Estimating snow water equivalent using cosmic‐ray neutron sensors from the <scp>COSMOS‐UK</scp> network

Wallbank, John; Cole, Steven J.; Moore, Robert J.; Anderson, Seonaid R.; Mellor, Edward J.

doi:10.1002/hyp.14048

Cited by 7 publications

(11 citation statements)

References 41 publications

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“…This dataset includes CRNS SWE. Methods for estimating SWE are available from Wallbank et al (2020) and discussed in more detail in Wallbank et al (2021).…”

Section: Derived Datamentioning

confidence: 99%

COSMOS-UK: national soil moisture and hydrometeorology data for environmental science research

Cooper

Bennett

Blake

et al. 2021

Earth Syst. Sci. Data

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Abstract. The COSMOS-UK observation network has been providing field-scale soil moisture and hydrometeorological measurements across the UK since 2013. At the time of publication a total of 51 COSMOS-UK sites have been established, each delivering high-temporal resolution data in near-real time. Each site utilizes a cosmic-ray neutron sensor, which counts epithermal neutrons at the land surface. These measurements are used to derive field-scale near-surface soil water content, which can provide unique insight for science, industry, and agriculture by filling a scale gap between localized point soil moisture and large-scale satellite soil moisture datasets. Additional soil physics and meteorological measurements are made by the COSMOS-UK network including precipitation, air temperature, relative humidity, barometric pressure, soil heat flux, wind speed and direction, and components of incoming and outgoing radiation. These near-real-time observational data can be used to improve the performance of hydrological models, validate remote sensing products, improve hydro-meteorological forecasting, and underpin applications across a range of other scientific fields. The most recent version of the COSMOS-UK dataset is publically available at https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185 (Stanley et al., 2021).

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“…This dataset includes CRNS SWE. Methods for estimating SWE are available from Wallbank et al (2020) and discussed in more detail in Wallbank et al (2021).…”

Section: Derived Datamentioning

confidence: 99%

COSMOS-UK: national soil moisture and hydrometeorology data for environmental science research

Cooper

Bennett

Blake

et al. 2021

Earth Syst. Sci. Data

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“…Though use of new sensors might help to reduce such uncertainties (e.g. Wallbank et al, 2021). This problem is for example reflected in the apparent wide variations in runoff coefficients, even in fast responding catchments (e.g.…”

Section: Some Key Knowledge Gaps In Our Perceptual Model Of Great Bri...mentioning

confidence: 99%

Knowledge gaps in our perceptual model of Great Britain's hydrology

Wagener

Dadson

Hannah

et al. 2021

Hydrological Processes

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There is a no lack of significant open questions in the field of hydrology. How will hydrological connectivity between freshwater bodies be altered by future human alterations to the hydrological cycle? Where does water go when it rains? Or what is the future space-time variability of flood and drought events? However, the answers to these questions will vary with location due to the specific and often poorly understood local boundary conditions and system properties that control the functional behaviour of a catchment or any other hydrologic control volume. We suggest that an open, shared and evolving perceptual model of a region's hydrology is critical to tailor our science questions, as it would be for any other study domain from the plot All our knowledge has its origins in our perception.

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“…Table 2 summarizes the uncertainties of each instrument and protocol (five cases: CRNP in and above ground, GMON, FMCW radar, and GNSSr) in relation to in situ manual measurements (snow pit method), as well as against snow pillow and snow scale data that were considered reference measurements by the authors of the publications consulted. The results from the COSMOS-UK network (Wallbank et al, 2021) were not included in the overall uncertainty analysis because, in this study, depth-based SWE estimate of fresh snow was used to assess the uncertainty of CRNP (R 2 of 0.53, in the range of 0-40 mm w.e.). Moreover, soil moisture is usually high and variable in the UK, which acts to increase uncertainties in the SWE estimate (Wallbank et al, 2021).…”

Section: Analysis Of Measurement Uncertaintymentioning

confidence: 99%

“…The results from the COSMOS-UK network (Wallbank et al, 2021) were not included in the overall uncertainty analysis because, in this study, depth-based SWE estimate of fresh snow was used to assess the uncertainty of CRNP (R 2 of 0.53, in the range of 0-40 mm w.e.). Moreover, soil moisture is usually high and variable in the UK, which acts to increase uncertainties in the SWE estimate (Wallbank et al, 2021).…”

Section: Analysis Of Measurement Uncertaintymentioning

confidence: 99%

Review article: Performance assessment of radiation-based field sensors for monitoring the water equivalent of snow cover (SWE)

Royer

Roy

Jutras³

et al. 2021

The Cryosphere

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Abstract. Continuous and spatially distributed data of snow mass (water equivalent of snow cover, SWE) from automatic ground-based measurements are increasingly required for climate change studies and for hydrological applications (snow hydrological-model improvement and data assimilation). We present and compare four new-generation sensors, now commercialized, that are non-invasive and based on different radiations that interact with snow for SWE monitoring: cosmic-ray neutron probe (CRNP), gamma ray monitoring (GMON) scintillator, frequency-modulated continuous-wave radar (FMCW radar) at 24 GHz and global navigation satellite system (GNSS) receivers (GNSSr). All four techniques have relatively low power requirements, provide continuous and autonomous SWE measurements, and can be easily installed in remote areas. A performance assessment of their advantages, drawbacks and uncertainties is discussed from experimental comparisons and a literature review. Relative uncertainties are estimated to range between 9 % and 15 % when compared to manual in situ snow surveys that are also discussed. Results show the following. (1) CRNP can be operated in two modes of functioning: beneath the snow, it is the only system able to measure very deep snowpacks (> 2000 mm w.e.) with reasonable uncertainty across a wide range of measurements; CRNP placed above the snow allows for SWE measurements over a large footprint (∼ 20 ha) above a shallow snowpack. In both cases, CRNP needs ancillary atmospheric measurements for SWE retrieval. (2) GMON is the most mature instrument for snowpacks that are typically up to 800 mm w.e. Both CRNP (above snow) and GMON are sensitive to surface soil moisture. (3) FMCW radar needs auxiliary snow-depth measurements for SWE retrieval and is not recommended for automatic SWE monitoring (limited to dry snow). FMCW radar is very sensitive to wet snow, making it a very useful sensor for melt detection (e.g., wet avalanche forecasts). (4) GNSSr allows three key snowpack parameters to be estimated simultaneously: SWE (range: 0–1000 mm w.e.), snow depth and liquid water content, according to the retrieval algorithm that is used. Its low cost, compactness and low mass suggest a strong potential for GNSSr application in remote areas.

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

Cited by 7 publications

References 41 publications

COSMOS-UK: national soil moisture and hydrometeorology data for environmental science research

COSMOS-UK: national soil moisture and hydrometeorology data for environmental science research

Knowledge gaps in our perceptual model of Great Britain's hydrology

Review article: Performance assessment of radiation-based field sensors for monitoring the water equivalent of snow cover (SWE)

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