This is a compilation of all presentations made at the TERN/ACEAS 'Smarter Workflows for Ecologists' workshop held at the ESA Conference in Melbourne, December 7th, 2012.

1. Smarter workflows for ecologists
A pathway through the data lifecycle of an ecologist, highlighting
new initiatives that will support and enhance practice

2. Agenda
Time item
09:05 – 09:10 Welcome (Alison Specht)
09:10 – 09:20 Introduction (Tim Clancy)
09:20 – 09:40 Data Collection (Nikki Thurgate)
09:40 – 09:50 Data Sharing and Citation (Alison Bradshaw)
09:50 – 10:00 Data Storage and Discovery (David Turner)
10:00 – 10:10 Data Analysis and Synthesis (Alison Specht)
10:10 – 10:20 Modelling (Gabriel Abramowitz)

3. Agenda
Time item
10:25 – 11:05 Discussion and investigation of facets of workflow
11:05 – 11:15 Question and answer (all presenters)
11:15 – 11:25 How good knowledge of Australia’s data environment will be
rewarded (Tim Clancy)

4. Smarter workflows for ecologists:
what are they?
Tim Clancy, Director of TERN

5. Overview
• Data management in the ecological
context
• Data infrastructure
• Why smarter workflows?
• Next steps

6. Overview
• Data management in the ecological
context
• Data infrastructure
• Why smarter workflows?
• Next steps

7. Sustaining Long Term Ecosystem Science & Research?

8. Ecology and the data deluge
Sensor Technologies

9. Modelled Data
High-resolution climate
surfaces
from Mike Hutchinson (ANU)
Ecology and the data deluge

10. Assembly of
Multi-Scale Remote Sensing Datasets
High-Resolution, Field-based Measurements
Low-spatial resolution, high temporal
resolution
Satellite Measurements
Continental Dynamics in Green Cover
ARA Cessna 404
Airborne
Systems
and new
sensor
technologies
Remote Sensing /High Res
Mapping
Ecology and the data deluge

11. Meta Analyses/Synthesis
Murphy B.P.et al.. (2011) Fire regimes: moving from a fuzzy
concept to geographic entity. New Phytologist 192: 316-318.
Bowman D.M.J.S. et al.. (in press) Forest fire management, climate
change and the risk of catastrophic carbon losses. Frontiers in
Ecology and Evolution.
Murphy B.P., et al. . (in press) Fire regimes of Australia, a
pyrogeographic model system. Journal of Biogeography
Historically, competitive research advantage
accrued to those individuals and groups who first
conducted the experiments and captured new
data, for they could ask and then answer
questions before others. The rise of large-scale,
shared instrumentation is necessitating new
models of sharing and collaboration across
disciplines and research cultures. When many
groups have access to the same data, advantage
shifts to those who can ask and answer better
questions.
-Daniel Reed, "My Scientific Big Data Are Lonely”
Ecology and the data deluge

12. Data Policy Environment
Expectations of top journals

13. Data Policy Environment
Expectations of top journals

14. Data Policy Environment
Expectations of top journals

15. Overview
• Data management in the ecological
context
• Data infrastructure
• Why smarter workflows?
• Next steps

16. IMOS Data Discovery Portal
Meta-data
from all Data Portals
Data Portals

17. IMOS Data Discovery Portal
Meta-data
from all Data Portals
Data Portals
ALA Data Discovery Portal
Meta-data
from all Data Portals

18. IMOS Data Discovery Portal
Meta-data
from all Data Portals
Data Portals
ALA Data Discovery Portal
Meta-data
from all Data Portals
TERN Data Discovery Portal
Meta-data
from all Data Portals

19. IMOS Data Discovery Portal
Meta-data
from all Data Portals
Data Portals
ALA Data Discovery Portal
Meta-data
from all Data Portals
TERN Data Discovery Portal
Meta-data
from all Data Portals

20. IMOS Data Discovery Portal
Meta-data
from all Data Portals
Data Portals
ALA Data Discovery Portal
Meta-data
from all Data Portals
TERN Data Discovery Portal
Meta-data
from all Data Portals

21. IMOS Data Discovery Portal
Meta-data
from all Data Portals
Data Portals
ALA Data Discovery Portal
Meta-data
from all Data Portals
TERN Data Discovery Portal
Meta-data
from all Data Portals
Data Sets
Meta-data - 1
Data Portal
Use Licence
Identifier - DOI

22. IMOS Data Discovery Portal
Meta-data
from all Data Portals
Data Portals
ALA Data Discovery Portal
Meta-data
from all Data Portals
TERN Data Discovery Portal
Meta-data
from all Data Portals
Data Sets
Meta-data - 1
Data Portal
Use Licence
Identifier - DOI
Data Sets
Meta-data - 2
Data Portal
Use Licence
Identifier - DOI

23. IMOS Data Discovery Portal
Meta-data
from all Data Portals
Data Portals
ALA Data Discovery Portal
Meta-data
from all Data Portals
TERN Data Discovery Portal
Meta-data
from all Data Portals
Data Sets
Meta-data - 1
Data Portal
Use Licence
Identifier - DOI
Data Sets
Meta-data - 2
Data Portal
Use Licence
Identifier - DOI
Data Sets
Meta-data - 1
Data Portal
Use Licence
Identifier - DOI

24. IMOS Data Discovery Portal
Meta-data
from all Data Portals
Data Portals
ALA Data Discovery Portal
Meta-data
from all Data Portals
TERN Data Discovery Portal
Meta-data
from all Data Portals
Data Sets
Meta-data - 1
Data Portal
Use Licence
Identifier - DOI
Data Sets
Meta-data - 2
Data Portal
Use Licence
Identifier - DOI
Data Sets
Meta-data - 1
Data Portal
Use Licence
Identifier - DOI

25. What is TERN?

26. Storing, sharing and building
data and knowledge
• TERN is providing infrastructure to enable transformational
change to ecosystem science and management in Australia
• TERN enables cooperative and collaborative collection,
storage, analysis and sharing of ecosystem data and
knowledge.
• TERN helps scientists, technicians and managers to be more
effective and efficient.
• TERN’s work helps to improve our understanding of Australian
environments, and therefore enables the Australian
community to make informed decisions about managing their
environments.

27. Overview
• Data management in the ecological
context
• Data infrastructure
• Why smarter workflows?
• Next steps

28. Collaborative and Networked
Research Infrastructure

29. Collaborative and Networked
Research Infrastructure
Ecosystem Science

30. Collaborative and Networked
Research Infrastructure
Ecosystem Science

31. Collaborative and Networked
Research Infrastructure
Ecosystem Science
Knowledge gap:
research
questions

32. Collaborative and Networked
Research Infrastructure
Ecosystem Science
Knowledge gap:
research
questions
Proposal and
planning

33. Collaborative and Networked
Research Infrastructure
Ecosystem Science
Knowledge gap:
research
questions
Proposal and
planning

34. Collaborative and Networked
Research Infrastructure
Ecosystem Science
Knowledge gap:
research
questions
Proposal and
planning

35. Collaborative and Networked
Research Infrastructure
Ecosystem Science
Knowledge gap:
research
questions
Proposal and
planning
Data collection,
verification,
quality assurance
and control

36. Collaborative and Networked
Research Infrastructure
Ecosystem Science
Knowledge gap:
research
questions
Proposal and
planning
Data collection,
verification,
quality assurance
and control

37. Collaborative and Networked
Research Infrastructure
Ecosystem Science
Knowledge gap:
research
questions
Proposal and
planning
Data collection,
verification,
quality assurance
and control
Data + meta-data,
licensing

38. Collaborative and Networked
Research Infrastructure
Ecosystem Science
Knowledge gap:
research
questions
Proposal and
planning
Data collection,
verification,
quality assurance
and control
Data + meta-data,
licensing

39. Collaborative and Networked
Research Infrastructure
Ecosystem Science
Knowledge gap:
research
questions
Proposal and
planning
Data collection,
verification,
quality assurance
and control
Data + meta-data,
licensing
Storage,
preservation and
discoverability
of data

40. Collaborative and Networked
Research Infrastructure
Ecosystem Science
Knowledge gap:
research
questions
Proposal and
planning
Data collection,
verification,
quality assurance
and control
Data + meta-data,
licensing
Storage,
preservation and
discoverability
of data

41. Collaborative and Networked
Research Infrastructure
Ecosystem Science
Knowledge gap:
research
questions
Proposal and
planning
Data collection,
verification,
quality assurance
and control
Data + meta-data,
licensing
Storage,
preservation and
discoverability
of data
Data analysis,
integration and
synthesis

42. Collaborative and Networked
Research Infrastructure
Ecosystem Science
Knowledge gap:
research
questions
Proposal and
planning
Data collection,
verification,
quality assurance
and control
Data + meta-data,
licensing
Storage,
preservation and
discoverability
of data
Data analysis,
integration and
synthesis

43. Collaborative and Networked
Research Infrastructure
Ecosystem Science
Knowledge gap:
research
questions
Proposal and
planning
Data collection,
verification,
quality assurance
and control
Data + meta-data,
licensing
Storage,
preservation and
discoverability
of data
Data analysis,
integration and
synthesis
Research output:
new data and
publications

44. Collaborative and Networked
Research Infrastructure
Ecosystem Science
Knowledge gap:
research
questions
Proposal and
planning
Data collection,
verification,
quality assurance
and control
Data + meta-data,
licensing
Storage,
preservation and
discoverability
of data
Data analysis,
integration and
synthesis
Research output:
new data and
publications

45. Collaborative and Networked
Research Infrastructure
Ecosystem Science
Knowledge gap:
research
questions
Proposal and
planning
Data collection,
verification,
quality assurance
and control
Data + meta-data,
licensing
Storage,
preservation and
discoverability
of data
Data analysis,
integration and
synthesis
Research output:
new data and
publications

46. Collaborative and Networked
Research Infrastructure
Ecosystem Science
Knowledge gap:
research
questions
Proposal and
planning
Data collection,
verification,
quality assurance
and control
Data + meta-data,
licensing
Storage,
preservation and
discoverability
of data
Data analysis,
integration and
synthesis
Research output:
new data and
publications
Enables large scale and
coordinated data
collection, sharing and
multiple re-uses

47. Collaborative and Networked
Research Infrastructure
Ecosystem Science
Knowledge gap:
research
questions
Proposal and
planning
Data collection,
verification,
quality assurance
and control
Data + meta-data,
licensing
Storage,
preservation and
discoverability
of data
Data analysis,
integration and
synthesis
Research output:
new data and
publications
Enables large scale and
coordinated data
collection, sharing and
multiple re-uses

48. Collaborative and Networked
Research Infrastructure
Ecosystem Science
Knowledge gap:
research
questions
Proposal and
planning
Data collection,
verification,
quality assurance
and control
Data + meta-data,
licensing
Storage,
preservation and
discoverability
of data
Data analysis,
integration and
synthesis
Research output:
new data and
publications
Enables large scale and
coordinated data
collection, sharing and
multiple re-uses
Enhanced ability to
revise, question and
expand knowledge

49. Collaborative and Networked
Research Infrastructure
Ecosystem Science
Knowledge gap:
research
questions
Proposal and
planning
Data collection,
verification,
quality assurance
and control
Data + meta-data,
licensing
Storage,
preservation and
discoverability
of data
Data analysis,
integration and
synthesis
Research output:
new data and
publications
Enables large scale and
coordinated data
collection, sharing and
multiple re-uses
Enhanced ability to
revise, question and
expand knowledge

50. Overview
• Data management in the ecological
context
• Data infrastructure
• Why smarter workflows?
• Next steps

51. Next Steps – Post the Workshop
• Apply Best Practice Data Management
• Appropriate Work Flow
• Participate in the Debate
• Data Discovery – AEKOS – Coming Soon
• Use the Resources including: TERN, ALA, IMOS
(Marine), BoM, ANDS etc.
• Discuss specific data licensing requirements
with relevant facility

52. The End
Enjoy the Workshop
www.tern.org.au
http://portal.tern.org.au

55. Data Collection and Sharing
Challenges and case study solutions
A/Prof Nikki Thurgate
University of Adelaide

56. The Scientific Method
• Ask a question
• Do background research
• Construct a hypothesis
• Test your hypothesis
• Analyse data
• Draw conclusion
• Communicate your results

57. The Scientific Method
• Ask a question
• Do background research
• Construct a hypothesis
• Test your hypothesis
• Analyse data
• Draw conclusion
• Communicate your results
• What can I do that’s new?
• How can I do a better job than
my peers?
• What cutting-edge process can
I use to explain my data?
• Can I prove existing thought
wrong?
• Publish and gloat

58. Ecological Data
Basal Area
Soils Photographs
Remote Sensing
Pressed Specimens
Point Intercept
Genetic Samples
Spatial Coordinates

59. Biogechemical Fluxes
Ecological Data
Histology
Models
Leaf Area Index
Wet Specimens
Lithology
Ecological Descriptions

60. Terrestrial LiDAR
Dendrochronology
Genetic Samples
Chemical Analysis
DNA Sequences
Dendrograms
Leaf Morphology
Population Dynamics
Mapping
Instruments
Ecological Data

61. Ecological Data
Basal Area
Soils Photographs
Remote Sensing
Pressed Specimens
Point Intercept
Genetic Samples
Spatial Coordinates
Biogechemical Fluxes
Histology
Models
Leaf Area Index
Wet Specimens
Lithology
Chemical Analysis
DNA Sequences
Dendrograms
Ecological Descriptions
Meteorology
Terrestrial LiDAR
Leaf Morphology
Population Dynamics
Dendrochronology
Instruments
Mapping
The message here is that
ecologists collect lots of data
for different purposes

62. Data Collection
Ad-hoc process to primary data collection
• Collected samples (flora, fauna, soils, etc)
• People-measured values (observations)
• Digitally-measured values (stored on a device)
• Where does all of this data go?
• Transcription error
• Does it have a use beyond the immediate project?
•SHOULD it have a use beyond the immediate project?

63. Data Collection
The answer is probably yes.
• Most scientific research is directly funded by
governments and should be of benefit to the
community and scientific community
• Analysis outputs alone are often insufficient. Primary
data is required to marry new and old data or conduct
a new analysis.
• So what’s the problem?

64. Data Sharing Challenges
The selfish: little incentive exists to share data
• Immature citation framework (you get no credit)
• Competitors can publish from your data
• Competitors have the ammunition to criticise your
methodology

65. Data Sharing Challenges
The selfless: a little knowledge is a dangerous thing
• Potential for data to be used to support poor science

66. Data Sharing Challenges
The selfless: a little knowledge is a dangerous thing
• Potential for data to be used to support poor science
• Data can be presented as “evidence” to support
perverse outcomes

67. Data Sharing Challenges
The selfless: a little knowledge is a dangerous thing
• Potential for data to be used to support poor science
• Data can be presented as “evidence” to support
perverse outcomes
• Data may not be appropriate for public
consumption for biosecurity, IP or
cultural reasons

68. Data Sharing Challenges
The selfless: a little knowledge is a dangerous thing
• Potential for data to be used to support poor science
• Data can be presented as “evidence” to support
perverse outcomes
• Data may not be appropriate for public
consumption for biosecurity, IP or
cultural reasons

69. Data Sharing Challenges
The selfless: a little knowledge is a dangerous thing
• Potential for data to be used to support poor science
• Data can be presented as “evidence” to support
perverse outcomes
• Data may not be appropriate for public
consumption for biosecurity, IP or
cultural reasons

70. Data Sharing Challenges
The selfless: a little knowledge is a dangerous thing
• Potential for data to be used to support poor science
• Data can be presented as “evidence” to support
perverse outcomes
• Data may not be appropriate for public
consumption for biosecurity, IP or
cultural reasons

71. Data Sharing Challenges
The selfless: a little knowledge is a dangerous thing
• Potential for data to be used to support poor science
• Data can be presented as “evidence” to support
perverse outcomes
• Data may not be appropriate for public
consumption for biosecurity, IP or
cultural reasons

72. Data Collection Challenges
The result:
• There is a great reluctance to share.

73. Back to Data Collection
• There’s lots of data being collected every day
• It’s becoming expected that data will be shared
• Plan what data you need
• Find out what others have that you can use
• Collect new data in the most sensible way
• But Nikki, that’s really hard! What’s the most
sensible way?

74. I’m glad you asked that question

75. The TERN Approach
• Standardised methodology

76. The TERN Approach
• Standardised methodology

77. The TERN Approach
• Standardised methodology
• Mobile data collection app

78. The TERN Approach
• Standardised methodology
• Mobile data collection app

79. The TERN Approach
• Standardised methodology
• Sample tracking
• Mobile data collection app

80. The TERN Approach
• Standardised methodology
• Online information repository
(ÆKOS)
• Sample tracking
• Mobile data collection app

81. The TERN Approach
• Standardised methodology
• Online information repository
(ÆKOS)
• Sample tracking
• Mobile data collection app

82. Data Collection
• More information about TERN (particularly
ÆKOS) in David Turner’s “Data Storage and
Discovery” talk
• Licensing
• The final consideration before you collect data
• Who will own your data?
• Who should be able to see it, use it or profit from
it?

84. Data sharing and citation
Alison Bradshaw, TERN Licensing Coordinator

85. Data Licensing Process
General process for data sharing – making data
available
• Create data;
• Identify any restrictions/conditions;
• Select publisher;
• Mint DOI;
• Select appropriate user licence; and
• Publish metadata & data with links to/detail of user
licence;

86. Data Licensing Process
General process for data sharing – using data
• Search data catalogue;
• Consider metadata;
• Check terms of licence;
• Download data and use.

87. Data Sharing
The key data licensing issues:
• PLAN, PLAN, PLAN
• Ownership
• Identifying data
 Digital Object Identifiers (DOIs)
• Licences
• Metadata

88. Data Sharing
Identifying data sets:
• Identify scope of data set
• Identify/remove sensitive data
• Clean/edit data set – quality assurance
• Mint DOI

89. Data Sharing
Licences:
• Open v conditions
• Check options available
 Owner limitations
 Publisher limitations
• Copyright?

90. Data Sharing
Metadata:
• Comprehensive
• Contemporaneous
• Scope
 Publisher limitations

91. Data Sharing
Data ownership
Principal Investigator

92. Data Sharing
Data ownership
Principal Investigator
Employer

93. Data Sharing
Data ownership
Principal Investigator
Employer
Research assistant/
undergraduate student

94. Data Sharing
Data ownership Principal Investigator
Employer
Research assistant
Postgraduate students

95. Data Ownership
Who owns data? Principal Investigator
Employer
Research assistant
Postgraduate students
Funder

97. Data storage and discovery
David Turner
Logos used with consent. Content of this presentation except logos is released under TERN Attribution Licence Data Licence v1.0

98. Characteristics of ecological data
www.nswrail.net
Fragmentation:
Many different ways of measuring/observing/
expressing similar ecological concepts
* Rapidly evolving
Dispersal:
Data is stored in many
storage locations and formats
Diversity:
Ecological Data covers a
wide range of topics
www.derm.qld.gov.au
©
Alamay
Source:Forestcheck:
www.dec.wa.gov.au
Complexity:
Data usually needs
explanation and context before
it can be accurately used

99. The Information Landscape

100. The Information Landscape
Plants Birds
Bats
© eResearchSA
©
eResearchSA

101. Preservation?

102. Principles of discovery
Search needs to be intuitive
Discovery needs to lead through to access
• As few steps as possible
• Access must be accessible
Sufficient description for understanding
• Access to SME*
• Authored description is for the benefit of others
• Define terms and or use vocabularies

103. Principles of capture and preservation
Capture needs to ensure:
• All relevant details are correctly recorded*
• Information is protected against accidental loss
Preservation can occur when the data:
• Has value beyond its immediate intended use
• Is in a readable storage format
• Is richly described
• Is intended to be published to facilitate discovery

104. Breakout session
An overview of the AEKOS system as a new
paradigm for improving discovery, access and
re-purposing of ecological data

106. Data analysis and synthesis
Alison Specht
Logos used with consent. Content of this presentation except logos is released under TERN Attribution Licence Data Licence v1.0

107. We are drowning in information while starving
for wisdom. The world henceforth will be run by
synthesizers, people able to put together the right
information at the right time, think critically about it,
and make important choices wisely.
Edward O. Wilson (1998) Consilience: The Unity of Knowledge

108. Scientific synthesis:
• provides a crucial counterweight to hyper-specialization in
science
• provides a method of coping with and capitalizing on the
data deluge, which allows analyses at previously
unimaginable scales and facilitates new discoveries
• enhances the capacity for transformative research and
serendipitous discoveries through the diversity of expertise,
skills, and data employed
• allows for the conceptualization of complex social and
environmental problems beyond the scope of any one
profession, discipline, data set, or research approach
(Hampton and Parker 2011)

115. The model

116. The model

117. The model

118. The model

119. The model

120. An example–C&N dynamics
Data from 6 long-term sites collated from many sources

121. An example–C&N dynamics
Data from 6 long-term sites collated from many sources
e.g. site 3: Kidman Springs Fire Regime Trial
The Kidman Springs Fire Regime established in 1993 A
factorial trial of 4 treatments varying in frequency of fire
(0, 2, 4, 6 years) and 2 treatments on season of fire – early
(June), and late dry season (October).

122. An example–C&N dynamics
Data from 6 long-term sites collated from many sources
e.g. site 3: Kidman Springs Fire Regime Trial
The Kidman Springs Fire Regime established in 1993 A
factorial trial of 4 treatments varying in frequency of fire
(0, 2, 4, 6 years) and 2 treatments on season of fire – early
(June), and late dry season (October).

123. An example–C&N dynamics
Data from 6 long-term sites collated from many sources
e.g. site 3: Kidman Springs Fire Regime Trial
The Kidman Springs Fire Regime established in 1993 A
factorial trial of 4 treatments varying in frequency of fire
(0, 2, 4, 6 years) and 2 treatments on season of fire – early
(June), and late dry season (October).
Synthesis, analysis and modelling

124. output

125. output

126. output

127. output
1 = 1-24%, 2 = 25-74%, 3
= 75-89%, 4 = 90-100%.

128. outreach
No point doing the work unless it is heard.
Traditional outputs:
• Refereed journal articles, conference
presentations
‘new’ outputs:
• Data deposition, portal visualisation, short
reports, web site news and entries etc etc

129. outreach
No point doing the work unless it is heard.
Traditional outputs:
• Refereed journal articles, conference
presentations
‘new’ outputs:
• Data deposition, portal visualisation, short
reports, web site news and entries etc etc

130. outreach
No point doing the work unless it is heard.
Traditional outputs:
• Refereed journal articles, conference
presentations
‘new’ outputs:
• Data deposition, portal visualisation, short
reports, web site news and entries etc etc

131. Breakout session
Check out some of the innovative outputs of
ACEAS projects and opportunity to ask
questions about the process

133. Connecting data streams
with the modelling community
Gab Abramowitz
Climate Change Research Centre, UNSW
ARC Centre of Excellence for Climate System Science

134. Outline
• Why care about models?
• Why model evaluation is complicated
• How models and observations interact (and how they don’t)
• How model-based experiments quantify uncertainty
• Why multiple data streams are particularly important
• How TERN eMAST is trying to help

135. Why care about models?
• Land surface models; hydrological models; ecosystem models.
• They drive: climate projections, weather forecasts, water resources
assessments, impacts assessments for natural systems
• Many models have a long history of development before high quality
observations became available – legacy code is still a real issue.
• Observations play a key role in reducing uncertainty in model predictions,
yet communication channels between modelling and observational
groups are often poor

136. Model evaluation is complicated
• Diagnostic model evaluation is contingent
upon the quality of the observations used
in all the four green categories.
• Model outputs may cover a wide range of
systems – e.g. carbon, water, energy
• Uncertainty in these is rarely low enough
to tightly constrain simulations
• Leads to “equifinality” – several different
combinations of inputs / initial states /
parameters give equally good results.
• This makes identification of the “best”
model structure very difficult
input
s
states
parameters
Outputs
MODEL
NASA LIS

137. How models and observations interact
• Simple comparison of observations with predictions (obs ≈ model output)
• Parameter estimation (obs ≈ model output)
• Direct restriction of parameter ranges (obs ≈ model parameters)
• Comparison with empirical approaches (obs ≈ model input / output / params)
• Data assimilation generating reanalysis products (obs ≈ model output / state)
All can give diagnostic information about model structure

138. How models and observations don’t interact
• Long pathways to data access / uncertainty in availability
• Physical inconsistencies in data (i.e. quality control)
• Data formatting and a lack of standardisation (file formats, standards within
formats, time and space sampling)
• e.g. CMIP5 database is anticipated to be ~50PB
• Lack of (and lack of standardisation) in gap-filling (where needed)
• Example – Fluxnet and the land surface modelling community

139. NASA LIS
Gauging uncertainty in model predictions
• Uncertainty in predictions is typically
estimated by sampling uncertainty in
parameters, inputs, and/or initial states
• Multiple streams of data mean that
uncertainty ranges can be better
constrained => more reliable predictions
• Examples: meteorology, C fluxes, water
and heat fluxes, biomass, carbon pool
sizes, physical soil properties, vegetation
characteristics, soil moisture and
temperature, streamflow, N, P etc
input
s
states
parameters
Outputs
MODEL

140. NPP (GtC y
-1
)
0 1 2 3 4
Eddy fluxes + Litterfall + Streamflow
Streamflow + Eddy fluxes
Streamflow + Litterfall
Eddy fluxes + Litterfall
Litterfall
Streamflow
Eddy fluxes
Prior estimate
Multiple data streams – an example
Model parameter estimation based on each
data type separately, and in combination
(error bars are uncertainty from propagated
parameter uncertainty – 1σ):
Haverd et al, BGD, 2012

141. How TERN eMAST addresses this issue
TERN eMAST has several projects aimed at bridging the observational-
modelling community divide:
• Convert existing datasets into standard modelling formats
• Package datasets into modelling experiments that constrain aspects of a
simulation so that diagnostic model evaluation is possible.
• Create key products for model evaluation where they compliment
important TERN datasets (high res climate forcing over Australia for 20C).

142. How TERN eMAST addresses this issue
TERN eMAST has several projects aimed at bridging the observational-
modelling community divide:
• Convert existing datasets into standard modelling formats
• Package datasets into modelling experiments that constrain aspects of a
simulation so that diagnostic model evaluation is possible.
• Create key products for model evaluation where they compliment
important TERN datasets (high res climate forcing over Australia for 20C).
One will be demonstrated in the
breakout: the Protocol for the Analysis
of the Land Surface (PALS) web
application:

143. The last slide
• Models that produce climate, weather, hydrological, ecological projections
are ripe to benefit from integration of ecological data streams
• “Benefit” can mean diagnostic model evaluation AND reduction in
projection uncertainty
• Concurrent multiple data streams are particularly useful
• To date the modelling and observation communities have not
communicated as well as they might – we’d like to try to help
• For more information: Gab Abramowitz: gabriel@unsw.edu.au
Colin Prentice: colin.prentice@mq.edu.au

146. thankyou