what lies behind domestic water use& a review essay on the drivers of domestic water consumption

what lies behind domestic water use& a review essay on the drivers of domestic water consumption

The multiple hypothesis approach is continued through model development and testing with the aim of identifying the model that best balances fit and parsimony. Here the potential drivers of consumption change, retained after the screening analysis, are used to develop a series of competing mathematical models of per capita consumption. Different combinations of these variables are tested and compared using the following metrics: root mean square error (RMSE), percent bias, Nash-Sutcliff efficiency (NSE), and the coefficient of determination (R2) (Moriasi et al. 2007 ).
Not all parameters can be readily measured or reasonably estimated based on prior investigation, therefore calibration and validation are needed. A challenge of calibrating and testing models that simulate the response to infrequent phenomena, such as drought or rapid population growth, is that there may only be one or two occurrences of the phenomena in the study period. The traditional approach to calibration and validation divides the historical data set into two periods, one for each calibration and validation. Using this approach to selecting calibration and validation periods, important but infrequent phenomena, or combinations of such phenomena, may be included the calibration but not validation periods hampering the researchers’ ability to assess model validity. For example, in the Las Vegas case the periods 1997 to 2006 and 2007 to 2012 were initially selected for calibration and validation respectively. There are fundamental differences between these periods. For example, population grows rapidly in the calibration period but slows during the validation period.

What lies behind domestic water use& a review essay on the drivers of domestic water consumption
Herrington (1996) is still one of the most widely cited studies of long-term water demand in the UK especially in relation to climate change. Per capita household demands were forecasted to the 2020s using water into supply data. It was assumed that past, present and future household water use could be understood through knowledge of appliance ownership, frequency and habits of appliance usage, and household occupancy rates. Data from Thames Water, Lee Valley, Cambridge and Southern Water’s three divisions were analysed for the period 1976–1991 to build a model and project future demand. Assuming no change in water policy or metering, it was forecasted that between 1991 and 2021 per capita household consumption in these regions could increase by 20 % (
50 %; without the temperature rise peak PCC was predicted to increase by

What lies behind domestic water use& a review essay on the drivers of domestic water consumption
1 Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA
The variable loading patterns to the PCs for the 14 demographic household groups are presented in Figure 5 . The PC loading patterns are similar for some of the household demographic groups. For example, the single household (Group 1) and couple with one child household (Group 6) are similar in PC loading patterns, because age, electricity, income, education, and poverty are significant contributors to PC1 while the rest are contributing to PC2 in both groups. Other groups that show similar PC loading patterns include: single-parent with two children households (Group 3) and single-parent with more than two children households (Group 4); couple with two children households (Group 7) and large household with more than two children (Group 12); three adult household with no child (Group 9), large household with two children (Group 11) and four adult household with no child (Group 13); large household with one child (Group 10) and large childless household (Group 14). Variation of PC loading patterns suggests that the weights of the variables differ among different household demographic groups.

Water demand in urban areas is rapidly changing due to urbanization and population growth, climate change, and land use change. Projections show that these changes will significantly affect water consumption in the residential sector, especially in densely populated urban environments and megacities. In order to develop descriptive and predictive water demand models to ultimately inform demand management, cost-effective conservation/efficiency measures, and water-energy management, it is key that water authorities and utilities acquire a clear understanding of household water use.
Especially with the increasing availability of intelligent metering and communication technologies, there is increasing interest and effort in the literature to identify insights on the predominant determinants of household water consumption. Research performed so far suggests that these latter are heterogeneous: some determinants are a function of technological factors (e.g., type and efficiency of appliances), others to environmental (e.g., climate, weather), and behavioural (e.g., attitude towards water use, demographics, income) factors. Yet, the majority of the literature analyzing household water use mainly focuses on only a few drivers per paper, with no clear consistent link emerging between drivers and their effects over a number of papers. Moreover, to our knowledge, a comprehensive review summarizing findings from the state-of-the-art literature is still missing.

What lies behind domestic water use& a review essay on the drivers of domestic water consumption
Mellor JE, Watkins DW, Mihelcic JR (2012) Rural water usage in East Africa: does collection effort really impact basic access? Waterlines 31(3):1756–3488
Otaki Y, Otaki M, Pengchai P, Ohta Y, Aramaki T (2008) Micro-components survey of residential indoor water consumption in Chiang Mai. Drink Water Eng Sci 1:17–25