Testing the assumptions in JULES: chalk soils

Nataliya Bulygina, Christina Bakopoulou, Adrian Butler, and Neil McIntyre

Outline

1)Assumptions

2)Methods• Data• Models

3)Results

4)Summary and conclusions

5)Ways forward

Assumptions

A1) The default parameter values are applicable at the point scale

A2) Free drainage lower boundary condition

A3) No subsurface lateral interactions

A4) No groundwater representation

Methods

A1) The default parameterisation is applicable at the point scaleJULES outputs are compared with observations of:• soil moisture measurements, and• EA fluxes.

A2) Free drainage lower boundary condition

Lower boundary effect investigation using a 1D Richards’ equation model

A3) No subsurface lateral interactions

Subsurface fluxes estimation using a 2D Richards’ equation model

A4) No groundwater representation• See A2)• A 2D Richards’ equation model simulated discharge vs. no GW routing• A 2D Richards’ equation model estimated EA fluxes from UZ and GW

Methods ▪Data

Grimsbury Wood (Pang catchment) (and other sites to be decided)

• 1 Jan, 2005 – 30 Jun, 2005 • Met. data (30-min)• AE fluxes from HYDRA• Soil moisture probes (.1, .2, .3, .4, .6, and 1 m)

Pang and Lambourne long term data

• Jan, 1961 – Mar, 2003• Met. Data (daily)

Methods ▪Models

1)1D Richards’ equation-based model (CUZ model of Ireson et al, 2009)

2)2D Richards’ equation-based model for a hillslope (2D CUZ of Ireson)

Hillslope topography Mesh used in the hillslope 2D model

ResultsA1) The default parameterisation is applicable at the point scale

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

0.05

0.1

Timestep

Soil

Mois

ture

(m3 /m

2 )

0 1000 2000 3000 4000 5000 6000 7000 8000 9000

0

0.5

1

Pre

cip

itation (

m/d

ay)

Observed sm for layer 1

Simulated sm for layer 1

• Soil moisture for the layer 1 exhibits the best agreement

• Soil moisture in the layer 2 and 3 is under-estimated

0 1000 2000 3000 4000 5000 6000 7000 8000 90000.05

0.1

0.15

Timestep

Soil

Mois

ture

(m3 /m

2 )

0 1000 2000 3000 4000 5000 6000 7000 8000 9000

0

0.5

1

Pre

cip

itation (

m/d

ay)

Observed sm for layer 2

Simulated sm for layer 2

0 1000 2000 3000 4000 5000 6000 7000 8000 90000.1

0.2

0.3

Timestep

Soi

l Moi

stur

e (m

3 /m2 )

0 1000 2000 3000 4000 5000 6000 7000 8000 9000

0

0.5

1

Pre

cipi

tatio

n (m

/day

)

Observed sm for layer 3

Simulated sm for layer 3

ResultsA1) The default parameterisation is applicable at the point scale

JULES under-estimates AE in winter-spring, and over-estimates in summer

Results A2) Free drainage lower

boundary condition

Free drainage condition at the bottom of 3m soil column (JULES type) vs. water table condition at the bottom of 40m column (the original CUZ setup)

The JULES column is drier: 4.5% more water drainage & EA is 1.2% lower.

More variable soil moisture in the JULES column (especially, in the deeper layers).

Results A3) No subsurface lateral

interactions

Total net and lateral net fluxes in the UZ located in the middle of a hillslope

Lateral fluxes in the UZ are close to 0.

Results A4) No groundwater representation

UZ drainage routed via GW vs. directly delivered to the river (no routing – as in JULES)

No GW routing leads to flushy response in rainy periods & flow underestimation in dry periods.

Results A4) No groundwater representation

EA fluxes from UZ and SZ

EA from the SZ is close to 0, and is negligible relative to EA from the UZ.

Summary and conclusions

In the context of these tests on chalk soils:

• The default JULES parameterisation is questionable.

• Improper boundary condition leads to inadequate soil moisture variability.

• Lower boundary condition has a little effect on EA rates.

• Lateral fluxes between columns can be neglected.

• Lack of groundwater leads to significantly different discharge patterns.

• EA extracted from the groundwater is negligible.

Therefore

• Other parameter estimation options are to be explored, and

• Groundwater representation and its interaction with the soil column can significantly improve soil moisture and discharge estimation in JULES, whilst it might only have minor effects on EA estimation.

• But wider range of sites need to be considered

Ways forward

• Is JULES model structure, or the default parameterisation flawed?• Parameter sensitivity analysis• Model structure supported prediction ranges• Model calibration

• JULES assumptions for low permeability soils in the Thames

(ANY suggestions about locations / data?)

• Consider coupling with a groundwater model• ZOOM (BGS)• Various simplifications