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Journal of Geo-information Science >

2016 , Vol. 18 >Issue 11: 1573 - 1580

DOI: https://doi.org/10.3724/SP.J.1047.2016.01573

Orginal Article

Snow Phenology Variability in the Qinghai-Tibetan Plateau and Its Response to Climate Change During 2002-2012

  • WANG Xiaoyue , 1, 2 ,
  • WANG Siyuan , 1, * ,
  • YIN Hang 1, 2 ,
  • PENG Yaoyao 1, 2
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  • 1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
*Corresponding author: WANG Siyuan, E-mail:

Received date: 2015-11-09

  Request revised date: 2015-12-20

  Online published: 2016-11-20

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Abstract

Snow cover is one of the most active natural components on Earth’s surface. The variability of snow phenology has a major impact on water cycle, climate change, environment and human activities. The Qinghai-Tibetan Plateau has a wide range of seasonal snow cover, and its accumulation and rapid meltdown can affect the regional and global climate change. Studying the snow variability in the Qinghai-Tibetan Plateau is therefore important. In this study, the MODIS snow product and IMS snow-ice product were used. Firstly, the Terra and Aqua satellite images were combined to reduce the proportion of cloud pixels. Secondly, the temporal combinations were employed to further reduce the cloud pixels. Finally, the processed MODIS snow product and IMS were fused to produce the daily cloud-free snow product of the Qinghai-Tibetan Plateau from 2002 to 2012. Then, the snow-covered days (SCD), snow cover start (SCS) and snow cover end (SCE) dates were calculated for each hydrological year, and their spatial and temporal variations in different eco-geographical regions were analyzed. The correlations among the SCS, SCE and climate factors were also investigated. The results show that the distribution of snow cover over the Qinghai-Tibetan Plateau was very uneven. The longest SCD, totalized to be more than 200 days, occurred in the Himalayas, Karakoram, Nyainqentanglha Mountains and the Pamirs Plateau. Up to 18.1% of the area of SCS showed a significantly advanced trend, which mainly occurred in the Golog-Nagqu high-cold region and the southern Qinghai high-cold region; while 8.5% of the area showed a slightly delayed trend. Up to 23.2% of the area of SCE was delayed, occurring mainly in the central and eastern Tibetan Plateau; while only 6.9% of the area showed an advanced trend. The SCS and SCE were greatly affected by temperature and precipitation, but showed different spatial patterns and evolution trends in different ecological zones. Generally, the higher temperature delayed the SCS and advanced the SCE, but more precipitation led to the earlier SCS and the later SCE.

Key words： Qinghai-Tibetan Plateau; snow-covered days; snow cover start date; snow cover end date; climate factors

Cite this article

WANG Xiaoyue , WANG Siyuan , YIN Hang , PENG Yaoyao . Snow Phenology Variability in the Qinghai-Tibetan Plateau and Its Response to Climate Change During 2002-2012[J]. Journal of Geo-information Science, 2016 , 18(11) : 1573 -1580 . DOI: 10.3724/SP.J.1047.2016.01573

1 引言

积雪作为地表覆盖的重要组成部分,是地表最活跃的自然要素之一,其相关参数（如积雪覆盖日数、积雪面积、雪深等）是全球能量平衡、气候、水文以及生态模型的重要输入参数[1-2]。同时,积雪对气候变化异常敏感,是气候变化最灵敏的指示器。对积雪参数进行有效的监测与分析,不仅对研究区域和全球气候变化有重要的科学意义,也能为农业、生态以及防灾减灾等众多领域提供信息服务。青藏高原是中国三大主要积雪区之一,也是北半球积雪时间最长、分布最广的地区之一,研究青藏高原积雪的动态变化对区域乃至全球气候变化的诊断分析具有重要意义[3]。
近年来,国内外许多学者针对不同地区的积雪时空变化进行了大量研究。Brown等[4]研究表明,随着全球变暖,过去40年北半球大部分地区春季积雪面积显著减少。Peng等[5]利用气象台站的数据研究发现,1980-2006年欧亚大陆积雪开始日期推后,结束日期提前,并指出该变化与气温的升高密切相关。Dietz等[6]基于MODIS每日积雪产品研究了2000-2011年中亚地区的积雪变化,发现积雪开始期和结束期均没有显著的变化趋势。白淑英等[3]通过对被动微波雪深数据分析发现,1979-2010年青藏高原雪深呈显著增加趋势,且以冬季增加最为明显。王叶堂等[7]利用MODIS积雪8天合成数据研究发现,青藏高原积雪面积总体上表现出冬春季减少,夏秋季增加的趋势,并指出气温和降水是影响高原积雪变化的基本因子。
虽然已有不少学者针对青藏高原的积雪变化进行了研究,但大多数都是针对积雪日数、积雪覆盖面积和雪深等参数,而对于高原积雪物候变化（即积雪开始期、结束期和持续时间）的研究还很少。积雪物候变化会改变土壤的冻融日期,从而对陆地生态系统的季节性变化产生重要影响,尤其是对高寒植被物候期的影响更为显著[8-9]。例如,春季积雪消融过早会导致土壤水分含量降低[10],增加霜冻事件出现的频率[11],对植被的正常生长有抑制作用。此外,对积雪开始期和结束期进行有效地监测,还能揭示气候变化,为河水径流量预测,以及洪水、泥石流等自然灾害预警方面提供重要的信息支持。而青藏高原作为地球的第三极,由于其独特的地理位置与脆弱的生态环境,受全球变化影响显著。因此,本文以青藏高原为研究区,首先利用MODIS积雪产品和IMS雪冰产品获得2002-2012年青藏高原逐日无云积雪覆盖图,然后逐像元计算每个水文年的积雪覆盖日数、积雪开始日期和积雪结束日期,从不同生态地理单元分析其时空变化特征,进而研究积雪开始期和结束期对温度、降水变化的响应关系。

2 研究区与研究方法

2.1 研究区域

青藏高原位于中国西南部,西起帕米尔高原,东至横断山脉,南自喜马拉雅山脉南缘,北至昆仑山-祁连山北侧,范围为26°00′12″~39°46′50″N,73°18′52″~104°46′59″E（图1）,平均海拔4000 m以上,是世界上最高的高原,有“世界屋脊”之称[12]。青藏高原积雪资源丰富,是长江、黄河、雅鲁藏布江等诸多河流的发源地。春季积雪融水是河流的主要补给来源,直接影响江河流量,甚至引发春汛。而秋季积雪开始时间的早晚不仅会影响季节性冻土的分布,还会通过改变地表反照率而影响地气间的能量传输和地表能量平衡[13],进而影响东亚大气环流和天气系统,最终对人们的生产、生活造成影响。
Fig.1 Location and eco-geographical regions of the Qinghai-Tibetan Plateau

图1 青藏高原地理位置及生态地理单元区划图

2.2 数据来源

本文采用美国国家冰雪数据中心（NSIDC）网站下载的MODIS Terra/Aqua每日积雪产品（V5版本）MOD10A1和MYD10A1,空间分辨率为500 m,投影方式为Sinusoidal。在晴空条件下,2种积雪产品的识别精度都能达到90%以上[14],但该产品极易受到云的影响,大大降低了产品的整体识别精度。为了消除MODIS积雪产品中云像素的影响,本文还使用了NSIDC网站下载的IMS雪冰产品（Interactive Multisensor Snow and Ice Mapping System）。该积雪产品由多种光学数据与微波数据融合而成,不受云层的影响,其空间分辨率在2006年以前为24 km,从2006年开始分辨率提高到了4 km[15]。相关研究表明,IMS雪冰产品有较高的积雪识别精度,可以与MODIS积雪产品融合以去除云层的 干扰[16-17]。
本文中使用的气象数据是由中国气象科学数据共享服务网提供的2002-2012年中国地面气温、降水逐月0.5°×0.5°格点数据集（V2.0）。该数据集根据中国2472个气象站记录的温度、降水数据,利用薄盘样条法（TPS,Thin Plate Spline）进行空间插值得到,数据经过交叉验证、误差分析,质量状况良好。

2.3 积雪参数提取

首先,根据云移动的特点及Terra和Aqua卫星过境时间的不同,对MOD10A1和MYD10A1积雪产品进行逐日合成,即对于MOD10A1中的云像元,如果MYD10A1对应像元为非云,则将该像元重分类为非云像元对应的地类值,如果也为云像元,则依然分为云像元;然后,利用临近日分析,进一步减少云像元的数量[18];最后,结合IMS雪冰产品,去除剩余的全部云像元,得到2002-2012年逐日无云积雪覆盖产品。
基于逐日无云积雪覆盖产品,根据Wang和Xie[19]提出的算法（式（1）-（3））逐像元分别计算每个水文年（当年9月1日至次年8月31日）的积雪覆盖日数（Snow-Covered Days,SCD）、积雪开始日期（Snow Cover Start,SCS）和积雪结束日期（Snow Cover Ending,SCE）。
SCD = ∑ i = 1 n ( S i ) （1）
SCS = Fd - SC D bFd （2）
SCM = Fd + SC D aFd （3）
式中： n 代表一个水文年内包含的天数; S i 为0或1,分别表示非雪像元和雪像元;Fd为固定日期。由于青藏高原积雪呈双峰分布,从秋季开始累积,春季开始消融[20],因此式（2）、（3）中分别把Fd设定为12月1日和3月1日; S C D bFd 和 SC D aFd 分别表示一个水文年内固定日期Fd之前和之后的积雪覆盖日数。该算法假设积雪从降落到融化是持续覆盖地表的,忽略了瞬时降雪的影响。

3 结果与讨论

3.1 青藏高原积雪空间分布特征

由青藏高原2002-2012年多年平均积雪日数的空间分布（图2（a））可看出,其积雪分布广泛,但受气候和地形等因素的影响,存在明显的地域性差异。总体来看,高原积雪日数存在2个高值区,年均积雪日数在200 d以上：南部高值区主要位于喜马拉雅山脉和念青唐古拉山地区,受印度洋和孟加拉湾暖湿气流的影响,降水较为充沛;西部高值区主要位于帕米尔高原和喀喇昆仑山脉,受西风带上升运动的影响,降水较多,加上海拔高、气温低,为积雪的持续发育创造了条件[20]。此外,昆仑山北翼地区,祁连山地,东部的唐古拉山和巴颜喀拉山以及西南部的冈底斯山脉积雪也较为丰富,年均积雪日数介于120~200 d之间。高原的中部地区受周围山地的影响,水汽输入较少,年均积雪日数相对较短,为20~90 d。积雪日数低值区则主要分布在藏南山地,藏北羌塘高原,柴达木盆地以及青东祁连山地南部,这些区域距离水汽源较远,降水稀少,导致积雪相对匮乏,年积雪日数不足20 d。从积雪日数的标准偏差和变化趋势（图2（b）、（c））可看出,青南高寒区、果洛那曲高寒区、念青唐古拉山地区以及祁连山西部地区积雪日数年际间变化较大,但均表现为增加的趋势,其中显著增加区域占高原面积的25.6%。12.3%的区域积雪日数表现为减少的趋势,主要分布在羌塘高原、藏南山地、柴达木盆地以及川西地区。
Fig.2 Spatial distribution, standard deviation and temporal trends of snow-covered days in the Qinghai-Tibetan Plateau during 2002-2012

图2 2002-2012年青藏高原积雪日数

3.2 积雪开始期与积雪结束期时空变化特征分析

青藏高原SCS的分布具有明显的空间异质性（图3（a））。高海拔的山区SCS普遍较早,9月开始出现积雪,其中包含了山顶部的永久积雪,主要分布在喀喇昆仑山、西昆仑山、喜马拉雅山和念青唐古拉山。随着海拔降低,SCS逐渐推迟,但整体上,山区在10月下旬就已经出现积雪。而在青藏高原中东部和川西地区,SCS普遍较晚,积雪期开始于11月中下旬。从SCS标准差的空间分布来看（图3（b））,高原北部的昆仑高寒荒漠区、中东部的青南高寒区和果洛那曲高寒区的SCS年际间波动较大,标准差超过了16 d。昆仑山北翼荒漠区、川西藏东高山深谷区和祁连山东北部地区的SCS年际间波动有所减弱,标准差为2~8 d。羌塘高原东部地区的SCS较为稳定,标准差仅为2 d左右。伯玥等的研究指出[21],秋季是大气环流从夏季型向冬季型转换的时期,从高原南侧北上的暖湿气流和西北部南下的冷空气在高原中东部交汇,使这些区域降雪年际间变化大,从而导致积雪开始期年际间的较大波动。
Fig.3 Spatial distribution, standard deviation and temporal trends of SCS in the Qinghai-Tibetan Plateau during 2002-2012

图3 2002-2012年青藏高原SCS

从2002-2012年SCS的变化趋势来看（图3（c））,73.4%的区域没有明显的变化,主要分布在积雪匮乏以及积雪开始期较晚的地区。18.1%的区域提前趋势较为明显,提前速率为2~4 d/a,主要分布在果洛那曲高寒区和青南高寒区,另外,在昆仑高寒荒漠区东部、祁连山西北部以及藏南山地的小部分区域也表现出提前趋势。而在羌塘高原南部、念青唐古拉山西段以及川西地区则表现为显著的推迟趋势,推迟速率在2 d/a左右,占高原总面积的8.5%。
青藏高原SCE的分布同样表现出显著的空间差异（图4（a））,在喀喇昆仑山、西昆仑山、喜马拉雅山、念青唐古拉山、巴颜喀拉山和祁连山等高海拔山区SCE普遍较晚,一般5月以后积雪才开始消融。随着海拔降低,SCE逐渐提前。从SCE标准差的空间分布来看（图4（b））,年际振荡大的区域主要分布在果洛那曲高寒区和川西藏东地区,尤其是念青唐古拉山以南地区,标准差超过了20 d。青南高寒区、昆仑高寒荒漠区和祁连山区的SCE年际振荡较小,标准差在4~12 d。积雪匮乏的地区SCE相对稳定,标准差小于4 d,包括羌塘高原、藏南山地和柴达木盆地。
从2002-2012年SCE的变化趋势来看（图4（c））,69.9%的区域没有显著变化趋势,主要分布在积雪稀少的地区。23.2%的区域SCE显著推迟,其中位于高原中东部的果洛那曲高寒地区推迟速率在1~4 d/a,位于西北部的昆仑山区以及南部的念青唐古拉山地区推迟速率超过了4 d/a。而SCE显著提前的区域仅占6.9%,主要分布在青藏高原西南部、祁连山西段以及川西藏东小部分区域。近年来,随着全
球变暖,青藏高原温度升高的同时,降水量也在增大,而高原春季温度相对偏低,多以固态降水为主,为积雪的进一步发育创造了条件,使得春季积雪日数有明显的增加[22-23],这可能是导致部分地区积雪结束日期推迟的原因。
Fig.4 Spatial distribution, standard deviation and temporal trends of SCE in the Qinghai-Tibetan Plateau during 2002-2012

图4 2002-2012年青藏高原SCE

3.3 积雪开始期和积雪结束期与温度、降水的相关性分析

积雪与气候变化之间的关系非常密切,其中温度和降水是积雪产生-维系-消融过程中的重要因素。青藏高原积雪从秋季开始累积,春季开始消融,因此,针对不同生态分区,对SCS与秋季温度降水的相关性以及SCE与春季温度降水的相关性进行分析（表1）,研究其对气候因子的响应特征。
Tab.1 The correlation analysis of SCS and SCE with temperature and precipitation in different eco-geographical regions

表1 不同生态分区SCS和SCE与温度、降水的相关性

生态分区 秋季温度 秋季降水 春季温度 春季降水
HIB1 0.32 -0.87** -0.54 0.66*
HIC1 0.22 -0.62* -0.19 0.82**
HIC2 0.67* -0.09 -0.68* 0.13
HID1 0.41 -0.01 -0.63* 0.12
HIID2 0.67* 0.19 -0.56* 0.20
HIIC1 -0.26 -0.14 0.02 0.74*
HIIC2 0.73* -0.05 -0.76* 0.03
HIID1 0.12 -0.50 -0.13 0.57
HIID2 0.26 -0.17 -0.57 0.42
HIID3 0.68* -0.30 -0.67* 0.23

注：*代表P<0.05;**代表P<0.01

Fig.5 Temperature and precipitation trends in spring and autumn for different eco-geographical regions

图5 不同生态地理单元春、秋季温度和降水变化趋势

由图5可看出,近10年来,除柴达木盆地和青东祁连山地的春季温度有轻微的减少趋势外,其余各生态单元春秋两季温度均表现为上升趋势,且秋季增温幅度明显高于春季。川西藏东地区秋季降水量有明显的减少趋势,羌塘高原秋季降水量有轻微的减少趋势,而其他各区春秋两季的降水量都表现为增加趋势。
总的来看,SCS与秋季温度呈正相关,与秋季降水呈负相关,说明秋季温度升高会导致积雪开始期推迟,降水量增大会使积雪开始期提前;SCE与春季温度呈负相关,与春季降水呈正相关,表明春季增温会使积雪结束期提前,降水增加又有助于积雪结束期推迟。然而,在不同生态区内SCS和SCE受温度、降水的影响差异很大。位于高原中部的果洛那曲高寒区和青南高寒区,SCS和SCE与降水有显著的相关性,而与温度的相关性相对较弱,这部分区域属于高原亚寒带,气候寒冷,春秋季的平均温度都在0 ℃以下,降水多以固态形式出现,因此降水量对该区域积雪开始与结束期的影响较大。高原东南部的川西藏东地区具有湿润、半湿润气候,降水量较为充沛,温度越低,积雪越不易融化,因此该区域积雪开始与结束期与温度有较显著的相关性。羌塘高原、藏南山地和阿里山地的积雪开始期和结束期与温度均有显著的相关性,而与降水的相关程度不大。昆仑高寒荒漠区仅在融雪期与温度有显著的负相关,青东祁连山地仅在融雪期与降水有显著的正相关,而昆仑北翼荒漠区和柴达木盆地的积雪开始期和结束期与温度降水均无明显的相关性。

4 结论

本文利用MODIS积雪产品和IMS雪冰数据得到的2002-2012年青藏高原逐日无云积雪覆盖产品计算了10个水文年的积雪覆盖日数、积雪开始日期以及积雪结束日期,从不同生态分区对其时空变化特征进行了分析,研究了积雪开始期和结束期对温度、降水变化的响应关系,主要结论包括：
（1）青藏高原积雪日数的空间分布极不均匀,南部喜马拉雅山脉和念青唐古拉山地区以及西部的帕米尔高原和喀喇昆仑山脉为积雪覆盖日数的2个高值区,年均积雪日数在200 d以上。而羌塘高原、藏南山地和柴达木盆地积雪匮乏,年均积雪日数不足20 d。
（2）积雪开始期与结束期存在显著的空间差异性,高原周围山地积雪开始时间早结束时间晚,而中部腹地积雪开始时间较晚结束时间较早。高原北部的昆仑高寒荒漠区、中东部的青南高寒区和果洛那曲高寒区的SCS年际间波动较大,标准差超过了16 d。SCE波动大的区域集中在果洛那曲高寒区和川西藏东地区,标准差超过了20 d。18.1%的区域SCS表现出明显的提前趋势,8.5%的区域有显著的推迟趋势;23.2%的区域SCE有所推迟,而仅有6.9%的区域表现出提前趋势。
（3）不同生态区内SCS和SCE受温度、降水的影响有所差异。属于高原亚寒带的青南高寒区和果洛那曲高寒区与降水量有显著的相关性,而与温度的相关性不大。降水量较为充沛的川西藏东地区和降水稀少的干旱半干旱地区的SCS和SCE主要受温度的影响,而受降水量的影响程度较小。

The authors have declared that no competing interests exist.

References

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[13]
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[14]
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DOI

[15]
刘洵,金鑫,柯长青.中国稳定积雪区IMS雪冰产品精度评价[J].冰川冻土,2014,36(3):500-507.IMS雪冰产品由多种光学与微波传感器数据融合而成,提供北半球每日无云的积雪范围,在积雪遥感研究中具有广阔的前景.以气象站实测雪深数据为真值,检验了2009-2010年IMS雪冰产品在中国三大稳定积雪区北疆、东北、青藏高原地区每月、积雪季以及全年的误判率、漏判率和总体准确率,并分析了IMS雪冰产品的准确率与雪深之间的关系.结果显示:IMS雪冰产品的年总体准确率在三大积雪区均超过了92％,积雪季总体准确率均超过了88％,利用IMS雪冰产品监测积雪范围是可靠的.然而,IMS雪冰产品精度具有区域差异性,北疆地区在1月和2月误判率偏高,青藏高原地区积雪季有严重的漏判现象.IMS雪冰产品的准确率在东北地区和北疆地区随着雪深的增加而升高,当东北地区雪深超过6 cm,北疆地区超过13 cm时,准确率接近100％,但是,青藏高原地区两者基本没有关系.通过在青藏高原地区与同时相的4景MODIS积雪产品对比分析发现,实际上IMS雪冰产品相对地高估了积雪面积,青藏高原地区漏判率高其原因是IMS对零碎积雪的识别能力不足并且气象站分布不均匀.

DOI

[ Liu X, Jin X, Ke C Q.Accuracy evaluation of the IMS snow and ice products in stable snow covers regions in China[J]. Journal of Glaciology and Geocryology, 2014,36(3):500-507. ]

[16]
Mazari N, Tekeli A E, Xie H J, et al. Assessment of ice mapping system and moderate resolution imaging spectroradiometer snow cover maps over Colorado Plateau[J]. Journal of Applied Remote Sensing, 2013,7(1):073540.Satellite snow cover area (SCA) mapping using optical sensors has been known to suffer severe obstruction due to vegetation canopy and cloud cover. Several algorithms have been developed to reduce cloud cover contamination and enhance the SCA mapping. In this study we introduce the use of a daily SCA product from the Multisensor Snow and Ice Mapping System (IMS) at a nominal resolution of 4 km, assess its accuracy and error levels against in situ observations, and compare the IMS SCA product with the SCA products from moderate resolution imaging spectroradiometer (MODIS), a combination of daily Terra and Aqua satellites. The results show that the snow accuracies are higher during winter for both IMS and MODIS, and that there is not much difference between MODIS at 500 m and upscaled at 4 km. The IMS SCA mapping accuracies are significantly higher than MODIS accuracies for all sky conditions, while they are similar to or slightly lower than MODIS accuracies in clear sky conditions. The overestimate error of snow cover using IMS is higher (lower) than that of MODIS during ablation (accumulation) periods. Both MODIS and IMS show a similar pattern of underestimation errors of snow cover with the IMS being slightly higher than the MODIS. It is concluded that the IMS SCA product has potential as a good alternative for the MODIS daily SCA products or replacing those cloud pixels in the MODIS daily or multiday products.

DOI

[17]
Yang J, Jiang L, Menard C B, et al. Evaluation of snow products over the Tibetan Plateau[J]. Hydrological Processes, 2015,29(15):3247-3260.Not Available

DOI

[18]
黄晓东,郝晓华,王玮,等.MODIS逐日积雪产品去云算法研究[J].冰川冻土,2012,34(5):1118-1126.由于积雪和云的反射特性,使用光学遥感监测积雪受到天气的严重干扰,对研究区云量的分析表明,无论是MOD10A1还是MYD10A1,云都是影响该产品对研究区积雪进行实时监测的最大影响因素.综合不同去云方法,利用MODIS逐日积雪产品和被动微波数据AMSR-E雪水当量产品,生成了MODIS逐日无云积雪图像,并利用研究区85个地面气象观测台站提供的雪深数据对合成的单日无云积雪产品进行验证.结果表明:当积雪深度3cm时,新产品的积雪分类精度达到91.7%,该产品对实时监测青藏高原积雪动态变化具有重要的使用价值.

[ Huang X D, Hao X H, Wang W, et al. Algorithms for cloud removal in MODIS daily snow products[J]. Journal of Glaciology and Geocryology, 2012,34(5):1118-1126. ]

[19]
Wang X, Xie H.New methods for studying the spatiotemporal variation of snow cover based on combination products of MODIS Terra and Aqua[J]. Journal of Hydrology, 2009,371(1):192-200.Based on multi-day combination of Terra and Aqua MODIS snow cover products (cloud cover less than 10%), this study developed new snow cover index (SCI), snow-covered duration/days (SCD) map, snow cover onset dates (SCOD) map and snow cover melting dates (SCMD) map, one each per hydrological year, to further examine the spatiotemporal variations of snow cover. Daily in situ snow depth observations in northern Xinjiang, China from 2001 to 2005 were used to validate the new maps. Our results indicate that the SCD maps had an overall agreement of 90% with in situ observations of snow cover days at 20 stations in the study area, and the SCOD and SCMD maps also had good agreements with the in situ measurements, with a mean value of 1 week forward shift and 1 week backward shift, respectively, due to transient snowfall events in early fall and in late spring. The snow cover index (SCI) (km

DOI

[20]
李培基,米德生.中国积雪的分布[J].冰川冻土,1983,5(4):9-18.早在建国初期,我国老一辈气候学家,为适应大规模社会主义建设的需要,面临资料缺乏的困难,利用平均最大积雪深度,探讨了我国积雪分布和区划问题<sup>[1、2]</sup>为我国积雪分布的研究奠定了基础。后来,中央气象局气候资料研究室根据全国350个地面台站最大积雪深度的观测资料,分析了我国最大积雪分布<sup>[3]</sup>。中国科学院兰州高原大气物理研究所绘制了青藏高原最大积雪深度分布图1)(1:1500万)。

[ Li P J, Mi D S.Distribution of snow cover in China[J]. Journal of Glaciology and Geocryology, 1983,5(4):9-18. ]

[21]
伯玥,李小兰,王澄海.青藏高原地区积雪年际变化异常中心的季节变化特征[J].冰川冻土,2014,36(6):1353-1362.利用青藏高原1980-2009年SMMR、SSM/I和AMSR-E被动微波遥感反演得到的逐日积雪深度资料,应用EOF方法分析了近30 a青藏高原地区冬春季积雪年际变化异常的时空变化.结果表明:青藏高原冬春季积雪年际异常敏感区随季节有着显著变化,并具有多尺度性.其在大尺度上最主要的空间特征是从秋末(10-12月)到隆冬(12-翌年2月)位于青藏高原腹地和东南缘的河谷;后冬和前春年际异常变化的敏感区显著变小,整个青藏高原地区的积雪稳定少变;而春季(3-5月),随着青藏高原气温的回升,敏感区出现在青藏高原东部.青藏高原冬春季积雪年际变化在局地尺度上存在着季节变化,表现为青藏高原积雪年际变化的异常与年际变化趋势相反的特征,以及积雪年际变化东西反向异常随季节的演变.青藏高原冬春季积雪年际变化的异常敏感区在空间范围上的变化,反映了冬春季积雪在季节尺度上受冬季风和南来的暖湿气流之间相互消长和进退影响的特征.青藏高原冬春季积雪具有显著的年代际变化,在20世纪80年代处于多雪期,80年代后期进入一个积雪较少期.秋末至隆冬(10-翌年2月)的积雪在20世纪90年代后期出现明显转折,进入多雪期,2000年后又进入一个少雪期.

DOI

[ Bo Y, Li X L, Wang C H.Seasonal characteristics of the interannual variations centre of the Tibetan Plateau snow cover[J]. Journal of Glaciology and Geocryology, 2014,36(6):1353-1362. ]

[22]
田柳茜,李卫忠,张尧,等.青藏高原1979-2007年间的积雪变化[J].生态学报,2014,34(20):5974-5983.利用雪深被动微波遥感数据产品,对青藏高原1979-2007年积雪深度、积雪日数的分布变化及其趋势进行了分析.结果表明:青藏高原积雪日数、积雪深度和海拔三者之间在空间上具有显著正相关;青藏高原积雪在1988年发生突变,该年前后积雪分布有显著不同,这与20世纪80年代中后期青藏高原由暖干时期进入暖湿时期有关;将青藏高原按夏季水汽来源不同将其分为南北两部分,发现29年来北部积雪日数变化与全国积雪变化相反呈极显著增加趋势(R2=0.39,P＜0.01),以1.40 d/a的趋势增加,主要原因是西北部地区冬季积雪日数增加;南部积雪深度与全国积雪变化一致呈极显著减少趋势(R2 =0.24,P＜0.01),以-0.04 cm/a的趋势减少,主要原因是东南部春、夏、秋三季积雪深度减少.

DOI

[ Tian L X, Li W Z, Zhang Y, et al. The analysis of snow information from 1979 to 2007 in Qinghai-Tibetan Plateau[J]. Acta Ecologica Sinica, 2014,34(20):5974-5983. ]

[23]
王春学,李栋梁.中国近50 a积雪日数与最大积雪深度的时空变化规律[J].冰川冻土,2012,34(2):247-256.通过REOF和非参数Mann-Kendall趋势检验法,以 1958/1959-2007/2008年度中国557个气象台站的积雪观测资料为基础,对中国积雪日数与最大积雪深度的时空演变规律进行分析.结果表 明：东北、新疆北部和青藏高原中东部为中国积雪日数和最大积雪深度的3个大值区;近50a来,春、秋季中国积雪日数和最大积雪深度在整体上呈现缓慢减少的 趋势,冬季积雪日数和最大积雪深度呈现增加的趋势.气温是影响积雪产生和维持的重要因素.

[ Wang C X, Li D L.Spatial-temporal variations of snow cover days and the maximum depth of snow cover in China during recent 50 years[J]. Journal of Glaciology and Geocryology, 2012,34(2):247-256. ]

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