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

Diel variability in stream NO3- concentration represents the sum of all processes affecting NO3- concentration along the flow path. Being able to partition diel NO3- 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 NO3- 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 NO3- 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 NO3- assimilation. The remaining patterns suggested that other processes (e.g., nitrification, denitrification, and heterotrophic assimilation) contributed to the formation of diel NO3- 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.

Automated summary

In this study, distinct diel patterns in high-frequency NO3- monitoring data were identified, with photoautotrophic NO3- assimilation being the dominant process contributing to diel variability. Other processes, such as nitrification, denitrification, and heterotrophic assimilation, also played a role in forming diel NO3- patterns. Seasonal trends showed variations in the importance of these processes throughout the year.