Innovations in Spatially Referenced Regression on Watershed Attributes (SPARROW) Modeling Toward Improved Monitoring, Estimation, and Understanding of Nutrients in Large Catchments
Recent and ongoing innovations in SPAtially Referenced Regression On Watershed attributes (SPARROW) modeling provide improved representation of nutrient dynamics and identify opportunities to optimize surface-water monitoring over large regional catchments. Efficient and effective monitoring, management, and restoration of surface-water quality requires adequate understanding of the upstream sources, fate, and transport of contaminants in contributing catchments. For nutrients, such understanding is often confounded by the multiple nitrogen and phosphorus sources and interacting hydrogeologic and geochemical conditions typical of large catchments. SPARROW models employ a hybrid empirical approach with deterministic constraints (such as flow routing and mass balance) to resolve such confounding factors affecting in-stream nutrient fluxes in large watersheds. SPARROW has been applied in many areas to a variety of water resources challenges, including predicting water quality in unsampled streams and into the future, targeting management investments, and improving the understanding of water-quality trends, contaminant fate and transport, and the effectiveness of management practices. Such models have traditionally been calibrated to mean-annual flow-normalized contaminant fluxes and therefore estimate mean fluxes over multiple years and during average hydrologic conditions. Recent advances in SPARROW, however, incorporate dynamic storage in terrestrial uplands and represent seasonal or annual fluxes over 21 years in catchments of the Illinois and Upper Colorado Rivers (respectively) in the central United States (US). Ongoing development of similar seasonal models for nitrogen and phosphorus over the conterminous US is intended to provide an improved representation and prediction of nutrient conditions affecting water availability as well as an improved understanding of observed changes in water quality over time. In a parallel effort, understanding of nutrient sources, fate, and transport quantified in calibrated SPARROW models is being used along with simulated annealing to demonstrate options for optimizing water-quality monitoring in support of regional flux estimates. Although monitoring is often conducted to address multiple objectives, such optimizations suggest opportunities for substantially improved efficiency in monitoring for the specific purpose of estimating regional fluxes to the Delaware River and to Chesapeake Bay along the mid-Atlantic US coast.