parameterisation by combination of different levels of process-based model physical complexity john...
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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