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Long‐Term Shifts in U.S. Nitrogen Sources and Sinks Revealed by the New TREND‐Nitrogen Data Set (1930–2017)
Author(s) -
Byrnes D. K.,
Van Meter K. J.,
Basu N. B.
Publication year - 2020
Publication title -
global biogeochemical cycles
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.512
H-Index - 187
eISSN - 1944-9224
pISSN - 0886-6236
DOI - 10.1029/2020gb006626
Subject(s) - environmental science , agriculture , population , land use , nitrogen balance , surface runoff , soil water , hydrology (agriculture) , geography , atmospheric sciences , nitrogen , ecology , soil science , chemistry , demography , geotechnical engineering , archaeology , organic chemistry , sociology , geology , engineering , biology
Reactive nitrogen (N) fluxes have increased tenfold over the last century, driven by increases in population, shifting diets, and increased use of commercial N fertilizers. Runoff of excess N from intensively managed landscapes threatens drinking water quality and disrupts aquatic ecosystems. Excess N is also a major source of greenhouse gas emissions from agricultural soils. While N emissions from agricultural landscapes are known to originate from not only current‐year N input but also legacy N accumulation in soils and groundwater, there has been limited access to fine‐scale, long‐term data regarding N inputs and outputs over decades of intensive agricultural land use. In the present work, we synthesize population, agricultural, and atmospheric deposition data to develop a comprehensive, 88‐year (1930–2017) data set of county‐scale components of the N mass balance across the contiguous United States (Trajectories Nutrient Dataset for nitrogen [TREND‐nitrogen]). Using a machine‐learning algorithm, we also develop spatially explicit typologies for components of the N mass balance. Our results indicate a large range of N trajectory behaviors across the United States due to differences in land use and management and particularly due to the very different drivers of N dynamics in densely populated urban areas compared with intensively managed agricultural zones. Our analysis of N trajectories also demonstrates a widespread functional homogenization of agricultural landscapes. This newly developed typology of N trajectories improves our understanding of long‐term N dynamics, and the underlying data set provides a powerful tool for modeling the impacts of legacy N on past, present, and future water quality.