Water, carbon and nutrients on the Australian continent: effects of climate gradients and land use...
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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
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
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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:
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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
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P/Ep
E/E
p
Forest
Mixed
Pasture
Forest: model
Mixed: model
Pasture: model
Annual mean, catchment-scale water balance
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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
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LE (average of NOAA and Licor 6262)
LE
(eq
uil
ibri
um
)
Daytime evaporation = 1.1 * equilibrium evaporation
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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
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gs (m/s)
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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
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Vast NPP [kgC/m2/year]
Bio
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PP
[kgC
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]
NPP: Bios and Vast
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Vast NPP [kgC/m2/year]
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il N
PP
[kgC
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]
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Testing predictions of NPPVast dataset (Barrett 2001)
NPP: dependence on saturation deficit
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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
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Bio
s A
G L
itter
mas
s [t
C/h
a]
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0.001 0.1 10 1000
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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
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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
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0.0015
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Flu
x (k
gN/m
2/ye
ar)
FNFertFNDepFNFixFNVolatFNLeachFNDisturbance
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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