1979-2020年天山地区积雪量估算及其特征分析北大核心CSCD

积雪作为干旱区的重要水源,深刻影响区域水资源及经济发展。决定积雪量的积雪深度、积雪面积和积雪密度在时空分布上存在不确定性,尤其是积雪密度难以获取。本文利用FY-3B/MWRI(Fengyun3B Microwave Radiation Imager)数据反演积雪密度,结合1979-2020年长时间序列遥感雪深数据集,对天山地区40多年来积雪期(11月-次年3月)及其不同时期(积累期、稳定期、消融期)的积雪量进行估算,并分析其时空分布及与地形、气象等因子之间的关系。结果表明:1979-2020年,天山地区积雪期不同时期积雪量存在差异,稳定期积雪量最大,消融期次之,积累期最小。研究时段内,积雪期积雪量最大值出现在1979年,最小值出现在1998年,积雪期积雪量呈微弱的下降趋势,消融期积雪量下降趋势显著。多年平均积雪量空间格局与积雪深度和积雪密度基本一致,主要呈现为西北多东南少的特点。天山地区积雪量空间分布主要受海拔、坡度影响,积雪量与海拔正相关,海拔越高,积雪量越丰富;在15°以下时,坡度对积雪的影响较大,且坡度越大,积雪量越大。不同时期积雪量的多年变化与气温关系密切,在一定温度范围内,气温越低,积雪量越大;稳定期积雪量变化同时受积累期降水影响,积累期降水越多,稳定期积雪量越大。本文基于遥感积雪深度和密度的天山积雪量研究结果,可供气候变化条件下新疆水资源利用和经济发展参考。     

青藏高原积雪被动微波遥感资料与台站观测资料的对比分析

积雪资料的可靠程度在反映积雪变化、预估后期气候变化时非常重要, 利用青藏高原74个气象台站资料与被动微波遥感资料进行对比分析. 结果表明: 两种积雪资料在高原南部边缘、高原东部唐古拉山与念青唐古拉山东部均表现为高值区, 在柴达木盆地、高原腹地及沿雅鲁藏布江一线表现为一致的少雪区,在青海南部和藏东南地区差异较大.遥感资料的积雪深度和积雪日数变化敏感区与台站观测资料存在差异.在积雪的显著季节性特征及气候尺度上的年际变化特征方面, 遥感资料与台站资料具有很好的一致性, 但遥感资料在刻画积雪季节内波动特征方面欠佳, 且年平均积雪深度和积雪日数遥感数据偏大.对AMSR-E逐日积雪资料进行评价发现, 高原腹地总精度大于高原边缘地区, 海拔3 000 m以下的反演精度较高, 雪深在9~10 cm时的反演精度较高.     

青海高原雪灾风险区划及对策建议

利用青海省50个气象台站逐日积雪深度资料、遥感监测积雪深度资料和牲畜死亡率资料,对遥感监测积雪数据进行了验证,证实遥感监测积雪数据能很好的反映青海积雪状况.利用积雪指标分析青海各地致灾强度大小.结果表明:青海三江源地区和祁连山区的部分地区致灾因子危险性最高,柴达木盆地的西部和东部农业区以及环湖的部分地区致灾因子危险性较低.通过分析积雪指标和牲畜死亡率的相关关系,确定了不同雪灾等级临界气象指标,对青海地区进行了雪灾风险区划.区划结果为:轻灾主要发生在柴达木盆地、东部农业区的大部和环湖的部分地区,这些地区发生轻灾的频率大都在50%以上;中灾和重灾在青海发生频率均不高,都在20%以下;三江源的大部尤其是囊谦、玉树和称多一带是特大雪灾的高发区,发生频率均在50%以上.     

中国积雪特性及分布调查

介绍了"中国积雪特性及分布调查"的背景、科学目标、调查内容及方案。调查的总体目标是建立中国全面而系统的积雪特性数据库,服务于气候变化、水资源调查和积雪灾害的数据需求。调查将从历史资料整编、典型积雪区积雪特性地面调查以及积雪遥感调查等方面展开。历史资料的整编包括收集气象站以及各单位已开展的积雪特性观测资料,并按照一定的规范进行整编;典型积雪区地面调查主要是在东北地区、新疆地区和青藏高原开展不同季节的积雪特性调查,以点、线、面3种方式开展,观测内容包括雪深、雪密度、雪水当量、积雪形态、表层硬度、液态水含量、雪粒径、雪层温度、雪土界面温度、介电常数以及积雪的若干化学特性;遥感积雪调查将利用地面调查的积雪特性信息改进已有的积雪参数反演算法,建立中国长序列的积雪面积、反照率以及雪水当量数据集。最终,利用地面和遥感调查所获取的积雪特性及分布数据集对中国进行积雪类型划分,并生产系列积雪特性及专题分布图。     

一种简化的MODIS亚像元积雪信息提取方法

遥感技术已逐步成为大范围内积雪信息提取的主要手段,但通常的遥感积雪像元识别算法使用二值判定的模式,这对于山区非连续分布的积雪监测能力较差.针对目前应用广泛的MODIS传感器数据,充分利用了雪盖指数在积雪监测中的重要性,并在考虑了地表覆盖的情况下,建立了像元雪盖率与雪盖指数、植被指数之间的线性关系模型,并利用ETM+数据对模型估算的雪盖率进行了验证.结果表明,该方法能有效地提取亚像元尺度的积雪信息.     

青藏高原多源雪深数据适用性综合评估北大核心CSCD

青藏高原积雪对区域气候及水循环有重要影响,现有积雪数据集在该区域存在很大不确定性,适用性评估工作不可或缺。基于气象站观测数据(OBS),采用秩评分方法对一套被动微波遥感(CHE)和两套再分析(ERA5-Land和MERRA2)积雪深度数据进行了多变量、多评价指标的综合定量评估。结果表明:从年平均积雪深度、年最大积雪深度、年积雪日数三个变量分别评价各数据,MERRA2对年最大积雪深度、年积雪日数模拟最好,CHE对年平均积雪深度描述最好;各数据在不同评价指标上的得分排名存在较大差异,CHE在描述线性变化趋势上具有优势,ERA5-Land在描述年际变化上具有优势,MERRA2在描述季节循环、多年平均值、极大值、标准差上具有优势;综合考虑,MERRA2在青藏高原适用性综合评分最高、ERA5-Land次之、CHE最低。三种数据均存在明显不足之处,MERRA2对积雪线性变化趋势的定性描述与OBS相反,对积雪年代际变化的模拟有待优化;ERA5-Land对各变量的多年平均值存在严重高估;CHE刻画积雪空间分布特征能力较差。由于青藏高原西部站点稀少,相关评估结论仅适用于高原中东部。基于遥感及再分析数据得到高原西部积雪变化趋势存在较大不确定性。     

植被覆盖度遥感估算研究进展

植被覆盖度是刻画地表植被覆盖的重要参数,在全球变化研究、地表过程模拟和水文生态模型中发挥着重要作用.遥感能够反映不同空间尺度的植被覆盖信息及其变化趋势,是获取区域及全球植被覆盖度参数的一个重要手段.综合分析了用于植被覆盖度估算的遥感数据源,包括高光谱数据、多光谱数据、微波数据和激光雷达数据.而且分析了各种常用的植被覆盖度遥感估算方法及其优缺点,并评价了现有基于遥感数据的植被覆盖度产品及存在问题.最后,针对目前研究中存在的问题,讨论了植被覆盖度遥感估算研究的发展趋势,指出高时空分辨率长时间序列的全球植被覆盖度数据集、多源遥感数据融合和同化技术是未来植被覆盖度遥感估算研究的主要方向.     

青藏高原积雪范围和雪深/雪水当量遥感反演研究进展及挑战

积雪是地表特征的重要参数,对辐射收支、气候和长期天气变化均有重要影响。雪本身又是一个重要的天气现象和水文气象参数,过量的降雪也会带来严重的雪灾,如牧区雪灾、雪崩和融雪洪水灾害等。因此对积雪的监测,尤其是对山区的积雪监测,具有多方面的意义。利用卫星遥感技术监测积雪已有50余年的历史,并已形成了系列业务产品。青藏高原平均海拔超过4 000 m,该地区的积雪具有重要的水文、气候和生态环境意义。由于地形复杂,人迹罕至,地面观测站点稀少,受较强太阳辐射的影响,积雪消融迅速、区域差异消融以及风吹雪等因素导致积雪分布破碎化严重,对使用遥感资料监测该地区的积雪造成的极大的困难和不确定性。随着国内外传感器技术的不断发展,光学和被动微波遥感数据的同步获取技术已经非常成熟,综合利用光学遥感数据高空间分辨率和被动微波数据不受云干扰的特点,结合机器学习、无人机等技术,将环境参数加入反演模型中,有助于提高青藏高原积雪参数反演精度。     

新疆积雪覆盖时空变异分析

利用2000-2010年MODIS积雪覆盖产品数据MOD10A2,提取了新疆近10年来积雪覆盖变化信息,并结合地面站点数据,对遥感积雪覆盖估算的精度进行了验证;分析了新疆积雪覆盖的年际、年内变化及南北疆积雪覆盖变化的差异;结合数字高程模型,分析不同高程带下积雪覆盖的时空变化规律,揭示高程因素对新疆积雪时空变化的影响。结果表明:MOD10A2提取的积雪信息能够反映新疆的积雪变化情况,总体精度达92.3%;近10年来,全疆年积雪覆盖率最大值范围为34.0%~51.7%,最小值范围为1.7%~2.6%;积雪覆盖比率的变化在南北疆差异明显,南疆区域积雪覆盖整体不高,年内积雪覆盖比率变化幅度低于50%;而北疆区域由于受复杂地形和气候带的影响,积雪覆盖比率大,年内的变化幅度强,除2008年均达到80%以上;在季节变化上,春季和秋季的积雪覆盖均值波动较为明显,夏季和冬季的积雪覆盖均值则波动较小,这一规律在北疆地区表现更为显著;积雪覆盖的时空分布与变化受高程的影响,在海拔4 000 m以下区域,夏季积雪覆盖比率低,冬季积雪覆盖比率高,而6 000 m以上海拔区域则表现出完全相反的特点,即夏季积雪覆盖比率高,冬季积雪覆盖比率低。     

水均衡法验证蒸散量计算的可靠性——以张掖盆地为例

蒸散量的计算方法有很多种,表面能量平衡系统(SEBS)是近年来应用较为广泛的计算蒸散量的方法之一。SEBS是应用卫星对地观测的可见光、近红外和热红外波段资料,结合实测气象数据或大气模式输出数据,根据表面能量平衡原理估算不同尺度的地表大气湍流通量,从而估算地表相对蒸散的一种方法。将水文数据与遥感数据相结合,运用SEBS方法对张掖盆地的区域蒸散量进行了估算,并在水均衡原理的基础上,对蒸散量计算结果的准确性进行了验证。结果表明:SEBS方法计算的蒸散量与水均衡法计算出的蒸散量结果吻合较好,从而验证了SEBS方法计算盆地蒸散量的准确性。     

Characteristics of wet-snow avalanche activity: 20 years of observations from a high alpine valley (Dischma,Switzerland)

The occurrence of wet-snow avalanches is, in general, poorly understood. For 20 years (winters of 1975–1976 to 1994–1995), the avalanche activity has been observed in the Dischma valley near Davos (Eastern Swiss Alps). The study area comprises a large starting zone of north-easterly aspect (2,300 m a.s.l.) with several avalanche paths. We have analyzed the occurrence data in combination with meteorological and snowpack data collected at an elevation of 2,090 m a.s.l. During the 20-year observation period, almost 800 wet-snow avalanches were observed, about 4.5 times more loose snow avalanches than slab avalanches. Considering both types of avalanches jointly, snow depth, precipitation and air temperature showed the highest correlation with avalanche activity. Most loose snow avalanches occurred when air temperature was high and/or after a precipitation period. Slab avalanches occurrence was primarily related to warm air temperatures and snowpack properties such as the isothermal state and the existence of capillary barriers. Radiation did not show up as a significant variable. The results suggest that in a transitional snow climate wet-snow avalanches are, as dry snow avalanches, often related to precipitation events, and that wet slab instability strongly depends on snowpack properties in relation to warming of the snowpack and melt water production.     

NOAA16卫星积雪识别和参数提取

通过对积雪、地物和云进行光谱分析,指出传统的NOAA-AVHRR可见光和近红外波段进行云雪识别存在困难,而雪在红外波段的低反射性特点是区分云雪的一个可行途径.利用NOAA16气象卫星新增的1.6μm红外波段,对中国北方冬季的卫星积雪图象进行识别,结果显示,云雪可以准确区分.同时,提出了利用AVHRR资料估算积雪面积、积雪深度和积雪时间的方法,并对积雪深度进行了精度检验.     

祁连山区冰沟流域积雪分布特征及其属性观测分析

以祁连山冰沟流域为研究区,通过在流域内布设花杆观测积雪深度,渊查了山区积雪分布情况;利用雪特性分析仪测量了区内积雪密度、介电常数、液念水含量等积雪参数,光谱仪测量了不同类型积雪的光谱特征,手持反照率测量计观测积雪表面反照率,带刻度手持放大镜测量积雪粒径,红外温度计和针式温度计测量雪层的温度和实地测量积雪属性.同时,在研究区内选择加强观测区挖雪坑,对雪层内部属性和雪剖面分层特性作了进一步研究,计算民流域内积雪等效密度;最后对试验中所使用的野外实测积雪的各种方法进行了评价.研究表明:山区积雪分布很不均匀,在阴坡山谷雪深最深,阳坡雪积累最少,即使在同一样区,积雪分布也小均匀;研究Ⅸ的积雪属于潮雪,体秋含水量在3%以下;不同粒径、类型和表面粗糙度的积雪反射率不同,验证了积雪光谱是雪颗粒、污染物和地面粗糙度的函数;积雪反照率随太阳高度角升高逐步降低,在没有新降雪的情况下,日反照率也逐渐降低;雪分层比较明显,雪下冰晶层发育良好.当深度达剑20 cm时,积雪具有保温作用;冰沟流域的积雪等效密度随时间和空间变化不大,经汁算为0.16 g·cm-3.     

A comparison of winter mercury accumulation at forested and no-canopy sites measured with different snow sampling techniques

Atmospheric mercury (Hg) is delivered to ecosystems via rain, snow, cloud/fog, and dry deposition. The importance of snow, especially snow that has passed through the forest canopy (throughfall), in delivering Hg to terrestrial ecosystems has received little attention in the literature. The snowpack is a dynamic system that links atmospheric deposition and ecosystem cycling through deposition and emission of deposited Hg. To examine the magnitude of Hg delivery via snowfall, and to illuminate processes affecting Hg flux to catchments during winter (cold season), Hg in snow in no-canopy areas and under forest canopies measured with four collection methods were compared: (1) Hg in wet precipitation as measured by the Mercury Deposition Network (MDN) for the site in Acadia National Park, Maine, USA, (2) event throughfall (collected after snowfall cessation for accumulations of >8 cm), (3) season-long throughfall collected using the same apparatus for event sampling but deployed for the entire cold season, and (4) snowpack sampling. Estimates (mean ± SE) of Hg deposition using these methods during the 91-day cold season in 2004–2005 at conifer sites showed that season-long throughfall Hg flux (1.80 μg/m²) < snowpack Hg (2.38 ± 0.68 μg/m²) < event throughfall flux (5.63 ± 0.38 μg/m²). Mercury deposition at the MDN site (0.91 μg/m²) was similar to that measured at other no-canopy sites in the area using the other methods, but was 3.4 times less than was measured under conifer canopies using the event sampling regime. This indicates that snow accumulated under the forest canopy received Hg from the overstory or exhibited less re-emission of Hg deposited in snow relative to open areas. The soil surface of field-scale plots were sprayed with a natural rain water sample that contained an Hg tracer (²⁰²Hg) just prior to the first snowfall to explore whether some snowpack Hg might be explained from soil emissions. The appearance of the ²⁰²Hg tracer in the snowpack (0–64% of the total Hg mass in the snowpack) suggests that movement of Hg from the soil into the snowpack is possible. However, as with any tracer study the ²⁰²Hg tracer may not precisely represent the reactivity and mobility of natural Hg in soils.     

Springtime changes in snow chemistry lead to new insights into mercury methylation in the Arctic

Seasonal snow is an active media and an important climate factor that governs nutrient transfer in Arctic ecosystems. Since the snow stores and transforms nutrients and contaminants, it is of crucial importance to gain a better understanding of the dynamics of contaminant cycling within the snowpack and its subsequent release to catchments via meltwater. Over the course of a two-month field study in the spring of 2008, we collected snow and meltwater samples from a seasonal snowpack in Ny-Ålesund, Norway (78°56′N, 11°52′E), which were analyzed for major inorganic ions and some organic acids, as well as total, dissolved, bioavailable mercury (THg, DHg, BioHg, respectively) and monomethylmercury (MMHg) species. We observe a seasonal gradient for ion concentrations, with surface samples becoming less concentrated as the season progressed. A significant negative correlation between BioHg and MMHg was observed in the snowpack. MMHg was positively and significantly correlated to methanesulfonate concentrations. Based on these results, we propose a new model for aerobic methylation of mercury involving species in the dimethylsulfoniopropionate cycle.     

Mercury distribution, partitioning and speciation in coastal vs. inland High Arctic snow

Atmospheric mercury deposition on snow at springtime has been reported in polar regions, potentially posing a threat to coastal and inland ecosystems receiving meltwaters. However, the post-depositional fate of Hg in snow is not well known, and no data are available on Hg partitioning in polar snow. During snowmelt, we conducted a survey of Hg concentrations, partitioning and speciation in surface snow and at depth, over sea ice and over land along a 100 km transect across Cornwallis Island, NU, Canada. Total Hg concentrations [THg] in surface snow were low (less than 20 pmol L⁻¹) and were significantly higher in marine vs. inland environments. Particulate Hg in surface snow represented up to 90% of total Hg over sea ice and up to 59% over land. At depth, [THg] at the snow/sea ice interface (up to 300 pmol L⁻¹) were two orders of magnitude higher than at the snow/lake ice interface (ca. 2.5 pmol L⁻¹). Integrated snow columns, sampled over sea-ice and over land, showed that particulate Hg was mostly bound to particles ranging from 0.45 to 2.7 μm. Moreover, melting snowpacks over sea ice and over lake ice contribute to increase [THg] at the water/ice interfaces. This study indicates that, at the onset of snowmelt, most of the Hg in snow is in particulate form, particularly over sea ice. Low Hg levels in surface snow suggest that Hg deposited through early spring deposition events is partly lost to the atmosphere from the snowpack before snowmelt. The sea ice/snow interface may constitute a site for Hg accumulation, however. Further understanding of the cycling of mercury at the sea ice/snow and sea ice/seawater interfaces is thus warranted to fully understand how mercury enters the arctic food webs.     

Investigation on Snow Characteristics and Their Distribution in China

The background, scientific objective, investigation contents and scheme of project “Investigation on snow characteristics and their distribution in China” was introduced in this paper. The general objective of the investigation is to build comprehensive and systematic database of snow characteristics in China, at the service of providing data for the climate change, water resource and snow disaster studies. The investigation will be performed on the three fields including the compilation of historical data, in situ measurement of snow characteristics in the typical regions, and investigation of snow characteristics using remote sensing methods. For the compilation of historical data, the historical snow data from the meteorological stations and research institutes will be firstly collected, and then they will be compiled based on a standard rule. In situ observation will be performed at point, line and area-scale on the typical regions which include Northeast region, Xinjiang Degion, and Qinghai-Tibet Plateau. The observation content will contain snow depth, snow density, snow water equivalent, snow particle shape, hardness of snowpack surface, liquid water content, grain size, snow temperature, snow/soil temperature, dielectric constant, and some chemical parameters. These snow characteristics are the priority information used for the modification of retrieval algorithm on snow parameters. Remote sensing methods will be used to build long-time series of snow cover, snow albedo and snow water equivalent datasets based on these modified algorithms. Finally, the snow characteristics from both in situ and remote sensing investigation will be used to classify snow types in China, and produce distribution maps of snow characteristic and other thematic maps.     

新疆伊犁地区季节冻土沿海拔的分布规律及其影响因素

基于2000 - 2014年新疆伊犁地区不同海拔区域观测的冻融期内的冻土、 积雪和气象数据, 应用相关性分析和回归分析方法, 分析该地区季节冻土沿海拔的分布规律, 以及气温、 积雪对季节冻土特征的影响。结果表明： 伊犁地区表层土壤存在着每年11月份开始结冻, 于次年4月份完全融化的周期性变化。每个周期内土壤冻结时长随海拔以4 d·（100m）^-1的趋势增加, 土壤最大冻结深度随海拔以3.9 cm·（100m）^-1的趋势增加。土壤冻结时长与冻结期的平均气温具有显著负相关关系, 相关系数为-0.98（P<0.05）。土壤冻结日数与积雪覆盖历时呈正相关关系, 土壤的最大冻结深度与最大雪深呈负相关关系。随着海拔升高, 温度递减, 导致伊犁地区土壤最大冻结深度和土壤冻结日数整体呈现增加趋势。但在海拔相对较高的地区, 由于相对较厚积雪的影响, 出现土壤最大冻结深度随海拔升高而减小的反常现象。研究结果可为新疆伊犁地区季节冻土的分布对气候变化的响应研究提供支持, 帮助研究区域生态规划和水资源管理, 为农业发展制定适应气候变化对策。     

Fuzzy factorial analysis of snow avalanches

The mutual influence of 21 factors pertaining to terrain, weather, forest and snowpack have been discussed by 10 experts. The semantic (nil, weak, medium, high) evaluations are translated as membership degrees of fuzzy sets, and averaged between the experts by taking their fuzzy expectation value, yielding a 21 × 21 fuzzy matrix for direct interactions. Fuzzy successive multiplications and additions of the matrix give the indirect interactions. The activity and the passivity of the factors with respect to avalanches is represented by directed weighted graphs, and the average semantic values by a 4 × 4 matrix: the activity (passivity) is weak (strong) for tree damage; medium (nil) for altitude; medium (weak) for ground shape; medium (medium) for vegetation, soil, wind, microclimate, tree type, vertical distribution and mechanics of trees, snowpack distribution and snow gliding; strong (nil) for ground inclination and exposition, weather; strong (medium) for heat, precipitation, horizontal distribution of trees, snowpack constitution and stability.