24/09/2015

Padova (IT)

On the role of subsurface

heterogeneity at hillslope

scale with Parflow

Gabriele Baroni

Sabine Attinger

Outline

Quick overview of the project

Motivations in our research unit

The effect of soil heterogeneity at field/hillslope

Outlook

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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

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?

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Test at

small

scales

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

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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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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

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

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)

Flat field: mean soil moisture and pressure

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• Differences if s2 increases

• To check effect of this dynamic

non-equilibrium in longer term

t0

t1

t2

t0

t1

t2

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)

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

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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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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…but a working hypothesis

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Model

results

GOOD

BAD

Fine

grid Coarse

grid

e.g.,

topography soil

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

Thank you for the attention

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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

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 vertical bedrock

1.4 m

18.6 m

50 m

50 m

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

Energy and mass fluxes in TerrSysMP

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Gasper al, 2015