2025 Orange County Water Demand Projection Model
Explanatory Variable
Relevance to Water Consumption
The representation of industries within a geographical area is related to the amount of water used within the CII sector. Housing density is negatively correlated with demand; on average, residences with more units per acre (or smaller parcel sizes relative to dwellings) tend to use less water for outdoor uses.
Mix of industries
Housing density
Positively correlated with demand; generally, residences with more people tend to use larger amounts of water on average.
Persons per Household
Higher units per account are associated with higher average demands per account.
Households per account
Conservation
Decreases the amount of water customers consume.
The presence of drought restrictions on water use tends to decrease the amount of water consumed by customers.
Drought Restrictions
The presence of COVID restrictions tends to increase the amount of water consumed by residential customers and decrease the amount used in the CII sector.
COVID Pandemic
The following sections document the raw data sources and the processing involved to derive each explanatory variable.
2.1.6
Historical Weather Data
Based on Hazen’s modeling experience, total monthly precipitation and maximum monthly temperature will have the greatest impact on demand. These weather characteristics best define demand when calculated for each retail service area boundary, which accounts for microclimates driven by elevation gradients and proximity to large water bodies. The Northwest Alliance for Computational Science and Engineering at Oregon State University produces the PRISM (PRISM Climate Group 2004) weather dataset from a wide monitoring network, including the California Data Exchange Center (CDEC) and the California Irrigation Management Information System (CIMIS) gages. PRISM provides gridded weather data at a 4-kilometer resolution, and Python scripts were then used to process the total monthly precipitation (inches per month) and monthly average maximum daily temperature (degrees Fahrenheit) for each member agency’s service area based on the coordinates of agency centroids. Weather data are normalized to average conditions to disconnect weather variations from systematic seasonal cycles. Historical normal weather values were calculated for each member agency as the average monthly values over the period 1991 to 2024. Departures from the historical normal were then calculated as the actual monthly value minus the monthly historical norm in the natural log-scale, as shown in Equation 2-6 .
𝐷𝑒𝑝𝑎𝑟𝑡𝑢𝑟𝑒 = ln 𝑋 ,,௧ −ln𝑋 ప, ത തതതതതതത
Equation 2-6
2-10
Appendix G - 29
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