Sensing area-average snow water equivalent with cosmic-ray neutrons: the influence of fractional snow cover
- Cosmic-ray neutron sensing (CRNS) is a promising non-invasive technique to estimate snow water equivalent (SWE) over large areas. In contrast to preliminary studies focusing on shallow snow conditions (SWE <130 mm), more recently the method was shown experimentally to be sensitive also to deeper snowpacks providing the basis for its use at mountain experimental sites. However, hysteretic neutron response has been observed for complex snow cover including patchy snow-free areas. In the present study we aimed to understand and support the experimental findings using a comprehensive neutron modeling approach. Several simulations have been set up in order to disentangle the effect on the signal of different land surface characteristics and to reproduce multiple observations during periods of snow melt and accumulation. To represent the actual land surface heterogeneity and the complex snow cover, the model used data from terrestrial laser scanning. The results show that the model was able to accurately reproduce the CRNS signal andCosmic-ray neutron sensing (CRNS) is a promising non-invasive technique to estimate snow water equivalent (SWE) over large areas. In contrast to preliminary studies focusing on shallow snow conditions (SWE <130 mm), more recently the method was shown experimentally to be sensitive also to deeper snowpacks providing the basis for its use at mountain experimental sites. However, hysteretic neutron response has been observed for complex snow cover including patchy snow-free areas. In the present study we aimed to understand and support the experimental findings using a comprehensive neutron modeling approach. Several simulations have been set up in order to disentangle the effect on the signal of different land surface characteristics and to reproduce multiple observations during periods of snow melt and accumulation. To represent the actual land surface heterogeneity and the complex snow cover, the model used data from terrestrial laser scanning. The results show that the model was able to accurately reproduce the CRNS signal and particularly the hysteresis effect during accumulation and melting periods. Moreover, the sensor footprint was found to be anisotropic and affected by the spatial distribution of liquid water and snow as well as by the topography of the nearby mountains. Under fully snow-covered conditions the CRNS is able to accurately estimate SWE without prior knowledge about snow density profiles or other spatial anomalies. These results provide new insights into the characteristics of the detected neutron signal in complex terrain and support the use of CRNS for long-term snow monitoring in high elevated mountain environments.…
Author details: | Paul SchattanORCiD, Markus KöhliORCiDGND, Martin SchrönORCiDGND, Gabriele BaroniORCiDGND, Sascha OswaldORCiDGND |
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DOI: | https://doi.org/10.1029/2019WR025647 |
ISSN: | 0043-1397 |
ISSN: | 1944-7973 |
Title of parent work (English): | Water resources research |
Publisher: | American Geophysical Union |
Place of publishing: | Washington |
Publication type: | Article |
Language: | English |
Year of first publication: | 2019 |
Publication year: | 2019 |
Release date: | 2020/09/14 |
Tag: | area-average snow monitoring; cosmic-ray neutron sensing; fractional snow cover; neutron simulations; spatial heterogeneity |
Volume: | 55 |
Issue: | 12 |
Number of pages: | 17 |
First page: | 10796 |
Last Page: | 10812 |
Funding institution: | Austrian Research Promotion Agency (FFG) [868034]; project "HoPI -Runoff Forecasting System for the Inn River" at alpS - Centre for Climate Change Adaptation under the scope of the COMET programme; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)German Research Foundation (DFG) [414050972, 413992326, FOR 2694, 357874777]; Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [AT 102/9-2, FOR 2131]; European UnionEuropean Union (EU) [213007] |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften |
DDC classification: | 5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften |
Peer review: | Referiert |
Publishing method: | Open Access |
Open Access / Hybrid Open-Access |