Science Inventory

Using monitoring and mechanistic modeling to improve understanding of eutrophication in a shallow New England estuary

Citation:

Cashel, Finnian S., Christopher D. Knightes, C. Lupo, T. Iott, K. Streich, C. Conville, Timothy W. Bridges, AND I. Dombroski. Using monitoring and mechanistic modeling to improve understanding of eutrophication in a shallow New England estuary. JOURNAL OF ENVIRONMENTAL MANAGEMENT. Elsevier Science Ltd, New York, NY, 355(March 2024):120478, (2024). https://doi.org/10.1016/j.jenvman.2024.120478

Impact/Purpose:

A multi-media, mechanistic modeling framework was developed and designed to simulate eutrophication in a small, shallow New England estuary. The Pawcatuck River and Little Narragansett Bay are a small, shallow estuarine system forming a border between Rhode Island and Connecticut. A one-dimensional model was developed using two models, HSPF (Hydrological Simulation Program - FORTRAN) and WASP (Water Quality Analysis Simulation Program), which Office of Water recognizes as supported by US EPA for TMDL development. This work serves to investigate how well a one-dimensional model can capture the processes governing dissolved oxygen, algae, and macroalgae in this system. This work investigates the use of discrete, grab samples and continuous data for model calibration and verification, as well as capturing the important processes to provide the groundwork for developing a TMDL. This systems has two waste water treatment plants, one in CT and one in RI, each releasing into the Pawcatuck River. The Pawcatuck River drains the Wood-Pawcatuck River watershed, which is a large, rural watershed, with high dissolved organic carbon concentrations. Using the modeling framework, this work serves to improve our understanding and model formulation of a small, shallow estuary, and to move towards improving our understanding on the appropriate level of model complexity.

Description:

Anthropogenic nutrient loading has resulted in eutrophication and habitat degradation within estuaries. Study of eutrophication in estuaries has often focused on larger systems, while there has been increasing interest in understanding the governing processes in smaller systems. In this study, we incorporate both monitoring data and mechanistic modeling to improve our understanding of eutrophication in a small, shallow New England estuary. High-frequency continuous and discrete water quality samples were collected from 2018 to 2020 along a salinity gradient and at varying depth to provide temporal and spatial resolution of the system. Conditions of this estuary were simulated using the Hydrological Simulation Program – FORTRAN (HSPF) and the Water Quality Analysis Simulation Program (WASP) to develop a mechanistic, numerical fate and transport model. Our findings suggest complex hydrodynamics with three distinct salinity gradients and variability in salinity concentration upstream. Simulated and observed nutrient trends demonstrated decreasing total nitrogen concentration moving downstream and low total phosphorus concentration throughout the system. Simulated nutrient depletion and shading via macroalgae suggest their importance in similar modeling initiatives. Dynamic spatiotemporal variability in dissolved oxygen concentrations ([DO]) resulted from hydrodynamic and ecological processes such as large, rapid swings in phytoplankton. Carbonaceous biological oxygen demand was suggested to be the driver of hypoxia in surface waters, while sediment oxygen demand may drive low [DO] in the stratified, benthic waters. These findings suggest that the coordination of monitoring and modeling was important to understanding the governing mechanisms of eutrophication and hypoxia. Insights from this study could be used to support regional management strategies to increase [DO], improve water clarity, and recover indigenous seagrass beds. This work has the potential to inform future study and management of small, complex estuaries.

Record Details:

Record Type:DOCUMENT( JOURNAL/ PEER REVIEWED JOURNAL)
Product Published Date:03/02/2024
Record Last Revised:04/11/2024
OMB Category:Other
Record ID: 361089