Near real-time observations of snow water equivalent for SIOS on Svalbard – (SWESOS)
Snow is an important component of the Svalbard permafrost and hydrological system due to its water storage and insulation properties. Continuous observations of snow properties in high-latitude such as Svalbard are almost non-existent, making it difficult to calibrate the current generation of snow,...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Conference Object |
| Language: | unknown |
| Published: |
2021
|
| Subjects: | |
| Online Access: | https://epic.awi.de/id/eprint/54923/ https://epic.awi.de/id/eprint/54923/1/JMartin_SWESOS.pdf https://hdl.handle.net/10013/epic.0d534fad-6a50-4ea2-aa79-54b65c4c766b https://hdl.handle.net/ |
| Summary: | Snow is an important component of the Svalbard permafrost and hydrological system due to its water storage and insulation properties. Continuous observations of snow properties in high-latitude such as Svalbard are almost non-existent, making it difficult to calibrate the current generation of snow, permafrost and hydrologic models, for these areas. The quantity of water contained within a snowpack, termed snow water equivalent (SWE), is an important variable to consider, but it is a difficult and time-consuming task to accurately measure and model SWE over broad spatial areas. Automated methods of SWE measurement can increase the ease with which seasonal SWE patterns can be monitored. We here evaluate an automated monitoring technique for measuring SWE using a passive gamma ray sensor at the Bayelva site (near Ny-Ålesund) installed in August 2019, validated with field data from 2019/2020 and 2020/2021. In March 2021 we performed a detailed SWE survey inside the sensor footprint that confirms a high spatial variability in the snowpack. Using independent wind, temperature and radiation data and automated photos from the nearby climate station, we could verify the onset and end of the snow-covered season as well as strong changes in snow depth and SWE as indicated by the new automated sensors. Thus, we conclude the new automated measurement system reliably captures the general evolution of these snow properties over the snow-covered season. One difficulty in comparing the automated and manual SWE measurements is the high spatial variability of SWE and snow depth within the footprint area of the sensor due to uneven snow cover associated with uneven terrain, wind drift and discontinuous snow cover due to patchy snow melt. Nevertheless, we see the automatic sensor as a good option to record continuous SWE data series in remote areas and with that fill data gaps to answer modeling questions. |
|---|