Cosmic-ray neutron method for the continuous measurement of Arctic snow accumulation and melt

The Arctic is warming at two to three times the rate of the global average, significantly impacting snow accumulation and melt. 15 Unfortunately, conventional methods to measure snow water equivalent (SWE), a key aspect of the Arctic snow cover, have numerous limitations that hinder our ability to document annual cycles, the impact of climate change, or to test predictive models. As a result, there is an urgent need for improved methods that measure Arctic SWE; allow for continuous, unmanned measurements over the entire winter; and allow measurements that are representative of spatially variable, Arctic snow covers. In-situ, or invasive, cosmic ray neutron sensors (CRNSs) may fill this observational gap, but few studies have tested these types of sensors or considered their applicability at remote sites 20 in the Arctic. During the winters of 2016/17 and 2017/18 we tested an in-situ CRNS system at two locations in Canada; a cold, lowto highSWE environment in the Canadian Arctic and at a warm, low-SWE landscape in Southern Ontario that allowed easier access for validation purposes. CRNS moderated neutron counts were compared to manual snow survey SWE values obtained during both winter seasons. Pearson correlation coefficients ranged from -0.89 to -0.98, while regression analyses provided R2 values from 0.79 to 0.96. RMSE of the CRNSmeasured SWE averaged 2 mm at the southern Ontario site and ranged from 28 to 40 mm at the Arctic site. These data show that in-situ 25 CRNS instruments are able to continuously measure SWE with sufficient accuracy, and have important applications for measuring SWE in a variety of environments, including remote Arctic locations. These sensors can provide important SWE data for testing snow and hydrological models, water resource management applications, and the validation of remote-sensing applications.


Introduction
The Arctic tundra snow cover is typified by low snow depth and low snow water equivalent (SWE) when averaged over areas of a few km 2 , but extreme spatial variability in depth and SWE over distances of less than 10 m (Sturm et, 1995(Sturm et, , 2001Rees et al., 2014). These features are due to a combination of low winter snowfall, wind that redistributes snow across the landscape, and high rates of sublimation during these blowing snow events. For example, total SWEsnowfall is often less 40 than 300 mm over the long winter, with up to 40% of this snowfall sublimating during blowing snow events. Blowing snow also results in wind scoured uplands characterized by shallow, low density snow cover (< 0.7 m; < 300 kg/m 3 ) and deep, high density snow drifts (up to 10 m; up to 600 kg/m 3 ) located on steep hillslopes (Marsh and Pomeroy, 1996). Within the tundrataiga ecotone, deep drifts also occur in small shrub or tree patches. Although deep drifts are small in area, they often contain a large portion of the total landscape SWE (Gray et al., 1974;Marsh and Woo, 1981;Gray et al. 1989;Marsh and Pomeroy, 45 1996;Sturm et al., 2001). This spatially variable snow cover exerts important controls on many aspects of the tundra environment, including soil and permafrost temperature, permafrost processes such as ice wedge cracking, streamflow hydrology, lake level, and wildlife habitat for example. However, monitoring this snow cover remains extremely challenging (Kinar and Pomeroy, 2015).
The Arctic snow observing system has very few ground-based monitoring stations, and these are often located in 50 areas not representative of the broader Arctic. For example, the majority of Arctic stations are typically chosen to be located at town sites; for search and rescue bases/stations; to improve military capabilities; to function as entities that legitimize national or sovereign claims; and to engage in multilateral actions to protect Arctic infrastructures (Goodsite et al., 2016).
Since the 1970's many purely research-purpose Arctic environmental monitoring stations have been permanently closed Formatted: Not Superscript/ Subscript (Schiermeier, 2006;Rees et al., 2014).at non-representative locations (Schiermeier, 2006;Rees et al., 2014;Goodsite et al., 55 2016). In additionAs such, standard measurements used at these stations are either prone to large considerable errors, not representative of the surrounding area, or not measured at all. For example, snowfall measurements are prone to large errors due to under catch during high winds (Pan et al., 2016), while sublimation is seldom measured. Measurements of snow depth are typically not representative of the surrounding natural terrain as they are limited to point observations using ruler measurements or acoustic distance systems (Kinar and Pomeroy, 2015). Recent advances in methodology allow measurement 60 of SWE using gamma attenuation (Kirkham et al., 2019) or global positioning systems (Koch et al., 2019)) for example, but again are limited to point or campaign-based measurementsmeasurements. To overcome these deficiencies, practitioners and researchers still use traditional, manual snow surveys in order to document average snow depth, density and SWE across Arctic landscapes. Snow survey methods have well known limited accuracy in tundra areas (Goodison et al., 1981;Pomeroy and Gray, 1995;Steufer et al., 2013;Kinar and Pomeroy, 2015) and do not allow for mapping snow cover as is needed for many 65 Arctic research studies.
Satellite and aircraft remote sensing provide methods to partially overcome some of the limitations outlined above through the mapping of both snow cover extent and SWE. Although current satellite methods are well suited to assessing climate change impacts on snow across the entire Arctic (Derksen and Brown, 2012;Rees et al., 2014;Hori et al., 2017;Tollefson, 2017;Bush and Lemmen, 2019) and for large scale water resource needs, they are not suited for providing snow 70 data at the high spatial resolution needed for many research needs. Airborne remote sensing methods are able to provide high resolution snow data, but also have certain limitations. For example, methods to map snow depth at high resolutions are available (Deems et al, 2013;Walker et al., 2020), but mapping of snow density or SWE are not (Koch et al., 2019). SWE along flight transects is available using airborne gamma methods but have limited applicability in the Arctic due to the high cost associated with campaign-based measurements. Airborne radar methods, such as LiDAR, have promise for mapping SWE 75 at moderate resolutions, but are also primarily utilized as campaign-based measurements and remain in the research stage (Derksen et al., 2017).
Grounded in-situ cCosmic ray attenuation methods -where the sensor is always in contact with the soil-interface, and specifically in our works, is not buried, have not been extensively tested but may fill a needed gap between existing groundbased and remote sensing snow monitoring methods. Kodama et al. (1979) first described the use of a groundedn in-situ, or 80 invasive, cosmic ray neutron sensor (CRNS) to measure SWE by burying a shielded neutron sensor below the ground surface and allowing snow to accumulate upon it. This method records neutrons in the fast (~1 MeV) to epithermal (~0.025 eV) range which are generated by galactic cosmic rays that interact with atmospheric particles, snow and soil (Kodama et al., 1979;Howat et al., 2018;Gugerli et al., 2019). As hydrogen in water molecules absorbs neutrons, higher SWE snowpacks will attenuate larger numbers of neutrons, leading to lower neutron counts below deeper snow packs. For a neutron sensor placed 85 at ground level, the sensor footprint is essentially a point source on the scale of the instrument (in our case, a 130 cm tube), and the relationship between neutron counts is inversely proportional to the amount of SWE on the ground. Currently, we are aware of two such grounded in-situ CRNS systems that are used operationally or are commercially available. One is deployed by Électricité de France (Paquet and Laval, 2005;Paquet et al., 2008;Delunel et al., 2014) in the French Alps and used in estimating snow cover runoff for operational hydroelectric power generation. A second CRNS system is the SnowFox TM (SF) 90 system commercially available from Hydroinnova (Howat et al., 2018;Gugerli et al., 2019). The SF uses a single neutron measuring tube placed immediately below or at the ground surface prior to winter, allowing the snowpack to accumulate atop of it. Hydroinnova also produces the CRS-1000™, a non-invasive CRNS, which uses two neutron measuring tubes located above the ground and snow surfaces. With sensor above the snow surface, the footprint of the system is increased substantially.
Although there remains some uncertainty in the size of the footprint, it may be as high as 300 m radius around the sensor 95 (Desilets et al., 2010;Zreda et al., 2012;Desilets and Zreda, 2013;Köhli et al., 2015;Sigouin and Si, 2016;Schattan et al. 2017). Further work is required on testing such a system in the Arctic.
The CRS-1000 and SF potentially complement each other for measuring snow, with the CRS-1000 providing an average SWE over a large area surrounding the sensor, and the SF measuring point SWE along a transect. Since the SF can measure SWE from near zero to as high as four meters, and potentially up to 10 meters (Howat et al., 2018;Gugerli et al., 100 2019), the SF could is capable of potentially measuringe SWE across deep snow drifts by employing multiple instruments in a transect. Such a network of CRNS sensors has the potential to fill a significant measurement gap between traditional groundbased measurement systems and remote sensing. This paper will focus on the in-situ type of CRNS system, the SF, simply called a CRNS for the remainder of the paper, with an objective to test the potential of this CRNS to provide continuous measurements of SWE accumulation and melt along transects where SWE varies greatly, and over full snow seasons. Future 105 research will focus on the CRS-1000 sensor.

Cosmic Ray Neutron Sensor (CRNS)
The CRNS has a single neutron sensor tube, installed on the ground surface, that provides an estimate of SWE across a small footprint that is assumed to be a "point" measurement. The CRNS used in this study has a 130 cm cylindrical neutron 110 detector tube with a separate control module incorporating a Hydroinnova QDL2100 data logger and an iridium satellite communication device. The neutron detector tube is moderated (shielded) by a polyethylene casing to reduce the sensitivity of the detector gas and to increase the sensitivity towards the fast and epithermal ranges -where the CRNS principally measures neutrons after they traverse the overlying snowpack (Delunel et al., 2014;Woolf et al., 2019). Between this energy range, a neutron collision with the CRNS polyethylene casing causes the neutron to reach thermal equilibrium with the moderator and 115 be easily absorbed by the detector. An absorber in the detector tube captures the neutron and splits into two charged particles which trigger an ionization pulse in the tube, this is noted as one neutron count (Bartol, 1999). Counts are recorded over a preset interval and the counting rate (i.e. relative neutron intensity) can be retrieved manually from the data logger and are also posted in near real-time on a private web portal hosted by the manufacturer. The fundamental process of the CRNS is that a baseline moderated neutron counting rate is established during the initial snow-free setup, and any deviations from this baseline 120 would be inversely proportional to the amount of near-surface water content. This near-surface water content is primarily attributed to SWE during snow covered periods, and to soil moisture during snow-free periods. A single neutron tube can be used individually, or a number of neutron sensor tubes can be connected to a single data logger to provide measurements along a transect up to several hundred meters in length. Due to the fundamental operation of the CRNS, when setting up multiple neutron sensor tubes in a transect, it is strongly recommended that a similar n identical moderated neutron counting rate is 125 used as the baseline for each unit.

Determination of Snow Water Equivalent using a Cosmic Ray System
To estimate SWE from the CRNS neutron data, the raw moderated neutron counts (NRAW) must be corrected for barometric pressure (Fp) and the temporal variation of incoming neutrons (Fi). Since these correction factors (Fp and Fi) represent a change from one point in time to another, they are unitless. The corrected moderated neutron counts (N) is 130 calculated as: N is then updated as a running average over 12 timesteps in order to reduce the noise associated with the hourly moderated 135 neutron data. Fp is given by: where is the natural exponential, P is the observed air pressure (hPa) recorded by a pressure sensor on the CRNS 140 instrument, and P0 represents a reference air pressure, set to 1000 hPa. The mass attenuation length, L (g/cm 2 ), was provided by the manufacturer and is based on latitude (Desilets, 2021). Fi is then calculated as: where Nref is the average incoming neutron count over an arbitrary counting period (e.g. the first month of data after the initial snow-precipitation of the winter season) and Nnm is the hourly incoming neutron count during the time of interest (snow covered season).. Numerous non-invasive CRNS studies (Zreda et al., 2012;Chrisman and Zreda, 2013;Schattan et al. 2017;Schattan et al., 2019) have used incoming cosmic ray fluxes from the Jungfraujoch Neutron Monitor in Switzerland to estimate Fi. However, incoming cosmic rays are location dependent, and neutron monitoring stations with higher geomagnetic latitudes 150 are known to have a greater sensitivity to the lower end of the neutron monitor energy range, when compared to midlatitude or low-latitude stations (Kuwabara et al., 2006). As a result, it is preferable to use a nearby neutron monitor, and we therefore use incoming neutron fluxes from the monitoring station located at the Aurora Research Institute, Inuvik, Northwest Territories, and available from the Neutron Monitor Database (Klein et al., 2010). SWE (mm) can then be estimated as follows (Desilets, 2010): 155 where ln is the natural logarithm, N is the corrected and 12-h averaged moderated neutron count from Eq. (1), and N0 represents the averaged neutron count 7-14 days prior to the initial snow accumulation of the season. N0 serves as the instrument's 160 moderated neutron count baseline, establishing a crucial initial relationship between the pre-snowfall neutron count and a nearsurface water content while the SWE is zero. Any deviations from the baseline counting rate are inversely proportional to the amount of near-surface water content. This is the fundamental operating process of the CRNS instrument. The near-surface water content range for this grounded in-situ CRNS has not been quantified in literature, however, it is primarily attributed to SWE during snow covered periods and soil moisture during snow-free periods (Paquet and Laval, 2005;Paquet et al., 2008;165 Howat et al, 2018). The attenuation coefficient, 1 , is then calculated as: The instrument manufacturer provided two sets of calibration parameters, used in Eq. (5), for the CRNS instrument. The Λmax 170 value represents the rapid attenuation of neutrons, while the Λmin value represents a more gradual attenuation. 1 , 2 and 3 are factory-fitting parameters determined by the manufacturer through calibration and field validation experiments.
For details regarding the CRNS parameters, refer to Sect. 3.3.

Study Sites 175
CRNSs were installed at two locations across Canada; a warm, low SWE agricultural field located in southern Ontario, and a cold, high SWE environment located within a tundra shrub patch in the western Canadian Arctic (Fig. 1). The southern site allowed frequent field visits during the winter period, and the combination of two sites allowed testing of the CRNS over a range of SWE, climate, and soil conditions. The southern Ontario study site is located at 300 masl, near Elora, Ontario (43.6˚ N, 80.3˚ W) (Fig. 1). This site typically has warm, shallow snowpacks with low SWE and low spatial variability. A dominant 180 feature of the Elora site is the absence of a consistent average annual snowpack, numerous snowfall events, and numerous melt and refreeze events that affect the SWE.
The Arctic study site is located at 30 masl in the Trail Valley Creek research observatory (TVC) ( Fig. 1) (68.4˚ N, 133.3˚ W), 50 km north of Inuvik, Northwest Territories. The TVC site is characterized by continuous permafrost with a shallow active layer. It is dominated by Arctic tundra vegetation, with the ground cover consisting of a highly porous organic 185 layer and a large water storage capacity (Quinton and Marsh, 1999;Wrona, 2016). Patches of tall shrubs (birch, alder, and willow) and black spruce trees are scattered across the tundra. Snow cover forms in October and persists until May, with few or no melt periods over the winter. This snow cover is shallow in the wind-blown upland areas and deep snow drifts form on lee hillslopes, along stream channels and lake edges, and in tall shrub patches (Marsh and Pomeroy, 1996).

. Locations of the southern Canada (Elora) and western Canadian Arctic (Trail Valley Creek) sites used in this study. The Elora site is located on an agricultural field and has a shallow, temperate snow cover. While theThe Trail Valley
Creek site is typical of the tundra-taiga ecotone with snow that is highly variable in depth, density and SWE.

CRNS Installations
A single CRNS was placed in the centre of the Elora field (  Five CRNSs were installed at TVC on August 5, 2016 along a 50 m transect that traversed from a tundra-shrub interface to alder shrubs (up to 2.5 m in height) and back to a tundra-shrub interface. The CRNSs were installed concurrently, approximately eight meters apart (Fig. 3) and were connected to a single data logger. This shrub patch accumulates a deep 205 snowdrift each winter that is representative of snow accumulation typical to shrub patches found in the tundra-taiga transition zone. Each CRNS was installed on the ground surface prior to the accumulation of snow. The batteries for both the Elora and TVC systems were recharged by solar panels. However, at TVC, they provided limited power to the batteries during much of the winter. From the start of the TVC snow season in October, until March 4, 2017 and May 3, 2018, a low power sampling mode was used, with four, one-hour recordings obtained per day. After these dates, sufficient sun allowed the solar panels to 210 recharge the batteries, and the CRNS system measurement frequency adjusted to 24, one-hour recordings per day. During the winter period, we used a 12-timestep running average to estimate SWE; resulting in a three-day averaged SWE which was used in our analysis. After March 4, 2017 and May 3, 2018, we used a 12-hour running average SWE. The TVC CRNS system experienced a power failure from November 10 to 27, 2017, and as a result, no data is available for this period.

Snow Surveys
A total of five snow surveys were conducted at the Elora site during accumulation and melt conditions from February 11 220 to March 14, 2017, and 11 surveys from December 23, 2017 to February 20, 2018. Snow surveys used an ESC30 style snowcorer from SnowHydro that features a cross sectional area of 30 cm 2 for measuring snow depth and density via snow cores.
The snow cores were transferred to a plastic bag and weighed on-site with an electronic scale (A&D HT-3000). The depth and density of each snow sample was recorded and used to calculate the SWE. Snow surveys at this site consisted of three to four snow core samples taken within a one meter proximity to the CRNS. Snow core results were averaged to represent a single 225 value for that date. Results from Turcan and Loijens (1975), Peterson and Brown (1975), Goodison et al. (1981), and Sturm et al. (2010) state that the standard measurement error associated with using this type of snow-corer ranges from 1-10 %. (approximately equally spaced apart) along the 50 meter transect (Fig. 3). Again, a SnowHydro snow-corer was used. Using the same approach as the Elora site, samples had their depth and weight recorded immediately after collection and were used to calculate SWE. Data from this site was used in two ways, 1) SWE calculated from the five CRNSs was averaged and this single value was used to represent the total snowdrift for that date, and 235 2) SWE calculated for each CRNS was compared with the snow survey measurement obtained nearest to the specific CRNS of interest. This allowed the CRNSs to be compared to one-another within the snowdrift over the course of the snow covered season.

3 CRNS Parameters
The instrument manufacturer provided two sets of calibration parameters, used in Eq. (5), for the CRNS instrument. 240 The Λmax value represents the rapid attenuation of neutrons, while the Λmin value represents a more gradual attenuation. 1 , 2 and 3 are fitting parameters determined by the manufacturer through calibration and field validation experiments (Howat et al., 2018;Gugerli et al., 2019). The standard terrestrial parameters (Table 1) were used for the Elora study site. However, the TVC snow cover is underlain by a high porosity soil matrix with an active layer thickness of 0.5 to 1.0 m (Wilcox et al., 2019), this active layer is typically saturated with liquid water prior to freeze up, and therefore has a high ice content during the winter 245 season (Wrona, 2016). As a result, we applied the manufacturer suggested glacier parameters (Table 2) to this Arctic site.
AdditionallyHowever, we increased the 1 parameter in order to create a site-specific calibration which addressed the factor that the TVC subsurface was not pure water/ice, but had mineral and organic properties and was highly porous and permeable -typical of an Arctic landscape. as well. We used a systematic approach on each of the parameters and observed a significant increase in data quality, relative to field measurements, when adjusting only the 1 parameter. Howat et al. (2018) and Gugerli 250 et al. (2019) tested a similar CRNS model on the Greenland Ice sheet and the Glacier de la Plaine Morte in Switzerland, and were successful using the manufacturer-provided glacier parameters. Although they tested a non-invasive CRNS model, findings from Schattan et al. (2017) and , Schrön et al. (2017), and Wallbank et al. (2021) suggest that adjusting the calibration fitting parameters may lead to improved results, and in discussion with the manufacturer, it was confirmed that adjusting the calibration fitting parameters for this grounded in-situ CRNS model may also lead to improved results. Future research is 255 recommended to investigate the impact of each parameter and to explore the potential of a standard set of factory-fitting parameters for an Arctic landscape.

Snow Surveys
A total of five snow surveys were conducted at the Elora site during accumulation and melt conditions from February 11 to March 14, 2017, and 11 surveys from December 23, 2017 to February 20, 2018. The snow surveys consisted of a snow-core campaign utilizing an ESC30 style snow-corer from SnowHydro which features a cross sectional area of 30 cm 2 for measuring 270 snow depth and density. The snow cores were transferred to a plastic bag and weighed on-site with an electronic scale (A&D HT-3000). The depth and density of each snow sample was recorded and used to calculate the SWE. Snow surveys at this site consisted of three to four snow core samples taken within a one meter proximity to the CRNS. Snow core results were averaged to represent a single value for that date. Results from Turcan and Loijens (1975), Peterson and Brown (1975), Goodison et al. (1981), Sturm et al. (2010) and Royer et al. (2021) state that the standard measurement error associated with using this type of 275 snow-corer ranges from 1-10 %. measurements (approximately equally spaced apart) along the 50 meter transect (Fig. 3). Again, a SnowHydro snow-corer was 280 used. Using the same approach as the Elora site, samples had their depth and weight recorded immediately after collection and were used to calculate SWE. Data from this site was used in two ways, Formatted: Normal 1) SWE calculated from the five CRNS instruments was averaged and this single averaged value was used to represent the total snowdrift for that date; and 2) SWE calculated for each CRNS was compared with the snow survey measurement obtained nearest to the specific 285 CRNS of interest. This allowed the CRNSs to be compared to one-another within the snowdrift over the course of the snow covered season.

Relationship between Neutron Counts and SWE
Corrected, moderated neutron counts, N from Eq. (1) (simply referred to as counts, or neutron counts, for the 290 remainder of the paper), were assessed in relation to SWE at both study sites as follows.

Elora:
The relationship between neutron counts and SWE at Elora was assessed in three two ways. First, the N0-calibration function (Eq. (4) and Eq. (5))Eq. (4) was used to estimate SWE from the neutron counts and compared to snow survey estimates measurements of SWE. Using this approach, the R 2 was 0.79 74 when combining data from the winters of 2016 and 2017 (Fig.   4), and improved to 0.93 with the exclusion of an outlier from the 2017/18 dataset.. 295  Second, we carried out a bivariate analysis directly between neutron counts and SWE from the snow surveys using a linear regression (Fig. 4a5a). Although the N0-calibration function (Eq. (4) and Eq. (5)) is commonly utilized due to the non-linearity 300 of the cosmic ray attenuation method for SWE, the manufacturer notes that a linear approximation may have potential to be effectively utilized for grounded in-situ CRNS up to 15 cm of SWE., Ppast this value, the non-linearity of the N0-calibration function becomes more prominentpronounced (Fig. A1) and should be accounted for. In addition,Additionally, although they tested a different CRNS model, findings from Siguoin and Si (2016) and Bogena et al. (2021) notestate that athe linear regression methodology is able to determine the SWE of the snow pack reasonably well. 305 The manufacturer notes that a linear approximation may have potential to be effectively utilized for up to 15 cm of SWE, past this value the non-linearity of the N0-calibration function becomes more prominent (Fig. A1) and should be accounted for.
Utilizing this approach Using this approach provided a best best fit linear regression that varied between each study year as follows: Where is the 12-h averaged counts (N) from Eq. (1) which has already been normalized by the snow-free near-surface water content from Eq. (4). The statistical analysis (Table 3) (6) and (7)) are considerably different. The discrepancy in the y-intercept is believed to be related to the CRNS being installed later in the winter season in 2016/17 (February 11, 2017) after the first accumulation of snow at the site. As a result, the sensors neutron count baseline does not incorporate the near-surface water content prior to the initial winter freeze-up, and therefore, based on Eq. (4), 330 underrepresents the actual SWE at this site. The majority of this unaccounted water content is likely stored in the first few centimeters of soil. In 2017/18 however, the CRNS was installed on December 4, 2017, before the first accumulation of snow on the ground and the initial soil freeze-up. This means the CRNSs' baseline between the two seasons are likely to be considerably different. To consider if this explanation is reasonable, we followed the approach of Sigouin and Si (2016), also noted by Royer et al. (2021), where the authors estimated applied a correction based on soil water storage in the top 10 cm of 335 the soil profile and adjusted their SWE values accordingly. To follow this approach, we used an estimated water capacity of the top 10 cm soil layer to be 1.3 to up to 2 mm/cm (Blencowe, 1960;Ball, 2001), and assumed a 50% soil moisture. This As shown in Fig. 4b5b, this adjusted equation provides a slope and y-intercept that is closer to that of the 2017/18 equation (Eq. (7)). The adjusted 2016/17 data yields an improved Pearson correlation coefficient of -0.98, an improved R 2 value of 345 0.96 and a slightly lower RMSE of 1.7 mm. This illustrates that it is extremely the significance important tofo installing CRNS prior to the start of the snow covered season. However, due to this late season installation, we were able to reasonably estimate the antecedent soil water capacity by comparing the regression trendlines from year-to-year. This comparison between snowseasons is possible because the soil water storage directly impacts the N0 value (or N when SWE is zero). In practice, this further indicates that a linear regression function is well transferable in time at sites with similar soil water storage capacity -350 such as Elora. In a broader approach, this allows researchers and operators to set up the CRNS, even after the initial snowfall and subsequent soil freeze-up, and capture accurate SWE data, so long as it is corrected for soil moisture conditions afterwards (Royer et al., 2021). Additionally, another significant advantage of utilizing a linear regression approach is that it is considerably more time efficient than fitting the full N0-calibration function.
This approach is most practical for cosmic ray neutron attenuation up to ~15 centimeters of SWE; past this point, the 355 non-linearity of the effective attenuation length vs. SWE (Eq. (5)) becomes more pronounced. Considering that the maximum SWE for both winter seasons at Elora was well below 15 centimeters, the linear regression approach provided reasonably accurate results. Bogena et al. (2021) notes that, to date, there is no consensus on which single method is best suited to convert neutron intensity data into SWE. This section demonstrates that a grounded in-situ CRNS utilizing a linear regression approach is able to reasonably measure SWE. Future 360 research is recommended to assess the linear regression analysis vs the non-linear approach in order to quantify the measurement accuracy discrepancy between the two approaches at low, moderate, and high SWE sites using grounded in-situ CRNS. Considering that the grounded in-situ CRNS requires virtually zero maintenance and can be set-up by one person in under an hour, the regression equation methodology may be an effective approach for quickly estimating SWE at remote sites and at sites where soil moisture is rather consistent. A third approach to estimating SWE follows that of Kodama et al. 365 (1979) to use an exponential relationship between SWE and neutron intensity. Prior to assessing an exponential curve, all zero values were changed to reflect non-zero values, as a result, these values were altered to 0.01 mm of SWE. An exponential regression was then performed and yielded an R 2 value of 0.55. Since this value is considerably lower than the two approaches noted above, it was not used for the remainder of this paper.

Temporal Snow Cover Development and Melt 415
Using Eq. (4), , or the relationships between neutron counts and SWE as described above (Eq. (7), (8), (9) and (10)), the CRNS instrument allowed for the continuous measurement of SWE over an entire winter accumulation and melt season at one point at the Elora site, and for multiple sites across the TVC snow drift. Figure 6 shows changes in SWE at the Elora site for both study years, and illustrates the potential for the CRNS approach to measure key aspects of the winter SWE, including: maximum SWE, rapid changes in SWE due to both snowfall accumulation and snowmelt, and the timing of snowpack removal 420 due to melt. For example, during the 2016/17 winter season the maximum SWE peaked briefly at 31 mm in mid-February and 42 mm in late January 2017/18 (Fig. 6), and then rapidly decreased over the next few days due to snowmelt. Measuring such rapid changes in SWE would be very challenging using manual snow survey measurements, and only a few other instruments, such as gamma snow sensors, allow this type of high-temporal resolution, point SWE observations. The CRNS also shows that in 2016/17, the site became snow free numerous times over the winter (Fig. 6), and the snow cover was removed for the 425 last time on March 14. In 2017/18 there was a continuous snow cover from December to late January, and the snow cover was then removed on February 20 and did not form again that winter. The small, short duration fluctuations in SWE in both years ( Fig. 6) likely represent the periods of snowfall, snowmelt, sublimation, and wind erosion/transport. In addition, small fluctuations are likely also due to the inherent measurement error of the CRNS. This error has yet to be definitively quantified but is assumed to average below 7% (Kodama et al., 1979;Howat et al., 2018;Gugerli et al., 2019). Figure 6, at some intervals, 430 shows negative SWE during both winters, this implies that the CRNS is recording a higher number of counts (N) than was originally measured during its baseline (N0). Meaning that the CRNS is sensing a lower amount of near-surface water content than was recorded at the start of the winter season. This is directly due to the CRNS fundamental measurement basis where any deviations from the baseline counting rate are inversely proportional to the amount of near-surface water content (Eq. (4)).
In these cases, the negative values imply that the snow has melted, infiltrated past the measurement scope of the CRNS, and 435 therefore the immediate surrounding environment is drier than it was just before the onset of the winter season's first snowfall and initial soil freeze-up. When SWE values are negative, the CRNS is recording a lower near surface water content than its baseline. CRNS SWE values were calculated using Eq. (4).

445
One example of the advantage of using a CRNS system is shown during 2017/18 (Fig. 6b) when there was a notable discrepancy on January 23, 2018 between the observed and estimated SWE. The CRNS estimated 16 mm of SWE, while the snow survey conducted on the same day resulted in a SWE of zero millimeters. This discrepancy occurred because a warm spell lead to rapid snowmelt between January 21-22, immediately followed by a return to below freezing temperatures. This resulted in the formation of a thick ice layer covering the site, which the snow survey was not able to measure. However, the 450 CRNS was able to measure record the SWE of this ice layer. May 9 th , a few weeks late in the season, one week prior to the onset of snowmelt. Small, high frequency SWE fluctuations during this period are primarily due to the change in the sampling rate of the CRNS. This change occurred when we switched the CRNS system from winter power conservation mode, where the sampling rate was four, one-hour interval recordings per 460 day, to the default sampling rate, which was 24, one-hour interval recordings per day. The maximum SWE at TVC in the 2017/18 season was 369 mm (May 13 th ), and once again, occurred immediately shortly prior to the initial onset of the spring snowmelt. Since the CRNS system measures total SWE (including liquid water within the snowpack), it does not identify when surface snowmelt begins, but instead detects when meltwater begins to leave the base of the snowpack and SWE begins to decline. This ability allows the direct measurement of snowmelt runoff from the snow cover and is an extremely 465 exceptionally useful parameter for studying snowmelt runoff and for testing the performance of snow models used for modelling snowmelt runoff.   Figure 8 shows snow accumulation and melt at each of the five CRNSs across the TVC drift (Fig. 3). In the winter of 2016/17 (Fig. 8a), the snow drift began to form in the centre of the shrub patch in early December, while significant accumulation did not begin in the southern edge of the patch until a few weeks later. Over the rest of the winter, the snow 475 cover in the centre of the shrub patch (CRNS 3 and 4) continued to accumulate rapidly as blowing snow was deposited in the drift, and these sites ended up with the largest SWE at the end of winter. In this case, the center centre of the patch had 555 mm of SWE at the end of winter in 2016/17 and 645 mm at the end of winter in 2017/18. Other parts of the shrub patch also had similar maximum SWE values in comparison to one another from both years ( Fig. 8a and 8b). As described earlier, the noisiness shown in Fig. 8 is due to a change in frequency of the sampling rate. 480

Snow Accumulation and Melt at locations across a Snow Drift
Spring snowmelt begins in mid-May at TVC, however, the early season melt is likely retained within the snowpack as liquid water is refrozen into ice (Wrona, 2016). Early seasonspring snowmelt, primarily from the snowpack surface or nearsurface, is known to refreeze whenduring infiltratinfiltrationing of deep snowpacks (Pomeroy and Gray, 1995;Marsh and Pomeroy, 1996), this infiltration refreeze is amplified when temperatures fluctuate between freezing and above freezing -as is common during spring snowmelt. Temperature data from 2017-2018 ( ( Fig. B1) confirm that early spring temperatures 485 tended to fluctuate between freezing and above freezing. Early into the spring melt season season snow-core campaign, we visually noticed the snowpack surface and near-surface melting, and later in the season, noticed distinct variability in the amount of water saturation within the snow-cores on different (Pomeroy and Gray, 1995;Marsh and Pomeroy, 1996;Wrona, 2016). Furtherdays. Further into the season, Aafter sufficient melt and subsequent snowpack saturation, water is available to infiltrate the soil or runoff laterally (Quinton et al., 2010), it is likely that at this point the melt began to exiting the measurement 490 footprint of the grounded in-situ CRNS and ledading to the rapid decrease in SWE (Fig. 8a).
Loss of SWE begins first at CRNS 1 (May 16), at the edge of the shrub patch where the snow is shallower. As melt progressed, snow mass is removed from each location in the following order: CRNS 5 on May 17, CRNS 2 and 3 on May 20 and lastly, CRNS 4 on May 23. By June 7, all five CRNSs indicated that the snow overlying them has melted. In both seasons,

Conclusion
Grounded iIn-situ CRNSs were tested at a temperate low-SWE agricultural field in Elora, Ontario and high-SWE Arctic tundra site in Trail Valley Creek, Northwest Territories. A strong negative correlation was found between the counts 515 and the manual SWE measurements obtained from snow surveying at both sites (Pearson correlation values between -0.89 and -0.98). The relationship implies that when SWE increases, the moderated neutron counts decrease. An empirical equation for estimating SWE at the Elora site appeared to indicate that low-SWE sites with similar annual soil water storage may provide reasonable SWE accuracy and are well transferable in time. Additionally, the comparison of annual regression trendlines at a single site may be used to reasonably estimate soil water storage. This allows researchers and operators to set up the CRNS, 520 even after the initial snowfall and subsequent soil freeze-up, and capture accurate SWE data, so long as it is corrected for soil moisture conditions afterwards. Another significant advantage of utilizing a linear regression approach is that it is considerably more time efficient than fitting the full N0-calibration function.
By applying five CRNS units in a transect, we were able to obtain continuous accumulation and melt data for a single snow feature, including a comparison of accumulation and melt within the snowdrift itself. We were able to determine the 525 exact date of the peak SWE and of the onset, and completion, of snowmelt. The transect data appeared to indicate that blowing snow and lateral redistribution of meltwater through infiltration have a considerable influence on Arctic snowdrifts, however, further research is needed to quantify the impact of each process. Future research is recommended to assess the linear regression analysis vs the non-linear formulation to quantify the measurement accuracy discrepancy between the two approaches at low, moderate, and high SWE sites using grounded in-situ CRNS. 530 A unique advantage of CRNS systems is that ice layers and wet-snow from mid-winter melt events do not impact the sensor measurement accuracy and the CRNS measures all components of the snowpack SWE, including dry snow, ice layers, and wet snow. Using a CRNS for monitoring SWE provides a unique ability to continuously measure SWE and these systems can be installed in remote locations and in areas where performing regularly scheduled manual measurements are costly and logistically impractical. As such, the CRNS system replaces the need of manually conducting snow surveys and requires 535 virtually zero operational maintenance.
Since it was found that soil water in the top soil profile directly surrounding the CRNS affected the neutron intensity, future research involving a CRNS should examine at what soil depth the CRNS is impacted by soil water content, or alternatively, should could be installed so that meltwater infiltration is shallow enough that water does not infiltrate past the base of the sensor. Additionally, we noted that the terrestrial set of parameters appeared to record low-SWE environments 540 exceptionally well, however, the glacier set of calibration parameters appeared to have some flexibility. This seems to indicate that site specific calibration may not apply only to the conventional parameters, such as the snow-free moderated neutron count (N0), but also for the CRNS calibration fitting parameters function as wellitself. Future research is recommended to investigate the impact of each factory-fitting parameter and to explore the potential of a standard set of factory-fitting parameters for an Arctic landscape. SWE data from the CRNS could be used for validating surface mass balance models, verifying remote 545 sensing approaches, and to better understand the effects of a changing climate on snowfall, mid-winter thaws, blowing snow, expanding shrubs capturing blowing snow, spatial variability in snow depth and snow water equivalent (SWE), and the rate of spring melt -all of which are poorly known. Future works are recommended to utilize grounded in-situ CRNS in a transect for a significant snowdrift by incorporating a CRNS unit at critical locations along several margin and semi-margin points, as well as in the relative centre to allow for the collection of continuous data in vital watersheds for water resource management 550 applications.

Data Availability
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Author Contributions 575
This paper is the result of cooperation and collaboration with the listed co-authors. Dr. Philip Marsh is the principal co-author.
Dr. Marsh assisted with the design of the sampling plan, design of the manuscript, and provided edits and expertise throughout the research process. Branden Walker reviewed the manuscript and provided support related to logistics and fieldwork. Darin Desilets assisted with troubleshooting the CRNS instruments , in-depth expertise related to cosmic ray sensors, and formulated the instrument weighting factory-fitting function parameters that were used in this works. . 580

Competing Interests
Dr. Philip Marsh is an editor for the peer-reviewed journal The Cryosphere.

Disclaimer 585
Any and all references made within this works to specific commercial products, services, manufacturers, trademarks, or otherwise, does not imply its recommendation or endorsement by the authors.