基于可解释机器学习的秦巴山区森林土壤有机碳动态及成因分析OACHSSCD

The Qinling-Daba Mountains,recognized as the Central Green Core and China's Carbon Reservoir,harbored a rich variety of unique forest ecosystems.Consequently,assessing the dynamics of forest soil organic carbon(SOC)in this region was pivotal for maintaining regional carbon balance.In this study,we compared six machine-learning algorithms and selected the optimal model to simulate the spatiotemporal distribution of forest SOC in the Qinling-Daba Mountains from 2000 to 2023.We subsequently applied the Shapley additive explanations(SHAP)method to elucidate the nonlinear relationships between environmental factors and surface SOC(0-20cm).The results showed that:(1)The XGBoost model demonstrated the best performance in spatial SOC simulation(R2=0.73,RMSE=21.98g/kg),confirming its strength in analyzing interactions among complex mountain environmental variables;(2)Environmental covariates and forest SOC exhibited nonlinear relationships,with solar radiation during the growing season,elevation,precipitation during the growing season,and mean temperature during the growing season contributing 26.18%,14.50%,8.76%,and 5.77%,respectively,and displaying threshold effects;(3)Between 2000 and 2023,surface forest SOC presented a spatial pattern of high values in the west and low values in the east,showed a generally increasing trend despite temporal fluctuations,and proved more sensitive to climate variations at higher elevations.These findings provided a scientific basis for a deeper understanding of the regional carbon cycle and offered theoretical support for developing precise forest management and carbon-sink enhancement strategies.