By R Sarker and S Gato-Trinidad.

First published in Water e-Journal Vol 2 No 1 2017.

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Abstract

Water demand modelling is traditionally based on population growth and standards based on historical records of water consumption. Climate change, drought, increasing growth in population, agriculture and economy resulted in competing uses of an already scarce water resource and to implementation of water conservation programs and water restrictions.

This led to changed water consumption patterns and the need to determine if the current water demand models are still adequate to handle these changes. An anonymous survey targeting relevant stakeholders involved in water supply and demand planning, modelling and management was undertaken to obtain their opinions on the adequacy of their current water demand models.

The results of the survey revealed that a number of respondents found their models to be adequate as these are being used only for financial and water accounting needs and could not see the impact of factors mentioned in the modelling process.

The other respondents recognised the effect of these changes and have already incorporated in their water demand models one or two of: end-use analysis, climatic variables, water conservation programs, alternative sources of water and water restrictions. However, none of the respondents reported that all of these factors were considered in their current water demand models.

The recommendations of some respondents in the survey included: (i) integrating demand model with a supply model, (ii) incorporating climate variability and impact of rainwater use, (iii) linking network data and demand models, (iv) using population growth within acceptable confidence limits and (v) simplification and better user interface to make the models user-friendly.

Introduction

Traditional water demand models calculate demand based mainly on population in a specific region. Climate change, drought, growth in population, economy and agriculture, along with environmental and social changes trigger changes in water use. As a consequence, alternative sources of water supply are identified to cope with the change.

Given these changes both in supply and demand, are the current models adequate enough to take into account climate change; water restrictions; and water conservation? Do all these drivers need to be incorporated into the current models used by entities in Australia? What are the limitations of the current models? What are the issues that should be addressed or incorporated to improve these water demand models to meet the needs of stakeholders?

To address these questions, an anonymous survey of urban and regional water authorities, agencies and consulting firms in Australia was conducted using a survey tool ‘Opinio’. The survey outcomes can benefit water resource planners and policy makers and other stakeholders involved in water modelling.

Literature review

Population growth, climate change and limited water resources exacerbated by the recent drought (late 1996-mid 2010, the Millennium Drought in Australia; BOM 2015) placed enormous pressure on traditional sources of water supply.

As water plays a crucial role in the location, function, and growth of communities (Marshall 1879), fresh water has become the source of increasing controversy, as supplies fail to meet demand in many areas (Arbués et al. 2003).

The effective supply of water is essential in geographical regions where the demand is increasing due to the growing population, and an improved living standard (Jacobs and Haarhoff 2002; Worthington and Hoffman 2008) along with economic growth and social and environmental changes.

Australia is moving towards more sustainable water management with rapid advances in the use of recycled water, greywater, rainwater and stormwater as alternatives to the use of traditional water supply augmentation approaches. Demand management is another technique to optimise the uses of limited water.

Modelling becomes an important part of demand management in the current climate of water restrictions and water conservation, e.g. in Melbourne, Australia. Many factors are involved in demand behaviour and can influence the water demand directly or indirectly (Herrera et al. 2010).

Water demand modelling has been undertaken by various researchers considering factors (Fox et al. 2009) that affect water demand, such as rainfall and temperature (Martinez-Espineira 2002; Zhou et al. 2002; Arbués et al. 2003; Neto et al. 2005; Gato et al. 2007a,b, Sarker et al. 2013); evapo-transpiration (Aly and Wanakule 2004; Syme et al. 2004; Taylor 2012); population (Koo et al. 2005; Rao 2005); income (Liu et al. 2003); household size (Martinez-Espineira 2002; Liu et al. 2003; Bradley 2004); dwelling or housing type (Troy and Holloway 2004; Kowalski and Marshalsay 2005); and water price (Agthe and Billings 2002; Martinez-Espineira 2002; Liu et al. 2003; Neto et al. 2005; Rinaudo et al. 2012; Yoo et al. 2014).

Methodology

Selection of respondents

The anonymous survey targeted managers of a total of 100 organisations both urban and regional water authorities, agencies and consulting firms in Australia, involved in water supply planning, demand modelling and management.

Anonymous survey questionnaire

The survey questions relate to the demand models currently being used, issues with these models, and appropriateness of the models; considering the effect of climate change, water restrictions, and water conservation practices. In this survey ‘climate change’ refers to consideration of climatic variables (rainfall, temperature and evapotranspiration).

A survey tool (Opinio) was used for the design of the questionnaire and for the implementation of the project.

A total of 100 representatives (managers) of both urban and rural water authorities, agencies and consulting firms in Australia, involved in water supply planning, demand modelling and management, were invited to respond to this anonymous survey through emails from December 1, 2014 to July 3, 2015. The email addresses of relevant representatives (managers) were collected from the corresponding websites of the organisations and from the Australian Water Directory published by Australian Water Association (AWA 2013).

The respondents were directed to “Opinio” by these emails and they completed the instrument online by ticking boxes and by inserting comments as required

The questionnaire was composed of five questions (Appendix A) and took approximately fifteen minutes to complete. Participants responded voluntarily and were free to withdraw.

Data collection and analysis

Responses of the survey were downloaded from Opinio and then analysed. All data is kept confidential according to Swinburne University’s Human Research Ethics Committee (SUHREC).

Survey result and discussion

A total of 16 responses out of 100 invitations were received from the anonymous survey and owing to the anonymity, the breakdown of respondents is not known.

The models in use were:

• End Use Model (EUM) – four respondents (includes two for iSDP model – one type of end use model)
• Resource Allocation Model (REALM) – two
• Demand Side Management Decision Support System (DSM DSS) – two.
• In-house – two.

The other six respondents use other types of model such as: (i) Projection of historic demands, (ii) State water supply-demand model, (iii) Bespoke demand forecast model (partnership with bulk water supplier), (iv) Statistical analysis in-house, (v) Spreadsheets, and (vi) Customised. The suitability and constraints of the models currently being used by the respondents have been collated in Table 1, taking into account climate change (climatic variables: rainfall, temperature and evapotranspiration), water conservation, rainwater tank supplies and end use of water. Table 1 also includes descriptions of the models and some recommendations received from the respondents.

Conclusions and recommendations

The following conclusions and recommendations can be drawn from the analysis of the anonymous survey results:

1. The EUM and iSDP models are suitable and relatively effective for estimating the impact of various water savings or recycling initiatives, although they are data-hungry and expensive to maintain. They do not account for the impact of climate change or alternative sources of water uses such as rainwater tanks. The relationship between household size and some end uses of water is non-linear (Roberts 2004), which is not adopted in the EUM.
2. DSM DSS integrates end-uses of water, alternative sources of water uses such as rainwater tanks, water restrictions and water conservation. The DSM DSS model includes appliance and stock modelling to identify change in behaviour considering the impact of different levels of efficiency of water appliances. However, it is a complex model and does not incorporate climate change implications.
3. The REALM model does not include end-use modelling; rather, it uses in-house demand models to generate the demand data as input. REALM is capable of modelling catchment yield and bulk distribution of water resources within a water supply system but is not able to provide reasonable forecasts considering the impact of water restrictions, water conservation (decline in consumption of customers using water savings devices and change in water-use behaviour affected by social awareness) and alternative sources of water. REALM is beneficial for allocating water for a supply system from a bulk water resource combining the demand criteria of all segments/ sectors of the system network. One of the two respondents (REALM users) uses its own growth figures of population, sourced from a variety of organisations, and estimates water demand from the extrapolated trends of historical average population ranging from 1.3 to 1.96%. This wide range of population growth makes it difficult to determine water demand with confidence and hence the model can be less reliable. It is therefore necessary to determine the factors affecting the varying population growth rates (land opening for more housing developments or government policy on migration) and the longevity of these growth rates. Are these growth rates temporary, short term or long-term? The uncertainty in population forecasting/water demand modelling would therefore require different modelling scenarios to cater for these varying growth rates.
4. Although some respondents deemed their models to be adequate in relation to their financial and water accounting requirements, the others acknowledged the impact of end–use analysis, climatic variables, water conservation programs, alternative sources of water and water restrictions in the demand modelling process and have already incorporated one or two of these factors separately in their water demand models. However, none of the respondents considered all these factors together in their current water demand models. The suggestions of other respondents include:
   • integrating demand model with a supply model;
   • incorporating climate variability and impact of rainwater use;
   • linking network data and demand models;
   • improving the capture and recording of metered water use;
   • good monitoring of demand and all relevant parameters to analyse what factors are influencing water consumption;
   • using population growth within acceptable confidence limits; focussing on the influence of marketing on demand; comparing new houses with older ones to identify changes in behaviour, and using more than one model to reach a balanced average water demand as an input to a resource allocation model;
   • simplifying the iSDP, REALM and DSM DSS models and;
   • better user interface to make the models user-friendly. Considering all these suggestions it can be argued that, for long term sustainable water management, there is a need for an improved water model, which can be accomplished through integration of end-use analysis, climatic variables, water conservation, water restrictions and alternative sources of water in total water demand modelling and forecasting.
5. It is recommended that the following be undertaken to improve water demand forecasting and for sustainable water management:
   • The population forecasting in water demand modelling needs to be accurate at least within an acceptable confidence limit to determine water demand with confidence.
   • The non-linear relationship between some end-uses and household size(s) should be considered in EUM.
   •  Demand models (such as EUM, iSDP and DSM DSS) can be integrated into supply models (such as REALM).
   • Climatic variables and rainwater tanks need to be incorporated in the water demand model.

Acknowledgements

The authors would like to acknowledge the contribution of all respondents to this survey. The authors are also thankful to Swinburne’s Human Research Ethics Committee (SUHREC) for the ethic clearance of the survey project (SUHREC Project 2013/256).

About the authors

Ram Chandra Sarker | Ram is a PhD Candidate in the Faculty of Science, Engineering and Technology, Swinburne University of Technology. He received a Master degree in Water Resources Engineering from the University of Tokyo. His research area relates to water demand modelling and management. He worked in civil engineering consulting and construction projects for three years in Bangladesh.

Dr Shirley Gato-Trinidad | Shirley is a Senior Lecturer and a Major Discipline Coordinator for the Civil Engineering Undergraduate Course at Swinburne University of Technology. Her research interests are in water resource engineering and management and urban water demand modelling. Prior to joining Swinburne University of Technology, Shirley was employed in different government and engineering consulting organisations in Australia, Thailand and Philippines for more than ten years.

References

Agthe, D., and Billings, R. 2002. Water Price Influence on Apartment Complex Water Use. Journal of Water Resources Planning and Management, 128(5), 366-369.

Aly, A. H., and Wanakule, N. 2004. Short-term forecasting for urban water consumption. Journal of Water Resources Planning and Management, 130(5), 405–410.

Arbués, F., Garcı-a-Valiñas, M. a. Á., and Martı-nez-Espiñeira R. 2003. Estimation of residential water demand: a state-ofthe-art review. The Journal of Socio-Economics, 32(1), 81-102.

AWA (Australian Water Association) 2013. Australian Water Directory 2013, AWA, Australia.

BOM (Bureau of Meteorology) 2015. Recent rainfall, drought and southern Australia’s long-term rainfall decline. Available at http://www.bom.gov.au/climate/updates/articles/a010-southern-rainfall-decline.shtml, viewed on 27/08/2016.

Bradley, R. 2004. Forecasting Domestic Water Use in Rapidly Urbanizing Areas in Asia. Journal of Environmental Engineering, 10.1061/(ASCE)0733-9372(2004)130:4(465), 465-471. DEUS (Department of Energy, Utilities and Sustainability) 2006. Integrated water cycle management: demand side management decision support system—simplified (version S1.1) manual.

DEUS, New South Wales Government, Sydney, Australia.

Dziegielewski, B., Kiefer, J. C., Opitz, E. M., Porter, G. A., Lantz, G. L., DeOreo W. B., Mayer, P. W., Nelson, J. O. 2000. Commercial and Institutional End Uses of Water. American Water Works Association Research Foundation (AWWARF), USA.

Fink, Z., Hawkins, E., Gray, T., Beatty, R., Beatty, K. 2009. Byron integrated water cycle management plan: final, Byron Shire Council, Australia.

Fox, C., McIntosh, B.S., and Jeffrey, P. 2009. Classifying households for water demand forecasting using physical property characteristics. Land Use Policy, 26, 558–568.

Gato, S., Jayasuriya, N., and Roberts, P. 2007a. Temperature and rainfall thresholds for base use urban water demand modelling. Journal of Hydrology, 337(3-4), 364-376.

Gato, S., Jayasuriya, N., and Roberts, P. 2007b. Forecasting Residential Water Demand: Case Study. Journal of Water Resources Planning and Management (ASCE), 133(4), 309-319.

Herrera, M., Torgo, L., Izquierdo, J., and Pérez-García, R. 2010. Predictive models for forecasting hourly urban water demand. Journal of Hydrology, 387(1-2), 141-150.

ISF (Institute for Sustainable Futures) 2011. Case study: Integrated resource planning for urban water - WaggaWagga, Waterlines Report Series No 41, Section 7, National Water Commission, Australia.

Jacobs, H., and Haarhoff, J. 2002. End-use modelling as a means to predict the effects of water demand management. Paper presented at the Biennial Conference of the Water Institute of Southern Africa (WISA), South Africa.

Koo, J., Yu, M., Kim, S., Shim, S., and Koizumi, A. 2005. Estimating regional water demand in Seoul, South Korea, using principal component and cluster analysis. Water Science and Technology: Water Supply, 5(1), 1–7.

Kowalski, M., and Marshalsay, D. 2005. Using measured micro component data to model the impact of water conservation strategies on the diurnal consumption profile. In: 3rd International Conference on the Efficient Use and Management of Water, ed., Santiago, Chile.

Liu, J., Savenije, H. H. G., and Xu, J. 2003. Forecast of water demand in Weinan City in China using WDF-ANN model. Physics and Chemistry of the Earth, Parts A/B/C, 28(4-5), 219-224.

Loh, M., and Coghlan, P. 2003. Domestic water use study in Perth, Western Australia 1998-2001. Water Corporation, Australia.

Marshall, A. 1879. Water as an element of national wealth, A. Pigou, Ed., Memorials of Alfred Marshall, Kelley &Millman, New York, reprinted in 1956, 134–141.

Martinez-Espineira, R. 2002. Residential water demand in the Northwest of Spain. Environmental and Resource Economics, 21(2), 161-187.

Neto, M. L. F., Naghettini, M., von Sperling, M., and Libanio, M. 2005. Assessing the relevance of intervening parameters on the per capita water consumption rates in Brazilian urban communities. Water Science and Technology: Water Supply, 5, 8–15.

Perera, B. J. C., James, B., and Kularathna, M.D.U. 2005. Computer software tool REALM for sustainable water allocation and management. Journal of Environmental Management, Elsevier 77 (2005) 291-300.

Rathnayaka, K., Malano, H., Maheepala, S., Nawarathna, B., George, B., and Arora, M. 2011. Review of residential urban water end-use modelling. In: 19th International Congress on Modelling and Simulation, Perth, Australia.

Rao, K.H.V.D. 2005. Multi-criteria spatial decision analysis for forecasting urban water requirements: a case study of Dehradun city, India. Landscape and Urban Planning, 71, 163-174.

Rinaudo, J.-D., Neverre, N., and Montginoul, M. 2012. Simulating the Impact of Pricing Policies on Residential Water Demand: A Southern France Case Study. Water Resources Management, (2012) 26: 2057. doi:10.1007/s11269- 012-9998-z.

Roberts, P. 2004. Yarra Valley Water 2003 Appliance Stock and Usage Patterns Survey. Yarra Valley Water, Victoria, Australia.

Sarker, R. C., Gato-Trinidad, S., and Imteaz, M. 2013. Temperature and rainfall thresholds corresponding to water consumption in Greater Melbourne, Australia. In: 20th international congress on Modelling and simulation (MODSIM2013), Adelaide Convention Centre, Adelaide, Australia.

Syme, G. J., Shao, Q., Po, M., and Campbell, E. 2004. Predicting and understanding home garden water use. Landscape and Urban Planning, 68(1), 121-128.

Taylor, B. A. 2012. Predicting normalised monthly patterns of domestic external water demand using rainfall and temperature data. Water Science and Technology: Water Supply, 12(2), 168.

Troy, P., and Holloway, D. 2004. The use of residential water consumption as an urban planning tool: a pilot study in Adelaide. Journal of Environmental Planning and Management, 47(1), 97–114.

Worthington, A. C., and Hoffman, M. 2008. An Empirical Survey of Residential Water Demand Modelling. Journal of Economic Surveys, 22(5), 842-871.

Yoo, J., Simonit, S., Kinzig, A. P., and Perrings, C. 2014. Estimating the Price Elasticity of Residential Water Demand: The Case of Phoenix, Arizona, Applied Economic Perspectives and Policy, 36(2), 333–350, doi:10.1093/aepp/ ppt054.

Zhou, S. L., McMahon, T. A., Walton, A., and Lewis, J. 2002. Forecasting operational demand for an urban water supply zone. Journal of Hydrology, 259(1–4), 189-202.