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FOREST PRODUCTION MODELS – Towards improved interactions between experimentalists and modellers

FOREST PRODUCTION MODELS – Towards improved interactions between experimentalists and modellers. Ross McMurtrie, Belinda Medlyn, Dave Pepper School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW, Australia. Themes in this talk.

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FOREST PRODUCTION MODELS – Towards improved interactions between experimentalists and modellers

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  1. FOREST PRODUCTION MODELS– Towards improved interactions between experimentalists and modellers Ross McMurtrie, Belinda Medlyn, Dave Pepper School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW, Australia

  2. Themes in this talk • Contrast models applied to Eddy-covariance data, versus, Forest-stands experiments

  3. Themes in this talk • Contrast models applied to Eddy-covariance data, versus, Forest-stands experiments • Plant & ecosystem feedbacks are crucial. • Can models be simplified? • Model parameterisation: • What parameters can be estimated from given data set? • If wish to estimate particular set of parameters, what data are required ?

  4. Timescales • Physiological: Minutes to Hours • Experimental: Months to Years • Human: Decades

  5. -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 Forest-growth models Models of growth but not biogeochemical cycling Forest-field Experiments Organisational Scale Water balance Nutrient availability Community / Competition Competition Ecosystem (short - lived lived species) (long- - species) Growth Allocation Plant Adaptation Turnover Nutrient uptake Photosynthesis Leaf / Organ Stomatal conductance Acclimation Adaptation Respiration Timescale (10 yrs) x Physiological Experimental Human Evolutionary (Minutes to Hours) (Months to Years) (Decades) (Centuries +)

  6. -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 Eddy-covariance data versus Forest-stand experiments Region EC Water balance Nutrient availability Community / Competition Competition Ecosystem (short - lived (long - lived species) species) Growth Allocation Plant Adaptation Turnover Nutrient uptake Photosynthesis Leaf / Organ Stomatal conductance Acclimation Adaptation Respiration Timescale (10 yrs) x Physiological Experimental Human Evolutionary (Minutes to Hours) (Months to Years) (Decades) (Centuries +)

  7. -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 Eddy-covariance data versus Forest-stand experiments Region EC EC Water balance Nutrient availability Community / Competition Competition Ecosystem (short - lived (long - lived species) species) Growth Allocation Plant Adaptation Turnover Nutrient uptake Photosynthesis Leaf / Organ Stomatal conductance Acclimation Adaptation Respiration Timescale (10 yrs) x Physiological Experimental Human Evolutionary (Minutes to Hours) (Months to Years) (Decades) (Centuries +)

  8. What do we learn from successes & failures in flux modelling? An example: Data set: Hourly Canopy CO2-flux data Timescale: 1 year Site: Sitka spruce, Griffin, Scotland Model: BEWDY sun-shade model + Farquhar photosynthesis model + autotrophic respiration Medlyn, Robinson, Clement, McMurtrie (2005) On the validation of models of forest CO2 exchange using eddy covariance data. Tree Physiol. (in press).

  9. Site: Griffin Sitka spruce forest, Scotland. 18 y.o. plantation.

  10. Respiration (2): Substrate-recycling model. Additional parameters: kc, kp, Wc0, Wp0 R 1-Yp P kc f(T) Wc Wc Yp kp f(T) Wp Wp E ap 1-ap Illustrative Model Respiration (1): Simple Q10 dependence on soil T R = R0Q10(T/10)

  11. Model 2: Carbon substrate dynamics Acclimation of respiration Feedback Timescale: 3-10 days

  12. Current Conditions vs. T + 2° Model 1 Model 2 Data Medlyn et al (in press) Tree Physiology

  13. Issues raised by respiration example: • We ignore feedbacks at our peril. • What extra data would be required to discriminate • between the 2 respiration models? • Potential for model simplification: • At acclimation NPP/GPP is conservative.

  14. Do similar ideas apply to other processes? • Respiration Y • Photosynthesis ? • Radiation interception ? • Stomatal conductance ? • Allocation ?

  15. First ask: Is model sensitive to parameters for photosynthesis, radiation, stomata, allocation? Parameter Sensitivity for BEWDY: Rank Name djmsqr 1 aJ 0.42 2 LAI 0.40 3 Jmax 0.24 4 Vcmax 0.22 5 Q10 0.21 8 g1 0.11 12 kext 0.032 1. Which of the “sensitive” parameters are identifiable? Calculations: UNCSIM www.uncsim.eawag.ch 2. Which of the “sensitive” parameters undergo acclimation? Medlyn et al (in press) Tree Physiology

  16. Do similar scaling issues apply to other processes?

  17. A model experiment with G’DAY ecosystem model - Response to 2 X CO2: Norway spruce, Flakaliden, Sweden Is simulated response to 2 X CO2 sensitive to following parameters? Respiration (Ratio NPP/GPP) Photosyn (Jmax, Vcmax) Radiation (kext, beam fraction) Stomata (Ci/Ca) Allocation (Root:Shoot allocn) Soil N feedbacks (Soil N:C, Ndep) CO2 chambers

  18. What is NPP response to 2 X CO2 sensitive to ? Relative sensitivity NPP/GPP ratio Vcmax, Jmax Ci/Ca kext beam fraction Root:Shoot allocation Ndeposition Soil N:C Calculations: UNCSIM package

  19. Conclusions • Applying ecosystem models to forest experiments is • a challenge because of complex interactions & feedbacks on various timescales. • Statistical methods are available to determine: • What parameters can and cannot be estimated from experimental data? • What extra data would make it possible to estimate specific parameters? • Potential for model simplification: • Provided NPP/GPP, LUE, WUE are well-behaved on longer timescales. • Links between models & experiments are improving.

  20. The End

  21. CO 2 N N in loss Foliage Branches NPP Stem Mineral N Fine roots CO 2 Litter pools CO 2 Microbial biomass & C ‘slow’ SOM CO 2 N ‘Old’ SOM An illustration with the G’DAY model –Generic Decomposition And Yield

  22. Sune Linder (2004)

  23. Summary Less complex formulations of canopy processes & feedbacks. Longer time-step More comprehensive. Longer time-scale

  24. Illustrative Model Photosynthesis: BEWDY model (sun-shade type) Leaf-level photosynthesis: Pl = (a Il Pmax,l )/ (a Il + Pmax,l) where a and Pmax,l determined from Farquhar model. Parameters: Jmax, Vcmax (T-dependent), aJ, Ci: Ca Canopy photosynthesis: assume Pmax,l varies with average irradiance level in canopy. Radiation incident on sunlit foliage: Isun(L) = a k [Ib + Id exp(-kL)] Radiation incident on shaded foliage: Ishade(L) = a k Id exp(-kL) Sunlit fraction at depth L = exp(-kL). Integrate from 0 to Lmax.

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