Social distancing policies (SDPs) implemented throughout the United States in response to COVID-19 have led to spatial and temporal shifts in drinking water demand and, for water utilities, created sociotechnical challenges. During this unique period, many water utilities have been forced to operate outside of design conditions with reduced workforce and financial capacities. Few studies have examined how water utilities respond to a pandemic; such methods are even absent from many emergency response plans. Here, we documented how utilities have been impacted by the COVID-19 pandemic. We conducted a qualitative analysis of 30 interviews with 53 practitioners spanning 28 U.S. water utilities. Our aim was to, first, understand the challenges experienced by utilities and changes to operations (e.g., demand and deficit accounts) and, second, to document utilities' responses. Results showed that to maintain service continuity and implement SDPs, utilities had to overcome various challenges. These include supply chain issues, spatiotemporal changes in demand, and financial losses, and these challenges were largely dependent on the type of customers served (e.g., commercial or residential). Examples of utilities' responses include proactively ordering extra supplies and postponing capital projects. Although utilities' adaptations ensured the immediate provision of water services, their responses might have negative repercussions in the future (e.g., delayed projects contributing to aging infrastructure).

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A state‐space representation of water quality (WQ) dynamics describing disinfectant (e.g., chlorine) transport dynamics in drinking water distribution networks has been recently proposed. Such representation is a byproduct of space‐ and time‐discretization of the partial differential equations modeling transport dynamics. This results in a large state‐space dimension even for small networks with tens of nodes. Although such a state‐space model provides a model‐driven approach to predict WQ dynamics, incorporating it into model‐based control algorithms or state estimators for large networks is challenging and at times intractable. To that end, this paper investigates model order reduction (MOR) methods for WQ dynamics with the objective of performing post‐reduction feedback control. The presented investigation focuses on reducing state‐dimension by orders of magnitude, the stability of the MOR methods, and the application of these methods to model predictive control.