ABSTRACT
Hydraulic models can provide efficient and cost-effective ways for water utilities to evaluate changes in operating conditions (e.g., population dynamics, disasters), thereby increasing system resiliency during crises. Unfortunately, model development remains out of reach for many utilities because of high software costs, data needs, or personnel requirements. This study seeks to classify hydraulic modeling data needs, identify success factors and challenges associated with model development, and determine whether modeling a subzone of a larger water distribution network can provide useful insights during a crisis, specifically the COVID-19 pandemic. At the pandemic onset, we began developing a hydraulic model of the water distribution system of the University of Texas at Austin campus—a subsystem of the water distribution network of Austin, Texas—to understand how spatiotemporal changes in water demands impacted system performance. We found that the completed model can offer useful insight into the impacts of demand changes within the modeled subsystem (e.g., potential locations of water stagnation). However, the data collection and processing challenges encountered (e.g., siloed collection efforts, lack of standardization, lengthy processing) reflect barriers to model development and use. The amount of time required to gather and process the necessary data shows that model development cannot occur during a time-sensitive crisis, likely rendering any insight too late for use. Here, we make recommendations to address data-related challenges and support utilities in incorporating hydraulic modeling into emergency planning.
ABSTRACT
When engineers design and manage a building's water and electricity utilities, they must make assumptions about resource use. These assumptions are often challenged when unexpected changes in demand occur, such as the spatial and temporal changes observed during the coronavirus (COVID-19) pandemic. Social distancing policies (SDPs) enacted led many universities to close their campuses and implement remote learning, impacting utility consumption patterns. Yet, little is known about how consumption changed at the building level. Here, we aim to understand how water and electricity consumption changed during the pandemic by identifying characteristic weekly demand profiles and understanding how these changes were related to regulatory and social systems. We performed k-means clustering on utility demand data measured before and as the pandemic evolved from five buildings of different types at the University of Texas at Austin. As expected, after SDPs were enacted both water and electricity use shifted, with most buildings seeing a sharp initial decline that remained low until the university partially reopened. In contrast to electricity use, we found that water use was tightly coupled with SDPs. Our study provides actionable information for managers to mitigate negative impacts (e.g., water stagnation) and capitalize on opportunities to minimize resource use.