Water Research：考虑大气N2O动态的SWAT模型可避免河流N2O排放被高估
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北京师范大学环境学院欧阳威教授课题组将溶解N2O浓度和大气N2O算法与空气-水气体交换模型（FN2O）与SWAT模型（土壤和水评估工具）相结合，模拟了河流N2O排放的时空动态，并分析了其对静态和动态大气N2O的响应。相关成果发表于Water Research（IF=7.913）。
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•The new model is key for simulating riverine N2O that combines terrestrial, riverine and atmospheric processes.
•The SWAT-FN2O model can capture the spatial pattern of riverine N2O well.
•The long-term riverine N2O emission can be overestimated under static atmospheric N2O.
•Rivers in the cold temperate area is an important N2O source during the snow melting period.
•Rivers near the forests or paddy fields could be an important N2O sink in a cold temperate area.
Modeling studies have focused on N2O emissions in temperate rivers under static atmospheric N2O (N2Oairc), with cold temperate river networks under dynamic N2Oairc receiving less attention. To address this knowledge and methodological gap, the dissolved N2O concentration (N2Odisc) and N2Oairc algorithms were integrated with an air-water gas exchange model (FN2O) into the SWAT (Soil and Water Assessment Tool). This new model (SWAT-FN2O) allows users to simulate daily riverine N2O emissions under dynamic atmospheric N2O. The spatiotemporal fluctuations in the riverine N2O emissions was simulated and its response to the static and dynamic atmospheric N2O were analyzed in a middle-high latitude agricultural watershed in northeastern China. The results show that the SWAT-FN2O model is a useful method for capturing the hotspots in riverine N2O emissions. The model showed strong riverine N2O absorption and weak N2O emissions from September to February, which acted as a sink for atmospheric N2O in this cold temperate area. High N2O 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 N2O/(m2·d) to the total riverine N2O emissions (15.02 t/a), respectively. The riverine N2O 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 N2O emissions can be overestimated under the static atmospheric N2O rather than under the increasing atmospheric N2O. This overestimation has increased from 1.52% to 23.97% from 1990 to 2016 under the static atmospheric N2O. The results of this study are valuable for water quality and future climate change assessments that aim to protect aquatic and atmospheric environments.
以往模型研究的重点是在静态大气N2O（N2Oairc）下的温带河流中的N2O排放，而在动态N2Oairc下的冷温带河流受到的关注较少。为了填补这一研究和方法上的空白，将溶解N2O浓度（N2Odisc）和N2Oairc算法与空气-水气体交换模型（FN2O）与SWAT模型（土壤和水评估工具）相结合。这种新模型（SWAT-FN2O）能够模拟在动态大气N2O下的每日河流N2O排放。本研究模拟了中国东北中高纬度农业流域河流N2O排放的时空动态，并分析了其对静态和动态大气N2O的响应。结果表明，SWAT-FN2O模型是捕获河流N2O排放热点的有效方法。模型显示，从9月到2月，河水N2O吸收强，排放弱，因此能够作为这个寒冷区域的大气N2O的汇。4月至7月，N2O排放较高，占全年排放量的83.34％。空间分析表明，相对于河流总N2O排放量（15.02 t/a），主要河流及其支流可分别贡献302.3~1043.7和41.5~163.4μg N2O/（m2·d）。在以森林和稻田为主的小流域中，河流的N2O排放量要低于以耕地和居住地为主的小流域中的河流。与N2O不断增长的动态大气N2O条件相比，静态大气N2O条件下河流的N2O排放可能会被高估。这种高估从1990年的1.52％增加到2016年的23.97％。这项研究的结果对于旨在保护水环境和大气环境的未来的水质和气候变化评估十分有价值。
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