On the role of subsurface heterogeneity at hillslope scale with Parflow

1. 24/09/2015
Padova (IT)
On the role of subsurface
heterogeneity at hillslope
scale with Parflow
Gabriele Baroni
Sabine Attinger

2. Outline
 Quick overview of the project
 Motivations in our research unit
 The effect of soil heterogeneity at field/hillslope
 Outlook
2

3. Overview project
German funded project (DFG)
"Data Assimilation for Improved Characterization of
Fluxes across Compartmental Interfaces“
Seven research units working on different compartments
of the terrestrial system
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http://www.for2131.de/home-en

4. Overview project and our unit
1. To create a virtual reality at catchment
scale with an integrated model (TerrSysMP)
2. Coarsening the model with different
upscaling rules and see the effects
3. To develop a unified data assimilation
framework to improve the performance of
the model
• which measurements to integrate?
• how?
4
Test at
small
scales

5. Motivations
heterogeneity and scaling effects
 Whenever we apply the current distributed
models (e.g. Richards eq.) we assume uniform
parameters within the grid
 Whatever is the scale of application (lab, field,
catchment) we do an upscaling exercise
5

6. Research questions
in this upscaling exercise?
 we have to find upscaling rules/effective
parameters
• different results functions of domain set-up,
parameters distributions, boundary conditions etc.
• Holy Grail of hydrology…worth searching for
even if a general solution might ultimate prove
impossible to find (Beven, 2006)
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7. Hillslope
Rain/runoff
Flat field
Infiltration/drainage
Tests at field/hillslope with Parflow
Variability in soil properties (~Fiori and Russo, 2007)
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1 Homogeneous
2
s2= 0.3
~ergodic domain = 33 * Integral scale
3 ~non-ergodic domain = 5 * Integral scale
4
s2= 1.0
~ergodic domain = 33 * Integral scale
5 ~non-ergodic domain = 5 * Integral scale

8. Same mean but variability
Flat field
Infiltration/drainage at 10 cm depths
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Homogeneous
Mean and variance of soil moisture
and pressure over the plan
- 10 cm - 10 cm

9. Flat field:
mean soil moisture and pressure
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• Soil moisture dynamics well
represented by the homogeneous
counterpart
• To check if differences are within
statistics of the random fields
(single realization vs. ensemble of
50 realizations)

10. Flat field:
mean soil moisture and pressure
10
• Differences if s2 increases
• To check effect of this dynamic
non-equilibrium in longer term
t0
t1
t2
t0
t1
t2

11. Hillslope
storage-discharge
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• Storage dynamics well
represented by the homogeneous
counterpart
• To check if differences are within
statistics of the random fields
(single realization vs. ensemble
of 50 realizations)

12. Hillslope
storage-discharge
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• Storage dynamics well
represented by the homogeneous
counterpart
• To check if differences are within
statistics of the random fields
(single realization vs. ensemble
of 50 realizations)
• Heterogeneity = faster responses
• Some differences especially if
non-ergodic

13. To summarize so far…
With an homogeneous counterpart
 state dynamic well represented (i.e., soil
moisture or water storage)
 variability increases dynamic non–equilibrium
but to test implications at longer term
 non-ergodicity - more than variability - precludes
the use of general upscaling rules
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14. To come…
 To generalize the tests at field/hillslope scale to
better understand the role of soil heterogeneity on
the hydrological responses
 To finalize the virtual reality and to analyse the
effect of coarsening with different upscaling rules
at catchment scale
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15. …but a working hypothesis
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Model
results
GOOD
BAD
Fine
grid
Coarse
grid
e.g.,
topographysoil
Ergodic D>>I
grid resolution
 From searching for effective parameters to search for best resolution?
grid resolution
GOOD
BAD
Fine
grid
Coarse
grid
Model
results
?
Best
resolution?
Non-ergodic D ~ I
 at this scale we might still have uncertainty in state and discharge
(fluxes): DA framework to integrate both measurements and to
compensate the model structure uncertainty

16. Thank you for the attention
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17. Virtual reality
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Neckar Catchment:
Location (Baden-Württemberg, Germany)
Area 14,000 km2
Temperate-Humid climate
Average annual precipitation 950mm
Medium groundwater depth (1-2m)
Model set-up
Different virtual realities
~ resolution 50 – 800 m
~ 30 million nodes
~ 5 -12 years of simulation runs
 We aim at a reasonable approximation
 Plausible check with measurements

18. Details of model set-up
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1 Homogeneous
2
s2= 0.3
~ergodic domain = 33 * Integral scale
3 ~non-ergodic domain = 5 * Integral scale
4
s2= 1.0
~ergodic domain = 33 * Integral scale
5 ~non-ergodic domain = 5 * Integral scale
Soil
Domain
50 x 50 x 50 nodes
1 m resolutions xy
dZ verticalbedrock
1.4 m
18.6 m
50 m
50 m

19. Details about soil variability (~Fiori and Russo, 2007)
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Homogeneous
s2 = 0.3 s2 = 1.0
s2 CV s2 CV
Ksat [m/h] 0.02 (geom.) 0.3 0.6 1.0 1.3
a [m-1] 3.5 (geom.) 0.2 0.4 0.5 0.8
n [-] 2.0 (arithmetic) 0.02 0.05 0.05 0.1
qs [-] 0.42 (arithmetic) 0.001 0.05 0.002 0.1
Ksat [m/h] a [m-1] n [-] qs [-]
Ksat [m/h] 1
a [m-1] 0.8 1
n [-] 0.4 0.5 1
qs [-] -0.4 -0.2 -0.6 1
Correlation matrix

20. Energy and mass fluxes in TerrSysMP
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Gasper al, 2015