A Data-Driven Framework to Characterize State-Level Water Use in the United States

Access to credible estimates of water use is critical for making optimal operational decisions and investment plans to ensure reliable and affordable provisioning of water. Furthermore, identifying the key predictors of water use is important for regulators to promote sustainable development policies to reduce water use. In this paper, we propose a data-driven framework, grounded in statistical learning theory, to develop a rigorously evaluated predictive model of state-level, per capita water use in the United States as a function of various geographic, climatic, and socioeconomic variables. Specifically, we compare the accuracy of various statistical methods in predicting the state-level, per capita water use and find that the model based on the random forest algorithm outperforms all other models. We then leverage the random forest model to identify key factors associated with high water-usage intensity among different sectors in the United States. More specifically, irrigated farming, thermoelectric energy generation, and urbanization were identified as the most water-intensive anthropogenic activities, on a per capita basis. Among the climate factors, precipitation was found to be a key predictor of per capita water use, with drier conditions associated with higher water usage. Overall, our study highlights the utility of leveraging data-driven modeling to gain valuable insights related to the water use patterns across expansive geographical areas.