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!