A Conditional Probability Interpolation Method Based on a Space-Time Cube for MODIS Snow Cover Products Gap Filling

Seasonal snow cover is closely related to regional climate and hydrological processes. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products from 2001 to 2018 were applied to analyze the snow cover variation in northern Xinjiang, China. As cloud obscuration causes significant spatiotemporal discontinuities in the binary snow cover extent (SCE), we propose a conditional probability interpolation method based on a space-time cube (STCPI) to remove clouds completely after combining Terra and Aqua data. First, the conditional probability that the central pixel and every neighboring pixel in a space-time cube of 5 × 5 × 5 with the same snow condition is counted. Then the snow probability of the cloud pixels reclassified as snow is calculated based on the space-time cube. Finally, the snow condition of the cloud pixels can be recovered by snow probability. The validation experiments with the cloud assumption indicate that STCPI can remove clouds completely and achieve an overall accuracy of 97.44% under different cloud fractions. The generated daily cloud-free MODIS SCE products have a high agreement with the Landsat–8 OLI image, for which the overall accuracy is 90.34%. The snow cover variation in northern Xinjiang, China, from 2001 to 2018 was investigated based on the snow cover area (SCA) and snow cover days (SCD). The results show that the interannual change of SCA gradually decreases as the elevation increases, and the SCD and elevation have a positive correlation. Furthermore, the interannual SCD variation shows that the area of increase is higher than that of decrease during the 18 years.

Evaluation of Remote Sensing and Reanalysis Snow Depth Datasets over the Northern Hemisphere during 1980–2016

Snow cover is a key parameter of the climate system and its significant seasonal and annual variability have significant impacts on the surface energy balance and global water circulation. However, current snow depth datasets show large inconsistencies and uncertainties, which limit their applications in climate change projections and hydrological processes simulations. In this study, a comprehensive assessment of five hemispheric snow depth datasets was carried out against ground observations from 43,391 stations. The five snow depth datasets included three remote sensing datasets, i.e., Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), Advanced Microwave Scanning Radiometer-2 (AMSR2), Global Snow Monitoring for Climate Research (GlobSnow), and two reanalysis datasets, i.e., ERA-Interim and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Assessment results imply that the spatial distribution of GlobSnow and ERA-Interim exhibit overall better agreements with ground observations than other datasets. GlobSnow and ERA-Interim exhibit less uncertainty during the snow accumulation and ablation periods, respectively. In plain and forested regions, GlobSnow, ERA-Interim and MERRA-2 show better performances, while in mountain and forested mountain areas, GlobSnow exhibits the best performance. AMSR-E and AMSR2 agree better with ground observations in shallow snow condition (0–10 cm), while MERRA-2 shows more satisfying performance when snow depth exceeds 50 cm. These systematic and integrated understanding of the five representative snow depth datasets provides information on data selection and data refinement, as well as data fusion, which is our next work of interest.

Evaluation and Intercomparison of Multiple Snow Water Equivalent Products over the Tibetan Plateau

Snow cover affects the thermal conditions of the Tibetan Plateau through snow–albedo feedback and snowmelt, which, in turn, modulates the Asian summer monsoon climate. An accurate estimation of the snow condition on the Tibetan Plateau is therefore of great importance in both seasonal forecasts and climate studies. Estimation of snow water equivalent (SWE) over the Tibetan Plateau is challenging due to the high altitude, complex terrain, and insufficient in situ observations. Multiple SWE products derived from satellite estimates, reanalyses, regional climate model simulations, and land data assimilations are intercompared in terms of daily, seasonal, and annual variations and are then evaluated against in situ SWE observations. The results show a relatively consistent seasonal to interannual variability of the SWE estimates among the products. The discrepancies in magnitude are large, however, especially in winter and spring. Evaluation against in situ SWE observations indicates that none of these products is capable of accurately characterizing both the spatial pattern and temporal variations.

Rainwater propagation through snow pack during rain-on-snow events under different snow condition

The mechanisms of rainwater propagation and runoff generation during rain-on-snow (ROS) are still insufficiently known. Understanding the behaviour of liquid water within the natural snowpack is crucial especially for forecasting of natural hazards such as floods and wet snow avalanches. In this study, rainwater percolation through snow was investigated by sprinkling deuterium enriched water on snow and applying an advanced hydrograph separation technique on samples collected from the snowpack runoff. This allowed quantifying the contribution of rainwater and snowmelt in the water released from the snowpack. Four field experiments were carried out during the winter 2015 in the vicinity of Davos, Switzerland. For this purpose, large blocks of natural snow were isolated from the surrounding snowpack to inhibit lateral exchange of water. These blocks were exposed to artificial rainfall with 41 mm of deuterium enriched water. The sprinkling was run in four 30 minutes periods separated by three 30 minutes non-sprinkling periods. The runoff from the snow block was continuously gauged and sampled for the deuterium concentration. At the onset of each experiment initially present liquid water content was first pushed out by the sprinkling water. Hydrographs showed four pronounced peaks corresponding to the four sprinkling bursts. The contribution of rainwater to snowpack runoff consistently increased over the course of the experiment but never exceeded 86 %. An experiment conducted on a cold snowpack suggested the development of preferential flow paths that allowed rainwater to efficiently propagate through the snowpack limiting the time for mass exchange processes to take effect. On the contrary, experiments conducted on ripe isothermal snowpack showed a slower response behaviour and resulted in a total runoff volume which consisted of less than 50 % of the rain input.

Research of Safe Speed at Snow on Freeway Based on Braking Model of Stopping Sight Distance

The brake model derivation based on the kinematics of the stopping sight distance analysis, considering the freeway visibility and road surface friction coefficient, put forward freeway safety speed calculation model in snow condition, reach maximum safe speed of snow freeway in a different visibility and road adhesion coefficient snow condition, provide a management reference for freeway snow driving safety.

Change in snow strength caused by rain

Rain-on-snow events can cause wet snow avalanches. Laboratory experiments were carried out to investigate the change in snow strength with increasing water content through rainwater percolation. Snowpack was artificially prepared consisting of a thin ice layer and fine compacted snow, and rainfall (2mmh–1) was artificially applied 22–25.5 and 49–52 hours after the snowpack was formed. Snow hardness was measured with a push–pull force gauge to indicate the snow strength before and after each rain-on-snow event. After the first rainfall, the upper half of the snowpack became wet and a rapid decrease in snow hardness was observed. After the second rainfall the rainwater penetrated the ice layer, high water content was observed above the ice layer but the hardness exceeded that estimated from an empirical relationship between hardness and water content. Micrographs of the snow particles suggest that the delay in grain coarsening observed near the wetting front induces the harder than estimated snow condition.