Mark Stafford Smith, Science DirectorClimate Adaptation Flagship

GEOSS/IPCC Workshop, Geneva, 1 Feb 2011

Climate Adaptation National Research Flagship

Anatomy of an integrated analysis involving adaptive capacity

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• 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??

From: http://geodata.grid.unep.ch/

Drylands x countries

Global Land Cover Facility, U Maryland

Land cover x aridity zone x country polygons

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

Trend in environmental

services per head

Pressure to migrate

National capacity/ interest to invest

in drylands

Variance in population National scale

Local adaptive capacity (to move

or to stay)

Recent environmental

conditions

Local scale

Conceptualisation - 3

Trend in environmental

services per head

Pressure to migrate

Local adaptive capacity (to move

or to stay)

Recent environmental

conditions

National capacity/ interest to invest

in drylands

Variance in population well-

being

GINI index

% urban GDP/capita

Corruption idx

Drought idx 1y Drought idx 10y

Child mortality Road density

(this polygon)

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

Complex sets of case studies without generalisability

Categories of regional GEC impacts

Typology of diverse systemsx

Broadly predictable sets of responses

Based on clear model of (different) systems functioning

Directions for the workshop?

• Long-term architecture and indicators needs • To deliver data for adaptation investment & evaluation for

decision-makers (e.g. Adaptation Fund, nations)• 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 AR51. Published description of needs; promote to parties

2. 2-3 proof-of-concept case studies, ??written up in time• 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…• Biophysical and social data at multiple scales, including ‘in

situ’, developed through demonstrators?

Climate Adaptation Flagship

Climate Adaptation Flagship Director: Andrew Ash [+61] 07 3214 2234 / andrew.ash@csiro.au

Science Director: Mark Stafford Smith[+61] 0408 852 082 / mark.staffordsmith@csiro.au