Climate change, including warmer winter temperatures, a
shortened snowfall season, and more rain-on-snow events, threatens nordic
skiing as a sport. In response, over-summer snow storage, attempted
primarily using woodchips as a cover material, has been successfully
employed as a climate change adaptation strategy by high-elevation and/or
high-latitude ski centers in Europe and Canada. Such storage has never been
attempted at a site that is both low elevation and midlatitude, and few
studies have quantified storage losses repeatedly through the summer. Such
data, along with tests of different cover strategies, are prerequisites to
optimizing snow storage strategies. Here, we assess the rate at which the
volume of two woodchip-covered snow piles (each ∼200 m3), emplaced during spring 2018 in Craftsbury, Vermont (45∘ N and
360 m a.s.l.), changed. We used these data to develop an optimized snow storage
strategy. In 2019, we tested that strategy on a much larger, 9300 m3
pile. In 2018, we continually logged air-to-snow temperature gradients under
different cover layers including rigid foam, open-cell foam, and woodchips
both with and without an underlying insulating blanket and an overlying
reflective cover. We also measured ground temperatures to a meter depth
adjacent to the snow piles and used a snow tube to measure snow density.
During both years, we monitored volume change over the melt season using
terrestrial laser scanning every 10–14 d from spring to fall. In 2018,
snow volume loss ranged from 0.29 to 2.81 m3 d-1, with the highest
rates in midsummer and lowest rates in the fall; mean rates of volumetric
change were 1.24 and 1.50 m3 d-1, 0.55 % to 0.72 % of initial
pile volume per day. Snow density did increase over time, but most volume
loss was the result of melting. Wet woodchips underlain by an insulating
blanket and covered with a reflective sheet were the most effective cover
combination for minimizing melt, likely because the aluminized surface
reflected incoming short-wave radiation while the wet woodchips provided
significant thermal mass, allowing much of the energy absorbed during the
day to be lost by long-wave emission at night. The importance of the pile
surface-area-to-volume ratio is demonstrated by 4-fold lower rates of
volumetric change for the 9300 m3 pile emplaced in 2019; it lost
<0.16 % of its initial volume per day between April and October,
retaining ∼60 % of the initial snow volume over summer. Together, these
data demonstrate the feasibility of over-summer snow storage at
midlatitudes and low elevations and suggest efficient cover strategies.
Introduction
Earth's climate is warming (Steffen et al., 2018). This warming is
expressed not only in warmer nights and days but also in the number of
winter rain and thaw events that degrade snowpacks (Climate Central, 2016).
The duration, extent, and thickness of both lake ice and snow have decreased
over the past several decades in response to increasing temperatures,
especially at high latitudes (Hewitt et al., 2018; Sanders-DeMott et al.,
2018). Winter recreation is particularly vulnerable to such warming. The ski
industry has responded by increasing snowmaking as well as attempting to
reduce melt by covering snow using various materials (Scott and McBoyle,
2007; Pickering and Buckley, 2010; Steiger et al., 2017). Over the past
several decades, ski centers have improved snowmaking strategies and
facility operations both to maintain financial stability and to decrease
their output of greenhouse gases (Koenig and Abegg, 1997; Moen and Fredman,
2007; Tervo, 2008; Kaján and Saarinen, 2013). Recent research focuses on
analyzing and optimizing stages in the snow production cycle to assist
industry efforts (Hanzer et al., 2014; Spandre et al., 2016; Grünewald
and Wolfsperger, 2019).
Many sites organizing major winter sports events, such as cross-country or
alpine world cup races, have adopted over-summer snow storage in response to
the unpredictability of snowmaking weather conditions. In areas of high
humidity and warm average fall temperatures, summer snow storage is more
reliable than expecting weather conditions to be sufficiently cold and dry
for making snow at the start of the winter ski season. For example, the 2014
Olympic Games at Sochi relied on 750 000 m3 of stored snow (Pestereva,
2014).
Over-summer storage of snow and ice is not a new idea; for example, ice
houses stored large blocks of lake ice beneath sawdust over the summer
(Nagnengast, 1999; Rees, 2013). Today, the ski industry uses stored snow to
support the early winter ski season. Modern over-summer snow storage
(sometimes referred to as “snow farming”) begins with the creation of snow
piles during winter months. Piles are covered (often with sawdust or woodchips and sometimes geotextiles) before the snow is stored over the summer
(Skogsberg and Lundberg, 2005). In the fall, the pile is uncovered and snow
spread onto trails. Nordic ski centers require less snow-covered area to
open than downhill ski centers, and so snow storage on the scale of thousands
of cubic meters is practical and cost-effective, allowing the center to open
on time instead of losing business, which occurs if centers are unable to make snow and thus must open
later. Snow storage has been employed predominately at high-elevation and/or
high-latitude ski centers (Fig. 1), many of which benefit from cool, dry
summers that minimize energy transfer to the snow, increase evaporative
cooling, and thus slow snowmelt.
Locations of known over-summer snow storage sites (both
currently active and inactive). (a) Conical projection shows known
locations of over-summer snow storage at nordic ski centers. The Craftsbury
Outdoor Center is highlighted with a blue arrow, which is labeled COC. The relative
elevations of ski centers are displayed as a color gradient, marked in the
legend. (b) Scatterplot of same locations as shown in (a). The Craftsbury
Outdoor Center (no. 3) is large yellow dot (COC). It has the lowest
combination of elevation and latitude of any snow storage yet attempted.
Here, we examine the feasibility of snow storage in the northern United
States at a midlatitude, low-elevation (45∘ N and 360 m a.s.l.) site with
a humid, temperate climate, including warm summer temperatures and high
relative humidity which limits evaporative cooling (Fig. 1). Out of the 28
known snow storage locations, our study location has the highest average
June–July–August temperature (24 ∘C) and highest solar-radiation
levels (Worldclim – Global Climate Data, http://worldclim.org/version2, last access: 14 September 2019). In this paper, we report data on the rate of
volumetric change of snow stored over the summer and consider those data in
the context of both ground temperature and meteorological data that together
help define the energy flux, which is responsible for melt into and out of
the snow piles. The goals of this research are to (1) determine the rate of
volumetric change of small experimental snow piles, (2) suggest an optimized
snow storage strategy based on those data, and (3) test the optimized
strategy on a larger snow pile sufficient for ski area opening. Our data
fill a research gap in measurements of volumetric change during snow storage
and provide a novel case study for snow storage at low-elevation and
midlatitude sites.
Background
Although the physics of snowmelt has been considered extensively (Dunne and
Leopold, 1978; Horne and Kavaas, 1997; Jin et al., 1999), there has been
limited application of physical and energy transfer knowledge to the problem
of over-summer snow storage (Grünewald et al., 2018). Snowmelt occurs
when the snowpack absorbs enough energy to raise snow temperature to the
melting point (0 ∘C) and then absorbs additional energy to enable
the phase change from solid to liquid water (0.334 MJ kg-1). The
snowpack gains energy from incoming short- and long-wave radiation, sensible
and latent heat transfer from condensation of atmospheric water vapor and
cooling and refreezing of rainwater, conduction from the underlying ground,
and advective heat transfer from wind (Dunne and Leopold, 1978). Loss of
energy from the snowpack occurs through convective and conductive heat
transfer to the air, evaporative cooling, and long-wave emission to the
atmosphere.
Both regional and local climatic factors influence the energy balance of
snow. Short-wave radiational gain is related to latitude (highest near
the Equator and least near the poles), elevation, time of year (greatest in
summer and least in winter), snow pile surface albedo, slope and aspect, and cloud and tree canopy cover. Long-wave radiation balance depends on
atmospheric emissivity, cloudiness, vegetation cover, and temperature of the
snow pile surface. Rain falling on the snowpack transfers heat. Conductive
heat transfer from the ground depends on soil thermal conductivity and
temperature (Kane et al., 2000; Abu-Hamdeh, 2003). Snowmelt typically
varies on a diurnal cycle, with melt increasing after sunrise, peaking in the
afternoon, and decreasing after sunset (Granger and Male, 1978). Once
surface melt occurs, water either refreezes if it percolates into a
sub-freezing snowpack, flows through an isothermal (0 ∘C) snowpack
and then infiltrates into the ground below, or flows along the ground
surface below the pile, depending on the soil infiltration rate (Schneebeli,
1995; Ashcraft and Long, 2005).
Recent research at nordic ski centers in Davos, Switzerland, and Martell,
Italy (Grünewald et al., 2018), has applied snowmelt physics to optimize
over-summer snow storage at high-elevation (∼1600 m) and midlatitude (∼46∘ N) sites. The Davos location has an average
summer relative humidity of 79 %. Each nordic center built piles of
machine-made snow and covered them with 40 cm of wet sawdust and woodchips;
researchers then used utilized terrestrial laser scanning to measure the
initial (spring) and final (fall) volumes of the two piles. These snow piles
retained 74 % and 63 % of their volume over the summer. Using a
physically based model, Grünewald et al. (2018) suggested that the most
effective cover, in relation to work and cost, was a 40 cm thick layer of
mixed wet sawdust and woodchips, which reduced energy input into the pile
by a factor of 12 (1504 MJ m-2 without woodchips as opposed to 128 MJ m-2 with woodchips). Deeper cover layers can save more snow, but costs
are higher. During the day, solar radiation caused evaporation from surface
woodchips while capillary flow continually supplied moisture from the
melting snow to the surface. The wet woodchips and sawdust
also provided
thermal mass, slowing the transfer of energy from the surface to the snow
beneath.
Lintzén and Knutsson (2018) reviewed current knowledge of snow storage
and experience from areas in Scandinavia and reported new results from an
experiment in northern Sweden, analyzing melt loss of stored snow. They
report that the most common snow storage method employs a breathable surface
layer over an insulating material. From field observations at multiple
nordic ski centers, they have found that the choice and age of covering
affects the melt rate; older woodchips were less effective at reducing melt
than fresh chips. Lintzén and Knutsson also determined that woodchips
were a more effective cover than bark. They measured snow volumes three
times over the summer and found that higher relative humidity increased the melt
rate. They also investigated the geometry of snow piles and determined that
shaping piles, in a way that maximized the ratio of volume to surface area, minimized melt
loss; however, steeper snow pile sides caused sliding and failure of cover
materials (Lintzén and Knutsson, 2018).
Data related to snow storage for the purpose of summer cooling to improve
energy efficiency and comfort supplements those gathered from ski centers.
In central Sweden, the Sundsvall Hospital conserves snow over the summer for
air conditioning with a 140 m ×60 m storage area (holding 60 000 m3
snow) underlain by watertight asphalt (Nordell and Skogsberg, 2000). After
covering with 20 cm of woodchips, the majority of natural snowmelt resulted
from heat transfer from air (83 %), while heat transfer from groundwater
drove 13 % of melt and heat from rain accounted for 4 % of melt. Similar
work was done by Kumar et al. (2016) and Morofsky (1982) in Canada
and by Hamada et al. (2010) in Japan.
Methods and settingStudy location
We conducted our experiment at the Craftsbury Outdoor Center (COC), a
sustainability-focused, full-year recreation venue located in northeastern
Vermont at 360 m a.s.l. (Fig. 1), an area with warm, humid summers and cold, dry
winters. The COC maintains 105 km of groomed nordic ski trails and hosts
national and international races several times each winter. Average maximum
monthly air temperature at St. Johnsbury, Vermont (closest National Oceanic
and Atmospheric Administration – NOAA – station to the COC about 30 km
southeast; at 215 m a.s.l.), between 1895 and 2018 ranges between 3.6 ∘C (January) and 29 ∘C (July), mean temperature ranges from
-8.3∘C (January) to 20.7 ∘C (July), and minimum air
temperature ranges between -34∘C (December) and 15 ∘C
(July, Climate Summary for Saint Johnsbury, VT,
https://www.fairbanksmuseum.org/eye-on-the-sky/summaries-for-st-js-climate/normals-and-extremes,
last access: 6 February 2019). Soils in the area are very rocky, silty loam, sandy loam, and loam
developed on glacial till (Web Soil Survey, https://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx, last access: 20 October 2018). Average summer precipitation is
∼300 mm (NOAA, 2019). The most common land-cover types are
forest and woodlands (USGS, https://mrdata.usgs.gov/geology/state/fips-unit.php?code=f50019, last
access: 15 October 2018).
Initial snow pile experiments
On 30 March 2018, two snow piles were emplaced at the COC using PistenBully snow groomers at two separate sites (Fig. 2). Site 1 is adjacent to
the COC's main campus buildings in direct sunlight, with minimal wind
protection. Site 2 is 1 km north of Site 1, within a cleared depression in
the forest which also in direct sunlight but more protected from wind than Site 1. At the time of emplacement, the snow was transformed and had a density of
>500 kg m-3 (see Sect. 3.5 for snow density measurement
methods). At Site 1, 225 m3 of machine-made snow was banked against a
north-facing slope. At Site 2, 210 m3 of natural snow was shaped into
a symmetrical, rounded pile. The two piles were draped with thin sheets of
clear plastic. The plastic sheets, about 0.15 mm thick, were impermeable
and emplaced to prevent woodchips from mixing with the snow. The piles were
then covered with an irregular layer of woodchips averaging 20±10 cm (1 SD) on 21 April 2018; chip thickness ranged from a minimum of 6 cm
to a maximum of 40 cm (Fig. 3). In early July, about 50 m3 of snow
were removed from the pile at Site 1 by COC personnel, the plastic was
removed, and the remaining snow was covered again with woodchips and left
for continued monitoring.
Snow storage at Craftsbury Outdoor Center. (a) Aerial
view of the Craftsbury Outdoor Center (COC) in Vermont, from http://maps.vcgi.vermont.gov (8 February 2019). Both study site locations
shown by number. (b) Site 1 (225 m3), covered in
woodchips on 21 April 2018, with trees and solar
panels for scale. (c) Site 2 (209 m3) when
installed. Site 1 received 24 m3 of woodchips, and
Site 2 received 42 m3 of woodchips. Person for scale.
(d) Site 2 in April 2019; 9300 m3 of snow,
eventually covered with 650 m3 of woodchips. (e) Site 2 in July 2019, the snow pile overlain by a reflective geofabric. Trees for
scale.
Weather stations
Weather stations adjacent to each pile and 3–4 m above the ground surface
(Davis Vantage Pro2) collected air temperature, humidity, precipitation,
solar-radiation, wind speed and direction, and barometric-pressure data. The
weather stations record data at 15 min intervals and transfer them to the
Web, where they are publicly accessible
(https://wunderground.com/personal-weather-station/dashboard?ID=KVTCRAFT2#history, last access: 23 October 2019).
Local soil temperature was measured with temperature sensors installed at
four depths within the soil (5, 20, 50, and 100 or 105 cm below the
surface) adjacent to each snow pile. Two HOBO Onset data loggers recorded
temperatures at four depths at 20 min intervals between June 2017 and
October 2018.
Terrestrial-laser-scanning field methods and processing
During spring and summer, the shape and volume of the piles were measured
every 10–14 d using a terrestrial laser scanner (RIEGL VZ-1000).
Terrestrial laser scanning (TLS) is an accurate method for obtaining digital
surface models (DSMs) of various terrain types, including snow surfaces
(Prokop et al., 2008; Molina et al., 2014). Six to ten permanent tie points
around each pile were established during the initial survey by fastening
reflective 5 cm disks to stable surfaces such as large trees and buildings.
The first survey was done prior to snow pile placement in order to establish
ground surface topography. Tie-point locations were determined and fixed
relative to the scanner GPS position during the initial scan. Each survey
consisted of three or four scans per site (depending on available vantage
points), which were combined in the RiSCAN Pro software version 2.6.2 (RIEGL Laser
Measurement Systems GmbH: RiScan Pro, 2011). Scan registration was done in
RiSCAN using a combination of tie-point registration (finding corresponding
points) and the multi-station adjustment routine using plane patches and
tie objects. Similar studies of monitoring bare and covered snow surfaces
with TLS have applied this technique (Prokop et al., 2008; Grünewald et
al., 2018; Grünewald and Wolfsperger, 2019). Scans were collected at a
horizontal and vertical angular resolution of 0.08∘. Scans
were collected from distances less than 100 m, resulting in average point
spacing over the pile <1 cm.
To calculate snow pile volumes and volumetric change over time (between
scans), point clouds of each pile were processed into DSMs. Processing
the workflow involved cropping the point cloud to the area of interest in RiSCAN
Pro and exporting cropped point clouds into LAS format, projected into
Vermont State Plane NAD83 coordinates. Point clouds were converted to a 10 cm resolution DSM using the min-Z filter and QT Modeler software (version
8.0.7.2) and adaptive triangulation to fill in small data gaps. Volume
calculations and differences in volume between sequential surveys were
calculated in QT Modeler using these DSMs.
Density
Snow density was measured using a Rickly Federal Snow Sampling Tube. The snow
tube was weighed, pushed into the snow, removed, and weighed again. The
weight of the tube was subtracted from the combined weight of the snow and
tube, and density was calculated by dividing the mass of snow by its volume
(length of snow within the tube multiplied by the area of the opening;
∼13 cm2). Density was collected three times (in
March, May, and July) at the top surface of pile 1 during 2018. In 2019,
density was collected once at the top of the pile in February.
Cover experiments
Cover experiments were performed at both sites in June and July 2018. At
Site 1, two 5 cm thick, impermeable, rigid foam boards (R=3.9 per 2.5 cm;
value expressing resistance to conductive heat flow) were stacked and
compared to a 20 cm, uniform, porous layer of woodchips both with and
without a reflective cover (aluminized space blanket). At Site 2, we covered
snow with a double-layered, 2.5 cm thick insulating concrete curing blanket
(R=3.3 per 2.5 cm) and overlaid the blanket with either open-cell,
permeable foam (R=3.5 per 2.5 cm) or a uniform, porous layer of woodchips
(20 cm thickness), both with a reflective cover. For both foam experiments, woodchips and plastic sheeting were removed from the test area. For woodchip
experiments, plastic sheeting was removed from the test area. Individual
cover experiments were conducted in areas of 1 m2 each, with
thermosensors placed in the center of each quadrat at varying depths between
layers (Table 2; Fig. 4).
Weather parameters measured between June 2017 and October 2018 at
the Craftsbury Outdoor Center, Craftsbury, VT.
Air temperatureRelative humidityPrecipitationSolar radiation(∘C)(%)(mm d-1)(W m -2)Minimum-281400Maximum3393221144Mean9790.1109Standard deviation12150.4205
Properties of the 2018 covering experiments.
Plot referenceSiteSnow-interfaceMiddle layerTop layerFig. 4number*layer(a)1None20 cm layer of woodchipsNone(b)1None20 cm layer of woodchipsReflective covering(c)1NoneTwo stacked rigid foam boardsNone(d)1NoneTwo stacked rigid foam boardsReflective covering(e)2Concrete curing blanket20 cm layer of woodchipsReflective covering(f)2Concrete curing blanket20 cm layer of open-cell foamReflective covering
* The experiment at Site 1 occurred in June 2018, while the experiment
at Site 2 occurred in July 2018.
Woodchip thickness distribution maps of pile 1 (a) and
pile 2 (b), with red indicating areas of high thickness and blue indicating
areas of low thickness. Panel (c) represents the chip thickness histogram
for pile 1, and (d) is chip thickness histogram for pile 2. Negative
thickness values likely represent snow settling between bare-snow survey and
survey after woodchip emplacement.
Cover experiments and resulting temperature records. (a) Site 1 – woodchips. (b) Site 1 – woodchips overlain by reflective cover. (c) Site 1 – foam. (d) Site 1 – foam overlain by reflective cover. (e) Site 2 –
woodchips underlain by concrete curing blanket and overlain by reflective
cover. (f) Site 2 – open-cell foam underlain by concrete curing blanket and
overlain by reflective cover.
Power spectral density function
We computed the power spectral density (PSD) function to determine relative
effectiveness of the different covers. The temperature signal is first
decomposed in a series of waves of well-defined frequencies:
Tt=1N∑k=0N-1T^kexpi2πfkt,
where T^k is the Fourier mode at frequency
fk=k/2ΔT, 1/ΔT is the sampling
frequency of temperature acquisition, and N is the number of samples in
the time series. The Fourier mode contains both amplitude and phase
information for each wave. The PSD is the power of the signal,
PSDT=ΔtN∑k=0N-1T^k2,
which is the sum of the contributions of each wave to the power (or
variance) of the signal. Typically plotted on a log–log plot, the norm of
the Fourier modes as a function of frequencies is a powerful tool for detecting
dominant frequencies (Welch, 1967). In the summer, the dominant oscillation
in temperature is diurnal; thus, using PSD, we can judge the effectiveness
of cover materials by their ability to damp the diurnal temperature signal
and relevant harmonics. We computed the PSD for all temperature records in
selected cover experiments (Fig. 4b, e, f).
Validating cover method, summer 2019
Based on data collected during summer 2018, the COC chose Site 2 (Fig. 2) as
their snow storage site for 2019. Cost and ease of installation mandated a
two-layer cover system – a ∼30 cm thick layer of woodchips
capped with a reflective, permeable covering. No plastic was placed between
the woodchips and the underlying snow. The 2019 snow pile filled a drained,
oblong pond basin and was gently sloped. During February, machine-made snow
was blown into the pile using fanless snowmaking wands. Snow density at and
just after emplacement was high, ranging between 500 and 600 kg m-3. In
March, the snow pile was shaped and further compacted with PistenBully
groomers and excavators; at that time, TLS showed that the pile had a volume of
about 9300 m3 without woodchips. During the next 6 weeks, the snow
pile was allowed to compact and grow denser. In late April, most of the pile
was covered in woodchips. By the end of May, additional woodchips were
obtained and snow pile covering was completed (total woodchip volume
∼650 m3). Using the exposed surface area of the pile
without woodchips (2300 m2) and the volume of woodchips, we calculate
that the average woodchip thickness was 28 cm. By the end of June, the snow
pile was covered in a white, 75 % reflective, breathable Beltech 2911
geofabric, secured by ropes and rocks to prevent wind disruption. Between
March and October, the pile was repeatedly scanned using TLS; data were
processed using methods described in Sect. 3.4.
ResultsMeteorological data and ground temperature data
Climate at the COC is strongly seasonal – such seasonality is clear in the
meteorological data collected between June 2017 and October 2018 (Fig. 5).
Between June 2017 and October 2018, air temperature varied between -28.2 and
33 ∘C (mean annual temperature of 9∘C). Precipitation fell at a
maximum rate of 22 mm d-1 (mean of 0.01 mm d-1), and relative
humidity ranged between 14 % and 93 % (mean of 79±15 %). Solar
radiation had a 24 h average of 109 W m -2 and maximum of 1144 W m-2 (Table 1). Air temperature and solar radiation followed similar trends
over the 16 months, decreasing during winter months and increasing
during summer months. Precipitation did not follow any significant pattern,
and relative humidity remained high (NOAA classifies above 65 % as high, and
relative humidity remained above this level for the summer), varying more
during summer than winter months. Average summer temperature in 2018 (June,
July, and August 2018; 22.4 ∘C) was ranked by NOAA as “Much above
the average of 20.7 ∘C”; in 2019, average summer temperature
ranked “above average” (21 ∘C). Both years had near-average
precipitation (National Oceanic and Atmospheric Administration Forecast Office, Burlington VT, 2018; Craftsbury Outdoor Center KVTCRAFT2,
https://www.wunderground.com/personal-weather-station/dashboard?ID=KVTCRAFT2#history,
last access: 12 December 2018).
Meteorological conditions and soil temperature between
11 June 2017 and 16 October 2018. Weather conditions were collected by a
Davis weather station at the Craftsbury Outdoor Center near Site 2. (a) Air
temperature (grey), collected at 30 min intervals plotted with ground
temperatures. Ground temperatures were collected at 20 min intervals
adjacent to Site 1 by four HOBO Onset data loggers at depths below the ground
surface of 5 cm (blue), 10 cm (orange), 50 cm (green), and 105 cm (red).
Ground temperature record ends on 2 September 2018. (b) Relative humidity (%). (c) Precipitation (mm d-1). (d) Solar
radiation (W m-2).
Ground temperature from all four depths at both locations followed similar
trends. The shallowest sensor (5 cm below the surface) recorded the greatest
variance over time (SD =7.4∘C for Site 1). Ground temperature
variations decreased in amplitude as soil depth increased; at 1 m in depth,
the atmospheric temperature signal was damped (SD =3.9∘C for Site 1). Ground temperatures for all depths showed consistent warming from
installation (11 June 2017) through late August 2017 and then decreased
through February 2018. The shallowest sensor revealed slight warming after
February, while the deeper sensors remained stable until May 2018. During
May, warming increased more noticeably for all four sensors. Ground
temperature depth trends inverted during both May and November. During the
winter, the coldest temperatures were at the surface; during summer, the
coldest temperatures were at depth. Figure 5 displays data from sensors
adjacent to pile 1 – data were collected at both sites but are missing from
Site 2 between 12 December 2017 and 21 April 2018.
Snow volume and density
Snow in both 2018 piles lasted until mid-September; however, snow volume
decreased consistently throughout the summer (Figs. 6 and 7). Comparing the
laser-scan survey completed just after woodchip emplacement with the
initial bare snow survey showed that the layer of chips ranged in depth
from 6 to 40 cm, with an average of 19±11 cm for pile 1 and 21±11 cm (1 SD) for pile 2 (Fig. 3). After the addition of woodchips, snow volume
in both piles decreased following similar trends (Fig. 7); initial decreases
in volume were partly related to compaction and increases in snow density, as
snow density was ∼500 kg m-3 at emplacement, 600 kg m-3 in May, and 700 kg m-3 in July. Relative to newly fallen
snow (100–200 kg m-3), the snow in these piles was closer in density
to ice (900 kg m-3). These measurements are supported by qualitative
observations of changes in snow crystal morphology over the summer
(increased rounding), increasing size (up to 5 mm by July), wetness (higher
liquid water content), and clarity (from white to clear by summer's end).
Continued volume loss over the summer was predominately the result of melt.
Average rates of volume change for both piles were relatively similar (1.24 and 1.50 m3 d-1), representing 0.55 % to 0.72 % of initial pile volume per day. Maximum loss rates, recorded in July,
reached 1.98 and 2.81 m3 d-1 (Fig. 7.) As
summer shifted into fall, the loss rate decreased (Fig. 7). Minimum rates of
change for both piles occurred in September and were 0.29 and 0.88 m3 d-1.
Snow pile topographic change over time in 2018. (a) Oblique view of digital surface model (1 m contours) of 2018 snow pile at
Site 1 with cross sections A–A′ and B–B′ (21 April 2018). (b) Profiles for
each terrestrial-laser-scan survey (21 April to 9 September 2018;
n=13) along section A–A′. (c) Profiles for each survey along
section B–B′. On 3 July 2018, 50 m3 of snow was removed from the pile at Site 1. (d) Oblique view of digital surface model (1 m contours) of 2018 snow pile
at Site 2 with cross sections C–C′ and D–D′ (21 April 2018). (e) Profiles
for each terrestrial-laser-scan survey (21 April to 9 September 2018;
n=12) along section C–C′. (f) Profiles for each survey along
section D–D′. Each scan represented by a line in panels (b), (c), (e), and (f) as
indicated in key.
Volume change over time for snow piles at sites 1 and 2
measured by terrestrial laser scanning. (a) Volume of snow piles from
placement in March 2018 until September 2018. Addition of woodchips in April
and removal of snow in July at pile 1 shown by black arrows. Volumes are
total, including woodchips. (b) Change in volume per unit time between
surveys. The rate of volume loss increases midsummer for both piles. Site 1
received about 24 m3 of woodchips, while Site 2
received about 42 m3 of woodchips – this difference
is due to pile geometry and the resulting difference in surface area. Site 1
snow was banked against the side of a hill, while the Site 2 pile was a
hemisphere in the middle of an open depression. (c) Volumes of snow pile
(2019) beginning in March and ending in October. Addition of woodchips throughout
May and addition of white tarp are indicated by black arrows. Volumes include
woodchip volume. (d) Change in volume per unit time between surveys.
As the piles decreased in volume over the summer, crevasses formed along the
edge of the plastic sheeting, which exposed the snow to direct sunlight and
thus increased rates of volumetric change (Fig. 6). On pile 1, a crevice
formed from east to west where the pile began to slope downward (Fig. 6b). Slope
failure was a potential catalyst for the formation of crevices. We did not
observe meltwater around either of the piles, suggesting that melt occurred at a
rate which allowed for infiltration into the rocky sandy loam soil below.
The woodchips deeper in the cover remained cold and wet throughout the
summer, while the woodchips on the surface were consistently dry in the
absence of rainfall.
Cover experiments
Thermal buffering is a function of air temperature, long-wave emissions, and
turbulent fluxes. We chose temperature at the snow–cover interface to
indicate cover efficiency because all experiments were subjected to similar
external conditions and because we have continuous data series of
temperature in, above, and below the cover during each of the
experiments. Two experiments preformed on 1 m2 plots on each snow pile
revealed that different combinations of cover materials resulted in a
variety of cover efficiencies (Fig. 4). Each experiment lasted between about
1 and 3 weeks and took place in June and July, respectively. We
assessed cover efficiency by determining which material combination
maintained the lowest and steadiest temperature at the snow–cover interface
and which most effectively damped the diurnal temperature signal (detected
using PSD analysis). On the rigid foam, open-celled foam, and woodchip
plots, the highest temperature was measured in the air above the surface (max of
51∘C; Fig. 4f). During the first experiment, air
temperatures above the reflective blanket were higher than above the
non-reflective surface. When all plots were covered with a reflective
blanket, all air temperatures above the pile were similar; however, temperatures
at lower depths, and under different cover materials (woodchips and open-cell
foam), varied significantly. The lowest and most stable
temperatures at the snow–cover interface resulted when the stored snow was
covered directly with an insulating concrete curing blanket, then with 20 cm of
wet woodchips, and finally with a reflective sheet.
Power spectral density
PSD analysis provides insight into the dynamics of heat transfer in the snow
piles. Figure 8 shows the log–log plot of temperature power spectral
densities for three different cover experiments. It is important to realize
that (i) each line represents the PSD at specific distance from the snow
surface, (ii) that the integral under each line is equal to the standard
deviation of the signal, or the energy of the signal fluctuations, and (iii)
that the horizontal axis is frequency, thereby breaking down the total
energy of the temperature signals into the individual contributions of each
frequency involved in the PSD. Furthermore, the frequency is normalized by
the frequency of 1 d or diurnal frequency fdiurnal=1/24×3600. Consequently, the horizontal coordinates 1,
2, and 4 are the diurnal (1 per 24 h), half-diurnal (1 per 12 h), and quarter-diurnal
(1 per 6 h) frequencies, with 2 and 4 being harmonics of the diurnal frequency. These
frequencies are highlighted by the peaks in the PSD of temperature outside
of the pile (the air T sensor at 46 cm). The PSD values at these frequencies
are much higher than the values at surrounding frequencies, indicating that
their contribution to the total energy of the signal, and therefore to the
dynamics of heat transfer, is significant.
Detection of diurnal temperature swings and their harmonics in temperature
records collected at different depths in the cover materials with various
relative strengths is critical to understanding how cover materials minimize
heat transfer. In the foam cover experiment (Fig. 8c), the diurnal
frequency and its harmonics are detectable in all layers; however, the
three-layer system (insulating blanket, wet woodchips, and reflective
cover; Fig. 8b) fully damps all oscillations, as shown by the flatness of
the PSD below the cover (0 cm; snow T sensor; thick blue line). In the
absence of an insulating blanket, the two-material cover system (reflective
cover and woodchips) is slightly less efficient at damping the diurnal
oscillation (Fig. 8a). In the case of foam, the dynamics of heat transfer at
the surface, or cyclic events that drive fluctuations of temperature, are
directly and efficiently transmitted to the snow surface. Such a response
can be modeled as quasi-steady heat transfer conduction, which is not
surprising for an inorganic dry material.
Woodchips profoundly affect the dynamics of heat transfer, and in the most
dramatic case (Fig. 8b), the snow surface temperature appears to be
insensitive to the diurnal and harmonic frequencies of atmospheric
temperature. This indicates that the system can no longer be modeled under
quasi-steady-state conduction but requires at least the time- and
depth-dependent heat transfer equations with a damping mechanism. The
damping might be storage and release of heat through convection and/or the
phase change of water from liquid to vapor and back within the woodchip
layer. Overall, relative cover material effectiveness can be ranked in
Fig. 8 as most efficient (Fig. 8b), efficient (Fig. 8a), and least
efficient (Fig. 8c).
Power spectral density of temperature records from three
different cover experiments (Fig. 4b, e, and f). PSD normalizes frequency
to 24 h =100 and displays the
magnitude of each temperature oscillation frequency for each sensor per
experiment (depth in centimeters measured above sensor at the snow – 0 cm). (a) Experiment with woodchips and reflective cover (Fig. 4b). (b) Experiment
with a concrete curing blanket, woodchips, and a reflective cover (Fig. 4e). (c) Experiment with concrete curing blanket, open-cell foam, and a
reflective cover (Fig. 4f). The lack of detectable signal (flat blue line)
at snow level (0 cm) in (b) demonstrates that three-layer configuration with
woodchips best damps the diurnal temperature signal. Colors correspond to
colors from Fig. 4.
Summer 2019
The 9300 m3 snow pile emplaced in 2019 lost volume at an
average rate of 15 m3 d-1 (min of 5 m3 d-1 in early April and max of 25 m3 d-1 in early July). Between the initial TLS survey
in March and the last survey in October, the pile lost 3700 m3 of
snow, a 40 % volume loss (not including woodchips). The average
percentage loss per day was 0.16 % of the initial volume. In comparison to
the 2018 snow piles, the pile lost volume more uniformly; no crevices formed
and no slumping occurred (Fig. 9), although the surface did become rougher by
October, and we noted more surface lowering near dark-colored rocks and logs
emplaced to hold down the white, reflective covering. Volume loss between
11 May and 25 August (the most intensive melt season) was similar in all
four quadrants of the pile, each of which experienced an average of 0.9 m
lowering. More lowering occurred on the pile boundaries, specifically along
the western margin, as shown clearly by the blue and purple colors in Fig. 9a.
Volume change of 2019 snow pile. (a) Spatial variability of
elevation change 2019 snow pile between 11 May and 25 August 2019. Cross
sections A–A′ and B–B′ are marked in black. (b) Profile for each terrestrial-laser-scan survey (3 March to 13 October 2019; n=12). (c) Profile for each terrestrial-laser-scan survey (2 March to 13 October 2019; n=12). Each scan represented by a colored line in panels (b)
and (c).
Discussion
Data we collected allow us to (1) determine the volumetric change rate of
small snow piles stored over summer with different coverings, (2) suggest an
optimal snow preservation strategy for low-elevation, midlatitude sites
based on these data, and (3) test this optimized snow storage strategy at
scale.
Experimental snow pile melt rate
The survival of small (200 m3) snow piles through the warmer-than-average summer of 2018 and the results of both repeated TLS surveys and
continuous in situ thermal data collected during a variety of different snow
cover experiments suggest ways of optimizing over-summer snow storage at
low elevations and midlatitudes. The 2018 snow piles experienced
nonuniform cover and nonideal geometry and developed crevices that exposed
snow to direct sunlight, all of which increased the rate of snowmelt and
thus volume loss. Field observations and TLS surveys demonstrated that the
thickness of woodchips covering the snow was not uniform and became less
uniform over time as melt changed the pile shape (Fig. 3). Woodchip depth
changed over the summer as crevices, which grew over time, exposed bare snow
to direct sunlight, which led to rapid and nonuniform pile melting (Fig. 6). Crevices
formed along boundaries of the large plastic sheets, which were emplaced to
prevent woodchips from mixing with the snow. Openings in the woodchip cover
also resulted from snow slumping within the pile – both piles had steep
sides, and the DSMs revealed snow moving downslope (Fig. 6). Lintzén and
Knutsson (2018) reference similar snow pile and cover failure due to steep
pile-side geometry.
Snow pile size impacts the rate of volumetric change significantly. The two test
piles were small, only a few percent of the volume of snow typically stored
over summer by Nordic ski areas. For example, in Davos, Switzerland, and
Martell, Italy, test piles were about 6000 and 6300 m3 (Grünewald et al., 2018). The Nordkette nordic ski operation
in Innsbruck, Austria, stores ∼13 000 m3 of snow, and
Östersund, Sweden, stores 20 000 to 50 000 m3 piles. Small piles have a
larger surface-area-to-volume ratio (SA /V), which allows more effective heat
transfer through radiation, conduction, and latent heat transfer. A simple
comparison of two hemispheres, one containing 200 m3 of snow and the
other containing 9000 m3 of snow, indicates that SA /V changes from 0.66
to 0.23 between the smaller and larger pile. As larger piles have a SA /V ratio that is 3 times lower in comparison to smaller piles, there is comparatively less
snow near the surface thermal boundary, which decreases heat transfer per
unit snow volume and thus the melt rate as a percentage of pile volume.
Optimal approach for over-summer snow preservation at
midlatitude and low-elevation sites
The 2018 survival of snow through the summer in small piles with only
simple woodchip, foam, and reflective coverings suggested that larger
piles, using an optimized cover strategy, will allow for practical
over-summer snow storage at midlatitude (<45∘ N) and
low-elevation (<350 m a.s.l.) locations. Our results are encouraging
given the relative warmth of the 2018 summer season, the simple and
spatially inconsistent nature of our cover material (20±10 cm of
woodchips), and the small size of the test piles (∼200 m3). Previous snow storage studies found success with woody covers as
well but in different geographic settings. Grünewald et al. (2018)
suggested that a 40 cm layer of sawdust sufficiently optimized snow
retention in Davos, Switzerland, and Martell, Italy. Skogsberg and Nordell (2001) reported that woodchips reduced snowmelt by 20 %–30 % at the
Sundsvall Hospital in Sweden. Lintzén and Knutsson (2018) built snowmelt
models and ran field tests in northern Scandinavia, revealing that thick
layers of woody materials successfully minimized snowmelt. In practice,
financial constraints often control the choice of cover strategies. For
example, the thicker the layer of woodchips, the better protected the pile
will be and the less over-summer melt will occur. However, using more chips increases
cost (Grünewald et al., 2018).
The experimental data (Fig. 4) show that the magnitude of daily temperature
oscillations at the snow surface below the covering (blue line in all
panels) is highly dependent upon the cover strategy. For example, in Fig. 4c, the temperature within the rigid foam board increases above air
temperature (purple line increasing above the yellow line). Due to the
rigidity of the foam boards and the nonuniform melting of the pile, the
foam shifted and exposed snow to direct solar radiation, allowing
warm air to move between the snow and the foam. Such failure of the cover
system allowed temperatures at the snow interface to rise significantly
above 0 ∘C. The three-layer cover (insulating blanket, wet woodchips, and
reflective cover) minimizes heat transfer into the stored snow, as evidenced
by the lack of diurnal temperature oscillations at the snow surface during
this and only this experiment (Fig. 4e). The comparison between foam and
saturated woodchips PSDs (Fig. 8) shows the dramatic effect on the heat
transfer from the atmosphere to the snow caused by the high heat capacity
and thus thermal inertia of wet woodchips. The damping of diurnal
temperature peaks by the three-layer cover system suggests that it will be the
most effective for preserving snow over the summer.
Although the relevant heat transfer mechanisms remain uncertain, Fig. 8
demonstrates the effectiveness of the three-layer cover approach to
buffering heat transfer from the environment to the snow. Deducing specific
heat transfer mechanisms will require different and more complex
measurements, as heat transfer is dependent on not only air temperature
but also surface temperature, long-wave radiation, and turbulent fluxes.
Perhaps evaporation of water from the wet woodchips absorbs thermal energy
during the day which is released as the latent heat of condensation at night
when the reflective blanket cools – effectively increasing the thermal mass
of the woodchip layer. Depending on weather conditions, which influence
long-wave radiation through cloudiness and turbulent fluxes through wind,
the heat transfer may be directed toward the snow pile (warm nights) or
radiated to the atmosphere (cold nights). In any case, the large thermal
mass of wet woodchips, in concert with an underlying layer (the concrete
curing blanket), and rejection of short-wave incident radiation from sunlight
by the reflective cover, appears more important than the insulating
capability (R value) of the cover material in damping daily temperature
fluctuations at the snow surface.
Summer 2019, testing the optimized snow storage strategy at
scale
Field data, TLS, and thermal observations from the 2018 experiments allowed
for a full-scale test of our optimized snow storage strategy in 2019.
Optimization began by further excavating the storage area so that the resulting
pile would sit within a pit and have gently sloping sides to reduce the
chance of mass movements and crevassing on the pile margins. Snowmaking was
tuned so that the density of the snow emplaced was already high; this
minimized settling after covering. The snow was then compacted by repeated
passes of large excavators and PistenBully groomers. Letting the snow
settle and transform before covering also reduced the chance of mass
movements which, in 2018, compromised pile and cover integrity. Results from
the 2018 cover material experiments (most effective was a reflective
cover, woodchips, and a concrete curing blanket) informed the 2019 covering
method (Fig. 8). Rather than use metallized cover material, which was
expensive, fragile, and impermeable, we used a high-albedo (0.75), white,
permeable geofabric that allowed rain to infiltrate, thus mitigating
regulatory concerns related to a large impermeable area. Concrete curing
blankets were not used in 2019 due to cost and logistical complications of
emplacement.
The 2019 pile, using an optimized strategy, confirmed the viability of snow
storage at the COC. The most rapid volume losses in
2019 were in the midsummer; while they were higher in
absolute terms than those in 2018 because the pile was 45 times larger, they were
more than 3 times lower in percentage terms. Most melt was focused along
the western boundary, perhaps because the snow here was thin or not as
thickly covered by woodchips or because western sun exposure occurs late in
the day when the air temperatures are warmer; there is likely less net
radiative cooling along the western side of the pile, as there is a steep,
forested slope immediately adjacent to the snow storage area. Compared with
the average percentage loss per day of the 2018 piles (0.64 % per day),
the 2019 snow pile average percentage loss per day was 0.16 %. We suspect
that the difference in volume loss reflects primarily the surface-area-to-volume ratio of the 2019 snow pile, which is about 3 times less than the
small piles tested in 2018. A 3-fold change in the SA /V ratio compared
with a 4-fold reduction in the percentage volumetric change rate suggests
the impact of an improved cover strategy. The complete covering of the 2019
pile with a reflective geofabric likely slowed melt by rejecting short-wave
radiation as well as protecting the snow even if the woodchips shifted. TLS
imagery from 2019 demonstrates that gentle side slopes of the pile prevented
any large mass movements of snow, indicating that pile shape and snow
pre-consolidation are important (Fig. 9).
TLS data show that from April until mid-October, about 60 % by volume
of the snow initially placed in the April 2019 pile remained. Considering the
snow density data gathered from the 2018 piles, which increased from 500 to
700 kg m-3 over the summer, some of this volume loss could be accounted
for by compaction rather than melting. This suggestion is supported by the
lack of surface water draining from the 2019 pile, which is underlain by
relatively impermeable rock and clay-rich glacial till. With fall
temperatures and the sun angle dropping, incident solar radiation as well as
convective and conductive heat transfer are diminished greatly from
midsummer values. This means that the COC will have >5000 m3 of snow to spread in November for early-season skiing. Covering 5 m wide trails 50 cm deep will allow at least 2 km of skiing at opening
and will provide a base so that any natural snow that does fall will be
retained.
Conclusions
Data presented here show that snow storage at midlatitudes and low
elevations is a practical climate change adaptation that can extend the
nordic ski season and the sport's viability as the climate continues to
warm. Using 14 terrestrial-laser-scan surveys between March and September 2018, we determined rates of volumetric change of two 200 m3 snow
piles covered in woodchips. Average volume loss rates were 1.24 and 1.50 m3 d-1, with the highest rates of volumetric change
in July and the lowest rates of volumetric change in September. A three-layer
cover approach was most effective: a concrete curing blanket, a 20 cm layer
of woodchips, and a reflective covering. This cover approach reduces solar
gain and buffers the effect of >30∘C summer daytime
temperatures and high (>78 %) relative humidity on stored
snow. Using data collected during summer 2018, we tested our experimental
results in summer of 2019 by creating a 9300 m3 snow pile. Due to cost
and logistical issues, we covered the pile using a two-layer approach – 650 m3 of woodchips and white, permeable geofabric. The average volume loss
rate between March and October was 15 m3 d-1 (or 0.16 % of
the initial volume per day). About 5600 m3 of snow remained as the
melt season ended in mid-October. This quantity of snow is sufficient for
the COC to open their 2019 season and represents ∼60 %
retention of snow by volume, comparable to storage losses at other storage
sites (at higher elevation and latitude). Future research could analyze
financial and environmental feasibility of snow storage at different global
locations and focus on heat transfer mechanisms of different cover
materials. Research could also explore other climate change adaptation
strategies for nordic ski centers that minimize carbon emissions and
maximize operational success.
Data availability
Data are available at
10.1594/PANGAEA.899744 (Weiss and Bierman, 2018).
Author contributions
PB and YD co-conceptualized the experiment. HW and YD curated the data. HW, YD, and SH conducted the
TLS and PSD analysis. HW and PB acquired funding, developed
the methodology (assisted by SH), conducted the investigation, and
validated data. HW, PB, and YD prepared data
visualizations. HW and PB wrote the original paper draft.
All authors contributed to the review and editing process.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank Judy Geer and Dick Dreissigacker, directors of the Craftsbury Outdoor Center, for enthusiastically supporting this project. We also thank our editor, Jürg Schweizer, and reviewers, Thomas Grünewald, Nina Lintzen, and one anonymous reviewer, for providing constructive criticism to strengthen this manuscript. Field work was generously assisted by Landon Williams and Amelia Murtha. We acknowledge that the land the research was preformed upon was once the land of the Abenaki people.
Financial support
This research has been financially supported by the University of Vermont's Department of Geology, Department of Mechanical Engineering, Rubenstein School of Environment and Natural Resources, the Office of Undergraduate Research, and the Graduate College. Additional support was provided by the National Science Foundation under CMMI-1229045.
Review statement
This paper was edited by Jürg Schweizer and reviewed by Thomas Grünewald, Nina Lintzen, and one anonymous referee.
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