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Seasonal variation in canopy conductance of urban green land and its environmental control.

BAI Yu-jie1, ZHU Yuan-jun2, MA Jing-yong1, LIU Peng1, YANG Rui-zhi1, LI Cheng1, ZHA Tian-shan1,3,4*   

  1. (1School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China; 2Institute of Institute of Desertification Studies, Chinese Academy of Forestry, Beijing 100091, China; 3Beijing Engineering Research Center of Soil and Water Conservation, Beijing 100083, China; 4Key Laboratory of State Forestry Administration on Soil and Water Conservation, Beijing 100083, China).
  • Online:2020-01-10 Published:2020-01-10

Abstract: Canopy conductance (gc) is a sensitive index indicating ecosystem response to environment. Understanding the response of canopy conductance to environmental factors is crucial for modelling ecosystem processes. We analyzed the seasonal dynamics of canopy conductance and its relationship with daily mean air temperature (Ta), photosynthetically active radiation (PAR), vapor pressure deficit (VPD) and soil water content (VWC) in Beijing Olympic Forest Park in the growing season from April to October 2012-2016 using eddy covariance technique. Regressions were used to test the effects of environmental factors on canopy conductance and the effects of canopy conductance on gross ecosystem productivity (GEP). The results showed that mean annual gc was 3.97, 3.28, 2.13, 3.95 and 5.071 mm·s-1 in 2012-2016, respectively, with a mean value of 3.69±1.99 mm·s-1. The gc started to increase in April and peaked in July orAugust, then gradually decreased at intraannual scale. VWC and Ta were the main factors affecting seasonal variation in gc, which increased as increasing VWC and Ta. The effects of PAR and VPD on gc varied yearly.GEP significantly increased with increasing gc during the five years. Elevated soil water content and air temperature enhanced canopy conductance, and thus promoted GEP on a seasonal scale in urban green land.

Key words: aggregation intensity, Juglans cathayensis, distribution pattern, pattern size, sampling scale.