scholarly journals Spatial distribution of snow cover and temperature in the upper layer of a polythermal glacier

2017 ◽  
Vol 57 (3) ◽  
pp. 373-380 ◽  
Author(s):  
T. V. Vshivtseva ◽  
R. A. Chernov
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Polythermal Glacier

Spatial Distribution of Snow Storage and Snowmelt Dynamics in Central Yenisei Siberia

2021 ◽  
pp. 82-92
Author(s):  
I. V. Danilova ◽  
◽  
A. A. Onuchin ◽  
◽  
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Sea Level ◽  
Thermal Regime ◽  
Regression Models ◽  
Weather Station ◽  
Digital Maps ◽  
Independent Variables ◽  
Snow Storage

In this paper the spatial distribution of water reserves in the snow cover and the dynamics of snow cover melting due to the peculiarity of the thermal regime were analyzed for the central part of Yenisei Siberia. To create digital maps of water reserves in the snow cover, regression models were developed. The geographic coordinates, elevation above sea level and the distance from the orographic boundaries were used as independent variables in regression models. Based on the created maps, the dynamics of snow cover melting was obtained in the study area, taking into account the thermal regime at a key weather station.

scholarly journals Synoptic and Mesoscale atmospheric features associated with an extreme Snowstorm over the Central Andes in August 2013

2019 ◽  
Author(s):  
Marcelo Zamuriano ◽  
Paul Froidevaux ◽  
Isabel Moreno ◽  
Mathias Vuille ◽  
Stefan Brönnimann
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Rossby Wave ◽  
Moisture Transport ◽  
Large Scale ◽  
Amazon Basin ◽  
Cold Front ◽  
Wave Train ◽  
Pressure System ◽  
The Andes

Abstract. We study the synoptic and mesoscale characteristics of a snowfall event over the Bolivian Altiplano in August 2013 that caused severe damage to people, infrastructure and livestock. This event was associated with a cold front episode following the eastern slope of the Andes-Amazon interface and a cut-off low pressure system (COL) over the Pacific Ocean. Large scale analyses suggest a two-stage mechanism: The first phase consisted of a strong cold surge to the east of the Andes inducing low level blocking of southward moisture transport over the SW Amazon basin due to post-frontal high-pressure up to 500 hPa synchronized to a Rossby wave train. The second stage was initiated by the displacement of 500 hPa anticyclone over the Andes due to a Rossby wave passage and a subsequent increase in north-easterly moisture transport, while another cold front along the eastern Andes provided additional lifting. We analyse an analog event (July 2010) to confirm the influence of these large-scale features on snow formation. We conduct a mesoscale analysis using the Weather Research and Forecasting (WRF-ARW) model. For this purpose, we perform a series of high-resolution numerical experiments that include sensitivity studies where we apply orographic and lake Titicaca temperature modifications. We compare our findings to MODIS snow cover estimates and in-situ measurements. The control simulation is able to capture the snow cover spatial distribution and sheds light over several aspects of the snowfall dynamics. In our WRF simulations, daytime snowfall mainly occurs around complex orography whereas nocturnal snowfall is concentrated over the plateau due to a combination of nocturnal winds and complex orography inside the plateau. The sensitivity experiments indicate the importance of the lake and mountain for thermal wind circulation affecting the spatial distribution of snowfall by shifting the position of the convergence zones. The influence of the lake's thermal effect is not evident around the regions surrounding the lake.

Measurement of snow cover depth using 100 × 100 meters sampling plot and Structure from Motion method in Adventdalen, Svalbard

2016 ◽  
Vol 6 (2) ◽  
pp. 155-168
Author(s):  
Radim Stuchlík ◽  
Jan Russnák ◽  
Tomáš Plojhar ◽  
Zdeněk Stachoň
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Structure From Motion ◽  
Snow Depth ◽  
Surface Model ◽  
Snow Surface ◽  
Reference Volume ◽  
Snow Cover Depth ◽  
Sampling Plot ◽  
Motion Method

We tried to verify the concept of Structure from Motion method for measuring the volume of snow cover in a grid of 100×100 m located in Adventdalen, Central Svalbard. As referencing method we utilized 121 depth measurements in one hectare area. Using avalanche probe a snow depth was measured in mentioned 121 nodes of the grid. We detected maximum snow depth of 2.05 m but snowless parts as well. From gathered depths’ data we geostatistically (ordinary kriging) interpolated snow surface model which we used to determine reference volume of snow at research plot (5 569 m3). As a result, we were able to calculate important metrics and analyze topography and spatial distribution of snow cover at the plot. For taking photos for Structure from Motion method, bare pole in hands with a camera mounted was used. We constructed orthomosaic of research plot.

scholarly journals Correcting basin-scale snowfall in a mountainous basin using a distributed snowmelt model and remote-sensing data

2014 ◽  
Vol 18 (2) ◽  
pp. 747-761 ◽  
Author(s):  
M. Shrestha ◽  
L. Wang ◽  
T. Koike ◽  
H. Tsutsui ◽  
Y. Xue ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Search Algorithm ◽  
Relative Volume ◽  
Shortwave Radiation ◽  
Topographic Effects ◽  
Hydrologic Modelling ◽  
Study Region ◽  
Basin Scale ◽  
Snowmelt Model

Abstract. Adequate estimation of the spatial distribution of snowfall is critical in hydrologic modelling. However, this is a well-known problem in estimating basin-scale snowfall, especially in mountainous basins with data scarcity. This study focuses on correction and estimation of this spatial distribution, which considers topographic effects within the basin. A method is proposed that optimises an altitude-based snowfall correction factor (Cfsnow). This is done through multi-objective calibration of a spatially distributed, multilayer energy and water balance-based snowmelt model (WEB-DHM-S) with observed discharge and remotely sensed snow cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The Shuffled Complex Evolution–University of Arizona (SCE–UA) automatic search algorithm is used to obtain the optimal value of Cfsnow for minimum cumulative error in discharge and snow cover simulations. Discharge error is quantified by Nash–Sutcliffe efficiency and relative volume deviation, and snow cover error was estimated by pixel-by-pixel analysis. The study region is the heavily snow-fed Yagisawa Basin of the Upper Tone River in northeast Japan. First, the system was applied to one snow season (2002–2003), obtaining an optimised Cfsnow of 0.0007 m−1. For validation purposes, the optimised Cfsnow was implemented to correct snowfall in 2004, 2002 and 2001. Overall, the system was effective, implying improvements in correlation of simulated versus observed discharge and snow cover. The 4 yr mean of basin-average snowfall for the corrected spatial snowfall distribution was 1160 mm (780 mm before correction). Execution of sensitivity runs against other model input and parameters indicated that Cfsnow could be affected by uncertainty in shortwave radiation and setting of the threshold air temperature parameter. Our approach is suitable to correct snowfall and estimate its distribution in poorly gauged basins, where elevation dependence of snowfall amount is strong.

scholarly journals Measurements of mid-winter spatial distribution of meltwater saturation

2010 ◽  
Vol 51 (54) ◽  
pp. 146-152
Author(s):  
J.C. Kapil ◽  
Anupam Kumar ◽  
P.S. Negi
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Snow Cover ◽  
Measurement Techniques ◽  
Three Dimensional ◽  
Dielectric Measurement ◽  
Snow Stratigraphy ◽  
Wet Snow ◽  
Water Contents ◽  
Saturation Profiles

AbstractUnder melt–freeze conditions crusts may evolve within a snowpack, which may favour avalanche initiation by forming a hard bed surface for weakly bonded faceted grains. We used a parallel-probe saturation profiler (PPSP) to record the distribution of water contents within the snowpack. Diurnal effects of melt–freeze action on the growth of crusts were monitored with the help of the PPSP device. Saturation profiles were collected from a partially wet snow cover. Snow stratigraphy was conducted manually in the morning, after overnight freezing, to identify the location and the granular compositions of the crusts that had evolved. A one-to-one correspondence between the saturation spikes collected using the PPSP and the actual positions of the crusts was established. The PPSP was also used to monitor three-dimensional variations in the maximum percolation depths within a south-facing snowpack. The operation of the PPSP is faster than existing dielectric measurement techniques, so it was applied to study the spatial variability of maximum percolation depths on the slopes of different aspects.

scholarly journals Modeling the Spatial Distribution of Snow Cover in the Dudhkoshi Region of the Nepal Himalayas

2012 ◽  
Vol 13 (1) ◽  
pp. 204-222 ◽  
Author(s):  
Maheswor Shrestha ◽  
Lei Wang ◽  
Toshio Koike ◽  
Yongkang Xue ◽  
Yukiko Hirabayashi
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Hydrological Model ◽  
Lapse Rate ◽  
Distributed Hydrological Model ◽  
Snow Cover Area ◽  
Temperature Lapse Rate ◽  
Snow Cover Extent

Abstract In this study, a distributed biosphere hydrological model with three-layer energy-balance snow physics [an improved version of the Water and Energy Budget–based Distributed Hydrological Model (WEB-DHM-S)] is applied to the Dudhkoshi region of the eastern Nepal Himalayas to estimate the spatial distribution of snow cover. Simulations are performed at hourly time steps and 1-km spatial resolution for the 2002/03 snow season during the Coordinated Enhanced Observing Period (CEOP) third Enhanced Observing Period (EOP-3). Point evaluations (snow depth and upward short- and longwave radiation) at Pyramid (a station of the CEOP Himalayan reference site) confirm the vertical-process representations of WEB-DHM-S in this region. The simulated spatial distribution of snow cover is evaluated with the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day maximum snow-cover extent (MOD10A2), demonstrating the model’s capability to accurately capture the spatiotemporal variations in snow cover across the study area. The qualitative pixel-to-pixel comparisons for the snow-free and snow-covered grids reveal that the simulations agree well with the MODIS data to an accuracy of 90%. Simulated nighttime land surface temperatures (LST) are comparable to the MODIS LST (MOD11A2) with mean absolute error of 2.42°C and mean relative error of 0.77°C during the study period. The effects of uncertainty in air temperature lapse rate, initial snow depth, and snow albedo on the snow-cover area (SCA) and LST simulations are determined through sensitivity runs. In addition, it is found that ignoring the spatial variability of remotely sensed cloud coverage greatly increases bias in the LST and SCA simulations. To the authors’ knowledge, this work is the first to adopt a distributed hydrological model with a physically based multilayer snow module to estimate the spatial distribution of snow cover in the Himalayan region.

scholarly journals LiDAR snow cover studies on glacier surface: significance of snow- and ice dynamical processes

2013 ◽  
Vol 7 (2) ◽  
pp. 1787-1832 ◽  
Author(s):  
K. Helfricht ◽  
M. Kuhn ◽  
M. Keuschnig ◽  
A. Heilig
Keyword(s):  
Spatial Distribution ◽  
Standard Deviation ◽  
Snow Cover ◽  
Snow Depth ◽  
Laser Scanning ◽  
Snow Accumulation ◽  
Surface Elevation ◽  
High Mountain ◽  
Dynamical Processes ◽  
Elevation Changes

Abstract. The storage of water within the seasonal snow cover is a substantial source for runoff in high mountain catchments. Information about the spatial distribution of snow accumulation is necessary for calibration and validation of hydro-meteorological models. Generally only a small number of precipitation measurements deliver precipitation input for modeling in remote mountain areas. The spatial interpolation and extrapolation of measurements of precipitation is still difficult. Multi-temporal application of Light Detecting And Ranging (LiDAR) techniques from aircraft, so-called airborne laser scanning (ALS), enables to derive surface elevations changes even in inaccessible terrain. Within one snow accumulation season these surface elevation changes can be interpreted as snow depths as a first assumption for snow hydrological studies. However, dynamical processes in snow, firn and ice are contributing to surface elevation changes on glaciers. To evaluate the magnitude and significance of these processes on alpine glaciers in the present state, ALS derived surface elevation changes were compared to converted snow depths from 35.4 km of ground penetrating radar (GPR) profiles on four glaciers in the high alpine region of Ötztal Alps. LANDSAT data were used to distinguish between firn and ice areas of the glaciers. In firn areas submerging ice flow and densification of firn and snow are contributing to a mean relative deviation of ALS surface elevation changes from actually observed snow depths of −20.0% with a mean standard deviation of 17.1%. Deviations between ALS surface elevation changes and GPR snow depth are small along the profiles on the glacier tongues. At these areas mean absolute deviation of ALS surface elevation changes and GPR snow depth is 0.004 m with a mean standard deviation of 0.27 m. Emergence flow leads to distinct positive deviations only at the very front of the glacier tongues. Snow depths derived from ALS deviate less from actually measured snow depths than expected errors of in-situ measurements of solid precipitation. Hence, ALS derived snow depths are an important data source for both, spatial distribution and total sum of the snow cover volume stored on the investigated glaciers and in the corresponding high mountain catchments at the end of an accumulation season.

scholarly journals Estimating spatial distribution of daily snow depth with kriging methods: combination of MODIS snow cover area data and ground-based observations

2015 ◽  
Vol 9 (5) ◽  
pp. 4997-5020 ◽  
Author(s):  
C. L. Huang ◽  
H. W. Wang ◽  
J. L. Hou
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Snow Depth ◽  
Western China ◽  
Kriging Interpolation ◽  
Snow Cover Area ◽  
Smoothing Effects ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Accurately measuring the spatial distribution of the snow depth is difficult because stations are sparse, particularly in western China. In this study, we develop a novel scheme that produces a reasonable spatial distribution of the daily snow depth using kriging interpolation methods. These methods combine the effects of elevation with information from Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover area (SCA) products. The scheme uses snow-free pixels in MODIS SCA images with clouds removed to identify virtual stations, or areas with zero snow depth, to compensate for the scarcity and uneven distribution of stations. Four types of kriging methods are tested: ordinary kriging (OK), universal kriging (UK), ordinary co-kriging (OCK), and universal co-kriging (UCK). These methods are applied to daily snow depth observations at 50 meteorological stations in northern Xinjiang Province, China. The results show that the spatial distribution of snow depth can be accurately reconstructed using these kriging methods. The added virtual stations improve the distribution of the snow depth and reduce the smoothing effects of the kriging process. The best performance is achieved by the OK method in cases with shallow snow cover and by the UCK method when snow cover is widespread.

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