| 引用本文: | 贺宁,黄来明.2026.2000—2020年天山北坡植被动态变化及影响因素[J].地球环境学报,17(3):669-682 |
| HE Ning,HUANG Laiming.2026.Dynamic change and driving factors of vegetation on the northern slope of the Tianshan Mountains from 2000 to 2020[J].Journal of Earth Environment,17(3):669-682 |
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| 2000—2020年天山北坡植被动态变化及影响因素 |
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贺宁1，2，黄来明1，2，3
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1.中国科学院地理科学与资源研究所 黄河三角洲现代农业工程实验室,北京 100101 ；2.中国科学院大学 资源与环境学院,北京 100049 ；3.中国科学院地球环境研究所 黄土科学全国重点实验室,西安 710061
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| 摘要: |
| 植被对维持生态系统稳定和促进社会经济可持续发展至关重要,明确植被长时序动态变化及其对气候因子和人类活动的响应可为生态系统合理管理提供依据。文章以植被净初级生产力(NPP)作为植被变化指标,分析了2000—2020年天山北坡(NSTM)植被动态变化及影响因素。结果表明:2000—2020年天山北坡植被NPP呈"一低多高"趋势,具体表现为东北区域高,西北、西南和东南区域低,这主要与不同地区土地利用类型有关。近21年,天山北坡植被恢复区面积(68.60%)远大于退化区域面积(31.40%),气候变化和人类活动分别是植被恢复和植被退化的主导因素。2000—2016年植被NPP呈波动上升趋势,增加速率为1.35 g/(m²·a)(以碳计,下同),生态恢复工程和农耕措施共同主导该时段植被恢复;2016—2020年植被NPP呈快速下降趋势,降低速率为11.35 g/(m²·a),降水量减少、土地利用类型转变、经济发展和人口密度增加主导该时段植被退化。人类活动对植被NPP的约束作用更强,随国内生产总值(GDP)和人口密度的增加,植被NPP潜在最大值呈非线性降低,降水量和温度对植被NPP的约束作用存在阈值,分别为378 mm和2 ℃。研究结果有助于揭示全球变暖背景下西北旱区植被对气候变化和人类活动的响应机制,进而为该区退化生态系统修复和管理提供依据。 |
| 关键词: 脆弱生态系统 植被动态变化 土壤水分 时空变化 驱动因素 |
| DOI：10.7515/JEE2024048 |
| CSTR：32259.14.JEE2024048 |
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| 基金项目:第三次新疆综合科学考察研究项目（2022XJKK0904）；黄土与第四纪地质国家重点实验室开放基金项目（SKLLQG2334） |
| 英文基金项目: |
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| Dynamic change and driving factors of vegetation on the northern slope of the Tianshan Mountains from 2000 to 2020 |
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HE Ning1，2，HUANG Laiming1，2，3
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1. Yellow River Delta Modern Agricultural Engineering Laboratory, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101 , China ；2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049 , China ；3. State Key Laboratory of Loess Science, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061 , China
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| Abstract: |
| Background, aim, and scope Vegetation is a fundamental component of terrestrial ecosystems, playing a key role in climate regulation and carbon sequestration, thereby contributing significantly to ecosystem quality and stability. Understanding the long-term dynamics of vegetation and its responses to climate change and human activities is essential for developing rational ecosystem management strategies. Net primary productivity (NPP) serves as a robust indicator of vegetation growth dynamics, offering valuable insights for regional vegetation restoration and ecosystem management. Previous studies have often simply attributed vegetation changes to either climate change or human activities, without sufficiently disentangling the combined mechanisms driven by both natural and socioeconomic factors. This study analyzed vegetation NPP changes and their influencing factors on the northern slope of the Tianshan Mountains (NSTM) (41°11′—46°12'N, 79°54′—91°34'E) from 2000 to 2020, aiming to (1) reveal the spatiotemporal patterns of vegetation NPP, (2) clarify the relative contributions of climate change and human activities to vegetation dynamics, and (3) elucidate the driving factors and constraint effects on vegetation NPP changes across different regions. Materials and methods Using an improved Carnegie-Ames-Stanford Approach (CASA) model, residual analysis, trend analysis, correlation analysis, and constraint line analysis, this study integrated meteorological data, land use types, and the normalized difference vegetation index (NDVI) to investigate vegetation dynamics and their influencing factors on the NSTM from 2000 to 2020. Results From 2000 to 2020, vegetation NPP on the NSTM was generally higher in the northeast and lower in the northwest, southwest, and southeast. Over the 21-year period, the area of vegetation restoration area (68.60%) was substantially larger than that of vegetation degradation (31.40%). Climate change and human activities were identified as the predominant drivers of vegetation restoration and degradation, respectively. Between 2000 and 2016, NPP showed a fluctuating upward trend, with an average increase rate of 1.35 g/(m²·a) (calculated as carbon, same below). However, from 2016 to 2020, vegetation NPP declined rapidly at an average rate of 11.35 g/(m²·a). Increasing gross domestic product (GDP) and population density were associated with a nonlinear decrease in the potential maximum value of vegetation NPP. Constraint effects of precipitation and temperature on vegetation NPP exhibited thresholds, approximately 378 mm and 2 ℃, respectively. Discussion The spatial distribution of vegetation NPP was mainly related to climate change and land use types on the NSTM. The fluctuating increase in NPP from 2000 to 2020 was attributed to the combined effects of ecological restoration projects and agricultural practices. In contrast, the decline from 2016 to 2020 was linked to reduced precipitation, land use conversion, rapid economic development, and population growth. Decreasing precipitation and rising temperature were found to be detrimental to long-term vegetation restoration. Human activities mainly affected vegetation changes through economic development and the implement of ecological protection measures. Conclusions Vegetation NPP on the NSTM exhibited significant spatiotemporal heterogeneity from 2000 to 2020, characterized by a fluctuating increase trend before 2016 followed by a rapid decline. Overall, the vegetation restoration area was more than double the vegetation degradation area during 2000—2020. Climate change and human activities were the leading factors driving vegetation restoration and degradation, respectively. However, the positive effects of ecological restoration projects may not always offset the negative impacts of rapid climate change, land use conversion and economic development, particularly during 2016—2020. Recommendations and perspectives This study helps reveal the response mechanism of vegetation to climate change and human activities in arid Northwest China under global warming. By applying an improved CASA model, it clarifies the spatiotemporal changes of vegetation NPP on the NSTM and its key influencing factors, confirming the differential effects and constraint thresholds of climate change and human activities. The findings provide a scientific basis for the recovery and management of degraded ecosystems in the region. |
| Key words: ecologically fragile region vegetation dynamics soil moisture spatiotemporal change driving factor |
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