Spatiotemporal simulation and projection of soil erosion as affected by climate change in Northeast China

Abstract

Long-term climate change significantly affects the spatiotemporal dynamics of soil erosion. To explore this, remote sensing technology, future climate scenarios, and deep learning are combined to model the historical and future variations in soil erosion, investigating its spatiotemporal dynamics influenced by climate change. This paper uses the Revised Universal Soil Loss Equation (RUSLE) to assess the historical changes in erosion in northeast China from 1980 to 2020. A soil erosion simulation (SES) model was developed, incorporating deep learning models, to forecast future trends in soil erosion under various climate scenarios. The SES model achieves an R-squared (R2) value of 0.7513. The SES model can simulate the Spatiotemporal dynamics of soil erosion influenced by long-term climate change. Soil erosion from 2001 to 2020 is lower than that from 1980 to 2000, indicating a decrease in soil erosion under natural variability conditions. Unlike historical trends, future soil erosion demonstrates significant variation across three scenarios: SSP1-RCP1.9 (SSP119), SSP2-RCP4.5 (SSP245), and SSP5-RCP8.5 (SSP585). The simulation results show that the SSP119 climate scenario has a minor impact on soil erosion, whereas the SSP245 scenario leads to a gradual increase in soil erosion. The SSP585 scenario, characterized by high social vulnerability and substantial radiative forcing, exacerbates the risk of soil erosion. The study provides valuable references for maintaining soil stability and managing surface runoff.

Keywords

• Soil erosion
• Climate change
• Deep learning
• RUSLE
• Climate scenario