Abstract. Pesticides may impact aquatic ecosystems when entering water bodies. Measures for mitigation against pesticide inputs include vegetated treatment systems (VTSs). Some of these systems have very short hydraulic retention time (< 1 h) but nevertheless manage to effectively reduce peak concentrations of contaminants as a result of dispersion. We hypothesize that the effect of dispersion on contaminant mitigation in VTSs depends on the shape of the contaminant input signal chemograph, which in turn is related to factors affecting contaminant mobilization in the contributing catchment. In order to test this hypothesis, we grouped chemographs of six contaminants originating from a viticultural catchment during 10 discharge events into clusters according to chemograph shape. We then compared peak concentration reduction and mass removal in a downstream VTS, both among clusters and in terms of compound properties and discharge dynamics. We found that chemograph clusters reflected combined effects of contaminant source areas, transport pathways, and discharge dynamics. While mass loss was subject to major uncertainties, peak concentration reduction rate was clearly related to chemograph clusters and dispersion sensitivity. These findings suggest that mitigation of acute toxicity in a VTS is stronger for compounds with sharp-peaked chemographs, whose formation is related to the contributing catchment and can be analyzed by chemograph clustering.

Abstract. 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 km2 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.

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