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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/

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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 tonne/ha) in this right-of-way. While tonne/ha is a commonly accepted unit for fuel loading in grass fuel types, we used kg/m2 as the convention for all fuel components in this project. Through sample processing, we estimated the total fuel load on this site as .60 kg/m2: vegetative fuel load was .08 kg/m2 and woody biomass was .52 kg/m2 (Table 4).

Table 4. Fuel load by fuel type and size class at the PGR sampling site (kg/m2).

Vegetative Biomass Woody Biomass

Grass Dead Shrub

Total Vegetative

Biomass

Size Classes 1 + 2

(0-0.5 cm, 0.5-1.0 cm)

Size class 3+ (>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

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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

(<0.5 cm,

0.5-1.0 cm)

Size Class 3+

(>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

(<0.5 cm,

0.5-1.0 cm)

Size Class 3+

(>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

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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

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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

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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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References

Baxter, G. 2002. All-terrain vehicles as a cause of fire ignition in Alberta forests. [online]For. Eng. Res. Inst. Can. (FERIC), Vancouver, BC. Advantage Report 3 ( 44). 7 p. Baxter, G. 2009. Photo-reference guide for estimating grass fuel loads in Alberta. [online] FPInnovations, Vancouver, BC. Web Report. Available: http://wildfire.fpinnovations.ca/70/PhotoGuideForGrassFuelLoads.pdf 14 p. BC Hydro. 2010a.Transmission System Map [online]. BCH DWG.NO.G-T06-E10. 2012/2013. Available: http://transmission.bchydro.com/NR/rdonlyres/83A5FDF4-F326-4AEC-ABCE-A13D2D00542B/0/BCH_Transmission_J1_2013.pdf BC Hydro. 2010b. Integrated Vegetation Management Plan for Transmission Rights-of-way [online]. #105-977- 2010/2015. Available: https://www.bchydro.com/content/dam/hydro/medialib/internet/documents/safety/pdf/safety_-_ivmp_web.pdf Beverly, JL; Wotton, BM. (2007) Modelling the probability of sustained flaming: predictive value of fire weather index components compared with observations of site weather and fuel moisture conditions. International Journal of Wildland Fire 16: 161–173.

Bradshaw, LS; Tirmenstein, D. 2010 (Draft) [online]. FireFamilyPlus user's guide, version 4.1. Available: http://www.firemodels.org/downloads/firefamilyplus/publications/FFP-4_1_Draft_Users_Guide.pdf

British Columbia. 2005. Wildfire Act [online]. B.C. Reg. 38/2005. Queen's Printer, Victoria, British Columbia, Canada. Available: http://www.bclaws.ca/EPLibraries/bclaws_new/document/ID/freeside/11_38_2005#section10

Burgan, RE. 1979. Estimating live fuel moisture for the 1978 National Fire Danger Rating System. USDA Forest Service Gen. Tech. Rep. INT-226. 23 p.

Coldham, D. 2011. Bushfire ignition from electric faults: a review of technical literature. [online]. Report No: HLC/2010/440 – Final. Energy Safe Victoria. Mulgrave, Victoria, Australia. Available: http://www.esv.vic.gov.au/Portals/0/Consumers/Files/BushfireIgnitionReview.pdf

de Groot, B. 2012. CANFIRE Canadian Fire Effects Model [online]. Natural Resources Canada Canadian Forest Service – Great Lakes Forestry Centre. Frontline Express, Bulletin Number 62. Available: http://cfs.nrcan.gc.ca/pubwarehouse/pdfs/34093.pdf

Fites, J; Campbell, M; Reiner, A; Decker, T. 2007. [online]. Fire behaviour and effects relating to suppression, fuel treatments, and protected areas on the Antelope Complex Wheeler Fire. Prepared by the Fire Behaviour Assessment Team. US Forest Service. Available: http://www.fs.fed.us/adaptivemanagement/projects/FBAT/docs/Antelope_FINAL3_12_04_07.pdf

Forestry Canada Fire Danger Group 1992. Development and structure of the Canadian Forest Fire Behaviour Prediction System. Forestry Canada, Ottawa, Canada. Inf. Rep. ST-X-3.

21 | P a g e

Glitzenstein, J; Streng, D; Achtemeier, G; Naeher, L; Wade, D. 2006. Fuels and fire behavior in chipped and unchipped plots: Implications for land management near the wildland/urban interface. Forest Ecology and Management 236(1): 18-29.

Heinsch, FA.; Andrews, PL. 2010. BehavePlus fire modeling system, version 5.0: Design and Features. General Technical Report RMRS-GTR-249. USDA, Forest Service, Rocky Mountain Research Station Fort Collins, CO. General Technical Report RMRS-GTR-249.

Hirsch, KG. 1996. Canadian Forest Fire Behavior Prediction (FBP) System: user’s guide. Nat. Resour. Can., Can. For. Serv., Northwest Reg., North. For. Cent., Edmonton, AB. Spec. Rep. 7.

Hvenegaard, S. 2012. Fire behaviour in mulched fuel beds along linear corridors [online]. FPInnovations Wildfire Operations Research, Hinton , AB. Project Note. Available: http://wildfire.fpinnovations.ca/120/FireBehaviourInMulchedFuelBedsAlongLinearCorridorsNWT2012.pdf

Kane, JM; Varner, JM; and Knapp, EE. 2009. Novel fuelbed characteristics associated with mechanical mastication treatments in northern California and southwestern Oregon, USA. IJWF 18:686 – 697.

Knapp, EE; Varner, JM; Busse, MD; Skinner, CN and Shestak, CJ. 2011. Behaviour and effects of prescribed fire in masticated fuelbeds. IJWF 20:932 - 945.

McRae, DJ; Alexander, ME; Stocks, BJ. 1979. Measurement and description of fuels and fire behavior on prescribed burns: a handbook. Environ. Can., Can. For. Serv., Great Lakes For. Res. Cent., Sault Ste. Marie, ON. Inf. Rep. O-X-287.

Scott, JH; Burgan, RE. 2005. Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO. Gen. Tech. Rep. RMRS-153. 72 p.

Schiks, T. 2013. Modelling the probability of sustained ignition in mulch fuel beds. FPInnovations Wildfire Operations Research, Hinton, AB. Project Report. Available: http://wildfire.fpinnovations.ca/3/ModellingProbabiliitySustainedIgnitionInMulchedFuels2013.pdf

Schroeder, D; Russo, G; Beck, J; Hawkes, B; Dalrymple, G. 2006. Modeling ignition probability of thinned lodgepole pine stands. For. Eng. Res. Inst. Can. (FERIC), Vancouver, BC. Advantage Report 7 (12).

Schroeder, MJ; and Buck, CC. 1970. Fire weather: a guide for application of meteorological information to forest fire control operations. Agric. Handb. 360. U.S. Dep. Agric., Washington, DC.

Van Wagner, CE. 1982. Practical aspects of the line intersect method. Natural Resources Canada, Canadian Forest Service, Petawawa National Forestry Institute, Chalk River, ON. Information Report PI-X-12.

Van Wagner, CE. 1987. Development and structure of the Canadian Forest Fire Weather Index System. Agriculture Canada, Canadian Forest Service, Ottawa, ON. For. Tech Rep. 35.

Wotton, BM. 2009. A grass moisture model for the Canadian Forest Fire Danger Rating System. In: Proceedings 8th Fire and Forest Meteorology Symposium, Kalispell, MT Oct 13-15, 2009. Paper 3-2. Available: https://ams.confex.com/ams/pdfpapers/155930.pdf