Long-Term Shifts in US Nitrogen Sources and Sinks Revealed by the New TREND-Nitrogen Data Set (1930-2017)
Long-Term Shifts in US Nitrogen Sources and Sinks Revealed by the New TREND-Nitrogen Data Set (1930-2017)
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DOI：
10.1029/2020gb006626
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发表时间：
2020-09-01
影响因子：
5.2
通讯作者：
Basu, N. B.
中科院分区：
文献类型：
--
作者：
Byrnes, D. K.;Van Meter, K. J.;Basu, N. B.
在人口增长、饮食变化和商业氮肥使用增加的推动下，活性氮(N)通量在上个世纪增加了十倍。集约化管理景观中过量氮的径流威胁饮用水质量，破坏水生生态系统。过量的氮也是农业土壤温室气体排放的主要来源。虽然已知农业景观的氮排放不仅源于当年的氮输入，还源于土壤和地下水中遗留的氮积累，但在几十年的集约化农业土地利用中，关于氮输入和输出的精细尺度、长期数据的获取有限。在目前的工作中，我们综合了人口、农业和大气沉积数据，开发了一个全面的、为期88年（1930-2017）的美国相邻县尺度氮质量平衡成分数据集（氮素轨迹营养数据集[TREND-nitrogen]）。使用机器学习算法，我们还开发了N质量平衡组件的空间显式类型学。我们的研究结果表明，由于土地利用和管理的差异，特别是由于人口稠密的城市地区与集约化管理的农业区相比，氮素动态的驱动因素非常不同，美国各地的氮素轨迹行为范围很大。我们对N轨迹的分析也表明了农业景观的广泛功能同质化。这种新开发的N轨迹类型学提高了我们对长期N动态的理解，基础数据集为模拟遗留N对过去、现在和未来水质的影响提供了强大的工具。在上个世纪，人们越来越多地使用氮肥来提高作物产量。在农业区，没有被作物吸收的氮从土地上流失，污染了河流、湖泊和沿海地区。过量的氮还会形成一种强大的温室气体，导致气候变化。随着时间的推移，过量的氮会在环境中积累，并在几十年内污染我们的水。因此，我们有必要知道几十年来额外使用了多少氮，以便更好地了解当前对环境的风险。在我们的研究中，我们使用了多个数据源来计算从1930年到2017年，美国相邻的每个县向景观中添加了多少氮，通过作物生产去除了多少氮，以及产生了多少人类废物。我们表明，氮的主要来源在全国不同地区可能是不同的。我们还表明，高水平的氮利用可以使不同气候条件下的景观看起来非常相似。这个新的数据集对于建立模型非常重要，这些模型可以预测几十年来高氮输入对水质和未来气候变化的影响。
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.Plain Language Summary Over the last century, people have increasingly used nitrogen fertilizer to increase crop yields. The nitrogen not taken up by crops in agricultural areas runs off of the land and pollutes rivers, lakes, and coastal areas. This excess nitrogen also forms a powerful greenhouse gas that contributes to climate change. Excess nitrogen can build up in the environment over time and pollute our water for decades. It is therefore necessary for us to know how much extra nitrogen has been applied over many decades to better understand current risks to the environment. In our study, we have used multiple data sources to calculate how much nitrogen has been added to the landscape, how much nitrogen has been removed through crop production, and how much human waste is produced, for every county in the contiguous United States from 1930 to 2017. We show that the main sources of nitrogen can be different in different areas of the country. We also show that high levels of nitrogen use can make landscapes in very different climates look very similar. This new data set will be very important for creating models that can predict how decades of high nitrogen inputs impact water quality and future changes in climate.