Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.1029/2019WR025647 |
Lizenz ![]() |
|
| Titel (primär) | Sensing area‐average snow water equivalent with cosmic‐ray neutrons: The influence of fractional snow cover |
| Autor | Schattan, P.; Köhli, M.; Schrön, M.; Baroni, G.; Oswald, S.E. |
| Quelle | Water Resources Research |
| Erscheinungsjahr | 2019 |
| Department | MET |
| Band/Volume | 55 |
| Heft | 12 |
| Seite von | 10796 |
| Seite bis | 10812 |
| Sprache | englisch |
| Keywords | area‐average snow monitoring; cosmic‐ray neutron sensing; neutron simulations; spatial heterogeneity; fractional snow cover |
| Abstract | 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 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. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=22588 |
| Schattan, P., Köhli, M., Schrön, M., Baroni, G., Oswald, S.E. (2019): Sensing area‐average snow water equivalent with cosmic‐ray neutrons: The influence of fractional snow cover Water Resour. Res. 55 (12), 10796 - 10812 10.1029/2019WR025647 |
|

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.