08 Nov 2022
 | 08 Nov 2022
Status: a revised version of this preprint is currently under review for the journal ESurf.

Optimising global landscape evolution models with 10Be

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

Abstract. By simulating erosion and deposition, landscape evolution models offer powerful insights to Earth surface processes and dynamics. These models are typically constructed from parameters describing drainage area (m), slope (n), substrate erodibility (K), hillslope diffusion (D), and a critical drainage area (Ac) that signifies the downslope transition from hillslope diffusion to advective fluvial processes. In spite of the widespread success of such models, the parameter values have high degrees of uncertainty mainly because the advection and diffusion equations amalgamate physical processes and material properties that span widely differing spatial and temporal scales. Here, we use a global catalogue of catchment-averaged cosmogenic 10Be-derived erosion (denudation) rates with the aim to optimise a set of landscape evolution models via a Monte Carlo based parameter search. We consider three model scenarios: advection-only, diffusion-only, and an advection-diffusion hybrid. In each case, we search for a parameter set that best approximates erosion rates at the global scale, and we directly compare erosion rates from the modelled scenarios with those derived from 10Be data. Optimised ranges can be defined for many LEM parameters at the global scale. In the absence of diffusion, n ~ 1.3, and with increasing diffusivity the optimal n increases linearly to a global maximum of n ~ 2. Meanwhile we find that the diffusion-only model somewhat outperforms the advection-only model and is optimised when concavity is raised to a power of 2. With these examples, we suggest that our approach provides baseline parameter estimates for large-scale studies spanning long timescales and diverse landscape properties. Moreover, our direct comparison of model-predicted versus observed erosion rates is preferable to methods that rely upon catchment-scale averaging or amalgamation of topographic metrics. We also seek to optimise K and D parameters in landscape evolution models with respect to precipitation and substrate lithology. These optimised models allow us to effectively control for topography and target specifically the relationship between erosion rate and precipitation. All models suggest a positive correlation between K or D and precipitation > 1500 mm yr–1, plus a local maximum at ~ 300 mm yr–1, which is compatible with the long-standing hypothesis that semi-arid environments are among the most erodible.

Gregory Ruetenik et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on esurf-2022-54', Richard Ott, 23 Nov 2022
  • RC2: 'Comment on esurf-2022-54', Boris Gailleton, 06 Jan 2023
  • EC1: 'Comment on esurf-2022-54', Jean Braun, 09 Jan 2023
  • AC1: 'Comment on esurf-2022-54', Gregory Ruetenik, 06 Mar 2023

Gregory Ruetenik et al.

Gregory Ruetenik et al.


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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 of parameters and find a correlation between precipitation and coefficients for optimised models.