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Modelling Australian Tropical Savanna

Modelling Australian Tropical Savanna

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Modelling Australian Tropical Savanna

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  1. 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

  2. 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

  3. 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) Methods ARC Discovery Project

  4. 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

  5. Howard Springs • 12º 29.655S 131º 09.143E • Open forest savanna • 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

  6. Howard Springs

  7. Above ground biomass 34 t ha-1 C3 overstorey Lai 0.6 - 1.0 hc 16 m C4 understorey Lai 0.08 - 1.4 hc 0.1 - 2 m Below ground biomass 17 t ha-1 Soil organic carbon 140 t ha-1 Savanna Canopy

  8. Leaf Area Index

  9. Drivers and Cycles

  10. 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 ?

  11. 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

  12. 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

  13. Savanna canopy ACASA C3 overstorey Lai 0.6 - 1.0 hc 16 m C4 understorey Lai 0.08 - 1.4 hc 0.1 - 2 m CABLE Sunlit C3 Shaded C4 roots shallow C4 C3 C3 roots deep Reality vs Model

  14. 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

  15. Out-of-the-box : CABLE

  16. Out-of-the-box : ACASA

  17. 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 mmolm-2s-1 • ACASA • set soil microbial respiration to 0

  18. Basic Tuning : CABLE

  19. Basic Tuning : ACASA

  20. Constant Lai : CABLE

  21. Constant Lai : ACASA

  22. Constant C4 fraction : CABLE

  23. Constant C4 fraction : ACASA

  24. Seasonal Variation in Fe and Fc

  25. 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 ?

  26. 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.

  27. 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.