Hydrological Modelling of a small Tundra-Taiga Basin in ... · tundra-taiga treeline. • Previous...

Sebastian Krogh and John PomeroyCentre for Hydrology, University of Saskatchewan

CCRN-CRHM Modelling WorkshopSaskatoon, Saskatchewan, June 6th – 7th

Hydrological Modelling of a small Tundra-Taiga Basin in the Continuous Permafrost Region

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MOTIVATION

Winter (1948-2012)

Vincent et al., 2015

Trend in mean temperature

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CCRN-CRHM Modelling Workshop 16

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MOTIVATION

Annual(1948-2012)

Change in Mean Annual Precipitation


Vincent et al., 2015

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MOTIVATION

Lantz et al. (2007)

Changes in Shrub Cover


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Woo et al., 2007

MOTIVATIONCCRN-CRHM Modelling Workshop 16

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MOTIVATION

CHANGES INTEMPERATURE

CHANGES IN:• ACTIVE LAYER

THICKNESS• ET• SOIL MOISTURE

STORAGE• SNOW REGIME• AND OTHERS

POTENTIAL CHANGES IN PRECIPITATION

CHANGES IN THE HYDROLOGICAL REGIME

Landcover Changes


RESEARCH GAP• There is a need to understand the hydrological regime of Arctic regions.

• Historical and future changes to these regimes may be most dramatic near the tundra-taiga treeline.

• Previous model results are uncertain because models either lacked representation of the key Arctic physical processes or were primarily conceptual and empirical.

OBJECTIVE• Create a physically based hydrological model that includes key Arctic winter and

summer physical processes, coupling surface and subsurface mass and energy fluxes.

• Use the model to diagnose the long-term hydrological regime, governing processes and energy and mass fluxes of an Arctic permafrost catchment in the tundra-taiga transition region.

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STUDY SITE: HAVIKPAK CREEK

Landcover based on the Earth Observation for Sustainable Development of Forest (EOSD) maps.

Digital Elevation Map based on the Canadian-DEM (20 x 20 m)

• Drainage area: 16.4 km2

• Elevation range: 60 – 240 masl

• Average slope = 2.1o, max slope = 9o

• Primarily South facing aspect

• Landcover:• Shrubs tundra• Tundra• Taiga Forest• Wetland Shrubs• Wetland Forest• Open Water

• Mean annual precipitation estimated in 300* [mm/yr]

• Mean annual temperature -8.2* [oC]

*between 1980 and 2010 8


STUDY SITE: HAVIKPAK CREEK

Landcover based on the Earth Observation for Sustainable Development of Forest (EOSD) maps.

Digital Elevation Map based on the Canadian-DEM (20 x 20 m)

• Drainage area: 16.4 km2

• Elevation range: 60 – 240 masl

• Average slope = 2.1o, max slope = 9o

• Primarily South facing aspect

• Landcover:• Shrubs tundra• Tundra• Taiga Forest• Wetland Shrubs• Wetland Forest• Open Water

• Mean annual precipitation estimated in 300* [mm/yr]

• Mean annual temperature -8.2* [oC]

*between 1980 and 2010 9


MODELLING FLOWCHART

VALIDATION• Daily streamflow NS

and BIAS• Snow accumulation

from snow survey

ANALYSIS• Snow ablation and energy

fluxes• Active layer development• Water fluxes and storage

CRHM-HPC MODEL SETUP1. Physical Processes:

• Blowing snow• Canopy Interception• Energy-balance snowmelt• Infiltration frozen/unfrozen soil• Flow through organic soil• Ground freeze/thaw• Hillslope flow• Streamflow routing

2. Parameterization• Field observation• Remote sensing• Previous studies in HPC, WCRB and

TVC• Calibration (DDS algorithm)

OUTPUTSThaw depthEvaporationEnergy fluxesStreamflowSWEAnd others

Observed1 and Raw Reanalysis2 Data• Precipitation1

• Temperature1

• Windspeed1

• Relative Humidity1

• Short wave radiation2

INPUTS PRE-PROCESSING- Precipitation undercatch

correction- Temporal disaggregation

of daily precipitation- Spatial distribution of

windspeed and temperature

INPUTS• DEM• Landcover: EOSD

PRE-PROCESSING• Basin drainage area• HRUs delineation

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WEATHER DATA PRE-PROCESSING

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1) Precipitation correction by wind undercatch for:

• Nipher (Goodison et al., 1998)

• Geonor Alter-Shield (Smith, 2008)

2) Temporal disaggregation of daily precipitation into hourly:

• Microcanonical cascade model (Güntner et al. (2001)

Observed Precipitation[mm/hr]

Disa

ggre

gate

d Pr

ecip

itatio

n[m

m/h

r]

5) Spatial wind speed distribution:

• Walmsley et al. (1989) – wind speed correction for

local topographic features

• Observed higher wind speed at wind-swept

upland tundra (Pomeroy and Marsh, 1997)

3) Spatial distribution of temperature

• Lapse rate: 7.5 [oC/km]

4) 3-hrs shortwave from ERA-I reanalysis is linearly

interpolated into hourly values


MODELLING FLOWCHART


• Temperature1

• Windspeed1











VALIDATION


• Daily streamflow NS and BIAS

• Snow accumulation from snow survey





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HYDROLOGICAL RESPONSE UNITS (HRUs) DELINEATION

• Landcover classes: Tundra, Shrubs, Taiga-Forest, Wetland Forest and Shrubs, Open Water. EOSD* maps

• Elevation (CDEM – 20m)• Aspect (CDEM – 20m)

* EOSD: Earth Observation for Sustainable Development of Forest

Upper Shrubs NF

Lower Shrubs NF

Upper Shrubs SF

Lower Shrubs SF

Upper Tundra NF

Lower Tundra NF

Upper Tundra SF

Lower Tundra SF

Forest NF

Forest SF

Wetland Tree

Wetland Shrub

Water

BasinOutlet

Based on the flow direction map

Drainage Sequence:


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HYDROLOGICAL RESPONSE UNITS (HRUs) DELINEATION

• Landcover classes: Tundra, Shrubs, Taiga-Forest, Wetland Forest and Shrubs, Open Water. EOSD* maps

• Elevation (CDEM – 20m)• Aspect (CDEM – 20m)

* EOSD: Earth Observation for Sustainable Development of Forest

Upper Shrubs NF

Lower Shrubs NF

Upper Shrubs SF

Lower Shrubs SF

Upper Tundra NF

Lower Tundra NF

Upper Tundra SF

Lower Tundra SF

Forest NF

Forest SF

Wetland Tree

Wetland Shrub

Water

BasinOutlet

Based on the flow direction map

Drainage Sequence:


MODELLING FLOWCHART


• Temperature1

• Windspeed1











VALIDATION




ANALYSIS• Snow ablation and

energy fluxes• Active layer development• Water fluxes and storage



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Infiltration frozen-unfrozen soils

Canopy Snow/Rain Interception and Sublimation/ET

Cold Regions Hydrological Modelling Platform (CRHM-HPC) SETUP:Physical Processes

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Permafrost

Active LayerGround freeze/Thaw

Flow through mineral soil

Precipitation Phase

Evapotranspiration

Blowing Snow Transport/Sublimation

Peat

Mineral SoilWater Level

Streamflow Routing

Overland flow

• Physical Processes

• Mass fluxes

Flow through organic terrain

• Soil Layers

Snowmelt energy-balance




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Permafrost



Precipitation Phase

Evapotranspiration


Peat


Streamflow Routing

Overland flow


• Mass fluxes


• Soil Layers


Precipitation Phase:

• Psychrometric Energy Balance (Harder and Pomeroy, 2013)

• Module: Obs (Snow-Rain Determination option 2)




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Permafrost



Precipitation Phase

Evapotranspiration


Peat


Streamflow Routing

Overland flow


• Mass fluxes


• Soil Layers


Snowmelt and Accumulation:

• Two layer energy balance model: SNOBAL (Marks et al., 1998)

• Module: SnobalCRHM#2




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Permafrost



Precipitation Phase

Evapotranspiration


Peat


Streamflow Routing

Overland flow


• Mass fluxes


• Soil Layers


Snowmelt and Accumulation:

• Two layer energy balance model: SNOBAL (Marks et al., 1998)

• Module: SnobalCRHM#2

Blowing Snow Transport and Sublimation

• Prairie Blowing Snow model (Pomeroy and Li, 2000; Fang and Pomeroy 2009)

• Module: PBSMSnobal#1




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Permafrost



Precipitation Phase

Evapotranspiration


Peat


Streamflow Routing

Overland flow


• Mass fluxes


• Soil Layers


Canopy Interception Snow/Rain:

• Sublimates/evaporates snow /rain from canopy based on canopy characteristics and weather conditions (Parviainen and Pomeroy, 2000; Valente et al., 1997)

• Module: CanopyClearing#1




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Permafrost



Precipitation Phase

Evapotranspiration


Peat


Streamflow Routing

Overland flow


• Mass fluxes


• Soil Layers


Soil Moisture and Hillslope:• 3-layers soil model (Pomeroy et al.,

2007)• Module: SoilX




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Permafrost



Precipitation Phase

Evapotranspiration


Peat


Streamflow Routing

Overland flow


• Mass fluxes


• Soil Layers


Evapotranspiration:

• Penman-Montieth and Priestley and Taylor (wetland and open water)

• Stomata resistance correction based on radiation, soil saturation, vapour pressure deficit and temperature (Verseghy et al., 1993)

• Module: Evap_Resist




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Permafrost



Precipitation Phase

Evapotranspiration


Peat


Streamflow Routing

Overland flow


• Mass fluxes


• Soil Layers


Streamflow Routing:

• Lag and Storage (Clark, 1945) • Module: Netroute




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Permafrost



Precipitation Phase

Evapotranspiration


Peat


Streamflow Routing

Overland flow


• Mass fluxes


• Soil Layers

Snowmelt energy-balanceGround Freeze/Thaw:

• Simplified Solution for Stefan’s Equation (Changwei and Gough, 2013)

• Module: XG#2

• More details next …

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The new ground Freeze/Thaw module (XG-Algorithm) included in CRHM (Changwei and Gough, 2013)

• It’s a simplified solution for Stefan’s Equation

• Allows simulating freeze/thaw depth using the ground surface temperature as a boundary condition

• It can be used with an n-number of soil layers allowing multiple soil configurations

• Required parameters:• Porosity• Layer depth• Dry soil thermal conductivity• Saturated frozen thermal conductivity• Saturated unfrozen thermal conductivity

(Williams and Smith, 1989)

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The XG-Algorithm (Changwei and Gough, 2013):

Stefan’s Equation (Juminikis, 1977):

k: Thermal Conductivity [W/m/K]F: Surface Freezing (Thawing) index [oC*days]L: Latent Heat of fusion of ice 3.35 x 105 [W/kg]

w: Volumetric water content []ρ: Bulk density of the soil [kg/m3]ξ: Freeze/Thaw depth [m]

Based only on the physical properties of the soils

For a given F in a two-layer configuration:

Compute ξ1If ξ1 < z1

Ground Surface (F)

Soil Layer 1

Soil Layer 2

K1, w1, ρ1

K2, w2, ρ2

z1

z2

ξ = ξ1

If ξ1 > z1 Compute residual depth:Δξ1 = ξ1 – z1

ξ2 = Δξ1/P12

ξ = z1 + ξ2

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The XG-Algorithm (Changwei and Gough, 2013):

Stefan’s Equation (Juminikis, 1977):

k: Thermal Conductivity [W/m/K]F: Surface Freezing (Thawing) index [oC*days]L: Latent Heat of fusion of ice 3.35 x 105 [W/kg]

w: Volumetric water content []ρ: Bulk density of the soil [kg/m3]ξ: Freeze/Thaw depth [m]

Based only on the physical properties of the soils

For a given F in a two-layer configuration:

Compute ξ1If ξ1 < z1

Ground Surface (F)

Soil Layer 1

Soil Layer 2

K1, w1, ρ1

K2, w2, ρ2

z1

z2

ξ = ξ1

If ξ1 > z1 Compute residual depth:Δξ1 = ξ1 – z1

ξ2 = Δξ1/P12

ξ = z1 + ξ2

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Soil Configuration used in the CRHM-HPC Model to Simulate Ground Freeze/Thaw

#5 organic layers

#15 organic layers

Ground Temperature:n-factor (Woo et al., 2007)

Layer #1

Layer #20

Soil Properties OrganicSoil

Mineral Soil

Porosity [m3/m3] 0.8 0.5

Depth [m] 0.1 0.1

Dry unfrozen TC [W/m/K]

0.06 0.25

Saturated unfrozen TC [W/m/K]

0.5 1.4

Saturated frozen TC [W/m/K]

1.9 1.8

b, c and s depend on landcover

Permafrost*TC: Thermal Conductivity

Changes in the soil thermal conductivity due to intermediate degrees of soil saturation are calculated based on on Johansen (1975, p. 221)

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Soil Configuration used in the CRHM-HPC Model to Simulate Ground Freeze/Thaw

#5 organic layers

#15 organic layers

Ground Temperature:n-factor (Woo et al., 2007)

Layer #1

Layer #20

Soil Properties OrganicSoil

Mineral Soil

Porosity [m3/m3] 0.8 0.5

Depth [m] 0.1 0.1

Dry unfrozen TC [W/m/K]

0.06 0.25

Saturated unfrozen TC [W/m/K]

0.5 1.4

Saturated frozen TC [W/m/K]

1.9 1.8

b, c and s depend on landcover

Permafrost*TC: Thermal Conductivity

Changes in the soil thermal conductivity due to intermediate degrees of soil saturation are calculated based on on Johansen (1975, p. 221)

Advance Parameters:

Trigger-Threshold: 50 [oC x days]

CRHM-HPC MODEL PARAMETERIZATION

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1. Remote sensing information (DEM landcover maps):

• Area, elevation, slope, aspect and landcover

2. Field observations and previous studies in HPC basin by Marsh and Pomeroy:

• Leaf Area Index, vegetation height and density, blowing snow fetch, snow surface

roughness, and others

3. Studies in other research basins with similar characteristics: Wolf Creek (Yukon) and Trail Valley (NWT):

• Soil properties (e.g. porosity, depth and hydraulic conductivity), canopy snow load capacity, etc.

(Carey, Quinton, Marsh, Pomeroy, and others)

4. Calibration: Restricted to surface and subsurface hydraulic and routing parameters

• The Dynamical Dimensioned Search (DDS) algorithm is used with 1000 iterations

• Lag and storage for surface and subsurface routing, depression storage in wetlands

and storage capacity of organic soils (ranges from previous studies)


CRHM-HPC MODEL PARAMETERIZATION

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1. Remote sensing information (DEM landcover maps):

• Area, elevation, slope, aspect and landcover

2. Field observations and previous studies in HPC basin by Marsh and Pomeroy:

• Leaf Area Index, vegetation height and density, blowing snow fetch, snow surface

roughness, and others

3. Studies in other research basins with similar characteristics: Wolf Creek (Yukon) and Trail Valley (NWT):

• Soil properties (e.g. porosity, depth and hydraulic conductivity), canopy snow load capacity, etc.

(Carey, Quinton, Marsh, Pomeroy, and others)

4. Calibration: Restricted to surface and subsurface hydraulic and routing parameters

• The Dynamical Dimensioned Search (DDS) algorithm is used with 1000 iterations

• Lag and storage for surface and subsurface routing, depression storage in wetlands

and storage capacity of organic soils (ranges from previous studies)


MODELLING FLOWCHART


• Temperature1

• Windspeed1











VALIDATION




ANALYSIS• Snow ablation and

energy fluxes• Active layer development• Water fluxes and storage



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MODEL VALIDATIONCCRN-CRHM Modelling Workshop 16

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MODEL VALIDATIONCCRN-CRHM Modelling Workshop 16

MODELLING FLOWCHART


• Temperature1

• Windspeed1











VALIDATION








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ANALYSIS: Snow accumulation, melt and redistribution

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HRUs* Max. SWE [mm]

Cum. Blowing Snow Transport [mm/yr]

Cum. Blowing Snow Sublimation [mm/yr]

Sublimationfrom Canopy Interception [mm/yr]

Upper Tundra 38 (22%) 40 (24%) 85 (50%) n/a

Lower Tundra 97 (57%) 12 (7%) 27 (16%) n/a

NF Shrubs 140 (82%) 15 (9%) 0 3 (2%)

SF Shrubs 130 (76%) 2 (1%) 0 3 (2%)

Forest 103 (61%) 0 0 32 (19%)

*Percentage with respect to the mean annual snowfall (170 [mm/yr])

ANAYSIS: ACTIVE LAYER DEVELOPMENT

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Differences in thaw depths are mainly explained by:

- Increasing of thermal conductivity with soil saturation (Johansen et al., 1975)

- The observed larger ground to air temperature ratio (n-factor) of vegetated surfaces (boreal forest) compared with non-vegetated tundra surfaces in Fort Simpson and Inuvik, respectively (Woo et al., 2007).


ANALYSIS: WATER FLUXES AND STORAGE

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Landcover-scale Water Fluxes:

include the contribution from up-hill HRUs.

*SWE is not presented as a cumulative value

Cum

ulat

ive

Wat

er F

luxe

s [m

m/y

r]

TUNDRA

SHRUBS

WETLAND-SHRUB

ANALYSIS: WATER FLUXES AND STORAGE

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Landcover-scale Water Fluxes:

include the contribution from up-hill HRUs.

*SWE is not presented as a cumulative value

Cum

ulat

ive

Wat

er F

luxe

s [m

m/y

r]

TUNDRA

SHRUBS

WETLAND-SHRUB

CONCLUSIONS

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1. A physically based hydrological model using the CRHM platform including the new freeze/thaw algorithm has shown to satisfactorily represent the streamflow and snow regime of an Arctic basin.

2. Wind speed correction by topographic features from the MSC weather station to the upland is critical to represent the wind regime snow redistribution and sublimation.

3. Sparsely-vegetated tundra surfaces can lose over 50% of the snowfall by blowing snow sublimation, whereas about 30% of the snowfall is lost by sublimation of intercepted snow in the taiga forest. Sublimation losses are negligible in shrub tundra.

4. Mean Active Layer Thickness varies significantly between landcover, with tundra showing the shallower ALT around 0.6 [m] and wetlands the deeper ALT with roughly 1.1 [m].

5. Mean annual evapotranspiration is about 58% of the mean annual precipitation, but varies widely. Wetland ET is about 400 [mm/yr], whereas for tundra and shrub-tundra ET is 100 and 150 [mm/yr], respectively.

6. The CRHM-HPC will be used to study the impact of climate and landcover changes over time

7. This model can be modified accordingly to study other Arctic basins underlined by permafrost


Hydrological Modelling of a small Tundra-Taiga Basin in ... · tundra-taiga treeline. • Previous...

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