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\begin{document}

\title{Enhancing Municipal Water System Planning and Operations Through
Climate-Sensitive Demand Estimates}



\author[1]{Ryan Curtis Johnson}%
\author[2]{Steven J. Burian}%
\author[3]{Carlos A Oroza}%
\author[4]{James Halgren}%
\author[3]{Trevor Irons}%
\author[3]{Daniyal Hassan}%
\author[3]{Jiada Li}%
\author[5]{Tracie Kirkham}%
\author[5]{Jesse Stewart}%
\author[5]{Laura Briefer}%
\author[4]{Danyal Aziz}%
\author[3]{Emily Baur}%
\affil[1]{University of Alabama, Tuscaloosa}%
\affil[2]{University of Arkansas at Fayetteville}%
\affil[3]{University of Utah}%
\affil[4]{University of Alabama}%
\affil[5]{Salt Lake City}%


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  \date{May 23, 2024}


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\begin{abstract}
High seasonality and interannual climate patterns drive the western
U.S.'s water supply and demand variability. While the mean and variance
of supply and demand drivers are changing with climate and urbanization,
the metrics of reliability, resilience, and vulnerability (RRV) that
guide urban water systems (UWS) seasonal management and operations tend
to be built on assumptions of stationarity. In this research, we use
documented performance of a real-world UWS as a testbed to investigate
how RRV metrics -- and therefore UWS planning and operations guidance --
change in response to demands modeled with and without assumptions of
stationarity during dry, average, and wet hydroclimate conditions. The
results indicate an assumption of stationary demands leads to large
differences between simulated and observed RRV metrics for all supply
scenarios, especially in supply-limiting conditions when the peak
severity is 129\% from the observed. The management implications of
relying on stationary demands are severe: if seasonal operational
decisions were made on these model results, managers might over-estimate
seasonal out-of-district water requests by 50\%. In contrast, when using
non-stationary demands, one can expect system performance error
reduction between 30\% to 60\% for average and dry climate conditions,
respectively, and accurate RRV metrics. Our results further indicate
that this UWS is more sensitive to percent changes in per-capita demand
relative to percent changes in supply, but because the supply
variability is so much greater (158\% vs. demand range of 28\%), we
suggest further work to examine the combined (and coupled) influence of
both factors in overall system performance.%
\end{abstract}%



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