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

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.

Download Full-text

Lidar snow cover studies on glaciers in the Ötztal Alps (Austria): comparison with snow depths calculated from GPR measurements

The Cryosphere ◽

10.5194/tc-8-41-2014 ◽

2014 ◽

Vol 8 (1) ◽

pp. 41-57 ◽

Cited By ~ 32

Author(s):

K. Helfricht ◽

M. Kuhn ◽

M. Keuschnig ◽

A. Heilig

Keyword(s):

Spatial Distribution ◽

Standard Deviation ◽

Snow Cover ◽

Snow Depth ◽

Snow Accumulation ◽

Surface Elevation ◽

Absolute Difference ◽

High Mountain ◽

Depth Maps ◽

Elevation Changes

Abstract. The storage of water within the seasonal snow cover is a substantial source of 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 modelling in mountain areas. The spatial interpolation and extrapolation of measurements of precipitation is still difficult. Multi-temporal application of lidar techniques from aircraft, so-called airborne laser scanning (ALS), provides surface elevations changes even in inaccessible terrain. These ALS surface elevation changes can be used to derive changes in snow depths of the mountain snow cover for seasonal or subseasonal time periods. However, since glacier surfaces are not static over time, ablation, densification of snow, densification of firn and ice flow contribute to surface elevation changes. ALS-derived surface elevation changes were compared to snow depths derived from 35.4 km of ground penetrating radar (GPR) profiles on four glaciers. With this combination of two different data acquisitions, it is possible to evaluate the effect of the summation of these processes on ALS-derived snow depth maps in the high alpine region of the Ötztal Alps (Austria). A Landsat 5 Thematic Mapper image was used to distinguish between snow covered area and bare ice areas of the glaciers at the end of the ablation season. In typical accumulation areas, ALS surface elevation changes differ from snow depths calculated from GPR measurements by −0.4 m on average with a mean standard deviation of 0.34 m. Differences between ALS surface elevation changes and GPR derived snow depths are small along the profiles conducted in areas of bare ice. In these areas, the mean absolute difference of ALS surface elevation changes and GPR snow depths is 0.004 m with a standard deviation of 0.27 m. This study presents a systematic approach to analyze deviations from ALS generated snow depth maps to ground truth measurements on four different glaciers. We could show that ALS can be an important and reliable data source for the spatial distribution of snow depths for most parts of the here investigated glaciers. However, within accumulation areas, just utilizing ALS data may lead to systematic underestimation of total snow depth distribution.

Download Full-text

Snow accumulation of a high alpine catchment derived from LiDAR measurements

Advances in Geosciences ◽

10.5194/adgeo-32-31-2012 ◽

2012 ◽

Vol 32 ◽

pp. 31-39 ◽

Cited By ~ 18

Author(s):

K. Helfricht ◽

J. Schöber ◽

B. Seiser ◽

A. Fischer ◽

J. Stötter ◽

...

Keyword(s):

Spatial Distribution ◽

Snow Accumulation ◽

Airborne Lidar ◽

Surface Elevation ◽

High Mountain ◽

Runoff Generation ◽

Spatial Characteristics ◽

Simple Regression Model ◽

Lidar Measurements ◽

Elevation Changes

Abstract. The spatial distribution of snow accumulation substantially affects the seasonal course of water storage and runoff generation in high mountain catchments. Whereas the areal extent of snow cover can be recorded by satellite data, spatial distribution of snow depth and hence snow water equivalent (SWE) is difficult to measure on catchment scale. In this study we present the application of airborne LiDAR (Light Detecting And Ranging) data to extract snow depths and accumulation distribution in an alpine catchment. Airborne LiDAR measurements were performed in a glacierized catchment in the Ötztal Alps at the beginning and the end of three accumulation seasons. The resulting digital elevation models (DEMs) were used to calculate surface elevation changes throughout the winter season. These surface elevation changes were primarily referred to as snow depths and are discussed concerning measured precipitation and the spatial characteristics of the accumulation distribution in glacierized and unglacierized areas. To determine the redistribution of catchment precipitation, snow depths were converted into SWE using a simple regression model. Snow accumulation gradients and snow redistribution were evaluated for 100 m elevation bands. Mean surface elevation changes of the whole catchment ranges from 1.97 m to 2.65 m within the analyzed accumulation seasons. By analyzing the distribution of the snow depths, elevation dependent patterns were obtained as a function of the topography in terms of aspect and slope. The high resolution DEMs show clearly the higher variation of snow depths in rough unglacierized areas compared to snow depths on smooth glacier surfaces. Mean snow depths in glacierized areas are higher than in unglacierized areas. Maximum mean snow depths of 100 m elevation bands are found between 2900 m and 3000 m a.s.l. in unglacierized areas and between 2800 m and 2900 m a.s.l. in glacierized areas, respectively. Calculated accumulation gradients range from 8% to 13% per 100 m elevation band in the observed catchment. Elevation distribution of accumulation calculated by applying these seasonal gradients in comparison to elevation distribution of SWE obtained from airborne laser scanning (ALS) data show the total redistribution of snow from higher to lower elevation bands. Revealing both, information about the spatial distribution of snow depths and hence the volume of the snow pack, ALS data are an important source for extensive snow accumulation measurements in high alpine catchments. These information about the spatial characteristics of snow distribution are crucial for calibrating hydrological models in order to realistically compute temporal runoff generation by snow melt.

Download Full-text

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

Applied Sciences ◽

10.3390/app11188365 ◽

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.

Download Full-text

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

Czech Polar Reports ◽

10.5817/cpr2016-2-14 ◽

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.

Download Full-text

Glacier surface elevation changes in Rongbuk Catchment of the Central Himalayas in the last four decades

10.5194/egusphere-egu2020-9125 ◽

2020 ◽

Author(s):

Qinghua Ye ◽

Wei Nie ◽

Yimin Chen ◽

Gang Li ◽

lide Tian ◽

...

Keyword(s):

Climate Modeling ◽

Melting Rate ◽

Surface Elevation ◽

High Mountain ◽

Glacier Surface ◽

Important Indicator ◽

Central Himalayas ◽

Glacier Melting ◽

Elevation Changes ◽

Thinning Rate

Glaciers in the central Himalayas are important water resources for the downstream habitants, and accelerating melting of the high mountain glaciers speed up with continuous warming. We summerized the geodetic glacier surface elevation changes (Dh) by 6 data sets at different time periods during 1974-2016 in RongbukCatchment(RC) on the northern slope of Mt. Qomolangma (Mt. Everest) in the Central Himalayas. The result showed that glacier Dh varied with altitude and time, from -0.29 &#177; 0.03m a-1 in 1974-2000, to -0.47 &#177;0.24 m a-1 in 1974-2006,and -0.48 &#177;0.16 m a-1 in 1974-2012. Dh increased to -0.60 &#177; 0.20 m a-1 in 2000-2012, then decreased to-0.46 &#177; 0.24 m a-1 in 2000-2014, and by -0.49 &#177; 0.08 m a-1 in 2000-2016, showing a diverse rate being up - down- a little up. However, it generally presented a similar glacier thinning rate by -0.46~-0.49 m a-1 in the last four decades since 1970s in RC according to Dh1974-2006, Dh1974-2012, Dh2000-2014, and Dh2000-2016. Local meteorological observations revealed that, to a first order, the glacier thinning rate was kept the same pace with the number of annual melting days (MD). In spite of the obviously arising summer air temperature (TS) in 2000-2014, a slowdown glacier melting rate by -391 mm w.e.a-1 occurred in 2000-2014 because of less melting days with more precipitation and less annual mean temperature(Tm). It shows that MD is another important indicator and controlling factor to evaluate or to estimate glacier melting trend, especially in hydrological or climate modeling.

Download Full-text

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

Hydrology Research ◽

10.2166/nh.2001.0011 ◽

2001 ◽

Vol 32 (3) ◽

pp. 181-194 ◽

Cited By ~ 13

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.

Download Full-text

Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data

The Cryosphere ◽

10.5194/tc-14-2925-2020 ◽

2020 ◽

Vol 14 (9) ◽

pp. 2925-2940 ◽

Cited By ~ 4

Author(s):

César Deschamps-Berger ◽

Simon Gascoin ◽

Etienne Berthier ◽

Jeffrey Deems ◽

Ethan Gutmann ◽

...

Keyword(s):

Satellite Imagery ◽

Snow Depth ◽

Laser Scanning ◽

Unmanned Aircraft ◽

Small Scale ◽

High Mountain ◽

Catchment Scale ◽

Absolute Deviation ◽

Covered Area ◽

Tuolumne River

Abstract. Accurate knowledge of snow depth distributions in mountain catchments is critical for applications in hydrology and ecology. Recently, a method was proposed to map snow depth at meter-scale resolution from very-high-resolution stereo satellite imagery (e.g., Pléiades) with an accuracy close to 0.5 m. However, the validation was limited to probe measurements and unmanned aircraft vehicle (UAV) photogrammetry, which sampled a limited fraction of the topographic and snow depth variability. We improve upon this evaluation using accurate maps of the snow depth derived from Airborne Snow Observatory laser-scanning measurements in the Tuolumne river basin, USA. We find a good agreement between both datasets over a snow-covered area of 138 km2 on a 3 m grid, with a positive bias for a Pléiades snow depth of 0.08 m, a root mean square error of 0.80 m and a normalized median absolute deviation (NMAD) of 0.69 m. Satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits at a typical scale of tens of meters. The random error at the pixel level is lower in snow-free areas than in snow-covered areas, but it is reduced by a factor of 2 (NMAD of approximately 0.40 m for snow depth) when averaged to a 36 m grid. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountain catchments.

Download Full-text

Recession and Ice Surface Elevation Changes of Baranowski Glacier and Its Impact on Proglacial Relief (King George Island, West Antarctica)

Geosciences ◽

10.3390/geosciences8100355 ◽

2018 ◽

Vol 8 (10) ◽

pp. 355 ◽

Cited By ~ 4

Author(s):

Joanna Sziło ◽

Robert Bialik

Keyword(s):

Laser Scanning ◽

Melting Process ◽

Middle Part ◽

Surface Elevation ◽

Climatic Fluctuations ◽

Aerial Photos ◽

Ice Surface ◽

Dynamic Landscapes ◽

Elevation Changes ◽

The Difference

Glacial forefields areas are dynamic landscapes, and due to the glacier frontal position changes, they are sensitive to climatic fluctuations. The results of the analysis of aerial photos, satellite imagery, archival maps, and terrestrial laser scanning surveys are presented. These investigations reveal that the ice surface decreased during the period 1989–2001, when almost the entire current forefield was already uncovered. Moreover, it is shown that, since 1969, there has been a relationship between the changes in air temperature and the changes of the annual front position rate of Baranowski Glacier. Specifically, the results demonstrate that during the cooling observed for the Antarctic Peninsula Regions since 2000, there is a deceleration of the recession rate and ice surface elevation changes of Baranowski Glacier. It is also shown that the fluctuation of the areal extent of the glacier as well as ice surface elevation changes are closely associated with proglacial relief. Moreover, it is shown that the difference in the retreat of the northern and southern tongue of the glacier can be explained by the presence of relatively warm water in the shallow bay, which can enhance the melting process of the northern part. In addition, existence of long flutes and crevasse fill ridges on the analyzed forefield of Baranowski Glacier suggest that the former episodes of its surge, which could happen at least in the northern part of the forefield and middle part of the southern forefield of the glacier.

Download Full-text

Impact of forcing on sublimation simulations for a high mountain catchment in the semiarid Andes

The Cryosphere ◽

10.5194/tc-14-147-2020 ◽

2020 ◽

Vol 14 (1) ◽

pp. 147-163 ◽

Cited By ~ 3

Author(s):

Marion Réveillet ◽

Shelley MacDonell ◽

Simon Gascoin ◽

Christophe Kinnard ◽

Stef Lhermitte ◽

...

Keyword(s):

Snow Cover ◽

Snow Depth ◽

Meteorological Data ◽

Wrf Model ◽

Annual Rainfall ◽

Cold Bias ◽

High Mountain ◽

Mountain Catchment ◽

Depth Sensitivity ◽

Sublimation Rates

Abstract. In the semiarid Andes of Chile, farmers and industry in the cordillera lowlands depend on water from snowmelt, as annual rainfall is insufficient to meet their needs. Despite the importance of snow cover for water resources in this region, understanding of snow depth distribution and snow mass balance is limited. Whilst the effect of wind on snow cover pattern distribution has been assessed, the relative importance of melt versus sublimation has only been studied at the point scale over one catchment. Analyzing relative ablation rates and evaluating uncertainties are critical for understanding snow depth sensitivity to variations in climate and simulating the evolution of the snowpack over a larger area and over time. Using a distributed snowpack model (SnowModel), this study aims to simulate melt and sublimation rates over the instrumented watershed of La Laguna (513 km2, 3150–5630 m a.s.l., 30∘ S, 70∘ W), during two hydrologically contrasting years (i.e., dry vs. wet). The model is calibrated and forced with meteorological data from nine Automatic Weather Stations (AWSs) located in the watershed and atmospheric simulation outputs from the Weather Research and Forecasting (WRF) model. Results of simulations indicate first a large uncertainty in sublimation-to-melt ratios depending on the forcing as the WRF data have a cold bias and overestimate precipitation in this region. These input differences cause a doubling of the sublimation-to-melt ratio using WRF forcing inputs compared to AWS. Therefore, the use of WRF model output in such environments must be carefully adjusted so as to reduce errors caused by inherent bias in the model data. For both input datasets, the simulations indicate a similar sublimation fraction for both study years, but ratios of sublimation to melt vary with elevation as melt rates decrease with elevation due to decreasing temperatures. Finally results indicate that snow persistence during the spring period decreases the ratio of sublimation due to higher melt rates.

Download Full-text

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

10.5194/egusphere-egu2020-1715 ◽

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

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 = &#8722;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&#8211;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&#8211;23% and 5&#8211;8% of the total maximum ice thickness on an annual basis in the case of initial ice thickness ranging from 0.05&#8211;2 m, respectively.

Download Full-text