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Abstract Combined sewer overflows (CSOs) occur when untreated raw sewage mixed with rainwater, runoff, or snowmelt is released during or after a storm in any community with a combined sewer system (CSS). Climate change makes CSOs worse in many locales; as the frequency and severity of wet weather events increases, so do the frequency and volume of CSO events. CSOs pose risks to humans and the environment, and as such, CSS communities are under regulatory pressure to reduce CSOs. Yet, CSS communities lack the tools needed, such as performance indicators, to assess CSS performance. Using the city of Cumberland, Maryland as a case study, we use public data on CSOs and precipitation over a span of 16 years to identify a new critical rainfall intensity threshold that triggers likely CSO incidence, and a multiple linear regression model to predict CSO volume using rainfall event characteristics. Together, this indicator and modeling approach can help CSS communities assess the performance of their CSS over time, especially to evaluate the effectiveness of efforts to reduce CSOs. 

As critical infrastructure systems consider whether and how to adapt and build resilience to climate variability and change, more research is needed to holistically explore the dynamics of resilience-building changes over time. We begin to fill this gap with a case study of the Rhode Island public wastewater sector. The Rhode Island Department of Environmental Management has invested significant funding, technical assistance, capacity building, and regulatory pressure to help publicly owned wastewater systems build resilience to climate challenges since 2010. To trace, assess, and understand the dynamics of resilience-building efforts over time, we interviewed wastewater utility and municipal personnel using event history calendars (EHCs). EHCs helped respondents recall details of relevant events, including potentially disruptive storms/incidents, and how they responded, including large- and small-scale adaptations, during the study period (2010–2023). We used EHCs to trace resilience and transformation capacities over time, and to analyze and predict movement toward transformational adaptation. We found that factors that best enable movement from incremental to transformational changes include unlocking capacity, or the organizational cultural value of in-depth learning/change, and a suite of contextual supports – new information, forward-looking collaborators, and stable funding sources – which require buy-in across levels of governance. We also found that, with organizational culture considered, experiencing disruption is not predictive of pursuing transformative adaptation. This suggests decision-making strategies for states, local jurisdictions, and utility managers to support climate adaptation and resilience in critical infrastructure, such as eliminating path-dependencies and silos, lowering thresholds for action, and leveraging networks to support moving toward transformation.