John Pomeroy John Pomeroy Canada Research Chair in Water Resources & Climate Change, Canada Research Chair in Water Resources & Climate Change, Centre for Hydrology, Univ Saskatchewan, Saskatoon, Canada Centre for Hydrology, Univ Saskatchewan, Saskatoon, Canada

www.usask.ca/hydrologywww.usask.ca/hydrology

and collaboratorsand collaboratorsRichard Essery (Richard Essery (UnivUniv Edinburgh), Chris Hopkinson (CGSEdinburgh), Chris Hopkinson (CGS--NS), Tim Link (Univ Idaho), NS), Tim Link (Univ Idaho), Danny Marks (USDA ARS), Al Pietroniro (Environment Canada), CherDanny Marks (USDA ARS), Al Pietroniro (Environment Canada), Cherie Westbrook ie Westbrook (Saskatchewan), (Saskatchewan), XulinXulin Guo (Saskatchewan)Guo (Saskatchewan)

and Centre for Hydrology Researchers and Studentsand Centre for Hydrology Researchers and StudentsRobert Armstrong, Tom Brown, Chris DeBeer, Pablo Robert Armstrong, Tom Brown, Chris DeBeer, Pablo DornesDornes, Logan Fang, Chad Ellis, , Logan Fang, Chad Ellis, Warren Warren HelgasonHelgason, Nicholas , Nicholas KinarKinar, Matt MacDonald, Jim MacDonald, Kevin Shook, Matt MacDonald, Jim MacDonald, Kevin Shook

Advancing Hydrological Advancing Hydrological Processes to Better Predict Processes to Better Predict Water Resources in Cold Water Resources in Cold RegionsRegions

Global Rationale for StudyGlobal Rationale for StudyUnsatisfactory density of measurements and Unsatisfactory density of measurements and inherently ungauged conditions (drought, winter, inherently ungauged conditions (drought, winter, climate change, land use change, extreme climate change, land use change, extreme events) require development of a capability for events) require development of a capability for prediction in ungauged basins.prediction in ungauged basins.Approach using physically based modelling Approach using physically based modelling requires improved measurement, understanding requires improved measurement, understanding and mathematical description of hydrological and mathematical description of hydrological systems in cold regions.systems in cold regions.

www.iahs-pub.orgDecade for Prediction in Ungauged Basins2002-2012

Canadian ImplementationCanadian Implementation

Need to better understand Need to better understand and predict hydrology of and predict hydrology of snow and ice in high snow and ice in high latitudes and altitudeslatitudes and altitudeswww.usask.ca/ip3www.usask.ca/ip3

Need to better understand Need to better understand and predict the hydrological and predict the hydrological impact of recent droughts in impact of recent droughts in Western CanadaWestern Canadawww.drinetwork.cawww.drinetwork.ca

Cold Regions Hydrological Model Cold Regions Hydrological Model Platform: CRHM Platform: CRHM

Modular Modular –– purpose built from C++ modulespurpose built from C++ modulesModules based upon +45 years of hydrology research at Univ of Modules based upon +45 years of hydrology research at Univ of Saskatchewan and Environment CanadaSaskatchewan and Environment CanadaParameters set by knowledge rather than optimizationParameters set by knowledge rather than optimizationHydrological Response Unit (HRU) basis Hydrological Response Unit (HRU) basis

landscape unit with characteristic hydrological processes/responlandscape unit with characteristic hydrological processes/responsesesingle parameter setsingle parameter sethorizontal interaction along flow cascade matrixhorizontal interaction along flow cascade matrixModel tracks state variables and flows for HRUModel tracks state variables and flows for HRU

Coupled energy and mass balance, physically based algorithms Coupled energy and mass balance, physically based algorithms applied to applied to HRUsHRUs via module selectionvia module selectionHRUsHRUs connected aerodynamically for blowing snow and via dynamic connected aerodynamically for blowing snow and via dynamic drainage networks for streamflowdrainage networks for streamflowFlexible Flexible -- can be configured for prairie, mountain, boreal, arctic basinscan be configured for prairie, mountain, boreal, arctic basinsSubSub--basins connected via Muskingum routingbasins connected via Muskingum routingVisualisationVisualisation tools, GIS interfacetools, GIS interface

Pomeroy et al., 2007 Hydrol. Proc. Tom Brown, CRHM Modeller

Saskatchewan and Assiniboine River BasinsSaskatchewan and Assiniboine River Basins

Marmot Creek

Smith Creek

Kernan FarmFlow Direction

Improved Snow Observations by Acoustic Sounding

• Centre for Hydrology research demonstrated the possibility of determining SWE by the use of an acoustic wave.

• Experimental apparatus has been confirmed at sites in Saskatchewan, Yukon Territory, and the Rocky Mountains.

Nicholas Kinar, PhD research

Acoustic Gauge TestingAcoustic Gauge Testing

Acoustic gauge

N = 20

RMSE = 18 mmMean Bias = −7%

Acoustic Snow Water Equivalent versus Acoustic Snow Water Equivalent versus Snow Pit MeasurementsSnow Pit MeasurementsMarch-June 2010 Kananaskis Country, Alberta

Can we close the Energy Balance Can we close the Energy Balance over Snow?over Snow?

soil

snow

atmosphere

Q*=QSW+QLW QH QE

QG

UTemp.profile

Warren Helgason, PhD research

Turbulent energy transfer to snow much less Turbulent energy transfer to snow much less than radiation loss than radiation loss –– energy imbalanceenergy imbalance

LongwaveLongwave radiation balance controls radiation balance controls the effective area for heat transferthe effective area for heat transfer

Marmot Creek Research BasinMarmot Creek Research Basin

Bow River valley

Kananaskis River valley

x x

x

xx x

x

Alpine Snow Accumulation, Ablation Alpine Snow Accumulation, Ablation and Runoff Contributing Areaand Runoff Contributing Area

Marmot Creek

Mt Allan Cirque, Marmot CreekMt Allan Cirque, Marmot Creek

2318 m2318 mRidge above Ridge above treelinetreelineWindblownWindblownU, T, RHU, T, RHPrecipPrecipRadiationRadiationSnowdepthSnowdepthCameraCamera2 outlier 2 outlier stationsstations

Snow SurveysSnow Surveys

Snow Hydrology Modelling in Snow Hydrology Modelling in Alpine BasinsAlpine Basins

Meteorological Inputs T, RH, U, P, K↓

Distributed K↓, L↓, u*, T, q, snowfall, rain

Blowing Snow Model ΔSWE, Sublimation, Transport

Latitude, elevation, slope, aspect, vegetation, fetch, area

Energy Balance Snowmelt ModelMelt, Sublimation

Albedo Decay

Snow Covered Area Depletion ModelSWE Variability

Runoff Contributing Area

Alpine Hydrological Response UnitsAlpine Hydrological Response Units

North Face

South Face(top)

South Face 

(bottom)

Forest

Snow Transport

Snow Deposition

Sublimation

Elevation ~2310 mASL

RidgeTop

Solar Radiation

Wind Direction

SourceSinkMatt MacDonald, MSc research

Alpine Snow Accumulation ModellingAlpine Snow Accumulation Modelling

0%50%100%150%200%250%

0

200

400

600

800

Forest SF bottom SF top Ridgetop NF Transect

SWE/Snow

fall

SWE (m

m)

SWE SWE/Snowfall

0%10%20%30%40%

0

50

100

150

Forest SF bottom SF top Ridgetop NF Transect Tran

sport 

Out/Sno

wfall

Tran

sport 

Out(m

m)

Transport Out Transport Out/Snowfall

0%

50%

100%

150%

0100200300400

Forest SF bottom SF top Ridgetop NF Transect

Tran

sport 

In/Sno

wfall

Tran

sport In 

(mm)

Transport In Transport In/Snowfall

0%

25%

50%

75%

050

100150200

Forest SF bottom SF top Ridgetop NF Transect Blow

ing Snow

 Sublim

ation/

Snow

fall

Blow

ing Snow

 Sublim

ation 

(mm)

Blowing Snow Sublimation Sublimation/Snowfall

0%50%100%150%200%250%

0

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Forest SF bottom SF top Ridgetop NF Transect

SWE/Snow

fall

SWE (m

m)

SWE SWE/Snowfall

0%

5%

10%

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20%

0

20

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60

Forest SF bottom SF top Ridgetop NF Transect

Melt/Snow

fall

Melt (mm)

Melt Melt/Snowfall (%)

0%10%20%30%40%

0

50

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150

Forest SF bottom SF top Ridgetop NF Transect Tran

sport 

Out/Sno

wfall

Tran

sport 

Out(m

m)

Transport Out Transport Out/Snowfall

0%

50%

100%

150%

0100200300400

Forest SF bottom SF top Ridgetop NF Transect

Tran

sport 

In/Sno

wfall

Tran

sport In 

(mm)

Transport In Transport In/Snowfall

0%

25%

50%

75%

050

100150200

Forest SF bottom SF top Ridgetop NF Transect Blow

ing Snow

 Sublim

ation/

Snow

fall

Blow

ing Snow

 Sublim

ation 

(mm)

Blowing Snow Sublimation Sublimation/Snowfall

1.0%1.1%1.2%1.3%1.4%1.5%

4.04.24.44.64.85.0

Forest SF bottom SF top Ridgetop NF Transect

Melt/Snow

fall

Melt (mm)

Melt Melt/Snowfall (%)

Alpine Snowmelt ModelingAlpine Snowmelt Modeling

SnobalSnobal mass and energy balance routine after Marks et mass and energy balance routine after Marks et al. (1999) incorporated into CRHM.al. (1999) incorporated into CRHM.Corrections for direct and diffuse shortwave and longwave Corrections for direct and diffuse shortwave and longwave radiation to slopes, including terrain emissionradiation to slopes, including terrain emission

Snowcovered area estimated using SWE frequency Snowcovered area estimated using SWE frequency distribution estimated from lidar snow depthsdistribution estimated from lidar snow depths

Snow layer 1

Snow layer 2

LvE H K↑ K↓ L↓ L↑ P E

Soil layer

G Q

Simulation of Snowcover Depletion and Simulation of Snowcover Depletion and Snowmelt RunoffSnowmelt Runoff

Simulation of incoming solar radiationJune 1, clear day

Image of snow depth from subtraction oftwo lidar DEMS (with and without snow)

Hopkinson, et al, IAHS Publ. 2010

Simulation of Snowcover Depletion and Simulation of Snowcover Depletion and Snowmelt Runoff Contributing AreaSnowmelt Runoff Contributing Area

Representation of differential melt over SWE distribution also iRepresentation of differential melt over SWE distribution also important for mportant for defining a snowmelt runoff contributing area (SRCA) and represendefining a snowmelt runoff contributing area (SRCA) and representing melt ting melt contribution over landscapecontribution over landscape

S-facing slope

N-facing slope

Chris DeBeer, PhD research

Snow Interception & SublimationSnow Interception & Sublimation

0

20

40

60

80

100

120

140

North South East West

Snow

Wat

er E

quiv

alen

t mm Forest

Clearing

Jim MacDonald, MSc research

Marmot Creek

Snow Interception Losses LargeSnow Interception Losses Large

Net Radiation to Forests: Net Radiation to Forests: Slope EffectsSlope Effects

Chad Ellis, PhD research

South FaceClearing

North & South Face Forests

North Face Clearing

Forest Snowmelt ModellingForest Snowmelt Modelling

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

SWE

[kg

m-2

]

0

50

100

150

200

level30o north-sloping30o south-sloping

Forest Snow Regime on SlopesForest Snow Regime on Slopes

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

SWE

[kg

m-2]

0

20

40

60

80

100

level30o north-sloping30o south-sloping

Open slopes highly sensitive to irradiationdifference, forests are not

Forest Cover ChangeForest Cover Change

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100

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300

350

400

01/10/07 20/11/07 09/01/08 28/02/08 18/04/08 07/06/08

mm

wat

er

tall cum subl

short cum subl

cum snowfall

tall SWE

short SWE

“tall” = 10 m, LAI 2, spruce“short” = 2 m, LAI 0.5, spruce

Winter Warming Impact on Winter Warming Impact on Alpine Snow AccumulationAlpine Snow Accumulation

0

50

100

150

200

30/11/06 30/12/06 29/01/07 28/02/07 30/03/07 29/04/07 29/05/07

SWE

(mm

)

reference simulation

+ 1°C

+ 2 °C

+ 3°C

+ 4 °C

Winter Warming Impact on Winter Warming Impact on Mountain Forest Snow RegimeMountain Forest Snow Regime

0

20

40

60

80

100

120

10/10/06 09/11/06 09/12/06 08/01/07 07/02/07 09/03/07 08/04/07 08/05/07 07/06/07 07/07/07

Snow

Acc

umul

atio

n (m

m) Current

+1 C +2 C +3 C + 4 C

Canadian Prairie Runoff GenerationCanadian Prairie Runoff GenerationSnow Redistribution to Channels

Spring melt and runoff

Water Storage in Wetlands

Dry non-contributing areas to runoff

Derivation of Wetland DepressionsDerivation of Wetland Depressions

Smith Creek SWE and Smith Creek SWE and θθ Prediction Prediction –– No CalibrationNo Calibration

Observed SWE vs Simulated SWE at Smith Creek Sub-basin 1

0

50

100

150

200

250

300

7-Feb 18-Feb 29-Feb 11-Mar 22-Mar 2-Apr 13-Apr

2008

Snow

Acc

umul

atio

n (m

m S

WE)

Fallow Obs. SWE Fallow Sim. SWEChannel Obs. SWE Channel Sim. SWEWetland Obs. SWE Wetland Sim. SWE

Volumetric Soil M oisture at Smith Creek during Spring Snowmelt Period

0

0.1

0.2

0.3

0.4

0.5

22-Mar 31-Mar 9-Apr 18-Apr 27-Apr 6-May

2008

Volu

met

ric S

oil

Moi

stur

e

ObservedSimulated

Runoff Prediction: Lidar = no calibration, Runoff Prediction: Lidar = no calibration, NonNon--Lidar = calibration of depressional storageLidar = calibration of depressional storage

MB RMSD (m3/sPeak Discharge (m3/s)Non-LiDAR Simulation -0.07 0.10 4.61LiDAR-based Simulation -0.39 0.12 4.17Observation 4.65

Smith Creek Spring Discharge near Marchwell

00.5

11.5

22.5

33.5

44.5

5

22-Mar 27-Mar 01-Apr 06-Apr 11-Apr 16-Apr 21-Apr 26-Apr 01-May 06-May

2008

Dai

ly M

ean

Dis

char

ge (m

3 /s) Observation

Non-LiDAR SimulationLiDAR-based Simulation

Xing Fang, research

CRHM Surface Water CRHM Surface Water Drought ModellingDrought Modelling

CRHM was used to create CRHM was used to create ““virtualvirtual”” model model of typical prairie upland basinof typical prairie upland basinModel was run over climate normal period Model was run over climate normal period (1961(1961--1990)1990)Output during drought period was Output during drought period was compared to normal period and spatially compared to normal period and spatially interpolatedinterpolated

Simulating Water Supply from Simulating Water Supply from ““VirtualVirtual””Prairie Drainage Basins over 46 yearsPrairie Drainage Basins over 46 years

14

2

3

Upland Drainage Basin Wetland Drainage Basin

Drought Hydrology Simulations Drought Hydrology Simulations Station locations, Prairie Station locations, Prairie ecozoneecozone and and

Palliser Triangle boundariesPalliser Triangle boundaries

Prairie Spring Discharge Early DroughtPrairie Spring Discharge Early DroughtWetlands Uplands

Kevin Shook, research

Wetlands Uplands

Prairie Spring Discharge Late DroughtPrairie Spring Discharge Late Drought

Spatial Variation of Prairie Soil Moisture Spatial Variation of Prairie Soil Moisture (Drought (Drought vsvs Wet)Wet)

Drought Wet

Mean for normal period 332 mm

Drought period: distribution wide, variance large, median > mean

Wetter period: distribution loses low soil moisture, variance smaller, median < meanProbability density of soil moisture

Robert Armstrong, PhD research

Spatial Variation of Evapotranspiration Spatial Variation of Evapotranspiration (Drought (Drought vsvs Wet)Wet)

Drought Wet

• Mean for normal period 352 mm• Drought period: distribution wide, variance large, median >> mean• Wetter period: distribution symmetric, variance greatly reduced

Probability density of evapotranspiration

Next Steps: Integrated Observing & Next Steps: Integrated Observing & Predicting SystemsPredicting Systems

Current high altitude Current high altitude observation network observation network in Canada is in Canada is inadequate, need a inadequate, need a network of stations, network of stations, remote sensing, remote sensing, modelling, data modelling, data assimilation in order assimilation in order to predict our cold to predict our cold regions water regions water resources resources adequately.adequately.

ConclusionsConclusionsChallenges remain in improving observations and our understandinChallenges remain in improving observations and our understanding g of snow hydrology systems.of snow hydrology systems.Better understanding of processes is the basis for more physicalBetter understanding of processes is the basis for more physically ly based models.based models.Remote sensing providing means for better parameterisation of Remote sensing providing means for better parameterisation of models and reduces need for calibration.models and reduces need for calibration.Improved models with enhanced remote sensing and good Improved models with enhanced remote sensing and good observations can be used to observations can be used to

Describe mountain alpine and forest hydrologyDescribe mountain alpine and forest hydrologyImpacts of forest cover change on hydrology Impacts of forest cover change on hydrology Climate impacts on cold regions hydrology Climate impacts on cold regions hydrology Impacts of wetland drainageImpacts of wetland drainageDrought hydrologyDrought hydrologyHydrological prediction without calibrationHydrological prediction without calibration

Integrated observation and prediction systems are needed to makeIntegrated observation and prediction systems are needed to makefurther advances, especially in high elevations and latitudesfurther advances, especially in high elevations and latitudes

Selected ReferencesSelected ReferencesMacDonald, M.K., Pomeroy, J.W. and A. Pietroniro. 2010. On the importance of sublimation to an alpine snow mass balance in the Canadian Rocky Mountains. Hydrol. Earth Syst. Sci., 14, 1401–1415. doi:10.5194/hess-14-1401-2010Armstrong, R.N., J.W. Pomeroy, LW. Martz. 2010. Estimating evaporation in a Prairie landscape under drought conditions. Canadian Water Resources Journal, 35(2), 173-186.Fang, X., J.W. Pomeroy, C.J. Westbrook, X. Guo, A.G. Minke and T. Brown. 2010. Prediction of snowmelt derived streamflow in a wetland dominated prairie basin. Hydrol. Earth Syst. Sci., 14, 991-1006. doi 10.5194/hess-14-1-2010.Ellis, C. R., Pomeroy, J. W., Brown, T., and MacDonald, J. 2010. Simulation of snow accumulation and melt in needleleaf forest environments, Hydrol. Earth Syst. Sci., 14, 925-940, doi:10.5194/hess-14-925-2010, 2010.DeBeer, C..M. and J.W. Pomeroy. 2009. Modelling snowmelt and snowcover depletion in a small alpine cirque, Canadian Rocky Mountains. Hydrological Processes, DOI:10.1002/hyp.7346.Fang, X. and J.W. Pomeroy. 2009. Modelling blowing snow redistribution to Prairie wetlands. Hydrological Processes, DOI: 10.1002/hyp.7348.MacDonald, M.K., Pomeroy, J.W. and A. Pietroniro. 2009. Parameterising redistribution and sublimation of blowing snow for hydrological models: tests in a mountainous subarctic catchment. Hydrological Processes, DOI:10.1002/hyp.7356.Reba, M. L., T. E. Link, D. Marks, and J. Pomeroy (2009), An assessment of corrections for eddy covariance measured turbulent fluxes over snow in mountain environments, Water Resources Research, 45, W00D38, doi:10.1029/2008WR007045.Kinar, N. and J.W. Pomeroy 2009. Automated determination of snow water equivalent by acoustic reflectometry. Institute of Electrical and Electronic Engineering, Transactions on Geoscience and Remote Sensing, 47(9). 3161-3167Pomeroy, J.W., Marks, D., Link, T., Ellis, C., Essery, R. Hardy, J., Rowlands, A. and R. Granger. 2009. The impact of coniferous forest temperatures on incoming longwave radiation to melting snow. Hydrological Processes, DOI 10.1002/hyp.7325.Pomeroy, J.W., Rowlands, A. Hardy, J., Link, T., Marks, D., Essery, R., Sicart, J-E., and C. Ellis. 2008. Spatial Variability of Shortwave Irradiance for Snowmelt in Forests. Journal of Hydrometeorology, 9(6), 1482-1490.Marks, D., M. Reba, J. Pomeroy, T. Link, A. Winstral, G. Flerchinger and K. Elder, 2008. Comparing simulated and measured sensible and latent heat fluxes over snow under a pine canopy. Journal of Hydrometeorology, 9(6), 1506-1522.Stewart, R., Pomeroy, J.W., and R. Lawford. 2008. A drought research initiative for the Canadian Prairies. CMOS Bulletin SCMO, 36(3), 87-96.Dornes, P.F, Pomeroy, J.W., Pietroniro, A. and D.L. Verseghy. 2008. Effects of spatial aggregation of initial conditions and forcing data on modelling snowmelt using a land surface scheme. Journal of Hydrometeorology, 9, 789-803.Dornes, P.F., Pomeroy, J.W., Pietroniro, A., Carey, S.K., and W. L. Quinton. 2008. Influence of landscape aggregation in modelling snow-cover ablation and snowmelt runoff in a sub-arctic mountainous environment. Hydrological Sciences Journal, 53(4), 725-740.Essery, R., Pomeroy, J.W., Ellis, C. and T. Link. 2008 Modelling longwave radiation to snow beneath forest canopies using hemispherical photography or linear regression. Hydrological Processes, 22(15). 2788-2800.Armstrong, R.L., Pomeroy, J.W. and L.W. Martz. 2008. Evaluation of three evaporation estimation methods in a Canadian prairie landscape. Hydrological Processes, 22(15). 2801-2815.Fang, X. and J.W. Pomeroy. 2008. Drought impacts on Canadian prairie wetland snow hydrology. Hydrological Processes, 22(15). 2858-2873.Kinar, N.J. and J.W. Pomeroy. 2008. Determining snow water equivalent by acoustic sounding. Hydrological Processes, 21, 2623-2640.Pomeroy, J.W., Gray, DM, Brown, T., Hedstrom, N.H., Quinton, W.L., Granger, R.J. and S.K. Carey. 2007. The cold regions hydrological model: a platform for basing process representation and model structure on physical evidence. Hydrological Processes, 21, 2650-2667.Fang, X and J.W. Pomeroy, 2007. Snowmelt runoff sensitivity analysis to drought on the Canadian Prairies. Hydrological Processes, 21, 2594-2609.Ellis, C.R. and J.W. Pomeroy. 2007. Estimating sub-canopy shortwave irradiance to melting snow on forested slopes. Hydrological Processes, 21, 2581-2593.