Simulating Areal Snowcover Depletion and Snowmelt Runoff in Alpine Terrain Chris DeBeer and John Pomeroy Centre for Hydrology, Department of Geography and Planning University of Saskatchewan
Jan 11, 2016
Simulating Areal Snowcover Depletion and Snowmelt Runoff in Alpine Terrain
Chris DeBeer and John Pomeroy
Centre for Hydrology,Department of Geography and Planning
University of Saskatchewan
Background
• Snowmelt runoff from Rocky Mountains is an important water resource
• High uncertainty in the future hydrological
response to climate and/or landcover change
• Important to be able to better understand and predict likely changes for future water management
– Requires robust and physically based models for simulating snow processes
Variability of Alpine Snow Processes
• Complexities in terrain and vegetation affect snow accumulation, redistribution, and melt
– High spatial variability in snow water
equivalent (SWE)– Large variation in energy for snowmelt during
the spring
• Leads to a patchy snowcover as the spring progresses
• Significantly affects timing, rate, and magnitude of meltwater generation
Areal Snowcover Depletion (SCD)
• Melt rate computations applied to a distribution of SWE yield snowcovered area (SCA) over time (SCD curve)
(SWE)dSWEpSCAaM
0
0.01
0.02
0.03
0.04
0.05
0.06
0 100 200 300 400 500 600
SWE (mm)
p (
SW
E)
Ma
100 mm
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 100 200 300 400 500 600
Melt depth (mm)
SC
A
Ma
100 mm
Ma
200 mm
Ma
350 mm
Ma
200 mm
Ma
350 mm
SCA = 0.92
SCA = 0.45
SCA = 0.05
Frequency distribution of SWE Derived SCD curve
Problems with SCD Approach in Alpine Terrain
• The approach assumes uniform melt rate over the SWE distribution– Energy balance melt rate computations depend
on snowpack state (e.g. depth, density, SWE, temperature, etc.)
– Melt rates are not uniform in alpine terrain
• Further problems with new snowfall part way through melt
Study Objectives
• Develop new theoretical framework for areal snowcover depletion (SCD) and meltwater generation
• Test framework using observations in alpine basin
• Determine how variability of SWE and snowmelt energy affect areal SCD and meltwater generation
• Incorporate framework within hydrological model and examine influence of variability on hydrograph
Development of Theoretical Framework
• Framework for areal SCD based on lognormal distribution
0
100
200
300
400
500
600
700
800
-4 -2 0 2 4 6
K
SW
E (
mm
)
2
5
10
15
SWE = 200(1+K(0.4))
days
0
0.25
0.5
0.75
1
0 5 10 15 20
Days
SC
A0
1
2
3
4
-3 -2 -1 0 1 2 3 4 5 6
K
SW
E /
SW
E
CV = 0.0
CV = 0.2
CV = 0.4
CV = 0.6
CV = 0.8
CV = 1.0
0 10 50 80 90 95 99 99.9
P
CV)K(1SWESWE
SCD curve from uniform 30 mm/day applied snowmelt
Development of Theoretical Framework
Frequency factor, K
SW
E (
mm
)
Kmin, i Kmin, ii 0
Initial distribution
50
150
200
100
Following melt
Melt depth for 50 mm initial SWE
Melt depth for 100 mm initial SWE
SW
E
Kmin, 1 Kmin, 2 0
Snowmelt
Subsequent accumulation
Foot
Line representing the distribution can be discretized
New snow added uniformly over remaining distribution
1
32
• Framework handles other important aspects of spatial snowmelt and new snowfall during spring
Field Study Site
• Marmot Creek Research Basin, Kananaskis Country, Alberta
Field Study Site
• Focused data collection at Fisera Ridge and Upper Middle Creek
Fisera Ridge
Mt. Allan
Field Methods and Observations
• Data collection over three years (2007-09) involved:– Meteorological observation– Snow surveys– Daily terrestrial photos– Lidar snowcover mapping– Streamflow measurement
Ridgetop Station
North Facing Station
Southeast Facing Station
Field Methods and Observations
• 100’s of snow surveys over 3 years • Setup and maintenance of many
instruments and met stations• Dozen’s of manual stream discharge
measurements
Terrestrial Oblique Photo Correction1) Viewshed mask created from camera perspective
2) DEM projection in camera coordinate system
3) Correspondence established between DEM cells and image pixels
4) Image reprojection in DEM coordinates
2 & 3
1
4
May 7, 2007May 10, 2007May 14, 2007May 17, 2007May 19, 2007May 22, 2007May 26, 2007May 29, 2007May 31, 2007June 2, 2007June 4, 2007June 7, 2007June 10, 2007June 13, 2007June 18, 2007June 21, 2007June 24, 2007June 27, 2007July 1, 2007July 4, 2007July 9, 2007July 13, 2007
Areal Snowcover Observations
Time lapse digital photography used to monitor areal SCD
Snowmelt Modelling and Validation
• Snowpack evolution simulated using the Snobal energy balance model within Cold Regions Hydrological Model (CRHM) platform
Active layer
Lower layer
LvE H K↑ K↓ L↓ L↑ P E
Soil layer
G R
U
U
Shortwave and longwave radiation inputs corrected for slope, aspect, skyview fraction using algorithms in CRHM (Qm = LVE + H - K↑ + K↓ + L↓ - L↑ + G - dU/dt)
Snowmelt Modelling and Validation
• Model performs well for depth, SWE, internal energy
-25
-15
-5
1-Apr 15-Apr 29-Apr 13-May 27-May 10-Jun 24-Jun
Date (2008)
Snow
tem
p (
°C)
Simulated active layer TMeasured active layer TSimulated lower layer TMeasured lower layer T
0
300
600
900
1-Apr 15-Apr 29-Apr 13-May 27-May 10-Jun 24-Jun
Date (2008)
Depth
(m
m)
Simulated SWE (mm)Measured SWE (mm)
0
1
2
3
1-Apr 15-Apr 29-Apr 13-May 27-May 10-Jun 24-Jun
Date (2008)
Depth
(m
)
Measured depth (m)
Simulated depth (m)
0
1
2
3
1-Apr 15-Apr 29-Apr 13-May 27-May 10-Jun 24-Jun
Date (2009)
Depth
(m
)
Measured depth (m)
Simulated depth (m)
0
300
600
900
1-Apr 15-Apr 29-Apr 13-May 27-May 10-Jun 24-Jun
Date (2009)
Depth
(m
m)
Simulated SWE (mm)
Measured SWE (mm)
-15
-10
-5
0
1-Apr 15-Apr 29-Apr 13-May 27-May 10-Jun 24-Jun
Date (2009)
Snow
tem
p (
°C)
Simulated active layer TMeasured active layer TSimulated lower layer TMeasured lower layer T
2008 2009
Effects of Snow Mass and Internal Energy
• Differences in initial state have large influence on computation of snowmelt timing and rate
-8
-6
-4
-2
0
22-Apr 23-Apr 24-Apr 25-Apr 26-Apr 27-Apr 28-Apr 29-Apr 30-Apr 1-May 2-May 3-May 4-May 5-May 6-May
Date (2008)
Co
ld c
on
ten
t (M
J/m
2 )
Sn
ow
pa
ck te
mp
era
ture
(°C
) Shallow CC Intermediate CC Deep CC Shallow T Intermediate T Deep T
0
5
10
15
20
22-Apr 23-Apr 24-Apr 25-Apr 26-Apr 27-Apr 28-Apr 29-Apr 30-Apr 1-May 2-May 3-May 4-May 5-May
Date (2008)
Me
lt ra
te (
mm
/da
y)
Shallow snow
Intermediate snow
Deep snow
Cold Content: Energy required to bring snowpack to 0 °C and satisfy liquid water holding capacity
Spatial – Temporal Snowmelt Variability
0
3
6
9
12
15
0 400 800 1200
SWE (mm)
Me
lt ra
te (
mm
/da
y) a
26-Apr
28-Apr
30-Apr
2-May
4-May
0
10
20
30
40
50
0 400 800 1200
SWE (mm)
Me
lt ra
te (
mm
/da
y) a 14-May 16-May
18-May 16-Jun
21-Jun
0
3
6
9
12
15
0 400 800 1200
SWE (mm)
Me
lt ra
te (
mm
/da
y) a 26-Apr
28-Apr
30-Apr
2-May
4-May
0
10
20
30
40
50
0 400 800 1200
SWE (mm)
Me
lt ra
te (
mm
/da
y) a
14-May 16-May18-May 16-Jun
21-Jun
SE-facing slope
N-facing slope
• Differences in melt energy and SWE lead to large differences in snowmelt that change over time
Landscape Disaggregation for SCD Simulation
• SWE values on different slopes fit theoretical lognormal distribution
SWE = 157.0K + 223.6
R2 = 0.96
0
500
1000
1500
2000
-2.0 0.0 2.0 4.0 6.0 8.0 10.0
K
SW
E (
mm
)
0
0.05
0.1
0.15
0.2
0.25
0.3
015
030
045
060
075
090
010
5012
0013
5015
00
SWE (mm)
f (S
WE
)
SWEmeasurements
Theoreticaldistribution
SWE = 273.5K + 290.9
R2 = 0.97
0
500
1000
1500
2000
2500
-5.0 0.0 5.0 10.0 15.0
K
SW
E (
mm
)
0
0.05
0.1
0.15
0.2
0.25
015
030
045
060
075
090
010
5012
0013
5015
00
SWE (mm)
f (S
WE
)
SWEmeasurements
Theoreticaldistribution
S-facing slope
N-facing slope
Simulation of Areal SCD over Landscape
• Framework applied to predict areal SCD
• Results were improved by considering separate distributions and melt rates on each slope
0
0.5
1
10-May 20-May 30-May 9-Jun 19-Jun 29-Jun 9-JulDate (2008)
SC
A fr
act
ion
Observed overall SCD curve Simulated overall SCD curve
NS = 0.78RMSE = 0.15
0
0.5
1
10-May 20-May 30-May 9-Jun 19-Jun 29-Jun 9-JulDate (2008)
SC
A fr
act
ion
Observed north facing SCD curve Simulated north facing SCD curve
NS = 0.89RMSE = 0.10
0
0.5
1
10-May 20-May 30-May 9-Jun 19-Jun 29-Jun 9-Jul
Date (2008)
SC
A fr
act
ion
Observed south facing SCD curve Simulated south facing SCD curve
NS = 0.84RMSE = 0.16
0
0.5
1
10-May 20-May 30-May 9-Jun 19-Jun 29-Jun 9-Jul
Date (2008)
SC
A fr
act
ion
Observed east facing SCD curve Simulated east facing SCD curve
NS = 0.94RMSE = 0.06
Overall basin
Northfacing
Southfacing
Eastfacing
0
0.5
1
10-May 20-May 30-May 9-Jun 19-Jun 29-Jun 9-Jul
Date (2008)
SC
A fr
act
ion
Simulated overal SCD curve Observed overall SCD curve
NS = 0.92
RMSE = 0.09
Influence of “Inhomogeneous” Snowmelt
• Earlier and more rapid melt of shallow snow on some slopes led to an initial acceleration of SCD
0.25
0.5
0.75
1
24-Apr 27-Apr 30-Apr 3-May 6-May
Date (2008)
SC
A fr
act
ion
Inhomogenous 150 mm initial 300 mm initial
850 mm initial Observed
S facing slope
0.25
0.5
0.75
1
24-Apr 27-Apr 30-Apr 3-May 6-May
Date (2008)
SC
A fr
act
ion
Inhomogenous 150 mm initial 300 mm initial
850 mm initial Observed
N facing slope
0.25
0.5
0.75
1
24-Apr 27-Apr 30-Apr 3-May 6-May
Date (2008)
SC
A fr
act
ion
Inhomogenous 150 mm initial 300 mm initial
850 mm initial Observed
Overall basin
• Variability in melt over landscape and SWE dist’s. affects location, extent, and timing of meltwater generating area
0 – 5mm/day
5 – 10mm/day
10 – 20mm/day
exposedground
forestedareas
cliffs
April 26 April 29
May 2 May 5
Spatial Variability of Meltwater Generation
• Process-based and conceptual model with spatial structure based on topography, land cover, and SWE distributions
Hydrological Model Development
SWE = 273.5K + 290.9R2 = 0.97
0
100
200
300
400
500
600
700
800
-1.5 0.0 1.5
SW
E (m
m)
K
33% of dist.
34% of distribution
19% of distribution
14% of distribution
• Model is capable of producing reasonable hydrographs with correct volume of runoff
Model Evaluation for Snowmelt Hydrograph
0
0.1
0.2
0.3
01-May 15-May 29-May 12-Jun 26-Jun 10-Jul 24-Jul
Date (2009)
Dis
cha
rge
ra
te (
m3 /s
)
Measured Hydrograph
Simulated Hydrograph
0
0.1
0.2
0.3
01-May 15-May 29-May 12-Jun 26-Jun 10-Jul 24-Jul
Date (2007)
Dis
cha
rge
ra
te (
m3 /s
)
Measured Hydrograph
Simulated Hydrograph
0
0.025
0.05
0.075
01-May 15-May 29-May 12-Jun 26-Jun 10-Jul 24-Jul
Date (2009)
Dis
char
ge ra
te (m
3 /s)
N-facing slopeS-facing slopeE-facing slopeCirque floorN-facing forestS-facing forest
0
0.025
0.05
0.075
01-May 15-May 29-May 12-Jun 26-Jun 10-Jul 24-Jul
Date (2007)
Dis
char
ge ra
te (m
3 /s)
N-facing slopeS-facing slopeE-facing slopeCirque floorN-facing forestS-facing forest
Total Discharge Component Hydrographs
• Various simulation approaches were used to examine influence on the basin hydrograph
Hydrograph Sensitivity Analysis
0
0.1
0.2
0.3
01-May 15-May 29-May 12-Jun 26-Jun 10-Jul 24-Jul
Date (2009)
Dis
cha
rge
ra
te (
m3 /s
)
Measured Hydrograph
Simulated Hydrograph
VariableSWE dist.Uniform Energy
0
0.1
0.2
0.3
01-May 15-May 29-May 12-Jun 26-Jun 10-Jul 24-Jul
Date (2009)
Dis
cha
rge
ra
te (
m3 /s
)
Measured Hydrograph
Simulated Hydrograph
Fixed SWE dist.Uniform Energy
0
0.1
0.2
0.3
01-May 15-May 29-May 12-Jun 26-Jun 10-Jul 24-Jul
Date (2009)
Dis
cha
rge
ra
te (
m3 /s
)
Measured Hydrograph
Simulated Hydrograph
Fixed SWE dist.Variable Melt
0
0.1
0.2
0.3
01-May 15-May 29-May 12-Jun 26-Jun 10-Jul 24-Jul
Date (2009)
Dis
cha
rge
ra
te (
m3 /s
)
Measured Hydrograph
Simulated HydrographVariable SWE dist.Variable Melt
NS = 0.62
RMSE = 0.03
NS = 0.53
RMSE = 0.03
NS = 0.39
RMSE = 0.04
NS = 0.23
RMSE = 0.04
• Other approaches were used to examine effects of forest canopy and soil depth, and inhomogeneous melt
Hydrograph Sensitivity Analysis
0
0.1
0.2
0.3
0.4
01-May 15-May 29-May 12-Jun 26-Jun 10-Jul 24-Jul
Date (2009)
Dis
cha
rge
ra
te (
m3 /s
)
Measured Hydrograph
Simulated Hydrograph
Fixed SWE dist. - Uniform Energy; Uniform soil and canopy
0
0.1
0.2
0.3
01-May 15-May 29-May 12-Jun 26-Jun 10-Jul 24-Jul
Date (2009)
Dis
cha
rge
ra
te (
m3 /s
)
Measured Hydrograph
Sim. - Inhomogeneous
Sim. - Homogeneous
NS = -0.28
RMSE = 0.06 NS = 0.47
RMSE = 0.04
NS = 0.62
RMSE = 0.03
Homogeneous Inhomogeneous
• Novel framework that allows for physical, yet spatially simple snowmelt and SCD simulation
– Incorporation of sub-grid distributions of internal energy for melt computation
• Application of the framework, together with a hydrological model showed the influence of the spatial variability of both SWE and snowmelt energy on areal SCD and snowmelt runoff in an alpine environment
Key Conclusions, Significance
• Important to take inhomogeneous melt into account for areal SCD simulations
– Implications for remote sensing, climate models and modelling applications using depletion curves
• Effects are not as important for snowmelt runoff and hydrograph simulation, as other processes tend to overwhelm the response
– Still important to account for spatial variability of snowmelt energy on the slope, and land cover scale
Key Conclusions, Significance
Thank You
• NSERC• CFCAS• Canada Research Chairs Programme• University of Calgary Biogeosciences
Institute• Nakiska Ski Resort• Applied Geomatics Research Group• Students and Staff of Centre for
Hydrology