Projection of water demand for river basins: case study in the Bloem Water service area, South Africa

Transcription

1 River Basin Management III 267 Projection of water demand for river basins: case study in the Bloem Water service area, South Africa J. C. de Klerk & E. Pretorius School of Civil Engineering and Built Environment, Central University of Technology, South Africa Abstract Climate variability and the consequent problems of continuous urbanization threaten water resource availability, particularly in less developed countries such as South Africa. This necessitates the pro-active determination of water needs to comply with the resource availability within a specified future period. Resulting from this is the need for purpose-built management plans and tools to enable water services providers to meet the water demand of the communities it serves. A comprehensive literature study regarding methods for the projection of future water needs has proven that very few of these models have been developed successfully. It also became clear that it is of the utmost importance to accommodate all possible relevant variables in such a model. The variables, to mention a few, are climatological, anthropological, demographic, socio-economic and economic factors, as well as water engineering related factors (e.g. water losses, water leakages etc.). Also very important are the effect of the HIV pandemic on population growth and consequently on water demand. The following actions were seen as important in the development of the model: selection of model structure (e.g. the choice of variables); choice of the functional format (e.g. linear or logarithmic); determining/calculation of the coefficients for the model (regression-analysis); verifying and testing of the model; application of the model; and sharing application methods with the client. The model furthermore has to comply with a variety of factors such as unexpected and extreme temperature changes with high peak demands, water tariffs, demographic movement, etc. and must be able to reflect reasons for historic water consumption tendencies and project expected water consumption using different predicted values for the relevant variables. The results from this study will put the water services provider in a pro-active position regarding prioritization of refurbishment of existing infrastructure, prioritizing needed capital work (extension of infrastructure), and more importantly, to be in a position to determine realistic tariffs and enable the organization to be managed and operated in an effective way. The study area consists of two river basins of which the water services provider (water board) is Bloem Water. Keywords: water demand, water services provider, forecasting, modelling, potable water.

2 268 River Basin Management III 1 Introduction Until relatively recently, the preferred approach to satisfying the water demand of growing communities has been to develop untapped supplies. As new watersupply sources have become less accessible, and as developing them has become more expensive and less acceptable environmentally, managing demand and enabling voluntary water reallocation has taken on increasing importance. Demand management and water markets could also be very important in coping with climate change, both because they promote efficiency and because they enable a considerable amount of flexibility in water resource management [9]. According to Tate [8], it is possible to draw a distinction between water conservation and water demand. Water conservation refers to efforts made to save water during situations of water shortage; it is normally a strategy reserved for short-term situations where water may be in short supply, usually as a result of drought conditions. Water demand, on the other hand, is a much broader concept, and Water Demand Management (WDM) is currently used throughout the world as a key strategy to manage water resources. WDM can be defined as any socially beneficial action that reduces or reschedules average or peak water withdrawals or consumption from either surface or groundwater, consistent with the protection or enhancement of water quality. The National Water Act [7] of the Republic of South Africa (RSA) is not only widely recognized as the most comprehensive water law in the world, but also underlines the concept of water demand management when it stipulates, that water is essentially a tool to transform society towards social and environmental justice and poverty eradication. The protection, use, development, conservation and management of the nation s water resources are therefore the main objectives of this Act, which considers the following amongst other factors: meeting the basic human needs of present and future generations; promoting the efficient, sustainable and beneficial use of water in the public interest; and providing for growing demand for water use. 2 Structure to the Water Demand Model 2.1 Approaches to forecasting demand The distribution of water consists of two interdependent systems, viz. the hydrological cycle on the one hand, and the manmade water supply and wastewater abstraction systems on the other hand. Noted methods for the projection of future water needs for a specific area are relatively scarce. A committee that investigated models to forecast the water demands in the Vaal River supply area [2], came to the conclusion that the majority of the models refer to water consumption and that relatively few models refer to water projections, and especially projections over medium to long periods.

3 River Basin Management III 269 As mentioned in Section 1, to be able to introduce WDM measures effectively, one would have to have knowledge of the future demand for water. According to Merrett [5] objective demand forecasting techniques, take two forms: extrapolation forecasts record past levels of aggregated consumption and their rate of growth is projected into the future; and component forecasts identify different categories of water consumption and their rate of growth into the future is estimated by using demographics and economic projections relevant to each category. The critique of forecasting is wide ranging, particularly with extrapolation since it ignores the components of consumption and assumes linear growth into the future. Merret [5] therefore pointed out that both techniques referred to above are guilty of two methodological errors. The first is the non-identification of supply losses as a component of consumption and the second is to regard consumption as a simple technical issue, exogenous to other social changes. These issues form a crucial part of demand management. Demand management has at least five strands: internal and external re-use, consumption technology, land-use planning, educational initiatives and water pricing. The fifth form of demand management, water pricing, brings us back to effective demand. The metering of water use, and setting of price per unit of quantity consumed, helps to underpin all the first four demand management forms. Water pricing is complementary to these other measures, and not a substitute for them. Current efforts of developing econometric water use models for a municipality, including a district or rural council, do not accommodate the needs of planning and evaluation of demand-side management programmes because they do not disaggregate water demands into levels of end-uses. For water planners to formulate, implement and evaluate various demand management alternatives, the observed sector demands during a defined season of use should be disaggregated into their applicable end uses [3]. Most of the water services providers in South Africa use the first mentioned technique (extrapolation forecasts), but considering the author s, Merret [5], negative comment and the fact that this method is not based on sound scientific principles it was ignored for the purpose of this research project. In the Water Services Act [11] of South Africa, a municipality, including a district or rural council is defined in the Local Government Transition Act [4], as a water services authority. The second demand forecasting technique mentioned in paragraph 3 of section 2 (component forecasts), is applicable to and is currently used by these water services authorities. Van Zyl et al. [10], refer to this method/technique in more detail. According to them, end-use water demand modelling generates water demand projections by modelling various end-uses, for example showers, toilets and washing machines. Figure 1 indicates the different components (elasticities) considered for end-use water demand modelling. End-use models estimates water demand changes due to various scenarios, such as price increases, housing densification and conservation programmes. The model includes elasticities of water demand with respect to variations in water

4 270 River Basin Management III price, household income, stand size and water pressure. Their study (Van Zyl et al. [10]) furthermore highlights many of the difficulties and limitations, but also the potential applications of end-use modelling as a water demand predictor. In their study, a special effort was made to explain the meaning and application of elasticity in end-use modelling. Various data sources were used to determine elasticities for the variables, and to identify minimum and maximum elasticity values. As Water Services Authorities, and not water services providers mainly use this technique, and the fact that this technique includes the two methodological errors mentioned in paragraph 4 (section 2), this method was therefore not considered for this research project. Household uses Bathroom utilities Toilets Showers Bathtubs Faucets END USE DEMAND Kitchen utilities Outdoor uses Municipal uses Faucets Dishwashing Washing Machine Vehicle washing Gardening Jacuzzi Evaporative cooling Boiler feed Process water Cooling & condensing Air conditioning Irrigation Figure 1: Schematic indication of end-uses. The Water Services Act [11] of the RSA, define the primary role player in the supply of potable water as a water board, having the responsibility to render water supply services to water services authorities, which entails the abstraction, conveyance, treatment and distribution of water intended to be converted to potable water (Figure 2). It is the duty of the water board, as a services provider, to ensure that water supply- and sanitation services provided are efficient, equitable and sustainable. Acknowledging the latter, the main objective of this research project was to develop a water demand prediction tool to enhance the responsibilities vested in such a water board

5 River Basin Management III 271 Figure 2: Example of a water treatment work. 2.2 Factors affecting water demand According to Van Zyl et al. [10], it is evident in order to conciliate the demand for and the availability of water, all the relevant variables, which might have an effect on, the demand/availability, be included in a water demand model. Metzner [6] also mentions that the quantity of water which is purchased by consumers is dependent on variables, and that a mathematical model (which brings the quantity of water purchased in context with the different variables) can be developed General factors For this research project cognizance was taken of the above two paragraphs. The actual water-needs of the river basins, which the study area consists of, are closely linked to various variables such as climate, population growth or decline, spendable income per capita, technological development, lifestyle and habits of consumers, the price of water, as well as water engineering related factors (e.g. water losses, water leakages etc.). One of the most important variables under investigation was the effect of the HIV pandemic on the population growth in the study area and consequently on the water demand Specific variables used in this study Public government policies; Demographic factors;

6 272 River Basin Management III Cultural and ethnic factors; Socio-economic factors; Economic factors; Techno-economic factors; Natural factors; Water restrictions; and Diverse factors (increasing peak demands). 3 Study area South Africa and specifically the river basins, which forms part of the study area under investigation, is a semi-arid area, with an average annual rainfall of approximately 500 mm, significantly less than the world average of 860 mm. Rainfall is irregular in both time and space and the area under consideration experiences frequent, unpredictable droughts and floods. The peak potable water demand during a continuous period of up to five days can exceed the mean annual demand for the same period by more than 50% and out of an engineering point of view, this is a challenging phenomenon, which must also be addressed in this water demand research project. The sub-division of the service area of the water board of Bloem Water, chosen for the purpose of this study, is depicted in Figure 3. Figure 3: Sub-division of the service area of Bloem Water (Source: [1]).

7 River Basin Management III 273 Important is the fact that the study area consists of two river basins with a raw-water transfer scheme from the one, to the other - needless to say that this is a contributory challenge in the project. For this study area, it has become a critical essential for the water board (Bloem Water) to predict the water demand and sudden changes in peak demands in the river basins as accurately as possible. The reasons are that: It is generally accepted that there is an ever increase in the demand for especially potable water, whilst the opposite can in actual fact be true and results in managerial decisions based on wrong information; Bloem Water would be in a position to prioritize maintenance and infrastructure development programmes; Bloem Water will be able to obtain funds in time and at competitive interest rates and conditions, and Bloem Water will be able to determine realistic and affordable potable water for their service area. 4 Projection methods With the decision taken that it was a priority to develop a water demand prediction tool to enhance the responsibilities vested in Bloem Water as a water board and water services provider, the variety of projection methods which could be used to predict the future demand for potable water were investigated and are as follows: Extrapolation; Regression models; Economical mathematical models; Interviews; Analogical models; and Structural design From the above variety of methods, the method of regression analysis was chosen as the most appropriate method to predict future water demand. This decision was taken after a thorough investigation of relevant literature, and the available data. 5 Results The diversity of variables between the municipal and/or rural areas, and the two river basins within the study area, resulted in a decision by the researchers to compile multiple regression equations for the different municipal and rural areas (refer to as sub-areas) in the study area. This was done by using the historical data for pre-determined sub-areas, develop a regression equation for them and then do water demand simulations. The next step was the verification and calibration of the equation for each sub area. The variables and regression-coefficients of the different equations (for each sub area) where then combined and resulted in one regression equation

8 274 River Basin Management III applicable for the whole area under research. The combined regression equation was then adapted into the water prediction model for Bloem Water as a service provider and the following equation obtained: y = 1, , x 1 + 0, x 2 + 0, x 3 0, x 4 (1) with: y = Monthly water demand in m 3 ; x 1 = Rainfall in mm per month; x 2 = Temperature in C; x 3 = Population (number of people); and x 4 = Water restrictions. For rainfall and temperatures, the monthly averages, maximum, minimum and standard deviations were determined, and in order to project the maximum expected demand, the minimum rainfall and temperature-values were used. With several simulations done and considering only the above four independent variables, the best reliability-coefficient of 86,38% was obtained. BLOEM WATER: WATER DEMANDS CONSUMPTION (million cubic m.) Figure 4: YEARS AVERAGE ACTUAL MAXIMUM Water demand projection. Predicted water demand

9 River Basin Management III 275 The research done complies with the priority requirements. Figure 4 is a graphical presentation of the application of the model and the outcome of the water demand projections up until Discussion and conclusion The projection of the water demand in the service area of the water board, Bloem Water leaves a few scenarios, which need to be investigated in more detail. These are: The population-tendencies in future i.e. are people going to seek for employment elsewhere vs. urbanisation; Upgrading and/or development water distribution networks in previously disadvantaged areas; and Lack of applicable relevant variable data, with specific reference to the long term, and HIV, which is a sensitive but serious matter; The number of relevant variables considered in a water prediction model does affect the reliability-coefficient and the more of these are included (as referred to in the above paragraph 2.2.2) the higher the reliability-coefficient would be. It has been negotiated with the water services provider, Bloem Water, that whilst the prediction model is refined to acceptable minimum requirements, they will utilize the current developed model for a pilot study and that this will be done with the full involvement of the research team. As mentioned above, the number of relevant variables considered in a water prediction model does affect the reliability of the model. Notwithstanding the fact that it is the intention to increase the reliabilitycoefficient, the current figure of 86,38 % is higher than was expected and can this Water Demand Management Tool be used by Bloem Water to the advantage of firstly, the institutions and community it serves, and secondly to their own advantage with reference to corporate governance. References [1] Bloem Water Annual Report, 2004/2005. [2] Department of Water Affairs and Forestry Water Demands in the Vaal River Supply Area, forecast to year [3] Dziegielewski, B.A Long-term forecasting of urban water demand. In Hall, D.C. (ed.) The economics of environmental resources: Marginal cost rate design and wholesale water markets, Volume 1. JAI PRESS INC, London. [4] Local Government Transition Act, 1993 (Act No. 209 of 1993). [5] Merrett, S Introduction to the economics of water resources: An international perspective. UCL Press Limited, London. [6] Metzner, R.C Demand forecasting: a model for San Francisco. Journal of the American Water Works Association, 59(3).

10 276 River Basin Management III [7] National Water Act (1998) of the Republic of South Africa (RSA, 1998). [8] Tate, D.M An Overview of Water Demand Management and conservation. Paper prepared for the World Water Supply and Sanitation Collaborative Council (WSSCC). [9] U.S. Office of Technology Assessment Preparing for an uncertain climate. Government printing office. ISBN Washington. [10] Van Zyl, J.E., Haarhof, J. and Husselmann, M.L Potential application of end-use demand modelling in South Africa. [11] Water Services Act (1997) of the Republic of South Africa (RSA, 1997).