Analysis of Potable Water Savings Using Behavioural Models

Abstract

The availability of drinking water in reasonable amounts is currently considered the most critical natural resource of the planet (United Nations Educational, Scientific and Cultural Organization [UNESCO], 2003). Studies show that systems of rainwater harvesting have been implemented in different regions such as Australia (Fewkes, 1999a; Marks et al., 2006), Brazil (Ghisi et al., 2009), China (Li & Gong, 2002; Yuan et al., 2003), Greece (Sazakli et al., 2007), India (Goel & Kumar, 2005; Pandey et al., 2006), Indonesia (Song et al., 2009), Iran (Fooladman & Sepaskhah, 2004), Ireland (Li et al., 2010), Jordan (Abdulla & Al-Shareef, 2009), Namibia (Sturm et al., 2009), Singapore (Appan, 1999), South Africa (Kahinda et al., 2007), Spain (Domenech & Sauri, 2011), Sweden (Villareal & Dixon, 2005), UK (Fewkes, 1999a), USA (Jones & Hunt, 2010), Taiwan (Chiu et al., 2009) and Zambia (Handia et al., 2003). One of the most important steps in planning a system for rainwater harvesting is a method for determining the optimal capacity of the rainwater tank. It should be neither too large (due to high costs of construction and maintenance) nor too small (due to risk of rainwater demand not being met). This capacity can be chosen from economic analysis for different scenarios (Chiu et al., 2009) or from the potential savings of potable water for different tank sizes (Ghisi et al., 2009). Several methodologies for the simulation of a system for rainwater harvesting have been proposed. The approaches commonly used are behavioural (Palla et al., 2011; Fewkes, 1999b; Imteaz et al., 2011; Ward et al., 2011; Zhou et al., 2010; Mitchell, 2007) and probabilistic (Basinger et al., 2010; Chang et al., 2011; Cowden et al., 2008; Su et al., 2009; Tsubo et al., 2005). One advantage of the behavioural methods is that they can measure several variables of the system over time, such as volumes of consumed and overflowed rainwater, percentage of days in which rainwater demand is met (Ghisi et al., 2009), etc. The main disadvantage of these methods is that as the simulation is based on a mass balance equation, there is no guarantee of similar results when using different rainfall data from the same region (Basinger et al., 2010). This problem can be avoided, in part, with the use of long-term rainfall time series. Probabilistic methods have the advantage of their robustness, for example, by using stochastic precipitation generators. A disadvantage of these methods is their portability. Several models adequately describe the rainfall process in one location but may not be satisfactory in another (Basinger et al., 2010).