Urban Poverty & Climate Change in Dar es Salaam, Tanzania:

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Estimates of Population and Asset Exposure 126 Annex 4a The sea-level rise scenarios considered are coupled with the A1 socio-economic (with rapid urbanisation) scenarios for estimating the future projected population and asset exposure. This follows the methodology used by Hanson et al. (2009). The simulations to estimate exposed number of people and associated economic assets that are located below the 1 in 100 year return period extreme water levels for each scenarios are performed based on a population distribution data available from the International Livestock Research Institute (ILRI) GIS-compatible online spatial data layers [http://www.ilri.org/GIS] (see Table 1 and Figure 5) and a Shuttle Radar Topography Mission (SRTM) elevation data of approximately 90m resolution obtained from the U.S. Geological Survey (USGS) server http://www.usgs.gov]. The population by elevation on a horizontal map of geographical cells is then estimated by mapping the population distribution over the wards of each district onto the SRTM, which allows the total population distribution against elevation to be estimated. In quantifying the infrastructure assets exposed to the 1 in 100 year extreme water levels, a method commonly used in the insurance industry and applied by Nicholls et al. (2008) is adopted which estimates the value of assets using the population exposed (Equation 2). Ea = Ep x GDPpercapita (PPP) x 5 ………………..……………………...............………………………. (2) Where, Ea is exposed asset (monetary value), Ep is exposed population, and GDPpercapita (PPP) is the national per capita Gross Domestic Product (GDP) Purchasing Power Parity (PPP). Source: Excerpted from Kebede and Nicholls (2010)
A simplified flowchart of the methodology 127 Annex 4b Source: Kebede and Nicholls (2010)
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Final Report Urban Poverty & Climat
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5.2 Relevant policies..............
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List of Boxes Box 1 Flood Early War
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NLFEP NSSCP NTD PMI PMORALG PRB PRE
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over coming decades in Dar es Salaa
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1 Introduction 1.1 Background to th
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water is unsafe for household use.
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Figure 1(a) shows Dar es Salaam’s
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For each location, the area leader
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displays the major zones. Natural v
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Figure 5: Trend of mean minimum tem
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Figure 9: Number of rain days per y
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Table 2: Information on significant
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Figure 12: Projected mean annual te
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Figure 15: Projections of monthly p
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Table 3 Population and assets expos
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climatic events (e.g., the droughts
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Bank, 2002). IFPRI (2006) also show
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quality. Olvera et al. (2002) state
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Several agencies are engaged in hou
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water table and poor drainage syste
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Figure 20: Illnesses and diseases s
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158 cases of cholera in 2009 (6 bel
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providing a suitable breeding groun
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asked about strategies used to comb
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From Table 5, it may be seen that a
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contaminated rivers that not only c
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The link between climate and income
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4 The Urban Poverty - Climate Chang
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(vi) Kunduchi and Bahari Beach Thes
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Figure 25: Flood hazard zone map ov
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Photo 3 Waste-dumping along storm-w
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disease incidence. Prolonged drough
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Figure 28 (b) Modeled flood extent
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4.3 Poverty, pollution and GHG emit
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households) and gas (3 percent). Ta
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Box 3: Roles of the City Council an
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Page 87 and 88: change, so that they could formulat
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Page 91 and 92: Kyessi, S and Kyessi, A. (2007). Re
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Page 95 and 96: Table 2: Construction Materials Wal
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Page 99 and 100: 31. How does your water taste: 1.Go
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Page 107 and 108: water drainage channel 2.1 Assessme
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Page 111 and 112: No 1 Name of City Program Instituti
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Page 115 and 116: 3 Environmental Monitoring 4 Beach
Page 117 and 118: No Type of poverty alleviation supp
Page 119 and 120: KEY INFORMANT INTERVIEW CHECKLIST 1
Page 121 and 122: Dar Es Salaam Administrative Struct
Page 123 and 124: CITY COUNCIL Officials Interviewed
Page 125: Ubungo Kibangu Paul Thomas Tabata-M
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