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- USE OF LAGRANGIAN METHODS TO SIMULATE HEAVY STORM-INDUCED RIVER PLUME DYNAMICS AND RECREATIONAL WATER QUALITY IMPACTS IN THE NEARSHORE REGION OF SOUTHWESTERN LAKE MICHIGAN
- Weiskerger, Chelsea
- Electronic Theses & Dissertations
The Great Lakes are the primary source of drinking water for nearly 30 million people in the region. During storm events runoff from upstream watersheds and (combined) sewer overflows delivers pathogens to the Lakes. The pathogens are then transported to beaches and water intakes by the lake circulation, posing risks to human health. Fecal indicator organisms such as Escherichia coli are used to track pollution levels and to take proactive measures to manage coastal resources and to safeguard...
Show moreThe Great Lakes are the primary source of drinking water for nearly 30 million people in the region. During storm events runoff from upstream watersheds and (combined) sewer overflows delivers pathogens to the Lakes. The pathogens are then transported to beaches and water intakes by the lake circulation, posing risks to human health. Fecal indicator organisms such as Escherichia coli are used to track pollution levels and to take proactive measures to manage coastal resources and to safeguard public health by closing beaches to the public, issuing swimming advisories, etc. Predictive modeling of coastal water quality continues to be an attractive approach to generate water quality forecasts and to gain insights into key processes. Although progress has been made in understanding and quantifying the impacts of tributary loading and river plumes on microbial pollution at beaches, the impacts of extreme storm events on coastal water quality are not well-understood. As the frequency and intensity of storm events increase, the pollution footprint of extreme storm events has not been quantified in a way that can be used to inform policy. Complex nearshore features, including irregular coastlines and coastal structures calls for high-resolution modeling that is computationally demanding. While traditional Eulerian approaches to plume modeling have been previously used, comparisons with available observed plume data indicated that Lagrangian particle tracking improves prediction of plume dimensions (and hence risks) in southwestern Lake Michigan. Therefore, coupled hydrodynamic and reactive particle tracking models were developed and tested to simulate the complex dynamics of multiple river plumes induced by extreme storm events in the Chicago area in southwestern Lake Michigan. The present-day Chicago River normally flows to the Mississippi River and discharges into Lake Michigan only during “backflow” events triggered by these storms. Simulations of extreme storm-induced river plumes during years 2008, 2010, 2011, 2013 and 2017 were reported and models tested using available data on currents, water temperatures, concentrations of indicator bacteria (E. coli) and the spatial extent of turbidity plumes using MODIS Terra satellite imagery. Results suggest that plumes associated with the extreme storms persist along the Chicago shoreline for up to 24 days after the commencement of backflow release and that plume areas of influence range from 7.9 to 291 km2 in the nearshore. Plume spatiotemporal dynamics were largely related to the volume of water released via backflow events and the duration of the backflow releases. Empirical relations were proposed to allow beach and stormwater managers to predict plume spatiotemporal dynamics in real time. Model results from a Lagrangian E. coli fate and transport model were compared against monitoring data collected at 16-18 beaches during and after backflow events in 2010 and 2011. Results indicate that all Chicago Park District beaches are susceptible to E. coli concentrations that exceed USEPA thresholds for safe recreation after extreme storms. Therefore, the current approach to beach management, which involves closing all beaches during and immediately after backflow events, is likely prudent. However, results also suggest that beaches are probably being reopened prematurely after storm events, as beaches may be at risk for degraded water quality for multiple days, post backflow event. To address data gaps, we recommend that future research focus on the collection of additional in situ hydrometeorological and water quality data during and after extreme storms and backflow events. These data may be collected using unmanned aerial vehicles or autonomous sensor systems.