An Empirical Formula to Compute Snow Cover Fraction in GCMs

There exists great uncertainty in parameterizing snow cover fraction in most general circulation models (GCMs) using various empirical formulae, which has great influence on the performance of GCMs. This work reviews the commonly used relationships between region-averaged snow depth (or snow water equivalent) and snow cover extent (or fraction) and suggests a new empirical formula to compute snow cover fraction, which only depends on the domain-averaged snow depth, for GCMs with different horizontal resolution. The new empirical formula is deduced based on the 10-yr (1978-1987) 0.5°× 0.5° weekly snow depth data of the scanning multichannel microwave radiometer (SMMR) driven from the Nimbus-7 Satellite. Its validation to estimate snow cover for various GCM resolutions was tested using the climatology of NOAA satellite-observed snow cover.

Evaluation of Snow Depth and Snow Cover Fraction Simulated by Two Versions of the Flexible Global Ocean–Atmosphere–Land System Model

Based on historical runs,one of the core experiments of the fifth phase of the Coupled Model Intercomparison Project （CMIP5）,the snow depth （SD） and snow cover fraction （SCF） simulated by two versions of the Flexible Global OceanAtmosphere-Land System （FGOALS） model,Grid-point Version 2 （g2） and Spectral Version 2 （s2）,were validated against observational data.The results revealed that the spatial pattern of SD and SCF over the Northern Hemisphere （NH） are simulated well by both models,except over the Tibetan Plateau,with the average spatial correlation coefficient over all months being around 0.7 and 0.8 for SD and SCF,respectively.Although the onset of snow accumulation is captured wellby the two models in terms of the annual cycle of SD and SCF,g2 overestimates SD/SCF over most mid-and high-latitude areas of the NH.Analysis showed that g2 produces lower temperatures than s2 because it considers the indirect effects of aerosols in its atmospheric component,which is the primary driver for the SD/SCF difference between the two models.In addition,both models simulate the significant decreasing trend of SCF well over （30°-70°N） in winter during the period 1971-94.However,as g2 has a weak response to an increase in the concentration of CO2 and lower climate sensitivity,it presents weaker interannual variation compared to s2.

Improved Assimilation of Fengyun-3 Satellite-Based Snow Cover Fraction in Northeastern China

Assimilation of snow cover is an important method to improve the accuracy of snow simulation. However, the effects of snow assimilation are poor because satellite observed snow cover data contain erroneous information, such as cloud contamination. In this paper, an improved approach is proposed to reduce the effects of observational errors during assimilation of snow cover fraction acquired by the Fengyun-3(FY-3) satellite in northeastern China. A snow depth constraint was imposed on quality control of a snow depth product from a microwave radiation imager. The assimilation experiments were carried out before and after quality control(denoted as SCFDA and SCFDA_WSD, respectively). The snow cover fraction results were evaluated against the Moderate Resolution Imaging Spectroradiometer(MODIS) snow cover products. When assimilating the snow cover fraction with the snow depth constraint(i.e., SCFDA_WSD), substantially larger improvement was obtained than that without such a constraint/quality control(SCFDA), and the deviation and root mean square error of the snow cover fraction were significantly reduced.The assimilation performance was also evaluated against in-situ snow depth observations. The SCFDA_WSD also showed greater improvements during the snow accumulation and snowmelt periods than the SCFDA. The SCFDA_WSD improvements in woodland and shrubland were the most obvious. At different altitudes, the effects of the SCFDA_WSD were basically equivalent, and the deeper the snow depth was, the better the effect. In addition, the SCFDA_WSD method was found in close agreement with the observations during a sudden snowfall event.

Spatiotemporal variation in snow cover and its effects on grassland phenology on the Mongolian Plateau

Snow cover is an important water source for vegetation growth in arid and semi-arid areas,and grassland phenology provides valuable information on the response of terrestrial ecosystems to climate change.The Mongolian Plateau features both abundant snow cover resources and typical grassland ecosystems.In recent years,with the intensification of global climate change,the snow cover on the Mongolian Plateau has changed correspondingly,with resulting effects on vegetation growth.In this study,using MOD10A1 snow cover data and MOD13A1 Normalized Difference Vegetation Index(NDVI)data combined with remote sensing(RS)and geographic information system(GIS)techniques,we analyzed the spatiotemporal changes in snow cover and grassland phenology on the Mongolian Plateau from 2001 to 2018.The correlation analysis and grey relation analysis were used to determine the influence of snow cover parameters(snow cover fraction(SCF),snow cover duration(SCD),snow cover onset date(SCOD),and snow cover end date(SCED))on different types of grassland vegetation.The results showed wide snow cover areas,an early start time,a late end time,and a long duration of snow cover over the northern Mongolian Plateau.Additionally,a late start,an early end,and a short duration were observed for grassland phenology,but the southern area showed the opposite trend.The SCF decreased at an annual rate of 0.33%.The SCD was shortened at an annual rate of 0.57 d.The SCOD and SCED in more than half of the study area advanced at annual rates of 5.33 and 5.74 DOY(day of year),respectively.For grassland phenology,the start of the growing season(SOS)advanced at an annual rate of 0.03 DOY,the end of the growing season(EOS)was delayed at an annual rate of 0.14 DOY,and the length of the growing season(LOS)was prolonged at an annual rate of 0.17 d.The SCF,SCD,and SCED in the snow season were significantly positively correlated with the SOS and negatively correlated with the EOS and LOS.The SCOD was significantly negatively correlated with the SOS and positively correlated with the EOS and LOS.The SCD and SCF can directly affect the SOS of grassland vegetation,while the EOS and LOS were obviously influenced by the SCOD and SCED.This study provides a scientific basis for exploring the response trends of alpine vegetation to global climate change.

Monitoring and analysis of snow cover change in an alpine mountainous area in the Tianshan Mountains,China

Estimating the snow cover change in alpine mountainous areas(in which meteorological stations are typically lacking)is crucial for managing local water resources and constitutes the first step in evaluating the contribution of snowmelt to runoff and the water cycle.In this paper,taking the Jingou River Basin on the northern slope of the Tianshan Mountains,China as an example,we combined a new moderate-resolution imaging spectroradiometer(MODIS)snow cover extent product over China spanning from 2000 to 2020 with digital elevation model(DEM)data to study the change in snow cover and the hydrological response of runoff to snow cover change in the Jingou River Basin under the background of climate change through trend analysis,sensitivity analysis and other methods.The results indicate that from 2000 to 2020,the annual average temperature and annual precipitation in the study area increased and snow cover fraction(SCF)showed obvious signs of periodicity.Furthermore,there were significant regional differences in the spatial distribution of snow cover days(SCDs),which were numerous in the south of the basin and sparse in the central of the basin.Factors affecting the change in snow cover mainly included temperature,precipitation,elevation,slope and aspect.Compared to precipitation,temperature had a greater impact on SCF.The annual variation in SCF was limited above the elevation of 4200 m,but it fluctuated greatly below the elevation of 4200 m.These results can be used to establish prediction models of snowmelt and runoff for alpine mountainous areas with limited hydrological data,which can provide a scientific basis for the management and protection of water resources in alpine mountainous areas.

Spatiotemporal variation of snow cover and its relationship with temperature and precipitation in the Yarlung Tsangpo-Brahmaputra River Basin

The snow cover over the Qinghai-Tibet Plateau(QTP)and its surrounding areas is very sensitive to climate changes.Due to the complexity of geographical environment in this large region,the response of snow cover to climate change should exhibit spatiotemporal differences.In this study,the spatiotemporal variations of snow cover from 2002-2015 in the Yarlung Tsangpo-Brahmaputra River Basin(YBRB)were analyzed using an optimized fractional snow cover(FSC)product derived from Moderate Resolution Imaging Spectroradiometer(MODIS).Additionally,the effects of temperature and precipitation on the variability of snow cover were also investigated.The results showed that:(1)The snow cover exhibited large spatial and temporal heterogeneity in the YBRB.High FSC was observed in the northeast of the basin and the south slope of Himalaya,while the lowest was concentrated in the broad valley of the upstream of YBRB.The FSC value reached its highest in winter and dropped to its lowest in summer,but the monthly change processes were different between upstream and downstream regions.(2)A slightly increasing tendency(3.76%/10 a)of snow cover was observed on basin-wide,but the changes varied through time and space.The FSC increased significantly in the source and midstream regions during winter to spring(10.5%-18.0%/10 a),while it changed slightly in summer over all parts of the basin(-0.4%-4.3%/10 a).(3)The study area generally became warm and wet,and the change trend of temperature was more significant than that of precipitation.Snow cover changes were weakly correlated with temperature in winter and precipitation in summer.But in spring and autumn,both precipitation and temperature were significantly related to snow cover change in most regions of the basin.(4)The dominant factor driving the changes of snow cover varied in seasons.The area dominated by temperature was slightly larger than that dominated by precipitation in spring,except that precipitation significantly dominated the snow cover changes in the source region;In summer and autumn,temperature contributed more to the snow cover change in most areas of the basin;However,in winter,precipitation played a leading role in the variations of snow cover.These findings help to understand the different performance of the snow cover in the QTP and its surrounding areas under future climate change.

MODIS observed snow cover variations in the Aksu River Basin, Northwest China

A major proportion of discharge in the Aksu River is contributed from snow-and glacier-melt water.It is therefore essential to understand the cryospheric dynamics in this area for water resource management.The MODIS MOD10A2 remotesensing database from March 2000 to December 2012 was selected to analyze snow cover changes.Snow cover varied significantly on a temporal and spatial scale for the basin.The difference of the maximum and minimum Snow Cover Fraction(SCF)in winter exceeded 70%.On average for annual cycle,the characteristic of SCF is that it reached the highest value of 53.2%in January and lowest value of 14.7%in July and the distributions of SCF along with elevation is an obvious difference between the range of 3,000 m below and 3,000 m above.The fluctuation of annual average snow cover is strong which shows that the spring snow cover was on the trend of increasing because of decreasing temperatures for the period of 2000-2012.However,temperature in April increased significantly which lead to more snowmelt and a decrease of snow cover.Thus,more attention is needed for flooding in this region due to strong melting of snow.

Optical remote sensing of snow fraction——status and future prospects

Fractional snow cover （FSC） is that fraction of the surface covered with snow in a remotely sensed image pixel. This subpixel snow fraction is useful for atmospheric correction and to retrieve various satellite products characterizing the land surface, including albedo, temperature, soil moisture, heat fluxes, and vegetation parameters.

Changes in snow parameterization over typical vegetation in the Northern Hemisphere

季节性降雪对气候变化很敏感,常被当作气候变化的信号.由于其局地特征差异显著,不同下垫面类型的积雪过程也不尽相同.北半球中高纬度的典型下垫面(开阔灌丛,常绿针叶林和混交林)在积雪覆盖率和雪深之间有着独特的关系曲线,这种关系不仅代表了积雪过程和融雪过程的特征变化,更能用于模式进行积雪预测.研究发现,北半球中高纬度的增温改变了积雪参数化关系,进一步影响了局地能量和水循环,造成开阔灌丛的北缩和常绿针叶林及混交林的扩张.然而,目前模式中的积雪参数化并不能很好地再现全球变暖影响下融雪阶段出现的加速融化过程,并且进一步影响对春季融雪的生态影响的理解.

基于积雪数据的HBV模型改进及应用

大渡河流域内站点分布较少,历史观测数据不足,给该地区的融雪径流预报带来困难。基于欧洲中期天气预报中心提供的最新一代高分辨率陆面再分析数据集ERA5-Land,将积雪覆盖率和积雪平均深度引入度日因子雪量计算公式中,对HBV模型的积融雪模块进行改进,以提升融雪径流计算的可靠性。以大渡河上游为研究对象,选取1961—2018年的水文气象资料对模型进行率定和验证,并以2019年为例进行试预报研究。结果表明,通过引入ERA5-Land再分析数据,以及对积融雪模块进行改进,发挥了其在模拟积融雪上的优势,有效提升了融雪径流预报精度,对大渡河流域具有适用性。研究成果可为稀缺资料地区融雪径流模拟预报提供经验。

基于MODIS积雪覆盖度数据的青藏高原两套被动微波雪深产品降尺度对比研究

积雪深度(雪深)是流域水量平衡、融雪径流模拟等模型的重要输入参数,被动微波雪深遥感产品被广泛用于雪深监测。然而,由于山区积雪时空异质性强,这些空间分辨率较粗的雪深产品受到极大限制。本研究基于MODIS积雪覆盖度数据,根据经验融合规则以及积雪衰退曲线对“中国雪深长时间序列数据集”的两套雪深产品(由SMMR、SSMI和SSMI/S反演的称为Che_SSMI/S产品;由AMSR-2反演称为Che_AMSR2产品)进行空间降尺度,最终获得青藏高原500 m降尺度雪深数据(Che_SSMI/S_NSD和Che_AMSR2_NSD)。利用6景Landsat-8影像对两套降尺度雪深数据进行对比分析,结果发现两套降尺度数据与Landsat-8影像积雪空间分布吻合度均较高。与29个气象站点雪深数据相比,Che_AMSR2_NSD与实测雪深更为接近,相关系数(R)达到0.72,均方根误差(RMSE)为3.21 cm;而Che_SSMI/S_NSD精度较低(R=0.67,RMSE=4.44 cm),可能是由于采用不同传感器亮温数据的两套原始雪深产品精度不同所致。除此之外,实验表明被动微波雪深产品降尺度精度还受积雪深度、积雪期等因素的影响。当积雪深度小于10 cm且在积雪稳定期时,两套雪深产品降尺度精度均最高;当积雪深度大于30 cm且在积雪消融期时,两套雪深产品降尺度精度均最低。通过对比两套降尺度雪深产品,有助于全面地了解青藏高原雪深时空分布变化及其应用提供数据支持。

2001-2012年青藏高原积雪覆盖率变化及地形影响

基于2001一2012年MODIS获取的青藏高原积雪面积比例(SCF)数据,利用统计方法分析了SCF时空分布特征及其变化趋势。结果表明,从10月至次年4月是青藏高原持续性积雪较多的月份,其中2月持续性积雪面积最大,达到92.93×10~4km^2。5月之后积雪面积开始递减,7月最低(仅12.37×10~4km^2);从9月起开始急剧增加,10月达到一个相对稳定的水平。2001—2012年间逐年平均持续性积雪面积在55×10~4~80×10~4km^2之间,并呈3年左右的周期振荡。高的山脉对应大的积雪面积比例,但高原内部积雪面积比例却较小。在5 km以下的地区基本上是冬季高、夏季低的趋势,5 km以上的地区平均积雪面积比例由于受常年积雪和降雪反馈的作用有双峰型的月变化特征。不同坡向的积雪面积比例也呈现较大差异:北向山坡最大,西向、东向次之,南向的最小,引起这个变化的主要原因是不同坡向条件下的太阳辐射差异导致的。10月至次年4月的平均积雪面积比例随坡度增加而急剧增加,在5—9月平均积雪面积比例随坡度变化较小。

基于MODIS数据的青藏高原积雪时空分布特征分析

利用2000―2014年间获取的MOD10A2积雪产品,结合数字高程模型数据,借助GIS空间分析方法,以积雪覆盖率(snow cover fraction,SCF)为指标,定量分析了青藏高原积雪的整体空间分布特征及高程、坡度和坡向等地形因素对高原积雪时空分布的影响。主要结论有:(1)青藏高原积雪覆盖具有高原周围和中部高大山脉积雪丰富、SCF高,内陆盆地和谷地积雪少、SCF低的特点;(2)海拔越高,SCF越高,积雪持续时间越长,年内变化越稳定;(3)海拔4 000 m以下年内积雪覆盖呈单峰型分布特点,海拔4 000 m以上则为双峰型;(4)SCF最低值在海拔6 000 m以下出现在夏季,而在海拔6 000 m以上则出现在冬季;(5)SCF在不同坡向中,北坡向最高,南坡向最低,东坡和西坡向居中。

气候模式中积雪覆盖率参数化方案的对比研究

利用基于NCEP再分析的近地面气候资料驱动陆面过程模型NCAR CLM3,检验了6种积雪覆盖率参数化方案(CLM3、Douville1995、Roesch2001、Wu2004、Yang1997、Niu2007)模拟的积雪覆盖率的季节变化,并与NOAA AVHRR得到的观测结果进行了对比分析。结果表明,在NCARCLM3的物理过程框架之下,CLM3、Douville1995、Roesch2001三种方案低估了广大地区的积雪覆盖率,模拟的雪线位置偏北,尤其是在秋季积雪初期;Wu2004方案低估了秋季欧亚大陆的积雪覆盖率;Yang1997方案模拟的积雪覆盖率有些偏高,尤其是在积雪覆盖区的南部边缘;考虑积雪密度变化的Niu2007方案一定程度上克服了Yang1997方案的正偏差。春季末期,6种方案模拟的雪线位置都偏北。在地形比较平缓的地区,Niu2007方案的整体效果最好。观测和模拟的积雪覆盖率的出现频数大部分集中在低(小于0.2)和高(大于0.8)覆盖率等级,中等覆盖率所占比例很少。

基于MODIS数据的玛纳斯河山区雪盖年内变化特征研究

基于2000年～2010年的MODIS/Terra积雪8d合成数据(MOD10A2)与DEM数据,通过计算和分析不同高程带、不同坡向和不同坡度的积雪覆盖率,研究了新疆玛纳斯河山区雪盖的年内变化特征。结果表明:①研究区平均积雪覆盖率最高为一月中旬的67.8%,最低为七月中旬的11.9%,年内变化总体上呈V字型,积雪分布与气温关系密切;②可将研究区雪盖年内分布情况归纳为1600m以下、1600m～3800m和3800m以上共三个高程带,各高程带内雪盖分布的年内变化较为相似,不同高程带则差异明显。从年内波动情况来看,低海拔地区年内波动幅度最大,随着海拔上升,波动幅度逐渐减小;③3800m以下各坡向和坡度地区积雪覆盖率均表现为一月最高,七月最低,四月和十月介于二者之间,而3800m以上地区积雪覆盖率全年最高值则出现在四月和十月;④各坡度和坡向区域雪盖的年内变化与所在高程带的总体情况基本相似,说明坡度和坡向对雪盖分布的影响是在高程影响的基础上产生的。

中国冬季多种积雪参数的时空特征及差异性

利用1979～2006年冬季中国站点最大雪深和站点雪日、卫星遥感雪深、积雪覆盖率和雪水当量5种积雪资料，从多角度深入细致地分析了我国冬季积雪的时空变化特征。结果表明：5种积雪资料的经验正交分解第一模态都表现为中国南、北方反位相的特征，即当新疆和东北三省-内蒙古地区积雪偏多（少）时，青藏高原和南方地区积雪偏少（多）。新疆和东北三省-内蒙古地区的雪深、积雪覆盖率和雪日随时间有逐渐增多的趋势，而其中边缘山区的雪水当量表现出减少的趋势，青藏高原地区的积雪表现出与其完全相反的特征。南方地区站点最大雪深和雪日表现出随时间减少的趋势，卫星遥感难以监测到该区积雪。相比较而言，卫星遥感资料比较适合高原和山区缺少气象站的地区及北半球更大区域积雪的研究，而站点资料更适用于中国中东部和平原地区积雪的区域研究。雪深、雪日、积雪覆盖率和雪水当量这些多样性积雪参数存在一定的差异性，因此5种积雪资料结合使用才能得到更准确的结论。

不同排放情景下模拟的21世纪东亚积雪面积变化趋势

利用WCRP CMIP3气候模式对SRES A2、A1B和B1排放情景下东亚地区积雪面积的未来变化趋势进行了预测,结果表明,未来东亚地区积雪面积将呈现减少趋势,在同一种排放情景下,春季的减小趋势最大,冬季次之,秋季再次之,夏季最小。比较不同区域之间的积雪面积变化,冬、春季青藏高原积雪面积变化趋势要明显大于东亚大陆北部,秋季二者差异不大。对不同排放情景而言,SRESA2情景减小趋势最大,A1B次之,B1最小。地表气温和降雪量变化是造成积雪面积减小的主要原因,但在不同的区域和季节,对积雪面积变化趋势的贡献有明显差异。

巴音布鲁克典型区MODIS亚像元积雪覆盖率估算

雪盖信息对气候变化研究、水资源利用和雪灾监测具有重要意义.亚像元积雪覆盖率(snow cover fraction,SCF)的估算可以减少混合像元造成的误差,提高雪盖监测精度.本文以新疆巴音布鲁克为研究区,以环境与灾害监测预报小卫星B星(HJ-1B)数据作为地面真值,计算出中分辨率成像光谱仪(moderate-resolution imaging spectroradiometer,MODIS)数据对应的雪盖率,建立所得雪盖率与归一化差分积雪指数(normalized difference snow index,NDSI)之间的逐像元估算模型,并对估算结果进行了验证.结果表明,与MODIS积雪产品数据相比,该模型估算的雪盖率精度明显改善,具有较强的精确性和实用性.但是相对于真实雪盖率,也存在总体高估的情况.模型误差的主要来源是积雪边缘区的破碎分布、阴坡和阳坡的太阳辐射差异以及林带积雪的复杂影响.逐像元估算模型能有效的提高巴音布鲁克地区雪盖率制图精度,为该地区水资源合理利用,牧区雪灾的抗灾减灾提供了依据.

积雪混合像元光谱特征观测及解混方法比较

积雪混合像元分解方法研究及积雪比例产品的发展是积雪遥感的重要研究方向。在我国北疆地区利用SVC HR-1024野外便携式光谱仪观测了已知积雪比例的混合像元光谱特征并进行系统分析,同时,采用四种混合像元分解模型对实测光谱进行解混及精度评价。结果表明反射率随积雪比例均匀下降并不呈均匀的线性变化,在不同波段呈非线性变化特征,积雪像元解混精度与观测尺度的不同有一定的联系,尺度越小,解混精度越低;进一步对实测光谱的解混结果表明,线性回归法精度较低,特别是对于积雪比例小于50%的解混结果不准确,稀疏回归解混法和非负矩阵解混法略高于线性混合像元分解法,但线性混合像元分解法运算效率最高,稀疏回归解混法运算效率最低,当对遥感图像进行解混时,要综合考虑四种方法的计算效率。通过将推动积雪混合像元分解定量遥感研究,并为遥感影像准确提取积雪比例提供理论依据。

基于BP神经网络的玛纳斯河流域雪盖率预测

为了实现对新疆玛纳斯河流域的雪盖率预测,为该区融雪径流预报提供数据支撑,本文选取反向传播(BP)神经网络模型时间序列预测的方法对研究区雪盖率的预测问题进行了研究.首先基于2001-2012年研究区中分辨率成像光谱仪(Moderate Resolution Imaging Spectroradiometer,MODIS)积雪数据产品MOD10A2提取研究区雪盖并计算雪盖率,分析研究区雪盖率变化在时间上的自相关性,据此选取对预测值影响较大的相邻若干个历史时刻的雪盖率以及往年同期雪盖率的均值作为模型输入变量,建立神经网络模型,并选取相关系数与确定系数作为模型预测效果的评价指标.结果表明:以BP神经网络时间序列预测方法预测2012年积雪覆盖率,输入变量为与待预测雪盖率相邻的前期雪盖率值与往年同期雪盖率均值,预测值与实际值的相关系数为0.80,确定系数为0.61,预测结果基本反映了2012年雪盖率的年内变化趋势,可以为融雪径流模型(SRM)径流预报提供一定的数据支撑.