Estimating snow depth or snow water equivalent from space

Satellite remote sensing is widely used to estimate snow depth and snow water equivalent(SWE)which are two key parameters in global and regional climatic and hydrological systems.Remote sensing techniques for snow depth mainly include passive microwave remote sensing,Synthetic Aperture Radar(SAR),Interferometric SAR(In SAR)and Lidar.Among them,passive microwave remote sensing is the most efficient way to estimate large scale snow depth due to its long time series data and high temporal frequency.Passive microwave remote sensing was utilized to monitor snow depth starting in 1978 when Nimbus-7 satellite with Scanning Multichannel Microwave Radiometer(SMMR)freely provided multi-frequency passive microwave data.SAR was found to have ability to detecting snow depth in 1980 s,but was not used for satellite active microwave remote sensing until 2000.Satellite Lidar was utilized to detect snow depth since the later period of 2000 s.The estimation of snow depth from space has experienced significant progress during the last 40 years.However,challenges or uncertainties still exist for snow depth estimation from space.In this study,we review the main space remote sensing techniques of snow depth retrieval.Typical algorithms and their principles are described,and problems or disadvantages of these algorithms are discussed.It was found that snow depth retrieval in mountainous area is a big challenge for satellite remote sensing due to complicated topography.With increasing number of freely available SAR data,future new methods combing passive and active microwave remote sensing are needed for improving the retrieval accuracy of snow depth in mountainous areas.

Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid region of Iran

Direct measurement of snow water equivalent(SWE)in snow-dominated mountainous areas is difficult,thus its prediction is essential for water resources management in such areas.In addition,because of nonlinear trend of snow spatial distribution and the multiple influencing factors concerning the SWE spatial distribution,statistical models are not usually able to present acceptable results.Therefore,applicable methods that are able to predict nonlinear trends are necessary.In this research,to predict SWE,the Sohrevard Watershed located in northwest of Iran was selected as the case study.Database was collected,and the required maps were derived.Snow depth(SD)at 150 points with two sampling patterns including systematic random sampling and Latin hypercube sampling(LHS),and snow density at 18 points were randomly measured,and then SWE was calculated.SWE was predicted using artificial neural network(ANN),adaptive neuro-fuzzy inference system(ANFIS)and regression methods.The results showed that the performance of ANN and ANFIS models with two sampling patterns were observed better than the regression method.Moreover,based on most of the efficiency criteria,the efficiency of ANN,ANFIS and regression methods under LHS pattern were observed higher than the systematic random sampling pattern.However,there were no significant differences between the two methods of ANN and ANFIS in SWE prediction.Data of both two sampling patterns had the highest sensitivity to the elevation.In addition,the LHS and the systematic random sampling patterns had the least sensitivity to the profile curvature and plan curvature,respectively.

Responses and changes in the permafrost and snow water equivalent in the Northern Hemisphere under a scenario of 1.5℃ warming

In this study, the period that corresponds to the threshold of a 1.5℃ rise (relative to 1861e1880) in surface temperature is validated using a multi-model ensemble mean from 17 global climate models in the Coupled Model Intercomparison Project Phase 5 (CMIP5). On this basis, the changes in permafrost and snow cover in the Northern Hemisphere are investigated under a scenario in which the global surface temperature has risen by 1.5℃, and the uncertainties of the results are further discussed. The results show that the threshold of 1.5℃ warming will be reached in 2027, 2026, and 2023 under RCP2.6, RCP4.5, RCP8.5, respectively. When the global average surface temperature rises by 1.5℃, the southern boundary of the permafrost will move 1e3.5
northward (relative to 1986e2005), particularly in the southern Central Siberian Plateau. The permafrost area will be reduced by 3.43x106 km2 (21.12%), 3.91x106 km2 (24.1%) and 4.15x106 km2 (25.55%) relative to 1986e2005 in RCP2.6, RCP4.5 and RCP8.5, respectively. The snow water equivalent will decrease in over half of the regions in the Northern Hemisphere but increase only slightly in the Central Siberian Plateau. The snow water equivalent will decrease significantly (more than 40% relative to 1986e2005) in central North America, western Europe, and northwestern Russia. The permafrost area in the QinghaieTibet Plateau will decrease by 0.15x106 km2 (7.28%), 0.18x 106 km2 (8.74%), and 0.17x106 km2 (8.25%), respectively, in RCP2.6, RCP4.5, RCP8.5. The snow water equivalent in winter (DJF) and spring (MAM) over the QinghaieTibet Plateau will decrease by 14.9% and 13.8%, respectively.

An enhanced method for estimating snow water equivalent in the central part of the Tibetan Plateau using raster segmentation and eigenvector spatial filtering regression model

Snow water equivalent(SWE)is an important factor reflecting the variability of snow.It is important to estimate SWE based on remote sensing data while taking spatial autocorrelation into account.Based on the segmentation method,the relationship between SWE and environmental factors in the central part of the Tibetan Plateau was explored using the eigenvector spatial filtering(ESF)regression model,and the influence of different factors on the SWE was explored.Three sizes of 16×16,24×24 and 32×32 were selected to segment raster datasets into blocks.The eigenvectors of the spatial adjacency matrix of the segmented size were selected to be added into the model as spatial factors,and the ESF regression model was constructed for each block in parallel.Results show that precipitation has a great influence on SWE,while surface temperature and NDVI have little influence.Air temperature,elevation and surface temperature have completely different effects in different areas.Compared with the ordinary least square(OLS)linear regression model,geographically weighted regression(GWR)model,spatial lag model(SLM)and spatial error model(SEM),ESF model can eliminate spatial autocorrelation with the highest accuracy.As the segmentation size increases,the complexity of ESF model increases,but the accuracy is improved.

Multi-Satellite and Sensor Derived Trends and Variation of Snow Water Equivalent on the High-Latitudes of the Northern Hemisphere

Utilizing more than 30 years of satellite-microwave sensor derived snow water equivalent data on the high-latitudes of the northern hemisphere we investigate regional trends and variations relative to elevation. On the low-elevation tundra regions encircling the Arctic we find high statistically significant trends of snow water equivalent. Across the high Arctic Siberia and Far East Russia through North America and northern Greenland we find increasing trends of snow water equivalent with local region variations in strength. Yet across the high Arctic of western Russia through Norway we find decreasing trends of snow water equivalent of varying strength. Power density spectra identify significant power at quasi-biennial and associated lunar nodal cycles. These cycles of the upper atmosphere circulation, ENSO and ocean circulation perturbations from tides forms the causative linkage between increasing snow water equivalent on low-elevation tundra landscapes and decreasing coastal sea ice cover as part of the Arctic system energy and mass cycles.

Remote Sensing, Model-Derived and Ground Measurements of Snow Water Equivalent and Snow Density in Alaska

Snow water equivalent (SWE) is important for investigations of annual to decadal-scale changes in Arctic environment and energy-water cycles. Passive microwave satellite-based retrieval algorithm estimates of SWE now span more than three decades. SWE retrievals by the Advanced Microwave Scanning Radiometer for the Earth Observation System (AMSR-E) onboard the NASA-Aqua satellite ended at October 2011. A critical parameter in the AMSR-E retrieval algorithm is snow density assumed from surveys in Canada and Russia from 1940s-1990s. We compare ground SWE measurements in Alaska to those of AMSR-E, European Space Agency GlobSnow, and GIPL model. AMSR-E SWE underperforms (is less than on average) ground SWE measurements in Alaska through 2011. Snow density measurements along the Alaska permafrost transect in April 2009 and 2010 show a significant latitude-gradient in snow density increasing to the Arctic coast at Prudhoe Bay. Large differences are apparent in comparisons of our measured mean snow densities on a same snow cover class basis March-April 2009-2011 Alaska to those measured in Alaska winter 1989-1992 and Canadian March-April 1961-1990. Snow density like other properties of snow is an indicator of climate and a non-stationary variable of SWE.

Discussion of Influences on Snow Water Equivalent at Utah Snow Courses

Snow data collection systems in the western United States were originally designed to forecast water supply and may be subject to several sources of bias. In addition to climate change and weather modification effects, site-specific effects may be introduced from vegetation changes, site physical changes, measurement technique, and sensor changes. This paper examines changes in Utah＇s snowpack conditions over the past decade compared with all previous measurement years, focusing on the 15 snow courses with the longest observational record within the state of Utah. Although patterns in snowpack data consistent with those that would be expected due to temperature h as greater declines at lower elevations and latitudes--were not identified, snow water equivalent decreased at sites with significant increases in vegetation coverage. Additionally, we provide a list of 22 snow courses in Utah that are best-suited for long-term climate analysis.

Snow Water Equivalent Estimation for a Snow-Covered Prairie Grass Field by GPS Interferometric Reflectometry

The amount of water stored in snowpack is the single most important measurement for the management of water supply and flood control systems. The available water content in snow is called the snow water equivalent (SWE). The product of snow density and depth provides an estimate of SWE. In this paper, snow depth and density are estimated by a nonlinear least squares fitting algorithm. The inputs to this algorithm are global positioning system (GPS) signals and a simple GPS interferometric reflectometry (GPS-IR) model. The elevation angles of interest at the GPS receiving antenna are between 50 and 300. A snow-covered prairie grass field experiment shows potential for inferring snow water equivalent using GPS-IR. For this case study, the average inferred snow depth (17.9 cm) is within the in situ measurement range (17.6 cm ± 1.5 cm). However, the average inferred snow density (0.13 g.cm-3) overestimates the in situ measurements (0.08 g.cm-3 ± 0.02 g.cm-3). Consequently, the average inferred SWE (2.33 g.cm-2) also overestimates the in situ calculations (1.38 g.cm-2 ± 0.36 g.cm-2).

Snow water equivalent over Eurasia in the next 50 years projected by aggregated CMIP3 models

Based on remote sensing snow water equivalent （SWE） data, the simulated SWE in 20C3M experiments from 14 models attend- hag the third phase of the Coupled Models for Inter-comparison Project （CMIP3） was first evaluated by computing the different percentage, spatial correlation coefficient, and standard deviation of biases during 1979-2000. Then, the diagnosed ten models that performed better simulation in Eurasian SWE were aggregated by arithmetic mean to project the changes of Eurasian SWE in 2002-2060. Results show that SWE will decrease significantly for Eurasia as a whole in the next 50 years. Spatially, significant decreasing trends dominate Eurasia except for significant increase in the northeastern part. Seasonally, decreasing proportion will be greatest in summer indicating that snow cover in wanner seasons is more sensitive to climate warming. However, absolute decreasing trends are not the greatest in winter, but in spring. This is caused by the greater magnitude of negative trends, but smaller positive trends in spring than in winter. The changing characteristics of increasing in eastern Eurasia and decreasing in western Eurasia and over the Qinghai-Tibetan Plateau favor the viewpoint that there will be more rainfall in North China and less in the middle and lower reaches of the Yangtze River in summer. Additionally, the decreasing rate and extent with significant decreasing trends under SRES A2 are greater than those under SRES B1, indicating that the emission of greenhouse gases （GHG） will speed up the decreasing rate of snow cover both temporally and spatially. It is crucial to control the discharge of GHG emissions for mitigating the disappearance of snow cover over Eurasia.

Combined effects of snow depth and nitrogen addition on ephemeral growth at the southern edge of the Gurbantunggut Desert,China

Water and nitrogen （N） inputs are considered as the two main limiting factors affecting plant growth.Changes in these inputs are expected to alter the structure and composition of the plant community,thereby influencing biodiversity and ecosystem function.Snowfall is a form of precipitation in winter,and snow melting can recharge soil water and result in a flourish of ephemerals during springtime in the Gurbantunggut Desert,China.A bi-factor experiment was designed and deployed during the snow-covering season from 2009 to 2010.The experiment aimed to explore the effects of different snow-covering depths and N addition levels on ephemerals.Findings indicated that deeper snow cover led to the increases in water content in topsoil as well as density and coverage of ephemeral plants in the same N treatment; by contrast,N addition sharply decreased the density of ephemerals in the same snow treatment.Meanwhile,N addition exhibited a different effect on the growth of ephemeral plants：in the 50％ snow treatment,N addition limited the growth of ephemeral plants,showing that the height and the aboveground biomass of the ephemeral plants were lower than in those without N addition; while with the increases in snow depth （100％ and 150％ snow treatments）,N addition benefited the growth of the dominant individual plants.Species richness was not significantly affected by snow in the same N treatment.However,N addition significantly decreased the species richness in the same snow-covering depth.The primary productivity of ephemerals in the N addition increased with the increase of snow depth.These variations indicated that the effect of N on the growth of ephemerals was restricted by water supply.With plenty of water （100％ and 150％ snow treatments）,N addition contributed to the growth of ephemeral plants; while with less water （50％ snow treatment）,N addition restricted the growth of ephemeral plants.

Temporal-spatial characteristics of observed key parameters of snow cover in China during 1957-2009

Using observed snow cover dam from Chinese meteorological stations, this study indicated that annual mean snow depth, Snow Water Equivalent （SWE）, and snow density during 1957-2009 were 0.49 cm, 0.7 ram, and 0.14 g/cm3 over China as a whole, re- spectively. On average, they were all the smallest in the Qinghai-Tibetan Plateau （QTP）, and were greater in northwestern China （NW）. Spatially, the regions with greater annual mean snow depth and SWE were located in northeastern China including eastern Inner Mongolia （NE）, northern Xinjiang municipality, and a small fraction of southwestern QTP. Annual mean snow density was below 0.14 g/cm3 in most of China, and was higher in the QTP, NE, and NW. The trend analyses revealed that both annual mean snow depth and SWE presented increasing trends in NE, NW, the QTP, and China as a whole during 1957-2009. Although the trend in China as a whole was not significant, the amplitude of variation became increasingly greater in the second half of the 20th century. Spatially, the statistically significant （95%-level） positive trends for annual mean snow depth were located in western and northem NE, northwestem Xinjiang municipality, and northeastem QTP. The distribution of positive and negative trends for annu- al mean SWE were similar to that of snow depth in position, but not in range. The range with positive trends of SWE was not as large as that of snow depth, but the range with negative trends was larger.

Measurements of physical characteristics of summer snow cover on sea ice during the Third Chinese Arctic Expedition

The spatial distribution of snow cover on the central Arctic sea ice is investigated here based on the observations made during the Third Chinese Arctic Expedition. Six types of snow were observed during the expedition： new/recent snow, melt-fi＇eeze crust, icy layer, depth hoar, coarse-grained, and chains of depth hoar. Across most measurement areas, the snow surface was covered by a melt-freeze crust 2-3 cm thick, which was produced by alternate strong solar radiation and the sharp temperature decrease over the summer Arctic Ocean. There was an intermittent layer of snow and ice at the base of the snow pack. The mean bulk density of the snow was 304.01~29.00 kg/m3 along the expedition line, and the surface values were generally smaller than those of the sub- surface, confirming the principle of snow densification. In addition, the thicknesses and water equivalents of the new/recent and total-layer snow showed a decreasing trend with latitude, suggesting that the amount of snow cover and its spatial variations were mainly determined by precipitation. Snow temperature also presented significant variations in the vertical profile, and ablation and evaporation were not the primary factors in the snow assessment in late summer. The mean temperature of the surface snow was -2.01±0.96℃, which was much higher than that observed in the interface of snow and sea ice.

Impact of forcing data and land surface properties on snow simulation in a regional climate model:a case study over the Tianshan Mountains,Central Asia

Snow is a key variable that influences hydrological and climatic cycles.Land surface models employing snow physics-modules can simulate the snow accumulation and ablation processes.However,there are still uncertainties in modeling snow resources over complex terrain such as mountains.This study employed the National Center for Atmospheric Research’s Weather Research and Forecasting(WRF)model coupled with the Noah-Multiparameterization(Noah-MP)land surface model to run one-year simulations to assess its ability to simulate snow across the Tianshan Mountains.Six tests were conducted based on different reanalysis forcing datasets and different land surface properties.The results indicated that the snow dynamics were reproduced in a snow hydrological year by the WRF/Noah-MP model for all of the tests.The model produced a low bias in snow depth and snow water equivalent(SWE)regardless of the forcing datasets.Additionally,the underestimation of snow depth and SWE could be relatively alleviated by modifying the land cover and vegetation parameters.However,no significant improvement in accuracy was found in the date of snow depth maximum and melt rate.The best performance was achieved using ERA5 with modified land cover and vegetation parameters(mean bias=−4.03 mm and−1.441 mm for snow depth and SWE,respectively).This study highlights the importance of selecting forcing data for snow simulation over the Tianshan Mountains.

Daily snow water equivalent product with SMMR,SSM/I and SSMIS from 1980 to 2020 over China

The reliable knowledge of seasonal snow volume and its trend is very important to understand Earth’s climate system.Thus,a long-time snow water equivalent(SWE)dataset is necessary.This work presents a daily SWE product of 1980-2020 with a linear unmixing method through passive microwave data including SMMR,SSM/I and SSMIS over China after cross-calibration and bias-correction.The unbiased root-mean-square error of snow depth is about 5-7 cm,corresponding to 10-15 mm for SWE,when compared with stations measurements and field snow course data.The spatial patterns and trends of SWE over China present significant regional differences.The overall slope trend presented an insignificant decreasing pattern during 1980-2020 over China;however,there is an obvious fluctuation,i.e.a significant decrease trend during the period 1980-1990,an upward trend from 2005 to 2009,a significant downward trend from 2009 to 2018.The increase of SWE occurred in the Northeast Plain,with an increase trend of 0.2 mm per year.Whereas in the Hengduan Mountains,it presented a downward trend of SWE,up to−0.3 mm per year.In the North Xinjiang,SWE has an increasing trend in the Junggar Basin,while it shows a decreasing trend in the Tianshan and Altai Mountains.

CMIP6模式对欧亚大陆冬季雪水当量的模拟能力评估及未来预估

基于第六次耦合模式比较计划(CMIP6)的模式模拟数据和欧洲宇航局GlobSnow卫星遥感雪水当量(Snow Water Equivalent,SWE)资料,评估了CMIP6耦合模式对1981~2014年欧亚大陆冬季SWE的模拟能力,并应用多模式集合平均结果预估了21世纪欧亚大陆SWE的变化情况。结果表明,CMIP6耦合模式对冬季欧亚大陆中高纬度SWE空间分布具有较好的再现能力,能模拟出欧亚大陆中高纬度SWE的主要分布特征;耦合模式对SWE变化趋势及经验正交函数主要模态特征的模拟能力存在较大差异,但多模式集合能提高模式对SWE变化趋势和主要时空变化特征的模拟能力;此外,多模式集合结果对欧亚大陆冬季SWE与降水、气温的关系也有较好的再现能力。预估结果表明,21世纪欧亚大陆东北大部分地区的SWE均要高于基准期(1995~2014年),而90°E以西的欧洲大陆SWE基本上呈现减少的特征;21世纪早期,4种不同排放情景下积雪变化的差异不大,但21世纪后期积雪变化的幅度差异较大,而且排放越高积雪变化的幅度越大,模式不确定性也越大;进一步的分析表明,欧亚大陆冬季未来积雪变化特征的空间分布与全球变化背景下局地气温、降水的变化密切相关,高温高湿的条件有利于欧亚大陆东北部积雪的增多。

极端生态环境水循环关键参量监测技术研究现状与进展

针对高寒和干旱极端生态环境水循环变化对国家生态安全的重要性,需要开展水循环关键参量监测设备与技术研究。本文重点围绕水循环关键参量野外原位/移动/非接触式、自动、稳定监测等技术难点,开展了一系列监测生态系统水-土-气-冰-雪的关键参量的新技术设备研究。通过构建基于物联网的天地一体化生态系统监测体系,打破行业技术壁垒,促进工程技术、地质学、计算机科学等不同领域的跨学科合作,共同开展极端生态环境水循环关键参量监测技术研究与创新,并将实现对重要生态功能区的大范围、全天候、立体化监测,对推进我国生态文明建设具有重要支撑作用,促进并实现我国生态监测技术的综合应用和发展。

Estimating Ground Snow Load Based on Ground Snow Depth and Climatological Elements for Snow Hazard Assessment in Northeastern China

Extreme snow loads can collapse roofs.This load is calculated based on the ground snow load(that is,the snow water equivalent on the ground).However,snow water equivalent(SWE) measurements are unavailable for most sites,while the ground snow depth is frequently measured and recorded.A new simple practical algorithm was proposed in this study to evaluate the SWE by utilizing ground snow depth,precipitation data,wind speed,and air temperature.For the evaluation,the precipitation was clas sified as snowfall or rainfall according to the air temperature,the snowfall or rainfall was then corrected for measurement error that is mainly caused by wind-induced undercatch,and the effect of snow water loss was considered.The developed algorithm was applied and validated using data from57 meteorological stations located in the northeastern region of China.The annual maximum SWE obtained based on the proposed algorithm was compared with that obtained from the actual SWE measurements.The return period values of the annual maximum ground snow load were estimated and compared to those obtained according to the procedure suggested by the Chinese structural design code.The comparison indicated that the use of the proposed algorithm leads to a good estimated SWE or ground snow load.Its use allowed the estimation of the ground snow load for sites without SWE measurement and facilitated snow hazard mapping.

黑龙江省粮食主产区雪水当量变化对土壤墒情的影响

土壤季节性冻融现象普遍存在于我国北方寒冷地区,是土壤水分时空分布剧烈变化的关键影响因素。雪水当量对积雪及冻土融化后土壤含水量有明显指示作用,对土壤墒情监测、预估和备春耕生产有指导作用。掌握季节性冻融过程中雪水当量、土壤水分状况和变化规律具有重要意义。利用全球环境变化观测卫星(GCOM-W1),分析并监测黑龙江省大豆、玉米、水稻产区的雪水当量和土壤含水量,空间分辨率为1 km。结果表明:2022年3月中下旬雪水当量高于历史平均,积雪积累丰富;4月初积雪迅速消融,雪水当量迅速减小,黑龙江省西北、东北和南部农区雪水当量在3月下旬和4月上旬高于历史同期,后逐渐与历史持平。2022年3—4月上旬,土壤含水量增高,变化趋势与雪水当量相反;3月下旬西部农区土壤含水率高于历史同期;4月上旬西部和东部大部分农区高于历史同期,4月中旬松嫩平原西部和三江平原中西部农区大风增温,失墒明显,部分农区土壤含水率低于2021年,但与近9a平均比仍持平或偏高。主要粮食产区雪水当量与土壤含水量呈显著负相关,即随着积雪融化,土壤含水量数值迅速增加;三种主要作物中,大豆产区随雪水当量减小,对土壤含水量影响最为显著。

雪数据集研究综述

大范围的雪盖变化是气候变化的指示剂,雪通过其自身的物理性质调节着地气之间的物质与能量循环,还能影响地表径流,调节水文循环,甚至在全球生态系统中发挥着重要的作用,因此建立数据集,实时监测雪盖的变化就变得非常必要。通过阅读大量文献,总结目前应用较为广泛的数据集,主要包括可见光/近红外数据集如NOAA数据集、MODIS数据集,微波数据集如SMMR数据集、AMSR-E数据集,以及多数据源的数据集。分析各种数据集的生成原理和优缺点,对目前雪数据集研究中可能存在的问题进行总结。

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.