Analyzing the impact of agricultural BMPs on stream nutrient load and biotic health in the Susquehanna-Chemung basin of New York.

Despite the widespread use of agricultural best management practices (BMPs) to reduce watershed scale nutrient loads, there remain few studies that use directly observed data - instead of models - to evaluate BMP effectiveness at the watershed scale. In this study, we make use of extensive ambient water quality data, stream biotic health data, and BMP implementation data within the New York State portion of the Chesapeake Bay watershed to assess the role of BMPs on reducing nutrient loads and modifying biotic health in major rivers. The specific BMPs considered were riparian buffers and nutrient management planning. A simple mass balance approach was used to evaluate the role of wastewater treatment plant nutrient reductions, agricultural land use changes, and these two agricultural BMPs in matching observed downward trends in nutrient load. In the Eastern nontidal network (NTN) catchment - where BMPs have been more widely reported - the mass balance model suggested a small but discernible contribution of BMPs in matching the observed downward trend in total phosphorus. Contrastingly, BMP implementations did not show clear contributions towards total nitrogen reductions in the Eastern NTN catchment nor for the total nitrogen and phosphorus in the Western NTN catchment, where BMP implementation data are more limited. Assessment of the relationship between stream biotic health and BMP implementation using regression models found limited connection between extent of BMP implementation and biotic health. In this case, however, spatiotemporal mismatches between the datasets and the relatively stable biotic health, typically of moderate to good quality even before BMP implementation, may reflect the need for better monitoring design to assess BMP effects at the subwatershed scale. Additional studies, perhaps using citizen scientists, may be able to provide more suitable data within the existing frameworks of the long-term surveys. Given the preponderance of studies that rely only on modeling to understand nutrient loading reductions achieved by implementation of BMPs, it is essential to continue to collect empirical data to meaningfully evaluate whether there are actual measurable changes due to BMPs.

Developing a watershed screening tool to estimate relative contribution of phosphorus to guide management planning.

To support the development of clean water plans, as required by the federal Clean Water Act 303(d) program, the New York State Department of Environmental Conservation (DEC) developed the Loading Estimator of Nutrient Sources (LENS) tool. DEC has prioritized clean water planning for fresh waterbodies experiencing negative impacts due to excessive phosphorus levels. LENS, an Excel based tool, combines several simple steady state models into a single screening tool that may be used to estimate the relative contribution of phosphorus sources within a watershed, waterbody response, and recovery potential of a waterbody. To validate that LENS results are reasonable approximations, LENS loading estimates were statistically compared with loading estimates from more complex watershed models that were used to develop existing clean water plans using simple linear regressions. For this comparison, DEC selected a variety of completed models that have modeled watershed phosphorus loads with different land use compositions and loading from both point and nonpoint sources. This analysis shows that LENS performs reasonably well at estimating the relative loading from a range of source sectors, though cannot replace more robust watershed models. DEC has used LENS to prioritize waterbodies for clean water plans and to guide management actions in watersheds where data is lacking to support more complex modeling efforts. Future expansions of LENS may include modifying the tool to estimate other pollutants (i.e. nitrogen), add the ability to account for internal loading of nutrients within waterbodies; and estimate the contribution of nutrients from groundwater.