Journal of Arid Land

, Volume 11, Issue 1, pp 15–28 | Cite as

Environmental factors influencing snowfall and snowfall prediction in the Tianshan Mountains, Northwest China

  • Xueting Zhang
  • Xuemei LiEmail author
  • Lanhai Li
  • Shan Zhang
  • Qirui Qin


Snowfall is one of the dominant water resources in the mountainous regions and is closely related to the development of the local ecosystem and economy. Snowfall predication plays a critical role in understanding hydrological processes and forecasting natural disasters in the Tianshan Mountains, where meteorological stations are limited. Based on climatic, geographical and topographic variables at 27 meteorological stations during the cold season (October to April) from 1980 to 2015 in the Tianshan Mountains located in Xinjiang of Northwest China, we explored the potential influence of these variables on snowfall and predicted snowfall using two methods: multiple linear regression (MLR) model (a conventional measuring method) and random forest (RF) model (a non-parametric and non-linear machine learning algorithm). We identified the primary influencing factors of snowfall by ranking the importance of eight selected predictor variables based on the relative contribution of each variable in the two models. Model simulations were compared using different performance indices and the results showed that the RF model performed better than the MLR model, with a much higher R2 value (R2=0.74; R2, coefficient of determination) and a lower bias error (RSR=0.51; RSR, the ratio of root mean square error to standard deviation of observed dataset). This indicates that the non-linear trend is more applicable for explaining the relationship between the selected predictor variables and snowfall. Relative humidity, temperature and longitude were identified as three of the most important variables influencing snowfall and snowfall prediction in both models, while elevation, aspect and latitude were of secondary importance, followed by slope and wind speed. These results will be beneficial to understand hydrological modeling and improve management and prediction of water resources in the Tianshan Mountains.


snowfall prediction snowfall fraction random forest multiple linear regression predictor variables Tianshan Mountains 


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This work was financially supported by the National Key Research and Development Program of China (2017YFB0504201), the National Natural Science Foundation of China (41761014, 41401050) and the Foundation of A Hundred Youth Talents Training Program of Lanzhou Jiaotong University.


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Copyright information

© China Science Publishing Media Ltd. (Science Press) and Xinjiang Institute of Ecology and Geography, the Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Xueting Zhang
    • 1
    • 2
  • Xuemei Li
    • 1
    • 2
    Email author
  • Lanhai Li
    • 3
  • Shan Zhang
    • 1
    • 2
  • Qirui Qin
    • 1
    • 2
  1. 1.Faculty of GeomaticsLanzhou Jiaotong UniversityLanzhouChina
  2. 2.Gansu Provincial Engineering Laboratory for National Geographic State MonitoringLanzhouChina
  3. 3.State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqiChina

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