Nonpoint Source Pollutant Monitering on the Ponaganset River Watershed

Abstract

The Ponaganset River Basin consists of an area of 14.4 mi2 located in the town of Foster, Rhode Island. This area is located within the northwest region of the Scituate Watershed. The source of this river comes from the Ponaganset Reservoir with an area of 2.1 mi2 and a storage capacity of 742 MG. Water quality samples were collected at United States Geological Survey (USGS) site (01115187) which is approximately 5 miles down stream from the reservoir and 0.4 miles upstream from Barden Reservoir. The Ponaganset River has the largest mean daily discharge of all the sampling locations in the Scituate Reservoir watershed. The concept of this analysis originated with the 1995 Water Quality Protection Plan which sited a lack of wet weather data on the Scituate Reservoir Watershed. No wet weather data was collected from the watershed between 1995 and 2003. In 2003, the Water Quality Protection Plan again sited a lack of wet weather data on the Scituate Reservoir Watershed as one of the major weaknesses. The plan recommended the need to determine potential wet weather impacts as well as the potential sources of those impacts on the environment. The objective of this analysis was to determine non-point sources of pollutants which contribute to the river and establish a preliminary wet weather monitoring program to determine pollutant loads contributed by stormwater runoff. In addition, this analysis was intended to establish a procedure to extrapolate wet and dry weather data from a characteristic sub watershed to the entire Scituate Watershed. In this study, the Ponaganset River site was selected based upon preliminary research, historical water data, and range of flows for the selected site. The data collected during wet weather sampling provided insight into the behavior of the sources during various storm events as well as storm characteristics. The information acquired for use in this analysis was used to explore load characteristics using linear and multiple regression models to predict loads then apply them to monthly and annual parameter data to determine if the site is either influenced more with dry weather or wet weather. As more stringent water quality standards continue to increase, monitoring the health of the watershed will increase as well. Evaluating the water quality under dry and wet weather conditions seems fitting to answer some of these questions in addition to fulfilling the requirements of this thesis. In this study, water quality results, loads, and