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 hydr...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.展开更多
利用1961年-2010年天山地区21站的冬季降雪资料,采用线性倾向估计法、反距离加权法和Morlet小波分析等方法,研究了天山地区近50a冬季降雪量及降雪的集中度与集中期的时空变化特征,并在此基础上应用Rescaled Range Analysis分析方法尝试...利用1961年-2010年天山地区21站的冬季降雪资料,采用线性倾向估计法、反距离加权法和Morlet小波分析等方法,研究了天山地区近50a冬季降雪量及降雪的集中度与集中期的时空变化特征,并在此基础上应用Rescaled Range Analysis分析方法尝试预测了未来该地区冬季降雪量变化的情形。结果表明:天山地区冬季降雪量呈明显上升趋势,未来天山地区冬季降雪量的变化趋势与过去50a冬季降雪量的变化趋势相同,仍将持续上升,冬季降雪的集中度和集中期也呈上升趋势,但空间差异明显;冬季降雪量及降雪的集中度和集中期在一定的时间序列中存在不同周期变化,且周期反映比较明显;此外研究还发现,天山地区南(北)坡冬季降雪量及降雪的集中度和集中期的年际变化也不尽相同。展开更多
基金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
文摘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.
文摘利用1961年-2010年天山地区21站的冬季降雪资料,采用线性倾向估计法、反距离加权法和Morlet小波分析等方法,研究了天山地区近50a冬季降雪量及降雪的集中度与集中期的时空变化特征,并在此基础上应用Rescaled Range Analysis分析方法尝试预测了未来该地区冬季降雪量变化的情形。结果表明:天山地区冬季降雪量呈明显上升趋势,未来天山地区冬季降雪量的变化趋势与过去50a冬季降雪量的变化趋势相同,仍将持续上升,冬季降雪的集中度和集中期也呈上升趋势,但空间差异明显;冬季降雪量及降雪的集中度和集中期在一定的时间序列中存在不同周期变化,且周期反映比较明显;此外研究还发现,天山地区南(北)坡冬季降雪量及降雪的集中度和集中期的年际变化也不尽相同。
