Abstract
Computational landscape evolution models (LEMs) typically comprise at least two interacting components: a flow hydraulic solver that routes water across a landscape and a fluvial geomorphological model that modifies terrain properties, primarily bed surface elevation. LEMs used in long-term simulations over large watersheds, including some available in the Landlab library, often assume that only erosive processes occur in rivers and that terrain elevation increases solely due to tectonic uplift. Consequently, these models cannot capture the dynamics of gravel-bedded rivers, lacking the capacity to include sediment mixtures, simulate sediment deposition, and track textural changes in substrate stratigraphy that result from varying flow characteristics. To address this limitation, we developed, implemented, and tested RiverBedDynamics, a new Landlab component that simulates the evolution of bed surface elevation and grain size distribution in 2D grids based on the Exner equation for sediment mass balance. By dynamically coupling RiverBedDynamics with Landlab's hydrodynamic flow solver, OverlandFlow, we created a new LEM capable of simulating the dynamics of local shear stresses, bed load transport rates, and grain size distributions. Comparisons of our LEM results with analytical and previously reported solutions demonstrate its ability to accurately predict time-varying local changes in bed surface elevation, including erosion and deposition, as well as grain size distribution. Furthermore, application of our LEM to a synthetic watershed illustrates how spatially variable rainfall intensity leads to varying discharge patterns, which in turn drive changes in bed elevation and grain size distribution across the domain. This approach provides a more comprehensive representation of the complex interactions between flow dynamics and sediment transport in gravel-bedded rivers at timescales ranging from individual flood events to yearly morphological changes, enhancing our ability to model landscape evolution across diverse geomorphic settings.