CAREER: Characterizing the Unseen Water Quality Consequences of Sunny-Day Floods in Nearshore Waters

Sea level rise has caused frequent tidal or “sunny-day” floods in coastal areas in the USA and around the world. Tidal floods occur when high tides rise up into sewers, streets, and sidewalks. During this process, the floods can move pollutants from land into coastal waters.

In spite of the strong potential for this pollution to impact human and ecological health, coastal water quality hazards caused by tidal floods are not well understood. The goal of this CAREER project is to study the ways in which tidal floods impact coastal water quality specifically focusing on fecal contamination. This goal will be achieved through the use of highly novel sensing and modeling systems that will be developed to monitor, explain, and predict water quality following tidal floods.

Successful completion of this project will help engineers, local governments, and policymakers understand and mitigate risks caused by persistent tidal floods. Science teachers and undergraduate students will participate in the research and engage middle and high school students in environmental engineering, coastal sustainability, and data sciences. These efforts will increase scientific literacy and build the Nation’s STEM workforce.

The overall goal of this project is to develop a fundamental understanding of how tidal floods and stormwater runoff interact to influence pollution loading in nearshore waters. The specific focus of this research is on Fecal Indicator Bacteria (FIB), a commonly used marker of sewage pollution, in order to develop explanatory and predictive models to inform the design of sustainable coastal stormwater infrastructure.

To achieve this goal, the project will carry out the following research objectives: (1) measure nearshore FIB concentrations at high frequencies using novel sensing technologies, (2) develop mechanistic and statistical models to explain FIB concentration dynamics during tidal floods and storm events, as well as over longer timescales of multiple tidal cycles; and (3) develop a predictive model framework for generating near-term forecasts of bacterial contamination risk in nearshore waters. Successful completion of this project will advance process-based understanding of how storms and tidal floods influence nearshore FIB loading, information that is needed to develop engineering solutions to mitigate hazards caused by climate change.

The use of FIB concentrations (a widely used pollution marker) will facilitate the use of this research by a diverse group of regulators, natural resource managers, and other decision-makers. Additionally, the investigator will develop educational programming that will: (1) train undergraduate students and secondary school science teachers in foundational principles of coastal observation and data science through involvement in research, (2) engage ~12,000 middle and high school students in coastal and data sciences, and (3) create an open-access digital repository of coding lessons that have learning objectives focused on environmental engineering foundations and data analysis skills.

Recruitment efforts will focus on engaging students from underrepresented groups in environmental engineering, as well as teachers who serve large student populations from historically marginalized groups.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.