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Anatomy of an integrated analysis involving
adaptive capacity
Climate Adaptation National Research Flagship
Mark Stafford Smith, Science Director
Climate Adaptation Flagship
GEOSS/IPCC Workshop, Geneva, 1 Feb 2011
Topics
• Relating an experience: A multi-level analysis of
drivers of migration from drylands globally
• Project, not yet public, for UK Foresight process
• Focus here on process and experience not results
• (Really only proof of concept)
• Characteristics
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Linked some environmental and social drivers
Considered adaptive capacity and multiple levels explicitly
Needed to focus on consistent biome within-country
Trying to detect and forecast trends over time
Project to 2030+2060
• Implications for data??
Drylands x countries
From: http://geodata.grid.unep.ch/
Land cover x aridity zone x country polygons
Global Land Cover Facility, U Maryland
Conceptualisation - 1
More movement is likely where there is:
1. more long term trend to less (environmental)
resources per head
2. less national capacity and interest to invest in dryland
regions
3. poorer investment outcomes in dryland regions
4. poorer recent or current environmental conditions,
5. all exacerbated by greater inequality
Conceptualisation - 2
Pressure to
migrate
Variance in
population
National capacity/
interest to invest
in drylands
Local adaptive
capacity (to move
or to stay)
Recent
environmental
conditions
National scale
Local scale
Trend in
environmental
services per head
Conceptualisation - 3
Pressure to
migrate
 GINI index
Variance in
population wellbeing
 Child mortality
 Road density
(this polygon)
Local adaptive
capacity (to move
or to stay)
 % urban
 GDP/capita
 Corruption idx
National capacity/
interest to invest
in drylands
Recent
 Drought idx 1y
environmental  Drought idx 10y
conditions
Trend in
environmental
services per head
 Pop’n increase
 Trend in NPP/capita
Slow variable: trends in environmental services
• Trend in NPP
• 1980-2000 AVHRR NDVI-derived NPP (Prince and
Goward 1995 GloPEM)
• Recognising MODIS would be better in the long run…
• Averaged across each polygon
• Future NPP: explored 5 DGVMs (Sitch et al. 2008)
• V. variable performance in drylands; & much coarser
resolution, so some polygons had to be dropped
• Population: GPW from CIESIN
• “Allocation gridding algorithm to assign population
values to grid cells” – may be least accurate in drylands
 NPP/population decadal trend
• Created ratios within a polygon over time
• Nb avoided comparing across space
Trend in NPP/hd in drylands 85-90 to 95-00
Environmental impacts
• Drought index (Sheffield & Wood)
• Indicator of acute drought and short-term
changes in production capital
• ‘Independent’ of NPP dataset
• Looked within polygon at periods >12 months in
its own lowest decile
• Assumes local society ‘in balance’ with the polygon’s
long-term median index
• Generally seems good but poor in hyperarid
• Long-term! 1948-2000 at 1° resolution
• But not yet available for future runs at higher resolution
Social projections
• Country GDP (SRES) and population &
urbanisation UN projections
• Actually usually false resolution in databases since
projected regionally
• Ie. not even at country level, let alone drylands within
country
• Used as indicators of proportional
change, not absolute
• No future projections of other indicators
• Sensitivity analysis instead
• (still useful for decision-making)
Case studies
• Easy 50% more if we could have gone back another decade in NPP…
Projected migration intensity: A1, 2030
Issues
• Need long time series, unavoidably
• Case studies over decades, + detection of change in
variable environments
• Historical and projections data need to be compatible
• Problems with definition typologies (cf. ‘forest’)
• Partially avoided by only looking at changes over time
within one pixel (what is ‘one pixel’?….)
• Data sets tuned for a particular purpose …
• e.g. tuned for C mitigation don’t do drylands well
• Adaptive capacity invariably multi-scaled!
• Sub-national social data hard to come by
• Not commensurate with environmental data
• In space, in time, in collection units
Some implications for adaptation research
• Matched nested data sets
• In space and time
• Multiple levels, multiple scales
• Accessibility
• Documentation of data-set prejudices
• What purpose in mind when it was cleaned up, etc?
• Commensurate sampling
• Especially social <-> environmental datasets
• Need to make mature: learn to walk before we run
• Work through in systematically chosen set of case
studies
Resolving antagonistic paradigms
• Adaptation – bottom-up local/regional/sectoral responses
• Participatory ownership vital
• Need a structured approach to extrapolation/scaling up
Generalisations & global statements
Typology of diverse systems
x
Based on
clear model
Categories of regional GEC impacts
of (different)
systems
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functioning
Broadly predictable sets of responses
Complex sets of case studies without generalisability
Directions for the workshop?
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Long-term architecture and indicators needs
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To deliver data for adaptation investment & evaluation
for decision-makers (e.g. Adaptation Fund, nations)
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What key decisions?
What key information for these decisions at what scales?
What architecture to aim towards? (i.e. Tues talk!)
Short term delivery to IPCC AR5
1. Published description of needs; promote to parties
2. 2-3 proof-of-concept case studies, ??written up in time
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Adaptations to changing water availability by basin?
Adaptation within mitigation actions of REDD+??
Adaptive DRR preparations for one class of disasters?
Monitoring to a purpose!! but not just mitigation…
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Biophysical and social data at multiple scales, including
‘in situ’, developed through demonstrators?
Climate Adaptation Flagship
Director: Andrew Ash
[+61] 07 3214 2234 / [email protected]
Science Director: Mark Stafford Smith
[+61] 0408 852 082 / [email protected]
Climate Adaptation Flagship