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《干旱区科学(英文版)》 2019-01
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Environmental factors influencing snowfall and snowfall prediction in the Tianshan Mountains, Northwest China

ZHANG Xueting;LI Xuemei;LI Lanhai;ZHANG Shan;QIN Qirui;Faculty of Geomatics, Lanzhou Jiaotong University;Gansu Provincial Engineering Laboratory for National Geographic State Monitoring;State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences;  
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 R~2 value(R~2=0.74; R~2, 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.
【Fund】: financially supported by the National Key Research and Development Program of China (2017YFB0504201);; the National Natural Science Foundation of China (41761014 41401050);; the Foundation of A Hundred Youth Talents Training Program of Lanzhou Jiaotong University
【CateGory Index】: P457
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