On GPS-based determination of snow cover properties
Abstract
Continuous information on snow cover properties is very important for various hydrological applications and forecasts of natural risks like avalanches and floods. Moreover, in situ measured snow data are requested for the validation of models and remote sensing products. However, they are often scarce, labor-intense, expensive, or invasive. During the ESA business applications demonstration project SnowSense (2015-2018), we developed a nondestructive approach based on signals of the Global Positioning System (GPS) to derive snow water equivalent (SWE), snow height (HS), and snow liquid water content (LWC) simultaneously using one sensor setup only. More recently, further signals of the Global Navigation Satellite System (GNSS) like Galileo were added. We mounted one GNSS antenna above the snowpack and another one on the ground, which was hence seasonally covered by snow. We derived the snow cover properties SWE, HS and LWC based on GNSS signal attenuation and time delay within the snowpack considering the varying dielectric properties of dry and wet snow. For dry-snow conditions, SWE was derived by solely exploiting GNSS carrier phases. Under wet-snow conditions, however, we combined carrier phase and signal strength information to jointly derive SWE, HS, and LWC as the GNSS signals are increasingly delayed and attenuated. So far, this approach was tested and validated over several years at the high-alpine site Weissfluhjoch, Switzerland (2.540 m asl). We observed a high agreement between our results and validation measurements, even for large values of SWE (>1,000 mm) and HS (> 3 m). Currently, we investigate this approach also along a steep elevation gradient in the Eastern Swiss Alps to target more frequent melt-freeze cycles with faster snow aging characteristics. Further, SWE monitoring (SnowSense) stations were installed and validated in Quebec and Newfoundland, Canada. These in situ GNSS measurements were combined with a hydrological model and remote sensing products to generate spatially distributed SWE and melt-onset maps as well as continuously assessed runoff information. With this approach, sensor networks can be established, which have the potential to spatially and temporally improve in situ snow data.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.C42B..06K
- Keywords:
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- 0736 Snow;
- CRYOSPHERE;
- 0740 Snowmelt;
- CRYOSPHERE;
- 0758 Remote sensing;
- CRYOSPHERE;
- 1863 Snow and ice;
- HYDROLOGY