Quantification of the effects of management programs on water quality is critical to agencies responsible for water resource protection. This research documents reductions in stream water phosphorus (P) loads resulting from agricultural best management practices (BMPs) implemented as part of an effort to control eutrophication of Cannonsville Reservoir, a drinking water supply for New York City. Dairy farms in the upstate New York reservoir basin were the target of BMPs designed to reduce P losses. A paired watershed study was established on one of these farms in 1993 to evaluate changes in P loading attributable to implementation of BMPs that included manure management, rotational grazing, and improved infrastructure. Intensive stream water monitoring provided data to calculate P loads from the 160-ha farm watershed for all runoff events during a two-year pre-treatment period and a four-year post-treatment period. Statistical control for inter-annual climatic variability was provided by matched P loads from a nearby 86-ha forested watershed, and by several event flow variables measured at the farm. A sophisticated multivariate analysis of covariance (ANCOVA) provided estimates of both seasonal and overall load reductions. Statistical power and the minimum detectable treatment effect (MDTE) were also calculated. The results demonstrated overall event load reductions of 43% for total dissolved phosphorus (TDP) and 29% for particulate phosphorus (PP). Changes in farm management practices and physical infrastructure clearly produced decreases in event P losses measurable at the small watershed scale.

Best management practices (BMPs) have been implemented on a farm-by-farm basis within the Cannonsville Reservoir watershed (CRW) as part of a New York City watershed-wide BMP implementation effort to reduce phosphorus (P) loads to the water supply reservoirs. Monitoring studies have been conducted at selected locations and at the watershed outlet on one of the farms, which spans an entire subwatershed within the CRW, with the aim of quantifying effectiveness of the BMPs installed on the farm. This study applied the Soil and Water Assessment Tool (SWAT) and a recently developed BMP characterization tool to the farm over pre-and post-BMP installation periods with the object of determining the extent to which model results incorporating all installed BMPs match observed data, and the individual impact of each of the BMPs installed on the farm. The SWAT model generally performed well at the watershed level for flow, sediment, and phosphorus simulations. Annual Nash-Sutcliffe (NS) coefficients for the components ranged between 0.56 and 0.80, while monthly NS coefficients ranged between 0.45 and 0.78. The model also performed well at the field level, with simulated in-field P loads closely matching observed data. Because the fields had various combinations of BMPs installed on them, it was difficult to separate out individual BMP impacts based on SWAT simulations. It was, however, possible to determine the effects of BMP combinations such as nutrient management plans and rotations (31% dissolved P; 25% total P). For dissolved P, integration of BMP tool efficiencies allowed individual BMP impacts to be incorporated while still maintaining the same level of representation as was obtained using model simulations. As the SWAT model is often used with little or no post-BMP data to verify simulation results, this study served to validate SWAT model suitability for evaluating BMP impacts. The BMP tool was found to suitably complement the model by providing insights into individual BMP impacts, and providing BMP efficiency data where the model was lacking.