ecotas13 bradevans e-mast unsw
DESCRIPTION
TERN's e-MAST Director Brad Evans's presentation on e_MAST at EcoTas13 in November 2013.TRANSCRIPT
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
eMAST : Data assimilation
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
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?
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?
What eMAST is delivering
High-resolution data products: climate, canopy conductance, water use, primary productionTools for interpolation, downscaling, upscaling, hindcasting, forecastingA state-of-the-art data assimilation system for ecosystem model optimizationSoftware for model evaluation (based on PALS)Top-level ecosystem drivers and targets for models
http://www.tern.org.au/e-MAST-Data-Products-pg26355.html
ANUClimate A NEW approach to interpolating our national network0.01 degree climate surfaces
Who? Professor Mike Hutchinson (ANU)
Climate data sets (1 km)Tmin Tmax vp P pan
evap.wet days
solar rad.
wind speed
daily1970-2011
✔ ✔ ✔ ✔
monthly 1970-2011
✔ ✔ ✔ ✔ ✔ ✔
mean monthly
✔ ✔ ✔ ✔ ✔ ✔ ✔
When? Delivery timeline…
30 Nov 2013
Data starts propagating to RDSI*ADVANCED USER ACCESSDOI’s NOT YET AVAILABLE = NO PUBLISH
*Currently experiencing delays in RDSI allocation – delays in the Raijin cloud roll out etc…
RDSI opendap netCDF CF & Metadata store complete= public release
24 Dec 2013
Complete set of Climate andBioclimatic data available on RDSI
31 Jan 2013
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 (non-anomaly) and current anomaly approach
• Coastal proximity: A new ‘proximity to coast’ modifier captures marine perturbation of climate
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 !
ANUClimate
How is it done?ANUClimate
AMOS 2014
Bioclimate data sets (1 km T, P and R)
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?
User workflow: ePiSaT GPP
Choose the ePiSaT model from the TERN
portal
Obtain OzFlux data via the TERN/ OzFlux
portals
Run the ePiSaT model – generate estimates of
ecosystem parameters, evaluate them
Obtain climate (eMAST) and satellite data
(AusCover) to scale the ePiSaT parameters
Produce continental scale estimates of GPP
and evaluate them
http://episat-software.blogspot.com.au/
OzFlux
ePiSaT : Flux tower scaling
OzFlux: Flux partitioning
Data filtering: Removal of outliers etc.. Gap filling of PAR (PPFD) for GPP
1
3
1R =
Assimilation
Amax = - 2
Efficiency
Φ =
2
2
3Amax *FC =
Rectangular Hyperbole
3 parameter
1 2 3
Respiration
Quantum
R -Φ I
Amax +Φ I
ePiSaT v 1.0 : Tower GPP
Amax *GPP = 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
ePiSaT v 1.0 : Map GPP
fAPAR *I* LUE GPP =
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
fAPAR *I* LUE*WUE*TrangeGPP =
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.
ePiSaT : Partitioning evaluation
ePiSaT : Partitioning evaluation
from Gab Abramowitz (UNSW)
Model data evaluation
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
Plant trait surfaces
NEON & TERN
TERN Data Discovery Portal
Summary: Data-model fusion toolsData 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
The future of eMASTContinue delivery of our key datasets through the RDSI, Data Discovery, Visualization & Exploitation… consolidation of our tools and porting them to Raijin.