The effect of climate change on tourism in the
Bavarian and Austrian Alps
Decision support
based on high resolution climate simulation
Dr. Alexander Dingeldey
University of Munich, Germany
Department of Geography
Chair of Economic Geography and Tourism Research
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
Climate change and tourism in the Alps
Bavarian and Austrian Tourism Regions – the future
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Overview of the project GLOWA-Danube
Investigation area
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Overview of the project GLOWA-Danube
Source: Final Report GLOWA Phase I, adapted.
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
Climate Change according to IPCC
 Different Scenarios
 Increase of the mean temperature from 1.4°C to 6.4°C till
2100
 Increase of the mean temperature from 0.1°C to 0.4°C
every 10 years
 Increase of daily maximum and minimum temperatures
 More “Hot days”
 Fewer “Cold Days”
 More humidity during the winter
 Increase of the altitude for „snow-reliability” form 1200m to
1500m
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
Challenges
 Climate-Simulation-Models work on a very large scale
 Climate-Simulation on small scales and mountain areas is
very complicated
 The impact of the climate change is regionally different
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
The GLOWA-Danube Team
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Models within GLOWA_Danube
Environment
Human
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Framework
Environment
Human
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Framework
Environment
Human
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Tourism Model
Actor classes

ski areas,

golf courses,

swimming pools,

gastronomy,

hotel business.
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Model concept
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Model concept
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Model concept
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Model concept
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Model concept
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Simulation Area – Golf Courses
Bavaria
Czech
Republic
Golf Courses
Number of Holes
Germany
Rivers
Simulation-Area
State-Boundaries
Austria
Country-Boundaries
Switzerland
Italy
Source: Own Research.
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
Kilometers
Kilometers
Simulation Area –
Swimming Pool
Bavaria
Czech
Republic
Swimming Pools
Germany
Outdoor Pool
In-&Outtdoor Pool
Indoor Pool
Water Park
Thermal Spas
Rivers
Simulation-Area
Austria
State-Boundaries
Country-Boundaries
Switzerland
Italy
Quelle: Eigene Recherchen.
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
Kilometers
Simulation Area – Skiing Areas
Bavaria
Czech
Republic
Capacity in Pers/h
Germany
Germany
No data
Rivers
Simulation-Area
State-Boundaries
Austria
Country-Boundaries
Switzerland
Italy
Source: Own Research.
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
Kilometers
Dependency on winter tourism
Ski dependant bednights:
None
Under 25%
25%-75%
75%-100%
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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How do guest inform themselfs?
Source: Own Research, Tyrol 2005 N=275
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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How do guests react?
Source: Own Research, Tyrol 2005 N=275
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Spatial concept
Proxel (Process-Pixel)
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Distribution on Proxels
Populalation
1 to 800
801 to 2900
Over 2900
Share of total
Population of
Starnberg
Community
Source: Topographical map with own adaptions
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
Example of a Deep Actor
Skiing-Area
Skiing Area
Out of Service
Start of Season?
Yes
No
Is there enough
Snow?
Yes
No
Sking area
In Service
IF
Current_date
>= Start of Season (15Dez)
AND
Current_date
<= End of Season (15Apr)
AND
Natural_Snow_Cover >= 30 cm
THEN SkiingArea.open
ELSE SkiingArea.close;
Source: Own Resaerch
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
Example of a Deep Actor
Skiing-Area
Skiing-Area with artificial Snowmaking
Open
SnowMaking
Open
Source: Own research
Artificial Snow
Status
Artificial Snow in m3
Snowmaking
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
Example of a Ski Area
Status of
Skiing areas
within 20 km Distance
Status of
Swimming Pools
within 20 km Distance
Status of
Golf Courses
within 30 km Distance
Availibility
of Drinking Water
within 30 km Discance
Monthly Average
Temperature
Simulation for ~2100 Communities
Source: Own Calculation – Climate Scenario: REMO, Climate Variant: Baseline
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Scenario kit
Climate trend
Climate variant
Societal scenario
IPCC regional
Baseline
Baseline
REMO regional
5 warm winters
Open
competition
MM5 regional
5 hot summers
Public Welfare
Extrapolation
5 dry years
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Simulated Opertation Days of Skiing Areas
Average Operating Days within the States 2012 - 2059
120
Bavaria South
Baseline
Bavaria South
Liberalisation
Average Operation Days
100
Bavaria South
Sustainibility
80
Bavaria North
Baseline
Bavaria North
Liberalisation
60
Bavaria North
Sustainibility
40
Tyrol Baseline
20
Tyrol Liberalisation
Tyrol Sustainibility
0
2012
2017
2022
2027
2032
2037
2042
2047
2052
Source: Own simulation, Remo Scenario, Baseline Variant
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
2057
Simulated Opertation Days of Skiing Areas
(Baseline-Scenario)
Average Operating Days of selected Skiing Areas 2012-2059
140
Hoher Bogen
120
Average Operation Days
Pröllerlifte
100
Brauneck
80
Christlum
60
Kitzbühel
Zugspitzplatt
40
Kitzbühel
20
Silvretta Arena
0
2012
2017
2022
2027
2032
2037
2042
2047
2052
Source: Own simulation, Remo Scenario, Baseline Variant
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
2057
Simulated Tourism Demand
in the Upper Danube Catchment
Simulated Number of Bednights per Year 2011- 2059
300000000.0
Baseline
Baseline
Number of Bednights
250000000.0
Hot Summers
Baseline
200000000.0
Baseline
Liberalisation
150000000.0
Hot Summer
Sustainibility
100000000.0
Baseline
Sustainibility
50000000.0
Hot Summer
Liberalisation
.0
2011
2021
2031
2041
Source: Own simulation, Remo Scenario
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
2051
Simulated Tourism Demand
in the Upper Danube Catchment
Simulated Number of Bednights per Month 2011-2059
40000000
211
Baseline
35000000
Number of Bednights
Liberalisation
212
30000000
Sustainibility
213
25000000
20000000
15000000
10000000
5000000
0
2059_1
2057_1
2055_1
2053_1
2051_1
2049_1
2047_1
2045_1
2043_1
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
2041_1
2039_1
2037_1
2035_1
2033_1
2031_1
2029_1
2027_1
2025_1
2023_1
2021_1
2019_1
2017_1
2015_1
2013_1
2011_1
Source: Own simulation, Remo Scenario, Baselnie Variant
Selected Results

Simulated threat level of climate change 2050/51 to 2059/60 – number of ski areas
300
Number of Ski Areas
250
Low
200
98
104
135
150
50
47
100
50
Elevated
36
102
82
105
Severe
0
Baseline
Open Competition
Public Welfare
Societal Scenario
Source: Own Calculation – Climate Scenario: REMO, Climate Variant: Baseline
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Selected Results

Simulated threat level of climate change 2050/51 to 2059/60 –
carrying capacity of ski areas
Capacity sum of skiing areas (pers/h)
3,000,000
2,500,000
Low
2,000,000
1,500,000
Elevated
1,000,000
Severe
500,000
0
Baseline
Open Competition
Public Welfare
Societal Scenario
Source: Own Calculation – Climate Scenario: REMO, Climate Variant: Baseline
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Operating Costs
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Operating Costs
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Selected Results
Threat level of ski areas and simulated
development of overnight tourism
Source: Own Calculation – Climate Scenario: REMO, Climate Variant: Baseline
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Selected Results – Water consumption of
Golf Courses
Average water consumption of golf courses per district in in m³/year from 2050
till 2059 in the scenaro REMO regional – baseline –
Societal Scenario
Performance
Societal Scenaro
Public welfare
Golf Course
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Selected Results – Water consumption of
Golf Courses
Average water consumption of golf courses per district in in m³/year from 2050
till 2059 in the scenaro REMO regional – 5 hot summers–
Societal Scenario
Performance
Societal Scenaro
Public welfare
Golf Course
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Perfect days for skiing
Variable
Value
No precipitation
precipitation sum = 0
Complete snow cover in the surrounding
snow height > 0
All lifts are operating
yes
Enough snow in the ski area
yes
Enogh artificial snow
yes
Comfortable temperature
-5 bis +5 °C
Sunshine duration (clear sky)
over 5 h/day
Wind speed
max 10 m/s
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Perfect days for skiing
Average number of perfect ski days per season
(REMO regional – Baseline – Baseline)
2011/12 bis 2018/19
2049/50 bis 2058/59
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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Conclusions


Different effects of the climate change on tourism
 The attractiveness of regions with a high percentage of leisure and recreational
tourism within Austria, Germany and Switzerland will increase with the rising
average temperature during the summer
 Bigger, better equipped and higher located skiing-areas will be more attractive
to tourists.
 The Climate change will enforce concentration of skiing areas
 Bigger, better equipped and higher located skiing-areas will get more
attractiveness
 Smaller skiing-areas with lower snow reliability will be very hard to operate
Climate change scenario leads the trend way,
but societal conditions wield relatively great influence
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
Further References

www.glowa-danube.de
© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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© 2011 University of Munich, Germany | Department of Geography | Dr Alexander Dingeldey
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2011 University
of Munich, Germany
| Department of Geography
| Dr Alexander
Dingeldey
© Lehrstuhl
für Wirtschaftsgeographie
und Tourismusforschung
Jahrestagung
Arbeitskreis
Freizeit- und Tourismusgeographie