Mark Stafford Smith, Science DirectorClimate Adaptation FlagshipGEOSS/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 industryImpacts and adaptation in the tropics

Engineering and technology solutions for adaptnCommunication of information for adaptation

EcosystemsConstructing and enabling local knowledge

National and international adaptation activitiesClimate extremes and disaster management

New concepts in adaptationAdaptation and development

Adapting to climate change in cities Public health adaptn to variability and change

Scenarios of the future for adaptationAdapting agriculture to climate change

Indigenous vulnerabilities and adaptationThe economics and costs of adaptation

Coasts, deltas and small islandsAdaptation and the communityClimate Information for users

National and sub-national case studies of adaptnThe interface of adaptation and mitigationHuman security, social and equity issues

Risk communication and behavioural changeWater 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 community2. 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’ - 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’ - 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 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 SummaryClimate 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 SouthWalesQueensland

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