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SIMULATING HYPOXIA IN NARRAGANSETT BAY: DEVELOPING AND APPLYING A MECHANISTIC WATER QUALITY FATE AND TRANSPORT MODEL
Citation:
Knightes, Chris. SIMULATING HYPOXIA IN NARRAGANSETT BAY: DEVELOPING AND APPLYING A MECHANISTIC WATER QUALITY FATE AND TRANSPORT MODEL. New England Estuarine Research Society Fall Meeting 2022, Providence, RI, November 17 - 19, 2022.
Impact/Purpose:
Narraganset Bay supports economic, recreation, and tourism in Rhode Island and New England. Over decades of human development and influence, Narragansett Bay has large zones of periodic low dissolved oxygen concentrations. In this work, we have developed a modeling framework, including hydrodynamics and water quality, to simulate nutrients, dissolved oxygen, and phytoplankton through the Bay, with time and space. Through these simulations, we hope to improve our understanding of the processes governing dissolved oxygen. The results suggest the importance of direct releases of nutrients (e.g., waste water treatment plant releases) controling dissolved oxygen concentrations in the surface waters while sediment processes goven dissolved oxygen concentrations in the deeper waters. Having a mechanistic modeling framework serves as a foundation for improving our understanding of the impact of climate change, land-use change, and management strategies on dissolved oxygen concentrations across the Bay.
Description:
Narraganset Bay has large zones of periodic observed hypoxia. To improve our understanding of processes governing dissolved oxygen concentrations [DO] in the Bay, a multi-media, mechanistic, mass balance fate and transport model has been developed and applied. Simulations captured the general spatial trends and patterns in [DO] and phytoplankton, though simulations were unable to capture large daily variations. Mechanistic scenario testing of nutrient loads investigated the drivers for hypoxia and showed that tributary sources of nitrogen affected upper layers of [DO], while sediment oxygen demand and nutrient fluxes affected deeper waters. These results suggest the importance of understanding and simulating the legacy effects of historic nutrient loading to the Bay to understand the magnitude and timing of long-term recovery due to management strategies. This work provides a foundation for future studies for longer term simulations incorporating management strategies, land-use change, acidification, and climate change.
