Mapping climate vulnerability and poverty in Africa - CGSpace Home

More documents

Recommendations

Info

confidence in the ability of that GCM to project future conditions under different scenarios of change, all other things being equal). McHugh (2005) identified five GCMs out of 19 that represent rainfall patterns in East Africa relatively well. Of those five, two are in the data set used here, HadCM3 and ECHam4. The preliminary results of work by Hanson et al. (2006) also tend to support the use of these two GCMs, for East African conditions at least. Liu et al. (2003) reported that for Sahelian conditions (rainfall of some 20 mm per year), these two GCMs were not the best performers in terms of reproducing current rainfall patterns, but they were better than several others. An advantage of using these two GCMs is that ECHam4 is what might be termed a “wet” model – the rainfall differences projected under the four SRES scenarios from 2000 to 2050 are the largest of all the five GCMs used here. On the other hand, the HadCM3 is a “drier” model; rainfall differences for the four scenarios between 2000 and 2050 are the lowest or among the lowest for all the GCMs used here. Accordingly, we use these two GCMs for the analyses presented below. In general, there seems to have been relatively little validation work carried out on GCMs for African conditions (Hulme et al., 2001; D Conway, personal communication), and this is an area that would benefit from further work. We also cut down the number of SRES scenarios that we looked at. As noted above, these scenarios cover a wide range of economic development, fossil fuel and population growth possibilities. Figure 4, slightly modified from Figure 9.14 in IPCC (2001), shows the envelope of global temperature increases associated with several GCMs and the various SRES scenarios. Most of the temperature range is covered by two scenarios, A1F1 and B1, and these were included in the TYN SC 2.0 data set used in this analysis. Accordingly, the vulnerability analysis results concentrate on the four combinations of two GCMS, HadCM3 and ECHam4, and two scenarios, A1F1 and B1. First, we show some results that characterize current conditions for Africa. Figure 5 shows the length of growing period (days per year) for current conditions (the year 2000), at a resolution of 10 arc-minutes. The coefficient of variation of annual rainfall (the standard deviation of annual rainfall divided by the mean, expressed as a percentage) is shown in 28
Figure 4. Estimated radiative forcing for the four illustrative SRES marker scenarios, illustrating different energy technology options; results relative to 1990. Slightly modified from Figure 9.14 in IPCC (2001). 29
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 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 46 and 47: Table 4. Country-by-system breakdow
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
Page 100 and 101:
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
Page 106 and 107:
Acronyms ACTS ASARECA CGIAR DFID EN
Page 108 and 109:
Appendix 2. Candidate vulnerability
Page 110 and 111:
Appendix 3. Country by system CV of
Page 112 and 113:
Appendix 4 List of contacted instit
Page 114 and 115:
112 Note 1. Indicators of Adaptive
Page 116 and 117:
Box 1: Source: IPCC, 2001 1. Adapti
Page 118 and 119:
irrigation and quality of infrastru
Page 120 and 121:
Irrigation rate is measured by net
Page 122 and 123:
moisture, especially in southern, N
Page 124 and 125:
Bryman, A and D. Cramer . 1997. Qua
Page 126 and 127:
Note 2. South-South Cooperation (pr
Page 128 and 129:
• Effective policy frameworks in
Page 130 and 131:
vulnerable countries in 12 selected
Page 132 and 133:
130 Note 3. Climate Change and Heal
Page 134 and 135:
impacts (developing nations). These
Page 136 and 137:
most relevant for the case of HIV/A
Page 138 and 139:
emerging infectious diseases; use o
Page 140 and 141:
Within the Sub-Saharan region, the
Page 142 and 143:
geographical shifts and yield reduc
Page 144 and 145:
142 Note 4. The climate, developmen
Page 146 and 147:
Figure 2 Percentage of undernourish
Page 148 and 149:
The Africa Environment Outlook desc
Page 150 and 151:
Figure 5 Total affected in hydro-me
Page 152 and 153:
Figure 5 Present and future freshwa
Page 154 and 155:
Turner B L, Kasperson R E, Matson P
Page 156 and 157:
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
Page 166 and 167:
Figure 2. Domains in Kenya with hig
Page 168 and 169:
Note 8. The SAKSS poverty targettin
Page 170 and 171:
168 Note 9. Simulating regional pro
Page 172 and 173:
Applications (ICASA). The results f
show all

Mapping climate vulnerability and poverty in Africa - CGSpace Home

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?