ghana climate change vulnerability and adaptation assessment

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in January through June rainfall and increases in July through December rainfall. The proportion of totalannual rainfall that falls in “Heavy” events tends towards increases in the ensemble projections. Seasonally,this varies between tendencies to decrease in January through April to increases in July through December,but the range of changes projected by the ensemble includes both increases and decreases in all seasons.Projected changes in 1‐ and 5‐day rainfall maxima tend towards increases, but projections range betweenincreases and decreases in all seasons.At the eco-climatic level (Minia, 2008), rainfall is projected to decrease at 2020, 2050, and 2080 in all regions.Percentage decrease in rainfall at each projection period tend to increase from north to south from -1.1, -6.7,-12.8 percent in the Sudan Savanna Zone to -3.1, -12.3, -20.5 percent in the Coastal Savanna Zone for periods2020, 2050, and 2080, respectively. Absolute change in rainfall is projected to be highest in the DeciduousForest and Rain Forest Zones (243 and 423 mm decrease by 2080, respectively). Upper boundary projectionsindicate an annual decrease of 32.6 percent annual precipitation in the Sudan Savanna Zone and a 26.8percent decrease in the remaining eco-climatic zones. Inspection of the projection tables (Minia 2008 andpers. comm., Appendix 1.2; see Appendices V-X) reveals the same monthly percentage changes inprecipitation were applied to all but the Sudan Savanna Zone. Similar to the caveats about temperatureprojections for eco-climatic zones (see previous), scaling and suitability of the rainfall projections at the ecoclimaticlevel is questionable.Our AOGCM modeling projections of rainfall among seven representative meteorological stations gavemixed and inconclusive results (Tables 3.1-3.7). AOGCM predictions of precipitation lack any sense ofconsistency, predicting both decreases and increases in rainfall in at each station. The results are summarizedfor each station as follows:Accra (Coastal Savanna Zone): Forecasted changes in precipitation ranged from 52 percentdecreases to 44 percent increases in wet season rainfall by 2080. The overall ensemble predictionacross emission scenarios gives a slight increase in wet season rainfall of 2.65 ± 13.96 percent by2050 and 3.88 ± 19.41 percent by 2080. The variability among the models' precipitation changes isnot very different from the inter-annual variability currently experienced in the region.Kumasi (Deciduous Forest Zone): Forecasted changes in precipitation range from 48 percentdecreases to 45 percent increases in wet season rainfall by 2080. The overall ensemble predictionacross emission scenarios gives a slight increase in wet season rainfall of 1.73 ± 13.42 percent by2050 and 3.19 ± 18.98 percent by 2080. The variability among the models' precipitation changes isnot very different from the inter-annual variability currently experienced in the region. The A2scenario, which generally shows the largest greenhouse gas impact, predicts the weakest increase inwet season rainfall, 1.13 percent.Tarkwa (Rain Forest Zone): Forecasted changes in precipitation range from 45 percent decreasesto 31 percent increases in wet season rainfall. The overall ensemble prediction across emissionscenarios gives a slight increase in wet season rainfall of 2.58 ± 12.83 percent by 2050 and 2.98 ±16.45 percent by 2080. The variability among the models' precipitation changes is not very differentfrom the inter-annual variability currently experienced in the region. Techiman (Forest-Savanna Transition Zone): Forecasted changes in precipitation range from 46percent decreases to 36 percent increases in wet season rainfall. The overall ensemble predictionacross emission scenarios gives a slight decrease in wet season rainfall of -1.06 ± 14.25 percent by2050 and -0.83 ± 19.71 percent by 2080. The variability among the models' precipitation changes isnot very different from the inter-annual variability currently experienced in the region. The A2scenario, which generally shows the largest greenhouse gas impact, predicts the largest decrease inwet season rainfall, -2.94 percent.Tamale (Guinea Savanna Zone): Forecasted changes in precipitation range from 36 percentdecreases to 32 percent increases in wet season rainfall. The overall ensemble prediction across34 GHANA CLIMATE CHANGE VULNERABILITY AND ADAPTATION ASSESSMENT
emission scenarios gives a decrease in wet season rainfall of -1.46 ± 10.35 percent by 2050 and -2.19± 15.25 percent by 2080. The variability among the models' precipitation changes is not verydifferent from the inter-annual variability currently experienced in the region. The Northern Regionwhere Tamale is located is the southern-most region in Ghana to show a consistent trend towarddecreased rainfall. Walembelle (northern Guinea Savanna Zone): Forecasted changes in precipitation range from 25percent decreases to 24 percent increases in wet season rainfall. The overall ensemble predictionacross emission scenarios gives a slight decrease in wet season rainfall of -0.42 ± 8.93 percent by2050 and 1.06 ± 11.79 percent by 2080. The variability among the models' precipitation changes isnot very different from the inter-annual variability currently experienced in the region.Bawku (Sudan Savanna Zone): Forecasted changes in precipitation range from 28 percentdecreases to 30 percent increases in wet season rainfall. The overall ensemble prediction acrossemission scenarios gives a slight decrease in wet season rainfall of -0.10 ± 9.63 percent by 2050 and -0.69 ± 12.94 percent by 2080. The variability among the models' precipitation changes is not verydifferent from the inter-annual variability currently experienced in the region.SEA-LEVEL PROJECTIONSThe coastal regions of Ghana may be vulnerable to sea‐level rise, and two different climate modeling effortsare in reasonably close agreement. By the 2090s, sea level is projected by the UNDP-NSCP climate models torise by the following levels relative to the 1980‐1999 sea level: 0.13-0.43 m under SRES B1; 0.16-0.53 munder SRES A1B; and 0.18-0.56 m under SRES A2 (McSweeney et al., no date a). With respect to the 1990mean sea-level, the EPA-NCAP climate modeling (Minia 2008) also projected sea levels to increase. Thoseestimates range from 0.36 m (lower and upper boundaries of 0.16-0.60 cm) in 2010 to 0.35 m (0.16-0.58 m) in2080 (under SRES A1F1).CLIMATE VARIABILITYThe climate of West Africa is subject to considerable variability across a range of space and time scales (Lebelet al., 2000). This variability is linked to variations in the movement and intensity of the ITCZ as well asvariations in the timing and intensity of the West African Monsoon. The most documented cause of thesevariations on an inter-annual timescale is the El Niño Southern Oscillation (ENSO). The West AfricanMonsoon is influenced either during the developing phase of ENSO or during the decay of some long-lastingLa Niña events (Joly and Voldoire, 2009). In general, El Niño (positive sea surface temperature anomalies inthe equatorial Pacific Ocean) is connected to below normal rainfall in West Africa (Janicot et al., 1998). Othersources of variability at decadal, annual and intra-seasonal time scales include land-atmosphere feedbacks(Taylor et al., 1997; Grodsky and Carton, 2001; Douville, 2002) and large-scale circulation features (Matthews,2004; Mournier et al., 2008; Lavender and Matthews, 2009). At intra-seasonal time scales, the West AfricanMonsoon system also exhibits variability, specifically at frequencies of 15 days and 25-60 days (Janicot andSultan, 2001). The longer of these periods is associated with the Madden-Julian Oscillation (a major source ofintra-seasonal variability in the tropical atmosphere with a period of 30-90 days) and variability in the Asiansummer monsoon (Matthews, 2004; Lavender and Matthews, 2009; Janicot et al., 2009).GHANA CLIMATE CHANGE VULNERABILITY AND ADAPTATION ASSESSMENT 35
Page 1 and 2: GHANA CLIMATE CHANGEVULNERABILITY A
Page 3: GHANACLIMATE CHANGEVULNERABILITY AN
Page 7 and 8: ACRONYMSCAADPCBOCCCDCSCEACEPFCFMCIC
Page 9: NGONCRCNREGNRMNTFPPAPAMSCPPGRCRAMSA
Page 13 and 14: EXECUTIVE SUMMARYCountries in Afric
Page 15 and 16: precipitation changes is not very d
Page 17 and 18: AGRICULTURE AND LIVELIHOODSAgricult
Page 19 and 20: would include concentrating access
Page 21 and 22: of transparency pervade the current
Page 23 and 24: alternate energy sources (i.e., fos
Page 25 and 26: affecting carbon sequestration. Adv
Page 27 and 28: Information and analysis needs for
Page 29 and 30: 1. INTRODUCTIONThe West African cou
Page 31: ABFigure 2.1 Two approaches to vuln
Page 35 and 36: Mean Annual Temperature (C)2928.528
Page 37 and 38: The UNDP-NSCP country-level climate
Page 39 and 40: For most eco-climatic zones, five-y
Page 41 and 42: increases generally were projected
Page 43 and 44: Table 3.2 Potential change in tempe
Page 45: parameter (temperature and precipit
Page 49 and 50: of finance and economic planning, f
Page 51 and 52: indigenous people and more recently
Page 53 and 54: Phase 1 REDD ReadinessConsultations
Page 55 and 56: SC. A New National Plantation Devel
Page 57 and 58: to be developed that provide rigoro
Page 59 and 60: LandownerTable 4.1 Land Ownership i
Page 61 and 62: ProblemTable 4.2 Problems Associate
Page 63 and 64: TENURE CONSIDERATIONS IN LIGHT OF C
Page 65 and 66: the south of the Ashanti Uplands re
Page 67 and 68: Figure 5.1 Ecological Zones of Ghan
Page 69 and 70: North latitude. Minia (2008) deline
Page 71 and 72: Table 5.1 Percentage of producer ho
Page 73 and 74: over a longer period. With the risk
Page 75 and 76: LAND SUITABILITYThe CSIR-Soil Resea
Page 77 and 78: Table 5.3 Crop Suitability by Soil
Page 79 and 80: MAJOR CROPSMAIZEMaize is the most i
Page 81 and 82: Source: Chamberlin, 2007, Figure 13
Page 83 and 84: Total area(ha)MangroveswampTable 5.
Page 85 and 86: Source: Chamberlin, 2007Figure 5.6
Page 87 and 88: Farmers who depend on annual rains
Page 89 and 90: Adjusting timing ofirrigationPricin
Page 91 and 92: Transportation networkChanging Crop
Page 93 and 94: Lower world food pricesAttitudes to
Page 95 and 96: POPULATION AND ECONOMYGhana contain
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Ghana is comprised of crop and live
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URBAN VERSES RURAL LIVELIHOODS 3Liv
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income; non-farm related enterprise
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6. VULNERABILITY TOCLIMATE CHANGETh
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DESERTIFICATION“Desertification
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In the National Action Plan to Comb
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significantly recovered by the late
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Source: Reich etal., 2001Figure 6.2
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Source: US Geological Survey, (http
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Two other proximate causes of defor
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extreme, fire is essential in fire-
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gill nets constructed from traditio
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upwelling strength) involved in reg
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completely dominate trawl catches b
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have increased substantially due to
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CategoryFishing effort andtechnolog
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(Binet, 1995). Even if the declinin
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METHODSAs described above, to asses
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Indicator DescriptionDistance fromd
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Indicator DescriptionUnimproveddrin
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lowest vulnerability of any distric
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Table 7.2 Social Vulnerability Inde
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Table 7.5 Incidence of poverty (per
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Table 7.6 Ghana District Names, Ref
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Figure 7.4 Percentage of district p
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Figure 7.6 Percentage of female-hea
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Figure 7.8 Percentage of the Distri
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Figure 7.10 Percentage of District
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Figure 7.12 Percentage of District
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Figure 7.14 Percentage of total Dis
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people residing in thesee regions a
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CASE STUDY: CLIMATE CHANGE ADAPTATI
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from friends and family to get by d
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CASE STUDY: CLIMATE CHANGE ADAPTATI
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At Mole National Park, managers exp
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ADAPTING TO CLIMATE CHANGE IN THE N
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(interview). An opportunity exists
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Upper West Region, 69.8 percent of
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Brong-Ahafo Region that entails ref
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Ghana Limited, 2009). Given the con
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positive impacts, and has upset com
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Basin (total area 416,382km 2 ) lie
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effect. In the drier scenario, the
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Communities are, rightly or wrongly
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carbon sequestration and maintenanc
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Information and analysis needs for
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Table 11.1 Options for intervention
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Barriers toAdaptation andMitigation
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Page 195 and 196:
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Amanor, K.S. 2001. Share contracts
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Braimoh, A. and P. Vlek (2006). "So
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Energy Commission. 2005. Strategic
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Gyau-Boakye P., and Tumbulto J.W. 2
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Koranteng, K.A. 1995. The Ghanaian
Page 207 and 208:
MSE (Ministry of Science and Agricu
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Rubin, J.A.; Gordon, C.; Amatekpor,
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Wagner, M.R. and Cobbinah, J.R., 19
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Date Organization Interviewee Posit
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APPENDIX 2. TEAM MEMBERSName Role B
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DP - contact person Activity Object
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OrganizationNameInstitutional objec
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APPENDIX 5. SCENARIOS OF TEMPERATUR
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e. RAIN FOREST ZONEBaseline Mean Te
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Aug 191.5 16 12.0 -0.1 -0.5 -1.0 19
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Feb 25.6 3 9.4 -9.1 -29.7 -58.9 23.
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c. TRANSITIONAL ZONEBaseline Mean T
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APPENDIX 8. SCENARIOS OF CHANGES IN
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d. DECIDUOUS FOREST ZONEBaseline Me
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APPENDIX 9. SCENARIOS OF MEAN SEA S
Page 258:
U.S. Agency for International Devel
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