CHONK 1.0: landscape evolution framework: cellular automata meets graph theory

Abstract

Abstract. Landscape Evolution Models (LEMs) are prime tools to simulate the evolution of source-to-sink systems through ranges of spatial and temporal scales. Plethora of different empirical laws have been successfully applied to describe the different parts of these systems: fluvial erosion, sediment transport and deposition, hillslope diffusion, or hydrology. Numerical frameworks exist to facilitate the combination of different subsets of laws, mostly by superposing grids of fluxes calculated independently. However the exercise becomes increasingly challenging when the different laws are inter-connected: for example when a lake breaks the upstream-downstream continuum of the amount of sediment and water it receives and transmits; or when erosional efficiency depends of the composition of a sediment flux affected by multiple processes. In this contribution, we present a method mixing the advantages of cellular-automata and graph theory to address such cases. We demonstrate how the former guarantees finite knowledge of all fluxes independently from the process-law implemented in the model while the latter offer a wide range of tools to process numerical landscapes, including landscapes with closed basins. We provide three scenario largely benefiting from our method: i) one where lake systems are primary controls on Landscape evolution, ii) one where sediment provenance is closely monitored through the stratigraphy and iii) one where heterogeneous provenance influences fluvial incision dynamically. We finally outline the way forward to make this method more generic and flexible.

Comment on egusphere-2024-1239

“…As stated in section 1.1, there are multiple ways to numerically solve for the SWE. Our developed scheme applies an explicit finite difference scheme on a graph (Braun and Willett, 2013;Barnhart et al, 2020;Gailleton et al, 2023). It aims to provide a reasonably efficient and scalable solution suitable for large-scale DEMs and LEMs.…”

“…Using the DAG structure, calculating drainage-area is very efficient and can be done in a single graph traversal following the downstream topological order (e.g. Anand et al, 2020;Braun and Willett, 2013;Gailleton et al, 2023;Hergarten and Neugebauer, 2001). Weighted by precipitation rates, drainage area determines the amount of water entering every cell of the system.…”

“…Topological sorting operations use a modified version of Braun and Willett (2013) for SFD and a variant of Anand et al (2020) for MFD. The modifications are minor changes of data structure that do not change the overall functioning while improving performance and readability (see Gailleton et al (2023) for detailed implementations). One advantage of GraphFlood is that it can be implemented using existing computational frameworks for DEM analysis and LEM simulation (e.g.…”

“…Anand et al, 2020;Braun and Willett, 2013;Carretier et al, 2016), the local minima resolver (e.g. Cordonnier et al, 2018;Barnes et al, 2014;Gailleton et al, 2023) and the receivers and donors computations as they need updtates at each iterations.…”

“…The process is repeate until emptying the queue. Note that if a node has no receiver and is not a model edge, we gradually fill the local depression until finding an outlet, in a similar way to Davy et al (2017) or Gailleton et al (2023).…”