Spatial-temporal variation of snow depth in Tibet and its response to climatic change in the past 30 years

doi:10.6046/gtzyyg.2014.01.25

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Abstract

Snow depth is an important parameter to characterize snow features, and is also one of the sensitive factors of regional response to climate change. Based on snow depth daily data and monthly temperature, precipitation, wind and sunshine hours data from meteorological stations during 1979 to 2010, the authors analyzed the spatial and temporal variation of snow depth in Tibetan Plateau and its response to climatic change by using methods of anomaly analysis, mutation analysis, spatial analysis and power spectral analysis. The results showed that, in the period from 1979 to 2010, the snow depth increased obviously and significantly with linear trend rate 0.26 cm/10a, but there was a pronounced decrease phase from 1999 to 2010, thus forming the situation that the snow depth increased first and then decreased in general in Tibetan Plateau. In the four seasons, winter mean snow depth contributed most significantly to the annual situation, with the correlation coefficient between them up to 0.88. The snow depth was extremely excessive in the 1990s but with no climate mutation. An analysis of power spectrum showed that the snow depth had quasi-periodic oscillation of 6-7 years. The results indicated that there were significant spatial differences in the snow depth of Tibetan Plateau. In the peripheral high mountains, snow depth was distributed extensively and had a long duration, but in the vast interior it was rare or even thin. The snow depth was significantly affected by the altitude with a steep step effect.And most of linear trend rates of snow depth in Tibetan Plateau were between -0.08 and 0.08 cm/a, with the percentage reaching 74.6%. The results of regression analysis indicated that the increased area of snow depth accounted for 76.9%, while the decreased area accounted for 23.1%. There was obvious statistical and spatial correlation between snow depth and temperature, precipitation, wind speed and sunshine duration in general; there was negative correlation between snow depth and temperature, wind speed and sunshine duration, but positive correlation between snow depth and precipitation. The results of multiple regression analysis showed that, in spring and autumn, the correlation coefficients between simulated snow depth and observation data were both above 0.6 and passed 0.01 significance test, while they were only between 0.4 and 0.5 in summer and winter and didn't pass the 0.05 significance test.

Keywords waste oil hyperspectral shortest distance method clustering analysis

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Issue Date: 08 January 2014

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	GUO Yi
	DING Haiyong
	XU Jingxin
	XU Hao

Cite this article:

GUO Yi,DING Haiyong,XU Jingxin, et al. Spatial-temporal variation of snow depth in Tibet and its response to climatic change in the past 30 years[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(1): 144-151.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2014.01.25 OR https://www.gtzyyg.com/EN/Y2014/V26/I1/144

[1] 李栋梁, 王春学.积雪分布及其对中国气候影响的研究进展[J].大气科学学报, 2011, 34(5):627-636. Li D L, Wang C X.Research progress of snow cover and its influence on China climate[J].Transactions of Atmospheric Sciences, 2011, 34(5):627-636.

[2] Liu X D, Chen B D.Climatic warming in the Tibetan Plateau during recent decades[J].International Journal of Climatology, 2000, 20(14):1729-1742.

[3] 孙鸿烈.青藏高原的形成演化[M].上海:科学技术出版社, 1996. Sun H L.Formation and evolution of Qinghai-Xizang Plateau[M].Shanghai:Science and Technology Press, 1996.

[4] 李小兰, 张飞民, 王澄海.中国地区地面观测积雪深度和遥感雪深资料的对比分析[J].冰川冻土, 2012, 34(4):755-764. Li X L, Zhang F M, Wang D H.Comparison and analysis of snow depth over China, observed and derived from remote sensing[J].Journal of Glaciology and Geocryology, 2012, 34(4):755-764.

[5] 李培基.中国季节积雪资源及其波动[J].科学通报, 1987, 32(17):1329-1332. Li P J.Resources and their fluctuations of seasonal snow in China[J].Chinese Science Bulletin, 1987, 32(17):1329-1332.

[6] 蔡迪花, 郭铌, 王兴, 等.基于MODIS的祁连山区积雪时空变化特征[J].冰川冻土, 2009, 31(6):1028-1036. Cai D H, Guo N, Wang X, et al.The spatial and temporal variations of snow cover over the Qilian Mountains based on MODIS data[J].Journal of Glaciology and Geocryology, 2009, 31(6):1028-1036.

[7] 纪鹏, 郭华东, 张露.近20年西昆仑地区冰川动态变化遥感研究[J].国土资源遥感, 2013, 25(1):93-98. Ji P, Guo H D, Zhang L.Remote sensing study of glacier dynamic change in West Kunlun Mountains in the past 20 years[J].Remote Sensing for Land and Resources, 2013, 25(1):93-98.

[8] 车涛, 李新, 高峰.青藏高原积雪深度和雪水当量的被动微波遥感反演[J].冰川冻土, 2004, 26(3):363-368. Che T, Li X, Gao F.Estimation of snow water equivalent in the Tibetan Plateau using passive microwave remote sensing data(SSM/I)[J].Journal of Glaciology and Geocryology, 2004, 26(3):363-368.

[9] 张佳华, 吴杨, 姚凤梅.卫星遥感藏北积雪分布及影响因子分析[J].地球物理学报, 2008, 51(4):1013-1021. Zhang J H, Wu Y, Yao F M.Study on the snow distribution and influencing factors in Northern Tibet based on remote sensing information[J].Chinese Journal of Geophysics, 2008, 51(4):1013-1021.

[10] 边多, 董妍, 边巴次仁, 等.基于MODIS资料的西藏遥感积雪监测业务化方法[J].气象科技, 2008, 36(3):345-348. Bian D, Dong Y, Bianba C R, et al.Practical method for monitoring snow in Tibet Plateau based on MODIS data[J].Meteorological Science and Technology, 2008, 36(3):345-348.

[11] 刘艳, 李杨, 张璞.基于实测光谱和SRF的稀疏植被区MODIS积雪信息提取[J].国土资源遥感, 2013, 25(1):26-32. Liu Y, Li Y, Zhang P.Extraction of snow cover information in sparse vegetation area based on spectral measurement and SRF by using MODIS data[J].Remote Sensing for Land and Resources, 2013, 25(1):26-32.

[12] 魏锋华, 李才兴, 扎西央宗.基于中巴资源卫星数据的积雪监测研究[J].国土资源遥感, 2007, 19(3):31-35. Wei F H, Li C X, Zaxi Y Z.Snow monitoring based on CBERS data[J].Remote Sensing for Land and Resources, 2007, 19(3):31-35.

[13] Che T, Li X, Jin R, et al.Snow depth derived from passive microwave remote-sensing data in China[J].Annual of Glaciology, 2008, 49(1):145-154.

[14] 杜军, 边多, 胡军, 等.西藏高原近35年日照时数的变化特征及其影响因素[J].地理学报, 2007, 62(5):492-500. Du J, Bian D, Hu J, et al.Climatic change of sunshine duration and its influencing factors over Tibet during the last 35 years[J].Acta Geographica Sinica, 2007, 62(5):492-500.

[15] 唐小萍, 闫小利, 尼玛吉, 等.西藏高原近40年积雪日数变化特征分析[J].地理学报, 2012, 67(7):951-959. Tang X P, Yan X L, Ni M J, et al.Changes of the snow cover days on Tibet Plateau in last 40 years[J].Acta Geographica Sinica, 2012, 67(7):951-959.

[16] Schulz M, Mudelsee M.Redfit:Estimatingred-noise spectra directly from unevenly spaced paleoclimatic time series[J].Computers and Geosciences, 2002, 28(3), 421-426.

[17] 韦志刚, 黄荣辉, 陈文.青藏高原冬春积雪年际振荡成因分析[J].冰川冻土, 2005, 27(4):491-497. Wei Z G, Huang R H, Chen W.The causes of the interannual variation of snow cover over the Tibetan Plateau[J].Journal of Glaciology and Geocryology, 2005, 27(4):491-497.

[18] 韦志刚, 黄荣辉, 陈文, 等.青藏高原地面站积雪的空间分布和年代际变化特征[J].大气科学, 2002, 26(4):496-508. Wei Z G, Huang R H, Chen W, et al.Spatial distributions and interdecadal variations of the snow at the Tibetan Plateau weather stations[J].Chinese Journal of Atmosphere Sciences, 2002, 26(4):496-508.

[19] 王叶堂, 何勇, 侯书贵.2000—2005年青藏高原积雪时空变化分析[J].冰川冻土, 2007, 29(6):855-861. Wang Y T, He Y, Hou S G.Analysis of the temporal and spatial variations of snow cover over the Tibetan Plateau based on MODIS[J].Journal of Glaciology and Geocryology, 2007, 29(6):855-861.

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