School of Environment, State Key Laboratory of Water Environment Simulation, Beijing Normal University, Beijing, 100875, China; College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China.
School of Environment, State Key Laboratory of Water Environment Simulation, Beijing Normal University, Beijing, 100875, China.
Water Res. 2020 May 1;174:115624. doi: 10.1016/j.watres.2020.115624. Epub 2020 Feb 16.
Modeling studies have focused on NO emissions in temperate rivers under static atmospheric NO (NO), with cold temperate river networks under dynamic NO receiving less attention. To address this knowledge and methodological gap, the dissolved NO concentration (NO) and NO algorithms were integrated with an air-water gas exchange model (F) into the SWAT (Soil and Water Assessment Tool). This new model (SWAT-F) allows users to simulate daily riverine NO emissions under dynamic atmospheric NO. The spatiotemporal fluctuations in the riverine NO emissions was simulated and its response to the static and dynamic atmospheric NO were analyzed in a middle-high latitude agricultural watershed in northeastern China. The results show that the SWAT-F model is a useful method for capturing the hotspots in riverine NO emissions. The model showed strong riverine NO absorption and weak NO emissions from September to February, which acted as a sink for atmospheric NO in this cold temperate area. High NO emissions occurred from April to July, which accounted for 83.34% of the yearly emissions. Spatial analysis indicated that the main stream and its tributary could contribute 302.3-1043.7 and 41.5-163.4 μg NO/(m·d) to the total riverine NO emissions (15.02 t/a), respectively. The riverine NO emissions rates in the subbasins dominated by forests and paddy fields were lower than those in the subbasins dominated by arable and residential land. Riverine NO emissions can be overestimated under the static atmospheric NO rather than under the increasing atmospheric NO. This overestimation has increased from 1.52% to 23.97% from 1990 to 2016 under the static atmospheric NO. The results of this study are valuable for water quality and future climate change assessments that aim to protect aquatic and atmospheric environments.
模型研究主要集中在静态大气 NO（NO）条件下的中温带河流的 NO 排放，而动态 NO 条件下的寒冷温带河网则受到较少关注。为了弥补这一知识和方法上的差距，将溶解态 NO 浓度（NO）和 NO 算法与气-液界面气体交换模型（F）集成到 SWAT（土壤和水评估工具）中。该新型模型（SWAT-F）允许用户模拟动态大气 NO 条件下的日河流 NO 排放。在中高纬度的中国东北地区的一个农业流域中，模拟了河流 NO 排放的时空波动，并分析了其对静态和动态大气 NO 的响应。结果表明，SWAT-F 模型是捕捉河流 NO 排放热点的有效方法。该模型显示 9 月至 2 月河流具有很强的 NO 吸收能力和较弱的 NO 排放能力，在这个寒冷的温带地区，河流成为大气 NO 的汇。4 月至 7 月 NO 排放量较高，占年排放量的 83.34%。空间分析表明，干流及其支流分别对总河流 NO 排放（15.02 t/a）的贡献为 302.3-1043.7 和 41.5-163.4 μg NO/（m·d）。森林和稻田占主导地位的子流域的河流 NO 排放率低于以耕地和居住用地为主导的子流域。在静态大气 NO 条件下，河流 NO 排放可能会被高估，而不是在大气 NO 增加的情况下。从 1990 年到 2016 年，在静态大气 NO 条件下，这种高估从 1.52%增加到 23.97%。本研究结果对于旨在保护水和大气环境的水质和未来气候变化评估具有重要价值。
