SSF Novel Tracers - Biogeochemical tracers (environmental DNA, dissolved organic matter) and their transport and transformation across the hillslope–riparian–stream continuum

• Applicants: Professor Dr. Peter Chifflard; Dr. Yvonne Schadewell
• Subject Area: Hydrogeology, Hydrology, Limnology, Urban Water Management, Water Chemistry, Integrated Water Resources Management
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 453746323

Inferences about subsurface flow paths, sources and connectivity of subsurface stormflow (SSF) remain constrained by assumptions and limited direct measurements. To overcome this, the potential of biological tracers - specifically environmental DNA (eDNA) and water-soluble organic carbon (WSOC) - based on their spatially distinct optical properties were explored in the first phase of the RU “Fast and invisible: Conquering Subsurface Stormflow (FOR 5288)”. It was demonstrated that catchments can be characterized spatially and vertically using these tracers. This highlighted their value in identifying source areas and subsurface flow paths at point and hillslope scales. However, it remains unclear whether biological signals from soil profiles at hillslopes are traceable to the stream, or how they are altered during transport via riparian zones. This project now aims to investigate and model the transport and transformation of eDNA and WSOC along the hillslope-stream continuum across spatial and temporal scales. We combine experimental and modeling strategies. Spatially, we focus on multiple intensively monitored hillslopes in four catchments across Germany and Austria, equipped with trenches for direct SSF sampling and wells above and below (riparian zone) as well as across the catchments (each 80 groundwater wells). Temporally, we integrate event-based and continuous stream sampling to assess variations in tracer export under different seasonal and hydrological conditions. Laboratory analysis includes TOC analysis, fluorescence spectroscopy, high-throughput sequencing, and qPCR. Multivariate statistics (e.g., PCA, PARAFAC, WGCNA) will reveal spatio-temporal patterns of source contributions and SSF signatures. To assess transformation processes of eDNA and WSOC, and to quantify their export, we apply machine learning and travel-time-based degradation models. These approaches will improve understanding of biogeochemical interactions and allow tracer-based calibration and validation of hydrological models, demonstrating the potential of eDNA and WSOC as novel tools in hydrological modeling.

• DFG Programme: Research Units
• Subproject of: FOR 5288: Fast and invisible: Conquering Subsurface Stormflow through an Interdisciplinary Multi-Site Approach
• International Connection: Austria, Italy
• Co-Investigator: Professor Dr. Florian Leese
• Cooperation Partners: Dr. Enrico Bertuzzo; Dr. Kyle Boodoo