The Cryosphere, 12, 1121–1135, 2018
https://doi.org/10.5194/tc-12-1121-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
Snowmobile impacts on snowpack physical and
mechanical properties
Steven R. Fassnacht1,2,3,4, Jared T. Heath1,5, Niah B. H. Venable1,3, and Kelly J. Elder6
1Department of Ecosystem Science and Sustainability – Watershed Science, Colorado State University,
Fort Collins, 80523-1476 CO, USA
2Cooperative Institute for Research in the Atmosphere, Fort Collins, 80523-1375 CO, USA
3Natural Resources Ecology Laboratory, Fort Collins, 80523-1499 CO, USA
4Geographisches Institut, Georg-August-Universität Göttingen, 37077 Göttingen, Germany
5City of Fort Collins, Water Resources & Treatment, Fort Collins, 80521 CO, USA
6Rocky Mountain Research Station, US Forest Service, Fort Collins, 80526 CO, USA
Correspondence: Steven R. Fassnacht (steven.fassnacht@colostate.edu)
Received: 21 February 2017 – Discussion started: 18 April 2017
Revised: 20 February 2018 – Accepted: n 6 March – Published: 28 March 2018
Abstract. Snowmobile use is a popular form of winter recre-
ation in Colorado, particularly on public lands. To examine
the effects of differing levels of use on snowpack properties,
experiments were performed at two different areas, Rabbit
Ears Pass near Steamboat Springs and at Fraser Experimen-
tal Forest near Fraser, Colorado USA. Differences between
no use and varying degrees of snowmobile use (low, medium
and high) on shallow (the operational standard of 30 cm) and
deeper snowpacks (120 cm) were quantified and statistically
assessed using measurements of snow density, temperature,
stratigraphy, hardness, and ram resistance from snow pit pro-
files. A simple model was explored that estimated snow den-
sity changes from snowmobile use based on experimental re-
sults. Snowpack property changes were more pronounced for
thinner snow accumulations. When snowmobile use started
in deeper snow conditions, there was less difference in den-
sity, hardness, and ram resistance compared to the control
case of no snowmobile use. These results have implications
for the management of snowmobile use in times and places
of shallower snow conditions where underlying natural re-
sources could be affected by denser and harder snowpacks.
1 Introduction
In the United States snowmobiling accounts for between
USD 7 billion (American Council of Snowmobile Associa-
tions, 2014) and USD 26 billion (International Snowmobile
Manufacturers Association, 2016) in annual revenue, and
much of the snowmobile use occurs on public land. The
United States National Forest System records about 6 million
snowmobile visits annually, accessing about 327 000 km2
of land (US Forest Service, 2010, 2013a). With continued
increases in the number of people participating in winter
recreation (Cook and Borrie, 1995; Winter Wildlands Al-
liance, 2006; US Forest Service, 2010, 2013a; Nagler et al.,
2012; Colorado Off-Highway Vehicle Coalition, 2016; Os-
terberg, 2016), activities including increased snowmobile use
may influence snowpack properties in these seasonally snow-
covered environments. Of additional concern, is that climate
change will result in reduced land available for snowmobil-
ing (Tercek and Rodman, 2016), likely increasing the impact
of snowmobile traffic.
There have been limited studies regarding the influence of
snowmobile use on snowpack properties (Keddy et al., 1979;
Thumlert et al., 2013; Thumlert and Jamieson, 2015). Stud-
ies have, however, examined how the snowpack changes due
to snow grooming at ski resorts (Fahey et al., 1999; Keller et
al., 2004; Spandre et al., 2016a), or to traction and mobility of
wheeled vehicles across a snowpack (Abele and Gow, 1975;
Shoop et al., 2006; Pytka, 2010). One of the few studies on
Published by Copernicus Publications on behalf of the European Geosciences Union.
1122 S. R. Fassnacht et al.: Snowpack changes due to snowmobile use
Figure 1. The snow compaction study plots are located in north-central Colorado. The Rabbit Ears Pass (REP) site is within the Routt
National Forest near the town of Steamboat Springs, as are the three operational (non-experimentally manipulated) sites (Walton Creek
with no use, Dumont Lakes with low to medium use, and Muddy Pass with high use based on field observations). The Columbine snow
telemetry (SNOTEL) station was used to identify the amount of annual snowfall in 2009–2010 compared to the long-term average. The
Fraser Experimental Forest (FEF) site is within the Arapaho–Roosevelt National Forest near the town of Fraser. The Middle Fork Camp
SNOTEL site was used to represent the year’s snowfall.
snowmobile use examined effects on very shallow snow (10–
20 cm deep) (Keddy et al., 1979). The authors found a dou-
bling of fresh snow density and a compression of the natural
vegetation below the snow (Keddy et al., 1979). Examining
deeper snow cover (> 20 cm deep), Thumlert et al. (2013)
and Thumlert and Jamieson (2015) examined the distribu-
tion of stresses through the snowpack due to type of loading,
depth and snowpack stratigraphy (Thumlert et al., 2013).
Changing snowpack conditions from snowmobile use will
have other impacts. Aside from the work done by Keddy
et al. (1979), there is limited research on how snowmobile
activity influences underlying vegetation. The addition of
snow due to snowmaking provides an indication of possible
changes. Changes from snowmaking include a greater occur-
rence of soil frost, ice layers may form at the base of the
snowpack, and there is often a delay in vegetative growth
due to extended snow cover (Rixen et al., 2003). Snowmelt
can occur later due to compaction and there is greater heat
loss from the densified snowpack and underlying soil, keep-
ing soil temperatures colder longer (Fassnacht and Soulis,
2002; Rixen et al., 2003).
In our research, we specifically examined the effect of
snowmobile use on the physical and material properties of
the snowpack. The objectives were as follows: (1) to quantify
changes to physical snowpack properties due to compaction
by snowmobiles; (2) to evaluate these changes based on the
amount of use, depth of snow when snowmobile use begins,
and the snowfall environment where snowmobiles operate;
and (3) to create a simple model to estimate the change in
snowpack density due to snowmobile use. This work ex-
amines not only changes to the basal snowpack layer, but
also to the entire snowpack. The positive economic impact
of snowmobiling and increasing winter recreation use from
non-motorized activities (such as backcountry skiers, snow-
shoers, and those on fat bikes) dictates a need to better un-
derstand impacts to snow and underlying natural resources
in multi-use areas, especially when the information may be
used by managers to reduce conflict among recreationists and
protect the resource.
2 Study sites
During the 2009–2010 snow season a set of snow compaction
plots were located near Rabbit Ears Pass (REP) in the Rocky
Mountains of northern Colorado to the southeast of the town
of Steamboat Springs. REP is within the Medicine Bow-
Routt National Forest (NF; Fig. 1) along the Continental Di-
vide encompassing over 9400 km2 of land in Colorado and
Wyoming. Rabbit Ears Pass is especially popular during the
winter season and is heavily used by snowmobilers and other
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S. R. Fassnacht et al.: Snowpack changes due to snowmobile use 1123
winter recreationists due to the ease of access to backcoun-
try terrain from Colorado Highway 40. Due to heavy use and
conflict among users during the winter season, the Forest Ser-
vice manages Rabbit Ears Pass for both non-motorized and
motorized uses. The west side of the pass is designated for
non-motorized use and prohibits motorized winter recreation
while the east side of the pass is a mixed-use area and is
open to motorized use (Fig. 1). This study area was selected
to determine if differences in snowpack properties will be ob-
served between the non-motorized and motorized use areas
(e.g., Walton Creek versus Dumont Lakes and Muddy Pass
in Fig. 1).
Two REP experimental snow compaction study plots were
located adjacent to one another within an open meadow
north of Colorado Highway 40 at an elevation of approx-
imately 3059 m (Fig. 1). The snow compaction sites were
established within an area that prohibits motorized use to
protect the study sites from unintended impacts of snow-
mobilers. Data from the Columbine snow telemetry (SNO-
TEL) station, located at an elevation of 2792 m, was used
to show how the 2009–2010 winter compared to other win-
ters at REP. The SNOTEL network was established in the
late 1970s across the western United States by the Natural
Resources Conservation Service to monitor snowpack prop-
erties. Initially snow water equivalent and precipitation were
monitored, however, temperature and snow depth were added
in the 1990s–2000s to aid in operational runoff volume fore-
casting (see https://www.wcc.nrcs.usda.gov/).
Three sites were not experimentally manipulated, i.e., the
specific amount of snowmobile use was unknown, and were
identified as operational sites along Colorado Highway 40 on
REP (Fig. 1 left inset). The ”natural” control site was Walton
Creek, located west of Rabbit Ears Pass in an open meadow
at an elevation of 2895 m within a managed area that pro-
hibits motorized use. Snowshoers, skiers, and snowboarders
primarily use this area in the winter to access backcountry
terrain. Two sites, Dumont Lakes and Muddy Creek, were
located east of REP at an elevation of about 2900 m within
an area managed for motorized and mixed uses; the sites
were located in open meadows near their respective trail-
heads (Fig. 1). These trailheads provide backcountry access
to snowmobilers. Snowmobile use in the meadows near the
trailheads is medium to high, especially on weekends and
over holidays (Robert Skorkowsky, personal communication,
2010). The meadow near the Muddy Creek trailhead is more
heavily used by snowmobiles than the meadow near the Du-
mont Lakes trailhead.
Another experimental snow compaction plot was estab-
lished during the same winter snow season of 2009–2010
at the Fraser Experimental Forest (FEF) near the town of
Fraser, Colorado in the Rocky Mountains of central Colorado
(Fig. 1). The 93 km2 experimental forest is a research unit
of the United States Forest Service (USFS) Rocky Moun-
tain Research Station (RMRS) located within the Arapaho
NF. The FEF snow compaction site was located in a small
meadow at an elevation of 2851 m surrounded by lodgepole
pine (Pinus contorta) forest. The Fraser Experimental Forest
is closed to snowmobile use, but is used to access backcoun-
try terrain by snowshoers, skiers, and snowboarders. The
Middle Fork Camp SNOTEL station, located at an elevation
of 2725 m, was used to characterize the 2009–2010 winter at
FEF.
3 Methods
3.1 Experimental snow compaction plots
Snow compaction study plots were established in undis-
turbed areas at the REP and FEF study areas. Each plot was
22 m wide and 15 m long (Fig. 2a and b). Plots were di-
vided into equal width transects (2 m) and treated with low,
medium (FEF only), or high snowmobile use, including a no
treatment control transect representing an undisturbed snow-
pack. Two control transects were used at FEF to represent
the undisturbed snowpack (Fig. 2b). Integrating two controls
in the FEF study plot allowed for replication and determina-
tion of variability. The location of control and treatment plots
across each study site were randomly selected. Each transect
was separated by a 3 m buffer to eliminate the influence of
compaction treatments on adjacent transects (Fig. 2a and b).
Transects were treated by driving a Ski-Doo brand snow-
mobile weighing about 300 kg including the rider (Fig. 2d) at
10 km h−1 over the length of each transect 5, 25 (FEF only),
or 50 times, representing low, medium (FEF only), and high
snowmobile use, respectively. Treatments began (Fig. 2c)
when non-compacted snow depths were approximately
30 cm (12 inches) for both locations, and when unpacked
snow depths equaled approximately 120 cm (48 inches) for
REP only (Fig. 2a). Treatments were implemented (Fig. 2e)
monthly thereafter, until peak accumulation (Fig. 3). Snow-
pack sampling was usually performed within a week after
each treatment (Figs. 2 and 3). At FEF, snowpack sampling
was performed prior to the first treatment to illustrate range
of spatial variability across the plots (first set of points in
Fig. 4b).
3.2 Snow pit analyses and data collection
Snow pit profiles were used to examine the physical prop-
erties of the snowpack at both the experimental and oper-
ational sites. A vertical snow face was excavated by dig-
ging a pit from the snow surface to the ground. Measure-
ments of snow density, temperature, stratigraphy, hardness,
and ram resistance were taken vertically along the snowpack
profile. Total snow depth was measured from the ground up,
and combined with density to yield snow water equivalent
(SWE). Physical snowpack properties were compared be-
tween non-snowmobile (control) and varying degrees (low,
medium (FEF), and high) of snowmobile use (treatment).
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1124 S. R. Fassnacht et al.: Snowpack changes due to snowmobile use
Figure 2. The sampling design for the snow compaction plots at (a) Rabbit Ears Pass, (b) Fraser Experimental Forest, and photographs of
the study plots (c) pre-treatment, (d) during treatment, and (e) after treatment. The colors used for the control and treatment plots are used in
Figs. 5–8.
Density was measured at 10 cm intervals, from the sur-
face of the snowpack to the ground, by extracting a 250
or 1000 mL snow sample using a stainless-steel wedge cut-
ter (http://snowmetrics.com/) and measuring the mass on an
electronic scale with a resolution of 1 g. At least two sam-
ples were taken per 10 cm interval. With the 1000 mL wedge
cutter, the density of the snow (ρs in kg m−3) was read di-
rectly from the scale as the volume of the cutter is 1/1000
of a cubic meter and a gram is 1/1000 of a kilogram. For
the 250 mL cutter, the mass measurement results were mul-
tiplied by four to obtain density. Snowpack density profiles
were created from samples extracted at discrete 10 cm inter-
vals vertically along the working face of the snow pit. The
bulk snowpack density was determined by averaging den-
sity measurements over the entire depth of the snowpack. A
mean of the density measurements for the bottom 10 cm of
the snowpack was used to evaluate changes near the snow
and ground interface (basal layer).
Temperature measurements were obtained at 5 cm inter-
vals from the top to the bottom of the snowpack using a
dial stem thermometer with ±1 ◦C accuracy. Temperature
gradients are well represented by this instrument, and the
repeatability of temperature measurements are better than
±1 ◦C (Elder et al., 2009; American Avalanche Association,
2016). Snowpack temperature profiles and the corresponding
bulk temperature gradient were compared. The temperature
gradient (TG in ◦C m−1) was calculated as the ratio of the
change in temperature (1T in ◦C) with the distance (d in m)
over which the change in temperature occurred. The snow-
pack temperature gradient was approximated as linear from
an upper boundary that was 25–30 cm below the surface to
the lower boundary at 0 cm. For this study, the depth below
the snow surface where temperature did not fluctuate diur-
nally was used as the upper boundary to remove bias from
diurnal fluctuations (Pomeroy and Brun, 2001). Basal layer
temperatures taken at 0 cm were used to compare tempera-
ture changes near the snow–ground interface.
Stratigraphic measurements were used to illustrate the
evolution of the snowpack over time through characteriza-
tion of the shape, size, and layering of snow crystals within
the snowpack. Classification of grain morphology was based
on The International Classification for Seasonal Snow on the
Ground (Fierz et al., 2009) and mean grain size was mea-
sured and recorded to the nearest 0.5 mm using a hand lens
and a crystal card. The crystal forms were identified as pre-
cipitation particles, rounded grains, faceted grains, and ice
layers.
Hardness is the penetration resistance of the snowpack
(Fierz et al., 2009), and is reported as the force per unit
area required to penetrate the structure of the snowpack
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S. R. Fassnacht et al.: Snowpack changes due to snowmobile use 1125
Table 1. Statistical differences (p values) between no snowmobile use (control) and varying snow compaction treatments on snowpack
properties at the study plots located at Rabbit Ears Pass (REP) and Fraser Experimental Forest (FEF), Colorado during the 2009–2010 winter
season for (a) density, (b) temperature, (c) hardness, and (d) ram resistance.
(a) Density Control Shallow initiation depth (30 cm)
Low Medium High
REP
Shallow initiation depth (30 cm)
Low < 0.01∗∗ < 0.01∗∗
High < 0.01∗∗ < 0.01∗∗
Deep initiation depth (120 cm)
Low 0.44 < 0.01∗∗ < 0.01∗∗
High 0.24 < 0.01∗∗ < 0.01∗∗
FEF Shallow initiation depth (30 cm)
Low < 0.01∗∗ 0.29 0.30
Medium < 0.01∗∗ 0.29 0.98
High < 0.01∗∗ 0.30 0.98
(b) Temperature No use
REP
Shallow initiation depth (30 cm)
Low 0.22 0.11
High 0.70 0.11
Deep initiation depth (120 cm)
Low 0.77 0.34 0.50
High 1.00 0.22 0.70
FEF Shallow initiation depth (30 cm)
Low 0.12 0.89 0.10
Medium 0.14 0.89 0.13
High 0.64 0.10 0.13
(c) Hardness No use
REP
Shallow initiation depth (30 cm)
Low < 0.01∗∗ 0.16
High < 0.01∗∗ 0.16
Deep initiation depth (120 cm)
Low 0.42 < 0.01∗∗ < 0.01∗∗
High 0.06 0.02∗ < 0.01∗∗
FEF Shallow initiation depth (30 cm)
Low < 0.01∗∗ 0.36 0.01∗
Medium < 0.01∗∗ 0.36 0.08
High < 0.01∗∗ 0.01∗ 0.08
(d) Ram resistance No use
REP
Shallow initiation depth (30 cm)
Low < 0.01∗∗ 0.08
High < 0.01∗∗ 0.08
Deep initiation depth (120 cm)
Low 0.32 < 0.01∗∗ < 0.01∗∗
High 0.07 0.01∗ < 0.01∗∗
FEF Shallow initiation depth (30 cm)
Low < 0.01∗∗ 0.33 < 0.01∗∗
Medium < 0.01∗∗ 0.33 < 0.01∗∗
High < 0.01∗∗ < 0.01∗∗ < 0.01∗∗
Statistically significant differences at the p < 0.05 confident level are denoted with an asterisk, and highly significant (p < 0.01)
difference are denoted with two asterisks.
(McClung and Schaerer, 2006); it is affected by snow-
pack microstructure and bonding characteristics of the snow
grains (Shapiro et al., 1997). Hardness measurements were
taken horizontally with a force gauge in each stratigraphic
layer using a Wagner Instruments Force Dial gauge (http:
//wagnerinstruments.com) with maximum force measure-
ments of 25 and 100 N, and fabricated circular metal plate
attachments of 20 cm2 in area. For each measurement, the
circular metal plate was pushed into the snow and the force
required to penetrate the snow was recorded. The snow hard-
ness (hi in N m−2) for each stratigraphic layer was calcu-
lated as the force required to penetrate the snow (F in N)
per unit area of the circular metal plate (A in m2). All layers
thicker than 5 cm were identified using the 5 cm diameter of
the plate. The bulk snowpack hardness (HB in N m−2) was
determined by weighting each stratigraphic layer hardness
measurement by the stratigraphic layer thickness. The hard-
ness associated with the bottom stratigraphic layer for each
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1126 S. R. Fassnacht et al.: Snowpack changes due to snowmobile use
transect was used to describe hardness changes in the basal
layer of the snowpack.
The standard ram penetrometer is an instrument with a
cone on the end of a rod onto which a hammer of defined
weight is dropped from a given height and the depth of pen-
etration is recorded; it was used here to vertically measure
the resistance of snow layers to assess the change in ram re-
sistance due to compaction (American Avalanche Associa-
tion, 2016). A ram profile measurement was taken 0.5 m from
the edge of the snow pit wall subsequent to snow pit profile
measurements. The mean ram resistance (SB in N) was de-
termined by weighting each ram resistance value obtained
from the standard ram penetrometer measurement with the
depth sampled. The ram resistance value associated with the
bottom layer was measured to describe changes in ram resis-
tance in the basal layer of the snowpack.
3.3 Statistical analyses
Data were analyzed using the Mann–Whitney–Wilcoxon
rank sum test (Wilcoxon, 1945; Mann and Whitney, 1947).
This statistical test is non-parametric and determines whether
two independent samples were selected from populations
having the same distribution. For this work, the sets of sam-
ples compared were density, temperature, hardness, and ram
resistance profiles for the five different monthly measure-
ments and the controls (Table 1). A statistical significance
was determined for the 95 % (significant) and 99 % (highly
significant) confidence interval (p < 0.05, and p < 0.01) and
noted with an asterisk in Table 1.
3.4 Bulk snowpack density change model
A multi-variate non-linear model was created to estimate the
change in bulk snowpack density for various treatments com-
pared to the control (no use) using the following snowpack
properties: depth, bulk density, and the number of passes
(Fig. 8). The cross-correlation between variables was consid-
ered to reduce model over-fitting. The model was calibrated
with the experimental data from REP and FEF, and evalu-
ated using data from the operational sites with Walton Creek
as the control, Dumont Lakes as medium use, and Muddy
Creek as high use. The Nash–Sutcliffe coefficient of effi-
ciency (NSCE, Nash and Sutcliffe, 1970) was used to evalu-
ate the fit of the model.
4 Results
4.1 The measurement winter
The 2009–2010 winter at REP had slightly below aver-
age snow depth compared to the 15-year mean, based on
the Columbine SNOTEL data averaged from 2003–2017
(Fig. 3a). A peak SWE value of 556 mm on 9 April was 93 %
of the historical average. Maximum snow depth measured
Figure 3. Mean snow depth from 2003–2017, and for the 2010 wa-
ter year (WY2010) measured at (a) the Columbine SNOTEL site
near Rabbit Ears Pass (REP), Colorado and (b) the Middle Fork
Camp SNOTEL near Fraser Experimental Forest (FEF), Colorado,
illustrating the dates of treatment and dates of sampling. Data were
obtained online from the Natural Resource Conservation Service
(NRCS) National Water and Climate Center (http://www.wcc.nrcs.
usda.gov/).
at the REP snow compaction study plot was approximately
1.5 m and represents a deeper snow cover environment for
Colorado. From the Middle Fork SNOTEL data, the 2009–
2010 winter snow depth at FEF was also below the 15-year
historical average (Fig. 3b). The measured snow depth at the
FEF snow compaction study plot never exceeded 1 m, similar
to the Middle Fork Camp, and therefore was used to repre-
sent a shallower snow cover environment.
4.2 Snowpack properties
4.2.1 Density
The natural variability in density was small at the test sites
(Fig. 4). At REP, deep snow (120 cm) compaction treat-
ments were not initiated until after the second sampling date
(Fig. 3a), so density for the deep snow low and high use
treatments could then be compared to the control; these show
minimal difference (Fig. 4). At FEF, there were two sets of
control snow pits, and sampling occurred before treatment at
all plots (Fig. 3b). These differences in density were greater
than those at REP but were still small (Fig. 4).
The mean density values at the FEF plots were almost the
same at the end of the sampling period in April (Fig. 5a, col-
umn ii). The mean snowpack density increased over the snow
season (Fig. 5a), with the exception of the FEF control and
at the high use site on 12 February 2010 due to fresh snow
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S. R. Fassnacht et al.: Snowpack changes due to snowmobile use 1127
Figure 4. Spatial variability of mean (yellow) and basal (blue)
snowpack density. At the Rabbit Ears Pass (REP shown with circles)
the deep snow (120 cm) compaction treatments (low and high use)
are compared to the control on the first two sampling dates (pre-
treatment, Fig. 3a). At the Fraser Experiment Forest (FEF shown
with triangles) the two sets of control snow pits were compared,
and all plots were sampled prior to the initial treatment and are com-
pared (see Fig. 5a and b, columns i and ii, respectively).
deposition. At the REP snow compaction study site, mean
density for high use compaction treatments starting on 30 cm
of snow was greater throughout the measurement period than
the no use treatment (Fig. 5a, column i, Fig. 6a, columns i,
and ii), while the density from low and high use starting on
the deeper snowpack of 120 cm was very similar to that mea-
sured for no use. The snowpack was more dense for low
use on the shallower snowpack (start at 30 cm) than the con-
trol for all sampling dates, with the exception of 13 March
(Fig. 5a, column i). Density differences are more pronounced
for the basal layer (Fig. 5b); for compaction treatments start-
ing at 30 cm, the lowest layers were much more dense than
the control (Fig. 6a). Densities for the compaction treatments
starting at 30 cm were significantly different than the con-
trol and compaction treatments beginning at 120 cm of snow
(Table 1a). The density differences between the treatments
on the deep snow (120 cm) and the control were not signifi-
cantly different (Table 1a).
Density increases due to snowmobile use were much
greater at Fraser (Fig. 5a, column i and Fig. 5b, column ii)
than Rabbit Ears. All treatments at FEF were significantly
different than the control, but the difference among treat-
ments was not significant (Table 1a). The density differ-
ences among treatments are highlighted in the 10 cm indi-
vidual density measurements (Fig. 6a) and in the basal layer
(Fig. 5b, column ii).
4.2.2 Temperature
Low and high use compaction treatments at the REP snow
compaction study site that began on both a shallow snow-
pack of 30 cm and on a deep snowpack of 120 cm did not re-
sult in significant changes in temperature gradient. The max-
imum temperature gradients were observed on the earliest
sampling date (12 December, Fig. 5c), while they were al-
most the same for the control, low use, and high use com-
paction treatments that began on a deep snowpack. Temper-
ature gradients for all treatments decreased throughout the
winter season, and were isothermal at 0 ◦C m−1 by mid to
late April (Fig., 5c, columns i and ii), since the snow had
stared to melt (Fig. 3). Overall, temperature gradients were
not very different (Fig. 5c) and the variations among treat-
ments were not found to be significant (Table 1b). At FEF,
gradients in the high use were greatest after the first treat-
ment and the temperature gradients were essentially the same
by March (Fig. 5c, column ii).
4.2.3 Hardness
The snowpack was harder for snowmobile use starting on
30 cm than the control (no use) for both sites (Fig. 5d and e).
Mean snowpack hardness did not change much over time
(Fig. 5d), except once high use treatments started (6 Febru-
ary) on a deeper snowpack. However, basal layer hardness
did decline at REP for both high and low use starting on
30 cm (Fig. 5e, column i). With treatments at FEF, the hard-
ness was always much higher than the control (Fig. 5d, col-
umn ii). Hardness initially increased at the REP snow com-
paction study site following low and high use compaction
treatments that began on 30 cm of snow (Fig. 5d, column i),
but these were about the same as the control by 17 April,
when melt had started. Significant increases in hardness were
observed between treatments that began on 30 cm of snow
and the control (Table 1c). There was also a significant dif-
ference in hardness for deep and shallow initiation depths
(Table 1c). In contrast, mean snowpack hardness was not sig-
nificantly impacted by snow compaction treatments that be-
gan on 120 cm of snow (Table 1c). Mean snowpack hardness
increased following the initial snow compaction treatments
for low starting on 30 cm and high use for starting on both
30 and 120 cm (Fig. 5d, column i). Subsequent compaction
treatments did not appear to have a large effect (Fig. 5d, col-
umn ii). There were minimal differences by the last sampling
date (Fig. 5e, column i).
Snow compaction treatments that began on 30 cm of snow
increased basal layer hardness (Fig. 5e, column i), but treat-
ments that began on 120 cm of snow did not impact basal
layer hardness (Fig. 5e, column i). For both controls and
all treatments that began on 120 cm of snow (Fig. 5e, col-
umn i), the maximum basal layer hardness was about 6 kPa.
Increased hardness due to snowmobile use showed similar
temporal patterns to densification (Fig. 5a and d). At REP,
snowmobile use compacted the second layer below the sur-
face, and high use (50 passes) made that layer about 10 times
harder than the low use (five passes) snowpack (Fig. 6b,
columns i and ii).
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1128 S. R. Fassnacht et al.: Snowpack changes due to snowmobile use
Figure 5.
There was more spatial variability in snowpack hardness
(NSCE of 0.50; results not shown graphically) than differ-
ences in density (NSCE of 0.93 in Fig. 4) for low and high
use compaction treatments versus the control on the first two
sampling dates at REP and for the control snow pits at FEF
on the pre-treatment date. These larger differences are both
attributed to spatial variability, but mostly to the low range
of non-treatment hardness values from 0.4 to 5.8 kPa com-
pared to the range of treatment hardness values from 30 to
1157 kPa (Fig. 5d and e).
4.2.4 Ram resistance
Low and high use compaction treatments at REP caused an
increase in mean snowpack ram resistance, but the differ-
ence was not significant for treatments that began on deep
snow (120 cm; Table 1d). After the initial snow compaction
treatments mean snowpack ram resistance for low and high
use was greater than the control for the entire study period,
but by the end of the study period minimal differences were
observed between treatments. Basal layer ram resistance in-
creased as a result of low and high use compaction treatments
that began on both 30 and 120 cm of snow. Snow compaction
treatments at the FEF snow compaction study site caused a
significant increase in mean snowpack ram resistance (Ta-
ble 1d, e.g., Fig. 6c, column iii for the February sampling
dates). Basal layer ram resistance increased following the ini-
tial snow compaction treatments and continued to increase
throughout the duration of the winter season.
4.2.5 Grain size
Smaller crystals in the basal layer were observed for snow-
mobile use starting on a shallow snowpack compared to the
control or starting on a deeper snowpack (Fig. 5f). Rounded
grains were observed during the first sampling at REP shal-
low depth snowmobile start, with faceted grains for the fol-
lowing three sampling dates (Fig. 5f, column i). Rounding
facets were observed on the last sampling day at both sites.
At FEF, there were 3 to 4 mm faceted crystals prior to the
treatments; fragmentation was noted in the faceted crystals
found in the basal layer of the treated plots, which began
rounding by the last sampling date (Fig. 5f, column ii). The
The Cryosphere, 12, 1121–1135, 2018 www.the-cryosphere.net/12/1121/2018/
S. R. Fassnacht et al.: Snowpack changes due to snowmobile use 1129
Figure 5. Time series for (i) Rabbit Ear Pass (REP) and (ii) Fraser Experimental Forest (FEF) at the different sampling dates of (a) mean
snowpack density, (b) basal snowpack density, (c) snowpack temperature gradient, (d) mean snowpack hardness, (e) basal layer hardness,
and (f) mean basal crystal size and shape. The crystal shape is included as per Fierz et al. (2009), with the exception of faceted crystals
that were fragmented. Note that the snowpack at the low and high use start at 30 cm could not be adequately tested for hardness on the first
sampling date at the REP treatment plots.
shallower snow at FEF enabled large faceted crystals to grow
in the basal layer, up to 9 mm in size (Fig. 5f, column ii).
4.3 Operational sites
As illustrated by SWE (Fig. 7d) and snow depth (Fig. 7e), the
amount of snow was comparable for the snow pits dug at the
three operational sites, even though they were located up to
6 km apart (Fig. 1). Also since these were operational sites,
the amount of treatment was not controlled and was based
solely on permitted snowmobile use. Generally, patterns of
increased density (Fig. 7a), hardness (Fig. 7b), and ram resis-
tance (Fig. 7c) seen at the REP operational sites were similar
to the overall patterns seen in the previously presented ex-
periments from REP and FEF (Figs. 5, and 6) with the non-
snowmobile impacted snow pits being less dense (Fig. 7a)
and having layers that were less hard (Fig. 7b). From visual
inspection of the sites and the measurement results, Muddy
Creek had the most snowmobile use and thus exhibited the
highest density throughout the winter, and the hardest snow-
pack for mid-winter (Fig. 7b), but at times the results for Du-
mont Lakes were similar.
4.4 Bulk snowpack density change model
A non-linear bulk snowpack density change model was cre-
ated using data from the experiments prior to onset of melt
conditions (Fassnacht et al., 2010); before the last sampling
date (Fig. 3) and prior to when the difference in density be-
tween the control and treatments was small (Fig. 5a). Ad-
ditionally, treatments starting on a deep snowpack at REP
were not significantly different than the control (Fig. 5a, Ta-
ble 1) and were not used in fitting the model. The variables of
number of passes per treatment, depth, and bulk density were
tested for correlations that might result in model over-fitting.
Cross-correlation results were small (R2 < 0.04), so these
variables were used to create the model. Difference in bulk
density compared to the control due to snowmobile use is a
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1130 S. R. Fassnacht et al.: Snowpack changes due to snowmobile use
Figure 6. (a) Density, (b) hardness, and (c) ram resistance profiles for the February sampling dates (6 February at REP and 12 February at
FEF) measured at the REP snow compaction study plot for no (control), low, and high use treatments beginning on (i) 30 cm and (ii) 120 cm
of snow, and (iii) the FEF snow compaction study plot for no (control), low, medium, and high use treatments beginning on 30 cm of snow.
Note that free floating measurements represent overlapping density measurements. The ground is at zero snow depth.
function of the number of passes per treatment and bulk den-
sity, but it is inversely related to snow depth (Fig. 8a). The
optimal model had a NSCE of 0.81 (Fig. 8a), which is con-
sidered very good (Moriasi et al., 2007). The model was cal-
ibrated on the experimental data (Fig. 8a) and applied to the
operational sites (Fig. 8b), with no passes occurring equiva-
lent to a density change of 0 kg m−3. The evaluation results
were less optimal, with a NSCE of −0.79 for the four dates
tested in December through March (Fig. 8b). The poorer
performance of the model at the operational sites is due to
an unknown number of snowmobile passes at each site and
from limited snowmobile use early in the season (Decem-
ber), resulting in minimal differences between compaction
levels at that time (Figs. 7 and 8b). Removing the December
data points and using only the January through March dates
improved the model fit to a NSCE of 0.34 (Fig. 8b).
5 Discussion
5.1 Observed changes to snowpack properties
Snowpack changes were observed for varying snowmobile
use beginning with two different snow depths (REP only in
Fig. 5 or 6, columns i and ii) and for two different snow-
covered environments (Figs. 5 and 6). A total of 101 snow
pits (50 at REP, 15 at the operational sites, and 36 at FEF)
were dug and sampled for this work. The increase in density
and hardness from snowmobile use is greatest compared to
an untreated snowpack in early to mid-season (January) for
a deeper snowpack at REP, with density increases of 7–33 %
and hardness 4 to 13 times greater than the control (Fig. 5a
and d, column i). For a shallower snowpack at FEF, den-
sity increased by 64–76 % and hardness was 500–2000 times
greater than the control (Fig. 5a and d, column i).
Similar differences were found from ski run grooming in
an Australia snowpack with a 400 % increase in hardness
early in the snow season but only about a 40 % increase later
in the winter (Fahey et al., 1999). Snow grooming increased
the average density by up to 36 % compared to non-groomed
ski slopes (Fahey et al., 1999; Rixen et al., 2001).
At rest, a snowmobile and its rider exert 4 to 10 kPa of
pressure on the underlying snowpack. This assumes a track
length from 0.9 to 1.4 m, width of 0.50 m, a snowmobile
weight of 200 to 350 kg, and a rider weight of about 100 kg
(data from http://www.polaris.com/). There is an increase of
less than an order of magnitude due to snowmobile move-
ment. Thumlert et al. (2013), measured stresses of about 10
to 20 kPa at a depth of 30 cm below the surface of a deep
snowpack. At 20 cm below the snow surface, the induced
stress from a snowmobile is already much less than 10 cm
below the surface (Thumlert et al., 2013). Grooming vehi-
cles add a force similar to snowmobiles (Pytka, 2010) based
on mass and track size. The snowpack property changes we
observed could, therefore, also be representative of impacts
from both types of vehicles. Snowpack loading by wheeled
The Cryosphere, 12, 1121–1135, 2018 www.the-cryosphere.net/12/1121/2018/
S. R. Fassnacht et al.: Snowpack changes due to snowmobile use 1131
Figure 7. Snow-pit data for Walton Creek (no snowmobile use), Dumont Lakes (moderate snowmobile use), and Muddy Creek (high snow-
mobile use) in the Rabbit Ears Pass recreational use areas illustrating (a) density, (b) hardness, (c) ram resistance, (d) SWE, and (e) snow
depth. For (a)–(c), column (i) is the mean snowpack value and column (ii) is the basal layer value.
vehicles on a shallow snowpack was much greater than that
of a snowmobile, peaking at about 350 kPa (Pytka, 2010). In
comparison, fresh snow with a density of 100 kg m−3 exerts a
pressure of 0.003 kPa on the underlying snowpack (Moynier,
2006).
Compaction due to snowmobile use increased density
of the snowpack which influenced snow hardness (Fig. 5d
and e) and ram resistance (Fig. 6c). Compaction altered
snow characteristics (Figs. 5–7), fragmented faceted grains
(Fig. 5f, column ii), and reduced the growth of faceted grains
(Fig. 5f). Density measurements for fresh snow (Fassnacht
and Soulis, 2002) and/or uncompacted snow (López-Moreno
et al., 2013) vary spatially and temporally (Fig. 4), these val-
ues can double with just one pass of a snowmobile on a very
shallow snowpack (Keddy et al., 1979). The snowpack prop-
erties of a shallow snow environment can be more greatly
affected by compaction from snowmobile use than those for
an area that receives more snow (e.g., Fig. 3b vs. Fig. 3a).
With more snow accumulation, density also increases, but
high levels of snowmobile use will tend to increase the den-
sity above what is observed with non-snowmobile impacted
snow (Figs. 5–7).
5.2 Limitations of the measurements
Although snowpack variability over space was limited
(Fig. 4), the properties of the snowpack change from site to
site and through time. For example, the mean snowpack den-
sity was less in February (Fig. 6) than January at FEF (Fig. 5,
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1132 S. R. Fassnacht et al.: Snowpack changes due to snowmobile use
Figure 8. Bulk snowpack density change model for different amounts of use compared to the control of no use (a) calibrated for the two
experiment sites (Rabbit Ears Pass, REP and Fraser Experimental Forest, FEF), and (b) applied to the operational sites (Dumont Lakes and
Muddy Creek), compared to the no use Walton Creek site. The calibrated model is presented in (a) with the Nash–Sutcliffe coefficient of
efficiency (NSCE). The NSCE is presented in (b) for two different time periods: the four pre-melt dates (December–March – 4 dates) and
the later three pre-melt dates (January–March – JFM).
column ii). From the operational sites, specific hard layers
and high values of ram resistance were measured that did not
persist until the next monthly sampling (observed in the ex-
perimental treatments – not shown graphically). These varia-
tions were possibly a combination of naturally occurring spa-
tiotemporal snowpack variability and sampling errors; it can
be difficult to obtain reliable hardness measurements in snow
disturbed by snowmobiles. Future investigations could focus
on specific aspects of this study, such as using a finer tempo-
ral resolution, but with fewer treatments.
Another source of variability or bias is the type of equip-
ment used for sampling. Density and temperature were mea-
sured at 10 cm intervals using the Snowmetrics wedge cutter
and dial gauge thermometers. A different sampler could be
used to measure the density over each layer and other types
of thermometers could be used. Snow-hardness gauges and
circular metal plates of known area were used for hardness
testing (McClung and Schaerer, 2006), rather than the more
simplistic in situ hand hardness test (American Avalanche
Association, 2016). However, the hardness of thin layers
could not be measured as the circular metal plate used for
measurements had a diameter of 5 cm, omitting the possible
measurement of these thin layers. Thus, bulk hardness was
possibly underestimated. Also, due to the compaction of the
snow grains by the high use 30 cm start treatment at REP the
hardness could not be measured (Fig. 5, column ii). Different
equipment may resolve this issue.
5.3 Significance of the changes to snowpack properties
from snowmobile use
Snowmobile use was found to have a highly significant effect
upon natural vegetation below the snow (Keddy et al., 1979),
and by extension from snowmaking as well (Rixen et al.,
2003). Ski grooming has been shown to delay the blooming
of alpine plants (Rixen et al., 2001) due to later snowmelt and
significantly cooler soil temperatures (Fassnacht and Soulis,
2002). Deeper snowpacks were found to not have cooler soil
temperatures under the snowpack (Keller et al., 2004), but
melted out four weeks later than thinner snowpacks (Keller
et al., 2004). Since the changes due to snowmobile traffic on
a shallow snowpack were significant (Table 1), the effects of
snowmobile use on the soil and vegetation underlying a shal-
low snowpack should be further investigated.
Snowmobile use, starting with a shallow or thin (30 cm)
snowpack, resulted in a denser and harder snowpack with a
decrease in grain size throughout the season, and rounded
crystals or facets observed with the last measurements
(Fig. 5f). If compaction penetrates deep enough into the
snowpack, it could affect weak layers that cause avalanches
(Saly et al., 2016), which are typically composed of soft lay-
ers consisting of large facetted grains (e.g., Schweizer and
Jamieson, 2003; van Herwijnen and Jamieson, 2007). While
this may be useful in very limited and small areas, such as
that performed in boot packing programs (e.g., Sahn, 2010)
to strengthen snowpacks likely to fail on basal facets, it is
very difficult to properly align and reproduce the intensity of
repetitive tracks, as done experimentally here (Fig. 2). The
effects of snowmobile use for avalanche hazard reduction
The Cryosphere, 12, 1121–1135, 2018 www.the-cryosphere.net/12/1121/2018/
S. R. Fassnacht et al.: Snowpack changes due to snowmobile use 1133
through changing snow stability properties requires more in-
vestigation.
Other factors acting in concert with snowmobile traf-
fic to affect snowpack properties include wind, snowmak-
ing/grooming, and a changing climate. Without the effects
of wind, snow depth will generally be lower for areas with
snowmobile traffic (Figs. 2d, e, and 7; Rixen et al., 2001;
Spandre et al., 2016a). However, wind is often present in
open areas where snowmobiling occurs. Local terrain fea-
tures and position and extent of canopy cover influence how
the wind interacts with the snowpack (Pomeroy and Brun,
2001). In an Australian case study, SWE increased by 45 %
in groomed areas (Fahey et al., 1999); at the Rabbit Ears
Pass recreational use areas, SWE also increased through time
(Fig. 7d) likely due to snow blowing into the depressions
created by snowmobile tracks (Fig. 2d); this increased load
could further impact the underlying snowpack properties.
Further, snowmaking (Spandre et al., 2016a) to supplement
natural snow conditions and /or grooming (Fahey et al., 1999;
Rixen et al., 2001; Spandre et al., 2016a) compacts the snow-
pack below it, and alters the underlying snowpack properties
(Howard and Stull, 2014; Spandre et al., 2016a, b). Also,
a changing climate will likely reduce the extent of snow-
covered terrain and decrease the length of the winter recre-
ation season (Lazar and Williams, 2008; Steiger, 2010; Daw-
son and Scott, 2013; Marke et al., 2015; Schmucki et al.,
2015; Tercek and Rodman, 2016; Marty et al., 2017). In addi-
tion to possible effects from a changing climate, inter-annual
variability of snowpack patterns can be large in Colorado
(Fassnacht and Hultstrand, 2015; Fassnacht and Records,
2015; Fassnacht et al., 2017). The effects of this variability
should be included in long term motorized use land manage-
ment considerations.
The significant change to snowpack properties by snow-
mobiles, except when treatments/use were initiated on a deep
snowpack (Table 1), could impact land management deci-
sions for multi-use public lands. The measured depth of in-
fluence for a snowmobile is about 90 cm according to work
done by Thumlert et al. (2013), but additional work could test
starting depths such as 30, 60, and 90 cm in differing snow
conditions to identify the depth when snowmobile use has
no significant impact. Most ski resorts in the French Alps re-
quired a minimum snow depth of 40 cm to offer skiing, with
a range from 60 cm in February to 40 cm in April (Spandre
et al., 2016b). The US Forest Service (2013b) recommends a
minimum of 30 cm before the use of snowmobiles. Increas-
ing the minimum snow depth before allowing snowmobile
traffic will reduce changes to the snowpack due to snow-
mobile use (Table 1). Additionally, the non-linear bulk den-
sity change model developed here and applied to operational
sites could be used predictively for management needs. This
model may be useful in terms of estimating when to limit
snowmobile use given changes in specific snow depth and
density conditions.
Where the experiments for this study were undertaken, on
public lands in Colorado, there are 1.1 to 1.6 million an-
nual snowmobile visits, with an increase from 580 thou-
sand to 690 thousand between 2010 to 2013 in north-
ern Colorado (Routt NF and Arapaho–Roosevelt NF) and
southern Wyoming (Medicine Bow NF) (US Forest Service,
2010, 2013a) alone. The annual economic impact of snow-
mobile use is more than USD 125 million to each state (Na-
gler et al., 2012; Colorado Off-Highway Vehicle Coalition,
2016). Snowmobile use is likely to continue to increase, and
economic gains need to be balanced with potential impacts
to the landscape, particularly in those times and places where
snowpacks are shallow.
6 Conclusion
Snowmobiling is a multimillion dollar industry that impacts
local and regional economies and public recreation lands.
There have been limited studies regarding the influence of
snowmobile use on snowpack properties. We examined the
effect of snowmobile use on the physical and material prop-
erties of the snowpack at sites with varying snowmobile
use and seasonal snow conditions. Low, medium, and high
snowmobile use was simulated on experimental transects and
snowpack sampling results from the treated sites were com-
pared to the snowpack properties observed at undisturbed
control sites, and at operational sites with varying levels of
use. A non-linear bulk snowpack density change model was
developed relating changes in bulk density to snowmobile
use as a function of the number of passes, snow depth (in-
verse relation), and bulk density. The largest differences in
snowpack properties occur with snowmobile use beginning
on a shallow snowpack (30 cm) compared to no use, which
increases snowpack density, hardness, and ram resistance.
These increases are directly related to increasing snowmo-
bile use (from low to medium to high). Conversely, snow-
mobile use that begins on a deep snowpack (120 cm) has a
limited effect on the snowpack properties of density, temper-
ature, hardness, and ram resistance as compared to an undis-
turbed snowpack. These results suggest that from a manage-
ment standpoint, it may be desirable to limit snowmobile use
in shallower snow conditions to avoid increases in density,
hardness, and ram resistance that could possibly impact land
resources below the snowpack.
Data availability. The experimental data are available in
Heath (2011). The snow depth time series data are available from
the US Department of Agriculture Natural Resources Conservation
Service at https://www.wcc.nrcs.usda.gov/snow/.
Author contributions. The experiments were designed by JTH and
SRF with input from KJE. JTH performed the experiments with
assistance from KJE at the Fraser site. The initial manuscript was
www.the-cryosphere.net/12/1121/2018/ The Cryosphere, 12, 1121–1135, 2018
1134 S. R. Fassnacht et al.: Snowpack changes due to snowmobile use
written by JTH, SRF, and KJE. The final version of the manuscript
was written by SRF and NBHV. SRF generated the figures and cre-
ated the density model.
Competing interests. The authors declare that they have no conflict
of interest.
Acknowledgements. Appreciation goes to Robert Skorkowsky,
Kent Foster and Becky Jones of the Hahns Peak/Bears Ears
Ranger District of the US Forest Service for their help and support
with compaction treatments at the Rabbit Ears Pass study site.
Additional thanks goes to James zum Brunnen of the Colorado
State University Statistics Department for his assistance with
statistical interpretation. Jared Heath would also like to recognize
the Colorado Mountain Club for their help supporting this project
with a generous grant. Jim Halfpenny, Ned Bair, and two anony-
mous reviewers provided insight into clarifying this paper. One
TC Discussion anonymous reviewer provided very thorough and
thoughtful comments that greatly improved this paper, and resulted
in the creation of new figures. While the comments from this
reviewer provided a challenge, they were appreciated after they had
been addressed. TC editor Guillaume Chambon provided additional
comments and an important citation that helped reformulate the
discussion.
This open-access publication was funded
by the University of Göttingen.
Edited by: Guillaume Chambon
Reviewed by: Edward Bair and one anonymous referee
References
Abele, G. and Gow, A.: Compressibility Characteristics of Undis-
turbed Snow, Research Report 336, US Army Cold Regions Re-
search and Engineering Laboratory, Hanover, New Hampshire,
1975.
American Avalanche Association: Snow, Weather and Avalanches:
Observation Guidelines for Avalanche Programs in the United
States, 3rd edn., Victor, ID, 104 pp., 2016.
American Council of Snowmobile Associations: Economic Im-
pact of the Snowmobiling Industry, available at: http://www.
snowmobilers.org/ (last access: 4 April 2017), 2014.
Colorado Off-Highway Vehicle Coalition: Economic Contribution
of Off-Highway Vehicle Recreation in Colorado, Report by
Pinyon Environmental, Lakewood, CO USA, available at: http:
//www.cohvco.org/ (last access: 4 April 2017), 2016.
Cook, B. and Borrie, W.: Trends in Recreation Use and Manage-
ment of Wilderness, Int. J. Wilderness, 1, 30–34, 1995.
Dawson, J. and Scott, D.: Managing for climate change in the alpine
ski sector, Tourism Manage., 35, 244–254, 2013.
Elder, K., Cline, D., Liston, G. E., and Armstrong, R.: NASA Cold
Land Processes Experiment (CLPX 2002/03): Field Measure-
ments of Snowpack Properties and Soil Moisture, J. Hydrome-
teor., 10, 320–329, 2009.
Fassnacht, S. R. and Hultstrand, M.: Snowpack variability and
trends at long-term stations in northern Colorado, USA, Proc.
IAHS, 371, 131–136, https://doi.org/10.5194/piahs-371-131-
2015, 2015.
Fassnacht, S. R. and Records, R. M.: Large snowmelt versus rainfall
events in the mountains, J. Geophys. Res.-Atmos., 120, 2375–
2381, https://doi.org/10.1002/2014JD022753, 2015.
Fassnacht, S. R. and Soulis, E. D.: Implications during transitional
periods of improvements of snow processes in the Land Surface
Scheme – Hydrological Model WATCLASS, Atmos.-Ocean, 40,
389–403, 2002.
Fassnacht, S. R., Heun, C. M., López-Moreno, J. I., and Latron, J. B.
P.: Variability of Snow Density Measurements in the Rio Esera
Valley, Pyrenees Mountains, Spain, Cuadernos de Investigación
Geográfica, J. Geogr. Res., 36, 59–72, 2010.
Fassnacht, S. R., López-Moreno, J. I., Ma, C., Weber, A. N.,
Pfohl, A. K. D., Kampf, S. K., and Kappas, M.: Spatio-
temporal Snowmelt Variability across the Headwaters of the
Southern Rocky Mountains, Front. Earth Sci., 11, 505–514,
https://doi.org/10.1007/s11707-017-0641-4, 2017.
Fahey, B., Wardle, K., and Weir, P.: Environmental effects associ-
ated with snow grooming and skiing at Treble Cone Ski Field,
Part 2. Snow properties on groomed and non-groomed slopes,
Science for Conservation, 120, 49–62, 1999.
Fierz, C., Armstrong, R. L., Durand, Y., Etchevers, P., Greene, E.,
McClung, D. M., Nishimura, K., Satyawali, P. K., and Sokra-
tov, S. A.: The International Classification for Seasonal Snow on
the Ground, IHP-VII Technical Documents in Hydrology No. 83,
IACS Contribution No. 1, UNESCO-IHP, Paris, 2009.
Heath, J. T.: Motorized winter recreation impacts on snowpack
properties, Order No. 1497962, Colorado State University,
available at: https://search.proquest.com/docview/889112499?
accountid=10223, 2011.
Howard, R. and Stull, R.: Piste: A snow-physics model incorporat-
ing human factors for groomed ski slopes, J. Hydrometeor., 15,
2429–2445, https://doi.org/10.1175/JHM-D-14-0013.1, 2014.
International Snowmobile Manufacturers Association: Snowmobil-
ing Fact Book, ISMA, available at: http://www.snowmobile.org/
(last access: 4 April 2017), 2016.
Keddy, P., Spavold, A., and Keddy, C.: Snowmobile impact
on old field and marsh vegetation in Nova Scotia, Canada
: An experimental study, Environ. Manage. 3, 409–415,
https://doi.org/10.1007/BF01866580, 1979.
Keller, T., Pielmeier, C., Rixen, C., Gadient, F., Gustafsson, D., and
Stähl, M.: Impact of artificial snow and ski-slope grooming on
snowpack properties and soil thermal regime in sub-alpine ski
area, Ann. Glaciol., 38, 314–318, 2004.
Lazar, B. and Williams, M. W.: Climate change in western ski ar-
eas: Potential changes in the timing of wet avalanches and snow
quality for the Aspen ski area in the years 2030 and 2100, Cold
Reg. Sci. Technol., 51, 219–228, 2008.
López-Moreno, J. I., Fassnacht, S. R., Heath, J. T., Musselman, K.,
Revuelto, J., Latron, J., Morán, E., and Jonas, T.: Spatial vari-
ability of snow density over complex alpine terrain: implications
for estimating snow water equivalent, Adv. Water Resour., 55,
40–52, https://doi.org/10.1016/j.advwatres.2012.08.010, 2013.
Mann, H. B. and Whitney, D. R.: On a Test of Whether
one of Two Random Variables is Stochastically
The Cryosphere, 12, 1121–1135, 2018 www.the-cryosphere.net/12/1121/2018/
S. R. Fassnacht et al.: Snowpack changes due to snowmobile use 1135
Larger than the Other, Ann. Math. Stat., 18, 50–60,
https://doi.org/10.1214/aoms/1177730491, 1947.
Marke, T., Strasser, U., Hanzer, F., Stötter, J., Wilcke, R.
A. I., and Gobiet, A.: Scenarios of future snow conditions
in Styria (Austrian Alps), J. Hydrometeor., 16, 261–277,
https://doi.org/10.1175/JHM-D-14-0035.1, 2015.
Marty, C., Schlögl, S., Bavay, M., and Lehning, M.: How much
can we save? Impact of different emission scenarios on fu-
ture snow cover in the Alps, The Cryosphere, 11, 517–529,
https://doi.org/10.5194/tc-11-517-2017, 2017.
McClung, D. and Schaerer, P.: The Avalanche Handbook, 3rd edn.,
The Mountaineers Books, Seattle, Washington, 342 pp., 2006.
Moriasi, D. N., Arnold, J. G., Liew, M. W., Bingner, R. L., Harmel,
R. D., and Veith, T. L.: Model Evaluation Guidelines for Sys-
tematic Quantification of Accuracy in Watershed Simulations,
T. ASABE, 50, 885–900, https://doi.org/10.13031/2013.23153,
2007.
Moynier, J.: Avalanche Aware: The Essential Guide to Avalanche
Safety, Morris Book Publishing, LLC, Guilford, Connecticut,
90 pp., 2006.
Nagler, A. M., Bastian, C. T., Taylor, D. T., and Foulke, T. K.: 2011–
2012 Wyoming Comprehensive Snowmobile Recreation Report,
Report to the State of Wyoming, Department of State Parks
and Cultural Resources, prepared by Department of Agricultural
and Applied Economics, University of Wyoming, Laramie WY,
172 pp., 2012.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through con-
ceptual models part I – A discussion of principles, J. Hydrol., 10,
282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Osterberg, J. N.: American Valhalla, in: Post Punk Depression,
edited by: Homme, J., Caroline International, Loma Vista, Bev-
erly Hills, California, 2016.
Pomeroy, J. W. and Brun, E.: Physical properties of snow, chap. 2,
in: Snow Ecology: An Interdisciplinary Examination of Snow-
Covered Ecosystems, edited by: Jones, H. G., Pomeroy, J. W.,
Walker, D. A., and Hoham, R. W., Cambridge, Cambridge Uni-
versity Press, 45–126, 2001.
Pytka, J.: Determination of snow stresses under ve-
hicle loads, Cold Reg. Sci. Technol., 60, 137–145,
https://doi.org/10.1016/j.coldregions.2009.10.002, 2010.
Rixen, C., Stoeckli, V., Huovinen, C., and Huovinen, K.: The phe-
nology of four subalpine herbs in relation to snow cover char-
acteristics, Soil-Vegetation-Atmosphere Transfer Schemes and
Large-Scale Hydrological Models, IAHS Pub., 270, 359–362,
2001.
Rixen, C., Stoeckli, V., and Ammann, W.: Does artificial snow pro-
duction affect soil and vegetation of ski pistes? A review, Per-
spect. Plant Ecol., 5, 219–230, 2003.
Sahn, K.: Avalanche risk reduction in the continental climate: How
to implement an effective boot packing program, Proceedings of
the 2010 International Snow Science Workshop, Squaw Valley,
CA, USA, 17–22 October 2010, 296–301, 2010.
Saly, D., Hendrikx, J., Birkeland, K., Challender, S., and Leonard,
T.: The Effects of Compaction Methods on Snowpack Stability,
Proceedings of the 2016 International Snow Science Workshop,
Breckenridge, CO, USA, 3–7 October 2016, 716–720, 2016.
Schmucki, E., Marty, C., Fierz, C., and Lehning, M.: Simulations
of 21st century snow response to climate change in Switzerland
from a set of RCMs, Int. J. Climatol., 35, 3262–3273, 2015.
Schweizer, J. and Jamieson, J. B.: Snowpack properties for snow
profile analysis, Cold Reg. Sci. Technol., 37, 233–241, 2003.
Shapiro, L. H., Johnson, J. B., Sturm, M., and Blaisdell, G. L.: Snow
Mechanics: Review of the State of Knowledge and Applications,
USA Cold Regions Research and Engineering Laboratory (CR-
REL), Research Report 97-3, 1997.
Shoop, S. A., Richmond, P. W., and Lacombe, J.: Overview of cold
regions mobility modeling at CRREL, Jo. Terramechanics, 43,
1–26, 2006.
Spandre, P., Morin, S., Lafaysse, M., Lejeune, Y., François, H., and
Lejeune, Y.: Integration of snow management processes into a
detailed snowpack model, Cold Reg. Sci. Technol., 125, 48–64,
https://doi.org/10.1016/j.coldregions.2016.01.002, 2016a.
Spandre, P., François, H., George-Marcelpoil, E., and Morin,
S.: Panel based assessment of snow management operations
in French ski resorts, J. Outdoor Rec. Tour., 16, 24–36,
https://doi.org/10.1016/j.jort.2016.09.002, 2016b.
Steiger, R.: The impact of climate change on ski season length and
snowmaking requirements in Tyrol, Austria, Clim. Res., 43, 251–
262, https://doi.org/10.3354/cr00941, 2010.
Tercek, M. and Rodman, A.: Forecasts of 21st Century Snow-
pack and Implications for Snowmobile and Snowcoach Use
in Yellowstone National Park, PLoS ONE, 11, e0159218,
https://doi.org/10.1371/journal.pone.0159218, 2016.
Thumlert, S. and Jamieson, B.: Stress measurements from common
snow slope stability tests, Cold Reg. Sci. Technol., 110, 38–46,
https://doi.org/10.1016/j.coldregions.2014.11.005, 2015.
Thumlert, S., Exner, T., Jamieson, B., and Bellaire, S.:
Measurements of localized dynamic loading in a moun-
tain snow cover, Cold Reg. Sci. Technol., 85, 94–101,
https://doi.org/10.1016/j.coldregions.2012.08.005, 2013.
US Forest Service: National Visitor Use Monitoring Results, USDA
Forest Service National Summary Report Data collected FY
2005 through FY 2009, US Department of Agriculture, available
at: http://www.fs.fed.us/recreation/programs/nvum/ (last access:
4 April 2017), 2010.
US Forest Service: National Visitor Use Monitoring Results, USDA
Forest Service National Summary Report Data collected FY
2008 through FY 2012, US Department of Agriculture, available
at: http://www.fs.fed.us/recreation/programs/nvum/ (last access:
4 April 2017), 2013a.
US Forest Service: Modifications Made to Medicine Bow Na-
tional Forest Winter Travel Special Order – Release date
15 November 2013, Medicine Bow National Forest, avail-
able at: https://www.fs.usda.gov/detail/mbr/news-events/?cid=
STELPRDB5440798 (last access: 7 April 2017), 2013b.
van Herwijnen, A. and Jamieson, J. B.: Snowpack properties asso-
ciated with fracture initiation and propagation resulting in skier-
triggered dry snow slab avalanches, Cold Reg. Sci. Technol., 50,
13–22, 2007.
Wilcoxon, F.: Individual comparisons by ranking methods, Biomet-
rics Bull., 1, 80–83, https://doi.org/10.2307/3001968, 1945.
Winter Wildlands Alliance: Winter Recreation on Western National
Forest Lands: A Comprehensive Analysis of Motorized and Non-
Motorized Opportunity and Access, Winter Wildlands Alliance,
Boise, Idaho, 44 pp., 2006.
www.the-cryosphere.net/12/1121/2018/ The Cryosphere, 12, 1121–1135, 2018