Advances in Climate Change Research (Sep 2018)

Temperature change along elevation and its effect on the alpine timberline tree growth in the southeast of the Tibetan Plateau

  • Bao-Xiong Chen,
  • Yu-Fang Sun,
  • Hong-Bin Zhang,
  • Zhi-Hua Han,
  • Jing-Sheng Wang,
  • Yao-Kui Li,
  • Xiao-Lin Yang

Journal volume & issue
Vol. 9, no. 3
pp. 185 – 191

Abstract

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Smith fir (Abies georgei var. smithii), which is the timberline constructive tree species in the cool slope of Mt. Sygera in the southeast of Tibet, plays a very important role in maintaining the timberline completeness and indicating global climate change. This study uses the instrumental recorded meteorological data along the altitude from 3600 to 4400 m at every 200 m in the growing season, investigates the smith fir growth biomass from 2006 to 2010 in the same timberline ecotone, and makes a non-linear regression analysis to determine the relationship between the alpine tree growth biomass and its in-situ environment condition. The results showed that the cool and warm slope share different air temperature lapse rates, which were −0.48 °C (100 m)−1 in the warm slope and −0.54 °C (100 m)−1 in the cool slope, respectively. However, the dominant timberline tree species in the warm slope was Sabina saltuaria, and it can reach as high as 4570 m, which is approximately 170 m higher than that in the cool slope. Moreover, the smith fir in the cool slope was only distributed in the range of elevation from approximately 3600 to 4400 m. The altitude of approximately 3800 m was the appropriate altitude for the growing smith fir, where the mean air temperature in the growing season was about 9.0 °C, and the young smith fir tree can form more biomass. The results suggested that alpine forest chose a suitable environment where trees can grow more in the prolonged succession, but not in the warmer or cooler condition, it could be seen as a biological evidence for climate change. Keywords: Abies georgei var. smithii, Growth biomass, Sabina saltuaria, In-situ environment condition, Non-linear regression analysis