
2
test fire database: historical overview and data documentation. Canadian Forest Service, Northern Forestry Centre, Information Report (in preparation).
Unpublished report. Lynham TJ, Martell DL (1989) Preliminary report on a national database of experimental fire in Canada. In ‘Proc. National Workshop
on Forest Fire Occurrence Prediction’ May 3–4, 1989, Forestry Canada.
Modelling the probability of sustained flaming
Int. J. Wildland Fire
site weather measurements were used to represent conditions for
the series (i.e. duplicated for each test fire record in the series).
In some cases the weather measurements were taken at the time
of the first fire in the series, while in other cases the measurement was taken mid-way through the series, which would have
improved the representativeness of the measurement. For example, the field notes for 5 July 1961 at site 90105 (Fort Smith)
indicate that test fires were conducted in moss and needle-leaf
ground fuels at 1435, 1440 and 1445 LST and site weather measurements recorded at 1440 were used for all three of these fires.
Ninety-three percent of test fires with duplicate site weather measurements occurred within 15 min of each other. At Fort Smith,
where each site was visited twice daily and consecutive test fires
conducted in the morning and afternoon were spaced with several hours between them, a new set of site weather measurements
were recorded for afternoon test fires.
Test fire procedures
Test fires were conducted between May and September (Table 2).
Once procedures at a site were initiated for a given year, the site
was visited daily with the exception of Sundays. Test fires were
attempted on all rain-free days provided that an informal on-site
assessment indicated that fuel moisture was not overly saturated.
Because researchers made subjective assessments about whether
or not conditions were appropriate for test fire experiments, our
restriction of the data to test fires conducted on days when the
FFMC was ≥70, which was introduced to account for increasing
variability in fuel moisture measurements at low FFMC values,
also provides an objective criterion for determining whether or
not a given day would be a test fire day.
Fires were ignited in both spring and summer seasons,
although the majority (88%) occurred during summer conditions. We used phenological records to categorise test fires in
grass and leaf fuels according to season. Spring fires represent
conditions before the onset of leaf flush, and summer fires represent conditions after leaf flush but before the onset of leaf fall.
Test fire procedures are described by Macleod (1948) and
Paul (1969). We acquired additional methodological details from
unpublished historical documents, which included test fire field
notebooks, original test fire field data cards and annual progress
reports that summarised fire research activities at test fire field
stations active in Canada between 1940 and 1961.
The majority of test fires (98%) occurred between 0800 and
1700 LST. Test fires at 100 Mile House sites were restricted to
the afternoon. Fort Smith test fires were conducted at each site
twice daily, in the morning and afternoon. Test fires were ignited
Table 2. Duration of sampling by site and year
Site
80108
80110
90101
90103
90105
90106
165
by placing the flame of a large, household sized wooden match
in contact with ground fuels considered to be representative of
fuel moisture content on the site. The temperature of a match
flame is ≥1260◦ C (Brown and Davis 1973).A match ignition can
be described as contact with a flame 35–40 mm in length for a
duration of 15–20 s. If the match extinguished before the ground
fuel ignited, the procedure was repeated. Sixty percent of the
1027 test fires were ignited with a single attempt, and 73% were
ignited with three attempts or less. In a small number of cases
(3%), match ignition of ground fuels could not be achieved after
repeated attempts. In these situations, we classified the outcome
of the test fire as ‘no sustained flaming’.
Once ground fuels were ignited, the fire was generally
observed for 120 s. Some fires became extinct before 120 s,
either as a result of poor burning conditions or through the
action of investigators seeking to limit aggressive fire behaviour.
Weakly burning fires were sometimes observed for more than
120 s to establish evidence of flame sustainability. The average observation period was 103 s with a range of 15 to 300 s.
Researchers documented observed test fire behaviour by assigning each fire a qualitative rating called the vigour code (Table 3).
We classified the outcome of a test fire ignition as achieving
‘sustained flaming’ if the vigour code was 3–5.
Statistical analysis
We modelled the probability of sustained flaming with logistic
regression by classifying the outcome of a test fire as either
‘sustained flaming’ (1), or ‘no sustained flaming’ (0):
P(sf ) −
1
1 + e−(β0 +β1 x1 +···+βn xn )
(1)
where P(sf ) is the probability of sustained flaming, x1−n are the
independent variables, and β0−n are regression coefficients.
For each of the ten fuel categories, the probability of sustained
flaming was modelled as a function of two separate groups of
independent variables: (1) FWI system components, and (2) site
measurements of weather and fuel moisture conditions.
The FWI system contains three fuel moisture codes that
account for daily and hourly changes in the fuel moisture content
of ground fuels layered at increasing forest floor depths. Moisture code values are calculated for individual weather stations
Table 3. Vigour code descriptions (Paul 1969)
Code
Description
1
At 2 min the fire is burning very weakly on
one front only and goes out by itself
At 2 min the fire is burning slowly and poorly on
two or more fronts and seems likely to go out on
its own accord rather than continue indefinitely
At 2 min no sign of fire going out by itself,
burning fairly briskly, but not on all fronts
Fire burning briskly at 2 min on all fronts with tendency
to become progressively stronger, but no difficulty
in putting it out with feet (stomping)
As for 4 but difficult or impossible to put out fire
with feet after 2 min
Fire goes out before 2 min
2
Year
1958
1959
1961
3
7 May–5 Sept
28 Aug–5 Sept
–
–
–
–
7 May–2 Sept
7 May–2 Sept
–
–
–
–
–
–
18 May–11 Sept
26 May–12 Sept
14 June–29 Aug
20 May–31Aug
4
5
9
166
Int. J. Wildland Fire
from consecutive, daily observations of weather conditions (dry
bulb temperature, relative humidity, 10-m open wind speed, and
precipitation) recorded throughout the fire season. Relative daily
ratings of potential fire intensity, spread rate, and fuel consumption are provided by three fire behaviour indices generated from
the moisture codes, for a total of six FWI system components
(Van Wagner 1987):
FFMC: represents the moisture content of litter and other cured
fine fuels.
Duff Moisture Code (DMC): represents the moisture content of
loosely compacted, decomposing organic matter.
Drought Code (DC): represents moisture conditions in a deep
layer of compact organic matter.
Initial Spread Index (ISI): a combination of wind and FFMC
that represents the rate of fire spread independent of fuel
quantities.
Buildup Index (BUI): a combination of the DMC and DC that
represents the total fuel available to the spreading fire.
Fire Weather Index (FWI): a combination of the ISI and BUI that
represents the intensity of the spreading fire as energy output
rate per unit length of fire front.
The FFMC value represents litter moisture content at peak
burning conditions, ∼1600 LST. We used documentation of the
timing of fires during the day to produce a diurnally adjusted
FFMC value for each test fire record (i.e. Van Wagner 1972;
Lawson et al. 1996). This FFMC value was then used to calculate
a diurnally adjusted Initial Spread Index (ISI) and a diurnally
adjusted FWI.
Fuel moisture content is commonly used to predict fire ignition potential and sustainability (e.g. Blackmarr 1973; Frandsen
1997; Lin 1999). We used records of fuel moisture content for
the ground fuel type consumed by the test fire (e.g. grass, lichen,
moss, needles, and leaf ) as an independent site variable.
We also included two site weather variables that are commonly used as predictors of fire ignition and sustainability:
relative humidity and temperature (e.g. Lin 1999). Vapour pressure deficit, a measure of evaporative drying potential, was
calculated from measurements of site relative humidity and temperature for each test fire record, and was included as a fourth
site variable in the analysis.
Wind is known to influence fire behaviour and has also been
used as a predictor in models of fire ignition and sustainability (e.g. Lawson et al. 1994; Lin 1999). It was not included as
a predictor of sustained flaming in this study because test fire
records do not include site wind speed measurements at the time
of the fire. Approximately 80% of the test fire records do contain a qualitative rating of site wind conditions, and 90% of these
fires were conducted with wind speeds of ≤4.8 km h−1 , likely
a result of the impracticality of conducting match ignition tests
under moderate or high wind conditions, which suggests that
wind speed likely did not have a major influence on test fire
outcomes.
Maximum likelihood estimates of model parameters were
computed with SAS LOGISTIC (SAS Institute 2004). Model
selection between and within the two groups of independent variables, (A) FWI system components and (B) site variables, was
based on Akaike’s information criterion (AIC). Model predictive
J. L. Beverly and B. M. Wotton
ability and goodness of fit was assessed by the likelihood ratio
χ 2 test, the Wald χ 2 test for individual parameters, and the
C statistic. For each model, validation was conducted with
cross-validated predicted probabilities computed in SAS. The
procedure involved withholding single observations from the
dataset, building the model without it, and then testing the
observation in the model. The process was repeated until all
observations had been tested, and cross-validation accuracy was
estimated from the frequency of times an observation was not
re-classified during the cross-validation procedure.
Results
Correspondence between data entered in the original test fire
field notes for a random sample of 100 test fires and data entered
for those 100 fires in the current database was 99.3% for the
seven variables that were assessed (i.e. ignition type, three fire
behaviour variables and three fuel type variables). This suggests
that data transformations and manipulation of the test fire data
over the past 60 years have not compromised data integrity.
Because of their insignificant numbers, it is most likely that
the infrequent errors observed in the database occurred during
the initial transfer of test fire field notes to punched cards in the
1960s.
The average moisture content of ground fuels, and relative
humidity at the time of the fire, were significantly lower for
fires that achieved sustained flaming as compared with fires
that did not achieve sustained flaming for all fuel categories
(Appendix 1). Average temperature and vapour-pressure deficit
at the time of the fire were significantly higher for fires that
achieved sustained flaming as compared with fires that did not
achieve sustained flaming, for all fuel categories, with the exception of average temperatures associated with test fires in FC9,
aspen grass fuels during summer conditions, which did not differ
between the two test fire outcomes.
Fires that achieved sustained flaming were associated with
a higher proportion of FWI system component values that
exceeded median values (calculated from all test fires and all
fuel categories), as compared with fires that did not achieve sustained flaming (Appendix 2). Exceptions were found in FC9,
aspen grass fuels during summer conditions, where the proportion of both the FFMC values and BUI values that exceeded
median values did not differ significantly between the two test
fire outcomes. For FC1, spring grass fuels, there were no significant differences in the proportion of FWI components that
exceeded median values between the two test fire outcomes, with
the exception of BUI.
The proportion of test fires associated with a DC that
exceeded the median value was not significantly different
between the two test fire outcomes for six of the ten fuel categories. For fuel categories 8–10, the proportion of fires with a
DC that exceeded the median value was significantly greater for
fires with no sustained flaming, which reflects a seasonal trend
rather than the influence of fuel moisture conditions in the deep
organic layer on ignition processes. These results are consistent
with other studies that indicate ignition outcomes are related to
all FWI system components except the DC (i.e. Tanskanen et al.
2005), and as a result, we limited further analysis to FFMC,
DMC, ISI, BUI and FWI.
Modelling the probability of sustained flaming
Int. J. Wildland Fire
167
Table 4. Correlations between independent variables: FWI system components and site weather variables
Numbers refer to fuel categories (1–10) where two independent variables were not significantly correlated. BUI,
Buildup Index; DMC, Duff Moisture Code, diurnally adjusted Initial Spread Index (ISI) calculated from FFMC
and the 10-m open wind speed; FFMC, diurnally adjusted Fine Fuel Moisture Code; FWI, Fire Weather Index
calculated from the BUI and diurnally adjusted ISI
Correlation
FFMC
DMC
ISI
BUI
DMC
ISI
BUI
FWI
Temperature
Vapour pressure deficit
Moisture content
2, 5–9
–
2, 5–9
–
5, 7, 9
–
–
5, 7, 9
–
–
Relative
humidity
9
–
2, 6, 8
Temperature
–
1, 2, 8, 9
Vapour pressure
deficit
2, 8
Table 5. Comparison of models composed of (A) FWI system components and (B) site variables, by fuel category
‘Variables’ are significant independent variables used in the model (P-values given in parentheses); ‘P (≤)’ is the P-value for the likelihood ratio χ 2 statistic;
AICB – AICA , the difference in Akaike’s Information Criterion between models A and B; C, the C statistic that indicates concordance between predicted
probabilities and observed outcomes; DMC, Duff Moisture Code; FFMC, diurnally adjusted Fine Fuel Moisture Code; FWI, Fire Weather Index calculated
from the Buildup Index and diurnally adjusted ISI; ISI, diurnally adjusted Initial Spread Index calculated from FFMC and the 10-m open wind speed; MC,
moisture content (%) of the ground fuels specified by the fuel category (grass, lichen, moss, needles and leaf); RH, relative humidity (%)
Fuel categoryA
n
Sustained flaming
(B) Site variables
No
Yes
Variables
P (≤)
C
–
FFMC (<0.0001)
FFMC (<0.0001)
FWI (<0.0001)
ISI (0.0028)
DMC (0.0118)
FFMC (0.0033)
DMC (<0.0001)
FFMC (0.0094)
FFMC (<0.0001)
DMC (<0.0001)
FWI (0.0053)
DMC (<0.0001)
–
0.0001
0.0001
0.0001
0.0001
FC1
FC2
FC3
FC4
FC5
52
118
190
129
53
9
21
31
42
25
43
97
159
87
28
FC6
111
63
48
FC7
FC8
54
158
49
64
5
94
FC9
FC10
31
131
18
101
13
30
A See
(A) FWI system components
AICB – AICA
Variables
P (≤)
C
–
0.87
0.92
0.92
0.90
RH (0.0022)
RH (<0.0001)
MC (<0.0001)
MC (<0.0001)
MC (0.003)
0.0001
0.0001
0.0001
0.0001
0.0001
0.90
0.88
0.88
0.92
0.87
–
−0.17B
47.91C
1.60B
5.94D
0.0001
0.89
0.0001
0.86
13.11C
0.0001
0.0001
0.96
0.92
0.0001
0.0001
0.97
0.82
−1.83B
47.48C
0.0004
0.0001
0.82
0.88
MC (0.001)
RH (0.0007)
VPD (0.0062)
MC (0.0002)
RH (<0.0001)
MC (0.0124)
MC (<0.0001)
0.0002
0.0001
0.85
0.89
−1.36B
−8.34E
Table 1 for definition of categories. B Substantial evidence for both models. C Model B is highly unlikely. D Considerably less support for model B.
less support for model A.
E Considerably
Correlation analysis indicated significant correlations
between many independent variables. Uncorrelated independent
variables for FWI system components and site weather variables are shown in Table 4. Only combinations of uncorrelated
independent variables were used in model building.
FWI components were as good as or better than site variables
at predicting the probability of sustained flaming for eight of the
ten fuel categories. For each fuel category, the independent variables included in the best FWI system component model and site
variables model are shown in Table 5. All models were highly significant with concordance between predicted probabilities and
observed outcomes that ranged from 82 to 96%. Correspondence between model accuracy and accuracy computed from
cross-validated predicted probabilities indicated that all models
are robust, with the exception of the site-variables model estimated for FC5, which exhibited slight differences between
model accuracy and that estimated in the cross-validation
processes. For one category (FC1, spring grass fuels) the FWI
system components were not useful for predicting sustained
flaming, and for another category (FC10, aspen leaf fuels during
summer conditions), the model based on FWI system components had considerably less support than the model based on site
variables. The probability of sustained flaming was predicted for
each fuel category (Figs 1–6) from the models listed in Table 6.
For fuel categories 2–9, only the models based on FWI system
components are shown, as these out-performed models based on
site-variables for these fuel categories.
Discussion
Results indicate that sustained flaming ignition is driven primarily by the moisture content of fine fuels. Duff moisture
content and relative humidity represent secondary influences
on sustained flaming ignition for some fuel categories. Models
168
Int. J. Wildland Fire
J. L. Beverly and B. M. Wotton
Fuel category 1: Grass (spring)
Fuel category 4: Pine, moss
Fuel category 9: Aspen, grass (summer)
1.00
1.00
0.90
0.80
Probability of sustained flaming
Probability of sustained flaming
0.90
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
0
20
40
60
Relative humidity (%)
80
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
0
Fig. 1. Predicted probability of sustained flaming in grass fuels during
spring conditions (FC1) as a function of relative humidity (%).
5
10 15 20 25 30 35 40 45 50 55 60
FWI
Fig. 3. Predicted probability of sustained flaming in pine moss ground
fuels (FC4) and aspen grass ground fuels during summer conditions (FC9)
as a function of the diurnally adjusted FWI.
Fuel category 10: Aspen, leaf (summer)
1.00
Fuel category 2: Grass (summer)
Fuel category 3: Pine, lichen
Fuel category 7: Mixed wood, needles–leaf (summer)
0.80
1.00
0.70
0.90
0.60
0.80
0.50
Probability of sustained flaming
Probability of sustained flaming
0.90
0.40
0.30
0.20
0.10
0.00
0
10
20
30
40
Moisture content (%)
Fig. 2. Predicted probability of sustained flaming in aspen leaf ground
fuels during summer conditions (FC10) as a function of leaf moisture
content (%).
developed from FWI system components were as effective as
models developed from site variables at predicting the probability of sustained flaming for eight of the ten fuel categories. In
five of these fuel categories, the probability of sustained flaming was driven by the diurnally adjusted FFMC. The diurnally
adjusted ISI or FWI were key determinants of the probability
of sustained flaming for three fuel categories. While a significant independent variable in four of the FWI system models,
the DMC tended to represent a secondary influence on the probability of sustained flaming. These results are consistent with
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
65
70
75
80
85
90
95
100
FFMC
Fig. 4. Predicted probability of sustained flaming in grass fuels during
summer conditions (FC2), pine lichen ground fuels (FC3), and mixed-wood
needle-leaf ground fuels during summer conditions (FC7) as a function of
the diurnally adjusted FFMC.
the findings of Wotton and Beverly (in press) that DMC has an
influence on surface fuel moisture that is not accounted for in
the FFMC model.
FWI system components were not useful to predict sustained
flaming in spring grass fuels (FC1), where test fire outcomes
were driven by site relative humidity at the time of the fire.
Modelling the probability of sustained flaming
Int. J. Wildland Fire
0.40
98
0.30
190
100
0.20
250
0.10
170
0.00
DMC
90
10
1
These results are not surprising. Drying processes in grass fuels
in open areas, which are exposed to solar radiation and wind,
differ markedly from those in closed canopy jack pine or lodgepole pine fuels on which the FWI fuel moisture codes are based.
Grass fuels are highly responsive to changes in relative humidity
because of its high surface area-to-volume ratio (de Groot et al.
2005). Test fire results indicate that with a relative humidity of
≤18%, there was a 99% chance or greater of sustained flaming in
grass fuels during spring conditions (Fig. 1). FWI system components had limited usefulness for modelling the probability of
sustained flaming in aspen leaf fuels during summer conditions
(FC10). Although a model using the DMC was developed for
this fuel category, it had considerably less support than one that
used observations of moisture contents of leaf ground fuels.
The probability of sustained flaming as a function of specific
FWI system components varied by fuel category. While aspen
grass fuels during summer conditions (FC9) and pine moss fuels
(FC4) are both modelled as a function of FWI, aspen grass fuels
require a much higher FWI value to achieve a 90% chance of
ignition compared with moss fuels (Fig. 3). The probability of
sustained flaming as a function of FFMC also varied by fuel
category. For example, there is a 90% chance of sustained flaming in pine lichen fuels (FC3) with an FFMC of 86; in grass
summer fuels (FC2) with an FFMC of 90; and in mixed-wood
needles–leaf fuels in summer conditions (FC9) with an FFMC
of 94 (Fig. 4). The probability of sustained flaming in moss fuels
differed between forest cover types (Fig. 6). With a DMC of 80
and an FFMC of 89, there is a 90% chance of sustained flaming in moss fuels in a mixed-wood stand (FC6) and a 96% of
sustained flaming in moss fuels in a spruce stand (FC8).
This study corroborates existing evidence that the probability of sustained flaming is determined by ground and canopy
fuel characteristics, fuel moisture content of ground fuels, and
site weather variables, and may be of practical importance for
predicting forest susceptibility to fire in research and operational
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
190
100
FFMC
10
DMC
70
Fig. 5. Predicted probability of sustained flaming in pine needle ground
fuels (FC5) as a function of the diurnally adjusted ISI and the DMC.
74
3
78
5
82
7
86
9
90
11
ISI
DMC
10
Fuel category 8: Spruce, moss
(b)
94
13
98
15
FFMC
70
0.50
74
0.60
78
0.70
82
0.80
Probability of sustained flaming
Probability of sustained flaming
0.90
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
86
1.00
Fuel category 6: Mixedwood, moss
90
Probability of sustained flaming
(a)
94
Fuel category 5: Pine, needles
169
Fig. 6. Predicted probability of sustained flaming in (a) mixed-wood moss
ground fuels (FC6) and (b) spruce moss ground fuels (FC8) as a function of
the diurnally adjusted FFMC and the DMC.
fire management applications. The short-duration flame ignition
source used in this analysis is not representative of all potential
ignition sources. Individual fire ignition points or events can
occur under a range of circumstances. Multiple, simultaneous
ignition points can occur as a result of firebrand transport from
convective processes on large fires. If successful, these firebrand
ignition points can collectively increase the rate of spread and
intensity of the main fire (Brown and Davis 1973). Firebrands
can occur in glowing and flaming states. Compared with the
surface temperature of a glowing firebrand (≤650◦ C), the temperature of a match flame is much higher (≥1260◦ C) (Brown and
Davis 1973). It has been suggested that firebrands fall at or near
their terminal settling velocities and are most likely to be in a
state of glowing combustion when impact with a fuel bed occurs,
however, the presence of an air flow can sustain firebrands in a
flaming state upon impact (Manzello et al. 2006).
Pérez-Gorostiaga et al. (2002) conducted laboratory tests that
compared the probability of ignition as a function of firebrand
characteristics and found that ignition probability was consistently higher for firebrands in the flaming state with an absence of
170
Int. J. Wildland Fire
J. L. Beverly and B. M. Wotton
Table 6. Parameters of the logistic regression equation listed for each fuel category
Model form: P(sf ) =
Fuel categoryA
β0
FC1
FC2
FC3
FC4
FC5
FC6
FC7
FC8
FC9
FC10
FC10
7.3703
−23.3755
−34.8731
−4.414
−4.8479
−24.3837
−127.8
−38.9
−3.6403
2.8566
−4.1990
β1
β2
B
1
1 + e−(β0 +β1 x1 +···+βn xn )
β3
β4
β5
β6
−0.1725
0.2895
0.4304
0.3368
0.2407
1.3902
0.4117
0.0258
0.0638
0.6032
0.0679
0.1558
−0.2456
0.0421
Table 1 for definition of categories. B Where P(sf) = predicted probability of sustained flaming, χ1 = the diurnally adjusted FFMC, χ2 = the DMC,
χ3 = the diurnally adjusted ISI, χ4 = FWI, χ5 = relative humidity (%), χ6 = fuel moisture content (%).
A See
wind (no airflow) compared with firebrands in the glowing phase
with wind (2.8–16 km h−1 ). This suggests an apparent compensation between oxygen supply from the wind and associated
decreases in heat transfer. Most test fires included in our analysis
were conducted with site wind conditions of ≤4.8 km h−1 , but
all would have occurred with some degree of air flow.
In addition to representing conditions associated with some
firebrand ignitions from large fires, the short-duration flame
ignition source used to ignite test fires in this analysis may also
approximate ignitions associated with some lightning fires, if for
example, a lightning strike ignites a tree crown resulting in the
deposition of flaming embers on the fuel bed beneath the tree.
Short-duration flame ignition sources may also be representative of conditions possible at the site of a smouldering ignition
from a lightning strike or at a point along a formerly active fire
perimeter where an isolated smouldering ignition remains. At
these locations, the presence of glowing fire embers could conceivably transition to a flaming state with the addition of wind or
other microclimatic changes to result in isolated points of small
flaming ignition sources in contact with adjacent unburned fuel.
Fuel-specific models of the probability of sustained flaming
presented here can provide valuable insight into spatial variation
in forest susceptibility to fire, but they have limitations for assessing the potential for flame propagation and surface fire spread
following establishment of the sustained flaming state. While a
positive test fire outcome indicated that the fire would continue to
spread if fuel and weather conditions were unchanged, in reality
the ability of the fire to continue to propagate will depend on its
ability to overcome discontinuities in fuels or variability in fuel
moisture. Experimental test fires have been used to explore the
transition from a sustained flaming ignition to actively spreading surface fire. For example, Fernandes et al. (2002) conducted
field trials using line ignitions of small experimental plots to
investigate the determinants of sustained fire propagation. Similarly, Weise et al. (2005) modelled the probability of successful
fire spread following line ignition of laboratory fuel beds.
This study has shown that FWI system components are
highly effective substitutes for site variables for modelling the
likelihood that short-duration sustained flaming will develop in
forest ground fuels that have direct contact with a small and
short-lived flame source. Future analysis of test fire data contained in the Canadian small-scale test fire database will focus
on developing a suite of fuel-specific models for modelling the
probability of sustained flaming as a function of FWI components. Development of a new experimental test fire program to
investigate the probability of sustained fire propagation would
complement the current analysis by addressing the transition of
a successful fire ignition from a sustained flaming state to that
of an actively spreading surface fire.
Acknowledgements
We thank the many forest fire researchers with the Canadian federal government who participated in the design, development, and implementation
of the small-scale test fire program and/or contributed to the maintenance
of the test fire database over the past 70 years. Special thanks to B. Todd
(Canadian Forest Service) for ensuring that key historical test fire documents
were preserved to support future analysis of the test fire data. We thank
T. Lynham (Canadian Forest Service), two anonymous reviewers and our
assigning editor for providing helpful comments on the manuscript.
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J. L. Beverly and B. M. Wotton
Appendix 1. Temperature, relative humidity, vapour pressure deficit, and fuel moisture content for the two test fire outcomes: no sustained flaming
and sustained flaming
Fuel
Conditions
Grass (spring)
Grass (summer)
Pine lichen
Pine moss
Pine needles
Mixed-wood moss
Mixed-wood needles/leaf (summer)
Spruce moss
Aspen grass (summer)
Aspen leaf (summer)
A Not
No sustained flaming
(◦ C)
Temperature
Relative humidity (%)
Vapour pressure deficit
Moisture content (%)
Temperature (◦ C)
Relative humidity (%)
Vapour pressure deficit
Moisture content (%)
Temperature (◦ C)
Relative humidity (%)
Vapour pressure deficit
Moisture content (%)
Temperature (◦ C)
Relative humidity (%)
Vapour pressure deficit
Moisture content (%)
Temperature (◦ C)
Relative humidity (%)
Vapour pressure deficit
Moisture content (%)
Temperature (◦ C)
Relative humidity (%)
Vapour pressure deficit
Moisture content (%)
Temperature (◦ C)
Relative humidity (%)
Vapour pressure deficit
Moisture content (%)
Temperature (◦ C)
Relative humidity (%)
Vapour pressure deficit
Moisture content (%)
Temperature (◦ C)
Relative humidity (%)
Vapour pressure deficit
Moisture content (%)
Temperature (◦ C)
Relative humidity (%)
Vapour pressure deficit
Moisture content (%)
Sustained flaming
n
Mean
s.d.
Range
n
Mean
s.d.
Range
9
9
9
9
21
21
21
21
31
31
31
31
42
42
42
42
25
25
25
25
63
63
63
63
49
49
49
49
64
64
64
64
18
18
18
18
101
101
101
101
14.9
41.7
10.5
16.2
17.7
44.3
12.6
16.6
19.4
52.5
11.2
24.7
20.1
50.5
12.3
56.1
21.0
48.4
13.4
17.1
21.8
49.7
13.8
62.1
22.2
46.8
14.9
17.1
21.0
50.4
13.0
82.9
19.6A
55.4
10.8
18.6
21.0
52.6
12.9
32.1
4.6
9.3
4.2
10.2
5.8
12.6
6.6
8.8
3.8
11.0
4.2
10.7
4.3
11.3
4.9
51.9
3.3
11.4
5.0
8.5
4.2
10.0
5.2
67.5
4.5
8.8
5.1
7.1
3.7
11.6
5.0
53.6
4.6
15.4
6.4
6.2
4.9
12.7
6.5
24.3
8.3–24.4
31.0–61.0
4.3–19.3
2.5–31.7
7.2–28.9
22.0–79.0
2.1–31.1
6.9–38.0
12.2–27.8
37.0–76.0
4.1–23.5
12.6–56.5
11.7–27.8
32.0–79.0
4.1–23.5
12.0–208.2
13.3–28.3
26.0–76.0
4.6–28.5
6.0–36.4
11.7–31.1
32.0–78.0
4.9–26.2
13.7–377.8
11.1–31.1
28.0–75.0
6.0–28.0
7.5–39.9
12.2–27.8
30.0–85.0
2.2–23.4
5.6–213.5
12.2–29.4
26.0–82.0
4.8–30.4
8.9–32.7
8.9–32.2
30.0–84.0
2.7–33.7
8.0–121.1
43
43
43
43
97
97
97
97
159
159
159
159
87
87
87
87
28
28
28
28
48
48
48
48
5
5
5
5
94
94
94
94
13
13
13
13
30
30
30
30
20.2
26.7
18.2
9.1
23.9
29.3
21.9
13.2
22.7
42.7
16.7
13.0
23.4
40.2
18.5
11.9
23.6
40.2
19.0
10.0
24.4
39.5
20.1
17.4
30.0
29.8
30.2
8.3
23.0
38.7
18.3
56.4
21.2A
42.2
16.2
11.8
23.5
39.4
19.0
12.0
4.4
8.5
6.1
5.5
4.3
7.9
7.2
18.9
4.5
11.4
6.5
9.3
5.4
11.5
7.8
5.1
5.4
12.7
8.9
2.7
5.9
11.3
8.8
16.2
2.0
11.4
7.1
2.5
4.9
10.3
7.3
51.6
5.7
13.1
8.9
3.0
5.5
10.3
8.0
4.6
11.7–29.4
12.0–48.0
7.7–32.9
1.9–36.6
12.8–32.2
17.0–63.0
8.4–36.6
1.7–149.1
11.7–33.3
24.0–79.0
3.7–37.8
2.9–108.8
8.9–33.3
22.0–77.0
3.7–37.8
2.7–36.4
13.3–33.3
19.0–77.0
3.7–37.8
5.9–19.6
11.1–32.8
20.0–62.0
6.0–37.3
8.3–91.2
27.8–32.8
20.0–49.0
19.0–37.3
6.6–12.7
11.1–33.3
18.0–64.0
6.1–38.4
5.6–215.3
12.2–29.4
26.0–74.0
5.1–30.4
8.9–19.1
12.2–32.2
29.0–68.0
6.5–33.7
6.7–31.4
significantly different between the two test fire outcomes: no sustained flaming and sustained flaming (Wilcoxon rank sum test, P > 0.05).
Modelling the probability of sustained flaming
Int. J. Wildland Fire
173
Appendix 2. FWI component values that exceeded median values (calculated for all fires and for all fuel categories) for the two test fire outcomes:
no sustained flaming and sustained flaming
BUI, Buildup Index; DC, Drought Code; DMC, Duff Moisture Code; FFMC, diurnally adjusted Fine Fuel Moisture Code; FWI, Fire Weather Index calculated
from the Buildup Index and diurnally adjusted ISI; ISI, diurnally adjusted Initial Spread Index calculated from diurnally adjusted FFMC and the 10-m open
wind speed
Fuel
Component
Grass (spring)
Grass (summer)
Pine lichen
Pine moss
Pine needles
Mixed-wood moss
Mixed-wood needles/leaf (summer)
Spruce moss
Aspen grass (summer)
Aspen leaf (summer)
A Not
FFMC ≥ 89
DMC ≥ 49
ISI ≥ 7
BUI ≥ 69
DC ≥ 333
FWI ≥ 17
FFMC ≥ 89
DMC ≥ 49
ISI ≥ 7
BUI ≥ 69
DC ≥ 333
FWI ≥ 17
FFMC ≥ 89
DMC ≥ 49
ISI ≥ 7
BUI ≥ 69
DC ≥ 333
FWI ≥ 17
FFMC ≥ 89
DMC ≥ 49
ISI ≥ 7
BUI ≥ 69
DC ≥ 333
FWI ≥ 17
FFMC ≥ 89
DMC ≥ 49
ISI ≥ 7
BUI ≥ 69
DC ≥ 333
FWI ≥ 17
FFMC ≥ 89
DMC ≥ 49
ISI ≥ 7
BUI ≥ 69
DC ≥ 333
FWI ≥ 17
FFMC ≥ 89
DMC ≥ 49
ISI ≥ 7
BUI ≥ 69
DC ≥ 333
FWI ≥ 17
FFMC ≥ 89
DMC ≥ 49
ISI ≥ 7
BUI ≥ 69
DC ≥ 333
FWI ≥ 17
FFMC ≥ 89
DMC ≥ 49
ISI ≥ 7
BUI ≥ 69
DC ≥ 333
FWI ≥ 17
FFMC ≥ 89
DMC ≥ 49
ISI ≥ 7
BUI ≥ 69
DC ≥ 333
FWI ≥ 17
No sustained flaming
n
Proportion
9
9
9
9
9
9
21
21
21
21
21
21
31
31
31
31
31
31
42
42
42
42
42
42
25
25
25
25
25
25
63
63
63
63
63
63
49
49
49
49
49
49
64
64
64
64
64
64
18
18
18
18
18
18
101
101
101
101
101
101
0.44A
0.33A
0.44A
0.22
0.00
0.44A
0.10
0.29
0.00
0.29
0.24
0.05
0.00
0.13
0.06
0.26
0.58A
0.03
0.02
0.26
0.07
0.26
0.64A
0.05
0.08
0.12
0.08
0.16
0.60A
0.20
0.17
0.21
0.14
0.25
0.68A
0.17
0.37
0.31
0.27
0.35
0.67A
0.35
0.11
0.20
0.13
0.27
0.63
0.16
0.06A
0.50
0.11
0.67A
0.44
0.22
0.19
0.44
0.29
0.46
0.63
0.39
Sustained flaming
Count
4
3
4
2
0
4
2
6
0
6
5
1
0
4
2
8
18
1
1
11
3
11
27
2
2
3
2
4
15
5
11
13
9
16
43
11
18
15
13
17
33
17
7
13
8
17
40
10
1
9
2
12
8
4
19
44
29
46
64
39
n
Proportion
Count
43
43
43
43
43
43
97
97
97
97
97
97
159
159
159
159
159
159
87
87
87
87
87
87
28
28
28
28
28
28
48
48
48
48
48
48
5
5
5
5
5
5
94
94
94
94
94
94
13
13
13
13
13
13
30
30
30
30
30
30
0.65A
28
8
21
1
0
15
61
57
37
57
47
64
67
87
63
88
77
83
57
59
48
55
43
67
16
17
15
17
12
22
29
37
25
37
28
35
5
4
4
4
4
5
57
71
47
65
35
66
4
11
8
11
2
12
18
30
19
30
9
29
significantly different between the two test fire outcomes: sustained flaming and no sustained flaming (χ 2 , 1 df, P > 0.05).
0.19A
0.49A
0.02
0.00
0.35A
0.63
0.59
0.38
0.59
0.48
0.66
0.42
0.55
0.40
0.55
0.48A
0.52
0.66
0.68
0.55
0.63
0.49A
0.77
0.57
0.61
0.54
0.61
0.43A
0.79
0.60
0.77
0.52
0.77
0.58A
0.73
1.00
0.80
0.80
0.80
0.80A
1.00
0.61
0.76
0.50
0.69
0.37
0.70
0.31A
0.85
0.62
0.85A
0.15
0.92
0.60
1.00
0.63
1.00
0.30
0.97