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2025 URBAN WATER MANAGEMENT PLAN <br /> MAY 2026/FINAL DRAFT/CAROLLO <br /> Table 4.7 Summary of Historical Data Collected for Model Development <br /> Dataset Data Source(s) <br /> Observed Weather(monthly precipitation, monthly PRISM <br /> maximum temperature) <br /> Water Price Retail agency-provided (2010-2024) <br /> Drought Restrictions State Water Resources Control Board <br /> GDP Federal Reserve Bank of St. Louis real GDP: all industries in <br /> Orange County,California <br /> Median Income US Census ACS <br /> Housing Density US Census ACS,CDR, SCAG land use data <br /> Persons Per Household US Census ACS,CDR, SCAG land use data <br /> Relative Sectoral Economic Activity US Census LODES, CDR <br /> Passive Efficiency Estimates Analysis of trend indicators and MWDOC/Flume Insight <br /> COVID Binary Indicator Assumed active from March 2020 to May 2023 <br /> Notes: <br /> ACS-American Community Survey;GDP-gross domestic product; LODES-Longitudinal Employer-Household Dynamics <br /> Origin Destination Employment Statistics; PRISM-parameter-elevation regressions on independent slopes model <br /> The MWDOC Water Use Efficiency Group provided annual water savings achieved by various active <br /> conservation measures.To avoid potential errors in the classification of historical conservation data, total <br /> historical conservation was modeled in each sectoral regression model using a linear trend to capture <br /> steady change over time.While historical conservation is captured in a linear trend, projected passive <br /> conservation is based on best available data from the 2021 Orange County Residential Water Efficiency <br /> Potential and Opportunities Study and assumes a 1.9 percent decrease in annual residential demand from <br /> 2025 to 2030, at which point passive conservation is projected to remain constant . Future active <br /> conservation is not accounted for in baseline demand forecast, as water savings from active programs <br /> (programs that require customers to change behavior) are highly specific to retail agencies and to the <br /> formulation and timing of their implementation. <br /> The process of identifying the explanatory variables to include in the regression equation and developing <br /> coefficients that accurately measure the response of water use to changes in these variables is the most <br /> time-intensive part of the demand forecasting process. <br /> Prior to developing the forecasts, model calibration and fine tuning for each of the four demand sectors <br /> occurred at the individual retail agency level. The modeling team worked with each retail agency to <br /> calibrate sectoral model equations and quantify other uses (those not included in the single-family, <br /> multi-family, irrigation, or CII demand sectors). <br /> CITY OF SANTA ANA <br />