SCC-IRG Track 2: Resilient Water Systems: Integrating Environmental Sensor Networks and Real-Time Forecasting to Adaptively Manage Drinking Water Quality and Build Social Trust

1737424 (Carey). The freshwater lakes and reservoirs that provide the majority of Americans with their drinking water face increasing threats to water quality. Nutrient pollution, contaminants, and land use change can lead to low oxygen concentrations and algal blooms, which can result in elevated metal concentrations, fish and bird kills, thick algal scums, noxious odors, and overall toxic water unsafe for drinking. These adverse outcomes may be prevented if drinking water managers have the information needed to act preemptively. To increase the resilience of water supplies, this project will develop a smart water system that integrates smart and connected (S&C) technology and adaptive management to ensure safe drinking water for communities. The smart water system will consist of sensor networks embedded in a drinking water reservoir to reduce delays and enhance feedbacks between the detection of water quality degradation and decisive management action to mitigate such threats. This increased capacity to ensure sustained water quality can in turn build both public confidence and meaningful engagement with drinking water institutions. This project will connect the networks of high-frequency sensors with secure cyberinfrastructure to develop innovative, real-time water quality prediction models and tools for more effective management. Finally, these models will be used to educate local residents and students about the use of S&CC technology to manage their drinking water.<br/> <br/>By embedding an integrated sensor network in a drinking water reservoir, this project integrates expertise from nine disciplines to study the complete feedback loop of how S&C technologies can improve drinking water management, water quality, and ultimately community well-being. The project will use novel sensor technology to monitor a drinking water supply reservoir and its catchment, and to develop and evaluate a new model-data fusion approach that will advance the field of environmental forecasting. These forecasts will be used to create decision-making tools for reservoir managers that will be evaluated for their usability. In addition, teaching materials will be developed to create a curriculum that exposes high school students to data emerging from S&C technology and increase their interest in and preparedness for careers in STEM. Finally, the project will assess public perception of the adoption and use of S&CC technologies by utilities to improve drinking water quality as well as the relationship between this perception, trust in the utility, and acceptance of the S&CC technology. This enhanced understanding and confidence may lead to increased social capital, permitting an evaluation of the degree to which S&CC technologies can increase both ecosystem resilience of drinking water quality in supply reservoirs and community resilience by increasing the public's trust in their water systems.