Optimising global landscape evolution models with <sup>10</sup>Be

Ruetenik, Gregory A.; Jansen, John D.; Val, Pedro; Ylä-Mella, Lotta

doi:https://doi.org/10.5194/esurf-11-865-2023

Articles | Volume 11, issue 5

https://doi.org/10.5194/esurf-11-865-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/esurf-11-865-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 5

Research article

|

18 Sep 2023

Research article |

| 18 Sep 2023

Optimising global landscape evolution models with ¹⁰Be

Gregory A. Ruetenik, John D. Jansen, Pedro Val, and Lotta Ylä-Mella

Supplement

https://doi.org/10.5194/esurf-11-865-2023-supplement

Data sets

Code and data for Ruetenik et al., (2023): Optimising global landscape evolution models with 10Be (v0.13) G. A. Ruetenik, J. D. Jansen, P. Val, and L. Ylä-Mella https://doi.org/10.5281/zenodo.8317033

PyGMT: A Python Interface for the Generic Mapping Tools D. Tian, L. Uieda, W. J. Leong, W. Schlitzer, Y. Fröhlich, M. Grund, M. Jones, L. Toney, J. Yao, Y. Magen, T. Jing-Hui, K. Materna, A. Belem, T. Newton, A. Anant, M. Ziebarth, J. Quinn, and P. Wessel https://doi.org/10.5281/zenodo.8303186

Model code and software

Code and data for Ruetenik et al., (2023): Optimising global landscape evolution models with 10Be (v0.13) G. A. Ruetenik, J. D. Jansen, P. Val, and L. Ylä-Mella https://doi.org/10.5281/zenodo.8317033

Short summary

We compare models of erosion against a global compilation of long-term erosion rates in order to find and interpret best-fit parameters using an iterative search. We find global signals among exponents which control the relationship between erosion rate and slope, as well as other parameters which are common in long-term erosion modelling. Finally, we analyse the global variability in parameters and find a correlation between precipitation and coefficients for optimised models.