Assessing Surface-Groundwater Interactions Using Time-Series Clustering and Convergent Cross Mapping: A Case Study of Saxony, Germany

The interaction between surface water and groundwater plays a crucial role in effective water resource management. In Saxony, Eastern Germany, lakes and reservoirs contribute significantly to the public water supply, alongside groundwater to a lesser extent. Despite growing attention to comprehensive studies of the region's water resources, the complex dynamics between streamflow and groundwater levels remain insufficiently explored. This research aimed to address this gap by providing a detailed analysis of these interactions using time series data.

To bridge this gap, the research framework integrated preprocessing techniques, feature-based characterization and clustering of groundwater level time series, and Convergent Cross-Mapping (CCM) applied to coupled groundwater level and discharge datasets. CCM, which uses time series data to identify causal relationships within dynamic systems, their direction and strength, was used to study the interactions between groundwater and streamflow in different regions of Saxony, Germany. Data from 597 groundwater level and 190 discharge time series have been used. The study also employed R, MATLAB, and QGIS for data processing and analysis, based on publicly available GitHub repositories and official documentation from previous studies.

The results revealed significant spatial variability in groundwater-stream interactions, with high levels of interaction identified in catchments such as the Elbe and Schwarze Elster, and lower levels of interaction in urban and agricultural areas. In regions such as Lausitz, geological and soil factors strongly influenced the streamflow-groundwater dynamic, with more complex interactions in areas with loess and highland soils. Factors like land cover and soil type played a significant role, as urbanization and land use changes can reduce groundwater recharge rates and disrupt natural water pathways. These findings underscore the importance of spatially distributed data for understanding the drivers of water system behavior and regional water resource management.

In conclusion, this study demonstrated the value of integrating time series data analysis methods, such as CCM, to enhance the understanding of hydrological dynamics in Saxony. The results provided insights into areas of high groundwater-streamflow interaction, highlighted the role of influencing factors, and emphasized the need for spatially detailed hydrological assessments to inform future water resource management strategies in the region.