Revising BMP Efficiencies for the Chesapeake Bay Watershed: Challenges and Lessons Learned

摘要

The Chesapeake Bay, the United State's largest, most productive estuary, experiences eutrophication from excess nitrogen and phosphorus resulting in algal blooms that impede clarity and create anoxic conditions. Bay watershed states have developed strategies based on BMP efficiency estimates to reduce nutrient loads by more than 50%. The Chesapeake Bay Program (CBP) used BMP-driven watershed model output to estimate progress given lag times and lack of monitoring response. By 1997, scientists questioned the assumed BMP efficiencies used in model runs. In 2003, Washington Post articles on the difference between modeled progress and actual water quality led to federal review of the CBP that renewed the call for revised BMP efficiencies. The CBP commissioned the authors to revise BMP definitions and efficiencies. A protocol for practice review was developed in which scientists with expertise on specific BMPs reviewed literature and other data, identified factors influencing operational effectiveness and recommended revisions. These were adjusted to account for differences between research- and field- scale effectiveness due to factors such as spatial, temporal, climatic and management variability. The draft efficiencies were reviewed by expert panelsand CBP technical workgroups where it became evident that both research and programmatic interests favored maintenance of optimistic efficiencies which necessitated debate and negotiation. The process to reach final efficiencies, causes of variability in BMP performance and the difficulty in lowering efficiencies will be discussed. Research on BMP effectiveness remains limited but data and experience indicate lower effectiveness at operational than research scales. Given resistance to lowering established efficiencies, BMP effectiveness estimates should be initially conservative that can be increased, if needed. The revised CBP efficiencies remain optimistic but are closer to actual.