In 2010 the U.S. Environmental Protection Agency established the Total Maximum Daily Load (TMDL) which is a “pollution diet” that aims to reduce the amount of nitrogen and phosphorus entering the Chesapeake Bay, the largest estuary in the United States, by 25 and 24% percent, respectively. To achieve this goal the TMDL requires the implementation of Best Management Practices (BMPs), which are accepted land management practices for reducing pollutant runoff to nearby bodies of water. While the TMDL requires that the necessary management actions be in place by 2025 to eventually reach targeted nutrient loads, the ability to detect an effect of BMPs while assuming that one has occurred (i.e. statistical power) is still not well understood. The goal of this study was to investigate the power and required timelines to detect nutrient reductions in streams and rivers as the result of BMP implementation at the Chesapeake Watershed scale. Power estimates were produced using SPAtially Referenced Regression On Watershed attributes (SPARROW) models, which offer a flexible statistical framework and were recently extended to allow for modeling multiple time steps. Nitrogen and phosphorus focused models were calibrated to estimate the power to detect reductions in flux from numerous constituent sources. To confidently detect a decrease in constituent flux reaching the Chesapeake Bay’s tidal waters from a specific constituent source, reductions ranging from 30–60% were required for the nitrogen model. In contrast, reductions of up to 80% were not detectable under the phosphorus model. The timelines necessary to detect reductions in nitrogen flux ranged from 11 to several hundred years under different rates-of-change and management scenarios. The approach proposed here can help better understand the ability to detect the effects of BMPs on a regional scale and help guide future management actions and monitoring programs.