Parameterisation by combination of different levels of process-based model physical

complexity

John Pomeroy1, Olga Semenova2,3, Lyudmila Lebedeva2,4 and Xing Fang1

1Centre for Hydrology, University of Saskatchewan, Saskatoon, Canada2Hydrograph Model Research Group, www.hydrograph-model.ru

3State Hydrological Institute, Saint Petersburg, Russia4Saint Petersburg State University, Russia

Levels of physical complexity in hydrological models

100 100

90 90 90 90

100 100

80 80 80 80

70 70 70 70

60 60 60 60

50 50 50 50

40 40 40 40

30 30 30 30

20 20 20 20

10 10 1010 10

organic layer

rocky/bouldery mineral soil, silty loam

alluvial fan of rocks and silt, sandy loam

silt loam or sandy loam

moss

river gravel

Alpine Tundra Buckbrush Taiga White Spruce Forest Gb2 Gb3 Gb4 Gb5

silty loampermafrost

Subsurface

Surface

Physically-basedConceptual

+

+ Is the process basis suitable to data availability?

Initially planned activities

***

*

* 1)Refine and confirm parameterisation of a physically-based model describing surface and near-surface processes at small-scale research basin

2) Use modelled outcomes to estimate the parameters of more conceptual process-based model

3) Apply the process-based model in larger scale where data availability is sparser

Study area

Forest

Subalpine

Alpine

Forest

Subalpine

Alpine

Study Site

Yukon River at Eagle, 345 000 km2

Wolf Creek Research Basin,195 km2

Granger watershed,8 km2

*

*

Similar principles of model development

The Cold Regions Hydrological Model (CRHM),

Canada

Hydrograph Model,Russia

• is distributed such that the water balance for selected surface areas can be computed;

• is sensitive to the impacts of land use and climate change;

• does not require the presence of a stream in each land unit;

• is flexible: can be compiled in various forms for specific needs;

• is suitable for testing individual process algorithms.

• DOES NOT REQUIRE CALIBRATION

• Single model structure for

watersheds of any scale• Adequacy to natural processes

while looking for the simplest

solutions

• Use of physically-

observable parameters

• MINIMUM OF MANUAL

CALIBRATION

Processes

Both models: precipitation, temperature, relative humidity, solar radiation

CRHM: wind speed

Slope transformationof surface flow

Initial surfacelosses

Infiltration andsurface flow

Heat dynamicsin soil

Snow coverformation

Heat energy

Interception

Heat dynamicsin snow

Snow melt andwater yield

EvaporationWater dynamics in soil

Channel transformation

Runoff at basin outlet

Underground flow

Transformation of underground flow

PrecipitationRain Snow

• Infiltration into soils (frozen and unfrozen)• Snowmelt (prairie & forest)• Radiation• Evapotranspiration• Wind flow over hills• Snow transport• Interception (snow & rain)• Sublimation (dynamic & static)• Soil moisture balance• Runoff, interflow• Routing (hillslope & channel)

Forcing data

HydrographCRHM

• Spatial variability of snow accumulation due to redistribution by blowing snow

• Infiltration of snowmelt water into frozen soils

• Actual evapotranspiration rates from different landscapes

Common Processes of the Hydrograph and CRHM models

Spatial variability of snow cover

physically-based two-dimensional blowing snow transport and

sublimation model

statistical accounting for snow redistribution at the moment of

snowfall

HydrographCRHM

0

50

100

150

200

250

01.10.98 01.12.98 01.02.99 01.04.99 01.06.99 01.08.99 01.10.99 01.12.99 01.02.00 01.04.00 01.06.00

SW

E (

mm

)

UB PLT NF SF VB

CRHM simulated variability of snow cover over different landscapes at the Granger watershed

Infiltration of snowmelt water into frozen soils

where C is a coefficient = 2, S0 is the

surface saturation (mm3·mm3), SI is

the average soil saturation (water + ice) of 0-40 cm soil at the start of infiltration (mm3·mm3), TI is the

average temperature of 0-40 soil layer at start of infiltration (K), and t0 is the

infiltration opportunity time (h).

44.00

45.064.192.2

0 15.273

15.273)1( t

TSSCINF II

North-facing slope

South-facing slope

Hydrograph

CRHMZhao and Gray approach

nS

TfHH)1(ff

/H

i0*

*2

q

where Hq is surface flow (mm), H – snowmelt depth (mm), f* - infiltration coefficient in frozen ground, f0 – infiltration coefficient in unfrozen ground, Si – ice content of a layer, n – coefficient (4 – sand, 5 – loam sand, 6 – loam, 7 – clay)

Assessment of evapotranspiration rates

Granger & Gray Actual ET method:Actual ET is calculated using a combination of energy balance, aridity feedback and aerodynamic tranfer, so no knowledge of soil moisture status is required for this module. To ensure continuity, evaporation is taken first from any intercepted rainfall store, then from the upper soil layer and then from the lower soil layer and restricted by water supply

Use of seasonal potential evaporation coefficients:

actual ET depends on air aridity and moisture availability in soil and interception storages. In this study evaporation rates were estimated by calibration of soil parameters according to soil moisture observations

HydrographCRHM

R2 = 0.7109

0

1

2

3

4

0 1 2 3 4

Hydrograph, mm

CR

HM

, m

m

Correlation between actual evaporation simulated by CRHM and the Hydrograph models

Verification of the Hydrograph model parameterization at point scale

observed simulated

09.200103.200109.200003.200009.1999

Vo

lum

etr

ic w

ate

r c

on

ten

t

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00

Forest Site: observed and simulated soil moisture content at 0.15 m depth

Alpine Tundra Site: observed and simulated soil temperature at 0.15 m depth

observed simulated

02.200408.200302.200308.200202.2002

Te

mp

era

ture

, d

eg

ree

C

12

10

8

6

4

2

0

-2

-4

-6

-8

-10

Results of runoff modelling at Granger watershed (8 km2), 1999 – 2001

simulated observed

12.199910.199908.199906.199904.1999

m3

/s

1 . 3

1. 2

1. 1

1. 0

0. 9

0. 8

0. 7

0. 6

0. 5

0. 4

0. 3

0. 2

0. 1

0. 0

simulated observed

01.200111.200009.200007.200005.2000

m3

/s

1 . 1

1.1

1.0

0.9

0.9

0.8

0.8

0.7

0.7

0.6

0.6

0.5

0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1

0.0

0.0

simulated observed

08.200107.200106.2001

m3

/s

2 . 2

2.1

2.0

1.9

1.8

1.7

1.6

1.5

1.4

1.3

1.2

1.1

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

1999

2000 2001

NS

1999 0.93

2000 0.73

2001 0.79

Wolf Creek basin, 195 km2

simulated observed

01.200307.200201.200207.200101.200107.200001.2000

m3

/s

10

9

8

7

6

5

4

3

2

1

0

Conclusions1. CRHM blowing snow transport and redistribution module was verified in Wolf

Creek basin and used to develop the information needed to set the Hydrograph

parameters.

2. The comparison of infiltration into frozen ground routine showed that both

models produce similar results in spite of application of different approaches.

3. The comparison of evaporation rates as well shows the coincidence between

the models approaches. It means that in case of absence of observed soil

moisture data the Hydrograph model could rely on the CRHM estimates.

4. The results of runoff and state variables simulations can be considered

satisfactory given the scarcity of the data.

5. The use of estimated parameters in upscaled application of the Hydrograph

model to the Yukon River will be explored as a next step.

Future collaboration may create new possibilities and opportunities which would not otherwise exist. Science should not know barriers for collaboration.

Acknowledgements

We appreciate invaluable help of Michael Allchin, Richard Janowicz, Sean Carey and Yinsuo Zhang in this project

The attendance to EGU was made possible only with the support of the German-Russian Otto-Schmidt Laboratory for Polar and Marine Research