antonio bombelli (project manager)

ClimAfrica Stakeholder Workshop Mombasa 21/06/2013

Antonio Bombelli (Project Manager)

ClimAfrica

ClimAfrica - Climate change predictions in Sub-Saharan Africa: impacts and adaptation


CLIMAFRICAClimate change predictions in Sub-Saharan Africa:impacts and adaptations

European Commission – FP73.5 M€ / 4.6 M€

48 months: 1 Oct 2010 – 30 Sep 2014

Key WordsClimate Predictions; Climate Impacts; Vulnerabilities; Adaptation; Case Studies; Agriculture and Water Resources;Socio-economic analysis



Acronym Participant organisation name Country1 CMCC CENTRO EURO-MEDITERRANEO per i CAMBIAMENTI CLIMATICI Italy2 ULUND LUNDS UNIVERSITET Sweden3 CEA COMMISSARIAT A L’ENERGIE ATOMIQUE France4 MPG MAX PLANCK GESELLSCHAFT ZUR FOERDERUNG DER WISSENSCHAFTEN Germany5 VUA VERENIGING VOOR CHRISTELIJK HOGER ONDERWIJS WETENSCHAPPELIJK

ONDERZOEK EN PATIENTEZORGNetherlands

6 CTFC CENTRE TECNOLOGIC FORESTAL DE CATALUNYA Spain7 PIC POTSDAM INSTITUTE FOR CLIMATE IMPACT RESEARCH Germany8 CIRAD CENTRE DE COOPERATION INTERNATIONAL EN RECHERCHE AGRONOMIQUE

POUR LE DEVELOPPEMENTFrance

9 FAO – GTOS FOOD and AGRICULTURE ORGANISATION of the UNITED NATIONS10 SOW – VU STICHTING ONDERZOEK WERELDVOEDSELVOORZIENING VAN DE VRIJE

UNIVERSITEITNetherlands

11 CRDPI CENTRE DE RECHERCHE SUR LA PRODUCTIVITE DES PLANTATIONS INDUSTRIELLES

Congo

12 UCT UNIVERSITY OF CAPE TOWN South Africa13 BCA UNIVERSITY OF MALAWI Malawi14 LBEV UNIVERSITY OF LOMÉ Togo15 ARC AGRICULTURAL RESEARCH CORPORATION Sudan16 ICPAC IGAD CENTRE FOR CLIMATE PREDICTION APPLICATION Kenya17 CSIR-CRI COUNCIL FOR SCIENTIFIC INDUSTRIAL RESEARCH – CROPS RESEARCH

INSTITUTEGhana

18 CERPINEDD CENTRE D’ETUDE DE RECHERCHE ET DE PRODUCTION EN INFORMATION POUR L’ENVIRONNEMENT ET LE DEVELOPPEMENT DURABLE

Burkina Faso

Partnership18 institutions:9 Europe8 Africa+ FAO

Project coordinator: CMCC – Italywww.cmcc-org

Local case studies:

1) Burkina Faso2) Congo3) Ghana4) Malawi 5) Sudan6) Togo7) Kenya8) Ethiopia9) Tanzania

ClimAfrica - The Project Consortium

http://www.cmcc-org/


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

direct linkage between climate, food production, economy and livelihoodnot enough or not adequate climate related info and products for Africa

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


1. Develop improved climate predictions on seasonal to decadal time scale

2. Assess climate impacts on water resources and agriculture3. Evaluate the vulnerability of ecosystems and population to inter-

annual variations and decadal trends in climate4. Suggest and analyse new adaptation strategies suited to SSA5. Develop a new concept of medium term warning system for food

and water security6. Analyse the economic impact of climate change on agriculture

and water resources and the cost-effectiveness of potential adaptation measures

ClimAfrica – Main Objectives


ClimAfrica – Advancements

Expected advancements

Improved climate-related information and products for Africa delivered on a time scale effective for timely adaptation:1. Improved climate predictions (seasonal/decadal)2. Assessment of climate impacts on water resources and

agriculture in the next 10-20 years3. New adaptation strategies from local to national level4. Assessment of economic implications of climate change impacts

and adaptation5. A prototype medium term warning system


WP titleLead

PartnerWP1 Effects of past climate variability on ecosystem

productivity and water cycleMPG

WP2 Feedbacks between climate variability / changes and the land surface. Improving modelling seasonal to decadal climate predictions

CMCC

WP3 Analysis of climate impacts on key ecosystem services (water, agriculture)

LU

WP4 Medium-term of Forecasting food and water vulnerabilities and recommending relevant adaptation measures

FAO

WP5 Socio-economic implications of climate change impacts and adaptation measures in SSA

CMCC

WP6 Regional case studies in SSA CTFC

WP7 Project Management CMCC

WP8 Dissemination and exploitation of project results CMCC

ClimAfrica – Work Packages


WP2climate

predictability and forecasts

WP1past climate variability

WP4 Medium-term

warning system

vulnerability, adaptation

WP3climate impacts

WP5 Socio- economic

implications

WP6case studies

WP7

pro

ject

man

agem

ent

WP8

dis

sem

inat

ion

ClimAfrica – Work Packages

WP1past climate variability

WP2climate

predictability and forecasts

WP3climate impacts

WP4 Medium-term

warning system

vulnerability, adaptation

WP6case studies

WP7

pro

ject

man

agem

ent

WP8

dis

sem

inat

ion

WP5 Socio- economic

implications


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 strategicpartnerships, also beyondthe ClimAfrica boundaries.

Any other?

Expectations from this Workshop


ClimAfrica Stakeholder Workshop Agenda

Time Friday 21st June 20139:00 – 10:30 Welcomes and introduction

9:00 – 9:10 Welcome (Dr. C. Oludhe, Dr. A. Bombelli)9:10 – 9:15 Round presentation of the participants

9:15 – 10:00 The ClimAfrica Project9:15 – 9:20 Introduction (A. Bombelli)9:20 – 9:25 WP1 – Past climate variability (E. Thomas)9:25 – 9:30 WP2 – Seasonal to decadal climate predictions (S. Materia)9:30 – 9:35 WP3 – Climate impacts on water and agriculture (P. Bodin)9:35 – 9:40 WP4 – Medium-term warning system, vulnerability, adaptation (R. Cumani)9:40 – 9:45 WP5 – Socio-economic implications (L. van Wesenbeeck)9:45 – 9:50 WP6 – Field case studies (M.T. Sebastià)

9:50 – 10:30 Questions10:30 – 11:00 Coffee break

11:00 – 12:30 Stakeholder’s Presentations(5 – 10 minutes max per Stakeholder)

12:30 – 14:00 Working Lunch14:00 – 15:30 Bilateral meetings15:30 – 16:00 Coffee break16:00 – 17:30 Plenary Discussion

17:30 Conclusions and close of the Workshop



ClimAfrica Stakeholder WorkshopMombasa, 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)

ClimAfrica Project Meeting Mombasa 19-20/06/2013

Importance of vegetation dynamics for Africa

anticipation & preparation strategies

StakeholdersDecision makers

CONTIGENT ONClimate

Land useHuman population

TechnologiesHuman activity

Precise forecasting of the future climate and their changes on vegetation productivity becomes

more & more important for policy &

management!!!

AFRICA

Depends on

require

Ecologicalforecasting

PREDICTION OFState of ecosystemsEcosystem services

Natural capital

Food insecurity

Water stress

Human needs


FAPAR as indicator for vegetation productivity

• 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

FAPAR - June 2006 (GOBRON et al 2006)


Approach of vegetation dynamics predictions

Meteorology

Land use

Soil

Topography

Fire

10

8

1

1

7x4 + 7x24

Variable selection based on Genetic Algorithm

Raw

Mea

n an

nual

Mea

n Se

ason

al C

ycle

Anom

alie

s

Vegetation state: f(climate, land cover, soil etc.)

FAPARRemoteSensingProduct

X Y


Results of vegetation dynamics predictions


II III

IV V

VI VII

I

III IVV

VIVII

IIPerformance of the vegetation dynamics predictions

in African ecoregions (full time series)


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


Thank you very much for your

attention!!!




WP3 – Climate impacts on water and agriculture

Per Bodin

Contributors: LU,PIK,MPG, CMCC,CEA,CIRAD


The models

WP3 – Climate Impacts on Agriculture

Crop Model

Climate Change Land Use Change(Cropland Expansion)

Crop yield Crop Water use

Crop Management


Crop models in ClimAfrica

WP3 – Climate Impacts on Agriculture

Global Models (LPJ models and ORCHIDEE)

Site based models(DSSAT and SIMETAW)

Run on a gridded scale (~50 km) (focus on regional to global scale)

Run at site level(focus on farm scale)

Requires a small number of model input variables and parameters

Requires a high number of model input variables and parameters

Integrates crop yield with biosphere processes (land use changes, carbon and water cycles, etc)

Crop yield in relation to management practices, nutrients and water availability.

Simulates a limited range of crops (or Crop Functional Types)

Simulates specific crops or cultivars

Fair predictive skill in relation to crop yields

Good predictive skill in relation to crop yields


WP3 – Example output: Global models

Net Ecosystem Exchange =Total flux of carbon from the ground to the atmosphere

Red means a carbon sourceGreen means a carbon sink


WP3 – Estimating the potential of on-field water management interventions in LPJmL

Changes in simulated yield (in calories) as affected by different management options


Example output: local/regional models

WP3 – Climate Impacts

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

MALAWI - Chancellor College experimental site

Maize simulations



Example output: local/regional models

KENYA - Makueni-Kitale experimental site

MALAWI - Chancellor College experimental site



Thanks




WP4 – Modeling for assessment of impacts on major agricultural production

Renato Cumani, Christelle Vancutsem , Selvaraju Ramasamyand John Latham

FAO


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)


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)

Group n° Group name LPJ-Guess Abbreviation FAOSTAT crop types1 Rice TrRi + irr Rice paddy2 Maize TeCo +irr Maize3 Tropical cereals (Millet, Sorghum) TrMi +irr Millet, Sorghum, Fonio4 Temperate cereals (Wheat, Barley) TeWW +irr Barley, Wheat, Mixed grain, Oats,…5 Pulses TePu + irr Pulses6 Root crops TrMa,TeSb,TeSo + irr Roots and Tubers7 Others (Oilcrops, Fibre crops, Fruits, …) TrPe, TeSf,TeRa + irr, 8 C4 crops TeCo, TrMi +irr Maize, Millet, Sorghum, Fonio9 C3 crops TrPe,TeSf,TeRa,TePu,TrRi,TeWW,TeSb,TrMa,TeSo + irr all except C4 crops

11 Tot irrigated Tot irr -12 Tot rainfed TrPe,TeSf,TeRa,TePu,TrRi,TeWW,TeSb,TrMa,TeSo,TeCo,TrMi -


WP4 – Task 4.1 Trend indicators

• 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)

• 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


WP4 – Methodology

Spatio-temporal analysis at the

zone levelIdentify the

possible causes of yield trend, areas of high vulnerability

Estimate yield changes after 2010 integrating technology trends

Harmonise datasets and

derive indicators

Agro-climatic and climatic time series

Suitability analysis (ECOCROP)

Agro-ecological zones changes

(GAEZ)1. Optimal2. Sub-optimal3. Suitable but not

optimal4. Partially unsuitable5. Unsuitable


Progress of work – TASK 4.2Mapping climate risks and trendsi) Mapping drought and wetness periods and

trendsii) Annual precipitation and variability trendsCalculation and documentation of Bio-Physical indicesiii) Climate resource indicesiv) Topographic resources indicesv) Soil resource indicesvi) Agricultural resources indicesPlanned tasksvii) Identification and mapping of socio-economic

factorsviii) Calculation of socio-economic indicesix) 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).


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)


• 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

WP4.3 – Evaluation and Analysis of Vulnerabilities:Mapping Outputs


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

IssueFinal 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)



WP4 – Achievements

Task 4.2 Create a Medium Term Warning System considering both “persistent” and “extreme” climate impact factors


Task 4.3 Identification of options for adaptation to climate change and development and dissemination of planning methods, tools and guides (with WP8)





WP5: Socio-economic assessments

Dr Lia van Wesenbeeck

Contributors: SOW-VU, CMCC



The aim of WP5 is two-fold:1. 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

2. Offer a spatially explicit assessment at the local scale of the impacts of climate change on the food system


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

WP5-2 – socio-economic effects:A spatial approach


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


Where are the vulnerable?Example for West Africa

Combine nutrition and health indicators

adults with a BMI of 16 - 18.3 (%)

children with w/a of -2sd to -3sd (%)

% of households with 3 diseases (%)

Vulnerable population (%)


Who are the vulnerable?Profiling: rural vulnerable West Africa

Frequency of occurrence of in top-6 for vulnerable

Characteristic Times in top-6Age head of household 20-24 6No education 6Agricultural self-employed 6No payment for work 5Agro-pastoral millet 3Cereal-root crops mixed 3Economic situation bad 4 (and 1 very bad)Ethnic group discriminated against 5 (1 occasional)Never received remittances 5 (1 less than once a year)


East Africa: vulnerability of food production system, shortening season of 1 LGP class

Vulnerable population (persons/km2)

Where to intervene?Example for Sudan, Uganda


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 startsCoping 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.

BUT: not only consider direct effects!

http://www.google.nl/url?sa=i&rct=j&q=domino%2Btumbling&source=images&cd=&cad=rja&docid=dJjnolNFc0WyOM&tbnid=MtnAy8MQnv6u_M:&ved=0CAUQjRw&url=http%3A%2F%2Fblendedfamilytoday.blogspot.com%2F2011%2F02%2Fblended-family-dominoes.html&ei=Lhm6Ubn6Koi80QXJjoDYAg&bvm=bv.47883778,d.ZWU&psig=AFQjCNGQLzM0zaG_6w-K_3bk5J50YnMxeQ&ust=1371237016223771


Thank you

Asante

Merci

Danke

Grazie

http://www.google.nl/url?sa=i&source=images&cd=&cad=rja&docid=HzdoefIXoHkkIM&tbnid=0vAST60jSf8lYM:&ved=0CAgQjRwwAA&url=http%3A%2F%2Ffishandbicycles.com%2F2012%2F04%2F27%2Fvideo-fridays-dispelling-stereotypes-of-african-men%2F&ei=7AnAUbvrPKik0AWa5IDwCg&psig=AFQjCNF86t56dBDEHC-ZniCAGIWU9O0W6w&ust=1371626349033076


WP6: CASE STUDIES

Ecological & Socio-economic surveys

Nine regions in Africa

ClimAfrica Stakeholder Workshop Mombasa 21/06/2013High diversity of social-agroecological systems

Highly experienced partnership

ClimAfrica Stakeholder Workshop Mombasa 21/06/2013Common methodology to survey plant diversity, productivity,…

ClimAfrica Stakeholder Workshop Mombasa 21/06/2013…soils,…

ClimAfrica Stakeholder Workshop Mombasa 21/06/2013…human responses


Main product: COMMON WP6 DATABASE


Results will allow better understanding of systems vulnerabilities

To allow the design of climate-smart adaptation and coping strategies


Thanks!


THANKS!

Antonio Bombelli (Project Manager)


[email protected]

http://www.cmcc.it/

mailto:[email protected]

antonio bombelli (project manager)

Documents

climate system

climate changekey role

subsaharan africa

adequate climate related

africa urgent need

global cemissions

global ccycle

global cbalance