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Table 4. Country-by-system breakdown of LGP change class to 2050: ECHam4, Scenario A1 EC A1 COAST FORST PEREN LGA LGH LGHYP LGT MIA MIH MIHYP MRA MRH MRHYP MRT OTHER RITRE TREEC URBAN Angola 1 2 2 1 2 2 1 1 2 Benin 2 2 2 2 2 2 2 Botswana 2 2 Burkina Faso 2 2 2 2 Burundi 2 1 1 1 Cameroon 1 2 2 1 1 2 1 2 Cent Afr Rep 1 2 1 2 1 1 Chad 2 2 2 2 Congo 1 1 1 1 1 1 1 DR of Congo 1 1 1 1 1 1 1 1 1 2 Cote D'Ivoire 2 2 2 2 2 2 1 Djibouti Equatorial Guinea Eritrea 2 2 2 Ethiopia 1 2 1 2 1 1 Gabon 1 1 1 1 1 1 1 Gambia 2 2 1 Ghana 1 2 2 2 2 1 1 2 Guinea Bissau 1 1 1 1 Guinea 1 1 1 1 1 1 1 1 Kenya 2 1 2 1 1 Lesotho 2 2 Liberia 1 1 1 1 Madagascar 1 2 1 2 1 1 2 1 2 2 Malawi 1 1 1 1 1 1 1 1 Mali 2 2 2 2 Mauritania 2 2 Mozambique 1 1 1 1 1 1 1 1 Namibia 2 2 Niger 2 2 2 2 Nigeria 2 1 2 1 2 1 1 Rwanda 2 2 Senegal 1 2 1 2 1 Sierra Leone 1 1 1 1 Somalia 2 2 2 2 South Africa 2 2 2 2 2 2 Sudan 2 1 2 2 1 2 2 Swaziland 2 1 1 1 2 Tanzania 2 1 1 1 1 1 1 1 1 1 2 Togo 2 2 2 2 1 Uganda 1 2 1 1 1 Zambia 1 1 1 1 1 1 1 1 Zimbabwe 1 1 1 “2” signifies substantial losses (>20%) in at least 50% of the system in that country, “1” signifies moderate losses (5-20%) in at least 50% of the system. 44
Table 5. Country-by-system breakdown of LGP change class to 2050: HadCM3, Scenario A1 HD A1 COAST FORST PEREN LGA LGH LGHYP LGT MIA MIH MIHYP MRA MRH MRHYP MRT OTHER RITRE TREEC URBAN Angola 1 2 2 1 2 2 1 1 2 Benin 2 2 2 2 2 2 2 Botswana 2 2 Burkina Faso 2 2 2 2 Burundi 2 1 1 1 Cameroon 1 2 1 1 1 2 1 1 1 1 2 Cent Afr Rep 1 1 1 2 1 1 Chad 2 2 2 2 Congo 2 2 1 2 2 2 2 DR of Congo 1 1 1 1 1 1 1 1 1 1 2 Cote D'Ivoire 2 2 2 2 2 1 1 Djibouti Equatorial Guinea 1 Eritrea 2 2 Ethiopia 1 1 1 1 Gabon 1 1 1 1 1 1 1 Gambia 1 1 1 Ghana 2 2 2 2 2 2 2 2 Guinea Bissau 1 1 1 1 Guinea 1 1 1 1 1 1 1 1 Kenya 2 1 2 1 2 1 2 1 1 Lesotho 2 2 Liberia 1 1 1 1 Madagascar 1 1 1 1 1 1 1 1 1 1 Malawi 1 1 1 1 1 1 1 1 Mali 2 2 2 Mauritania 2 2 Mozambique 1 1 1 1 1 1 1 1 Namibia 2 2 Niger 2 2 2 2 Nigeria 1 2 1 2 1 2 1 1 1 1 Rwanda 2 2 2 Senegal 1 1 1 2 1 Sierra Leone 1 1 1 1 1 Somalia 2 2 2 2 South Africa 2 2 2 2 2 2 2 2 Sudan 2 1 2 2 1 2 2 Swaziland 2 2 2 2 2 Tanzania 2 2 1 1 1 1 1 1 2 1 1 2 Togo 2 2 2 2 2 Uganda 2 1 2 2 2 1 2 1 Zambia 1 1 1 1 1 1 1 1 Zimbabwe 1 1 1 1 2 1 “2” signifies substantial losses (>20%) in at least 50% of the system in that country, “1” signifies moderate losses (5-20%) in at least 50% of the system. 45
Page 1: Mapping climate vulnerability and p
Page 4 and 5: Citation Thornton PK, Jones PG, Owi
Page 6 and 7: egion of eastern Africa, the coasta
Page 8 and 9: Contents Executive summary 3 1 Back
Page 10 and 11: Tables Table 1. AOGCMs in the TYN S
Page 12 and 13: • Research to reduce uncertainty,
Page 14 and 15: 2 Project objectives and activities
Page 16 and 17: 3 Framework for the study The liter
Page 18 and 19: Figure 1. Representation of vulnera
Page 20 and 21: Conceptually, there is still a cons
Page 22 and 23: 4. Climate impacts in sub-Saharan A
Page 24 and 25: Table 1. AOGCMs in the TYN SC 2.0 d
Page 26 and 27: We then prepared similar climate gr
Page 28 and 29: Mapping changes to the probability
Page 30 and 31: confidence in the ability of that G
Page 32 and 33: Figure 5. Length of growing period
Page 34 and 35: Figure 6. Coefficient of variation
Page 36 and 37: Figure 7 (B). Percentage changes in
Page 38 and 39: Figure 7 (D). Percentage changes in
Page 40 and 41: Figure 8 (B). Percentage changes in
Page 42 and 43: • For human population, we now us
Page 44 and 45: Figure 9. Farming/livelihood system
Page 48 and 49: latter, the highland systems in sou
Page 50 and 51: Table 6. Country-by-system breakdow
Page 52 and 53: Table 8. Positive LGP changes to 20
Page 54 and 55: Figure11. Number of growing seasons
Page 56 and 57: 4.3 Uncertainties in the analysis T
Page 58 and 59: 5. Poverty and vulnerability The se
Page 60 and 61: Table 9. Vulnerability indicators u
Page 62 and 63: datasets (more information on this
Page 64 and 65: political process, civil liberties
Page 66 and 67: the major development issue facing
Page 68 and 69: scenarios. For this synthesis, the
Page 70 and 71: Table 11. Rotated component (or “
Page 72 and 73: Table 12. Synthesis of possible reg
Page 74 and 75: climate change-vulnerability hotspo
Page 76 and 77: contain components that draw far mo
Page 78 and 79: 6.1 A survey of information needs o
Page 80 and 81: unit; the International Red Cross;
Page 82 and 83: predominantly in southern Africa. I
Page 84 and 85: Table 13. Institutional preparednes
Page 86 and 87: particularly so for government and
Page 88 and 89: 6.2 Prospects for a decision suppor
Page 90 and 91: Figure 14. Stages of impact assessm
Page 92 and 93: An ex ante impact assessment might
Page 94 and 95: A second issue relates to the setti
Page 96 and 97:
A second key point that these resul
Page 98 and 99:
References AchutaRao K, Covey C, Do
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Food and Agriculture Organisation o
Page 102 and 103:
JRL (2005). GLC 2000 (Global Land C
Page 104 and 105:
Roeckner E, Oberhuber J M, Bacher A
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Acronyms ACTS ASARECA CGIAR DFID EN
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Appendix 2. Candidate vulnerability
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Appendix 3. Country by system CV of
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Appendix 4 List of contacted instit
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112 Note 1. Indicators of Adaptive
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Box 1: Source: IPCC, 2001 1. Adapti
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irrigation and quality of infrastru
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Irrigation rate is measured by net
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moisture, especially in southern, N
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Bryman, A and D. Cramer . 1997. Qua
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Note 2. South-South Cooperation (pr
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• Effective policy frameworks in
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vulnerable countries in 12 selected
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130 Note 3. Climate Change and Heal
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impacts (developing nations). These
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most relevant for the case of HIV/A
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emerging infectious diseases; use o
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Within the Sub-Saharan region, the
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geographical shifts and yield reduc
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142 Note 4. The climate, developmen
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Figure 2 Percentage of undernourish
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The Africa Environment Outlook desc
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Figure 5 Total affected in hydro-me
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Figure 5 Present and future freshwa
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Turner B L, Kasperson R E, Matson P
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Note 5. The Sub-Saharan Africa Chal
Page 158 and 159:
Several useful lessons were learned
Page 160 and 161:
Note 6. The ASARECA priority settin
Page 162 and 163:
Figure 1. Development domains and a
Page 164 and 165:
Note 7. The SLP’s food-feed impac
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Figure 2. Domains in Kenya with hig
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Note 8. The SAKSS poverty targettin
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168 Note 9. Simulating regional pro
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Applications (ICASA). The results f
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