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q.1 C -IS <br />1.Objective and Approach <br />The objective of the research described in this report :s to develop water use forecasts for <br />the City of Redwood City (RWC) forthe 20- yearpetiod 2000 to 2020. The resulting <br />water use forecasts can assist RWC with a vadety of water planning decisious/foncdons, <br />including supporting the decciaton- in&ing.proccu underlying construction of water <br />recycling facilities in the Redwood Shores area <br />A variety of methods are available to forecast water use. There are pros and cons <br />associated with each, and data availabihty is often an important selection determinant <br />Previously, RWC has used simple extrapolation methods for water we forecasting. This <br />approach has minimal data and analytic requirements, but an extrapolation of the past is <br />not necessarily the best predictor of the future. <br />Because RWC has available several key sources of information regarding future d rivers <br />of water use (e.g., housing runts and employment), it is logical to make use of this <br />information as is done via the following single variable model- <br />WATER,, = DRIVER,,, • COEFFICIW A <br />where <br />WATER.,, = water use of sector s in time t <br />DRIVER,. - data driver of sector s in time t <br />COEFFIC1ENT - water use per driver coefficient of sector s in time t <br />In RWC's case there are seven sectors identified in the billing system as follows: <br />• single family residential <br />• multiple family residential <br />• commercial <br />• commercial Irrigation <br />• government <br />• other <br />• residential irrigation <br />As described in Section 2, the data drivers are number of housing units for the residential <br />sectors, number of employees for the commercial sectors, and population for the other <br />sectors. Section 3 describes the development of the coefficients that are based on <br />historical water use correlations, as potentially adjusted for weather, water prices, and <br />expected water conservation transformations. Once the data drivers and their associated <br />coefficients are identified, water use forecasts over a 20 -year period are developed as <br />described in Section 4. Lastly, Section 5 descr sensitivity analyses of the water use <br />forecasts to underlying assumptions. <br />Page 3 <br />