An improved cellular automata model for soil erosion in coastal areas based on discrete physical variables. (27th June 2022)

• Record Type: Journal Article
• Authors: Tang, Shengqiang; She, Dongli; Wang, Hongde; Lv, Haishen
• Is Part Of: European journal of soil science. Volume 73:Number 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-27
• Subjects: cellular automaton -- discrete variable -- model improvement -- soil erosion; Soil science -- Periodicals; 631.4
• DOI: 10.1111/ejss.13261
• Languages: English
• ISSNs: 1351-0754

Abstract

Abstract: Soil erosion is a major and costly environmental hazard worldwide. Obtaining a thorough understanding of the soil erosion process is essential for erosion control. The high erodibility of the saline‐sodic soil in coastal areas causes rills to develop rapidly and erosion patterns change substantially during the rainfall process. Understanding the temporal and spatial dynamics of the soil surface morphology is crucial for revealing the mechanism behind the severe soil erosion process, but simulating these dynamics with many existing empirical or physical models is difficult. Cellular automata (CA) provides a simple alternative approach with high computational efficiency for modelling the complex patterns of soil erosion evolution using local interactions rules. However, previously established CA models poorly simulated the lateral erosion. By dividing the runoff amount into a discrete variable of every constituent cell and considering the runoff flow velocity when calculating the flow direction, the CA model developed here, based on discrete physical variables (CADV), could simulate soil erosion rate dynamics and rill morphological development with low relative errors. The variations in the simulated sediment yield rates during rainfall events were consistent with the experimental measurements, and the relative errors of cumulative sediment yields between the simulated and measured values were lower than 5% from the 38th to 60th min (the end of rainfall event) during the rainfall. The morphological characteristics of slope erosion with relative errors in rill length, rill width, and rill depth were lower than 5% at specific times during rainfall events. Specifically, regarding the rill width parameter, the relative errors of the maximum and average rill widths were both lower than 5% when the rainfall event ended at the 60th min. The results show that the CADV is an appropriate tool for describing the soil erosion process and improving our understanding of the erosion process characteristics of saline‐sodic soil slopes in a coastal region of China. Highlights: Cellular automata based on discrete variables (CADV) improves soil erosion simulation. CADV simulates soil loss and rill morphological development with low relative errors. CADV improves insight into the mechanism of soil erosion in coastal area. CADV provides a new approach to improve soil erosion modelling with cellular automata.