A greater negative impact of future climate change on vegetation in Central Asia: Evidence from trajectory/pattern analysis

被引:3
作者
Han, Wanqiang [1 ,2 ]
Zheng, Jianghua [1 ,2 ]
Guan, Jingyun [2 ,3 ]
Liu, Yujia [1 ,2 ]
Liu, Liang [1 ,2 ]
Han, Chuqiao [1 ,2 ]
Li, Jianhao [1 ,2 ]
Li, Congren [1 ,2 ]
Tian, Ruikang [1 ,2 ]
Mao, Xurui [1 ,2 ]
机构
[1] Xinjiang Univ, Coll Geog & Remote Sensing Sci, Urumqi 830046, Peoples R China
[2] Xinjiang Univ, Key Lab Oasis Ecol, Urumqi 830046, Peoples R China
[3] Xinjiang Univ Finance & Econ, Coll Tourism, Urumqi 830012, Peoples R China
关键词
Central asia; Climate change; Vegetation estimation; Change trajectory; Machine learning; WATER-USE EFFICIENCY; DROUGHT; SHIFTS; PRODUCTIVITY; ADAPTATION; TREND;
D O I
10.1016/j.envres.2024.119898
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In the context of global warming, vegetation changes exhibit various patterns, yet previous studies have focused primarily on monotonic changes, often overlooking the complexity and diversity of multiple change processes. Therefore, it is crucial to further explore vegetation dynamics and diverse change trajectories in this region under future climate scenarios to obtain a more comprehensive understanding of local ecosystem evolution. In this study, we established an integrated machine learning prediction framework and a vegetation change trajectory recognition framework to predict the dynamics of vegetation in Central Asia under future climate change scenarios and identify its change trajectories, thus revealing the potential impacts of future climate change on vegetation in the region. The findings suggest that various future climate scenarios will negatively affect most vegetation in Central Asia, with vegetation change intensity increasing with increasing emission trajectories. Analyses of different time scales and trend variations consistently revealed more pronounced downward trends. Vegetation change trajectory analysis revealed that most vegetation has undergone nonlinear and dramatic changes, with negative changes outnumbering positive changes and curve changes outnumbering abrupt changes. Under the highest emission scenario (SSP5-8.5), the abrupt vegetation changes and curve changes are 1.7 times and 1.3 times greater, respectively, than those under the SSP1-2.6 scenario. When transitioning from lower emission pathways (SSP1-2.6, SSP2-4.5) to higher emission pathways (SSP3-7.0, SSP5-8.5), the vegetation change trajectories shift from neutral and negative curve changes to abrupt negative changes. Across climate scenarios, the key climate factors influencing vegetation changes are mostly evapotranspiration and soil moisture, with temperature and relative humidity exerting relatively minor effects. Our study reveals the negative response of vegetation in Central Asia to climate change from the perspective of vegetation dynamics and change trajectories, providing a scientific basis for the development of effective ecological protection and climate adaptation strategies.
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页数:14
相关论文
共 112 条
[1]   A review of the global climate change impacts, adaptation, and sustainable mitigation measures [J].
Abbass, Kashif ;
Qasim, Muhammad Zeeshan ;
Song, Huaming ;
Murshed, Muntasir ;
Mahmood, Haider ;
Younis, Ijaz .
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2022, 29 (28) :42539-42559
[2]   The human-environment nexus and vegetation-rainfall sensitivity in tropical drylands [J].
Abel, Christin ;
Horion, Stephanie ;
Tagesson, Torbern ;
De Keersmaecker, Wanda ;
Seddon, Alistair W. R. ;
Abdi, Abdulhakim M. ;
Fensholt, Rasmus .
NATURE SUSTAINABILITY, 2021, 4 (01) :25-U150
[3]   Importance of vegetation dynamics for future terrestrial carbon cycling [J].
Ahlstrom, Anders ;
Xia, Jianyang ;
Arneth, Almut ;
Luo, Yiqi ;
Smith, Benjamin .
ENVIRONMENTAL RESEARCH LETTERS, 2015, 10 (05)
[4]  
[Anonymous], 2018, Global Warming of 1.5C. An IPCC Special Report on the impacts of global warming of 1.5C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development
[5]   Prevalence and drivers of abrupt vegetation shifts in global drylands [J].
Berdugo, Miguel ;
Gaitan, Juan J. ;
Delgado-Baquerizo, Manuel ;
Crowther, Thomas W. ;
Dakos, Vasilis .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2022, 119 (43)
[6]   Global ecosystem thresholds driven by aridity [J].
Berdugo, Miguel ;
Delgado-Baquerizo, Manuel ;
Soliveres, Santiago ;
Hernandez-Clemente, Rocio ;
Zhao, Yanchuang ;
Gaitan, Juan J. ;
Gross, Nicolas ;
Saiz, Hugo ;
Maire, Vincent ;
Lehman, Anika ;
Rillig, Matthias C. ;
Sole, Ricard V. ;
Maestre, Fernando T. .
SCIENCE, 2020, 367 (6479) :787-+
[7]   Global-scale characterization of turning points in arid and semi-arid ecosystem functioning [J].
Bernardino, Paulo N. ;
De Keersmaecker, Wanda ;
Fensholt, Rasmus ;
Verbesselt, Jan ;
Somers, Ben ;
Horion, Stephanie .
GLOBAL ECOLOGY AND BIOGEOGRAPHY, 2020, 29 (07) :1230-1245
[8]   Improved prediction of tree species richness and interpretability of environmental drivers using a machine learning approach [J].
Brugere, Lian ;
Kwon, Youngsang ;
Frazier, Amy E. ;
Kedron, Peter .
FOREST ECOLOGY AND MANAGEMENT, 2023, 539
[9]   Vegetation as the catalyst for water circulation on global terrestrial ecosystem [J].
Chen, Jinlong ;
Shao, Zhenfeng ;
Deng, Xiongjie ;
Huang, Xiao ;
Dang, Chaoya .
SCIENCE OF THE TOTAL ENVIRONMENT, 2023, 895
[10]   Disentangling the relative impacts of climate change and human activities on arid and semiarid grasslands in Central Asia during 1982-2015 [J].
Chen, Tao ;
Bao, Anming ;
Jiapaer, Guli ;
Guo, Hao ;
Zheng, Guoxiong ;
Jiang, Liangliang ;
Chang, Cun ;
Tuerhanjiang, Latipa .
SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 653 :1311-1325