We analyzed seven years of high-frequency water quality data from the Bode River, Germany, using Principal Component Analysis on multivariate time series (EOF analysis). We successfully separated broad impacts like storms and pollution from local biological processes like algae growth. Our results prove that advanced statistical tools are essential to untangle complex environmental data, providing the vital insights needed to monitor and protect natural river systems.

This study aimed at a better understanding why time series of stream discharge and groundwater head differ that much in spite of spatial proximity and similar boundary conditions. Time series a 36,000 km² region in Germany was analysed. Climate patterns explained 22 % of the spatial variance. There was no significant difference between major land use patterns. Damping of the input signal in the subsoil explained another 32 % of the spatial variance. It was closely related to long-term trends.

Sustainable management schemes in agriculture require knowledge of site-specific soil hydrological processes, especially the interplay between soil heterogeneities and crops. We disentangled such effects on soil moisture in a diversified arable field with different crops and management schemes by applying a principal component analysis. The main effects on soil moisture variability were quantified. Meteorological drivers, followed by different seasonal behaviour of crops, had the largest impact.