Snow Hydrology
and Modelling
in Alpine, Arctic and
Forested Basins
John Pomeroy
and collaborators
Richard Essery (Edinburgh), Chris Hopkinson (CGS-NS), Rick Janowicz (Yukon Env), Tim Link
(Univ Idaho), Danny Marks (USDA ARS), Phil Marsh (Env Canada), Al Pietroniro (Env Canada),
Diana Verseghy (Env Canada), Jean Emmanual Sicart (IRD France
and Centre for Hydrology Faculty, Researchers and Students
Tom Brown, Kevin Shook, Warren Helgason, Chris DeBeer, Pablo Dornes, Chad Ellis, David
Friddell, Warren Helgason, Edgar Herrera, Nicholas Kinar, Jimmy MacDonald, Matt
MacDonald, Chris Marsh, Stacey Dumanski, Brad Williams, May Guan
Mountain Snow
vast water reserves in winter
snowpack
Snow depth in January
summer snow water reserves
Snow depth in June
Study Elements
•
Processes
– Snow accumulation, structure and observation
– Turbulent transfer to snow
– Radiation effects on snowmelt under tundra shrubs and evergreen forests
•
Parameterisations
–
–
–
–
–
–
•
Blowing snow over complex terrain
Irradiance in complex terrain – longwave from terrain, shortwave shadows
Forest snow interception, unloading and sublimation
Sub-canopy snowmelt
SCA Depletion in complex terrain,
Contributing area for runoff generation in snowmelt period
Prediction
–
–
–
–
–
Wind and atmospheric modelling over complex terrain
Level of spatial complexity necessary in models
Regionalisation of CLASS parameters
Snow modelling contribution to MESH
CRHM
•
•
•
Arctic and sub-arctic snow hydrology, Wolf Creek & Trail Valley Creek
Alpine snow hydrology, Marmot Creek
Montane forest snow hydrology, Marmot Creek
Blowing Snow in Complex Terrain
Inter-basin water
transfer
Transport of snow
to drifts
Supports glaciers,
late lying snowfields,
hydrological
contributing areas
dSWE
  S    T  ES
dt
SF
LiDAR used to develop
topography and vegetation DE
NF
Granger Basin,
Wolf Creek,
Yukon Territory
Essery and Pomeroy, in preparation
Computer simulation of wind flow over mountains
Windspeed
Direction
3000
2500
2000
1500
1000
500
0
0
500
1000
3 km
Granger Basin, Wolf Creek, Yukon
1500
2000
2500
3000
Simulation of Hillslope Snowdrift
3 km
Marmot Creek Research Basin
x x
x
xx
x
x
CRHM Mountain Structure
Alpine Hydrological Response Units
Sublimation
Wind Direction
Solar Radiation
Snow
Deposition
South
Face
(bottom)
South
Face
(top)
Ridge
Top
Snow
Transport
North
Face
Forest
Sink
Source
Winter Snow Redistribution Modelling
250%
200%
150%
100%
50%
0%
600
400
200
0
Forest
SF bottom
SF top
Blowing Snow
Sublimation
(mm)
SWE
Ridgetop
NF
Transect
SWE/Snowfall
200
150
100
50
0
75%
50%
25%
0%
Forest
SF bottom
SF top
Blowing Snow Sublimation
Ridgetop
NF
Transect
Sublimation/Snowfall
Blowing Snow
Sublimation/
Snowfall
SWE (mm)
800
SWE/Snowfall
Winter Snow Redistribution and Sublimation
Point Evaluation of Snowmelt Model
2008
2009
Depth (m)
2
1
Measured depth (m)
3
Depth (m)
Measured depth (m)
Simulated depth (m)
3
0
1-Apr 15-Apr 29-Apr 13-May 27-May 10-Jun 24-Jun
Simulated depth (m)
2
1
0
1-Apr 15-Apr 29-Apr 13-May 27-May 10-Jun 24-Jun
Date (2009)
Simulated SWE (mm)
Measured SWE (mm)
900
600
300
0
1-Apr 15-Apr 29-Apr 13-May 27-May 10-Jun 24-Jun
Depth (mm)
Depth (mm)
Date (2008)
600
300
0
1-Apr 15-Apr 29-Apr 13-May 27-May 10-Jun 24-Jun
-15
Simulated active layer T
Measured active layer T
Simulated lower layer T
Measured lower layer T
-25
1-Apr 15-Apr 29-Apr 13-May 27-May 10-Jun 24-Jun
Date (2008)
Date (2009)
Snow temp (°C)
Snow temp (°C)
Date (2008)
-5
Simulated SWE (mm)
Measured SWE (mm)
900
0
-5
-10
Simulated active layer T
Measured active layer T
Simulated lower layer T
Measured lower layer T
-15
1-Apr 15-Apr 29-Apr 13-May 27-May 10-Jun 24-Jun
Date (2009)
Frequency Distributions of SWE from LiDAR Depths
and Measured Density
N facing slope
SWE
measurements
0.1
0.05
0.25
0.25
SWE
SWE
measurements
measurements
Theoretical
0.2
0.2
f (SWE)
Theoretical
distribution
0.15
0.15
0.1
distribution
Theoretical
distribution
0.15
0.1
0.05
00
15
50
13
00
12
50
0
10
90
SWE
(mm)
SWE distribution within HRU fit log-normal density
distribution
00
15
50
13
00
12
50
10
0
90
0
75
0
60
0
45
0
0
15
0
0
75
0
60
0
45
0
30
15
0
SWE (mm)
0
30
00
15
50
13
00
12
50
0
10
90
0
75
0
60
0
0
30
0
15
45
SWE (mm)
0.05
0
0
0
0
f (SWE)
0.2
0.3
f (SWE)
0.25
S facing slope
0.3
Snowcovered Area from Oblique Terrestrial Photographs,
Aerial Photographs and LiDAR DEM
Snow-covered Area Depletion Modelling
Four HRU (NF, SF, EF, VB) with modelled melt applied to SWE frequency distributions.
SCA fraction
Fully distributed
Uniform
Variable snowmelt
Variable SWE dist.
Observed
1
0.5
0
10-May
20-May
30-May
9-Jun
19-Jun
29-Jun
9-Jul
Date (2007)
SCA fraction
Fully distributed
Uniform
Variable snowmelt
Variable SWE dist.
Observed
1
0.5
0
10-May
20-May
30-May
9-Jun
Date (2008)
19-Jun
29-Jun
9-Jul
Observed – using oblique photography
Uniform – spatially uniform SWE distributions and applied melt rates for each HRU
Variable SWE dist. – each HRU has a distinct distribution of SWE
Variable snowmelt – each HRU has a distinct melt rate applied
Fully distributed – each HRU has a distinct distribution of SWE and applied melt rate
Snowmelt Runoff Intensity by HRU
South facing slope
Area fraction
1
0.5
0
25-Apr
Area fraction
1
5-May
15-May
25-May
4-Jun
Date (2008)
14-Jun
North facing slope
Area fraction
1
5-May
1
15-May
25-May
4-Jun
Date (2008)
14-Jun
East facing slope
5-May
15-May
25-May
4-Jun
Date (2008)
14-Jun
Overall alpine basin
5-May
24-Jun
0 - 5 mm/day
5 - 10 mm/day
10 - 20 mm/day
>20 mm/day
0.5
0
25-Apr
24-Jun
0 - 5 mm/day
5 - 10 mm/day
10 - 20 mm/day
>20 mm/day
0.5
0
25-Apr
24-Jun
0 - 5 mm/day
5 - 10 mm/day
10 - 20 mm/day
>20 mm/day
0.5
0
25-Apr
Area fraction
0 - 5 mm/day
5 - 10 mm/day
10 - 20 mm/day
>20 mm/day
15-May
25-May
4-Jun
Date (2008)
14-Jun
24-Jun
Visualisation of Snowmelt Runoff Intensity
Early Snowmelt Period - 2008
26-Apr
29-Apr
02-May
05-May
0-5
Melt rates (mm/day)
5-10
10-20
bare forest
cliff
Snow Interception & Sublimation
Net Radiation to Forests:
Slope Effects
South Face
Clearing
North & South
Face Forests
North Face
Clearing
Forest Snow Regime on Slopes
200
level
30o north-sloping
30o south-sloping
SWE [kg m-2]
150
100
50
0
10/1/07
Open slopes highly
sensitive to irradiation
difference, forests are not
11/1/07
12/1/07
1/1/08
SWE [kg m-2]
3/1/08
4/1/08
5/1/08
6/1/08
7/1/08
8/1/08
5/1/08
6/1/08
7/1/08
8/1/08
Date (M/D/YY)
100
80
2/1/08
level
30o north-sloping
30o south-sloping
60
40
20
0
10/1/07
11/1/07
12/1/07
1/1/08
2/1/08
3/1/08
4/1/08
HRU Delineation
• Driving meteorology:
temperature, humidty,
wind speed, snowfall,
rainfall, radiation
• Blowing snow,
intercepted snow
• Snowmelt and
evapotranspiration
• Infiltration &
groundwater
• Stream network
Model Structure
Model Tests - SWE
Streamflow Prediction 2006
Mean Bias = -0.13
all parameters estimated from basin data
Streamflow Prediction 2007
Mean Bias = -0.068
all parameters estimated from basin data
Conclusions
• Appropriate process based models driven by
enhanced remote sensing and good observations can
be used to achieve adequate hydrological prediction
in the alpine.
• Model process and spatial structure must be
appropriate to the complexity of the energy and
mass exchange processes as they operate on the
landscape.
• It is possible to test for the most appropriate
structure for balance between model complexity and
predictive ability.