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Projected climate change impacts on skiing and snowmobiling: A case study of the United States

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We use a physically-based water and energy balance model to simulate natural snow accumulation at 247 winter recreation locations across the continental United States. We combine this model with projections of snowmaking conditions to determine downhill skiing, cross-country skiing, and snowmobiling season lengths under baseline and future climates, using data from five climate models and two emissions scenarios. Projected season lengths are combined with baseline estimates of winter recreation activity, entrance fee information, and potential changes in population to monetize impacts to the selected winter recreation activity categories for the years 2050 and 2090. Our results identify changes in winter recreation season lengths across the United States that vary by location, recreational activity type, and climate scenario. However, virtually all locations are projected to see reductions in winter recreation season lengths, exceeding 50% by 2050 and 80% in 2090 for some downhill skiing locations. We estimate these season length changes could result in millions to tens of millions of foregone recreational visits annually by 2050, with an annual monetized impact of hundreds of millions of dollars. Comparing results from the alternative emissions scenarios shows that limiting global greenhouse gas emissions could both delay and substantially reduce adverse impacts to the winter recreation industry.
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Global Environmental Change
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Projected climate change impacts on skiing and snowmobiling: A case study
of the United States
Cameron Wobus
, Eric E. Small
, Heather Hosterman
, David Mills
, Justin Stein
Matthew Rissing
, Russell Jones
, Michael Duckworth
, Ronald Hall
, Michael Kolian
Jared Creason
, Jeremy Martinich
Abt Associates, 1881 Ninth Street, Suite 201, Boulder, CO, USA
University of Colorado Boulder, Geological Sciences, Boulder, CO, USA
U.S. Environmental Protection Agency, Climate Change Division, Washington, DC, USA
Climate change
We use a physically-based water and energy balance model to simulate natural snow accumulation at 247 winter
recreation locations across the continental United States. We combine this model with projections of
snowmaking conditions to determine downhill skiing, cross-country skiing, and snowmobiling season lengths
under baseline and future climates, using data from ve climate models and two emissions scenarios. Projected
season lengths are combined with baseline estimates of winter recreation activity, entrance fee information, and
potential changes in population to monetize impacts to the selected winter recreation activity categories for the
years 2050 and 2090. Our results identify changes in winter recreation season lengths across the United States
that vary by location, recreational activity type, and climate scenario. However, virtually all locations are
projected to see reductions in winter recreation season lengths, exceeding 50% by 2050 and 80% in 2090 for
some downhill skiing locations. We estimate these season length changes could result in millions to tens of
millions of foregone recreational visits annually by 2050, with an annual monetized impact of hundreds of
millions of dollars. Comparing results from the alternative emissions scenarios shows that limiting global
greenhouse gas emissions could both delay and substantially reduce adverse impacts to the winter recreation
1. Introduction
Projected climate change through the 21st century will generate
warmer temperatures and changes in precipitation that are expected to
decrease the duration and extent of natural snow cover across the
northern hemisphere (e.g., Dyer and Mote, 2006; Brown and Mote,
2009; Dienbaugh et al., 2013). A number of studies have examined
how climate change could inuence seasonal snowpack in the western
United States (e.g., Barnett et al., 2005; Mote et al., 2005; Pierce and
Cayan, 2013), with the aim of understanding impacts on water
resources. However, the geographic extent and economic impacts of a
changing snowpack are likely to extend well beyond the western United
States (e.g., Hayhoe et al., 2007; Campbell et al., 2010). In particular,
large components of the winter recreation industry will face challenges
without reliable access to snow. This could threaten tens of millions of
current annual recreational visits and have important repercussions in
areas where winter recreation is central to economic activity.
This study follows a series of reports that quantify and monetize the
potential for climate change impacts to a number of sectors in the
United States (e.g., Walsh et al., 2014; U.S. EPA, 2015a,b). In particular,
U.S. EPA (2015a) quanties and monetizes impacts from climate
change under a range of scenarios in terms of anticipated impacts to
human health and labor, electricity, forestry and agriculture, water
resources, ecosystems, and the built infrastructure. Here we model
potential changes in snowpack at sites across the United States, and
calculate the eects of changing season lengths on the number of
recreational visits and direct revenue associated with entrance fees. Our
study expands on previous impacts work related to winter recreation by
combining the geographic breadth of previous studies (e.g., Burakowski
and Magnusson, 2012) with the detail that has historically been applied
only to site- or regionally-specic studies (e.g., Burakowski et al., 2008;
Lazar and Williams, 2008, 2010; Scott et al., 2008; Dawson and Scott,
2013). We consider a range of future climate scenarios by examining
outputs from ve global climate models (GCMs), two representative
Received 30 November 2016; Received in revised form 14 April 2017; Accepted 17 April 2017
Corresponding author.
E-mail address: (D. Mills).
Global Environmental Change 45 (2017) 1–14
0959-3780/ © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (
concentration pathways (RCPs), and both mid- and late 21 st century
time periods. As a result, this study helps expand the type and number
of sectors with national-scale estimates of quantied and monetized
impacts from climate change.
The foundation of our method is a water and energy balance model
that accounts for simplied, but site-specic climatic and topographic
characteristics to project natural snow accumulation at 247 locations in
the continental United States (CONUS). For downhill skiing and
snowboarding, we combine results from this natural snow model with
projections of how resortsabilities to make articial snow will change
in the future. For Nordic (i.e., cross-country) skiing and for snowmobil-
ing, we use the model outputs without consideration for snowmaking,
since these activities are typically more reliant on natural snow. For all
three winter recreation activities, we estimate season length during a
baseline period, and then project the impact of climate change on
season lengths through the 21st century. We summarize these impacts
as anticipated changes in future season lengths, levels of recreational
activity, and entry-based expenditures. Although our results are specic
to the United States, our methods could be easily adapted and extended
to address other international regions (e.g., the Alps, Scandinavia, New
Zealand) where snow-dependent winter recreation is an important
cultural or economic activity.
2. Methods
To produce nationally informative results on climate changes
potential impacts on winter recreation, we considered available sources
of recreational and meteorological data to select representative sites
across the CONUS. We then used a well-vetted snow model to project
natural snow accumulation and melt at each site. We used modeled
future temperatures to project changing snowmaking conditions at each
downhill skiing location. Snow modeling and snowmaking projections
were repeated for baseline and future climate conditions. We then
quantied and monetized the resulting changes in snow conditions by
integrating available baseline recreational and population data with a
series of reasonable, but simplifying, assumptions about future recrea-
tional participation and expenditures.
2.1. Recreational site selection
The rst step in our work was to identify reliable recreational
participation data for multiple locations, ideally with multiple observa-
tions over time, which could be paired with observed and projected
climate data. To link climate projections to specic winter recreation
locations, we downloaded publicly available ski area information (i.e.,
polylines) to create footprints for winter recreation sites in the CONUS
(OpenSnowMap, 2016). We then merged this information with ski area
site names and locations from the National Oceanic and Atmospheric
Administration geospatial data portal (NOAA, 2016). We compared ski
area polygons to aerial photography to verify ski area names, ensure a
match of the area footprints, and record the type of skiing for each area
(i.e., downhill, cross-country, or both) making any edits as necessary
in the review process. Our nal sample included 247 ski locations,
distributed across the 6 National Ski Areas Association (NSAA) regions
and across private and public lands, as shown in Fig. 1.
2.2. Natural snow accumulation and snowmaking model
2.2.1. Utah energy balance model
We chose the Utah Energy Balance (UEB) model (Tarboton and
Luce, 1996), a physically-based model that simulates the water and
energy balance of a seasonal snowpack. We used several criteria in the
model selection process: (1) high computational eciency, as the study
design required over 300,000 years of model simulations; (2) minimal
parameters, given the broad range of site conditions that exist across
the CONUS; and (3) acceptable performance. UEB is a single-layer snow
model, and thus is more ecient and has fewer parameters than more
complex, multi-layer models (e.g., Flerchinger et al., 1996). Even
though the UEB model is relatively simple, its performance is on par
with more complex models, as determined through snow model
intercomparison eorts (e.g., Rutter et al., 2009; Förster et al., 2014).
As discussed below, the selection of a meteorological dataset is even
more important than the model, given that uncertainty from forcing
data often exceeds that from errors due to model physics or parameters
(Raleigh et al., 2015). We used the current version of the model
(UEBveg; Mahat and Tarboton, 2012, 2014) to simulate natural snow
accumulation and snowmelt at two elevations, representing the bottom
and top of a ski area, for our selected locations. The UEB model tracks
three state variables: snow-water-equivalent (SWE), internal energy of
the snowpack, and snow surface age, the latter which aects surface
For implementation, we set the vegetation fractional cover input to
zero to simulate the open areas that predominate on wide ski area
slopes. The shortwave radiation input is calculated based on date/time,
latitude, and slope angle and azimuth. The diurnal cycle of the surface
energy balance, and thus melt, is represented because we used hourly
meteorological forcing. However, we exported only daily SWE from the
UEB model.
2.2.2. Meteorological forcing and topographic adjustments
We used hourly North American Land Data Assimilation System
(NLDAS-2) meteorological forcing data (Xia et al., 2012) to drive the
UEB snow model. NLDAS-2 data were selected because they provide
physically-consistent forcing elds for the entire United States. NLDAS-
2 forcing data are provided on a 1/8th degree (12 km) grid for the
interval from January 1, 1979 through the present. No other multi-
decadal, high-spatial resolution, continental-scale datasets exist, and
thus NLDAS-2 data have been used in hundreds of snow and hydrology
studies (e.g., Sultana et al., 2014; Fu et al., 2015; Raleigh et al., 2015).
We used data for the following NLDAS-2 variables: air temperature,
specic humidity, wind speed, downward shortwave radiation, and
downward longwave radiation. The four non-precipitation variables
were generated on a 32-km grid at 3 hourly intervals as a part of the
National Centers for Environmental Predictions North American Re-
gional Reanalysis, and then interpolated to the NLDAS-2 grid (Cosgrove
et al., 2003). The NLDAS-2 precipitation data are based on daily
weather gauge values gridded to 1/8 °, utilizing information from the
Parameter-Elevation Relationships on Independent Slopes Model
(PRISM; Daly et al., 1994) and disaggregated through time using radar
analyses when available. The UEB snow model has previously been
driven with North American Land Data Assimilation System (NLDAS)-2
meteorological forcing data (as in this study), yielding a reasonable
time series of SWE as observed at individual California Department of
Water Resources Snow Telemetry (SNOTEL) sites (Sultana et al., 2014).
The accuracy of the NLDAS-2 input data aects the SWE simulated
by the UEB model. It is challenging to model SWE in mountain ranges:
precipitation, temperature, and radiation vary dramatically on length
scales from meters to kilometers, resulting in extreme spatial variability
in SWE (e.g., Clark et al., 2011). Even though NLDAS-2 data are
relatively high resolution (1/8th degree), it is impossible for this
dataset to represent the extreme range of conditions that exist within
individual grid cells. In order to represent topographic eects at scales
ner than the NLDAS grid cells, we applied site-specic adjustments in
temperature and precipitation as a function of elevation within each ski
area boundary. To do this, we extracted monthly climate normals for
19812010 from PRISM, and regressed both temperature and precipita-
tion against elevation for each cardinal direction within each ski area
boundary. We used these regression results to calculate an average
temperature and precipitation lapse rate for each ski area. Using these
calculated lapse rates, we adjusted the baseline NLDAS forcing to
estimate precipitation and temperature at the bottom and top of each
ski area.
C. Wobus et al. Global Environmental Change 45 (2017) 1–14
The topographic adjustment accounts for dierences in elevation
between the ski area and the NLDAS model grid cell in which the ski
area exists. However, this adjustment does not account for the low bias
in NLDAS precipitation that exists in mountainous regions (e.g., Pan
et al., 2003; Argus et al., 2014; Fu et al., 2015). In two similar studies
that used NLDAS data as inputs for snow models, a comparison to
SNOTEL data was used to adjust the input precipitation to account for
this bias (Pan et al., 2003; Sultana et al., 2014). We take the same
approach here. We compared both precipitation and temperature from
NLDAS-2 to that measured at individual SNOTEL sites. We identied
SNOTEL sites within NLDAS-2 grid cells containing one of our target
skiing locations and that were within 100 m of the specied NLDAS-2
elevation. Only 27 SNOTEL stations met this criterion. Other SNOTEL
sites were too dierent in elevation, so the comparison would have
been obfuscated by topographic gradients in precipitation and tem-
perature. NLDAS-2 precipitation is 10% lower and temperature is 0.5 °C
warmer than observed at SNOTEL stations, averaged across these 27
sites. We accounted for both this 10% under-prediction relative to
SNOTEL and the documented 20% undercatch of precipitation at
SNOTEL gauges (e.g., Serreze et al., 2001) by multiplying the NLDAS
precipitation by 1.3 prior to use in the UEB model. We also subtracted
0.5 °C from the NLDAS temperature to remove the bias relative to
SNOTEL. The precipitation adjustment factor is smaller than that used
by Pan et al. (2003) and similar to that from Sultana et al. (2014).
The 27 SNOTEL sites used for this meteorological forcing adjust-
ment are all in the western United States. Homogenous monitoring
networks comparable to SNOTEL do not exist in the Midwest or East,
where meteorological observations are not made in environments and
at elevations similar to ski areas. Although SNOTEL stations are in the
western United States, we show below that the UEB model driven by
this bias-corrected NLDAS-2 forcing provides a reasonable ski season
length throughout the United States (see Section 3.1).The slope and
aspect of ski slopes each play an important role in controlling seasonal
snow accumulation and melt, due to the change in net solar radiation
per square meter depending on the incident angle of sunlight relative to
the surface. To account for these topographic inuences, we also
calculated the mean slope and modal aspect of each ski area using a
90 m digital elevation model (USGS, 2008). Our modeling includes
snow accumulation and melt at the average slope and aspect for the top
and bottom of each skiing location.
2.2.3. Modeling hours suitable for snowmaking
Snowmaking is already a critical operational feature for many
downhill skiing locations, and helps to ensure that an area is open for
the Christmas/New Year holidays (e.g., Dawson and Scott, 2013).
Typically, downhill ski areas require between 400 and 500 h of suitable
snowmaking conditions before they can open (Robin Smith, TechnoAl-
pin, personal communication, October 13, 2016), which requires a wet
bulb temperature of 28 °F or less (e.g., Scott et al., 2003; Robin Smith,
TechnoAlpin, personal communication, October 13, 2016). We calcu-
lated cumulative hours of wet bulb temperature below 28 °F beginning
on October 1 of each year as a proxy for snowmaking potential at each
location. We calculated wet bulb temperature from NLDAS-2 humidity
and air temperature (e.g., Stull, 2011) at the lowermost elevation for
each location, under the assumption that most resorts need to cover
their lowest slopes to open. In each simulation year, we recorded the
Fig. 1. Map of skiing locations, by NSAA reporting region, to project climate change impacts on natural snow accumulation and potential snowmaking conditions.
C. Wobus et al. Global Environmental Change 45 (2017) 1–14
date when each location reached 450 h of cumulative snowmaking
conditions. We calculated an average date to reach 450 snowmaking
hours for each location from each of the individual years in the 30-year
climate dataset.
2.3. Climate change scenarios
Computational and resource constraints required that we select a
subset of GCMs from the full suite of the fth Coupled Model
Fig. 2. Comparisons of UEB model SWE estimates with independent measures of season length from SNODAS, averaged over water years 20042010. Small dots represent individual
sites; large dots are regional averages with 1σerror bars.
Fig. 3. Baseline season lengths for cross-country skiing and snowmobiling.
C. Wobus et al. Global Environmental Change 45 (2017) 1–14
Intercomparison Project (CMIP5; Taylor et al., 2012) models. We chose
ve GCMs (CCSM4, GISS-E2-R, CanESM2, HadGEM2-ES, and MIROC5)
with the intent of ensuring that (1) the subset captured a large range of
variability in climate outcomes observed across the entire CMIP5
ensemble, and (2) the models were independent and broadly used by
the scientic community. For each GCM, we chose two RCPs that
captured a range of plausible emissions futures. The RCPs, originally
developed for the Intergovernmental Panel on Climate Changes Fifth
Assessment Report, are identied by their approximate total radiative
forcing in the year 2100, relative to 1750: 8.5 W/m
(RCP8.5) and 4.5
(RCP4.5). RCP8.5 implies a future with continued high emissions
growth with limited eorts to reduce greenhouse gases (GHGs),
whereas RCP4.5 represents a global GHG mitigation scenario.
To provide localized climate projections and to bias correct the
projections to improve consistency with the historical period, we used
the LOCA dataset (Pierce et al., 2014, 2015; USBR et al., 2016). The
LOCA downscaled dataset provides daily minimum and maximum
temperatures (T
and T
), and daily precipitation values at 1/16°
resolution from 2006 to 2100. For each climate scenario, we calculated
an average daily change factor for temperature and precipitation at
each grid cell by comparing 20 years of LOCA projections centered on
2050 and 2090 to a historical 1/16° gridded dataset from the period
19862005 (Livneh et al., 2015). We calculated these daily change
factors as a spatial average of nine 1/16° LOCA grid cells (3 × 3
window) surrounding each location.
We calculated hourly temperature change factors based on model-
projected changes in T
and T
by assuming these temperatures
occur at midnight and noon, respectively, and interpolating between
and T
values over the course of each day. These hourly changes
were then added to the baseline NLDAS-2 temperature time series. For
precipitation, we used the GCM outputs to calculate a multiplier to
apply to the hourly NLDAS-2 precipitation time series. In some cases,
the LOCA-modeled precipitation led to unrealistically high change
factors. To eliminate these outliers, we rst discarded values that
exceeded the 90th percentile of all change factors for each station. We
then calculated daily change factors as a 31-day moving average ratio
of this ltered time series, and applied them to the NLDAS baseline.
Additional details regarding our GCM selection process, an overview of
the selected models, and processes for producing the relevant tempera-
ture and precipitation measures are provided in Supplementary in-
formation le #1.
2.4. Winter recreation activity, season length, and monetization approach
We combined the physical modeling, described above, with avail-
able recreational visit and entrance fee data to advance the under-
standing of how climate change aects winter recreation. First, we
created baseline recreational activity levels for downhill skiing, cross-
country skiing, and snowmobiling using NSAA and National Visitor Use
Monitoring (NVUM) program data (NSAA and RRC, 2016; USFS, 2016).
NSAA provides a comprehensive, annual dataset that uses survey data
from approximately 195 ski resorts to report downhill ski visits by state
(consistent with the NSAA survey, we use the term visitthroughout to
represent one persons activity for a single day of a given type of
recreation). To produce the downhill skiing baseline, we constructed a
decadal average of skier visits by state using visit data from the
20062007 season through the 20152016 season (NSAA and RRC,
2016). The United States Forest Service (USFS) uses onsite surveys to
Fig. 4. Average percent change in annual cross-country skiing and snowmobiling season lengths across GCMs. A) Results for RCP4.5 in 2050. B) Results for RCP4.5 in 2090. C) Results for
RCP8.5 in 2050. D) Results for RCP8.5 in 2090.
C. Wobus et al. Global Environmental Change 45 (2017) 1–14
estimate the level of recreation use on national forests through the
NVUM program. NVUM has a time-series of recreation visits and
expenditure data for cross-country skiing and snowmobiling, as well
as downhill skiing and other recreational activities (USFS, 2016). To
produce the cross-country and snowmobiling baseline, we used the
average of visits from two rounds of survey data (Round 1 from 2005 to
2009 and Round 2 from 2010 to 2014). For national forests that span
multiple states, we allocated baseline visits according to the distribu-
tion of the forest area in the respective states. The NSAA data are
representative of visits in 2011 and the NVUM data are representative
of visits in 2010.
We applied changes in season length, as modeled at each of the 247
locations in the CONUS, to estimate changes in winter recreation visits.
For downhill skiing season lengths, we incorporated snowmaking,
which is consistent with Scott et al. (2003, 2008).Wedened the start
of each season as the earlier of 10cm SWE or 450 h of snowmaking at
the base of each location, and the end of each season as the last date
with 10 cm SWE at the upper elevation of each location. For Nordic
skiing and snowmobiling, the use of snowmaking is relatively uncom-
mon (Reese Brown, Cross Country Ski Areas Association, personal
communication, July 7, 2016) and, therefore, we did not incorporate
snowmaking into our analysis. We used the direct outputs from the UEB
model to simulate snowpack, and determined season length as the
dierence between the rst and last dates with 10 cm SWE at the base
elevation of each location.
Winter recreation use is strongly correlated to season length; this
correlation is particularly strong at the regional level for downhill
skiing (correlation coecients for season length versus total annual
visits range from 0.60 in the Rocky Mountains to 0.99 in the Southeast
NSAA regions). To project potential impacts of climate change on
winter recreation activities, we assumed recreational visits will change
in direct proportion to the length of the associated recreational season.
This assumption is consistent with NSAA data that show the percent
visitation by month is approximately even throughout the ski season,
particularly in the Rocky Mountain region (NSAA and RRC, 2016).
Finally, we monetized the impacts of climate change on winter
recreation using ticket prices for downhill skiing and entry fees at
national forests for cross-country skiing and snowmobiling to reect the
price of access to each recreational opportunity. For downhill skiing, we
used the average of reported adult ticket prices, by region, for the
20142015 through 20152016 seasons (NSAA and RRC, 2016).
Regional ticket prices ranged from approximately $59 in the Midwest
region to $127 in the Rocky Mountain region (NSAA and RRC, 2016; all
prices in year 2015 dollars). For comparability, we used entry fees from
the NVUM data to monetize projected changes in cross-country skiing
and snowmobiling visits in national forests. Entry fees are one
component of USFSs NVUM trip spending proles and include site
admission, parking, and recreation use fees (Stynes and White, 2005;
Dan McCollum, U.S. Forest Service, personal communication, October
12, 2016). The NVUM data ask visitors surveyed about trip-related
spending. We converted trip spending to visitor spending by dividing
the trip entry fees by the average number of people per trip. We use
these entry fee measures to monetize recreational impact given the
scale and goals of the analysis, and to avoid a number of more complex
economic issues discussed in greater detail in the Conclusions section.
For all three winter recreational activities, we rst evaluated
impacts holding population constant over time to isolate the impact
of climate change. A second set of impact estimates were then
calculated to account for projected population growth. We used the
Integrated Climate and Land Use Scenarios (ICLUS) v2.0 (U.S. EPA,
2016) county-level, all-age population projections, to project 2050 and
2090 populations by state. To estimate future visitation, we multiplied
Fig. 5. Baseline and future cross-country skiing and snowmobile season length for Bretton Woods resort in New Hampshire. Boxplots represent the distribution of 30 annual UEB model
simulations for baseline conditions and each of the future scenarios specied. A) Results for RCP4.5 in 2050. B) Results for RCP4.5 in 2090. C) Results for RCP8.5 in 2050. D) Results for
RCP8.5 in 2090.
C. Wobus et al. Global Environmental Change 45 (2017) 1–14
the ICLUS state population projections by the average annual number of
visits in each state per resident calculated for the baseline period, and
then scaled the resulting product by the estimated proportional change
in season length for the activity in that state. The change in season
length used in this calculation represents an average of change for all
sites in each state, by activity type.
3. Results
Below, we summarize results for each NSAA region by RCP and time
period and provide detailed baseline and future snow modeling results
for representative sites across the United States. Detailed annual season
length results for all locations for the 21 simulations (one baseline and
20 future projections) are included in Supplementary information le
#2. We also provide detailed baseline and future winter recreational
activity levels under climate change scenarios. Detailed state-level
changes in winter recreation visits and dollars are included in
Supplementary information le #3.
3.1. UEB model validation
We used season length derived from the Snow Data Assimilation
System (SNODAS; Barrett, 2003) to validate our baseline simulations,
by examining how UEB-simulated season length varies from region to
region across the United States. SNODAS provides daily SWE at 1km
resolution nationwide, based on a multi-layer snow model that is forced
to be consistent with remotely-sensed observations of snow extent.
SNODAS data are available beginning in 2003, so we compared season
length (duration of SWE > 10 cm) at each of the 247 sites averaged
over the 7 overlapping seasons between SNODAS and our baseline
simulations (water years 20042010). The correspondence between
UEB and SNODAS season length estimates at the ski area scale is
reasonable given the coarseness of the NLDAS-2 forcing: including all
outliers, the r
is approximately 0.6 and there is little bias (Fig. 2). The
only clear dierence between UEB and SNODAS is in the Pacic
Southwest, where the UEB season length is longer than SNODAS by
30 days.
We also compared UEB SWE with SNOTEL data at the 27 sites that
are within 1 km of ski areas. Although dierences at individual SNOTEL
sites are in some cases substantial, the average season length from UEB
at these 27 sites is nearly the same as from SNOTEL (UEB season length
for these 27 sites is 112 +/30 days; SNOTEL is 125 +/40 days).
3.2. Baseline and projected season lengths
3.2.1. Cross-country skiing and snowmobiling
For cross-country skiing and snowmobiling, season lengths vary
from less than 1 week for some sites in the Northeast and upper
Midwest to more than 24 weeks for many sites in the western United
States (Fig. 3). These season length projections assume no adaptation
from snowmaking.
Average annual changes in cross-country skiing and snowmobiling
season lengths across the GCMs range from small increases in some
locations, to declines of more than 80% under the RCP4.5 scenarios in
2050 (Fig. 4A). In general, the most signicant reductions in season
length occur in the upper Midwest and the Northeast, and the smallest
Fig. 6. Average date to reach 450 cumulative hours with conditions suitable for snowmaking (Twb < 28 °F), based on 30 years of baseline NLDAS-2 forcing data.
C. Wobus et al. Global Environmental Change 45 (2017) 1–14
reductions occur at locations in the central Rockies and Sierras. The few
locations with increases in season length are generally in arid regions of
the Southwest and parts of the upper Midwest. These increases in
season length are driven by projected increases in precipitation, which
oset projected increases in temperature by mid-century.
The general regional pattern of changes in cross-country skiing and
snowmobiling season length persists across GCMs into the late century
under the higher emissions scenario (i.e., RCP8.5 in 2090; Fig. 4D).
However, under this scenario a much larger fraction of the modeled
locations are projected to see average annual reductions from their
baseline season length of > 80% compared to the RCP4.5 estimates in
2090 (Fig. 4B).
Beneath these regional trends, there is substantial variability across
the GCMs. Fig. 5 illustrates projected changes in cross-country skiing
and snowmobiling season length at the Bretton Woods resort in New
Hampshire for each climate model/RCP combination. For this resort,
the average projected decrease in season length ranges from approxi-
mately 65% by 2050 under RCP4.5, to more than 90% by 2090 under
RCP8.5. While inter-annual variability remains high in 2050 under
some of the models (e.g., CCSM4, GISS), this variability eectively
collapses in the relatively unconstrained RCP 8.5 emissions scenarios,
particularly late in the century.
3.2.2. Downhill skiing and snowboarding
The length of the winter season for downhill skiing reects the
combined inuence of early season temperatures, which modulate
resortsability to make snow; and natural precipitation and tempera-
ture throughout the ski season, which control the water and energy
balance that drive the natural snowpack. Under baseline conditions,
locations with the highest base elevations (e.g., those in the central
Rocky Mountains) typically reach the 450 cumulative hours of snow-
making threshold by late October, whereas this snowmaking threshold
is not reached until late January or later in some locations in the
Southeast (Fig. 6). Including snowmaking, baseline season lengths for
alpine skiing range from just 12 weeks in some resorts in the
Southeast, to more than 28 weeks in the highest elevations of the
Rocky Mountains and Sierras (Fig. 7; Supplementary information File
Under climate change scenarios, the average date to reach the
cumulative 450 h snowmaking threshold increases by approximately
1020 days by mid-century under RCP4.5, and by 3070 days by late
century under RCP8.5 (Fig. 8). Winter recreation impacts are regionally
variable, with the largest delays in reaching this snowmaking threshold
occurring in the Pacic Northwest and smaller delays in the Rocky
Mountain region.
For most downhill skiing locations, opening prior to the Christmas
and New Years holidays is critical to remaining protable and staying
in business (e.g., Dawson and Scott, 2013; Robin Smith, TechnoAlpin,
personal communication, October 13, 2016). While approximately 70%
of modeled downhill skiing sites can reach 450 h of snowmaking by
December 15 under baseline climate conditions, this percentage
declines markedly under each of the future scenarios (Fig. 9). By
2050 this percentage is reduced by nearly half under both RCPs. In
2090 the contrast is sharper, as only 23% of locations would meet the
December 15 date under the RCP4.5 scenarios and only 11% of
locations under the RCP8.5 scenarios.
Nationally, changes in projected downhill skiing season lengths
range from slight increases at a few areas (10 areas and 6 areas,
respectively, for RCP4.5 and RCP8.5 in 2050; and 4 areas for RCP4.5 in
2090) to declines of more than 80% under RCP8.5 in 2050 for some
Fig. 7. Modeled baseline season lengths for downhill skiing, including snowmaking.
C. Wobus et al. Global Environmental Change 45 (2017) 1–14
locations (Fig. 10). As with the cross-country skiing and snowmobiling
season length results, the general spatial patterns of changes in season
length are largely preserved under RCP8.5 in 2090, but are amplied
relative to the 2050 results. Specically, the projected changes in
season length are most dramatic in the Northeast and upper Midwest,
and are less dramatic in the higher elevations in the Rockies and
Sierras. Further, in 2090 under RCP8.5, no areas are projected to have
an increased season and the smallest projected reduction in season
length is 15%.
As with the cross-country skiing and snowmobiling season results,
there is also considerable inter-model variability in climate change
results for downhill skiing season lengths. At Aspen Mountain, for
example, average season lengths decrease by 1020 days under RCP4.5
in 2050 and by 2575 days under RCP8.5 in 2090 (Fig. 11).
While Figs. 8 and 9 highlighted potential delays in opening dates
relative to the critical Christmas and New Years holidays, climate
change generally has a larger impact on closing dates than opening
dates across the combinations of RCPs and future years (Fig. 12). In the
most extreme reductions (RCP8.5 projections for 2090), the median
closing date is more than a month earlier than the baseline, moving
from early April to the end of February. In contrast, the largest shift in
the start date from the baseline involves several weeks from the
beginning to the end of December. Although we did not incorporate
detailed data on user visits by month, we do know that revenue from
spring break is important for some resorts, particularly in the Rockies.
Thus, scaling user visits and entry fee revenue linearly with changes in
season length (see Section 3.3) is likely to be conservative.
3.3. Quantifying and monetizing potential changes in future winter
3.3.1. Baseline winter recreation activity levels
Nationally, we estimated a baseline winter recreational activity
level of approximately 56.0 million downhill skiing visits from the
available NSAA data, with an additional 3.6 million cross-country ski
visits and 2.8 million snowmobile visits from the available NVUM data
(Table 1). Using calculated regional average adult weekend ticket
Fig. 8. Lost season days due to additional time required to reach 450 h of potential snowmaking time, by region. A) Results for RCP4.5 in 2050. B) Results for RCP4.5 in 2090. C) Results
for RCP8.5 in 2050. D) Results for RCP8.5 in 2090. (MW = Midwest, NE = Northeast, PNW = Pacic Northwest, PSW = Pacic Southwest, RM = Rocky Mountain, SE = Southeast; see
Fig. 1 for regions).
Fig. 9. Percentage of modeled areas able to reach 450 h of snowmaking before December
C. Wobus et al. Global Environmental Change 45 (2017) 1–14
prices from the NSAA data, we monetized baseline downhill skiing at
$5.4 billion; using the conceptually-equivalent entry fees results in
$32.4 million for cross-country skiing and $12.6 million for snowmo-
3.3.2. Monetized impact holding populations constant
Holding population constant at baseline levels, we project climate
change would reduce national downhill skiing visits to 35.4 million
visits under RCP4.5 and 19.8 million under RCP8.5 by 2090; this is a
decrease of 20.6 million and 36.3 million visits from the baseline,
respectively (Table 1). Holding population constant, national cross-
country skiing visits would be projected to decrease to 2.7 million
under RCP4.5 and 1.5 million under RCP8.5 by 2090, and national
snowmobiling visits would decrease from approximately 2.8 million in
2010 to 1.9 million under RCP4.5 and 1.0 million under RCP8.5 by
3.3.3. Monetized impact including population growth
In our approach, population growth increases projected winter
recreation visits. As a result, population growth dampens the projected
adverse impacts of climate change on the winter recreation industry.
Nationally, downhill skiing visits decrease slightly after adjusting for
changes in climate and population to 52.8 million under RCP4.5 and
30.6 million under RCP8.5 by 2090; a decrease in 3.2 million and 25.4
million visits from baseline, respectively (Table 1). Under RCP4.5,
cross-country skiing visits increase slightly in 2050 and 2090, and
snowmobiling visits increase slightly in 2090 (Table 1). However, for
RCP8.5, which reects a higher emissions scenario, the shortened
seasons overwhelm the increase in visits driven by population growth,
resulting in an overall decrease in recreational visits in both 2050 and
2090 (Table 1).
To clearly demonstrate the osetting impact of population growth
on these recreational visit results, we aggregated state-level results for
downhill skiing to the NSAA regions and compared projected regional
visits with and without population growth for 2050 and 2090 (Fig. 13).
The eect of population growth on winter recreation visits is most
clearly seen in the results for the Rocky Mountain and Pacic Southwest
regions. Under the RCP4.5 scenarios, downhill skiing visits in these
regions increase in 2050 and 2090 when we account for the combined
impacts of climate change and population growth. However, when we
hold population growth constant and account for only the impacts of
climate change, downhill skiing visits decline in both regions in the
RCP4.5 scenario. In the RCP8.5 scenario, this impact is still observable
in these regions. In all cases, projected visitation at each time period is
larger when population change is included.
As shown in Table 1, holding the population constant at baseline
values, the projected impacts of climate change alone could result in the
loss of tens of millions of winter recreation visits with an undiscounted
annual impact measured in the billions of dollars. Integrating the
impacts of projected climate change and population growth complicates
these results, as seen in Fig. 13. In general, our assumption that winter
recreation visits will increase with population, all else equal, mitigates
but does not fully oset the projected adverse impacts of climate
change at a national level. Regional trends in projected population
growth are also critical. Specically, the combination of relatively large
population increases in the Rocky Mountain and Pacic Southwest
regions, which have the highest average ticket prices, mitigate pro-
jected national-scale losses in visits and ticket revenue, especially under
Fig. 10. Average percent change in downhill skiing season length based on a combination of UEB model and snowmaking results. A) Results for RCP4.5 in 2050. B) Results for RCP4.5 in
2090. C) Results for RCP8.5 in 2050. D) Results for RCP8.5 in 2090.
C. Wobus et al. Global Environmental Change 45 (2017) 1–14
Fig. 11. Example output for Aspen Mountain showing change in season length for downhill skiing across all GCMs under A) RCP4.5 in 2050, B) RCP4.5 in 2090, C) RCP8.5 in 2050, and
D) RCP8.5 in 2090.
Fig. 12. Average baseline and projected season start and end dates for the downhill ski season, across all modeled resorts. Median opening date is represented by the red line at the bottom
of the box and whiskers plot, and median closing date is represented by the red line at the top of the box. Boxes enclose the middle 50% of the season length distribution, and whiskers
extend to the 5th and 95th percentiles. (For interpretation of the references to colour in this gure legend, the reader is referred to the web version of this article.)
C. Wobus et al. Global Environmental Change 45 (2017) 1–14
RCP 4.5 (Table 1).
4. Conclusions
Physical models that account for changes in natural snow and ski
resortsability to make snow demonstrate that season lengths for winter
recreation activities will decline at nearly all sites in the CONUS under
the considered future climate scenarios. In each region of the United
States, these impacts increase in severity over time for a given emissions
scenario, and also increase in severity with GHG emissions for a given
time period: impacts are more severe under RCP8.5 vs RCP4.5 at any
point in time and more severe in 2090 compared to 2050 for a given
Underlying these national results, we found considerable variability
at all levels of the analysis, particularly with respect to the spatial
distribution of impacts. In general, sites at higher elevations (such as
the Rocky Mountains and Sierras) tend to be more resilient to projected
changes in temperature and precipitation, whereas sites at lower
elevations (generally in the upper Midwest and New England) are more
sensitive to climate change. Based on our modeling, the dierence
between RCP4.5 and RCP8.5 could represent the dierence between
preserving skiing and snowmobiling in the eastern half of the country
and losing these activities almost completely by 2090 (Fig. 10). When
these physical modeling results are monetized using current prices for
recreational entry while accounting for population change, we nd that
the changes in winter recreation season lengths under RCP8.5 could
result in a loss of more than $2 billion annually for downhill skiing, and
an additional $5 million and $10 million for snowmobiling and cross-
country skiing, respectively (Table 1).
These results include a number of important caveats. On the
physical modeling side, our snow model was simplied to simulate
average conditions at the top and bottom of 247 areas across the
CONUS, and was driven by a relatively coarse-scale representation of
climate. The UEB model framework is exible enough that it could be
rened on a site by site basis to generate an improved calibration for
each individual site. However, this added level of specicity would
have made both the data requirements and the computational burden
too high for this national study.
Our monetization approach also required a number of simplifying
assumptions. For example, not all downhill ski areas participate in the
NSAA data collection or are located on national forest lands, so our
impacts on estimated visits may be understated. Similarly, considerable
cross-country skiing and snowmobiling activity occurs outside of
national forests, so those impacted visit estimates are likely also
understated. Our entry fee also does not measure the implicit value of
winter recreation or the full monetary impact of these activities in a
specic region or collection of regions. While alternative economic
approaches could be incorporated to try to fully monetize the projected
impacts of climate change on winter recreation, we did not attempt to
do that here.
We also have not attempted to account for the complete loss of
recreational activity with the closure of facilities, as this would require
consideration and development of business models or general operating
rules that are beyond the scope of this study. However, our modeling
does suggest increased pressure on downhill ski facility operators in
general as sequences of what would currently be considered marginal
snow seasons increase over time. This is particularly relevant when
recognizing that revenue and prot for downhill ski operators is often
concentrated into the start and end of the current winter season (e.g.,
Christmas/New Years holiday and spring break). Over time, pressure
on these important revenue periods could result in a facilitys closure.
Since we have not attempted to project potential closures, our projected
estimates of downhill skiing visits could be conservative if visits to a
closed area are not transferred to those that remain open.
Finally, we have not accounted for the dierent types of substitution
that could arise with climate change. The impacts of climate change on
Table 1
National projected impacts in terms of visits by recreational activity averaged across models for dierent time periods and RCPs.
Baseline Impacts in 2050 Impacts in 2090
Visits Dollars Change in visits
Change in visits
Equivalent monetized
impact (RCP4.5)
Equivalent monetized
impact (RCP8.5)
Change in visits
Change in visits
Equivalent monetized
impact (RCP4.5)
Equivalent monetized
impact (RCP8.5)
National impacts of climate change holding population constant
Downhill skiing 56,028,000 $5,400,134,000 (16,131,000) (19,772,000) ($1,367,232,000) ($1,716,806,000) (20,608,000) (36,259,000) ($1,783,996,000) ($3,255,810,000)
3,590,000 $32,368,000 (636,000) (873,000) ($5,949,000) ($8,037,000) (935,000) (2,055,000) ($8,623,000) ($18,711,000)
Snowmobiling 2,821,000 $12,641,000 (681,000) (844,000) ($3,072,000) ($3,798,000) (912,000) (1,802,000) ($4,106,000) ($8,102,000)
Total 62,439,000 $5,445,143,000 (17,448,000) (21,489,000) ($1,376,251,000) ($1,728,641,000) (22,455,000) (40,116,000) ($1,796,725,000) ($3,282,623,000)
National impacts of climate change with anticipated population growth
Downhill skiing 56,028,000 $5,400,134,000 (6,845,000) (11,270,000) ($345,580,000) ($778,142,000) (3,228,000) (25,448,000) $125,888,000 ($2,029,791,000)
3,590,000 $32,368,000 220,000 (87,000) $1,635,000 ($1,060,000) 628,000 (1,106,000) $5,226,000 ($10,299,000)
Snowmobiling 2,821,000 $12,641,000 (113,000) (318,000) ($525,000) ($1,442,000) 83,000 (1,212,000) $352,000 ($5,469,000)
Total 62,439,000 $5,445,143,000 (6,738,000) (11,675,000) ($344,470,000) ($780,644,000) (2,517,000) (27,766,000) $131,466,000 ($2,045,559,000)
C. Wobus et al. Global Environmental Change 45 (2017) 1–14
future winter recreation season lengths and associated conditions raise
the potential for three general types of substitution, including (1)
temporal, where the timing of future recreation will change, generally
shifting to later in the season; (2) spatial, where recreators will change
travel patterns to access dierent areas; and (3) activity, where some
recreators may switch to dierent recreational activities altogether. By
adjusting future recreation for projected season length, we are imposing
a strong assumption that captures some, but not all of these substitution
All of these caveats represent simplications that were required to
complete this national-scale analysis. Despite these simplications,
however, our approach represents an important step forward in that
it combines detailed physical modeling with a nationally consistent
monetization approach to evaluate how climate change might aect
this important industry in the United States. The methodology we have
employed in this study is also easily transferable, and could be rened
and adapted for further insight within the United States or for
applications elsewhere. For example, we could gather more detailed
meteorological, topographic and spending data from a single resort to
develop a site-specic model to dive deeper into the potential impacts
for a specic location. Alternatively, we could synthesize national-scale
meteorological and topographic data from other parts of the world to
develop scoping analyses of climate change impacts on winter recrea-
tion for other countries or regions. In any case, it is clear from this study
that climate change will have profound impacts on usersability to
enjoy skiing and snowmobiling over the 21st century in the United
States. These impacts will ripple through the economies of regions that
depend strongly on these activities, and indicate signicant challenges
for snow-dependent communities under these, and similar, climate
change scenarios.
Funding sources
This work was funded by the U.S. Environmental Protection
AgencysOce of Atmospheric Programs through contract No. EP-
This work greatly beneted from information provided by Robin
Smith of TechnoAlpin and Harry Lynk of the Aspen Skiing Company
with respect to snowmaking and downhill ski area operations. The work
also beneted from discussions with U.S. Forest Service (USFS) sta
including Dan McCollum, Don English, David Chapman, and Eric White
over the use and interpretation of dierent USFS recreational survey
data and reports. The views expressed in this article are solely those of
the authors, and do not necessarily represent the views of their
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in the
online version, at
Fig. 13. Comparison of projected impact of climate change on downhill skiing visits holding population constant and allowing for population changes over time.
C. Wobus et al. Global Environmental Change 45 (2017) 1–14
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... Industry leaders emphasize snowmaking is a central adaptation strategy to future climate change in US regional markets (Knowles, 2019, Wilkins et al., 2021Wobus et al., 2017), including further declines in the length of midlatitude winters (Wang et al., 2021) and changes in the timing and reductions in natural snowpacks in eastern (Ning & Bradley, 2015) and western US Figure 1. Expansion of snowmaking capacity in the US regional markets. ...
... Without snowmaking, Parthum and Christensen (2022) project climatological changes in winter could reduce total skier visit revenue in the US ski industry between À35% and À50% by mid-century and À40% to À60% by late-century (under RCP4.5 and RCP8.5 scenarios respectively). Facing such large potential economic losses from changes in the natural snowpack, reliance on snowmaking will likely increase substantially in all US ski regions in the decades ahead Wobus et al., 2017); highlighting the need to determine where such an expansion might be maladaptive. ...
... Another important tourism system question raised by Figure 3 is that if snowmaking were curtailed for economic or regulatory reasons and ski operations and associated tourism were no longer viable at specific destinations, what would replace this activity and what would be the emission outcomes of potential substitutions (e.g., carbon-intense long-haul flight or cruise)? This is a salient question in US regional ski markets such as the Midwest, Southeast, and Northeast where almost all skiable terrain has snowmaking capacity and average seasons without snowmaking would not be physically and economically viable at most locations Wobus et al., 2017). Studies of skiers observed and self-reported substitution behaviours (spatial, temporal, and activity) in North American markets provide insight to this question. ...
Snowmaking has been an integral part of the multi-billion-dollar ski industry in most regional markets for more than 20 years and is one of the most visible and widespread forms of climate adaptation in the tourism sector. Under accelerating climate change, snowmaking is projected to increase at most destinations - some substantially. Snowmaking has come under increasing criticism in recent years and branded by some scholars and ski industry observers as unsustainable and maladaptive as a climate change response. Using data on snowmaking from across the diverse US ski market, this study assesses snowmaking against multiple established criteria that define maladaptation. The analysis demonstrates that snowmaking is highly place-context specific, varying at the individual operator and regional market scales, and represents a continuum from successful (and sustainable) adaptation to maladaptation. Regions of the US where snowmaking is most likely to be maladaptive are identified (water insecure and carbon intense electricity grids). The framework highlights the importance of scale and a tourism system perspective when assessing (mal)adaptation and provides decision-makers with a tool to evaluate the compatibility of snowmaking with climate action plans at the destination and regional scale.
... Moreover, this massive influence on SCA might directly impact hydrological droughts, with an overall rise in droughts' extent, severity, and length [62]. Several researches has shown that climate change substantially impacts SCA in different mountain ranges [63][64][65]. Our findings also support prior regional and local climate change impact assessment studies. ...
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Mountainous basins are frequently called “natural water towers” because they supply essential water to downstream regions for irrigation, industrial–municipal use, and hydropower generation. The possible implications of climate change on water supplies have gained prominence in recent years, particularly in snow-dominated mountainous basins. The Euphrates River, a snow-fed transboundary river that originates from the Eastern part of Türkiye with several large dam reservoirs downstream, was chosen within this scope. The study reveals the impact of climate change on two snow-dominated headwaters, namely Karasu and Murat, which have a basin area of 41,109 km2. The impact of climate change is assessed across runoff regimes and snow dynamics for future periods (2024–2099). Global Climate Model (GCM) data sets (CNRM-CM5, IPSL-CM5A, EC-EARTH, MPI-ESM-LR, NorESM1-M, HadGEM2-ES) were downscaled by Regional Circulation Models (RCMs), provided from CMIP5 EURO-CORDEX domain for climate projections under RCP4.5 and RCP8.5 scenarios. Future projections of runoff and snow variables are predicted by two conceptual hydrological models, HBV and HEC-HMS. The results indicate a dramatic shrink in snow cover extents (>65%) and snow duration (25%), a decrease in snow water equivalent (>50%), and a timely shift (up to a month) in peak runoff through early spring in the runoff hydrograph for the last future period (2075–2099). The overall assessment shows that operations of downstream water systems should be reconsidered for future changes.
... At high-elevation parks and wilderness areas, snow plays a primary role in park and wilderness management (Bales et al. 2006), particularly in the western United States where in the past half-century, mountain snowpack has decreased (Mote et al. 2018). Climate projections suggest continued contraction of mountain snowpack in the region, resulting in less wildernessdependent winter recreation activities such as cross-country skiing and backcountry snowshoeing (Wobus et al. 2017). Reduced spring snowpack has also been associated with earlier peak runoff and lower streamflow, lake, and reservoir levels, all of which can alter the availability of water-dependent outdoor recreation activities (Cutler et al. 2017;Jenkins 2022). ...
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Wilderness visitation, particularly overnight use, is reactive to climate variability as backpackers face greater exposure to and dependence on environmental conditions. This study examines the effect that spring snowpack has on the timing and volume of permits issued for overnight use of the Yosemite Wilderness during peak and shoulder season months (April-October) from 2002-2019. We categorize April 1 st snowpack at Tuolumne Meadows into snow drought (<75%), high snowpack (>125%), and near average snowpack (75-125%). Results confirm Wilderness-wide differences between snowpack categories, including change in spring overnight visitors (April-June: +20% snow drought, −28% high snowpack). Our findings confirm that snow drought allows for more access to high elevation trailheads when seasonal roads are open earlier in spring (May-June: +74% Tioga Road, +81% Tuolumne Meadows). Mid-to-high elevation trailheads experience a sustained increase in use during high snowpack years (June-October: +12% Yosemite Valley and Big Oak Flat, +15% Glacier Point Road and Wawona; +32% Hetch Hetchy) as a narrower seasonal access window leads to filled permit quotas in the high country and displaces use to lower elevation trailheads. These findings have implications for wilderness stewards, including biophysical and experiential impacts to wilderness character from earlier and longer seasons, especially at higher elevation and fragile alpine and sub-alpine areas, as snow drought in mountain protected areas becomes more common. Recommendations to address greater early season use and its attendant impacts include adaptively managing permits for different types of snowpack years, including potential changes in the number, timing, and destination of select trailhead quotas.
... There will be more rain than snow with increasing temperature, with the most significant changes occurring in the northern Sierra Nevada at lower elevations (Jardine and Long 2014) (chapter 3). Snow-dependent recreation across Sierra Nevada national forests will be especially sensitive to this shift (Reynier et al. 2015), reducing the length of the winter recreation season and participation in snow-dependent activities (Wobus et al. 2017). Although projected changes in the Sierra Nevada in cross-country skiing, snowmobiling, and downhill skiing are smaller than projected for other Resources Planning Act assessment regions (USDA FS 2016), the benefits to recreationists and economies from snowbased activities in the Sierra Nevada require consideration. ...
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Chapter 4 discusses Climate Change impacts on Infrastructure, particularly roads, and methods for assessment and adaptation measures to deal with climate change, fires, and storms and floods in mountains.
... Moreover, the chances for marginal snow levels will also rise in key winter months and start later and finish sooner. In other study conducted by Wobus et al. (2017), they found that a certain percentage change in the duration of a ski season is also assumed to mean a change in demand of the same degree. ...
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Climate change is a critical issue today which significantly affected not just the ecosystem of the community but also the sustainability of tourism industry. Climate change consequences on sustainable tourism are crucial because it increases the danger of species extinction, decreases freshwater, increases wildfire accidents, heat waves, and illnesses, all of which cause visitors to avoid certain places. This study surveyed the community awareness toward climate change and also their perceptions regarding the adaptation strategies for impacts of climate change on sustainable tourism in Malaysia. A number of 400 respondents living in the area of sustainable tourism in Malaysia which are directly and indirectly affected by climate change was selected to fulfill the objectives of the study. An online survey was implemented for the purpose of distributing questionnaire during the pandemic. The findings indicated that two domains derived as adaptation strategies and were named as enhancing awareness and capacity development and also diversification of sustainable tourism activities. This study significantly provides the policymakers a comprehensive adaptation plan to overcome the impacts of climate change on sustainable tourism in Malaysia through the community perspectives. It also assists the policymakers to strongly understand the consequences of adaptation measures of climate change for the future sustainability of tourism industry in Malaysia.
... The variation in the distribution of the impacts of desertification was displayed through box plots, district-wise, since they are ideally used in various researches to show the distribution between scale and categorical variables (Cammarano et al. 2019;Gornott and Wechsung 2016;Munir et al. 2019;Wobus et al. 2017). New mean variables were computed for the above-mentioned Likert scales, in SPSS 26, for the box plots to be prepared, as box plots work with scale and categorical variables. ...
Anthropogenic activities and climatic variations continue to aggravate desertification in the drylands of the world. This study is aimed to explore the perceptions of local residents in the drylands of Bahawalpur, Rahim Yar Khan and Rajanpur districts, lying in the drylands of South Punjab, regarding the impacts of desertification on humans, finances, animals and the environment of the study area. In addition , we explored possible relations between these impacts and adaptive capacity of the local population. Primary data was collected from 399 respondents in a survey conducted during Feb-July 2019 using disproportionate stratified random sampling techniques. The Rajanpur District suffered the most in terms of human and environmental impacts, while Rahim Yar Khan experienced the lowest financial and human impacts, but most severe livestock impacts due to desertification. We also found that increases in water scarcity of surface water bodies and decline in groundwater levels , along with an increase in unemployment and delayed repayment of loans, all led to reduced adaptive capacity of the respondents. These results are helpful for policy makers to plan desertification control policies, that are region specific and focus on the main impacts being faced by each district.
... First, snow production and grooming demands have increased both for climate change effects (Pickering & Buckley, 2010) and tourist demand for ski season extension (Spandre, et al., 2016). The effects of climate change include less reliable snow cover (Steiger, et al., 2019) and shorter snow season (Wobus, et al., 2017). Second, the number of ski lifts have increased (Falk, 2015), with a preference for the energy-intensive ones (e.g. ...
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Nowadays, ski resorts represent an energy-intensive industry in the mountain environment. On the one hand, their energy consumptions (and costs) have greatly increased in the last 40 years, linked to growing: (I) snow production and grooming demands, facing climate change and ski season extension, (II) number of ski lifts, with a share expansion of the energy-intensive ones (e.g. gondola), (III) need of operational buildings (e.g. warehouses, workshops). On the other hand, their energy transition calls for energy efficiency improvement and renewable sources integration, to be part of cleaner energy systems with low emissions and low environmental impacts (e.g. water use). This paper describes the developing and testing of an "Integrated Energy Management System - IEMS" in the "Living Lab Madonna di Campiglio", as part of the Interreg Alpine Space - Smart Altitude project. After a detailed characterization of the case study, this paper describes the development of the new IEMS as an integration of existing and new monitoring systems and platforms. This IEMS aims to facilitate and stimulate ski resort technicians and managers in the continuous analysis of energy, environmental and economic performance, paving the way for greater awareness and targeted interventions.
Seasonal snowpack in the Western United States (WUS) is vital for meeting summer hydrological demands, reducing the intensity and frequency of wildfires, and supporting snow-tourism economies. While the frequency and severity of snow droughts (SD) are expected to increase under continued global warming, the uncertainty from internal climate variability remains challenging to quantify. Using a 30-member large ensemble from a state-of-the-art global climate model, the Seamless System for Prediction and EArth System Research (SPEAR), and an observations-based dataset, we find WUS SD changes are already significant. By 2100, SPEAR projects SDs to be nearly 9 times more frequent under shared socioeconomic pathway 5-8.5 (SSP5-8.5) and 5 times more frequent under SSP2-4.5. By investigating the influence of the two primary drivers of SD, temperature and precipitation amount, we find the average WUS SD will become warmer and wetter. To assess how these changes affect future summer water availability, we track April 15th snowpack across WUS watersheds, finding differences in the onset time of a “no-snow” threshold between regions and large internal variability within the ensemble that are both on the order of decades. For example, under SSP5-8.5, SPEAR projects California could experience no-snow anywhere between 2058 and 2096, while in the Pacific Northwest, the earliest transition happens in 2091. We attribute the inter-regional uncertainty to differences in the regions’ mean winter temperature and the intra-regional uncertainty to irreducible internal climate variability. This analysis indicates that internal climate variability will remain a significant source of uncertainty for WUS hydrology through 2100.
The successful bid for the 2022 Winter Olympics has provided a strong impetus for the development of China's ski industry. Ski areas have sprung up throughout the country, even in the low latitudes south of 30°N. However, ski tourism is extremely susceptible to weather and climate conditions. In the context of global warming, it has become important to assess the climate reliability of ski areas. Therefore, this study demonstrates a novel approach to assessing the ski tourism sector's climate risks, which can be easily applied in other markets catering to the same industry. Using the random forest regression model based on climate projections and survey data, we projected the ski season start dates, end dates and season lengths of 694 existing ski areas in China under three emission scenarios (RCP2.6, RCP4.5 and RCP8.5). Climate projections, including air temperature, snowfall, rainfall, wind speed and air humidity, were the ensemble means from five climate models. Results indicate that ski areas in China may have later start dates, earlier end dates and shorter ski seasons before 2099. By the 2090s, under RCP2.6, RCP4.5 and RCP8.5, 20% (139), 28% (195) and 35% (245) of ski areas are projected to be at high climate risk (ski season less than 60 d), respectively, while 28% (197), 23% (157) and 8% (56) of ski areas are at low climate risk (ski season with at least 100 d), respectively. The climate risks are ranked from the highest to lowest in East, Central, Southwest, North, Northwest and Northeast China. Furthermore, the ski tourism sector in the latitudes south of 40°N is exposed to much higher climate risks than in areas north of 40°N. Therefore, climatic reliability should be carefully considered before establishing or expanding ski areas to avoid unnecessary resource waste and ecological damage, as well as to promote sustainable development in mountain areas.
Technical Report
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The Earth's climate is changing. Temperatures are rising, snow and rainfall patterns are shifting, and more extreme climate events – like heavy rainstorms and record high temperatures – are already happening. Many of these observed changes are linked to the rising levels of carbon dioxide and other greenhouse gases in our atmosphere, caused by human activities. EPA partners with more than 40 data contributors from various government agencies, academic institutions, and other organizations to compile a key set of indicators related to the causes and effects of climate change. The indicators are published in EPA's report, Climate Change Indicators in the United States. Order print copies or send inquiries by emailing:
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Physically based models provide insights into key hydrologic processes, but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure. Forcing uncertainty is enhanced in snow-affected catchments, where weather stations are scarce and prone to measurement errors, and meteorological variables exhibit high variability. Hence, there is limited understanding of how forcing error characteristics affect simulations of cold region hydrology. Here we employ global sensitivity analysis to explore how different error types (i.e., bias, random errors), different error distributions, and different error magnitudes influence physically based simulations of four snow variables (snow water equivalent, ablation rates, snow disappearance, and sublimation). We use Sobol' global sensitivity analysis, which is typically used for model parameters, but adapted here for testing model sensitivity to co-existing errors in all forcings. We quantify the Utah Energy Balance model's sensitivity to forcing errors with 1 520 000 Monte Carlo simulations across four sites and four different scenarios. Model outputs were generally (1) more sensitive to forcing biases than random errors, (2) less sensitive to forcing error distributions, and (3) sensitive to different forcings depending on the relative magnitude of errors. For typical error magnitudes, precipitation bias was the most important factor for snow water equivalent, ablation rates, and snow disappearance timing, but other forcings had a significant impact depending on forcing error magnitudes. Additionally, the relative importance of forcing errors depended on the model output of interest. Sensitivity analysis can reveal which forcing error characteristics matter most for hydrologic modeling.
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Global climate model (GCM) output typically needs to be bias corrected before it can be used for climate change impact studies. Three existing bias correction methods, and a new one developed here, are applied to daily maximum temperature and precipitation from 21 GCMs to investigate how different methods alter the climate change signal of the GCM. The quantile mapping (QM) and cumulative distribution function transform (CDF-t) bias correction methods can significantly alter the GCM's mean climate change signal, with differences of up to 2 degrees C and 30% points for monthly mean temperature and precipitation, respectively. Equidistant quantile matching (EDCDFm) bias correction preserves GCM changes in mean daily maximum temperature but not precipitation. An extension to EDCDFm termed PresRat is introduced, which generally preserves the GCM changes in mean precipitation. Another problem is that GCMs can have difficulty simulating variance as a function of frequency. To address this, a frequency-dependent bias correction method is introduced that is twice as effective as standard bias correction in reducing errors in the models' simulation of variance as a function of frequency, and it does so without making any locations worse, unlike standard bias correction. Last, a preconditioning technique is introduced that improves the simulation of the annual cycle while still allowing the bias correction to take account of an entire season's values at once.