Disentangling Spatiotemporal Water Quality Dynamics in a Heterogeneous Catchment Using High-Frequency Data and Principal Component Analysis

High-frequency sampling enables the observation of rapid and subtle variations that can be missed with less frequent observations, which is crucial for understanding the complex interplay of factors influencing water quality. This research analyzes high-frequency data to understand water quality dynamics in the Bode river basin in central Germany, a region characterized by diverse climatic, geological, and land-use conditions.

Using data collected from five monitoring stations over seven years (2013–2020), six variables (electrical conductivity, nitrate, turbidity, water discharge, water temperature, and pH) were analyzed with Principal Component Analysis (PCA). The first principal component (PC1) explained 46% of the variance, and described the typical effect of stream discharge fluctuations throughout the seasons. PC2 highlighted the influence of saline groundwater upwelling during low-flow conditions, while PC3 revealed the role of photosynthetic activity in driving diurnal and seasonal pH fluctuations. Other components unraveled localized processes, including turbidity variability during discharge peaks (PC4, PC5 and PC6), anthropogenic effects, such as the discharge of treated acid mine drainage into the river system (PC7), and agricultural runoff influencing nitrate dynamics (PC8). Together, these components demonstrated how PCA can disentangle diverse influences on water quality, from climatic patterns to human interventions.

Collectively, the PCA results elucidated a wide range of factors influencing water quality, encompassing climatic variations and anthropogenic impacts. High-resolution temporal data revealed intricate dynamics that would otherwise remain undetected with less frequent sampling intervals. PCA proved to be an effective quantitative tool for synthesizing complex, multivariate datasets across multiple monitoring sites, enabling the identification of dominant hydrological controls and interactions between natural and human-driven processes. This methodological framework is adaptable to larger datasets, offering the potential for pattern recognition at regional or global scales and advancing hydrological synthesis. Its application can support adaptive water resource management in regions subject to diverse environmental and anthropogenic pressures.