Date of Award
Level of Access Assigned by Author
Master of Science (MS)
Second Committee Member
Third Committee Member
Over the past two decades, total nitrogen (TN) concentrations have increased in Casco Bay (CBEP 2015). The sources of the increased nitrogen are poorly understood but occur with simultaneous population growth and land use changes. The total riverine nitrogen load to Casco Bay was previously estimated by Liebman and Milstead (2012) using the United States Geologic Survey’s (USGS) SPAtially Referenced Regression On Watershed attributes (SPARROW) model. The SPARROW model uses watershed characteristics, regional monitoring data and nitrogen source data to estimate nitrogen loading but was not validated using measurements of nitrogen in the Casco Bay watershed. This study attempts to estimate the nitrogen load from three rivers (Presumpscot, Royal and Capisic Brook), that together account for 78% of Casco Bay’s watershed (87% of the freshwater flow) and generally represent two distinct types of sub basins in the larger watershed (i.e., forested and urban) (Liebman and Milstead 2012). The TN loading estimates from the three rivers were then extrapolated to provide an estimate for the total riverine load to Casco Bay and compared to the previously modeled TN load estimates. Additionally, the riverine TN load was compared to other known TN loads from the other major sources such as atmospheric deposition, combined sewage outfalls (CSO) and waste water treatment facility (WWTF) effluent.
Loading estimates for the three rivers were based on discharge and nitrogen concentration data from June 2017 – May 2018. We used Presumpscot River discharge from USGS gauge 01064118 near Westbook, Maine. Discharge for the Royal River was estimated using a historic watershed yield relationship with the nearby Sheepscot River which is still gauged. Capisic Brook discharge was estimated using the USGS Streamstats model. Water samples were collected at least monthly with an attempt to collect at both high and low flows. Water samples were analyzed for TN, Nitrate/Nitrite, and Ammonium. Water samples were not collected from December – March; concentrations for that time period are based on a discharge-concentration relationship, if present, or are assumed to be the average concentration of all data.
Collectively, the rivers in this study load less TN than is discharged by the area’s five largest WWTFs. Presumpscot River, while loading the greatest total mass of nitrogen (173 Mg N yr-1), loads the least per hectare (1.16 kg ha-1). Capisic Brook loads the most total nitrogen per hectare (7.71 kg N ha-1) and Royal River loads more nitrogen than Presumpscot but less than Capisic (3.79 kg N ha-1). Land use is correlated with the mass of nitrogen per hectare exported via the rivers. For example, Capisic Brook has the greatest percentage of developed land use types followed by Royal then Presumpscot. For comparison, if we assume the WWTF’s discharge to their permit limit, the total nitrogen load from these three rivers accounts for less than half of the total nitrogen mass discharged into Casco Bay from WWTFs (902 Mg N yr-1).
This study’s findings suggest that while non-point loading from river systems in Casco Bay contribute to the nitrogen content in the bay, they load less nitrogen than the areas of WWTFs. The amount of developed and agricultural land is correlated with the amount of nitrogen delivered to the bay by a river, which means that population growth will increase diffuse and point source loading in the future. And finally, this study’s estimates are in fair agreement with SPARROW’s TN loading estimate. More specifically, all estimates are within the same order of magnitude, but SPARROW’s estimates are a factor of two greater for the Presumpscot River and Capisic Brook. This study represents an important first step in understanding nitrogen loading to Maine’s most populous watershed and can be used to prioritize management of the largest nitrogen sources.
Gray, Whitley J., "Improved Estimates of Tributary Nitrogen Load to Casco Bay, Maine" (2019). Electronic Theses and Dissertations. 3087.