TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-11-101-2017An assessment of two automated snow water equivalent instruments during the
WMO Solid Precipitation Intercomparison ExperimentSmithCraig D.craig.smith2@canada.cahttps://orcid.org/0000-0002-6552-1486KontuAnnahttps://orcid.org/0000-0001-6880-6260LaffinRichardPomeroyJohn W.https://orcid.org/0000-0002-4782-7457Environment and Climate Change Canada, Saskatoon, S7N 3H5, CanadaFinnish Meteorological Institute, Sodankylä, 99600, FinlandCampbell Scientific, Edmonton, T5L 4X4, CanadaCentre for Hydrology, University of Saskatchewan, Saskatoon, S7N 5C8, CanadaCraig D. Smith (craig.smith2@canada.ca)16January20171111011161March201624March201622November201625November2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://tc.copernicus.org/articles/11/101/2017/tc-11-101-2017.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/11/101/2017/tc-11-101-2017.pdf
During the World Meteorological Organization (WMO) Solid Precipitation
Intercomparison Experiment (SPICE), automated measurements of snow water
equivalent (SWE) were made at the Sodankylä (Finland), Weissfluhjoch
(Switzerland) and Caribou Creek (Canada) SPICE sites during the northern
hemispheric winters of 2013/14 and 2014/15. Supplementary intercomparison
measurements were made at Fortress Mountain (Kananaskis, Canada) during the
2013/14 winter. The objectives of this analysis are to compare automated SWE
measurements with a reference, comment on their performance and, where
possible, to make recommendations on how to best use the instruments and
interpret their measurements. Sodankylä, Caribou Creek and Fortress
Mountain hosted a Campbell Scientific CS725 passive gamma radiation SWE
sensor. Sodankylä and Weissfluhjoch hosted a Sommer Messtechnik SSG1000
snow scale. The CS725 operating principle is based on measuring the
attenuation of soil emitted gamma radiation by the snowpack and relating the
attenuation to SWE. The SSG1000 measures the mass of the overlying snowpack
directly by using a weighing platform and load cell. Manual SWE measurements
were obtained at the intercomparison sites on a bi-weekly basis over the
accumulation–ablation periods using bulk density samplers. These manual
measurements are considered to be the reference for the intercomparison.
Results from Sodankylä and Caribou Creek showed that the CS725 generally
overestimates SWE as compared to manual measurements by roughly 30–35 %
with correlations (r2) as high as 0.99 for Sodankylä and 0.90 for
Caribou Creek. The RMSE varied from 30 to 43 mm water equivalent (mm w.e.)
and from 18 to 25 mm w.e. at Sodankylä and Caribou Creek, which
had respective SWE maximums of approximately 200 and 120 mm w.e. The
correlation at Fortress Mountain was 0.94 (RMSE of 48 mm w.e. with a
maximum SWE of approximately 650 mm w.e.) with no systematic
overestimation. The SSG1000 snow scale, having a different measurement
principle, agreed quite closely with the manual measurements at Sodankylä
and Weissfluhjoch throughout the periods when data were available (r2 as
high as 0.99 and RMSE from 8 to 24 mm w.e. at Sodankylä and from
56 to 59 mm w.e. at Weissfluhjoch, where maximum SWE was
approximately 850 mm w.e.). When the SSG1000 was compared to the
CS725 at Sodankylä, the agreement was linear until the start of ablation
when the positive bias in the CS725 increases substantially relative to the
SSG1000. Since both Caribou Creek and Sodankylä have sandy soil, water
from the snowpack readily infiltrates into the soil during melt, even if the
soil is frozen. However, the CS725 does not differentiate this water from the
unmelted snow. This issue can be identified, at least during the late spring
ablation period, with soil moisture and temperature observations like those
measured at Caribou Creek. With a less permeable soil and surface runoff, the
increase in the instrument bias during ablation is not as significant, as
shown by the Fortress Mountain intercomparison.
Introduction
The measurement of snow water equivalent (SWE) is vital for
flood and water resource forecasting, drought monitoring, climate trend
analysis, and hydrological and climate model initialization (Barnett et
al., 2005; Bartlett et al., 2006; Gray et al., 2001; Laukkanen, 2004). Many
of these applications require accurate and timely information about how much
water is being held within the snowpack (Pomeroy and Gray, 1995). SWE
measurements can be made in situ, either manually or via automated
instrumentation, or derived from remote sensing platforms, and they are usually
expressed as units of mass per area (kgm-2) or in equivalent
units of millimetres of water equivalent (mm w.e.). Manual measurements of
SWE are typically made using a multi-point bulk density sampling technique
along an established transect or snow course (WMO, 2008). Snow course
measurements are often time consuming and expensive, especially if required
in remote locations (Pomeroy and Gray, 1995). This means that manual SWE
measurements may be infrequent or only undertaken when the snowpack is
estimated to be at its seasonal maximum. Prohibitive costs of manual snow
course observations have led to the reduction of these measurements by many
agencies, including Environment and Climate Change Canada, where operational
snow course numbers have decreased from over 100 in the 1980s to less than 30
(Barry, 1995; Brown et al., 2000). Since the early 1990s, manual SWE
measurements have been augmented or replaced by remote sensing techniques
such as passive microwave retrievals (Goodison and Walker, 1995) but these
techniques still require accurate and reliable in situ measurements for
ground truthing and retrieval development (Derksen et al., 2005; Takala et
al., 2011).
With the reduced availability of manual SWE measurements, automated
instruments for the measurement of SWE are becoming more necessary and more
commonplace. Snow pillows have been used for the automated measurement of SWE
in remote locations since the 1960s (Beaumont, 1965) by measuring the
overlying pressure of the snowpack on a fluid-filled bladder. The SNOTEL
network in the United States is based on snow pillow measurements (Serreze et
al., 1999). More recently, similar measurements are obtained using snow
scales that use a weighing surface and load cell to measure the weight of the
overlying snow (Beaumont, 1966a; Johnson et al., 2007, 2015). Several
indirect methods exist to measure SWE that include the use of neutron probes
(Harding, 1986) in which a radiation source is placed under the snowpack and
the scattering of neutrons through the snow is measured by a detector. Cosmic
ray proton probes (Kodama et al., 1979; Rasmussen et al., 2012) work in a
similar manner but do not require an active source. The probes described by
Kodama et al. (1979) are installed under the snow while the system described
by Rasmussen et al. (2012) (called COSMOS) is installed above the snow. Kinar
and Pomeroy (2007, 2015a) outline a method of non-invasive sonic
reflectometry through the snowpack to determine snow density, liquid water
content and temperature. Other passive radiation sensors are mounted above
the surface and measure the attenuation of naturally emitted radiation from
the soil as it passes through the snowpack and then relate this attenuation
to SWE content (Choquette et al., 2008; Martin et al., 2008). Each of these
instruments and techniques have advantages and disadvantages, which are not
discussed here (see Kinar and Pomeroy, 2015b, for a more comprehensive
description of snow measurement methods and related issues). Rather, this
analysis assesses the use and accuracy of two instruments that were tested
during the World Meteorological Organization (WMO Solid Precipitation
Intercomparison Experiment (SPICE) (Nitu et al., 2012; Rasmussen et
al., 2012), namely the Campbell Scientific CS725 and the Sommer Messtechnik
SSG1000 snow scale.
The CS725 (previously known as GMON or GMON3) has been previously field
tested by Hydro Québec (Choquette et al., 2008; Martin et al., 2008) as
well as by Wright et al. (2011). Results by Choquette et al. (2008) showed an
average error of +18 % when comparing to eight manual snow cores over
three seasons in Québec. They obtained a somewhat better agreement with
total SWE calculated from density profiles (with an average error of
+5 %) but only had four samples over two seasons. Wright et al. (2011)
showed intercomparison results between GMON3 sensors and snow pillows,
precipitation gauges and snow courses at Sunshine Village (Alberta, Canada)
and Tony Grove Ranger Station (Utah, USA). Results showed high correlations
between the sensor and (unadjusted) accumulated precipitation (0.99) and
between the sensor and snow pillow observations (0.99) but lower correlations
(0.83) with snow course observations (during one season at Sunshine Village).
The authors question the quality and inherent biases in the snow course
samples but do not comment on the sources of error or the proximity of the
snow course to the instrument.
Instrument intercomparisons that included the SSG1000 have been limited but
some results are reported by Stranden and Grønsten (2011), who showed
parallel SWE measurements between snow pillows, snow scales and manual snow
courses. With mitigating circumstances (e.g. snow drifting and scale issues),
they concluded that the measurement surface area had an impact on the
measurement quality and that the Sommer scale gave “promising results” but
that further intercomparison was required.
One of the overall objectives of the WMO-SPICE project is to assess the
performance of automated instrumentation for the measurement of snow,
including snow on the ground (SoG). This is accomplished by comparing the
tested instruments to an established reference measurement. In total, 15
countries are participating in the WMO-SPICE project with about 20
intercomparison sites. Of these, seven countries and nine intercomparison
sites are hosting SoG instrumentation. The instrumentation for WMO-SPICE has
either been provided by the instrument manufacturers or by the site hosts.
For SoG, 13 different instruments are under test with 9 measuring snow depth
and 4 measuring SWE. The CS725 and the SSG1000 SWE instruments examined here
were installed at the Sodankylä (Finland), Caribou Creek (Canada) and
Weissfluhjoch (Switzerland) intercomparison sites (Fig. 1). To supplement the
CS725 data collected for WMO-SPICE, data were added from an additional CS725
instrument installed at the Fortress Mountain ski area in the Kananaskis
region of the Canadian Rocky Mountains.
Instrumentation and methodsCampbell Scientific CS725
The CS725 (Fig. 2, left) is a passive gamma sensor developed by Hydro
Québec in collaboration with Campbell Scientific (Canada) Corp.
(Choquette et al., 2008; Martin et al., 2008). The instrument is installed
above the snow surface and determines SWE by measuring naturally emitted
gamma radiation from potassium (K) and thallium (Tl) sources in the soil that
is attenuated by the snowpack. Each gamma ray detected by the sensor element
is counted over a user defined period, the resulting distribution is compared
to the distribution when there was no snow cover, and the difference is used
to calculate SWE. The sensor field of view is approximately 120∘,
resulting in a measurement area of approximately 80 m2 when
installed 3 m above the snowpack and with the collimator attached.
The collimator serves to shield the instrument from gamma rays emitted from
sources that are not in the target area. The effective range of the
instrument is 0–600 mm w.e. with a measurement accuracy of
±15 mm w.e. from 0 to 300 mm w.e. and 15 % from
300 to 600 mm w.e. (Campbell Scientific CS725 manual,
https://s.campbellsci.com/documents/ca/manuals/cs725_man.pdf).
The two CS725 instruments for WMO-SPICE were both installed in October 2013
at Sodankylä, Finland, and Caribou Creek, Canada, and operated over the
northern hemispheric winters of 2013/14 and 2014/15. Both instruments were
mounted so that the bottom of the instrument was approximately 2 m
above the ground and both were installed with the manufacturer provided
collimator. Data were output every 6 h. The instrument at
Sodankylä was moved approximately 10 m during the summer of 2014
to avoid some buried cables in the measurement area, but any potential impact
of the move is considered to be negligible because of the consistency in the
snowpack at this site. The impact of spatial variability is addressed in
Sect. 4.
The third CS725 used in this analysis was not a WMO-SPICE instrument, but it
was loaned to the University of Saskatchewan for testing and intercomparison
by the instrument manufacturer. This instrument was installed in a clearing
near the Fortress Mountain ski resort in the Kananaskis Valley, Alberta,
Canada. The CS725 was mounted at a height of approximately 3.5 m
above the ground. The distance to the trees around the instrument was
approximately 10 m from the centre of the instrument, putting them
outside of the response area. Data collected by this instrument from October
2013 through June 2014 are used in this analysis. Like the other CS725
instruments, SWE data were output every 6 h.
Location of the CS725 (Sodankylä, Caribou Creek, Fortress
Mountain) and SSG1000 (Sodankylä and Weissfluhjoch) instrument
intercomparisons.
The Campbell Scientific CS725 (left) installed at Caribou Creek and
the Sommer Messtechnik SSG1000 (right) installed at Sodankylä.
Sommer SSG1000
The SSG1000 snow scale (Fig. 2, right) manufactured by Sommer Messtechnik,
Austria, measures SWE through the use of a weighing platform and load cells.
Unlike the CS725, it makes a direct measurement of the weight of the snowpack
on top of the weighing platform and converts this weight to SWE. The entire
platform consists of seven perforated panels, each 0.8m×1.2m, that are attached to a frame and installed level with the
surface of the ground. The entire instrument surface is 2.8m×2.4m (6.72 m2) but only the centre panel is weighed by the
load cell. According to the manufacturer, the purpose of the larger surface
surrounding the centre measurement panel is to “stabilize” the overlying
snowpack and prevent ice bridging
(http://www.sommer.at/en/products/snow-ice/snow-scales-ssg). The
SSG1000, as tested for WMO-SPICE, has a measurement range of 0 to
1000 mm w.e., and a manufacturer-stated resolution and accuracy of
0.1 mm w.e. and 0.3 % of full scale (3 mm w.e.),
respectively.
The SSG1000 snow scales in this analysis were installed in the Sodankylä
and Weissfluhjoch SPICE sites. The Weissfluhjoch instrument was provided by
the WSL Institute for Snow and Avalanche Research SLF. Data collection from
the instrument started in October 2013 and continued for the 2013/14 and
2014/15 northern
hemispheric winters. The SSG1000 at Sodankylä was located
in the northeast quadrant of the SPICE Field, approximately 22 m
southeast of the original location of the CS725. At Weissfluhjoch, it is
located in the southwest corner of the instrument field. SWE observations
from the instruments were recorded once per minute during the two
intercomparison seasons.
Reference SWE measurements
The reference SWE manual measurements for this intercomparison differed by
site. All were bulk density snow samples made with a snow sampling tube of a
known diameter that has one end capable of penetrating and cutting into the
snowpack. The tube was inserted into the snowpack either down to the surface
of the ground or to a plate inserted into the snowpack, and the sample was
extracted. Along with the sample, the depth of the snowpack was also
obtained. The sampled snow was then either bagged and weighed or was weighed
inside the tube using a cradle and balance. The snow sampler used in Canada
is different than the tube used in Finland and these differences, as well as
any other differences in sampling technique, are described below.
At Caribou Creek, the reference SWE measurements were obtained using an
ESC-30 snow tube with a 30 cm2 cutting area. Farnes et al. (1983)
and Goodison et al. (1987) show that the ESC-30, when used correctly and in
ideal conditions, has a mean measurement error of less than 0.5 % of the
true SWE. Errors associated with sampling in less than ideal conditions are
discussed later. Bulk density samples at Caribou Creek were taken just inside
the response area of the CS725, bagged and weighed. A 30 cm2 sample
from within the response area is assumed to have a negligible impact on
future sensor measurements considering the total sensor response area is
80 m2, but it was filled in with discarded snow when possible. These
manual SWE measurements were made about every 2 weeks in conjunction with a
full five-point snow course across the intercomparison field and into the
forest canopy on each side.
At Sodankylä, the reference SWE measurement was made using a Finnish bulk
density sampling tube, with a sampling area of 78.54 cm2, and
balance (Kuusisto, 1984) at roughly the same location in the intercomparison
field every 2 weeks. Only one sample was measured at a time. During the
winter of 2013/14, the bulk density SWE sample was obtained approximately
12 m from the centre of the CS725 measurement area and approximately
16 m from the centre of the SSG1000. In 2014/15, after the CS725 was
moved, the manual sampling was done approximately 6 m from the CS725
measurement area and approximately 25 m from the SSG1000.
An ESC-30 snow tube was used at the Fortress Mountain site. A full snow
survey was conducted at the site once per month, transitioning to bi-weekly
during the ablation period. Although the actual snow survey course was
through the forested area, supplemental measurements were taken in the
clearing where the instrumentation is located. The distance between the
sensor and the manual measurements was approximately 10 m.
The manual SWE measurements at Weissfluhjoch were performed
bi-weekly on the SLF study plot
using a bulk density aluminum sampling tube with a sampling area of
70 cm2 and length of 55 cm. The weight was measured with a
cradle and balance (Jonas et al., 2009). The distance between the sensor and
manual snow measurement varied from observation to observation as the
location of the snow pit was relocated for each bi-weekly measurement. The
average distance was approximately 20 m.
Intercomparisons
The intercomparisons are not completely consistent amongst the four sites
because of the different instrumentation and manual methods for measuring
reference SWE. At Sodankylä and Weissfluhjoch, the sensors can both be
compared with the manual SWE measurements made nearby, although the manual
measurements are not within the measuring area of either instrument. The
timestamps of both instruments were matched as closely as possible to the
manual observation time. Since the CS725 only reports every 6 h, the
measurement output closest to the manual observation time was used for the
intercomparison. Since the SSG1000 reports every minute, no time adjustment
was necessary. The same procedure was used to compare the CS725 to the
SSG1000. No SSG1000 was present at Caribou Creek or Fortress Mountain and no
CS725 sensors were installed at Weissfluhjoch.
For the CS725, which outputs a SWE value derived from both the K
and Tl counts, the manufacturer suggests that the output with the
higher count is generally the most reliable. For Sodankylä, the
K/Tl ratio is always greater than 1 (varying from 3.5 to
8.0), indicating that the potassium counts are greater than the thallium
counts. For Caribou Creek, the ratio varies from 2.8 to 4.0. For Fortress
Mountain, the ratio varies from 0.3 to 8.5 but is above 1 approximately
70 % of the time. Therefore, the CS725 analysis is based on the potassium
output although the statistics for thallium are shown in parenthesis in
Table 1. This will allow us to determine if there were any obvious
differences in the statistics related to the output derived from one source
or the other.
Regression coefficients and other statistical measures for the
multi-season intercomparison of the CS725 with manual SWE at Sodankylä,
Caribou Creek and Fortress Mountain (where β and ε are the
slope and intercept of the regression line). Values inside and outside of the
parenthesis represent thallium and potassium output, respectively, from the
sensor. “Accumulation” indicates that data occurring after maximum seasonal
SWE is omitted from the analysis. “Combined” indicates that data from both
seasons are included, and n represents the sample size.
CS725 vs. manual SWE for Sodankylä (top) and Caribou Creek
(middle) for the 2013/14 and 2014/15 seasons and Fortress Mountain (bottom)
for the 2013/14 season. Potassium output in red and thallium output in blue.
Black line is 1:1. Error bars represent manufacturer's stated sensor
accuracy.
Time series of the CS725 SWE sensors and manual SWE measurements at
Caribou Creek (top), Sodankylä (middle) for the 2013/14 (left) and
2014/15 (right) seasons, and Fortress Mountain (bottom) for the 2013/14
season.
ResultsCS725 vs. manual
The comparison between the CS725 measurements and the manual SWE observations
are shown in Fig. 3 with the potassium output in red circles and the thallium
output in blue triangles. The black line in the figure represents the 1:1
line and the error bars represent the manufacturer's stated sensor accuracy.
Figure 4 shows the time series of automated and manual SWE measurements.
Figure 5 shows the difference between the CS725 and the manual measurement
(red) and the measured air temperature (blue) through the two seasons. The
regression analysis coefficients and summary statistics are listed in
Table 1. The statistics are provided for each individual season and for the
two seasons combined. The statistics for the individual seasons are also
refined further to show results for the accumulation period (delineated from
the ablation period by the timing of maximum seasonal SWE). This will help to
eliminate the effects of snowmelt on both the manual measurement and the
various potential impacts on the CS725 measurement. These figures and tables
are further analyzed for each site in the following subsections.
Sodankylä
Throughout the intercomparison periods at Sodankylä, the CS725
overestimated SWE on average by 30 % (mean relative bias or MRB) as
compared to the manual measurements. From Table 1, the regression analysis
for the CS725 as compared to manual SWE over the entire season results in a
slope (β) of 1.24 for 2013/14 and 1.06 for 2014/15. The difference in
β between the K and Tl outputs is small. The intercepts (ε)
for the entire seasons are 8.77 mm w.e. for 2013/14, increasing to
26.9 mm w.e. for 2014/15. This difference might be in part a result
of moving the instrument to a new location. The correlation coefficient,
r2, is 0.92 for 2013/14 and 0.96 for 2014/15. With the period of ablation
eliminated from the analysis, the impact on β and ε are
relatively small although the intercept ε decreases almost 9 and
4 mm w.e. for the respective seasons. The accumulation period r2
increases to 0.97 and 0.99 for the 2013/14 and 2014/15 seasons, respectively,
suggesting that more scatter is introduced into the relationship during the
ablation period. This is discussed further below.
Figure 4 (top) shows the time series for the 2013/14 (left) and 2014/15
(right) seasons at Sodankylä. In this figure, the overestimation of the
CS725 (red and blue lines) can be seen when compared to manual SWE (black
circles). In general, the instrument trends are the same as for the manual
measurements with differences between the measurements increasing after the
start of the ablation periods and in January 2014 and December 2014. Although
it appears from Fig. 5 that the difference between the measurements is simply
increasing with time (or SWE amount), we believe that at least part of this
increase is a result of melting in the snowpack which occurs during some
relatively warm days. In 2013/14 (Fig. 5, left), a large increase in the
difference occurs after the > 0 ∘C temperatures in mid- to late
April. In 2014/15 (Fig. 5, right), there is a moderate increase after some
> 0 ∘C temperatures in March but a much larger jump after the
beginning of the ablation period in April.
Caribou Creek
The comparison of the CS725 instrument and the manual SWE measurements made
at Caribou Creek are shown in Fig. 3 (middle) and summarized in Table 1. As with Sodankylä, the
difference between the two sensor outputs (potassium vs. thallium) is
negligible. Also like Sodankylä, the CS725 at Caribou Creek consistently
overestimates total SWE such that the MRB is 35 %. However, the
relationships between the instrument and the manual SWE measurements are
different than at Sodankylä. At Caribou Creek, the slopes of the
regression line, β, are less than 1 for all scenarios in Table 1 with
the exception of the accumulation period in 2014/15. The intercepts
(ε) are all larger than seen at Sodankylä, with the
accumulation period in 2014/15 being the exception once again. The r2
values range from 0.90 for the combined (2013/14 and 2014/15) data to 0.55
for the accumulation period in 2014/15.
For both the 2013/14 and 2014/15 seasons, the time series for Caribou Creek
(Fig. 4, middle) shows a rapid increase in SWE in early winter related to
heavier, wet snowfall events that most likely began as rain and transitioned
to snow. For 2013/14, the CS725 time series generally follows the trend of
the manual SWE measurements with a large deviation developing mid- to late
March with the onset of seasonal ablation. Figure 5 (middle) shows the time
series of the difference between the CS725 and manual SWE (red) and the
temperature time series (blue) for both seasons. In 2013/14 (Fig. 5, middle
left), there is an increase in the difference that occurs in late January.
This could be due to a melt period where temperatures at the site exceeded
4 ∘C preceding the increase in the instrument bias. A much larger
jump in the difference occurs mid-March possibly due to significantly higher
temperatures (exceeding 10 ∘C) earlier that month. In 2014/15
(Fig. 5, middle right), the
deviation between the measurements occurs earlier in the season (mid- to late
January) coinciding with a January snowmelt period characterized by above
0 ∘C air temperatures and high wind speeds (not shown) that resulted
in ice layers on top and within the snowpack (which make accurate manual SWE
measurements more difficult) and possibly infiltration of meltwater into the
frozen sandy soil. Differences decrease after snowfall events in February
only to increase again after the start of ablation in March.
Time series of 1.5 m air temperature (blue, left axis) and
difference between CS725 and manual measurements (red, right axis) at
Sodankylä (top) and Caribou Creek (middle) for the 2013/14 (left) and
2014/15 (right) seasons and at Fortress Mountain (bottom) for the 2013/14
season.
Time series of near-surface (0–5 cm) soil moisture
(volumetric water content, blue) and the difference between the CS725 and
manual measurements (dashed line and black boxes) at Caribou Creek for the
2014/15 season. Red markers show where near-surface soil temperatures are
above 0 ∘C.
In reaction to an observed offset after the 2013/14 intercomparison season,
soil moisture and temperature probes were installed at the Caribou Creek site
with the objective of correlating post-calibration, overwinter and ablation
soil moisture changes with sensor offsets. The instruments were installed at
three depths: 0–5 cm (vertically), 5 cm (horizontally) and
20 cm (horizontally). Unfortunately, the probes only measure liquid
water (volumetric water content, or VWC) so the analysis is mostly limited to
when the soil temperatures (also measured by the probe) are above
0 ∘C when we assume that most of the water in the soil is unfrozen.
Figure 6 shows the time series of soil moisture near the surface
(0–5 cm) along with the difference between the CS725 and manual
measurements (scaled by a factor of 100 for visualization) for the 2014/15
season. The red markers indicate when the soil temperature at this level is
above 0 ∘C. It is easy to see from the time series when the liquid
soil moisture (near the surface) freezes in late fall, resulting in a rapid
drop in measured VWC. Following the freezing of the near-surface layer, which
occurs on 8 November 2014, the measured soil moisture in this layer remains
static until mid-March 2015, when a period of positive air temperatures
(Fig. 5, middle right) raises the near-surface soil temperatures above
freezing, transitioning frozen soil moisture to liquid and allowing for
further infiltration of snowmelt water into the sandy soil. The near-surface
(0–5 cm) soil temperatures rose above freezing even with snow on the
surface. The snowpack was patchy (verified from hourly photos) and shallow,
and meltwater was likely percolating through the snow and into the top layers
of the soil.
The freezing of the 0–5 cm depths in early November is preceded by
rain–snow events in late October that are represented by the large jump in
CS725 SWE shown in Fig. 4 (middle right) and confirmed with snow depth
measurements (not shown). During the transition from rain to snow and prior
to the surface freezing, Fig. 6 shows fluctuations in near-surface soil
moisture (measured by the soil moisture sensor as VWC) related to the precipitation events in late October and early November.
The soil moisture calibration of the CS725 sensor was entered as a
gravimetric water content (GWC) of 0.10, which can be converted to VWC by
multiplying by the specific gravity of the soil (Lambe and Whitman, 1969).
The specific gravity of the loose sand near the surface at Caribou Creek was
estimated to be 1.4 based on nearby measurements taken during the BOREAS
campaign (Anderson, 2000). The increase in measured VWC from the calibration
value to 0.18 (GWC of 0.13) prior to freezing has the potential to create a
small but potentially perpetuating offset of up to 3 mm w.e. in the
CS725 SWE estimates and may explain at least some of the bias shown by the
instrument beginning in mid-December.
In addition to the offset in the CS725 SWE measurements that occurs at the
beginning of the season, it was anticipated that the rapid increase in the
difference between the CS725 and manual SWE at the end of January 2015 could
also be attributed to a change in near-surface soil moisture, as this was a
time of mid-season snowmelt. However, a change in the liquid soil moisture
during the melt period could not be detected by the soil moisture sensors so
it is unlikely that the increase in the instrument offset can be attributed
to infiltration of meltwater into the sandy soil. A more plausible
explanation is manual measurement errors that could result from attempting
to sample a complex snowpack containing ice layers in the pack or at the
snow–soil interface. Ice layers would have formed due to mid-season melt and
refreezing. The increase in the difference between the manual measurement
and CS725 in mid- to late March could be a result of snowmelt infiltrating
into the top layers of the sandy soil as the soil thaws or forming a basal
ice layer (Woo et al., 1982; Lilbaek and Pomeroy, 2008) on top of the soil.
A corresponding spike in measured soil moisture during early spring snowmelt is shown in Fig. 6.
Fortress Mountain
The intercomparison of the CS725 instrument and the manual SWE measurements
made at Fortress Mountain are shown in Fig. 3 (bottom) and summarized in
Table 1. Unlike the other two sites, the CS725 and manual SWE measurements
generally fall on the 1:1 line with no systematic overestimation
(MRB <-5 %). This can also be seen in the time series shown in
Fig. 4 (bottom). The slope of the regression line is 0.88 with a small
decrease to 0.76 when excluding the ablation period. The intercept is
32.4 mm w.e. increasing to 84.4 mm w.e. when excluding the
ablation period. The r2 is comparable to Sodankylä at 0.92 (increasing
to 0.94 by excluding the ablation period). It is unfortunate that the sample
size is relatively small (n=8) but, regardless, the instrument compares
quite well to the manual measurements at this site.
SSG1000 vs. manual
The regression analysis for the SSG1000 intercomparisons is shown in Fig. 7
with the time series for both seasons shown in Fig. 8. The comparison
statistics are in Table 2. This analysis, as for the CS725 above, is
organized by site.
Regression coefficients and other statistical measures for the
multi-season intercomparison of the SSG1000 with manual SWE at Sodankylä
and Weissfluhjoch (where β and ε are the slope and
intercept of the regression line). “Combined” indicates that data from both
seasons are included and n indicates the sample size.
The SSG1000 regression analysis with the manual SWE measurements shown in
Fig. 7 (top) and summarized in Table 2 has an r2 for the entire 2014/15
period of 0.99 but is only 0.84 for the 2013/14 period. However, the SWE data
from the SSG1000 are not available for the ablation period in 2014/15 due to
an instrument malfunction. To have a consistent intercomparison for the two
seasons, the ablation period (post maximum SWE) was removed from the 2013/14
period and the r2 becomes 0.97, very similar to 2014/15. Combining the two
seasons, the slope of the regression, β, becomes 0.99 with an offset
ε of -7.27 mm w.e. with an r2 of 0.88. The MRB for
the two seasons combined is -11 %.
The time series of these data are shown in Fig. 8 (top) for both the 2013/14
(left) and 2014/15 (right) seasons. For both seasons, the sensor measurements
track quite well with the manual measurements. The outliers that appear in
Fig. 7 (top) can also be seen in the 2013/14 time series (Fig. 8, top left)
beginning midway through the ablation period. It is unknown whether this occurs
during the 2014/15 ablation period because the data are missing due to a
sensor failure caused by water damage to the electronics (an issue later
remedied by the manufacturer).
SSG1000 vs. manual SWE at Sodankylä (top) and Weissfluhjoch
(bottom) for the 2013/14 and 2014/15 seasons. Black line is 1:1. Error bars
represent manufacturer's stated sensor accuracy.
Time series of the SSG1000 SWE sensors and manual SWE measurements
at Sodankylä (top) and Weissfluhjoch (bottom) for the 2013/14 (left) and
2014/15 (right) seasons.
Weissfluhjoch
The regression analysis for the SSG1000 and the manual SWE measurements is
shown in Fig. 7 (bottom) with the time series in Fig. 8 (bottom). This alpine
site has a much deeper snowpack than either Caribou Creek or Sodankylä
but comparable to Fortress Mountain, which unfortunately did not have
concurrent SSG1000 measurements. The r2 for both seasons is quite high at
0.97, similar to the accumulation period intercomparison at Sodankylä,
but β is less (0.72 and 0.82) and ε is much higher (91.7
and 79.0 mm w.e.) for both seasons (2013/14 and 2014/15). The
outliers are obvious in Fig. 8 (bottom) when the manual SWE measurements are
substantially higher than the sensor measurements. Unlike Sodankylä,
these outliers mostly occur before maximum seasonal SWE, which is why we do
not break the season down as we do with Sodankylä. They are, however,
likely a result of sensor bridging which is discussed more in Sect. 4. There
are also outliers that occur late in the ablation periods, where the sensor
substantially overestimates SWE, and these are perhaps due to issues with the
manual sampling of a complex (melting or melting–refreezing) snowpack. When
combining the two seasons, the resulting low MRB of 8 % (for combined
seasons) is somewhat surprising given the obvious outliers. Perhaps the low
combined MRB is a reflection of errors in one season compensating the errors
in the other season.
CS725 vs. SSG1000
The intercomparison with manual measurements for both the CS725 and the
SSG1000 suggests that the agreements are the most favourable during
accumulation rather than during ablation. Figure 7 shows the relationship
between the CS725 and the SSG1000 for both seasons at Sodankylä with the
2014/15 season shown in red circles and the 2013/14 season shown in blue dots
(changing to blue triangles at the approximate onset of ablation). The
relationship for both years appears to be linear up to the time when maximum
SWE is reached. At the onset of ablation, the relationship between the
instruments (shown by the blue triangles) deviates substantially from linear.
This is confirmed by Table 3, which shows a higher r2 when the 2013/14
ablation period is not included in the analysis. This analysis could only be
completed for the 2013/14 season since the sensor data are missing for the
2014/15 ablation period due to malfunction.
Regression coefficients for the multi-season intercomparison of the
CS725 with the SSG1000 SWE measurements at Sodankylä (where β and
ε are the slope and intercept of the regression line).
“Accumulation” indicates that data occurring after maximum seasonal SWE are
omitted from the analysis.
Data quality control metrics for the CS725 sensors at each of the two SPICE
sites demonstrated that the instruments performed at a high level of
reliability, such that over 95 % of the sensor measurements were usable
for intercomparison. No malfunctions were noted and no maintenance was
required at any of the sites.
For the SSG1000, data quality control metrics show that the sensors performed
reliably during the accumulation periods but malfunctioned at Sodankylä
late in the spring of 2014 and again early spring of 2015. At Weissfluhjoch,
99 % of the 1 min data were usable for intercomparison. At Sodankylä,
the malfunctions resulted in only 83 and 67 % of the 1 min data, for
the 2013/14 and 2014/15 seasons, respectively, being available for
intercomparison. The sensor malfunctions at Sodankylä were determined to
be related to water damage to the electronics. Other than this, no other
malfunctions were reported or maintenance required during the
intercomparison.
Discussion
The regression analysis between the CS725 and the manual SWE measurements
resulted in r2 values ranging from 0.55 to 0.99, depending on site and
season. Combined season r2 values ranged from 0.90 to 0.92. Although
generally lower than the correlations of 0.99 reported for intercomparisons
with other instruments by Wright et al. (2011), our correlations (averaged by
season) are similar to the r2 of 0.83 that they reported for snow tube
measurements. The (combined season) bias shown here, which was between 30 and
35 %, is substantially higher than the 18 % reported by Choquette et
al. (2008). The exception to this is the CS725 at Fortress Mountain which has
a mean negative bias less than 5 % when compared to the manual
measurements. Besides the maximum SWE, the two major differences that
Fortress Mountain has from Caribou Creek and Sodankylä are the soil and
the topography. Soils at the Fortress Mountain site have higher clay and loam
content, overlain with a layer of organics, and generally remain frozen and
saturated for the duration of the winter. These, combined with the sloping
terrain and faster meltwater runoff via drainage channels, likely minimizes
the change in soil moisture during the transition seasons and thereby
minimizes potential offsets in the CS725 measurements. Furthermore, the
correlations for the CS725 for Caribou Creek are substantially lower than for
Sodankylä and Fortress Mountain. This could be for several reasons. The
spatial and seasonal variability are quite high at Caribou Creek and the
sample size is low. This is especially the case for 2014/15 where sample size
is small due to a shorter and more variable winter where melt and refreeze
occurred several times over the course of the season (Fig. 5, middle right).
Melting and refreezing generally makes the manual SWE measurements more
difficult and prone to error, creates basal ice and results in higher
spatial variability. Eliminating the ablation period improved the comparison
statistics for 2013/14 but made the statistics for 2014/15 much worse due to
the reduced sample size. Potential sources of error in the CS725
intercomparison are discussed further in the following sections.
The SSG1000 was quite highly correlated with the manual SWE measurements at
both Sodankylä and Weissfluhjoch with r2 values as high as 0.99 at
Sodankylä (when excluding the ablation period) and 0.97 at Weissfluhjoch.
However, when the ablation period is included in the intercomparison for
2013/14 at Sodankylä (it is not present in 2014/15 at Sodankylä due
to sensor malfunction), the r2 drops to 0.84. The more significant result
at Sodankylä is the smaller MRB as compared to the CS725, which is -2
to -15 % (depending on the exclusion of ablation). The magnitude of the
MRB is similar at Weissfluhjoch but the bias here is a positive 8 %. This
is surprising considering the many occurrences of negative sensor bias (as
seen in Fig. 8, bottom) but these negative outliers are balanced by some
large (albeit inconspicuous) positive outliers at the end of the ablation
periods. The outliers for Sodankylä in Fig. 7 (top) occur during the
ablation period in late April–May 2014 but it is difficult to ascertain
whether
the errors are related to the instrument or to the manual measurement. The
most likely explanation is that these are related to the occurrence of
bridging. Bridging is also suspected as the cause of the pre-ablation
outliers at Weissfluhjoch since the sensor seems to agree quite well with the
manual measurements up to mid-March and early April for both seasons. An
intercomparison with a collocated snow pillow (not shown here) suggests a
similar albeit smaller negative bias during the same period. Errors
associated with bridging are discussed further in this section.
The CS725 and SSG1000 measurements at Sodankylä correlate very well with
each other showing correlations as high as 0.99 when excluding the ablation periods. The key result here, as
shown in Fig. 9, is the deviation from this linear correlation at the onset
of melt in the 2013/14 season. Although some of this deviation can be blamed
on differential melting at the site, we attribute a large portion of the
deviation to the different measurement principles of the sensors. At the
onset of melt and the ripening of the snowpack, meltwater drains out of the
snowpack towards the ground surface. Once reaching the surface, the meltwater
can pool and refreeze (potentially forming a basal layer of ice), runoff from
the measurement area or infiltrate into the soil. Due to the flat measurement
area and the sandy soil at Sodankylä, runoff is unlikely; therefore the
meltwater is either infiltrating into the sandy soil or refreezing at the
surface. Either way, the same meltwater is likely draining through and away
from the measurement plate of the SSG1000 and therefore no longer being
measured as SWE in the snowpack. However, this meltwater, whether infiltrated
into the top layer of the sandy soil or pooling at the surface, is still
being registered by the CS725 as SWE. This contributes to the overestimation
of SWE by the CS725 as compared to the SSG1000 and to the non-linearity of
the intercomparison shown in Fig. 9 after ablation. Also, this meltwater is
either difficult or impossible to include in a snow tube sample, increasing
the bias between the CS725 and the manual measurements.
Sources of error
There are several possible sources of error that affect both the automated
and manual SWE measurements. They are discussed and analyzed for each
instrument and method in this section.
CS725 vs. SSG1000 for the 2013/14 (blue dots/blue triangles) and
2014/15 (red) seasons at Sodankylä. Black line is 1:1.
Soil moisture (CS725)
A potential source of error for the CS725 can arise from a poor pre-snowpack
soil moisture calibration or a large post-calibration change in soil moisture
prior to the freezing of the ground surface. Overwinter soil moisture changes
(Gray et al., 1985) or infiltration of snowmelt water into soils (Gray et
al., 2001) could also result in deviation between the manual and CS725 SWE
measurements. Since the CS725 calculation of SWE is based on gamma ray counts
during wet and dry periods with no snow cover, incorrect measurements or
faulty assumptions with respect to the soil moisture calibrations could
result in a sensor offset. Furthermore, if soil moisture levels change
significantly prior to freeze-up, during winter or during ablation, then the
SWE estimates derived from the sensor are less reliable. The approximate
error associated with an inaccurate gravimetric soil moisture calibration, as
provided by the manufacturer, is roughly 10 mm w.e. of SWE for a
0.10 change in GWC. Figure 6 shows an increase in soil moisture at Caribou
Creek up to a VWC of 0.18 (GWC of 0.13) prior to freeze-up in the fall of
2014, an increase of 0.03 GWC and approximately 3 mm w.e.
The resulting calibration offset could explain up to 30 % of the early
season difference between the instrument and the manual measurement shown in
Figs. 4 (middle right) and 5 (middle right). This calibration issue would
then perpetuate through the winter period and grow with any additional
infiltration into the soil beneath the snowpack. It is unfortunate that this
same soil moisture and soil temperature data are not available for
Sodankylä or for the first season at Caribou Creek as this would have
provided some verification for the calibration offset.
From Fig. 6, there appears to be a coinciding jump in the CS725 bias and the
jump in soil moisture (due to above freezing soil temperatures and
infiltration) in the spring of 2015 at Caribou Creek. Although the bias is
not as large as that seen in midwinter, it is a significant increase of
approximately 10 mm w.e. for each of the final two intercomparison
points in mid-March and early April. Much of this 20 mm w.e.
increase could be explained by a corresponding increase in soil moisture from
0.18 VWC (0.13 GWC; estimated at freeze-up) to
0.45 VWC (0.32 GWC; spike at thaw) or approximately
19 mm w.e., assuming that the CS725 is interpreting this
near-surface soil moisture as SWE.
There is some ambiguity in the soil moisture results because the soil
moisture sensors are incapable of measuring moisture content below
0 ∘C and because this is not the only source of error. However, we
think that these soil measurements are useful for explaining at least some of
the offsets seen between the sensor and the manual SWE measurements,
especially during the transition periods. More work is needed on these
linkages before a reliable sensor adjustment can be derived.
Ice bridging (SSG1000)
Ice bridging is a known issue affecting SWE measurements that are made by
weight, such as snow pillows or the snow scale (e.g. Engeset et al., 2000).
Bridging typically occurs when air temperature reaches 0 ∘C and then
cools creating a melt–refreeze crust layer on the snow surface. This layer
is very hard and supports the weight of the snow, thus causing an
underestimate of measured SWE with further accumulation on the surface.
Probable bridging situations can be seen in Fig. 7 both at Sodankylä and
at Weissfluhjoch. At Sodankylä, in December 2013, March 2014 and
February–March 2015, the SWE values measured by the SSG1000 do not increase
as quickly as the manual measurements. At the same time, air temperature
first goes above 0 ∘C and then cools to as low as -30 ∘C
creating perfect conditions for ice bridging. At Weissfluhjoch, the cause of
potential ice bridging is not so obvious, but it is difficult to explain the
differences between manual and SSG1000 measurements otherwise. The snowpack
was homogeneous (verified with terrestrial laser scans) and even though a
co-located snow pillow (not shown here) showed some underestimation compared
to the manual measurements, the underestimation was much smaller than by the
SSG1000. However, snow pillows have been found to be less prone to ice
bridging issues due to their larger surface area (Beaumont, 1966b; Tollan,
1970). A more comprehensive description of the physical processes that cause
measurement errors in SWE pressure sensors can be found in Johnson (2004).
Snow spatial variability
Another potential source of error in this analysis is due to the spatial
variability at the intercomparison sites impacting the relative SWE between
the sensor and manual measurement locations. At Sodankylä, the maximum
distance between the sensors and the manual SWE measurements was 12 m
for the CS725 (6 m after the move prior to the 2014/15 season) and
25 m (16 m in 2013/14) for the SSG1000. Unfortunately, only
1 SWE measurement is made at the intercomparison site, but generally the
spatial variability is low with snow depth exhibiting a coefficient of
variation (COV) under 6 % (with a maximum snow depth of just over
80 cm). Therefore, the impact of spatial variability in SWE, even
with a 25 m separation, is likely quite small for most of the season.
However, both webcam photos and snow depth measurements provide evidence that
snowmelt rates during ablation vary across the site, largely dependent on
exposure. Manual snow depth measurements suggest that spatial differences in
the area around the SWE measurements are small and are perhaps as high as
4 cm in mid-April of 2014 and less in mid-April of 2015. These
differences obviously account for very little of the late season SWE
deviation shown in Fig. 5 (top). This also suggests that the CS725 move prior
to the 2014/15 season had a low impact on sensor bias from one season to the
next.
Caribou Creek, with maximum snow depths of 56 and 41 cm for the two
consecutive seasons, exhibits a much higher spatial variability. Here, COV is
about 15 % (19 %) at peak snow depth but increases to 30 %
(90 %) during ablation for 2013/14 (2014/15). With a full five-point snow
course performed here, mean SWE maximum is approximately 125 mm w.e.
in 2013/14 and 75 mm w.e. in 2014/15 with COV very similar to those
shown for snow depth. The manual measurement used in the intercomparison is
made just inside the measurement area of the sensor, approximately
5 m from the centre. Although relatively close, the higher spatial
variability could result in a spatial bias, especially during ablation. For
example, in 2013/14, we estimate SWE to increase across the sensor
measurement area by approximately 10 mm w.e. in late April due to
differential melting as a result of exposure. With the manual measurement
closer to the lower SWE estimate in the sensor measurement area, up to
25 % of the difference in SWE between the sensor and the manual
measurement (as shown in Fig. 5, middle left) could be explained.
The spatial variability is not assessed for Fortress Mountain or
Weissfluhjoch.
Experiment design
Some aspects of the design of the SWE intercomparison are less than ideal and
often were a result of compromise amongst the overall SPICE objectives, site
host resources and nationally accepted practices. These compromises
potentially contribute to some ambiguity of the study results and this
commentary could form the basis for recommendations on the design of future
SWE sensor intercomparisons.
Ideally, the manual reference at each site should have been identical using
the same sampling equipment at a prescribed offset distance from each SWE
sensor. Rather, each site host used their nationally accepted method of
sampling SWE (as described in Sect. 2.3). Distances between the manual SWE
measurement and the sensor varied from 5 to 25 m, depending on site,
but perhaps more significantly, the variation within the sensor measurement
area (especially for the CS725) was not properly assessed. This could
certainly have been a factor at Caribou Creek but the intense sampling within
the measurement area of the sensor would have caused too much disturbance and
impacted sensor measurements. Also, increased frequency (i.e. weekly) of
manual measurements is desirable especially after significant changes in the
snowpack, albeit at the risk of disturbance. In the future, manual observers
should pay special attention to the existence of basal ice layers which may
have an impact on the overall accuracy of the manual SWE estimate.
Another ideal situation would have been the co-location of both SWE sensor
types at each site. This, in combination with soil moisture and temperature
sensors within the measurement area of the CS725 sensors, would have provided
additional information for the assessment of sensor bias. Another good
addition would be the automated and high frequency measurement of snow depth
within the sensor measurement areas to provide an indicator of snow density
and melt rates and perhaps an indicator of snow bridging on the weighing SWE
sensors.
Manual SWE measurements
As noted above, the manual SWE measurements differed by site, the exception
being Caribou Creek and Fortress Mountain that both used the ESC-30 snow tube
and bagged and weighed the sample. We will not comment further on possible
bias associated with different samplers (Farnes et al., 1983; Goodison et
al., 1987), as these are generally small as compared to the differences in
the measurements shown in these results. We do, however, want to address
possible errors associated with the manual measurement of a complex snowpack
(i.e. a snowpack with ice layers or during melt), especially with a snow
tube.
During the intercomparison, both Caribou Creek and Sodankylä experienced
several freeze and thaw cycles over the course of the winter (as seen in
Fig. 5 top and middle) but one was especially pronounced at Caribou Creek
during mid- to late January 2015 (Fig. 5, middle right). The result of
freeze–thaw is usually a “crusty” snowpack with several ice layers. In
general, these characteristics make a snowpack difficult to sample with a
snow tube as the tube cutters need to cut through multiple ice layers without
snow escaping from the bottom of the tube (Powell, 1987; Sturm et al., 2010).
It is anticipated that even an expert user will have difficulties obtaining
an accurate sample in these conditions, exacerbated even more by the shallow
pack found at Caribou Creek in 2014/15. It is difficult even at the time of
the sample to estimate measurement error, but it could easily result in a
5–10 % underestimate of SWE. Sturm et al. (2010) reported an average
underestimate from a snow tube of 7.1 % as compared to layer-integrated
snow pit measurements. Although this may explain some of the bias in the
CS725 measurements, especially at Caribou Creek, it is countered by the
relatively good agreement between the manual and SSG1000 measurements for
Sodankylä. However, midwinter melting could also result in basal ice as
the meltwater percolates through the snow and refreezes at the surface
(providing that the surface is below 0 ∘C) or in the top layer of
the sandy substrate. Not only would this ice layer be difficult to measure
with a snow tube (which is difficult to cut through and often results in an
underestimate), the meltwater may drain off of the SSG1000 measurement
surface and be underestimated by that measurement as well. This may partially
explain the often (but sometimes inconsistent) increase in sensor bias shown
by manual SWE measurements following midwinter freeze–thaw cycles in Fig. 5
(top and middle). Unfortunately, the observer's notes did not indicate when a
basal ice layer was observed so much of this is speculation.
During ablation, measures were taken to sample the snowpack before it ripened
but this could not always be accomplished due to travel time to the site
(especially for Caribou Creek). Because the sample was bagged and weighed
rather than weighed in the tube, a wet sample would experience some errors
because of the bagging process (liquid water or sticky snow left in the tube)
and result in an underestimate of SWE (perhaps 5 % as a rough estimate).
Summary and conclusions
Two automated SWE sensors were tested
at three WMO-SPICE sites (Sodankylä, Weissfluhjoch and Caribou Creek) and
at one additional Canadian site (Fortress Mountain) during the WMO-SPICE
intercomparison (northern
hemispheric) winters of 2013/14 and 2014/15.
Instrument measurements were compared to periodic manual measurements of SWE
at the sites and cross referenced with ancillary measurements of air
temperature and soil moisture and soil temperature (at Caribou Creek) to try
to determine causality for some of the bias seen in the intercomparison. The
objective was not necessarily to determine which instrument makes the most
accurate measurement, but to inform users of potential measurement issues
that may influence their data interpretation.
Intercomparison results for the CS725 show that it overestimates SWE on
average by 30 and 35 % at Sodankylä and Caribou Creek, respectively,
with combined season correlations (r2) of 0.92 at Sodankylä and 0.90
at Caribou Creek. Interseasonal variability in both the MRB and the
correlations were higher at Caribou Creek, the differences attributed to
smaller sample sizes, higher spatial variability of SWE and ice layers in
the snowpack. Offsets were generally higher at Caribou Creek, which could be
indicative of an inaccurate soil moisture calibration of the instrument, a
change in soil moisture relative to the calibration prior to or after the
soil freezing or sampling errors in the manual SWE measurement due to a more
complex snowpack. Correlations at Fortress Mountain are also quite high over
the single intercomparison season (r2=0.92) with a mean negative bias of
approximately 5 %, which is more comparable to the results of Wright et
al. (2011) in similar conditions. At the two sandy SPICE sites, the agreement
between the CS725 and the manual SWE measurements are generally better prior
to the start of seasonal ablation. We believe this occurs largely because of
early spring melt percolating through the snowpack and either forming a basal
ice layer or infiltrating into the sandy substrate. Either way, this water is
difficult or impossible to measure with a snow tube. However, because this
water continues to attenuate the gamma radiation signal detected by the
CS725, the sensor still interprets this water as SWE and therefore appears to
overestimate as compared to the manual measurements. Seasonal ablation has no
significant impact on the agreement at Fortress Mountain due to saturated
frozen soils that restrict infiltration and a mild slope that promotes runoff
of meltwater from the site.
The SSG1000 at both Sodankylä and Weissfluhjoch compared quite well to
the manual SWE measurements showing mean biases of -11 and 8 % at the
respective sites. It did, however, experience some technical issues at
Sodankylä early in the 2014/15 snowmelt period which limited the
intercomparison for that season. The correlations were quite high with the
combined season r2 ranging from 0.88 at Sodankylä to 0.96 at
Weissfluhjoch. Many of the outliers in the SSG1000 intercomparisons are most
likely due to bridging of the snowpack on the weighing plate but we also have
to consider errors related to the manual measurements and other processes
going on at the snow–soil–sensor interface (as outlined in Johnson, 2004). At
Weissfluhjoch, these outlier events occurred prior to maximum seasonal SWE
while at Sodankylä they occurred during ablation. Removing the ablation
period in the 2013/14 Sodankylä data resulted in a substantial increase
in r2 from 0.84 to 0.97.
The SSG1000 correlated very well with the CS725 at Sodankylä during the
accumulation period. Although the overestimation of SWE by the CS725 is
quite apparent when compared against the SSG1000, the accumulation period
r2 was 0.98 and 0.99 for the two respective seasons. Intercomparison of
the two sensors clearly shows how the overestimation of SWE by the CS725
increases at the onset of ablation in March/April of the 2013/14 season.
Independent of the manual measurements, this indicates that the deviation of
the CS725 from manual SWE during ablation is most likely instrument related
and a result of the CS725 misinterpreting the meltwater infiltrated into
the sandy soils as SWE.
When comparing SWE instruments to a manual reference, there are several
considerations that must be made that ultimately impact the interpretation
of the results. We know that the manual measurements of SWE are not free of
error. Experience proves that making a snow tube bulk density sample in a
snowpack containing ice layers or during melt is difficult and inherently
prone to errors. We also have to consider the spatial variability of the
snow that we are sampling as the CS725 (and the SSG1000 to a lesser degree)
have a much larger measurement area than the manual point sample. Taking
this and the technical capabilities of the instruments into consideration,
both sensors have high correlations (generally higher than 0.90, Caribou
Creek being the exception) with the manual reference measurements. We have
identified that the SSG1000 has had some technical issues during snowmelt
but are satisfied that these issues can be overcome with some installation
modifications. The SSG1000 may also underestimate SWE on occasion due to
bridging so users need to be aware of this potential error. We have
identified the potential for the CS725 measurements to be misinterpreted,
especially when deployed over sandy soils and during melting conditions.
Although more verification work is required on the impact of soil moisture
change on the CS725 bias, aggregating subsurface moisture in the SWE
estimate could potentially be useful from a hydrological perspective as it
ultimately impacts the amount of water available for runoff. Nevertheless,
it is recommended to co-locate the CS725 with ancillary measurements of soil
moisture, soil temperature and snow depth to guide the user in interpreting
the data.
Data availability
Much of the data used in this analysis were collected during the SPICE
project on behalf of the WMO Commission for Instruments and Methods of
Observations (CIMO). At the time of publication, the SPICE final report
remains in progress and the project data protocol limits data availability
until the final report is released, at which time the data can be obtained by
contacting the corresponding author.
Acknowledgements
We wish to thank the WSL Institute for Snow and Avalanche Research SLF for
kindly providing the SSG1000 and manual SWE measurements from Weissfluhjoch
as well as the countless other contributors to SPICE who helped to make the
project a success. We would like to express our appreciation for the effort
that the reviewers and the special issue editor provided to help us improve
this paper with a special thanks to Charles Fierz (WSL-SLF, Davos), who
provided a very thorough review with substantial and helpful
feedback.
Edited by: S. Morin
Reviewed by: C. Fierz and two anonymous referees
Many of the results presented in this work were obtained as part
of the Solid Precipitation Intercomparison Experiment (SPICE) conducted on
behalf of the World Meteorological Organization (WMO) Commission for
Instruments and Methods of Observation (CIMO). The analysis and views
described herein are those of the authors at this time and do not necessarily
represent the official outcome of WMO-SPICE. Mention of commercial companies
or products is solely for the purposes of information and assessment within
the scope of the present work and does not constitute a commercial
endorsement of any instrument or instrument manufacturer by the authors or
the WMO.
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