Water Management Implications of Water Management Implications of CRHM: Mountains to Prairies to ArcticCRHM: Mountains to Prairies to Arctic

John Pomeroy and colleaguesJohn Pomeroy and colleaguesCentre for Hydrology, Centre for Hydrology,

University of Saskatchewan, SaskatoonUniversity of Saskatchewan, Saskatoon

ProblemRocky Mountain Water Resources are Declining

WHY?

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10

20

30

40

50

60

1900 1920 1940 1960 1980 2000 2020

Year

Mea

n Y

earl

y Fl

ow m

3/se

c

Bow River at Banff, Mean Annual Flow

Trend -11.5% since 1910, Statistically Significant at 99% Probability

Flows in Late Summer

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20

40

60

80

100

120

1900 1920 1940 1960 1980 2000 2020

Year

Mea

n Au

gust

Flo

w m

3/se

c Bow River at Banff, Mean August Flow

Trend -24.9% since 1910, Statistically Significant at 99.9% Probability

Late summer flows large and dropping rapidly

Early Spring Flow Increasing

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4

6

8

10

12

1900 1920 1940 1960 1980 2000 2020

Year

Mea

n M

arch

Flo

w m

3/se

c

Bow River at Banff, Mean March Flow

Trend +15.2% since 1910, Statistically Significant at 99.9% Probability

Winter flows small and rising somewhat

Marmot Creek Research Basin• 1450-2886 m.a.s.l. Kananaskis Valley, Bow River• Alpine• Subalpine• Montane• Clearcut• Meadow• 900 mm

precipitation• 70% snowfall• ~50% runoff

Marmot Basin

Bow River valley

Kananaskis River valley

Temperature Trends at High Elevation in Marmot Creek, Rocky Mountains

Winters are warmer by 3 to 4 oC since 1962

Harder & Pomeroy

Upper Clearing

• 1844 m• Small forest

clearing• Sheltered by fir

and spruce forest

Fisera Ridge, Mt Allan Cirque

• 2318 m• Alpine

Ridge• Windblown

CRHM for Alpine Terrain

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

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01/11/06 01/12/06 31/12/06 30/01/07 01/03/07 31/03/07 30/04/07 30/05/07

snow

dep

th (m

)

observedsnow depth

calculatedsnow depth

Alpine Ridge Snow Modelling

Fisera Ridge

rms error = 0.134 m, mean error = 0.074 m

• Dominant windflow: north to south• Flow over ridgetop and into forest

ForestSouth Face 

(bottom)

South Face(top)

RidgeTop

North Face

Sublimation Loss

Downwind Transport

Windflow

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100

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600

700

24-Sep-08 24-Oct-08 24-Nov-08 24-Dec-08 24-Jan-09 24-Feb-09 24-Mar-09

SWE

(mm

)

Snowfall NF SWE (SIM) Ridge SWE (SIM)

SF Upper SWE (SIM) SF Lower SWE (SIM) Forest SWE (SIM)

NF (OBS) Ridge (OBS) SF Upper (OBS)

SF Lower (OBS) Forest (OBS)

RMSE = 9.7 mmMB = 0.00

0%10%20%30%40%50%

050

100150200

Forest SF bottom SF top Ridgetop NF Transect Tran

sport 

Out/Sno

wfall

Tran

sport 

Out(m

m)

Transport Out Transport Out/Snowfall

0%10%20%30%40%50%

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%25%50%75%100%

0100200300400

Forest SF bottom SF top Ridgetop NF Transect

Tran

sport 

In/Sno

wfall

Tran

sport In 

(mm)

Transport In Transport In/Snowfall

Winter Warming Impact on Alpine Ridge Snow Accumulation

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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

Impact of Warming on Blowing Snow Fluxes

80

90

100

110

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130

140

150

160

0.0 +0.5 +1.0 +1.5 +2.0 +2.5 +3.0 +3.5 +4.0

Temperature Increase ( ºC)

Cumulative Blow

ing Snow

 Transpo

rt or 

Sublim

ation (m

m)

Transport Sublimation

Impact of Winter Warming on Maximum Snow Accumulation

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200

0 1 2 3 4air temperature increase (°C)

max

imum

SW

E (m

m)

Impact of Winter Warming on Snowmelt Rate

0123456789

0 1 2 3 4temperature increase (°C)

mel

t rat

e (m

m S

WE

/day

)

Impact of Winter Warming on Spring Snowmelt Duration

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30

35

0 1 2 3 4

temperature increase (°C)

dura

tion

of s

prin

g m

elt (

days

)

Impact of Winter Warming on Date of Snowpack Depletion

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0.5

1

1.5

2

2.5

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3.5

4

03-May 08-May 13-May 18-May 23-May 28-May 02-Jun

date of snowcover depletion

tem

pera

ture

incr

ease

(°C

)

CRHM for Mountain Forests

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01/11/06 01/12/06 31/12/06 30/01/07 01/03/07 31/03/07 30/04/07 30/05/07

snow

dep

th (m

)

observed snowdepthcalculated snowdepth

Forest Snow Modelling

Upper Clearing

Winter Warming Impact on Mountain Forest Snow Regime

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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

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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

mm

wat

er e

quiv

alen

t

SnowfallSnowmeltSublimationSnow Accumulation

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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

mm

wat

er e

quiv

alen

t

SnowfallSnowmeltSublimationSnow Accumulation

Current ClimateForest Snow Processes

+4 C ClimateForest Snow Processes

Change in Melt with Temperature

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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

Cum

ulat

ive

Snow

mel

t (m

m)

Current +0.5 C +1 C +1.5 C +2 C +2.5 C +3 C +3.5 C + 4 C

Change in Snowfall with Temperature

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50

100

150

200

250

Current +0.5 C +1 C +1.5 C +2 C +2.5 C +3 C +3.5 C + 4 C

Seas

onal

Sno

wfa

ll (m

m)

Change in Sublimation with Temperature

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50

60

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80

Current +0.5 C +1 C +1.5 C +2 C +2.5 C +3 C +3.5 C + 4 C

Inte

rcep

ted

Snow

Sub

limat

ion

(mm

)

Change in Maximum Accumulation with Temperature

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120

Current +0.5 C +1 C +1.5 C +2 C +2.5 C +3 C +3.5 C + 4 C

Max

imum

Sno

w A

ccum

ulat

ion

(mm

)

Water Management Implications• These results show that intact mountain forests

have a mitigating effect on some aspects of climate variability and that wind-swept open environments are highly sensitive to climate warming.

• Full consideration of blowing and intercepted snow processes along with energy balance snowmelt calculations must be given for credible climate change impact studies of mountain snow hydrology.

Effects of Forest Cover Change

• Evergreen forest canopy is associated with two primary hydrological effects– Snow and rainfall interception and subsequent

sublimation and evaporation resulting in reduced sub-canopy snowfall or rainfall,

– Alteration of sub-canopy radiation and turbulent transfer affecting the snowmelt rate.

Forest Sky View for Maximum Melt Energy from Net Radiation

2/1/07 3/1/07 4/1/07 5/1/07 6/1/07

v [ ]

0.0

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0.8

1.0

south-sloping site (αs= 0.7)

south-sloping site (αs= 0.8)

level site (αs= 0.7)

level site (αs= 0.8)

north-sloping site (αs= 0.7)

north-sloping site (αs= 0.8)

There is no simple relationship between forest density and melt rate.Influence of slope, aspect, solar elevation, weather and albedo are overwhelming.

Ellis and Pomeroy, in preparation

CRHM Forest

Observed and Modelled Forest Energetics – Marmot Creek

CRHM Forest Tests –Colorado, Switzerland, Alberta

Slope and Forest Density Effect on Net Radiation for Snowmelt - Rockies

Clearing Mature Forest

Net radiation = solar + thermal radiation

Ellis &Pomeroy, in preparation

Water Management Implications

• Forest clearing increases snow accumulation

• Forest clearing accelerates snowmelt rates on south facing slopes and level sites, BUT

• Forest clearing reduces snowmelt rates on north facing slopes

Prairie Runoff GenerationSnow Redistribution to Channels

Spring melt and runoff

Water Storage in Wetlands

Dry non-contributing areas to runoff

PRAIRIE HYDROLOGY – Limited Contributing Areas for Streamflow

Non-contributing areas for streamflowextensive in Canadian Prairies

Localized hydrology affected by poor drainage,storage in small depressions

Modelling Prairie Hydrology• Need a physical basis to calculate the effects of

changing climate, land use, wetland drainage• Need to incorporate key prairie hydrology processes:

snow redistribution, frozen soils, spring runoff, wetland fill and spill, non-contributing areas

• Frustration that hydrological models developed elsewhere do not have these features and fail in this environment

• Streamflow calibration does not provide information on basin non-contributing areas and is not suitable for change analysis

Smith Creek Hydrology Study• Problem: Inability to reliably model the basins of the

Upper Assiniboine River and other prairie basins where variable contributing area, wetlands, nonsaturatedevapotranspiration, frozen soils, snow redistribution and snowmelt play a major role in hydrology.

• Objectives– Develop a Prairie Hydrological Model computer program that

can simulate the response of streams, wetlands, and soil moisture to weather inputs for various basin types.

– Evaluate the model performance in Smith Creek by comparing to observations of streamflow, wetland extent, and snowpack.

– Use the Prairie Hydrological Model to estimate the sensitivity of streamflow, wetland water storage, and soil moisture to changes in drainage and land use.

Smith Creek – extreme interannual and seasonal variability

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31-Jan

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27-Dec

Stre

amflo

w m

3 pe

r se

cond

Average 1975-20061995 High Year2000 Low Year

Smith Creek, Saskatchewan, ~400 km2 basin area

Streamflow over Time

Smith Creek Annual Streamflow

0

5000000

10000000

15000000

20000000

25000000

3000000019

75

1977

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Ann

ual S

tream

flow

(m3 )

Top 4 years for streamflow since 1995

Peak Flow over Time

Maximum Daily Discharge of Smith Creek during 1975-2006

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1975

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Max

imum

Dai

ly D

isch

arge

(m3 /s

) Top 6 peak daily flows since 1995

Changing Climate?Mean Annual Air Temperature at Yorkton

y = 0.0239x + 1.4259R2 = 0.0404

-1

0

1

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4

519

75

1977

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1987

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Mea

n A

nnua

l Tem

pera

ture

(°C

)

An n u a l R a in fa ll an d S n o w fa ll a t Yo rk to n

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600

1975

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2005

Ra inf a ll (mm)

Snow f a ll (mm)

Warming but high variability

No trend in rainfall and snowfall

Drainage of Wetlands?

Drainage of Wetlands?

Modelling Approach

CRHM – Prairie Hydrological Model Configuration

Flow Chart in Cold Regions Hydrological Model Platform

(CRHM)

HRU Configuration for Smith Creek

Small scale Processes Large Scale Processes

HRUs “grouped”into “representativebasins”, RBs, that are repeated forsub-basins but withindividual parametersets. Routingbetween RBspermits large scaleprocess estimation.

Routing

Fallow

River Channel

Open Water

Stubble Grassland

Woodland

WetlandRB outlet

RB 1

RB 2

RB 4

RB 3RB 5 Smith Creek

basin outlet

(a) (b)

Amongst HRU in a Representative Basin

Amongst Representative Basins

Instrumentation of Smith Creek

Hydrometeorological Station11 dual rain gauges

7 wetland level recorders

Completed Summer 2007

Main Hydrometeorological StationTemperature, humidity, wind speed,

shortwave radiation, longwave radiation, soil moisture,

soil temperature,soil heat flux, snow depth, rainfall,

snowfall

Snow and Wetland Surveys

Smith Creek Basin CharacteristicsDrainage Network Spot Image

SCR-9

SCR-8LR-5

LT-2/SCR-4

SCR-9

SCR-8LR-5

LT-2/SCR-4

SCR-9SCR-9

SCR-8SCR-8LR-5LR-5

LT-2/SCR-4LT-2/SCR-4

Remote Sensing Supervised Classification

SPOT5Field tests ofvegetationclassification

vegetationused to defineHRU area,HRU location &vegetationparameters

LiDAR-Derived DEM Drainage Network

LiDAR DEM to Calculate Depression Storage using pond volume-depth-area relationship

Derivation of Wetland Depressions

CRHM Tests Smith Creek – No CalibrationObserved SWE vs Simulated SWE at Smith Creek Sub-basin 1

0

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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

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

2008

Volu

met

ric S

oil

Moi

stur

e

ObservedSimulated

Runoff Prediction 2008

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

Runoff Prediction 2009

MB RMSD (m3/Peak Discharge (m3/s)Non-LiDAR Simulation -0.21 0.28 7.83LiDAR-based Simulation -0.57 0.31 5.37Observation 6.22

Smith Creek Spring Discharge near Marchwell

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23-Mar 28-Mar 02-Apr 07-Apr 12-Apr 17-Apr 22-Apr 27-Apr 02-May 07-May

2009

Daily

Mea

n Di

scha

rge

(m3 /s

) ObservationNon-LiDAR SimulationLiDAR-based Simulation

Sensitivity Analysis: Change in Spring Discharge

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-05

Spri

ng D

isch

arge

(100

0 da

m3)

"normal condition"complete forest coverprimarily agricultural land usehigh wetland extentminimal wetland extent

Sensitivity of Spring Discharge Volume to Land use and Drainage

-10

-8

-6

-4

-2

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6

0 5 10 15 20 25 30 35 40

Spring Discharge Volume (1000 dam3)

Cha

nge

in d

isch

arge

vol

ume

(100

0 da

m3)

Agricultural Conversion

Forest Conversion

Wetland Restoration

Wetland Drainage

Long-term Impact of Land Use and Drainage Change

-40-30-20-10

010203040506070

ForestConversion

AgriculturalConversion

WetlandRestoration

Wetland Drainage% C

hang

e in

Dis

char

ge V

olum

e

Wetland Change in Low Discharge Volume Year

Scenarios of Smith Creek Spring Discharge near Marchwell

0

0.2

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1

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1.4

1.6

11-Feb 20-Feb 29-Feb 09-Mar 18-Mar 27-Mar 05-Apr 14-Apr 23-Apr 02-May

2000

Daily

Mea

n Di

scha

rge

(m3 /s

)

"Normal Condition"high natural wetland extentminimum wetland extent

2000 Drought: Lowest Discharge Volume on Record

Wetland Change in High Discharge Volume Year

Scenarios of Smith Creek Spring Discharge near Marchwell

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25

30

01-Mar 10-Mar 19-Mar 28-Mar 06-Apr 15-Apr 24-Apr 03-May 12-May 21-May 30-May

1995

Daily

Mea

n Di

scha

rge

(m3 /s

) "Normal Condition"high natural wetland extentminimum wetland extent

1995 Flood: Record High Discharge Volume

Discussion on Scenarios• Changes in wetland extent often are accompanied by

changes to land use.• Increasing forest cover decreases discharge volume.• Increasing agricultural land increases discharge volume.• Increasing wetland area reduces discharge volume, whilst

decreasing wetland area results in an increase. • The changes to discharge volume due to decreasing

wetland area are similar for almost all discharge volumes, but changes due to increasing wetland area tend to increase with discharge volume.

• In dry conditions, when storage is small, wetland drainage increases discharge volume, whilst wetland restoration has little impact.

• In flooding conditions, when storage is filled, neither wetland drainage nor restoration has an effect on the hydrograph.

Conclusions• Consideration of snow, frozen soil and surface storage

processes are essential to calculating spring runoff in the Prairies.

• Depressional storage is exceedingly difficult to calculate in this flat, poorly drained environment – LiDAR permits estimation of depressional and wetland storage volumes.

• It is possible to model prairie snowpack, soil moisture and streamflow without calibration using physically based simulations that aggregate landscape scale hydrological cycle calculations, if high resolution information is available on catchment characteristics.

• There is moderate sensitivity of streamflow volumes to changes in agricultural and forest land use.

• There is strong sensitivity of streamflow volumes to wetland drainage and restoration.