EGU24-20146, updated on 11 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
PICO | Friday, 19 Apr, 08:49–08:51 (CEST)
PICO spot 2, PICO2.8

Erosion rate estimation across a large-scale domain from surface analyses, thermochronology and cosmogenic nuclide concentrations: A case study from Germany.

Thomas Bernard1, Christoph Glotzbach1, Alexander Neely1, Daniel Peifer1, Alexander Beer1, Mirjam Schaller1,2, Yanqing Shi3, and Todd A. Ehlers1,2
Thomas Bernard et al.
  • 1University of Tübingen, GUZ, Department of Geosciences, Tübingen, Germany (
  • 2School of Geographical and Earth Sciences, University of Glasgow, Glasgow, UK
  • 3College of Geosciences, China University of Petroleum, Beijing 102219, China

Topography and relief reflect the long-term competition between tectonics and surface processes linked to climate. The integration of tectonics, climate and surface processes in numerical modelling have the potential to quantify landscape evolution over large periods of time. Analytical methods such as 1) low-temperature thermochronology sensible to long timescale (i.e. ~Myrs) and 2) cosmogenic nuclide concentrations sensible to shorter time scales (i.e., ~Kyrs) allow the calibration of such models. Analyses of a catchment’s topography combined with the previous analytical data can, therefore, be used to reconstruct the continuous uplift or erosion history of this specific landscape. However, reconstruction at larger spatial scale where numerous catchments are involved remains challenging. Difficulties arise from different base-levels, tectonics and climate settings that control the different catchments forming the vast landscape.

            In this study, we reconstruct the erosion rate of Germany. The numerical model solves river erosion, hillslope diffusion and 1D heat transfer to predict river profiles, cosmogenic nuclide concentrations and low-temperature thermochronological ages. The model algorithm utilizes the efficient inverse modelling scheme “Simulation-Based Inference”. Simulations of the inverse modelling use neural networks to learn the observed data in order to predict high-dimensional unknown parameters such as uplift and erodibility. River profiles extracted from a DEM are combined with pre-existing and new low-temperature thermochronological data as well as cosmogenic nuclide concentrations across entire Germany. We perform individual inverse modelling of the different types of datasets for the main catchments in Germany in order to estimate erosive parameters. Initial results suggest highly variable uplift and erodibility between the different catchments. Hence, further analyses have to be performed in order to combine the different results over a large-scale domain.

How to cite: Bernard, T., Glotzbach, C., Neely, A., Peifer, D., Beer, A., Schaller, M., Shi, Y., and Ehlers, T. A.: Erosion rate estimation across a large-scale domain from surface analyses, thermochronology and cosmogenic nuclide concentrations: A case study from Germany., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20146,, 2024.

Presentation file