Snow Wetness Influence on Impulse Radar Snow Surveys Theoretical and Laboratory Study

Lundberg, Angela; Thunehed, Hans

doi:10.2166/nh.2000.0007

Cited by 31 publications

(24 citation statements)

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“…On the other hand, the LWC directly upon upGPR and GPS can easily vary due to rapid melting processes within the ripe snowpack (end of June 2013 and beginning of June 2014). Moreover, the formulas applied for the complex permittivity are only valid up to a LWC of approximately 8% [ Lundberg and Thunehed , ; Mitterer et al , ]. However, these deviations were only observed at the very end of the snow‐covered season; overall, the agreement between both systems was very good.…”

Section: Discussionmentioning

confidence: 99%

A novel sensor combination (upGPR‐GPS) to continuously and nondestructively derive snow cover properties

Schmid¹,

Koch

Heilig

et al. 2015

Geophysical Research Letters

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Monitoring seasonal snow cover properties is critical for properly managing natural hazards such as snow avalanches or snowmelt floods. However, measurements often cannot be conducted in difficult terrain or lack the high temporal resolution needed to account for rapid changes in the snowpack, e.g., liquid water content (LWC). To monitor essential snowpack properties, we installed an upward looking ground‐penetrating radar (upGPR) and a low‐cost GPS system below the snow cover and observed in parallel its evolution during two winter seasons. Applying external snow height (HS) information, both systems provided consistent LWC estimates in snow, based on independent approaches, namely measurements of travel time and attenuation of electromagnetic waves. By combining upGPR and GPS, we now obtain a self‐contained approach instead of having to rely on external information such as HS. This allows for the first time determining LWC, HS, and snow water equivalent (SWE) nondestructively and continuously potentially also in avalanche‐prone slopes.

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Section: Discussionmentioning

confidence: 99%

A novel sensor combination (upGPR‐GPS) to continuously and nondestructively derive snow cover properties

Schmid¹,

Koch

Heilig

et al. 2015

Geophysical Research Letters

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“…However, they reported that quantitative interpretation is challenging due to, for example, air pockets that easily form around the sensors. Nondestructive measurements of wet-snow or even LWC alongside with the determination of further snow cover properties were obtained with different radar systems (e.g., Bradford et al, 2009;Heilig et al, 2015;Lundberg & Thunehed, 2000;Mitterer et al, 2011;Schmid et al, 2014) but are rather expensive for operational application.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay

Koch

Henkel

Appel

et al. 2019

Water Resources Research

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For numerous hydrological applications, information on snow water equivalent (SWE) and snow liquid water content (LWC) are fundamental. In situ data are much needed for the validation of model and remote sensing products; however, they are often scarce, invasive, expensive, or labor‐intense. We developed a novel nondestructive approach based on Global Positioning System (GPS) signals to derive SWE, snow height (HS), and LWC simultaneously using one sensor setup only. We installed two low‐cost GPS sensors at the high‐alpine site Weissfluhjoch (Switzerland) and processed data for three entire winter seasons between October 2015 and July 2018. One antenna was mounted on a pole, being permanently snow‐free; the other one was placed on the ground and hence seasonally covered by snow. While SWE can be derived by exploiting GPS carrier phases for dry‐snow conditions, the GPS signals are increasingly delayed and attenuated under wet snow. Therefore, we combined carrier phase and signal strength information, dielectric models, and simple snow densification approaches to jointly derive SWE, HS, and LWC. The agreement with the validation measurements was very good, even for large values of SWE (>1,000 mm) and HS (> 3 m). Regarding SWE, the agreement (root‐mean‐square error (RMSE); coefficient of determination (R2)) for dry snow (41 mm; 0.99) was very high and slightly better than for wet snow (73 mm; 0.93). Regarding HS, the agreement was even better and almost equally good for dry (0.13 m; 0.98) and wet snow (0.14 m; 0.95). The approach presented is suited to establish sensor networks that may improve the spatial and temporal resolution of snow data in remote areas.

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“…Alternatively, Lundberg et al () assumed density to be linearly dependent on snow depth. However, when liquid water is present in the snowpack, data analysis methods become more complex and snow water equivalent estimations are typically more uncertain (Lundberg & Thunehed, ). Bradford and Harper () determined the liquid water content by using the frequency shift method to estimate the complex electrical permittivity and by using the common midpoint (CMP) method to estimate the real part of the electrical permittivity.…”

Section: Introductionmentioning

confidence: 99%

Measuring snow ablation rates in alpine terrain with a mobile multioffset ground‐penetrating radar system

Mohr

Jonas

2018

Hydrological Processes

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Ground‐penetrating radar (GPR) has become a promising technique in the field of snow hydrological research. It is commonly used to measure snow depth, density, and water equivalent over large distances or along gridded snow courses. Having built and tested a mobile lightweight set‐up, we demonstrate that GPR is capable of accurately measuring snow ablation rates in complex alpine terrain. Our set‐up was optimized for efficient measurements and consisted of a multioffset radar with four pairs of antennas mounted to a plastic sled, which was small enough to permit safe and convenient operations. Repeated measurements at intervals of 2 to 7 days were taken during the 2014/2015 winter season along 10 profiles of 50 to 200 m length within two valleys located in the eastern Swiss Alps. Resulting GPR‐based data of snow depth, density, and water equivalent, as well as their respective change over time, were in good agreement with concurrent manual measurements, in particular if accurate alignment between repeated overpasses could be achieved. Corresponding root‐mean‐square error (RMSE) values amounted to 4.2 cm for snow depth, 17 mm for snow water equivalent, and 22 kg/m3 for snow density, with similar RMSE values for corresponding differential data. With this performance, the presented radar set‐up has the potential to provide exciting new and extensive datasets to validate snowmelt models or to complement lidar‐based snow surveys.

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Snow Wetness Influence on Impulse Radar Snow Surveys Theoretical and Laboratory Study

Cited by 31 publications

References 0 publications

A novel sensor combination (upGPR‐GPS) to continuously and nondestructively derive snow cover properties

A novel sensor combination (upGPR‐GPS) to continuously and nondestructively derive snow cover properties

Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay

Measuring snow ablation rates in alpine terrain with a mobile multioffset ground‐penetrating radar system

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