Sensing Area‐Average Snow Water Equivalent with Cosmic‐Ray Neutrons: The Influence of 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.

Large-Scale Boron-Lined Neutron Detection Systems as a 3He Alternative for Cosmic Ray Neutron Sensing

“…It uses the validated near-ground cosmic-ray neutron spectrum by Sato (2016). The code was employed for CRNS footprint revision by Köhli et al (2015) and Schrön et al (2017), in roving (Schrön et al, 2018) and irrigation studies (Li et al, 2019) as well as understanding the signal for snow height measurements (Schattan et al, 2019). It also features special input options for conducting detector-related neutron transport studies (Köhli et al, 2018).…”

“…For simplicity, all hydrogen content is considered to be bound in soil moisture. The calculation still holds true if using the total water equivalent approaches like Franz et al (2013) or Schattan et al (2019). However, here the uncertainty analysis of specific further parameters such as air humidity or snow is neglected.…”

A dense network of cosmic-ray neutron sensors for soil moisture observation in a highly instrumented pre-Alpine headwater catchment in Germany

“…-Irkutsk NM, R cut ≈ 3.64 GV, altitude 435 m; and Nevertheless, open questions about the suitability of neutron monitor data for local CRNS applications still exist, as pointed out by Schrön (2017) and Baroni et al (2018). To build the basis for a more thorough analysis in future studies, we take advantage of additional neutron detector instruments during the study period in Fendt, the so-called mini-NM (Krüger et al, 2008), and Bonner spheres (Bramblett et al, 1960;Rühm et al, 2012).…”

A dense network of cosmic-ray neutron sensors for soil moisture observation in a pre-alpine headwater catchment in Germany

“…One of the challenges in using CRNS for soil moisture observation is thus to separate the part of the signal that is related to soil moisture from those parts affected by other hydrogen pools such as vegetation and soil organic carbon (Baatz et al, 2015;Andreasen et al, 2017;Jakobi et al, 2018). Further prerequisites include the correction of atmospheric effects such as air pressure, air humidity, and fluctuations of incoming neutron radiation (Rosolem et al, 2013;Schrön et al, 2016;Hawdon et al, 2014), and a better, physically-based understanding of the spatial sensitivity of the neutron sensors, both vertically and horizontally, and how it is influenced by dynamic environmental conditions in the footprint (Köhli et al, 2015;Schrön et al, 2017;Schattan et al, 2019).…”