Continuous snowpack monitoring using upward-looking ground-penetrating radar technology
Snow 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 b...
| Published in: | Journal of Glaciology |
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| Main Authors: | , , , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
INT GLACIOL SOC
2014
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| Subjects: | |
| Online Access: | https://epic.awi.de/id/eprint/35405/ https://epic.awi.de/id/eprint/35405/1/j13J084.pdf http://www.igsoc.org/journal/60/221/j13J084.html https://hdl.handle.net/10013/epic.43558 https://hdl.handle.net/10013/epic.43558.d001 |
| Summary: | Snow 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. |
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