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CLIMATE, ADAPTATION AND SUSTAINABILITY OF WESTERN U.S. SKI AREAS
IN THE 21st CENTURY AND A COMPARSION WITH SKI AREAS IN THE ALPS
Brian Lazar
1
1,*
2
3
Mark Williams and Carmen de Jong
Stratus Consulting Inc., Boulder, Colorado and American Institute of Avalanche Research and
Education, Gunnison, Colorado
2
Department of Geography and Institute of Arctic and Alpine Research, University of Colorado,
Boulder, Colorado
3
The Mountain Institute, University of Savoy, Bourget du Lac, France
ABSTRACT
We evaluate how climate change resulting from increased greenhouse gas (GHG) emissions
may affect snow coverage for two case studies: Aspen Mountain and Park City Mountain in the
years 2030 and 2100. Snow coverage was evaluated using the Snowmelt Runoff Model (SRM).
We estimated climate changes (temperature and precipitation) using MAGICC/SCENGEN and
the output from five General Circulation Models (GCMs). We bracketed potential climate
changes by using the relatively low, mid-range, and high GHG emissions scenarios known as
B1, A1B, and A1FI.
By 2030, temperatures are estimated to increase 1.8 to 2.5 oC at Aspen Mountain and
Park City Mountain. The length of the ski season is estimated to decrease by approximately 1 to
1.5 weeks at both ski areas, and the snowline is estimated at 2250 m; an increase of
approximately 200 m from current conditions. In 2100, average annual temperatures are
projected to increase 2.9 to 9.4 oC at Aspen Mountain and 4.2 to 8.9 oC at Park City Mountain.
The snowline is estimated at 2800 to 2900 m at both ski areas for the A1B and B1 scenarios in
2100, and 3100 to 3200 m for the A1FI scenario. Issues of adaptability and sustainability are
discussed with relation to ski areas from several regions in the European Alps.
KEYWORDS: climate change, snow, Aspen, Park City, ski areas, General Circulation Models,
downscaling
1. INTRODUCTION
Potential impacts of climate change on snow has long been a concern for a variety of
snow dependent industries, as snow and ice are often viewed as early indicators of a warming
climate (Armstrong and Brun, 2008; Lemke et al. 2007; Barry et al., 2007). Changes to
snowpack impact a range of industries from water resource management to ski area operation
(Lemke et al. 2007; Barry et al., 2007; Tegart et al., 1990; Watson et al., 1996; National
Assessment Synthesis Team, 2000; McCarthy et al., 2001). An increasing number of studies
have investigated the potential hydrologic effects of climate change on snow (Rango et al. 2007;
Mote 2006; Mote el al., 2005; Barnett et al., 2005; Rango and Martinec, 1997, 1999, 2000;
Seidel et al., 1998; and others). Similarly, a number of studies have analyzed the effects of
potential climate change on ski areas and winter tourism, all of which project negative
consequences for the industry (Scott et al., 2008; Agrawala, 2007; Climate Impacts Group, 2006;
Nolin and Daly, 2006; Scott et al., 2003, Forthcoming; Scott and Jones, 2005; Hennessy et al.,
2003, and others). In contrast to studies of snowmelt runoff, research on potential climate change
impacts at ski areas are concerned primarily with snowpack characteristics during the snow
accumulation season.
Evaluating potential changes in snow properties that are important to managing ski areas,
such as snow coverage and depth, requires an approach to modeling these properties during the
accumulation season in a changed climate. There is a need to develop easily employed and sitespecific techniques for estimating potential changes in snow properties in response to future
climate change scenarios. Ski area managers need to be able to address issues such as the ability
to open in the early season, snow depths during the Christmas holidays, and the likelihood of ski
seasons ending before the highly profitable spring break period in late March. Similarly, towns
and businesses that depend on ski areas for their economic viability need very specific
information on how snow properties may change in the future so as to be able to make economic
adjustments.
Here, we present two case studies on the results of GCM projections for three GHG
emission scenarios on snow coverage for the Aspen Mountain and Park City Mountain Resort ski
areas for 2030 and 2100. We then address questions of adaptation and sustainability of ski areas
in the face of these challenges, such as production of artificial snow. Last, we compare and
contrast these results with ski areas in the European Alps.
2. STUDY SITES
Aspen Mountain and Park City Mountain Resort lie within the Rocky Mountains in the
western USA. Aspen Mountain is located in the Elk Mountains of western Colorado (Figure 1).
The ski area ranges in elevation from the 2,422 m base area to the 3,418 m summit, for a total
vertical rise of 996 m. Park City Mountain Resort is located in the Wasatch Mountains of north
central Utah (Figure 1). The property boundary encompasses an area of 17.5 km2 , and has a
vertical relief of approximately 1,067 m (3,500 ft), from the base area at 2,100 m (6,890 ft) to the
highest elevation at 3,170 m (10,400 ft).
Figure 1: Location of Aspen Mountain and Park City Mountain Resort.
The operational season at both ski areas generally begins in mid-November and ends
mid-April. The beginning of the operational season is dictated by adequate snowfall, while the
end of the season is driven by a decrease in skier visits. Snow depths in early April are generally
at or near their annual maximum.
Meteorological data suitable for use in the SRM are available from several sources in and
around both ski areas; including weather stations maintained by the ski areas, the Western
Regional Climate Center (www.wrcc.dri.edu/index.html), highway departments, local
municipalities, and Natural Resources Conservation Service SNOpack TELemetry (SNOTEL)
sites. The SRM requires full-year temperature and precipitation datasets, at daily time steps,
which are not available from the ski area weather stations, which only operate during the ski
season (mid-November to mid-April).
3. METHODS
3.1
Climate modeling
Future changes in GHG emissions depend on complex social, economic, and technological
relationships that underlie energy use and resulting emissions. We relied on emission scenarios
described by the Intergovernmental Panel on Climate Change (IPCC) in its Special Report on
Emission Scenarios (Nakićenović et al., 2000) to develop climate scenarios for two twenty-year
time periods centered on the years 2030 and 2100. The scenarios result in a wide range of GHG
emissions and atmospheric concentrations.
We used the relatively low, mid-range, and high GHG emissions scenarios known as B1,
A1B, and A1FI to bracket the range of potential GHG emissions and concentrations. For one of
our modeling approaches, we used B2 as a moderate projection, and A2 as a high projection.
Current atmospheric CO2 concentrations are approximately 380 ppm. In 2030, there is little
divergence of GHG concentrations between scenarios, with all emission scenarios projecting
approximately 450 ppm CO2. We therefore bracketed potential climate changes in 2030 using
the average of five selected GCMs, the driest model, and the wettest model. By 2100, the B1
scenario has the lowest emissions, resulting in 540 ppm of CO2. The A1B scenario projects CO2
concentrations (700 ppm) and temperature warming close to the middle of the range for 2100
described in the IPCC Fourth Assessment Report (Houghton et al., 2007). The A1FI yields 930
ppm CO2 by 2100. The A1FI and B1 scenarios present a stark contrast between development
paths. The A2 scenario results in CO2 concentrations of about 850 ppm by 2100, which is
between A1B and A1FI. The B2 scenario produces CO2 concentrations of about 600 ppm by
2100, which is between A1B and B1. We decided to use 3°C as the central estimate of GCM
sensitivity to GHG emissions (how much global mean temperature would increase for a doubling
of CO2) based on a recent review by Kerr (2004).
We used a dynamic downscaling approach to evaluate how changes in global GHG
concentrations translate to regional climate responses. We used the model
“MAGICC/SCENGEN” to project changes in temperature and precipitation across 17 GCMs,
relative to the projected increase in global mean temperature (Wigley, 2004). Using relative
change is preferable to averaging projected regional GCM output because it avoids results from
models having a high sensitivity dominating the regional projections. MAGICC/SCENGEN
reports changes in regional climate in 5° by 5° grid boxes (approximately 480 km on a side).
We were most interested in the GCMs that best simulate the climate over the central
Rocky Mountains. In an evaluation of the ability of 17 existing GCMs to simulate current
climate in western North America, Wigley (2006) concluded that the following five models
performed best, and were therefore used in our climate scenarios for this manuscript.

CSIRO—Australia

ECHAM3—Max Planck Institute for Meteorology, Germany

ECHAM4—Max Planck Institute for Meteorology, Germany

HadCM2—Hadley Model, United Kingdom Meteorological Office
HadCM3—Hadley Model, United Kingdom Meteorological Office.
3.2 Snow modeling
We modeled snow coverage using the SRM (Martinec, 1975; Martinec et al., 1994;
http://hydrolab.arsusda.gov/cgi-bin/srmhome).
because the required drivers are compatible with GCM outputs: air temperature and
precipitation. The model is based on the concept that changes in air temperature provide an index
of snowmelt. The modeled domain used in evaluating Aspen Mountain snowpack encompassed
the upper portion of the Roaring Fork watershed, which drains snow melt from Aspen Mountain
and other nearby ski areas. The domain was 942 km2 in area, ranging in elevation from 2,225 m
to the 4,348 m. The domain was broken into seven elevation bands of approximately 305 m
each. The modeled domain used in evaluating Park City Mountain Resort snowpack was the
current (2008) Park City ski area property boundary area (17.5 km2). The property boundary
encompasses a vertical relief of approximately 1,067 m from the base area at 2,100 m to the
highest elevation at 3,170 m. We created four elevation zones of approximately 265 m each.
The SRM stores any precipitation event recognized as snow, thereby calculating the
maximum snow stored for each elevation band on the defined winter end date. Beyond the winter
end date, SRM models the melting process and the subsequent depletion of snow-covered area
(SCA). To model the rate and spatial distribution of snowpack buildup during the fall and early
winter months, we developed an additional module for use with the SRM. Since snowpack
buildup is dictated by temperature and precipitation, we used changes in temperature to
determine the change in timing at which snow begins to accumulate. We scaled the rates of
change in snow-covered area by changes in precipitation.
We used years for which snowfall and temperature were representative of historical
average as calibration years for the SRM. Daily air temperature for the selected representative
year was distributed over the defined elevation bands using a developed lapse rate
(0.65°C/100 m for Aspen and 0.4°C/100 m for Park City). We imposed the projected changes in
air temperature and precipitation from the GCMs on the climate data from the representative year
to generate future climate scenarios.
SCA was estimated approximately once per month using Landsat imagery from 19992000 and 2000-2001 for Park City Resort and Aspen Mountain, respectively. A binary
classification scheme was used to classify each 30-m pixel as either snow-covered or nonsnowcovered (Klein et al., 1998; Dozier and Painter, 2004). Linear interpolation between estimated
SCA values from Landsat was employed to generate the required daily SCA time series.
4. RESULTS
4.1 Projected changes in climate
Figure 2 presents estimated changes in temperature for Aspen and Park City in (A) 2030
and (B) 2100 (B) (relative to 1990) using the middle-emissions A1B scenario. Under this
scenario, the average model warming is 2.1C in Aspen and 2.0C in Park City, with a range for
both areas of 1.8 to 2.5C by 2030. By 2100 the average annual temperature for Aspen increases
to 4.8C with a range of 3.9 to 5.9C. Park City is projected to experience more warming with
an average annual temperature increase of 5.8C and a narrower range of 5.2 to 6.2C. There is
little variance among the GCMs with respect to temperature projections.
Aspen
7
Park City
A
6
Temperature change, deg C
5
4
3
2
1
7
6
B
5
4
3
2
1
C
S
EC IRO
H
EC AM
H 3
A
H M
ad 4
C
H M
ad 2
M CM
O
D 3
B
AR
C
S
EC IR
H O
EC AM
H 3
A
H M
ad 4
C
H M
ad 2
M CM
O
D 3
B
AR
0
Figure 2: The projected annual changes for
Aspen and Park City in (A) 2030 (B) 2100 for five GCMs for the A1B scenario. The first five
bars are results for individual models within MAGICC/SCENGEN; the last bar is the model
average.
Figure 3 displays the projected average monthly temperature changes for (A) Aspen and
(B) Park City for the B1, A1B, and A1FI scenarios in 2100. Temperature increases are larger in
the summer months, with summer temperature increases about 50% greater than during the
winter months for all scenarios. Projected warming under the A1FI scenario is approximately
twice as much as projected under the B1 scenario. Park City is projected to experience about
1C more warming by 2100 than Aspen for all scenarios.
For the low-emissions B1 scenario, annual average temperature is projected to increase
by 3.5C (range of 2.9 to 4.3C) in Aspen and 4.7C (range of 4.2 to 4.9C) in Park City. For the
high-emissions A1FI scenario, annual average temperature is projected to increase by 7.6C
(range of 6.3 to 9.4C) in Aspen and 8.4C (range of 7.4 to 8.9C) in Park City. As with the
projections under the A1B scenario (Figure 2), there is little variance in temperature projections
among the GCMs.
By contrast, there is more variance among GCMs for projections of changes in
precipitation. Under the A1B scenario in 2030, all five models estimate a decrease in annual
precipitation for Aspen with decreases ranging from 1% to 18% and average of 7%. Model
average also projects a 7% decrease in precipitation for Park City in 2030 under A1B ranging
from a 1% increase to a 16% decrease.
Monthly climate changes by scenario, 2100
12
Temperaure change, deg C
10
B1
A1B
A1FI
A
A
8
6
4
2
12
10
B
February value for
A1FI is assumed
8
6
4
2
JA
N
FE
B
M
A
R
A
PR
M
A
Y
JU
N
JU
L
A
U
G
SE
P
O
C
T
N
O
V
D
EC
0
Figure 3: Projected model average monthly
temperature changes in 2100 for (A) Aspen and (B) Park City
Aspen and Park City are both projected to experience further decreases in precipitation
by 2100 (Table 1). The average decrease in precipitation is projected to be smaller for Aspen
and the range is greater for all scenarios. Decreases in precipitation are projected to be minor (2
to 4%) in Aspen, while decreases are projected to be 14 to 17% greater in Park City for all
scenarios. All models show an increase in monthly precipitation during January and February,
followed by strong declines in precipitation during April, May, and June.
Projected Change in Total Annual
Precipitation (%) in 2100
A1FI
A1B
B1
Aspen
Avg.
Avg.
Avg.
(range)
(range)
(range)
-4
-3
-2
(-49 to 24) (-31 to 15) (-23 to 11)
Park
-21
-17
-16
City
(-49 to 15)
(-38 to 5)
(-32 to 1)
Table 1: Projected changes in annual precipitation (%) for Aspen and Park City in 2100 for the
A1FI, A1B, and B1 emission scenarios.
4.2 Projected changes to snowpack
We imposed the projected changes in air temperature and precipitation (Figures 2 and 3,
Table 1)on the climate data from the representative years to model snowpack under future
climate scenarios. Figure 4 displays projected changes in SCA relative to the selected
representative year at the base area for Aspen and Park City in (A) 2030 and (B) 2100. The base
area elevation zone has a hypsometric mean of 2684 m at Aspen Mountain and 2250 m at Park
City Mountain Resort.
Since there was little difference in SCA between emission scenarios in 2030, we compare
the GCM average with the wettest and driest GCMs under the A1B scenario. Historically, the
start of snowpack buildup at both ski areas begins in the first week of November. The start of
snowpack buildup at the base area is delayed by approximately one week at Aspen Mountain and
by three to four days at Park City Mountain Resort by 2030 (Figure 4(A)). Snow melt at the base
area historically begins in the third week of March at both ski areas. Snow melt is projected to
begin four to five days earlier at Aspen Mountain and one week to 10 days earlier at Park City
Mountain Resort. The snowline, defined as the elevation below which a seasonally persistent
snowpack will not develop, rises about 200 m from current conditions to an elevation of
approximately 2250m for both ski areas. This implies the snowline has moved into the lower
end of the base area elevation zone at Park City Mountain by 2030.
By 2100 projected SCA suggest a very strong sensitivity to the emission scenario. The
base area of both ski areas have essentially lost a skiable snowpack, with the exception of the
low-emission B1 scenario (Figure 4 (B)). There is virtually no snow cover under the A1B and
A1FI model average scenarios. Snowfall for these scenarios is projected to be infrequent or to
not occur at all throughout the winter. The winter will be punctuated by frequent and sustained
periods of melt. Under the B1 scenario, the snow coverage at the base area is substantially
reduced, but not completely obliterated.
The start of snowpack buildup at the base area of Aspen Mountain is delayed
anywhere from 1.5 weeks for the B1 scenario to 4.5 weeks for the A1FI scenario in 2100. The
start of snowpack buildup at the base area of Park City Mountain Resort in 2100 is projected to
begin from one to two months later than the historical start date under the B1 and A1B scenarios,
respectively. For the A1FI scenario, snowpack buildup will not occur at all, and all winter
precipitation will come as rain. At Aspen Mountain, snowmelt at the base area begins 2.5 weeks
earlier for the B1 scenario and 5 weeks for the A1FI scenario. At Park City Mountain resort,
snowmelt at the base area will occur throughout the winter for the A1B and B1 scenarios, while
all precipitation will come as rain under the A1FI scenario.
Park City
Aspen
A
0.9
Avg. year
A1B_2030_avg
A1B_2030_WET
A1B_2030_DRY
0.7
Snow covered area
0.5
0.3
0.1
0.9
B
Avg. year
A1B_2100_avg
B1_2100_avg
A1FI_2100_avg
0.7
0.5
0.3
Jun-16
Apr-21
May-19
Mar-24
Jan-27
Feb-24
Dec-30
Dec-02
Oct-07
Nov-04
Jun-01
Apr-01
May-01
Mar-01
Jan-01
Feb-01
Dec-01
Oct-01
Nov-01
0.1
Figure 4: Projected SCA at the base areas of Aspen and Park City relative to selected
representative year in (A) 2030 and (B) 2100.
The snowline is estimated at 2800 to 2900 m at both ski areas for the A1B and B1
scenarios in 2100, and 3100 to 3200 m for the A1FI scenario. This implies that Aspen Mountain
will retain skiable snow from the mid-mountain elevation and above for the A1B and B1
scenarios in 2100, while only retaining skiable snow at the top of the mountain under A1FI. For
Park City Mountain Resort, skiable snow would probably exist only at the top of the mountain
under the A1B and B1 scenarios, while snow will not be present at any elevation under the A1FI
scenario. BRIAN, PUT YOUR NEW FIGURE HERE WITH ADDITIONAL TEXT.
5. DISCUSSION
Here we address some of the challenges of adaptation and sustainability of ski areas in the face
of these future climatological changes. We then compare and contrast these conditions in the
Western US to those of the European Alps for insight.
BRIAN: Paragraph on response to snowline moving upward. Ski areas need to expand operations
at higher elevation where that is a possibility, etc.
Production of artificial snow is a major form of adaptation to these changing climatological
which provides a guarantee for both the spatial coverage of snow-covered area for skiing and the
temporal window for skiing to satisfy consumer preferences. Wolfsegger et al. (2008) found
snowmaking was the most preferred climate adaptation strategy by Austrian ski area managers
and that the majority believed that further adaptation would allow their business to remain
economically viable until at least mid-century, regardless of the magnitude of climatic change.
Their findings support Scott’s (2005) contention that future studies of the potential impact of
climate change on skiing should anticipate the future development of snowmaking. The
production of person-make snow involves three dominant cost-drivers: adequate liquid water,
infrastructure to deliver the water to specific sites at the ski area and convert the liquid water to
snow, and power to operate the snow-making equipment.
Here we use Aspen Mountain as a case study to evaluate how much water and power may be
needed to produce additional snow in response to these changing climatic conditions. The skiable
terrain at Aspen Mountain is about 675 acres. Annual snowfall averages near 300 inches. The
snowmaking period runs generally from the beginning of November through the end of January.
About a third of the skiable terrain is presently covered by snow-making activities. In 20002001, snow production was 325 acre-feet or 105,901,000 gallons (Edwards et al., 2001). The
325 acre-feet of snow produced at Aspen Mountain is comparable to other ski areas in Colorado.
For example, Keystone Mountain Ski Area in 2000 produced 485 acre-feet of snow and currently
Arapahoe Basin Ski Area produces about 350 acre-feet of snow each year (UPCO, 2000).
Our modeling results suggest that future changes in climate will reduce the length of the ski
season, the snow-covered area, and the depth of snow on the ground (eg Fig. 4). If in response to
changes in climate, ski area operators expand the coverage to the entire mountain using the same
proportion of man-made snow as in 2000-2001, the amount of water needed would increase by a
factor of two to an additional 650 acre-feet. The total amount of person-made snow needed for
total coverage of the mountain is thus 975 acre-feet.
To compensate for the future decrease in snowfall, the length of the snow-making period will
most likely have to be increased as well as the depth of snow coverage, with the magnitude of
this increase in the volume of person-make snow varying as a function of future CO2 emission
scenarios. As a back-of-the-envelope estimate, let's assume that in future years the operators of
Aspen Mountain need to triple the amount of person-made snow relative to that necessary to
fully cover the mountain in 2000-2001. Operators would need to produce about 3,000 acre-feet
of snow. A rule of thumb is that 2 gallons of snow are produced from each gallon of liquid water.
In this scenario, mountain operators will need to have access to about 1,500 acre-feet of liquid
water, or the equivalent of stream discharge of about 20 cfs for a month.
The 20 cfs for a month of water needed in the future is problematic for several reasons. Almost
all surface water in the Rocky Mountains is over-appropriated. Obtaining the water rights to this
large volume of will be expensive. The 20 cfs for a month is about the winter flow for larger
mountain streams in the Colorado Rocky Mountains, such as Deer Creek near Keystone
Mountain Resort (Todd, 2005). Appropriating this amount of water during the winter season
would not leave sufficient water flow to maintain the integrating of these aquatic ecosystems. If
this amount of water can be obtained, adequate storage facilities on or near the mountain need to
be developed.
The cost of power needed for the snow-making apparatus increases as the amount of snow
produced increases, generating a large expense. In 2000-2001, Aspen Mountain used 2,606,00
kwHr of electricity, at a cost of $207,000. Snow-making was the major user of this energy
(Edwards et al., 2001). A six-fold increase in energy use to produce the future snow-making
scenario above would thus increase electrical costs to about $1,200,000 dollars per year at the
current electrical rates.
However, the cost of making snow is in part a function of air temperature. Warmer air
temperature results in less efficient nucleation of the expelled compressed water and air from
snow-making apparatus, increasing the power cost for the same amount of snow. Hartman
(2000) developed an exponential function that estimates the cost to make snow over a range of
ambient air dry bulb temperatures based on empirical data at Aspen Mountain as follows:
C = ($0.0027845/gal)e(0.0322/ °F )´T
eq 1
where the cost, C, is expressed in units of dollars per gallon of water converted into snow and T
is the ambient air dry bulb temperature in units of degrees Fahrenheit. An increase in the dry
bulb temperature from 10 °F to 30 °F causes almost a doubling in the cost of producing artificial
snow. Warmer air temperatures in the future will result in an increase in the cost of producing
artificial snow.
The Alps provide insight into how the ski industry in the Western US may change in the future.
The Alps are experiencing a change in air temperature, which has increased more than 2°C on in
the last 30 years for many areas such as Bourg St. Maurice, French Alps (de Jong and Barth,
2008). Average snow depths are decreasing, with more than a 50 cm decrease at the Col de Porte
near Grenoble at 1326 m altitude over the last 45 years and a 4 m decrease over the last 30 years
at 3000 m above Bourg St. Maurice, French Alps (de Jong and Barth 2008). Similar trends are
captured by other alpine stations, together with an increased variability in frequency of snowfall
events (de Jong et al., in press). These changes in climate result in longer snow-free periods
(several weeks on end) during peak holiday seasons such as December or February.
In contrast to ski areas in the intercontinental US, these changes in climate have resulted in ski
managers in the Alps increasingly depending on artificial snow for extended periods of time. For
example, in Slovenia, up to 80% of the skiable terrain in the major ski resorts is covered with
artificial snow, compared to 70% in the Italian Alps and 30% in the French Alps. The differences
in the percentage of snowmaking is primarily a reflection of altitude, with ski areas at lower
altitudes forced to cover more of the skiable terrain with artificial snow.
Projected temperature increase up to the year 2030 for the Alps are on the order of 1.8 °C, which
will cause a decrease in the number of snow days from 170 to 130 days at 1500 m altitude in the
northern French Alps. Projections for the potential of artificial snow production under these
warming temperatures show a reduction by one third of the potential snowmaking days at 2000
m altitude (Steiger and Mayer, in press).
There are two main forms of adaptation in the Alps to these changes in climate and the need for
extensive production of artificial snow. Firstly, since the water traditionally tapped from springs
is no longer sufficient to support the growing tourist industry, water is increasingly transported
via inter-basin pipeline transfer over long distances, or pumped from groundwater to be stored in
water reservoirs and re-distributed through pipelines to the local tourist areas (de Jong et al
2008). Secondly, “soft” adaptation methods are being developed that take into account socioeconomic adaptation, such as controls on household water demand, the development of less
water-intensive tourism and an increased focus on summer tourism (ClimAlpTour). However,
seen from the perspective of long-term climate change, there is concern that technological
adaptations may not be an acceptable long-term solution.
5. CONCLUSIONS
Aspen Mountain and Park City Mountain Resort present two comparable case studies for
evaluating the potential impacts of climate change on western U.S. ski areas. The warmer baseline
temperatures in the Park City region, the lower elevation of the ski area, and the warmer and drier climate
projections combine to produce greater projected losses to snowpack at Park City Mountain Resort than
at Aspen Mountain. Both ski areas are projected to maintain adequate snow coverage for ski operations
at all elevations through 2030. By 2100, there is strong sensitivity to the emission scenario. Park City
Mountain Resort will only maintain adequate snow at the base area during mid-winter under the B1
scenario, while it will lose all its snowpack at all elevations under the A1FI scenario. Aspen mountain will
not lose all its snowpack under A1FI in 2100, but the rise in snowline will confine a seasonal snowpack to
the top quarter of the mountain.
Here, we have introduced a method for estimating site-specific impacts to snow coverage during
the ski area operating season that can be tuned for individual ski areas. By using measured SCA from
increasingly available high resolution satellite imagery, we avoid the potential pitfalls of estimating snow
pack conditions with precipitation data and arbitrarily selected temperature thresholds. By relying on a
physically based model, we are able to estimate spatially distributed snow coverage using only
temperature, precipitation, and SCA data as model inputs. Requiring only these few input parameters
allows us to effectively incorporate the site-specific GCM outputs for monthly climate change, where
temperature and precipitation are often the only available or reliable parameters. This methodology is
easily applied to other ski areas around the globe.
We need one more paragraph that summarizes the new material. Something like: These
climatological changes in the future present challenges for adaptation and sustainability of ski areas. Ski
areas will need to consider expansion to higher elevation areas. The future loss of consistent snow at
current base areas necessitates adaptations such loading customers in gondolas at base areas that then
transport customers to skiable terrain. Increased production of artificial snow mitigates some of the
sustainability problems caused by climate change, and provides surety for operating days, length of the
ski season, and the amount of skiable terrain. However, increased production of artificial snow
necessitates the diversion and storage of large amounts of water that is problematic. Super-imposed on
this water availability and storage problem are the high costs of snow-making, which calls into the
question the economic feasibility of ski areas.
6. REFERENCES
Agrawala, S. (ed.) 2007. Climate Change in the European Alps: Adapting Winter Tourism and Natural
Hazards Management. Organisation for Economic Co-operation and Development, Paris
Alp-Water-Scarce, Alpine Space project on Water Management Strategies against Water Scarcity in the
Alps, 2008-2011. www.alpwaterscarce.eu
Armstrong, R.L. and E. Brun.(eds.) 2008. Snow and Climate: Physical Processes, Surface Energy
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