Snowmelt energy balance in a burned forest stand, Crowsnest Pass, Alberta, Canada
Katie Burles and Sarah Boon, Dept. of Geography, University of Lethbridge, katie.burles@uleth.ca
1-D ENERGY BALANCE SIMULATION
Latent Advective
Energy Energy
(LHF)
(Qr)
Snow
Change in Internal Energy (Qθ)
Ground
Ground Heat
(GHF)
Energy balance components (Figure 4)
were simulated hourly for each stand
during the snow melt period (Apr 1- May
25) using the above equation. Advective
energy (energy supplied to the snow pack
by rainfall) was not considered as it was
not observed during the 2009 snow melt
period. Additionally, internal snow pack
processes are not physically represented
however, cold content was calculated and
incorporated empirically into the
simulation to account for energy required
to warm the snow pack to the melting
point (0ºC).
2.5
No research has been conducted to quantify the effects of forest cover change following
wildfire on snow processes. The purpose of this research is to quantify the effects of
wildfire on snow melt energy balance and seasonal timing of melt in a burned versus
healthy mature forest stand in the Crowsnest Pass, AB.
STUDY SITE
r2
Peak snow accumulation and snow pack
depletion were monitored by measuring
snow water equivalence. Snow density
was measured at 36 snow survey sites
on a 50 m x 50 m grid. Snow depth (n =
121) was measured at 5 m intervals in
the same grid. Snow surveys took place
in mid February, early March, and
weekly throughout the melt period. All
measurements were made in 2009, 5years post-fire.
Figure 3. Meteorological stations in the burned (left) and healthy (right) forest
stands.
1271
0.94
1439
0.96
Slope
1.09
1.10
Intercept
-0.33
-2.65
E
0.81
0.94
RMSE (cm)
2.48
1.01
10
1-Apr-09 11-Apr-09 21-Apr-09 1-May-09 11-May-09 21-May-09 31-May-09
Time (Hours)
B_Observed
B_Simulated
H_Observed
H_Simulated
Snow water equivalent
(SWE) (cm)
Healthy
15
0
SHF
-0.5
GHF
K*
GHF
K*
Qm
2.0
Healthy (Reference)
1.5
1.0
0.5
LHF
0.0
SHF
-0.5
Qm
L*
Burned
Healthy
Tss (oC)
Ta (oC)
μ (m s-1)
τc
τL
α
Ts (oC)
-3.5
1.5
1.2
0.82
0.82
0.62
1.1
-1.5
1.8
0.4
0.085
0.18
0.35
0.1
VWC(%)
13
30
Maximum observed SWE was greater in
the burned site than in the healthy site
(Figure 8). In 2009 and 2010, SWE peaked
in both stands just before April 1st . The
snow pack in the burned stand melted more
rapidly; complete snow pack removal
occurred seven days sooner than in the
healthy site. More snow water equivalent
was observed in 2009 relative to 2010
(Figure 8).
How does snow water equivalent (SWE) in 2009 compare to 2010?
Date of snow pack removal
Measured
143
151
Simulated
141
151
Figure 6. Simulated and observed snow water equivalent (Apr 1-May 31, 2009).
Simulation performance was assessed by comparing the rate and timing of continuous records of
observed versus simulated melt (Figure 6). Observed SWE was derived from the continuous
snow depth record and averaged snow survey density measurements. Goodness-of-fit between
observed and simulated SWE was determined using three quantitative measures of performance:
coefficient of determination (r2), coefficient of efficiency (E), and root mean square error (RMSE)6
(Table 1).
Snow water equivalent
(SWE) (cm)
Figure 2. Study area region (inset) and location of forest stands.
n (hours)
5
0.0
Table 2. Average meteorological conditions (Apr 1- May 25, 2009)
and forest structure parameters at both sites.
Burned
20
L*
Figure 7. Cumulative (Apr 1 – May 25, 2009) energy balance components: burned and healthy forest stand.
Table 1. Comparison of simulated vs. measured SWE (Apr 1May 25, 2009).
25
LHF
Components of the snowmelt energy balance
SIMULATION VALIDATION AND PERFORMANCE
30
0.5
2.5
Figure 5. Hemispherical photographs taken in the burned
(top) and healthy (bottom).
35
1.0
-1.0
Canopy density parameters were calculated using
hemispherical photos taken in close proximity to the
meteorological stations. Hemi-photos (Figure 5) were
processed using SideLook edge-detection software 4,
and Gap Light Analyzer (GLA)5 to determine the sky
view factor (τL :fraction of hemisphere visible from
beneath the canopy). Ability of the canopy to transmit
radiation (canopy transmissivity: τc) (K↓) in the burned
site was assumed to equal τL because there are no
needles or branches to restrict transmissivity of K↓. τc
was calculated in the healthy stand using a 60-day
average ratio of 20-minute measurements of K↓ in the
burned stand to K↓ measured beneath the healthy
forest canopy.
Snow water equivalent (SWE) (cm)
Two north-facing 2500 m2 stands at the northern
edge of the 2003 Lost Creek wildfire boundary in
the Crowsnest Pass(Figure 2), were selected for
study. The healthy (control) stand is located at
~1680 m and is representative of the mature
forest of the region and the burned stand is
located at ~1775 m, ~1km away is representative
of the most severely burned forest in the Lost
Creek fire. The dominant tree species is
subalpine fir with small portions of white spruce
and lodgepole pine.
10 m meteorological towers were installed in
each site to measure air pressure (P), air
temperature (Ta), snow surface temperature (Tss),
sonic air temperature (θ), relative humidity (RH),
wind speed (μ), ground heat flux (GHF), soil
temperature (Ts), soil volumetric water content
(VWC), incoming short-wave (K↓)and long-wave
radiation (L↓), snow surface albedo (α), and
snow depth (Figure 3).
Burned
1.5
-1.0
Figure 4. Schematic of the vertical energy fluxes during snow melt in a forested
environment.
Figure 1. Burned forest stand in the Crowsnest Pass, AB.
2.0
40
35
30
25
20
15
10
5
Burned
0
16-Feb-09
40
35
30
25
20
15
10
5
0
16-Feb
2009
Snow water equivalent
(SWE) (cm)
Removal of the forest canopy by wildfire
reduces the interception capacity of
forests, increasing snow accumulation in
burned stands3. Reduced forest canopy
increases short-wave radiation reaching
the snow surface, increases wind speeds,
increases vapour pressure and
temperature gradients between the snow
surface and the atmosphere, decreases
long-wave radiation emissions towards the
snow surface, and subsequently
enhances the energy available for snow
melt.
Sensible
Net
Radiation Energy
(SHF)
(K*+L*)
Differences in micrometeorological variables and forest structure parameters between
stands (Table 2) resulted in larger energy balance fluxes in the burned than the healthy
stand (Figure 7). 83% more energy was available for melt in the burned stand. Snow
melt was largely driven by K* and SHF in the burned stand, and a combination of K*,
L* and SHF in the healthy stand.
Healthy (Reference)
7-Apr-09
Time (Days)
27-May-09
Burned
2009
Snow water equivalent
(SWE) (cm)
Southwestern Alberta (AB) has seen a mean annual temperature increase of
in the last
century1. Predicted shifts in climate may increase the susceptibility of forest environments
to natural disturbances such as insect infestation and wildfire, and associated
anthropogenic disturbances such as salvage harvesting. Wildfire frequency and area
burned in Canada have been increasing since the early 20th century2. Forest disturbance
ultimately opens the forest canopy; however, specific structural impacts vary between
disturbance types, with difference effects on snow processes. Burned forest stands are a
unique disturbance type, where needles and small branches are completely removed and
all that remains are dead standing trunks and branches (Figure 1).
in MJ m-2 h-1
Energy (MJ m-2 d-1)
Q m = K * +L * +LHF + SHF + GHF + Q r + Q Θ
2 ºC
RESULTS & DISCUSSION
Energy (MJ m-2 d-1)
BACKGROUND
2010
7-Apr
Time (Days)
27-May
40
35
30
25
20
15
10
5
Burned
0
16-Feb-10
40
35
30
25
20
15
10
5
0
16-Feb
2010
Healthy (Reference)
7-Apr-10
Time (Days)
27-May-10
Healthy (Reference)
2009
2010
7-Apr
Time (Days)
27-May
Figure 8. Snow survey results. 2009 (top left) and 2010 (top right) snow water equivalent (SWE) in the burned and healthy stand. SWE between
years in the burned (bottom left) and healthy (bottom right).
ACKNOWLEDGEMENTS & REFERENCES
Funding was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC) - Alexander Graham
Bell Canadian Graduate Scholarship (CGS-M), University of Lethbridge – Startup Funds granted to Dr. Sarah Boon, and Alberta
(AB) Sustainable Resource Development (SRD) Forest Management Branch.
Thank you to the University of Alberta – Forest Hydrology, and the University of Lethbridge – Mountain Hydrology Research
Group for invaluable field assistance.
References: 1 Schindler, D.W., and Donahue, W.F. 2006. ‘An impending water crisis in Canada’s western prairie provinces’, P. Natl. Acad. Sci. USA. 103, 7210–
7216; 2 Podur, J., Martell, D. L.,and Knight, K. 2002. ‘Statistical quality control analysis of forest fire activity in Canada’, Can. J. Forest Res. 32, 195–205; 3
Farnes, P.E., and Hartmann, R.K. 1989. ‘Estimating the effects of wildfire on water supplies in the Northern Rocky Mountains’. Proc 57th Western Snow Conf.,
CO, Apr 18-20, 90-99; 4 Nobis, M., and Hunkizer, U. 2005. ‘Automatic thresholding for hemispherical canopy-photographsbased on edge detection’, Agric. For.
Meteor. 128, 243-250; 5 Frazer, G.W., Canham, C.D., and Lertzman, K.P. 1999. ‘Gap light analyzer (GLA), Version 2D0: Users manual and program
documentation’, Burnaby: Simon Fraser University and Millbrook: Institute of Ecosystem Studies; 6 Confalonieri, R., Bregaglio, S., and Acutis, M. 2010. ‘A
proposal of an indicator for quantifying model robustness based on the relationship between variability of errors and of explored conditions’, Ecol. Model. 21,
960-964.
University of Lethbridge Mountain Hydrology Research Group