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INVESTIGATION ON THE SOIL THERMAL CONDUCTIVITY OF DIFFERENT UNDERLYING SURFACES IN THE NORTHERN QINGHAI-TIBETAN PLATEAU, CHINA

Session: In Situ Testing, Instrumentation, and Monitoring in Cold Regions / Essais insitu et instrumentation en milieu nordique

Ren Li, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences (China)
Tonghua Wu, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences (China)
Lin Zhao, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences (China)
Changwei Xie, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences (China)
Yao Xiao, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences (China)
Guojie Hu, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences (China)
Yizhen Du, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences (China)

Several numerical methods are applied and compared in the calculation of soil thermal conductivity (STC) in the Kekexili (QT01), Beiluhe (QT02, QT03), Kaixinling (QT05), and Tongtianhe (QT06) regions in the northern Qinghai-Tibetan Plateau based on data from January 2004 to December 2013. The results show that the STC of the active layer in the study region exhibited marked seasonal variations: it was low during the cold season but high in the warm season. Averagely, the mean value was 1.080 Wm-1K-1, ranging from 0.752 to 1.371 Wm-1K-1. In the frozen state (FS), STC was 0.955 W.m-2.K-1 while in the unfrozen state (UFS), it was 1.204 W.m-2.K-1. STC increased with increasing soil bulk density but decreased with increasing vegetation cover of the underlying surface; STC of alpine frost meadow soil was greater than that of alpine frost steppe soil; fine-grained soil with low unfrozen water content and a low saturation degree resulted in low STC in the cold season; and monthly mean STC can be well expressed as a function of conventional meteorological data. Verification results further ensured that the proposed model accurately predicts monthly STC values.