Heuristic cellular automaton model for simulating soil organic carbon under land use and climate change: A case study in eastern China

Abstract

The concentration of soil organic carbon (SOC) is one of the most important soil properties, and its spatio-temporal variability greatly affects the global climate and agroecology. To investigate the effects of land use and climate change on SOC, a heuristic cellular automaton (HCA) model was proposed and applied to a plains area in eastern China with a high population density and rapid urbanization rate. The HCA model was designed to simulate the geographical variation in SOC dynamics over the long term (2080), and lateral carbon (C) migration is represented by revised neighbourhood variables at the macro scale. Three widely used soil mapping techniques were applied for comparison: multiple linear regression (MLR), support vector machine (SVM) and kriging with external drift (KED). The HCA model enhanced the accuracy of the predicted SOC by 15.27% over MLR, 12.31% over SVM and 10.98% over KED. Future land use maps were produced using legacy land use data and artificial neural network-based cellular automata (CA), and the simulation results showed the rapid urbanization of this area, where the percentage of cropland declined by 23.75% and that of village/urban areas increased by 22.90% from 2010 to 2080. The overall SOC concentrations are anticipated to increase by 2080 given the rising mean annual air temperature and mean annual precipitation. Our results also suggested that land use change clearly influenced the change in soil C, with village/urban areas exhibiting higher SOC than cropland. To provide stakeholders with accurate soil information, it is important to understand the comprehensive impacts of land use and climate change on soil evolution; this study illustrates the value of integrating pedogenetic information in soil C simulation models.