The effect of climate change on tourism in the Decision support

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 = 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

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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

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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

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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