scholarly journals Continuous snowpack monitoring using upward-looking ground-penetrating radar technology

2014 ◽  
Vol 60 (221) ◽  
pp. 509-525 ◽  
Author(s):  
Lino Schmid ◽  
Achim Heilig ◽  
Christoph Mitterer ◽  
Jürg Schweizer ◽  
Hansruedi Maurer ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Snow Water Equivalent ◽  
Temporal Evolution ◽  
Liquid Water Content ◽  
Internal Layers ◽  
Contributing Factors ◽  
Height Measurement ◽  
Additional Information ◽  
Snow Properties ◽  
Ground Penetrating

AbstractSnow stratigraphy and water percolation are key contributing factors to avalanche formation. So far, only destructive methods can provide this kind of information. Radar technology allows continuous, non-destructive scanning of the snowpack so that the temporal evolution of internal properties can be followed. We installed an upward-looking ground-penetrating radar system (upGPR) at the Weissfluhjoch study site (Davos, Switzerland). During two winter seasons (2010/11 and 2011/12) we recorded data with the aim of quantitatively determining snowpack properties and their temporal evolution. We automatically derived the snow height with an accuracy of about ± 5 cm, tracked the settlement of internal layers (± 7 cm) and measured the amount of new snow (± 10 cm). Using external snow height measurements, we determined the bulk density with a mean error of 4.3% compared to manual measurements. Radar-derived snow water equivalent deviated from manual measurements by 5%. Furthermore, we tracked the location of the dry-to-wet transition in the snowpack until water percolated to the ground. Based on the transition and an independent snow height measurement it was possible to estimate the volumetric liquid water content and its temporal evolution. Even though we need additional information to derive some of the snow properties, our results show that it is possible to quantitatively derive snow properties with upGPR.

Field evaluation of a new method for estimation of liquid water content and snow water equivalent of wet snowpacks with GPR

2012 ◽  
Vol 44 (4) ◽  
pp. 600-613 ◽  
Author(s):  
Nils Sundström ◽  
David Gustafsson ◽  
Andrey Kruglyak ◽  
Angela Lundberg
Keyword(s):  
Water Content ◽  
Liquid Water ◽  
Ground Penetrating Radar ◽  
Snow Water Equivalent ◽  
Field Evaluation ◽  
Liquid Water Content ◽  
Snow Water ◽  
The Mean ◽  
Path Dependent ◽  
Ground Penetrating

Estimates of snow water equivalent (SWE) with ground-penetrating radar can be used to calibrate and validate measurements of SWE over large areas conducted from satellites and aircrafts. However, such radar estimates typically suffer from low accuracy in wet snowpacks due to a built-in assumption of dry snow. To remedy the problem, we suggest determining liquid water content from path-dependent attenuation. We present the results of a field evaluation of this method which demonstrate that, in a wet snowpack between 0.9 and 3 m deep and with about 5 vol% of liquid water, liquid water content is underestimated by about 50% (on average). Nevertheless, the method decreases the mean error in SWE estimates to 16% compared to 34% when the presence of liquid water in snow is ignored and 31% when SWE is determined directly from two-way travel time and calibrated for manually measured snow density.

scholarly journals Estimating snow water equivalent over long mountain transects using snowmobile-mounted ground-penetrating radar

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. WA183-WA193 ◽  
Author(s):  
W. Steven Holbrook ◽  
Scott N. Miller ◽  
Matthew A. Provart
Keyword(s):  
Ground Penetrating Radar ◽  
Snow Water Equivalent ◽  
Ground Surface ◽  
Snow Accumulation ◽  
Accurate Estimation ◽  
Snow Density ◽  
Snow Stratigraphy ◽  
Flood Estimation ◽  
Snow Water ◽  
Ground Penetrating

The water balance in alpine watersheds is dominated by snowmelt, which provides infiltration, recharges aquifers, controls peak runoff, and is responsible for most of the annual water flow downstream. Accurate estimation of snow water equivalent (SWE) is necessary for runoff and flood estimation, but acquiring enough measurements is challenging due to the variability of snow accumulation, ablation, and redistribution at a range of scales in mountainous terrain. We have developed a method for imaging snow stratigraphy and estimating SWE over large distances from a ground-penetrating radar (GPR) system mounted on a snowmobile. We mounted commercial GPR systems (500 and 800 MHz) to the front of the snowmobile to provide maximum mobility and ensure that measurements were taken on pristine snow. Images showed detailed snow stratigraphy down to the ground surface over snow depths up to at least 8 m, enabling the elucidation of snow accumulation and redistribution processes. We estimated snow density (and thus SWE, assuming no liquid water) by measuring radar velocity of the snowpack through migration focusing analysis. Results from the Medicine Bow Mountains of southeast Wyoming showed that estimates of snow density from GPR ([Formula: see text]) were in good agreement with those from coincident snow cores ([Formula: see text]). Using this method, snow thickness, snow density, and SWE can be measured over large areas solely from rapidly acquired common-offset GPR profiles, without the need for common-midpoint acquisition or snow cores.

scholarly journals Sodankylä manual snow survey program

2016 ◽  
Vol 5 (1) ◽  
pp. 163-179 ◽  
Author(s):  
Leena Leppänen ◽  
Anna Kontu ◽  
Henna-Reetta Hannula ◽  
Heidi Sjöblom ◽  
Jouni Pulliainen
Keyword(s):  
Snow Water Equivalent ◽  
Measurement Techniques ◽  
Liquid Water Content ◽  
The Arctic ◽  
Research Centre ◽  
Future Research ◽  
Forest Zone ◽  
Finnish Meteorological Institute ◽  
Snow Properties ◽  
Snow Water

Abstract. The manual snow survey program of the Arctic Research Centre of the Finnish Meteorological Institute (FMI-ARC) consists of numerous observations of natural seasonal taiga snowpack in Sodankylä, northern Finland. The easily accessible measurement areas represent the typical forest and soil types in the boreal forest zone. Systematic snow measurements began in 1909 with snow depth (HS) and snow water equivalent (SWE). In 2006 the manual snow survey program expanded to cover snow macro- and microstructure from regular snow pits at several sites using both traditional and novel measurement techniques. Present-day snow pit measurements include observations of HS, SWE, temperature, density, stratigraphy, grain size, specific surface area (SSA) and liquid water content (LWC). Regular snow pit measurements are performed weekly during the snow season. Extensive time series of manual snow measurements are important for the monitoring of temporal and spatial changes in seasonal snowpack. This snow survey program is an excellent base for the future research of snow properties.

scholarly journals Imaging the structure of cave ice by ground-penetrating radar

2011 ◽  
Vol 5 (2) ◽  
pp. 329-340 ◽  
Author(s):  
H. Hausmann ◽  
M. Behm
Keyword(s):  
Electromagnetic Wave ◽  
Internal Structure ◽  
Liquid Water ◽  
Ground Penetrating Radar ◽  
Wave Speed ◽  
Liquid Water Content ◽  
Thin Layers ◽  
Ice Formation ◽  
Alpine Regions ◽  
Ground Penetrating

Abstract. Several caves in high elevated alpine regions host up to several meters thick ice. The age of the ice may exceed some hundreds or thousands of years. However, structure, formation and development of the ice are not fully understood and are subject to relatively recent investigation. The application of ground-penetrating radar (GPR) enables to determine thickness, volume, basal and internal structure of the ice and provides as such important constraints for related studies. We present results from four caves located in the Northern Calcareous Alps of Austria. We show that the ice is far from being uniform. The base has variable reflection signatures, which is related to the type and size of underlying debris. The internal structure of the cave ice is characterized by banded reflections. These reflection signatures are interpreted as thin layers of sediments and might help to understand the ice formation by representing isochrones. Overall, the relatively low electromagnetic wave speed suggests that the ice is temperate, and that a liquid water content of about 2% is distributed homogenously in the ice.

scholarly journals Southeast Greenland high accumulation rates derived from firn cores and ground-penetrating radar

2013 ◽  
Vol 54 (63) ◽  
pp. 322-332 ◽  
Author(s):  
Clément Miège ◽  
Richard R. Forster ◽  
Jason E. Box ◽  
Evan W. Burgess ◽  
Joseph R. McConnell ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Climate Model ◽  
Regional Climate ◽  
Elevation Gradient ◽  
Greenland Ice Sheet ◽  
Snow Accumulation ◽  
Internal Layers ◽  
Accumulation Rates ◽  
High Accumulation ◽  
Ground Penetrating

AbstractDespite containing only 14% of the Greenland ice sheet by area, the southeastern sector has the highest accumulation rates, and hence receives ∼30% of the total snow accumulation. We present accumulation rates obtained during our 2010 Arctic Circle Traverse derived from three 50 m firn cores dated using geochemical analysis. We tracked continuous internal reflection horizons between the firn cores using a 400 MHz ground-penetrating radar (GPR). GPR data combined with depth-age scales from the firn cores provide accumulation rates along a 70 km transect. We followed an elevation gradient from ∼2350 to ∼1830m to understand how progressive surface melt may affect the ability to chemically date the firn cores and trace the internal layers with GPR. From the firn cores, we find a 52% (∼0.43 m w.e. a-1) increase in average snow accumulation and greater interannual variability at the lower site than the upper site. The GPR profiling reveals that accumulation rates are influenced by topographic undulations on the surface, with up to 23% variability over 7 km. These measurements confirm the presence of high accumulation rates in the southeast as predicted by the calibrated regional climate model Polar MM5.

A critical analysis on the uncertainty computation in ground-penetrating radar-retrieved dry snow parameters

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. H39-H49
Author(s):  
Federico Di Paolo ◽  
Barbara Cosciotti ◽  
Sebastian E. Lauro ◽  
Elisabetta Mattei ◽  
Elena Pettinelli
Keyword(s):  
Wave Velocity ◽  
Ground Penetrating Radar ◽  
Snow Water Equivalent ◽  
Radar Data ◽  
Published Data ◽  
Physical Parameters ◽  
Large Area ◽  
Essential Information ◽  
Two Parameters ◽  
Ground Penetrating

The use of the ground-penetrating-radar (GPR) technique to estimate snow parameters such as thickness, density, and snow water equivalent (SWE) is particularly promising because it allows for surveying a large area in a relatively short amount of time. However, this application requires an accurate evaluation of the physical parameters retrieved from the radar measurements, which requires estimating each quantity involved in the computation along with its associated uncertainty. Conversely, the uncertainties are rarely reported in GPR snow studies, even if they represent essential information for data comparisons with other techniques such as the snow rod or snow pit methods. Snow parameters can be estimated from radar data as follows: The snow thickness can be computed from two-way traveltime if the snow average wave velocity is known; the snow density can be estimated from wave velocity using an appropriate mixing formula, and SWE can be computed once these two parameters have been calculated. Starting from published data, we have estimated the accuracy achievable by computing the overall uncertainty for each GPR-retrieved snow parameter and evaluated the influence of the different sources of uncertainties. The computation was made for three antenna frequencies (250, 500, and 1000 MHz) and various snow depths (0–5 m). We find that for snow thicknesses of less than 3 m, the main contribution to the uncertainties associated with snow parameters is given by the uncertainty on two-way traveltime estimation, especially for low antenna frequencies. However, for thicker snow depths, other factors such as the uncertainty on the antenna separation affect the overall accuracy and cannot be neglected. Our studies highlight the importance of the uncertaintiy assessment and suggest a rigorous way for their computation in the field of quantitative geophysics.

scholarly journals Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions

2020 ◽  
Vol 14 (3) ◽  
pp. 935-956 ◽  
Author(s):  
Carlo Marin ◽  
Giacomo Bertoldi ◽  
Valentina Premier ◽  
Mattia Callegari ◽  
Christian Brida ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Melting Process ◽  
Liquid Water Content ◽  
Sar Images ◽  
Distributed Information ◽  
Snow Modeling ◽  
Alpine Regions ◽  
Snow Properties ◽  
Wet Snow ◽  
Spatially Distributed

Abstract. Knowing the timing and the evolution of the snow melting process is very important, since it allows the prediction of (i) the snowmelt onset, (ii) the snow gliding and wet-snow avalanches, (iii) the release of snow contaminants, and (iv) the runoff onset. The snowmelt can be monitored by jointly measuring snowpack parameters such as the snow water equivalent (SWE) or the amount of free liquid water content (LWC). However, continuous measurements of SWE and LWC are rare and difficult to obtain. On the other hand, active microwave sensors such as the synthetic aperture radar (SAR) mounted on board satellites are highly sensitive to LWC of the snowpack and can provide spatially distributed information with a high resolution. Moreover, with the introduction of Sentinel-1, SAR images are regularly acquired every 6 d over several places in the world. In this paper we analyze the correlation between the multitemporal SAR backscattering and the snowmelt dynamics. We compared Sentinel-1 backscattering with snow properties derived from in situ observations and process-based snow modeling simulations for five alpine test sites in Italy, Germany and Switzerland considering 2 hydrological years. We found that the multitemporal SAR measurements allow the identification of the three melting phases that characterize the melting process, i.e., moistening, ripening and runoff. In particular, we found that the C-band SAR backscattering decreases as soon as the snow starts containing water and that the backscattering increases as soon as SWE starts decreasing, which corresponds to the release of meltwater from the snowpack. We discuss the possible reasons of this increase, which are not directly correlated to the SWE decrease but to the different snow conditions, which change the backscattering mechanisms. Finally, we show a spatially distributed application of the identification of the runoff onset from SAR images for a mountain catchment, i.e., the Zugspitze catchment in Germany. Results allow us to better understand the spatial and temporal evolution of melting dynamics in mountain regions. The presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.

scholarly journals Sodankylä manual snow survey program

2015 ◽  
Vol 5 (2) ◽  
pp. 405-426
Author(s):  
L. Leppänen ◽  
A. Kontu ◽  
H.-R. Hannula ◽  
H. Sjöblom ◽  
J. Pulliainen
Keyword(s):  
Snow Water Equivalent ◽  
Measurement Techniques ◽  
Liquid Water Content ◽  
The Arctic ◽  
Research Centre ◽  
Future Research ◽  
Forest Zone ◽  
Finnish Meteorological Institute ◽  
Snow Properties ◽  
Snow Water

Abstract. The manual snow survey program of the Arctic Research Centre of Finnish Meteorological Institute (FMI-ARC) consists of numerous observations of natural seasonal taiga snowpack in Sodankylä, northern Finland. The easily accessible measurement areas represent the typical forest and soil types in the boreal forest zone. Systematic snow measurements began in 1909 with snow depth (SD) and snow water equivalent (SWE); however some older records of the snow and ice cover exists. In 2006 the manual snow survey program expanded to cover snow macro- and microstructure from regular snow pits at several sites using both traditional and novel measurement techniques. Present-day measurements include observations of SD, SWE, temperature, density, horizontal layers of snow, grain size, specific surface area (SSA), and liquid water content (LWC). Regular snow pit measurements are performed weekly during the snow season. Extensive time series of manual snow measurements are important for the monitoring of temporal and spatial changes in seasonal snowpack. This snow survey program is an excellent base for the future research of snow properties.

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