青藏高原积雪深度时空分布与地形的关系

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白淑英, 吴奇, 史建桥, 顾海敏. 青藏高原积雪深度时空分布与地形的关系[J]. 国土资源遥感, 2015,27(4): 171-178
BAI Shuying, WU Qi, SHI Jianqiao, GU Haimin. Relationship between the spatial and temporal distribution of snow depth and the terrain over the Tibetan Plateau[J]. REMOTE SENSING FOR LAND & RESOURCES,2015,27(4): 171-178 复制到剪切板

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《国土资源遥感》编辑部

青藏高原积雪深度时空分布与地形的关系

白淑英^1,², 吴奇¹, 史建桥³, 顾海敏⁴

1.南京信息工程大学遥感学院,南京 210044

2.环境保护部南京环境科学研究所,南京 210042

3.94783部队61分队,长兴 313111

4.涪陵区气象局,涪陵 408000

第一作者简介: 白淑英(1973-),女,博士,副教授,研究方向为遥感与GIS在资源环境中的应用。Email: baishu-ying@163.com。

收稿日期: 2014-06-12

基金项目: 国家环保公益性行业科研专项项目“我国国土生态安全格局构建关键技术和保护战略研究”(编号: 201209027)资助

摘要

利用1979—2010年逐日中国雪深长时间序列数据集,采用GIS空间分析和Mann-Kendall检验等方法,分析了青藏高原雪深时空分布及其与地形的关系。结果表明: 青藏高原雪深分布受地形影响明显,在5—9月平均雪深主要受到高程影响,在其他月份则受气温和高程共同影响; 同一高程带雪深的变幅反映坡度和坡向对雪深的影响,变幅越宽坡向影响越大; 最大雪深随高程和坡度的增加而增加,空间变异随高程的增加而下降,随坡度的增加而呈上升趋势; 从10月至翌年5月,平均雪深在高程82~2 482 m和6 082~7 682 m受坡度和坡向影响较高程2 482~6 082 m要偏大; 回归分析表明,高原雪深分布受到高程和坡度的双重影响,高程是雪深分布的主要影响因子,在高程82~3 282 m区间,坡度的空间差异对平均雪深空间变异的影响具有明显正效应。

关键词: 青藏高原; 被动微波遥感; Mann-Kendall检验; 地形因子

中图分类号:TP 79 文献标志码:A 文章编号:1001-070X(2015)04-0171-08 doi: 10.6046/gtzyyg.2015.04.26

Relationship between the spatial and temporal distribution of snow depth and the terrain over the Tibetan Plateau

BAI Shuying^1,², WU Qi¹, SHI Jianqiao³, GU Haimin⁴

1. College of Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044, China

2. Nanjing Institute of Environmental Science, Ministry of Environmental Protection, Nanjing 210042, China

3. Unit 61, No.94783 Troop of PLA, Changxing 313111, China

4. Meteorological Bureau of Fuling District, Fuling 408000, China

Abstract

Using the daily dataset of snow depth from 1979 to 2010, the authors analyzed the relationship between the spatial and temporal distribution of snow depth and the terrain factors over the Tibetan Plateau by means of Mann-Kendall test and GIS space analysis. According to the results achieved, the distribution of snow depth was significantly affected by topography, the average snow depth was affected mainly by elevation from May to September, whereas in other months it was under the common influence of temperature and elevation. The snow depth amplitude of the same segment elevation reflected the impact of the aspect on snow depth, and the influence was greater when the amplitude was wider. The maximum snow depth kept increasing with the increment of elevation and slope, while the spatial heterogeneity decreased with the increment of elevation but increased with the decrement of slope. From October to May of the next year, the average snow depth at the elevation from 82 to 2 482 meter and 6 082 to 7 682 meter was more affected by slope and aspect than that at the elevation from 2 482 to 6 082 meter. Multiple regression analysis showed that the distribution of snow depth over the Tibetan Plateau was influenced by elevation and slope; furthermore, spatial differences of slope at the elevation of 82~3 282 meter had significant positive effect on the spatial variability of average snow depth; nevertheless, elevation was the main limiting factor of the average snow depth.

Keyword: Tibetan Plateau; passive microwave remote sensing; Mann-Kendall test; terrain factor

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0 引言

越来越多的证据表明全球气候正在变暖, 积雪变化必然对气候变化特别是区域气候产生重要影响。青藏高原作为北半球中纬度海拔最高、积雪覆盖最大的地区, 其积雪时间长、空间分布广^[1], 是北半球积雪异常变化最强烈的区域。积雪变化会对青藏高原靠积雪融化补给的河流和湖泊产生不容忽视的影响。卫星遥感技术具有快速、高效及便捷等优点, 为积雪实时动态监测提供了有效的观测手段^{[2, 3]}, 特别是在地面站点分布稀疏, 甚至无站点的高原和山区, 可以弥补地面站观测数据缺乏等不足。被动微波积雪数据^{[3, 4, 5]}是全天候地表积雪的观测信息, 不但能够用于区分地表有积雪与无积雪区域, 还可以用于反演积雪深度或积雪水当量, 基本不受云的干扰。经过很多学者的不断改进^{[6, 7, 8]}, 被动微波雪深反演数据的精度有所提高, 已经能够较好地反映高原积雪的时空变化特征。

以往对高原积雪的影响因素研究主要从气候变化角度展开^{[9, 10, 11]}, 如降水和气温等, 但气候因子对积雪变化的影响是作用在地形因子影响的基础上, 随着海拔升高, 气温逐渐降低, 积雪融化变缓, 从而影响雪深和雪盖的分布。青藏高原冰雪覆盖区是我国和南亚许多大江大河的发源地。气候变暖导致的降水重新分配、冰川加速消融、积雪提前融化、土地荒漠化和草场退化^[12]等都会对积雪地区水资源数量与河川径流量季节分配的变化, 以及周边与下游地区的气候变化产生重要影响。因此, 本文以青藏高原为研究区, 利用1979— 2010年逐日中国雪深长时间序列数据集, 采用GIS空间分析和Mann-Kendall检验, 对雪深随高程、坡度的趋势和突变进行分析, 揭示雪深对地形因子的响应, 为进一步开展积雪时空变化规律、驱动因子及融雪径流模拟等研究提供参考信息。

1 数据与方法

1.1 数据来源与处理

积雪数据来源于中国西部环境与生态科学数据中心的“ 中国雪深长时间序列数据集(1978— 2010)” 。该数据集是由SMMR(1978— 1987年)、SSM/I(1987— 2008年)及AMSR-E(2002— 2010年)3种被动微波遥感数据经交叉订正一致后, 根据地面观测站实测雪深数据反演得到的逐日雪深资料, 空间分辨率25 km, 覆盖范围E60° ~140° , N15° ~55° , 采用全球等积圆柱EASE-GRID投影。首先, 利用ArcGIS软件将1979— 2010年逐日雪深数据转化为栅格数据; 然后, 根据栅格统计求得逐年逐月雪深(算术平均)和逐年逐月最大雪深数据(最大合成法); 最后, 在此基础上得到月平均雪深、年平均雪深(对月雪深求算术平均)、月最大雪深及年最大雪深数据(对月最大雪深进行最大合成)。

DEM是由CGIAR ICT- ICT(http://srtm.csi.cgiar.org)发布的SRTM3数据, 分辨率为 90 m, 利用ArcGIS软件对DEM进行拼接、裁切及投影处理, 得到青藏高原DEM数据, 并对其进行坡度提取, 结果见图1。

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	图1 青藏高原高程(左)和坡度(右)空间分布Fig.1 Spatial distribution of elevation(left) and slope(right) in the Tibetan Plateau

1.2 研究方法

1.2.1 Mann-Kendall检验

Mann-Kendall检验^[13]不需要样本遵从一定的分布, 不受少数异常值的干扰, 计算较方便, 常用于类型变量和顺序变量的趋势检验和突变分析^{[14, 15, 16]}。

在Mann-Kendall趋势检验中, 原假设H₀为时间序列数据(x₁, x₂, …, x_n), 其为n个独立的、随机变量同分布的样本; 备选假设H₁是双边检验, x_k和x_j的分布是不相同的, 其中样本序列k≤ n, j≤ n, 且k≠ j, 检验的统计变量S计算公式为

$\begin{matrix} S = \overset{n - 1}{\sum_{k = 1}} \overset{n}{\sum_{j = k + 1}} Sgn (x_{j} - x_{k}) \end{matrix}$ , (1)

式中Sgn()为符号函数, 即

$\begin{matrix} Sng (x_{j} - x_{k}) = \{\begin{matrix} 1, & x_{j} - x_{k} > 0 \\ 0, & x_{j} - x_{k} = 0 \\ - 1, & x_{j} - x_{k} < 0 \end{matrix} \end{matrix}$ 。 (2)

确定方差Var和标准正态系统变量U, 公式分别为

$\begin{matrix} Var = [n (n - 1) (2 n + 5) / 18] \end{matrix}$ , (3)

$\begin{matrix} U = \{\begin{matrix} (S - 1) / \sqrt{Var}, & S > 0 \\ 0, & S = 0 \\ (S + 1) / \sqrt{Var}, & S < 0 \end{matrix} \end{matrix}$ , (4)

式中: 如果U> 0, 表明序列有上升或增加的趋势; 如果U< 0, 则表明序列有下降或减少的趋势。在给定显著性水平α 下, 当|U|> U₁_{-α /}₂时, 表示序列趋势变化显著。当确定有显著趋势时, 可以用线性方程或多项式方程模拟趋势方程。|U|≥ 1.28, 1.96, 2.32时, 分别表示通过了信度90%, 95%, 99%的显著性检验。

在Mann-Kendall突变分析中, 设时间序列x₁, x₂, …, x_n; 构造秩序列r_i, 其表示x_i> x_j(i≥ j≥ 1)的样本累积数。定义S_t为

$\begin{matrix} S_{t} = \overset{t}{\sum_{i = 1}} r_{i} \end{matrix}$ , (5)

其中t=2, 3, …, n,

$\begin{matrix} r_{i} = \{\begin{matrix} 1, x_{i} > x_{j} \\ 0, x_{i} \leq x_{j} \end{matrix} \end{matrix}$ 。 (6)

S_t均值E(S_t)以及方差Var(S_t)定义为

$\begin{matrix} E (S_{t}) = \frac{t (t - 1)}{4} \end{matrix}$ , (7)

$\begin{matrix} Var (S_{t}) = \frac{t (t - 1) (2 t + 5)}{72} \end{matrix}$ 。 (8)

在时间序列随机独立的假定下, 定义统计量为

$\begin{matrix} U F_{t} = [S_{t} - E (S_{t})] / \sqrt{Var (S_{t})} \end{matrix}$ , (9)

式中: UF₁=0; UF_t为标准正态分布的统计量(t=1, 2, …, n), 给定一显著水平α , 查正态分布表得到临界值U_α。当|UF_t|> U_α, 表明序列存在显著增长或减少趋势, 通过信度检验可知其是否具有显著趋势。按时间序列x逆序, 再重复上述计算过程, 得到逆序的标准正态分布的统计量UB_t, 同时使

$\begin{matrix} U B_{t} = - U F_{t} \end{matrix}$ , (10)

然后绘出UF_t和UB_t曲线图。若UF_t> 0, 表明序列呈上升趋势, 若UF_t< 0表明呈下降趋势; 当超过信度线时, 表示存在显著的上升或下降趋势; 若2条曲线出现交点, 且位于信度线之间, 那么交点对应的时刻就是突变开始的时刻。

本文分别利用雪深均值及标准差作为表征积雪的总体情况和空间异质性的变量, 运用Mann-Kendall趋势检验进行雪深随高程和坡度的趋势分析, 利用Mann-Kendall突变检验进行雪深对高程和坡度响应的突变分析。

1.2.2 多元回归分析

设雪深因变量为Y, n个自变量(气温、降水量、风速、日照时数、高程及坡度等)分别为X₁, X₂, …, X_n, 则多元线性回归模型的一般形式为

$\begin{matrix} Y = β_{0} + β_{1} X_{1} + β_{2} X_{2} + \dots + β_{n} X_{n} + ε \end{matrix}$ , (11)

式中: ε 为随机误差; β ₀为回归常数; β _j为总体回归系数, j=1, 2, …, n。

实际中需要用样本信息对因变量进行估计。假设已给出n(样本容量)个雪深值, 则多元回归线性模型的样本回归模型为

$\begin{matrix} Y_{i} = b_{0 i} + b_{1 i} X_{1 i} + \dots + b_{ji} X_{ji} + \dots + b_{ni} X_{ni} + ε_{i} \end{matrix}$ , (12)

样本回归方程为

$\begin{matrix} {\hat{Y}}_{i} = b_{0 i} + b_{1 i} X_{1 i} + b_{2 i} X_{2 i} + \dots + b_{ji} X_{ji} + \dots + b_{ni} X_{ni} \end{matrix}$ , (13)

式中: $\begin{matrix} {\hat{Y}}_{i} \end{matrix}$ 为X_i第i值时对Y的估计值; b₀_i, b₁_i, b₂_i, …, b_ji, …, b_ni为总体回归参数的估计; ε _i为Y_i与估计值 $\begin{matrix} {\hat{Y}}_{i} \end{matrix}$ 的残差。b₀, b₁, b₂, …, b_n一定程度反映了自变量X₁, X₂, …, X_n对Y的解释程度, 但解释程度是否显著还需T检验。在给定的显著水平下, 自变量X_j对应的T值越大, 则说明X_j对因变量Y的解释能力越强。本文拟将高程和坡度作为雪深的因变量, 对雪深进行多元线性回归分析。

2 结果与分析

2.1 雪深空间分布

青藏高原1979— 2010年间的年平均雪深分布如图2所示。

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	图2 1979— 2010年青藏高原年平均雪深分布Fig.2 Spatial distribution of annual mean snow depth in the Tibetan Plateau during 1979 to 2010

从图2可以看出, 青藏高原积雪分布受气候和地形的影响显著, 雪深分布的空间地域性和差异性明显, 主要特征是中间小、四周大, 多年平均雪深为2.03 cm, 积雪深度1~3 cm所占百分比为54.8%, 5 cm以上仅占5.3%。其中川西藏东山地、阿里山地半荒漠和荒漠地带积雪深度分布密集且较厚, 雪深平均厚度在3 cm以上, 而中部积雪疏散且雪深相对较浅, 平均在0~3 cm之间。受高原地形阻挡作用, 空中西风带冷槽分支沿高原西侧南下, 高原西部处于西风带上升区, 降水较多^[17]; 高原南部受印度洋和孟加拉湾暖湿气流影响, 降水充沛; 中部广阔的高原腹地由于受周围高山的地形影响, 加之海拔较高, 季风影响减弱, 水汽输送较少, 形成雪深低值区。

2.2 雪深与地形因子的响应关系

2.2.1 月最大雪深、月平均雪深与高程之间的关系

按月将雪深数据与对应高程进行空间分析, 发现月最大雪深随海拔分布呈类似“ 凸” 字型分布特征, 并伴随有明显的陡坎效应, 而这种陡坎效应在不同月份又有所差异。图3为青藏高原12个月份的雪深(月平均雪深、月最大雪深)与高程的关系图。可以看出, 1, 2, 3, 11, 12月份最大雪深的分布规律基本相似, 最大雪深随海拔升高逐渐增加。在高程82~482 m, 最大雪深增幅速率达到5 cm/400 m, 命名为“ 第一陡坡” ; 在高程482~2 082 m, 最大雪深上升较为缓慢, 增幅速率在1.6 cm/400 m左右, 命名为“ 第二陡坡” ; 随后出现差异, 1, 11, 12月的最大雪深随高程(2 482~3 282 m)变化表现为先平稳增加, 在3 282 m附近存在陡坎, 最大雪深突然大幅增长, 3 682 m之后达到稳定状态。而2月与3月在2 482 m附近出现陡坎, 最大雪深突然增加达6~7 cm, 随后马上达到稳定状态。二者在5 682 m和6 882 m附近再次出现2个陡坎, 最大雪深大幅减小; 4, 5, 6月分布规律相近, “ 第一陡坡” 和“ 第二陡坡” 增幅速率大幅减小。5月与6月482 m以下基本没有雪, 在高程2 482 m陡坎向高位推移变化到2 882 m(6月), 5 682 m处陡坎消失, 6月份6 882 m处陡坎消失, 最大雪深峰值也大幅减小。7月与8月“ 第一陡坡” 和“ 第二陡坡” 消失, 高程2 082 m以下基本没有雪, 在高程2 082~3 682 m和3 682~4 882 m雪深呈增加态势, 在高程4 882 m附近存在陡坎, 高程5 682 m以上雪深又逐步减小。9月高程2 082 m以下雪深稍有增加, 最大雪深小于1 cm, 在4 082 m最大雪深达到最大值4.2 cm左右, 为年内最小值。10月份“ 第一陡坡” 和“ 第二陡坡” 重新建立, 在高程3 282~5 682 m雪深变化平缓, 高程5 682 m以上雪深呈缓慢下降趋势。另外, 年内各个月份在高程7 682 m处均有陡坎, 在此高程以上雪深突然大幅减小, 但雪深基本保持不变。

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	图3 青藏高原月尺度的雪深(月平均雪深、月最大雪深)与高程关系Fig.3 Relationships between monthly snow depth (monthly mean snow depth and monthly maximum snow depth) and elevation in the Tibetan Plateau

从月平均雪深分布来看(图3), 1— 5月雪深随高程呈“ 三峰” 型分布, 峰值高度在2 682 m, 4 282 m和6 282 m附近, 随着温度升高各高程带内的平均雪深逐渐减少, 各峰值也在逐渐减小, 但4 882 m以上平均雪深变化幅度较少; 从5— 9月, 第一峰值和第二峰值逐渐消失, 在高程4 882 m以下基本没有雪, 平均雪深基本不受温度影响; 随着温度下降, 10— 12月雪深“ 三峰” 型分布重建, 随高程升高月平均雪深逐渐增加。这说明平均雪深在5— 9月主要受到高程的影响, 在其他月份则主要受气温和高程共同的影响。

2.2.2 年最大雪深、年平均雪深与高程之间的关系

为研究青藏高原积雪信息对不同高程带的响应机制, 对研究区高程进行400 m等密度分割。以年最大雪深的均值和标准差为参数分别与高程进行相关分析(图4), 相关系数分别为0.438和-0.609, 均达到了0.05显著性水平, 说明年最大雪深的均值分布随高程上升而增加, 而年最大雪深的空间异质性则随高程逐渐减小。

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	图4 青藏高原年尺度的雪深与高程的关系Fig.4 Relationships between annual snow depth and elevation in the Tibetan Plateau

从图4可以看出, 年平均雪深和年最大雪深均值随高程的变化趋势十分明显, 二者变化趋势比较一致, 相关系数达0.875, 表现为明显分段特征, 在高程82~2 482 m和2 882~4 482 m雪深逐步增加, 但在高程2 482~2 882 m存在趋势转折, 即峰值向谷值的急剧反转, 之后雪深随高程上升而增加, 在高程4 082~4 482 m年最大雪深的均值达到峰值10 cm; 在高程4 482 m以上年最大雪深均值又转为波动减少趋势, 在高程8 482 m以上雪深维持不变。年最大雪深的空间异质性随着高程上升也逐步增大, 但在高程4 482 m以上年最大雪深空间差异性逐步减小。

Mann-Kendall趋势检验结果表明, 青藏高原年最大雪深均值高程序列U₁=1.551(1.28< U₁< 1.96), 年最大雪深标准差高程序列U₂=-3.074(U₂< -2.32), 说明年最大雪深随高程的增加呈不显著上升趋势, 年最大雪深的空间差异性随高程增加而呈显著下降趋势。在95%的置信水平下, U_a/₂=1.96, 利用Mann-Kendall法对年最大雪深高程序列的计算结果见图5。

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	图5 年最大雪深随高程的Mann-Kendall法突变分析结果Fig.5 Annual maximum snow depth with elevation mutation analysis results by Mann-Kendall

从图5可以看出, 年最大雪深的均值随高程上升呈增加趋势, 其中在高程小于1 682 m区域均呈不显著增加趋势, 在高程1 682 m以上区域呈显著增加趋势, 其随高程的突变点出现在882 m处; 年最大雪深的空间差异性在高程6 082 m以下区域呈不显著增加趋势, 在高程6 082 m以上区域呈减少趋势, 高程7 682 m以上减少显著, 空间差异性随高程的突变点出现在7 282 m处, 说明高程7 282 m以下区域年最大雪深空间差异性大, 相对而言, 高程7 282 m以上区域年最大雪深空间差异较小。

2.2.3 雪深与坡度之间的关系

分析青藏高原积雪信息与坡度的响应机制时, 对研究区坡度进行5° 等密度分割。图6为青藏高原雪深与坡度的关系图。

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	图6 青藏高原雪深与坡度的关系Fig.6 Relationships between snow depth and slope in the Tibetan Plateau

从图6可以看出, 年平均雪深在各坡度带均有分布, 而且雪深随坡度的变化趋势比较明显, 年平均雪深和年最大雪深的分布特征较一致, 相关系数达0.926, 均表现为单峰型特征, 即雪深随坡度先增加后减少, 峰值出现在40° ~45° 坡度带, 年最大雪深平均值为12.5 cm。坡度在50° 以下, 雪深的空间异质性随着坡度增加也逐步增大, 其中在20° ~40° 坡度带年最大雪深标准差变化不大, 在45° ~50° 坡度带雪深的空间异质性最大, 年最大雪深标准差达6.0 cm, 随后雪深的空间异质性迅速减小, 坡度在60° 以上, 雪深不存在空间差异。Mann-Kendall趋势检验结果表明, 青藏高原最大雪深均值坡度序列U₃=1.403(1.28< U₃< 1.96), 年最大雪深标准差坡度序列U₄=2.013(1.96< U₄< 2.32), 说明年最大雪深随坡度的增加呈不显著上升趋势, 年最大雪深的空间差异性随坡度增加而呈显著上升趋势。在95%的置信水平下, U_a/₂=1.96, 利用Mann-Kendall法对年最大雪深坡度序列的计算结果见图7。

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	图7 年最大雪深随坡度的Mann-Kendall法突变分析结果Fig.7 Annual maximum snow depth with slope mutation analysis results by Mann-Kendall

从图7可以看出, 年最大雪深及其空间差异性随坡度的上升均表现为增加趋势, 其中坡度在20° 以下区域二者呈不显著增加趋势, 在坡度20° 以上则呈显著增加趋势, 不同的是年最大雪深随坡度的突变点出现在坡度10° , 年最大雪深的空间差异性的突变点出现在坡度15° , 说明坡度小于15° 以下的年最大雪深空间差异小, 相对而言, 坡度大于15° 区域的年最大雪深空间变异性较大。

3 雪深与高程、坡度的回归分析

通过对研究区不同高程带与分布坡度均值和雪深均值的相关分析得知, 雪深均值与坡度的相关性较小, 未通过0.05显著性检验, 说明坡度并非影响雪深分布的限制因子; 雪深均值与高程的关系密切, 相关系数达0.767, 通过了0.001显著性检验。在高程固定的条件下, 雪深均值与坡度的偏相关系数为-0.333, 表明高原雪深分布受到高程和坡度的双重影响, 高程是雪深分布的主要影响因子。

为更好地揭示高程和坡度因子对积雪深度的影响, 在相关分析基础上, 对积雪深度与高程、坡度进行多元回归分析, 标准化回归方程为

$\begin{matrix} D = 0.873 + 0.907 E - 0.256 S \end{matrix}$ , (14)

式中: D为雪深; E为高程; S为坡度。该回归方程的复相关系数达0.796, 通过了0.001的显著性检验。可以看出, 高程对积雪深度的影响有正效应, 坡度对积雪深度的影响表现为负效应, 高程是影响积雪深度分布的主要地形驱动因子。综上所述, 不同高程带坡度的分布会造成雪深空间差异性的变化。

不同高程带坡度标准差和雪深标准差的分布情况如图8所示。

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	图8 坡度标准差、雪深标准差与高程分布的关系Fig.8 Relationship between elevation distribution and slope standard deviation, snow depth standard deviation

从图8可以看出, 在高程82~3 282 m区间, 平均雪深标准差和坡度标准差变化趋势比较一致, 相关系数达0.937, 达到了0.001显著性水平, 说明坡度的空间差异对平均雪深空间变异的影响具有明显正效应; 在高程3 282 m以上, 坡度标准差呈“ 下降— 上升— 下降” 大幅波动态势, 而平均雪深标准差变化平稳, 二者呈负相关, 相关系数为-0.315, 说明坡度的空间差异对平均雪深空间变异的影响较小。

4 结论与讨论

1)青藏高原雪深分布存在2个高值区: ①东南高值区, 集中在唐古拉山、念青唐古拉山东段山区带和巴颜喀拉山, 年均雪深最大值可达10 cm; ②西部和西南部高值区, 位于喀喇昆仑山山脉、喜马拉雅山西段, 多年平均积雪深度的范围在3~7 cm之间。

2)月最大雪深受高程影响显著, 并伴随有明显的陡坎效应; 在5— 9月平均雪深主要受高程影响, 在其他月份则受气温和高程共同影响; 高原雪深分布受高程和坡度的双重影响, 高程是雪深分布的主要影响因子, 但在高程82~3 282 m区间, 坡度的空间差异对平均雪深空间变异的影响具有明显正效应。

3)年最大雪深的均值随高程增加呈不显著上升趋势, 其随高程的突变点在882 m处, 年最大雪深的空间差异性随高程增加而呈显著下降趋势, 高程7 282 m以下区域年最大雪深空间差异性大; 年最大雪深的均值随坡度增加呈不显著上升趋势, 年最大雪深的空间差异性随坡度增加而呈显著上升趋势, 年最大雪深空间差异性的突变点出现在坡度15° 处, 坡度在15° 以下年最大雪深空间差异小。

4)从10月至翌年5月, 平均雪深在高程82~2 482 m和6 082~7 682 m区间, 受坡度影响较高程2 482~6 082 m大, 同一高程上雪深变化明显。回归分析表明, 高原雪深分布受到高程和坡度的双重影响, 高程是雪深分布的主要影响因子, 在高程82~3 282 m区间, 坡度的空间差异对平均雪深空间差异性的影响具有明显正效应。

5)地形因素中坡向对雪深的分布有很大影响, 正常情况下, 阳坡的雪比阴坡融化得快, 因此很多地区同一高程, 阴面有雪, 但阳面没有雪, 所以还需更高空间分辨率的DEM数据与卫星积雪数据进行局地分析, 进而得出更精确的结论。不同高程带上的气候、坡度、坡向、地表温度及地表覆盖等有很大差异, 因此研究不同高程带上积雪的时空动态变化及影响因素, 需综合应用地面台站观测资料、多源遥感积雪数据及各类数值模拟产品, 以便提高卫星遥感积雪数据反演雪深的精度。

The authors have declared that no competing interests exist.

参考文献

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[1]	徐兴奎. 1970—2000年中国降雪量变化和区域性分布特征[J]. 冰川冻土, 2011, 33(3): 497-503. Xu X K. Spatiotemporal variation and regional distribution characteristics of snowfall in China from 1970 to 2000[J]. Journal of Glaciology and Geocryology, 2011, 33(3): 497-503. [本文引用:1]
[2]	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. doi: DOI: 106046/gtzyyg. 2013. 01. 17. 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. doi: DOI:10.6046/gtzyyg.2013.01.17. [本文引用:1]
[3]	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. [本文引用:2]
[4]	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. [本文引用:1]
[5]	Che T, Li X, Jin R, et al. Snow depth derived from passive microwave remote-sensing data in China[J]. Annals of Glaciology, 2008, 49(1): 145-154. [本文引用:1]
[6]	柏延臣, 冯学智, 李新, 等. 基于被动微波遥感的青藏高原雪深反演及其结果评价[J]. 遥感学报, 2001, 5(3): 161-165. Bo Y C, Feng X Z, Li X, et al. The retrieval of snow depth in Qinghai_Xizang(Tibet) Plateau from passive microwave remote sensing data and its results assessment[J]. Journal of Remote Sensing, 2001, 5(3): 161-165. [本文引用:1]
[7]	车涛, 李新. 利用被动微波遥感数据反演我国积雪深度及其精度评价[J]. 遥感技术与应用, 2004, 19(5): 301-306. Che T, Li X. Retrieval of snow depth in China by passive microwave remote sensing data and its accuracy assessment[J]. Remote Sensing Technology and Application, 2004, 19(5): 301-306. [本文引用:1]
[8]	车涛. 积雪被动微波遥感反演与积雪数据同化方法研究[D]. 兰州: 中国科学院寒区旱区环境与工程研究所, 2006. Che T. Study on Passive Microwave Remote Sensing of Snow and Snow Data Assimilation Method[D]. Lanzhou: Cold and Arid Regions Environmental and Engineering Research Institute, 2006. [本文引用:1]
[9]	白淑英, 史建桥, 沈渭寿, 等. 近30年西藏雪深时空变化及其对气候变化的响应[J]. 国土资源遥感, 2014, 26(1): 144-151. doi: DOI: 106046/gtzyyg. 2014. 01. 25. Bai S Y, Shi J Q, Shen W S, 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 and Resources, 2014, 26(1): 144-151. doi: DOI:10.6046/gtzyyg.2014.01.25. [本文引用:1]
[10]	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. [本文引用:1]
[11]	杨倩, 陈圣波, 路鹏, 等. 2000—2010年吉林省积雪时空变化特征及其与气候的关系[J]. 遥感技术与应用, 2012, 27(3): 413-419. Yang Q, Chen S B, Lu P, et al. Spatio-temporal variation of snow cover in Jilin province and its relationships to climate change in recent 10 years[J]. Remote Sensing Technology and Application, 2012, 27(3): 413-419. [本文引用:1]
[12]	孙鸿烈. 不断攀登探索世界屋脊奥秘[J]. 中国西藏, 1998(6): 46-48. Sun H L. Climbing up and exploring mysteries concerning the roof of world[J]. China’s Tibet, 1998(6): 46-48. [本文引用:1]
[13]	魏凤英. 现代气候统计诊断与预测技术[M]. 北京: 气象出版社, 2007. Wei F Y. Modern Climatic Statistical Diagnosis and Prediction Technology[M]. Beijing: Meteorological Press, 2007. [本文引用:1]
[14]	徐宗学, 米艳娇, 李占玲, 等. 和田河流域气温与降水量长期变化趋势及其持续性分析[J]. 资源科学, 2008, 30(12): 1833-1838. Xu Z X, Mi Y J, Li Z L, et al. Long-term trends and sustainability analysis of air temperature and prediction in the Hotan river basin[J]. Resources Science, 2008, 30(12): 1833-1838. [本文引用:1]
[15]	白淑英, 史建桥, 相栋, 等. 近50年青海降水时空格局变化[J]. 干旱区资源与环境, 2013, 27(6): 148-153. Bai S Y, Shi J Q, Xiang D, et al. Spatiotemporal pattern change of precipitation in Qinghai in the last 50 years[J]. Journal of Arid Land Resources and Environment, 2013, 27(6): 148-153. [本文引用:1]
[16]	康淑媛, 张勃, 柳景峰, 等. 基于Mann-Kendall法的张掖市降水量时空分布规律分析[J]. 资源科学, 2009, 31(3): 501-508. Kang S Y, Zhang B, Liu J F, et al. Analysis of the spatiotemporal distribution of precipitation in Zhangye City using Mann-Kendall method[J]. Resources Science, 2009, 31(3): 501-508. [本文引用:1]
[17]	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. [本文引用:1]

2011

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徐兴奎. 1970—2000年中国降雪量变化和区域性分布特征[J]. 冰川冻土, 2011, 33(3): 497-503.

Xu X

. Spatiotemporal variation and regional distribution characteristics of snowfall in China from 1970 to 2000[J]. Journal of Glaciology and Geocryology, 2011, 33(3): 497-503.

源自NOAA-NESDIS北半球积雪覆盖数据显示，20世纪70年代至2000年期间，我国降雪覆盖范围基本没有出现明显变化. 依据全国无缺测、具有连续日降雪观测的台站资料波谱分析显示，1970-2000年我国降雪量年变化波谱组成比较简单，但具有明显的区域性分布特征和两种相反的变化趋势. 划分出4个降雪量年变化相似区域：除东北少部分地区、内蒙中部至华北地区、青藏高原东缘至四川盆地和新疆部分地区，降雪量持续呈线性和非线性减少外，我国绝大部分地区年降雪量总体呈增加趋势，但80年代中后期开始减少. 降雪量月变化也存在区域性分布特征，并与区域气温和地理特征有关. 在年平均气温较低的区域，降雪量月波动变化呈双峰分布，双峰之间的时间间隔与年平均气温成正比，在气温较高的华南沿海地区，降雪量月变化呈单峰波动.

... 青藏高原作为北半球中纬度海拔最高、积雪覆盖最大的地区,其积雪时间长、空间分布广^[1],是北半球积雪异常变化最强烈的区域 ...

2013

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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. doi: DOI: 106046/gtzyyg. 2013. 01. 17.

, Guo H

, Zhang

. 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. doi: DOI:10.6046/gtzyyg.2013.01.17.

Based on the Landsat TM/ETM + remote sensing images from 1991 to 2009, the authors obtained the dynamic change of the West Kunlun glaciers as well as the Gozha Co and the Aksayqin Lake, with the climatic data, analyzed the change relationship between the glaciers, the lakes and the local climate in different regions. The conclusions of this paper are as follows: 1 In the period of 1991 to 2009, the West Kunlun glaciers were changing obviously, but the east part and the west part show different characteristics. That is, the glaciers of the east part firstly decreased, then increased, and decreased again. The change was fluctuated. However, the situation of the west part was different: the glaciers were decreasing continuously. Meanwhile the Gozha Co was firstly enlarged, then narrowed, and enlarged again while the Aksayqin Lake was narrowed continuously. 2 In the West Kunlun glaciers, there existed important interaction relationship between the Gozha Co and the Aksayqin Lake, showing an inverse ratio in quantity. 3 From 2000 to 2005, the main factor responsible for the difference in area between the West Kunlun glaciers and the lakes was the temperature. With the growing temperature, the area of the glaciers decreased while that of the lakes increased. The variation showed linear relationship.

1. Key Laboratory of Digital Earth, Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China; 2. Shanghai Institute of Satellite Engineering, Shanghai 200240, China

依据西昆仑地区1991—2009年Landsat TM/ETM + 遥感图像,获取近20 a西昆仑地区冰川及其冰川融水形成的郭扎错和阿克赛钦湖的动态变化信息,并结合气候资料,分区域综合分析冰川、湖泊变化与气候变化的关系。研究结果表明:1在1991—2009年间,西昆仑冰川有显著变化,但东、西区存在较大差异。其中东区处于先减少,后增加,再减少的波动状态,而西区则处于持续减少状态。与此同时,郭扎错具有先扩大,后缩小,再扩大的变化现象,而阿克赛钦湖则在不断地缩小。2西昆仑冰川与郭扎错、阿克赛钦湖之间,存在着重要的相互作用关系,在数量上成反比。3在2000—2005年间,西昆仑冰川与湖泊面积变化差异产生的主要原因是温度,随着温度的升高,冰川面积减少,湖泊面积增大,三者的变化具有线性关系。

... 卫星遥感技术具有快速、高效及便捷等优点,为积雪实时动态监测提供了有效的观测手段^[2,3],特别是在地面站点分布稀疏,甚至无站点的高原和山区,可以弥补地面站观测数据缺乏等不足 ...

2004

0.0

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

, Li

, Gao

. 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.

Snow depth and snow water equivalent are the most import factors in the hydrologic model and climate model. So far, there is not an operational algorithm to estimate the snow water equivalent from passive microwave remote sensing data (SSM/I) in the Tibetan Plateau. In this study the SSM/I brightness temperature data in January 1993 are used to estimate the snow water equivalent SWE in the plateau. The frequencies of SSM/I data used to retrieve snow depth are 19 and 37 GHz in horizontal polarization. The results show that all available algorithms overestimate the snow depth in the Tibetan Plateau. In this paper the reasons of overestimation of snow depth from several aspects are analyzed, such as the water content of snow pack, large water bodies (e.g. lakes), and the abnormal field snow depth data. After eliminating some futile data (including the passive microwave brightness temperature values and snow depth data in the weather stations), an improved algorithm has been established to retrieve the snow depth from the difference of 19 and 37 GHz brightness temperatures in horizontal polarization. Here, snow density is obtained by a time function of fresh snow density. The snow depth and density were converted to the snow water equivalent, and are regarded as the ground truth. In finally, the TB vertically polarized differences of 19 and 37 GHz are regressed with the SWE. Using the statistical method, a simple and practical algorithm is developed to estimate the snow water equivalent from the differences of 19 and 37 GHz in vertical polarization.

Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou Gansu 730000, China

利用1993年1月份的SSM/I亮度温度数据反演了青藏高原的雪水当量,首先使用被动微波SSM/I数据19和37GHz的水平极化数据来反演雪深,根据积雪时间的函数来计算实时的雪密度,由雪的深度和密度计算出雪水当量.最后,利用SSM/I数据的19和37GHz的垂直极化亮度温度梯度对计算出的雪水当量进行回归分析,得到了利用SSM/I数据直接反演雪水当量的算法.

... 被动微波积雪数据^[3,4,5]是全天候地表积雪的观测信息,不但能够用于区分地表有积雪与无积雪区域,还可以用于反演积雪深度或积雪水当量,基本不受云的干扰 ...

2008

0.0

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

, Wu

, Yao F

. 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.

Based on two kinds of remote sensing data (snow depth of Special Sensor Microwave/ Imager SSM/I from 1993 to 2004, snow cover of NOAA/AVHRR from 1996 to 2004) and snow depth and snow cover days of six stations over northern Tibet from 1966 to 2003, we studied snow cover distribution in temporal and spatial scales, and analyzed the influencing factors of snow in Northern Tibet region. The result showed that the remote sensing snow data (retrieval from SSM/I, NOAA/AVHRR) was in general agreement with the weather stations data. In temporal scale, for the seasonal cycle, during 1966 to 2003, the snow cover extent increased rapidly in autumn and winter seasons in Northern Tibet. However, in spring (March-April-May), snow cover extent did not show a continual decrease，whereas indicated a positive feedback. For the interannual cycle, from the end of 1960s the snow cover of Northern Tibet region reduced, but in 1980s, the snow cover increased as a whole, and from 1990s it decreased again. Thus, the snow cover variation showed a reducing-increasing-reducing dynamic process in the study area. The result of a wavelet analysis on temporal distributions of annual cumulative snow cover indicated that there were mainly oscillation periods of quasi-2~3 yr, quasi-9 yr, quasi-13 yr, and from the beginning of 1970s to the middle of 1990s, there was an oscillation period about quasi-5 yr. In spatial scale, the east part of Northern Tibet region was the main snow cover area, where the snow cover days exceeded 15 ten-days in each winter-spring, and were obviously distinguished from the western part of Northern Tibet region. The majority of snow cover pixels exist in altitude of 4900~5600 m. The greatest variance of snow cover was found in middle and eastern part of the Northern Tibet Plateau, i.e. Andu and Nierong regions. We selected two important factors influencing snow-cover (i.e., temperature, precipitation) and calculated multiple linear regressions. The result showed that the correlation coefficient between simulated annual cumulative snow days and observation data was 0.74. It revealed that temperature and precipitation are the main factors influencing the snow cover in Northern Tibet region. Global warming might be one reason causing snow cover declining in recent years in Northern Tibet region. However, the effect of precipitation seems larger than that of temperature.

1 Lab. for Remote Sensing and Climate Information Sciences, Chinese Academy of Meteorological Science, Beijing 100081, China;2 Zhejiang Meteorological Information Center, Hangzhou 310017，China; 3 College of Earth Sciences, the Graduate University of the Chinese Academy of Sciences, Beijing 100049, China

利用1993~2004年SSM/I被动微波辐射仪反演的雪深资料，1996~2004年NOAA/AVHRR可见光和红外反演的积雪覆盖面积资料，1966~2003年藏北地区6个地面台站的积雪观测资料来检验卫星资料的可用性，并研究近年来藏北积雪的时空分布和影响因素.结果表明，SSM/I, NOAA/AVHRR和实际观测的积雪资料具一致性.从积雪时间变化看：季节尺度上，藏北地区秋冬季积雪迅速增加，但春季(3~5月)融雪速度不快，呈现正反馈特征；年际尺度上，藏北地区20世纪60年代末期起积雪开始减少，80年代积雪增加，90年代起到2003年积雪总体上减少，呈现出减少—增加—减少趋势.采用小波分析发现积雪振荡周期存在着一个准2~3年，准9年和13年的周期，从20世纪70年代初到90年代中期还有一个5年的周期.积雪空间上看，藏北地区积雪主要集中在东部地区，该区每个冬春年积雪覆盖旬数超过15旬，显著高于西部少雪区，大部分积雪集中在4900~5600 m的高度左右；藏北高原积雪变动的显著区位于藏北中东部的安多和聂荣地区.利用藏北地区1966~2003年的地面温度和降水资料建立回归方程模拟年累积雪日，结果表明模拟值与实测值之间的相关系数达0.74.积雪时空分布受温度、降水等因子影响明显.1998~2003年藏北积雪的减少与全球变暖有关，但降水的减少可能是导致近年来藏北积雪减少的更主要因素.

2008

0.0

2001

0.0

柏延臣, 冯学智, 李新, 等. 基于被动微波遥感的青藏高原雪深反演及其结果评价[J]. 遥感学报, 2001, 5(3): 161-165.

Bo Y

, Feng X

, Li

, et al. The retrieval of snow depth in Qinghai_Xizang(Tibet) Plateau from passive microwave remote sensing data and its results assessment[J]. Journal of Remote Sensing, 2001, 5(3): 161-165.

Snow cover extension and snow depth information may be useful indicators of regional and global climate change and of basin_scale water storage in mountainous areas as well as snow disaster monitoring, forecasting and the loss assessment in pastoral areas. Thus, it is important to ensure that they are accurate and as free as possible of any known biases.Though it is practical to get snow extension from the visible and infrared remotely sensed data such as NOAA_AVHRR, TM and the like, it is nearly impossible to get snow depth information from such data set. Passive microwave remotely sensed data such as SMMR and SSM/I make it possible to derive snow depth in a large_scale area. Some algorithms have been developed to retrieve the snow depth from SMMR and SSM/I data, but those algorithms didn't perform well in many studies.In this paper, using a revised Chang's algorithm and SSM/I 37GHz and 19GHz horizontally polarized brightness temperature, we retrieved the snow depth distribution in Qinghai_Xizang (Tibet) plateau, assessed the results' accuracy, and analyzed their error sources. It showed that the revised Chang's algorithm described the general trend of snow depth of this area, in spite of overestimation in whole and large errors in local areas. The errors are mainly from the effects of the existence of frozen ground, depth_hoar, vegetation cover and the lacking of considering the effects of the liquid water content of snow and the changing of shape and grain size of snow. The coarse spatial resolution of SSM/I remotely sensed data makes the retrieved snow depth hard to compare with the ones from ground observation, thus, makes it difficult to evaluate the accuracy. Some suggestions for further investigation were put forward at last.

采用修正的张氏雪深反演算法，用ＳＳＭ／Ｉ３７ＧＨz和19GHz水平极化亮温值计算了青藏高原及其毗邻地区的积雪深度，对其精度进行了评价，并对误差来源进行了分析，结果显示，此算法能够较好地反映研究区的雪深分布，但局部地区误差较大，总体上雪深被高估，其误差主要来源于冻土，深霜层，植被以及雪层中液态水含量，雪粒的形状和粒径的变化带来的影响，SSM／I数据较低的分辨率和研究区复杂的地形使反演的雪深与观测的雪深缺少可比性，给精度的评价带来影响。

... 经过很多学者的不断改进^[6,7,8],被动微波雪深反演数据的精度有所提高,已经能够较好地反映高原积雪的时空变化特征 ...

2004

0.0

车涛, 李新. 利用被动微波遥感数据反演我国积雪深度及其精度评价[J]. 遥感技术与应用, 2004, 19(5): 301-306.

Che

, Li

. Retrieval of snow depth in China by passive microwave remote sensing data and its accuracy assessment[J]. Remote Sensing Technology and Application, 2004, 19(5): 301-306.

Considering the overestimation of snow depth in West China using the global algorithm, thegeographic region is divided into two parts by 105°E. The modified and global algorithms are used toretrieve the snow depth in West and East part, respectively. However, the retrieval of snow cover anddepth will be overestimated due to the influence of precipitation, cold desert, and frozen ground.Therefore, for setting up the operational scheme, a land surface type classification tree based on the SSM/I data has been used to remove these factors. In final, the MODIS snow products in 2000/2001 wintershave been collected for the assessment of accuracy from passive microwave remote sensing data. In theninety days, the means of overall accuracy and Kappa coefficient are 86% and 66%, respectively.

(Cold and Arid Regions Environmental and Engineering Research Institute,Chinese Academy of Sceiences,Lanzhou Gansu730000,China)

考虑到我国西部地区使用SSM/I全球算法将高估积雪深度,故以东经105°为界将我国分为东部和西部。在西部地区采用修正后的雪深算法,东部地区沿用全球算法。对散射系数较高,容易和积雪相混淆的降雨、寒漠和冻土地表类型,通过积雪分类树进行剔除,进而发展了一套适用于全国积雪深度的业务化反演方案。最后利用MODIS积雪产品对冬季90天的结果进行了精度评价,总体精度平均达到86.4%,最高精度达到95.5%,Kappa系数均值为65.5%,最大值达到86.2%。

... 经过很多学者的不断改进^[6,7,8],被动微波雪深反演数据的精度有所提高,已经能够较好地反映高原积雪的时空变化特征 ...

2006

0.0

... 经过很多学者的不断改进^[6,7,8],被动微波雪深反演数据的精度有所提高,已经能够较好地反映高原积雪的时空变化特征 ...

2014

0.0

白淑英, 史建桥, 沈渭寿, 等. 近30年西藏雪深时空变化及其对气候变化的响应[J]. 国土资源遥感, 2014, 26(1): 144-151. doi: DOI: 106046/gtzyyg. 2014. 01. 25.

Bai S

, Shi J

, Shen W

, 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 and Resources, 2014, 26(1): 144-151. doi: DOI:10.6046/gtzyyg.2014.01.25.

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.

1. College of Remote Sensing, Nanjing University of Information Science and Technology, Nanjing 210044, China; 2. Environmental Protection Department of Nanjing Institute of Environmental Science, Nanjing 210042, China; 3. Unit 61, No.94783 of PLA, Changxing 313111, China

积雪深度是表征积雪特征的重要参数，也是气候变化区域响应敏感因素。利用1979—2010年逐日雪深被动微波遥感数据以及同期气象资料，对西藏雪深时空变化特征及其与气候因子的响应关系进行了分析。结果表明：32 a来，西藏雪深呈显著增加趋势，气候倾向率为0.26 cm/10 a；1999年以后，雪深则表现为下降趋势，气候倾向率为-0.35 cm/10 a。四季平均雪深中，春季雪深的变化对年平均贡献最大，二者相关系数高达0.88。高原雪深异常偏多年份主要在20世纪90年代，但并未发生气候突变。周期分析表明，西藏雪深存在准6~7 a振荡的显著周期。西藏雪深呈四周山地雪深大，中部腹地雪深小的空间格局，且受海拔影响有明显的陡坎效应，绝大部分地区雪深变化趋势倾向率在-0.08~0.08 cm/a，百分比达到74.6%；逐像元回归分析表明，雪深呈增加趋势的像元数占全区像元总数的76.9%，有减少趋势的仅占23.1%。西藏雪深与气温、降水、风速和日照时数存在明显的统计和空间相关性，整体表现为雪深与气温、风速、日照时数呈负相关，而与降水呈正相关。多元回归分析表明，春秋季雪深模拟值与实测值的相关系数均达0.6以上，通过了0.01的显著性检验；夏冬季雪深回归模型的复相关系数只有0.4~0.5，且未通过0.05显著性检验。

... 以往对高原积雪的影响因素研究主要从气候变化角度展开^[9,10,11],如降水和气温等,但气候因子对积雪变化的影响是作用在地形因子影响的基础上,随着海拔升高,气温逐渐降低,积雪融化变缓,从而影响雪深和雪盖的分布 ...

2005

0.0

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

, Huang R

, Chen

. The causes of the interannual variation of snow cover over the Tibetan Plateau[J]. Journal of Glaciology and Geocryology, 2005, 27(4): 491-497.

Analyzing the station snow, air temperature, precipitation and reanalyzed NECP/NCAR data demonstrates: The winter-spring snow in Tibetan Plateau has obvious negative correlation to the air temperature but positive correlation to the precipitation in the pre-winter (November and December) in Plateau according to its interannual variation. In the pre-winter of the rich (poor) winter-spring snow years in the plateau, the subtropical anticyclone is strong (weak), the polar vortex over Europe and Asia is strong (weak), and the trough over Europe is deep (shallow), the ridge over Ural is strong (weak), the trough line over East Asia inclines much (little) from northeast to southwest. Therefore, the warm and wet air from ocean flows easily (hard) to the plateau and the cold air from Siberia flows easily (hard) to the plateau, and the snowfall falls much (little) and the snow exists long (short).

1. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou Gansu 730000, China; 2. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

通过对高原冬春积雪异常年气温、降水和环流特征的分析,结果发现:从年际变化来讲,高原冬春积雪和冬春气温是明显的负相关,与降水呈正相关,高原冬春积雪的年际变化与前冬11月、12月高原降水的变化基本一致;1983年前,高原冬春积雪的偏多主要对应于高原冬春气温的偏低,积雪的偏少则主要对应于高原冬春降水的偏少;而自1984年后,高原冬春积雪的偏多主要对应于高原冬春降水的偏多,积雪的偏少则主要对应于高原冬春气温的偏高.多雪年前冬,副热带高压明显偏强,欧洲槽加深,乌山脊加强,东亚大槽从东北向西南明显倾斜,我国南海和阿拉伯海西岸各有一反气旋距平环流,而高原南部、印度半岛到孟加拉湾为一明显的气旋距平环流,有利于洋面暖湿气流抬升爬上高原;另一方面,从西伯利亚向我国出现北风距平,同时我国北方地区出现东风距平,这一形势使得西伯利亚冷空气多流向高原,冷暖空气在高原交汇,产生降雪.同时这种冷空气流保证了高原温度偏低,因而冬春高原多雪;少雪年前冬,副热带高压明显偏弱,欧洲槽变浅,乌山脊减弱,东亚大槽比较竖直,南海地区和阿拉伯海为气旋环流距平,而高原南侧为反气旋环流距平,西伯利亚为南风距平,形势基本与多雪年相反.

2012

0.0

杨倩, 陈圣波, 路鹏, 等. 2000—2010年吉林省积雪时空变化特征及其与气候的关系[J]. 遥感技术与应用, 2012, 27(3): 413-419.

Yang

, Chen S

, Lu

, et al. Spatio-temporal variation of snow cover in Jilin province and its relationships to climate change in recent 10 years[J]. Remote Sensing Technology and Application, 2012, 27(3): 413-419.

Based on MODIS/Terra Snow Cover Daily Data MOD10A1,the snow spatial-temporal information of Jilin province including snow cover duration (SCD),snow cover onset dates (SCOD) and snow cover melting date (SCMD),are extracted and the variation characteristics are also analyzed.Then,the relationship between snow cover and climate are analyzed.And the climate data covers the temperature and precipitation data of 23 base stations in recent 10 years.The result shows that snow cover duration spans from 30 to 90 days in most regions of Jilin;it is in the eastern area that snow cover lasts for a longer time,SCOD begins earlier and SCMD ends later.There is a negative relationship between SCD and temperature and a positive relationship between SCD and precipitation,with coefficients of -0.7407 and 0.6875 of temperature in snow season and precipitation in summer respectively;there is a negative relationship between SCMD and temperature and a positive relationship between SCMD and precipitation,with coefficients of -0.5414 and 0.4647;on the contrary,with coefficients of 0.7430 and 0.5479 respectively.In all,snow remote sensing information were more sensitive to temperature than to precipitation,as well as to snow season than to summer;The lower the temperature was and the more the precipitation was,the longer the duration of snow cover.Temperature is more essential for maintaining snow cover.

(College of Geo-exploration Science and Technology,Jilin University,Changchun 130026,China)

利用Terra卫星提供的2000年10月1日到2010年4月30日每日雪覆盖产品MOD10A1,提取研究区积雪覆盖指数SCI、积雪日数SCD、积雪初日SCOD及积雪终日SCMD遥感信息,结合同期吉林省界内23个地面气象观测站的同期气温和降水资料,分析该区积雪的变化特征与气温和降水的关系。结果表明:① 吉林省大部分地区积雪日数为30~90 d,东部山区积雪持续时间长、积雪初日日期早以及积雪终日日期晚,中西部地区变化情况相反;② 积雪覆盖指数SCI呈波浪式变化,与积雪季气温呈负相关;③ 积雪日数与气温呈反相关、与降水量呈正相关,与积雪季气温、夏季降水量的相关系数分别为-0.7407、0.6875;积雪初日情况相反,与积雪季气温、夏季平均气温为0.743、0.5479;积雪终日与气温呈反相关、与降水量呈正相关,与积雪季气温、夏季降水量为-0.5214、0.4647。积雪指数均对气温的变化更敏感,气温升高导致积雪初日推迟、积雪终日提前,从而使积雪日数减小;积雪季降水量的增加有利于积雪日数增大,而积雪终日的推迟有利于夏季降水量的增加。

1998

0.0

... 气候变暖导致的降水重新分配、冰川加速消融、积雪提前融化、土地荒漠化和草场退化^[12]等都会对积雪地区水资源数量与河川径流量季节分配的变化,以及周边与下游地区的气候变化产生重要影响 ...

2007

0.0

... Mann-Kendall检验^[13]不需要样本遵从一定的分布,不受少数异常值的干扰,计算较方便,常用于类型变量和顺序变量的趋势检验和突变分析^[14,15,16] ...

2008

0.0

徐宗学, 米艳娇, 李占玲, 等. 和田河流域气温与降水量长期变化趋势及其持续性分析[J]. 资源科学, 2008, 30(12): 1833-1838.

Xu Z

, Mi Y

, Li Z

, et al. Long-term trends and sustainability analysis of air temperature and prediction in the Hotan river basin[J]. Resources Science, 2008, 30(12): 1833-1838.

Long-term trends for the time series of air temperature and precipitation at two meteorological stations in the Hotan River basin were detected using the nonparametric Mann-Kendall technique. In order to estimate future trends of air temperature and precipitation, R/S method was used to estimate the Hurst index. The reliability of this estimation was tested by comparing H values obtained from original time series and from stochastically reset ones. The results showed that during the past half century, air temperature increased in the whole Hotan River basin, especially in autumn and winter. The separate test results for temperature at two stations also exhibited an increasing trend. All the H values estimated from original time series and from stochastically reset ones are greater than 0.5, which indicates that the air temperature in the Hotan River basin will continue to increase in the future. During the past 50 years, precipitation in the Hotan River basin showed a general increasing trend, with a decrease of precipitation in spring and a strong increase in summer. The trends for autumn and winter are not obvious. Two separate time series of seasonal precipitation in summer from the two stations showed a strong increasing trend, and the β values for annual average precipitation are positive, which indicates an increasing trend. H values for precipitation time series showed long-range dependence, which indicates that in the future, precipitation will continue to increase. In the process to estimate Hurst index using R/S method, the least squares method was used to estimate slope. There were some human factors in the estimation, which may result in subjective errors. R/S method is efficient for testing the long-range dependence of large samples, but errors may exist when relative small samples are used. In this study, a relatively short time series of less than 50 years was used, which may result in estimation errors. However, this result should be helpful for local water resources planning and management, and also should be significant for the rational allocation of water resources to different sectors including industrial, agricultural and urban users.

采用Mann-Kendall非参数检验方法对和田河流域两个主要气象站点月、年平均气温序列和逐月、年平均降水量序列进行趋势检验，在此基础上运用R/S分析方法，估算各时间序列的Hurst指数，预测该流域气温、降水量的未来变化趋势及其持续性，并采用随机重排法对估算结果进行了稳定性检验。分析结果表明：和田河流域在过去近50年内，气温变化趋势以增温为主，秋、冬季增温趋势尤为显著；降水量变化总体上呈增加趋势，夏季降雨量增加趋势较明显；未来的气温、降水量变化趋势表现为较强的长程依赖性，说明在未来一段时间内，其上升趋势还将持续一段时间。

... Mann-Kendall检验^[13]不需要样本遵从一定的分布,不受少数异常值的干扰,计算较方便,常用于类型变量和顺序变量的趋势检验和突变分析^[14,15,16] ...

2013

0.0

... Mann-Kendall检验^[13]不需要样本遵从一定的分布,不受少数异常值的干扰,计算较方便,常用于类型变量和顺序变量的趋势检验和突变分析^[14,15,16] ...

2009

0.0

康淑媛, 张勃, 柳景峰, 等. 基于Mann-Kendall法的张掖市降水量时空分布规律分析[J]. 资源科学, 2009, 31(3): 501-508.

Kang S

, Zhang

, Liu J

, et al. Analysis of the spatiotemporal distribution of precipitation in Zhangye City using Mann-Kendall method[J]. Resources Science, 2009, 31(3): 501-508.

Heihe River locates in the arid region of northwest China. In the last five decades, the climate in Heihe River Basin changed significantly. Zhangye City lies in the middle reaches of Heihe River with little rainfall and its average annual precipitation is 198mm. Precipitation is not only one of main sources of water resources , but also one of the main factors affecting the periodic change of water resources over this area. According to the precipitation data of six typical meteorological stations from 1956 to 2004, this paper analyzed the changing characteristics of precipitation in Zhangye City by using Mann-Kendall rank analysis method to finish examination of annual and seasonal precipitation trends in Zhangye City. The Mann-Kendall statistic test is a rank-based and non-parametric approach which has been widely used to estimate the significance of long-term trends in hydro-meteorological time series. The results showed that the change of precipitation fluctuated in the last 49 years. The minimum and maximum precipitation happened in 1962 and 1979, which were 122mm and 289mm respectively. The precipitation of the 1990s was higher than that in the 1960s. The change of precipitation synchronized with temperature change. In other words, if precipitation increases, the temperature would be higher, and if precipitation decreases, the temperature would become lower. The general tendency of annual runoff kept fluctuating while the mean annual precipitation tended to increase (β＝4.7mm/a), especially in autumn (β＝3.9mm/a). The precipitation in summer and spring showed a declining trend, however, the precipitation in summer was 51.9% of the annual precipitation. Spatial distribution of precipitation showed that there was a gradual decreasing trend from southeast to northwest, with the elevation increasing precipitation gradually increasing. Topographic differentiation has led to such kind of spatial distribution of precipitation. Seasonal rainfall varied in different regions, and there was also an obvious increasing center in Minle District. The precipitation of Gaotai District and Linze District in summer tended to decline and so did the precipitation of Ganzhou District in spring(β＝-5.1 mm/a). There was an obvious increasing tendency in the autumn precipitation of Shandan District(β＝4.2 mm/a), and so did the winter precipitation of Minle District(β＝0.65 mm/a). In the last part, some problems need solving in the future were briefly analyzed.

本文依据张掖市6县（区）6个气象观测站点近50年（1956年～2004年）来逐月的降水量观测资料，运用非参数检验方法（Mann-Kendall法），分析了张掖市降水量的时空分布规律。结果表明：①张掖市的降水量总体上呈现增加的趋势（β值为4.7mm/a），其中秋季降水量增加明显（β值为3.9mm/a），春夏季降水量呈现减少趋势，但降水量主要集中在夏季，占全年的59.1%；②降水量空间分布不均，呈现出由东南向西北逐渐递减的趋势，而且随着海拔的升高降水量逐渐增大；③春季和夏季都形成以民乐县为中心的降水量高值区；春季只有高台和临泽县降水量呈下降趋势，夏季甘州区降水量下降趋势最明显（β值为-5.1 mm/a），秋季降水量增幅最大的地区位于山丹县（β值为4.2 mm/a），冬季降水量民乐县增加趋势最明显（β值为0.65 mm/a）

... Mann-Kendall检验^[13]不需要样本遵从一定的分布,不受少数异常值的干扰,计算较方便,常用于类型变量和顺序变量的趋势检验和突变分析^[14,15,16] ...

2007

0.0

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

, He

, Hou S

. 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.

A study is presented of the spatial distribution,seasonal and inter-annual variability of snow cover over the Tibetan Plateau from 2000 to 2005,and their relation to corresponding temperature and precipitation.The data used consist of MODIS 8-day snow cover extent chart and the daily temperature and precipitation data from meteorological stations.The distribution of the snow cover over the Tibetan Plateau is far from uniformity.In the peripheral high mountains snow cover distributes extensively and has a long duration,but in the vast interior it is rare or even thin.The duration of snow cover in the Tibetan Plateau is from October to May of the next year.Inter-annual change of snow cover is evident during 2000-2005.The Plateau snow cover extent slowly increases in summer and autumn,but decreases in winter and spring.Temperature and precipitation are the main factors affecting the change of snow cover in the Tibetan Plateau.In winter,precipitation has more significant effect on snow cover extent than temperature does.But in spring,temperature has more important impact on snow cover.

1. State Key Laboratory of Cryospheric Science, CAREERI, CAS, Lanzhou Gansu 730000, China; 2. National Climate Center, Beijing 100081, China

利用MODIS卫星反演的积雪资料以及同期气象资料,分析了2000-2005年青藏高原积雪分布特征、年际变化及其与同期气温和降水的关系,结果表明:青藏高原积雪分布极不均匀,四周山区多雪,腹地少雪;高原积雪期主要集中在10月到翌年5月;2000-2005年高原积雪年际变化差异较大,积雪面积总体上呈现冬春季减少、夏秋季增加的趋势;气温和降水是影响高原积雪变化的基本因子.冬季,高原积雪面积变化对降水更为敏感;春季,气温是影响高原积雪面积变化更主要的因素.

... 受高原地形阻挡作用,空中西风带冷槽分支沿高原西侧南下,高原西部处于西风带上升区,降水较多^[17] ...