SSF SUBSURFACE CONTROLS
Project staff:
Mag. Dr. Bernhard Kohl
Assoc.-Prof. DI Dr. Stefan Achleitner
Prof. Dr. Stefan Hergarten
Prof. Dr. Anja Klotzsche
Nicole Höring
Description:
Subsurface Controls investigates the spatial and temporal dynamics of subsurface stormflow (SSF) at the hillslope scale using high-resolution geophysical imaging. Despite its critical role in runoff generation and water quality, SSF remains poorly understood due to its hidden, transient, and heterogeneous nature. Traditional methods like trenches and point sensors provide limited spatial coverage, while conventional geophysical techniques often lack the resolution to capture small-scale flow paths. This project addresses these limitations by combining semi-invasive electrical resistivity tomography (ERT), ground-penetrating radar (GPR), and electromagnetic induction (EMI) to image subsurface flow dynamics with unprecedented detail. The project focuses on four diverse catchments (Black Forest, Gotznerberg, Sauerland, Ore Mountains), where SSF has been previously observed. Two innovative ERT setups are tested: borehole-based (deep, stable electrodes) and rod-based (partially insulated, minimally invasive rods), allowing for high-resolution monitoring of flow activation and saturation changes. GPR, particularly using full-waveform inversion (FWI), enables decimeter-scale imaging of soil structure and water content dynamics. These methods are integrated with artificial rainfall simulations and trench-based observations to validate findings under controlled conditions. The project also develops digital shadows—3D physical models based on petrophysical relationships—to simulate and optimize measurement configurations. These models are used to generate synthetic data for testing inversion algorithms and improving interpretation. By combining multi-method geophysics with process-based hydrological modeling (HYDRUS, GeoTOP, RoGeR), Subsurface Controls aims to identify the depth, lateral extent, and threshold behavior of SSF, and to understand how soil heterogeneity and temporary storage influence flow patterns. The results will provide a mechanistic understanding of SSF, enabling improved parameterization in catchment-scale models and advancing the science of subsurface hydrology.
Research Questions
1. Is SSF typically limited to a certain (site-specific) depth range?
2. Given that there is at least some preferred depth range, is it just site-specific or does it strongly depend on event size?
3. How inhomogeneous is the SSF pattern in the lateral direction (preferential layers vs. distinct paths)?
4. What is the effect of internal thresholds and temporary storage on SSF discharge curves?
Methods/ Approach
Subsurface Controls employs a multi-method, integrated approach to image subsurface flow dynamics at high spatial and temporal resolution. The core methods include semi-invasive ERT (borehole and rod-based), GPR with full-waveform inversion (FWI), and EMI, deployed across four catchments. Borehole ERT uses 2 m deep, filled boreholes with steel net electrodes for stable, long-term monitoring. Rod-based ERT uses partially insulated electrodes driven into the subsurface, minimizing disturbance. GPR is applied via surface, crosshole, and vertical radar profiling (VRP), with FWI used to reconstruct permittivity and conductivity fields at decimeter resolution. EMI maps electrical conductivity at multiple depths, guiding transect placement. Field campaigns combine these methods with artificial rainfall simulations and trench discharge measurements to validate flow responses. Digital shadows—3D models based on soil and hydrological data—are developed using gprMax and pyGIMLi to simulate geophysical responses and optimize measurement designs. Time-lapse monitoring captures seasonal and event-scale dynamics. Inversion of ERT and GPR data is performed using advanced algorithms, with deep learning used to accelerate FWI workflows. The results are used to calibrate and validate hydrological models (HYDRUS, GeoTOP, RoGeR), enabling the development of threshold-based, 2D plan-view models for efficient simulation of lateral fast-flow activation. The project integrates geophysical and hydrological data across scales, contributing to digital twins and improving the representation of SSF in catchment models. This approach enables a mechanistic understanding of subsurface connectivity and flow path dynamics