  Considering the interdependencies between water and electricity use is
critical for ensuring conservation measures are successful in lowering the net
water and electricity use in a city. This water-electricity demand nexus will
become even more important as cities continue to grow, causing water and
electricity utilities additional stress, especially given the likely impacts of
future global climatic and socioeconomic changes. Here, we propose a modeling
framework based in statistical learning theory for predicting the
climate-sensitive portion of the coupled water-electricity demand nexus. The
predictive models were built and tested on six Midwestern cities. The results
showed that water use was better predicted than electricity use, indicating
that water use is slightly more sensitive to climate than electricity use.
Additionally, the results demonstrated the importance of the variability in the
El Nino/Southern Oscillation index, which explained the majority of the
covariance in the water-electricity nexus. Our modeling results suggest that
stronger El Ninos lead to an overall increase in water and electricity use in
these cities. The integrated modeling framework presented here can be used to
characterize the climate-related sensitivity of the water-electricity demand
nexus, accounting for the coupled water and electricity use rather than
modeling them separately, as independent variables.
