modelling australian tropical savanna peter isaac 1, jason beringer 1, lindsay hutley 2 and stephen...

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Modelling Australian Tropical Savanna Peter Isaac 1 , Jason Beringer 1 , Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science, Monash University, Melbourne 2 School of Science and Primary Industry, Charles Darwin University, Darwin

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Page 1: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Modelling Australian Tropical Savanna

Peter Isaac1, Jason Beringer1, Lindsay Hutley2 and Stephen

Wood1

1 School of Geography and Environmental Science, Monash University, Melbourne

2 School of Science and Primary Industry, Charles Darwin University, Darwin

Page 2: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Introduction• Savanna occupies ~20% of the Earth’s

surface and ~25% of Australia

• Undisturbed Australian savanna is a sink of CO2, -3 to -4 tCha-1yr-1

• Tropical savanna is a highly dynamic ecosystem– mix of C3 and C4 plant species

– large annual variation in Fe and Fc in response to wet season/dry season climate

– large inter-annual variation due to fire

Page 3: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

ARC Discovery Project• Title

– “Patterns and Processes of Carbon, Water and Energy Cycles Across Northern Australian Landscapes: From Point to Region”

• People– Beringer (Monash), Hacker (ARA), Paw U

(UCD), Neininger (MetAir AG), Hutley (CDU)

• MethodsFlux towers (Monash, CDU)

6 sitesRemote sensing

MODIS, LandsatAircraft (ARA, MetAir)

IOP September 2008LSM (UCD, CC)

ACASA, CABLE

Page 4: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Sites• Howard Springs

– open forest savanna

• Fogg Dam– wetland

• Adelaide River– woody savanna

• Daly River Uncleared– open forest savanna

• Daly River 5 year– regrowth

• Daly River 25 year– pasture

Page 5: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Howard Springs• Fluxes

– Fsd, Fsu, Fld, Flu, Fn, PAR– Fm, Fe, Fh, Fc, Fg

• Meteorology– Ta, RH, WS, WD

• Concentrations– CO2, H2O

• Precipitation– Rainfall

• Soil– moisture (10 & 40 cm)– temperature

• 12º 29.655S 131º 09.143E • Open forest savanna

Page 6: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Howard Springs

Page 7: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Savanna Canopy

C3 overstoreyLai 0.6 - 1.0hc 16 m

C4 understoreyLai 0.08 - 1.4hc 0.1 - 2 m

Above ground biomass 34 t ha-1

Below ground biomass 17 t ha-1

Soil organic carbon 140 t ha-1

Page 8: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Leaf Area Index

0.0

0.5

1.0

1.5

2.0

2.5

1 2 3 4 5 6 7 8 9 10 11 12

Month

LAI

Overstorey

Understorey

Overstorey(Fit)

Total

C4 fraction

Page 9: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Drivers and Cycles

0

0.5

1

1.5

2

2.5

LAI (to ta l)

LA I (U S)

C 4 fraction

-50

0

50

100

150

200

(Wm

-2)

N et radiation

Latent heat flux

C O 2 flux

- 5

0

(m

olm

-2s-1

)

0 3 6 9 12M onth

0

0.1

0.2

(m3 m

-3)

Soil m oisture (10cm )

0 3 6 9 12M onth

0

100

200

300

400(m

m)

R ainfa ll

a) b)

c) d)

Page 10: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Questions

To accurately model the seasonal variation in Fe and Fc over tropical savanna, is it necessary:1) for the data input to the model to resolve

the seasonal change in Lai ?

2) for the data input to the model to resolve the seasonal change in C4 ?

3) to use a multi-layer model to resolve changes in the canopy ?

Page 11: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

CABLECSIRO Atmosphere Biosphere Land Exchange model

• Big leaf model– Kowalczyk et al. (2006), CMAR Paper 013– coupled assimilation/transpiration– one sunlit leaf, one shaded– mixed C3/C4 canopy by specifying C4 fraction

– seasonally varying Lai and C4 fraction

– radiation in IR, near IR and visible– 13 vegetation types, 9 soil types, 6 soil layers– destined to be the LSM in ACCESS

Page 12: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

ACASAAdvanced Canopy Atmosphere Soil Algorithm

• Multi-layer model– University of California, Davis– Pyles et al., 2000, QJRMS, 126, 2951-2980– coupled assimilation/transpiration– third-order closure turbulence sub-model– 100 canopy layers for radiation– 20 canopy layers for turbulence/fluxes– 15 soil layers– no C4 pathway

Page 13: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Savanna canopy

C3C4

Sunlit

Shaded

CABLE C3

ACASA

C3 overstoreyLai 0.6 - 1.0hc 16 m

C4 understoreyLai 0.08 - 1.4hc 0.1 - 2 m

C4 roots shallow

C3 roots deep

Reality vs Model

Page 14: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Results

1) Out-of-the-box– all defaults except LAI

2) Basic Tuning– “educated guess”

3) Constant Lai

– as for 2), Lai = 1.4

4) Constant C4 fraction– as for 2), C4 fraction = 0.39

Page 15: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Out-of-the-box : CABLE

0 6 12 18 24H o u r

0

200

400

600

800

Wm

-2

O bs

C ABLE

0

100

200

300

400

Wm

-2

O bs

C ABLE

0

100

200

300

400

Wm

-2

O bs

C ABLE

-15

-10

-5

0

5

mo

lm-2

s-1

O bs

C ABLE

0 6 12 18 24H o u r

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

0 6 12 18 24H o u r

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

0 6 12 18 24H o u r

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

W et D raw -dow n D ry R e-charge

C ABLE : H ow ard Springs ; 1 /6 /2004 to 31/5 /2005

N et R adiation

Sensib le

Latent

N EE

Page 16: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Out-of-the-box : ACASA

0 6 12 18 24H o u r

0

200

400

600

800

Wm

-2

O bs

A C ASA

-100

0

100

200

300

Wm

-2

O bs

A C ASA

0

100

200

300

400

Wm

-2

O bs

A C ASA

-15-10

-505

1015

mo

lm-2

s-1

O bs

A C ASA

0 6 12 18 24H o u r

O bs

A C ASA

O bs

A C ASA

O bs

A C ASA

O bs

A C ASA

0 6 12 18 24H o u r

O bs

AC ASA

O bs

AC ASA

O bs

AC ASA

O bs

AC ASA

0 6 12 18 24H o u r

O bs

AC ASA

O bs

AC ASA

O bs

AC ASA

O bs

AC ASA

W et D raw -dow n D ry R e-charge

AC AS A : H ow ard Springs ; 1 /6/2004 to 31/5 /2005

N et R adiation

Sensib le

Latent

N EE

Page 17: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Basic Tuning

• Soil moisture at wilting point reduced from 0.135 to 0.08 m3m-3 based on observations

• Root fraction for E. tetradonta according to Eamus et al. (2002)

• CABLE– vcmax increased from 10 to 30 molm-2s-1

• ACASA– set soil microbial respiration to 0

Page 18: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Basic Tuning : CABLE

0 6 12 18 24H o u r

0

200

400

600

800

Wm

-2

O bs

C ABLE

0

100

200

300

400

Wm

-2

O bs

C ABLE

0

100

200

300

400

Wm

-2

O bs

C ABLE

-15

-10

-5

0

5

mo

lm-2

s-1

O bs

C ABLE

0 6 12 18 24H o u r

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

0 6 12 18 24H o u r

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

0 6 12 18 24H o u r

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

W et D raw -dow n D ry R e-charge

C ABLE : H ow ard Springs ; 1 /6 /2004 to 31/5 /2005

N et R adiation

Sensib le

Latent

N EE

Page 19: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Basic Tuning : ACASA

0 6 12 18 24H o u r

0

200

400

600

800

Wm

-2

O bs

A C ASA

-100

0

100

200

300

Wm

-2

O bs

A C ASA

0

100

200

300

400

Wm

-2

O bs

A C ASA

-15

-10

-5

0

5

mo

lm-2

s-1

O bs

A C ASA

0 6 12 18 24H o u r

O bs

A C ASA

O bs

A C ASA

O bs

A C ASA

O bs

A C ASA

0 6 12 18 24H o u r

O bs

AC ASA

O bs

AC ASA

O bs

AC ASA

O bs

AC ASA

0 6 12 18 24H o u r

O bs

AC ASA

O bs

AC ASA

O bs

AC ASA

O bs

AC ASA

W et D raw -dow n D ry R e-charge

AC AS A : H ow ard Springs ; 1 /6/2004 to 31/5 /2005

N et R adiation

Sensib le

Latent

N EE

Page 20: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Constant Lai : CABLE

0 6 12 18 24H o u r

0

200

400

600

800

Wm

-2

O bs

C ABLE

0

100

200

300

400

Wm

-2

O bs

C ABLE

0

100

200

300

400

Wm

-2

O bs

C ABLE

-15

-10

-5

0

5

mo

lm-2

s-1

O bs

C ABLE

0 6 12 18 24H o u r

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

0 6 12 18 24H o u r

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

0 6 12 18 24H o u r

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

W et D raw -dow n D ry R e-charge

C ABLE : H ow ard Springs ; 1 /6 /2004 to 31/5 /2005

N et R adiation

Sensib le

Latent

N EE

Page 21: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Constant Lai : ACASA

0 6 12 18 24H o u r

0

200

400

600

800

Wm

-2

O bs

A C ASA

-100

0

100

200

300

Wm

-2

O bs

A C ASA

0

100

200

300

400

Wm

-2

O bs

A C ASA

-15

-10

-5

0

5

mo

lm-2

s-1

O bs

A C ASA

0 6 12 18 24H o u r

O bs

A C ASA

O bs

A C ASA

O bs

A C ASA

O bs

A C ASA

0 6 12 18 24H o u r

O bs

AC ASA

O bs

AC ASA

O bs

AC ASA

O bs

AC ASA

0 6 12 18 24H o u r

O bs

AC ASA

O bs

AC ASA

O bs

AC ASA

O bs

AC ASA

W et D raw -dow n D ry R e-charge

AC AS A : H ow ard Springs ; 1 /6/2004 to 31/5 /2005

N et R adiation

Sensib le

Latent

N EE

Page 22: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Constant C4 fraction : CABLE

0 6 12 18 24H o u r

0

200

400

600

800

Wm

-2

O bs

C ABLE

0

100

200

300

400

Wm

-2

O bs

C ABLE

0

100

200

300

400

Wm

-2

O bs

C ABLE

-15

-10

-5

0

5

mo

lm-2

s-1

O bs

C ABLE

0 6 12 18 24H o u r

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

0 6 12 18 24H o u r

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

0 6 12 18 24H o u r

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

O bs

C ABLE

W et D raw -dow n D ry R e-charge

C ABLE : H ow ard Springs ; 1 /6 /2004 to 31/5 /2005

N et R adiation

Sensib le

Latent

N EE

Page 23: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Constant C4 fraction : ACASA

0 6 12 18 24H o u r

0

200

400

600

800

Wm

-2

O bs

A C ASA

-100

0

100

200

300

Wm

-2

O bs

A C ASA

0

100

200

300

400

Wm

-2

O bs

A C ASA

-15

-10

-5

0

5

mo

lm-2

s-1

O bs

A C ASA

0 6 12 18 24H o u r

O bs

A C ASA

O bs

A C ASA

O bs

A C ASA

O bs

A C ASA

0 6 12 18 24H o u r

O bs

AC ASA

O bs

AC ASA

O bs

AC ASA

O bs

AC ASA

0 6 12 18 24H o u r

O bs

AC ASA

O bs

AC ASA

O bs

AC ASA

O bs

AC ASA

W et D raw -dow n D ry R e-charge

AC AS A : H ow ard Springs ; 1 /6/2004 to 31/5 /2005

N et R adiation

Sensib le

Latent

N EE

Page 24: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Seasonal Variation in Fe and Fc

0 3 6 9 12M o n th

0

40

80

120

160

200

Wm

-2

-5

-4

-3

-2

-1

0m

olm

-2s-1

O bs

Tuned

LA I constant

C 4 constant

0 3 6 9 12M o n th

O bs

Tuned

LA I constant

C 4 constant

C ABLE AC ASAN EE N EE

Latent

Latent

Page 25: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Summary• Tropical savanna is a dynamic system

– mix of C3 overstorey and C4 understorey

– Lai and C4 fraction respond mainly to soil moisture

– soil moisture driven by bi-modal rainfall

• Questions– do we need a multi-layer model ?

– do we need seasonally varying Lai ?

– do we need seasonally varying C4 fraction ?

Page 26: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Conclusions (ACASA)

• The multi-layer model (ACASA) did not perform better than the single layer model (CABLE) for this study– Raupach and Finnigan (1988)

• The multi-layer model did not perform well enough to make conclusions about the necessity of resolving seasonal changes in Lai and C4 fraction in the input data.

Page 27: Modelling Australian Tropical Savanna Peter Isaac 1, Jason Beringer 1, Lindsay Hutley 2 and Stephen Wood 1 1 School of Geography and Environmental Science,

Conclusions (CABLE)• Basic tuning significantly improves model

performance.

• When tuned, CABLE over-predicts Fc in the wet season and under-predicts Fe in the dry season.

• CABLE is sensitive to both Lai and C4 fraction when predicting Fc but is not sensitive to either when predicting Fe.

• C4 fraction must vary by season to correctly predict seasonal changes in Fc.