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

ABSTRACT: 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 (RF) classification - can be combined to better understand the regional patterns and drivers of total nitrogen (TN) trends in large monitoring networks, resulting in information useful for watershed management. Cluster analysis revealed regional patterns of short-term TN trends (2007-2018) and categorized the stations into three distinct trend clusters, namely, V-

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC9743807 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature