Water, carbon and nutrients on the Australian continent: effects of climate

gradients and land use changes

Michael Raupach, Damian Barrett, Peter Briggs and Mac KirbyCSIRO Land and Water, Canberra, Australia

[michael.raupach@csiro.au]

Outline:Models, data, constraints

ResultsUncertainty and synthesis

Acknowledgments: Helen Cleugh, John Finnigan, Roger Francey, Dean Graetz, Ray Leuning, Peter Rayner, Hilary Talbot

IGBP Global Change Science Conference, Amsterdam, July 2001

Two points on the landscape

100

200

300

400

500

600

800

1000

1200

1600

2000

2400

3200

3500

100

200

300

400

500

600

800

1000

1200

1600

2000

2400

3200

3500

Old-growth forest

Rainfall 600 mm

Savannah woodland

Rainfall 800 mm

Linked terrestrial cyclesof water, C, N and P

C flowN flow

P flow

Water flow

PLANTLeaves, Wood, Roots

ORGANIC MATTERLitter: Leafy, WoodySoil: Active (microbial)

Slow (humic)Passive (inert)

Photosynthesis

Respiration

ATMOSPHERECO2 H2O N2, N2O

SOILSoil water

Mineral N, P

Product offtake

Leaching Sediment transport

Fertiliser inputs

N fixation,N deposition,N volatilisation

N,P Cycles

C Cycle

Rain

Transpiration

Runoff

WaterCycle

Modelling water, carbon and nutrient cycles Framework: the dynamical system

Variables: X = {Xr} = set of stores (r) including all water, C, N, P, …

stores F = {Frs} = set of fluxes (affecting store r by process s)

M = set of forcing climate and surface forcing variablesP = set of process parameters

Stores obey mass balances (conservation equations) of form (for store r)

Statistical steady state or quasi-equilibrium solutions:

Fluxes are described by scale-dependent phenomenological equations of form

1 2r r r rss

X t F F F

, ,rs rsF F X M P

0rss

F

Problem: find these for large scales!

Used here!

Scaling: a general viewStatistical averaging of phenomenological equations

Requirement for scale consistency:

• X, F, M and P are all defined with the same space and time averaging

• Related to smaller-scale process descriptions by statistical averaging

Statistical averaging process: space or time averages of fluxes are

rs rsF F p d V V V

Coarse-scale average flux

Fine-scale model PDF of (X,M,P) = V

2 2 2

21 1 1

...2!

M M Mi rs rs

rs rs i j iji i j ii i j

F FF F

V V V

V

Bias = [(co)variance] * [second derivative of Frs(V) ]Fine-scale model with coarse data

Rain > Ksat1 Rain > Ksat2Duplex soil Ksat1 >> Ksat2

Rain < Ksat 2 looks like clay

Evaporation and TranspirationSimplifying infiltration models to 2-layer soil, daily time step (Mac Kirby)

Evaporation and TranspirationA simple statistical-steady-state model

Evaporation is determined by (rainfall, energy) in (dry, wet) environments [Energy-limited Evaporation] = [Priestley-Taylor Evaporation]

= constant * [Available Energy] A single-parameter hyperbolic function interpolates between dry and wet limits [Total Evaporation] = [Plant Transpiration] + [Soil Evaporation] Time average of [Soil Evaporation] / [Total Evaporation] = exp(-c*LAI)

Annual mean, catchment-scale water balance:

1

Runoff

1aa

p

p a

p

P E

P EE E

P E

precipitation

total evaporation

Priestley-Taylor evaporation

1.5 (grass) to 2.5 (forest)

p

P

E

E

a

Evaporation and TranspirationTests of statistical-steady-state model

0

0.2

0.4

0.6

0.8

1

1.2

0 0.5 1 1.5 2 2.5 3

P/Ep

E/E

p

Forest

Mixed

Pasture

Forest: model

Mixed: model

Pasture: model

Annual mean, catchment-scale water balance

1

Runoff

1aa

a

p

p

P E

pE

p

e E E

p P E

Evaporation and energy: forest sytemsRay Leuning and Helen Cleugh (CLW), Tumbarumba flux site

y = 1.0914x

R2 = 0.7815

0

100

200

300

400

500

0 100 200 300 400 500

LE (average of NOAA and Licor 6262)

LE

(eq

uil

ibri

um

)

Daytime evaporation = 1.1 * equilibrium evaporation

100

200

300

400

500

600

800

1000

1200

1600

2000

2400

3200

3500

Mar

-99

May

-99

Jul-9

9Sep

-99

Nov

-99

Jan-

00

Evap

otr

an

s pir

at i

on

(m

m/w

eek )

0

10

20

30

40

50

60Triticale, 1999

Mar-

00

May

-00

Jul-0

0

Sep-0

0

Nov-0

0

Jan-0

10

10

20

30

40

50

60Lupin, 2000

Priestley Taylor Lysimeters

Evaporation and energy: cropping sytemsChris J Smith and Frank Dunin, CSU Site, Wagga

Steady-state PT ratio alfa(Dz)/alfaq plotted against gsParameter = relDe

0

0.5

1

1.5

0.00 0.01 0.02 0.03 0.04 0.05

gs (m/s)

1

0.8

0.6

0.4

0.2

VBT data

parameter = relative deficit of entrained air

Quasi-steady surface energy balance in an entraining convective boundary layer

( )

Priestley-Taylor

ratio q

Why Priestley-Taylor evaporation is a good measure of potential evaporation over a moist region

Net Primary Productivity (NPP) [NPP] = [Photosynthetic Assimilation] - [Autotrophic Respiration]

A simple, linearised model for light and water limited NPP:

* ** 1.6

; ;s s sC sC pC

sC pC

C D CF R R

R R E

*

2

2

*2

, climate-limited NPP, actual NPP

, resistances to CO transfer

, surface saturation deficit, [CO ]

CO compensation point

canopy water flux (transpiration)

canopy flux of absorbed PA

C C

sC pC

s s

F F

R R

D C

E

R

canopy-scale quantum yield [C/PAR] *plant substrate

atmosphere at plant surface ;s sC D

atmosphere inside stomata ; 0i iC D

sCR

pCR

linear axes logarithmic axes

Testing predictions of NPPVast dataset (Barrett 2001)

NPP: Bios and Vast

0

0.5

1

1.5

0 0.5 1 1.5

Vast NPP [kgC/m2/year]

Bio

sEqu

il N

PP

[kgC

/m2/

year

]

NPP: Bios and Vast

0.01

0.1

1

10

0.01 0.1 1 10

Vast NPP [kgC/m2/year]

Bio

sEqu

il N

PP

[kgC

/m2/

year

]

-45

-40

-35

-30

-25

-20

-15

-10

112 117 122 127 132 137 142 147 152

Testing predictions of NPPVast dataset (Barrett 2001)

NPP: dependence on saturation deficit

0

0.5

1

1.5

2

2.5

0 0.01 0.02 0.03Surface saturation deficit [molW/molA]

NP

P [k

gC/m

2/ye

ar]

Vast

Bios

NPP depends on saturation deficit, through water use efficiency

MeasurementsModel

Testing predictions of C storesVast dataset (Barrett 2001)

Biomass Litter C Soil C

Bio

s

Vast

0.01

0.1

1

10

100

1000

0.01 0.1 1 10 100 1000

0.01

0.1

1

10

100

0.01 0.1 1 10 100

Bio

s A

G L

itter

mas

s [t

C/h

a]

0.001

0.01

0.1

1

10

100

1000

0.001 0.1 10 1000

-45

-40

-35

-30

-25

-20

-15

-10

112 117 122 127 132 137 142 147 152

Data requirements

Climate Rainfall; solar irradiance; temperature; humidity

Land cover and land use Vegetation properties (leaf area index; height) Land use (forest / rangeland / crop / pasture / horticulture)

Land management Fertiliser application rate (N, P) N fixation by legumes Irrigation

Soils Soil type (via pedotransfer functions) => Soil depth; soil texture; hydraulic properties; bulk density

C, N and P balances with present climate and agricultural nutrient inputs Net Primary Production

NPP broadly follows rainfall, with additional modulation by saturation deficit (through water use efficiency). Hence there is less NPP per unit rainfall in north than in south.

Effect of agricultureExample: ratio of (NPP with agriculture) / (NPP without agriculture)

NPP has increased locally (at scale of 5 km cells) by up to a factor of 2 in response to the nutrient inputs associated with European-style agriculture

Largest regional-scale increases occur in the WA, SA, Victorian and NSW wheatbelts

Mineral N balance

Without agriculture

• IN: fixation, small deposition

• OUT: leaching, volatilisation, disturbance

With agriculture

• More fixation (x 2)

• More disturbance

Mineral Nitrogen Balance: 12 Drainage Divisions

-0.003

-0.002

-0.001

0

0.001

0.002

0.003

0.004

0.005

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

x (k

gN/m

2/ye

ar)

FNFertFNDepFNFixFNVolatFNLeachFNDisturbance

Mineral Nitrogen Fluxes: 12 Drainage Divisions

-0.0015

-0.001

-0.0005

0

0.0005

0.001

0.0015

0.002

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

Flu

x (k

gN/m

2/ye

ar)

FNFertFNDepFNFixFNVolatFNLeachFNDisturbance

16

712

8

9

10

1

4

11

2

3

5

Summary

A formal dynamical-system framework

• rigorous treatment of scaling, uncertainty, synthesis

Information flow: evaporation -> NPP -> fluxes and stores of C,N,P

Effects of agriculture on NPP, nitrogen and phosphorus:

• Agricultural nutrient inputs (fertiliser, legumes) have led to regional-scale increases (relative to pre-agricultural conditions) of up to factor of 2 for NPP, and up to a factor of 5 for mineral N, labile P

• Largest changes in N balance are fixation (sown legumes) and disturbance (herbivory)

Continental aggregates:

• Mean continental NPP without agriculture is 0.96 GtC/year

• Continental changes induced by agriculture: NPP + 4.8% mineral N + 13%labile P + 7.6%N budget (in, out) + factor 2

SynthesisA multiple-constraint approach (1)

Problem: What is the space-time distribution of the sources and sinks of CO2 (water, CH4, N2O, dust …) across a large region?

Available information from observations:

• C(i) atmospheric concentrations: provide budget constraint

• E(j) eddy fluxes: provide accurate point checks

• R(k) remotely sensed data: provide indirect continental coverage

• S(m) carbon stocks: provide biological linkage

Model:

• Includes a (small) set of N parameters p which are poorly known

• Predicts flux distribution F with given parameters p

• Can also predict observable quantities (C, E, R, S)

• How can we use observations (of C, E, R, S) to constrain p?

SynthesisA multiple-constraint approach (2)

Approach:

• Use the model to predict the observed quantities C, E, R and S, and also the regional flux distribution F(p), using a consistent small set of parameters p

• Determine p by minimising a multiple objective function Jmult

Jmult = sum of several single objective functions(each a sum-of-squared errors)

2

( ) (meas)( ) i i

i j k mCi

C CJ E R S

p

p

• Use p to determine regional flux distribution F(p)

Keys to approach:

• Multiple (not necessarily direct) observations

• A model which predicts F and all observables with common parameters p

• Consistency check: use single objective functions (for C, E, R, S) separately