Increasing Snow–Soil Interface Temperature in Farmland of Northeast China from 1979 to 2018

Xiuxue Chen, Xiaofeng Li, Lingjia Gu, Xingming Zheng, Guangrui Wang and Lei Li
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Xiuxue Chen: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, No. 4888 Shengbei Street, Gaoxinbei District, Changchun 130102, China
Xiaofeng Li: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, No. 4888 Shengbei Street, Gaoxinbei District, Changchun 130102, China
Lingjia Gu: College of Electronic Science and Engineering, Jilin University, Changchun 130012, China
Xingming Zheng: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, No. 4888 Shengbei Street, Gaoxinbei District, Changchun 130102, China
Guangrui Wang: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, No. 4888 Shengbei Street, Gaoxinbei District, Changchun 130102, China
Lei Li: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, No. 4888 Shengbei Street, Gaoxinbei District, Changchun 130102, China

Agriculture, 2021, vol. 11, issue 9, 1-18

Abstract: The presence of seasonal snow cover in the cold season can significantly affect the thermal conditions of the ground. Understanding the change of the snow–soil interface temperature ( T SS ) and its environmental impact factors is essential for predicting subnivean species changes and carbon balance in future climatic conditions. An improved Snow Thermal Model (SNTHERM) is employed to quantify T SS in farmland of Northeast China (NEC) in a 39-year period (1979–2018) firstly. This study also explored the variation tendency of T SS and its main influencing factors on grid scale. The result shows that annual average T SS and the difference between T SS and air temperature ( T DSSA ) increased rapidly between 1979 and 2018 in the farmland of NEC, and we used the Mann–Kendall test to further verify the increasing trends of T SS and T DSSA on aggregated farmland of NEC. The correlation analysis showed that mean snow depth ( M SD ) is the most pivotal control factor in 95% of pixels and T DSSA increases as M SD increases. Snow depth can better predict the change of T SS in deep–snow regions than average winter temperature ( T SA ). The results of this study are of great significance for understanding the impact of snow cover on the energy exchange between the ground and the atmosphere in the cold climate.

Keywords: snow cover; snow–soil interface temperature; climate change; SNTHERM; Northeast China (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
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