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Time series of nutrient concentrations and related water quality parameters taken at several locations along the River Elbe were subjected to multivariate statistical analysis. The main question underlying this study is concerned with whether known interactions between water quality variables can be recovered as statistically significant covariance patterns. For this purpose, the standard technique of principal component analysis (PCA) was applied. Raw data and deviations from an estimated seasonal cycle were analysed. In both cases, two leading patterns of covariance was obtained, one discharge-dependent and the other related to biological activities. Linear regression modelling based on discharge and temperature was used to approximately eliminate the impact of meteorological forcing; this led to a large reduction of the seasonal component. The remaining partial variance of water-quality variables could be …