Abstract. Humanity's footprint on Earth systems has engendered water quality impoverishment in streams, lakes, and coastal waters globally. In agricultural areas, stream nitrogen concentrations are often high where excess nitrogen fertilization and wetland loss via artificial drainage degrade water quality. While the watershed-scale influence of fertilization and wetland loss on annual nitrogen loads has been studied, little is known about the watershed-scale effects of these wetland losses at seasonal time scales. Here we apply machine learning and linear statistical analyses in a big data framework to improve understanding of the role wetlands play in influencing the seasonality of down-gradient water quality. We confirm the seasonal role of wetlands in improving water quality at the watershed scale and uncover evidence demonstrating the importance of contemporary watershed nitrogen inputs to in-stream total nitrogen concentrations [TN]. We observe that in the Upper Mississippi River Basin, United States, after the application of spring fertilizers, [TN] drops by 70 % from June to September suggesting the importance of seasonal nutrient loading. Our data mining approach affords exploration of the potential influence of numerous landscape and wetland hydrologic processes on [TN], some of which are shown to exert seasonal influence. Our counterfactual analysis–in which wetlands are restored to their historic extent–points to the substantial water quality benefits of wetland restoration, including particular water quality improvements in the spring when [TN] are highest. Water quality benefits due to wetland restoration would make water safer for human consumption and improve the security of aquatic ecosystems.
Supplementary material to "Seasonal watershed-scale influences on nitrogen concentrations across the Upper Mississippi River Basin"
