Dynamic Assessment and Predictive Modeling of Vulnerability of Karst Springs: Implementation and Application of non-source specific and source specific Indicators into a Hybrid Discharge Model (MOIN)

• Applicants: Professor Dr. Traugott Scheytt; Dr. Ferry Schiperski
• Subject Area: Hydrogeology, Hydrology, Limnology, Urban Water Management, Water Chemistry, Integrated Water Resources Management
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 565179611

Groundwater serves as a vital source of drinking water, yet the increasing demand for freshwater resources is exerting increasing pressure on local aquifers. Of particular concern are karst groundwater catchments, characterized by rapid groundwater recharge and fast contaminant transport. The vulnerability assessment of karst aquifers and springs has predominantly relied on static methodologies that are seldomly integrated. Additionally, there exists a multitude of promising indicators, both non-source specific (e.g., electrical conductivity, turbidity, dissolved organic carbon) for identifying rapid discharge, and source-specific (e.g., metazachlor, cyclamate, caffeine, acesulfame) for discerning contaminant origins within karst aquifers. However, these indicators lack predictive capabilities, being limited to real-time contamination identification or contaminant origin tracking. This project aims to enhance and develop new methods for real-time vulnerability assessment and predictive modeling in karst aquifers by integrating a cutting-edge hybrid discharge model (incorporating spatially resolved recharge and lumped groundwater) with an indicator-based approach utilizing both non-source specific and source-specific indicators. To achieve this, we will calibrate the hybrid discharge model to identify rapid discharge based on non-source specific indicators. Furthermore, we will incorporate contamination hotspots, such as point-source inputs from sewer failures and diffuse inputs from agricultural areas, into the model, coupled with a reactive transport model. This comprehensive approach will enable us to identify periods of heightened rapid discharge, often associated with contamination from bacteria, viruses, organic compounds, and specific sources. One particularly noteworthy feature of this approach is its capacity to predict potential impacts on water quality by leveraging weather forecasts up to 24 hours in advance. The outcomes of this project hold immense relevance for contemporary, model-driven groundwater management in karst aquifers, addressing the pressing need for sustainable resource utilization and environmental protection.

• DFG Programme: Research Grants