1 / 27

Modelling Australian Tropical Savanna

Modelling Australian Tropical Savanna. Peter Isaac 1 , Jason Beringer 1 , Lindsay Hutley 2 and Stephen Wood 1. 1 School of Geography and Environmental Science, Monash University, Melbourne 2 School of Science and Primary Industry, Charles Darwin University, Darwin. Introduction.

misha
Télécharger la présentation

Modelling Australian Tropical Savanna

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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.

More Related