Temporal and Spatial Variation Characteristics of Snow Cover Area in the Pamirs from 2010 to 2020

Scientific and comprehensive monitoring of snow cover changes in the Pamirs is of great significance to the prevention of snow disasters around the Pamirs and the full utilization of water resources. Utilize the 2010-2020 snow cover product MOD10A2, Synthesis by maximum, The temporal and spatial variation characteristics of snow cover area in the Pamirs in the past 11 years have been obtained. Research indicates: In terms of interannual changes, the snow cover area of the Pamir Plateau from 2010 to 2020 generally showed a slight decrease trend. The average snow cover area in 2012 was the largest, reaching 54.167% of the total area. In 2014, the average snow cover area was the smallest, accounting for only 44.863% of the total area. In terms of annual changes, there are obvious changes with the change of seasons. The largest snow area is in March, and the smallest snow area is in August. In the past 11 years, the average snow cover area in spring and summer showed a slow decreasing trend, and there was almost no change in autumn and winter. In terms of space, the snow cover area of the Pamirs is significantly affected by altitude, and the high snow cover areas are mainly distributed in the Karakoram Mountains and other areas with an altitude greater than 5000 meters.

A decline in snow cover area of Passu and Ghulkin valleys between 1995 and 2022

The global cryosphere is experiencing accelerated melting due to climate change.Currently,the Karakoram anomaly is under discussion with a debate about the possibility that the anomaly may have recently ended.This study aims to evaluate the up-to-date changes in snow cover in the western Karakoram region.We observed the snow cover changes in Passu and Ghulkin valleys in the Hunza River basin(HRB)of the Karakoram through multitemporal Landsat satellite data between 1995 and 2022.We found a significant reduction in snow cover in these valleys,with an average reduction rate of 0.42 km~2/yr,resulting in a total reduction of~11.46 km~2 between 1995 and 2022.This reduction in snow cover is consistent with the mass loss of glaciers in the Karakoram region in recent years.The decline in snow cover in these valleys is also consistent with the meteorological data.The temperature in summer(June)has significantly increased whereas the precipitation in the accumulation season(March)has decreased.These rapid changes suggest that it is crucially important to monitor the snow cover on a regular basis to support downstream management of snowmelt runoff.In addition,there is a need of planning for mitigation and adaptation strategies for snow-related hazards.

Using Landsat images to monitor changes in the snow-covered area of selected glaciers in northern Pakistan

Landsat satellite images were used to map and monitor the snow-covered areas of four glaciers with different aspects(Passu: 36.473°N, 74.766°E;Momhil: 36.394°N, 75.085°E; Trivor: 36.249°N,74.968°E; and Kunyang: 36.083°N, 75.288°E) in the upper Indus basin, northern Pakistan, from 1990-2014. The snow-covered areas of the selected glaciers were identified and classified using supervised and rule-based image analysis techniques in three different seasons. Accuracy assessment of the classified images indicated that the supervised classification technique performed slightly better than the rule-based technique. Snow-covered areas on the selected glaciers were generally reduced during the study period but at different rates. Glaciers reached maximum areal snow coverage in winter and premonsoon seasons and minimum areal snow coverage in monsoon seasons, with the lowest snow-covered area occurring in August and September. The snowcovered area on Passu glacier decreased by 24.50%,3.15% and 11.25% in the pre-monsoon, monsoon and post-monsoon seasons, respectively. Similarly, the other three glaciers showed notable decreases in snow-covered area during the pre-and post-monsoon seasons; however, no clear changes were observed during monsoon seasons. During pre-monsoon seasons, the eastward-facing glacier lost comparatively more snow-covered area than the westward-facing glacier. The average seasonal glacier surface temperature calculated from the Landsat thermal band showed negative correlations of-0.67,-0.89,-0.75 and-0.77 with the average seasonal snowcovered areas of the Passu, Momhil, Trivor and Kunyang glaciers, respectively, during pre-monsoon seasons. Similarly, the air temperature collected from a nearby meteorological station showed an increasing trend, indicating that the snow-covered area reduction in the region was largely due to climate warming.

Support Vector Machines for Cloud Detection over Ice-Snow Areas

In polar regions, cloud and underlying ice-snow areas are difficult to distinguish in satellite images because of their high albedo in the visible band and low surface temperature of ice-snow areas in the infrared band. A cloud detection method over ice-snow covered areas in Antarctica is presented. On account of different texture features of cloud and ice-snow areas, five texture features are extracted based on GLCM. Nonlinear SVM is then used to obtain the optimal classification hyperplane from training data. The experiment results indicate that this algorithm performs well in cloud detection in Antarctica, especially for thin cirrus detection. Furthermore, when images are resampled to a quarter or 1/16 of the full size, cloud percentages are still at the same level, while the processing time decreases exponentially.

SNOW COVER MONITORING BY REMOTE SENSING AND SNOWMELT RUNOFF CALCULATION IN THE UPPER HUANGHE RIVER BASIN

The upper Huanghe(Yellow) River basin is situated in the northeast of the Qinghai Xizang(Tibet)Plateau of China. The melt water from the snow cover is main water supply for the rivers in the region during springtime and other arid regions of the northwestern China, and the hydrological conditions of the rivers are directly controlled by the snowmelt water in spring. So snowmelt runoff forecast has importance for hydropower, flood prevention and water resources utilization. The application of remote sensing and Geographic Information System (GIS) techniques in snow cover monitoring and snowmelt runoff calculation in the upper Huanghe River basin are introduced amply in this paper. The key parameter-snow cover area can be computed by satellite images from multi platform, multi temporal and multi spectral. A cluster of snow cover data can be yielded by means of the classification filter method. Meanwhile GIS will provide relevant information for obtaining the parameters and also for zoning. According to the typical samples extracting snow covered mountainous region, the snowmelt runoff calculation models in the upper Huanghe River basin are presented and they are mentioned in detail also. The runoff snowmelt models based on the snow cover data from NOAA images and observation data of runoff, precipitation and air temperature have been satisfactorily used for predicting the inflow to the Longyangxia Reservoir , which is located at lower end of snow cover region and is one of the largest reservoirs on the upper Huanghe River, during late March to early June. The result shows that remote sensing techniques combined with the ground meteorological and hydrological observation is of great potential in snowmelt runoff forecasting for a large river basin. With the development of remote sensing technique and the progress of the interpretation method, the forecast accuracy of snowmelt runoff will be improved in the near future. Large scale extent and few stations are two objective reality situations in China, so they should be considered in simulation and forecast. Apart from dividing, the derivation of snow cover area from satellite images would decide the results of calculating runoff. Field investigation for selection of the learning samples of different snow patterns is basis for the classification.

Application of snowmelt runoff model(SRM) in mountainous watersheds:A review

The snowmelt runoff model （SRM） has been widely used in simulation and forecast of streamflow in snow-dominated mountainous basins around the world. This paper presents an overall review of worldwide applications of SRM in mountainous watersheds, particularly jn data-sparse watersheds of northwestern China. Issues related to proper selection of input climate variables and parameters, and determination of the snow cover area （SCA）using remote sensing data in snowmelt runoff modeling are discussed through extensive review of literature. Preliminary applications of SRM in northwestern China have shown that the model accuracies are relatively acceptable although most of the watersheds lack measured hydro-meteorological data. Future research could explore the feasibility of modeling snowmelt runoff in data-sparse mountainous watersheds in northwestern China by utilizing snow and glacier cover remote sensing data, geographic information system （GIS） tools, field measurements, and innovative ways of model parameterization.

Application of Snow Melt Runoff Model in a Mountainous Basin of Iran

Simulation and modeling the stream flow provide major data while it is a challenge in mountainous basins with regard to the important role of snowmelt runoff as well as the data scarcity in these places. The main purpose of this paper is to examine the capability of an integrated application of remote sensing data and Snowmelt Runoff Model (SRM) to simulate scheme of daily stream flow in the snow-dominated catchment, located in the North-East region of Iran. The main parameters of the model are Snow Cover Area (SCA), temperature and participation. Regarding to the lack of measured data, the input variable and parameters of the model are extracted or estimated based on accessible maps, satellite data and available meteorological and hydrological stations. The changes of snow-cover, as spatial-temporal data, which are the most effective variable in performance of SRM, are obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) eight-day composite snow cover images. The evaluation of the model application efficiency was tested by the coefficient of determination and the volume difference, which are 0.85% and -4.6% respectively. The result depicts the relative capability of SRM though it is evident that the more accurate the estimation of model parameters, the more efficient simulation results can be obtained.

Integration of aspect and slope in snowmelt runoff modeling in a mountain watershed

This study assessed the performances of the traditional temperature-index snowmelt runoff model(SRM) and an SRM model with a finer zonation based on aspect and slope(SRM + AS model) in a data-scarce mountain watershed in the Urumqi River Basin,in Northwest China.The proposed SRM + AS model was used to estimate the melt rate with the degree-day factor(DDF) through the division of watershed elevation zones based on aspect and slope.The simulation results of the SRM + AS model were compared with those of the traditional SRM model to identify the improvements of the SRM + AS model's performance with consideration of topographic features of the watershed.The results show that the performance of the SRM + AS model has improved slightly compared to that of the SRM model.The coefficients of determination increased from 0.73,0.69,and 0.79 with the SRM model to 0.76,0.76,and 0.81 with the SRM + AS model during the simulation and validation periods in 2005,2006,and 2007,respectively.The proposed SRM + AS model that considers aspect and slope can improve the accuracy of snowmelt runoff simulation compared to the traditional SRM model in mountain watersheds in arid regions by proper parameterization,careful input data selection,and data preparation.

基于多源数据的三江源区雪灾风险评估

三江源区是青海省雪灾最为严重的地区,对畜牧业影响较大。利用1961—2020年逐日积雪深度气象资料分析了三江源区冬季雪灾时空分布特征,运用MOD10A1积雪遥感数据提取积雪覆盖天数,选择最低气温、雪灾强度及积雪覆盖天数作为致灾因子,高程和坡向作为孕灾环境,地均人口、牲畜存栏及土地利用类型作为承灾体,构建雪灾风险评估模型,并评估三江源区冬季雪灾风险。结果表明:三江源区冬季雪灾呈增加趋势,称多地区最为严重,进入21世纪后雪灾极端性明显加剧;称多、玛多等地因最低气温低、雪灾强度大及积雪覆盖天数多导致致灾因子危险性最高,孕灾环境敏感性自东向西增强,人口及牲畜存栏均在东部地区较多,而草地在中、东部地区广泛分布,因此三江源区以中等承灾体脆弱区为主;基于所选因子建立三江源区冬季雪灾风险评估模型,并进行雪灾风险评估,三江源区中部称多、达日、甘德、同德、泽库及玛沁西部为雪灾风险较高的地区。

2003-2010年青藏高原积雪及雪水当量的时空变化

利用MODIS逐日无云积雪产品与AMSR-E雪水当量产品进行融合,获取了青藏高原500 m分辨率的高精度雪水当量产品,通过研究青藏高原积雪时空动态变化特征,分析了积雪覆盖日数、雪水当量以及总雪量的季节及年际变化.结果表明:青藏高原地区降雪主要集中在高海拔山区,而高原腹地降雪较少,降雪在空间上分布极为不均;2003-2010年期间,平均积雪日数呈显著减少趋势,稳定积雪区面积在逐渐扩大,常年积雪区面积在不断缩小.与积雪日数时空变化相比,雪水当量增加的区域与积雪日数增加的区域基本一致,但喜马拉雅山脉在积雪日数减少的情况下雪水当量却在逐年增加,表明该地区温度升高虽然导致部分常年积雪向季节性积雪过渡,但降雪量却在增加.总的积雪面积年际变化呈波动下降的趋势,但趋势不显著,且减少的比例很少.最大积雪面积呈现波动上升后下降的趋势,平均累积积雪总量呈明显的波动下降趋势,年递减率为1.0×103m3·a-1.

基于多源数据的西藏地区积雪变化趋势分析

利用1980—2009年气象台站的观测数据、北半球NOAA周积雪产品和2001—2010年500m分辨率的EOS/MODIS积雪产品等多源资料,从不同角度对近30a来西藏区域积雪变化趋势进行了分析.结果表明:不同资料分析均显示,近30a来西藏地区积雪不断减少,尤其以近些年较为明显.近30a积雪日数、最大积雪深度总体上呈现下降趋势,尤其是进入21世纪以来,下降趋势非常明显.从秋冬春季节的积雪变化趋势来看,冬、春两季的积雪在减少,而秋季在增多,这些变化趋势都与各季节的气温和降水密切相关.NOAA资料显示,近30a来西藏地区的积雪覆盖面积正在逐步减少;季节变化略有不同,春、秋两季略呈上升趋势,冬、夏两季在减少,且夏季减少趋势较明显.MODIS资料分析表明,近10a来西藏地区的积雪总体呈下降趋势,尤其是2007年下半年开始下降明显.秋季的积雪在增加,冬、春、夏三季的积雪趋于减少,且春季的下降趋势最明显,其次为冬季,夏季的减少幅度最小.不同海拔的积雪都有减少趋势,最明显的是海拔4 000～5 000m的积雪,其次是海拔5 000～6 000m段.按地理区域分析,近10a来西藏东、西、中3个区域的积雪都呈减少趋势,其中西部的下降趋势最明显,其次为中部,东部相对较稳定.

45a来塔里木河流域气温、降水变化及其对积雪面积的影响

对塔里木河流域19个台站45a（1958—2002年）的气温、降水序列进行非参数检验，查明其变化趋势及特征，在此基础上，对近20a（1982—2001年）流域的积雪面积（SCA，％）变化进行趋势与相关分析．结果表明：流域的气温和降水均在20世纪80年代中期发生了阶段式的跳跃增长，气温和降水增加的主要季节分别为冬季和夏季．流域总体的积雪面积呈缓慢增加态势，其中北区和西区增加较为稳定，而南区相对不稳定．在垂直方向上，海拔〈2500m的区域积雪面积表现缓慢增加，而海拔≥2500m的区域则减少．相比较，低海拔区域更易受降水影响，而高海拔区域更易受气温影响．海拔2500～5000m的高度带是对气候变化较为敏感的区域．20世纪90年代与80年代相比，降雪和融雪的速度都更快．积雪与冷季降水呈正相关，但与冷季气温没有明显的相关关系．

基于MODIS数据的新疆叶尔羌河流域山区积雪特征分析

科学监测新疆叶尔羌河流域山区积雪面积及其变化特征对该区域的气候研究、雪水资源开发利用、环境灾害预报和生态环境保护等方面有重要意义.利用2000-2012年近13 a的MOD10A2积雪产品提取研究区域内积雪,结合DEM数据分析研究区内积雪面积的动态变化特征.结果显示:新疆叶尔羌河流域山区的积雪面积的年际变化幅度较大,其中,2005年和2009年积雪面积较大,2007年则为典型少雪年;年内变化差异显著,总体上呈现"M"型的特点,12月和3月处于高位,2月和8月处于低谷.叶尔羌河流域山区积雪覆盖率随着海拔的上升逐渐增大,稳定积雪主要分布在海拔5 000 m以上的地区;不同坡向的积雪覆盖率差异比较明显,西北坡、东坡、东北坡的积雪覆盖率比北坡、东南坡、西坡、南坡的积雪覆盖率高,西北坡高达52.8%,南坡仅为20%.叶尔羌河流域山区的积雪面积与气温呈负相关,与降水量呈正相关,积雪面积变化对气温因素更为敏感.

2002-2011年新疆积雪时空分布特征研究

基于2002-2011年的MODIS积雪产品数据, 对新疆积雪的年际变化特征、年内变化特征及空间分布特征进行了分析.结果表明： 年内积雪从10月中旬开始建立, 于1月面积达到最大, 7月面积达到最小.其中, 冬季积雪面积所占比例最大, 夏季最小. 2002-2011年新疆积雪面积总体上呈减少趋势. 其中, 春季和冬季为减少趋势;夏季的积雪由于其基本上都是高海拔的永久性积雪, 故比较稳定, 变化趋势不明显;秋季为上升趋势.新疆积雪空间分布极不均匀, 北疆积雪分布明显多于南疆.山区为积雪覆盖频次的高值区, 盆地为积雪覆盖频次的低值区.永久性积雪在阿尔泰山脉分布较少, 主要分布在天山山脉和昆仑山脉.就永久性积雪面积而言, 分布在海拔5 000~6 000 m的面积最大, 其次是海拔4 000~5 000 m, 再次是海拔6 000~7 000 m.

祁连山区1997—2004年积雪面积和雪线高度变化分析

利用1997-2004年5～8月的NOAA-AVHRR和EOS-MODIS卫星资料、周遍气象台站气象数据、人工增雨雪等相关资料,对河西内陆河流域上游的祁连山区积雪、冰川的光谱特征进行了判识,并分析了积雪面积和雪线高度变化.结果表明:6～8月祁连山西、中、东部的积雪面积都呈下降趋势,5月积雪面积有所升高;雪线高度处的气温在5月为降低趋势,6月和8月略有升高,7月升高最快;5～8月随时间的变化,祁连山区累计降水量都呈现出不同程度的增加;祁连山西、中部积雪面积和雪线高度随降水和气温的变化有明显的响应,并且中部较西部明显.人工增雪作业对祁连山雪消融具有缓冲作用.

基于SSM/I和MODIS数据的天山山区积雪深度时空特征分析

以天山山区为研究区,利用MODIS 8d最大积雪合成数据MOD10A2,分析天山山区积雪的时间变化和空间变化情况以及不同高程带的积雪覆盖率的变化情况;结合SSM/I亮温数据和站点观测数据建立的雪深反演模型并反演研究区的雪深,根据研究区的地势起伏情况,提取特殊地形进行分析其雪深变化情况,进一步分析整个天山山区的积雪深度的时空特征,并对结果进行验证,并且对不同高程带的积雪深度进行分析.研究结果表明：1）天山山区积雪面积分布的趋势表现为自西向东、自北向南减少,总体是呈波动中减少的趋势,到了2012年天山山区年最大积雪面积为37.69×104km2.2）积雪覆盖率与高程呈正比,在高山区可达70%以上.积雪深度分布呈自西向东、由北向南减少,深度最大的是在天山北部的博格达峰、河源峰附近,可以达到80 cm以上,最小在哈密地区的托木尔提峰附近积雪深度仅在10 cm左右.积雪深度与海拔呈正相关,最大雪深出现在4 500 m以上的高山区.3）对雪深反演结果的精度评价表明,模型在10～30 cm雪深范围内,反演平均误差为-2.47 cm;在雪深〈10 cm或〉30 cm的局部地区存在较大偏差.

我国东北及邻近地区年平均气温异常及其对北半球气候变暖和欧亚雪盖面积的响应

根据我国东北及邻近地区201个常规气象台站的年平均气温资料,利用EOF和分段线性拟合等方法分析了该地区气温异常的年代际特征及其对北半球气温和欧亚雪盖面积的响应.结果表明:我国东北及邻近地区年平均气温异常主要有全区一致型和南北反向型两种.近50 a来气温总体呈单调上升趋势,尤其是1990年代后期增温趋势更加明显,时间上东部早于西部.结合降水,1980年代起东北北部由冷干向暖湿转变,而华北区则由暖湿向暖干转变.东北北部升温单调、剧烈、显著,南部在1968和1985年曾两次发生突变,经历过1970—1980年代初的低温后增温趋势更加剧烈,其增温率几乎是第一次突变前的两倍.分析认为,我国东北及邻近地区大范围整体变暖与北半球平均气温的升高相一致;东北部气温的年际变化还受到欧亚雪盖面积的影响.

青海湖流域径流变化趋势及归因分析

针对青海湖流域近年来径流量增加的现状,根据流域内1976~2016年水文、遥感观测数据,采用滑动平均法、Mann-Kendall趋势检验及突变检验法、距平累积法和双累积曲线法研究了流域气温、降水、径流及积雪等的时空特性。结果表明,青海湖流域气温总体变化呈上升趋势,并在1998年前后发生突变;1976~2016年气温上升率为0.06℃/a;流域内降水量呈缓慢上升,2014年后降水量显著增加。布哈河流域近5年冰雪覆盖面积缩小较迅速且入湖径流量上升趋势显著;通过径流影响因素分析,2005~2015年冻土及冰雪融化对径流影响占主导地位,达75%,降水量占25%。这说明青海湖流域正向暖湿化发展,近年来青海湖湖泊的扩张主要是由降水和径流增加所致。

SRM融雪径流模型在黑河流域上游的模拟研究

以黑河流域上游作为典型干旱区流域,探讨了SRM(Snowmelt Runoff Model)融雪径流模型的可操作性.在论述模型结构、参数意义的基础上,重点研究以MODIS雪产品为基础的积雪面积衰减曲线的获取方法,细化了退水系数的获取,并分析了其中存在的问题.通过利用WinSRM1.10版本对黑河上游2004年融雪期进行径流量模拟,结果表明:SRM模型在以融雪水为主要补给的黑河流域上游有着较好的径流量模拟精度,拟合优度确定系数R2为0.020,体积差Dv为7.124%.在实际应用中应最大限度利用已有的气象水文资料,以更为准确地描述融雪径流过程.针对数据稀缺的问题,需要加强小流域的观测,改进SRM模型以更适用于以融雪为主的我国西北山区流域.

从AVHRR到MODIS的雪盖制图研究进展

雪盖面积是水文、气候模型中重要的输入参数之一。使用遥感方法能够有效获取大范围的雪盖信息,弥补地面观测资料在空间上的不足。阐述积雪遥感基本原理以及积雪遥感监测的研究进展,介绍现阶段应用较广的基于AVHRR和MODIS数据的雪盖制图算法,并通过对制图算法的比较和分析得出,MODIS雪盖制图算法的精度和效率都比AVHRR算法高。最后讨论现阶段雪盖制图算法误差的主要来源:大气状况、地表覆盖、积雪物理特性的复杂性以及使用相同的检测阈值等,并提出解决方法。