EcoTas13 BradEvans e-Mast UNSW


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TERN's e-MAST Director Brad Evans's presentation on e_MAST at EcoTas13 in November 2013.

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EcoTas13 BradEvans e-Mast UNSW

  1. 1. ecosystem Modelling And Scaling infrasTructure (eMAST) - Where models and data become one Presentation by Brad Evans based on contributions by Colin Prentice, Michael Hutchinson, Gab Abramowitz, Ben Evans, Rhys Whitley, Julie Pauwels
  2. 2. eMAST : Data assimilation
  3. 3. eMAST’s objectives 2013-2015 DELIVER research data infrastructure to integrate TERN (and other) data streams on the National Computing Infrastructure ENABLE data assimilation, model evaluation and accreditation and ecosystem model optimization DRIVE advances in ecosystem science, impact assessment and land management
  4. 4. Driving science questions CARBON: How much CO2 is exchanged? How much carbon can be stored and where? WATER: What drives water use by ecosystems, and runoff in rivers? CLIMATE CHANGE: How does it change the rules? LAND MANAGEMENT: What will work, in a changing climate?
  5. 5. More driving science questions FIRE: What are the risks? How can they be mitigated? CLIMATE FEEDBACKS: How will ecosystem changes influence the exchanges of carbon, water and energy with the atmosphere? BIODIVERSITY: What species are threatened? Where are likely refugia? Is there a tipping point?
  6. 6. What eMAST is delivering High-resolution data products: climate, canopy conductance, water use, primary production Tools for interpolation, downscaling, upscaling, hindcasting, forecasting A state-of-the-art data assimilation system for ecosystem model optimization Software for model evaluation (based on PALS) Top-level ecosystem drivers and targets for models
  7. 7.
  8. 8. ANUClimate A NEW approach to interpolating our national network 0.01 degree climate surfaces Who? Professor Mike Hutchinson (ANU)
  9. 9. Climate data sets (1 km) Tmin Tmax vp P daily ✔ 1970-2011 ✔ ✔ ✔ monthly ✔ 1970-2011 ✔ ✔ ✔ ✔ mean monthly pan evap. wet days ✔ ✔ ✔ ✔ ✔ ✔ solar rad. wind speed ✔ ✔
  10. 10. ANUClimate When? Delivery timeline… Complete set of Climate and Bioclimatic data available on RDSI RDSI opendap netCDF CF & Metadata store complete = public release Data starts propagating to RDSI* ADVANCED USER ACCESS DOI’s NOT YET AVAILABLE = NO PUBLISH 30 Nov 2013 24 Dec 2013 31 Jan 2013 *Currently experiencing delays in RDSI allocation – delays in the Raijin cloud roll out etc…
  11. 11. ANUClimate What is different? • Improved ‘background-anomaly-interpolation’ approach • • • Temperature and both positive and zero rainfall can be effectively interpolated by the thin plate splines method - with adaptive capacity ! Monthly means, topographically corrected yield influence of atmospheric processes and terrain Significant improvement over both direct (nonanomaly) and current anomaly approach • Coastal proximity: A new ‘proximity to coast’ modifier captures marine perturbation of climate
  12. 12. ANUClimate What can we expect? • Temperature estimates improved by around 25% compared to Jones et al. 2009 (RMSE cross validation) • Precipitation estimates a modest, but significant, improvement (7-15% RMSE cross validation) The model makes no further improvement on accuracy beyond the 1km mark !
  13. 13. ANUClimate How is it done?
  14. 14. AMOS 2014
  15. 15. Bioclimate data sets (1 km T, P and R)
  16. 16. ecosystem Production in Space and Time: ePiSaT eMAST: How does gross primary productivity (GPP) vary in space and time across Australia? Colin: How can we ‘simply’ estimate GPP across Australia? What data does TERN provide that might be useful for addressing this research question?
  17. 17. User workflow: ePiSaT GPP Choose the ePiSaT model from the TERN portal Produce continental scale estimates of GPP and evaluate them Obtain OzFlux data via the TERN/ OzFlux portals Obtain climate (eMAST) and satellite data (AusCover) to scale the ePiSaT parameters Run the ePiSaT model – generate estimates of ecosystem parameters, evaluate them
  18. 18.
  19. 19. OzFlux
  20. 20. ePiSaT : Flux tower scaling
  21. 21. OzFlux: Flux partitioning 1 Respiration R= 1 Data filtering: Removal of outliers etc.. Gap filling of PAR (PPFD) for GPP Amax = - 2 Quantum 3 Rectangular Hyperbole 3 parameter 1 Assimilation 2 2 Efficiency 2 Φ= 3 3 FC = R - Amax * Φ I Amax +Φ I
  22. 22. ePiSaT v 1.0 : Tower GPP GPP = Amax * I Amax + C Where: Amax is the maximum rate of carboxylation, I is PAR (PPFD) and C = parameter 3 from the rectangular hyperbola described in the previous slide
  23. 23. ePiSaT v 1.0 : Map GPP GPP = fAPAR *I* LUE Where: fAPAR is the fraction of absorbed photosynthetic active radiation, I is PAR (PPFD) and LUE is light use efficiency derived from the relationship of Tower GPP (previous slide) and fAPAR and I. ePiSaT v 2.0 : Map GPP GPP = fAPAR *I* LUE*WUE*Trange Where: fAPAR is the fraction of absorbed photosynthetic active radiation, I is PAR (PPFD) and LUE is light use efficiency derived from the relationship of Tower GPP (previous slide) and fAPAR and I. WUE and Trange are derived similarly.
  24. 24. ePiSaT : Partitioning evaluation
  25. 25. ePiSaT : Partitioning evaluation
  26. 26. Model data evaluation from Gab Abramowitz (UNSW)
  27. 27. Plant trait surfaces • • • • • • • • Leaf nitrogen Leaf phosphorus Specific leaf area Leaf area Maximum plant height Photosynthesis per leaf area Photosynthesis per leaf dry mass Leaf stomatal conductance Dr. Rhys Whitley
  28. 28. Plant trait surfaces
  29. 29. NEON & TERN
  30. 30. TERN Data Discovery Portal
  31. 31. Summary: Data-model fusion tools Data assimilation collaboration with NEON and NCAR, CSIRO, Macquarie University and the Australian National University - ACEAS workshop on data assimilation early 2014 eMAST : An R-Package ‘emast’ for the computation and visualization of bioclimatic indices ePiSaT : Collaboration with OzFlux and AusCover to model Gross Primary Production across the landscape, another R-Package ‘ePiSaT’ -ACEAS worskshop on SPEDDEXES Protocol for the Analysis of Land Surface Models (PALS) for evaluation of data and models
  32. 32. The future of eMAST Continue delivery of our key datasets through the RDSI, Data Discovery, Visualization & Exploitation… consolidation of our tools and porting them to Raijin.