Spatiotemporal variations of snow characteristics in Xinjiang, China over 1961–2013
Spatiotemporal variations of snow characteristics in Xinjiang, China over 1961–2013
复制标题
DOI：
10.2166/nh.2017.035
复制
发表时间：
2018-10
影响因子：
2.7
通讯作者：
Yangmei Ding;Yi Li;Linchao Li;Ning Yao;Wei Hu;Daqing Yang;Chunyan Chen
中科院分区：
文献类型：
--
作者：
Yangmei Ding;Yi Li;Linchao Li;Ning Yao;Wei Hu;Daqing Yang;Chunyan Chen
利用新疆105个气象站1961-2013年逐日积雪资料，分析了新疆积雪起止日期、持续时间、年、月平均积雪深度和最大积雪深度等参数的时空变化特征。应用修正的Mann-Kendall检验、经验模态分解、经验正交函数（ERF）和反距离权重插值。降雪持续了71至120天。积雪深度由北向南逐渐减小。日积雪深度具有周期性变化，可分为四种典型类型，平峰、多峰、尖锐单峰和右偏斜。日积雪深度分解为17个固有模态函数（IMF）后，IMF 9，IMF 10和IMF 11超过189，302，437天的尺度占总的时空变化的79%的积雪深度。年开始日数和结束日数均呈减少趋势，而持续天数呈增加趋势。无论是考虑到12月、1月、2月的季节性还是年度值，大多数地点的平均和最大积雪深度都有所增加。EOF 1占空间变异的70%，时间系数EC 1呈周期性变化。积雪特性的时空分析为积雪融化的认识提供了基础。
Daily snow data during 1961–2013 at the 105 meteorological stations in Xinjiang, China were used to investigate the spatiotemporal variations of several parameters, including starting and ending dates, duration, annual and monthly average and maximum snow depths. The modified Mann–Kendall test, empirical mode decomposition, empirical orthogonal function (EOF), and the inverse distance weight interpolation were applied. Snow lasted for 71 to 120 days. Snow depth decreased from north to south. Daily snow depth had periodical variations and were classified as four typical types, i.e., flat peak, multi-peak, sharp single-peak, and right-skewed. After daily snow depth was decomposed into 17 intrinsic mode functions (IMFs), IMF9, IMF10, and IMF11 over 189, 302, and 437 days of scales accounted for 79% of the total spatiotemporal variance in snow depth. Both annual starting and ending day numbers had decreasing trends, while the duration in days had an increasing trend. The average and maximum snow depth increased in most sites whether considering the seasonality in December, January, February, or annual values. EOF1 accounted for 70% of spatial variability and the temporal coefficient EC1 varied periodically. The spatiotemporal analysis of snow properties provides a basis for snowmelt understanding.