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Scott Worland: "Exploring the drivers of municipal water use in the U.S. using hierarchical-Bayesian models"
Scott Worland is a hydrologist who applies statistical learning methods to answer pressing earth science questions. He hopes to bring strong disciplinary knowledge to interdisciplinary research teams. His research interests include statistical hydrology, machine learning, and Bayesian statistics.

Population growth and climate change are often cited as the likely drivers of future increases in municipal water withdrawals in the U.S. Are there other important explanatory variables? Are the relationships between water-use and explanatory variables uniform across the nation? This talk explores these questions by analyzing the relationship between municipal water-use and multiple environmental, social, economic, behavioral, and policy variables in addition to population growth and changes in the local climate. These additional variables include, among others, water yield, income inequality, regional price parity, education attainment, voting habits, water price, and water conservation policies. We also explore how three different grouping variables (climate region, urban class, and primary economic sector) affect the associations between water-use and the explanatory variables. The results indicate that most important explanatory variables are average precipitation, person per household, partisan voting, water price, and regional price parity. Although, the results also suggests that the controls on water use are not uniform across the contiguous U.S., and national-scale water use assessments must account for regional variability in order to understand the present drivers of water use, and project likely changes for the future.

Apr 13, 2018 1:30 PM in Eastern Time (US and Canada)

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