Black soil layer thickness prediction and soil erosion risk assessment in a small watershed in Northeast China.

Black soil has good properties and high fertility. Understanding the spatial distribution of black soil layer thickness is of great significance in promoting regional agricultural development, ecological environmental protection, and soil erosion control. However, traditional soil investigation methods often fail to provide detailed soil thickness information. This study focuses on a small watershed in Northeast China's black soil region. By integrating topographical parameters and vegetation-climate indicators, random forest and kriging methods (classical bayesian, ordinary, and simple) were used to estimate the spatial distribution of thickness of black soil layer. An integrated evaluation framework was developed by combining RUSLE-derived erosion estimates with black soil layer thickness, systematically incorporating both external erosive forces and inherent soil erosion resistance attributes. The results show that the random forest model outperformed the kriging models, with smaller RMSE (34.05 cm) and larger R² (0.57), especially when handling nonlinear, high-dimensional data. The predicted thickness of the black soil layer ranged from 16.2 cm to 107 cm, with a mean of 48.31 cm, closely matching the measured value of 48 cm. Elevation (EL) was found to be the most significant factor affecting the thickness of black soil layer. Soil erosion risk assessment revealed that areas with no risk and low risk accounted for 21.91% and 62.21%, respectively, while medium and high-risk areas made up 15.87% and 0.01%. No-risk areas were soil accumulation zones, while low-risk areas were mainly sloped farmland, where measures like terracing, adjusting crop ridge directions, and planting pedunculated vegetation were recommended. Medium- and high-risk areas should be addressed by returning farmland to forests and implementing engineering practices. This study offers a reference for thickness of black soil layer estimation and provides valuable insights for soil erosion risk management.