Impact of a snow avalanche against an obstacle. Formation of shock waves

The paper is devoted to the effect of compressibility of the avalanche snow impacting an obstacle. Compression shocks generated by obstacle cause high pressure peaks at first instants of impact. That is why the account of compressibility is essential for the understanding of measurements and the design of structures. The main problem in calculation compression shocks in avalanches is to formulate an equation of state for moving snow in impact. Two different types of equations of state are proposed depending on the type of the avalanche (low-density and high-density flows). The approach is not totally new. It was earlier proposed mainly in Russian literature. Here a brief review of the previous work is given with discussion of some gaps in it. The theory is reformulated and further developed to account thermodynamical equations. The simplest case of a normal compression shock in an avalanche flow is studied. Examples of estimations of pressure and density behind a shock are given. It is important to emphasize that the Mach number plays an important role in the theory of compressible flows so it should be taken into account (together with the Froude number) in calculation and modelling an avalanche impact pressure.     

Evaluation of passive microwave snow water equivalent algorithms in the depth hoar-dominated snowpack of the Kuparuk River Watershed, Alaska, USA

This research investigates the utility of passive microwave remote sensing instruments to accurately determine snow water equivalent (SWE) over large spatial extents. Three existing Special Sensor Microwave Imager (SSM/I) snow water equivalent algorithms produced by Chang, Tait and Goodison were evaluated for their ability to determine snow water equivalent in a snowpack containing substantial depth hoar, large faceted snow crystals. The Kuparuk River Watershed (8140 km²) test site on the North Slope of Alaska was chosen for its snowpack containing a think depth hoar layer and long history of ground truth data. A new regional snow water equivalent algorithm was developed to determine if it could produce better results than the existing algorithms in an area known to contain significant depth hoar. The four algorithms were tested to see how well they could determine snow water equivalent: (1) on a per pixel basis, (2) across swath-averaged spatial bands of approximately 850 km², and (3) on a watershed scale. The algorithms were evaluated to see if they captured the annual spatial distribution in snow water equivalent over the watershed. Results show that the algorithms developed by Chang and from this research are generally within 3 cm of the spatially averaged snow water equivalents over the entire watershed. The algorithms produced by Chang, Tait, and in this research were able to predict the basin-wide ground measured snow water equivalent value within a percent error range from −32.4% to 24.4% in the years with a typical snowpack. None of the algorithms produce accurate results on a pixel-by-pixel scale, with errors ranging from −26% to 308%.     

Cellular-Automata Model for Dense-Snow Avalanches

This paper introduces a three-dimensional model for simulating dense-snow avalanches, based on the numerical method of cellular automata. This method allows one to study the complex behavior of the avalanche by dividing it into small elements, whose interaction is described by simple laws, obtaining a reduction of the computational power needed to perform a three-dimensional simulation. Similar models by several authors have been used to model rock avalanches, mud and lava flows, and debris avalanches. A peculiar aspect of avalanche dynamics, i.e., the mechanisms of erosion of the snowpack and deposition of material from the avalanche is taken into account in the model. The capability of the proposed approach has been illustrated by modeling three documented avalanches that occurred in Susa Valley (Western Italian Alps). Despite the qualitative observations used for calibration, the proposed method is able to reproduce the correct three-dimensional avalanche path, using a digital terrain model, and the order of magnitude of the avalanche deposit volume.     

A survey of models and algorithms for winter road maintenance. Part IV: Vehicle routing and fleet sizing for plowing and snow disposal

This is the last part of a four-part survey of optimization models and solution algorithms for winter road maintenance planning. The two first parts of the survey address system design problems for winter road maintenance. The third part concentrates mainly on vehicle routing problems for spreading operations. The aim of this paper is to provide a comprehensive survey of optimization models and solution methodologies for the routing of vehicles for plowing and snow disposal operations. We also review models for the fleet sizing and fleet replacement problems.     

Snowpack response in the Assiniboine-Red River basin associated with projected global warming of 1.0 °C to 3.0 °C

This study assesses snow response in the Assiniboine-Red River basin, located in the Lake Winnipeg watershed, due to anthropogenic climate change. We use a process-based distributed snow model driven by an ensemble of eight statistically downscaled global climate models (GCMs) to project future changes under policy-relevant global mean temperature (GMT) increases of 1.0 °C to 3.0 °C above the pre-industrial period. Results indicate that basin scale seasonal warmings generally exceed the GMT increases, with greater warming in winter months. The majority of GCMs project wetter winters and springs, and drier summers, while autumn could become either drier or wetter. An analysis of snow water equivalent (SWE) responses under GMT changes reveal higher correlations of snow cover duration (SCD), snowmelt rate, maximum SWE (SWE_max) and timing of SWE_max with winter and spring temperatures compared to precipitation, implying that these variables are predominantly temperature controlled. Consequently, under the GMT increases from 1.0 °C to 3.0 °C, the basin will experience successively shorter SCD, slower snowmelt, smaller monthly SWE and SWE_max, earlier SWE_max, and a transition from snow-dominated to rain-snow hybrid regime. Further, while the winter precipitation increases for some GCMs compensate the temperature-driven changes in SWE, the increases for most GCMs occur as rainfall, thus limiting the positive contribution to snow storage. Overall, this study provides a detailed diagnosis of the snow regime changes under the policy-relevant GMT changes, and a basis for further investigations on water quantity and quality changes.     

基于高斯差分模型的雪地扰动痕迹遥感识别

雪面上人或动物的活动信息涉及冬季动物迁移追踪、人员活动轨迹检测、安全防卫等各领域应用,利用遥感技术监测人或动物的活动痕迹可以及时掌握动物的迁移情况、江上人员活动范围等信息,进而可为相应的管理决策提供依据。利用高分辨率遥感技术,通过图像滤波等增强处理,可以识别冰雪覆盖区域经人或动物活动后产生的微弱线状扰动痕迹,为后续分析提供帮助。提出了利用高斯差分滤波器(DoG)进行冬季冰雪覆盖区域扰动痕迹识别的监测方法。通过对吉林省龙井市结冰江面上活动痕迹的实验表明,当σ取1.5时,DoG带通滤波频率与人员过江痕迹频率一致,对痕迹增强效果最明显,此时的滤波器为最佳滤波器,痕迹总体提取精度达到83.67%,优于传统的Laplacian算子、Sobel算子和Prewitt算子滤波方法。说明通过DoG滤波器处理,能够有效地突出雪面上人或动物的活动痕迹,可为进一步识别人或动物活动类型、追踪路线以及相关部门的安全巡逻提供服务。     

NASA系列雪参数反演算法在单像元内的时间序列验证与分析

NASA系列算法(Chang,NASA96和Foster算法)是被动微波遥感反演雪深、雪水当量的简单、实用的经验算法,并经过了很多学者大范围的算法验证和改进。为了进一步评价NASA系列算法在东北地区的时空适用性,于长春净月潭区域选定了一个以农田和森林为主的10km×10km被动微波遥感混合像元,在时间上连续观测整个干雪期(2014年12月至次年2月)的积雪参数和气象数据,结合FY3B卫星搭载的微波成像仪(MWRI)亮温数据,对NASA系列算法精度进行了评价分析。结果表明:对于雪深的反演,Chang算法和NASA 96算法前期反演效果较好,后期随着时间的推进高估雪深的趋势愈加明显。由于考虑了森林覆盖率的影响,NASA 96算法的反演精度更高。两种算法最大高估值分别是24.46和14.62cm,这是因为期间雪性质不断变化,尤其是雪粒径不断增大的缘故。Foster算法也严重高估了雪水当量,可能是由于积雪类型的分类系统未必适合于东北地区的积雪特征。本文的积雪连续观测数据为认识东北地区的积雪特性奠定了基础,对算法的时间序列验证与分析为雪参数反演算法的进一步改进提供了可靠依据。     

An application of MODIS data to snow cover monitoring in a pastoral area: A case study in Northern Xinjiang, China

Snow is an important land cover on the earth's surface. It is characterized by its changing nature. Monitoring snow cover extent plays a significant role in dynamic studies and prevention of snow-caused disasters in pastoral areas. Using NASA EOS Terra/MODIS snow cover products and in situ observation data during the four snow seasons from November 1 to March 31 of year 2001 to 2005 in northern Xinjiang area, the accuracy of MODIS snow cover mapping algorithm under varied snow depth and land cover types was analyzed. The overall accuracy of MODIS daily snow cover mapping algorithm in clear sky condition is high at 98.5%; snow agreement reaches 98.2%, and ranges from 77.8% to 100% over the 4-year period for individual sites. Snow depth (SD) is one of the major factors affecting the accuracy of MODIS snow cover maps. MODIS does not identify any snow for SD less than 0.5 cm. The overall accuracy increases with snow depth if SD is equal to or greater than 3 cm, and decreases for SD below 3 cm. Land cover has an important influence in the accuracy of MODIS snow cover maps. The use of MOD10A1 snow cover products is severely affected by cloud cover. The 8-day composite products of MOD10A2 can effectively minimize the effect of cloud cover in most cases. Cloud cover in excess of 10% occurs on 99% of the MOD10A1 products and 14.7% of the MOD10A2 products analyzed during the four snow seasons. User-defined multiple day composite images based on MOD10A1, with flexibilities of selecting composite period, starting and ending date and composite sequence of MOD10A1 products, have an advantage in effectively monitoring snow cover extent for regional snow-caused disasters in pastoral areas.     

Development and evaluation of a cloud-gap-filled MODIS daily snow-cover product

The utility of the Moderate Resolution Imaging Spectroradiometer (MODIS) snow-cover products is limited by cloud cover which causes gaps in the daily snow-cover map products. We describe a cloud-gap-filled (CGF) daily snow-cover map using a simple algorithm to track cloud persistence, to account for the uncertainty created by the age of the snow observation. Developed from the 0.05° resolution climate-modeling grid daily snow-cover product, MOD10C1, each grid cell of the CGF map provides a cloud-persistence count (CPC) that tells whether the current or a prior day was used to make the snow decision. Percentage of grid cells “observable” is shown to increase dramatically when prior days are considered. The effectiveness of the CGF product is evaluated by conducting a suite of data assimilation experiments using the community Noah land surface model in the NASA Land Information System (LIS) framework. The Noah model forecasts of snow conditions, such as snow-water equivalent (SWE), are updated based on the observations of snow cover which are obtained either from the MOD10C1 standard product or the new CGF product. The assimilation integrations using the CGF maps provide domain-averaged bias improvement of ~11%, whereas such improvement using the standard MOD10C1 maps is ~3%. These improvements suggest that the Noah model underestimates SWE and snow depth fields, and that the assimilation integrations contribute to correcting this systematic error. We conclude that the gap-filling strategy is an effective approach for increasing cloud-free observations of snow cover.     

Validation of the Climate-SAF surface broadband albedo product: Comparisons with in situ observations over Greenland and the ice-covered Arctic Ocean

This paper describes a validation study performed by comparing the Climate-SAF Surface Albedo Product (SAL) to ground truth observations over Greenland and the ice-covered Arctic Ocean. We compare Advanced Very High Resolution Radiometer (AVHRR)-based albedo retrievals to data from the Greenland Climate Network (GCN) weather stations and the floating ice station Tara for polar summer 2007. The AVHRR dataset consists of 2755 overpasses. The overpasses are matched to in situ observations spatially and temporally. The SAL algorithm presented here derives the surface broadband albedo from AVHRR channels 1 and 2 using an atmospheric correction, temporal sampling of an empirical Bidirectional Reflectance Distribution Function (BRDF), and a narrow-to-broadband conversion algorithm. The satellite product contains algorithms for snow, sea ice, vegetation, bare soil, and water albedo. At the Summit and DYE-2 stations on the Greenland ice sheet, instantaneous SAL RMSE is 0.073. The heterogeneous surface conditions at satellite pixel scale over the stations near the Greenland west coast increase RMSE to > 0.12. Over Tara, the instantaneous SAL RMSE is 0.069. The BRDF sampling approach reduces RMSE over the ice sheet to 0.053, and to 0.045 over Tara. Taking into account various sources of uncertainty for both satellite retrievals and in situ observations, we conclude that SAL agrees with in situ observations within their limits of accuracy and spatial representativeness.