Downscaling snow deposition using historic snow depth patterns: Diagnosing limitations from snowfall biases, winter snow losses, and interannual snow pattern repeatability

2021 ◽  
Author(s):  
J.M. Pflug ◽  
M. Hughes ◽  
J.D. Lundquist
Keyword(s):  
Snow Depth ◽  
Winter Snow ◽  
Snow Deposition

Impacts of 1.5 °C and 2 °C global warming on winter snow depth in Central Asia

2019 ◽  
Vol 651 ◽  
pp. 2866-2873 ◽  
Author(s):  
Yun Li ◽  
Hui Tao ◽  
Buda Su ◽  
Zbigniew W. Kundzewicz ◽  
Tong Jiang
Keyword(s):  
Global Warming ◽  
Central Asia ◽  
Snow Depth ◽  
Winter Snow

scholarly journals Effects of Winter Snow Cover on Spring Soil Moisture Based on Remote Sensing Data Product over Farmland in Northeast China

2020 ◽  
Vol 12 (17) ◽  
pp. 2716
Author(s):  
Shuang Liang ◽  
Xiaofeng Li ◽  
Xingming Zheng ◽  
Tao Jiang ◽  
Xiaojie Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Linear Regression ◽  
Snow Cover ◽  
Snow Depth ◽  
Northeast China ◽  
Regression Models ◽  
Linear Regression Models ◽  
Winter Snow ◽  
The Relationship

Spring soil moisture (SM) is of great importance for monitoring agricultural drought and waterlogging in farmland areas. While winter snow cover has an important impact on spring SM, relatively little research has examined the correlation between winter snow cover and spring SM in great detail. To understand the effects of snow cover on SM over farmland, the relationship between winter snow cover parameters (maximum snow depth (MSD) and average snow depth (ASD)) and spring SM in Northeast China was examined based on 30 year passive microwave snow depth (SD) and SM remote-sensing products. Linear regression models based on winter snow cover were established to predict spring SM. Moreover, 4 year SD and SM data were applied to validate the performance of the linear regression models. Additionally, the effects of meteorological factors on spring SM also were analyzed using multiparameter linear regression models. Finally, as a specific application, the best-performing model was used to predict the probability of spring drought and waterlogging in farmland in Northeast China. Our results illustrated the positive effects of winter snow cover on spring SM. The average correlation coefficient (R) of winter snow cover and spring SM was above 0.5 (significant at a 95% confidence level) over farmland. The performance of the relationship between snow cover and SM in April was better than that in May. Compared to the multiparameter linear regression models in terms of fitting coefficient, MSD can be used as an important snow parameter to predict spring drought and waterlogging probability in April. Specifically, if the relative SM threshold is 50% when spring drought occurs in April, the prediction probability of the linear regression model concerning snow cover and spring SM can reach 74%. This study improved our understanding of the effects of winter snow cover on spring SM and will be beneficial for further studies on the prediction of spring drought.

Change in winter snow depth and its impacts on vegetation in China

2010 ◽  
pp. no-no ◽  
Author(s):  
SHUSHI PENG ◽  
SHILONG PIAO ◽  
PHILIPPE CIAIS ◽  
JINGYUN FANG ◽  
XUHUI WANG
Keyword(s):  
Snow Depth ◽  
Winter Snow

Factors influencing permafrost temperatures across tree line in the uplands east of the Mackenzie Delta, 2004–20101This article is one of a series of papers published in this CJES Special Issue on the theme of Fundamental and applied research on permafrost in Canada.2Polar Continental Shelf Contribution 03611.

2012 ◽  
Vol 49 (8) ◽  
pp. 877-894 ◽  
Author(s):  
M.J. Palmer ◽  
C.R. Burn ◽  
S.V. Kokelj
Keyword(s):  
Snow Depth ◽  
Applied Research ◽  
Abrupt Change ◽  
Late Winter ◽  
Mackenzie Delta ◽  
Near Surface ◽  
Ground Temperatures ◽  
Winter Snow ◽  
Shrub Tundra ◽  
Snow Conditions

Air and near-surface ground temperatures, late-winter snow conditions, and characteristics of the vegetation cover and soil were measured across the forest–tundra transition in the uplands east of the Mackenzie Delta, Northwest Territories, in 2004–2010. Mean late-winter snow depth decreased northward from 73 cm in the subarctic boreal forest near Inuvik to 22 cm in low-shrub tundra. Annual near-surface ground temperatures decreased northward by 0.1–0.3 °C/km near the northern limit of trees, in association with an abrupt change in snow depth. The rate decreased to 0.01–0.06 °C/km in the tundra. The freezing season is twice as long as the thawing season in the region, so measured differences in the regional ground thermal regime were dominated by the contrast in winter surface conditions between forest and tundra.

scholarly journals Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

2016 ◽  
Author(s):  
Haruko M. Wainwright ◽  
Anna K. Liljedahl ◽  
Baptiste Dafflon ◽  
Craig Ulrich ◽  
John E. Peterson ◽  
...  
Keyword(s):  
High Precision ◽  
Snow Depth ◽  
Spatial Sampling ◽  
Multi Scale ◽  
Arctic Alaska ◽  
Winter Snow ◽  
Digital Elevation ◽  
Tundra Ecosystem ◽  
Ice Wedge ◽  
Ground Penetrating

Abstract. This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes, and estimated using ground penetrating radar (GPR) surveys and the Photogrammetric Detection and Ranging (PhoDAR) technique with an unmanned aerial system (UAS). We found that GPR data provided high-precision estimates of snow depth (RMSE = 2.9 cm), with a spatial sampling of 10 cm along transects. UAS-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing while yielding a high precision (RMSE = 6.0 cm) and a fine spatial sampling (4 cm by 4 cm). We then investigated the spatial variability of snow depth and its correlation to micro- and macrotopography using the snow-free LiDAR digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free LiDAR DEM and multi-scale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high precision estimates of snow depth (RMSE = 6.0 cm), at 0.5-meter resolution and over the LiDAR domain (750 m by 700 m).

scholarly journals Topographical effects of Snow Deposition on Restructured Land

1980 ◽  
Vol 26 (94) ◽  
pp. 518-518
Author(s):  
E. Chaco ◽  
M. Molnau
Keyword(s):  
Snow Depth ◽  
Winter Season ◽  
Surface Mining ◽  
Snow Accumulation ◽  
Grid Data ◽  
Grid Pattern ◽  
Contour Maps ◽  
Snow Properties ◽  
Waste Dumps ◽  
Snow Deposition

AbstractThe measurement of snow accumulation and distribution is one of the primary objectives of a study on the melt of snow-drifts and erosion in the phosphate mining region of south-eastern Idaho. The study area is located in an active phosphate mine and is limited to the sites of waste dumps, a product of the surface mining technique used in this area. Four sites are included in the overall study with one dump selected for intensive snow measurement. Snow deposition data have been collected for one winter season (November 1977—June 1978) on a grid pattern over this dump. The area of the study site has been expanded and similar measurements are planned for the coming snow season.The snow measurements were made monthly on a pre-established 23 m (75 ft) square grid overlaid on the dump. The analysis of the snow data consists of contour mapping of any one or all the snow properties measured—snow depth, density, or water equivalent. In addition, since the measurements are made on the same grid each month, mathematical manipulation of grid data allows contour maps of the residual of the monthly snow properties to be plotted. A similar analysis of terrain properties collected on the same grid results in contour maps displaying ground slope, concavity-convexity of the surface, aspect, or distance from snow- deposition obstacles.The aim of the analysis using these types of data is to arrive at a model which will compute patterns of snow accumulation and distribution on the ground surface given a description of terrain type and probable meteorological properties of the region. A preliminary comparison of the maps shows a similar pattern of snow deposition occurring each month with the exposed areas of the dump swept clean and the greatest snow depth occurring in the sheltered concavities.

scholarly journals Winter snow depth on Arctic sea ice from satellite radiometer measurements (2003‐2020): Regional patterns and trends

2021 ◽  
Author(s):  
Sang‐Moo Lee ◽  
Hoyeon Shi ◽  
Byung‐Ju Sohn ◽  
A. J. Gasiewski ◽  
Walter N. Meier ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
Regional Patterns ◽  
Winter Snow ◽  
Arctic Sea

scholarly journals Accuracy assessment of late winter snow depth mapping for tundra environments using Structure-from-Motion photogrammetry

2020 ◽  
pp. 1-17
Author(s):  
Branden Walker ◽  
Evan J. Wilcox ◽  
Philip Marsh
Keyword(s):  
Structure From Motion ◽  
Snow Depth ◽  
Accuracy Assessment ◽  
In Situ Measurements ◽  
Surface Elevation ◽  
Late Winter ◽  
Unmanned Aerial Systems ◽  
Snow Surface ◽  
Winter Snow

Arctic tundra environments are characterized by a spatially heterogeneous end-of-winter snow depth resulting from wind transport and deposition. Traditional methods for measuring snow depth do not accurately capture such heterogeneity at catchment scales. In this study we address the use of high-resolution, spatially distributed, snow depth data for Arctic environments through the application of unmanned aerial systems (UASs). We apply Structure-from-Motion photogrammetry to images collected using a fixed-wing UAS to produce a 1 m resolution snow depth product across seven areas of interest (AOIs) within the Trail Valley Creek Research Watershed, Northwest Territories, Canada. We evaluated these snow depth products with in situ measurements of both the snow surface elevation (n = 8434) and snow depth (n = 7191). When all AOIs were averaged, the RMSE of the snow surface elevation models was 0.16 m (<0.01 m bias), similar to the snow depth product (UASSD) RMSE of 0.15 m (+0.04 m bias). The distribution of snow depth between in situ measurements and UASSD was similar along the transects where in situ snow depth was collected, although similarity varies by AOI. Finally, we provide a discussion of factors that may influence the accuracy of the snow depth products including vegetation, environmental conditions, and study design.

scholarly journals The smoothing of landscapes during snowfall with no wind

2019 ◽  
Vol 65 (250) ◽  
pp. 173-187 ◽  
Author(s):  
SIMON FILHOL ◽  
MATTHEW STURM
Keyword(s):  
Snow Depth ◽  
Cumulative Effect ◽  
Snow Accumulation ◽  
Narrow Gap ◽  
Lateral Transport ◽  
Directional Bias ◽  
Strobe Light ◽  
Artificial Surfaces ◽  
Topographic Relief ◽  
Snow Deposition

ABSTRACTEvery winter, snowy landscapes are smoothed by snow deposition in calm conditions (no wind). In this study, we investigated how vertically falling snow attenuates topographic relief at horizontal scales less than or approximately equal to snow depth (e.g., 0.1–10 m). In a set of three experiments under natural snowfall, we observed the particle-scale mechanisms by which smoothing is achieved, and we examined the cumulative effect at the snowpack scale. The experiments consisted of (a) a strobe-light box for tracking the trajectories of snowflakes at deposition, (b) allowing snow to fall through a narrow gap (40 mm) and examining snow accumulation above and below the gap, and (c) allowing snow to accumulate over a set of artificial surfaces. At the particle scale, we observed mechanisms enhancing (bouncing, rolling, ejection, breakage, creep, metamorphism) and retarding (interlocking, cohesion, adhesion, sintering) the rate of smoothing. The cumulative effect of these mechanisms is found to be driven by snowpack surface curvature, introducing a directional bias in the lateral transport of snow particles. Our findings suggest that better quantification of the mechanisms behind smoothing by snow could provide insights into the evolution of snow depth variability, and snow-vegetation interactions.

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