Many coastal ecosystems have suffered from cultural eutrophication and dead zones. In the Chesapeake Bay, water quality degradation is manifested in low dissolved oxygen, poor water clarity, and decreased submerged aquatic vegetation acreage. This research combines long-term monitoring data, science-based assessment methods, and novel data analysis approaches (i.e., machine learning) toward understanding the geography, trajectories, and controls of water quality in the Chesapeake Bay, which provides an example for the assessment and management of complex coastal ecosystems. Results showed that the attainment of water quality standards has improved in both the deep zone and the shallow zone since 1985, but the shallow zone has improved more rapidly. In addition, the attainment trajectory has been affected by mainly external drivers (i.e., nutrient reductions) and, to a lesser extent, internal drivers (i.e., water temperature and stratification). Reductions in nutrient loads would improve attainment, whereas warming and stratification would decrease attainment. Furthermore, scenario analyses demonstrated the importance of managing both nitrogen and phosphorus loads. Overall, the deep zone and the shallow zone showed different trajectories and controls, emphasizing the importance of geographical targeting with management actions.
Keywords: climate change; coastal ecosystems; deep zone; nutrient reductions; shallow zone; water quality.