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1. Objective and Approach <br /> <br /> The objective of thc research described in this report is to develop water use forecasts for <br /> the City of Redwood City (RWC) for the 20-year period 2000 to 2020. The resulting <br /> water use forecasts can assist RWC with a variety of water planning decisions/functions, <br /> including supporting the decision-making process underlying construction of water <br /> recycling facilities in the Redwood Shores area. <br /> <br /> A variety of methods are available to forecast water use. There are pros and cons <br /> associated with each, and data availability is often an important selection determinant. <br /> Previously, RWC has used simple extrapolation methods for water use 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 /> <br /> Because RWC has available several key sources of information regarding future drivers <br /> of water use (e.g., housing units and employment), it is logical to make use of this <br /> information as is done via the following single variable model: <br /> <br /> WATERs,t = DRIVERs.t * COEFFICIENTs.t <br /> <br /> where <br /> <br /> WATERs,t = water use of sector s in time t <br /> DRIVERs.t = data driver of sector s in time t <br /> COEFFICIENTs.i = water use per driver coefficient of sector s in time t <br /> <br /> In RWC's case there are seven sectors identified in the billing system as follows: <br /> <br /> · single family residential <br /> · multiple family residential <br /> · commercial <br /> · commercial Irrigation <br /> · government <br /> · other <br /> · residential irrigation <br /> <br />As described in Section 2, the data drivers are number of housing units for the residential <br />sectors, number of employees tbr 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 describes sensitivity analyses of the water use <br />forecasts to underlying assumptions. <br /> <br /> Page 3 <br /> <br /> <br />