Stream water quality data provide essential insights into catchment biogeochemical processes. Continuous high-resolution measurements offer significant potential to differentiate between catchment-scale inputs and in-stream biogeochemical processing. Nevertheless, extracting clear, meaningful information from complex, multivariate time series spanning multiple variables and locations poses significant challenges. Using Principal Component Analysis (PCA) on high-resolution multivariate water quality data, this study aims to (1) separate catchment-scale contributions from in-stream biogeochemical processes, and (2) evaluate the dominant environmental drivers of spatial and temporal variability. The data were collected at five monitoring stations located in the Bode River, Germany. At each station, six variables were measured at 15-minute intervals over a period of seven years (2013–2020). The first principal component (PC1) accounted for 46 % of the variance, capturing the typical seasonal impacts of stormflow dynamics. The second principal component (PC2) revealed the influence of saline groundwater upwelling, particularly during lowflow periods at specific sites. Diurnal fluctuations in pH, driven primarily by algal photosynthetic activity, were identified by the third component (PC3). Additional components highlighted localized processes: PC4, PC5, and PC6 were linked to turbidity variability during discharge peaks, while PC7 reflected anthropogenic influences, notably treated acid mine drainage entering the river. Lastly, PC8 described distinct nitrate dynamics observed at downstream monitoring sites. The application of PCA to high-resolution multivariate data proved to be very helpful in disentangling various catchment and within-stream effects on stream water quality. These findings emphasize the importance of advanced analytical techniques in unravelling complex hydrobiogeochemical dynamics.