071 DEVELOPMENT OF A PRACTICAL GUIDELINE FOR ESTIMATING NON-DOMESTIC WATER USE IN SOUTH AFRICA BASED ON DATA FROM THE NATIONAL WATER CONSUMPTION ARCHIVE BJ Kriegler* and HE Jacobs** *Africon Engineering International (Pty) Ltd, Africon Centre, Century Falls, Century Boulevard, Century City, 7441; Tel: 021 526 9477; Fax: 021 526 9500; email:
[email protected] **University of Stellenbosch
ABSTRACT Methods for estimating non-domestic water demand are often based on out-dated methods, merely because these older guidelines are practical and easy to use. Most of these existing guidelines group the non-domestic user types into generalized categories, not providing a diversified data range. Experience has shown that these guidelines are also conservative and do not encourage efficient and effective system designs. Recent advances by the WRC in this regard have provided substantial improvements for empirically estimating non-domestic demand, based on analysis of millions of data points. This study provides increased resolution by classifying the use according to the following specific types of categories, like business-commercial, education, hospitals and industries (dry and wet industries). The result is a practical guideline in the form of a table that is intended for use by Consultants in South Africa. In essence it represents a regrouping of the WRC-results in a practical user-friendly format. The quantity of data points available provides statistically adequate design guideline to be developed, which also include probabilistic design procedures. 1.PURPOSE OF STUDY South Africa is a water scarce country; therefore the management of our available water resources is of utmost importance. To ensure effective management of this scarce commodity, proper planning is required. The foundation of effective planning is accurate information. The purpose of this study is produce first order estimates for non-domestic water consumption as recorded in the National Water Consumption Archive (NWCA) (1). These design parameters will be based on the stand size of a property, whereas most non-domestic design parameters are based on other measurable units, which are not always readily available during high level planning exercises, e.g. the compilation of a Spatial Development Framework for a municipal area. 2.BACKGROUND Numerous design guidelines for estimating non-domestic water demand in South Africa are available; the most wellknown one is the “Blue Book” (2), the latest version of which is called the “Red Book” (3). These guidelines are probably the most widely used in South Africa. The guidelines for non-domestic use have remained unchanged since the first publication in 1983, as summarised in Table 1. These references do provide guidelines for water demand, expresse d in terms of area or other specific units. The Red Book does not provide design guidelines for industrial land uses and for these other guidelines should be used. In the wine industry, for example, the specific water intake (SWI) is related either to mass of grapes processed (winemaking) or volume of absolute alcohol produced (distilling). The average demand values are 1.8 m 3/t and 0.35 m 3/hℓAA (hectolitre Absolute Alcohol produced) for wine-making and distilling respectively (4). In the red meat industry the SWI are related to water-related cattle unit (wrcu). For A-grade abattoirs the SWI is 1.1 m 3/wrcu and 1.75 m3/wrcu for other grades of abattoirs (5). During high level planning detail information such as this is not readily available. A water demand guideline based on area would be more applicable and easily applied. In a study on the water use in Gauteng day schools (6), a recommended average water demand of 10 l/capita/day is recommended. Table 1 : Non-domestic water use guidelines - adopted from Red Book Land use
Unit
Offices and shops Government and municipal Clinic Church Day School
100m 2 of gross floor area (a) 100m 2 of gross floor area (a) 100m 2 of gross floor area (a) Erf Hectare of erf area
(2)
Annual Average Water Demand (l/day) 400 400 500 2000 ≤2 ha : 15 kℓ(b)(c) >2 ha≤10ha : 12.5 kℓ(b)(c)
>10 ha : 10 kℓ(b)(c) Non-Domestic Users Schools : Day Boarding Hospitals Clinics Bus Stations Community Halls/Restaurants
Water Demand 15-20 90-140 litres/pupil/day 220-300 litres/bed/day 5 – out patients 40-60 – in-patients litre/bed/day 15 – for those persons outside the community litres/user/day 65-90 litres/seat/day
Notes (a) Gross Floor Area obtained using applicable floor space ratio from the town planning scheme (b) Demand for developed parks to be considered as drawn over six hours on any particular day in order to obtain the peak demand (c) Where the designer anticipates the development of parks and sport grounds to be of a high standard, e.g. 25mm of water applied per week, the annual average water demand should be taken as follows: ≤2ha : 50kℓ(b); >2ha ≤10ha : 40kℓ(b); >10ha : 30kℓ(b) In some cases (e.g. schools and clinics) the guidelines currently in use (3) make provision for both area based demand estimates and estimates based on other parameters. This is evident from the information in Table 1. These demand values are easy to use in the planning stages to determine water demand. One of the most recent studies conducted to determine non-domestic water demand guidelines was based on data in the NWCA (7), but the study only used the NWCA land use categories (refer to Section 3 of paper) and compared it to the Red Book (3) design guidelines. The purpose of this study is to expand the land use categories recorded in the NWCA to be used as a practical design tool. The proposed outcome would be in a format similar to Table 1. 3.METHODOLOGY This paper focuses on using data from the NWCA to produce water demand guidelines for non-domestic water demand based on area (stand size). The NWCA is a database compiled by the Water Research Commission from treasury data of several South African municipalities (1). There are a number of different land uses and zoning categories used in the NWCA, shown in Table 2, but these do not necessarily correspond to common categories used by town planners and found in previous guidelines for non-domestic water demand. These categories are indicated by the abbreviated codes in the NWCA, shown in Table 2. Table 2 : Non-Domestic Land Use and Zoning Categories and Codes Land Use / Zoning Category Business/Commercial Education Government/Institutional Industrial Recreational Agricultural Unknown
NWCA Code BUS_COMM EDU GOVT_INST or GOVT/LOCAL IND RECR AGRIC UNKNOWN
The data is imported and manipulated by a software programme, Swift, developed by consulting firm GLS. Within Swift, queries can be run in order to produce reports that allow the mass of data to be interpreted. One of the reports produced within Swift is the large users report. This report includes all consumers whose annual average daily demand (AADD) is above a specified value, typically 20 kl/day. In some smaller towns, this value is reduced (say to 3 kl/day) due to the relatively low water demand in some small towns. From experience, the majority of users included in the large user report are non-domestic. For the purpose of this paper, the large user reports of 15 Western Cape municipalities were used as database, including those form the City of Cape Town. The total number of data points within this database was 2644 users (Table 3).
Table 3 : Large User Reports Statistics Number of Entries in Large User Report Municipality
Beaufort West**** Bergriver** Breederiver** Cape Agulhas*** Cederberg* City of Cape Town**** Drakenstein** George**** Hessequa** Matzikama** Mosselbay**** Stellenbosch** Swartland** Swellendam** Theewaterskloof**** Total Number of Users Large Users AADD boundary * ≥ 3 kℓ/day ** ≥ 10
BUS_ COMM
IND
EDU
OTHER
TOTAL
0 4 1 4 4 316 14 19 11 11 21 0 2 1 0 408
7 5 6 0 8 317 13 14 0 0 0 0 1 1 0 372
0 2 0 0 0 109 0 10 1 0 1 0 0 0 1 124
3 28 8 7 27 1203 136 29 64 30 32 89 21 30 33 1740
10 39 15 11 39 1945 163 72 76 41 54 89 24 32 34 2644
kℓ/day
*** ≥ 15 kℓ/day **** ≥ 20 kℓ/day
As these large user reports do not include the plot/stand size, this was sourced directly from the NWCA, or from Global Information Systems (GIS) Western Cape Erven Database. Of the 2644 data points, only 2189 stand sizes could be sourced. This is due to the NWCA entry for the specific plot not containing the Stand size or the property’s Surveyor General Code (SG). The SG code is an alpha-numerical code unique to each erf within the GIS-database. The Large User Report also contains the Consumer, Owner, Land Use and Zoning data for the stand. These entries were used to determine the specific land use per stand as far as possible. All stands with land use category other than the three categories (Bus_Comm, Ind and Edu) were grouped together under “Other” in Table 3. These land use categories included mostly domestic (Res, Cluster and Flats) land use categories and some non-domestic categories (Agric and Govt_Inst). These non-domestic categories have not been included in the analysis for this study. As part of this study it was necessary to break down each NWCA land use category into land use sub-categories. The reason for this is that land uses, although grouped into one common land use category, could have very different water demand. To break down the land use into homogenous sub-categories, the owner or consumer field of each stand has to be scrutinised. This is only possible by visual inspection of each consumer record in the database! From studying the dataset and previous guideline categories, the following eight land use sub-categories were identified for further investigation. These are indicated in Table 4, with the NWCA land use category, the sub-category and the number of data points for each sub-category. Table 4 : NWCA Land use Categories and Sub-Categories NWCA Category
Land use Category (This study)
Commercial
Businesses Hotels Schools Churches Hospitals Abattoirs Manufacturing Wine Cellars
Education Government/Institutional Industrial
Number of data points in each category 257 32 123 10 45 12 140 16
More data points are required for each chosen land use category before any conclusion can be made as to whether the category posse sses a clear trend between water demand and stand size, or any other parameter. Research in this regard is currently being extended by the authors.
4.RESULTS 4.1 Final Land use selection criteria The land use categories indicated in Table 5 are just some of the non-domestic land use categories that could be investigated as part of ensuing research, but for this paper the focus was only on those shown in Table 4. Table 5 : Conceptual Sub-Category Land uses Land use Category (This study) Business
Conceptual Sub-category land uses Offices Shops/Supermarkets Nurseries Fuel Stations Primary Schools Secondary Schools Food Processing Packaging Building Materials
Schools Manufacturing
4.2 Overview In the following figures (Figures 1-9), the results of the analysis of the stands from the Large User Reports are included. As can be seen from all the Figures, the data indicates a wide spread of water demand to stand size and no clear trend is obvious. t Figure 2Business : Business Data 900 800
AADD (kl/day)
'
700 600 500 400 300 200 100 0 0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
2
Stand Size ( x1000 m )
Figure 1Abattoirs : Abattoir Data 300
'
250
AADD (kl/day)
200
150
100
50
0 0
500000
1000000
1500000
Stand Size ( m2)
2000000
2500000
Hotels
Figure 3Hospitals : Hospitals Data
200
AADDAADD (kl/day) (kl/day) '
'
180 1600 160 1400 140 1200 120 1000 100 80 800 60 600 40 400 20 0 200 0
2000000
400000 0
600 0000
80 00000
0 0
2 000000
4 000000
600 0000
1000 0000
St and Size (m2 )
8000 000
1 0000000
120 00000
1 4000000
16000000
180000 00
12 000000
1400 0000
1600 0000
180000 00
Stand Size ( m2)
Figure 4 Churches : Churches Data 100 90
AADD (kl/day)
'
80 70 60 50 40 30 20 10 0 0
500000
1000000
1500000
2000000
2500000
3000000
St and Size (m2 )
Manufacturing
Figure 5 : Manufacturing Data 2500
AADD (kl/day)
'
2000
1500
1000
500
0 0
5 000000
1000000 0
15000000
2 0000000
25 000000
300 00000
35000 000
400000 00
Stand Size ( m2)
Figure 6 : Schools Data
Wine Cellars 600
AADD (kl/day)
'
500 400 300 200 100 0 0
10000000
20000000
30000000
40000000
50000000
60000000
2
St and Size (m )
Schools 300
AADD (kl/day)
'
250 200 150 100 50 0 0
5000000
10000000
15000000
20000000
25000000
2
St and Size (m )
4.3 Abattoirs It is clear from the abattoir data shows a wide spread of stand size relative to water demand (Figure 2). One of the reasons for this wide spread is that abattoirs also contain areas for live cattle storage (grazing fields) which could have an impact on the water demand. However, this is not always the case since some use off-site storage for livestock. Another reason for the scatter of data is that there are different gradings of abattoirs (5). These gradings are issued to the abattoirs by the Department of Health and determine the amount of meat the specific abattoir is allowed to process per day. This grading is considered to have a substantial impact on the water use of each abattoir property. Further studies would involve the determination of each abattoir’s grading as well as which abattoirs have on-site live cattle storage. This additional information could be used in conjunction with stand area to update existing guidelines for water demand of abattoirs. 4.4 Business The initial data analysed under the business land use, included three outliers. They were: Owner : University of Stellenbosch, AADD : 33 kl/day, stand size : 18 235 000 m2 Owner : University of Stellenbosch, AADD : 149 kl/day, stand size : 18 235 000 m 2 Owner : V&A Waterfront, AADD : 2257 kl/day, stand size : 1 018 606 m2. These three stands were removed from the analysis, as they were seen as “special” consumers and did not relate to the other data points. It is very probable that such “special” consumers are found in large user reports, for example refineries (Caltex refinery in Milnerton, AADD = 5177 kl/day), large scale industrial complexes (Saldanha Steel, AADD = 5561 kl/day) or power stations (Koeberg nuclear power station, AADD = 1239 kl/day). These types of consumers should be considered separately and their data should not be included in general guidelines. The water demand for business indicates no clear trend between water demand and stand size (Figure 1). Further studies would involve the breaking down of the data set into the proposed sub-categories as discussed indicated in Table 5. 4.5 Churches The water demand to stand size for churches identified within the current data set does not produce a clear possible trend (Figure 4). What is of interest is that none of the stands’ water demand is below 2kl/day, which is the “Red Book” (3)
guideline for Church plots. The water demand of churches could largely be influenced by the area of their gardens and whether the rectory is also supplied from the same supply. 4.6 Hospitals The data set for hospitals do not indicate a clear trend for hospitals (Figure 3). What is of interest, is that for most of the hospital stands, the water demand per plot does not exceed 200 kl/day (41 of 45 stands) or 100 kl/day (31 of 45 stands). As some of these hospitals are multi-storey buildings, this would also have an effect on the demand-stand size trend. Further studies should also include the update of the demand guideline based number of beds or other factors. 4.7 Hotels The data set for hotels also do not indicate a definite trend between water demand and stand size (Error! Reference source not found.). There are also a large number of data points with high water demands on a small stand size, this is possibly due to large number of hotels that are multi-storey buildings. Future studies could determine whether there exist a suitable trend between water demand and number of rooms or beds. 4.8 Manufacturing The manufacturing data set also do not present a definite trend in the water demand to stand size (Figure 5). What is interesting is, with the exception of number of outliers that the water demand threshold for manufacturing sites, irrespective of stand size seems to be 500 kl/day. Further studies should focus on expanding the data set into the different sub-categories within the manufacturing land use to determine if an each sub-category produces a suitable trend to use as a design guideline. 4.9 Schools The schools data set do not indicate a clear trend to be used as design guideline, with a number of small stands (
