Drought Risk of Global Terrestrial Gross Primary Productivity Over the Last 40 Years Detected by a Remote Sensing-Driven Process Model

被引:52
|
作者
He, Qiaoning [1 ,2 ,3 ]
Ju, Weimin [1 ,3 ]
Dai, Shengpei [1 ,3 ,4 ]
He, Wei [1 ,2 ]
Song, Lian [1 ,5 ]
Wang, Songhan [1 ,3 ]
Li, Xinchuan [2 ]
Mao, Guangxiong [2 ]
机构
[1] Nanjing Univ, Int Inst Earth Syst Sci, Nanjing, Peoples R China
[2] Huaiyin Normal Univ, Sch Urban & Environm Sci, Huaian, Peoples R China
[3] Nanjing Univ, Sch Geog & Ocean Sci, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Minist Nat Resources,Key Lab Land Satellite Remot, Nanjing, Peoples R China
[4] Chinese Acad Trop Agr Sci, Inst Sci & Tech Informat, Haikou, Hainan, Peoples R China
[5] Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing, Peoples R China
基金
中国国家自然科学基金;
关键词
NET PRIMARY PRODUCTION; CARBON-CYCLE; INTERANNUAL VARIABILITY; VEGETATION PRODUCTIVITY; CLIMATE EXTREMES; RECENT TRENDS; ECOSYSTEMS; SENSITIVITY; REDUCTION; IMPACTS;
D O I
10.1029/2020JG005944
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 the largest flux in the global terrestrial carbon cycle. Drought has significantly impacted global terrestrial GPP in recent decades, and has been projected to occur with increasing frequency and intensity. However, the drought risk of global terrestrial GPP has not been well investigated. In this study, global terrestrial GPP during 1981-2016 was simulated with the process-based Boreal Ecosystem Productivity Simulator model. Then, the drought risk of GPP was quantified as the product of drought probability and reduction of GPP caused by drought, which was determined using the standardized precipitation evapotranspiration index. During the study period, the drought risk of GPP was high in the southeastern United States, most of South America, southern Europe, central and eastern Africa, eastern and southeastern Asia, and eastern Australia. It was low at some high latitudes of the Northern Hemisphere and in part of tropical South America, where terrestrial GPP increased slightly in drought years. The drought risk of terrestrial GPP was greater during 2000-2016 than during 1981-1999 in 21 out of 24 climatic zones. The global mean drought risk of GPP increased from 13.6 g C m(-2) yr(-1) during 1981-1999 to 19.3 g C m(-2) yr(-1) during 2000-2016. The increase in drought risk of GPP was mainly caused by the increase in drought vulnerability. Simulation experiments indicated that the drought vulnerability of GPP was mainly induced by climatic variability. This study advances our understanding on the impact of drought on GPP over the globe. Plain Language Summary Drought generally affects gross primary productivity (GPP) of terrestrial ecosystems. However, the drought risk of global terrestrial GPP has not been well investigated. We assessed the drought risk of GPP based on the simulation by a process-based model for the years 1981-2016. The drought risk of GPP was quantified in terms of the chances of drought occurring and the anticipated reduction of GPP that would be caused by the drought. The results were variable across the world, being serious in the southeastern United States, most of South America, southern Europe, central and eastern Africa, eastern and southeastern Asia, and eastern Australia. The drought risk of GPP at some high latitudes of the Northern Hemisphere and in part of tropical South America was not found to be problematic. In those areas, GPP increased slightly in drought years in comparison with that in normal years. The drought risk of terrestrial GPP was greater during 2000-2016 than during 1981-1999 in 21 out of 24 climatic zones. The global mean drought risk of GPP increased from 13.6 g C m(-2) yr(-1) during 1981-1999 to 19.3 g C m(-2) yr(-1) during 2000-2016. The increase in drought risk of GPP was mainly caused by climatic variability.
引用
收藏
页数:16
相关论文
共 12 条
  • [1] Estimation and analysis of terrestrial net primary productivity over India by remote-sensing-driven terrestrial biosphere model
    Nayak, Rabindra K.
    Patel, N. R.
    Dadhwal, V. K.
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2010, 170 (1-4) : 195 - 213
  • [2] Estimation and analysis of terrestrial net primary productivity over India by remote-sensing-driven terrestrial biosphere model
    Rabindra K. Nayak
    N. R. Patel
    V. K. Dadhwal
    Environmental Monitoring and Assessment, 2010, 170 : 195 - 213
  • [3] Net primary productivity of China's terrestrial ecosystems from a process model driven by remote sensing
    Feng, X.
    Liu, G.
    Chen, J. M.
    Chen, M.
    Liu, J.
    Ju, W. M.
    Sun, R.
    Zhou, W.
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2007, 85 (03) : 563 - 573
  • [4] Gross primary productivity of terrestrial ecosystems: a review of observations, remote sensing, and modelling studies over South Asia
    Pandey, Varsha
    Harde, Sakshi
    Rajasekaran, Eswar
    Burman, Pramit Kumar Deb
    THEORETICAL AND APPLIED CLIMATOLOGY, 2024, 155 (09) : 8461 - 8491
  • [5] Net primary productivity distribution in China from a process model driven by remote sensing
    Feng, XF
    Liu, GH
    Zhou, WZ
    Chen, JM
    Chen, MZ
    Ju, WM
    Liu, J
    Sun, R
    IGARSS 2005: IEEE International Geoscience and Remote Sensing Symposium, Vols 1-8, Proceedings, 2005, : 3055 - 3058
  • [6] Global-scale improvement of the estimation of terrestrial gross primary productivity by integrating optical and microwave remote sensing with meteorological data
    Zhang, Shuyu
    Yang, Shanshan
    Huang, Jiaojiao
    Yang, Danni
    Zhang, Sha
    Zhang, Jiahua
    Bai, Yun
    ECOLOGICAL INFORMATICS, 2024, 83
  • [7] ESTIMATION OF FOREST GROSS PRIMARY PRODUCTIVITY IN NORTH-EAST CHINA BY A PHYSIOLOGICALLY-BASED MODEL DRIVEN WITH REMOTE SENSING DATA
    Liu, Yanan
    Gong, Weishu
    Hu, Xiangyun
    2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), 2019, : 7375 - 7378
  • [8] Development of a coupled carbon and water model for estimating global gross primary productivity and evapotranspiration based on eddy flux and remote sensing data
    Zhang, Yulong
    Song, Conghe
    Sun, Ge
    Band, Lawrence E.
    McNulty, Steven
    Noormets, Asko
    Zhang, Quanfa
    Zhang, Zhiqiang
    AGRICULTURAL AND FOREST METEOROLOGY, 2016, 223 : 116 - 131
  • [9] Improved modeling of gross primary productivity (GPP) by better representation of plant phenological indicators from remote sensing using a process model
    Wang, Jian
    Wu, Chaoyang
    Zhang, Chunhua
    Ju, Weimin
    Wang, Xiaoyue
    Chen, Zhi
    Fang, Bin
    ECOLOGICAL INDICATORS, 2018, 88 : 332 - 340
  • [10] A remote sensing-based two-leaf canopy conductance model: Global optimization and applications in modeling gross primary productivity and evapotranspiration of crops
    Bai, Yun
    Zhang, Jiahua
    Zhang, Sha
    Yao, Fengmei
    Magliulo, Vincenzo
    REMOTE SENSING OF ENVIRONMENT, 2018, 215 : 411 - 437