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Diel patterns in stream nitrate concentration produced by in-stream processes

机译：流动硝酸盐浓度的二极管图案通过内部生产

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Diel variability in stream NO 3 - concentration represents the sum of all processes affecting NO 3 - concentration along the flow path. Being able to partition diel NO 3 - signals into portions related to different biochemical processes would allow calculation of daily rates of such processes that would be useful for water quality predictions. In this study, we aimed to identify distinct diel patterns in high-frequency NO 3 - monitoring data and investigated the origin of these patterns. Monitoring was performed at three locations in a 5.1?km long stream reach draining a 430?km 2 catchment. Monitoring resulted in 355 complete daily recordings on which we performed a k -means cluster analysis. We compared travel time estimates to time lags between monitoring sites to differentiate between in-stream and transport control on diel NO 3 - patterns. We found that travel time failed to explain the observed lags and concluded that in-stream processes prevailed in the creation of diel variability. Results from the cluster analysis showed that at least 70?% of all diel patterns reflected shapes typically associated with photoautotrophic NO 3 - assimilation. The remaining patterns suggested that other processes (e.g., nitrification, denitrification, and heterotrophic assimilation) contributed to the formation of diel NO 3 - patterns. Seasonal trends in diel patterns suggest that the relative importance of the contributing processes varied throughout the year. These findings highlight the potential in high-frequency water quality monitoring data for a better understanding of the seasonality in biochemical processes.
机译：流中的Diel可变性No 3 - 浓度表示影响沿着流动路径3浓度的所有过程的总和。能够将Diel No 3 - 与不同的生物化学过程相关的部分将允许计算用于水质预测的这些过程的日常率。在这项研究中，我们旨在识别高频NO 3监测数据中的不同DIEL模式，并调查了这些模式的起源。监测在5.1 km长溪流中达到5.1 km长溪流，达到430 km 2集水区。监控导致355个完整的每日记录，我们执行了K-Means集群分析。我们比较旅行时间估计到监测站点之间的时间滞后，以区分在DIEL NO 3模式上的流中和传输控制。我们发现旅行时间未能解释观察到的滞后并得出结论，在Diel可变性的创建中占流入的流程。聚类分析结果表明，所有DIEX模式的至少70倍的反射形状通常与光摄逸术NO 3 - 同化相关。其余的模式表明其他方法（例如，硝化，脱氮和异养同性化）有助于形成Diel No 3 - 图案。 Diel模式的季节性趋势表明，贡献过程的相对重要性全年各种各样的变化。这些发现突出了高频水质监测数据的潜力，以更好地了解生化过程中的季节性。

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