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Journal of Arid Land  2019, Vol. 11 Issue (1): 15-28    DOI: 10.1007/s40333-018-0110-2
Orginal Article     
Environmental factors influencing snowfall and snowfall prediction in the Tianshan Mountains, Northwest China
Xueting ZHANG1,2, Xuemei LI1,2,*(), Lanhai LI3, Shan ZHANG1,2, Qirui QIN1,2
1 Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
2 Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
3 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
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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.

Key wordssnowfall prediction      snowfall fraction      random forest      multiple linear regression      predictor variables      Tianshan Mountains     
Received: 20 December 2017      Published: 10 February 2019
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Cite this article:

Xueting ZHANG, Xuemei LI, Lanhai LI, Shan ZHANG, Qirui QIN. Environmental factors influencing snowfall and snowfall prediction in the Tianshan Mountains, Northwest China. Journal of Arid Land, 2019, 11(1): 15-28.

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[1] Anderson B T, McNamara J P, Marshall H P, et al.2014. Insights into the physical processes controlling correlations between snow distribution and terrain properties. Water Resources Research, 50(6): 4545-4563.
[2] Anderton S P, White S M, Alvera B.2004. Evaluation of spatial variability in snow water equivalent for a high mountain catchment. Hydrological Processes, 18(3): 435-453.
[3] Antipov E A, Pokryshevskaya E B.2012. Mass appraisal of residential apartments: An application of random forest for valuation and a CART-based approach for model diagnostics. Expert Systems with Applications, 39(2): 1772-1778.
[4] Asaoka Y, Kominami Y.2012. Spatial snowfall distribution in mountainous areas estimated with a snow model and satellite remote sensing. Hydrological Research Letters, 6(6): 1-6.
[5] Breiman L.2001. Random forests. Machine Learning, 45(1): 5-32.
[6] Chen Y N, Li Z, Fan Y T, et al.2014. Research progress on the impact of climate change on water resources in the arid region of Northwest China. Acta Geographica Sinica, 69(9): 1295-1304. (in Chinese)
[7] Chen Y N, Li W H, Deng H J, et al.2016. Changes in Central Asia's water tower: past, present and future. Scientific Reports, 6(1): 35458.
[8] Chen Y N, Li Z, Fang G H, et al.2017. Impact of climate change on water resources in the Tianshan Mountians, Central Asia. Acta Geographica Sinica, 72(1): 18-26. (in Chinese)
[9] Clark M P, Andrew G S.2006. Probabilistic quantitative precipitation estimation in complex terrain. Journal of Hydrometeorology, 7(1): 3-22.
[10] Cutler D R, Edwards T C, Beard K H., et al.2007. Random forests for classification in ecology. Ecology, 88(11): 2783-2792.
[11] Dai A.2008. Temperature and pressure dependence of the rain-snow phase transition over land and ocean. Geophysical Research Letters, 35(12): 1-6.
[12] Davis R E, Lowit M B, Knappenberger P C, et al.1999. A climatology of snowfall-temperature relationships in Canada. Journal of Geophysical Research, 104(D10): 11985-11994.
[13] Erickson T A, Williams M W, Winstral A.2005. Persistence of topographic controls on the spatial distribution of snow in rugged mountain terrain, Colorado, United States. Water Resources Research, 41(4): 1-17.
[14] Füssel H M, Jol A.2012. Climate change, impacts and vulnerability in Europe 2012 an indicator-based report. Luxembourg: Publications Office of the European Union.
[15] Genuer R, Poggi J M, Tuleau-Malot C.2010. Variable selection using random forests. Pattern Recognition Letters, 31(14): 2225-2236.
[16] Goudarzi N.2016. Free variable selection QSPR study to predict 19F chemical shifts of some fluorinated organic compounds using Random Forest and RBF-PLS methods. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 158: 60-64.
[17] Grömping U.2006. Relative importance for linear regression in R: the package relaimpo. Journal of Statistical Software, 17(1): 139-147.
[18] Guo L P, Li L H.2015. Variation of the proportion of precipitation occurring as snow in the Tian Shan Mountains, China. International Journal of Climatology, 35(7): 1379-1393.
[19] Hu R J.2004. Physical Geography of the Tianshan Mountains in China. Beijing: China Environmental Science Press, 2-4. (in Chinese)
[20] Ikeda K, Rasmussen R, Liu C, et al.2010. Simulation of seasonal snowfall over Colorado. Atmospheric Research, 97(4): 462-477.
[21] Ji X, Chen Y F.2012. Characterizing spatial patterns of precipitation based on corrected TRMM 3B43 data over the mid Tianshan Mountains of China. Journal of Mountain Science, 9(5): 628-645.
[22] Kapnick S B S, Delworth T L T, Ashfaq M, et al.2014. Snowfall less sensitive to warming in Karakoram than in Himalayas due to a unique seasonal cycle. Nature Geoscience, 7(11): 834-840.
[23] Karl T R, Groisman P Y.1993. Recent variations of snow cover and snowfall in North America and their relation to precipitation and temperature variations. Journal of Climate, 6(6): 1327-1344.
[24] Kousari M R, Ekhtesasi M R, Tazeh M, et al.2011. An investigation of the Iranian climatic changes by considering the precipitation, temperature, and relative humidity parameters. Theoretical and Applied Climatology, 103(3-4): 321-335.
[25] Kovdienko N A, Polishchuk P G, Muratov E N, et al.2010. Application of random forest and multiple linear regression techniques to QSPR prediction of an aqueous solubility for military compounds. Molecular Informatics, 29(5): 394-406.
[26] Krasting J P, Broccoli A J, Dixon K W, et al.2013. Future changes in northern hemisphere snowfall. Journal of Climate, 26(20): 7813-7828.
[27] Kwon T J, Fu L.2013. Evaluation of alternative criteria for determining the optimal location of RWIS stations. Journal of Modern Transportation, 21(1): 17-27.
[28] Legates D R, McCabe Jr G J.1999. Evaluating the use of ''goodness of fit'' measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1): 233-241.
[29] Li B F, Chen Y N, Shi X, et al.2013. Temperature and precipitation changes in different environments in the arid region of northwest China. Theoretical and Applied Climatology, 112(3-4): 589-596.
[30] Li X M, Gao P, Li Q, et al.2016. Muti-paths impact from climate change on snow cover in Tianshan Mountainous area of China. Climate Change Research, 12(4): 303-312. (in Chinese)
[31] Li X S, Zhang M J, Wang B L, et al.2012. The change characteristics of winter snowfall, snow concentration degree and concentration period in the Tianshan Mountains. Resources Science, 34(8): 1556-1564. (in Chinese)
[32] Liu Y L, Ren G Y, Yu H M.2012. Climatology of snow in China. Scientia Geographica Sinica, 32(10): 1176-1185. (in Chinese)
[33] Lopatin J, Dolos K, Hernández H J, et al.2016. Comparing generalized linear models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile. Remote Sensing of Environment, 173(315): 200-210.
[34] Lu H, Wei W S, Liu M Z, et al.2016. Variations in seasonal snow surface energy exchange during a snowmelt period: an example from the Tianshan Mountains, China. Meteorological Applications, 23(1): 14-25.
[35] Marks D, Winstral A, Reba M, et al.2013. An evaluation of methods for determining during-storm precipitation phase and the rain/snow transition elevation at the surface in a mountain basin. Advances in Water Resources, 55(3): 98-110.
[36] Mir R A, Jain S K, Saraf A K, et al.2015. Decline in snowfall in response to temperature in Satluj basin, western Himalaya. Journal of Earth System Science, 124(2): 365-382.
[37] Moriasi D N, Arnold J G, Van Liew M W, et al.2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3): 885-900.
[38] Muñoz J, Felicísimo A M.2004. Comparison of statistical methods commonly used in predictive modelling. Journal of Vegetation Science, 15(2): 285-292.
[39] Nair H C, Padmalal D, Joseph A, et al.2017. Delineation of groundwater potential zones in river basins using geospatial tools—an example from southern western Ghats, Kerala, India. Journal of Geovisualization and Spatial Analysis, 1:5, doi:
[40] Ning L K.2013. Study on the influence of iopography and geomorphology on precipitation over Tianshan Mountains, Central Asia. MSc Thesis. Shihezi: Shihezi University. (in Chinese)
[41] O'Gorman P A, Muller C J.2010. How closely do changes in surface and column water vapor follow Clausius-Clapeyron scaling in climate change simulations? Environmental Research Letters, 5(5): 025207.
[42] Oliveira S, Oehler F, San-Miguel-Ayanz J, et al.2012. Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest. Forest Ecology and Management, 275(4): 117-129.
[43] Padoan S A, Ribatet M, Sisson S A.2009. Likelihood-based inference for max-stable processes. Journal of the American Statistical Association, 105(489): 263-277.
[44] Palmer D, O'boyle N, Glen R, et al.2007. Random forest models to predict aqueous solubility. Journal of Chemical Information and Modeling, 47(1): 150-158.
[45] Perry L, Konrad C.2006. Relationships between NW flow snowfall and topography in the Southern Appalachians, USA. Climate Research, 32(1): 35-47.
[46] Piazza M, Boé J, Terray L, et al.2014. Projected 21st century snowfall changes over the French Alps and related uncertainties. Climatic Change, 122(4): 583-594.
[47] Rahman K, Maringanti C, Beniston M, et al.2013. Streamflow modeling in a highly managed mountainous glacier watershed using SWAT: The upper Rhone River watershed case in Switzerland. Water Resources Management, 27(2): 323-339.
[48] Rasmussen R, Liu C, Ikeda K, et al.2011. High-resolution coupled climate runoff simulations of seasonal snowfall over Colorado: A process study of current and warmer climate. Journal of Climate, 24(12): 3015-3048.
[49] Roebber P J, Bruening S L, Schultz D M, et al.2003. Improving snowfall forecasting by diagnosing snow density. Weather and Forecasting, 18(2): 264-287.
[50] Scipiõn D E, Mott R, Lehning M, et al.2013. Seasonal small-scale spatial variability in alpine snowfall and snow accumulation. Water Resources Research, 49(3): 1446-1457.
[51] Serquet G, Marty C, Dulex J P, et al.2011. Seasonal trends and temperature dependence of the snowfall/precipitation-day ratio in Switzerland. Geophysical Research Letters, 38(7): 14-18.
[52] Shen Y J, Shen Y, Goetz J, et al.2016. Spatial-temporal variation of near-surface temperature lapse rates over the Tianshan Mountains, Central Asia. Journal of Geophysical Research: Atmospheres, 121(23): 14006-14017.
[53] Shi Y F, Shen Y P, Kang E S, et al.2007. Recent and future climate change in northwest China. Climatic Change, 80(3-4): 379-393.
[54] Sorg A, Bolch T, Stoffel M, et al.2012. Climate change impacts on glaciers and runoff in Tien Shan (Central Asia). Nature Climate Change, 2(10): 725-731.
[55] Strobl C, Boulesteix A L, Kneib T, et al.2008. Conditional variable importance for random forests. BMC Bioinformatics, 9(1): 307.
[56] Tang Z G, Wang J, Wang X, et al.2017. Spatiotemporal variation of snow cover in Tianshan Mountains based on MODIS. Remote Sensing Technology and Application, 32(3): 556-563. (in Chinese)
[57] Tinkham W T, Smith A M S, Marshall H P, et al.2014. Quantifying spatial distribution of snow depth errors from LiDAR using Random Forest. Remote Sensing of Environment, 141(2): 105-115.
[58] Vrotsou K, Fuchs G, Andrienko N, et al.2017. An interactive approach for exploration of flows through direction-based filtering. Journal of Geovisualization and Spatial Analysis, 1(1-2): 1, doi:
[59] Wang L, Liu H L, Bao A M, et al.2016. Estimating the sensitivity of runoff to climate change in an alpine-valley watershed of Xinjiang, China. Hydrological Sciences Journal, 61(6): 1069-1079.
[60] Wetzel M, Meyers M, Borys R, et al.2004. Mesoscale snowfall prediction and verification in mountainous terrain. Weather and Forecasting, 19(5): 806-828.
[61] Wi S, Dominguez F, Durcik M, et al.2012. Climate change projection of snowfall in the Colorado River Basin using dynamical downscaling. Water Resources Research, 48(5): 205-210.
[62] Willmott C J.1981. On the validation of models. Physical Geography, 2(2): 184-194.
[63] Xu J R, Qiu J Q.1996. A study on snowfall variation in the Tianshan Mountains during the recent 30 winters. Journal of Glaciology and Geocryology, 18(S1): 123-128. (in Chinese)
[64] Xu L G, Zhu M L, He B, et al.2014. Analysis of water balance in Poyang Lake Basin and subsequent response to climate change. Journal of Coastal Research, 68: 136-143.
[65] Yang J, Fang G H, Chen Y N, et al.2017. Climate change in the Tianshan and northern Kunlun Mountains based on GCM simulation ensemble with Bayesian model averaging. Journal of Arid Land, 9(4): 622-634.
[66] Yang Q, Cui C X, Sun C R, et al.2007. Snow cover variation during 1959-2003 in Tianshan Mountains, China. Advances in Climate Change Research, 3(2): 80-84. (in Chinese)
[67] Yu J Y, Zhang G Q, Yao T D, et al.2015. Developing daily cloud-free snow composite products from MODIS Terra-Aqua and IMS for the Tibetan Plateau. IEEE Transactions on Geoscience & Remote Sensing, 54(4): 2171-2180.
[68] Zhang F Y, Bai L, Li L H, et al.2016. Sensitivity of runoff to climatic variability in the northern and southern slopes of the Middle Tianshan Mountains, China. Journal of Arid Land, 8(5): 681-693.
[69] Zhang G, Xie H, Yao T, et al.2012. Snow cover dynamics of four lake basins over Tibetan Plateau using time series MODIS data (2001-2010). Water Resources Research, 48(10): 10529.
[70] Zhang H, Wu P B, Yin A J, et al.2017. Prediction of soil organic carbon in an intensively managed reclamation zone of eastern China: A comparison of multiple linear regressions and the random forest model. Science of the Total Environment, 592: 704-713.
[71] Zhang X T, Li X M, Gao P, et al.2017. Separation of precipitation forms based on different methods in Tianshan Mountainous Area, Northwest China. Journal of Glaciology and Geocryology, 39(2): 235-244. (in Chinese)
[72] Zhang Z F, Xi S, Liu N,et al.2015. Snowfall change characteristics in China from 1961 to 2012. Resources Science, 37(9): 1765-1773. (in Chinese)
[73] Zhang Z Y, He H L, Liu L, et al.2015. Spatial distribution of rainfall simulation and the cause analysis in China's Tianshan Mountains area. Advances in Water Science, 26(4): 500-508. (in Chinese)
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