Mark Stafford Smith, Science Director Climate Adaptation Flagship

1. Data for adaptation decision-making Climate Adaptation National Research Flagship Mark Stafford Smith, Science Director Climate Adaptation Flagship GEOSS/IPCC Workshop, Geneva, 1 Feb 2011

2. Data for adaptation decision-making • Where I’m coming from • 2010 Climate Adaptation Futures conference, Australia • Lessons from global desertification data needs?

3. CSIRO’s Climate Adaptation Flagship 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

4. 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 conference • Gold Coast, Australia, Jun-Jul 2010 • ~1000 attendees, >30% overseas • http://www.nccarf.edu.au/conference2010/ • 24 parallel sessions

5. 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

6. 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”

7. 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)

8. Purposes of monitoring data (my view!) • Determine where/how to invest resources (state) • …from local decisions of a household or farmer to investments of nations and the global community • Determine whether past such investments have been successful (detect trend, signal from ‘noise’) • …and change them if not • 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

9. 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

10. 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: 111-125 Verstraete et al (2009) Frontiers in EcolEnvir 7: 421-8

11. Verstraete et al (2009): Fig. 3 Developed dryland country with land abandonment Developing dryland countrywith increasing population Conservation /amenity province Miningprovince Mixed subsistenceagriculture province Water supply catchment withgrazing Area dominated bysmallholder grazingand cropping 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 areas where uses prioritise different ecosystem services within the country’s general trajectory Dryland countries experiencing major regional syndromesat global scale Dryland provinceswithin countries with different trajectories

12. Household level adaptive capacity for drought Global NPP datasets National $$ State/trend of vegetation for primary production Household food capital Remittances from outside Palatable perennial forage Crop weed invasions # grazing animals Stored grain stock Palatable shrub cover Perennial grass cover Scaling up through major themes e.g. forage; management responses; governance capacity; household economics

13. 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: 111-125 Verstraete et al (2009) Frontiers in Ecol.Envir 7: 421-8 Verstraete et al (2011) Land Deg. Dev. 21 in press

14. 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

15. 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 Rangelands 2008 – Taking the PulseWhat we learned: some headlines - 4 CSIRO. Insert presentation title

16. $$ from states and federal govts Steering Committee Users: Federal and State Governments, regional ‘NRM Bodies’ Sources of on-ground monitoring data+ some regional socio-economic & R/S data+ regional interpretative ‘local’ knowledge WesternAustralia NorthernTerritory SouthAustralia New SouthWales Queensland Hierarchical system of diverse sources of data Sources of national data (CSIRO, Queensland, Federal Govt) – rainfall, land use, remote sensing, dust, socio-economic stats, etc, Management Unitto collate, interpret and synthesise,and return to users CSIRO. Insert presentation title

17. 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!

18. 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