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Antonio Bombelli (Project Manager)

ClimAfrica - Climate change predictions in Sub-Saharan Africa: impacts and adaptation. ClimAfrica. Antonio Bombelli (Project Manager). ClimAfrica - Climate change predictions in Sub-Saharan Africa: impacts and adaptation. CLIMAFRICA Climate change predictions in Sub-Saharan Africa:

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Antonio Bombelli (Project Manager)

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  1. ClimAfrica - Climate change predictions in Sub-Saharan Africa: impacts and adaptation ClimAfrica Antonio Bombelli (Project Manager)

  2. ClimAfrica - Climate change predictions in Sub-Saharan Africa: impacts and adaptation CLIMAFRICA Climate change predictions in Sub-Saharan Africa: impacts and adaptations European Commission – FP7 3.5 M€ / 4.6 M€ 48 months: 1 Oct 2010 – 30 Sep 2014 Key Words Climate Predictions; Climate Impacts; Vulnerabilities; Adaptation; Case Studies; Agriculture and Water Resources; Socio-economic analysis

  3. ClimAfrica - The Project Consortium • Partnership • 18 institutions: • 9 Europe • 8 Africa • + FAO • Project coordinator: CMCC – Italy • www.cmcc-org • Local • case studies: • Burkina Faso • Congo • Ghana • Malawi   • Sudan • Togo • Kenya • Ethiopia • Tanzania

  4. ClimAfrica – Rationale • Why ClimAfrica? • Africa and Climate Change • key role in the global C-cycle and climate system •  50% of interannual variability of the global C-balance • > 50% of global fire emissions •  1/5 of the global C-emissions from land use change • Weakness • most vulnerable continent to climate change/variability • population mostly depend on the rain fed rural sector • economy relies mainly on natural resources • less covered region by climate change studies • climate models developed outside the African context • current climate scenarios consider long term trends, with less focus to the shorter time frame Urgent need for the most up-to date and appropriate information and products, developed specifically for Africa, to better understand and predict climate change and its impacts in Sub- Saharan Africa for the next 10-20 years direct linkage between climate, food production, economy and livelihood not enough or not adequate climate related info and products for Africa

  5. ClimAfrica – Main Objectives Develop improved climate predictions on seasonal to decadal time scale Assess climate impacts on water resources and agriculture Evaluate the vulnerability of ecosystems and population to inter-annual variations and decadal trends in climate Suggest and analyse new adaptation strategies suited to SSA Develop a new concept of medium term warning system for food and water security Analyse the economic impact of climate change on agriculture and water resources and the cost-effectiveness of potential adaptation measures

  6. ClimAfrica – Advancements Expected advancements Improved climate-related information and products for Africa delivered on a time scale effective for timely adaptation: Improved climate predictions (seasonal/decadal) Assessment of climate impacts on water resources and agriculture in the next 10-20 years New adaptation strategies from local to national level Assessment of economic implications of climate change impacts and adaptation A prototype medium term warning system

  7. ClimAfrica – Work Packages

  8. ClimAfrica – Work Packages WP2 climate predictability and forecasts WP2 climate predictability and forecasts WP7 project management WP8 dissemination WP7 project management WP8 dissemination WP6 case studies WP6 case studies WP3 climate impacts WP1 past climate variability WP3 climate impacts WP1 past climate variability WP5 Socio- economic implications WP5 Socio- economic implications WP4 Medium-term warning system vulnerability, adaptation WP4 Medium-term warning system vulnerability, adaptation

  9. Expectations from this Workshop Briefly present main achieved and/or expected results from ClimAfrica that may be of interests and have practical application for the stakeholders. Receive feedbacks from stakeholders to improve ClimAfrica outputs and produce more practical ones. Understand which outputs (if any) stakeholders can provide to contribute to ClimAfrica. Explore the possibility to develop collaborations, synergies and strategic partnerships, also beyond the ClimAfrica boundaries. Any other?

  10. ClimAfrica Stakeholder Workshop Agenda

  11. ClimAfrica - Climate change predictions in Sub-Saharan Africa: impacts and adaptation ClimAfrica Stakeholder Workshop Mombasa, 21 June 2013 WP1 – An approach to predict the African vegetation response to climate anomalies Martin Jung, Eric Thomas, Ulrich Weber Max Planck Institute for Biogeochemistry Jena (Germany)

  12. Importance of vegetation dynamics for Africa Human needs Food insecurity Water stress AFRICA PREDICTION OF State of ecosystems Ecosystem services Natural capital Depends on Precise forecasting of the future climate and their changes on vegetation productivity becomes more & more important for policy & management!!! require anticipation & preparation strategies CONTIGENT ON Climate Land use Human population Technologies Human activity Ecological forecasting Stakeholders Decision makers

  13. FAPAR as indicator for vegetation productivity FAPAR - June 2006 (Gobron et al 2006) • Fraction of Absorbed Active Radiation • Dimensionless variable quantifying the fraction of the incoming solar radiation at the top of the canopy • Controls the photosynthetic activity of plants • Key variable for estimating the presence & vegetation productivity • Operational remote sensing FAPAR products on a regional to global scale (MERIS, MODIS, MISR etc.) • Temporal resolution: daily to monthly • Spatial resolution: 250m up to 0.5deg

  14. Approach of vegetation dynamics predictions Variable selection based on Genetic Algorithm Vegetation state: f(climate, land cover, soil etc.) Raw Mean annual Mean Seasonal Cycle Anomalies 7x4 + 7x24 Random forests FAPAR Remote Sensing Product 8 10 1 1 X Y

  15. Results of vegetation dynamics predictions

  16. I Performance of the vegetation dynamics predictions in African ecoregions (full time series) II IV III V VII VI II III IV V VI VII

  17. Conclusion • We developed a phenology model to predict vegetation dynamics on seasonal to decadal scale • Data driven approach as seasonal forecasting tool might be promising • The tool can be tailored to specific applications within Sub-Saharan Africa • Spatial resolution: regional to continental scale • Temporal resolution: currently we are working with monthly values, but can be expanded up to weekly time ranges • Crop yields are possible to obtain if necessary data is available • A good performance of the approach is strongly dependent on weather data input quality • Prototype application which is not operational yet

  18. Thank you very much for your attention!!!

  19. ClimAfrica - Climate change predictions in Sub-Saharan Africa: impacts and adaptation ClimAfrica Stakeholder Workshop Mombasa, 21 June 2013 WP3 – Climate impacts on water and agriculture Per Bodin Contributors: LU,PIK,MPG, CMCC,CEA,CIRAD

  20. WP3 – Climate Impacts on Agriculture The models Land Use Change (Cropland Expansion) Climate Change Crop Management Crop Model Crop Water use Crop yield

  21. WP3 – Climate Impacts on Agriculture Crop models in ClimAfrica

  22. WP3 – Example output: Global models Net Ecosystem Exchange = Total flux of carbon from the ground to the atmosphere Red means a carbon source Green means a carbon sink

  23. WP3 – Estimating the potential of on-field water management interventions in LPJmL Changes in simulated yield (in calories) as affected by different management options

  24. WP3 – Climate Impacts Example output: local/regional models MALAWI - Chancellor College experimental site Maize simulations Mean observed rainfed yield = 5.8 t ha-1 Mean yield reduction = 7% Mean full irrigated yield = 6.2 t ha-1 Mean yield loss = 400 kg ha-1

  25. WP3 – Climate Impacts Example output: local/regional models MALAWI - Chancellor College experimental site KENYA - Makueni-Kitale experimental site

  26. WP3 – Climate Impacts Thanks

  27. ClimAfrica - Climate change predictions in Sub-Saharan Africa: impacts and adaptation ClimAfrica Stakeholder Workshop Mombasa, 21 June 2013 WP4 – Modeling for assessment of impacts on major agricultural production Renato Cumani, Christelle Vancutsem , Selvaraju Ramasamy and John Latham FAO

  28. WP4 - Objectives • Main FAO’s objectives: • To develop the methodology and the prototype of a Medium-term (10 years) Warning System • To provide options for adaptation to climate change and development and dissemination of planning methods, tools, guides and best practices for adaptation planning • 1. Understand the current dynamics of major food production systems in Africa and develop a set of conditional vulnerability scenarios based on current agricultural and socio-economic trends to be used to assess impacts • 2. Create a Medium Term Warning System considering both “persistent” and “extreme” climate impact factors • 3. Identification of options for adaptation to climate change and development and dissemination of planning methods, tools and guides (with WP8)

  29. WP4 – Task 4.1 Inputs • Period of analysis: 1961-2050 • Selection of relevant climatic, agro-climatic, and socio-economic parameters Precipitation, temperature, yield, GPP, soil water content, runoff, carbon, evapotranspiration, seasonality, soil quality,… • Crops grouping for yield • Deriving indicators for 22 crops and 12 groups • The length of the growing seasons • The cumulated & mean precipitation over the growing period (GP) • The annual mean precipitation • The average min and max temperature over the GP • The crop area (ha) and production (hg/ha) per pixel • The temperature growing period (LGPt) • The water use efficiency index (WUE) = GPPs/AETs (crop specific) • The water requirement satisfaction index (WRSI) =AETs/PETs (crop specific) • The aridity index (AI) = P / PETs (crop specific) • The LGP equivalent (LGPeq)

  30. WP4 – Task 4.1 Trend indicators • Anomaly maps on the LTA and the standard-deviation : • the baseline anomaly, i.e. the relative difference between the current average (CUR) and the reference average (HIS75) • the future anomaly 2015 • the future anomaly 2025 • the future anomaly 2035 • Timelines: • Historical reference (1961-1990) (HIS75) • Historical (1971-2000) (HIS85) • Historical (1981-2010) (HIS95) • Current (1991-2020) (CUR) • Future (2001-2030) (FUT15) • Future (2010-2040) (FUT25) • Future (2020-2050) (FUT35)

  31. WP4 – Methodology • Agro-climatic and climatic time series • Spatio-temporal analysis at the zone level • Identify the possible causes of yield trend, • areas of high vulnerability • Harmonise datasets and derive indicators • Agro-ecological zones changes (GAEZ) • Estimate yield changes after 2010 integrating technology trends • Suitability analysis (ECOCROP) • Optimal • Sub-optimal • Suitable but not optimal • Partially unsuitable • Unsuitable

  32. Progress of work – TASK 4.2 Mapping climate risks and trends • Mapping drought and wetness periods and trends • Annual precipitation and variability trends Calculation and documentation of Bio-Physical indices • Climate resource indices • Topographic resources indices • Soil resource indices • Agricultural resources indices Planned tasks • Identification and mapping of socio-economic factors • Calculation of socio-economic indices • Update of indices by incorporating additional indices from other work packages These thematic indices are combined as raster themes, with the same spatial scope and resolution, in a GIS to produce either an Agricultural Resource Availability Index (ARAI) or an Agricultural Resource Poverty Index (ARPI).

  33. WP4 – Task 4.3 • Task 4.3 Evaluation and analysis of vulnerabilities • Assessment of the vulnerability of society to changes in food security • Part of the Risk calculation outlined in the following expression: • Risk = f(H,V) • H = hazard posed by pressures on physical resources; • V = vulnerability to changes in food security • (source: IPCC; 2006) • Analysis of varied data sources (socio-economic, transport infrastructures, health) • Highlight associations within data to social vulnerability • Select appropriate datasets for establishment of statistical data model • Use data model to validate primary dataset (Prevalence of Stunting Among Children in Developing Countries) • Assess the potential for primary dataset as a proxy for Food Security • Geodata Institute and FAO to liaise with policy makers on adaptation scenarios • Planned delivery date: 36 months (instead of 30 months)

  34. WP4.3 – Evaluation and Analysis of Vulnerabilities: • Mapping Outputs • Map Units • Admin Level 1 – GAUL • 4534 zones • Hot-spot Mapping • Phase 1: Vulnerability to Food Insecurity • Phase 2: Combine with data on Hazards to Food Security from the physical environment • Phase 3: Risk to food insecurity hot spot map for Sub-Saharan Africa

  35. WP4 – Achievements • Task 4.1 Understand the current dynamics of major food production systems in Africa and develop a set of conditional vulnerability scenarios based on current agricultural and socio-economic trends to be used to assess impacts • Design of the processing chain for producing trend indicators • Design of the methodology for the analysis : suitability classification, AEZ changes, yield correction, regression,…) • Preparation of input datasets: Spatial unit, FAOSTAT, ECOCROP, Soil datasets • Test of the methodology on v0 of agro-climatic and climatic time series: production of anomaly maps (v0) for various parameters, Yield correction by country • Issue • Final analysis should be done on v1 of agro-climatic and climatic outputs. • V1 not provided yet (planned for June 2013) • Partially solved with some independent analysis (water availability, GAEZ changes) • Planned delivery date: 36 months (instead of 18 months)

  36. WP4 – Achievements • Task 4.2 Create a Medium Term Warning System considering both “persistent” and “extreme” climate impact factors • Planned delivery date: 36 months (instead of 30 months) • Task 4.3 Identification of options for adaptation to climate change and development and dissemination of planning methods, tools and guides (with WP8) • Planned delivery date: 36 months (instead of 30 months)

  37. ClimAfrica - Climate change predictions in Sub-Saharan Africa: impacts and adaptation ClimAfrica Stakeholder Workshop Mombasa, 21 June 2013 WP5: Socio-economic assessments Dr Lia van Wesenbeeck Contributors: SOW-VU, CMCC

  38. ClimAfrica - Climate change predictions in Sub-Saharan Africa: impacts and adaptation The aim of WP5 is two-fold: Provide a socio-economic assessment of climate change impacts and adaptation in the agricultural sector in Sub Saharan Africa (SSA). Its economies are detailed at the country level. The analysis is however global => it considers SSA trade relations and economic feedback with the rest of the world economic systems Offer a spatially explicit assessment at the local scale of the impacts of climate change on the food system

  39. WP5-2 – socio-economic effects: A spatial approach • Climate change • More volatile weather, climate disasters • Longer-term change in temperature, moisture • Effects on population? • Usual approach (FEWS, GIEWS): • Vulnerability of area affected • With limited attention for diversity of people • With no attention for indirect effects • Our approach: • Find out where and who the vulnerable are • Follow how disaster spreads • Design optimal prevention and coping policies

  40. Climate change and optimal response • Climate related policies needed • Prevention • Coping • BUT: • Where? • How? • For whom? • Within budgetary restrictions • Hence: • Combine as much information as possible • Be spatially explicit • Design method to define optimal interventions

  41. Where are the vulnerable? Example for West Africa • Combine nutrition and health indicators % of households with 3 diseases (%) adults with a BMI of 16 - 18.3 (%) children with w/a of -2sd to -3sd (%) Vulnerable population (%)

  42. Who are the vulnerable? Profiling: rural vulnerable West Africa Frequency of occurrence of in top-6 for vulnerable

  43. Where to intervene? Example for Sudan, Uganda East Africa: vulnerability of food production system, shortening season of 1 LGP class Vulnerable population (persons/km2)

  44. BUT: not only consider direct effects! • Think of a line of domino stones, standing straight • A small push may have some effect on the first stone, making it wobble, but a certain minimal strength is needed to make it tumble • If the next stone is nearby and not supported: tumbling starts • Coping mechanisms fail! • If the next stone stands too far away or if the next stone is supported: • tumbling ends • Model determines optimal policies to prevent or stop • tumbling.

  45. Asante Grazie Thank you Danke Merci

  46. WP6: CASE STUDIES Nine regions in Africa Ecological & Socio-economic surveys

  47. Highly experienced partnership High diversity of social-agroecological systems

  48. Common methodology to survey plant diversity, productivity,…

  49. …soils,…

  50. …human responses

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