scholarly journals Mapping snow cover and snow depth across the Lake Limnopolar watershed on Byers Peninsula, Livingston Island, Maritime Antarctica

2013 ◽  
Vol 25 (2) ◽  
pp. 157-166 ◽  
Author(s):  
S.R. Fassnacht ◽  
J.I. López-Moreno ◽  
M. Toro ◽  
D.M. Hultstrand
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Regression Trees ◽  
Snow Accumulation ◽  
Maritime Antarctica ◽  
Density Profiles ◽  
Livingston Island ◽  
Digital Elevation ◽  
Depth Classification ◽  
Elevation Model

AbstractFew parts of Antarctica are not permanently covered in ice. The retreat of the ice sheet from Byers Peninsula on western Livingston Island, Maritime Antarctica, has provided a new area of seasonal snow cover. Snow surveys were conducted in late November 2008 at the time of peak accumulation across the 1 km2 Lake Limnopolar watershed. Topographic variables were derived from a digital elevation model to determine the variables controlling the presence or absence of snow and the distribution of snow depth. Classification with binary regression trees showed that wind related variables dominated the presence and depth of snow. The product of the sine of aspect and the sine of slope was the first variable in both regression trees. Density profiles were also measured and illustrated a relatively homogeneous snowpack over space at peak snow accumulation.

scholarly journals GIS spatial analysis of the distribution of snow depth: A study of western Rhodopes, Bulgaria

2016 ◽  
Vol 96 (1) ◽  
pp. 46-55
Author(s):  
Valentina Nikolova ◽  
Aleksandar Penkov
Keyword(s):  
Spatial Analysis ◽  
Snow Cover ◽  
Snow Depth ◽  
Kriging Interpolation ◽  
Map Algebra ◽  
Explanatory Variables ◽  
Distance Weighted ◽  
Point Data ◽  
Gis Environment ◽  
Elevation Model

The aim of the present research is to show the advantages of information technology in investigating the snow cover. The snow data is usually taken from the measurement in meteorological stations which are often sparsely and insufficient. The problem in the analysis of the snow cover is how to present point data spatially and what is the most appropriate model. The area of the present research is the western part of Rhodopes mountain (Southern Bulgaria). The relief is variable from low to high mountainous and the climate is influenced by the high altitude and Mediterranean air advections. The spatial analysis of the distribution of snow depth is done in ArcGIS by application of Spatial Statistics Tools and Geostatistical Analyst. We considered altitude, aspect and slope as explanatory variables that could be used for determination of the territorial distribution of the snow depth. These factors are determined on the base of digital elevation model and the relationship between variables is evaluated by application of regression analysis, ordinary less squares (OLS) analysis and geographically weighted regression (GWR). The high values of R2 (above 0.7) show the representativeness of the model. A map of spatial distribution of snow depth is created by Map algebra in GIS environment, applying the regression equation of the relation snow depth - altitude. Inverse distance weighted and ordinary kriging interpolation are also carried out. The research shows that spatial presentation of point snow data and its interpretation should be done taking into account the relief and the exposition of the territory.

scholarly journals Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China

2021 ◽  
Vol 11 (18) ◽  
pp. 8365
Author(s):  
Liming Gao ◽  
Lele Zhang ◽  
Yongping Shen ◽  
Yaonan Zhang ◽  
Minghao Ai ◽  
...  
Keyword(s):  
Snow Cover ◽  
River Basin ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Implicit Scheme ◽  
Snow Water ◽  
Irtysh River ◽  
Simulation Results ◽  
The Irtysh River

Accurate simulation of snow cover process is of great significance to the study of climate change and the water cycle. In our study, the China Meteorological Forcing Dataset (CMFD) and ERA-Interim were used as driving data to simulate the dynamic changes in snow depth and snow water equivalent (SWE) in the Irtysh River Basin from 2000 to 2018 using the Noah-MP land surface model, and the simulation results were compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD), the long-term series of daily snow depth dataset in China (LSD), and China’s daily snow depth and snow water equivalent products (CSS). Before the simulation, we compared the combinations of four parameterizations schemes of Noah-MP model at the Kuwei site. The results show that the rainfall and snowfall (SNF) scheme mainly affects the snow accumulation process, while the surface layer drag coefficient (SFC), snow/soil temperature time (STC), and snow surface albedo (ALB) schemes mainly affect the melting process. The effect of STC on the simulation results was much higher than the other three schemes; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is much greater than that of a semi-implicit scheme. At the basin scale, the accuracy of snow depth modeled by using CMFD and ERA-Interim is higher than LSD and CSS snow depth based on microwave remote sensing. In years with high snow cover, LSD and CSS snow depth data are seriously underestimated. According to the results of model simulation, it is concluded that the snow depth and snow water equivalent in the north of the basin are higher than those in the south. The average snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly during the study period, but the end time of snow melting was significantly advanced.

scholarly journals Comparing Linear Regression and Regression Trees for Spatial Modelling of Soil Reaction in Dobrovăţ Basin (Eastern Romania)

2011 ◽  
Vol 68 (1) ◽  
Author(s):  
Cristian Valeriu PATRICHE ◽  
Radu Gabriel PÎRNĂU ◽  
Bogdan ROŞCA
Keyword(s):  
Linear Regression ◽  
Digital Elevation Model ◽  
Spatial Models ◽  
Selection Procedure ◽  
Regression Trees ◽  
Training Sample ◽  
Soil Reaction ◽  
Soil Profiles ◽  
Digital Elevation ◽  
Elevation Model

Our study compares the performances of two statistical methods, namely multiple linear regression and classification and regression trees, for deriving spatial models of soil reaction in the surface horizon. The applications were carried out within a 186 km2 hydrographic basin situated in eastern Romania. Statistical models were computed from a sample of 235 soil profiles, scattered in the eastern half of the basin. An independent sample of 237 expeditionary pH measurements was used to validate the results within the interpolation area, whereas an independent sample of 50 soil profiles was used to validate the results within the extrapolation area (the western half of the basin). The predictors included geomorphometrical parameters, derived from a 10x10 m digital elevation model, X and Y coordinates of soil profiles and the main soil types for the regression trees approach. The stepwise selection procedure indicated Y coordinate, digital elevation model, wetness index and surface ratio as the best predictors for soil reaction. The correlation between observed and predicted pH values for the training sample suggests a much higher quality of the regression trees spatial model. However, the validation using the two independent samples points out the instability of this model and recommends the regression model more reliable.

scholarly journals Contribution of glacier melt to stream runoff: if the climatically extreme summer of 2003 had happened in 1979…

2007 ◽  
Vol 46 ◽  
pp. 303-308 ◽  
Author(s):  
Gernot R. Koboltschnig ◽  
Wolfgang Schöner ◽  
Massimiliano Zappa ◽  
Hubert Holzmann
Keyword(s):  
Water Balance ◽  
Catchment Area ◽  
Meteorological Data ◽  
Snow Accumulation ◽  
Balance Model ◽  
High Temporal Resolution ◽  
Ice Melt ◽  
Digital Elevation ◽  
Dry Conditions ◽  
Elevation Model

AbstractThis paper presents a comparative study at a small and highly glacierized catchment area in the Austrian Alps, where runoff under the extreme hot and dry conditions of summer 2003 was simulated based on two different glacier extents: the 2003 glacier extent and the 29% larger 1979 extent. Runoff was simulated applying the hydrological water balance model PREVAH at a high temporal resolution. For this purpose, the catchment area was subdivided into hydrological response units based on digital elevation model and land-cover data. The model was driven by meteorological data from the observatory at Hoher Sonnblick, situated at the highest point of the catchment area (3106ma.s.l.). We were interested in the effect the change in glacier extent would have on the annual and monthly water balance and the hydrograph of hourly discharges. Results of the 2003 and the hypothetical 1979 simulation show main differences in runoff for the period July–August depending on a higher ice-melt contribution. Due to the same meteorological input, both simulations calculate the same snow accumulation and snowmelt. Annual discharge in 1979 would have been 12% higher and hourly runoff up to 35% higher than in 2003.

scholarly journals Spatial and Temporal Variation of Net Snow Accumulation in a Small Alpine Watershed, Emerald Lake Basin, Sierra Nevada, California, U.S.A.

1989 ◽  
Vol 13 ◽  
pp. 56-63 ◽  
Author(s):  
K. Elder ◽  
J. Dozier ◽  
J. Michaelsen
Keyword(s):  
Sierra Nevada ◽  
Field Measurements ◽  
Snow Accumulation ◽  
Spatial And Temporal Variation ◽  
Lake Basin ◽  
Snow Properties ◽  
Digital Elevation ◽  
Snow Water ◽  
Elevation Model ◽  
Topographic Parameters

Distribution of snow-water equivalence (SWE) in the Emerald Lake watershed located in Sequoia National Park, California, U.S.A, was examined during the 1987 water year. Elevations at this site range from 2780 to 3416 m a.s.l., and the total watershed area is about 122 ha. A stratified sampling scheme was evaluated by identifying and mapping zones of similar snow properties, based on topographic parameters that account for variations in both accumulation and ablation of snow. Elevation, slope, and radiation values calculated from a digital elevation model were used to identify these zones. Field measurements of SWE were combined with characteristics of the sample locations and clustered to identify similar classes of SWE. The entire basin was then partitioned into zones for each set of survey data. The topographic parameters of the basin used in classification, namely slope and elevation, are constant in time and did not change between survey dates. The radiation data showed temporal variability providing a physically justified basis for changes in SWE distribution through time. Although results do not identify which of the classification attempts is superior to the others, net radiation is clearly of primary importance, and slope and elevation appear to be important to a lesser degree. The peak accumulation for the 1987 water year was 598 mm SWE, which is about half the 50 year mean.

scholarly journals Improved Measurements and Analysis of Spatial Snow Cover by Combining a Ground Based Radar System With a Differential Global Positioning System Receiver

2001 ◽  
Vol 32 (3) ◽  
pp. 181-194 ◽  
Author(s):  
Wolf-Dietrich Marchand ◽  
Oddbjørn Bruland ◽  
Ånund Killingtveit
Keyword(s):  
Snow Cover ◽  
Global Positioning System ◽  
Snow Depth ◽  
Control Unit ◽  
Snow Accumulation ◽  
Positioning System ◽  
Global Positioning ◽  
Log File ◽  
Ground Penetrating ◽  
Differential Global Positioning System

The paper describes the realization of a new snow measurement system where a Ground Penetrating Radar (GPR) is connected to a Differential Global Positioning System (DGPS) receiver. A snow scooter pulled a radar antenna, a distance wheel triggered the radar pulses and the reflections were stored in a control unit. A marker was set on the radar file each time a position was logged on the DGPS receiver. Thus, each position was directly related to a snow depth measured by the GPR. The obtained accuracy of the position was in the range of 5-10 m and manual calibration measurements were used to ensure good quality of the snow depth data. The system was tested in the Norwegian catchment Aursunden during the period of maximum snow accumulation, 12th – 23rd April 1999. Landscape features were analyzed with a Geographic Information System (GIS) and extensive snow measurements were worked out in representative areas. The obtained data on the snow cover were later used for statistical analysis. In addition to the efficiency which makes it possible to measure large areas in a relatively short time, the major advances in the described system is that the obtained data can be used directly in a computer aided GIS. Nevertheless, further improvement is needed because of 1) the possibility for ambiguous connection between snow depth log and position log, 2) the distance between consecutive positions is not constant since it is time dependent, 3) the algorithm for automatically detection of the ground reflection from the radar log-file still needs interference from the user.

Observation and thermodynamic modeling of the influence of snow cover on landfast sea ice thickness in Prydz Bay, East Antarctica

2020 ◽  
Author(s):  
Jiechen Zhao ◽  
Bin Cheng ◽  
Timo Vihma ◽  
Qinghua Yang ◽  
Fengming Hui ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Annual Cycle ◽  
Snow Depth ◽  
Snow Accumulation ◽  
East Antarctica ◽  
Prydz Bay ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Landfast Sea Ice

<p>The observed snow depth and ice thickness on landfast sea ice in Prydz Bay, East Antarctica, were used to determine the role of snow in (a) the annual cycle of sea ice thickness at a fixed location (SIP) where snow usually blows away after snowfall and (b) early summer sea ice thickness within the transportation route surveys (TRS) domain farther from coast, where annual snow accumulation is substantial. The annual mean snow depth and maximum ice thickness had a negative relationship (r = −0.58, p < 0.05) at SIP, indicating a primary insulation effect of snow on ice thickness. However, in the TRS domain, this effect was negligible because snow contributes to ice thickness. A one-dimensional thermodynamic sea ice model, forced by local weather observations, reproduced the annual cycle of ice thickness at SIP well. During the freeze season, the modeled maximum difference of ice thickness using different snowfall scenarios ranged from 0.53–0.61 m. Snow cover delayed ice surface and ice bottom melting by 45 and 24 days, respectively. The modeled snow ice and superimposed ice accounted for 4–23% and 5–8% of the total maximum ice thickness on an annual basis in the case of initial ice thickness ranging from 0.05–2 m, respectively.</p>

scholarly journals Impact of Surface Albedo Assimilation on Snow Estimation

2020 ◽  
Vol 12 (4) ◽  
pp. 645 ◽  
Author(s):  
Sujay Kumar ◽  
David Mocko ◽  
Carrie Vuyovich ◽  
Christa Peters-Lidard
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Surface Albedo ◽  
Land Surface Model ◽  
Snow Accumulation ◽  
High Plains ◽  
Surface Model ◽  
Fractional Snow Cover ◽  
The Impact

Surface albedo has a significant impact in determining the amount of available net radiation at the surface and the evolution of surface water and energy budget components. The snow accumulation and timing of melt, in particular, are directly impacted by the changes in land surface albedo. This study presents an evaluation of the impact of assimilating Moderate Resolution Imaging Spectroradiometer (MODIS)-based surface albedo estimates in the Noah multi-parameterization (Noah-MP) land surface model, over the continental US during the time period from 2000 to 2017. The evaluation of simulated snow depth and snow cover fields show that significant improvements from data assimilation (DA) are obtained over the High Plains and parts of the Rocky Mountains. Earlier snowmelt and reduced agreements with reference snow depth measurements, primarily over the Northeast US, are also observed due to albedo DA. Most improvements from assimilation are observed over locations with moderate vegetation and lower elevation. The aggregate impact on evapotranspiration and runoff from assimilation is found to be marginal. This study also evaluates the relative and joint utility of assimilating fractional snow cover and surface albedo measurements. Relative to surface albedo assimilation, fractional snow cover assimilation is found to provide smaller improvements in the simulated snow depth fields. The configuration that jointly assimilates surface albedo and fractional snow cover measurements is found to provide the most beneficial improvements compared to the univariate DA configurations for surface albedo or fractional snow cover. Overall, the study also points to the need for improving the albedo formulations in land surface models and the incorporation of observational uncertainties within albedo DA configurations.

scholarly journals Spatial and Temporal Variation of Net Snow Accumulation in a Small Alpine Watershed, Emerald Lake Basin, Sierra Nevada, California, U.S.A.

1989 ◽  
Vol 13 ◽  
pp. 56-63 ◽  
Author(s):  
K. Elder ◽  
J. Dozier ◽  
J. Michaelsen
Keyword(s):  
Sierra Nevada ◽  
Field Measurements ◽  
Snow Accumulation ◽  
Spatial And Temporal Variation ◽  
Lake Basin ◽  
Snow Properties ◽  
Digital Elevation ◽  
Snow Water ◽  
Elevation Model ◽  
Topographic Parameters

Distribution of snow-water equivalence (SWE) in the Emerald Lake watershed located in Sequoia National Park, California, U.S.A, was examined during the 1987 water year. Elevations at this site range from 2780 to 3416 m a.s.l., and the total watershed area is about 122 ha. A stratified sampling scheme was evaluated by identifying and mapping zones of similar snow properties, based on topographic parameters that account for variations in both accumulation and ablation of snow. Elevation, slope, and radiation values calculated from a digital elevation model were used to identify these zones. Field measurements of SWE were combined with characteristics of the sample locations and clustered to identify similar classes of SWE. The entire basin was then partitioned into zones for each set of survey data. The topographic parameters of the basin used in classification, namely slope and elevation, are constant in time and did not change between survey dates. The radiation data showed temporal variability providing a physically justified basis for changes in SWE distribution through time. Although results do not identify which of the classification attempts is superior to the others, net radiation is clearly of primary importance, and slope and elevation appear to be important to a lesser degree. The peak accumulation for the 1987 water year was 598 mm SWE, which is about half the 50 year mean.

scholarly journals Seasonal cycle of the snow coverage in the former Soviet Union and its relation with atmospheric circulation

2004 ◽  
Vol 38 ◽  
pp. 106-114 ◽  
Author(s):  
Kunio Rikiishi ◽  
Junko Sakakibara
Keyword(s):  
Soviet Union ◽  
Arctic Ocean ◽  
Snow Cover ◽  
Snow Depth ◽  
Former Soviet Union ◽  
Snow Accumulation ◽  
River Valley ◽  
The Arctic ◽  
Lena River ◽  
The Arctic Ocean

AbstractHistorical snow-depth observations in the former Soviet Union (FSU) during the period September 1960–August 1984 have been analyzed in order to understand the seasonal cycle of snow coverage in the FSU. Snow cover first appears in September in northeastern regions, and spreads over the entire territory before early January. Snowmelt begins in mid-January in the southern regions and then snow cover retreats rapidly northward until it disappears completely before late June. Northward of 60°N, the land surface is snow-covered for more than half the year. The longest snow-cover duration is observed on the central Siberian plateau (about 9.5 months) and along the Arctic coastal regions (about 8.5 months). One of the most conspicuous features of the snow coverage in the FSU is that the length of the snow-accumulation period differs considerably from region to region (2–7 months), while the length of the snowmelt period is rather short and uniform over almost the entire territory (1–2 months). Although the maximum snow depths are 20–50 cm in most regions of the FSU, they exceed 80 cm in the mountainous regions in central Siberia, Kamchatka peninsula, and along theYenisei river valley. Values for the maximum snow depth are very small along the Lena river valley in spite of the air temperature being extremely low in winter. By calculating correlation coefficients between the snowfall intensities and the sea-level pressures or 500 hPa heights, it is shown that deep snow along the Yenisei river valley is caused by frequent migration of synoptic disturbances from the Arctic Ocean. Snowfalls along the Lena river valley are also caused by traveling disturbances from the Arctic Ocean. Snow accumulation is suppressed after the Arctic Ocean has been frozen.

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