Articles | Volume 11, issue 5
https://doi.org/10.5194/esurf-11-865-2023
https://doi.org/10.5194/esurf-11-865-2023
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

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Cited articles

Adams, B. A., Whipple, K. X., Forte, A. M., Heimsath, A. M., and Hodges, K. V.: Climate controls on erosion in tectonically active landscapes, Sci. Adv., 6, 3166, https://doi.org/10.1126/sciadv.aaz3166, 2020. 
Ahnert, F.: Functional relationships between denudation, relief, and uplift in large mid-latitude drainage basins, Am. J. Sci., 268, 243–263, 1970. 
Barnes, R., Lehman, C., and Mulla, D.: Priority-flood: An optimal depression-filling and watershed-labeling algorithm for digital elevation models, Comput. Geosci., 62, 117–127, 2014. 
Barnhart, K. R., Tucker, G. E., Doty, S. G., Shobe, C. M., Glade, R. C., Rossi, M. W, and Hill, M. C.: Inverting topography for landscape evolution model process representation: 1. Conceptualization and sensitivity analysis, J. Geophys. Res.-Earth, 125, 1–31, https://doi.org/10.1029/2018JF004961, 2020. 
Beven, K. and Binley, A.: The future of distributed models: model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, 1992. 
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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.