Welcome to Chinese Journal of Applied Ecology! Today is

Chinese Journal of Applied Ecology ›› 2026, Vol. 37 ›› Issue (6): 1763-1774.doi: 10.13287/j.1001-9332.202606.028

• Special Features of Ecosystem Remote sensing and AI Services for Ecology Science • Previous Articles     Next Articles

Spatiotemporal variations and driving mechanisms of carbon storage in the mountain-basin systems of arid regions: A case study of Fuyun County, Xinjiang, China

BI Xu1, SHI Kailong1, FU Yongyong1, ZHAO Ruoning1, LI Jian1, NAN Bo2, LI Bo3*   

  1. 1College of Resource and Environment, Shanxi University of Finance and Economics, Taiyuan 030006, China;
    2Institute of Geographical Sciences, Hebei Academy of Sciences, Shijiazhuang 050011, China;
    3Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • Received:2025-12-27 Revised:2026-04-29 Online:2026-06-18 Published:2026-12-18

Abstract: Under the “dual carbon” strategy, elucidating carbon storage dynamics and their driving mechanisms in the mountain-basin systems of Northwest China’s arid regions is crucial for enhancing regional ecosystem services and maintaining the stability of ecological barriers. We used the InVEST and PLUS models to analyze the spatio-temporal variations of ecosystem carbon storage in Fuyun County of Xinjiang from 2000 to 2020. Furthermore, we simulated the spatial patterns of carbon storage in 2030 under four distinct scenarios: natural development (ND), rapid development (RD), cropland protection (CP), and ecological protection (EP), and employed the XGBoost machine learning algorithm and the SHAP interpretability framework to quantitatively identify the driving factors for the spatial differentiation of carbon storage. From 2000 to 2020, the total carbon storage in Fuyun County fluctuated between 192.01×106 and 194.67×106 t, peaking in 2010 (194.67×106 t). Spatially, it exhibited a “high in the north, low in the south” pattern, with high-value areas concentrated in northern high-altitude mountains and river valley oasis belts. Natural factors dominated the spatial pattern of carbon storage, with net primary productivity was the core driver (mean SHAP value of 26.59, accounting for 54.8%), followed by soil erosion and slope. Among the human and socio-economic factors, grazing intensity exerted the most significant contribution (5.4%), and its nonlinear response characteristics revealed the impact of grazing pressure on grassland carbon pool. The global explanatory power of socio-economic factors, such as economic development and population density, was relatively low, which exhibited positive synergistic effects in river valley oasis areas. Multi-scenario simulation results showed that carbon storage in 2030 was projected to decrease by approximately 1.0% compared to 2020 (dropping to 190.07×106 t), whereas under the ecological protection scenario, it was expected to rise to 196.04×106 t (an increase about 2.1%). Carbon storage in arid regions would be significantly constrained by water condition, and therefore implementing strict ecological protection measures would be an effective pathway to increase regional carbon sink.

Key words: arid region, carbon storage, PLUS-InVEST model, multi-scenario simulation, XGBoost-SHAP model