Monitoring recent changes in snow cover in Central Asia using improved MODIS snow-cover products

Snow cover plays an important role in the fields of climatology and cryospheric science. Remotely-sensed data have been proven to be effective in monitoring snow covers. Improved methods to process the 8-day snow-cover products derived from MODIS Terra/Aqua data can dramatically increase the data quality and reduce noise. A five-step algorithm for removing cloud effects was designed to improve the quality of MODIS snow products, and the overall accuracy of the MODIS snow data without cloud（defined as cloud-free snow-cover dataset） was enhanced by more than 90% based on direct and indirect validation methods. The snow-cover frequency（SCF） and snow-cover rate（SCR） of Central Asia were analyzed from 2000 to 2015 using trend analysis and empirical orthogonal functions（EOFs）. Over the plain regions, the SCF displayed a significant north-south declining trend with a rate of 0.03 per degree of latitude, and the SCR showed a similar north-south gradient. In the mountainous areas, the SCF significantly increased with altitude by 0.12 per kilometer. Within the study area, the SCF in 65% of the study area experienced an increasing trend, but only 4.3% of the SCF-increasing pixels passed a significance test. The remaining 35% of the area underwent a decreasing trend of SCF, but only 5.2% of the SCF-decreasing pixels passed a significance test. For the entire Central Asia, the inter-annual variations of snow-cover presented a slight and insignificant increase trend from 2000 to 2015. However, the change trends of snow cover are different between the plain and mountainous regions. That is, the annual mean SCR in the plain areas displayed an increasing trend, but a decreasing trend was found in the mountainous areas.

Spectral Reflectance Characteristics of Different Snow and Snow-Covered Land Surface Objects and Mixed Spectrum Fitting

The field spectroradiometer was used to measure spectra of different snow and snow-covered land surface objects in Beijing area.The result showed that for a pure snow spectrum,the snow reflectance peaks appeared from visible to 800 nm band locations;there was an obvious absorption valley of snow spectrum near 1 030 nm wavelength.Compared with fresh snow,the reflection peaks of the old snow and melting snow showed different degrees of decline in the ranges of 300～1 300,1 700～1 800 and 2 200～2 300 nm,the lowest was from the compacted snow and frozen ice.For the vegetation and snow mixed spectral characteristics,it was indicated that the spectral reflectance increased for the snow-covered land types(including pine leaf with snow and pine leaf on snow background), due to the influence of snow background in the range of 350～1 300 nm.However, the spectrum reflectance of mixed pixel remained a vegetation spectral characteristic.In the end,based on the spectrum analysis of snow,vegetation,and mixed snow/vegetation pixels,the mixed spectral fitting equations were established,and the results showed that there was good correlation between spectral curves by simulation fitting and observed ones(correlation coefficient R2=0.950 9).

Characterization and quantification of within-year variation of snow-cover elevation in mountainous regions, eastern Tibet

Fluctuating snow-covered elevation （FSCE） was conceptualized and proposed to characterize within-year variation of the border between snow-covered and snow-free area in mountainous regions. Two parameters, namely median of snow flee period （Tm） and snow free duration （AT）, were defined to quantify FSCE. A regression model of FSCE was developed for the Lhasa River Basin, Niyang River Basin, and Changdu region in eastern Tibet. Statistical analysis of the snow-products data with a spatial resolution of 25km×25km shows that： （1） Tm correlates weakly with geographical and topographic factors, having the yearly mean value of July 31; （2） △T correlates significantly with the average elevation of the snow-products cell, having the yearly mean value of nearly five months （i.e., 151 days）; （3） the region begins snow disappearance in late April and finishes snow coverage in mid November, being snow-free from late June to mid September and snow-covered from December to March in the next year. In addi- tion, snow-products with higher spatial resolution will be helpful to characterize FSCE in smaller spatial scales.

Treasure of the Snow-covered Highland

Tibet, known as the“roof of the world”, has long been a destination of pilgrims from around the globe for its unique and mysterious culture. And antiquities and relics excavated from this area, which are testament to the Tibetans’ diligence and intelligence, have been an abundant resource of the famous Tibetan culture. About 50,000 years ago , the ancestors of the Tibetans entered the Stone Age. The pottery shown in picture one, a monkey-faced sculpture excavated in Qu Gong, is the remains of the decorative part of a large piece of earthenware. It illustrates the widespread idea that the

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.

NORMALIZED DIFFERENCE SNOW INDEX SIMULATION FOR SNOW-COVER MAPPING IN FOREST BY GEOSAIL MODEL

The snow-cover mapping in forest area is always one of the difficult points for optical satellite remote sensing. To investigate reflectance variability and to improve the mapping of snow in forest area, GeoSail model was used to simulate the reflectance of a snow-covered forest. Using this model, the effects of varying canopy density, solar illumination and view geometry on the performance of the MODIS (Moderate-resolution Imaging Spectroradiometer) snow-cover mapping algorithm were investigated. The relationship between NDSI (Normalized Difference Snow Index), NDVI (Normalized Difference Vegetation Index) and snow fraction was discussed in detail. Results indicated that the weak performance would be achieved if fixed criteria were used for different regions especially in the complicated land cover components. Finally, some suggestions to MODIS SNOWMAP algorithm were put forward to improve snow mapping precision in forest area based on the simulation, for example, new criteria should be used in coniferous forest, that is, NDSI greater than 0.3 and NDVI greater than zero. Otherwise, a threshold on view zenith angle may be used in the criteria such as 45°.

Monitoring snow-cover area change in Antarctic coastline region using MODIS product data

Based on MODIS snow products, this article studied the changes of snow cover area during 2003--2006 along the coastline of the Antarctic, and 18 typical regions were chosen for further analysis. The result showed that the change of snow cover area was in a fluctuant downward trend as a whole, and more fluctuated obviously in warm season than in cold season. In temporal scale ： for the season cycle, the snow cover extent increased rapidly in cold season （Apr-Oct） , while the performance in warm season （Nov-Mar） was not exactly the same during the four years, the snow cover extent decreased in the first and then increased in 2004 and 2006, however, increased firstly and then decreased but reduced as a whole in 2005, for the inter-annual cycle, snow cover extent was the largest in 2003, but reached to the lowest level in 2004, and then increased gradually in 2005 and 2006, whereas, it declined with fluctuant as a whole. In spatial scale, changes mainly centralized along the coastline, moreover, it was more remarkable in the West Antarctic than in the East Antarctic, especially in the Antarctic Peninsula region.

Snow-covered pavilions

on Gushan Hill on Hangzhou’s West Lake;fabled as the place where the couple in the Legend of White Snake met for the first time,the Broken Bridge(right]is famous for its snowy scenery and is popular with young lovers.

CMIP6耦合模式对青藏高原积雪的未来预估

基于第六次国际耦合模式比较计划(CMIP6)的历史模拟试验以及情景预估试验数据,分析了21世纪中(2035—2064年)、后期(2070—2099年)青藏高原积雪相对于参考期(1985—2014年)的变化。结果表明:相对于参考期,21世纪中、后期青藏高原平均年积雪日数、平均积雪期均表现为减少,减少幅度总体随着人为辐射强迫的增加而加大;除低强迫情景外,21世纪后期的减少幅度均大于21世纪中期;空间上总体表现为青藏高原东南部的减少幅度大于西北部。21世纪中、后期青藏高原积雪初日均表现为推迟、积雪终日均表现为提前,积雪初日推迟天数是积雪终日提前天数的1.5~2.0倍;人为辐射强迫越高,积雪初(终)日推迟(提前)天数越多;相同情景下21世纪后期积雪初(终)日推迟(提前)天数均多于21世纪中期。降雪(气温)与年积雪日数呈正(负)相关;随着人为辐射强迫的增加,降雪对年积雪日数的相对贡献率总体呈增加趋势;空间特征表现为降雪(气温)对青藏高原南部和北部(东部和西部)的年积雪日数的相对贡献更大。7—12月降雪的减少幅度大于1—6月,这可能是积雪初日推迟天数多于积雪终日提前天数的重要原因。不同情景下青藏高原未来积雪变化差异明显,由此可见,控制温室气体排放对减缓未来青藏高原积雪的减少速率至关重要。

不均匀积雪致大跨网架结构局部损伤分析

网架结构管球连接处局部损伤是此类结构的重要安全隐患。在风致雪漂移引起的不均匀积雪荷载作用下,局部损伤会加剧,最终导致结构局部破坏。因此很有必要分析不均匀积雪荷载作用下带伤服役网架结构局部损伤劣化情况。采用CFD数值模拟技术,以一正放四角锥网架结构为研究背景,分析了在90°风向角、12 m/s风速下,持续降雪24 h中网架结构屋面不均匀积雪分布的变化情况,并建立可表征网架结构管球连接处存在裂纹损伤的多尺度模型,分析了网架结构节点存在不同裂纹尺寸局部损伤时,不均匀积雪致网架结构局部损伤劣化程度。结果表明:网架结构风致积雪不均匀程度非常显著,而且当网架结构管球连接处存在局部裂纹损伤时,在降雪中后期,有管球连接损伤的节点大多都出现了不同程度的裂纹扩展,节点为最不利分布的穿透型裂纹时,裂纹扩展最大为15.64 mm。说明在持续特大降雪这种极端荷载作用下,带伤服役网架结构局部损伤将进一步加剧,危及结构使用安全。

雪被厚度对土壤温湿度的影响

以牡丹江森林生态系统国家定位观测研究站为平台,针对雪被覆盖期(积雪形成期、积雪稳定期、积雪融化期)雪被厚度、土壤温度、土壤含水量三者间的相关关系开展研究。结果表明,土壤含水量在积雪期呈现先降低再升高趋势,同一时期的不同雪被厚度土壤含水量无显著差异;积雪形成期及稳定期,雪被厚度与土壤温度呈显著正相关,随着雪被厚度增加,土壤温度升高。该研究对加深非生长季土壤生态认知、解释温度升高、林窗或林冠下生境变化等规律具有重要意义。

积雪变化对陆地生态系统植被特征和土壤碳氮过程的影响

在季节性积雪地区,冬季气候变暖导致积雪变薄、积雪不连续、融雪提前及雪盖面积缩小等现象。然而相较于氮沉降、增温、降水变化等全球变化因子,目前尚缺乏积雪因子对陆地生态系统过程和功能影响的系统报道。为加深人们对积雪特征变化生态后果的认知,综述了积雪深度和融雪时间变化对植被物候和群落组成、凋落物分解、土壤碳氮过程、温室气体排放和土壤微食物网(土壤动物和微生物)的影响。由于模拟积雪变化手段不同和复杂的气候、土壤背景,生态系统各要素对积雪特征变化的响应规律存在较大的分异和不确定性。例如,在未来气候变暖导致积雪变薄和融雪提前情景下,植被物候提前,生长季延长,导致生产力增加和凋落物数量增加,禾草比例减少导致凋落物质量增加,早春温度高刺激微生物活性,凋落物分解速率高,促进土壤碳氮周转过程。但积雪减少和融雪提前导致的早春低温和夏季干旱也可能引起植被生产力下降,凋落物数量减少质量降低,土壤微生物活性低,分解速率低,从而减缓碳氮周转过程。此外,积雪特征变化对植被特征和土壤碳氮过程影响相关研究目前还存在以下问题:1)积雪深度和融雪时间对生态系统的影响是否存在交互效应仍缺乏关注,且积雪变化对后续生长季是否存在持续效应也不明确;2)积雪因子对植被、土壤碳氮动态过程和土壤生物的影响,各生态要素研究相对较为独立;3)积雪变化引起对土壤地化循环过程影响的微生物驱动机制缺乏组学数据支撑;4)缺乏遥感手段反演各类影响生态系统功能和过程的积雪参数。应加强植被群落-土壤碳氮过程-土壤微食物网生态关联研究、基于基因组学的土壤微生物群落组成和生态功能研究和遥感相关技术研究,以期为发展积雪生态学提供参考。

青藏高原地区积雪与雪线高度时空变化研究

积雪对气候变化具有高度敏感性,研究积雪变化对区域水循环及生态环境演变具有重要意义。基于遥感数据和河流水系分布情况,将青藏高原划分为12个子流域,分析了青藏高原及其子流域的积雪深度、积雪覆盖率、雪线高度的时空变化特征。结果表明:①1979—2020年青藏高原积雪深度呈明显降低趋势,空间上积雪深度由中心区域向四周递增,阿姆河流域多年平均积雪深度最大,印度河流域的次之。②2000-2015年青藏高原多年平均积雪覆盖率为29.66%,呈平缓的下降趋势,印度河流域的积雪覆盖率最大,高达39.83%,塔里木河的次之。③青藏高原雪线高度的变化范围为[4700,5000]m,夏季的雪线高度整体偏高,在8月达到最大值;各子流域雪线高度由大到小的排序依次为雅鲁藏布江流域、印度河流域、河西流域、恒河流域、长江流域、怒江流域、阿姆河流域、塔里木河流域、柴达木河流域、内河流域、黄河流域、澜沧江流域。研究结果对寒区水资源管理和生态环境可持续发展具有重要意义。

2000~2021年北半球多年冻土区NDVI变化趋势及其影响因素

气候变暖正在导致北半球多年冻土区的土地覆被类型和植被生物量发生快速变化,而不同冻土类型区和不同土地覆被类型区对气候变化的响应程度尚不清楚。基于Slope趋势分析和皮尔逊相关性分析,量化了2000~2021年北半球多年冻土区归一化植被指数(NDVI)的时空变化及其对气候变化的响应。结果表明:约21.43%的多年冻土区NDVI值表现出显著增长趋势,其中连续和不连续多年冻土区的NDVI值增长速率是零星多年冻土区的2~3倍。在月尺度上,约33.75%多年冻土区的NDVI值在6月呈显著增长趋势,其中连续多年冻土区和灌丛植被类型区的增长速率最快。气温、降水量和活动层厚度均呈显著上升趋势,积雪覆盖率呈下降趋势。气温升高对俄罗斯等低纬度冻土区的植被生长起到了促进作用;降水在蒙古高原等一些特定干旱区对植被生长具有促进作用,但在俄罗斯中部和加拿大南部存在不利影响;积雪对于俄罗斯南部等积雪覆盖较低地区的植被生长有促进作用,而对于北极等积雪覆盖较高的地区存在不利影响;活动层厚度的增加有助于俄罗斯北部等冻土区的植被加速生长。总之,北半球多年冻土区植被整体呈增长趋势,气温升高仍然是北半球多年冻土区植被生长的主控因素,但不同多年冻土类型区的NDVI值增长有着明显的月份差异,因此在以后植被模型的发展和改进时需要考虑月份的差异。

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。

2002—2021年北半球中高纬度典型山脉积雪的时空变化对比分析

积雪是冰冻圈的重要组成部分,其对气候变化的响应存在明显的空间差异。阿尔泰山脉、阿尔卑斯山脉和喀斯喀特山脉分别是亚洲、欧洲和北美洲最重要的山脉之一,三条山脉同处于北半球中高纬度,具有丰富的积雪资源。本文基于MODIS每日积雪产品,获得阿尔泰山脉、阿尔卑斯山脉和喀斯喀特山脉的积雪覆盖率、积雪日数、积雪初日和积雪终日四个积雪参数,对比分析了三条山脉2002—2021年积雪的时空变化,并研究了气候因子对积雪参数的影响。结果表明,三条山脉积雪参数的空间分布差异明显。阿尔泰山脉积雪覆盖率最大,积雪日数最长,积雪初日最早,积雪终日最晚,分别为38.00%、141 d、66 d、207 d;阿尔卑斯山脉的积雪覆盖率、积雪日数、积雪初日、积雪终日分别为21.68%、79 d、97 d、194 d;喀斯喀特山脉的积雪覆盖率最小,积雪日数最短,积雪初日最晚,积雪终日最早,分别为15.18%、56 d、103 d、183 d。就趋势而言,阿尔泰山脉积雪覆盖率增大,更早的积雪和更晚的融雪使积雪日数增加;阿尔卑斯山脉积雪覆盖率减小,更早的融雪使得积雪日数减少;喀斯喀特山脉积雪覆盖率增大,更晚的融雪使得积雪日数增加。各气候因子中,地表温度对三条山脉积雪的影响比降水大。对三条山脉积雪时空变化的一致性对比分析,有助于全面了解北半球中高纬度山区积雪对气候变化响应的时空差异。

青藏高原冬春多源积雪资料年际变化尺度上的适用性分析

青藏高原冬春积雪变化具有显著的年际变化特征,其对中国东部夏季降水预测具有一定指示意义。由于特殊的复杂地形,青藏高原气象站点分布稀疏且不均匀,再分析数据和卫星数据提供的高原积雪资料的不确定性是影响和制约积雪变化及其天气气候效应研究中的一个关键问题。本文基于青藏高原台站观测、再分析(ERA5和NOAA-V3)和卫星反演(MODIS雪盖以及IMS雪盖)的多源积雪资料,采用偏差分析、均方根误差以及相关分析等多元统计方法重点检验了多源高原积雪数据在描述积雪年际变化特征方面的不确定性。通过比较不同积雪资料的时空分布和变化特征,以期提升多源高原积雪资料适用性的认知,并为相关研究提供有意义的参考。分析结果表明:(1)就再分析数据给出的积雪资料而言,ERA5雪深资料相较NOAA-V3雪深,对高原站点观测雪深的描述效果更好。除了高原中东部分站点外,ERA5雪深数据的平均偏差和平均均方根误差均较小,而NOAA-V3雪深数据的平均偏差和均方根误差在整个高原范围内均存在一定程度的高估;(2)再分析(ERA5和NOAA-V3)和卫星反演(MODIS雪盖以及IMS雪盖)积雪数据和高原站点雪深均在年际变化特征上具有较好的一致性;其中ERA5再分析积雪数据与高原站点观测雪深数据在长期趋势上更为类似;另外,北半球雪盖与站点观测雪深的年代际变化趋势更加类似;(3)再分析资料(ERA5、NOAA-V3)的雪深和雪盖在高原的分布存在空间差异性,其中NOAA-V3在多雪年和少雪年的大值区都位于高原南部、北部边缘以及高原中东部,而ERA5的大值区主要分布在高原的中东部、西部以及南部边缘。然而,每种再分析数据各自的雪深和雪盖之间差异较小,且积雪异常年份的差值分布较为一致,无论是雪深还是雪盖,正异常区都位于高原中部、西部和南部边缘,负异常区则都在高原北部;(4)三种卫星的雪盖在多雪年大值区都位于高原中东部、南部以及西部地区。不同卫星数据在积雪异常增多年份雪盖的差值分布也较为一致,但再分析资料体现出的高原北部边缘的负异常并未在卫星雪盖资料上体现出来,此结果可能与卫星反演积雪数据的涵盖时间短以及处理方式不同有关。

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。

冀西北地区高速公路防雪网板数值模拟研究

公路是现代文明的重要标志,也是经济和社会发展的根本需求。确保公路的正常运行是社会发展的基础。冀西北地区特殊的地形特征和气候条件,使得该地区公路极易形成风积雪灾害,造成交通瘫痪,道路封闭,影响居民正常出行,给该地区人民的生活带来了极大的不便。针对冀西北寒冷地区的地形地貌和气候特点,借助Fluent软件,对不同形状、不同孔隙率的防雪网板前后风速流场变化进行数值模拟,得出防雪网板设计的最佳参数,并运用到实际工程中,对改善冀西北地区高速公路风积雪灾害,保障公路正常通行具有重要实际意义。为冀西北地区高速公路风积雪的防治措施提供一个切实可行的方案。通过分析研究得出以下结论:孔隙率为20%和30%的网板防雪效果优于其他孔隙率网板;有折角的蝶形网板在相同孔隙率情况下比平面网板防雪效果好。