干旱区山盆系统碳储量时空变化及驱动机制: 以新疆富蕴县为例

doi:10.13287/j.1001-9332.202606.028

应用生态学报 ›› 2026, Vol. 37 ›› Issue (6): 1763-1774.doi: 10.13287/j.1001-9332.202606.028

• 生态系统遥感与人工智能服务生态科学专栏 • 上一篇下一篇

干旱区山盆系统碳储量时空变化及驱动机制: 以新疆富蕴县为例

毕旭¹, 史凯龙¹, 付勇勇¹, 赵若宁¹, 李建¹, 南箔², 李波^3*

¹山西财经大学资源环境学院, 太原 030006;
²河北省科学院地理科学研究所, 石家庄 050011;
³北京师范大学地理科学学部, 北京 100875

收稿日期:2025-12-27 修回日期:2026-04-29 出版日期:2026-06-18 发布日期:2026-12-18
通讯作者: *E-mail: libo@bnu.edu.cn
作者简介:毕旭, 女, 1987年生, 副教授, 博士。主要从事土地利用与生态响应研究。E-mail: 20181078@sxufe.edu.cn
基金资助:
山西省中央引导地方科技发展资金项目(YDZJSX2024D066)、国家自然科学基金项目(42107498,42401376)和教育部人文社会科学研究青年基金项目(25YJCZH050)

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 Bo^3*

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

摘要/Abstract

摘要： 在“双碳”战略背景下,探明西北干旱区山盆系统的碳储量演变特征和驱动机制,对于提升区域生态服务功能及维护生态屏障稳定性至关重要。本研究以新疆富蕴县为研究区,耦合InVEST与PLUS模型,分析2000—2020年生态系统碳储量时空演变特征,模拟2030年自然发展、快速发展、耕地保护及生态保护4种情景下的碳储量空间格局,并引入XGBoost机器学习算法和SHAP可解释性框架定量识别碳储量空间分异的驱动因素。结果表明:2000—2020年,富蕴县碳储量总量在192.01×10⁶ ~194.67×10⁶ t,于2010年达到峰值(194.67×10⁶ t);空间上呈显著的北高南低格局,高值区集中于北部高海拔山区及河谷绿洲带。驱动机制解析显示,自然要素主导碳储量格局,其中,植被净初级生产力为核心驱动因子(SHAP均值26.59,占比54.8%),其次为土壤侵蚀量和坡度。在人文社会经济因子中,放牧强度贡献最显著(5.4%),其非线性响应特征揭示了放牧压力对草地碳库的影响。经济发展和人口密度等社会经济因子的全局解释力虽较低,但在河谷绿洲区表现出正向协同效应。多情景模拟结果表明:2030年,快速发展情景下碳储量较2020年减少约1.0%(降至190.07×10⁶ t);生态保护情景下碳储量预计提升至196.04×10⁶ t(增幅约2.1%)。干旱区碳储量受水分条件显著制约,实施严格的生态保护措施是实现区域碳汇增长的有效途径。

关键词: 干旱区, 碳储量, PLUS-InVEST模型, 多情景模拟, XGBoost-SHAP模型

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

毕旭, 史凯龙, 付勇勇, 赵若宁, 李建, 南箔, 李波. 干旱区山盆系统碳储量时空变化及驱动机制: 以新疆富蕴县为例[J]. 应用生态学报, 2026, 37(6): 1763-1774.

BI Xu, SHI Kailong, FU Yongyong, ZHAO Ruoning, LI Jian, NAN Bo, LI Bo. Spatiotemporal variations and driving mechanisms of carbon storage in the mountain-basin systems of arid regions: A case study of Fuyun County, Xinjiang, China[J]. Chinese Journal of Applied Ecology, 2026, 37(6): 1763-1774.

参考文献

[1] Cramer W, Guiot J, Fader M, et al. Climate change and interconnected risks to sustainable development in the Mediterranean. Nature Climate Change, 2018, 8: 972-980
[2] Schiermeier Q. At least three-quarters of climate change is man-made. Nature, 2011, 480: 299
[3] Bryan BA, Gao L, Ye YQ, et al. China’s response to a national land-system sustainability emergency. Nature, 2018, 559: 193-204
[4] Wang WJ, Guo HC, Chuai XW, et al. The impact of land use change on the temporospatial variations of ecosystems services value in China and an optimized land use solution. Environmental Science & Policy, 2014, 44: 62-72
[5] Ali G, Pumijumnong N, Cui S. Valuation and validation of carbon sources and sinks through land cover/use change analysis: The case of Bangkok metropolitan area. Land Use Policy, 2018, 70: 471-478
[6] Madigan AP, Zimmermann J, Krol DJ, et al. Full inversion tillage (FIT) during pasture renewal as a potential management strategy for enhanced carbon sequestration and storage in Irish grassland soils. Science of the Total Environment, 2022, 805: 150342
[7] Levy PE, Friend AD, White A, et al. The influence of land use change on global-scale fluxes of carbon from terrestrial ecosystems. Climatic Change, 2004, 67: 185-209
[8] Shah MI, Abbas S, Olohunlana AO, et al. The impacts of land use change on biodiversity and ecosystem ser-vices: An empirical investigation from highly fragile countries. Sustainable Development, 2023, 31: 1384-1400
[9] Winkler K, Fuchs R, Rounsevell M, et al. Global land use changes are four times greater than previously estimated. Nature Communications, 2021, 12: 2501
[10] Mahmoud SH, Gan TY. Impact of anthropogenic climate change and human activities on environment and ecosystem services in arid regions. Science of the Total Environment, 2018, 633: 1329-1344
[11] He YL, Ma JM, Zhang CS, et al. Spatio-temporal evolution and prediction of carbon storage in Guilin based on FLUS and InVEST models. Remote Sensing, 2023, 15: 1445
[12] Qiao XR, Li ZJ, Lin JK, et al. Assessing current and future soil erosion under changing land use based on InVEST and FLUS models in the Yihe River Basin, North China. International Soil and Water Conservation Research, 2024, 12: 298-312
[13] Yang Q, Huang Z, Wu L, et al. Resilience changes of carbon stocks to quantify the long-term effects of ecological engineering projects in subtropical forests of China based on satellite-derived net ecosystem production time series and inventory data. Land Degradation & Development, 2024, 35: 2329-2344
[14] Nie X, Lu B, Chen ZP, et al. Increase or decrease? Integrating the CLUMondo and InVEST models to assess the impact of the implementation of the major function oriented zone planning on carbon storage. Ecological Indicators, 2020, 118: 106708
[15] Lai JL, Qi S, Chen JD, et al. Exploring the spatiotemporal variation of carbon storage on Hainan Island and its driving factors: Insights from InVEST, FLUS models, and machine learning. Ecological Indicators, 2025, 172: 113236
[16] Guo H, Zhou X, Dong Y, et al. On the use of machine learning methods to improve the estimation of gross primary productivity of maize field with drip irrigation. Ecological Modelling, 2023, 476: 110250
[17] Liang X, Guan QF, Clarke KC, et al. Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China. Computers, Environment and Urban Systems, 2021, 85: 101569
[18] Ren YX, Zhang LY, Li X, et al. Spatiotemporal variations and driving mechanisms of carbon storage in Central Asia: Insights from the PLUS-InVEST models and machine learning. Journal of Environmental Management, 2025, 389: 126123
[19] 杨红霞, 何浩, 刘蓓, 等. 基于PLUS-InVEST-Geodetector模型的西北干旱区土地利用多情景模拟与碳储量评估. 环境科学, 2026, 47(5): 3049-3060
[20] 李玉洁, 江洪, 刘宣广. 基于CatBoost的植被总初级生产力遥感模拟方法及在福建省的应用. 地球信息科学学报, 2023, 25(9): 1908-1922
[21] Liao DH, Niu J, Ciais P, et al. Changing climate threatens irrigation benefits of maize gross primary productivity in China. Earth’s Future, 2024, 12: e2022EF003474
[22] Qu JS, Xu ZL, Dong B, et al. Analyzing trade-offs, synergies, and driving factors of ecosystem services in Anhui Province using spatial analysis and XG-boost modeling. Ecological Indicators, 2025, 171: 113098
[23] Zhu CX, Wang GJ, Shao YH, et al. Disentangling gross primary productivity drivers of forested areas in China and its climate zones from 1990 to 2018. Journal of Cleaner Production, 2025, 509: 145616
[24] 韩楚翘, 郑江华, 王哲, 等. 基于PLUS-InVEST模型吐哈盆地陆地生态系统碳储量时空变化及多情景模拟. 干旱区地理, 2024, 47(2): 260-269
[25] 杨顺法, 昝梅, 袁瑞联, 等. 基于PLUS与InVEST模型的新疆碳储量变化及预测. 环境科学, 2025, 46(1): 378-387
[26] Li HJ, Zhang K, Liu YQ, et al. Spatiotemporal evolution of land use and carbon storage in China: Multi-scenario simulation and driving factor analysis based on the PLUS-InVEST model and SHAP. Environmental Research, 2025, 279: 121860
[27] 张新时. 天山北部山地-绿洲-过渡带-荒漠系统的生态建设与可持续农业范式. 植物学报, 2001, 43(12): 1294-1299
[28] 牛飞飞, 郭靖, 罗杰, 等. 基于GeoSOS-FLUS和InVEST模型的新疆地区土地利用变化模拟及碳储量预测. 干旱区地理, 2025, 48(12): 2169-2182
[29] Alam SA, Starr M, Clark BJF. Tree biomass and soil organic carbon densities across the Sudanese woodland savannah: A regional carbon sequestration study. Journal of Arid Environments, 2013, 89: 67-76
[30] 张坤, 吴新萍, 刘永强, 等. 基于PLUS-InVEST模型的新疆碳储量时空演变与预测. 干旱区研究, 2025, 42(9): 1715-1725
[31] 傅楷翔, 贾国栋, 余新晓, 等. 耦合PLUS-InVEST-Geodector模型的新疆地区碳储量时空变化及驱动机制分析. 环境科学, 2024, 45(9): 5416-5430
[32] 张顺鑫, 吴子豪, 闫庆武, 等. 基于PLUS-InVEST模型的天山北坡生态系统碳储量时空变化与预测. 干旱区研究, 2024, 41(7): 1228-1237
[33] 新疆维吾尔自治区人民政府. 新疆维吾尔自治区国土空间总体规划(2021—2035年). (2024-05-17) [2025-10-11]. https://www.xinjiang.gov.cn/xinjiang/gtkjgh/dpwz.shtml
[34] Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. (2016-03-08) [2025-10-15]. https://arxiv.org/abs/1603.02754v3
[35] Lundberg SM, Lee SI. A unified approach to interpreting model predictions. 31st International Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, California, USA, 2017: 4768-4777
[36] Nan B, Li B, Yang ZH, et al. Sustainability of sown systems of cultivated grassland at the edge of the Junggar Desert Basin: An integrated evaluation of emergy and economics. Journal of Cleaner Production, 2020, 276: 122800
[37] Bi X, Li B, Zhang LX, et al. Response of grassland productivity to climate change and anthropogenic activities in arid regions of Central Asia. PeerJ, 2020, 8: e9797
[38] Zhang HW, Bie Q, Shi Y, et al. Analysis of sustained growth characteristics of carbon storage in Central Asia under multi-scenario simulation. Land, 2025, 14: 848
[39] 黄艳, 刘晓曼, 袁静芳, 等. 2000—2020年华北干旱半干旱区碳储量变化特征及影响因素. 环境科学研究, 2024, 37(4): 849-861
[40] 韩敏, 徐长春, 隆云霞, 等. 西北干旱区不同土地利用情景下的碳储量及碳源/汇变化模拟与预估. 水土保持通报, 2022, 42(3): 335-344
[41] Wang JX, Zhao CY, Shi ZY, et al. Spatiotemporal variation in carbon storage in the Central Plains Urban Agglomeration under multi-scenario simulations. Land, 2025, 14: 1594
[42] Wondimu MT, Yusuf MM, Gulti MB, et al. Unveiling woody species diversity and carbon storage potential of agroforestry systems in Southeast Ethiopia. Ecological Processes, 2026, 15: 29
[43] 赵佳俊, 侯鹏, 陈妍. 中国草地植被碳储量估算研究进展. 生态学杂志, 2025, 44(5): 1678-1687
[44] 贺国凯, 王凯博, 任宗萍, 等. 黄土高原典型小流域水土保持土壤碳库演变特征及其碳汇潜力. 水土保持学报, 2024, 38(6): 161-169
[45] Huang JP, Yu HP, Guan XD, et al. Accelerated dryland expansion under climate change. Nature Climate Change, 2016, 6: 166-171
[46] Bi X, Li B, Fu Q, et al. Effects of grazing exclusion on the grassland ecosystems of mountain meadows and temperate typical steppe in a mountain-basin system in Central Asia’s arid regions, China. Science of the Total Environment, 2018, 630: 254-263
[47] 李雪婷, 杨亮洁, 杨永春, 等. 河西走廊经济带城镇化与生态韧性的耦合协调分析. 应用生态学报, 2025, 36(2): 547-558
[48] 库勒米拉·赛力克. 富蕴县牧民定居现状分析. 当代经济, 2014, 31(2): 94-96
[49] 杨嘉晖, 刘晓煌, 时鹏, 等. “三生”功能视角下额尔齐斯河流域空间优化调控研究. 干旱区地理, 2026, 49(3): 581-593
[50] 任玥颖, 贾夏, 赵永华, 等. 未来气候变化情景下河西走廊生态系统服务变化及其驱动力. 生态学报, 2025, 45(18): 8821-8836
[51] 王婉莹, 黄佛君, 王华美. 天山北坡山地-绿洲-荒漠系统碳储量梯度效应时空演变及机理. 中山大学学报:自然科学版, 2026, 65(2): 1-14

干旱区山盆系统碳储量时空变化及驱动机制: 以新疆富蕴县为例

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

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