Water demand prediction by end-use at an appropriate spatial and temporal resolution is essential for planning water supply systems that will supply water from a diversified set of sources on a fit-for-purpose basis. Understanding seasonal, daily and sub-daily water demand including peak demand by end-uses is an essential planning requirement to implement a fit-for-purpose water supply strategy. Studies in the literature assume that all indoor water uses except evaporative cooler water use are weather independent and do not exhibit seasonal variability. This paper presents an analysis undertaken to examine seasonal variability of residential water end-uses. The analysis was repeated using two sets of data to ensure the validity of findings. The study shows that shower water use is significantly different between winter and summer, in addition to irrigation, evaporative cooler and pool water end-uses, while other water end-uses are not. Weather is shown to be a significant determinant of shower water use; in particular it affects shower duration which increases with lower temperature. Further analysis on OPEN ACCESSWater 2015, 7 203shower water use suggests that it is driven by behavioural factors in addition to weather, thus providing useful insights to improve detailed end-use water demand predictions.

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Detailed prediction of water demand by their end-uses at multiple scales is essential to support planning of Integrated Urban Water Management, an increasingly applied approach to deal with the problem of water scarcity. This paper presents an urban residential water demand modeling framework that can predict end-use water demand at multiple scales, especially at small scales with a robust explanatory capacity. This is achieved by integrating the complex water demand dynamics of urban residential water use and their underlying variables into a single model. The model described in this study can predict shower, toilet, tap, dishwasher, clothes washer, irrigation, evaporative cooler, bath, and other uses which account for the entire household water use. The model aims to predict water demand at multiple spatial (household/cluster/suburb) and temporal scales (hourly, daily, weekly and seasonal) by considering behavioral differences triggered by factors such as seasonality and presence of people at home. The model incorporates an improved representation of spatial variability by considering behavioral differences between customer groups, and improves the capability to deal with areas with different demographic and housing characteristics. This research confirms the capacity of stochastic modeling methods to represent unexplained behavior of water consumers.