• Journal of Natural Resources
  • Vol. 35, Issue 6, 1484 (2020)
Zheng-chao REN1、2、*, Hua-zhong ZHU3, Hua SHI4, and Xiao-ni LIU5
Author Affiliations
  • 1College of Finance and Economics, Gansu Agricultural University, Lanzhou 730070, China
  • 2Research Center of Ecological Construction and Environmental Conservation in Gansu Province, Lanzhou 730070, China
  • 3Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • 4Earth Resources Observation and Science (EROS) Center, ASRC Federal InuTeq, Contractor to the U.S. Geological Survey (USGS), Sioux Falls 57198, USA
  • 5College of Pratacultural Science, Gansu Agricultural University, Lanzhou 730070, China
  • show less
    DOI: 10.31497/zrzyxb.20200619 Cite this Article
    Zheng-chao REN, Hua-zhong ZHU, Hua SHI, Xiao-ni LIU. Spatio-temporal distribution pattern of potential natural vegetation and its response to climate change from Last Interglacial to future 2070s in China[J]. Journal of Natural Resources, 2020, 35(6): 1484 Copy Citation Text show less

    Abstract

    Regarded as the final evolutionary status with its standing environment, potential natural vegetation plays a key role in ecological reconstruction, design of natural reserve, and development of agriculture and livestock farming. Based on the Comprehensive and Sequential Classification System model, in combination of climatic datasets containing temperature and precipitation in periods of Last Interglacial, Last Glacial Maximum, Mid Holocene, Present-Day, and project climate in the 2050s and 2070s, the spatio-temporal distribution pattern of potential natural vegetation in China and its response to climate change during the six periods were analyzed. The results showed that: (1) 39, 37, 38, 40, 40, 40 and 40 classes, and 10 super-classes were classified for potential natural vegetation by CSCS model from Last Inter-Glacial to future 2070s in China. (2) The frigid-arid super-classes were mainly distributed in northwest China, but warm-humid super-classes and tropical-perhumid super-classes appeared in the central-east China and southern China, respectively. The area was following a descending order: temperate zonal forest steppe, tundra and alpine steppe, sub-tropical zonal forest steppe, frigid desert, semi desert, steppe, temperate zonal humid grassland, tropical zonal forest steppe, warm desert and savanna during the six periods. Tundra and alpine steppe, frigid desert, semi desert, and temperate zonal forest steppe presented a decreasing trend, but the other super-classes showed an increasing trend. (3) The conversion of temperate zonal forest steppe to sub-tropical zonal forest steppe had the biggest area, accounting for 35.4% of total changed area, which meant that the climate shifted sharply and the response of terrestrial vegetation to climate change was sensitive during the period from Last Glacial Maximum to Mid Holocene. (4) CSCS, with more detailed features for classifying grassland vegetation than other models such as RT, excluding the human activity from its classification system, could simulate the long-time series succession of potential natural vegetation. (5) With the global warming, forest shifted to northern China and Tibet with much higher latitude and elevation. The geometrical center, shifting direction and distance of super-classes revealed more offset with more serious impact of climate change. The results further clarified the concept of potential natural vegetation, explored the impact mechanism of climatic change on succession of potential natural vegetation, and enriched the research contents of potential natural vegetation, which could be taken as a reference for construction of regional natural reserve, ecological reconstruction and promotion for agriculture and animal husbandry.
    Zheng-chao REN, Hua-zhong ZHU, Hua SHI, Xiao-ni LIU. Spatio-temporal distribution pattern of potential natural vegetation and its response to climate change from Last Interglacial to future 2070s in China[J]. Journal of Natural Resources, 2020, 35(6): 1484
    Download Citation