Seasonally varying contributions of contemporaneous and lagged sources of instream total nitrogen and phosphorus load across the Illinois River basin.

Quantifying nutrient sources in streams, their temporal and spatial variability, and drivers of that variability can support effective water resources management. Yet a lack of data and modeling capabilities has previously prevented comprehensive quantification across both space and time. Here a dynamic SPARROW (Spatially Referenced Regressions on Watershed attributes) model that accounts for a lagged delivery of nutrients to streams was developed and applied to simulate seasonal and source-specific total nitrogen (TN) and total phosphorus (TP) loads in streams across the Illinois River basin (IRB).

Evaluating the suitability of large-scale datasets to estimate nitrogen loads and yields across different spatial scales.

Decision makers are often confronted with inadequate information to predict nutrient loads and yields in freshwater ecosystems at large spatial scales. We evaluate the potential of using data mapped at large spatial scales (regional to global) and often coarse resolution to predict nitrogen yields at varying smaller scales (e.g.

SPARROW Models Used to Understand Nutrient Sources in the Mississippi/Atchafalaya River Basin.

Nitrogen (N) and phosphorus (P) loading from the Mississippi/Atchafalaya River Basin (MARB) has been linked to hypoxia in the Gulf of Mexico. To describe where and from what sources those loads originate, SPAtially Referenced Regression On Watershed attributes (SPARROW) models were constructed for the MARB using geospatial datasets for 2002, including inputs from wastewater treatment plants (WWTPs), and calibration sites throughout the MARB. Previous studies found that highest N and P yields were from the north-central part of the MARB (Corn Belt).

Vulnerability of streams to legacy nitrate sources.

The influence of hydrogeologic setting on the susceptibility of streams to legacy nitrate was examined at seven study sites having a wide range of base flow index (BFI) values. BFI is the ratio of base flow to total streamflow volume. The portion of annual stream nitrate loads from base flow was strongly correlated with BFI.

Nutrient Inputs to the Laurentian Great Lakes by Source and Watershed Estimated Using SPARROW Watershed Models.

Nutrient input to the Laurentian Great Lakes continues to cause problems with eutrophication. To reduce the extent and severity of these problems, target nutrient loads were established and Total Maximum Daily Loads are being developed for many tributaries. Without detailed loading information it is difficult to determine if the targets are being met and how to prioritize rehabilitation efforts.

A Multi-Agency Nutrient Dataset Used to Estimate Loads, Improve Monitoring Design, and Calibrate Regional Nutrient SPARROW Models.

Stream-loading information was compiled from federal, state, and local agencies, and selected universities as part of an effort to develop regional SPAtially Referenced Regressions On Watershed attributes (SPARROW) models to help describe the distribution, sources, and transport of nutrients in streams throughout much of the United States. After screening, 2,739 sites, sampled by 73 agencies, were identified as having suitable data for calculating long-term mean annual nutrient loads required for SPARROW model calibration. These sites had a wide range in nutrient concentrations, loads, and yields, and environmental characteristics in their basins.

Incorporating Uncertainty Into the Ranking of SPARROW Model Nutrient Yields From Mississippi/Atchafalaya River Basin Watersheds.

Excessive loads of nutrients transported by tributary rivers have been linked to hypoxia in the Gulf of Mexico. Management efforts to reduce the hypoxic zone in the Gulf of Mexico and improve the water quality of rivers and streams could benefit from targeting nutrient reductions toward watersheds with the highest nutrient yields delivered to sensitive downstream waters. One challenge is that most conventional watershed modeling approaches (e.

Agriculture-related trends in groundwater quality of the glacial deposits aquifer, central Wisconsin.

Measuring and understanding trends in groundwater quality is necessary for determining whether changes in land-management practices have an effect on groundwater quality. This paper describes an approach that was used to measure and understand trends using data from two groundwater studies conducted in central Wisconsin as part of the USGS NAWQA program. One of the key components of this approach, determining the age of sampled groundwater, gave a temporal component to the snapshots of water quality that were obtained through synoptic-sampling efforts.

Linking ground-water age and chemistry data along flow paths: implications for trends and transformations of nitrate and pesticides.

Tracer-based ground-water ages, along with the concentrations of pesticides, nitrogen species, and other redox-active constituents, were used to evaluate the trends and transformations of agricultural chemicals along flow paths in diverse hydrogeologic settings. A range of conditions affecting the transformation of nitrate and pesticides (e.g.

A regional classification scheme for estimating reference water quality in streams using land-use-adjusted spatial regression-tree analysis.

Various approaches are used to subdivide large areas into regions containing streams that have similar reference or background water quality and that respond similarly to different factors. For many applications, such as establishing reference conditions, it is preferable to use physical characteristics that are not affected by human activities to delineate these regions. However, most approaches, such as ecoregion classifications, rely on land use to delineate regions or have difficulties compensating for the effects of land use.

Environmental water-quality zones for streams: a regional classification scheme.

Various approaches have been used to classify large geographical areas into smaller regions of similar water quality or extrapolate water-quality data from a few streams to other unmonitored streams. A combination of some of the strengths of existing techniques is used to develop a new approach for these purposes. In this new approach, referred to here as SPARTA (SPAtial Regression-Tree Analysis), environmental characteristics for each monitored stream are first quantified using a Geographic Information System (GIS) and then regression-tree analysis is used to determine which characteristics are most statistically important in describing the distribution of a specific water-quality constituent.