scholarly journals Estimating snow water equivalent using cosmic‐ray neutron sensors from the COSMOS‐UK network

2021 ◽  
Author(s):  
J. R. Wallbank ◽  
S. J. Cole ◽  
R. J. Moore ◽  
S. R. Anderson ◽  
E. J. Mellor
Keyword(s):  
Snow Water Equivalent ◽  
Cosmic Ray ◽  
Snow Water ◽  
Neutron Sensors

scholarly journals Snow water equivalent measurement in the Arctic based on cosmic ray neutron attenuation

2021 ◽  
Vol 15 (11) ◽  
pp. 5227-5239
Author(s):  
Anton Jitnikovitch ◽  
Philip Marsh ◽  
Branden Walker ◽  
Darin Desilets
Keyword(s):  
Water Resource Management ◽  
Snow Water Equivalent ◽  
Cosmic Ray ◽  
The Arctic ◽  
Sensing Applications ◽  
Neutron Attenuation ◽  
Snow Water ◽  
Remote Sites ◽  
Neutron Sensors

Abstract. Grounded in situ, or invasive, cosmic ray neutron sensors (CRNSs) may allow for continuous, unattended measurements of snow water equivalent (SWE) over complete winter seasons and allow for measurements that are representative of spatially variable Arctic snow covers, but few studies have tested these types of sensors or considered their applicability at remote sites in the Arctic. During the winters of 2016/2017 and 2017/2018 we tested a grounded in situ CRNS system at two locations in Canada: a cold, low- to high-SWE environment in the Canadian Arctic and at a warm, low-SWE landscape in southern Ontario that allowed easier access for validation purposes. Five CRNS units were applied in a transect to obtain continuous data for a single significant snow feature; CRNS-moderated neutron counts were compared to manual snow survey SWE values obtained during both winter seasons. The data indicate that grounded in situ CRNS instruments appear able to continuously measure SWE with sufficient accuracy utilizing both a linear regression and nonlinear formulation. These sensors can provide important SWE data for testing snow and hydrological models, water resource management applications, and the validation of remote sensing applications.

scholarly journals Analysis of the Snow Water Equivalent at the AEMet-Formigal Field Laboratory (Spanish Pyrenees) During the 2019/2020 Winter Season Using a Stepped-Frequency Continuous Wave Radar (SFCW)

2021 ◽  
Vol 13 (4) ◽  
pp. 616
Author(s):  
Rafael Alonso ◽  
José María García del Pozo ◽  
Samuel T. Buisán ◽  
José Adolfo Álvarez
Keyword(s):  
Software Defined Radio ◽  
Continuous Wave ◽  
Snow Water Equivalent ◽  
Cosmic Ray ◽  
Relative Dielectric Permittivity ◽  
Near Surface ◽  
Wave Radar ◽  
Spanish Pyrenees ◽  
Snow Water ◽  
Stepped Frequency

Snow makes a great contribution to the hydrological cycle in cold regions. The parameter to characterize available the water from the snow cover is the well-known snow water equivalent (SWE). This paper presents a near-surface-based radar for determining the SWE from the measured complex spectral reflectance of the snowpack. The method is based in a stepped-frequency continuous wave radar (SFCW), implemented in a coherent software defined radio (SDR), in the range from 150 MHz to 6 GHz. An electromagnetic model to solve the electromagnetic reflectance of a snowpack, including the frequency and wetness dependence of the complex relative dielectric permittivity of snow layers, is shown. Using the previous model, an approximated method to calculate the SWE is proposed. The results are presented and compared with those provided by a cosmic-ray neutron SWE gauge over the 2019–2020 winter in the experimental AEMet Formigal-Sarrios test site. This experimental field is located in the Spanish Pyrenees at an elevation of 1800 m a.s.l. The results suggest the viability of the approximate method. Finally, the feasibility of an auxiliary snow height measurement sensor based on a 120 GHz frequency modulated continuous wave (FMCW) radar sensor, is shown.

scholarly journals Sensing Area‐Average Snow Water Equivalent with Cosmic‐Ray Neutrons: The Influence of Fractional Snow Cover

2019 ◽  
Vol 55 (12) ◽  
pp. 10796-10812 ◽  
Author(s):  
P. Schattan ◽  
M. Köhli ◽  
M. Schrön ◽  
G. Baroni ◽  
S. E. Oswald
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Cosmic Ray ◽  
Fractional Snow Cover ◽  
Snow Water

scholarly journals Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier

2019 ◽  
Vol 13 (12) ◽  
pp. 3413-3434 ◽  
Author(s):  
Rebecca Gugerli ◽  
Nadine Salzmann ◽  
Matthias Huss ◽  
Darin Desilets
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Cosmic Ray ◽  
Snow Accumulation ◽  
Scaling Factor ◽  
High Temporal Resolution ◽  
High Mountain ◽  
Alpine Glacier ◽  
Snow Drift ◽  
Snow Water

Abstract. Snow water equivalent (SWE) measurements of seasonal snowpack are crucial in many research fields. Yet accurate measurements at a high temporal resolution are difficult to obtain in high mountain regions. With a cosmic ray sensor (CRS), SWE can be inferred from neutron counts. We present the analyses of temporally continuous SWE measurements by a CRS on an alpine glacier in Switzerland (Glacier de la Plaine Morte) over two winter seasons (2016/17 and 2017/18), which differed markedly in the amount and timing of snow accumulation. By combining SWE with snow depth measurements, we calculate the daily mean density of the snowpack. Compared to manual field observations from snow pits, the autonomous measurements overestimate SWE by +2 % ± 13 %. Snow depth and the bulk snow density deviate from the manual measurements by ±6 % and ±9 %, respectively. The CRS measured with high reliability over two winter seasons and is thus considered a promising method to observe SWE at remote alpine sites. We use the daily observations to classify winter season days into those dominated by accumulation (solid precipitation, snow drift), ablation (snow drift, snowmelt) or snow densification. For each of these process-dominated days the prevailing meteorological conditions are distinct. The continuous SWE measurements were also used to define a scaling factor for precipitation amounts from nearby meteorological stations. With this analysis, we show that a best-possible constant scaling factor results in cumulative precipitation amounts that differ by a mean absolute error of less than 80 mm w.e. from snow accumulation at this site.

Cosmic-ray neutron sensing based monitoring of snowpack dynamics: A comparison of four conversion methods

2020 ◽  
Author(s):  
Heye Reemt Bogena ◽  
Frank Herrmann ◽  
Jannis Jakobi ◽  
Vassilios Pisinaras ◽  
Cosimo Brogi ◽  
...  
Keyword(s):  
Snow Cover ◽  
Energy Transport ◽  
Snow Water Equivalent ◽  
Ground Surface ◽  
Cosmic Ray ◽  
Snow Pack ◽  
Wind Fields ◽  
Neutron Intensity ◽  
Epithermal Neutrons ◽  
Snow Water

<p>Snow monitoring instruments like snow pillows are influenced by disturbances such as energy transport into the snowpack, influences from wind fields or varying snow properties within the snowpack (e.g. ice layers). The intensity of epithermal neutrons that are produced in the soil by cosmic radiation and measured above the ground surface is sensitive to soil moisture in the upper decimetres of the ground within a radius of hectometres. Recently, it has been shown that aboveground cosmic ray neutron sensors (CRNS) are also a promising technique to monitor snow pack development thanks to the larger support that they provide and to the lower need for maintenance compared to conventional sensor systems. The basic principle is that snow water moderates neutron intensity in the footprint of the CRNS probe. The epithermal neutrons originating from the soil become increasingly attenuated with increasing depth of the snow cover, so that the neutron intensity measured by the CRN probe above the snow cover is directly related to the snow water equivalent.</p><p>In this paper, we use long-term CRNS measurements in the Pinios Hydrologic Observatory, Greece, to test different methods for the conversion from neutron count rates to snow pack characteristics, namely: i) linear regression, ii) the standard N<sub>0</sub>-calibration function, iii) a physically-based calibration approach and iv) the thermal to epithermal neutron ratio. The latter was also tested for its reliability in determining the start and end of snowpack development, respectively. The CRNS-derived snow pack dynamics are compared with snow depth measurements by a sonic sensor located near the CRNS probe. In the presentation, we will discuss the accuracy of the four conversion methods and provide recommendations for the application of CRNS-based snow pack measurements.</p>

scholarly journals Calibration of a non-invasive cosmic-ray probe for wide area snow water equivalent measurement

2016 ◽  
Vol 10 (3) ◽  
pp. 1181-1190 ◽  
Author(s):  
Mark J. P. Sigouin ◽  
Bing C. Si
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Soil Water ◽  
Snow Water Equivalent ◽  
Pearson Correlation ◽  
Water Storage ◽  
Cosmic Ray ◽  
Soil Water Storage ◽  
Neutron Intensity ◽  
Snow Water

Abstract. Measuring snow water equivalent (SWE) is important for many hydrological purposes such as modelling and flood forecasting. Measurements of SWE are also crucial for agricultural production in areas where snowmelt runoff dominates spring soil water recharge. Typical methods for measuring SWE include point measurements (snow tubes) and large-scale measurements (remote sensing). We explored the potential of using the cosmic-ray soil moisture probe (CRP) to measure average SWE at a spatial scale between those provided by snow tubes and remote sensing. The CRP measures above-ground moderated neutron intensity within a radius of approximately 300 m. Using snow tubes, surveys were performed over two winters (2013/2014 and 2014/2015) in an area surrounding a CRP in an agricultural field in Saskatoon, Saskatchewan, Canada. The raw moderated neutron intensity counts were corrected for atmospheric pressure, water vapour, and temporal variability of incoming cosmic-ray flux. The mean SWE from manually measured snow surveys was adjusted for differences in soil water storage before snowfall between both winters because the CRP reading appeared to be affected by soil water below the snowpack. The SWE from the snow surveys was negatively correlated with the CRP-measured moderated neutron intensity, giving Pearson correlation coefficients of −0.90 (2013/2014) and −0.87 (2014/2015). A linear regression performed on the manually measured SWE and moderated neutron intensity counts for 2013/2014 yielded an r2 of 0.81. Linear regression lines from the 2013/2014 and 2014/2015 manually measured SWE and moderated neutron counts were similar; thus differences in antecedent soil water storage did not appear to affect the slope of the SWE vs. neutron relationship. The regression equation obtained from 2013/2014 was used to model SWE using the moderated neutron intensity data for 2014/2015. The CRP-estimated SWE for 2014/2015 was similar to that of the snow survey, with an root-mean-square error of 8.8 mm. The CRP-estimated SWE also compared well to estimates made using snow depths at meteorological sites near (< 10 km) the CRP. Overall, the empirical equation presented provides acceptable estimates of average SWE using moderated neutron intensity measurements. Using a CRP to monitor SWE is attractive because it delivers a continuous reading, can be installed in remote locations, requires minimal labour, and provides a landscape-scale measurement footprint.

scholarly journals Evaluating continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on a Swiss glacier

2019 ◽  
Author(s):  
Rebecca Gugerli ◽  
Nadine Salzmann ◽  
Matthias Huss ◽  
Darin Desilets
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Mean Absolute Error ◽  
Cosmic Ray ◽  
Absolute Error ◽  
Snow Accumulation ◽  
Scaling Factor ◽  
High Temporal Resolution ◽  
Snow Drift ◽  
Snow Water

Abstract. Snow water equivalent (SWE) measurements are crucial in many research fields. Yet accurate measurements at a high temporal resolution are difficult to obtain in high mountain regions. With a cosmic ray sensor (CRS), SWE can be directly derived from neutron counts. In this study, we present the analyses of temporally continuous SWE measurements by a CRS on a Swiss glacier (Glacier de la Plaine Morte) over two winter seasons (2016/17 and 2017/18), which were markedly different in terms of amount and timing of snow accumulation. By combining the SWE values with snow depth measurements, we calculate the daily mean density of the snowpack. The autonomous measurements overestimate SWE by +2 % ± 12 % compared to manual field observations (snow pits). Snow depth and mean density agree with manual in situ measurements with a standard deviation of ±6 % and ±8 %, respectively. In general, the cosmic ray sensor measured with high reliability during these two winter seasons and is, thus, considered an effective method to measure SWE at remote high alpine sites. We use the daily observations to break down the winter season into days either dominated by accumulation (solid precipitation, snow drift), ablation (snow drift, melt) or snow densification. The prevailing meteorological conditions of these periods are clearly distinct for each of the classified processes. Moreover, we compare daily SWE amounts to precipitation sums from three nearby weather stations located at lower elevations, and to a gridded precipitation dataset. We determine the best-possible scaling factor for these precipitation estimates in order to reproduce the measured accumulation on the glacier. Using only one scaling factor for the whole time series, we find a mean absolute error of less than 8 cm w.e. for the reproduced snow accumulation. By applying temperature-specific scaling factors, this mean absolute error can be reduced to less than 6 cm w.e. for all stations.

scholarly journals Calibration of a non-invasive cosmic-ray probe for wide area snow water equivalent measurement

2016 ◽  
Author(s):  
M. J. P. Sigouin ◽  
B. C. Si
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Soil Water ◽  
Snow Water Equivalent ◽  
Water Storage ◽  
Cosmic Ray ◽  
Measurement Scale ◽  
Soil Water Storage ◽  
Neutron Intensity ◽  
Snow Water

Abstract. Measuring snow water equivalent (SWE) is important for many hydrological purposes such as modeling and flood forecasting. Measurements of SWE are also crucial for agricultural production in areas where snowmelt runoff dominates spring soil water recharge. Typical methods for measuring SWE include point measurements (snow tubes) and large-scale measurements (remote sensing). We explored the potential of using the cosmic-ray soil moisture probe (CRP) to measure average SWE at a measurement scale between those provided by snow tubes and remote sensing. The CRP measures above ground moderated neutron intensity within a radius of approximately 300 m. Using snow tubes, surveys were performed over two winters (2013/2014 and 2014/2015) in an area surrounding a CRP in an agricultural field in Saskatoon, Saskatchewan, CAN. The raw moderated neutron intensity counts were corrected for atmospheric pressure, water vapor, and temporal variability of incoming cosmic ray flux. The mean SWE from manually measured snow surveys was adjusted for differences in soil water storage before snowfall between both winters because the CRP reading appeared to be affected by soil water below the snowpack. The SWE from the snow surveys was negatively correlated with the CRP-measured moderated neutron intensity, giving Pearson correlation coefficients of −0.92 (2013/2014) and −0.94 (2014/2015). A linear regression performed on the manually measured SWE and moderated neutron intensity counts for 2013/2014 yielded an r2 of 0.84. Linear regression lines from the 2013/2014 and 2014/2015 manually measured SWE and moderated neutron counts were very similar, thus differences in antecedent soil water storage did not appear to affect the slope of the SWE vs. neutron relationship. The regression equation obtained from 2013/2014 was used to model SWE using the moderated neutron intensity data for 2014/2015. The CRP-estimated SWE for 2014/2015 was similar to that of the snow survey, with a RMSE of 7.7 mm. The CRP-estimated SWE also compared well to estimates made using snow depths at meteorological sites near (< 10 km) the CRP. Overall, the empirical equation presented provides acceptable estimates of average SWE using moderated neutron intensity measurements. Using a CRP to monitor SWE is attractive because it delivers a continuous reading, can be installed in remote locations, requires minimal labour, and provides a landscape-scale measurement footprint.

scholarly journals Multi-sensor analysis of monthly gridded snow precipitation on alpine glaciers

2021 ◽  
Vol 18 ◽  
pp. 7-20
Author(s):  
Rebecca Gugerli ◽  
Matteo Guidicelli ◽  
Marco Gabella ◽  
Matthias Huss ◽  
Nadine Salzmann
Keyword(s):  
Snow Water Equivalent ◽  
Cosmic Ray ◽  
Remotely Sensed ◽  
High Elevation ◽  
High Mountain ◽  
Data Set ◽  
Novel Approach ◽  
Spatially Distributed ◽  
Precipitation Estimates ◽  
Snow Water

Abstract. Accurate and reliable solid precipitation estimates for high mountain regions are crucial for many research applications. Yet, measuring snowfall at high elevation remains a major challenge. In consequence, observational coverage is typically sparse, and the validation of spatially distributed precipitation products is complicated. This study presents a novel approach using reliable daily snow water equivalent (SWE) estimates by a cosmic ray sensor on two Swiss glacier sites to assess the performance of various gridded precipitation products. The ground observations are available during two and four winter seasons. The performance of three readily-available precipitation data products based on different data sources (gauge-based, remotely-sensed, and re-analysed) is assessed in terms of their accuracy compared to the ground reference. Furthermore, we include a data set, which corresponds to the remotely-sensed product with a local adjustment to independent SWE measurements. We find a large bias of all precipitation products at a monthly and seasonal resolution, which also shows a seasonal trend. Moreover, the performance of the precipitation products largely depends on in situ wind direction during snowfall events. The varying performance of the three precipitation products can be partly explained with their compilation background and underlying data basis.

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