• Geographical Research
  • Vol. 39, Issue 3, 735 (2020)
Junjie LIU1、1、2、2, Ziwu PAN1、1、2、2, Fen QIN1、1、2、2、3、3, Jiangyan GU1、1, Mingyang ZHU1、1, and Fang ZHAO1、1、*
Author Affiliations
  • 1College of Environment and Planning, Henan University, Kaifeng 475004, Henan, China
  • 1河南大学环境与规划学院,开封 475004
  • 2Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Kaifeng 475004, Henan, China
  • 2黄河中下游数字地理技术教育部重点实验室,开封 475004
  • 3Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Zhengzhou 450000, China
  • 3河南省时空大数据产业技术研究院,郑州 450000
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    DOI: 10.11821/dlyj020190164 Cite this Article
    Junjie LIU, Ziwu PAN, Fen QIN, Jiangyan GU, Mingyang ZHU, Fang ZHAO. Estimation of air temperature based on MODIS and analysis of mass elevation effect in the Qinling-Daba Mountains[J]. Geographical Research, 2020, 39(3): 735 Copy Citation Text show less

    Abstract

    As a huge mountain range in the North-South boundary of China, the Qinling-Daba Mountains are characterized by prominent mass elevation effect (MEE) and play an important role in the azonality pattern of climate and ecology in central China. The essence of the MEE is the warming effect of mountains, as huge mountain and plateau absorbs more solar radiation compared with the free atmosphere of the same altitude and then releases in the form of long-wave radiation external heat, making the internal mountain temperature higher than the external in the same altitude of free atmosphere. Therefore, the temperature difference between the mountain interior and the periphery has been suggested as an appropriate indicator to quantify the MEE. To analyze MEE of the Qinling-Daba Mountains, MODIS land surface temperature (LST) data, STRM-1 DEM data and observation data from 118 meteorological stations were combined to estimate monthly mean air temperature by ordinary linear regression (OLS) and geographical weighted regression (GWR) methods in the Qinling-Daba Mountains. Air temperature at an altitude of 1500 m (the average elevation of the Qinling-Daba Mountains) in the interior of the Qinling-Daba Mountains was calculated by a fixed lapse rate and compared with that in the periphery. The results show that: (1) Compared with OLS method, the GWR method has higher accuracy with R 2 > 0.89 and the root mean squared error (RMSE) = 0.68-0.98 ℃. (2) The monthly mean temperature at the altitude of 1500 m estimated by GWR presents a gradual upward trend from east to west. In the western Qinling Mountains, the annual average temperature and temperature in July at the altitude of 1500 m increase about 6 ℃ and 4.5 ℃ compared with the eastern flank, while in the Daba Mountains, they are about 8°C and 5 ℃ higher in the west than in the east. (3) From south to north, with the Hanjiang River as the boundary, the monthly mean temperature at the altitude of 1500 m tends to rise from the rim of the mountains to the ridge. (4) Compared with the lower valleys in Hanzhong and western Henan, the monthly mean temperatures at the altitude of 1500 m are approximately 3.85-9.28 ℃, 1.49-3.34 ℃ and 0.43-3.05 ℃ higher those in the great undulating high mountains in the western Qinling-Daba Mountains, the great undulating middle-high mountains in the Qinling Mountains and the great undulating middle mountains in the Daba Mountains, respectively, and the average temperature difference is about 3.50 ℃. This shows that the MEE of the Qinling-Daba Mountains is obvious and its impact on the distribution patterns of mountain climate and ecology needs to be further studied.
    Junjie LIU, Ziwu PAN, Fen QIN, Jiangyan GU, Mingyang ZHU, Fang ZHAO. Estimation of air temperature based on MODIS and analysis of mass elevation effect in the Qinling-Daba Mountains[J]. Geographical Research, 2020, 39(3): 735
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