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 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.

Soil Moisture and Soil Water Storage Using Hydrological Modeling and Remote Sensing

2014 ◽  
pp. 307-345 ◽  
Author(s):  
Otto Corrêa Rotunno Filho ◽  
Afonso Augusto Magalhães de Araujo ◽  
Luciano Nóbrega Rodrigues Xavier ◽  
Daniel Medeiros Moreira ◽  
Rafael Carneiro Di Bello ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Soil Water ◽  
Hydrological Modeling ◽  
Water Storage ◽  
Soil Water Storage

scholarly journals Water storage change estimation from in situ shrinkage measurements of clay soils

2012 ◽  
Vol 9 (11) ◽  
pp. 13117-13154 ◽  
Author(s):  
B. te Brake ◽  
M. J. van der Ploeg ◽  
G. H. de Rooij
Keyword(s):  
Soil Water ◽  
Water Storage ◽  
Soil Surface ◽  
Surface Elevation ◽  
Measurement Scale ◽  
Soil Water Storage ◽  
Soil Layers ◽  
Shrinkage Curve ◽  
Elevation Changes ◽  
Storage Change

Abstract. Water storage in the unsaturated zone is a major determinant of the hydrological behaviour of the soil, but methods to quantify soil water storage are limited. The objective of this study is to assess the applicability of clay soil surface elevation change measurements to estimate soil water storage changes. We measured moisture contents in soil aggregates by EC-5 sensors, and in volumes comprising multiple aggregates and intra-aggregates spaces by CS616 sensors. In a prolonged drying period, aggregate-scale storage change measurements revealed normal shrinkage for layers ≥ 30 cm depth, indicating volume loss equalled water loss. Shrinkage in a soil volume including multiple aggregates and voids was slightly less than normal, due to soil moisture variations in the profile and delayed drying of deeper soil layers upon lowering of the groundwater level. This resulted in shrinkage curve slopes of 0.89, 0.90 and 0.79 for the layers 0–60, 0–100 and 0–150 cm. Under a dynamic drying and wetting regime, shrinkage curve slopes ranged from 0.29 to 0.69 (EC-5) and 0.27 to 0.51 (CS616). Alternation of shrinkage and incomplete swelling resulted in an underestimation of volume change relatively to water storage change, due to hysteresis between swelling and shrinkage. Since the slope of the shrinkage relation depends on the drying regime, measurement scale and combined effect of different soil layers, shrinkage curves from laboratory tests on clay aggregates require suitable modifications for application to soil profiles. Then, the linear portion of the curve can help soil water storage estimation from soil surface elevation changes. These elevation changes might be measurable over larger extents by remote sensing.

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

&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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&lt;sub&gt;0&lt;/sub&gt;-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.&lt;/p&gt;

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 Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Tomás de Figueiredo ◽  
Ana Caroline Royer ◽  
Felícia Fonseca ◽  
Fabiana Costa de Araújo Schütz ◽  
Zulimar Hernández
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Regression Models ◽  
Meteorological Data ◽  
Water Storage ◽  
Model Performance ◽  
Soil Water Balance ◽  
Soil Water Storage ◽  
The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

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