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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 Kirby CSIRO Land and Water, Canberra, Australia [michael.raupach@csiro.au] Outline: Models, data, constraints Results
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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 • [michael.raupach@csiro.au] • Outline: • Models, data, constraints • Results • Uncertainty 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 Savannah woodland Rainfall 800 mm Old-growth forest Rainfall 600 mm
ATMOSPHERE CO2 H2O N2, N2O Photosynthesis Rain WaterCycle N fixation,N deposition,N volatilisation C Cycle Transpiration PLANTLeaves, Wood, Roots Product offtake Respiration Fertiliser inputs N,P Cycles ORGANIC MATTER Litter: Leafy, Woody Soil: Active (microbial) Slow (humic) Passive (inert) SOIL Soil water Mineral N, P Sediment transport Runoff Leaching Water flow Linked terrestrial cyclesof water, C, N and P C flow N flow P flow
Modelling water, carbon and nutrient cyclesFramework: 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 Used here! Problem: find these for large scales!
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 Coarse-scale average flux Fine-scale model PDF of (X,M,P) = V Fine-scale model with coarse data Bias = [(co)variance] * [second derivative of Frs(V) ]
Evaporation and TranspirationSimplifying infiltration models to 2-layer soil, daily time step (Mac Kirby) Rain > Ksat2 Rain > Ksat1 Duplex soil Ksat1 >> Ksat2 Rain < Ksat 2 looks like clay
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:
Evaporation and TranspirationTests of statistical-steady-state model • Annual mean, catchment-scale water balance
Evaporation and energy: forest sytemsRay Leuning and Helen Cleugh (CLW), Tumbarumba flux site • Daytime evaporation = 1.1 * equilibrium evaporation
Evaporation and energy: cropping sytemsChris J Smith and Frank Dunin, CSU Site, Wagga 60 Triticale, 1999 Lysimeters Priestley Taylor 50 40 30 20 10 0 Jul-99 Jan-00 Mar-99 Nov-99 May-99 Sep-99 Evapotranspiration (mm/week) 60 Lupin, 2000 50 40 30 20 10 0 Jul-00 Jan-01 Mar-00 Nov-00 Sep-00 May-00
Quasi-steady surface energy balance in an entraining convective boundary layer • Why Priestley-Taylor evaporation is a good measure of potential evaporation over a moist region parameter = relative deficit of entrained air
Net Primary Productivity (NPP) • [NPP] = [Photosynthetic Assimilation] - [Autotrophic Respiration] • A simple, linearised model for light and water limited NPP:
Testing predictions of NPPVast dataset (Barrett 2001) linear axes logarithmic axes
Testing predictions of NPPVast dataset (Barrett 2001) • NPP depends on saturation deficit, through water use efficiency MeasurementsModel
Biomass Litter C Soil C Bios Vast Testing predictions of C storesVast dataset (Barrett 2001)
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 inputsNet 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
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 JmultJmult = sum of several single objective functions (each a sum-of-squared errors) • 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