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1.
1951～1997年中国西北地区积雪水资源的变化 总被引：16，自引：0，他引：16
采用美国宇航局微波候积雪深度连续 1 0a观测结果与地面气象台站积雪观测记录相结合 ,估算出西北地区季节积雪水贮量 ,揭示出它的空间分布 ,季节变化与年际波动特征 .研究结果表明 ,季节积雪是西北干旱区重要淡水资源 ,冬季积雪鼎盛时期平均积雪水贮量达 36 1 .0× 1 0 8m3 ,相当于该区地表年径流总量的 38.2 % .近来引起学术界广泛关注的有关全球变暖导致北半球大陆积雪自 1 987年以来显著减少 ,春季积雪提前消失并造成土壤干旱化的现象 ,在新疆地区并不存在 .相反 ,近 5 0a来 ,新疆年积雪日数 ,消融期积雪日数和年累积雪深分别增加了 8.9d、1 .6d和2 0 8cm .积雪的增加与冬季降水量的增加相一致 . 相似文献
2.
FY3B-MWRI中国区域雪深反演算法改进 总被引：1，自引：0，他引：1
基于2002～2009年全国753个国家基本气象站观测的地面雪深和温度资料,以及同期的高级微波扫描辐射计(Advanced Microwave Scanning Radiometer for EOS,AMSR-E)亮温数据,利用不同频率亮温对雪深的敏感性差异,建立了中国区域雪深半经验统计反演算法.经2006年地面台站观测雪深验证,其反演均方根误差为5.6 cm.具体反演思路如下:根据全国1 km网格土地利用覆盖度数据,结合中国区域的下垫面微波辐射特征,划分成森林、农田、草地和裸地四种主要地物类型;首先建立这四种主要地物类型相对较纯像元下的雪深反演算法,然后利用线性混合像元分解技术,建立微波像元下高精度的雪深反演算法.将本算法分别应用于风云三号B星搭载的微波成像仪(Fengyun-3B/Mcirwoave Radiation Imagery,FY3BMWRI)和AMSR-E数据,进行了2010～2011年冬季雪盖制图,与相应时段的MODIS日积雪产品(MYD10C1)相比,尽管两者数据源有所不同,本算法估算雪盖的精度均达到84%以上.此外,利用本算法和FY3B-MWRI数据在北半球进行了雪当量估算测试,与AMSR-E标准雪当量产品进行了比较,发现二者结果较为一致.但在中国地区,AMSR-E雪当量值明显高于FY3B-MWRI估算值,这与目前已有AMSR-E雪当量产品的验证结果较为一致,FY3B-MWRI雪深估算值与站点观测值更为吻合.该算法已被作为国家卫星气象中心FY3B-MWRI雪深产品的业务化算法. 相似文献
3.
《地球物理学进展》2019,(5)
积雪不仅作为一种关键的淡水存储方式,也是全球气候系统的重要组成部分之一.目前,基于大地测量型接收机的GNSS-MR(Global Navigation Satellite System Multipath Reflectometry)技术遥测地表特征参数的研究已广泛开展.然而,先前大多的研究利用GPS (Global Positioning System)卫星探测积雪深度并获取了良好的结果,但是其仍存在观测数据单一、反演精度偏低等问题.为增加GNSS-MR技术所使用的数据源且GLONASS卫星数量接近GPS系统,本文提出了基于GNSS-MR算法,采用GLONASS卫星L1和L2载波低卫星高度角(小于25°)的信噪比数据测量了加拿大YEL2跟踪站2015年7月至2016年6月的逐日雪深.该反演值分别与GPS的结果和实测雪深对比,从多路径反射信号与雪深变化量的关系、反演精度以及相关性等多方面进行详细分析,验证了基于GLONASS卫星信号反演地表雪深的适用性和可靠性.结果显示:利用GLONASS卫星信噪比数据的反演雪深与实测雪深间具有高一致性.其中,GPS和GLONASS卫星L1载波的反演雪深无明显差异,两者的RMSE皆在4 cm左右;而GLONASS卫星L2-RMSE为2.6 cm,相关系数达到0.98,其雪深反演的效果明显优于前两种方法.因此,该结果表明采用GLONASS卫星反射信号探测地表雪深能进一步扩展GNSS-MR技术的应用. 相似文献
4.
本文应用欧洲中期预报中心（ECMWF,European Centre for Medium\|Range Weather Forecasts—ERA\|40）资料和美国国家环境预测中心和国家大气研究中心(NCEP/NCAR, National Centers for Environmental Prediction/National Center for Atmospheric Research)资料,研究了青藏高原雪深变化对南海夏季风爆发的影响和ENSO对青藏高原降雪的影响.结果表明：（1）ECMWF的雪深资料是可信的,可以用来研究青藏高原雪深变化对南海夏季风爆发的影响;（2）青藏高原的积雪异常影响到500 hPa以上的温度异常和印度洋与大陆间的气温对比,一方面使上层的南亚高压移动速度发生变化,另一方面也影响到低层大气的运动和东西向风异常,在青藏高原少雪年,东印度洋产生西风异常和一个气旋对,而在青藏高原多雪年,东印度洋产生东风异常和一个反气旋对;（3）ENSO与青藏高原春季积雪关系密切.东太平洋SST正异常时,东印度洋和南海气压偏高,从而导致该区海陆经向压强梯度增强和西风异常.另外,此时青藏高原北部气压偏高,北风偏强,副热带锋面增强,同时,印度洋的SST偏高,为青藏高原降雪提供了水汽保障,这些都有利于青藏高原的降雪. 相似文献
5.
积雪是西北干旱地区河流的主要补给源,是绿洲的生命线.积雪的时空变化是全球变化的区域响应敏感因子之一,同时也是影响西北干旱地区地表水资源变化的主要因子之一.本研究利用MODIS雪盖产品、地表温度、SSM/I雪深、DEM等数据,通过GIS空间分析及地统计分析功能,系统分析了博斯腾湖流域雪盖、雪深的时空变化规律及其与影响因素之间的关系.研究表明,研究区雪深和雪盖多年月平均值从8月份到1月份达到最大值,到7月份降到最低值.但月最大雪深却出现在3月份.雪盖、雪深与地温相关系数分别达到-0.878、-0.853,与分布高程均值相关系数分别达到-0.626和-0.791.雪深最大值受海拔影响有明显的陡坎效应.从12月到8月份随着时间的推移雪的深度在降低,陡坎向高海拔方向移动.9-11月份雪深在加深,陡坎向低海拔方向移动.同一高程段雪深的变幅反应坡向对雪深的影响,变幅越宽坡向影响越大.并且变幅也有先从低海拔到高海拔移动,然后再回到低海拔的特点.本研究对了解该研究区积雪特性的研究有很大作用,可为在该地区开展融雪径流模拟等研究提供重要的参考信息. 相似文献
6.
《地球物理学报》2017,(9)
本文利用1948—2010年Global Land Data Assimilation System(GLDAS)NOAH陆面模式资料、GPCC月平均降水资料和NCAR/NCEP全球月平均再分析资料,采用滤波、距平合成和线性相关等方法,分析了El Nino成熟位相冬季欧亚大陆积雪异常的分布特征,研究了关键区积雪融化对后期春、夏季土壤湿度、土壤温度以及大气环流与降水的影响,揭示了El Nino事件通过关键区积雪储存其强迫信号并影响东亚夏季气候异常的机制和过程.主要结论如下:El Nino成熟阶段冬季伊朗高原、巴尔喀什湖东北部和青藏高原南麓区域是雪深异常的三个关键区,这些区域的雪深、雪融和土壤湿度有明显的正相关;这三个关键区雪深异常通过春季融雪将冬季El Nino信号传递给春、夏季局地土壤湿度,通过减少感热通量和增加潜热通量对大气环流产生影响;春末夏初伊朗高原土壤湿度异常对东亚夏季气候异常的影响最大,其引起的降水异常与El Nino次年夏季降水异常分布基本一致,春夏季青藏高原南麓和巴尔喀什湖附近土壤湿度也都明显增加,均会对中国华北降水增加有显著正贡献.总之,在利用El Nino事件研究和预测东亚夏季气候异常时,还应考虑关键区雪深异常对El Nino信号的存储和调制作用. 相似文献
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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. 相似文献
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本文利用1948-2010年Global Land Data Assimilation System（GLDAS）NOAH陆面模式资料、GPCC月平均降水资料和NCAR/NCEP全球月平均再分析资料，采用滤波、距平合成和线性相关等方法，分析了El Niño成熟位相冬季欧亚大陆积雪异常的分布特征，研究了关键区积雪融化对后期春、夏季土壤湿度、土壤温度以及大气环流与降水的影响，揭示了El Niño事件通过关键区积雪储存其强迫信号并影响东亚夏季气候异常的机制和过程.主要结论如下：El Niño成熟阶段冬季伊朗高原、巴尔喀什湖东北部和青藏高原南麓区域是雪深异常的三个关键区，这些区域的雪深、雪融和土壤湿度有明显的正相关；这三个关键区雪深异常通过春季融雪将冬季El Niño信号传递给春、夏季局地土壤湿度，通过减少感热通量和增加潜热通量对大气环流产生影响；春末夏初伊朗高原土壤湿度异常对东亚夏季气候异常的影响最大，其引起的降水异常与El Niño次年夏季降水异常分布基本一致，春夏季青藏高原南麓和巴尔喀什湖附近土壤湿度也都明显增加，均会对中国华北降水增加有显著正贡献.总之，在利用El Niño事件研究和预测东亚夏季气候异常时，还应考虑关键区雪深异常对El Niño信号的存储和调制作用. 相似文献
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湖冰光谱特征是湖冰遥感反演的物理基础，是研究湖冰光学特性和空间分布的理论依据。本文以查干湖为例，使用ASD Field Spec 4便携式地物光谱仪采集冰封期不同类型湖冰、积雪和水体光谱，利用Savitzky-Golay滤波法和包络线去除法分析白冰、灰冰、黑冰、雪冰、积雪和水体的反射光谱特征，探索气泡对湖冰反射光谱特征的影响。积雪和雪冰、白冰和灰冰、黑冰和水体的反射特征随着波长的变化特征基本一致，冰的反射率介于积雪和水体之间，其中白冰的反射率高于灰冰和黑冰，在包络线去除结果中，黑冰和水体在440 nm吸收谷处的吸收面积为5.184和10.878、吸收深度为0.052和0.106，雪、雪冰、白冰、灰冰在800和1030 nm吸收谷处的吸收面积和吸收深度的变化表现为雪<雪冰<灰冰<白冰。气泡是影响湖冰光谱特征的重要因素，气泡使白冰反射率减小和黑冰反射率增大，并且气泡使得白冰在800/1030nm和黑冰在440 nm处的吸收面积和吸收深度减小，其中气泡大小和疏密程度的不同会导致湖冰反射率的影响程度存在差异。同时，本文选取时间同步的Landsat 8 OLI遥感影像，在完成辐... 相似文献
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《地球物理学进展》2016,(4)
降雪是重要的淡水资源,也是全球气候系统中重要的组成部分.目前全球雪深探测主要依靠站点稀疏的人工探测仪或低时空分辨率的遥感卫星.近几年,基于不同地表介质引起GPS多路径效应的GPS-MR技术以其全天候,自动化,时空分辨率高、无需建网等诸多优点成为雪深探测领域的一个新型探测手段,本文在国内首次开展了基于测量型接收机的GPS-MR探测雪深研究.首先详细给出了基于GPS信噪比观测值的GPS-MR技术进行雪深探测的基本原理;其次结合实测GPS观测数据和雪深测量数据,分别利用单颗和多颗GPS卫星对GPS-MR探测雪深的可行性与探测精度进行深入研究与分析.初步实验结果表明基于信噪比观测值的GPS-MR技术可用于雪深探测,与实测雪深数据相比其探测精度约为10cm左右,尤其可用于暴雪监测.GPS-MR技术不仅可充分挖掘全球密集GNSS网络用于地表环境监测(雪深探测、海平面变化、土壤湿度、植被变化、火山活动等)的潜能,而且可进一步精化GNSS多路径模型误差,以期进一步提高GNSS用于精密定位与地表环境监测的精度. 相似文献
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Diagnosing snow accumulation errors in a rain‐snow transitional environment with snow board observations 
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下载免费PDF全文 Diagnosing the source of errors in snow models requires intensive observations, a flexible model framework to test competing hypotheses, and a methodology to systematically test the dominant snow processes. We present a novel process‐based approach to diagnose model errors through an example that focuses on snow accumulation processes (precipitation partitioning, new snow density, and snow compaction). Twelve years of meteorological and snow board measurements were used to identify the main source of model error on each snow accumulation day. Results show that modeled values of new snow density were outside observational uncertainties in 52% of days available for evaluation, while precipitation partitioning and compaction were in error 45% and 16% of the time, respectively. Precipitation partitioning errors mattered more for total winter accumulation during the anomalously warm winter of 2014–2015, when a higher fraction of precipitation fell within the temperature range where partition methods had the largest error. These results demonstrate how isolating individual model processes can identify the primary source(s) of model error, which helps prioritize future research. 相似文献
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Snow interception is a crucial hydrological process in cold regions needleleaf forests, but is rarely measured directly. Indirect estimates of snow interception can be made by measuring the difference in the increase in snow accumulation between the forest floor and a nearby clearing over the course of a storm. Pairs of automatic weather stations with acoustic snow depth sensors provide an opportunity to estimate this, if snow density can be estimated reliably. Three approaches for estimating fresh snow density were investigated: weighted post-storm density increments from the physically based Snobal model, fresh snow density estimated empirically from air temperature (Hedstrom, N. R., et al. [1998]. Hydrological Processes, 12, 1611–1625), and fresh snow density estimated empirically from air temperature and wind speed (Jordan, R. E., et al. [1999]. Journal of Geophysical Research, 104, 7785–7806). Automated snow depth observations from adjacent forest and clearing sites and estimated snow densities were used to determine snowstorm snow interception in a subalpine forest in the Canadian Rockies, Alberta, Canada. Then the estimated snow interception and measured interception information from a weighed, suspended tree and a time-lapse camera were assimilated into a model, which was created using the Cold Regions Hydrological Modelling platform (CRHM), using Ensemble Kalman Filter or a simple rule-based direct insertion method. Interception determined using density estimates from the Hedstrom-Pomeroy fresh snow density equation agreed best with observations. Assimilating snow interception information from automatic snow depth measurements improved modelled snow interception timing by 7% and magnitude by 13%, compared to an open loop simulation driven by a numerical weather model; its accuracy was close to that simulated using locally observed meteorological data. Assimilation of tree-measured snow interception improved the snow interception simulation timing and magnitude by 18 and 19%, respectively. Time-lapse camera snow interception information assimilation improved the snow interception simulation timing by 32% and magnitude by 7%. The benefits of assimilation were greatly influenced by assimilation frequency and quality of the forcing data. 相似文献
13.
The snow water equivalent (SWE) characterizes the hydrological significance of snow cover. However, measuring SWE is time-consuming, thus alternative methods of determining SWE may be useful. SWE can be calculated from snow depth if the bulk snow density is known. Thus, a reliable estimation method of snow densities could (a) potentially save a lot of effort by, at least partly, sampling snow depth instead of SWE, and would (b) allow snow hydrological evaluations, when only snow depth data are available. To generate a useful parameterization of the bulk density a large dataset was analyzed covering snow densities and depths measured biweekly over five decades at 37 sites throughout the Swiss Alps. Four factors were identified to affect the bulk snow density: season, snow depth, site altitude, and site location. These factors constitute a convenient set of input variables for a snow density model developed in this study. The accuracy of estimating SWE using our model is shown to be equivalent to the variability of repeated SWE measurements at one site. The technique may therefore allow a more efficient but indirect sampling of the SWE without necessarily affecting the data quality. 相似文献
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Leah Meromy Noah P. Molotch Timothy E. Link Steven R. Fassnacht Robert Rice 《水文研究》2013,27(17):2383-2400
The spatial distribution of snow water equivalent (SWE) is a key variable in many regional‐scale land surface models. Currently, the assimilation of point‐scale snow sensor data into these models is commonly performed without consideration of the spatial representativeness of the point data with respect to the model grid‐scale SWE. To improve the understanding of the relationship between point‐scale snow measurements and surrounding areas, we characterized the spatial distribution of snow depth and SWE within 1‐, 4‐ and 16‐km2 grids surrounding 15 snow stations (snowpack telemetry and California snow sensors) in California, Colorado, Wyoming, Idaho and Oregon during the 2008 and 2009 snow seasons. More than 30 000 field observations of snowpack properties were used with binary regression tree models to relate SWE at the sensor site to the surrounding area SWE to evaluate the sensor representativeness of larger‐scale conditions. Unlike previous research, we did not find consistent high biases in snow sensor depth values as biases over all sites ranged from 74% overestimates to 77% underestimates. Of the 53 assessments, 27 surveys indicated snow station biases of less than 10% of the surrounding mean observed snow depth. Depth biases were largely dictated by the physiographic relationship between the snow sensor locations and the mean characteristics of the surrounding grid, in particular, elevation, solar radiation index and vegetation density. These scaling relationships may improve snow sensor data assimilation; an example application is illustrated for the National Operational Hydrologic Remote Sensing Center National Snow Analysis SWE product. The snow sensor bias information indicated that the assimilation of point data into the National Operational Hydrologic Remote Sensing Center model was often unnecessary and reduced model accuracy. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
16.
J. Ignacio López-Moreno Leena Leppänen Bartłomiej Luks Ladislav Holko Ghislain Picard Alba Sanmiguel-Vallelado Esteban Alonso-González David C. Finger Ali N. Arslan Katalin Gillemot Aynur Sensoy Arda Sorman M. Cansaran Ertaş Steven R. Fassnacht Charles Fierz Christoph Marty 《水文研究》2020,34(14):3120-3133
Manually collected snow data are often considered as ground truth for many applications such as climatological or hydrological studies. However, there are many sources of uncertainty that are not quantified in detail. For the determination of water equivalent of snow cover (SWE), different snow core samplers and scales are used, but they are all based on the same measurement principle. We conducted two field campaigns with 9 samplers commonly used in observational measurements and research in Europe and northern America to better quantify uncertainties when measuring depth, density and SWE with core samplers. During the first campaign, as a first approach to distinguish snow variability measured at the plot and at the point scale, repeated measurements were taken along two 20 m long snow pits. The results revealed a much higher variability of SWE at the plot scale (resulting from both natural variability and instrumental bias) compared to repeated measurements at the same spot (resulting mostly from error induced by observers or very small scale variability of snow depth). The exceptionally homogeneous snowpack found in the second campaign permitted to almost neglect the natural variability of the snowpack properties and focus on the separation between instrumental bias and error induced by observers. Reported uncertainties refer to a shallow, homogeneous tundra-taiga snowpack less than 1 m deep (loose, mostly recrystallised snow and no wind impact). Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even in the case when observers were not familiar with a given snow core sampler. 相似文献
17.
Changes of snow cover in Poland 总被引：2，自引：2，他引：0
Małgorzata Szwed Iwona Pińskwar Zbigniew W. Kundzewicz Dariusz Graczyk Abdelkader Mezghani 《Acta Geophysica》2017,65(1):65-76
The present paper examines variability of characteristics of snow cover (snow cover depth, number of days with snow cover and dates of beginning and end of snow cover) in Poland. The study makes use of a set of 43 long time series of observation records from the stations in Poland, from 1952 to 2013. To describe temporal changes in snow cover characteristics, the intervals of 1952–1990 and of 1991–2013 are compared and trends in analysed data are sought (e.g., using the Mann–Kendall test). Observed behaviour of time series of snow-related variables is complex and not easy to interpret, for instance because of the location of the research area in the zone of transitional moderate climate, where strong variability of climate events is one of the main attributes. A statistical link between the North Atlantic Oscillation (NAO) index and the snow cover depth, as well as the number of snow cover days is found. 相似文献
18.
Monitoring and estimation of snow depth in alpine catchments is needed for a proper assessment of management alternatives for water supply in these water resources systems. The distribution of snowpack thickness is usually approached by using field data that come from snow samples collected at a given number of locations that constitute the monitoring network. Optimal design of this network is required to obtain the best possible estimates. Assuming that there is an existing monitoring network, its optimization may imply the selection of an optimal network as a subset of the existing one (if there are no funds to maintain them) or enlarging the existing network by one or more stations (optimal augmentation problem). We propose an optimization procedure that minimizes the total variance in the estimate of snowpack thickness. The novelty of this work is to treat, for the first time, the problem of snow observation network optimization for an entire mountain range rather than for small catchments as done in the previous studies. Taking into account the reduced data available, which is a common problem in many mountain ranges, the importance of a proper design of these observation networks is even larger. Snowpack thickness is estimated by combining regression models to approach the effect of the explanatory variables and kriging techniques to consider the influence of the stakes location. We solve the optimization problems under different hypotheses, studying the impacts of augmentation and reduction, both, one by one and in pairs. We also analyse the sensitivity of results to nonsnow measurements deduced from satellite information. Finally, we design a new optimal network by combining the reduction and augmentation methods. The methodology has been applied to the Sierra Nevada mountain range (southern Spain), where very limited resources are employed to monitor snowfall and where an optimal snow network design could prove critical. An optimal snow observation network is defined by relocating some observation points. It would reduce the estimation variance by around 600 cm2 (15%). 相似文献
19.
L. Merlivat J. Ravoire J.P. Vergnaud C. Lorius 《Earth and Planetary Science Letters》1973,19(2):235-240
Deuterium and tritium measurements were made on firn samples taken along a central axis across Greenland. Accumulation values determined from tritium profiles decrease regularly from 54.7 cm of water per year on the west coast to 18.1 cm of water per year on the east coast. A correlation is demonstrated between the tritium deposition rate and the altitude of the precipitation collection site. 相似文献
20.
Melted snow volume control in the snowmelt runoff model using a snow water equivalent statistically based model 总被引：1，自引：0，他引：1
Snowmelt is an important component of the river discharge in mountain environments. In the past 40 years, the snowmelt dynamics has been mostly evaluated using degree‐day‐based models like the snowmelt runoff model (SRM). This model has no control on the volume of the melting snow, even if SRM includes as data input the snow‐covered area. This lack explains why the application of SRM may lead to inaccurate snowmelt volume estimations, even if the discharge volumes are accurately reproduced. Here we introduce in SRM the control on the melted snow volume and consider it in the determination of SRM parameters. The total snow volume, accumulated at the end of winter season, is evaluated by a snow water equivalent statistically based model, SWE‐SEM, and used as an estimate of the melting snow during the summer season. The benefit derived from the introduction of the control on the melting snow volume was investigated in the Mallero basin (northern Italy) for the 2003 and 2004 snow melting seasons. The analysis compares the model's results adopting different parameter sets, both considering and ignoring the control on the melting snow volume. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献