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Mulched fuels and potential fire behaviour in BC Hydro
rights-of-way
Steven Hvenegaard; Tom Schiks
Introduction
BC Hydro operates and maintains an extensive network of
hydroelectric transmission lines across British Columbia (BC Hydro
2010a) that spans vast areas of vegetated landscape. BC Hydro
implements an Integrated Vegetation Management Program (IVMP) (BC
Hydro 2010b), which is critical in mitigating the risk of power
interruption resulting from line contact with adjacent vegetation.
Manual and mechanical vegetation treatments (such as hand slashing,
mowing, and mulching) are used as vegetation control measures as
part of the IVMP. These operations result in an accumulation of
woody debris, which may alter the risk of fire in the hydro
rights-of-way (ROWs). BC Hydro recognizes fuel loading as a factor
in determining vegetation management cycles for various vegetation
types in ROWs. Increasing the frequency of ROW maintenance prevents
excessive growth in target species and reduces the deposits of
debris produced in maintenance operations. Mulching treatments in
hydro ROWs are costly and create, at least in the short-term, an
increased environmental disturbance. Vegetation management programs
attempt to balance their cost and environmental effects with the
positive outcomes of reduced fuel loading and consequent fire risk
in ROWs. Other considerations in vegetation management include the
proximity of combustible fuels and legislation under the British
Columbia Wildfire Act (British Columbia 2005). This legislation
requires a utility transmission operation adjacent to forest land
or grass land to “maintain the site in a manner that prevents any
fire from spreading from the site.”
FPInnovations is interested in evaluating wildland fuel
conditions in mulched fuel along linear corridors to assess
quantity and combustibility of fuels, and quantify the potential
fire spread and ignition probability. In May 2012, we studied BC
Hydro ROWs and wildland fuel environments at a number of sites in
Northern and Southern Interior BC to evaluate the accumulation of
woody debris (specifically mulch) resulting from ROW maintenance
operations.
Objectives
Determine the potential behaviour of a fire originating from a
point-source ignition within hydro rights-of-way under different
weather scenarios.
Evaluate the ignition potential of fuels in hydro rights-of-way
to determine a weather/fuel moisture threshold that would support
ignition.
Assess the effect of a modified fuel environment in a hydro
right-of-way on the behaviour of an encroaching wildfire.
Final Report July 2013
Wildfire Operations Research
1176 Switzer Drive Hinton, AB T7V 1V3
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Methods
BC Hydro representatives in the Northern Interior and Southern
Interior management areas assisted FPInnovations in selecting study
sites that represent various vegetation types encountered in ROWs
(Table 1). Site selection criteria for the study sites included
measurable accumulations of mulch or woody debris in the ROW and
accessibility.
Table 1. Information for Northern and Southern Interior BC Hydro
study sites.
Site Code
Site Name and BC Hydro
Line/Structure Identifier
Location Treatment
Date Treatment
Type Sample
Date
PGR
Prince George
5L061 Structure 28-2
40 km west of Prince George; adjacent to Highway 16
2008
2010
Mowinga
Herbicide
May 2012
SSR
Sunny Slope Road
60L358 Structure 38-8
60 km west of Prince George; junction of Highway 16 and Sunny
Slope Road
2008 Chipping May 2012
KLR
Keefer Lake Road
1L201/202 Structure 21-9
90 km east of Vernon on Highway 6
Spring 2008
Mowing and Hand
Slashing
May 2012
CCR
Cooke Creek Road
5L075/77 Structure 42-2
25 km east of Enderby; access on Cooke Creek Forest Service
Road
Spring 2010
Mowing and Hand
Slashing
May 2012
a A tractor or tracked vehicle equipped with a horizontal rotary
blade (1 – 2 m in diameter), often referred to as a Hydro-axe, is
used to
reduce (mow) vegetation in ROWs.
Fuel Load Estimation
Each study site was selected in a relatively homogeneous area as
a representative mulched right-of-way for the region and
surrounding forest fuel type. We established one 50 x 50 m plot for
fuel load estimation within the ROW. Site descriptions and
photographs were collected. We used a destructive plot-based
sampling method (Kane et al. 2009) to collect vegetation and dead
woody debris from each sampling site. Two 20 m transects were set
up at random azimuths, randomly traversing the site. Along each
transect, five 50 x 50 cm square quadrats were spaced 5 m apart
with random orientations. Four 20 cm spikes were driven down to the
mineral soil at the corners of each quadrat (Figure 1) for the
purpose of measuring depth of the fuel bed layer. All vegetation
and woody material was collected within the quadrat, bagged and
labeled by each separate fuel component. Any material intersecting
the quadrat was cut along the inside of the square, and the inner
portion bagged. All samples were transported to the lab where they
were oven-dried at 95°C to a constant weight, and the dry weight of
each fuel component was recorded. All dry weights were converted to
kg/m2.
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Figure 1. Fuel sampling quadrat in a mulched fuel bed.
Fire Behaviour Predictions
Head Fire Intensity and Rate of Spread
We estimated potential fire behaviour by using the fuel load
data from the processed samples and historical percentile weather
data as inputs to appropriate fire behaviour prediction models. For
three of our fuel sampling sites, we used the Canadian Fire Effects
Model (CanFIRE) (deGroot 2012) to calculate fire behaviour
characteristics including rate of spread (ROS) and head-fire
intensity1 (HFI). The CanFIRE model applies a grass fuel model and
a grass fuel model with standing timber model, which closely
replicate these sites. CanFIRE allows for variable fuel load inputs
and we applied measured loadings for vegetation and different size
classes of debris to produce fire behaviour predictions. We
processed hourly weather data for the last ten years from
representative weather stations (Table 2) operated by the British
Columbia Wildfire Management Branch to determine Fire Weather Index
(FWI) (Van Wagner 1987) values for the 50th, 75th, and 90th
percentile weather scenarios for each site (Table 3).
Existing fuel models within fire behaviour prediction models do
not appropriately represent the mulched fuel bed sampled at the
Sunny Slope Road site. We modeled the mulched fuel bed following an
approach implemented by Glitzenstein (2006) where an existing
representative slash fuel model (Scott and Burgan 2005) was
customized using measured loadings of different sized fuel
particles in the fuel bed. We processed the hourly weather data in
Fire Family Plus (Bradshaw and Tirmensten 2010) in order to
determine moisture content for 1-hour, 10-hour, and 100-hour time
lag fuels at the 50th, 75th, and 90th percentiles. We input these
fuel moisture contents and customized fuel loadings into the BEHAVE
Plus 5 (Heinsch and Andrews 2010) fire modeling system to determine
fire behaviour predictions for the mulched fuel bed at Sunny Slope
Road site.
We estimated potential fire behaviour in adjacent forest stands
using the REDApp Fire Behaviour Calculator.2 We input the FWI
values and wind speed for the 75th and 90th percentiles for the
sampling site areas. Due to
1 Head Fire Intensity is the predicted intensity, or energy
output, of the fire at the front or head of the fire. (Canadian
Forest Service Canadian Wildfire Information System).
http://cwfis.cfs.nrcan.gc.ca/en_CA/background/summary/fbp 2 REDApp.
The Universal Fire Behaviour Calculator. http://redapp.org/
http://cwfis.cfs.nrcan.gc.ca/en_CA/background/summary/fbphttp://redapp.org/
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time constraints, we were unable to sample and intensively
characterize the adjacent forest stands to provide site-specific
inputs for the fire behaviour prediction models. We chose one or
two of the benchmark FBP fuel types that best matched the adjacent
forest fuels.
Table 2. Representative weather stations for sampling sites.
Sampling Site
Location Altitude (m) Weather Station Location Altitude (m)
PGR N53 52.018
W123 17.410 801 Bednesti
N53 51.92
W123 19.38 858
SSR N53 53.453
W123 30.541 810 Bednesti
N53 51.92
W123 19.38 858
KLR N50 03.846
W118 26.857 1218 Kettle 2
N49 57.58
W118 37.55 1389
CCR N50 36.483
W118 51.589 538 Mabel Lake 2
N50 21.1
W118 46.40 488
Table 3. Percentile weather scenarios and associated FWI
values.
Weather Station
Percentile Wind Speed
Fine Fuel Moisture
Code
Duff Moisture
Code
Drought Code
Initial Spread Index
Buildup Index
Fire Weather
Index
Bednesti
50 5.0 75 21 326 1.3 37 3
75 7.0 87 27 227 4.0 40 9
90 10.0 90 45 362 7.0 67 18
Mabel Lake 2
50 9.0 76 19 245 1.3 32 2
75 8.0 89 51 370 5.7 73 17
90 9.0 92 79 367 9.0 100 28
Kettle 2
50 8.0 78 27 243 2.0 38 5
75 7.5 90 46 326 6.0 67 18
90 9.0 93 68 301 11.0 85 30
Ignition Probabilty
We did not conduct ignition probability testing in these hydro
ROWs since the short time frame for testing would not provide a
sufficient data set to capture a broad range of fuel moisture
values and ignition outcomes. Several research projects have
documented ignition testing results in uniform surface fuels
including duff, grass and needle litter. However, literature that
documents ignition potential in heterogeneous fuel beds (such as
those studied in the PGR, KLR, and CCR sites) is limited. We
reviewed literature that developed models for open
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or thinned fuel types with similar fuel characteristics as those
in the sample sites. Documentation of ignition potential in mulched
fuel beds is limited; however, we applied preliminary results from
ongoing research (Schiks 2013) to the SSR study site.
Results
Each of the surveyed sites has unique vegetation
characteristics, debris accumulations, and weather conditions,
which contributed to varied potential fire behaviour at each site.
Given the variations in weather conditions and broad range of
survey data collected in these sampled sites, we have presented
fire behaviour predictions for each site separately in this
section. Without ignition test data for the sampling sites, it is
impossible to quantify the probability of ignition and sustained
burning for each of the specific study sites. A collective summary
of results from the literature review that apply to these study
sites is presented at the end of the Results section.
PGR - 5L061 – Structure 28-2
Site Description
This 500 kV hydro line ROW runs along Highway 16 west of Prince
George through continuous coniferous forest mixed with a lesser
deciduous component (Figure 2). With the exception of watercourses
intersecting this section of ROW, the chosen study site represented
a very consistent grass–shrub fuel environment.
Figure 2. The 5L061 right-of-way.
Vegetation in the study area was primarily short grass with
minor herbaceous and dead-and-down woody debris components (Figure
3). Recent treatments on this section of the ROW included mowing in
2008 and herbicide application in 2010. Herbicide was applied to
treat deciduous target re-sprouts to reduce their densities on the
ROW. Treatments in hydro ROWs are applied very selectively to
target selected species and allow compatible vegetation such as
shrubs or herbaceous layers to grow. Conifers do not require
treatment because they do not re-sprout if cut below the lowest
living branch, or whorl. The 5L061 ROW has 5 different management
areas with varying maintenance cycles. The maintenance cycle for
this study site is 10–12 years.
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Figure 3. Light fuel loading in the 5L061 right-of-way.
Fuel Loading
Initial onsite observations and use of a photo reference guide
(Baxter 2009) suggested a light grass loading (1 cm)
Total Woody Biomass
.07 .01 .08 .21 .31 .52
Fire Behaviour Predictions
We chose matted grass as the most representative subset of the
FBP grass fuel type for the fire behaviour predictions at the PGR
sampling site. Two different degrees of curing (100% and 80%) were
applied as inputs in CanFIRE to produce two sets of fire behaviour
outputs for each weather percentile (Table 5).
Table 5. Fire behaviour predictions for the PGR sampling site
using CanFIRE model.
Percentile Weather Scenario
Head Fire Intensity (kW/m) Rate of Spread (m/min)
100% cured grass 80% cured grass 100% cured grass 80% cured
grass
50th 94 44 1.4 0.8
75th 447 242 9.3 5.5
90th 1167 650 19.6 11.7
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SSR - 60L358 – Structure 38-8
Site Description
This 69 kV hydro line ROW is 20–25 m wide with surface fuels
consisting of a thick layer of chipped debris (Figure 4) produced
from a 2008 treatment using a Rolly Chipper.3 This treatment was
used to mitigate the risk of Lodgepole pine in the red and grey
stages of Mountain Pine Beetle infestation. Even though some
affected trees were harvested from this section of ROW, a large
number of dead trees remained. The Rolly Chipper was used to mulch
these hazardous fuels and efficiently remove the risk. The ROW is
adjacent to coniferous forest on the south side and cured grass
along Highway 16 on the north side. Given the narrow width of this
short section of ROW, the sample plot was located in the widest
part of this area.
Figure 4. Sunny Slope Road study site adjacent to Highway
16.
Fuel Loading
Biomass at this study site was primarily a thick layer of
chipped debris with willow stems sprouted sparsely throughout the
ROW. The measured depth of debris accumulations in the sample
quadrats varied from 1.5 to 49.6 cm with an average depth of 18.3
cm. The average fuel load (oven-dried) of chipped material
collected from the sample quadrats was 17.7 kg/m2. The debris in
the top 2–4 cm of study site was dry while debris at lower depths
were saturated and in the early stages of decomposition (Figure 5).
We estimated the quantity of available fuel by considering this dry
layer of fuel as the only portion of the fuel bed available for
consumption. We extended previous observations (Hvenegaard 2012) of
mulched fuel beds under extended drought conditions to the SSR
site, and estimated that the upper 5 cm of this fuel bed could be
available for consumption. The average bulk density of the mulched
fuel bed was 125.94 kg/m3 and it follows that the available fuel
loading in the top 5 cm layer is 6.3 kg/m2. The loadings of the
different sized fuel components are outlined in Table 6.
3 Rolly Chipper is a vegetation management implement
manufactured by Risley Equipment.
http://www.risleyequipment.com/products.aspx?Product=RollyChipper&Gallery=Main
http://www.risleyequipment.com/products.aspx?Product=RollyChipper&Gallery=Main
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Figure 5. Fuel profiles in deep chip accumulation at the SSR
sampling site.
Table 6. Fuel loads at the SSR sampling site (kg/m2).
Mulch Particles Needles
Size Classes 1 and 2
(0-0.5 cm, 0.5-1.0 cm)
Size Class 3+
(>1 cm)
4.4 (69.9%) 1.42 (22.6%) .47 (7.4%)
Fire Behaviour Predictions
A close range of fuel moisture content values for the 1-hour and
10-hour time lag fuels at the three weather percentiles produce
similar fire behaviour characteristics when input into BEHAVE Plus
5 (Table 7). However, large differences in wind speed from the 50th
to the 90th percentile resulted in the rate of spread being
doubled.
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Table 7. Fuel moisture, wind speed and fire behaviour prediction
for the SSR sampling site using the BEHAVE model.
Percentile Weather
Fuel Moisture (%)
Wind Speed (m/min)
Fire Behaviour Prediction
1-hour 10-hour Rate of Spread
(m/min) Flame Length
(m) Head Fire Intensity
(kW/m)
50th 7.95 8.95 5 0.2 0.2 5
75th 7.47 8.48 7 0.3 0.2 7
90th 7.22 8.29 10 0.4 0.3 10
KLR - 1L201/202 Structure 21-9 to 22-1
Site Description
This 138 kV hydro line ROW traverses several vegetation types
(agricultural and forested) from Vernon along Highway 6 to Lower
Arrow Lake. The study site is located 90 km east of Vernon in
forested mountain terrain. This section of the ROW was treated in
spring 2008 using a tracked mower with hand slashing in rocky or
steep areas. Regeneration in the survey site is primarily Lodgepole
pine 0.5 to 1.5 m tall with some broadleaf species (Figure 6). The
study site represented the consistent fuel environment along this
section of ROW, which included saplings as contributing fuel
component.
Figure 6. The 1L201/201 right-of-way.
Fuel Loading
We estimated the vegetative fuel load and the woody debris fuel
load at .37 kg/m2 and .54 kg/m2, respectively (Table 8) for a total
of .91 kg/m2. The primary vegetative component (Figure 7) was live
shrubs (.27 kg/m2) while grass made up a small portion of the
vegetative load (.02 kg/m2). We inventoried the regeneration within
a 12 m diameter circle and estimated the live stem density of
saplings at 2,475 stems/ha with an average height of 0.8 m.
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Figure 7. Predominance of live shrubs in the vegetative fuel
component at the KLR sampling site.
Table 8. Fuel loads at the KLR sampling site (kg/m2).
Vegetative Biomass Woody Biomass
Live Shrub
Grass Herb Litter Total
Size Classes 1 and 2
(1 cm) Total
.27 .02 .01 .07 .37 .21 .33 .54
Fire Behaviour Predictions
At the 90th percentile with 100% cured grass and a live shrub
component, the CanFIRE model calculated rate of spread and head
fire intensity outputs of 31 m/min and 6,142 kW/m (Table 9). The
grass with standing timber fuel model in CanFIRE has provision for
including the saplings as fuel which was accounted for in this
prediction.
Table 9. Fire behaviour predictions for the KLR sampling
site.
Percentile Weather Scenario
Head Fire Intensity (kW/m) Rate of Spread (m/min)
100% cured grass 80% cured grass 100% cured grass 80% cured
grass
50th 327 186 2 1
75th 3000 1762 17 10
90th 6142 3120 31 19
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CCR - 5L075/77 Structure 42-2
Site Description
These twinned 500 kV hydro lines cross steep forested terrain
from Revelstoke to the Ashton Creek substation with the majority of
the ROW surrounded by a mix of coniferous and deciduous vegetation
(Figure 8). The sample site is accessed from the Cooke Creek Forest
Service Road. This section of ROW was recently treated in spring
2010 using a tracked mower and hand slashing. We chose this
sampling site for its uncharacteristic heavier fuel loading which
could produce more vigorous or ‘worst-case scenario’ fire behaviour
for this section of ROW.
Figure 8. The 5L075/077 right-of-way from Cooke Creek Forest
Service Road.
Fuel Loading
Live and dead shrubs were the predominant vegetative fuel in
this ROW with a very light grass load (Table 10). Accumulations of
dead fern and cured grass (Figure 9) created a relatively high
loading of matted fine fuels. The sparse regeneration of broadleaf
species was not considered as fuel.
Figure 9. Vegetation and woody debris in the CCR sampling
site.
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Table 10. Fuel loads at the CCR sampling site (kg/m2).
Vegetative Biomass Woody Biomass
Dead Shrub
Live Shrub
Grass Litter Total
Size Classes 1 and 2
(1 cm) Total
.17 .17 .04 .18 .56 .21 .44 .65
Fire Behaviour Predictions
In the fire behaviour predictions for the CCR site (Table 11),
we used the grass model in CanFIRE and included the live and dead
shrub loadings in the overall matted grass loading. Since the model
does not account for live herbaceous as fuel, this fuel component
was included as part of the grass loading. Fuel moisture content in
the live shrubs is likely higher than that in the grass component
and the predictions likely over predict. This issued is addressed
in the Discussion section.
Table 11. Potential fire behaviour for the CCR sampling
site.
Percentile Weather Scenario
Head Fire Intensity (kW/m) Rate of Spread (m/min)
100% cured grass 80% cured grass 100% cured grass 80% cured
grass
50th 278 153 1.5 1.2
75th 2145 1189 14 8.6
90th 5242 2809 26 16
Effect of Fuel Modification in ROW on Behaviour of Encroaching
Fire on ROW
The estimated fire behaviour characteristics (rate of spread and
head fire intensity) calculated in REDApp for the adjacent forest
stands, and estimates for fuel conditions in each of the sampling
sites are presented in Table 12. In each of the comparisons between
head fire intensity in the adjacent forest stand and the
fuel-reduced ROW, there is a notable drop in HFI. With the
exception of the SSR sampling site, the rate of spread was greater
in the open fuel environment of the ROW.
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Table 12. Effect of fuel treatment on fire behaviour in hydro
rights-of-way.
Site Code
Predicted Fire Behaviour
Sampling Sitea Adjacent Forest Stand
90th percentile
75th percentile
90th percentile
75th percentile
HFI ROS HFI ROS FBP Fuel Type HFI ROS HFI ROS
PGR 1167 19.6 447 9 M-1 (75:25) 5296 6.7 1340 2.8
M-2 (75:25) 5030 6.4 1286 2.7
SSR 10 0.4 7 0.3 C-3 1335 2.2 118 0.5
O-1b 1426 19 582 7.7
KLR 6142 31 3000 17 M-1 (75:25) 10487 11.1 4335 5.7
M-2 (75:25) 9977 10.6 4117 5.4
CCR 3900 26 2145 14 M-1 (50:50) 5846 7.6 2254 3.8
M-2 (50:50) 5048 6.7 1994 3.3 a100% cured grass rate is applied
to fire behaviour predictions in the sampling sites.
Ignition Potential in Hydro ROWs
An abundance of fine fuels (cured grass and litter) in hydro
ROWs presents an ideal receptor for ignition sources (fire brands,
line contact or mechanical). The strongest influence on sustained
flaming ignition in fine fuels such as grass is fuel moisture
content (Beverly and Wotton 2007). Once grass becomes snow-free in
early spring, it dries much more quickly than fuels in
closed-canopy forest types. The very open and exposed conditions
(i.e., higher winds and greater solar radiation) increase the
drying rates of surface fuels. Generally, it is assumed that grass
fuels dry more quickly than forest litter in closed canopies
(Wotton 2009) and, therefore, becomes dry enough for combustion
sooner. Fuel moisture content in cured grasses and other fine fuels
are highly responsive to relative humidity and can change
dramatically through the day (Schroeder and Buck 1970).
Daily or hourly monitoring of fuel moisture content is difficult
to implement, and models have been developed that apply surrogate
measures such as FWI values or site weather conditions to predict
probability of ignition and sustained flaming. Schroeder et al.
(2006) evaluated the predictive value of input variables including
fuel moisture content, FWI values, and site weather conditions as
indicators of ignition probability in thinned Lodgepole pine
stands. Relative humidity was found to be the best predictor of
ignition probability in this fuel type. The ignition probability
model developed through this study suggests a probability of
ignition of 90% when relative humidity is below 30%. Relative
humidity is also the most reliable predictor of ignition in cured
grass fuel types (Beverly and Wotton 2007). The ignition
probability model developed for cured grass fuels indicates outputs
similar to those in the model for thinned Lodgepole pine stands.
Both of these models indicate that probability of ignition is
greater than 50% when relative humidity falls below 42%.
Baxter (2002) studied ignitions in Alberta forests resulting
from all-terrain vehicles (ATV) and found that the majority of ATV
caused wildfires occurred in April and May when grass fuels are
almost 100% cured. The
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median FWI fine fuel moisture code (FFMC) on the days of these
ATV-caused ignitions was 87. This study also suggests that an FFMC
of 75 is a minimum value for ignitions in these fuel types.
The current fine-fuel moisture model (FFMC) was developed as an
indicator of moisture levels in a forest litter layer (dead needles
and twigs) in a closed canopy conifer stand. Moisture content in
exposed open grass fuel types are not affected by the sheltering
effect of a closed canopy, or by the exchange of moisture with a
sub-surface organic layer. Surface fuel beds in open fuel types, as
in hydro ROWs, receive a greater amount of solar radiation and are
more exposed to winds than forest stands with a closed canopy.
Wotton (2009) developed a moisture model that more accurately
tracks the fast changing moisture condition of open cured grass
fuels. This model includes adjustments for exposure to solar
radiation and a response time specific to fine grass fuels.
The mulched material observed in this study had a mostly
discontinuous distribution of material within a ROW, and the
quantity and size classes present more closely resemble light
downed woody debris. Grasses and shrubs dominate the ROWs in terms
of percent cover. Logically, it is more likely that potential
ignition sources (e.g., cigarettes, matches, machinery sparks) will
come into contact with the most abundant fuels. Given that grasses
and shrubs were the most dominant cover on the ROWs, a grass
ignition probability model should be applied.
However, in areas where mulch is continuous and arranged in a
thick layer (SSR site), a mulch ignition probability model would be
applicable. Schiks (2013) conducted ignition probability tests in
mulched fuels and developed a model which presents a starting point
for assessing ignition probability in 100% continuous mulch. The
mulched fuel beds under study had lower probability of ignition
than grass and moss fuel beds and higher FFMC values are required
for successful ignition. Results from this study show that a
threshold value for medium probability of sustained ignition
(>50%) in mulched fuels is at an FFMC of 90.
Varying loadings of different sized fuel particles and the
continuity of fine fuels in hydro ROWs will influence sustained
burning. While it is difficult to quantify the influence of fine
fuel continuity in a fuel environment, one could surmise that ROWs
with a heavier grass load and relatively lighter loadings of live
shrub and debris may be more prone to ignition and sustained
burning.
Questions addressing probability of ignition and wildfires
resulting from arcing hydro lines and the requisite parameters of
ignition source and receptive fuels are discussed in a literature
review by Coldham (2011). The findings suggest that there is no
published research work that addresses the ignition of wildland
fuels and wildfires resulting from arcing of hydro lines. The
author suggests that there are several parameters (voltage, current
or duration of arc) that may influence probability of ignition from
arcing hydro lines and that further research is required to
quantify how the many different parameters will influence ignition.
One conclusive discussion surrounds the nature of fuel beds and the
well-documented research that directly relates the probability of
ignition in fuel beds to low fuel moisture content.
Discussion
Fire Behaviour From a Point-Source Ignition
The first objective of this research was to determine the
potential fire behaviour characteristics of a fire originating from
a point-source ignition within hydro rights-of-way under different
weather scenarios. Fires in open fuel types in linear corridors are
more exposed to ambient winds than closed canopy forests and will
initially develop a higher rate of spread. It is important to note
that fire originating from a point-source ignition develops through
an acceleration phase with increasing intensity and rate of spread
until the fire reaches its maximum output (equilibrium) that can be
achieved under the current fuel and weather conditions. The
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acceleration approach built into the FBP system assumes that a
fire in an open fuel type will “reach 90% of its equilibrium rate
of spread 20 minutes after ignition” (Forestry Canada Fire Danger
Group 1992). For example, for a predicted equilibrium rate of
spread of 15 m/min, the rate of spread after 10 and 20 minutes will
be 10.3 m/min and 13.5 m/min, respectively (Hirsch 1996). This has
implications for industry operators in fire prone fuel types. Most
importantly, operators need to have basic fire suppression
equipment that allows them to suppress small fires quickly and
aggressively. High rate of spread and fire intensity predicted for
the hydro ROWs under the 90th percentile will create serious
suppression challenges and safety concerns when a fire reaches
equilibrium.
Operators in BC Hydro ROWs maintain awareness of weather and
fuel conditions by checking daily the Fire Danger Class for their
work zone as determined by Schedule 2 of the British Columbia
Wildfire Act (British Columbia 2005). Operators modify activities
in ROWs to adhere to Restrictions on High Risk Activities as
outlined in Schedule 3 of this legislation. These restrictions
describe appropriate precautions including using a fire watcher,
reducing working hours, and stopping work activities. Ongoing
observations of localized weather and fuel conditions (seasonal
curing) will aid in evaluating the ignition and fire behaviour
potential in rights-of-way. Crews working in BC Hydro ROWs are
trained in basic fire suppression4 and can respond to fire starts
that are within their capabilities. Crews are also equipped with
firefighting hand tools and water delivery equipment appropriate to
the risk of ignition in their work activities and the fire danger
class.
Ignition Potential for Fuels in ROWs
In spite of the varying nature and loadings of surface fuels
found in hydro ROWs, the universal factor influencing probability
of ignition and sustained burning is fuel moisture content in fine
fuels. Cured grasses present in ROWs in early spring or late summer
will dry quickly in low humidity conditions and will be easily
ignited. While it is not operationally practical to measure fuel
moisture in fine fuels, other indicators such as relative humidity
and FFMC can be used as indicators of the potential for ignition
and sustained burning.
The influence of relative humidity on fuel moisture is
indirectly implied through the FWI values (BUI and FWI) in the Fire
Danger Class tables in Schedule 2 of the BC Wildfire Act. While the
fast changing fuel moisture in cured grass may not be fully
reflected, these tables provide the basis for the Restrictions on
High Risk Activities (Schedule 3). Schedule 3 provides guidance on
operational measures required to mitigate wildfire risk and
directives on when high risk activities must cease.
An updated grass fuel moisture model will provide a more
accurate representation of the moisture content and drying response
times for open cured grass fuels typically found in linear
corridors. However, until this fuel model is implemented
operationally, the most relevant indicators of ignition probability
and sustained burning in this fuel type will be relative humidity
and the current FFMC model.
While the majority of BC Hydro ROWs have well-established
maintenance cycles to maintain vegetation and debris loadings,
other newer treatments of standing forest can produce heavy
loadings of chipped debris which has different ignition potential
and fire behaviour characteristics. Documented research and fire
behaviour predictions from this study indicate that ignition
potential, rate of spread and fire intensity will be less than what
can be expected in a grass/debris fuel loading found in other
ROWs.
4 S-100 Basic Fire Suppression and Safety training is the
standard of training used by BC Wildfire Management Branch for
entry level firefighters.
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Hydro ROWs as Potential Fuelbreaks
Mooney (2010) developed a collective definition of an effective
fuelbreak as a distinct area of modified forest fuels adjacent to
values at risk that can significantly alter fire behaviour such
that suppression forces can safely mitigate the fire spread to
values. Fire behaviour predictions for fuel modified ROWs
contrasted with those in adjacent natural forest stands do not
indicate an appreciable reduction in fire behaviour at the 90th
percentile that would guarantee safe and effective fire suppression
in the ROWs. However, it should be noted that predictions in this
percentile range are calculated using a 100% cured grass input
creating a worst-case fire behaviour scenario during the peak
burning period (midafternoon to early evening). Ignitions outside
this timeframe in fuels which are not fully cured may not reach the
intensity suggested by the predictions. Fire behaviour predictions
using a cured grass content of 80% resulted in a 40% reduction in
predicted head fire intensity and rate of spread.
Some Community Wildfire Protection Plans (CWPPs) recognize hydro
ROWs as potential fuelbreaks and recommend ongoing maintenance and
evaluation of wildfire risk.5 Protecting hydro infrastructure and
power supply to essential values during wildfire events is also a
critical part of CWPPs. Communities and municipalities that rely on
hydro ROWs or other linear corridors as fuelbreaks should recognize
that these may not provide an effective fuelbreak at all weather
percentiles and need to determine an acceptable degree of
protection that can be achieved within the community protection
budget.
Fire Behaviour Modelling
Fire behaviour predictions for the KLR and CCR sites were
estimated in CanFIRE by including dead and live shrub loads in the
grass load input. These inclusive fuel models likely overestimate
fire behaviour predictions since variations in seasonal fuel
moisture content in live herbaceous plants and woody plants will
determine whether these fuels act as a heat source or heat sink
(Burgan 1979). The exclusion of the live shrub components in the
grass loading reduces predicted fire intensity by approximately 30%
and interpolations may provide an intuitive compromise to the high
estimates of fire intensity.
Fire behaviour predictions based on FWI values are produced for
the peak burning period. Maximum temperature and minimum relative
humidity compounded with stronger winds during the late afternoon
create a more active period of fire behaviour. Diurnal fluctuations
in fine fuel moisture content tend to temper fire behaviour outside
the peak burning period.
Fuel Models for Mulched Fuels
Documented fire behaviour in mulched fuels is limited. Hence,
there is little empirical data to develop and validate mulch fuel
models used in fire behaviour models. However, documented
observations of fire behaviour in chipped fuels (Glitzenstein 2006)
suggest that fire behaviour modeling in BEHAVE Plus 5 for the SSR
mulch site under similar wind conditions produces realistic
estimates. Mulched fuels have a unique compacted structure, which
affects fuel moisture, oxygen supply, and, ultimately, fire
propagation. A specific fuel model for mulched fuels has not been
developed for use in fire behaviour models, but modifications to a
slash fuel model in BEHAVE Plus 5 have been implemented. Custom
fuel models developed to reflect actual loadings of different sized
mulched fuel particles can predict fire behaviour more accurately
than standard fuel models (Knapp et al. 2011). Continued research
and collection of empirical data of fire behaviour in mulched fuel
is necessary to create a data set that can be used to develop and
validate a fuel model for mulch.
5City of Kelowna Policy and Planning. 2011. Community Wildfire
Protection Plan.
http://www.kelowna.ca/CityPage/Docs/PDFs/Parks/11-05-11%20DHC%20Report%20-%20Kelowna%20CWP%20Electronic.pdf
http://www.kelowna.ca/CityPage/Docs/PDFs/Parks/11-05-11%20DHC%20Report%20-%20Kelowna%20CWP%20Electronic.pdf
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Suppression Considerations
The nature of the open fuel environment in ROWs creates fire
suppression challenges and opportunities. The reduced resistance to
wind in an open fuel type in linear corridors allows the fire to
spread to available fuels and reach greater rates of spread as
predicted in Table 12. The majority of fuels in the ROWs are fine
fuels such as grass or shrubs which respond quickly to changes in
relative humidity and increased solar radiation. The reduced fire
intensity as fire moves from a forest stand to fuel-reduced ROWs
may allow fire crews to action fires in the ROW under moderate
intensities or implement indirect attack strategies under extreme
conditions.
Firefighter observations provide valuable feedback on the fire
behaviour in mulched fuel treatments and the difficulty in
suppressing fire in these fuel treatments during wildfire
operations. Wildfire in the Antelope Complex (Fites et al. 2007)
was observed approaching a treated area where it transitioned from
a crown fire to a moderate intensity surface allowing firefighters
to directly suppress the fire and conduct burn operations.
Observations of spot fires in treated areas, resulting from ember
transfer, in the Wheeler Fire were contained or self-extinguished.
Post-burn documentation also provides valuable information on burn
severity including tree mortality and depth of burn in mulched fuel
treatments.
Fire intensity and rate of spread outputs produced by the fire
behaviour prediction models are for the head of the fire, which is
the more vigorous than other parts of the fire (flanks and rear).
Under extreme fire conditions, direct attack with ground crews on
the head of a fire will not be safe or productive, but alternate
strategies, such as anchor-and-hold, on different parts of a fire
may be effective and safe. Under extreme fire conditions, aerial
attack of fire in fuel-reduced areas along hydro ROWs may be a
viable suppression strategy for reducing fire intensity to a level
that can be managed by other resources. Aerial suppression using
airtankers or bucket-equipped helicopters close to hydro lines has
special considerations including effect of retardants on hydro
lines or water contact with charged hydro lines.
Responders to wildfires in volatile fuels found in the ROWs
should have basic wildfire suppression training, use appropriate
PPE, and recognize the potential for sudden flare-ups in fire
behaviour with changing weather conditions. Working in hydro ROWs
has inherent risks and during fire suppression operations these
risks are increased. Line contact with vegetation or the ground
creates a serious hazard and responders must follow safe work
procedures and observe limits of approach6 when suppressing fires
near hydro lines. Water spray or mist created during suppression
operations has the potential to conduct electricity from hydro
lines and appropriate training7 in safe work procedures around
hydro lines is recommended.
Conclusions
The fuel sampling sites studied in this study represent a small
portion of BC Hydro ROWs and these results should not be applied to
other areas with different vegetation types, fuel loading, and
weather patterns. Further sampling and wildfire risk evaluation
should be conducted in areas with denser vegetation types and
heavier accumulations of woody debris in the ROWs.
The fire behaviour predictions generated using our measured fuel
loads as inputs suggest that vegetation management in the surveyed
ROWs was successful in reducing the potential intensity of
wildfires approaching
6 Limits of approach are the safe distances that you must
maintain between your body and your equipment, and live
electrical lines or apparatus (from BC Hydro Utility Tree
Workers Safety Guide. 2004). 7 BC Hydro. Electrical Safety Training
for Fire Fighter Training Officers.
http://www.bchydro.com/safety-outages/safety/workplace_safety/electrical_safety0.html
http://www.bchydro.com/safety-outages/safety/workplace_safety/electrical_safety0.html
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18 | P a g e
a ROW. However, since light, flashy fuels in the open ROWs are
more exposed to the effects of wind, there is a potential for
higher rates of spread under conditions of cured fine fuels and low
fuel moisture in live fuels.
Curing of grass fuels is a critical fuel condition closely
monitored by some fire zones to more accurately evaluate fire
hazard. Fire Danger Class ratings do not fully incorporate grass
curing rates and operators in hydro ROWs should be aware of the
increased wildfire hazard in cured grass fuels in early spring and
late summer. Operators can also monitor other localized conditions
such as relative humidity and wind speed which may not be fully
reflected in regional FWI values and general Fire Danger Classes.
Point-source ignitions starting on ROWs have the potential to
rapidly accelerate to a high rate of spread in open linear
corridors and onsite operators should be prepared with basic fire
suppression tools to contain spot fires before they gain momentum
and reach full intensity and rate of spread.
Some CWPPs8 incorporate recommendations to work with BC Hydro to
maintain transmission ROWs to a fuelbreak standard that will
provide the community with a reliable power supply that is less
likely to fail during a fire event and reduce the threat of
wildfire impacting the community. Further research studying fire
behaviour in linear corridors and ongoing monitoring of fuels
treatments to evaluate fuelbreak effectiveness will be valuable to
communities that rely on hydro ROWs or other linear corridors as
integral components of a values protection strategy in their
community.
Mulched debris particles in surface fuel beds have a lower
probability of ignition than other fine fuels in linear corridors.
Chipping operations are often used to increase line clearance with
forest fuels adjacent to hydro ROWs. The fire behaviour modifying
effects of chipping operations can be optimized by broadcasting
chipped debris to produce a more uniform ground cover of mulch.
Since the majority of surface fuels in established BC Hydro ROWs
are not uniformly mulched fuels, the potential for ignition and
sustained burning in heterogeneous fuel beds including mulched
debris should be addressed as a research question. Continued
documentation of fire behaviour in mulched fuels is necessary to
gain a better understanding of ignition probabilities and sustained
burning in this fuel type and how fire behaviour is influenced by
mulched fuels.
8 City of Fernie. 2005. Community Wildfire Protection Plan.
https://fernie.civicweb.net/Documents/DocumentList.aspx?ID=6371
https://fernie.civicweb.net/Documents/DocumentList.aspx?ID=6371
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Participating Members/Collaborators
BC Hydro
University of Toronto
Acknowledgements
Thank you to BC Hydro representatives for assistance in
coordinating this research project, and locating suitable sampling
plots. Tom Wells, Geoff Helfrich, Wayne Clarke, Ben Cave, and Ian
Boyd provided us with initial and ongoing support in clarifying
site and vegetation management details.
Thank you to the following people who assisted in so many ways
to provide technical assistance, fuel-modeling advice, and data
processing and management assistance:
Alan Cantin and Bill DeGroot at the Canadian Forest Service
assisted with technical assistance and application of fuel modeling
inputs of the CanFIRE fire behaviour model.
Marty Alexander provided advice in the use of fire behaviour
models and additional resources to be explored in preparing fire
behaviour predictions.
Eric Meyer at British Columbia Wildfire Management Branch
provided historical data for the weather stations in the study
areas.
Larry Bradshaw and staff at Rocky Mountain Research Station,
Missoula Fire Sciences Lab processed historical weather data
through Fire Family Plus to produce time lag fuel moisture contents
that were used in BEHAVE fire behaviour predictions.
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20 | P a g e
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