Articles | Volume 13, issue 5
https://doi.org/10.5194/esurf-13-845-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-13-845-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Translating deposition ages into erosion rates: inverse landscape evolution modelling and uncertainty analysis
W. Marijn van der Meij
CORRESPONDING AUTHOR
Institute of Geography, University of Cologne, Zülpicher Straße 45, 50674 Cologne, Germany
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W. Marijn van der Meij, Svenja Riedesel, and Tony Reimann
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Soil mixing (bioturbation) plays a key role in soil functions, but the underlying processes are poorly understood and difficult to quantify. In this study, we use luminescence, a light-sensitive soil mineral property, and numerical models to better understand different types of bioturbation. We provide a conceptual model that helps to determine which types of bioturbation processes occur in a soil and a numerical model that can derive quantitative process rates from luminescence measurements.
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We present our model ChronoLorica. We coupled the original Lorica model, which simulates soil and landscape evolution, with a geochronological module that traces cosmogenic nuclide inventories and particle ages through simulations. These properties are often measured in the field to determine rates of landscape change. The coupling enables calibration of the model and the study of how soil, landscapes and geochronometers change under complex boundary conditions such as intensive land management.
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The development of soils and landscapes can be complex due to changes in climate and land use. Computer models are required to simulate this complex development. This research presents a new method to analyze and visualize the results of these models. This is done with the use of evolutionary pathways (EPs), which describe how soil properties change in space and through time. I illustrate the EPs with examples from the field and give recommendations for further use of EPs in soil model studies.
W. Marijn van der Meij, Svenja Riedesel, and Tony Reimann
SOIL, 11, 51–66, https://doi.org/10.5194/soil-11-51-2025, https://doi.org/10.5194/soil-11-51-2025, 2025
Short summary
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Soil mixing (bioturbation) plays a key role in soil functions, but the underlying processes are poorly understood and difficult to quantify. In this study, we use luminescence, a light-sensitive soil mineral property, and numerical models to better understand different types of bioturbation. We provide a conceptual model that helps to determine which types of bioturbation processes occur in a soil and a numerical model that can derive quantitative process rates from luminescence measurements.
W. Marijn van der Meij, Arnaud J. A. M. Temme, Steven A. Binnie, and Tony Reimann
Geochronology, 5, 241–261, https://doi.org/10.5194/gchron-5-241-2023, https://doi.org/10.5194/gchron-5-241-2023, 2023
Short summary
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We present our model ChronoLorica. We coupled the original Lorica model, which simulates soil and landscape evolution, with a geochronological module that traces cosmogenic nuclide inventories and particle ages through simulations. These properties are often measured in the field to determine rates of landscape change. The coupling enables calibration of the model and the study of how soil, landscapes and geochronometers change under complex boundary conditions such as intensive land management.
W. Marijn van der Meij
SOIL, 8, 381–389, https://doi.org/10.5194/soil-8-381-2022, https://doi.org/10.5194/soil-8-381-2022, 2022
Short summary
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The development of soils and landscapes can be complex due to changes in climate and land use. Computer models are required to simulate this complex development. This research presents a new method to analyze and visualize the results of these models. This is done with the use of evolutionary pathways (EPs), which describe how soil properties change in space and through time. I illustrate the EPs with examples from the field and give recommendations for further use of EPs in soil model studies.
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Short summary
A soil-landscape evolution model was used to calculate hillslope erosion rates from OSL-based (Optically Stimulated Luminescence) deposition ages through inverse modelling, with consideration of uncertainties in model input. The results show that erosion rates differ systematically from the deposition rates, highlighting important shortcomings of assessing land degradation through measurable deposition rates.
A soil-landscape evolution model was used to calculate hillslope erosion rates from OSL-based...