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  • 标题:Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach
  • 本地全文:下载
  • 作者:Jianwei Yang ; Lingmei Jiang ; Kari Luojus
  • 期刊名称:The Cryosphere
  • 印刷版ISSN:1994-0416
  • 电子版ISSN:1994-0424
  • 出版年度:2020
  • 卷号:14
  • 期号:6
  • 页码:1763-1778
  • DOI:10.5194/tc-14-1763-2020
  • 出版社:Copernicus Publications
  • 摘要:20 cm), with biases of −10.4, −8.9, and −34.1 cm for northeast China (NEC), northern Xinjiang (XJ), and the Qinghai–Tibetan Plateau (QTP), respectively. Additionally, the long-term snow depth datasets (station observations, RF estimates, and WESTDC product) were analyzed in terms of temporal and spatial variations over China. On a temporal scale, the ground truth snow depth presented a significant increasing trend from 1987 to 2018, especially in NEC. However, the RF and WESTDC products displayed no significant changing trends except on the QTP. The WESTDC product presented a significant decreasing trend on the QTP, with a correlation coefficient of −0.55, whereas there were no significant trends for ground truth observations and the RF product. For the spatial characteristics, similar trend patterns were observed for RF and WESTDC products over China. These characteristics presented significant decreasing trends in most areas and a significant increasing trend in central NEC.
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