SSF NOVEL TRACERS
Project staff:
Dr. Yvonne Schadewell
Prof. Dr. Peter Chifflard
Abstract:
Description:
The Novel Tracers project aims to advance the understanding of subsurface stormflow (SSF) by investigating how biological tracers—environmental DNA (eDNA) and water-soluble organic matter (WSOM)—move and transform along the hillslope–riparian–stream continuum. A central challenge in hydrology is identifying where water originates and how it travels through subsurface systems, especially given the spatial and temporal variability of flow paths. Existing tracers can distinguish general water sources but lack the resolution to pinpoint precise origins within soil layers or across catchments.
This project addresses that gap by leveraging the unique spatial signatures of eDNA and WSOM. Soil layers and habitats host distinct biological and chemical compositions, which can act as natural tracers when mobilized by subsurface flow. Building on successful preliminary work, the project will examine whether these signatures can be reliably detected in streams and how they are altered during transport.
The research combines field experiments across multiple scales—from soil layers and hillslopes to entire catchments—with high-resolution temporal sampling under both seasonal (non-event) and rainfall-driven (event) conditions. Four main objectives guide the work: understanding seasonal variability, quantifying transport and transformation processes, determining tracer contributions to streamflow, and integrating tracer data into hydrological models.
Ultimately, the project seeks to establish eDNA and WSOM as robust tools for tracing SSF pathways and improving model calibration. By linking hydrology with biogeochemistry and biodiversity, it contributes to a more detailed and predictive understanding of subsurface connectivity in catchments.
Research Questions
- How do seasonal changes in species composition influence the temporal dynamics in flow paths and fluxes of eDNA and WSOM at the hillslope scale?
- How do biogeochemical processes along the hillslope–riparian–stream continuum impact the original signature of eDNA and WSOM starting from specific source areas on the hillslope?
- What is the contribution of SSF-derived WSOM and eDNA to streamflow, and how does this change with hydrological conditions?
- Can we model the transport processes of eDNA and WSOM in soils to provide continuous time series of their export for temporally and spatially high-resolution, multi-criteria calibration of SSF in hydrological models?
- How can we address the transport and transformation processes of eDNA and WSOM along streams at the catchment scale to enable their use as biological tracers at larger spatial scales?
Methods/Approach
The technological approach is designed as an exploratory and integrative framework combining field measurements, laboratory analyses, and modeling techniques. At this stage, the focus is on developing and testing methods to capture and interpret eDNA and WSOM signals in hydrological systems.
We plan to conduct field sampling across our well-instrumented catchments, using existing infrastructure such as trenched hillslopes, groundwater wells, and stream monitoring stations. Sampling strategies will target both spatial variability (e.g., soil depth and landscape position) and temporal dynamics, including seasonal baseline conditions and rainfall events. For WSOM, spectroscopic techniques will be applied to characterize organic matter composition, while eDNA will be analyzed using metabarcoding approaches to identify biological signatures.
In parallel, we aim to explore different modeling strategies. Data-driven methods, such as machine learning, will be tested to identify relationships between hydrological conditions and tracer signals. We will also develop conceptual models to describe the transport and transformation of WSOM and eDNA, including initial approaches to represent degradation processes and travel times.
Overall, the approach is intentionally exploratory, with the objective of assessing methodological feasibility and building the foundation for future, more predictive applications.