Determinants of domestic water demand
Introduction: Recently, several models for domestic water forecasting have been developed and published in literature. The model from Archibald is a simple component model for calculating the domestic water demand per household.
One of the components in the model is “bathing and showering”. The problem with this type of model is that every component has several factors, which should be determined and in most applications there is not enough information to every component and if one component cannot be modeled the whole model would be insufficient. Eqns (1) and (2) describe some of the most used models for domestic water demand forecasting.
Test for determinants: Correlation and significance tests: To test the determinants, correlation tests are used in this paper. A correlation describes the strength of an association between variables. An association between variables means that the value of one variable can be predicted, to some extent, by the value of the other. A correlation is a special kind of association: there is a linear relation between the values of the variables. A non-linear relation can be transformed into a linear one before the correlation is calculated.
For a set of variable pairs, the correlation coefficient gives the strength of the association. The square of the size of the correlation coefficient is the fraction of the variance of the one variable that can be explained from the variance of the other variable. The relation between the variables is called the regression line. The regression line is defined as the best fitting straight line through all value pairs, i.e., the one explaining the largest part of the variance. The correlation coefficient is calculated with the assumption that both variables are stochastic (i.e., bivariate Gaussian). If one of the variables is deterministic, e.g., a time series or a series of doses, this is called regression analysis
Determinants for Beijing domestic water demand: Possible influencing factors for the domestic water demand for the Beijingregion were selected and are listed inform the period from 1996-2003.They are factors from the weather, population and economy, which are thoughtto be obviously linked to the domestic water demand. The main objective of thisstudy is to find out, which factor has the greatest influence and which ones arenegligible in the models, and therefore simplify them.
Conclusions: The applied study is an important starting point for the development of simpleand robust models. For the residential sector the variables of the economic wateruse models include income, household size, housing density, air temperature,rainfall, marginal price, and fixed charges for water and wastewater. In mostregions there is no data available for most of these parameters. It is thereforevery important to know which parameter, can be used at the minimum toproduce some reliable results. In this study several variables have been tested fortheir influence on the domestic water demand. It has been shown that to predictdomestic water reliably at least the gdpc, the previous water demand,employment rate,
the time and the number of households must be included. Theestimation can be improved by using panel data covering a longer time period ormore disaggregated sub-regional level analyses. It would also be useful to extendthe study with more adequate data especially regarding time series water pricesfor the domestic sector. Well-designed household surveys would provide richerinformation and greater insights into the factors influencing domestic waterdemand. The results of the comparison between Canada, Beijing and Germanyshows that in water abundant areas more water will be used and also theincreased positive correlation coefficient of income (Beijing 0.345; Canada0.578; Germany 0.623) implies that consumers who have a high income tend toconsume more water.