Oral Presentation Society for Freshwater Science 2025 Annual Meeting

Regional patterns and drivers of nutrient trends across the Chesapeake Bay watershed: Machine-learning insights and management implications (116758)

Qian Zhang 1 2 , Joel T. Bostic 3 , Robert D. Sabo 4
  1. Oak Ridge Institute for Science and Education Fellow c/o U.S. EPA Office of Research and Development, Washington, D.C.
  2. University of Maryland Center for Environmental Science / EPA Chesapeake Bay Program Office, Annapolis, MD, United States
  3. University of Maryland Center for Environmental Science, Frostburg, MD, United States
  4. U.S. Environmental Protection Agency, Washington, D.C., United States

Reduction of nutrient loads has long been a management focus of watershed restoration efforts in many regions, including the Chesapeake Bay. To better understand the regional patterns and drivers of total nitrogen (TN) and total phosphorus (TP) load trends across the Bay watershed, we analyzed TN and TP load data from the Chesapeake Bay Non-Tidal Network (NTN) stations, respectively, using advanced machine learning approaches1,2. Cluster analysis revealed regional patterns of short-term trends and categorized the NTN stations into distinct clusters. In addition, random forest models identified regional drivers of the trend clusters by quantifying the effects of major nutrient sources and watershed characteristics (i.e., land use, geology, physiography). Results showed evidence that improved agricultural nutrient management has resulted in declines in agricultural nonpoint sources, which in turn contributed to water-quality improvement in the period of analysis. Results also showed that water-quality improvements are less likely to occur in the Coastal Plain areas, reflecting the effect of legacy nutrients. To provide spatially explicit information for the entire watershed including unmonitored areas, the developed models were used to predict TN and TP trend clusters at the fine scale of river segments, which are more relevant to watershed planning. Across the Bay watershed, about two thirds of the river segments had declines in TN, TP, or both. Despite such progress, continued nutrient reductions, especially from agricultural nonpoint sources, are imperative in order to achieve the nutrient reduction goals. Overall, this work demonstrates that advanced machine learning approaches can help better understand the regional patterns and drivers of nutrient trends in large monitoring networks, providing insights directly applicable to watershed management and planning.

  1. Zhang, Q., J.T. Bostic, and R.D. Sabo, 2024. “Effects of point and nonpoint source controls on total phosphorus load trends across the Chesapeake Bay watershed,” Environmental Research Letters, 19:014012, doi: 10.1088/1748-9326/ad0d3c.
  2. Zhang, Q., J.T. Bostic and R.D. Sabo, 2022. “Regional patterns and drivers of total nitrogen trends in the Chesapeake Bay watershed: Insights from machine learning approaches and management implications”, Water Research, 218:118443, doi: 10.1016/j.watres.2022.118443.