Comment on esurf-2022-54

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.