Regional patterns and drivers of total nitrogen trends in the Chesapeake Bay watershed: Insights from machine learning approaches and management implications

Anthropogenic nutrient inputs have led to nutrient enrichment in many waterbodies worldwide, including Chesapeake Bay (USA). River water quality integrates the spatial and temporal changes of watersheds and forms the foundation for disentangling the effects of anthropogenic inputs. We demonstrate with the Chesapeake Bay Non-Tidal Monitoring Network that machine learning approaches – i.e., hierarchical clustering and random forest – can be combined to better understand the regional patterns and drivers of total nitrogen (TN) trends in large monitoring networks. Cluster analysis revealed regional patterns of short-term TN trends (2007-2018) and categorized the stations into three distinct clusters, namely, V-shape (n = 23), monotonic decline (n = 35), and monotonic increase (n = 26). Random forest models were developed to predict the clusters using watershed characteristics and major N sources, providing information on regional drivers of TN trends. Results show encouraging evidence that improved agricultural nutrient management has contributed to water-quality improvement. Moreover, water-quality improvements are more likely in watersheds underlain by carbonate rocks, reflecting the relatively quick groundwater transport. By contrast, water-quality improvements are less likely in Coastal Plain watersheds, reflecting the effect of legacy N in groundwater. Notably, TN trends are degrading in forested watersheds, suggesting new and/or remobilized sources that may compromise management efforts. Finally, the developed random forest models were used to predict TN trend clusters for the entire Chesapeake watershed at the scale of river segments (n = 979), providing fine-level information that can facilitate targeted watershed management, especially in unmonitored areas. More broadly, this combined use of clustering and classification approaches can be applied to other monitoring networks to address similar questions.