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2025 URBAN WATER MANAGEMENT PLAN <br /> MAY 2026/FINAL DRAFT/CAROLLO <br /> By statistically linking water use to explanatory variables, the econometric models provide a robust <br /> foundation for understanding variability and projecting future consumption patterns. Modeled water use <br /> is the product of the driver count (e.g., number of accounts), and the rate of water use per driver: <br /> Water Use = Driver Count x Rate of Use per Driver <br /> Each of the four demand sectors modeled (single-family, multi-family, CII, and irrigation) has a separate <br /> equation. Driver units change into the future based on housing, employment, and population projections. <br /> The rate of water use per driver is based on the historical response of the use rate to explanatory variables <br /> (measured by coefficients) and the future values of those same explanatory variables. <br /> Linear regression produces the coefficients for each explanatory variable to closely reproduce the <br /> historical rate of use per driver unit. The coefficients explain how (both in terms of magnitude and sign) <br /> water use responds to changes to explanatory variables. <br /> MWDOC identified driver units based on data provided by agencies and the Center for Demographic <br /> Research at Cal State Fullerton (CDR) that can be easily projected into the future.The rate of water use per <br /> driver is based on agency-provided billing sector uses from 2010 through 2024.Table 4.6 shows the driver <br /> units and rate of use for each of the four models. <br /> Table 4.6 Summary of Demand Sectors <br /> Sector Driver Units - of Definition <br /> Single-Family Residential Accounts Gallons/account/day <br /> Multi-Family Residential Accounts Gallons/account/day <br /> CII Jobs Gallons/job/day <br /> Dedicated Irrigation (potable, recycled, and raw water) Accounts Gallons/account/day <br /> The rates of water use for each sector model are based on the historical responses to explanatory <br /> variables and the future values of those explanatory variables. Addressing multiple influences on demand <br /> improves the accuracy and precision of all estimated parameters, and the modeling team identified a <br /> large range of explanatory variables based on past experience with demand modeling and available data. <br /> Table 4.7 displays the explanatory variables. <br /> CITY OF SANTA ANA <br />