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.

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.

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.

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.

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.

基于MOD10A1 V6产品下青海省各片区积雪的分布气候特征

利用MOD10A1 V6版积雪产品获取青海省及省内各重要生态功能区的积雪信息,并对积雪变化与气候因子间的响应关系进行分析。结果表明:(1)16年来青海省内积雪覆盖面积变化存在空间差异性,可可西里积雪覆盖面积显著增加(p<0.05)而其他生态功能区变化不显著,从阶段变化来看青海省及各生态功能区在2016积雪季后积雪覆盖面积大幅增加,而在2016积雪季前仅柴达木盆地积雪覆盖面积显著减少(p<0.05),其余地区减少趋势不显著。(2)16年来青海省内积雪日数变化存在空间差异性,可可西里内26.8%的面积积雪日数显著增加(p<0.05),而三江源腹部、柴达木盆地东部和祁连山地区西部积雪日数显著减少(p<0.05)。(3)近16年来青海省及各生态功能区积雪覆盖面积与降水显著正相关(r>0.65,p<0.01),与气温显著负相关(r>0.5,p<0.05),其中在青海省、祁连山、可可西里和柴达木盆地的积雪覆盖面积变化主要受降水主导,而三江源和青海湖流域则是由气温和降水共同影响。

Distribution assessment of soil erosion with revised RUSLE model in Tianshan Mountains

aSoil degradation caused by soil erosion is one of the world's most critical environmental issues.Soil erosion in the Tianshan Mountains has caused various environmental problems in the surrounding areas.This study used remote sensing data to analyze the distribution of the factors influencing soil erosion,and the revised universal soil loss equation(RUSLE)to calculate the total amount and distribution characteristics of soil erosion in the Tianshan Mountains in 2019.Due to the large error of RUSLE in soil erosion estimation in mountainous areas,this study modified RUSLE equation based on the characteristics of snow cover in the Tianshan Mountains.The results show that the average soil erosion was 1690.3 t/(km^(2)·year),of which insignificant erosion,slight erosion and moderate erosion accounted for 42,8%,22.4%and 9.9%,respectively.Severe erosion and above accounted for 13.3%.The accuracy of the soil erosion modulus calculated by the RUSLE was only 61.9%,with an average error of 1631.9 t/(km^(2)·year).The average error of the double-coefficient correction method was 1259.1 t/(km^(2)·year),and the average error of the modified formula method was reduced by 40.3%compared with the RUSLE,reaching 973.7 t/(km^(2)·year),and its accuracy reached 76.2%.Very severe erosion and catastrophic erosion are distributed on mountain ridges with higher elevation and on the northern area with higher precipitation.Snow cover has a certain inhibitory effect on soil erosion,and snow cover in alpine mountains is a factor that cannot be ignored in soil erosion research.

昆仑山北坡区域积雪时空变化特征

积雪是水循环的重要组成部分,积雪的积累消融对下游社会经济发展具有重要影响。采用趋势分析、相关性分析等方法分析2000—2020年昆仑山北坡积雪面积、积雪日数(SCD)在空间和时间上的分布特征和变化趋势,并分析积雪的海拔效应及气候对积雪的影响。结果表明:(1)2000—2020年昆仑山北坡积雪面积呈减少趋势(-152.4 km^(2)·a^(-1)),2010年以前积雪面积距平偏正,2010年后偏负。(2)月尺度上,积雪覆盖率(SCF)从8月到翌年7月呈先增后减的趋势,冬、春季高于夏、秋季。(3)SCD分布具有明显的空间异质性,中部地区SCD的变化影响该地的年总SCD。(4)2000—2020年的年均SCD随海拔升高而增加,海拔>6.0 km为331.6 d,说明高海拔地区存在常年性积雪。(5)气温与积雪面积呈显著负相关(相关系数为-0.68,P<0.01),降水与积雪面积呈不显著正相关(相关系数为0.14,P>0.05),气温对积雪的影响高于降水。

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．

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.

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.

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.

不同积雪深度与面积对积雪覆盖遥感反演的影响

针对利用多源遥感数据进行积雪覆盖监测的需要,在分析不同深度与面积的积雪实测光谱基础上,探讨了在利用NDSI方法进行积雪覆盖遥感监测时,不同积雪深度对其的影响,并以HJ-1B卫星以及MODIS卫星为例,研究了不同积雪面积对遥感积雪反演的影响。结果表明,积雪深度几乎对遥感积雪覆盖监测的结果没有影响,而在积雪面积方面,受所使用的遥感影像空间分辨率的制约,对遥感积雪监测的结果有一定影响。

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.

2001—2008年天山西部山区积雪覆盖及NDVI的时空变化特性

对于以融雪及融冰补给为主的山区河流,融雪及融冰量的多少对当地可供利用水资源量的大小起着决定作用,对河流所在的水库的正常蓄水、防洪及发电产生一定的影响.积雪时空变化规律的影响因素较多,除气温这个主要因素外,还与当地植被覆盖情况、风向、风速及太阳辐射等因素有关,因此,基于2001—2008年的MODIS积雪数据和NDVI数据分析了研究区的积雪覆盖度与NDVI时空变化特性.结果表明:天山西部山区积雪分布极不均匀,边缘山区多雪,腹地少雪,边缘山区南坡比北坡积雪多;积雪期主要集中在10月到翌年5月,积雪年际变化差异较大,积雪有减少趋势.近8a来研究区的植被有较好的改善且与降水有一定的联系,但部分区域NDVI也有减小的趋势,不同区域植被返青时间不同.通过对比分析发现,除积雪消融与NDVI有其自有的变化规律外,二者之间也有很好的相关性,但关于植被覆盖是否会对积雪的消融起加速或减缓的作用,基于此两种MODIS数据产品无法得知,有待于通过其它方法或进行野外实验确定.

近10年石羊河流域水资源与生态环境遥感监测

在气候变暖的大背景下,石羊河生态环境发生了很大变化。针对流域生态环境的定量监测策略及关键技术,提出石羊河流域尺度的植被覆盖、水库面积、积雪面积等要素因子的快速监测方法,并具体通过实际监测水库蓄水前后面积、植被覆盖时空变化、积雪分布特征,分析石羊河流域生态环境。研究发现:1997-2006年,整个祁连山区域冰川积雪面积呈多波形变化,有线性增加趋势。其中,祁连山东段(石羊河流域上游)和中段积雪面积呈减少趋势,西段呈增加趋势。石羊河流域植被总面积整体上呈持续减少趋势,其中稀疏植被和茂密植被退化最严重,适中植被面积变化不大。红崖山水库面积基本呈增加趋势,最大值出现在2005年,是19.35km2;最小值出现在2001年,为7.4km2。

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.

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。