Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: Instrumental bias and variability induced by observers

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.     

基于IAP AGCM4的欧亚大陆春季雪水当量可预报性评估

欧亚大陆积雪是重要的气候预测因子,评估其在气候模式中的预测潜力可为季节气候预测和模式发展提供重要参考。本文利用IAP AGCM4的多年集合后报结果,分析了欧亚大陆春季雪水当量的可预报性。结果表明该模式对提前1月后报的欧亚大陆春季雪水当量的空间分布,主要模态及变化趋势具有较好的可预报能力。此外模式对欧亚中高纬积雪的年际异常也具有较高的预报技巧,特别是高纬度区域。可预报性来源分析则表明,大气初始异常对欧亚中高纬积雪可预报性的影响与海温异常相比显得更为重要。     

A temperature‐precipitation‐based model of thirty‐year mean snowpack accumulation and melt in Oregon,USA

High‐resolution, spatially extensive climate grids can be useful in regional hydrologic applications. However, in regions where precipitation is dominated by snow, snowmelt models are often used to account for timing and magnitude of water delivery. We developed an empirical, nonlinear model to estimate 30‐year means of monthly snowpack and snowmelt throughout Oregon. Precipitation and temperature for the period 1971–2000, derived from 400‐m resolution PRISM data, and potential evapotranspiration (estimated from temperature and day length) drive the model. The model was calibrated using mean monthly data from 45 SNOTEL sites and accurately estimated snowpack at 25 validation sites: R² = 0·76, Nash‐Sutcliffe Efficiency (NSE) = 0·80. Calibrating it with data from all 70 SNOTEL sites gave somewhat better results (R² = 0·84, NSE = 0·85). We separately applied the model to SNOTEL stations located < 200 and ≥ 200 km from the Oregon coast, since they have different climatic conditions. The model performed equally well for both areas. We used the model to modify moisture surplus (precipitation minus potential evapotranspiration) to account for snowpack accumulation and snowmelt. The resulting values accurately reflect the shape and magnitude of runoff at a snow‐dominated basin, with low winter values and a June peak. Our findings suggest that the model is robust with respect to different climatic conditions, and that it can be used to estimate potential runoff in snow‐dominated basins. The model may allow high‐resolution, regional hydrologic comparisons to be made across basins that are differentially affected by snowpack, and may prove useful for investigating regional hydrologic response to climate change. Published in 2011 by John Wiley & Sons, Ltd.     

被动微波遥感积雪参数反演方法进展

雪深(snow depth,SD)和雪水当量(snow water equivalent,SWE)是气候水文研究中的重要参数,在雪灾监测中尤为重要。首先,简要介绍了被动微波遥感SD和SWE反演算法的物理基础——积雪微波辐射传输模型,分析了不同微波频段、不同特点的积雪微波辐射和散射特性。然后,根据前人的研究从数学角度将反演算法分为线性亮温梯度法和基于先验知识法,总结了2类算法的优势和局限性:线性亮温梯度法相对简单、速度快,一般只适用于特定的研究区;先验知识法需要获取研究区的样本数据,并反复训练才能达到较好的精度,但对样本的独立性及其均值差异显著性的要求较高。最后,重点介绍了我国风云三号微波成像仪(FY-3 MWRI)的全球SD和SWE反演算法和针对中国区域的改进算法,并对未来的研究热点进行了展望。     

被动微波遥感反演中国西部地区雪深、雪水当量算法初步研究

雪深、雪水当量是积雪研究中重要参数，其在流域水量平衡和融雪径流预报以及雪灾监测与评价中起着重要作用。Chang等(1987)以辐射传输理论和米氏散射为理论基础，假定积雪密度和颗粒大小为常数，利用实测雪深数据和SMMR的亮温数据，通过统计回归方法，建立了雪深与18 GHz和37 GHz水平极化的亮温梯度之间的关系，发展了SMMR半经验的反演雪深的算法。后在此基础上又发展了针对SSM/I的半经验反演雪深算法。2002年发射的装载于Aqua卫星上的AMSR E是新一代的被动微波辐射计，性能较以往星载被动微波辐射计有较大提高，采用了改进后的SSM/I的半经验算法作为其估算全球雪水当量的反演算法。 将AMSR E的雪水当量产品与气象台站观测的雪水当量进行比较，发现在新疆地区和青藏高原地区雪水当量的RMSE分别达到31.8 mm和21 mm。本研究旨在建立基于AMSR E亮温数据，适用于中国西部地区的雪深和雪水当量反演算法。首先收集整理了2003年新疆地区的雪深、雪水当量数据和AMSR E亮温数据，去除错误样本，利用统计回归的方法，建立了新疆的反演雪深、雪水当量的半经验算法，算法中加入积雪覆盖度参数，较以往的算法有所改进，与气象台站观测数据比较，结果也表明新疆地区建立的经验算法较AMSR E的雪水当量算法有较大改进，RMSE为15.7 mm。但青藏高原地区因海拔高，地形复杂，大部分地区积雪较浅,空间分布不均和冻土存在等诸多因素运用同样的方法建立反演算法，结果不甚理想，以后的研究将重点消除这些干扰因素。     

On the evaluation of snow water equivalent estimates over the terrestrial Arctic drainage basin

Comparisons between snow water equivalent (SWE) and river discharge estimates are important in evaluating the SWE fields and to our understanding of linkages in the freshwater cycle. In this study, we compared SWE drawn from land surface models and remote sensing observations with measured river discharge (Q) across 179 Arctic river basins. Over the period 1988‐2000, basin‐averaged SWE prior to snowmelt explains a relatively small (yet statistically significant) fraction of interannual variability in spring (April–June) Q, as assessed using the coefficient of determination (R²). Averaged across all basins, mean R²s vary from 0·20 to 0·28, with the best agreement noted for SWE drawn from a simulation with the Pan‐Arctic Water Balance Model (PWBM) forced with data from the European Centre for Medium‐Range Weather‐Forecasts (ECMWF) Re‐analysis (ERA‐40). Variability and magnitude in SWE derived from Special Sensor Microwave Imager (SSM/I) data are considerably lower than the variability and magnitude in SWE drawn from the land surface models, and generally poor agreement is noted between SSM/I SWE and spring Q. We find that the SWE versus Q comparisons are no better when alternate temporal integrations–using an estimate of the timing in basin thaw–are used to define pre‐melt SWE and spring Q. Thus, a majority of the variability in spring discharge must arise from factors other than basin snowpack water storage. This study demonstrates how SWE estimated from remote sensing observations, or general circulation models (GCMs), can be evaluated effectively using monthly discharge data or SWE from a hydrological model. Copyright © 2007 John Wiley & Sons, Ltd.     

Projected 21st century climate change on snow conditions over Shasta Dam watershed by means of dynamical downscaling

Snow is an important component of the Earth's climate system and is particularly vulnerable to global warming. It has been suggested that warmer temperatures may cause significant declines in snow water content and snow cover duration. In this study, snowfall and snowmelt were projected by means of a regional climate model that was coupled to a physically based snow model over Shasta Dam watershed to assess changes in snow water content and snow cover duration during the 21st century. This physically based snow model requires both physical data and future climate projections. These physical data include topography, soils, vegetation, and land use/land cover, which were collected from associated organizations. The future climate projections were dynamically downscaled by means of the regional climate model under 4 emission scenarios simulated by 2 general circulation models (fifth‐generation of the ECHAM general circulation model and the third‐generation atmospheric general circulation model). The downscaled future projections were bias corrected before projecting snowfall and snowmelt processes over Shasta Dam watershed during 2010–2099. This study's results agree with those of previous studies that projected snow water equivalent is decreasing by 50–80% whereas the fraction of precipitation falling as snowfall is decreasing by 15% to 20%. The obtained projection results show that future snow water content will change in both time and space. Furthermore, the results confirm that physical data such as topography, land cover, and atmospheric–hydrologic data are instrumental in the studies on the impact of climate change on the water resources of a region.     

Analysing the contribution of snow water equivalent to the terrestrial water storage over Canada

In this study, the spatial and temporal variabilities of terrestrial water storage anomaly (TWSA) and snow water equivalent anomaly (SWEA) information obtained from the Gravity Recovery and Climate Experiment (GRACE) twin satellites data were analysed in conjunction with multisource snow products over several basins in the Canadian landmass. Snow water equivalent (SWE) data were extracted from three different sources: Global Snow Monitoring for Climate Research version 2 (GlobSnow2), Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and Canadian Meteorological Centre (CMC). The objective of the study was to understand whether SWE variations have a significant contribution to terrestrial water storage anomalies in the Canadian landmass. The period was considered from December 2002 to March 2011. Significant relationships were observed between TWSA and SWEA for most of the 15 basins considered (53% to 80% of the basins, depending on the SWE products considered). The best results were obtained with the CMC SWE products compared with satellite-based SWE data. Stronger relationships were found in snow-dominated basins (R_s > = 0.7), such as the Liard [root mean square error (RMSE) = 21.4 mm] and Peace Basins (RMSE = 26.76 mm). However, despite high snow accumulation in the north of Quebec, GRACE showed weak or insignificant correlations with SWEA, regardless of the data sources. The same behaviour was observed in the Western Hudson Bay basin. In both regions, it was found that the contribution of non-SWE compartments including wetland, surface water, as well as soil water storages has a significant impact on the variations of total storage. These components were estimated using the Water-Global Assessment and Prognosis Global Hydrology Model (WGHM) simulations and then subtracted from GRACE observations. The GRACE-derived SWEA correlation results showed improved relationships with three SWEA products. The improvement is particularly important in the sub-basins of the Hudson Bay, where very weak and insignificant results were previously found with GRACE TWSA data. GRACE-derived SWEA showed a significant relationship with CMC data in 93% of the basins (13% more than GRACE TWSA). Overall, the results indicated the important role of SWE on terrestrial water storage variations.     

Deriving snow‐cover depletion curves for different spatial scales from remote sensing and snow telemetry data

During the melting of a snowpack, snow water equivalent (SWE) can be correlated to snow‐covered area (SCA) once snow‐free areas appear, which is when SCA begins to decrease below 100%. This amount of SWE is called the threshold SWE. Daily SWE data from snow telemetry stations were related to SCA derived from moderate‐resolution imaging spectroradiometer images to produce snow‐cover depletion curves. The snow depletion curves were created for an 80 000 km² domain across southern Wyoming and northern Colorado encompassing 54 snow telemetry stations. Eight yearly snow depletion curves were compared, and it is shown that the slope of each is a function of the amount of snow received. Snow‐cover depletion curves were also derived for all the individual stations, for which the threshold SWE could be estimated from peak SWE and the topography around each station. A station's peak SWE was much more important than the main topographic variables that included location, elevation, slope, and modelled clear sky solar radiation. The threshold SWE mostly illustrated inter‐annual consistency. Copyright © 2015 John Wiley & Sons, Ltd.     

Improved Parameterization of Snow Albedo in WRF + Noah: Methodology Based on a Severe Snow Event on the Tibetan Plateau

Snowfall and the subsequent evolution of the snowpack have a large effect on the surface energy balance and water cycle of the Tibetan Plateau (TP). The effects of snow cover can be represented by the WRF coupled with a land surface scheme. The widely used Noah scheme is computationally efficient, but its poor representation of albedo needs considerable improvement. In this study, an improved albedo scheme is developed using a satellite-retrieved albedo that takes snow depth and age into account. Numerical experiments were then conducted to simulate a severe snow event in March 2017. The performance of the coupled WRF/Noah model, which implemented the improved albedo scheme, is compared against the model’s performance using the default Noah albedo scheme and against the coupled WRF/CLM that applied CLM albedo scheme. When the improved albedo scheme is implemented, the albedo overestimation in the southeastern TP is reduced, reducing the RMSE of the air temperature by 0.7°C. The improved albedo scheme also attains the highest correlation between the satellite-derived and the model-estimated albedo, which provides for a realistic representation of both the snow water equivalent (SWE) spatial distribution in the heavy snowbelt (SWE > 6 mm) and the maximum SWE in the eastern TP. The underestimated albedo in the coupled WRF/CLM leads to underestimating the regional maximum SWE and a consequent failure to estimate SWE in the heavy snowbelt accurately. Our study demonstrates the feasibility of improving the Noah albedo scheme and provides a theoretical reference for researchers aiming to improve albedo schemes further.