Predicting Gross Primary Productivity under Future Climate Change for the Tibetan Plateau Based on Convolutional Neural Networks

被引:1
|
作者
Li, Meimei [1 ]
Zhu, Zhongzheng [2 ]
Ren, Weiwei [2 ]
Wang, Yingzheng [3 ]
机构
[1] Sun Yat Sen Univ, Sch Ecol, State Key Lab Biocontrol, Shenzhen Campus, Shenzhen 518107, Peoples R China
[2] Chinese Acad Sci, Inst Tibetan Plateau Res, Natl Tibetan Plateau Data Ctr TPDC, State Key Lab Tibetan Plateau Earth Syst Sci Envir, Beijing 100101, Peoples R China
[3] Lanzhou Univ, Coll Earth & Environm Sci, Lanzhou 730000, Peoples R China
基金
中国国家自然科学基金;
关键词
Tibetan Plateau; gross primary productivity; climate change; spatiotemporal variation; convolutional neural networks; MODEL; COVARIATION; ECOSYSTEM; IMPACTS;
D O I
10.3390/rs16193723
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Gross primary productivity (GPP) is vital for ecosystems and the global carbon cycle, serving as a sensitive indicator of ecosystems' responses to climate change. However, the impact of future climate changes on GPP in the Tibetan Plateau, an ecologically important and climatically sensitive region, remains underexplored. This study aimed to develop a data-driven approach to predict the seasonal and annual variations in GPP in the Tibetan Plateau up to the year 2100 under changing climatic conditions. A convolutional neural network (CNN) was employed to investigate the relationships between GPP and various environmental factors, including climate variables, CO2 concentrations, and terrain attributes. This study analyzed the projected seasonal and annual GPP from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four future scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The results suggest that the annual GPP is expected to significantly increase throughout the 21st century under all future climate scenarios. By 2100, the annual GPP is projected to reach 1011.98 Tg C, 1032.67 Tg C, 1044.35 Tg C, and 1055.50 Tg C under the four scenarios, representing changes of 0.36%, 4.02%, 5.55%, and 5.67% relative to 2021. A seasonal analysis indicates that the GPP in spring and autumn shows more pronounced growth under the SSP3-7.0 and SSP5-8.5 scenarios due to the extended growing season. Furthermore, the study identified an elevation band between 3000 and 4500 m that is particularly sensitive to climate change in terms of the GPP response. Significant GPP increases would occur in the east of the Tibetan Plateau, including the Qilian Mountains and the upper reaches of the Yellow and Yangtze Rivers. These findings highlight the pivotal role of climate change in driving future GPP dynamics in this region. These insights not only bridge existing knowledge gaps regarding the impact of future climate change on the GPP of the Tibetan Plateau over the coming decades but also provide valuable guidance for the formulation of climate adaptation strategies aimed at ecological conservation and carbon management.
引用
收藏
页数:21
相关论文
共 50 条
  • [31] Assessing responses of hydrological processes to climate change over the southeastern Tibetan Plateau based on resampling of future climate scenarios
    Gao, Chao
    Liu, Li
    Ma, Di
    He, Keqi
    Xu, Yue-Ping
    SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 664 : 737 - 752
  • [32] Predicting the future direction of cell movement with convolutional neural networks
    Nishimoto, Shori
    Tokuoka, Yuta
    Yamada, Takahiro G.
    Hiroi, Noriko F.
    Funahashi, Akira
    PLOS ONE, 2019, 14 (09):
  • [33] Decadal change and inter-annual variability of net primary productivity on the Tibetan Plateau
    Lan Cuo
    Yongxin Zhang
    Bingrong Xu-Ri
    Climate Dynamics, 2021, 56 : 1837 - 1857
  • [34] Predicting the responses of subalpine forest landscape dynamics to climate change on the eastern Tibetan Plateau
    Liu, Junyan
    Zou, Heng-Xing
    Bachelot, Benedicte
    Dong, Tingfa
    Zhu, Zhongfu
    Liao, Yuchen
    Plenkovic-Moraj, Andelka
    Wu, Yan
    GLOBAL CHANGE BIOLOGY, 2021, 27 (18) : 4352 - 4366
  • [35] Decadal change and inter-annual variability of net primary productivity on the Tibetan Plateau
    Cuo, Lan
    Zhang, Yongxin
    Xu-Ri
    Zhou, Bingrong
    CLIMATE DYNAMICS, 2021, 56 (5-6) : 1837 - 1857
  • [36] Increasing lake water storage on the Inner Tibetan Plateau under climate change
    Jia, Binghao
    Wang, Longhuan
    Xie, Zhenghui
    SCIENCE BULLETIN, 2023, 68 (05) : 489 - 493
  • [37] Amphibians rise to flourishing under climate change on the Qinghai-Tibetan Plateau
    He, Fangfang
    Liang, Lu
    Wang, Huichun
    Li, Aijing
    La, Mencuo
    Wang, Yao
    Zhang, Xiaoting
    Zou, Denglang
    HELIYON, 2024, 10 (16)
  • [38] Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks
    Marcolongo, Aris
    Vladymyrov, Mykhailo
    Lienert, Sebastian
    Peleg, Nadav
    Haug, Sigve
    Zscheischler, Jakob
    ENVIRONMENTAL DATA SCIENCE, 2022, 1
  • [39] Modeling gross primary production of alpine ecosystems in the Tibetan Plateau using MODIS images and climate data
    Li, Zhengquan
    Yu, Guirui
    Xiao, Xiangming
    Li, Yingnian
    Zhao, Xinquan
    Ren, Chuanyou
    Zhang, Leiming
    Fu, Yuling
    REMOTE SENSING OF ENVIRONMENT, 2007, 107 (03) : 510 - 519
  • [40] Spatiotemporal variations in water conservation function of the Tibetan Plateau under climate change based on InVEST model
    Wang, Yunfei
    Ye, Aizhong
    Peng, Dingzhi
    Miao, Chiyuan
    Di, Zhenghua
    Gong, Wei
    JOURNAL OF HYDROLOGY-REGIONAL STUDIES, 2022, 41