Simulation of loess gully evolution based on geographic cellular automata

Abstract

Gully development is an important topic in the evolution of modern geomorphology. The study of the development process of gullies is key to understanding the genesis, mechanism and spatial differentiation of loess geomorphology. This study explored the mechanism of the development process of gullies whilst taking into account the erosion characteristics of the gully head area, gully slope area and gully bed area. Based on geographic cellular automata (Geo‐CA), the transition rules were interacted with the fundamental hydraulic principles for gully evolution simulation. Different sediment transfer rules apply to the gully slope area and gully bed area. A simulation model of the loess gully evolution was constructed and implemented based on the loess simulation small watershed. The superior performance of the model was demonstrated by the accuracy evaluation of the model structure and the comparative analysis of the modelling results. Moreover, the model was validated by natural watershed data, and the analytical results indicated the feasibility of the model.

An improved cellular automata model for soil erosion in coastal areas based on discrete physical variables

“…Streeter and Cutler (2020) used CA to replicate the spatial characteristics of eroded landscapes and assess the changing ecological stress on soil erosion at a landscape scale. Luo et al (2022) developed a geographic CA model for simulating loess gully evolution at a watershed scale.…”

“…As a self‐organisation approach, the cellular automaton (CA) model has considerable advantages in simulating complex physical patterns of soil erosion evolution from simple rules with high computational efficiency (Heung et al, 2013; Luo et al, 2022), providing an easy and robust approach for investigating the mechanism of sheet erosion and rill erosion development under various hydrology or underlying surface conditions. A CA model is a system in which the automation of every component evolves iteratively by communicating with its neighbouring automata according to local interactions rules (Caviedes‐Voullième et al, 2018; Han et al, 2021; Wolfram, 1984).…”