2025 Orange County Water Demand Projection Model
Table ES-2: Summary of Historical Data Collected for Model Development
Dataset
Data Source(s)
Observed weather (monthly precipitation, monthly maximum temperature)
Parameter-elevation Regressions on Independent Slopes Model (PRISM)
Water Price
Retail agency provided (2010 – 2024)
Drought Restrictions
State Water Resources Control Board
Federal Reserve Bank of St. Louis Real Gross Domestic Product: All Industries in Orange County, CA
Gross Domestic Product (GDP)
Median income
US Census American Community Survey (ACS)
Housing density
US Census American Community Survey (ACS), California State University Fullerton Center for Demographics Research (CDR), Southern California Association of Governments (SCAG) land use data
Persons Per Household
Relative sectoral economic activity
US Census LODES, CDR
Analysis of trend indicators and MWDOC/FLUME insight
Passive Efficiency Estimates
COVID Binary Indicator
Assumed active from March 2020 – May 2023
The MWDOC Water Use Efficiency Group provided annual savings achieved by various active conservation measures. To avoid additional calculations and potential errors in the classification of historical conservation data, total historical conservation was captured in each sectoral model using a linear trend term. While historical conservation is captured in a linear trend, projected passive conservation is based on the 2021 Orange County Residential Water Efficiency Potential and Opportunities Study (2021 Study) and assumes a 1.9% decrease in annual residential demand from 2025 to 2030, at which point passive conservation will remain constant going forward. Active conservation is not accounted for in the models. The process of identifying the explanatory variables to include in the regression equation and developing coefficients that accurately measure the response of water use to changes in these variables is the most time-intensive part of the demand forecasting process.
Econometric Model Development
Orange County water use varies by time with changes to the price of water, drought restrictions, and weather, as well as by geography. For example, different agency income levels and household densities result in different water use rates. Hazen fit the four sectoral econometric models using a panel regression approach to account for explanatory variables that vary primarily over geography as well as those that
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Appendix G - 12
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