Identifying water price and population criteria for meeting future urban water demand targets

Predictive models for urban water demand can help identify the set of factors that must be satisfied in order to meet future targets for water demand. Some of the explanatory variables used in such models, such as service area population and changing temperature and rainfall rates, are outside the immediate control of water planners and managers. Others, such as water pricing and the intensity of voluntary water conservation efforts, are subject to decisions and programs implemented by the water utility. In order to understand this relationship, a multiple regression model fit to 44 years of monthly demand data (1970-2014) for Los Angeles, California was applied to predict possible future demand through 2050 under alternative scenarios for the explanatory variables: population, price, voluntary conservation efforts, and temperature and precipitation outcomes predicted by four global climate models with two CO2 emission scenarios. Future residential water demand in Los Angeles is projected to be largely driven by price and population rather than climate change and conservation. A median projection for the year 2050 indicates that residential water demand in Los Angeles will increase by approximately 36 percent, to a level of 620 million m3 per year. The Monte Carlo simulations of the fitted model for water demand were then used to find the set of conditions in the future for which water demand is predicted to be above or below the Los Angeles Department of Water and Power 2035 goal to reduce residential water demand by 25%. Results indicate that increases in price can not ensure that the 2035 water demand target can be met when population increases. Los Angeles must rely on furthering their conservation initiatives and increasing their use of stormwater capture, recycled water, and expanding their groundwater storage. The forecasting approach developed in this study can be utilized by other cities to understand the future of water demand in water-stressed areas. Improving water demand forecasts will help planners understand and optimize future investments in water supply infrastructure and related programs.