Content uploaded by Jürgen Schmude
Author content
All content in this area was uploaded by Jürgen Schmude on Nov 19, 2019
Content may be subject to copyright.
Tourism Economics, 2014, 20 (2), 323–336 doi: 10.5367/te.2013.0272
The Christmas–Easter shift: simulating
Alpine ski resorts’ future development under
climate change conditions using the
parameter ‘optimal ski day’
A
NJA
B
ERGHAMMER
AND
J
ÜRGEN
S
CHMUDE
Department of Geography, University of Munich, Luisenstraße 37, 80333 Munich,
Germany. E-mail: juergen.schmude@geographie.uni-muenchen.de.
(Corresponding author: Jürgen Schumde.)
Ski tourism is strongly influenced by climate change. The economic
success of ski tourism regions depends on both the quantity and
quality of the ski resorts’ opening days. However, to date there has
been little research on the quality of ski season opening days. The
purpose of this paper is to develop and apply a parameter that enables
us to assess the future development of ski seasons in a differentiated
way. The results show a decrease of ski area opening days from 2011
to 2060 in the investigation area in general and a trend to an intra-
seasonal postponing of optimal ski days, labelled the ‘Christmas–
Easter shift’. The parameter developed contributes to our understand-
ing of climate change effects on the tourism supply-side at a regional
scale and offers decision support to ski lift operators or tourism
associations in terms of adaptation measures.
Keywords: winter sports tourism; climate change; climate impact
research; regional economic effects; season length
The ski tourism industry in the Alps is very heterogeneous in terms of the size,
structure and profitability of ski areas and their adaptability to climatic changes
(Abegg and Elsasser, 2007). While ski resorts that have well developed infra-
structure, are located at high altitudes and therefore rated as snow-reliable are
mostly successful, smaller ski areas in disadvantaged locations often struggle to
make a profit. Since ski area operators largely depend on the quality and
quantity of snow conditions, they are highly sensitive to snow deficient winters
(Falk, 2011). To date, the decline of snow reliability at lower altitudes has
started to lead to a concentration process in the number of ski operators and
destinations (Müller and Weber, 2008). It must be assumed that this process
will become even more marked under climate change conditions in the future
(Elsasser and Bürki, 2002; Dawson and Scott, 2010).
Furthermore, climate change leads to an intra-seasonal postponing of snow
T
OURISM
E
CONOMICS
324
reliability. Historically, the seasonal peak days throughout much of Europe and
North America are – because of holidays – those either side of Christmas and
during February (Bark et al, 2010). As tourists start to shift the date of travel
to prevent unfavourable climate conditions affecting their holiday or completely
avoid destinations affected by climate change (Harrer, 1996), a spatial and/or
temporal shift of tourism patterns is expected (OECD and UNEP, 2011). At
the same time, consumer perception of destinations is also changing because
of media reporting of climate change and related events (Jones and Phillips,
2011).
To counteract these demand-side adaptations in terms of modified travel
periods, destinations and travel frequencies (Berrittella et al, 2006) and to stay
competitive, ski area operators try to maintain their attractiveness to tourists
even under changing climate conditions by using technical adaptation measures.
Such measures include the grooming of ski slopes, the development of terrain
at higher altitudes and artificial snow-making (Scott et al, 2009). Over the last
five years, the artificially snowed areas in Bavaria increased by 170% to 711
ha (Frenzel, 2011). The total ski slope area in Bavaria amounts to 3,700 ha
(Hahn, 2004), so that about 19% is artificially snowed. This share is relatively
small compared with Austria (67%) or Switzerland (36%) (VDS, 2012). Pro-
ducing artificial snow is costly and small single operators fairly quickly reach
the limits of their financial resilience. Moreover, as ski tourism in the Alps is
focused on four to five months (between November and April), it requires, on
the supply-side, seasonal staff in hotels (Boon, 2006), distributors, transport
operators and of course precisely timed advertising.
To assess the profitability of ski areas, the literature refers to the 100-day
rule (Witmer et al, 1986), which states that a ski resort reaches its breakeven
point if snow reliability (snow depth of at least 30 cm for alpine skiing) is
achieved on 100 days per season. If this condition is met in 7 of 10 consecutive
years, the ski area is said to be economically viable. Abegg (1996) points out
that the 100-day rule functions as a reference point for practitioners. This rule
of thumb is regarded as valid for each ski area, independent of its geographic
position or specific local climatic factors. As the rule is oriented only to the
snow depth, it can be assumed that the threshold of 100 days is set too high:
economically successful ski areas can assert themselves on the market even with
a smaller number of open days. In contrast, for smaller, less rentable ski areas
100 open days per season might be insufficient in perpetuity (Soboll and
Dingeldey, 2012). If, additionally, the investment volume for snow making and
lift facilities is taken into account, it is more specifically the breakeven point
of modern, well equipped ski resorts which is higher during the depreciation
period (Dawson et al, 2009). Thus, 100 days might not be enough to be
economically successful. However, smaller ski areas with facilities already
amortized and with lower overheads could be cost effective even with a dis-
tinctly reduced number of open days (Sax, 2008).
Consequently, tourism practitioners are not only interested in the number
of open days per season; the intra-seasonal distribution and the quality of open
days are also important (Fukushima et al, 2002). In the light of a changing
climate, a ski area’s particular future development could be assessed in a more
realistic way using knowledge of these additional factors in a new key figure.
The aim of this paper is to present the development, implementation and
325Simulating Alpine ski resorts’ future development
application of such a parameter – the ‘optimal ski day’ (OSD). Therefore, in
Methodology, we briefly introduce the superior modelling concept and the
climate scenarios developed in the project GLOWA-Danube that are necessary for
the OSD design. (For more detailed information about the project, see http:/
/www.glowa-danube.de or Barthel et al ((2008). Next, we describe the param-
eter’s conceptualization, also considering the requirements and input of tourism
practitioners: ski lift operators, chairs of skiing clubs, ski instructors and
representatives of tourism associations who were involved in our project. For
this purpose, the variables included in the parameter OSD are outlined, before
the relevance of an open day’s quality for the ski area’s turnover is expressed
in an equation. The Results section introduces results from different climate
scenario simulations. Finally, these results and the parameter’s possibilities and
limitations are discussed and conclusions are drawn.
Methodology
Project background – modelling concept, climate scenarios and multi-agent models
The quantity and distribution of open days within a season can be simulated
in terms of a fully coupled simulation system developed in the interdisciplinary
project GLOWA-Danube (GLObal change of the WAter cycle in the Upper
Danube catchment; http://www.glowa-danube.de). In this 10-year research project
(2001 to 2010) funded by the German Federal Ministry of Education and
Research, the effects of climate and societal change on a broad range of sectors,
such as tourism, households or farming, have been intensively analysed (Barthel
et al, 2008). To enable a wide analysis of the effects of climate change on a
broad range of sectors, different climate and societal scenarios can be simulated
within the period between 2011 and 2060 (Soboll and Schmude, 2011). The
scenarios can be combined modularly to met the needs of scientific or practical
users. As a further advantage, using different scenarios, a wide range of possible
future developments can be investigated. Simulations are run for the Upper
Danube catchment, an area of 77,000 km², including parts of Germany, Austria
and Switzerland (Mauser et al, 2008).
According to the modular principle, one can choose a climate scenario and a
societal scenario for a simulation run. A climate scenario consists of a direction-
giving climate trend and a further modifying climate variant. The four climate
trends available for the investigation area are all based on the moderate global
Intergovernmental Panel on Climate Change (IPCC) emission scenario A1B
which is regarded as the most established and highest developed scenario for
Europe concerning quality as well as uncertainty level (Solomon et al, 2007).
This global climate model (GCM) sketches a development assuming that the
world’s population will grow until the mid-21st century and decline afterwards,
a fast economic growth and a rapid spread of new and efficient technologies.
Concerning energy supply, shares of fossil and renewable energy sources are
deemed to be equivalent.
Based on this GCM, four regional climate models (RCMs) have been devel-
oped that vary in terms of the level of temperature increase (from +3.3°C to
+ 6.8°C in winter between 1990 and 2100, and from +3.3°C to +5.2°C in
T
OURISM
E
CONOMICS
326
summer between 1990 and 2100) and precipitation alteration (from –5% to
+47% in winter between 1990 and 2100, and from –69% to –14% in summer
between 1990 and 2100) (Mauser et al, 2009) and thus offer a broad range of
possible climatic futures. The RCMs have a spatial resolution of 1 km² and a
temporal resolution of a day and offer a variety of climate variables, all of which
make the model unique (Barthel et al, 2010).
Depending on the hypothesis, a climate variant can be chosen additionally
(Mauser, 2010). These variants specify the selected climate trend and are derived
from a statistical climate forcing generator that produces synthetic meteorologi-
cal time series stochastically from historical measurement series (Mauser, 2010).
From the large number of synthetic time series (5,000 in each case), climate
variants with a probability of occurrence of 5% are selected. In the project, four
climate variants (‘baseline’, ‘five warm winters’, ‘five hot summers’ and ‘five dry
years’) are available that were designated as relevant by the project’s stakeholders.
If, for instance, a ski area operator wants to know whether planned investments
in lift facilities are economically reasonable, even if five consecutive warm
winters are expectable in the near future, an adapted simulation can be run.
As climate change and societal development are interacting parts of a com-
plex system, a complete GLOWA-Danube scenario is a combination of a climate
trend, a climate variant and a societal scenario. The societal scenario describes
different possible trajectories of the demographic, economic and political de-
velopment. The societal scenarios are defined independently of the climate scenarios
and no assumptions are made as to their respective probabilities of occurrence.
Beyond a ‘baseline’ scenario, which assumes a continuation of present societal
conditions, two contrary scenarios, ‘open competition’ and ‘public welfare’, are
distinguished. The scenario simulations run for the paper are all based on the
societal scenario ‘baseline’ and vary only in response to the chosen climate trend.
For further information on the configuration of societal scenarios, refer to Kuhn
and Ernst (2009).
The use of multi-agent models allows for a regionally differentiated simu-
lation and assessment of diverse adaptation strategies for various affected actor
groups in a bottom-up approach (Soboll et al, 2011). One of these multi-agent
models, the tourism model, investigates the operability of the tourism supply
system in the research area with sophisticated temporal (daily changes) and
spatial (1 km grid) focus (Soboll et al, 2012). In concrete terms, we have
modelled hotels and restaurants as well as public swimming pools, golf courses
and ski areas with regard to climate change and tourism demand development
(Soboll and Schmude, 2011). In the case of the ski areas, each real ski resort
in the area under investigation is represented by a modelled resort, which
corresponds to the original in its location and other attributes, namely the
length of slopes, the number of snow cannons and the water reservoir volume.
To enable this, we determined the geographical coordinates of each ski resort
in order to locate it exactly in the model grid (Dingeldey, 2008). During a
simulation, every single modelled ski area reacts to the arising environmental
conditions and opens, for example, if the snow surface reaches a predefined
sufficient depth or closes if too much snow has melted following a rise in
temperature. Accordingly, we can calculate the evolution of the number of days
opened during the simulation period not only for aggregated levels, such as
states or districts, but also for single ski resorts.
327Simulating Alpine ski resorts’ future development
Thus the model allows for the differentiation of required open days for a
long-term economic operation. According to a survey of 93 ski area operators
(Sax, 2008), Swiss ski resorts need 107 open days per season on average for
economic survival because of their high investment volume. In contrast, for the
mostly amortized ski areas in the Bavarian Forest, 73 open days suffice.
At an early stage of the project GLOWA-Danube, we engaged in a stakeholder
dialogue to integrate practical knowledge in the model and the scenarios. The
stakeholders were asked to review critically the applicability and closeness to
reality of our concepts from their point of view to ensure the models function
as a decision support tool for practitioners (Ludwig et al, 2003). For this
purpose, meetings and conferences were organized regularly with stakeholders
of all or of specifically relevant fields. This consultation led to ski area operators
requesting a more detailed parameter to reflect the quality of an open day. On
the one hand, this parameter has to meet the requirement of being spatially
explicit. On the other hand, the parameter must allow for a distinction between
economically valuable open days – such as holidays – and less valuable days,
namely, working days.
Parameter ‘optimal ski day’
Essentially, climatic factors determine whether a ski area might open on a
certain day. Moreover, if these factors generally allow for opening, the quality
of the respective day is of importance. Therefore, given the framework condi-
tions as outlined in the introduction section, establishing the parameter OSD
should identify those open days which offer the best conditions for skiing.
The selection of variables that make up the OSD is based on a comprehensive
literature review and the model requirements outlined above. It has been
validated in terms of 15 expert interviews with selected stakeholders, including
ski lift operators, chairs of skiing clubs, ski instructors and representatives of
tourism associations. Table 1 gives the factored variables and their required
values. Only when a day meets all of these conditions concomitantly, is it
categorized as OSD.
An OSD must be free of precipitation. Moreover, for an OSD, we expect all
slopes of the ski area to be opened. In addition, according to Hall and Higham
(2005) who list several ideal climatic conditions for alpine skiing, a minimum
snow depth of 30 cm is required. This snow blanket may consist of natural
or artificial snow. Additionally, full snow coverage of the surrounding landscape
is necessary for pleasant scenery (Tuppen, 2000). Though this is more of a
psychological factor, it is considered of importance for the perception of a day
as an OSD: imagine a white strip of artificial snow amid barren brown sur-
roundings.
There are different indexes for characterizing the thermal bioclimate on a
thermal sensation scale, among them the physiological equivalent temperature
(PET) (Matzarakis et al, 1999). The advantage of PET is its use of an established
unit for the ranges of perceived temperature (°C), which makes it relatively easy
to apply. Each PET-range refers to an adjective describing the thermal percep-
tion – for example, ‘slightly cool’ or ‘comfortable’. For an OSD, the consulted
experts valued a band of pleasantly perceived temperatures from –5 to +5 °C.
This corresponds to a slightly cool thermal perception of PET.
T
OURISM
E
CONOMICS
328
Table 1. Parameter ‘optimal ski day’: variables considered and required values.
Variable Value
Precipitation = 0 mm
Operating state Ski area completely operating
(Artificial) snow depth on slopes ≥ 30 cm
Surrounding snow coverage (scene function) > 0 cm
Perceived temperature between –5 and +5 °C
Sunshine duration ≥ 5 h/day
Wind speed ≤ 10 m/s
Type of day Weekend day/holiday
The perceived temperature depends among other things directly on the daily
sunshine duration. The actual sunshine duration is influenced by the climatic
zone, the sky cover and the obscuration (for example, by smog or tree cover).
It contains time periods that reach or exceed a solar radiation threshold of 120
W/m² (WMO, 2008). We rate a day as an OSD if the local actual sunshine
duration is at least five hours. Moreover, a maximum wind speed of 5 metres
per second (18 km/h) as a climatic requirement for an OSD was set according
to the literature (Hall and Higham, 2005) and the experts consulted.
Finally, all interviewees confirmed the relevance of the type of day for the
parameter OSD: days which meet all of the above requirements and are in
addition a weekend day or holiday will attract a considerably higher number
of visitors and thus more turnover.
After having shaped the parameter OSD, the following revenue function is
set up assuming a positive linear correlation between the quality of an open
day and the turnover generated on this day:
OSDs · a
R = 1 – ———— · s · k,
x
where OSDs are the number of OSDs; x the number of open days; a the
weighting factor of an OSD; s the maximum possible length of the season
(days); and k the expenditure per visitor and day
where:
a ≥ 0, ∈.
According to this equation, the season’s revenue R of a ski area is traditionally
determined by the maximum possible length of a season (s) and the expenditure
per visitor and day (k). But, in addition, it is boosted by the seasonal share
of OSDs in the actual number of open days, where the relevance of OSDs in
the specific ski area is weighted by a factor (a). Thus, the turnover of a ski
area depends on the quantity and the quality of open days, where the weighting
factor a of an OSD determines the average increase in turnover on an OSD
compared to a ‘normal’ open day. Figure 1 schematically illustrates the turnover
of a fictional ski area for three numbers of OSDs (0, 15 and 30 OSDs)
depending on the total number of open days per season. The solid line (0 OSDs)
represents the commonly used 100-day-rule. In this case, it can be assumed that
329Simulating Alpine ski resorts’ future development
Figure 1. Schematic correlation between number of open days, number of
OSDs and achievement of breakeven point in one season (exemplary for 0, 15
and 30 OSDs).
per open day 1% of the necessary turnover is generated and that breakeven point
is reached on the hundredth open day.
On the supposition that the quality of an open day has a decisive influence
on the number of visitors and therefore on turnover, an OSD is more valuable
for a ski lift operator than a ‘normal’ open day. At a given weighting factor
of 2, implying that the turnover is two times higher on an OSD compared to
a ‘normal’ open day, the fictional ski area reaches the breakeven point after about
77 days, if 15 days are OSDs (dashed line). In the case of 30 OSDs (dash-dotted
line), breakeven is achieved after approximately 62 open days. This weighting
factor can be set by the particular ski lift operator according to the specific
conditions of their ski area.
Figure 1 illustrates the basic idea that more OSDs led to a ski area achieving
its breakeven point sooner. While the 100-day-rule assumes a constant prof-
itability level, the differentiated consideration of open day quality in the OSD
concept allows variable individual breakeven points to be considered.
Subsequently, we validated the OSD range of climate variable values by
comparison with reference data from the German Meteorological Service (2011).
For instance, the Zugspitze ski resort (administrative district Garmisch-
Partenkirchen, Germany, located between 2,057 and 2,962 m above sea level)
achieves in the simulation on average 33 OSDs per season during the decade
2001–2010. Reference data for the period from 2006 to 2009 give an average
0 20 40 60 80 100 120
Number of opening days
1
breakeven point
Turnover standardized at the breakeven point
Number of OSDs: 0
Number of OSDs: 15
Number of OSDs: 30
T
OURISM
E
CONOMICS
330
of 30 OSD-equivalents. As the review of other ski areas led to similar results,
we consider the included variable values confirmed.
Results
The initial results of the six different climate scenario simulations show a strongly
differentiated regional picture of the occurrence of OSDs even in the first
simulation decade, 2001–2010: ski areas in the investigation area achieved
between 2 and 16 OSDs per season on average. In Figure 2, the number of
OSDs is aggregated at the administrative district level, so that spatial differ-
ences become more distinct. Figure 2 shows a north–south gradient trend: the
further an administrative district is located to the south (and further in to the
Alps), the higher it is and the more OSDs it is likely to register. Moreover,
Figure 2. Simulated development of OSDs: average number (2010s) and av-
erage percentage deviation (2050s compared to 2010s) per season and admin-
istrative district.
Simulated number of OSDs
per season and administrative district –
average of 2010s seasons
10
1
Selected city
Ski area
Danube
Administrative district border
State border
Investigation area
Simulated average percentage
deviation of number of OSDs per
administrative district in 2050s seasons
compared to 2010s seasons
More than –70
> –55 to –70
> –40 to –55
–40 and less
No data
km
0
50 100 150
331Simulating Alpine ski resorts’ future development
the use of chemical additives for artificial snow making is prohibited in
Germany, but not in Switzerland (Müller, 2007; BayWG, 2010). Thus, snow
can still be produced in Switzerland even if temperatures reach 3°C or higher.
This may attenuate warm seasons.
Also ski areas in the Bavarian and Palatinate Forests are at relatively high
altitudes and so can expect an acceptable number of OSDs per season in the
coming years. In contrast, lower altitude ski areas, especially those in the
Swabian Jura region and the Innviertel, will have to manage with a considerably
lower number of OSDs. This is mainly down to unfavourable local climatic
conditions.
Throughout the simulation period, the absolute number of OSDs decreases
over the entire investigation area. The average percentage deviation per admin-
istrative district in the 2050s seasons compared to the 2010s seasons amounts
to between –35 and –91%. In the course of this, the spatial distribution of
OSDs per seasons turns out to be relatively stable (Spearman coefficient 0.92
for p < 0.01). In the southern half of the investigation area, those ski areas that
currently achieve the highest numbers of OSDs per season will probably also
be in the leading group in future. In contrast, for the northern ski areas,
regardless of how high the average number of OSDs per season is in the first
decade, all districts have to expect relative declines, which are highest for the
administrative districts of the Bavarian Forest, Palatinate Forest, Innviertel and
Swabian Jura. In this context, the parameter OSD specifies the sheer number
of expected open days per season.
While the interseasonal development of OSDs over the simulation period is
of interest, so too is their intraseasonal distribution. To illustrate this aspect,
we selected one ski area in Berchtesgadener Land, the southeastern most ad-
ministrative district of Bavaria, Germany. The ski area is located between 900
and 1,300 metres above sea level. For this ski area, we have run simulations
with six different climate trend–climate variant combinations to show the range
of possible future developments. Figure 3 shows the average share of OSDs per
month on all OSDs per season (December to April). To minimize the potential
impact of outliers, we averaged the results for every decade. In order to
accentuate the scenario character of our simulation, which is in contrast to any
kind of forecast, the confidence intervals of six different scenario runs are
depicted. The lines represent the respective average of these six scenario results.
For clarity, only the results for the 2010s, 2030s and 2050s are depicted.
Even within the relatively short time span of the next 50 years, a trend
towards a temporal shift of OSDs in terms of an intraseasonal postponing is
evident. This shift is due to climate change effects including alterations of
precipitation patterns and a tendentially later occurrence of freezing days in the
ski season. Averaged over the 2010s, the OSD peak lies in December and
January with, jointly, about 60% of the seasonal count of OSDs (solid line,
Figure 3). Also for February, the share is relatively high for the 2010s. Mean-
while, in the second half of the season, the monthly shares of OSDs slump. The
2030s seasons already show considerable differences compared to the 2010s: the
seasonal distribution of OSDs becomes broader, and December, March and April
reach approximately the same shares of OSDs (dashed line). The peak is deferred
to February, which registers about one-third of all OSDs per season. Within
the 2050s seasons, the highest shares of OSDs per month are in March and
T
OURISM
E
CONOMICS
332
Figure 3. Simulated monthly shares of OSDs per ski season for a ski area in
Berchtesgadener Land, Bavaria, Germany: confidence intervals and averages of
six different scenarios.
April, where the intraseasonal postponing of OSDs shares becomes particularly
obvious (dash-dotted line).
This trend, which is also pointed out by analyses for the other ski areas in
the investigation area, can be labelled the ‘Christmas–Easter shift’, since the
former seasonal focus around Christmas tends more and more towards Easter.
Discussion and conclusion
Our initial results illustrate that the absolute number of OSDs decreases over
the simulation period in all ski areas while the spatial distribution remains
relatively stable. Not only does the number of OSDs decrease per season, but
also the general number of open days per season decreases over the simulation
period in all administrative districts. Therefore, the temporal setting of OSDs
is particularly important as it emphasizes the increasing ‘turnover seasonality’,
which for ski area operators implies that in the future, the necessary turnover
has to be generated in fewer days and later in the season. This will lead to a
further intensification of the concentration of winter sports tourism on the
premier regions, a process that is already observable today.
Several implications arise from these findings. First, the commonly used 100-
day rule implies a one-dimensional correlation of snow reliability and profit-
ability and does not allow for regional differentiation. As opposed to this, the
parameter OSD enables a spatially explicit estimation of a ski area’s develop-
ment by integrating various climatic factors as well as the economic value of
the type of open day. This allows for a consideration of individual breakeven
Confidence interval
2010s average 2030s average 2050s average
December
January
February
March
April
Month
Share of OSDs per month on OSDs per season
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0
333Simulating Alpine ski resorts’ future development
points. Second, the OSD provides more specified information than the sheer
number of open days per season and thus helps, on the one hand, to raise
awareness among tourism policymakers of ‘climate change impact’, while on the
other hand, the concept creates awareness of the importance of distinguishing
between ‘normal’ open days and OSDs for ski area operators, tourism associa-
tions and hotels. In that regard, the parameter OSD can be deployed to optimize
targeted timing of snow-making periods and advertising budgets. Third, the
indicator OSD can offer a decisive advantage to those actors who recognize at
an early stage that, for example, new concepts or products have to be developed
for the Christmas period. Tourists’ image of a destination may change because
of climate change. For example, the Allgaeu is an Alpine region, which tourists
associate with green grass and grazing cows. In the future, because of climate
change, the image of the Allgaeu will change, with a change to agrarian
monoculture. This new image has to be marketed in a different way and
stakeholders need to assess different adaptation strategies in order to stay
competitive in the light of a changing climate (Zhang et al, 2011). Fourth, the
OSD delivers implications for the local labour market and human resource
planning, as those working in the tourism sector will be needed later in the
winter sports season in the future. And fifth, simulations based on the intraseasonal
development of OSDs, in relation to the potentially expected general number
of days opened, can inform decisions on whether it is worthwhile investing in
snow-making facilities and other technical adaptation measures or whether it
is potentially necessary to diversify tourism supply.
The results emphasize that ski tourism should primarily be concentrated and
promoted in those regions that have high numbers of open days and OSDs.
Elsewhere, it seems appropriate to reduce the reliance on ski tourists. Those
winter tourism destinations that are (potentially) disadvantaged by climate
change have to diversify and attract alternative market segments. However, for
those destinations that are economically highly dependent on tourism, a rapid
reorientation may be unrealistic or even risky (Landauer et al, 2012). Hence,
any alternative offer should initially be established with the objective of reduc-
ing the destination’s seasonality. At least in the short to medium term, these
extensions should be supplementary rather than substituting.
Such information may help particularly tourism suppliers to gain knowledge
of climate change impacts and may constitute a crucial competitive advantage.
However, there arises the question of whether skiers are willing to follow the
expected Christmas–Easter shift. From a perceptual point of view, which plays
a major role in tourists’ decision making (Gössling et al, 2012), skiing may not
be suitable in late winter as tourists may be put off by the image of a white
strip of artificial snow in a non-wintry surrounding (Unbehaun et al, 2008).
In March or April, tourists might already be attuned to spring and be tired
of winter.
It should be borne in mind that the results presented in this paper are of
a rather explorative character: the parameter OSD could be further shaped with
more variables to make it even more realistic. The tourism experts interviewed
in the course of the parameter development, for instance, mentioned additional
variables such as snow quality (for example, powder snow, glacier snow, wet
snow), the variety of catering services in the ski area, or special promotions and
discounts including ‘ladies’ days’ or night skiing. However, such amendments
T
OURISM
E
CONOMICS
334
require suitable and available data to support them. Moreover, it is questionable
whether the extra effort leads to a significant improvement of the parameter.
By all means, the parameter OSD in its current version is spatially and
temporally much more detailed and complex than most models that have been
developed so far in this field. The main achievement of the parameter OSD is
the information it offers to stakeholders on the significance of an open day’s
quality.
References
Abegg, B. (1996), Klimaänderung und Tourismus. Klimafolgenforschung am Beispiel des Wintertourismus
in den Schweizer Alpen, vdf, Zurich.
Abegg, B., and Elsasser, H. (2007), ‘Wintertourismus im klimastress’, in Egger, R., and Herdin,
T., eds, Tourismus. Herausforderung Zukunft. Wissenschaftliche Schriftreihe des Zentrums für
Tourismusforschung, Vol 11, Lit, Salzburg, pp 219–230.
Bark, R.H., Colby, B.G., and Dominguez, F. (2010), ‘Snow days? Snowmaking adaptation and the
future of low latitude, high elevation skiing in Arisona, USA’, Climatic Change, Vol 102, pp 467–
491.
Barthel, R., Janisch, S., Schwarz, N., Trifkovic, A., Nickel, D., Schulz, C., and Mauser, W. (2008),
‘An integrated modelling framework for simulating regional-scale actor responses to global
change in the water domain’, Environmental Modelling and Software, Vol 23, pp 1095–1121.
Barthel, R., Janisch, S., Nickel, D., Trifkovic, A., and Hörhan, T. (2010), ‘Using the multiactor-
approach in GLOWA-Danube to simulate decisions for the water supply sector under conditions
of global climate change’, Water Resources Management, Vol 24, No 2, pp 239–275.
BayWG (Bayerisches Wassergesetz des Landtags des Freistaats Bayern) (2010), ‘Bayerisches
Wassergesetz’ (http://www.gesetze-bayern.de/jportal/portal/page/bsbayprod.psml?showdoccase=
1&doc.id=jlr-WasGBY2010rahmen&doc.part=X&doc.origin=bs, accessed 15 October 2012).
Berrittella, M., Bigano, A., Roson, R., and Tol, R. (2006), ‘A general equilibrium analysis of climate
change impacts on tourism’, Tourism Management, Vol 27, pp 913–924.
Boon, B. (2006), ‘When leisure and work are allies. The case of skiers and tourist resort hotels’,
Career Development International, Vol 11, No 7, pp 594–608.
Dawson, J., Scott, D., and McBoyle, G. (2009), ‘Climate change analogue analysis of ski tourism
in the northeastern USA’, Climate Research, Vol 39, pp 1–9.
Dawson, J., and Scott, D. (2010), ‘Systems analysis of climate change vulnerability for the US
northeast ski sector’, Tourism and Hospitality Planning & Development, Vol 7, No 3, pp 219–235.
Dingeldey, A. (2008), Modellierung der touristischen Attraktivität zur Bestimmung der Übernachtungsnachfrage
im Einzugsbereich der Oberen Donau unter Berücksichtigung von Umwelteinflüssen, Dr Hut, Munich.
Elsasser, H., and Bürki, R. (2002), ‘Climate change as a threat to tourism in the Alps’, Climate
Research, Vol 20, pp 253–257.
Falk, M. (2011), ‘Impact of long-term weather on domestic and foreign winter tourism demand’,
International Journal of Tourism Research, Online First DOI: 10.1002/jtr.865.
Frenzel, V. (2011), ‘Skifahren ohne Schnee. Sinkende Niederschläge stellen Bayerns Wintersportgebiete
vor Probleme’, Bayerische Staatszeitung (http://www.bayerische-staatszeitung.de/staatszeitung/politik/
detailansicht-politik/artikel/skifahren-ohne-schnee/, accessed 23 December 2011).
Fukushima, T., Kureha, M., Ozaki, N., Fujimori, Y., and Harasawa, H. (2002), ‘Influences of air
temperature change on leisure industries. Case study on ski activities’, Mitigation and Adaptation
Strategies for Global Change, Vol 7, pp 173–189.
German Meteorological Service (2011), ‘Tageswerte station Zugspitze’ (http://www.dwd.de/bvbw/
appmanager/bvbwdwdwwwDesktop?_nfpb=trueand_pageLabel=_dwdwww_klima_umwelt_
klimadaten_deutschlandand T82002gsbDocumentPath=Navigation%2FOeffentlichkeit%2
FKlima__Umwelt%2FKlimadaten%2Fkldaten__kostenfrei%2Fausgabe__tageswerte__node.html%
3F__nnn%3Dtrue, accessed 15 October 2012).
Gössling, S., Scott, D., Hall, C.M., Ceron, J.-P., and Dubois, G. (2012), ‘Consumer behaviour and
demand response of tourists to climate change’, Annals of Tourism Research, Vol 39, No 1, pp 36–
58.
Hahn, F. (2004), ‘Künstliche Beschneiung im Alpenraum. Ein Hintergrundbericht’ (http://
www.cipra.org/de/alpmedia/dossiers/11/?set_language=de, accessed 15 October 2012).
335Simulating Alpine ski resorts’ future development
Hall, C.M., and Higham, J. (2005), ‘Introduction: tourism, recreation and climate change’, in Hall,
C.M., and Higham, J., eds, Tourism, Recreation and Climate Change, Channel View Publications,
Clevedon, pp 3–28.
Harrer, B. (1996), Wirtschaftsgeographische Auswirkungen einer veränderten ökologischen Situation. Konsequenzen
für den Wintertourismus in Deutschland, dwif, Munich.
Jones, A., and Phillips, M., eds (2011), Disappearing Destinations. Climate Change and Future Challenges
for Coastal Tourism, CABI, Oxford.
Kuhn, S., and Ernst, A. (2009), ‘Gesellschaftsszenarien in GLOWA-Danube’, in GLOWA-Danube
Project, Global Change Atlas Einzugsgebiet Obere Donau (http://www.glowa-danube.de/atlas/s6.php,
accessed 15 October 2012).
Landauer, M., Pröbstl, U., and Haider, W. (2012), ‘Managing cross-country skiing destinations under
the conditions of climate change. Scenarios for destinations in Austria and Finland’, Tourism
Management, Vol 33, No 4, pp 741–751.
Ludwig, R., Mauser, W., Niemeyer, S., Colgan, A., Stolz, R., Escher-Vetter, H., Kuhn, M., Reichstein,
M., Tenhunen, J., Kraus, A., Ludwig, M., Barth, M., and Hennicker, R. (2003), ‘Web-based
modelling of energy, water and matter fluxes to support decision making in mesoscale catch-
ments. The integrative perspective of GLOWA-Danube’, Physics and Chemistry of the Earth, Vol
28, pp 621–634.
Matzarakis, A., Mayer, H., and Iziomon, M.G. (1999), ‘Applications of a universal thermal index.
Physiological equivalent temperature’, International Journal of Biometeorology, Vol 43, pp 76–84.
Mauser, W. (2010), ‘Der statistische Klimaantriebs-Generator’, in GLOWA-Danube Project, Global
Change Atlas Einzugsgebiet Obere Donau (http://www.glowa-danube.de/atlas/s3.php, accessed 15
October 2012).
Mauser, W., Weidinger, R., and Stöber, S. (2008), ‘Das Projekt GLOWA-Danube’, in GLOWA-
Danube Project, Global Change Atlas Einzugsgebiet Obere Donau (http://www.glowa-danube.de/atlas/
e1.php, accessed 15 October 2012).
Mauser, W., Marke, T., Reiter, A., Jacob, D., and Preuschmann, S. (2009), ‘Die GLOWA-Danube
Klimatrends’, in GLOWA-Danube Project, Global Change Atlas Einzugsgebiet Obere Donau (http:/
/www.glowa-danube.de/atlas/s2.php, accessed 15 October 2012).
Müller, H. (2007), Tourismus und Ökologie. Wechselwirkungen und Handlungsfelder, Oldenbourg, Munich.
Müller, H., and Weber, F. (2008), ‘Climate change and tourism. Scenario analysis for the Bernese
Oberland in 2030’, Tourism Review, Vol 63, No 3, pp 57–71.
OECD (Organization for Economic Cooperation and Development), UNEP (United Nations Envi-
ronment Programme) (2011), ‘Climate change and tourism policy in OECD countries’ (http://
www.oecd.org/dataoecd/8/28/48681944.pdf, accessed 15 October 2012).
Sax, M. (2008), ‘Entwicklung eines Konzepts zur computergestützten Modellierung der touristischen
Wassernutzung im Einzugsgebiet der Oberen Donau unter Berücksichtigung des Klimawandels’,
Beiträge zur Wirtschaftsgeographie Regensburg, Vol 11, pp 1–236.
Scott, D., de Freitas, C., and Matzarakis, A. (2009), ‘Adaptation in the tourism and recreation sector’,
in Ebi, K.L., Burton, I., and McGregor, G.R., eds, Biometeorology for Adaptation to Climate
Variability and Change, Springer, Berlin, pp 171–194.
Soboll, A., Elbers, M., Barthel, R., Schmude, J., Ernst, A., and Ziller, R. (2011), ‘Integrated regional
modeling and scenario development to evaluate future water demand under global change
conditions’, Mitigation and Adaptation Strategies for Global Change, Vol 16, No 4, pp 477–498.
Soboll, A., and Schmude, J. (2011), ‘Simulating tourism water consumption under climate change
conditions using agent-based modeling. The example of ski areas’, Annals of the Association of
American Geographers, Vol 101, No 5, pp 1049–1066.
Soboll, A., and Dingeldey, A. (2012), ‘The future impact of climate change on Alpine winter
tourism. A high resolution simulation system in the German and Austrian Alps’, Journal of
Sustainable Tourism, Vol 20, No 1, pp 101–120.
Soboll, A., Klier, T., and Heumann, S. (2012), ‘The prospective impact of climate change on tourism
and regional economic development. A simulation study for Bavaria’, Tourism Economics, Vol 18,
No 1, pp 139–157.
Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L.,
and Chen, Z., eds (2007), ‘Climate change 2007. The physical science basis. Contribution of
working group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate
Change’, IPCC, New York.
Tuppen, J. (2000), ‘The restructuring of winter sports resorts in the French Alps. Problems, processes
and policies’, International Journal of Tourism Research, Vol 2, No 5, pp 327–344.
T
OURISM
E
CONOMICS
336
Unbehaun, W., Pröbstl, U., and Haider, W. (2008), ‘Trends in winter sport tourism. Challenges for
the future’, Tourism Review, Vol 63, No 1, pp 36–47.
VDS (Verband Deutscher Seilbahnen und Schlepplifte e.V.) (2012), ‘Beschneiungsanlagen in
Deutschland’ (http://www.seilbahnen.de/beschneiungsanlagen-in-deutschland, accessed 15 Octo-
ber 2012).
Witmer, U., Filliger, P., Künz, S., and Kung, P. (1986), Erfassung, Bearbeitung und Kartierung von
Schneedaten in der Schweiz, Geographical Institute University of Bern, Bern.
WMO (World Meteorological Organization) (2008), ‘WMO guide to meteorological instruments
and methods of observation. CIMO-Guide’ (http://www.wmo.int/pages/prog/www/IMOP/publi-
cations/CIMO-Guide/CIMO_Guide-7th_Edition-2008.html, accessed 15 October 2012).
Zhang, H., Lu, L., Cai, L., and Huang, Z. (2011), ‘Tourism destination image structural model and
visitors’ behavioral intentions. Based on a confirmatory study of localization of potential consum-
ers’, Tourism Science, Vol 1, pp 35–45.
