Researchers are investigating the problem of estimating households with potable water service outages soon after an earthquake. Most of these modeling approaches are computationally intensive, have large proprietary data collection requirements or lack precision, making them unfeasible for rapid assessment, prioritization, and allocation of emergency water resources in large, complex disasters. This study proposes a new simplified analytical method—performed without proprietary water pipeline data—to estimate water supply needs after earthquakes, and a case study of its application in the HayWired earthquake scenario. In the HayWired scenario—a moment magnitude (M_w) 7.0 Hayward Fault earthquake in the San Francisco Bay Area, California (USA)—an analysis of potable water supply in two water utility districts was performed using the University of Colorado Water Network (CUWNet) model. In the case study, application of the simplified method extends these estimates of household water service outage to the nine counties adjacent to the San Francisco Bay, aggregated by a ~250 m² (nine-arcsecond) grid. The study estimates about 1.38 million households (3.7 million residents) out of 7.6 million residents (2017, ambient, nighttime population) with potable water service outage soon after the earthquake—about an 8% increase from the HayWired scenario estimates.

Water insecurity has emerged as a global issue, leading to inadequate access to clean water, increased health risks, and barriers to sustainable development. While most research on household-level water insecurity has focused on the Global South, these challenges are also present in high-income countries due to economic issues, aging infrastructure, climate change, and other extreme events. For instance, the 2023 East Palestine, Ohio chemical spill and fires—the focus of the current study—raised widespread concerns about water safety and long-term contamination to private wells. Despite increasing water crises in the United States (e.g. Flint, MI; Jackson, MS), limited research has explored how household water insecurity is experienced in high-income countries impacted by disasters. To address this gap, we investigated factors associated with household water insecurity following the East Palestine crisis. Departing from existing work, our study applies the Household Water Insecurity Experiences (HWISE) scale in a high-income country during an industrial disaster. To do so, we conducted a community survey (n = 259) and used a combination of logistic regression and gradient boosting machine learning (ML) models to identify key predictor variables and complex, non-linear interactions between households and their water resources. The integration of traditional statistical methods with advanced ML techniques in this context represents a novel methodological contribution. Results showed that 31% of respondents experienced household water insecurity, with factors such as changing water sources, perceptions of water safety, bottled water source, and renter status significantly influencing these experiences. Notably, we found that the source of bottled water matters—purchasing bottled water can lead to increased household water insecurity, whereas water received through donations and aid can reduce these experiences. This work reveals critical leverage points for policy change to ensure access to safe drinking water after industrial disasters.