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
27
Embed
Snow Hydrology and Modelling in Alpine, Arctic and ... · Snow Hydrology and Modelling in Alpine, Arctic and Forested Basins John Pomeroy and collaborators Richard Essery (Edinburgh),
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
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
Snow depth in January Snow depth in June
summer snow water reservesvast water reserves in winter
snowpack
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
Granger Basin,Wolf Creek,
Yukon Territory
NF
SF
LiDAR used to develop
topography and vegetation DEM
SS ETdt
dSWE
Essery and Pomeroy, in preparation
0 500 1000 1500 2000 2500 3000
0
500
1000
1500
2000
2500
3000
Computer simulation of wind flow over mountains
Windspeed Direction
3 km
Granger Basin, Wolf Creek, Yukon
3 km
Simulation of Hillslope Snowdrift
Marmot Creek Research Basin
x x
x
xx x
x
CRHM Mountain Structure
Alpine Hydrological Response Units
North Face
South Face(top)
South Face
(bottom)
Forest
Snow Transport
Snow Deposition
Sublimation
RidgeTop
Solar Radiation
Wind Direction
SourceSink
Winter Snow Redistribution Modelling
Winter Snow Redistribution and Sublimation
0%
50%
100%
150%
200%
250%
0
200
400
600
800
Forest SF bottom SF top Ridgetop NF Transect
SW
E/S
no
wfa
ll
SW
E (
mm
)
SWE SWE/Snowfall
0%
25%
50%
75%
050
100150200
Forest SF bottom SF top Ridgetop NF Transect Blo
win
g Sn
ow
Subl
imat
ion/
Snow
fall
Blo
win
g Sn
ow
Subl
imat
ion
(mm
)
Blowing Snow Sublimation Sublimation/Snowfall
Point Evaluation of Snowmelt Model2008 2009
-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)D
epth
(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
0
0.05
0.1
0.15
0.2
0.25
015
030
045
060
075
090
0
1050
1200
1350
1500
SWE (mm)
f (S
WE
)
SWE
measurements
Theoretical
distribution
Frequency Distributions of SWE from LiDAR Depths and Measured Density
N facing slope
0
0.05
0.1
0.15
0.2
0.25
0.3
015
030
045
060
075
090
0
1050
1200
1350
1500
SWE (mm)
f (S
WE
)
SWE
measurements
Theoretical
distribution
0
0.05
0.1
0.15
0.2
0.25
0.3
015
030
045
060
075
090
0
1050
1200
1350
1500
SWE (mm)
f (S
WE
)
SWE
measurements
Theoretical
distribution
S facing slope
SWE distribution within HRU fit log-normal density distribution
Snowcovered Area from Oblique Terrestrial Photographs, Aerial Photographs and LiDAR DEM
Snow-covered Area Depletion Modelling
0
0.5
1
10-May 20-May 30-May 9-Jun 19-Jun 29-Jun 9-Jul
Date (2008)
SC
A fra
ctio
n
Fully distributed Uniform Variable snowmelt Variable SWE dist. Observed
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
Four HRU (NF, SF, EF, VB) with modelled melt applied to SWE frequency distributions.
0
0.5
1
10-May 20-May 30-May 9-Jun 19-Jun 29-Jun 9-Jul
Date (2007)
SC
A fra
ctio
n
Fully distributed Uniform Variable snowmelt Variable SWE dist. Observed
• 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.