Snow Hydrology and Modelling

in Alpine, Arctic and Forested Basins

John Pomeroy

and collaboratorsRichard 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 StudentsTom 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 snowto drifts

Supports glaciers,late lying snowfields,hydrologicalcontributing areas

Granger Basin, Wolf Creek,

Yukon Territory

NF

SF

LiDAR used to develop

topography and vegetation DEM

Wind Direction

SS ETdt

dSWE

Essery and Pomeroy, in preparation

0 500 1000 1500 2000 2500 30000

500

1000

1500

2000

2500

3000

0 500 1000 1500 2000 2500 30000

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

Bow River valley

Kananaskis River valley

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

SWE/

Snow

fall

SWE (

mm

)

SWE SWE/Snowfall

0%

25%

50%

75%

050

100150200

Forest SF bottom SF top Ridgetop NF Transect Blo

win

g Sn

ow

Subl

imati

on/

Snow

fall

Blo

win

g Sn

ow

Subl

imati

on

(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)

Sno

w te

mp

(°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)

Dep

th (

mm

)

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)

Dep

th (

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)

Dep

th (

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)

Sno

w te

mp

(°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

010

5012

0013

5015

00

SWE (mm)

f (S

WE

)

SWEmeasurements

Theoreticaldistribution

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

SWE (mm)f (

SW

E)

SWEmeasurements

Theoreticaldistribution

0

0.05

0.1

0.15

0.2

0.25

0.3

SWEmeasurements

Theoreticaldistribution

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 fr

act

ion

Fully distributed Uniform Variable snowmelt Variable SWE dist. Observed

Observed – using oblique photographyUniform – spatially uniform SWE distributions and applied melt rates for each HRUVariable SWE dist. – each HRU has a distinct distribution of SWEVariable snowmelt – each HRU has a distinct melt rate appliedFully 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 fr

act

ion

Fully distributed Uniform Variable snowmelt Variable SWE dist. Observed

Snowmelt Runoff Intensity by HRU

0

0.5

1

25-Apr 5-May 15-May 25-May 4-Jun 14-Jun 24-JunDate (2008)

Are

a fr

act

ion

0 - 5 mm/day5 - 10 mm/day10 - 20 mm/day>20 mm/day

East facing slope

0

0.5

1

25-Apr 5-May 15-May 25-May 4-Jun 14-Jun 24-JunDate (2008)

Are

a fra

ctio

n

0 - 5 mm/day5 - 10 mm/day10 - 20 mm/day>20 mm/day

South facing slope

0

0.5

1

25-Apr 5-May 15-May 25-May 4-Jun 14-Jun 24-JunDate (2008)

Are

a fra

ctio

n

0 - 5 mm/day5 - 10 mm/day10 - 20 mm/day>20 mm/day

North facing slope

0

0.5

1

25-Apr 5-May 15-May 25-May 4-Jun 14-Jun 24-JunDate (2008)

Are

a fra

ctio

n

0 - 5 mm/day5 - 10 mm/day10 - 20 mm/day>20 mm/day

Overall alpine basin

Visualisation of Snowmelt Runoff Intensity

26-Apr 29-Apr

02-May 05-May

0-5 5-10 10-20 bare forest cliffMelt rates (mm/day)

Early Snowmelt Period - 2008

Snow Interception & Sublimation

Net Radiation to Forests: Slope Effects

South Face Clearing

North & South Face Forests

North Face Clearing

Date (M/D/YY)

10/1/07 11/1/07 12/1/07 1/1/08 2/1/08 3/1/08 4/1/08 5/1/08 6/1/08 7/1/08 8/1/08

SW

E [

kg m

-2]

0

50

100

150

200

level

30o north-sloping

30o south-sloping

Forest Snow Regime on Slopes

Date (M/D/YY)

10/1/07 11/1/07 12/1/07 1/1/08 2/1/08 3/1/08 4/1/08 5/1/08 6/1/08 7/1/08 8/1/08

SW

E [

kg m

-2]

0

20

40

60

80

100

level

30o north-sloping

30o south-sloping

Open slopes highly sensitive to irradiationdifference, forests are not

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.