Drivers of Variability in Public‐Supply Water Use Across the Contiguous United States

Abstract

This study explores the relationship between municipal water use and an array of climate, economic, behavioral, and policy variables across the contiguous U.S. The relationship is explored using Bayesian‐hierarchical regression models for over 2,500 counties, 18 covariates, and three higher‐level grouping variables. Additionally, a second analysis is included for 83 cities where water price and water conservation policy information is available. A hierarchical model using the nine climate regions (product of National Oceanic and Atmospheric Administration) as the higher‐level groups results in the best out‐of‐sample performance, as estimated by the Widely Available Information Criterion, compared to counties grouped by urban continuum classification or primary economic activity. The regression coefficients indicate that the controls on water use are not uniform across the nation: e.g., counties in the Northeast and Northwest climate regions are more sensitive to social variables, whereas counties in the Southwest and East North Central climate regions are more sensitive to environmental variables. For the national city‐level model, it appears that arid cities with a high cost of living and relatively low water bills sell more water per customer, but as with the county‐level model, the effect of each variable depends heavily on where a city is located.

Spatial and Seasonal Response of Municipal Water Use to Weather across the Contiguous U.S.

“…The effects of mean temperature were also explored, but ultimately eliminated from multiple regression models due to collinearity since mean and maximum temperature were over 99% correlated. Maximum temperature was chosen since it is more prevalent in the literature (Maidment and Miaou 1986;Kenney et al 2008;Worland et al 2018). The best multiple regression model for each city was selected to include a subset of the terms in Equation (A1) based on the corrected Akaike information criterion (AIC c ), a model selection tool used to balance the tradeoff between model fit and simplicity, with a correction for small sample sizes to prevent overfitting.…”

“…Other factors such as irrigated area, net evapotranspiration (ET), water price, use of in-ground sprinklers, and excess irrigation together explained 45% of the variation in outdoor water use (DeOreo et al 2016). An analysis using U.S. Geological Survey (USGS) county-level water use found that climate region groupings were more explanatory than primary economic activity or urban gradient, and emphasized that predictive capabilities of social and environmental variables differ across climate regions (Worland et al 2018). An analysis using U.S. Geological Survey (USGS) county-level water use found that climate region groupings were more explanatory than primary economic activity or urban gradient, and emphasized that predictive capabilities of social and environmental variables differ across climate regions (Worland et al 2018).…”

“…In nine cities across Texas, Florida, and Pennsylvania, municipal seasonal water use was responsive to temperatures above a threshold of 70°F and precipitation events >0.05 inches, with precipitation effects that were nearly 6 times greater in Florida and Texas compared to Pennsylvania (Maidment and Miaou 1986). An analysis using U.S. Geological Survey (USGS) county-level water use found that climate region groupings were more explanatory than primary economic activity or urban gradient, and emphasized that predictive capabilities of social and environmental variables differ across climate regions (Worland et al 2018).…”

Drivers of Interbasin Transfers in the United States: Insights from Sampling

“…Other works have also examined the drivers of future water withdrawals in general (Worland et al. ).…”

“…Examples include interactions between IBTs and the Clean Water Act (Schroeder and Woodcock 2011), effects of IBT connectivity on fish biodiversity (Grant et al 2012), and the economic feasibility of IBTs utilizing optimization models, with maximization of net benefits as the objective (Karamouz et al 2010). Other works have also examined the drivers of future water withdrawals in general (Worland et al 2018).…”