Estimation of Total Nitrogen and Phosphorus in New England Streams Using Spatially Referenced Regression Models

Abstract

The U.S. Geological Survey (USGS), in cooperation with the U.S. Environmental Protection Agency (USEPA) and the New England Interstate Water Pollution Control Commission (NEWIPCC), has developed a water-quality model, called SPARROW (Spatially Referenced Regressions on Watershed Attributes), to assist in regional total maximum daily load (TMDL) and nutrient-criteria activities in New England. SPARROW is a spatially detailed, statistical model that uses regression equations to relate total nitrogen and phosphorus (nutrient) stream loads to nutrient sources and watershed characteristics. The statistical relations in these equations are then used to predict nutrient loads in unmonitored streams. The New England SPARROW models are built using a hydrologic network of 42,000 stream reaches and associated watersheds. Watershed boundaries are defined for each stream reach in the network through the use of a digital elevation model and existing digitized watershed divides. Nutrient source data is from permitted wastewater discharge data from USEPA's Permit Compliance System (PCS), various land-use sources, and atmospheric deposition. Physical watershed characteristics include drainage area, land use, streamflow, time-of-travel, stream density, percent wetlands, slope of the land surface, and soil permeability. The New England SPARROW models for total nitrogen and total phosphorus have R-squared values of 0.95 and 0.94, with mean square errors of 0.16 and 0.23, respectively. Variables that were statistically significant in the total nitrogen model include permitted municipal-wastewater discharges, atmospheric deposition, agricultural area, and developed land area. Total nitrogen stream-loss rates were significant only in streams with average annual flows less than or equal to 2.83 cubic meters per second. In streams larger than this, there is nondetectable in-stream loss of annual total nitrogen in New England. Variables that were statistically significant in the total phosphorus model include discharges for municipal wastewater-treatment facilities and pulp and paper facilities, developed land area, agricultural area, and forested area. For total phosphorus, loss rates were significant for reservoirs with surface areas of 10 square kilometers or less, and in streams with flows less than or equal to 2.83 cubic meters per second. Applications of SPARROW for evaluating nutrient loading in New England waters include estimates of the spatial distributions of total nitrogen and phosphorus yields, sources of the nutrients, and the potential for delivery of those yields to receiving waters. This information can be used to (1) predict ranges in nutrient levels in surface waters, (2) identify the environmental variables that are statistically significant predictors of nutrient levels in streams, (3) evaluate monitoring efforts for better determination of nutrient loads, and (4) evaluate management options for reducing nutrient loads to achieve water-quality goals.

Relating land cover to stream properties in southern Chilean watersheds: trade-off between geographic scale, sample size, and explicative power

“…Geomorphology deserves attention because some of its components, such as the watersheds' slopes, can also control the delivery of nutrients to streams (Dillon and Molot 1992;Moore et al 2004). All these variables were calculated using the Spatial Analyst extension of ArcView 3.2.…”

“…Precipitation is an important predictor because it can affect the delivery of nutrients from the land surface to streams by determining the volume and rate of runoff (Moore et al 2004). Rainfall was recorded with rain gauge collectors connected to a HOBO Data Logger (Onset Computer Corporation, Massachusetts, USA).…”

“…However, the percentage of variance explained by land cover is usually moderate to low (R 2 £ 0.55) (Herlihy et al 1998;Strayer et al 2003). On the other hand, Moore et al (2004) used a more restricted data set, from 67 watersheds, which produced a much better fit using a more complete set of predictors and non-linear models. We hypothesize that the reduced explicative power of studies that encompass more extensive areas is due to the amount of variation, which increases with larger sample sizes and with the broader geographic scales of the areas in which the watersheds are distributed.…”

“…Further, plantations also produce sediment exports to the water that are between 15 and 81% greater than those from native forests (Otero et al 1994). In addition to land cover, other factors, such as soil characteristics, slope, precipitation, and in-stream-losses of nutrients can influence concentrations of chemicals in streams (Dillon and Molot 1992;Moore et al 2004). …”

A combined watershed–water quality modeling analysis of the Lake Waco reservoir: I. Calibration and confirmation of predicted water quality

“…Differences exist between models that are important for different goals. Diagnostic hydrologic models have been developed to rigorously assess watershed properties important to water nutrients based on statistical analyses (Moore et al 2004). In contrast, prognostic models are based on physical representations of movement of materials, energy transfer and biogeochemistry and are characteristically predictive and mechanistic (Arnold and Fohrer 2005).…”