Future burn probability in south-central
British Columbia
Xianli Wang1,2, Marc-André Parisien3, Steve Taylor4, Dan Perrakis5,
John Little3, Mike D. Flannigan2
1.
Canadian Forest Service, Sault Ste. Marie, ON
Department of Renewable Resources, University of Alberta. Edmonton, AB.
3. Canadian Forest Service, Edmonton, AB.
4. Canadian Forest Service, Victoria, BC.
5. Wildfire Management Branch, Victoria, BC.
2.
1
Where are wildfires most likely to burn?
2
Burn-P3 General Design
INPUT
BP MODEL
OUTPUT
Ignition *
Fuels (“snapshot”)
Topography
Fire spread
simulation
(Prometheus)
Fire
perimeter
Weather
duration*
daily conditions*
BP =
50
× 100 = 0.1%
50,000
*probabilistic
3
Research Goal*
Assess the spatial changes in fire likelihood as a
function of a changing climate in the ThompsonOkanagan region, British Columbia.
1) Fuels
2) Fire Weather
3) Number of Fire Ignitions
4
Study area
*Wang et al. 2016. International Journal of Wildland Fire
5
What do we want to achieve?
Treatments†
Factors
FUEL
IGN
WX
FUEL
baseline*
2080s
baseline
baseline
baseline
baseline
IGN
baseline
2080s
baseline
WX
baseline
baseline
2080s
FUEL × IGN
2080s
2080s
baseline
FUEL × WX
2080s
baseline
2080s
WX × IGN
baseline
2080s
2080s
2080s
2080s
2080s
CONTR
FUEL × IGN × WX
†
CONTR=Control, FUEL=Fuel, IGN=Number of fire ignitions, WX=Fire weather
*baseline = 1961~1990; 2080s= 2071~2100 projection
6
Generating future daily fire weather
Software GCM
CO2 emission Number of stations Time periods
scenario
188 pseudo-stations Baseline: 1961 – 1990
BioSIM* CGCM3.2 A2
2080s: 2071 – 2100
*Régnière and Saint-Amant (2008)
7
Map inputs
* SAF=Subalpine
forest; GDF=Grassland and dry forest; MTF= Moist temperate forest
8
Map inputs
* SAF=Subalpine
forest; GDF=Grassland and dry forest; MTF= Moist temperate forest
9
Subalpine Forest
Grassland and Dry Forest
Moist Temperate Forest
10
Modified factors
11
Modified factors
12
Modified factors
13
Modified factors
14
Burn P3 runs
Treatments
Factors
FUEL
IGN
WX
FUEL
baseline*
2080s
baseline
baseline
baseline
baseline
IGN
baseline
2080s
baseline
WX
baseline
baseline
2080s
FUEL × IGN
2080s
2080s
baseline
FUEL × WX
2080s
baseline
2080s
WX × IGN
baseline
2080s
2080s
2080s
2080s
2080s
CONTR†
FUEL × IGN × WX
†
CONTR=Control, FUEL=Fuel, IGN=Number of fire ignitions, WX=Fire weather
*baseline = 1961~1990 baseline; 2080s= 2071~2100 projection
15
Burn probabilities (BP)
16
Impacts of treatments
17
The effect of the ignitions, weather, and fuels on BP
Treatment
F-value
p-value
F
56.30
0.00
W
16.83
0.00
I
8.52
0.01
F×W
4.94
0.05
F×I
3.48
0.09
W×I
0.45
0.58
F×W×I
1.93
0.21
F = fuel; W = weather; I = ignition
18
Similarity in spatial patterns of BP: correlation test
Baseline F
I
W
F+I
F+W I+W
F
0.41*
I
0.97*
0.42*
W
0.96*
0.40*
0.96*
F+I
0.42*
0.97*
0.42*
0.41*
F+W
0.47*
0.95*
0.48*
0.48*
0.96*
I+W
0.96*
0.40*
0.97*
0.99*
0.40* 0.48*
0.61*
0.04
–0.01
0.62* 0.52* –0.01
F+I+W 0.04
F = fuel; W = weather; I = ignition
19
Fire size changes
20
Take home message
1. Ignition, Fuel, and weather are all significant in
affecting future BP; Fuel > Weather > Ignition
2. Spatial distribution of BP varies the most when
fuel is changed
3. BP are significantly different among all
treatments
4. Weather and ignition affect BP uniformly
across the study area; weather affects middleupper elevation more than lower elevation
21
Future work:
Mapping current and future wildfire risk in Canada
1. Develop a nationally consistent
procedure to map current and future BP
2. Develop a method to portray wildfire risk
by combining BP with wildland and
urban interface (WUI) and infrastructure
maps
3. Deliver these products to key
stakeholders nationally
22
Thank you!
23
Ignition grids
24
Fire zone
BEC zone
Description
Subalpine
Engelmann Spruce and
Elevation > 1200 m, dominant tree species
Forest (SAF)
Subalpine Fir (ESSF),
include spruces (Picea engelmannii), fir
Montane Spruce (MS),
(Abies lasiocarpa), and mountain hemlock
Mountain Hemlock (MH),
(Tsuga mertensiana).
Alpine Tundra (AT)
Grassland and Ponderosa Pine (PP),
Elevation < 1200 m, dominated by
Dry Forest
Interior Douglas-Fir (IDF), bunchgrass, ponderosa pine (Pinus
(GDF)
Bunchgrass (BG)
ponderosa) and Douglas-fir (Pseudotsuga
menziesii). Residential and agricultural
activities mainly occur in this zone.
Moist
Interior Cedar Hemlock
Mainly distributed in the deep valley area in
Temperate
(ICH), Coastal Western
the northeastern corner of the study area.
Forest (MTF)
Hemlock (CWH)
Dominant tree species include red cedar,
mountain hemlock and western hemlock.
25
Defining a potential spread day
 Fire expiration day
threshold:
 DMC = 20
 Fire spread day
threshold (Podur and
Wotton 2011):
 FWI = 19
26
Simulating potential spread days
27
Simulating potential spread days
28
Simulating potential spread days
29
Simulating potential spread days
30
Generating realized spread days
31
Generating realized spread days
Step 1: Mapping daily
fire growth from
remotely sensed data
(Parks 2014)
32
Generating realized spread days
Step 1: Mapping daily
fire growth from
remotely sensed data
(Parks 2014)
Step 2: Generating
realized spread days
33
Potential and realized spread day distribution
34
Predicting realized spread days
A
B
35
Predicting realized spread days
A
B
36
Predicting realized spread days
A
B
37
Location
--- Average monthly temperature C ---
(Fire zone)
Lumby
559 m
(MTF)
Princeton
701 m
(GDF)
Brenda Mines
1520 m
(SAF)
A
1961-1990
M
J
J
A
S
--- Average monthly precipitation (mm) --O
7.2 11.3 15.2 17.6 17.3 12.6 6.2
2070-2100 10.1 15.2 18.4 21.7 21.3 16.0 9.0
∆
2.9
4.0
3.3
4.1
4.0
3.5 2.8
A
M
J
J
A
S
O
41.8 61.8 72.1 58.8 48.3 46.4 42.8
56.7 78.6 91.7
59 41.9 50.5 65.4
14.9 16.8 19.6
0.2
-6.4
4.1 22.6
1961-1990
6.4 10.7 14.8 17.6 17.5 12.8 6.6
18.1 23.4 29.5 28.6 25.6 21.5 20.5
2071-2100
9.7 14.0 17.7 22.0 21.8 16.9 9.4
19.5 31.7 40.8 27.7 19.3 26.9 30.7
∆
3.3
3.3
4.3
4.1 2.8
1961-1990
2.0
6.6 10.7 13.9 13.9
9.2 3.6
36.6 47.3 44.7 38.9 38.6 36.9 41.4
2071-2100
5.2 10.0 13.5 17.2 18.2 13.2 6.5
45.8 68.5 68.5 57.5 33.6 41.6 62.1
∆
3.2
3.4
2.9
2.8
4.4
3.3
4.3
4.1 2.9
1.4
8.3 11.3
-0.9
9.2 21.2 23.8 18.6
-6.3
-5.0
5.4 10.2
4.7 20.7
38