Chin J Plant Ecol ›› 2024, Vol. 48 ›› Issue (4): 428-444.DOI: 10.17521/cjpe.2022.0352

Special Issue: 全球变化与生态系统 生态系统碳水能量通量 碳储量

• Research Articles • Previous Articles     Next Articles

Dynamics of ecosystem carbon storage of potential natural vegetation in China under climate change

ZHANG Ji-Shen1, SHI Xin-Jie1, LIU Yu-Nuo1, WU Yang3,4, PENG Shou-Zhang2,*()()   

  1. 1College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China
    2College of Soil and Water Conservation Science and Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
    3Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi 712100, China
    4University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2022-08-30 Accepted:2023-05-15 Online:2024-04-20 Published:2024-05-11
  • Contact: * (szp@nwafu.edu.cn)
  • Supported by:
    National Natural Science Foundation of China(U2243226);National Natural Science Foundation of China(42077451);Second Tibetan Plateau Scientific Expedition and Research Program (STPE)(2022QZKK0101);Key R&D Plan Project of Ningxia Huizu Autonomous Region(2020BCFO1001)

Abstract:

Aims Carbon sequestration in terrestrial ecosystems is one of the important ways to slow down the rise of atmospheric CO2 concentration. Therefore, understanding the natural vegetation ecosystems carbon storage (ECS) in response of future climate change is critical for making of regional land management policies.
Methods In this study, the sensitive parameters of LPJ-GUESS model are calibrated based on genetic algorithm. Using the downscale climate data-driven model, combined with Mann Kendall test, Sen’s slope estimation and partial correlation analysis, the temporal and spatial patterns, trend change characteristics and climate dominant factors of China’s ECS from 2001 to 2100 are analyzed.
Important findings The Nash-Sutcliffe efficiency coefficient and Pearson correlation coefficient of the calibrated LPJ-GUESS model in simulating ECS are 0.751 and 0.901, respectively, indicating that the LPJ-GUESS model can simulate China’s ECS well. During 2001-2020, China’s ECS decreased from southeast to northwest, with a total amount of 156.06 Pg. Vegetation, litter and soil carbon storage accounted for 34.2%, 1.9% and 63.8% of total ECS, respectively. The ECS in 2081-2100 shows similar spatial pattern with that in historical periods. The total amount of ECS at the end of this century are expected to increase by 0.51-11.16 Pg. The growth rates of China’s ECS was 8.5 g·m-2·a-1 and 3.7-21.0 g·m-2·a-1 during 2001-2020 and 2021-2100, respectively. During 2021-2100, significant increases of ECS are observed in southeast China, Nei Mongol Plateau, Qingzang Plateau (37-44 g·m-2·a-1), while obvious decreases (45-72 g·m-2·a-1, in the southern Yunnan-Guizhou Plateau, hilly areas in Guangxi and Guangdong. In northwest China, temperature is the dominant factor affecting ECS. The influences of precipitation on ECS are strengthened from the southeast to northwest. In high latitude and high-altitude areas, radiation is the dominant factor of ECS. CO2 plays the most important role on ECS across about 47.9%-56.1% of China’s area.

Key words: climate change, potential natural vegetation, ecosystem carbon storage, LPJ-GUESS model, climatic driver factor