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Stats SA’s IsiBalo Conference Break-Away 3: Service Delivery in a Municipal Context
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Transcript of Stats SA’s IsiBalo Conference Break-Away 3: Service Delivery in a Municipal Context
Calculation of Preliminary Water Demand Projections for
the Mhlabatshane Bulk Water Supply Scheme
Stats SA’s IsiBalo ConferenceBreak-Away 3: Service Delivery in a Municipal Context
Moses Mabhida StadiumDurban
13 September 2013
Alka RamnathUmgeni Water
Purpose
• Show how the preliminary water demand projections for the
Mhlabatshane Bulk Water Supply Scheme were calculated.
Methodology: Delineation of the Supply Zones
Demographics: - Census 2001
Not to scale
Zone Name
Number of People
per Zone (Area
Weighted)
2001
Number of Households per
Zone (Area Weighted)
2001
Approximate HH Size
2001
Bhekani 1052 228 4.8
Frankland 1426 272 5.4
Hlubi 8674 1769 4.9
KwaCele 1 11257 2358 4.7
KwaCele K 11266 2150 5.2
KwaMadlala 10125 1964 5.0
Mabheleni
(West) 10239 1933 5.1
Nhlangwini
(West) 14783 3139 4.7
Qwabe P 5502 1071 5.2
Shabeni 6094 1029 5.1
Table 1 Number of people and households as per Census 2001.
Demographics: - DWA’s WSNIS (2011)
Not to scale
Zone Name
Number of People per
Zone (Area Weighted)
2011
Number of Households per Zone
(Area Weighted)
2011
Bhekani 967 203
Frankland 2687 556
Hlubi 9182 1900
KwaCele 1 11767 2440
KwaCele K 20062 4272
KwaMadlala 15569 3840
Mabheleni (West) 16212 3360
Nhlangwini (West) 13414 2836
Qwabe P 6432 1336
Shabeni 7565 1851
Table 2 Number of people and households as per WSNIS 2011.
Eskom Building Count 2008
Not to scale
Zone NameNumber of Buildings
2008
Bhekani 150
Frankland 373
Hlubi 1363
KwaCele 1 1964
KwaCele K 1574
KwaMadlala 1891
Mabheleni (West) 1394
Nhlangwini (West) 2915
Qwabe P 643
Shabeni 1266
Table 3 Number of buildings per supply zone (Eskom 2008).
Demographics: - Ugu’s Infrastructure Audit 2011
Not to scale
Zone Name
Number of Households (Area
Weighted)
Bhekani 139.2290
Frankland 353.0500
Hlubi 1113.7770
KwaCele 1 1870.2230
KwaCele K 1405.5300
KwaMadlala 1775.3440
Mabheleni (West) 1003.0550
Nhlangwini (West) 2769.2360
Qwabe P 630.6660
Shabeni 1130.0820
Table 4 Number of households per Supply
Zone (Ugu Infrastructure Audit 2011).
Comparison of the Demographic Datasets
Zone
SBA Report
(after Census
2001)
Census 2001
Number of People per
Zone (Area Weighted)
WSNIS 2011
Number of People per
Zone (Area Weighted)
Bhekani 1305 1052 967
Frankland 4200 1426 2687
Hlubi 4880 8674 9182
KwaCele 1 11934 11257 11767
KwaCele K 9012 11266 20062
KwaMadlala 10500 10125 15569
Mabhaleni
(West) 10432 10239 16212
Nhlangwini
(West) 25400 14783 13414
Qwabe P 6360 5502 6432
Shabeni 14760 6094 7565
Table 5 Comparison of the number of people per Supply Zone.
Zone
SBA
Report
(after
Census
2001)
Census 2001
Number of
Households
per Zone (Area
Weighted)
WSNIS 2011
Number of
Households
per Zone
(Area
Weighted)
Eskom
Building Count
2008
Number of
Buildings
Ugu Infrastructure Audit
2011 (after Ugu's SDF
2011)
Number of Households
(Area Weighted)
Bhekani 261 228 203 150 139
Frankland 600 272 556 373 353
Hlubi 976 1769 1900 1363 1114
KwaCele 1 1989 2358 2440 1964 1870
KwaCele K 2253 2150 4272 1574 1406
KwaMadlala 2100 1964 3840 1891 1775
Mabhaleni
(West) 1304 1933 3360 1394 1003
Nhlangwini
(West) 3175 3139 2836 2915 2769
Qwabe P 795 1071 1336 643 631
Shabeni 1845 1029 1851 1266 1130
Table 6 Comparison of the number of households per Supply Zone.
Demographic Projections
• Projection horizon = 30 years.
• Rates
Scenario 2010 2015 2020 2025 2030 2035 2040
DBSA Average Annual
Growth Factor: KZN High
Aids Impact
0.0095 0.0058 0.0008 0.0008 0.0008 0.0008 0.0008
DBSA Average Annual
Growth Factor: KZN Low
Aids Impact
0.0178 0.0151 0.0134 0.0134 0.0134 0.0134 0.0134
Growth Rate = 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
Table 7 Rates used for the population scenarios.
• Scenarios
Zone 2008 2010 2015 2020 2025 2030 2035 2040
Bhekani 750.0 1,012.4 1,042.2 1,046.4 1,050.6 1,054.8 1,059.0 1,063.2
Frankland 1,865.0 2,517.5 2,591.6 2,601.9 2,612.4 2,622.8 2,633.4 2,643.9
Hlubi 6,815.0 9,199.3 9,470.0 9,507.9 9,546.0 9,584.3 9,622.7 9,661.3
KwaCele 1 9,820.0 13,255.6 13,645.7 13,700.3 13,755.3 13,810.4 13,865.7 13,921.3
KwaCele K 7,870.0 10,623.4 10,936.0 10,979.8 11,023.8 11,068.0 11,112.4 11,156.9
KwaMadlala 9,455.0 12,762.9 13,138.5 13,191.1 13,244.0 13,297.1 13,350.4 13,403.9
Mabhaleni (West) 6,970.0 9,408.5 9,685.4 9,724.2 9,763.2 9,802.3 9,841.6 9,881.0
Nhlangwini (West) 14,575.0 19,674.2 20,253.1 20,334.3 20,415.8 20,497.6 20,579.8 20,662.2
Qwabe P 3,215.0 4,339.8 4,467.5 4,485.4 4,503.4 4,521.4 4,539.5 4,557.7
Shabeni 6,330.0 8,544.6 8,796.0 8,831.3 8,866.7 8,902.2 8,937.9 8,973.7
Table 8 DBSA High AIDS Scenario (Eskom Building Count 2008
and Assuming Average Household Size of 5).
Zone 2008 2010 2015 2020 2025 2030 2035 2040
Bhekani 750.0 1,012.4 1,091.8 1,167.4 1,248.3 1,334.8 1,427.3 1,526.3
Frankland 1,865.0 2,517.5 2,714.9 2,903.0 3,104.2 3,319.3 3,549.3 3,795.3
Hlubi 6,815.0 9,199.3 9,920.7 10,608.2 11,343.3 12,129.3 12,969.8 13,868.6
KwaCele 1 9,820.0 13,255.6 14,295.2 15,285.8 16,345.0 17,477.6 18,688.7 19,983.8
KwaCele K 7,870.0 10,623.4 11,456.5 12,250.4 13,099.3 14,007.0 14,977.6 16,015.5
KwaMadlala 9,455.0 12,762.9 13,763.8 14,717.6 15,737.5 16,828.0 17,994.1 19,241.0
Mabhaleni (West) 6,970.0 9,408.5 10,146.4 10,849.5 11,601.3 12,405.2 13,264.8 14,184.0
Nhlangwini (West) 14,575.0 19,674.2 21,217.1 22,687.4 24,259.5 25,940.6 27,738.1 29,660.2
Qwabe P 3,215.0 4,339.8 4,680.1 5,004.4 5,351.2 5,722.1 6,118.6 6,542.6
Shabeni 6,330.0 8,544.6 9,214.7 9,853.2 10,536.0 11,266.1 12,046.8 12,881.6
Table 9 DBSA Low AIDS Scenario (Eskom Building Count 2008
and Assuming Average Household Size of 5).
Zone 2008 2010 2015 2020 2025 2030 2035 2040
Bhekani 750.0 1,012.4 1,067.0 1,106.9 1,149.4 1,194.8 1,243.2 1,294.7
Frankland 1,865.0 2,517.5 2,653.2 2,752.5 2,858.3 2,971.1 3,091.3 3,219.6
Hlubi 6,815.0 9,199.3 9,695.3 10,058.1 10,444.7 10,856.8 11,296.3 11,764.9
KwaCele 1 9,820.0 13,255.6 13,970.4 14,493.0 15,050.1 15,644.0 16,277.2 16,952.5
KwaCele K 7,870.0 10,623.4 11,196.2 11,615.1 12,061.6 12,537.5 13,045.0 13,586.2
KwaMadlala 9,455.0 12,762.9 13,451.1 13,954.4 14,490.7 15,062.5 15,672.2 16,322.4
Mabhaleni
(West) 6,970.0 9,408.5 9,915.9 10,286.8 10,682.2 11,103.7 11,553.2 12,032.5
Nhlangwini
(West) 14,575.0 19,674.2 20,735.1 21,510.8 22,337.6 23,219.1 24,158.9 25,161.2
Qwabe P 3,215.0 4,339.8 4,573.8 4,744.9 4,927.3 5,121.7 5,329.1 5,550.1
Shabeni 6,330.0 8,544.6 9,005.4 9,342.3 9,701.4 10,084.2 10,492.4 10,927.7
Table 10 Middle Scenario (Eskom Building Count 2008 and
Assuming Average Household Size of 5).
Zone 2008 2010 2015 2020 2025 2030 2035 2040
Bhekani 750.0 1,012.4 4,537.2 20,334.5 91,132.8 408,428.9 1,830,451.5 8,203,514.4
Frankland 1,865.0 2,517.5 11,282.6 50,565.1 226,616.9 1,015,626.6 4,551,722.7 20,399,405.8
Hlubi 6,815.0 9,199.3 41,228.3 184,772.6 828,093.5 3,711,257.6 16,632,702.5 74,542,600.9
KwaCele 1 9,820.0 13,255.6 59,407.5 266,246.1 1,193,232.3 5,347,696.2 23,966,711.4 107,411,348.6
KwaCele K 7,870.0 10,623.4 47,610.7 213,376.5 956,287.0 4,285,780.9 19,207,537.6 86,082,211.2
KwaMadlala 9,455.0 12,762.9 57,199.4 256,350.0 1,148,881.0 5,148,927.4 23,075,891.7 103,418,971.6
Mabhaleni (West) 6,970.0 9,408.5 42,166.0 188,975.1 846,927.6 3,795,666.2 17,010,995.8 76,237,993.9
Nhlangwini (West) 14,575.0 19,674.2 88,173.6 395,166.7 1,771,014.3 7,937,135.6 35,571,773.8 159,421,630.0
Qwabe P 3,215.0 4,339.8 19,449.6 87,167.1 390,656.0 1,750,798.7 7,846,535.4 35,165,731.8
Shabeni 6,330.0 8,544.6 38,294.3 171,623.0 769,160.9 3,447,140.2 15,449,010.5 69,237,661.6
Table 11 Growth Rate = 0.3 (Eskom Building Count 2008 and
Assuming Average Household Size of 5).
Water Projections
Water consumption pattern – income levels.
Average daily consumption per capita (kl/c/day) = 0.075
Water losses = 1.3
• Assumptions:
Results
2005 2010 2015 2020 2025 2030 2035 20400.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
20.0
Projected Water Consumption for the Mhlabatshane Study Area(Eskom Building Count Dataset assuming Average Household = 5 and Consumption = 0.075kl/
c/day)
High AIDS ScenarioLow AIDS ScenarioMiddle ScenarioGrowth Rate = 0.3
Year
Proj
ecte
d W
ater
Con
sum
ption
(Ml/
day)
• Household size and income distribution useful.
• Mid-Year Population Estimates.
• Evidence-based planning, users appear more comfortable with a Building
Count dataset.
• Unavailability of datasets at required scale for the Component Method
of population projections.
Lessons Learnt
Thank you!