mark stafford smith, science director climate adaptation flagship
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Mark Stafford Smith, Science DirectorClimate Adaptation Flagship
GEOSS/IPCC Workshop, Geneva, 1 Feb 2011
Climate Adaptation National Research Flagship
Data for adaptation decision-making
Data for adaptation decision-making
• Where I’m coming from
• 2010 Climate Adaptation Futures conference, Australia
• Lessons from global desertification data needs?
Goal: Equip Australia with practical and effective adaptation options to climate change and variability and in doing so
create $3 billion per annum in net benefits by 2030
Ca. 160 full time equivalent researchers (~300 individuals),
established 2008
Engaged with policy, industry and community decision-makers
in Australia and Asia-Pacific
CSIRO’s Climate Adaptation Flagship
Climate Adaptation Futures conference
• Gold Coast, Australia, Jun-Jul 2010• ~1000 attendees, >30% overseas
• http://www.nccarf.edu.au/conference2010/
• 24 parallel sessions
Research meets business and industry
Impacts and adaptation in the tropics
Engineering and technology solutions for adaptn
Communication of information for adaptation
Ecosystems
Constructing and enabling local knowledge
National and international adaptation activities
Climate extremes and disaster management
New concepts in adaptation
Adaptation and development
Adapting to climate change in cities
Public health adaptn to variability and change
Scenarios of the future for adaptation
Adapting agriculture to climate change
Indigenous vulnerabilities and adaptation
The economics and costs of adaptation
Coasts, deltas and small islands
Adaptation and the community
Climate Information for users
National and sub-national case studies of adaptn
The interface of adaptation and mitigation
Human security, social and equity issues
Risk communication and behavioural change
Water sector adaptation: innovations
Climate Adaptation Futures: some lessons
• Some sessions dealt with data needs• Climate services for early warning systems
• Often about communication of data in the right format• Simplified datasets for many users, overload on local
government decision-makers, handling uncertainty• “reframing information for risk management and away from
dependency on climate uncertainty” UKCIP• “rapid assessment of climate uncertainty in evaluating [ag]
adaptation options”
• Emphasis on adaptation at multiple levels• Also on measuring the effectiveness of adaptation
Mostly supporting decisions, not providing data• In development, disasters, agriculture, water, cities,
health, engineering, business, human security, coasts and small islands, policy, indigenous communities, etc
Some implications for adaptation datasets
• “Most of our current investment is [still] in defining the problem, not in finding the solutions. A decisions and outcomes focus is needed. ”
• A focus on use means dealing with:• Multiple scales – geographic, governance, institutions,
industry sectors, etc• Multiple levels in all of them – local, provincial, national,
supra-national, global
• Measuring the amount and effectiveness of action• “Knowledge that is coproduced is more likely to be used”
Parallels in desertification
• 30 years of debate on desertification• Plenty of political aspects though <<$$ than adaptation• Persistent uncertainty even in how much (17-70% of
drylands desertified in late 1990s!), leading to policy paralysis, loss of confidence in donors for action, etc
• Key gulf:• Top-down universal indicators ~readily obtained
• But lacking credibility locally so not supporting action• e.g. cover can increase or decrease with degradation
• Focused on biophysical or aggregated social indicators
• Bottom-up schemes engaging communities• Locally credible indicators engendering local action
• But generally impossible to aggregate up• measuring different things x different places (for good reason)
Purposes of monitoring data (my view!)
1. Determine where/how to invest resources (state)
• …from local decisions of a household or farmer to investments of nations and the global community
2. Determine whether past such investments have been successful (detect trend, signal from ‘noise’)
• …and change them if not
3. Understand cause and effect (causation)
• …to improve conceptual models driving investment, the monitoring system or even the whole institutional set-up around the monitoring (‘triple loop learning’).
• Science helps #1 and #2 but mainly through #3. • Scientists tend to design monitoring systems for #3….• Decision-makers actually need #1 and #2
Towards a ‘Global Drylands Observing System’ - 1Towards a ‘Global Drylands Observing System’ - 1
• Need to sort out the clients for the data– International, national, sub-national levels
– Different regions will care about different measures• Some consistent meta-themes, other sensitive to locale• On-ground measures legitimately differ by system
• Need to combine remote sensing (etc) and local ground data for credible measures of change– Tracking statistically significant change is much
harder than assessing state• But needed to determine whether investments are
working, & to contribute to adaptive decision-making
Verstraete et al (2009) Frontiers in EcolEnvir 7: 421-8
Towards a ‘Global Drylands Observing System’ - 2Towards a ‘Global Drylands Observing System’ - 2
• Need the right (multi-scaled) governance of the system to be effective, owned, credible– To sustain valuing of results and consequent
investment in collecting data
• All suggests a nested system:– Nested clients, purposes (some generic data)
– Nested measures (mostly generic themes but different indicators, able to be logically collated upscale)
– Nested governance
– Nested, iterative development – can’t do it all at once!
Bastin et al (2009.) ACRIS. The Rangeland Journal 31: 111-125Verstraete et al (2009) Frontiers in EcolEnvir 7: 421-8
Dryland areas where uses prioritise different ecosystem services within the country’s general trajectory
Verstraete et al (2009): Fig. 3
Developing dryland countrywith increasing population
Developed dryland country with land
abandonment
Miningprovince
Mixed subsistenceagriculture province
Conservation /amenity province
Area dominated bysmallholder grazing
and cropping
Water supply catchment with
grazing
Nested monitoring of human and environmental slow variables, chosen so local data systematically contributes to broader scale data, with remote sensing providing context at broader scales
Data aimed at primary dryland syndromes, e.g. population, poverty, market orientation, access to finance, health, food reserves in developing country, age of managers, NRM investment, pests and weeds, indigenous minority access in developed country
Indicator tailored to provinces’ trajectories, eg. population density, food and water per head, net agricultural productivity per unit area in agricultural region, endangered species, weed invasions, fire regimes, tourist income in amenity region
Data on locally important ecosystem services, e.g. pasture productivity, soil nitrogen, household poverty in agricultural area, involvement of women; water quality, pasture cover in catchment. Measures suited to different ecosystems
Dryland provinces within countries with different trajectories
Dryland countries experiencing major regional syndromes
at global scale
Scaling up through major themesScaling up through major themes
e.g. forage; management responses; governance capacity; household economics
Palatable shrub cover
Perennial grass cover
Palatable perennial forage
State/trend of vegetation for primary production
Crop weed invasions
# grazing animals
Stored grain stock
Household food capital
Household level adaptive capacity for drought
Remittances from outside
Global NPP datasets
National $$
Is it happening at UNCCD?
• Many attempts to do global assessments• Mostly fail to account for different local causation and
hence can’t tell what management/investments are needed or responsible for change
• But provide vital context if interpreted correctly
• Various more locally-sensitive systems• Aim to support action on the ground/within the nation
• e.g. LADA (Land Degradation Assessment in Drylands) http://www.fao.org/nr/lada/
• Papers emerging with a new architecture• Not universally accepted (or even understood) yet
• But important lessons for adaptation
Bastin et al (2009.) ACRIS. The Rangeland Journal 31: 111-125Verstraete et al (2009) Frontiers in Ecol.Envir 7: 421-8Verstraete et al (2011) Land Deg. Dev. 21 in press
CSIRO. Insert presentation title
1955 1992
Australian Collaborative Rangelands Information System - ACRIS
• Why a national system for reporting change?• There are reasons for having nationally comparable information!
• Investment planning and evaluation• SoE, international reporting• Sustaining a sustainable image
• Who for?• National, state-level, regional stakeholders (different needs)
• What’s the challenge?• Spatial and temporal variability, and sparse resources
• Detecting change, then attributing it
CSIRO. Insert presentation title
Rangelands 2008 – Taking the PulseWhat we learned: some headlines - 4
Theme Summary
Climate variability • Seasonal quality: above-average in the north and north-west; variable in central Australia; above average then dry in most of WA & SA shrublands; below average then drought in eastern grasslands & mulga lands
Total grazing pressure
• Mapped, including 20-40% by roos in S & E; feral densities still poorly tracked• In some pastorally important bioregions, domestic stock remain high despite declining seasons
Landscape function • WA, SA, NSW & NT: generally positive signs given seasonal quality.• Queensland: 6 of 11 bioregions - decreased landscape function
Landscape function provides a measure of the landscape’s capacity to capture rainfall and nutrients, the essential resources for plant growth.
functional – non leaky dysfunctional –leaky landscape
CSIRO. Insert presentation title
Users:Federal and
State Governments, regional ‘NRM
Bodies’
Hierarchical system of diverse sources of data
WesternAustralia
NorthernTerritory
SouthAustralia
New SouthWales
Queensland
Sources of on-ground monitoring data+ some regional socio-economic & R/S data+ regional interpretative ‘local’ knowledge
Management Unitto collate, interpret
and synthesise,and return to users
Sources of national data (CSIRO,
Queensland, Federal Govt) – rainfall, land use, remote sensing, dust, socio-economic
stats, etc,
$$ from states and federal govts
Steering Committee
Implications: adaptation info system architecture
• Think multiple levels in multiple scales
• Hierarchically nested structure with data themes• Data themes relevant to decision-makers
• Focus on indicators that can be acted upon (decisions)
• Support with indicators of causation (science; =PSR+)
• Flexible indicators locally, within agreed themes• Design processes to engender ownership at each level
• Design and resource mechanisms for upscaling• At least at national and global levels
• Meta-analysis for consistency at that level in the theme
• Bring in global datasets for context, extrapolation, truthing
• Expect iterative system development • Can’t be done in a day, but worth it eventually!
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
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