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 Topographical effects of Snow Deposition on Restructured Land

1980 ◽  
Vol 26 (94) ◽  
pp. 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

Abstract The 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 Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1027 ◽  
Author(s):  
Theodora Lendzioch ◽  
Jakub Langhammer ◽  
Michal Jenicek
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Snow Depth ◽  
Control Point ◽  
Winter Season ◽  
Snow Accumulation ◽  
Plant Canopy ◽  
Coverage Area ◽  
Area Index ◽  
Uav Images

This study presents a novel approach in the application of Unmanned Aerial Vehicle (UAV) imaging for the conjoint assessment of the snow depth and winter leaf area index (LAI), a structural property of vegetation, affecting the snow accumulation and snowmelt. The snow depth estimation, based on a multi-temporal set of high-resolution digital surface models (DSMs) of snow-free and of snow-covered conditions, taken in a partially healthy to insect-induced Norway spruce forest and meadow coverage area within the Šumava National Park (Šumava NP) in the Czech Republic, was assessed over a winter season. The UAV-derived DSMs featured a resolution of 0.73–1.98 cm/pix. By subtracting the DSMs, the snow depth was determined and compared with manual snow probes taken at ground control point (GCP) positions, the root mean square error (RMSE) ranged between 0.08 m and 0.15 m. A comparative analysis of UAV-based snow depth with a denser network of arranged manual snow depth measurements yielded an RMSE between 0.16 m and 0.32 m. LAI assessment, crucial for correct interpretation of the snow depth distribution in forested areas, was based on downward-looking UAV images taken in the forest regime. To identify the canopy characteristics from downward-looking UAV images, the snow background was used instead of the sky fraction. Two conventional methods for the effective winter LAI retrieval, the LAI-2200 plant canopy analyzer, and digital hemispherical photography (DHP) were used as a reference. Apparent was the effect of canopy density and ground properties on the accuracy of DSMs assessment based on UAV imaging when compared to the field survey. The results of UAV-based LAI values provided estimates were comparable to values derived from the LAI-2200 plant canopy analyzer and DHP. Comparison with the conventional survey indicated that spring snow depth was overestimated, and spring LAI was underestimated by using UAV photogrammetry method. Since the snow depth and the LAI parameters are essential for snowpack studies, this combined method here will be of great value in the future to simplify snow depth and LAI assessment of snow dynamics.

scholarly journals Microscale variability of snow depth using U.A.S. technology

2015 ◽  
Vol 9 (1) ◽  
pp. 1047-1075 ◽  
Author(s):  
C. De Michele ◽  
F. Avanzi ◽  
D. Passoni ◽  
R. Barzaghi ◽  
L. Pinto ◽  
...  
Keyword(s):  
Snow Depth ◽  
Depth Distribution ◽  
Winter Season ◽  
Aerial Survey ◽  
Snow Accumulation ◽  
Volume Estimation ◽  
Accurate Estimation ◽  
Unmanned Aerial Systems ◽  
Spatial Integration ◽  
Interpolation Techniques

Abstract. We investigate the capabilities of photogrammetry-based surveys with Unmanned Aerial Systems (U.A.S.) to retrieve the snow depth distribution at cm resolution over a small alpine area (~300 000 m2). For this purpose, we have designed two field campaigns during the 2013/2014 winter season. In the first survey, realized at the beginning of the accumulation season, the digital elevation model of bare soil has been obtained. The second survey, made at the end of the accumulation season, allowed to determine the snow depth distribution as difference with respect to the previous aerial survey. 12 manual measurements of snow depth were collected at random positions in order to run a point comparison with U.A.S. measurements. The spatial integration of U.A.S. snow depth measurements allowed to estimate the snow volume accumulated over the area. We compare this volume estimation with the ones provided by classical interpolation techniques of the 12 point measurements. Results show that the U.A.S. technique provides an accurate estimation of point snow depth values (the average difference with reference to manual measurements is of −7.3 cm), and a distributed evaluation of the snow accumulation patterns. Moreover, the interpolation techniques considered return average differences in snow volume estimation, with respect to the one obtained through the U.A.S. technology, equal to ~21%.

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.

scholarly journals Inferring Watershed-scale Mean Snow Magnitude and Distribution Using Multidecadal Snow Reanalysis Patterns and Snow Pillow Observations

2021 ◽  
Author(s):  
Justin Pflug ◽  
Steven Margulis ◽  
Jessica Lundquist
Keyword(s):  
Spatial Heterogeneity ◽  
Sierra Nevada ◽  
Snow Depth ◽  
Snow Accumulation ◽  
Airborne Lidar ◽  
Watershed Scale ◽  
Mountainous Regions ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Deposition

The magnitude and spatial heterogeneity of snow deposition are difficult to model in mountainous terrain. Here, we investigated how snow patterns from a 32-year (1985 – 2016) snow reanalysis in the Tuolumne, Kings, and Sagehen Creek, California Sierra Nevada watersheds could be used to improve simulations of winter snow deposition. Remotely-sensed fractional snow-covered area (fSCA) from dates following peak-snowpack timing were used to identify dates from different years with similar fSCA, which indicated similar snow accumulation and depletion patterns. Historic snow accumulation patterns were then used to 1) relate snow accumulation observed by snow pillows to watershed-scale estimates of mean snowfall, and 2) estimate 90 m snow deposition. Finally, snow deposition fields were used to force snow simulations, the accuracy of which were evaluated versus airborne lidar snow depth observations. Except for water-year 2015, which had the shallowest snow estimated in the Sierra Nevada, normalized snow accumulation and depletion patterns identified from historic dates with spatially correlated fractional snow-covered area agreed on average, with absolute differences of less than 10%. Watershed-scale mean winter snowfall inferred from the relationship between historic snow accumulation patterns and snow pillow observations had a ±13% interquartile range of biases between 1985 and 2016. Finally, simulations using 1) historic snow accumulation patterns, and 2) snow accumulation observed from snow pillows, had snow depth coefficients of correlations and mean absolute errors that improved by 70% and 27%, respectively, as compared to simulations using a more common forcing dataset and downscaling technique. This work demonstrates the real-time benefits of satellite-era snow reanalyses in mountainous regions with uncertain snowfall magnitude and spatial heterogeneity.

scholarly journals GNSS-R-Based Snow Water Equivalent Estimation with Empirical Modeling and Enhanced SNR-Based Snow Depth Estimation

2020 ◽  
Vol 12 (23) ◽  
pp. 3905
Author(s):  
Kegen Yu ◽  
Yunwei Li ◽  
Taoyong Jin ◽  
Xin Chang ◽  
Qi Wang ◽  
...  
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Depth Estimation ◽  
Snow Accumulation ◽  
Empirical Models ◽  
Empirical Modeling ◽  
Fusion Model ◽  
Snow Water

Snow depth and snow water equivalent (SWE) are two parameters for measuring snowfall. By exploiting the Global Navigation Satellite System reflectometry (GNSS-R) technique and thousands of existing GNSS Continuous Operating Reference Stations (CORS) deployed in the cryosphere, it is possible to improve the temporal and spatial resolutions of the SWE measurement. In this paper, a fusion model for combining multi-satellite SNR (Signal to Noise Ratio) snow depth estimations is proposed, which uses peak spectral powers associated with each of the snow depth estimations. To simplify the estimation of SWE, the complete snowfall period over a winter season is split into snow accumulation, transition, and melting period in accordance with the variation characteristics of snow depth and SWE. By extensively using in situ snow depth and SWE observations recorded by snow telemetry network (SNOTEL) and regression analysis, three empirical models are developed to describe the relationship between snow depth and SWE for the three periods, respectively. Based on the snow depth fusion model and the SWE empirical models, an SWE estimation algorithm is proposed. Three data sets recorded in different environments are used to test the proposed method. The results demonstrate that there exists good agreement between the in situ SWE measurements and the SWE estimates produced by the proposed method; the root-mean-square error of SWE estimations is smaller than 6 cm when the SWE is up to 80 cm.

scholarly journals Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska

2021 ◽  
Vol 13 (15) ◽  
pp. 2922
Author(s):  
Yang Song ◽  
Patrick D. Broxton ◽  
Mohammad Reza Ehsani ◽  
Ali Behrangi
Keyword(s):  
Snow Water Equivalent ◽  
Stage Iv ◽  
Microwave Remote Sensing ◽  
Rain Gauge ◽  
Snow Accumulation ◽  
Correction Factors ◽  
Rainfall Analysis ◽  
Rain Gauges ◽  
Snow Properties ◽  
Precipitation Estimates

The combination of snowfall, snow water equivalent (SWE), and precipitation rate measurements from 39 snow telemetry (SNOTEL) sites in Alaska were used to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges. Observation of precipitation from two water years (2018–2019) of a high-resolution radar/rain gauge data (Stage IV) product was also utilized to give insights into the scaling differences between various products. The outcomes were used to assess two popular methods for rain gauge undercatch correction. It was found that SWE and precipitation measurements at SNOTELs, as well as precipitation estimates based on Stage IV data, are generally consistent and can provide a range within which other products can be assessed. The time-series of snowfall and SWE accumulation suggests that most of the products can capture snowfall events; however, differences exist in their accumulation. Reanalysis products tended to overestimate snow accumulation in the study area, while the current combined passive microwave remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumulation. We found that correction factors applied to rain gauges are effective for improving their undercatch, especially for snowfall. However, no improvement in correlation is seen when correction factors are applied, and rainfall is still estimated better than snowfall. Even though IMERG-HQ has less skill for capturing snowfall than rainfall, analysis using Taylor plots showed that the combined microwave product does have skill for capturing the geographical distribution of snowfall and precipitation accumulation; therefore, bias adjustment might lead to reasonable precipitation estimates. This study demonstrates that other snow properties (e.g., SWE accumulation at the SNOTEL sites) can complement precipitation data to estimate snowfall. In the future, gridded SWE and snow depth data from GlobSnow and Sentinel-1 can be used to assess snowfall and its distribution over broader regions.

Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska

2021 ◽  
Author(s):  
Thomas Douglas ◽  
Caiyun Zhang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Snow Depth ◽  
Spatial Scales ◽  
Critical Role ◽  
Winter Season ◽  
Thermal Conditions ◽  
Near Surface ◽  
Lidar Measurements ◽  
Remote Sensing Methods

The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.

Critical snow density threshold for Dall’s sheep (Ovis dalli dalli)

2018 ◽  
Vol 96 (10) ◽  
pp. 1170-1177 ◽  
Author(s):  
Kelly J. Sivy ◽  
Anne W. Nolin ◽  
Christopher L. Cosgrove ◽  
Laura R. Prugh
Keyword(s):  
Snow Depth ◽  
Animal Movement ◽  
Population Declines ◽  
Snow Density ◽  
Energetic Costs ◽  
Snow Properties ◽  
Ovis Dalli ◽  
Snow Conditions ◽  
Dall’S Sheep ◽  
Density Threshold

Snow cover can significantly impact animal movement and energetics, yet few studies have investigated the link between physical properties of snow and energetic costs. Quantification of thresholds in snow properties that influence animal movement are needed to help address this knowledge gap. Recent population declines of Dall’s sheep (Ovis dalli dalli Nelson, 1884) could be due in part to changing snow conditions. We examined the effect of snow density, snow depth, and snow hardness on sinking depths of Dall’s sheep tracks encountered in Wrangell–St. Elias National Park and Preserve, Alaska. Snow depth was a poor predictor of sinking depths of sheep tracks (R2 = 0.02, p = 0.38), as was mean weighted hardness (R2 = 0.09, p = 0.07). Across competing models, top layer snow density (0–10 cm) and sheep age class were the best predictors of track sink depths (R2 = 0.58). Track sink depth decreased with increasing snow density, and the snowpack supported the mass of a sheep above a density threshold of 329 ± 18 kg/m3 (mean ± SE). This threshold could aid interpretation of winter movement and energetic costs by animals, thus improving our ability to predict consequences of changing snowpack conditions on wildlife.

scholarly journals Seasonal influence of snow conditions on Dall’s sheep productivity in Wrangell-St Elias National Park and Preserve

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0244787
Author(s):  
Christopher L. Cosgrove ◽  
Jeff Wells ◽  
Anne W. Nolin ◽  
Judy Putera ◽  
Laura R. Prugh
Keyword(s):  
Snow Depth ◽  
National Park ◽  
Meteorological Data ◽  
Multiple Regression Model ◽  
Snow Accumulation ◽  
Population Declines ◽  
Climate Conditions ◽  
Remote Cameras ◽  
Snow Conditions ◽  
Dall’S Sheep

Dall’s sheep (Ovis dalli dalli) are endemic to alpine areas of sub-Arctic and Arctic northwest America and are an ungulate species of high economic and cultural importance. Populations have historically experienced large fluctuations in size, and studies have linked population declines to decreased productivity as a consequence of late-spring snow cover. However, it is not known how the seasonality of snow accumulation and characteristics such as depth and density may affect Dall’s sheep productivity. We examined relationships between snow and climate conditions and summer lamb production in Wrangell-St Elias National Park and Preserve, Alaska over a 37-year study period. To produce covariates pertaining to the quality of the snowpack, a spatially-explicit snow evolution model was forced with meteorological data from a gridded climate re-analysis from 1980 to 2017 and calibrated with ground-based snow surveys and validated by snow depth data from remote cameras. The best calibrated model produced an RMSE of 0.08 m (bias 0.06 m) for snow depth compared to the remote camera data. Observed lamb-to-ewe ratios from 19 summers of survey data were regressed against seasonally aggregated modelled snow and climate properties from the preceding snow season. We found that a multiple regression model of fall snow depth and fall air temperature explained 41% of the variance in lamb-to-ewe ratios (R2 = .41, F(2,38) = 14.89, p<0.001), with decreased lamb production following deep snow conditions and colder fall temperatures. Our results suggest the early establishment and persistence of challenging snow conditions is more important than snow conditions immediately prior to and during lambing. These findings may help wildlife managers to better anticipate Dall’s sheep recruitment dynamics.

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