Berichte des Meteorologischen Institutes der Universität Freiburg

Nr. 12

A. Matzarakis, C. R. de Freitas and D. Scott

(Eds.)

Advances in Tourism Climatology

Freiburg, November 2004

2

ISSN 1435-618X

Alle Rechte, insbesondere die Rechte der Vervielfältigung und Verbreitung sowie der

Übersetzung vorbehalten.

Eigenverlag des Meteorologischen Instituts der Albert-Ludwigs-Universität Freiburg

Druck: Druckerei der Albert-Ludwigs-Universität Freiburg

Herausgeber: Prof. Dr. Helmut Mayer und PD Dr. Andreas Matzarakis Meteorologisches Institut der Universität Freiburg Werderring 10, D-79085 Freiburg Tel.: 0049/761/203-3590; Fax: 0049/761/203-3586 e-mail: meteo@meteo.uni-freiburg.de

http://www.mif.uni-freiburg.de

Dokumentation: Ber. Meteor. Inst. Univ. Freiburg Nr. 12, 2004, 259 S.

3

CONTENTS Page Acknowledgements

5

Tourism and recreation climatology. A. Matzarakis, C. R. de Freitas, D. Scott

6

Mapping the thermal bioclimate of Austria for health and recreation tourism. A. Matzarakis, M. Zygmuntowski, E. Koch, E. Rudel

10

A new generation climate index for tourism and recreation. C. R. de Freitas, D. Scott and G. McBoyle

19

Estimation and comparison of the hourly discomfort conditions along the Mediterranean basin for touristic purposes. Ch. Balafoutis, D. Ivanova and T. Makrogiannis

27

Weather and recreation at the Atlantic shore near Lisbon, Portugal: A study on applied local Climatology. M. J. Alcoforado, H. Andrade, and M.J. Viera Paulo

38

Impact of Climate Change on Recreation and Tourism in Michigan. S. Nicholls and C. Shih

49

Climate change: The impact on tourism comfort at three Italian tourist sites. M. Morabito, A. Crisci, G. Barcaioli and G. Maracchi

56

Trends of thermal bioclimate and their application for tourism in Slovenia. T. Cegnar and A. Matzarakis

66

Variation and trends of thermal comfort at the Adriatic coast. K. Zaninovic and A. Matzarakis

74

The impacts of global climate change on water resources and tourism: The responses of Lake Balaton and Lake Tisza. T. Rátz and I. Vizi

82

Climate change and the ski industry in eastern north America: A reassessment. D. Scott, G. McBoyle, B. Mills and A. Minogue

90

Approaches to offsetting greenhouse gas emissions from tourism. P. Hart, S. Becken, and I. Turney

97

The Eco-efficiency of Tourism. P. Peeters, S. Gössling, J.-P. Ceron, Gh. Dubois, T. Patterson and R. Richardson

105

Methods of sensitivity analysis to assess impacts of climate change on tourism at the regional scale. C. R. de Freitas

116

Alternative futures for coastal and marine tourism in England and Wales. M.C. Simpson and D. Viner

123

4

Evaluation of the potential economic impacts of climate change on Caribbean tourism Industries. M.C. Uyarra, I.M. Côte, J.A. Gill, R.R.T. Tinch, D. Viner and A.R. Watkinson

134

Interactions between tourism, biodiversity and climate change in the coastal zone. E. Coombes, A. P. Jones, W. Sutherland and I. J. Bateman

141

The development prospects of Greek health tourism and the role of the bioclimate regime of Greece. E. A. Didaskalou, P. Th. Nastos and A. Matzarakis

149

The impact of hot weather conditions on tourism in Florence, Italy: The summer 2002-2003 experience. M. Morabito, L. Cecchi, P. A. Modesti, A. Crisci, S. Orlandini, G. Maracchi, G. F. Gensini

158

Managing weather risk during major sporting events: The use of weather derivatives. S. Dawkins and H. Stern

166

Sports tourism and climate variability. A. Perry

174

A developing operational system to support tourism activities in Tuscany region. D. Grifoni, G. Messeri, M. Pasqul, A. Crisci, M. Morabito, B. Gozzini, G. Zipoli

180

Visitor Motivation and dependence on the weather of recreationists in Viennese recreation areas. Ch. Brandenburg, A. Matzarakis and A. Arnberger

189

Tourism stakeholders' perspectives on climate change policy in New Zealand. S. Becken and P. Hart

198

Climate and the destination choices of German tourists: A segmentation approach. J. M. Hamilton, D. J. Maddison and R. S. J. Tol

207

Knowledge management for tourism, recreation and bioclimatology: Mapping the interactions (Part II). T. Patterson

215

Boat tourism and greenhouse gas emissions: contributions from downunder. T. A. Byrnes and J. Warnken

223

A bibliography of the tourism climatology field to 2004. D. Scott, B. Jones and G. McBoyle

236

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ACKNOWLEDGEMENTS

Figure 1: View of the Orthodox Academy of Crete (foreground)

The Commission on Climate, Tourism and Recreation is grateful to the International Society of Biometeorology for financial assistance and to the Orthodox Academy of Crete for hosting the CCTR Workshop. The editors wish to thank Mark Storey (University of Waterloo) for his contribution to proof-reading and formatting articles that appear here. Andreas Matzarakis, Chris de Freitas and Daniel Scott November 2004

6

TOURISM AND RECREATION CLIMATOLOGY

Andreas Matzarakis1, C. R. de Freitas2, Daniel Scott3

1Meteorological Institute, University of Freiburg, 79085 Freiburg, Germany 2 School of Geography and Environmental Science, University of Auckland, PB 92019, Auckland,

New Zealand. 3 Department of Geography, University of Waterloo, 200 University Avenue West, Waterloo,

Ontario, Canada, N2L 3G1

Email Addresses:

andreas.matzarakis@meteo.uni-freiburg.de (Andreas Matzarakis);

c.defreitas@auckland.ac.nz (C R de Freitas);

dj2scott@fes.uwaterloo.ca (Daniel Scott).

THE ISB COMMISSION ON CLIMATE, TOURISM AND RECREATION

This publication grew out of the Second International Workshop of the International Society of

Biometeorology, Commission on Climate Tourism and Recreation (ISB-CCTR) that took place at

the Orthodox Academy of Crete in Kolimbari, Greece, 8-11 June 2004. The aim of the meeting was

to a) bring together a selection of researchers and tourism experts to review the current state of

knowledge of tourism and recreation climatology and b) explore possibilities for future research and

the role of the ISB-CCTR in this.

A total of 40 delegates attended the June 2004 ISB-CCTR Workshop. Their fields of expertise

included biometeorology, bioclimatology, thermal comfort and heat balance modelling, tourism

marketing and planning, urban and landscape planning, architecture, climate change, emission

reduction and climate change impact assessment. Participants came from universities and research

institutions in Australia, Austria, Canada, Croatia, France, Germany, Greece, Hungary, Italy, the

Netherlands, New Zealand, Portugal, Slovenia, United Kingdom and United States of America.

Business conducted at the Workshop was divided between five sessions: assessment of climatic

resources; climate change; health; weather, sports and risk forecasts; and behaviour and perception.

However, the content of this publication is organised so that it reflects the new perspectives and

methods that have evolved since the ISB-CCTR was established. This is the reason for using

“Advances” in the title. In order for all this to be achieved in one volume, the individual research

articles were limited in most cases to 8 pages. Only those articles that were recommended for

publication by three reviewers were included.

7

THE GROWTH OF TOURISM CLIMATOLOGY

An inspiration for the activities of the CCTR was the recent rapid growth and diversification of the

research activity in the field of tourism and recreation climatology. Scott et al. (page 237-258 of this

volume) have compiled a comprehensive bibliography for this field, containing over 330

publications (current to December 2004). Figures 1 and 2 are based on this comprehensive

bibliography and put this recent rapid growth into the context of the historical development of the

field.

The first phase

The field of tourism and recreation climatology has a 30 year history. The earliest tourism and

recreation climatology research began in what Lamb (1) called the ‘climate revolution’ during the

1960s and 1970s. Government investment in the expansion of climate station networks and climate

research provided applied climatologists the opportunity to exam how climate affected a wide range

of economic sectors, including the rapidly growing tourism and recreation industry. As de Freitas

(2:p89) noted, “much of the [early] research in recreation climatology appears to be motivated by

the potential usefulness of climatological information within planning processes for tourism and

recreation.”

0

20

40

60

80

100

120

1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04

Num

ber o

f Pub

licat

ions

Journals

Book Chapters

Reports

Conference Proceedings

Figure 1: Number of Publications on Climate-Weather and Tourism-Recreation

8

0

5

10

15

20

25

30

35

40

45

1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04

Jour

nal A

rticl

esClimate Change

Climate & Weather

Figure 2: Journal Articles on Climate-Weather and Tourism-Recreation

The second phase

The initial development phase peaked in the late 1970s and was followed by a notable decline in

research activity. As Figure 1 indicates, publication of research in this field almost stopped during

the early 1980s and did not regain the level of activity of the late 1970s until the early 1990s. A

possible explanation for the lack of continued development in the 1980s was that climate scientists,

who were almost exclusively responsible for the early research in this field, were deflected into

new, salient and better funded atmospheric science issues, such as acid rain, ozone depletion, and

air pollution.

The third phase

A new phase of growth began in the early 1990s and has continued through to the present. The

volume of journal articles related to climate and tourism-recreation increased three-fold between

1990-94 and 1995-99 (Figure 2). Recognising the need for an organization to help the growing

number of researchers with interests in tourism and recreation climatology share their ideas, the ISB

Commission on Climate, Tourism and Recreation was established early in this growth phase, at the

14th Congress of the International Society of Biometeorology, held in September 1996 in Ljubljana,

Slovenia.

9

CURRENT TRENDS AND THE WAY AHEAD

The onset of the third phase and the rapid growth in the tourism and recreation climatology

coincided with emerging interest in the potential implications of global climate change for national

economies and societies worldwide. Much of the earliest empirical studies on climate change and

tourism-recreation borrowed on the methods and findings of the pioneering work in the field of

tourism and recreation climatology. Figure 1 demonstrates that the proportion of journal papers in

the field of tourism and recreation climatology that have focused on climate change has increased

over the past 10 years.

A second important trend not apparent in Figures 1 and 2, but that is clearly evident in the

bibliography (pages 237-257), is the diversification of research questions and methodologies in the

field over the past decade. As this volume clearly demonstrates, the field of tourism and recreation

climatology has become truly multidisciplinary, with researchers from a number of disciplines

bringing fresh perspectives and new methods to the task of advancing the field of tourism and

recreation climatology. Many of the new perspectives and methods are being employed by young,

emerging scholars. These are tremendous strengths that portend a very positive future for the field.

It is a truly exciting time in the field of tourism and recreation climatology, and as the title suggests,

the purpose of this volume is to showcase the diversity of on-going research in this rapidly

advancing field of inquiry and provide a benchmark to which research in this field 20 years hence

can be compared.

REFERENCES 1. Lamb, P. 2002 The climate revolution: a perspective. Clim. Change 54: 1-9. 2. De Freitas, C.R. 1990. Recreation climate assessment. Int. J. Climatol. 10:89-103.

10

MAPPING THE THERMAL BIOCLIMATE OF AUSTRIA

FOR HEALTH AND RECREATION TOURISM

Andreas Matzarakis1, Markus Zygmuntowski1, Elisabeth Koch2 and Ernest Rudel2

1. Meteorological Institute, University of Freiburg, Germany, D-79085 Freiburg, Germany

2. Central Institute for Meteorology and Geodynamics, Vienna, Austria

E-mail address: andreas.matzarakis@meteo.uni-freiburg.de (Andreas Matzarakis)

ABSTRACT

This paper analysed the thermal human bioclimate in Austria. Data covering the period of 1991 to

2000 was collected from Austria’s dense network of 201 meteorological stations, and was used to

compute the Physiological Equivalent Temperature (PET). Daily measurements and observations, at

various times, of air temperature, relative humidity, wind velocity and mean cloud cover were the

required data for the PET calculation. The results were compared with the outcome of a

computation using synoptic data, not only from Austria but also from surrounding countries.

The mean radiant temperature, an important factor in the energy balance of humans, was calculated

using the well established RayMan model. It was determined on the basis of the maximum possible

global radiation to a certain time and place, and the existing mean cloud cover from the

observations of the climatic network, as well as those computed for current conditions.

Statistical and GIS procedures were applied to the PET computation of the single climatic station in

order to transfer the point into aerial values. The results give fundamental information often

demanded by health, recreation, and tourism authorities.

KEYWORDS: Physiological Equivalent Temperature, Recreation, Austria

INTRODUCTION

The thermal bioclimate is of high interest for decision makers in the public health and recreation

tourism sectors, as well as for the general public. The first and only existing description of the

thermal human bioclimate, the "bioclimatic map of Austria", had its origin in the 1983 work of

Rudel et al. (1). This description was based on the combination of equivalent temperature

(representing the thermal load) and cooling power (measuring cooling stress using both ‘simple’

and ‘complex’ parameters). Annual mean values of different so called “Reizstufen” (Reizstufe can

be translated as phases of stimulation of thermal stress) were also presented.

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Current investigation into the thermal complex of human bioclimate uses more scientific methods.

A large disadvantage of the older ‘simple’/‘complex’ indices is that they disregarded the extensive

interactions of all meteorological parameters affecting the thermophysiology of humans. The human

organism is influenced by radiant fluxes, air temperature, water vapour pressure, wind velocity,

physiological parameters (weight, size, and activity) and clothing, all of which are part of the

human energy balance equation. Human beings react to the environment by adjusting both skin

temperature and sweat rate, to keep core temperature constant (stationary condition). Thus, one of

the new thermal indices, the Physiologically Equivalent Temperature (PET), in contrast to older

indices (e.g. the Predicted Mean Vote (PMV)), is applicable to the more complex context of outdoor

conditions.

Transferring this human adaptation for outdoor conditions into indoor conditions (with a clothing

insulation of 0.9 clo, metabolic rate of 80 W, water vapour pressure of 12 hPa, wind velocity of 0.1

m/s and provided that the indoor air temperature corresponds to the mean radiant temperature)

results in a PET value that is equivalent to the respective air temperature (degrees Celsius), which

fulfills the energy balance equation in the outdoor conditions. This is useful because using the

Celsius scale, instead of PMV or similar indices, makes the results much more understandable. In

this paper the calculation of PET, and of bioclimatic maps based on PET, are applied for Austria.

INVESTIGATION AREA

Geographically situated between 46.5° and 49° northern latitude, and 9.5° and 17° eastern

longitude, Austria covers 83855 km². Distributed throughout this area are an extensive series of 201

meteorological stations, making Austria a perfect country for bioclimate investigations and case

studies. Not only does Austria collect much climatic data, but is also has an extremely differentiated

climate for its relatively small size. This diversity of climatic zones is caused by various orographic

characteristics, and by the interaction of atlantic and continental climatic influences (1). Also, its

central geographical location in Europe increases the attractiveness of the country for a broad

population spectrum, so that numerous groups have a high need for a bioclimatic zoning of Austria.

METHODS

The well being and health of humans depends on the close linkage between thermal regulation and

circulation (2).The thermal bioclimatic complex comprises the meteorological variables that affect

human beings in a thermo-physiologically manner: air temperature, air humidity, and wind speed,

as well as short and long-wave radiation from the surrounding area. In order to consider the thermal

environment of humans in a relevant way it is necessary to use evaluation methods that

12

• deal with the atmospheric environment as a whole and not with single meteorological

components, as humans do not have receptors for such singular components

• have a thermo-physiologically relevance

Thus ‘simple’/‘complex’ indices that were often used in older publications (e.g. effective

temperature or the equivalent temperature) do not fulfil the above criteria (3,4).

The VDI-guideline 3787, part 2 (2) recommends methods for the assessment of the thermal

component of the human climate, which takes into account the complexity of this inquiry. The

human energy balance equation (5,6,7) is the basis of these recommended methods, one of them

being the thermal index PET, derived from the model MEMI.

Much analysis has been carried out with synoptic data (8,9,10,11). For the current investigation a

modified method was chosen, using data from the Austrian climatic network (Figure 1), as well as

the synoptic observations for the greater area. The number of climatic stations is much higher than

the synoptic ones, and therefore has an excellent aerial coverage. Climatic observations were

carried out at 7, 14 and 19 CET, and synoptic observations at 6, 12 and 18 UTC. The

meteorological elements air temperature (Ta), relative air humidity (RH), wind velocity (v) and

mean cloud cover (c) are the necessary inputs for the calculation of PET. Mean radiant temperature

can be calculated be applying the radiation and bioclimate model RayMan (2) to the theoretical

maximum global radiation in combination with the mean cloud cover.

A statistical model was used for the generation of spatially detailed bioclimatic data. This multiple

regression model has demonstrated its suitability in former investigations (9,13). PET is the

dependent variable, and the independent predictors are latitude, longitude, height above mean sea

level, exposure and land use.

The multiple regression model (1) has the following form:

Y = f (X1,X2,..., X5) = a0 + a1*X1 +...+ a6*X6 (1)

where:

Y = mean monthly PET (oC) or amount of days

aι = regression coefficients (i = 0,...,6)

Χ1 = latitude (degrees, minutes)

X2 = longitude (degrees, minutes)

Χ3 = elevation above mean sea level (meters)

Χ4 = slope angle (°)

Χ5 = orientation (°)

Χ6 = land use

13

RESULTS

Figure 1 shows all of the stations used for the PET calculations. A bioclimate diagram based on the

PET-classes (14) for the period 1.1.1991 to 31.12.2000 was developed in order to quantify the

bioclimate of recreation areas and health spas.

Figure 2 gives an example for Vienna; it contains additional average values of PET classes (14) for

14 CET, extreme values, as well as mean frequencies of days with excesses of PET threshold

values. In detail, the following values are to be found in this figure:

• annual average value of PET for the examined period (PETa)

• absolute maximum of PET for the examined period (PETmax)

• absolute minimum of PET for the examined period (PETmin)

• mean amount of days with PET < - 10,0 °C for 7 CET (PETd < - 10)

• mean amount of days with PET < 0,0 °C for 7 CET (PETd < 0)

• mean amount of days with PET < 5,0 °C for 7 CET (PETd < 5)

• mean amount of days with PET > 30,0 °C for 14 CET (PETd > 30)

• mean amount of days with PET > 35 °C for 14 CET (PETd > 35)

PET mapping is presented in the form of:

• mean monthly and daily average values for the climatic dates 7, 14, 19 CET

• absolute monthly maximums and minimums

• annual frequencies of PET classes for climatic observations 7, 14, 19 CET

• mean monthly frequencies on the daily basis of PET classes

The linear regression model calculated the corresponding PET value for each grid point of the

digital terrain model and, applying an interpolation method, allowed the plotting of maps for

monthly mean PET-values at 7, 14, and 19 CET, as well as maps with number of PET days above

or below a certain threshold. An additional analysis using synoptic data for 6, 12 and 18 UTC from

a bigger area (not shown here) was also carried out. The comparison of the synoptic and climatic-

based maps showed that the differences were small and explainable.

In figure 3 the geographical distribution of the PET values for July at 14 CET is shown. Areas with

high heat load can be identified in the outer alpine regions and in the big valley systems of the Alps

during summer conditions.

Figure 4 gives the distribution of the amount of days with PET values exceeding 35 °C, thus

providing information on frequencies of heat waves and heat stress areas.

14

Figure 1: Digital terrain model and distribution of synoptical and climatic stations used for the PET

calculations

Figure 2: Thermal bioclimate diagram for Vienna, period 1991-2000

15

Figure 3: Geographical distribution of PET for Austria, July, at 14 CET, period 1991-2000

Figure 4: Geographical distribution of the amount of days with PET > 35.0 °C for Austria for 14

CET, period 1991-2000

16

Figure 5: Geographical distribution of the amount of days with PET > 21.0 °C for Austria for 7 CET,

period 1991-2000

Furthermore, figure 5 offers more detailed information about the thermal bioclimate, especially for

recovery conditions during the night; it shows the number of days with a PET > 21 °C at 7 CET,

which can be taken as an indicator of heat stress conditions.

DISCUSSION

The method used of analyzing the thermal bioclimatic conditions with specific bioclimate diagrams,

including relevant information for tourism and recreation, presents an excellent way of transferring

complex scientific information into a form that can be easily understood by decision makers and the

general public. The Physiological Equivalent Temperature (PET), using the well known Celsius

scale, can be easily applied and interpreted by anyone who is acquainted with this temperature

scale. The method for regionalization of the PET-values, with its high statistical regression

coefficients, allows the construction of bioclimate maps.

The mapping of modern bioclimatic indices, based on the human energy balance, presents an

adequate method for the quantification of the human thermal bioclimate that can be applied for

different uses and requirements. The need for bioclimatic information for health tourism and for

tourism and recreation in general is very high. The results of our investigation are strongly

demanded by decision makers because of the preparation of new legal regulations for Austrian

17

health resorts, where the assessment of the human bioclimate plays only one, but nevertheless an

important, role.

ACKNOWLEDGEMENTS

This study is part of the Austrian Climate and Tourism Initiative (ACTIVE) funded by the Austrian

Federal Ministry of Transport, Innovation and Technology.

REFERENCES 1. Rudel, E., at al. 1983. Eine Bioklimakarte von Österreich. Mitteilungen der Österreichischen

Geographischen Gesellschaft. Band 125, 1983.

2. VDI, 1998. Methoden zur human-biometeorologischen Bewertung von Klima und

Lufthygiene für die Stadt- und Regionalplanung, Teil I: Klima. VDI-Richtlinie 3787 Blatt 2.

3. Hammer, N., Koch, E., and Rudel, E., 1986. Die thermisch hygrische Behaglichkeit in der

Großstadt, beurteilt nach einem menschlichen Energiebilanzmodell, der Schwüle und der

Abkühlungsgröße. Archiv für Meteorologie und Geophysik.Teil B. 343-355.

4. Matzarakis, A., 2001. Die thermische Komponente des Stadtklimas. Ber. Meteor. Inst. Univ.

Freiburg Nr. 6.

5. Höppe, P., 1984. Die Energiebilanz des Menschen. Wiss. Mitt. Meteor. Inst. Univ. München

Nr. 49.

6. Höppe, P.R., 1993. Heat balance modelling. Experientia. 49:741-746.

7. Höppe, P., 1999. The physiological equivalent temperature – a universal index for the

biometeorological assessment of the thermal environment. Int. J. Biometeorol. 43:71-75.

8. Jendritzky, G., et al. 1990. Methodik zur raumbezogenen Bewertung der thermischen

Komponente im Bioklima des Menschen (Fortgeschriebenes Klima-Michel-Modell). Beitr.

Akad. Raumforsch. Landesplan. Nr. 114.

9. Matzarakis, A., 1995. Humanbiometeorological assessment of the climate of Greece.

Dissertation, Aristotelian University of Thessaloniki. (in greek).

10. Matzarakis, A. and Mayer, H., 1997. Heat stress in Greece. Int. J. Biometeorol. 41:34-39.

11. Matzarakis, A., Mayer, H. and Iziomon, M., 1999. Applications of a universal thermal

index: physiological equivalent temperature. Int. J. Biometeorol. 43:76-84.

12. Matzarakis, A., Rutz, F. and Mayer, H., 2000. Estimation and calculation of the mean

radiant temperature within urban structures. Biometeorology and Urban Climatology at the

Turn of the Millenium, edited by R.J. de Dear, et al. Selected Papers from the Conference

ICB-ICUC’99, Sydney. WCASP-50, WMO/TD No. 1026, 273-278.

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13. Matzarakis, A., Balafoutis, Ch. and Mayer, H., 1998. Construction of Bioclimate and

Climate maps of Greece (in greek). Proc. 4the Panhellenic Congress Meteorology-

Climatology-Physics of the Atmosphere. Athens September 1998, Volume 3, 477-482.

14. Matzarakis, A. and Mayer, H., 1996. Another kind of environmental stress: Thermal stress.

WHO Newsletter No. 18:7-10.

19

A NEW GENERATION CLIMATE INDEX FOR TOURISM

C. R. de Freitas1, Daniel Scott2 and Geoff McBoyle2

1 School of Geography and Environmental Science, University of Auckland, PB 92019, Auckland,

New Zealand. 2 Department of Geography, University of Waterloo, 200 University Avenue West, Waterloo,

Ontario, Canada, N2L 3G1

E-mail addresses: c.defreitas@auckland.ac.nz (C R de Freitas), dj2scott@fes.uwaterloo.ca (Daniel

Scott), gmcboyle@fes.uwaterloo.ca (Geoff McBoyle)

ABSTRACT

Climate is important to tourism, but the relationship between the two is complex. This is because of

the multifaceted nature of climate and the complicated way these variables come together to give

meaning to a particular weather or climate condition for tourism. Researchers have attempted to

tackle the problem by integrating relevant climate and tourism variables into a single index for ease

of interpretation. However, these indices have been largely reliant on subjective judgements of the

researcher(s) and not validated through field investigation. In the present study we aim to address

this limitation by devising and then testing a theoretically informed and practically useful climatic

index for tourism. The Climate Index for Tourism (CIT) can be derived using either standard

climate data or, for short-time forecasts, weather variables. In either case the CIT relies on actual

observations rather than on averaged data. The CIT combines three conceptual attributes of climate

for tourism and recreation: the thermal, aesthetic and physical/mechanical. Unlike some existing

climate indices for the tourism-recreation sector that rated the climate for broad-based “cultural

tourism” or “urban tourism”, the CIT rates the climate resource for activities that are highly

climate/weather sensitive (e.g., beach holidays, resort tourism, water-based sporting holidays). The

theoretical basis and structure of CIT are explained and the results of a preliminary validation

exercise presented.

KEYWORDS: Tourism climate index, Tourism climate, Recreation climate, Destination image

INTRODUCTION

Climate is a dominant attribute of a tourist destination and has a major effect on tourism demand

and satisfaction, but its relationship with tourism is complex. Because of this, considerable effort

20

has gone into devising climate indices that summarise the significance of climate for tourism. An

index approach is required because of the multifaceted nature of weather and climate and the

complex ways they come together in a social and cultural context to give meaning to a particular

weather or climate condition for tourism.

An important limitation of most existing climate indices for tourism is that their rating schemes for

individual climate variables and the weighting of climate variables in the index were largely based

on the subjective opinion of the researcher(s) and not empirically tested on tourists or within the

tourism marketplace. Other weaknesses of existing indices stem from their failure to address the

essential requirements of an ideal index, which are discussed in detail later in this paper. In the

present study we aim to address the deficiencies of past indices for tourism by devising a

theoretically informed and practically useful climatic index called the Climate Index for Tourism

(CIT). CIT facilitates interpretation of the integrated effects of climate and has a range of possible

applications for both tourists and the tourism industry. Tourists and tour operators could use CIT to

select the best time and place for a vacation travel or plan activities appropriate to the expected

climate. Tourism planners could use the index to promote visitation outside the peak period and, if

necessary, discourage it during the peak; or it could be used to assess the potential visitor numbers

to assist in planning resort development programmes. The index, having validated the current

climate preferences of tourists, could also be used to assess possible impacts of climate change on

the climate resource of tourism destinations worldwide.

ESSENTIAL CHARACTERISTICS OF A NEW GENERATION INDEX

Rather than simply build on previous climate indices for the tourism, we began this study by

considering the essential characteristics of a theoretically sound and practically useful index. After a

detailed review of the literature and consideration of the needs of tourism stakeholders, six essential

characteristics for a new generation climate index were identified:

1) Theoretically sound

A new generation index must incorporate the results of recent multi-disciplinary research (tourism,

biometeorology, resource management, psychology, geography) that has contributed to an

improved understanding of tourism-climate relationships.

2) Integrates the effects of all facets of climate

Tourists respond to the integrated effect of various facets of climate (1, 2). De Freitas (2) identified

these facets the thermal, physical and aesthetic (Figure 1). Analysis of the thermal facet involves

three steps. i) Integrate the factors that influence the body-atmosphere thermal state using a method

21

that takes account of both the attributes of those exposed and the functional attributes of the

environment. Ideally this would include the following variables: air temperature, humidity, wind,

solar radiation and nature of the physical surroundings, and for the body, level of activity and

clothing. ii) Provide a rational index with sound physiological basis that adequately describes the

net thermal effect on the human body. iii) Identify relationships between the thermal state of the

body and the condition of mind that expresses the thermal sensation associated with this state. There

are a range of methods to analyse the thermal facet. To maximize flexibility and potential

application, the index should be able to accommodate input from any analysis of the thermal facet.

To achieve this, the final output of the thermal facet of the index is expressed using the

internationally standardised and recognised ASHRAE thermal sensation scale (see column [A] of

Table 1). The physical facet covers meteorological elements such as rain and wind that directly or

indirectly affect tourist satisfaction other than in a thermal sense. The occurrence of high wind, for

example, can have either a direct mechanical effect, causing inconvenience (personal belongings

having to be secured or weighted down) or an indirect effect such as blowing sand along the beach

causing decreased staisfaction. The aesthetic facet relates to the appealing attributes of the non-

thermal and non-physical components of the atmospheric environment. Included within this

category are factors such as sunshine or cloud.

3) Simple to calculate and uses readily available data

To maximize application, the index should be designed so that it can use either standard climate

data or, for short-time forecasts, weather variables. In either case, the index should rely on actual

observations rather than on averaged data. The temporal resolution of climatic data must be daily, in

order that the index values can be expressed as probability estimates of likelihood of occurrence

(e.g., there is a 90% chance of experiencing ‘ideal’ conditions during each day of a specified

holiday period).

4) Easy to use and understand

Importance should be placed on the nature and form of the index output, which should be presented

in a form that can be readily interpreted and understood by users in the tourism-recreation sector.

Much research has been done on the international application and communication of the UV index

and the lessons learned about the simplicity of the rating system and messaging are highly

applicable to designing a climate index for the tourism-recreation sector. The end product of the

index should be a rating system with five to seven classes, with clear descriptors of the quality of

the climate conditions for the tourism activities the index was specifically designed for. In the case

of CIT, the highly climate/weather sensitive activities of beach holidays are the focus.

22

Figure 1: Various facets of tourism climate, their significance and impact (from 1)

23

5) Recognise overriding effect of certain weather facets

This requirement takes into account that the combined effect of a given weather or climate

condition is not necessarily the sum total of its various facets. Under certain conditions and at

certain thresholds, the physical facet has an overriding influence on the thermal and aesthetic facets.

For example, heavy rain or high winds will cause people to leave the beach even if the thermal

conditions are excellent and the sun is shining. No previous climate index for tourism and recreation

recognized this overriding characteristic of the physical facet and thus tended to overrate days when

rain or wind dominated.

6) Empirically tested

Unlike most previous climate indices for the tourism-recreation sector, the performance of the index

and its thresholds should be validated against measures of tourist satisfaction with weather climate

conditions. Index validation presents several challenges. Use of the usual ‘demand’ indicators such

as attendance/visitation numbers, traffic flows, or campsite / motel occupancy rates can be

inappropriate. This is because these are not necessarily a measure of tourist satisfaction with climate

conditions. For example, peak demand is strongly influenced by state holidays (institutional

seasonality), not just climate (natural seasonality). In fact, peak demand is observed to sometimes

occur outside of the period when optimal climate occurs (2, 3). This means statistical models of

climate and tourism demand can be calibrated to non-optimal climate and thus may not predict

‘optimal climate for generating tourism’ as claimed. Self-reported tourist satisfaction with climate is

a more reliable ‘validator’ for a tourism climate index. It is also important that a climate index for

tourism be cross-culturally validated, as climatic preferences might differ.

STRUCTURE OF CIT

CIT is an integrated index for tourism and recreation that rates climate and weather along a

favourable-to-unfavourable spectrum. It is defined as:

CIT =ƒ [(T, A) * P]

where T is a measure of thermal sensation using the ASHRAE scale (column [A] in Table 1), A is

the aesthetic appeal of the sky condition ranging from clear to overcast (column [B+C] in Table 1),

and P is the physical thresholds of high wind and rain (column [D+E] in Table 1). Thermal and

aesthetic states are combined in a holiday weather typology matrix to produce a climate satisfaction

rating class, ranging from 1 to 7 (Table 2). If either physical threshold is exceeded, then P over-

rides T and A to reduce the satisfaction rating.

24

Table 1: CIT ratings (1 to 7) based on thermal state of the human body expressed as thermal sensation (TSN) on the standard ASHRAE scale, the aesthetic quality (cloud/sun), and physical factors (wind and rain). Bold values are theoretical ratings based on the work of de Freitas (2). Bracket values are ratings based on a limited validation exercise from an interview survey using the questionnaire shown as Table 3

ASHRAE TSN

[A]

Cloud ≤ 0.4 (n/N ≤ 0.4)

[B]

Cloud ≥0.5 (n/N ≥ 0.5)

[C]

Rain (>3mm, or >1hr

duration) [D]

Wind ≥ 6 m s-1

at ground [E]

Very hot 4 (3.8) 3 (3.1) 2 2 Hot 5 (5.4) 3 (4.2) 2 2 Warm 6 (6.2) 4 (4.6) 2 2 Sl. Warm 7 (5.8) 5 (4.0) 2 2 Indifferent 6 (5.0) 4 (3.2) 2 2 Sl. Cool 4 (3.4) 3 (2.2) 2 2 Cool 3 2 1 1 Cold 2 1 1 1 Very cold 1 1 1 1

Table 2: CIT rating scale and interpretation for holiday travel or tourism development

Satisfaction Class 1 Very poor Unacceptable 2 Poor Unacceptable 3 Fairly poor Marginal 4 Okay Suitable 5 Fairly good Good 6 Good Excellent 7 Very good Ideal

The initial development of the climatic thresholds and satisfaction ratings (bold font in Table 1) for

the CIT were based on the work of de Freitas (2, 4). In this detailed work, beach users were

interviewed on-site over a period of 18 months and their responses compared with detailed climate

data monitored on-site. De Freitas (2) showed that ideal atmospheric conditions are those producing

“slightly warm” conditions in the presence of scattered cloud (0.3 cover) and with wind speeds of

less than 6 m s-1, and that rain of greater than 30 minutes duration or wind speeds of over 6 m s-1

had an overriding effect on reducing tourist satisfaction. Cloud cover greater than about 0.4 had the

effect of reducing the aesthetic appeal of the weather condition for the beach user by 30%. The

occurrence of wind greater than or equal to 0.6 m s-1, or the occurrence of more than half an hour of

rain or 1 mm had an overriding effect on CIT.

The work by de Freitas (2, 4) identified the contribution of the thermal component to the overall

climate rating by first using a detailed body-atmosphere energy balance model to describe the net

thermal state in calorific terms, which, in turn, were correlated with the standardised ASHRAE

scale thermal sensation responses (TSN). Based on these findings, the contribution of the thermal

component of CIT (CITTSN) is given by:

CITTSN = 6.4 + 0.4 TSN – 0.281 TSN2

25

The effect of cloud cover greater than about 0.4 reduces the aesthetic appeal of the weather

condition for the beach user by 30%. The occurrence of wind great than or equal to 0.6 m s-1, or the

occurrence of more than half an hour of rain or 1 mm had an overriding effect. The thermal,

aesthetic and physical states are combined in holiday weather typology matrix to produce CIT index

rating in classes 1 to 7 shown in Table 1.

Table 3: Beach weather questionnaire ________________________________________________________________________________________________

The aim of this questionnaire is to identify levels of satisfaction with beach weather.

Assume you are at the beach, how would you rate each of the following weather scenarios using the scale:

1 = Very poor; 2 = Poor, 3 = Fairly poor; 4 Just OK; 5 = Fairly good; 6 = Good; 7 = Very good

Slightly cool weather Lots of blue sky visible Rating: 1..2..3..4..5..6..7

Indifferent Lots of blue sky visible Rating: 1..2..3..4..5..6..7

Slightly warm weather Lots of blue sky visible Rating: 1..2..3..4..5..6..7

Warm weather Lots of blue sky visible Rating: 1..2..3..4..5..6..7

Hot weather Lots of blue sky visible Rating: 1..2..3..4..5..6..7

Very hot weather Lots of blue sky visible Rating: 1..2..3..4..5..6..7

Slightly cool weather Most of sky cloud covered Rating: 1..2..3..4..5..6..7

Indifferent Most of sky cloud covered Rating: 1..2..3..4..5..6..7

Slightly warm weather Most of sky cloud covered Rating: 1..2..3..4..5..6..7

Warm weather Most of sky cloud covered Rating: 1..2..3..4..5..6..7

Hot weather Most of sky cloud covered Rating: 1..2..3..4..5..6..7

Very hot weather Most of sky cloud covered Rating: 1..2..3..4..5..6..7

You are at the beach and it rains for about an hour and you do not know when

or if it will stop, are you likely to leave the beach? Yes / No

You are at the beach and wind is a nuisance. For example, it blows personal

belongs away, blows sand onto your beach towel, into your clothing, food

and drink. Are you likely to leave the beach? Yes / No

________________________________________________________________________________

VALIDATION OF CIT

The work of de Freitas (2) reported on the results of empirical field data to identify the main

components of tourism climate and climatic thresholds that affect tourist satisfaction for beach

activities. To build on these results and examine how tourists discriminate between the finer

26

amenity attributes of weather types, questionnaire surveys in controlled settings were used to

measure satisfaction for a range of hypothetical atmospheric environmental conditions. A prototype

questionnaire was developed and tested on 20 respondents for clarity, ease of use and timing. The

final version of this survey is shown in Table 3. A preliminary survey of 34 adults was conducted in

Southern Ontario, Canada during May 2004. The results of this preliminary analysis are shown in

Table 1. While very preliminary, the findings were positive, as the stated satisfaction ratings of the

sample group (brackets in Table 1) approximated the theoretical satisfaction ratings (bold font in

Table 1) based on the field work of de Freitas (2). Further cross-cultural testing is underway with

surveys being conducted in Australia, Canada, Germany, Hungary, Italy, New Zealand, Portugal

and the United Kingdom as part of a collaborative project by members of the International Society

of Biometeorology’s, Commission on Climate, Tourism and Recreation.

REFERENCES

1. De Freitas, C.R. 2003. Tourism climatology: evaluating environmental information for

decision making and business planning in the recreation and tourism sector. Int. J.

Biometeorol. 48: 45-54.

2. De Freitas, C.R. 1990. Recreation climate assessment. Int. J. Climatol. 10:89-103.

3. Yapp G.A and McDonald N.S. (1978) A recreation climate model. J. Env. Mgmt.

7:235-252.

4. De Freitas, C.R. 1985. Assessment of human bioclimate based on thermal response. Int. J.

Biometeorol. 29: 97-119.

27

ESTIMATION AND COMPARISON OF HOURLY THERMAL DISCOMFORT ALONG

THE MEDITERRANEAN BASIN FOR TOURISM PLANNING

Christos Balafoutis1, Dafinka Ivanova2 and Timos Makrogiannis1

1. Department of Meteorology and Climatology, Aristotle University of Thessaloniki, 54124 Greece

2. University of Plovdiv-Bulgaria

E-mail address: balas@geo.auth.gr (Christos Balafoutis)

ABSTRACT

Tourists need accurate, easy to interpret information about the climate at their holiday destinations

to assist in the choice of the location and timing of their holidays. We used the Relative Strain

Index (RSI) to interpret the thermal biometeorological conditions of nine Mediterranean tourist

destinations. RSI values were calculated using hourly temperature and humidity data for July 2003

at nine locations: Malaga and Barcelona in Spain, Pisa and Venice in Italy, Corfu (Kerkyra),

Alexandroupolis, Rhodes and Heraklion in Greece, and Larnaca in Cyprus. The hourly values of

RSI ≥ 2 (value 2 represents the threshold for discomfort) were examined. The results show that the

climate at all of these nine locations causes thermal discomfort during the daytime period from

about 10:00 to 23:00 (Local Time). Most of the RSI values are 2 or 3, but on some days the values

are higher and the discomfort conditions extend over an entire day. Generally Malaga is more

comfortable than Barcelona, where some days have very unpleasant conditions (RSI =5). Pisa is

more comfortable than Barcelona, but is less comfortable than Venice. Conditions in Corfu are

similar to Barcelona’s. The results for the remaining Greek locations show that Rhodes and

Heraklion are similar and generally more comfortable than Corfu. Alexandroupolis is characterized

as the most pleasant location of those studied. Finally, Larnaca in Cyprus has the least attractive

thermal climatic conditions of the nine destinations studied.

KEYWORDS: Discomfort Indexes, Relative Strain Index, Hourly Data, Mediterranean resorts

INTRODUCTION

The Mediterranean shores and coastal cities are among the most favourite leisure destinations for

Europeans. The tourist industry in these areas has developed heavily over the years, offering to the

thousands of central and north European visitors a great number of alternatives for their summer

vacation, at the vast number of hotel units, organized camping infrastructures, and marina facilities.

28

However, the hot and highly humid weather conditions prevailing at these areas of the world may

create discomfort to central and north European visitors, due to the fact that they are not

acclimatized to these conditions. As is known, humans can cope easier with extreme cold rather

than with extreme heat - where in cases of extreme cold extra clothing can be added, when facing

extreme heat there is an absolute limit to the amount that can be removed. Therefore, a very

important question arises when one chooses a vacation destination: Which is the most suitable

destination in terms of weather conditions and how can one recognize it?

Due to the different levels of heat the discomfort conditions vary across the Mediterranean basin,

ranking a number of summer resorts as more comfortable than others. These differences were the

incentive to study and compare, in as much detail as possible, the discomfort conditions that prevail

in many coastal and highly touristy Mediterranean cities, using hourly temperature and humidity

data for the month July. July was selected as usually the warmest month of the year, and the busiest

in terms of tourists’ visits.

In estimating discomfort conditions, the Relative Strain Index (RSI) was considered as the most

appropriate for this paper. According to Lee and Henschel (1) there are three sets of variables

involved in any assessment of the effects of heat on a person: (i) the environmental conditions, (ii)

human factors (age, sex, metabolic, etc.) and (iii) the definition of reaction-effect criteria

(sensations, tolerance, etc.) These three sets can be quantified in terms of six variables: air

temperature, air humidity, air movement, radiant heat, metabolic rate, and clothing. Since these

variables could not be dealt with simultaneously, a measure of the relative strain imposed on an

individual was developed, based on various modifications to a series of heat transfer equations

proposed by Burton (2). By defining a set of standard conditions [a person wearing a light suit,

walking at 4 km/h, with a wind speed set at 0.5 m/s] the following equation was produced:

Relative Strain Index = (10.7 + 0.74 (T-35))/(44 – e) (1)

Where: e = partial water vapour pressure (mmHg), T = Air Temperature (° C)

Due to the fact that vapour pressure’s data collection is complex, this magnitude was estimated with

the use of temperature and relative humidity data by applying the following empirical formula

(Bloutsos, 1976):

e = 0.254 H (0.00739 T + 0.807 ) 8 (in mmHg) (2)

Or with the use of Dew Point temperature (Td), by applying the following equation:

e = 4.58 x 10 ((7.5 Td / (237.3+Td)) ( in mmHg) (3)

Where: T=Air Temperature (°C), H=Rel. Humidity (%) and Td=Dew Point Temperature (°C)

29

Lee and Henschel (1) defined the following terms qualitatively:

Comfort – thermal neutrality; general satisfaction; no anxiety.

Discomfort – sensations of heat and cold; uncomfortable; feeling of unease.

Distress – Physical strain; lack of concentration and unsteadiness; pain and suffering.

Failure – loss of physiological equilibrium; changes in pulse rate and temperature possible leading

to collapse; hospitalisation.

Using the literature survey and their own experience they applied the RSI to each of the terms

described above, and to different types of people. Giles et al. (3) utilized their results and unified

them into a single table (Table 1), which represents the relative strain values that correspond to the

four terms defined and to each category of the population. The population was divided into three

categories: the first category, labelled ‘Average Person’ includes people whose characteristics

match those of a typical young and healthy central European; the second category, under the name

‘Acclimatized Person’, describes people that are acclimatized in these weather conditions - for

example a Greek resident; while the third category, ‘Old Person’, includes anyone over 65 years

old.

Table 1: Values that give the limits of various effects of relative strain index for average, acclimatized

and old people

Sensation % of

Population

Average

Person

Acclimatized

Person

Old

Person

Comfortable 100 < 0.1 <0.2 <0.1

Discomfort 100 0.2 – 0.3 0.3 – 0.5 0.1- 0.2

Distress 100 0.4 – 0.5 0.6 –1.0 0.3

Failure 100 >0.5 >1.0 >0.3

DATA AND RESULTS

In order to estimate the hourly values of RSI, we used hourly temperature and vapour pressure data

for nine Mediterranean cities: Malaga and Barcelona in Spain, Pisa and Venetia in Italy, Kerkyra,

Alexandroupolis, Rhodes, and Heraklion in Greece, and Larnaca in Cyprus (Figure 1). This data

was retrieved from the Internet site of NOAA (http://weather.noaa.gov/weather/GR_cc.html).

30

Figure 1: The positions of the used stations around the Mediterranean basin (Larnaca is out of the

frame)

Using the above-mentioned equations (1,2,3), we calculated the hourly RSI values for July 2003.

The RSI value 0.2 was plotted as the lower hourly threshold value. Choosing all values equal or

greater than the threshold value, we constructed the monthly graphs, analyzed on an hourly basis,

presenting detailed information on discomfort conditions 24 hours a day.

The graphed analysis results in very worthy information about the prevailing bioclimatic conditions

in these Mediterranean resorts. These conditions were analyzed, following a west to east sequence

moving along the Mediterranean basin.

The first station examined was Malaga, Spain. This area, as Figure 2(left) shows, is generally

characterized by comfort conditions. During the after midnight hours and until 10 in the morning

(Local Time) conditions were comfortable. Only for a few days, at the end of the month, was there

discomfort during these hours. (RSI = 0.2). The same applies for the early evening hours, where in

most days conditions were comfortable. On the other hand, noon and afternoon hours throughout

the month corresponded to the discomfort sensation scale (0.2≤RSI=0.3), with a distress sensation

on the 27th of the month.

Barcelona’s conditions (Figure 2, right) differentiated significantly from Malaga’s. The discomfort

sensation prevailed all day long from 10:00 in the morning to 02:00 at night, with some small

exceptions during the morning hours of the first fortnight of the month. In addition, distress

conditions were more frequent than in Malaga. Thus when it comes to Spain, according to this

analysis, Malaga was more comfortable than Barcelona.

Moving eastward, to Italy, two cities were examined. Venice (Figure 3, left) shared almost the same

bioclimatic conditions as Malaga; where discomfort conditions (RSI ≥0.2) started at 10:00 Local

Time, and distress conditions were absent. Pisa (Figure 3, right), which is the only inland city

among the nine examined (but it is usually included in the travelling schedule of many tourists), had

more discomfort days than Venice, and the bioclimatic conditions were close to Barcelona’s. The

31

differences were spotted in the morning hours where Pisa was more comfortable, and during

midday where the feeling of distress was not apparent in Venice.

MALAGA JULY 20031 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,22 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,23 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,24 0,2 0,25 0,2 0,2 0,2 0,2 0,2 0,26 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,27 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,28 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,29 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2

10 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,211 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,212 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,213 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,2 0,214 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,215 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,216 0,2 0,2 0,2 0,2 0,2 0,217 0,2 0,2 0,318 0,2 0,2 0,2 0,2 0,2 0,219 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,220 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,221 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,222 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,223 0,2 0,2 0,3 0,3 0,3 0,4 0,3 0,3 0,4 0,3 0,3 0,3 0,3 0,2 0,224 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,3 0,3 0,3 0,2 0,225 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,226 0,2 0,2 0,2 0,2 0,2 0,2 0,227 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,4 0,4 0,4 0,4 0,4 0,4 0,3 0,3 0,3 0,328 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,229 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,2 0,230 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,3 0,3 0,3 0,2 0,2 0,2 0,3 0,2 0,2 0,2 0,2 0,231 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2

BARCELONA JULY 20031 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,2 0,22 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,23 0,24 0,2 0,2 0,2 0,25 0,2 0,2 0,2 0,2 0,26 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,27 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,28 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,29 0,2 0,2 0,3 0,3 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2

10 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,211 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,212 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,213 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,214 0,2 0,3 0,3 0,3 0,3 0,4 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,215 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,4 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,216 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,217 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,218 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,219 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,4 0,4 0,3 0,3 0,3 0,3 0,2 0,220 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,321 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,4 0,4 0,4 0,4 0,4 0,5 0,4 0,4 0,4 0,3 0,3 0,3 0,222 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,4 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,223 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,2 0,224 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,225 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,4 0,3 0,3 0,3 0,3 0,2 0,2 0,226 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,4 0,4 0,5 0,4 0,4 0,3 0,3 0,3 0,3 0,2 0,227 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,4 0,4 0,4 0,3 0,4 0,3 0,3 0,3 0,2 0,2 0,2 0,228 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,229 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,3 0,3 0,2 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,230 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,3 0,3 0,3 0,3 0,4 0,3 0,3 0,3 0,3 0,2 0,2 0,231 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,2 0,2

Figure 2: Unpleasant hot conditions (Relative Strain Index values ≥ 0.2) during July 2003 in the west

Mediterranean (Malaga, Barcelona)

32

VENECIA JULY 20031 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,2 0,3 0,3 0,3 0,2 0,2 0,2 0,22 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,23 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,245 0,2 0,26 0,27 0,2 0,2 0,2 0,2 0,2 0,2 0,28 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,2 0,2 0,29 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2

10 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,211 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,212 0,2 0,2 0,2 0,2 0,3 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,213 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,2 0,2 0,2 0,3 0,2 0,2 0,214 0,2 0,2 0,2 0,2 0,215 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,216 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,2 0,2 0,2 0,2 0,217 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,4 0,4 0,3 0,3 0,3 0,3 0,3 0,218 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,219 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,2 0,2 0,220 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,3 0,2 0,2 0,221 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,4 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,222 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,223 0,2 0,2 0,2 0,2 0,3 0,3 0,2 0,2 0,3 0,3 0,2 0,3 0,3 0,2 0,2 0,22425 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,226 0,2 0,2 0,3 0,3 0,2 0,3 0,3 0,3 0,2 0,3 0,2 0,2 0,2 0,227 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,228 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,229 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,2 0,2 0,2 0,2 0,230 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,231 0,2 0,2 0,2 0,2 0,2

PISA JULY 20031 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,4 0,4 0,3 0,3 0,3 0,3 0,3 0,2 0,22 0,2 0,2 0,2 0,4 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,23 0,2 0,2 0,2 0,2 0,3 0,3 0,2 0,3 0,2 0,2 0,2 0,2 0,2 0,24 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,25 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,26 0,2 0,2 0,3 0,2 0,2 0,3 0,2 0,2 0,2 0,2 0,2 0,2 0,27 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,28 0,2 0,2 0,3 0,3 0,2 0,3 0,2 0,2 0,2 0,3 0,3 0,3 0,2 0,29 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2

10 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,4 0,4 0,4 0,4 0,5 0,3 0,4 0,3 0,3 0,311 0,3 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,2 0,3 0,2 0,2 0,3 0,2 0,2 0,2 0,2 0,2 0,212 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,213 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,214 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,215 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,3 0,216 0,2 0,2 0,2 0,3 0,3 0,3 0,4 0,4 0,4 0,4 0,3 0,3 0,317 0,3 0,3 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,218 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,2 0,2 0,2 0,219 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,220 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,2 0,221 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,222 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,4 0,4 0,4 0,4 0,4 0,3 0,2 0,223 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,224 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,225 0,2 0,2 0,2 0,2 0,3 0,3 0,2 0,2 0,2 0,2 0,2 0,2 0,226 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,227 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,228 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,229 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,230 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,2 0,2 0,2 0,231

Figure 3: Unpleasant hot conditions (Relative Strain Index values ≥ 0.2) during July 2003 in the

central Mediterranean (Venice, Pisa)

33

ALEXANDROUPOLIS -GREECE: JULY 20031 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,22 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,2 0,2 0,2 0,23 0,2 0,2 0,3 0,3 0,3 0,4 0,4 0,4 0,3 0,2 0,2 0,2 0,24 0,2 0,3 0,3 0,4 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,25 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,26 0,2 0,2 0,2 0,2 0,2 0,27 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,28 0,2 0,2 0,2 0,29 0,2 0,2 0,2 0,2 0,2 0,2 0,2

10 0,2 0,2 0,2 0,2 0,2 0,2 0,211 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,212 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,213 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,214 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,215 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,216 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,217 0,2 0,2 0,2 0,2 0,3 0,3 0,2 0,2 0,2 0,2 0,2 0,218 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,2 0,3 0,3 0,219 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,220 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,221 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,2 0,3 0,2 0,2 0,2 0,222 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,223 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,224 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,225 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,2 0,2 0,2 0,2 0,226 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,227 0,2 0,2 0,3 0,2 0,3 0,3 0,3 0,3 0,2 0,228 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,2 0,229 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,230 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,231 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2

KERKYRA JULY 20031 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,4 0,3 0,3 0,3 0,3 0,2 0,2 0,22 0,2 0,2 0,3 0,3 0,3 0,3 0,4 0,4 0,4 0,4 0,4 0,4 0,3 0,3 0,2 0,33 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,4 0,4 0,4 0,4 0,3 0,4 0,3 0,3 0,2 0,2 0,24 0,2 0,2 0,3 0,2 0,3 0,4 0,4 0,4 0,4 0,3 0,3 0,3 0,3 0,2 0,2 0,25 0,2 0,2 0,3 0,3 0,4 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,26 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,2 0,2 0,2 0,2 0,27 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,28 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,29 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2

10 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,211 0,2 0,2 0,2 0,2 0,3 0,2 0,3 0,3 0,3 0,2 0,2 0,2 0,212 0,2 0,2 0,2 0,3 0,3 0,2 0,2 0,2 0,2 0,2 0,2 0,213 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,214 0,2 0,2 0,2 0,3 0,2 0,2 0,2 0,2 0,215 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,2 0,2 0,2 0,216 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,217 0,2 0,3 0,3 0,4 0,4 0,3 0,3 0,4 0,4 0,3 0,2 0,2 0,218 0,2 0,2 0,2 0,3 0,4 0,3 0,4 0,4 0,4 0,3 0,3 0,3 0,2 0,2 0,2 0,219 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,4 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,220 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,221 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,222 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,4 0,4 0,4 0,3 0,3 0,3 0,3 0,2 0,223 0,3 0,3 0,3 0,3 0,4 0,4 0,4 0,4 0,4 0,4 0,4 0,3 0,2 0,224 0,2 0,2 0,3 0,3 0,3 0,4 0,4 0,4 0,4 0,4 0,3 0,3 0,2 0,2 0,2 0,225 0,2 0,2 0,2 0,3 0,3 0,4 0,4 0,4 0,4 0,4 0,4 0,3 0,3 0,2 0,2 0,226 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,4 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,227 0,2 0,2 0,3 0,3 0,3 0,4 0,4 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,228 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,229 0,2 0,3 0,3 0,3 0,3 0,4 0,4 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,230 0,2 0,2 0,3 0,3 0,3 0,4 0,3 0,3 0,4 0,3 0,3 0,3 0,2 0,2 0,231 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2

Figure 4: Unpleasant hot conditions (Relative Strain Index values ≥ 0.2) during July 2003 in the north

Greece (Alexandroupolis, Corfu (Kerkyra))

In the Eastern Mediterranean we examined five stations. Four of them were located in Greece, and

one in Cyprus.

34

Alexandroupolis’ station (Figure 4, left), located in Alexandroupolis, a North Aegean coastal city,

generated the most favourable results of all cities examined in terms of comfort. In Alexandroupolis

the discomfort sensation scale (Table 1) prevailed (during July only for 2 days and for a time period

of one to three hours conditions caused distress) between 11:00 and 23:00 hours Local Time,

offering comfortable nights during July.

In the island of Corfu (Figure 4, (right-Kerkyra)), located at the same parallel with Alexandroupolis,

the RSI values were higher. Discomfort conditions emerged from 09:00 o’clock to midnight.

Additionally, distress conditions appeared during a few days at the beginning of the month, and

became common in the second fortnight. On the other hand, morning hours were comfortable

throughout July.

To summarize, Corfu had the most uncomfortable conditions compared to Malaga, Barcelona, Pisa

and Venice.

The other two Greek stations (Heraklion and Rhodes) are located in the southern Greek Islands of

Crete and Rhodes. In the city of Heraklion, located at the northern part of Crete (Figure 5, left) the

discomfort sensation (Table 1) was very common throughout the month, from 09:00 to 23:00, but

these values were usually equal to 0.2, with some exceptions where these values were equal to 0.3.

Thus Heraklion was more comfortable than Corfu, Barcelona, and Pisa.

In Rhodes (Figure 5, right) the discomfort sensation was present 24 hours a day, except for the

morning hours of the first fortnight; but the majority of these values were equal to 0.2 RSI value,

and only during the midday become equal to 0.3. Thus Rhodes was more comfortable than Corfu

and shared a similar bioclimatic behavior with Heraklion.

In Larnaca, located at southern Cyprus, the daily bioclimatic conditions were characterized as

Discomfort from 08:00 to 01:00 (local Time) and Distress (Table 1) during daytime. Thus, as it is

concluded from Figure 6, Larnaca was the most unpleasant place in terms of bioclimatic conditions

compared to all eight stations examined.

DISCUSSION

For the purposes of this paper, data for July 2003 was used. For the success of this methodology,

the use of a greater time period (greater that 5 years) is essential. This way, one can define the

bioclimatic behavior of each location for, at least, the warmest six months of the year. The RSI that

will be generated from this procedure will define the ‘normal’ bioclimatic condition of each

location. Seasonally, the RSI values of the warmest month can be compared to the corresponding

normal values and assist in identifying possible inter-annual fluctuations, which characterize the

bioclimatic nature of an area as stable or unstable.

35

HERAKLION JULY 20031 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,22 0,2 0,2 0,3 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,23 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,24 0,2 0,3 0,3 0,3 0,3 0,3 0,4 0,4 0,4 0,3 0,3 0,3 0,3 0,3 0,2 0,25 0,2 0,3 0,3 0,3 0,3 0,3 0,4 0,4 0,3 0,4 0,3 0,3 0,2 0,2 0,26 0,2 0,2 0,2 0,1 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,27 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,28 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,29 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2

10 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,211 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,2 0,2 0,212 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,213 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,214 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,2 0,2 0,2 0,2 0,2 0,215 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,216 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,217 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,218 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,219 0,2 0,2 0,3 0,3 0,2 0,3 0,2 0,2 0,2 0,3 0,3 0,2 0,2 0,2 0,2 0,220 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,221 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,222 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,223 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,224 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,225 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,226 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,227 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,228 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,229 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,230 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,3 0,2 0,3 0,2 0,231 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,3 0,2 0,2 0,2 0,2

RHODOS JULY 20031 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,2 0,2 0,22 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,23 0,2 0,2 0,3 0,3 0,3 0,3 0,4 0,3 0,3 0,4 0,3 0,3 0,24 0,2 0,2 0,3 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,25 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,4 0,4 0,3 0,3 0,4 0,3 0,3 0,3 0,2 0,2 0,26 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,27 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,28 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,29 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2

10 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,211 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,212 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,2 0,2 0,2 0,213 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,214 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,215 0,2 0,2 0,2 0,2 0,3 0,3 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,216 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,217 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,4 0,3 0,3 0,3 0,3 0,2 0,2 0,218 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,219 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,220 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,2 0,221 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,222 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,3 0,3 0,3 0,3 0,3 0,2 0,3 0,2 0,2 0,2 0,223 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,224 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,3 0,2 0,2 0,2 0,225 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,226 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,227 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,2 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,228 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,229 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,230 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,231 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,2 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,3 0,2 0,2 0,2 0,2 0,2

Figure 5: Unpleasant hot conditions (Relative Strain Index values ≥ 0.2) during July 2003 in the south

Greek Islands (Crete (Heraklion), Rhodes)

36

L A R N A C A J U L Y 2 0 0 31 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4

1 0 , 2 0 , 3 0 , 3 0 , 4 0 , 4 0 , 4 0 , 3 0 , 3 0 , 3 0 , 4 0 , 3 0 , 3 0 , 2 0 , 2 0 , 22 0 , 2 0 , 3 0 , 3 0 , 3 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 3 0 , 3 0 , 3 0 , 2 0 , 2 0 , 2 0 , 23 0 , 2 0 , 2 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 4 0 , 4 0 , 4 0 , 3 0 , 3 0 , 2 0 , 2 0 , 24 0 , 2 0 , 3 0 , 3 0 , 3 0 , 4 0 , 2 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 3 0 , 3 0 , 25 0 , 2 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 3 0 , 3 0 , 2 0 , 2 0 , 26 0 , 2 0 , 2 0 , 2 0 , 2 0 , 2 0 , 2 0 , 2 0 , 2 0 , 2 0 , 3 0 , 3 0 , 4 0 , 4 0 , 4 0 , 5 0 , 4 0 , 5 0 , 4 0 , 4 0 , 3 0 , 3 0 , 3 0 , 3 0 , 37 0 , 2 0 , 2 0 , 2 0 , 2 0 , 3 0 , 3 0 , 3 0 , 3 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 28 0 , 2 0 , 2 0 , 3 0 , 3 0 , 3 0 , 3 0 , 4 0 , 4 0 , 4 0 , 4 0 , 3 0 , 3 0 , 3 0 , 3 0 , 2 0 , 3 0 , 3 0 , 29 0 , 2 0 , 2 0 , 2 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 4 0 , 3 0 , 3 0 , 3 0 , 3 0 , 2 0 , 2 0 , 2 0 , 2 0 , 2

1 0 0 , 2 0 , 2 0 , 2 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 2 0 , 2 0 , 2 0 , 21 1 0 , 2 0 , 2 0 , 2 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 2 0 , 2 0 , 21 2 0 , 2 0 , 2 0 , 2 0 , 3 0 , 3 0 , 3 0 , 4 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 2 0 , 21 3 0 , 2 0 , 2 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 2 0 , 2 0 , 21 4 0 , 2 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 2 0 , 2 0 , 2 0 , 21 5 0 , 2 0 , 2 0 , 3 0 , 3 0 , 3 0 , 3 0 , 4 0 , 4 0 , 4 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 21 6 0 , 2 0 , 2 0 , 2 0 , 2 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 2 0 , 2 0 , 21 7 0 , 2 0 , 3 0 , 3 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 3 0 , 3 0 , 3 0 , 2 0 , 2 0 , 21 8 0 , 3 0 , 3 0 , 3 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 3 0 , 3 0 , 3 0 , 2 0 , 2 0 , 2 0 , 21 9 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 4 0 , 4 0 , 4 0 , 4 0 , 3 0 , 3 0 , 3 0 , 3 0 , 2 0 , 2 0 , 22 0 0 , 2 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 4 0 , 3 0 , 4 0 , 4 0 , 4 0 , 3 0 , 3 0 , 3 0 , 2 0 , 2 0 , 22 1 0 , 2 0 , 2 0 , 2 0 , 3 0 , 3 0 , 3 0 , 3 0 , 4 0 , 4 0 , 4 0 , 5 0 , 4 0 , 4 0 , 3 0 , 3 0 , 3 0 , 2 0 , 2 0 , 22 2 0 , 2 0 , 2 0 , 2 0 , 2 0 , 3 0 , 4 0 , 3 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 3 0 , 3 0 , 3 0 , 2 0 , 2 0 , 22 3 0 , 2 0 , 2 0 , 2 0 , 2 0 , 2 0 , 2 0 , 3 0 , 3 0 , 3 0 , 3 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 3 0 , 4 0 , 3 0 , 3 0 , 2 0 , 2 0 , 22 4 0 , 2 0 , 2 0 , 2 0 , 2 0 , 2 0 , 2 0 , 2 0 , 3 0 , 3 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 3 0 , 3 0 , 2 0 , 2 0 , 22 5 0 , 2 0 , 2 0 , 2 0 , 2 0 , 3 0 , 3 0 , 4 0 , 3 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 3 0 , 3 0 , 2 0 , 2 0 , 2 0 , 22 6 0 , 2 0 , 2 0 , 2 0 , 2 0 , 2 0 , 2 0 , 3 0 , 3 0 , 3 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 3 0 , 4 0 , 4 0 , 3 0 , 3 0 , 3 0 , 2 0 , 22 7 0 , 2 0 , 2 0 , 2 0 , 2 0 , 2 0 , 2 0 , 3 0 , 3 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 3 0 , 4 0 , 3 0 , 3 0 , 3 0 , 2 0 , 2 0 , 22 8 0 , 2 0 , 2 0 , 3 0 , 3 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 2 0 , 2 0 , 2 0 , 22 9 0 , 2 0 , 2 0 , 2 0 , 2 0 , 2 0 , 3 0 , 3 0 , 3 0 , 3 0 , 4 0 , 4 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 3 0 , 2 0 , 2 0 , 23 0 0 , 2 0 , 2 0 , 2 0 , 2 0 , 3 0 , 3 0 , 3 0 , 4 0 , 4 0 , 3 0 , 4 0 , 4 0 , 3 0 , 4 0 , 3 0 , 3 0 , 2 0 , 2 0 , 2 0 , 23 1 0 , 2 0 , 2 0 , 3 0 , 3 0 , 3 0 , 3 0 , 4 0 , 4 0 , 4 0 , 4 0 , 4 0 , 3 0 , 3 0 , 3 0 , 2 0 , 2 0 , 2 0 , 2

Figure 6: Unpleasant hot conditions (R. S. Index values ≥ 0.2) during July 2003 in Larnaca - Cyprus

Knowledge of the RSI can be very beneficial for both tourists and the tourist industry. Being aware

in which category of the population one is classified under (average person, acclimatized person,

old person), and also being familiar with the levels of comfort in certain cities as they derive from

the RSI analysis, tourists are able to make efficient planning decisions tailored to their

individualistic needs. Depending on the level of tolerance one has, and the effect of bioclimatic

conditions in each destination, tourists can determine the appropriate accommodation (camping,

marina or hotel), attire, transportation, and agenda. Sight seeing, shopping, sports and other

activities can be scheduled in time periods where the least discomfort and distress is evident.

Moreover, the RSI can be helpful to senior citizens who may have health concerns. By choosing the

destination with the least discomfort conditions, or by planning their agenda during the comfortable

hours as projected by the RSI, senior travelers may minimize any possible risks.

The RSI results can also be beneficial for the tourist industry, by integrating the results into their

marketing strategies. Specific resorts, municipalities, or countries can present their level of comfort

as a competitive advantage in their advertising campaigns in an attempt to attract more tourists.

Hotel owners can manage the capacity of their units by launching campaigns aiming at specific

categories of the population that match the comfort conditions of their location, while tailoring their

pricing strategy in specific time periods. Places with comfort conditions can emphasize specific

competencies of their areas while being more persuasive in promoting them. Outdoor camping,

outdoor sporting activities, sight seeing, and shopping can become the competitive advantage of an

area when sided with their premium bioclimatic conditions. Travel agencies organizing group

vacations can increase customer satisfaction by promoting destinations, and scheduling agenda, at a

time and place where comfort conditions prevail. In conclusion, the RSI is an informative and

37

useful tool for both tourists and the tourist industry that can assist in managerial and personal

decision-making.

CONCLUSIONS

It is clear that on an hourly basis all Mediterranean cities examined presented unpleasant conditions

during the daytime for the studied month. Discomfort and Distress conditions appeared during these

hours, but their length varied from one station to the other - where they usually lasted from 10:00 to

23:00 and in some stations extended beyond midnight. The RSI, which was fitted to estimate the

bioclimatic conditions in nine Mediterranean cities, worked efficiently, as it described effectively

the human sensations which relate to weather conditions. Based on the hourly distribution of the

RSI values we can rank these cities from the most comfortable to the most uncomfortable. The

research showed that the north shores of the Greek Peninsula (Alexandroupolis) were more

comfortable compared to the other resorts; Malaga and Venice followed. Preceding Malaga and

Venice were Heraklion, Rhodes, and Pisa - all classified in the same group, followed by the resorts

of Barcelona and Corfu. Lastly, Larnaca in Cyprus was characterized as the most uncomfortable

resort of all.

REFERENCES

1. Lee D.H.K., Henschel A. 1966. Effects on physiological and clinical factors on response to

heat. Ann. NY Acad. Sci. 134:743-749.

2. Burton A.C. 1944. An analysis of the physiological effects of clothing in hot atmospheres.

Report of Aviation Medical research Association Committee.

3. Giles B.D. and Balafoutis Ch. 1990. The Greek heatwaves of 1987 and 1988. Int. J.

Climatol. 10:505-517.

4. Giles B.D., Balafoutis Ch. and Maheras P. (1990) To hot for comfort: The heatwaves in

Greece in 1987 and 1988. Int. J. Biometeorology 34: 98-104.

5. Bloutsos A.A. (1976) The climate in the upper atmosphere over Athens. PHD, pp 210 (p 26)

38

WEATHER AND RECREATION AT THE ATLANTIC SHORE NEAR LISBON,

PORTUGAL: A STUDY ON APPLIED LOCAL CLIMATOLOGY

M. J. Alcoforado1, H.Andrade1 and M. J. Vieira Paulo 2

1. University of Lisbon, Centre of Geographical Studies, Faculdade de Letras, 1600-214 Lisboa,

Portugal

2. Escola Secundária Maria Amália Vaz de Carvalho, Lisboa

E-mail addresses: mjalcoforado@mail.telepac.pt (M.J. Alcoforado), henriqueandrade@fl.ul.pt

(H.Andrade), mjvp@iol.pt (M.J. Paulo)

ABSTRACT

The main objective of this paper was to investigate how individuals enjoying summer leisure

activities at the seaside respond to weather. Praia Grande, a seaside resort near Lisbon (Portugal),

was selected as the study area. Most of the previous research has been carried out at the regional

scale, while this was an attempt to carry out a local study with applied purposes. On site

meteorological data were measured during 120 field surveys, carried out in August 1994, 1995 and

1996. For the same time periods two types of attendance indicators were selected: number of cars

parked by the beach, and subjective classification of business by two restaurants/coffee shops. In

order to describe weather in a holistic way, weather type classification was carried out for each

survey. Significant correlation was established between weather types and attendance factors. The

two restaurants/coffee shops showed different “responses” depending on their distance from the

beach. Thermal (using PET) and aesthetic factors proved to be the largest influences on beach

activity (excluding surf), as Praia Grande has very infrequent strong winds in the summer. This

method proved to be convenient for the study case, but highlighted that generalisation must only be

done with care. Data from the nearest meteorological station cannot be used before verification of

local scale climate variation. We are convinced that the weather type method (with subsequent

frequency calculations) expresses reality more accurately than averages of meteorological

parameters, or single numerical indices, calculated from meteorological averages.

KEYWORDS: Climate, Leisure activities, Tourism, Weather-type, Portugal

39

INTRODUCTION

Climate is an important part of the environmental context in which recreation takes place (1).

According to Perry (1) there are three main areas of research within the interaction between climate

and tourism: forecasting how weather and climate affects the participation rates for different types

of leisure activities, improving the weather and climate information for the leisure industry, and

investigating the likely impacts of climate change on tourism and recreational activities. More

recently, some studies have been published on the inverse relation: how does the growing tourism

industry, particularly the increase of GHG due to tourist long-haul travel, influence climate change

and global warming (2).

The present study refers mostly to the first area of research. Our main objective was to investigate

how individuals engaged in leisure activities at the seaside respond to weather, and to give

information to beach users. A seaside resort near Lisbon, Praia Grande, was selected as the study

area (Fig.1). Another objective of this study was to draw attention to the pitfalls of using data from

meteorological stations to assess climate elements at nearby seaside resorts.

Figure 1: Location map and fog distribution in the Lisbon Region There are not many studies on this subject that refer to Portugal. The two following research works

were carried out on a regional scale. Ferreira et al. (3) calculated the Terjung index of coastal

standard meteorological stations. Terjung classes were subdivided according to wind and nebulosity

40

values. Besancenot (4) dealt with the summer tourism of the Iberian Peninsula coastal areas using

weather type methodology (5), and data from coastal meteorological stations. A map of the summer

frequency of “favourable” weather types gives information at a regional scale for the coastal areas

of Iberia. Praia Grande lies in an intermediate class (75 to 80% of favourable days), between the

Algarve (≥ 80%) and the coastal areas of northern Portugal (≤ 75 %). On a more local scale, Paulo

(6) studied the relation between Praia Grande attendance indicators and atmospheric conditions,

using different methods; the results presented in this paper are based on that data.

Praia Grande (large beach) is a seaside resort used all year by surfers. In summer it is a traditional

resort for Portuguese families that rent houses in the vicinity, or travel from Lisbon (circa 30km) or

other nearby areas. It is located at the NW side of the Serra de Sintra, a 500m high, 12km long and

5km wide range. In spite of its small dimensions, the Serra de Sintra has an enormous influence on

the weather of the nearby areas (fig. 1).

The advection fog, frequent during summer dawns and morning hours, is “very frequent” to the

north of this range, although it hardly ever occurs at its southern hill foot (7). Both N and NW

winds prevail in spring and summer in this area. Wind shaped trees have permitted the study of the

direction and relative intensity of these winds (8). One of the conclusions was that there is a

sheltered area at the windward side of the Serra de Sintra where N and NW winds are less frequent

(9), and their speed is lower than on the leeward side (10). This fact may seem strange at first sight,

but was verified by field measurements carried out between Praia Grande and Guincho (half an

hour drive) during N wind afternoons. For example, on 10-8-83 there was no wind at Praia Grande,

while at the leeward side of the mountain the wind speed was up to 7-12 m/s. On 10-9-83 the wind

speed increase was from 2m/s at Praia Grande to up to 18m/s at the windward side of the mountain

(9). Daytime air temperature was lower at Praia Grande than at the leeward side of the mountains

during field surveys (-5ºC to -2.4 ºC difference, (9)).

MATERIALS AND METHODS

As was indicated by Besancenot (11), beach climate is characterised by large amplitudes of spatial

and temporal variations. To restrain seasonal variations, our study only refers to summer. August

was chosen because it corresponds to the month where most Portuguese people are on holidays. In

August most of the people at Praia Grande seek sun bathing, sea bathing and some sports carried

out on the large sand areas of this beach resort (fig.2).

1. Meteorological data acquisition at Praia Grande

This study was based on 120 field surveys, which took place on 40 summer days in August of 1994,

1995 and 1996. The decision to carry out measurements was based on Alcoforado (9) and on

41

empirical knowledge of the difference between the airport (for which daily data are available) and

Praia Grande weather conditions. This difference was later confirmed by the comparison of Praia

Grande and airport conditions during the study period. For example, the average temperature at 12h

was 1.4ºC higher at the airport than at Praia Grande. However, the largest differences pertained to

wind speed: 1.5m high wind speed average was much lower at Praia Grande (1.3m/s) than at the

Airport (3.5m/s). The frequency of wind speed >3m/s was only 7% at Praia Grande, during field

surveys, while it was up to 58% at the airport for the same time periods. No wind velocities >5m/s

occurred at Praia Grande, while at the airport they were present 15% of the time. Due to such

variation, meteorological measurements were carried out directly on the beach, as there is a very

sharp spatial modification of weather elements inland (12). Measurements of air temperature,

relative humidity and wind speed took place on the beach at 9h, 12h, 15h and 17h. Visual sky

observation was used to assess cloudiness (in octas) and fog (when the visibility was inferior to

100m).

Figure 2: View from Praia Grande and Coffee-shop location

2. Attendance and business indicators

2.1. Number of cars parked by the beach as an attendance indicator

To assess the effects of the atmosphere on visitors to Praia Grande we have tried to monitor

behavioural responses. As there is not very frequent public transport to this seaside resort many

people take their own car. Therefore, one of the indicators used was the number of cars parked in

the vicinity of Praia Grande. The parking lots and the nearby road were divided into sectors, in

42

order to permit a quick assessment of the number of cars during each survey. The number of cars

varied according to weather type, day of the week and time of day. In order to be able to establish

the relation between weather type and number of cars, some decisions were made:

1) Sundays and public holidays were withdrawn from the sample, as the number of cars

reached twice the average number of weekday cars, nearly independent of the weather-type.

2) There was also a large variation in the number of cars at each survey time: the average number of

cars attained its lowest value at 9h (100) and its highest value at 15h (650). As the size of the

sample for each period of day was too small to carry out a separate analysis, the following

procedure was followed to permit a consistent statistical data analysis: the difference between the

number of cars at a certain moment and the average number of cars at all the surveys at the same

moment of the day was calculated and referred to as “relative number of cars” (RNC).

2.2. Subjective classification of business

A second type of indicator of beach attendance was obtained by daily inquiries at two food

establishments by the beach: a coffee-shop right on the sand and a snack-bar/restaurant located on

the road, opposite to the beach and 20m away from it. Although it was impossible to gather

information on business amounts, the owners of the coffee shop and of the restaurant accepted to

classifying business from the previous days in qualitative terms (good, normal, weak). Sundays and

public holiday data were rejected.

3. Weather type classification

The climatic environment of beach users of Praia Grande was defined in a holistic way, which is as

an integration of all the atmospheric factors influencing the body thermal state and the perception

that an individual may have of weather.

Weather-type classification was the selected methodology. The weather type method was first used

in applied biometeorology by Jean-Pierre Besancenot in different works (4, 5, 11). In the first stage,

the same classes identified by Besancenot, Mounier and Lavenne (5) were used by Paulo (6). In this

research, the methodology was modified to consider three types of factors: thermal, aesthetic, and

physical (13).

Thermal factors - When comparing air temperature with RNC present near Praia Grande, no

significant relation was found (fig. 3a). Another attempt was then made: air temperature was

replaced by a thermo-physiologic indicator, the Physiologic Equivalent Temperature (PET), that

integrates the influence of air temperature, wind speed, vapour pressure and mean radiant

temperature (MRT), assuming a clothing insulation equivalent to 0.1 CLO and a production of

43

metabolic heat of 80 W/m2 (14). MRT was computed through the Rayman model (15), based on

solar altitude and cloudiness. The correlation between PET and the RNC (fig. 3b) was positive and

significant (r2=0.31).

Through a variance analysis (16), PET and air temperature values corresponding to different

demand indicators were tested. We concluded that PET values vary significantly from each

attendance class to another (F=7.9, for a critical value of 2.5 and an error probability of 5%), while

the same does not occur in regards to air temperature.

RNC

Air

tem

pera

ture

(ºC

)

PET

(º)

RNC

Figure 3: Air temperature and PET versus relative number of cars (RNC) Aesthetic factors - Subjective observation has shown that in the summer at Praia Grande beach

leisure depends to a great extent on cloud cover and/or presence of fog, and therefore the “aesthetic

natural milieu” (13) was included in the weather-type definition: cloudiness and presence of fog

were assessed subjectively. We are aware that nebulosity is already included in the computation of

MRT for PET calculation. However, the same PET value may correspond to different nebulosity

and, on the other hand, the state of the sky has a psychological influence on the well being of

individuals. High nebulosity and fog occasions are repulsive factors for beach leisure activities.

Therefore, nebulosity values and presence/absence of fog were included in the final weather type

classification.

Physical factors - The physical factors referred to by Freitas (13) are mainly rainfall and high wind

speeds. They did not vary significantly during the study period as no rain occurred and wind speed

was always inferior to 5m/s. As referred to before, when the prevailing summer N or NW wind is

blowing, Praia Grande is a relatively sheltered location. No rainy days were included in the sample

for two main reasons: a summer dry period is a common characteristic of the Mediterranean climate

(17), and it is well known that rainfall acts per se as a repulsive factor for attendance.

Final weather types - PET values were divided into three groups (< 30ºC; 30ºC-40ºC and > 41ºC).

The 40ºC threshold was indicated by Mayer and Matzarakis (18) as the lower limit of the

44

“extremely hot” PET values; 40ºC is a value which was daily exceeded during the heat wave that

occurred in Athens in 1987 (19). The 30ºC threshold was subjectively chosen for the present

research. Each of the PET groups was subdivided following the nebulosity criteria (>4/8; 4/8-6/8;

>6/8). However, as some of the weather types were rare (e. g. the class with PET > 40 ºC and

cloudiness > 6) the number of weather type classes was reduced from 9 to 5 (fig.4).

Figure 4: Weather type classification at Praia Grande The most frequent weather type was warm and clear (class 3, 30%), while the less frequent was the

warm and cloudy (class 4). Cool (either cloudy or clear) weather types were relatively frequent

(classes 1 and 2, respectively 23 % and 21 %), while hot weather types (almost always cloudless)

represented 15 % of the sample.

RESULTS

1. Weather type versus relative number of cars (RNC, fig. 4)

From figure 5, where the frequency of weather types are plotted for each class of RNC, several

conclusions may be drawn. First, the weakest beach attendance class (RNC < -100) corresponded to

the weather types cool and cloudy (class 2, 55 % of the cases), and clear (classes 1 and 2, 45%).

Second, weak beach attendance occasions (RNC between –100 and –50) were mostly cool and

cloudy.

For the opposite situation, beach attendance was the highest (RNC > 100) when the weather was

either hot (class 5, 43 %), warm and clear (class 3, 32 %), or cool and clear (class 1, 27 %). The

days with average RNC were mostly warm and clear.

45

Figure 5: Weather types versus relative number of cars (RNC) 2. Weather type versus business

When comparing information about business and weather types the following conclusions were

highlighted.

- There was a different relation between business and weather type according to the items that were

sold, and particularly according to the location of the two establishments.

- At the coffee shop, located right on the beach, the sales increased when the weather was hot or

warm as well as clear (weather types 5, 3 and 1, fig.6a). Therefore the relationship was similar to

the one between weather-types and the RNC.

- Sales at the restaurant seemed to be less dependent on weather. There were higher percentages of

days where business was considered “good” that occurred during cool and cloudy weather (weather

type 2, fig.6b). On the other hand, low sales occurred mostly during hot and warm cloudless

weather.

The small dimensions of the sample does not permit this paper to draw final conclusions, but it

seems that there was an inversion in the behaviour of the customers of both establishments. When

the weather was fine people stayed on the beach, buying cake, ice-cream, etc., while when the

weather was “bad” for beach activities people went to the restaurant and waited for the weather to

get better (which happens quite frequently in the afternoon at this seaside resort).

46

igure 6: Weather types versus business (a = coffee shop, b = restaurant)

ISCUSSION

s concentrated on a well-defined human activity, beach recreation (excluding wind-

om the studied period, the main factors that

F

D

Our research wa

surf and surf), and was carried out on a local scale.

If rainy days (rare in August) are withdrawn fr

contributed to the “overall desirability” (13) of on-site conditions at Praia Grande were the

aesthetic (presence of sunshine versus cloud cover) and thermal factors. Strong winds that would

hinder most beach activities are relatively rare in summer at Praia Grande.

47

The weather types were defined in a holistic manner for each survey. Thermal and aesthetic factors

proved to be the most important, as weather type depended mostly on cloud cover and on an index

(PET) that expresses the integrated thermal environment. For other seaside resorts (e.g. Guincho,

fig.1), where wind is frequently very strong, wind speed will have to be included in the weather

type definition.

As satisfaction with weather affects participation, we tried to assess the latter as a measure of

demand for the climatic resource. The attendance and business indicators were related to the

weather types. The finding indicate that the method used is a convenient one for assessing beach

activity in summer at this particular seaside resort. We are aware that extrapolation can only occur

with seaside resorts that have a similar local climate.

We also consider that the data used was adequate for our purpose. It would make no sense to use

data from the airport or even from the nearest meteorological station (6). This should be taken into

consideration in other locally applied climatology research. Finally, no average data were

combined: one weather-type was assigned to each survey based on meteorological parameters,

occurring simultaneously. Subsequently, frequencies were calculated instead of average values,

which can distort reality. Thus thresholds, namely the one of PET, may be debated but they proved

to be appropriate in this case.

REFERENCES

1. Perry, A. 1997. Recreation and Tourism. Applied Climatology, edited by Thompson, R.D.

and Perry, A. (London, Routledge):240-248.

2. Nicholls, S. 2004. Climate Change and Tourism. Annals of Tourism Research. 31(1):238-

240.

3. Ferreira, A. et al. 1983. Ambiência atmosférica e recreio ao ar livre. Duas tentativas de

classificação e sua aplicação a estações litorais portuguesas. Lisboa, CEG, Linha de Acção

de Geografia Física, nº17.

4. Besancenot, J. P. 1985. Climat et tourisme estival sur les côtes de la Péninsule Ibérique.

Rev. Géographique des Pyrénées et du Sud-Ouest. 56(4):427-449.

5. Besancenot, J. P., Mounier, J., and Lavenne, J. 1978. Les conditions climatiques du tourisme

littoral: une méthode de recherche compréhensive. Norois. 99:357-382.

6. Paulo, M.J. Vieira. 1997. Clima e turismo: Ambiências atmosféricas estivais e conforto na

Praia Grande. Master Thesis, University of Lisbon.

7. Daveau, S. et al. 1985. Mapas climáticos de Portugal. Nevoeiro e Nebulosidade. Contrastes

térmicos. Memórias do Centro de Estudos Geográficos, Lisboa.

48

8. Alcoforado, M. J. 1986. Les vents dominants autour de la Serra de Sintra, Lisbonne.

Représentation cartographique de la déformation des arbres. Proceedings of the International

Symposium on Topoclimatology and its applications. Liège. U.G.I. : 13-25

9. Alcoforado, M. J. 1992. O clima da região de Lisboa. Contrastes e Ritmos térmicos.

Memórias do Centro de Estudos Geográficos.vol.15. Lisboa.

10. Oke, T.R. 1987. Boundary Layer Climates. (London, Routledge).

11. Besancenot, J.-P. 1990. Climat et tourisme. (Dijon, Masson, Collection Géographie).

12. Jehn, K. H. and Jehn, M. S. 1979. Beach atmosphere. Weather 34(6):223-232.

13. Freitas, C.R. 2003. Tourism Climatology: evaluating environmental information for decision

making and business planning in the recreation and tourism sector. International Journal of

Biometeorology. 48:45-54.

14. Matzarakis, A., Mayer, H., Iziomon, M. 1999. Applications of a universal thermal index:

physiological equivalent temperature. Int. J. Biometeorol. 43:76-84.

15. Matzarakis, A., F. Rutz, Mayer, H., et al. 1999. Estimation and calculation of the mean

radiant temperature within urban structures. Proceedings of the 15 th International Congress

of Biometeorology & International Conference on Urban Climatology, Sydney, Australia,

Macquarie University.

16. Wilks, D. S. 1995. Statistical Methods in the Atmospheric Sciences. (San Diego, Academic

Press).

17. Alcoforado, M. J. et al. 1983. Les indices de Gaussen et d'Emberger appliqués au Portugal.

Recherches Géographiques à Strasbourg. 22-23:1-13.

18. Mayer, H. and A. Matzarakis. 1997. The urban heath island seen from the angle of human-

biometeorlogy. Proc. Intern. Sympos. Monit. Urban Heath Island. Fujisawa (Japan) Keio

Univ.:84-95.

19. Matzarakis, A. and Mayer, H. 1997. Heat stress in Greece. Int. J. Biometeorol. 41(1):34-39.

49

IMPACT OF CLIMATE ON RECREATION AND TOURISM IN MICHIGAN

S. Nicholls1 and C. Shih2

1. Departments of Community, Agriculture, Recreation, & Resource Studies, and Geography,

Michigan State University, East Lansing, MI 48824-1222, USA

2. Department of Community, Agriculture, Recreation, & Resource Studies, Michigan State

University, East Lansing, MI 48824-1222, USA

E-mail addresses: nicho210@msu.edu (S. Nicholls), shrjuany@msu.edu (C. Shih)

ABSTRACT

Outdoor recreation and tourism (ORT) together constitute one of the three largest industries in

Michigan, and the provision of ORT opportunities to the traveling public represents a vital source of

income and jobs within the state. Many of the activities provided depend heavily upon appropriate

climatic, and associated environmental, conditions. However, many of these conditions are

projected to change, possibly quite substantially, in future decades. The purpose of the study

discussed here is to develop a web-based tool that will enable stakeholders in Michigan’s ORT

industries to examine the potential impacts of a range of futures (climatic, technological,

socioeconomic, and demographic) on the financial viability of their businesses, so as to improve

future planning and enable more informed decision-making. Construction of such a tool first

requires development of valid statistical models of historical relationships between ORT activity,

climatic conditions, and other factors likely to influence ORT use or participation, and it is this

topic that forms the basis of the present contribution. Models of participation in downhill skiing

and in general ORT activity (as measured by tourist traffic) are presented; a model of camping

activity remains under construction. Upon development of valid representations of historical ORT

activity, these models will be integrated with a suite of climate change scenarios and a web-based

interface that will allow users to access both historical and projected data. ORT stakeholders will

then be able to convert projected levels of activity at their site under a range of future climatic

conditions into financially meaningful figures, thereby allowing them to consider multiple future

scenarios and, thus, make more informed planning and management decisions.

KEYWORDS: Outdoor recreation and tourism (ORT), Climate, Michigan

50

INTRODUCTION

Outdoor recreation and tourism (ORT) are vital elements of Michigan’s economy and society. In

2001, Michigan welcomed 67 million leisure visitors, who spent over US$10.8 billion. The state

accounts for 3.4% of leisure trips in the United States, placing it seventh in the nation in terms of

leisure travel activity (1). Mid-Westerners are avid outdoor lovers, and there are more registered

boaters and more daily fee and municipal golf courses in Michigan than in any other state in the

union. Other activities engaged in at above national average levels include hunting, ice fishing,

snowmobiling, and skiing, all of which exhibit heavy dependence on weather/climatic conditions

and the environmental conditions created by them (snow and ice depth, vegetation patterns, lake

levels, etc.).

The current climate in the Great Lakes region consists of warm summers, cold winters, and

substantial year-round precipitation. The Great Lakes themselves have a significant impact on local

and regional weather conditions. Areas leeward of the lakes experience intense lake-effect storms;

such storms currently contribute up to 50% of annual snowfall in these areas (2). Climate in the

region may be warmer and wetter in the future, according to the Great Lakes Regional Assessment

(3), part of the US Global Change Research Program’s National Assessment. Output from the

Canadian (CGCM1) and Hadley (HadCM2) general circulation models (GCMs) suggests increases

of between 1-2ºC in minimum summer temperature, and between 0-1ºC in maximum summer

temperatures, by 2025-2034, with more warming in the western part of the region than the east.

Increases in summer precipitation of 15-25% are also projected. Expected changes in 2025-2034

winter conditions include increases in minimum temperature of between 4-6ºC according to

CGCM1, and 0.5-2.5ºC according to HadCM2, and in maximum temperature of between 2-3ºC

(CGCM1) and 0.5-2.5ºC (HadCM2). While the CGCM1 scenario suggests winter precipitation

levels similar to present day levels, the HadCM2 prediction is slightly lower. Projections for 2090-

2099 suggest even more substantial increases in summer and winter temperatures, with an increase

of approximately 20% in winter precipitation according to both the CGCM1 and HadCM2 models.

The direction and magnitude of predicted climate change in the Great Lakes region offers both

threats and opportunities for outdoor recreation and tourism. While shorter, less severe winters may

be damaging for winter activities such as ice fishing and skiing, longer summers may bode well for

golfing, boating, fishing, camping, etc. To date, however, impacts of both current and future climate

on ORT in the region remain under-investigated, especially from the perspective of those most

likely to be directly affected by such change: ORT participants and providers. For providers, the

economic ramifications of climate variability and change are particularly pertinent, yet little

research has addressed the financial viability of this industry in the face of changing conditions,

climatic and otherwise.

51

The study, described below, attempts to redress this shortcoming through construction of a series of

models that will enable ORT stakeholders in Michigan to evaluate the effects of weather and

climate on their business or activity from the perspective of its financial viability. The primary

objective of the study is to develop and monitor the use of a web-based tool that will enable

stakeholders in Michigan’s ORT industries to examine the potential impacts of a range of futures

(climatic, technological, socioeconomic, and demographic) on the viability of their business, so as

to aid their planning activities and enable more informed decision-making. Secondary objectives

include: (i) the fostering of increased interaction and collaboration between researchers, policy

makers, and ORT industry members in Michigan; (ii) increased knowledge regarding decision-

making in the face of uncertainty such as that surrounding the issue of climate variability and

change; and, (iii) improved understanding of the impacts (economic, environmental, and others) of

climate change and variability in the Great Lakes region.

METHODS

In recognition of the likely differential impacts of climate change on the various sectors of

Michigan’s ORT industry, most particularly depending upon their season of offering, the study

focuses on two distinct outdoor recreation activities – downhill skiing (a popular winter activity)

and camping (a popular summer activity) – in addition to the industry as a whole (on a year-round

basis, as measured by traffic volume on major tourist routes). Figure 1 illustrates the five major

stages envisaged for each of the three analyses (skiing, camping, and general industry), the first

three of which, focusing on the development of valid statistical models of historical relationships

between ORT activity, climatic conditions, and other factors likely to influence ORT use or

participation, form the basis of this paper.

Location of industry stakeholders and identification of their information needs has been a crucial

first stage in each of the three activity analyses (of skiing, camping, and the industry as a whole).

Methods have included the convening of special advisory committees, composed of key players in

Michigan’s ORT sector, as well as the involvement of the project team in numerous industry events

and meetings where the project has been introduced and assistance solicited. These preliminary

contacts have enabled identification of industry collaborators for each of the three activities: those

government agencies, industry organizations, and private businesses willing to share with the

project team the historical use/participation data needed to construct the statistical models of past

conditions which will then be integrated with various climate change scenarios. In this paper, results

from two of the three sets of analyses (of skiing and the industry as a whole) are presented.

Collaborators to date for these two areas have been various individual ski resorts, and the Michigan

Department of Transportation (MDOT), respectively. Use/participation was measured on a daily

52

basis in both cases, by lift tickets sold for skiing, and by traffic counts (as a general proxy of overall

tourism activity).

Create web-based tool with which stakeholders can assess likely impacts of range of future scenarios

(climatic, economic, technological, demographic, etc.) on use and business viability

Integrate models of use/participation with a suite (minimum of forty) of climate change scenarios

Develop location-specific models of use/participation and validate using historical data (use, climate, prices, etc.)

Identify industry collaborators and collect daily use/participation data from them

Locate recreation and tourism industry stakeholders and identify their information needs

STAGE ONE

STAGE TWO

STAGE THREE

STAGE FOUR

STAGE FIVE

Figure 1: Project stages

Collection of these data has enabled construction of a series of site-specific regression models

designed to account for as much of the daily variation in lift ticket sales and general tourism traffic

(the dependent variables) as possible, based on inclusion of a series of independent variables

relating to as many potentially influential factors as are measurable and able to be entered into such

analyses (climate, prices, other economic and social conditions, etc.). Upon development of

statistically valid representations of historical patterns, these models will then be integrated with a

suite of climate change scenarios so as to enable assessment of the potential impacts of projected

change. Individual users will then be able to convert projected levels of use/participation into

financially meaningful terms, thereby allowing them to make more informed planning and

management decisions.

53

RESULTS

Construction of valid models of historic patterns that explain as much variation in use/participation

as is possible is essential before they can be integrated with future climate scenarios, and it is upon

this task, for the skiing, and general tourism sectors, that these results focus.

Table 1: Multiple regression results, spring traffic volume (log of daily traffic count, 1991-2000)

unstandardized coefficients

standardized coefficients t sig.

predictors b std. error beta (constant) -80.418 14.989 -5.365 0.000 max. temperature 0.017 0.001 0.318 19.470 0.000 precipitation -0.003 0.001 -0.030 -1.890 0.059 gas price 0.275 0.098 0.046 2.797 0.005 CCI -0.001 0.001 -0.040 -0.804 0.422 Friday or Sunday 0.800 0.017 0.791 48.380 0.000 Saturday 0.309 0.021 0.236 14.471 0.000 public holiday 1.145 0.078 0.234 14.761 0.000 year 0.044 0.008 0.291 5.839 0.000 R2 = 0.81

Table 2: Multiple regression results, fall traffic volume (log of daily traffic count, 1991-2000)

unstandardized coefficients

standardized coefficients t sig.

predictors b std. error beta (constant) -100.772 18.288 -5.510 0.000 max. temperature 0.015 0.001 0.264 15.046 0.000 precipitation -0.003 0.001 -0.047 -2.675 0.008 gas price -0.120 0.123 -0.019 -0.980 0.328 CCI -0.002 0.001 -0.154 -2.600 0.010 Friday or Sunday 0.854 0.019 0.826 45.633 0.000 Saturday 0.303 0.024 0.228 12.610 0.000 public holiday 0.916 0.064 0.253 14.382 0.000 year 0.055 0.009 0.354 5.919 0.000 R2 = 0.79

Tables 1 and 2 illustrate results of regression analysis of daily traffic flow as measured at an MDOT

recording station on a major route (US 27) to the north-western portion of Michigan’s lower

peninsula. The route experiences little daily commuter traffic, and the recording device

differentiates between motorcycles, cars, pickups, minivans, and large trucks and trailers (by

number of axles). Thus, a good proportion of non-tourist industrial traffic can be excluded from the

54

count. The majority of the remaining traffic consists of travelers accessing the many outdoor

recreation opportunities offered in the area. To account for differences in tourist traffic throughout

the year, separate models have been constructed for each season. Tables 1 and 2 represent spring

(March-May) and fall (September-November), respectively. Table 3 shows regression results for ski

lift ticket sales at a popular ski resort in the north-western part of the lower peninsula.

Table 3: Multiple regression results, ski resort (log of daily lift tickets sold, 1996-2002)

unstandardized coefficients standardized coefficients t sig. predictors b std. error beta (Constant) 3.790 0.230 16.513 0.000 CCI 0.001 0.002 0.022 0.795 0.427 min. temperature -0.055 0.012 -0.310 -4.720 0.000 min. temperature square -0.001 0.000 -0.185 -2.910 0.004 snow depth 0.002 0.000 0.165 5.839 0.000 public holiday 1.478 0.123 0.314 11.974 0.000 slope 0.210 0.069 0.084 3.035 0.002 weekend 1.111 0.061 0.463 18.086 0.000 peak season 0.858 0.070 0.328 12.333 0.000 R2 = 0.55

DISCUSSION

Results suggest that there are statistically significant relationships between weather conditions and

both general tourist traffic (maximum temperature and precipitation) and ski participation

(minimum temperature and snow depth). Spring and fall traffic levels experience statistically

significant increases with rising daily maximum temperature, and decreases with increasing daily

precipitation, as expected. Lift ticket sales increase as snow depth rises, and also increase as daily

minimum temperature drops, though in a non-linear, decreasing fashion. In all three regressions,

however, temporal factors appear to have the most substantial impacts on traffic and lift ticket sales.

Spring and fall traffic increases significantly on weekends, with Fridays and Sundays (the most

typical days of arrival and departure) showing even more substantial activity than Saturdays.

Significant increases in traffic are also suggested on public holidays. Similarly, ski activity

increases significantly on weekends, public holidays, and in the industry-defined peak season

(January and February). The relationships between tourist traffic, ski activity and economic

conditions, as measured by gas prices and the Consumer Confidence Index (CCI), are less clear,

since neither appears significant on a consistent basis.

55

The overall explanatory power exhibited by the models is substantially better for the traffic models

than the ski model (R2 equals 0.81 for spring traffic and 0.79 for fall traffic, but only 0.55 for

skiing), which suggests that the ski model in particular requires significant improvement before it

can be integrated with any scenarios of future climate change. One variable under consideration for

incorporation in the ski model is weather conditions in major markets (to enable testing of the

hypothesis that conditions at the skier’s point of origin may influence their propensity to

participate).

ACKNOWLEDGEMENTS

The work presented results from U.S. Environmental Protection Agency funding for the project,

“Improving the Utility of Regional Climate Change Information from a Stakeholder Perspective,”

submitted by Sousounis, P.J., Andresen, J.A., Black, J.R., Holecek, D., and Winkler, J.A.

REFERENCES

1. D.K. Shifflet & Associates Ltd. 2003. Michigan 2001 Travel Summary. Report prepared for

Travel Michigan. Falls Church, Virginia: D.K. Shifflet & Associates Ltd. Available online at

http://www.travelmichigannews.org/pdf/MICHIGAN%202001%20Report.pdf

2. Sousounis, P.J. and Albercook, G.M. 2000a. Historical overview and current situation.

Preparing for a Changing Climate: The Potential Consequences of Climate Variability and

Change – Great Lakes Overview, edited by Sousounis, P.J. and Bisanz, J.M. (Ann Arbor,

MI, Atmospheric, Oceanic and Space Sciences Department, University of Michigan), 13-

17.

3. Sousounis, P.J. and Albercook, G.M. 2000b. Potential futures. Preparing for a Changing

Climate: The Potential Consequences of Climate Variability and Change – Great Lakes

Overview, edited by Sousounis, P.J. and Bisanz, J.M. (Ann Arbor, MI, Atmospheric,

Oceanic and Space Sciences Department, University of Michigan), 19-24.

56

CLIMATE CHANGE: THE IMPACT ON TOURISM COMFORT AT THREE ITALIAN

TOURIST SITES

Marco Morabito1, Alfonso Crisci2, Giacomo Barcaioli2, Giampiero Maracchi2

1. Interdepartmental Centre of Bioclimatology - University of Florence - Piazzale delle Cascine18

Florence, 50144, Florence, Italy

2. Institute of Biometeorology, CNR, Via Caproni 8, 50145, Florence, Italy

E-mail address: m.morabito@ibimet.cnr.it (M. Morabito)

ABSTRACT

A large number of studies have shown that climate change has a great impact on human health, and

on other living organisms. In the Mediterranean area, in particular, the fact that heat-waves are

frequent and persistent, often associated with low water availability, and that winter precipitation

has undergone modification related to the rising altitude of the thermal zero, highlights concerns

that such change could have an increasing impact on tourism. Rather than studying the

Mediterranean as a whole, this paper focused on Italy. Many Italian cities are characterised by a

mild climate, generally without temperature extremes. Together with other attractive attributes, such

as history, architecture and favourable geographical position, climate helps to make Italy an

important destination for tourists. This study was based on a biometeorological approach to tourist

activities in all seasons by using climatological scenarios in three Central Italian tourist sites:

Firenze, an important city for cultural and architectural tourism, Grosseto, a city involved in

summer tourism and connected with environmental activities during all seasons, such as agro-

tourism, and Monte Cimone, an important site for sports in winter and mountain holidays in

summer. Local climatic scenarios, derived from a downscaled HadCM3 Global Model series for the

period 2001-2080, were carried out for these three localities. Local scenarios consisted of: a daily

series of maximum and minimum temperatures, amount of precipitation, average relative humidity,

average wind velocity and global radiation. A biometeorological index based on the human energy

balance, the PET, was applied. Trend analysis of seasonal precipitation was also performed for each

site. The main results were represented by favourable winter conditions for tourist activity, but a

large and unexpected increase in extreme discomfort caused by hot conditions for summer tourist

activity. This was particularly true for tourists that were not acclimatized to such weather

conditions.

KEYWORDS: Climate, Tourism, PET, Biometeorological index

57

INTRODUCTION

One of the major concerns about the potential for climate change is that variation in extreme

climatic events will occur. Climatic change due to the enhanced greenhouse effect is likely to have

substantial impacts on human beings, other living organisms, and activities such as tourism (1, 2, 3).

This is especially true regarding the choice of destination for seasonal activities. For example,

traditional beach resorts may become too hot and humid for summer holidays, because they will

cause climatic stress on tourists. On the other hand, insufficient snow precipitation on mountain

sites may severely affect winter sport resorts.

Most studies have shown climatic changes on historical series in terms of increases in extreme high

temperatures, decreases in extreme low temperatures and increases in intense precipitation events

(4, 5, 6, 7, 8). These results are unable to evaluate the real influence of the atmospheric environment

on humans, in particular on tourists who need information about physiological strain, especially

when they are not acclimatized to specific local weather conditions. Relatively little is known about

the effects of climate on tourism or the role it plays (9). Only a few studies (10, 11) have

investigated the climate change effect from a biometeorological point of view, mostly by using

simple biometeorological indices, such as the Apparent Temperature index (12, 13). Also, studies

on the application of biometeorological indices on climatological scenarios are also few and far

between (14).

The aim of this study was to evaluate the future seasonal variations of extreme biometeorological

discomfort, caused by hot and cold conditions, in three Italian sites that are characterized by a great

reliance on tourism. Since tourists respond to the integrated effects of the atmospheric environment

rather than to climatic averages (9), a thermal index based on the energy balance model for humans

was employed. The three areas studied are situated in Central Italy: Firenze (λ = 11°11' E; Φ =

43°47' N) at 76m a.s.l, Grosseto (λ = 11°70' E; Φ = 42°45' N) at 10m a.s.l., and Monte Cimone (λ =

10°42' E; Φ = 44°11' N) at 2,165m a.s.l. The first two sites are located in the Region of Tuscany,

while the third site is situated in the Apennine Mountains in the Region of Emilia Romagna, on the

border with Tuscany.

METHODS

Climatological scenarios

A climatological series of daily maximum and minimum air temperatures (°C), daily average

relative humidity (%), wind velocity (ms-1), global radiation (Wm-2) and daily cumulative

precipitation (mm) were derived by a downscaling technique from the Hadley Centre’s HadCM3

scenario series (15). This series corresponds to the Summary for policymakers-Emission Scenarios

(SRES) (16) classes A2 and B2, obtained under the CLIMAGRI project (www.climagri.it). The

58

HadCM3 GCM model is able to represent the main physical and chemical atmospheric processes,

taking into consideration both economic development and the emission rate of greenhouse gases

(Fig. 1).

Figure 1: Scenarios’s emission SRES hypothesis (16)

The main advantage of using the HadCM3 model was its consideration of the interaction between

atmosphere and ocean (Atmosphere-Ocean General Circulation Model: AOGCM).

The downscaled series concerned the three sites in the Region of Tuscany. The methodology

adopted to produce the local series, relative to atmospheric variables, was carried out by the

following steps:

• A linear interpolation was calculated on the HadCM3 daily series using irregular

triangulation, TIN. The results were represented by the series which has the geographical

location of the specific sites, and corresponds to a real observed meteorological series. The

products of interpolation kept the statistical proprieties of the original series of scenarios

owing to the linearity of this interpolator.

• A numerical calibration of the obtained series was applied using a historical series

recorded in each specific site. Among the innumerable techniques which could be used, the

most effective seems to be the application of a linear regression model for each month,

where the daily values were selected. Each monthly model was able to assess the relation

among the quantile of the distribution of the series of scenarios interpolated, and also the

observed series for each parameter involved. This kind of analysis was time invariant

59

because the regression model works on selected series. Finally the models were applied to

the unselected series of scenarios. The results were a series of calibrated scenarios that have

a daily variability similar to real observations, but have maintained the information about

trend provided by the GCM model.

Local climatic scenario data so obtained was used for climatological inference for the local sites.

The calibration procedures are written in PERL language, and are an internal product of

CLIMAGRI project.

Biometeorological index

Both climatological data scenarios (A2 and B2) were utilized to assess the Physiological Equivalent

Temperature (PET) (17, 18) by using the RayMan model (19). The male average used was: 35 years

old; 1.75 m in height; 75 kg in weight; with moderate clothing (0.9 clo); standing and with a

metabolic level corresponding to light activities (80 W). This index was applied to assess two daily

conditions:

1. Diurnal PET (at 16:00 hours), by using the daily maximum air temperature (°C); the

corresponding relative humidity (%), assessed by using the empirical formula provided by

the National Weather Service-Alabama University (http://www.srh.noaa.gov/bmx/tables/rh.

html), replacing the dew point temperature with the available daily minimum air

temperature; the daily average wind velocity (ms-1); the daily global radiation (Wm-2); and

the daily average cloud cover (in eighths) assessed by the percentage of solar radiation

extinction.

2. Nocturnal PET (at 08:00 hours), by using the daily minimum air temperature (°C); the daily

average relative humidity (%); the daily average wind velocity (ms-1); the daily global

radiation (Wm-2); and the daily average cloud cover (in eighths) assessed as by the

percentage of solar radiation extinction.

Statistical analyses

A statistical analyses was made for each site and for each season for the period 2001-2080. The

seasons were considered as follows: winter (December, January and February); spring (March,

April and May); summer (June, July and August); autumn (September, October and November).

The diurnal and nocturnal daily PET were assessed on a decadal basis (8 decades) and the relative

frequencies as compared to the first decade were assessed. For winter and summer all days with

diurnal or nocturnal extreme discomfort caused by cold (PET ≤ 4°C), or hot (PET > 41°C),

conditions were considered. For spring and autumn all days with extreme discomfort caused by

cold, or hot, conditions were considered using slightly less severe criteria (cold= PET ≤ 8°C, and

60

hot= PET > 34°C). Also, the number of days per decade with a daily amount of precipitation over

0.2 mm (rainy days) was assessed on a seasonal basis and relative frequencies, to the first decade,

were calculated. With the aim of the detection of trends in the time series, a parametric method of

linear correlation analyses was applied. The Pearson product moment correlation coefficient (r) was

assessed and the statistical significance (P) was tested by using the Student t-test.

RESULTS

Winter

All sites showed negative and significant linear trends of diurnal (Tab. 1) and nocturnal (Tab. 2)

extreme discomfort caused by cold conditions. The decrease in the number of days with

uncomfortable conditions was higher during the diurnal period than during the nocturnal period.

The maximum diurnal decrease for the three sites was observed for Grosseto (A2: 13.2% per

decade; B2: 7.3% per decade), followed by Firenze, while the minimum was observed for M.

Cimone (Fig. 2). Regarding the trends of the number of days with an amount of precipitation over

0.2 mm, Grosseto showed a significant (P<0.001) linear decrease of 2.5% per decade. The other

sites showed negative but not significant trends.

Table 1: Correlation coefficients of the seasonal trends of diurnal extreme discomfort conditions

evaluated by using the Physiological Equivalent Temperature (PET) in the three sites of interest for

tourism. Legend: * Statistically significant P<0.05; ** Statistically significant P<0.01; Statistically

significant P<0.001; No D.: No extreme discomfort conditions measured

Pearson’s correlation coefficients for diurnal discomfort conditions Winter Spring Summer Autumn Discomfort Sites A2 B2 A2 B2 A2 B2 A2 B2

M.Cimone -0.86** -0.79** -0.88*** -0.88*** -0.91*** -0.91*** -0.97*** -0.84** Grosseto -0.90*** -0.71* -0.75* -0.82** No D. No D. -0.71* -0.80** Cold

conditions Firenze -0.81** -0.70* -0.71* -0.42 No D. No D. -0.87*** -0.93** M.Cimone No D. No D. No D. No D. No D. No D. No D. No D. Grosseto No D. No D. 0.91*** 0.91*** 0.98*** 0.96*** 0.96*** 0.83** Hot

conditions Firenze No D. No D. 0.88*** 0.96*** 0.98*** 0.97*** 0.96*** 0.74*

Spring

Diurnal extreme discomfort caused by cold conditions showed a prevalence towards significant

linear decrease in all sites (Tab. 1). The same situation was also observed regarding nocturnal

discomfort conditions (Tab. 2), where a slight linear decrease (A2 4% per decade; B2 3% per

decade), which was statistically significant for Grosseto and Firenze (P<0.001), was observed. On

the other hand, high significant increases (P<0.001) in diurnal discomfort caused by hot conditions

were observed for Grosseto (A2: 66.3% per decade; B2: 69.5% per decade) and for Firenze (A2:

61

73.5% per decade; B2: 77.6% per decade) (Fig. 3). The mountain site, M. Cimone, showed a

significant negative trend (P<0.01) for rainy days. The only site which showed a positive trend was

Firenze, which showed a significant increase (P<0.05) in days with precipitation, mostly evident in

the second half of the 21st century.

Table 2: Correlation coefficients of the seasonal trends of nocturnal extreme discomfort conditions

evaluated by using the Physiological Equivalent Temperature (PET) in the three sites of interest for

tourism. Legend: * Statistically significant P<0.05; ** Statistically significant P<0.01; Statistically

significant P<0.001; No D.: No extreme discomfort conditions measured

Pearson’s correlation coefficients for nocturnal discomfort conditions Winter Spring Summer Autumn Discomfort Sites A2 B2 A2 B2 A2 B2 A2 B2

M.Cimone -0.35 -0.43 -0.55 -0.63 -0.99*** -0.96*** -0.94*** -0.69* Grosseto -0.94*** -0.92*** -0.91*** -0.95*** No D. No D. -0.94*** -0.89** Cold

conditions Firenze -0.85** -0.73* -0.90*** -0.91*** No D. No D. -0.93*** -0.82** M.Cimone No D. No D. No D. No D. No D. No D. No D. No D. Grosseto No D. No D. No D. No D. No D. No D. No D. No D. Hot

conditions Firenze No D. No D. No D. No D. No D. No D. No D. No D.

Figure 2: Relative frequencies of winter days with extreme diurnal discomfort caused by cold

conditions evaluated by using the Physiological Equivalent Temperature (PET) to the climatological

series of scenarios of class A2 evaluated with the HadCM3

62

Summer

A highly significant (P<0.001) decadal increase in extreme diurnal discomfort (Tab. 1), caused by

hot conditions, was observed for Grosseto, ranging from 83.0% (B2) to 99.2% (A2) per decade, and

for Firenze, ranging from 41.8% (B2) to 88.5% (A2) per decade. On the other hand a significant

decrease (P<0.001) of extreme diurnal (Tab. 1) and nocturnal (Tab. 2) discomfort caused by cold

conditions was only observed for M. Cimone. Negative trends for rainy days were observed in all

sites. Linearly significant trends were observed for Firenze (P<0.05), with a decrease of 3.8% per

decade, and for M. Cimone (P<0.05), with a decrease of 3.0% per decade (Fig. 4).

Figure 3: Relative frequencies of spring days with extreme diurnal discomfort caused by hot

conditions evaluated by using the Physiological Equivalent Temperature (PET) to the climatological

series of scenarios of class A2 evaluated with the HadCM3

Autumn

Significant linear decreases of diurnal (Tab. 1) and nocturnal (Tab. 2) discomfort caused by cold

conditions were observed in all sites. On the other hand great increases in diurnal discomfort caused

by hot conditions were observed for Firenze (A2: 122.1% per decade; B2: 23.2% per decade) and

especially for Grosseto (A2: 170.4% per decade; B2: 62.9% per decade). The trends for rainy days

were prevalently positive and statistically significant only for M. Cimone (P<0.05), with an increase

of 1.2% per decade.

63

Figure 4: Relative frequencies of rainy days in summer obtained by using the climatological series of

scenarios of class A2 evaluated with the HadCM3 for the city of Firenze and for M. Cimone

DISCUSSION

These surveys will be useful to explore perceptions of seasonal extremes of weather, and for their

potential impacts on vacation planning. The reduction of extreme discomfort caused by cold

conditions shows that winter will be favourable for tourist activity. On the other hand, summers will

show more frequent extreme discomfort conditions, especially in urban environments. These results

confirm those pointed out by the Third Assessment Report of IPCC (20), which showed an increase

in the frequency of days with very high maximum temperatures during the 21st century. They also

corroborate another study (21), carried out in Thessaloniki, in Northern Greece, which

demonstrated that the temperature-humidity index (THI) will rise above a value for which everyone

feels uncomfortable, for more than twice as long as present, by 2050. All of these conditions are

very dangerous for the health of tourists who tend to be more vulnerable than locals, as they are not

acclimatized to the place they are visiting (9). If summer became warmer and/or drier, tourists

might suffer great discomfort and may be encouraged to remain closer to home.

The tourists that visit cities in Tuscany during the seasons of transition, such as spring and autumn,

generally characterized by mild weather, will more often find extreme and unexpected hot

conditions. Several authors (3), in a recent preliminary study, have already shown that the decisions

64

of tourists are affected by weather fluctuations, especially with regard to short breaks in spring and

autumn.

The present study shows that autumn, caused by the fact that the mountain site will present an

increasing number of days with precipitation, will be favourable for tourists who practise sports on

snow. On the other hand, the reduction in spring precipitation will anticipate the dry and hot

summer season.

It will be necessary to extend these surveys to other sites, which will be fundamental for the

identification and the evaluation of environmental information for business planning and decision-

making in the recreation and tourism industry (9).

ACKNOWLEDGEMENTS

The authors wish to thank Dr. P. Bonasoni of the National Research Council (CNR) of the Institute

of Atmospheric Sciences and Climate (ISAC) for the precious collaboration, and Dr F. Giovannini

of ARPAT-Firenze (Agenzia Regionale per la Protezione Ambientale della Toscana) for providing

meteorological data.

REFERENCES

1. Viner, D. and Agnew, M. 1999. Climate Change and its Impacts on Tourism. Climatic

Research Unit, UEA. Report commissioned for WWF UK, WWF.

http://www.wwf.org.uk/filelibrary/pdf/ tourism_and_cc_full.pdf.

2. Agnew, M.D. and Viner, D. 2001. Potential impacts of climate change on international

tourism. Int. J. Tour. Hosp. Res. 3:37-60(R).

3. Palutikof, J.P. and Agnew, M.D. 2002. Climate change and the potential impacts on tourism.

Proc. of the 15th Conference on Biometeorology and Aerobiology. 27 Oct./1 Nov. Kansas

City, Missoury.

4. Easterling, D.R., et al. 1997. Maximum and minimum temperature trends for the globe.

Science. 277:364-367.

5. Karl, T.R. and Knight, R.W. 1998. Secular trends of precipitation amount, frequency, and

intensity in the USA. Bull. Amer. Meteor. Soc. 79:231-241.

6. Heino, R., et al. 1999. Progress in the study of climatic extremes in Northern and Central

Europe. Clim. Change. 42:151-181.

7. Easterling, D.R., et al. 2000. Climate Extremes: Observations, Modelling, and Impacts.

Science. 289:2068-74.

8. DeGaetano, A.T. and Robert J.A. 2002. Trends in Twentieth-Century Temperature Extremes

across the United States. J. Climate. 15:3188-3205.

65

9. de Freitas, C.R. 2003. Tourism climatology: evaluating environmental information for

decision making and business planning in the recreation and tourism sector. Int. J.

Biometeorol. 48:45-54.

10. Gaffen, D.J. and Ross, R.J. 1998. Increased summertime heat stress in the U.S. Nature.

396:529-530.

11. Wang, X.L. and Gaffen D.J. 2001. Trends in extremes of surface humidity, temperature and

summertime heat stress in China. Advances in Atmospheric Sciences. 18:742-751.

12. Steadman, R.G. 1979. The assessment of sultriness. Part I: A temperature-humidity index

based on human physiology and clothing science; Part II: Effect of wind, extra radiation

and barometric pressure on apparent temperature. Journal of Applied Meteorology. 18:863-

885.

13. Steadman, R.G. 1984. A universal scale of apparent temperature. Journal of Climate and

Applied Meteorology. 23:1674-1687.

14. Delworth, T.L., Mahlman, J.D. and Knutson, T.R. 1999. Changes in heat index associated

with CO2-induced global warming. Climatic Change. 43:369-386.

15. Johns, T.C., et al. 2003. Anthropogenic climate change for 1860 to 2100 simulated with the

HadCM3 model under updated emissions scenarios. Climate Dynamics. (Online First, 18

Feb 2003), DOI 10.1007/s00382-002-0296-y.

16. IPCC. 2000. Summary for policymakers-Emission Scenarios, Special Report of IPCC

Working Group III, ISBN:92-9169-113-5.

17. Höppe, P. 1999. The physiological equivalent temperature - a universal index for the

biometeorological assessment of the thermal environment. Int. J. Biometeorol. 43:71-75.

18. Matzarakis, A., Mayer, H. and Iziomon, M. 1999. Applications of a universal thermal index:

physiological equivalent temperature. Int. J. Biometeor. 43:76-84.

19. Matzarakis, A., Rutz, F. and Mayer, H. 2000. Estimation and calculation of the mean radiant

temperature within urban structures. Biometeorology and Urban Climatology at the Turn of

the Millenium, edited by R.J. de Dear, et al. Selected Papers from the Conference ICB-

ICUC'99, Sydney, WCASP-50, WMO/TD No. 1026:273-278.

20. IPCC3. 2001. Third Assessment Report - Climate Change 2001

http://www.ipcc.ch/pub/online.htm (last access 15 April 2004).

21. Gawith, M.K., Downing, T. and Karacostas. 1999. Heatwaves in a changing climate.

Climate, Change and Risk, edited by T. Downing, A. Oissthoorn and R.Toll, Routledge,

279-307.

66

CLIMATE AND BIOCLIMATE VARIATIONS IN SLOVENIA AND

THEIR APPLICATION FOR TOURISM

Tanja Cegnar1 and Andreas Matzarakis2

1. Environmental Agency, Meteorological Office, SI-1000 Ljubljana, Slovenia

2. Meteorological Institute, University of Freiburg, D-79085 Freiburg, Germany

E-mail address: tanja.cegnar@rzs-hm.si (Tanja Cegnar)

ABSTRACT

Slovenia has a well-developed winter and summer tourism industry, which benefits many economic

branches. Climatic and bioclimatic conditions are of importance for such tourism. Although there

are plenty of places with pleasant thermal conditions even during high summer, heat waves can be

oppressive from time to time for the tourist destinations in Slovenia. This paper will examine

climate and bioclimate variations in Slovenia, and their application for tourism. For assessing these

conditions meteorological stations with long data series were selected. Three lowland stations, and

one in the Julian Alps, were selected to represent the variety of climatic conditions in Slovenia. We

expect that climate change will not have the same impact on the tourism industry in all regions; the

most vulnerable branch of tourism seems to be winter tourism.

The daily mean, maximum and minimum air temperature, relative humidity, wind speed and cloud

cover have been included in the analysis. From these parameters we have calculated, on a daily

basis, the mean radiant temperature, and also one of the most used thermal bioclimatic indices,

derived from the human energy balance: Physiological Equivalent Temperature (PET). There were

significant differences in seasonal trends of the input parameters and PET. On the annual scale a

rather sharp change in trend sign was noticed for all stations. One of our aims was also to compare

variability of basic climatic variables with variability of derived thermo-physiological relevant

variables, and their change over the last fifty years - but the main question was which period was

the most representative for describing the ongoing changes.

KEYWORDS: Climate, Climate variability and change, Thermal comfort, Bioclimate, Tourism

INTRODUCTION

Four major European geographic regions meet in Slovenia: the Alps, the Dinaric area, the

Pannonian plain, and the Adriatic sea. The highest peak is Mt. Triglav (2864 m). As a small,

67

beautiful and picturesque country with a well-preserved environment, Slovenia makes a great

tourist destination. Mountains, lakes, waterfalls, forests, caves, hills, plains, rivers and the sea can

be found within a modest 20.273 km2. Forests cover half of the country; remains of primeval forests

are still to be found, the largest in the Kocevje area. Approximately 8 % of Slovenia's territory is

formally protected - the largest area with such a regime is the Triglav national park, with a surface

area of 848 km2. You can ski in the morning and surrender yourself to the luxury of the Adriatic Sea

in the afternoon. In most of Slovenia a continental climate of cold winters and warm summers

prevails. The average rainfall is 1000 mm for the coast, up to 3500 mm for the Julian Alps, 800 mm

for the northeast, and 1400 mm for central Slovenia. Climate had a dominant impact on architecture

and agriculture in the past, and the landscape of regions was determined by climate.

Tourism is one of the most promising sectors of the economy in Slovenia. Every year 1.2 million

foreign tourists visit Slovenia. Tourism represents 9.1 % of GDP (1). It is enhancing the economic

value of natural resources and cultural heritage. Tourism is a highly adaptive and responsive branch

of the economy, because it depends upon many external factors. One of them is climate change, but

right now tourist resorts are more concerned about climate variability.

For the tourism industry the extremely warm, sunny and dry summer of 2003 was welcome.

However, green winters, one after another, at the end of eighties and beginning of nineties were

almost a disaster for winter tourism. Even with the existing climate variability the tourism industry

has to be flexible and adaptive, looking to develop alternative programs. Thus, general knowledge

about past, and current, climate and bioclimate conditions is relevant and important for the tourism

industry because of its dependence on weather and climate.

METHODS Methods used to assess climatic conditions, based on half a century of data, from selected stations

are common in everyday climatological praxis at the Meteorological Office in Slovenia (2) e.g.

anomalies, moving averages, and trends. Because at the annual time scale data showed a changing

point around the beginning of the eighties (in some seasons also a well pronounced periodicity), and

it is well known that climate is a complex nonlinear system, we are trying to make

recommendations to the tourism industry based on moving average features. Using only the linear

trend would, most of the time, lead to an underestimation of ongoing change. For example, in the

case of periodic changes in the winter season, the length of the analysed period could have a

dominant impact on the calculated trend magnitude.

The climatic analysis was complemented with Physiological Equivalent Temperature (PET)

calculated by means of the RayMan model which takes into account all of the relevant

68

meteorological variables that have an impact on human thermal comfort/discomfort (3). The

methodology of adapting human-biometeorological methods for the assessment of thermal

bioclimate is described and explained in this book (4, 5). For the application of tourism climatology

results, in tourism and recreation, we included PET and the precipitation in this analysis. Of course

additional parameters like sunshine duration and basic climatological parameters were relevant, but

they were included in an indirect way: through the thermal comfort analysis. In conclusion, we

assume that a quantification of the tourism climate, in a basic manner, can be accessed by the

combination of PET and precipitation.

RESULTS There is evidence that mean annual temperature is increasing in the mountains and in low-land

areas. Also, absolute humidity is showing an increasing trend, although not so relevant as air

temperature. No change can be detected in mean annual cloudiness; it seems that mean annual

sunshine duration is increasing too. Positive trends have also been observed for some

bioclimatological indicators (Figures 1 and 2). PET increased by almost 3 °C during the last 25

years in Ljubljana and on Kredarica, and these trends are significant.

KREDARICA

-20

-15

-10

-5

0

5

10

1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002

YEAR

PET

(°

C)

YEAR WINTER SPRING SUMMER AUTUMN

Figure 1: Mean annual and seasonal PET for Kredarica

Comparing seasonal and annual PET values for different locations it becomes evident that values,

cycles, and tendencies show significant differences. It is clear that for assessment of climatic and

thermal conditions it is necessary to take into account local conditions and local meteorological

69

data. Local peculiarities are often neglected, sometimes because of lack of data or poor knowledge

of local geographic conditions.

LJUBLJANA

-10

-5

0

5

10

15

20

25

30

35

1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002

YEAR

PET

(°

C)

YEAR WINTER SPRING SUMMER AUTUMN

Figure 2: Mean annual and seasonal PET for Lubljana

There is an evident difference between the autumn trend in Ljubljana and Kredarica. In the high

mountains there is no trend, but in Ljubljana there is an increasing tendency after the mid seventies.

The magnitude of the summer trend in Ljubljana during the last three decades was almost twice the

one observed in high mountains, although on the annual time scale there was almost no difference

between these two stations in trend magnitude. Seasonal features for Murska Sobota show a strong

periodic component (Figure 3); in some seasons these periodic changes are also present at other

stations.

Figure 3: Mean winter (left) and summer (right) PET for Murska Sobota

It has to be noticed that at both stations, Ljubljana and Kredarica, the observing site is the same

during the analysed period and the observing method is the conventional one (6), so there is no

internal cause for inhomogenity. However, in Ljubljana, a station in the central part of Slovenia,

70

there is an impact of increasing urbanization around the station, which could contribute to the

magnitude of the increasing trend during the last decades.

Figure 4: Mean annual temperature (red line), average of the reference period 1961-1990 (black line)

and moving average (yellow line)

Murska Sobota is representative of the plane in the north east of Slovenia - the region with the most

pronounced continental component in climate. Portorož lies on the Slovenian coast; the station has

been moved several times during the last decades, but data has been carefully homogenized. In all

of the stations the moving average rose above the reference value in the eighties, and remained

above for the rest of the period (Figure 4). This increase in mean air temperature was concentrated

during the last 25 years - during the first half of the analysed period there was no significant trend.

A trend calculated upon the whole period of data was much smaller than the one observed during

the last two decades. Which of them could best fit the needs of the tourism industry? According to

the forecasted climate changes the one calculated over the whole period would correspond to the

most optimistic IPCC forecasted increase in temperature, and the one based on the last two decades

is close to the upper limit of forecasted increase. Our advice to the tourism industry is to take into

account the trends during the last half of the analysed period, because they are closer to the worst-

case scenarios and they fit better the ongoing changes.

During the end of the seventies and beginning of the eighties there were winters with abundant

snow cover, so many ski resorts at a relatively low altitude were established. They were popular

71

because they were easily accessible, but many of them were closed during the series of green

winters at the end of the eighties and beginning of the nineties. It should be noted that there are

several processes that combine to create what we experience as our climate: climate change (most

of the people associate it to slowly warming atmosphere due to increasing concentration of green

house gases), and coincidental variability and cycles (although not enough understood or studied).

Reliance only on linear trends is not enough - on the contrary, it sometimes could even be

misleading.

Not only do temperature and thermal bioclimatic indicators show fluctuations and trends, there are

also pronounced year-to-year fluctuations in the amount of precipitation and sunshine duration for

all time resolutions (Figure 5 and 6) as well.

Figure 5: Kredarica annual precipitation (left) and sunshine duration (right), 1961-1990 mean (black line) and

moving average (light blue and red lines)

0

100

200

300

400

500

600

700

PREC

IPIT

ATI

ON

(mm

)

1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003

WINTER

0

200

400

600

800

1000

1200

1400

PREC

IPIT

ATI

ON

(mm

)

1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003

AUTUMN

Figure 6: Kredarica seasonal precipitation, 1961-1990 mean and moving average

Analysis of extreme short-term precipitation for a large number of stations indicates mostly positive

trends, but not for all time intervals. Only very few trends are statistically significant. For a very

short duration, in some cases 5 minutes, of precipitation negative trends were noticed.

72

Not only are average values for bioclimatic indices of interest to the tourism industry, but frequency

of different degrees of comfort/discomfort is useful. Frequency distribution is certainly more useful

for end users than mean value, because it carries more information. An example of frequency

representing the occurrence of selected comfort degrees is on Figure 7.

Figure 7: Frequency distribution of different comfort classes in Ljubljana

CONCLUSION Climate change has enhanced interest in climatic conditions in almost all economic sectors, and the

tourism industry is no exception: it is looking to be informed, adapted and prepared for change. In

the future climatologists expect: more frequent and intense extreme events, higher air temperature,

more hot days, decreased daily amplitude, more droughts, and more floods. Even though

uncertainty is related with local predictions, and with phenomena on the local scale, end users,

among them the tourism industry, are mainly interested in such local features. They need and expect

from the National Meteorological Services detailed descriptions of climatic and bioclimatic

features, but also some guidelines about the expected future conditions. One of the possible tools to

assess the future climatic and bioclimatic conditions is the use of trends, and the extrapolation of

moving averages. It sounds very simple, but there are several problems incorporated in such a

projection. The first one is a homogenization of the input data; often there is insufficient metadata

available, and even with use of homogenization tests some subjective touch in the final result

remains. The second problem is the length of the time series under investigation. We have shown

that there are several sub-periods with quite different trend magnitudes, and sometimes even with

trends in the opposite direction. Even more confusion is introduced when there is a well-

pronounced periodicity in the time series. The climate system is complex; there are many feedbacks

and non-linear connections.

73

Results, based on analysis of historical data, showing that Slovenia’s local climate is changing:

results show significant spatial and seasonal differences. The magnitude of change in

meteorological and also complex measures like PET is such that it should be taken into account

when planning future investments and activities. Spatial and seasonal differences suggest that

analysis should be made for each individual region, or maybe even each individual location.

It is evident that a change has already started and the projected magnitude is close to the upper

predicted interval for the global change. We should take into account: annual as well seasonal

variations; possible periodicity when detected; and in the case of data with subsets showing

distinctively different trends, the last subset of data. Such rapid changes as we have observed during

the last two decades are likely to produce significant changes in the environment and ecosystems. It

seems necessary for all climate and weather related economic branches, and tourism industry

certainly is on of them, to start adaptation programs, and to take into account the changing climate

when planning new infrastructure.

REFERENCES

1. Statistical Office of the Republic of Slovenia, 2003: Statistical Yearbook 2003 of the

Republic of Slovenia, Ljubljana.

2. Environmental Agency of the Republic of Slovenia, 2003: Monthly bulletin, Ljubljana, No.

1-12.

3. Matzarakis, A.; Rutz., F.; Mayer, H., 2000. Estimation and calculation of mean radiant

temperature within urban structures. Biometeorology and Urban Climatology at the

Turn of the millennium, Selected Papers form the Conference ICB-ICUC99, Sydney,

WCASP-50, WMO/TD No. 1026, 273-278.

4. Matzarakis, A., Zygmuntowski, M., Koch, E., Rudel, E., 2004. Mapping of the thermal

bioclimate of Austria for recreation tourism. Wiss. Ber. Meteorol. Inst. Univ. Freiburg No.

12, 10-18.

5. Zaninovic, K., Matzarakis, A., 2004. Trends and variations of thermal comfort at the

Adriatic Coast. Wiss. Ber. Meteorol. Inst. Univ. Freiburg No. 12, 71-81.

6. Cegnar T., Roskar J., 2004. Meteorološka postaja Kredarica 1954-2004, Meteorological

Office, Environmental Agency of the Republic of Slovenia, Ljubljana.

74

VARIATIONS AND TRENDS OF THERMAL COMFORT

AT THE ADRIATIC COAST

Ksenija Zaninovic1 and Andreas Matzarakis2

1. Meteorological and hydrological service, Climatological research department,

10000 Zagreb, Gric 3, Croatia

2. Meteorological Institute, University of Freiburg, Werderring 10,

D-79085 Freiburg, Germany

E-mail address: zaninovic@cirus.dhz.hr (Ksenija Zaninovic)

ABSTRACT

Knowledge about the thermal comfort of humans plays an important role in health and activities,

especially in tourism, recreation, leisure and sport. In spite of the efforts that have been made to

investigate temperature changes during the last century, results cannot completely clarify their

impacts on humans. This paper analysed variations and trends of the Physiologically Equivalent

Temperature (PET) and Predicted Mean Vote (PMV), two thermal bioclimate indices based on

human energy balance models. Climatic changes were analysed using data from the period of 1867-

2000, taken from the meteorological station at Hvar,Croatia, a popular tourist destination island in

the Adriatic Sea. This analysis of thermal bioclimate conditions has showd that changes in the

examined period were within the range of one class of physiological strain for humans.

KEYWORDS: Thermal bioclimate, Binomial moving average filter, Trend analysis, Progressive

analysis

INTRODUCTION

Until now climate change in Croatia has been investigated using minimum and maximum

temperatures, daily temperature range and precipitation from inland lowland and coastal stations.

The results indicate decreasing maximum temperatures and increasing minimum temperatures,

leading to a significant decrease in daily temperature range (1). Hvar was not included in those

investigations due to missing data for certain years. However, the changes in thermal comfort for

Hvar (Fig. 1) have been investigated for the periods of 1858-1995 (2), and 1901-2000 (3). The

results for the first show increasing thermal comfort trends being significant in winter, autumn and

annually as the result of positive temperature trends and negative wind speed trends. The analysis

75

for the second period show a negative trend for thermal indices in all seasons as a result of the

increasing trend in wind speed, despite positive temperature trends. Similar investigations have

been made for the mountainous meteorological station Zavizan (1594 m), for the shorter period of

1954-1993 (4). The trends of thermal comfort were positive in all seasons as well, but significant

for summer, autumn and annually.

HVAR

Figure 1: The location of Hvar

METHODS

2.1 Thermal environment

The thermal effective complex deals with the influences of the thermal environment on the well-

being and health of human beings. The basis for this is the close relationship between the human

thermoregulatory mechanism and the human circulatory system. For the physiologically significant

assessment of the thermal environment, some thermal indices are available which are derived from

the human energy balance (5, 6, 7, 8, 9).

Several investigations have been performed which use thermal indices, such as PMV or PET, for

the human-biometeorological assessment of the thermal environment in different scales. Results

from case studies (9) enable a process analysis, e.g. in the form of regressions between PET and

meteorological input parameters such as single radiative fluxes, mean radiant temperature, air

temperature, vapour pressure and wind speed. For calculating the mean radiant temperature, the

human-biometeorological radiation model RayMan (10) was used, which is well suited for

application in applied climatological and meteorological studies.

76

2.2. Trend analysis

The fluctuations and trends of seasonal and annual values of the thermal comfort indices PET and

PMV, as well as the meteorological parameters that influence thermal comfort (air temperature,

relative humidity, wind speed and cloudiness), were determined. Variations and trends were

analysed during the period of 1867-2000, in spite of some missing data in the 20’s and 40’s. In

order to remove short-term fluctuations the data series was smoothed by means of the weighted 11-

year binomial moving average filter.

The linear trend has been tested for significance by means of the nonparametric Mann-Kendall rank

statistics t (11, 12). For the series, which showed the significant trend identified by the Mann-

Kendall coefficient t, a progressive analysis of the time series by means of the statistic u(t) was

performed in order to determine the beginning of this phenomenon by means of a sequential

analysis (12).

RESULTS

According to the mean annual and seasonal PET and PMV values, the mean annual thermal

sensation in Hvar from 1867-2000 was slightly cool (17.4°C PET, -0.8 PMV), varying from cool

winters (6.5°C PET, -3.1 PMV) to slightly warm summers (30.7°C PET, 1.6 PMV). Because of the

maritime influence, autumn was warmer (19.3°C PET, -0.4 PMV) than spring (15.9°C PET, -1.2

PMV).

The PET and PMV fluctuations showed a visible warming at both the beginning of the century and

around the 1950’s (Fig. 2), related to decreases in wind speeds in the same periods. After the

warming in the 50’s, a cooling period occurred until the beginning of 80’s, as the result of a

decrease in temperature and simultaneous increase in wind speed. The warming in PET and PMV

from the beginning of the 80’s until the end of century was the result of an increase in air

temperature and decrease in wind speed, but also a decrease in cloudiness from the end of the 70’s

until the end of the 80’s.

Both human-biometeorological indices, PET and PMV, showed increasing trends in all seasons,

significant for winter, autumn and annual values. These positive trends were the result of increasing

temperature and decreasing wind speed. The greatest change was temperature in winter (around

0.4°C/100 years), and the smallest was temperature in spring (0.2°C/100 years). However, only the

increasing trend of mean annual values of 0.4°C per 100 years was significant (Tab. 1). The wind

speed decreasing trends were significant for the winter, autumn and annual values, the same as for

the human biometeorological indices. Vapour pressure also contributed to the increasing trend in

thermal sensation, because of increasing trends in all seasons (although statistically insignificant).

77

Finally, cloudiness showed positive trends in all seasons, and only the winter trend was not

significant. Physiological equivalent temperature

PET = 17.0 + 0.0068 (t-1867)14

16

18

20

22

1867 1887 1907 1927 1947 1967 1987

°C

t (years) Air temperature

t = 16.1+ 0.0035 (t-1867) 14

16

18

20

1867 1887 1907 1927 1947 1967 1987

°C

t (years)

Wind speed

v = 3.2 - 0.0037 (t-1867)

0

2

4

6

1867 1887 1907 1927 1947 1967 1987t (years)

m/s

Vapour pressure

VP = 12.5 + 0.002 (t-1867)

10

12

14

16

1867 1887 1907 1927 1947 1967 1987t (years)

hPa

Cloudiness

C = 3.8 + 0.0058 (t-1867)

3

4

5

6

1867 1887 1907 1927 1947 1967 1987t (years)

Tenths

Figure 2: Annual variations of the physiologically equivalent temperature (°C), the air temperature

(°C), wind speed (m/s), vapour pressure (hPa), wind speed (m/s) and cloudiness C (in tenths), including

a weighted 11-year binomial moving average series, and linear trends during the period of 1867-2000

at Hvar

78

Table 1: Seasonal and annual trends (per 100 years) of mean physiologically equivalent temperature

(PET in °C) and predicted mean vote (PMV), temperature (t in °C), vapour pressure (VP in hPa),

wind speed (v in m/s) and cloudiness C (in tenths). Shading denotes trends significant at the 0.05

level according to Mann-Kendall rank statistics. Period: 1867-2000

Seasons PET PMV t VP V CWinter 0,67 0,16 0,43 0,28 -0,41 0,59Spring 0,35 0,08 0,17 0,12 -0,39 0,81Summer 0,56 0,10 0,34 0,06 -0,11 0,73Autumn 0,99 0,20 0,36 0,10 -0,52 0,39Annual 0,68 0,14 0,35 0,20 -0,40 0,58

The progressive trend test was applied to the annual values of PET, which had significant increasing

trend, as did the parameters influencing thermal comfort - temperature, wind speed and water

vapour pressure (Fig. 3). From the graphical representation of the onward (u) and backward (u’) test

series of PET it can be seen that during the analysed period they overlap several times, implying the

absence of a trend. The last intersection point between u and u’ occurred in 1988, while u exceeded

the 1.96 limit value in 1994, suggesting the beginning of a significant positive trend. However, as

the effect was very recent it is advisable to await confirmation from future observations, especially

because of the many changes in the previous periods. The progressive trend test for temperature

shows that the increasing trend in temperature began in 1946. The increasing trend of temperature

became significant in 1959 and, besides some fluctuations in the 70’s, stayed significant until the

end of the observing period. In spite of some similarities between PET and temperature trends, it is

obvious that variations in the PET trend were the result of other meteorological parameters

important for thermal comfort.

The progressive trend test for wind speed showed that, in spite of a significant negative trend for the

whole period, wind speed changed from the beginning of the 30’s. However, the intersection

between onward and backward test series for wind speed in 1976 cannot be taken as the beginning

of an increasing trend, but it is obviously corresponded with the retard in the increase of PET.

Finally, the increase of PET in the last decade of the century was the result of simultaneous sharp

increases in temperature.

79

-3-2-10123456

1867 1877 1887 1897 1907 1917 1927 1937 1947 1957 1967 1977 1987 1997

u

u'

Physiological equivalent temperature

-4

-3

-2

-1

0

1

2

3

4

1867 1877 1887 1897 1907 1917 1927 1937 1947 1957 1967 1977 1987 1997

Air temperature

u

u'

-8

-6

-4

-2

0

2

4

6

1867 1877 1887 1897 1907 1917 1927 1937 1947 1957 1967 1977 1987 1997

u

u'

Wind speed

-8

-6

-4

-2

0

2

4

6

1867 1877 1887 1897 1907 1917 1927 1937 1947 1957 1967 1977 1987 1997

u

u'

Vapour pressure

-6

-4

-2

0

2

4

6

8

1867 1877 1887 1897 1907 1917 1927 1937 1947 1957 1967 1977 1987 1997

u

u'

Cloudiness

Figure 3: Progressive trend test for annual values of mean physiologically equivalent temperature, air

temperature, wind speed, vapour pressure and cloudiness at Hvar, during the period of 1867-2000

80

CONCLUSIONS

For the assessment of the thermal environment on human beings in different scales, human-

biometeorology provides well-suited thermal indices on the basis of the human energy balance.

Investigations of thermal bioclimate for the quantification of the effects of atmospheric conditions

on human beings require long data series to check trends, and to see if the trends are significant.

The analysis of climate change through thermal indices in Hvar from the middle 19th century

showed a positive trend in all seasons as a result of positive temperature trends and decreasing

trends in wind speed. This result coincides with earlier investigations into the trends in thermal

comfort at Hvar, calculated with a different thermal comfort index (2). On the other hand, the

analysis of thermal comfort changes in Hvar from the beginning of 20th century showed the

opposite trend as the result of increasing wind speed trend in the century (3). Trends of the

bioclimatic conditions of tourism areas provide information for the tourism industry and

govermental authorities, allowing adequate planning for the expected changes in the nature and

length of the tourism season.

REFERENCES

1. Zaninovic, K. and Gajic-Capka, 1995. M. Extreme Temperature Changes in this Century

in Croatia. Hrv. Meteor. Cas. 30:21-26.

2. Zaninovic, K. 1998. Secular Variations and Trends of Thermal Comfort at the Adriatic

Coast. Book of abstracts IGU, Climate and Environmental Change. 1998, 24-30 August,

Evora, Portugal, 181-182.

3. Zaninovic, K. and Matzarakis A. 2004. Climatic changes in thermal comfort at the

Adriatic coast, Advanced Research workshop Climate change and Tourism. Warsaw (in

print)

4. Zaninovic, K. 1996. Trends of Thermal Comfort on Dinaric Alps. Proceedings of the 24th

International Conference on Alpine Meteorology ICAM 96. September 9-13, Bled,

Slovenia, 1996:425-430.

5. Mayer, H. 1993. Urban bioclimatology. Experientia. 49:957-963.

6. Matzarakis, A. and Mayer, H. 1997. Heat stress in Greece. Int. J. Biometeorol. 41: 34-39.

7. VDI, 1998. Methods for the human-biometerological assessment of climate and air hygiene

for urban and regional planning. Part I: Climate, VDI guideline 3787. Part 2. Beuth, Berlin,

1998.

8. Höppe, P. 1999. The physiological equivalent temperature – a universal index for the

biometeorological assessment of the thermal environment. Int J Biometeorol. 43:71–75.

81

9. Matzarakis, A., Mayer, H. and Iziomon, M.G. 1999. Applications of a universal thermal

index: physiological equivalent temperature. Int. J. Biometeorol. 43:76- 84.

10. Matzarakis, A., Rutz, F. and Mayer, H. 200. Estimation and calculation of the mean

radiant temperature within urban structures. WCASP-50, WMO/TD No. 1026:273-278.

11. Mitchell, J. M.Jr. et al. 1966. Climatic Change. WMO. Tech. Note No. 79.

12. Sneyers, R. 1999. On the Statistical Analysis of Series of Observations, WMO, Technical

Note No 143.

82

THE IMPACTS OF GLOBAL CLIMATE CHANGE ON WATER RESOURCES AND

TOURISM: THE RESPONSES OF LAKE BALATON AND LAKE TISZA

Tamara Rátz1 and István Vizi1

1. Department of Tourism, Kodolányi János University College 8000 Székesfehérvár, Irányi D. u.

4. Hungary

Email address: ratztamara@freemail.hu

ABSTRACT

In Hungary, tourism is an important economic and social activity. Leisure tourism is typically

seasonal and highly dependent on water and climatic resources. Lake Balaton is one of the largest

bodies of freshwater in Europe and the Lake Balaton Region is the oldest and most established

holiday destination in Hungary. Lake Tisza, on the other hand, is a recently developed tourist

destination; however, it does share one common characteristic with Lake Balaton: tourism is a

dominant economic sector in both areas. Climate change impacts have been observed on both lakes

and these changes significantly influence the development of tourism in the regions. This study,

based on secondary research, observation and interviews, provides an overview of tourism

development at Lake Balaton and Lake Tisza, and examines the major negative and positive

impacts brought about by climate change.

KEYWORDS: Tourism, Climate change, Hungary, Lake Balaton, Lake Tisza

INTRODUCTION

Hungary is located in the heart of Europe, in the Carpathian Basin. It is a typical low-lying country:

73 per cent of its territory is flatland less than 200 meters above the sea level. The country is located

at the frontier between the temperate continental and the Mediterranean climate zones, with

complementary effects of the temperate oceanic climate.

In Hungary, tourism is an important economic and social activity. Leisure tourism is typically

seasonal and highly dependent on water and climatic resources. The Lake Balaton Region is the

oldest holiday destination in Hungary. The lake itself is a particularly significant attraction for

Hungary, a country with no access to the sea. The Lake Tisza Region is a recently established

tourist destination. Tourism is a dominant economic sector in both areas, with a significant

83

proportion of the local population being (over)dependent on its use. This dependence obviously

makes tourism development and its environmental preconditions crucially important to these areas.

TOURISM AND CLIMATE CHANGE

Tourism is a major sector of the global economy, with global receipts from international tourism of

US$464 billion in 2002. With a projected annual growth rate of 6.7%, annual international tourism

expenditures are expected to surpass US$2 trillion by 2020 (1). Domestic tourism is many times

more important than international tourism in terms of participation and economic activity. The

magnitude of the implications of climate change for tourism will depend on the distribution and

importance of the sector, and the characteristics of climate change (2,3).

When considering the impact of global climate on tourism a duality becomes apparent: on the one

hand, tourist destinations and the tourism industry are potential victims of climate change. On the

other hand, the industry contributes to global warming in various ways, the best known being the

emission of greenhouse gases by road and air travel (4,5).

One critically important dimension of the tourism sector that will be sensitive to climate change is

the length of the operating season. Any changes in season length have considerable implications for

the short- and long-term viability of tourism and recreation enterprises (2,6,4,7).

In addition to changes in season length, climate change may have an impact on the availability and

quality of the resource base upon which recreational activities depend. For example, below-average

water levels reveal the sensitivity of water-based destinations to climate variability. Global warming

is anticipated to modify many other ecosystems on which outdoor recreation depends.

THE STUDY AREAS: LAKE BALATON AND LAKE TISZA

Lake Balaton is the biggest freshwater lake in Central Europe. It is a typical shallow lake of 588.5

km2 surface, 3.25 m average depth and 236 km shoreline length, with high sensitivity to the

fluctuation of hydro-meteorological factors (1). In winter the lake is generally covered by ice. In

summer the average water temperature is 23Cº (8).

The water catchment area of the lake is approximately 5774 km2. The main inflow is the Zala River

at the south-western end, while the Sió-canal drains the water from the eastern basin into the River

Danube. However, the most significant part of the lake’s water supply comes from two sources, the

approximately 130 underwater springs, and precipitation in the form of rain and snow. The

catchment area receives on average 621 mm of precipitation each year (9).

The existence itself of Lake Balaton is due to climate change: the lake was born about 21 thousand

years ago as a consequence of the Last Glacial Maximum. As a particularly shallow lake, Lake

84

Balaton is a perfect indicator of the stability of the climate over the last 10,000 years. Without this

climatic balance the lake would not have survived.

Lake Tisza is the second largest freshwater body in Hungary and the largest artificial lake in the

country. The original Kisköre Reservoir was built in 1973, as part of the River Tisza flood control

project, and its filling was finished in the 1990s. The completed reservoir - renamed as Lake Tisza -

is 27 km long with a 127 km2 surface. The River Tisza’s length within the reservoir is 33.6 km.

Lake Tisza is also a typically shallow lake, with an average depth of 1.3 m and a maximum depth of

17 m. Unlike Lake Balaton, Lake Tisza contains several small islands of 43 km2 total surface (10).

The development of the lake’s local ecology has been a gradual process and it has resulted in highly

differentiated areas: swamps, shallow and deep water, and water inflow basins are all found in its

mosaic structure. In the reservoir the share of macrophytes has been continuously growing

compared to that of open water (10).

METHODS

Various research methods were used for the completion of this paper. Statistical data and qualitative

information were gathered from secondary sources such as: the tourist authorities of the Lake

Balaton and the Lake Tisza regions, the Hungarian Tourist Board, and various environmental

agencies. Data collected from such sources were analysed in order to understand the current tourism

situation in the study areas, and to comprehend the potential impacts of climate change on the lakes.

The conclusions of impact assessment projects carried out by the authors in the Lake Balaton region

were also applied to gain further evidence.

Information on tourism supply and on international and Hungarian tourism demand helped to

visualize the responses of tourists and tourist enterprises to climate change. Secondary information

was complemented by personal observation in both areas and by interviews with one tourism

representative in each region.

RESULTS

It is obvious that natural features play a significant role in the destination choice of leisure tourists:

sunshine and beach attract the majority of tourists all around the world (1). In Hungary, most

domestic trips are made with the motivation of recreation, and a significant proportion of all foreign

visits are also made by leisure tourists (11,9). The main destinations of pleasure seekers are lake

resorts which are mostly visited during the summer.

Analysis shows that the two lakes significantly differ in size, population and popularity among

tourists as well as in natural characteristics. Despite its artificial background and its unique situation

as a paradise for motorboats and jet skis (the use of which are strictly forbidden on Lake Balaton),

85

today Lake Tisza is the wilder natural area. This difference in environmental characteristics and

image is mostly explained by the history of the lakes' development: Lake Balaton is an almost 200

year old resort, while Lake Tisza was born only 30 years ago and has just recently become an

established tourist destination. While today Lake Balaton is a classic water-based family destination

for all generations who mainly look for passive enjoyment (although this market situation is slowly

changing), Lake Tisza has attracted two very different segments since the beginning of its

development: the physically active, more adventurous kind on the one hand, and the

environmentally conscious ecotourist on the other.

TOURISM AT LAKE BALATON AND LAKE TISZA

Lake Balaton is a popular summer destination due to its warm, shallow water and sandy beaches.

The lake is about an hour’s drive from Budapest and attracts approximately 1 million tourists each

year, as well as day and weekend visitors. Registered tourists spend approximately 4 million nights

around the lake annually. The magnitude of unregistered tourism is very difficult to estimate, but is

considered to be substantial.

The peak season for tourism is short, comprising only eight weeks. The peak corresponds to the

summer vacation period for schools in the sending countries, and to the period when the lake is

warm and amenable for swimming. In practice, depending on the weather, the period of intense use

is often shorter, only four or five weeks during July and August. During this time extremely high

numbers of people visit the lake, and the destination becomes quite crowded (approximately 55% of

all the visitors arrive during these summer weeks) (6). As a result, many commercial establishments

are only economically viable during this short period, and are only marginally viable during the

shoulder season. Seasonality, therefore, puts pressure on the infrastructure, facilities and

establishments around the lake for a short time and leads to poor economic prospects during the off-

season. While tourism brings jobs to the region, many cease at the end of the season. Even so, this

seasonal employment remains very important for a region where most local communities are

dependent upon tourism.

During the last decades Lake Tisza has become a popular holiday destination, especially among

Hungarian tourists. The Kisköre reservoir has attracted visitors since its completion, as it compared

favourably with the crowded and expensive Lake Balaton, the traditional holiday site. As a result of

this popularity the more appealing "Lake Tisza" name was suggested, general and tourism

infrastructure has been developed, and the government has designated the area an official tourist

destination.

Lake Tisza consists of five water basins which offer different activities and services. The southern

bays are mainly used for water sports and for beach activities, while the quiet backwaters are

86

popular for anglers, birdwatchers and all those who are interested in nature (12). This double profile

makes the lake a very attractive destination, but it also leads to conflicts between users, especially

between those who support and those who oppose the use of motor boats.

In 2001 the region experienced a 30 per cent growth in tourist numbers compared to 2000: nearly

60,000 guests spent approximately 300,000 nights at Lake Tisza. Foreign guests accounted for 25

per cent of all arrivals. The majority of Hungarian visitors (93 per cent) arrived from the

neighbouring regions and Budapest (10).

CLIMATE CHANGE AT LAKE BALATON AND LAKE TISZA

Tourism at both lakes is dependent upon water quality and quantity. If the beaches are not attractive

enough tourists turn elsewhere. Less demand means less income on both the individual and

community level. Unfortunately, water characteristics are vulnerable to climatic factors, particularly

to warming and precipitation.

Water quality is an ongoing concern for both lakes, and measures have been taken to reduce the

loadings of phosphorus and to control direct pollution from nearby urban, rural and resort areas.

Contamination from waterborne waste is not considered to be a major problem as all waste from

sewers is treated and much of the treated water is not returned to the lakes. Turbidity remains a

source of discomfort for many tourists, although it is mostly natural due to the shallow nature of the

lakes. In warm years, algae is a major concern in Lake Balaton, with eutrophication and blooms

causing distress to beach users in late summer (4). Lake Tisza, as a shallow lake, is also threatened

by eutrophication, though the problem has not been as serious as in the case of Lake Balaton.

The chemical, physical and biological quality of the lakes’ water is measured by regional

organisations of the Ministry of Environment and Water Management and the Ministry for Health,

Social and Family Affairs in co-operation with the National Public Health and Medical Officers'

Service (13).

Excellent water quality at Lake Balaton indicates chlorophyll content lower than 25 mg/m3, while

quality is unacceptable if the chlorophyll content exceeds 75 mg/m3. Warming increases the amount

of algae present, which is an indicator of the development of chlorophyll-a concentrations. The

presence of algae in the water has various effects: green colouring as a visual effect, perceived low

quality as an impact on visitors’ satisfaction, and the development of allergic symptoms in cases of

sensitivity as a health impact.

According to measurements carried out at 35 beaches during the summer of 2003, water quality at

Lake Balaton was suitable for recreation (excellent at 22 beaches and satisfactory at 13 beaches)

(13). According to perception research however, visitors in general are only moderately content

with the quality of Lake Balaton’s water: in a recent survey, the average satisfaction value was 3.4

87

on a scale 1 to 5 (5 indicating the highest satisfaction) (11). This value is partly explained by the

visual impact of algae. In addition, Lake Balaton is usually less transparent than any sea, due to

sand particles that improve circulation but blur the water.

Water quality at four Lake Tisza beaches, as measured by the National Public Health and Medical

Officers' Service, generally improved during the period of 2001-2003. However, by the end of the

2003 season the quality of the water only proved acceptable at two beaches, due to the high summer

month temperatures and to the impact of tourist use (14).

Another significant variable affecting tourism is the quantity of water in the lakes. As has already

been mentioned, Lake Balaton’s major sources of water supply are precipitation, the Zala River,

and underwater springs. In the case of Lake Tisza, due to its nature as a reservoir, water is steadily

supplied by the River Tisza.

The water level of Lake Balaton has been regulated since 1977: it must be between 70 cm and 110

cm (measured at Siófok at 103.41 m above sea level). In case the water level rises above 110 cm,

the Sió-canal is opened in order to protect the lakeshore resorts and infrastructure. However, in the

last three years, water quantity has been continuously decreasing at Lake Balaton, so the canal has

been closed since April 2000 (15).

During April 2003 water level was 70-71 cm, following an unusually low 40 cm in September 2002

and 54 cm in August 2001. While these figures are far from being the lowest ever measured, this is

the first time in the history of recorded measurements that the lake has experienced three dry years

in a row. Though it may be debated whether these unusual data are simply due to natural fluctuation

or caused by global warning, the ecological and economic consequences prompted decision makers

to consider possible solutions. Such interest has been expedited by the dynamic interaction between

water quantity and quality: low water quantity allows algae to grow faster, mostly due to more light

and higher temperature, leading to poorer water quality. In addition to ecological damage, a low

water level may have long-term negative impacts on tourism, fishing and recreational boating (15).

Artificial water supply has been suggested as one possible solution to increase water level in the

lake. At the moment this solution has been declared unfeasible by researchers, as the

microbiological and chemical characteristics of most possible sources are too different than the

lake’s water. In the future the development of a high quality control mechanism may make such

water input feasible (although other questions may arise regarding the effect on the Little-Balaton

wetland area, and on the artificial water supply source) (16).

Another idea is to construct large ponds along the Sió-canal (at the outflow of the water from the

lake) that would serve as water reservoirs to store water for the dry periods. Obviously, this solution

would involve huge construction costs and damage the natural environment of the area.

88

DISCUSSION

Climate change directly effects the natural supply of the Lake Balaton and Lake Tisza regions:

temperature increase lengthens the season as late spring and early autumn also become suitable for

bathing, but at the same time July and August may be uncomfortably hot for tourists. Water quality

is directly affected, particularly through eutrophication and by threatening the lakes' fish stock.

Considering the man-made supply of the regions, temperature and water quality changes require

effective and efficient co-operation between tourist businesses and local authorities. This may

involve promotional activities, and/or development of less climate-dependent tourist services.

As a consequence of climate change, tourist demand is indirectly affected since changing natural

resources either attracts different segments or discourages potential visitors to the lakes. However,

if global warming similarly influences the Mediterranean region, some of the Southern European

beaches may lose their competitiveness, and existing demand may turn toward Central and Northern

European beach resorts.

At the moment, the impacts brought about by climate change are partly negative, such as decreasing

water quality and quantity, and partly positive, such as longer seasons and increased demand from

city dwellers for water-based escapes.

The responses of Lake Balaton and Lake Tisza differ to a certain extent. While eutrophication is

already a relatively serious problem in the case of Lake Balaton, it is more aptly characterised as a

threat than a present danger in Lake Tisza. Due to the different nature of the lakes, Lake Balaton

suffers more from low levels of precipitation. Lake Tisza, as a reservoir, has a significant supply

source which makes water level regulation an easier task. The ecosystems of both lakes are

threatened by global warming, but Lake Balaton has experienced more serious ecological problems

as non-endemic fish species are more apt to suffer from natural changes.

REFERENCES

1. World Tourism Organisation. http://www.world-tourism.org.

2. IPCC 2001. Climate Change 2001. Impacts, Adaptation and Vulnerability.

http://www.grida.no/climate/ipcc_tar/wg2.

3. Puczkó L. and Rátz T. 2002. The Impacts of Tourism. (Hämeenlinna, HP).

4. Wall, G. 1998a. Implications of Global Climate Change for Tourism and Recreation in

Wetland Areas. Climatic Change 40:371-389.

5. Wall, G. 1998b. Climate Change, Tourism, and the IPCC. Tourism Recreation Review.

23(2):65-68.

6. Smith, K. 1993. The Influence of Weather and Climate on Recreation and Tourism.

Weather. 48:398-404.

89

7. WWF Climate Change and Its Impacts on Tourism 1999.

http://www.wwf.org.uk/filelibrary/pdf/tourism_and_cc_full.pdf.

8. ILEC World Lakes Database, http://www.ilec.or.jp/database/database.html.

9. Lake Balaton. http://www.livinglakes.org/balaton.

10. Tisza tó Régió. 2003. http://www.tisza-to-info.hu.

11. Belföldi forgalom a Balaton kiemelt üdülőkörzetben. 2002. Balatoni Integrációs és

Fejlesztési Ügynökség Kht. Társadalomtudományi Kutatócsoportja, Balatonfüred.

12. Michalkó G. 2002: Árvíz és turizmus: A szatmár-beregi térség komplex turisztikai

vizsgálata a Tisza 2001. évi áradása tükrében. Földrajzi Értesítő. 51(3-4):365-383

13. Water Quality at Selected Lake Balaton and Lake Tisza Beaches. 2004.

http://www.kvvm.hu/szakmai/balaton/lang_en/vizmb_tb.htm.

14. Water Quality at Lake Tisza Beaches. 2004. http://www.antsz.hu/oki/tis.htm.

15. Kiapad a Balaton? Interjú Zágoni Tamás fizikussal. Heti Válasz, 2002. November 29.

16. Vituki Rt. 2002. A Balatoni vízpótlás lehetőségeinek vizsgálata. (Budapest, Vituki Rt.).

90

CLIMATE CHANGE AND THE SKI INDUSTRY IN EASTERN NORTH AMERICA:

A REASSESSMENT

Daniel Scott 1, Geoff McBoyle 2, Brian Mills 3 and Alanna Minogue 2

1. Canada Research Chair in Global Change and Tourism, University of Waterloo, Canada

2. Department of Geography, University of Waterloo, Canada

3. Meteorological Service of Canada

E-mail addresses: dj2scott@fes.uwaterloo.ca (Daniel Scott), gmcboyle@fes.uwaterloo.ca (Geoff

McBoyle), brian.mills@ec.gc.ca (Brian Mills)

ABSTRACT

The ski industry and winter tourism more generally has been repeatedly identified as vulnerable to

global climate change. An important limitation of previous climate change impact assessments of

the ski industry is their incomplete consideration of snowmaking as a climate adaptation strategy.

This limitation is particularly problematic in areas of eastern North America, where snowmaking

has been an integral component of the ski industry for more than 20 years. This study examined

how current snowmaking capacity affects the climate change vulnerability of ski areas in six

locations in Ontario, Quebec, Vermont and Michigan where previous climate change assessments

did not incorporate snowmaking. The study used a model developed and previously applied in

areas of Ontario and Quebec (Canada) to project the length of ski seasons under a range of climate

change scenarios. The findings suggest that in the 2020s, even the high impact climate change

scenario poses only a minor risk to ski areas at five of the six study areas. The reassessment for the

2050s period with snowmaking found that the impacts of climate change were not as severe as

projected in earlier studies. The low impact climate change scenario would pose a challenging

business environment for ski areas, but would not pose a serious risk to their economic

sustainability. Conversely if the high impact climate change scenario was realized the reduction in

the length of the ski season, combined with projected increases in snowmaking requirements, could

jeopardise ski operations in some ski areas (southern Michigan and Ontario) by the 2050s.

However, this high impact scenario for the 2050s did not pose a threat to the regional ski industry.

Instead, certain ski areas will likely benefit economically from reduced competition brought about

by climate change induced contraction in the industry.

KEYWORDS: Climate change, Ski industry, Tourism, Recreation, Canada, United States

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INTRODUCTION

The winter tourism industry, in particular alpine skiing, has been repeatedly identified as highly

vulnerable to climate change. Studies (1) indicate that the Swiss tourism industry has not fully

recovered from low snowfall years during the late 1980s and project that climate change could

jeopardise the industry by reducing the number of ‘snow reliable’ ski resorts from 85% to between

44%-63%. In Austria, changes in snow cover could put several major low elevation resorts at risk,

resulting in winter tourism revenue losses of 10% (2). An assessment of the Japanese ski industry

(61 ski areas) estimated that increased winter temperatures of 3°C would reduce skier visitation by

30% (3). The impact of climate change on Australia’s snowfields and its three main ski areas is

also expected to be substantial, with the average ski season reduced by 54-81% (4).

The earliest studies on the potential impact of climate change on the ski industry were completed in

the Great Lakes and New England regions of North America (5, 6, 7, 8). Although snowmaking

has been an integral component of the ski industry in eastern North America for more than 20 years,

an important limitation of these first generation climate change impact assessments of the ski

industry was the omission of snowmaking.

Scott et al. (9) were the first to fully integrate snowmaking as an adaptation strategy to climate

change. They found substantially lower climate change impacts relative to previous assessments of

the ski industry in central Ontario that did not incorporate snowmaking. The authors subsequently

recommended that similar reassessments be completed in areas of eastern North America where

previous, and widely cited, climate change studies projected very large impacts on the ski season.

Building on this recommendation and the methods developed by Scott et al. (10), this study

examined the impact of projected climate change on six ski areas in eastern North America where

previous climate change impact studies had been completed without consideration of snowmaking.

METHODS

The ski areas examined in the study are located in the provinces of Ontario and Quebec (Canada),

and the states of Michigan and Vermont (USA). The selection of the climate stations for this study

was based on two considerations: proximity of the station to the ski area of interest and the quality

of the climate record (minimally 1961-1990). For each location a complete record of daily

temperature and precipitation was obtained for the 1961-1990 period. The results of this analysis

are only valid for the climate station location and surrounding areas that exhibit similar

climatological characteristics. The ski area(s) in the vicinity are usually several kilometres away and

may have microclimatic features that enhance or reduce its natural snowfall or suitability for

snowmaking.

92

The climate change scenarios used in this analysis were obtained from the Canadian Climate Impact

Scenarios project and were constructed in accordance with the methodological recommendations of

the United Nations Intergovernmental Panel on Climate Change (IPCC) Task Group on Scenarios

for Climate Impact Assessment. A total of 25 possible scenarios from global climate models

(GCMs) were considered for this analysis. In order to limit the number of scenarios to a manageable

number, while still considering the full range of potential climate futures, five scenarios

representing the upper and lower bounds of change in mean temperature and precipitation, for

December-January-February (DJF), were analysed. For concise presentation only the results of two

scenarios, a low impact scenario (least change in climate - NCARPCM-B2) and high impact

scenario (greatest change in climate - CCSRNIES-A1), are reported. Changes are relative to the

1970s baseline (average 1961-90). Results for the 2020s are of greatest relevance to ski area

operators due to the smaller range in uncertainty of climate change projections, and because they

are within the lifetime of existing infrastructure and long-term business and investment planning

horizons.

To produce daily data for the two climate change time series, (2010-2039 and 2040-2069), monthly

climate change scenarios from the two GCM scenarios were downscaled using the LARS stochastic

weather generator. The weather generator was parameterized to the climate station at each location

using climate data from the baseline period 1961-1990.

Daily temperature and precipitation data downscaled with the LARS weather generator were used

to drive a locally calibrated snow depth model that was based largely on methods used to develop

the Canadian Daily Snow Depth Database and Water Balance Tabulations for Canadian Climate

Stations. This technique involved estimating three parameters: 1) amount of precipitation that falls

as snow and rain, 2) snow accumulation, and 3) snowmelt. Historical precipitation data was

analyzed for each station to determine the minimum, maximum and/or mean daily temperature

thresholds that best-predicted observed snowfall amounts over a 30-year period. Snowfall was

added to the snow pack assuming a constant density of 400kgm-3. A US Army Corps of Engineers

equation was used for daily snowmelt calculations. The snow model was evaluated by comparing

the predicted and observed number of days with snow, and days when snow depth met or exceeded

the assumed operational requirement (30cm) over the baseline period.

To complete the modelling of snow conditions at each ski area, a snowmaking module was

integrated with the natural snow cover model. The estimated technical capacities and decision rules

were derived from communications with ski industry stakeholders.

The climatic criteria for a skiable day were adopted from Scott et al. (10), which were derived from

an examination of 20 years of daily observed ski operations data from ski areas in the province of

Ontario, and communications with ski industry stakeholders. Ski areas were assumed to close if

93

any of the following climatic conditions occurred: snow depth less than 30cm, maximum

temperature greater than 15°C for two consecutive days and accompanied by measurable liquid

precipitation, or when the two-day liquid precipitation exceeded 20 mm. It is acknowledged that

these criteria may differ slightly in the other ski regions, but data were not available to make

regional adjustments.

In order to compare the relative impact of projected climate change at the six locations in this study,

it was decided to model the impact of climate change on a single hypothetical ski area (identical in

skiable terrain and snowmaking capacity) at each study area. This approach isolates the importance

of climate and projected climate change at each location, rather than the advantages of the

snowmaking systems in place at each ski area.

RESULTS

Table 1 presents the modelled baseline ski season (in days) at the six study areas as well as the

projected impact of climate change in the 2020s and 2050s. The modelled average baseline ski

season was the longest at more northerly (Quebec City =160, Ste. Agathe-des-Monts and Thunder

Bay = 163 days) locations, while the most southerly and lower elevation study area had the shortest

average baseline ski season (Brighton = 114 days).

The projected impact of climate change on average ski season length differed substantially among

the high and low impact scenarios and among the six study areas (Table1). Unlike earlier climate

change impact studies, which could only examine doubled-carbon dioxide conditions (~2050s), this

analysis was able to examine the impact of climate change scenarios for the early decades of this

century, which are the most relevant to business planning and investment time frames. The 2020s

low impact climate change scenario produced minor impacts on the length of the average ski season

(less than -10% at all locations). Under the much warmer high impact scenario more serious

impacts were realized at Brighton (-28%) and Rutland (-25%) relative to other locations where

projected season losses ranged from 13-19%.

The range of projected impacts on the average ski season length increased substantially in the

2050s, portraying two distinct operational futures for the six ski areas examined. Once again, the

low impact scenario projected only minor impacts on the average ski season length (<10%

reduction) at four of the six study areas. Only the southern Michigan and low elevation Vermont

locations had losses of greater than 10%. Notably the range of season losses projected under the

2050s low impact scenario were less than the high impact scenario for the 2020s, indicating the

importance of uncertainty related to climate change scenarios. The 2050s high impact climate

change scenario presented a much more challenging scenario for the ski industry. Substantial

season losses (32-65%) were projected for the six study areas. It is questionable whether ski

94

operations at some of these locations would be sustainable under the high impact climate change

scenario of the 2050s. The two Quebec locations appeared to be the least vulnerable to climate

change.

Table 1: Modelled Ski Season Length

Baseline Climate % Change from 1970s Baseline % Change in Study Area Average Change Earlier Studies

(days) Scenario 2020s 2050s (~2050s) Brighton, Michigan 114 -59 to -100 (7)

Low Impact -5 -12 High Impact -28 -65

Orillia, Ontario 149 -40 to -100 (8)

Low Impact -3 -8 High Impact -19 -46

Quebec City, Quebec 160 -42 to -70 (6)

Low Impact -1 -5 High Impact -13 -34

Rutland, Vermont 119 -56 to –92 (5)

Low Impact -5 -14 High Impact -25 -60

Ste. Agathe-des-Monts,

Quebec 163 -48 to -87 (6)

Low Impact -0 -4 High Impact -13 -32

Thunder Bay, Ontario 163 -30 to -40 (8)

Low Impact -2 -4 High Impact -17 -36

Consistent with Scott et al. (10) the range of season losses projected by this study in the 2050s were

substantially lower than earlier studies that did not account for snowmaking (Table 1). In most

cases the losses projected under the high impact 2050s scenario in this study (‘high impact’)

approximated the low end of the impact range (‘low impact’) from earlier studies. This reinforces

the importance of incorporating snowmaking in climate change impact assessments, particularly

where snowmaking systems are already an integral component of ski operations.

95

DISCUSSION

This study reassessed the potential impact of projected climate change on the ski industry at six

locations in eastern North America. A central conclusion of this study is that rather than ‘crippling’

the ski industry as some media reports have suggested, climate change will create winners and

losers in the ski industry of eastern North America. The confluence of climatic changes and other

factors (access to capital, demand trends, energy prices, water supply, etc.) will advantage certain

ski areas and likely result in further contraction and consolidation in the industry. The findings

suggest that in the 2020s even the high impact climate change scenario poses only a minor risk to

ski areas examined, except southern Michigan, where a series of poor winters could pose a

reasonable business risk to smaller, less diversified ski areas. Consistent with Scott et al. (2003), a

major finding of the reassessment was that ski season losses to climate change in the 2050s were

not as severe as projected in earlier studies that did not include snowmaking. Nonetheless, the

projected average season length reductions in the high impact 2050s scenario were not insignificant.

When reductions in the season length are combined with the projected increases in snowmaking

costs, the sustainability of ski operations could be jeopardised by the 2050s if the high impact

climate change scenario were realized. This scenario is currently thought to have a low probability

however.

REFERENCES

1. Elsasser, H and Bürki, R. 2002. Climate change as a threat to tourism in the Alps. Clim Res.

20:253-257.

2. Breiling M., Charamza P., and Skage O. 1997. Klimasensibilitat Osterreichischer

Bezirke mit besonderer Berucksichtigung des Wintertourismus. Report 97:1. Institute for

Landscape Planning. (Alnarp, Austria).

3. Fukuskima, T., et al. 2003. Influences of air temperature change on leisure industries: case

study on ski activities, Mit & Ad Strat. for Climate Change, 7:173-189.

4. Galloway RW. 1988. The potential impact of climate changes on Australian ski fields.

Greenhouse: Planning for climatic change, edited by Pearman GI. (CSIRO, Melbourne,

Australia), 428-437.

5. Badke, C. 1991. Climate change and tourism: the effect of global warming on Killington,

Vermont. Senior Honours Thesis, Department of Geography, University of Waterloo,

Waterloo, Ontario, Canada.

6. Lamothe and Periard. 1988. Implications of climate change for downhill skiing in

Quebec. Climate Change Digest 88-03, Environment Canada, Ottawa, Canada.

96

7. Lipski, S. and McBoyle, G. 1991. The impact of global warming on downhill skiing in

Michigan. East Lakes Geog. 26:37-51.

8. McBoyle, G., et al. 1986. Recreation and climate change: a Canadian case study, Ont

Geog. 23:51-68.

9. Scott, D., et al. 2001. Assessing the sensitivity of the alpine skiing industry in Ontario,

Canada to climate variability and change. Proc. of the First Int’l Workshop on Climate,

Tourism and Recreation., edited by A. Matzarakis and C. de Frietas. (Int’l Soc. of Biomet. -

Commission on Climate, Tourism and Recreation. 5-10 October 2001. Halkidiki, Greece),

153-170.

10. Scott, D., McBoyle, G., Mills, B. 2003. Climate change and the skiing industry in Southern

Ontario (Canada): Exploring the importance of snowmaking as a as a technical adaptation.

Clim Res, 23, 171-181.

97

OFFSETTING CARBON DIOXIDE EMISSIONS FROM TOURISM

P. Hart1, S. Becken1 and I. Turney1

1. Landcare Research, Lincoln 8152, New Zealand

E-mail addresses: hartp@landcareresearch.co.nz (P. Hart), beckens@landcareresearch.co.nz (S.

Becken), turneyi@landcareresearch.co.nz (I. Turney)

ABSTRACT

Despite energy-efficiency and renewable-energy initiatives, tourism remains an energy-intensive

industry and a significant emitter of greenhouse gases, especially CO2. Air travel in particular is a

major contributor to CO2 emissions, and travel to a destination can contribute as much as 80% of an

international tourists’ total CO2 emissions. Carbon-neutral transport technology is not expected to

be mainstream in the near future. Other initiatives are needed to offset tourism’s emissions, at least

in the short and medium term, to move tourism towards being ecologically sustainable. A few

schemes for reducing energy use and/or carbon offsetting now exist. We describe a New Zealand

carbon-offsetting scheme and its application to tourism. The EBEX21® (Emissions Biodiversity

Exchange) scheme sequesters carbon and at the same time enhances biodiversity by regenerating

native forests on marginal land in New Zealand. Using EBEX21 we calculated the cost of CO2

emissions, as well as the native forest area needed to offset CO2 emissions from international

tourism to and within New Zealand. Two models are suggested: firstly, a voluntary mechanism for

tourists to offset their emissions. Secondly, regulatory models that charge a ‘carbon tax’ on every

tourist, based on average emissions resulting from travel within New Zealand, and an ‘ecotax’ that

compensates for other environmental costs, including CO2 emissions from international travel to

New Zealand. For both models sufficient marginal land (one million hectares) is available for

offsetting every international tourist’s CO2 emissions on an annual basis.

KEYWORDS: Carbon dioxide emissions, Offsetting, Sinks, Biodiversity, Emission tax

INTRODUCTION

Tourism is attracting increasing attention as an emitter of greenhouse gases and a consequent

contributor to climate change. Gössling (1) suggested that CO2 emissions (adjusted for nitrous

oxides and water emitted by aircraft) from tourism may be in the order of 5.3% of the global total.

Ninety per cent of these emissions come from transport. Within transport there is now a growing

98

debate on aviation’s role in climate change. Penner et al. (2) estimate that international aviation

contributes about 3.5% to climate change. Most attention has focused on the emission of CO2 and

neglected other effects, including emission of ozone and nitrous oxides, condensation trails and

increased cirrus cloudiness, which in total contribute to climate change significantly more than do

CO2 emissions (3). So far the international aviation industry has not had to face up to fuel taxes or

other emission reduction schemes, as bunker fuels lie outside the Kyoto Protocol. The European

Union is now actively considering steps towards curbing greenhouse gas emissions from transport,

and recently the International Civil Aviation Organisation’s Committee on Aviation Environmental

Protection agreed on a model for voluntary measures to reduce emissions from aeroplanes (3). In

New Zealand, planning is underway to introduce a carbon tax in 2007 capped at NZ$25 a tonne of

CO2 (4) subject to the Kyoto Protocol coming into force.

Mitigation options for the tourism industry include improving energy efficiency, replacing fossil

fuel with renewable energy sources, and carbon offsetting. Continuing improvements in transport

fuel efficiency are expected and hydrogen-powered cars are being developed. In the medium term

there are no practical alternatives to kerosene-based fuels for aircraft. The offsetting or

sequestration of CO2 as biomass (usually trees) is termed ‘mitigation’ because the growth of new

forests will reduce the amount of atmospheric CO2 (Kyoto Protocol, Article 3). Little is known

about tourist awareness of the impacts of travel on climate change, or tourist willingness to mitigate

their impact by, for example, sequestering carbon. Tourists may not see the link between tourism

and climate change, although they may still be favourably disposed to planting trees for their

benefits in relation to biodiversity, hydrology, and soil retention (5). This paper describes existing

carbon offsetting schemes for tourists, and in particular a case study approach in New Zealand. Two

models to fund such an approach are discussed: a voluntary mechanism for tourists to offset their

emissions, and regulatory models that charge eco/emission taxes. It is beyond the scope of this

paper to consider the role of tourist operators or emissions trading schemes.

CARBON OFFSETTING SCHEMES IN TOURISM

A small number of web-based carbon offsetting schemes tailored to various markets are now

available (e.g. Climate Care, Climate Protection Partnership, Business Enterprises for Sustainable

Travel, Future Forests, 500 ppm, Trees for Travellers, Emissions Biodiversity Exchange

(EBEX21®)1). Most offer individual travellers the opportunity to work out their travel greenhouse

gas emissions with an online calculator. Tourists can then invest either in energy-efficiency

measures (e.g. low-energy light bulbs), energy renewal (e.g. hydro-turbines), or carbon

1 Climate Care (www.climatecare.org.uk); Climate Protection Partnership (www.clipp.org); Business Enterprises for Sustainable Travel (www.sustainabletravel.org); Future Forests (www.futureforests.com); 500 ppm, http://travel.500ppm.com); Trees for Travellers (www.treesfortravellers.co.nz); Emissions Biodiversity Exchange (EBEX21) (www.ebex21.co.nz)

99

sequestration (e.g. by projects for restoring forests). Often these projects are in developing

countries, in order to empower communities through commerce and tackle climate change at the

same time. Climate Care also works with tour operators who include offsetting in their package.

An approach in New Zealand being led by the Kaikoura District Council is providing tourists with

the opportunity to plant a tree during their visit to Kaikoura. The tree is numbered, its exact

location recorded, and it can be revisited by the tourist.

Concerns are sometimes raised about the effectiveness of tree planting in mitigating CO2. Some of

the difficulties are forest pests, diseases and fire, the difficulty of measuring carbon uptake (6),

sinks being a short-term solution, insecurity of projects, and costs of administration. Moreover, in

tree-planting schemes the initial rates of sequestration are low and, therefore, it can be some years

before travel emissions are actually offset. EBEX21 takes a different approach and promotes the

retirement of marginal (not suited for farming) hill farmland to promote natural regeneration of

shrublands and native forests, a process that can deliver high initial sequestration rates. Hence,

EBEX21 invests in carbon sequestration and the permanent restoration of native biodiversity (7).

Tourists or organisations that want to offset their CO2 emissions provide funding via EBEX21 to

landowners for regeneration work. Natural regeneration will sequester carbon for 150–300 years

depending on site selection, species range, fertility, rainfall, and nearby seed sources (8). Because

New Zealand is also seeking to enhance its landscapes and biodiversity (which underpin its 100%

Pure NZ brand), these new native forest sinks provide multiple environmental benefits, as well as

potentially providing new economic opportunities for the more remote parts of New Zealand.

A NEW ZEALAND CASE-STUDY

METHOD

By using arrival figures for international tourists (9), and estimated energy use, CO2 emissions from

international air travel can be calculated. Average travel distances for one-way travel to New

Zealand are available (10), as well as energy intensities for air travel (1.75 MJ per passenger-

kilometre (11)) and CO2 conversion factors (69 g CO2 per MJ (12)). Energy use and emissions were

calculated for tourists from the 36 countries that made up 94% of all international arrivals in 2002

(9). Results were linearly extrapolated to estimate CO2 emissions for all arrivals. In addition, the

energy use and CO2 emissions associated with tourist activity within New Zealand were estimated.

Only transport and accommodation behaviour were taken into account, as those two sectors

contribute over 90% of the total (internal) energy use of an international tourist (13). We considered

energy and emission rates for different tourist types, and weighted them according to their

representation among all international tourists to New Zealand (14).

100

Having derived CO2 emissions for international air travel, internal transport and accommodation, it

was possible to estimate the emission costs and the native forest area required to offset these

emissions. To this end, we used a cost of NZ$25 per tonne of CO2 (4). The required area of land

was derived by assuming a minimum carbon sequestration rate of 3 t CO2 per hectare of land per

annum. Calculations in this paper focused on CO2 and did not include other greenhouse gases. The

overall radiative forcing, however, is estimated to be 2.7 times higher than the mere effect of CO2 as

a result of other effects specific to aviation, such as the formation of ozone and contrails (2).

RESULTS

The analysis of 2002 arrival figures showed an energy use of 36.4 PJ and emissions of 2513 kt of

CO2 (Table 1). On average, an international tourist consumed 17 800 MJ of energy for their one-

way flight, which resulted in 1.2 t of CO2 and a CO2 emission cost of NZ$30. The average area

required to offset the one-way air travel emissions would be 0.4 ha per tourist.

Table 1: Arrival numbers (9), travel distances and energy use for one-way air travel to New Zealand (10 largest markets)

Country of origin Arrival number

in 2002

Average travel

distance *

Energy use per

tourist (MJ **)

Energy use per

country (PJ)

CO2 emissions

per country (t)***

Australia 632 470 3 372 5 900 3.73 257 497 United Kingdom 236 986 19 955 34 922 8.28 571 042 USA 205 289 11 146 19 506 4.00 276 295 Japan 173 567 9 931 17 379 3.02 208 132 Korea, Republic of 109 936 10 684 18 697 2.06 141 830 China, PR 76 534 13 874 24 279 1.86 128 216 Germany 48 951 20 701 36 228 1.77 122 363 Canada 39 669 15 172 26 550 1.05 72 673 Taiwan 38 358 9 579 16 764 0.64 44 368 Singapore 34 019 8 514 14 899 0.51 34 974 Total (including

other countries) 2 044 962 - - 36.41 2 513 120

* Based on 1999 calculations (10); ** One-way energy use at an energy intensity of 1.75 MJ / passenger-km (11);

*** Carbon dioxide emissions with an emission factor of 69 g / MJ (12).

This analysis of energy use and CO2 emissions resulting from international tourist transport and

accommodation within New Zealand showed that tourism uses about 8 PJ of energy annually within

New Zealand, resulting in 532 kt of CO2 emissions (Table 2). Tourist types differed markedly, with

camping tourists, for example, emitting about 0.44 t of CO2 per person, compared with coach

tourists emitting only 0.24 t of CO2. An average international tourist (emitting 0.26 t of CO2 within

New Zealand) would have to pay NZ$7 for their CO2 emissions and would require a native forest

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area of 0.09 ha to offset these emissions. In total, an average international tourist would pay NZ$37

to offset CO2 emissions from their New Zealand holiday under the assumption of an equal share of

international emissions between country of origin and destination.

Table 2: Energy use within New Zealand for transport and accommodation for different tourist types (data from 2001 International Visitor Survey (IVS) (14))

International

tourists 2001

Number of

touring

tourists

Energy use per

tourist within NZ

(MJ)

Total energy use

(PJ)

CO2 per tourist

within NZ (t)

Total CO2 (t)

Coach tourist 429 159 3538 1.52 0.244 104 767 VFR 343 577 3649 1.25 0.252 86 506 Auto tourist 247 972 3440 0.85 0.237 58 859 Backpacker 131 419 3657 0.48 0.252 33 161 Camper 84 195 6306 0.53 0.435 36 634 Soft comfort 42 966 5035 0.22 0.347 14 927 TOTAL* 1 279 288 na 4.85 na 334 855 Extrapolated to

2002 arrivals 2 044 962 7.70 531 516

*Note: The IVS does not include all tourists and the above tourist types do not include ‘gateway-only’ tourists (21% of all

international tourists). Energy use for attractions is also not included. Results from the IVS analysis were extrapolated to 2002

arrivals provided by Statistics New Zealand. These are minimum estimates for energy use and emissions only.

The total area required to offset all emissions from international tourism (including the one-way

flight) to and within New Zealand would be 1 014 800 ha. With about one million hectares of

marginal land potentially available for native forest regeneration in New Zealand (7), it is just

possible to offset tourism’s emissions and restore biodiversity at the same time. However, the

radiative forcing of CO2 is only about one-third of the total radiative effect of all climate-impacting

aviation emissions, and if the total aviation effect was taken into account the land available for

regenerating native forest sinks would not be enough.

FUNDING MODELS FOR OFFSETTING TOURIST EMISSIONS IN NEW ZEALAND

Voluntary schemes: Tourists can calculate their emissions according to country of origin and travel

style (Tables 1 and 2), and then use EBEX21 or one of the other schemes to offset their emissions.

Although there has been some interest by international tourists in EBEX21, few have offset their

emissions. Educational campaigns about the benefits of carbon sequestration may persuade more

tourists to offset their emissions. In-flight videos showing this kind of information en route to New

Zealand could increase the awareness of the issue. However, a study carried out in the tourism

information centre of Christchurch, New Zealand (A Reiser, Lincoln University pers. comm.)

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showed no preference by tourists for eco-labelled operators’ products compared with those of other

operators. This suggests voluntary schemes might struggle without ongoing educational campaigns,

marketing and government support. It is possible that tourists are more amenable to offsetting their

emissions early in their decision making, for example when booking their flights. This would

require the cooperation of travel agents, tour wholesalers and airlines. Further research is needed to

answer these questions, and to learn how they might complement operator-led and regulatory

approaches.

Regulatory schemes: There are two approaches whereby tourists could be levied or taxed for their

emissions. A general ‘ecotax’ (15) imposed on entry to New Zealand would directly target

international tourists but would probably attract negative reaction from the tourism industry, even

though the ecotax would meet the aim of sustainability of the New Zealand Tourism Strategy 2010.

A NZ$100 ecotax is already being mentioned by some politicians in New Zealand. About a third of

this (totalling an estimated NZ$60 million) would be needed to carbon-offset the trip with co-

benefits for restoring biodiversity. The remaining amount could go towards national park

management and infrastructure improvements. Since the New Zealand Government aims to

internalise carbon costs by introducing a carbon tax in 2007, tourists would automatically pay for

their internal emissions (e.g. calculated to be a six cent increase in the price of petrol). The

calculations in this paper indicate that the Government could raise at least NZ$13 million for

climate change mitigation from international tourists’ emissions within New Zealand. The

Government could use these funds to purchase credits in schemes like EBEX21, thereby creating

new tourist attractions in the form of native ‘tourist forests’.

CONCLUSION

Non-harvested native forest sinks are an important part of the New Zealand carbon cycle and could

be increased in area to buy time for tourism to come up with more effective energy use, allowing

the New Zealand tourism industry to become carbon neutral. We suggest that New Zealand (and

maybe some other countries) can capture benefits from offsetting CO2 emissions by restoring

biodiversity, and potentially creating additional tourist attractions around restored native forest

areas, thus effectively underpinning its 100% Pure NZ brand.

However, as has happened with ecolabels in tourism (16), the proliferation of a diversity of

worldwide voluntary schemes may confuse tourists such that they prefer to ignore them. Voluntary

schemes that stress multiple benefits (i.e. carbon sequestration and restoring forest ecosystems) may

find favour and acceptance from tourists. For New Zealand, a regulatory model is likely to be more

effective, especially given that a carbon tax is already planned. Another option is to impose a

general ecotax on entry to New Zealand, or some combination of both an entry ecotax and an

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internal carbon tax. The ecotax may cause some negative reaction and strong resistance amongst

tourists and tourism stakeholders since their awareness of climate change issues remains poor (17).

A strong education campaign will be needed in any case.

ACKNOWLEDGEMENTS

This study was partially supported under Foundation for Research, Science and Technology

Contract C09X0207.

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10. Becken, S. 2002a. Analysing international tourist flows to estimate energy use associated

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14. Becken, S. and Cavanagh, J. 2003. Energy efficiency trend analysis of the tourism sector.

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prospects. Tourism Manage. 23:197-205.

17. Becken, S. and Hart, P. 2004. Tourism stakeholders’ perspectives on climate change policy

in New Zealand. In: A. Matzarakis, C. R. de Freitas, D. Scott (eds.) Advances in tourism

climatology. Ber. Meteor. Inst. Univ. Freiburg Nr. 12, 199-207.

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THE ECO-EFFICIENCY OF TOURISM

Paul Peeters1, Stefan Gössling2, Jean-Paul Ceron3, Ghislain Dubois4,

Trista Patterson5 and Robert B. Richardson6

1. NHTV Breda University, Centre for Sustainable Tourism and Transport, P.O. Box 3917, 4800

DX Breda, The Netherlands.

2. Department of Service Management, Lund University, Box 882, 251 08 Helsingborg, Sweden

3. Centre de Recherche en Droit de l’Environnement, de l’Aménagement et de l’Urbanisme

(CRIDEAU), Université de Limoges, 34 Rue Dupleix, 87000 Limoges, France

4. Tourisme Environnement Consultants (TEC), 89 Rue de la République, 13002 Marseille, France

5. University of Siena, Department of Science and Technology for Physical Chemistry in

Biosystems, Via Della Diana 2A, 53100 Siena, Italy

6. Centre for Rainforest Studies, The School for Field Studies, Australia/Center for Energy and

Environmental Studies, Boston University, Boston, USA

E-mail addresses: peeters.p@nhtv.nl (Tel.: +31-76-530 22 03; Fax: +31-76-530 22 05)

ABSTRACT

The use of fossil energy is one of the major environmental problems associated with tourism and

travel. Consequently, the need to limit fossil energy use has been highlighted as a precondition for

achieving sustainable tourism development. However, tourism is also one of the most important

sectors of the world economy, and fears have thus been expressed by the tourist industry and its

organisations that increasing energy prices (for example, as a result of eco-taxes) could substantially

decrease the economic welfare of countries and destinations. In this article, the interplay of

environmental damage and economic gains is thus analysed within the context of tourism. Carbon

dioxide-equivalent emissions are assessed in relation to the revenues generated, leading to

conclusions about the eco-efficiency of tourism.

KEYWORDS: Air travel, Energy, Climate change, Policy making, Sustainable tourism, Taxation,

Transport

INTRODUCTION

There is broad consensus that tourism development should be sustainable; however, the question of

how to achieve this remains an object of debate. It is clear that in order to be sustainable,

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environmental effects of tourism need to be kept below critical threshold levels, which can only be

achieved if these environmental effects can be quantified. Several conclusions can be drawn from

studies attempting to quantify environmental impacts of tourism (1, 2, 3, 4, 5, 6). First, whether

using energy consumption, greenhouse gas emissions or area-equivalents as a basis for calculations,

a substantial share of tourism is seen to conflict with sustainability goals. Second, the use of fossil

fuels and related emissions of greenhouse gases is, from a global point of view, the most pressing

environmental problem related to tourism (7). Third, transport contributes disproportionably to the

overall leisure-related environmental impact of tourism: up to 95% per trip.

In the light of these insights there is a given need to reduce greenhouse gas emissions, particularly

in the transport sector. Policy changes designed to reduce emissions from the tourism industry are

frequently seen as unpalatable, especially in light of the widely held belief that environmental levies

could reduce tourism revenues. In countering the image of an environmentally harmful industry,

tourism lobbyists seek to establish and maintain a discourse portraying tourism as an

environmentally neutral, if not beneficial industry, claiming its ecological performance to be

superior to other sectors of the global economy (8). The analysis of the tourist industry from an

ecological efficiency (or eco-efficiency) perspective may provide new insights into these claims.

Eco-efficiency is a term coined by the World Business Council for Sustainable Development in

1995, and based on a lifecycle analysis approach (9).

For the purpose of this article, environmental damage per unit of value generation has been chosen

as the basis for calculations. Carbon dioxide equivalent (CO2-e) emissions are used as a proxy for

environmental damage. The use of equivalent emissions allows consideration of the impacts of air

travel, which is important because emissions (nitrogen oxides, water vapour and other pollutants)

released at cruise altitude have a larger effect on radiative forcing than those emitted at ground level

(10). As a proxy for value generation, turnover is used. Thus eco-efficiency (EE) is defined as the

ratio of CO2-e (kg) to turnover (€). Note that we describe eco-efficiencies as

“favourable/unfavourable”. Calculations do not consider indirect ecological and economic effects.

Based on these premises, the analysis will focus on the following questions:

What is the eco-efficiency of the tourism industry and how does this vary per market?

How does the eco-efficiency of tourism compare to other sectors?

How can eco-efficiency be used i) to judge the environmental impact of different source markets or

forms of tourism, ii) to assess the sustainability of tourism, and iii) to develop more sustainable

tourism products?

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METHOD

The calculation of eco-efficiency ratios requires two data-sets: one for CO2–e emissions and one for

turnover. Indirect energy requirements, costs or multiplier effects are not considered in this analysis,

as none of the existing databases is detailed enough for such advanced calculations. As most data

available are for energy consumption, these had to be converted to CO2–e emissions using

appropriate conversion factors.

Transport emissions can be calculated for different transport modes and connections, using the

following equation:

( )∑ ∗∗=m

mmmel VE εβ

In which Eel is CO2–equivalent emissions in kg, mβ specific emissions of CO2 in grams per

passenger kilometre (pkm), mε equivalence factor and Vm total transport volume for transport mode

(m) in passenger kilometres (pkm). The mβ are based on occupancy rates of 70% for intra-EU air,

75% for ICA air, 50% for car, 60% for long-distance rail and 75% for coach. The emission factors

vary between an average 0.018 kg CO2–e/pkm for coaches and an average 0.14 kg CO2-e/pkm for

air transport within Europe (based on 11, 12 and others). Equivalence factors ( mε ) are used to

include the climate-relevant effects of other emissions than carbon dioxide. For surface transport

(road, rail and shipping) this factor is about 1.05 (13). At cruise altitude, emissions of NOx, H2O and

soot cause positive additional radiative forcing (14). The equivalence factor for air transport is

estimated at 2.7 (10). The total transport volume for transport mode m (Vm) is calculated using the

following equation:

nn

mnnm WFDFSNV ∗∗∗= ∑*2

In which Vm is total transport volume for transport mode (m) in passenger kilometres (pkm), Nn total

number of tourists travelling with transport mode m on connection n, Sn great circle distance for

relation n, DFm average detour factor for mode m and WFn generalised weight factor for multi-

destination travel calculations at journey, region or country level.

The total number of tourists travelling with transport mode m includes all travellers arriving with a

certain means of transport (aircraft, car, etc.). The great circle distance Sn is the shortest distance

between two locations. The detour factor DFm gives the average ratio between the real distance

covered and the theoretical shortest great circle distance (as estimated by Peeters). These vary

between 1.05 for air transport and 1.15 for ground based transport. The weight factor WFn is used to

indicate that long-distance tourists may visit several countries during their stay. Only part of their

travel impact should therefore be allocated to the country where the tourists arrived. Using

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Amsterdam as an example, WFn is calculated as n

nn TLOS

ALOSWF__

= , with LOS_An being the average

length of stay within Amsterdam and LOS_Tn being the average total length of the trip. Finally, to

capture return-trips, results have to be multiplied by two.

The amount of energy consumed in different hotels as well as the environmental impact of their

production may thus vary considerably. Average energy consumption per bed night in hotels might

be in the order of 130 MJ (3). Hotels use generally more energy per visitor, as they have energy

intense facilities, such as bars, restaurants, pools, and more spacious rooms. For accommodation

establishments in the category 'pensions' an average value of 50 MJ is used. Campsites were

assumed to have the lowest energy use of all categories with 25 MJ per bed night, while holiday

villages were calculated with 90 MJ per bed night. It should be noted that there is a moderate degree

of uncertainty, as scientific data on energy use in accommodation establishments is limited. No data

is available for self-catering facilities and vacation homes. These are assumed to consume 120 MJ

and100 MJ per bed night.

On holiday, tourists are usually engaged in activities. Becken and Simmons (15) identified activities

of New Zealand tourists and calculated their energy-intensity, which ranged between 7 MJ per

tourist (visitor centers) to 1,300 MJ per tourist (heli-skiing). Given the differences in energy-

intensity, it seems difficult to allocate an average amount of energy to each tourist. Gössling (3)

estimated that, on average, 250 MJ per tourist (corresponding to 39.6 kg CO2) are used for

'activities' during a longer vacation of international tourists, which might be a rather conservative

estimate (15).

CASE STUDIES

Five case studies were used for this paper: Amsterdam, France, Seychelles, Valle di Merse

(Tuscany) and Rocky Mountains National Park (RMNP). In the first four cases the market has been

divided by country of origin of the tourists. Typical results are shown for the Amsterdam inbound

tourism case (figure 1). The left picture shows the total amount of CO2–e emissions per market and

the right one the revenues. From the figure the economically best performing market (The

Netherlands) does not show on the emissions graph, being too small. On the other hand, an

intermediate emitter like Japan is not on the emissions graph. The overall eco-efficiency is 1.1 kg

CO2–e/€. This is 20% more than for the rural destination of Valle di Merse.

Val di Merse is located in the Province of Siena in Tuscany, Italy. 250,113 bed nights were

recorded in the study area in 2003 (16). The eco-efficiency of tourism to Val di Merse is better with

respect to other case studies presented in this study due in part to the relatively small share (10%) of

non-European tourists visiting the area. Eco-efficiency varies between 0.4 kg CO2-e/€ for Italian

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visitors to 4.0 kg CO2-e/€ for Australians and New Zealanders (including the revenues from

transport). The second reason why tourism to Val di Merse can be seen to have more favourable

eco-efficiency than other cases presented are the low on-site emissions, because of the small scale

low energy country home rentals typical of the area. Emissions from transport of food products also

tends to be lower, as tourists to Tuscany tend to eat Tuscan foods. Activities of tourists in the area

such as shopping, farm visits, museums, and horseback riding generally have low emissions.

Finally, the production of products typically purchased by tourists (i.e., olive oil, pasta, wine,

cheeses, etc.) are relatively low in energy intensity and greenhouse gas emissions.

The Seychelles show on average a seven times higher EE (seven time less favourable). This is

because the islands depend almost entirely on long distance tourism. The analysis of CO2-e

emissions for tourism on the Seychelles shows that transport to the destination accounts for 96% of

the total, while accommodation contributes 2%, other transport 2%, and activities less than 1% (4).

Calculated per tourist, EE values range between 3.19 kg CO2-e/€ for visitors from La Reunion to

13.03 kg CO2-e/€ for visitors from Italy. The large differences between the European source

countries depend on two factors: expenditure per day and average length of stay. For example, in

2002 Swiss visitors stayed on average 11.9 days, spending €57 per day, while Italian tourists stayed

9.2 days, spending €42 per day. These differences seem to be marginal, but result in 58% higher

CO2-e emissions per Euro revenue for Italian visitors in comparison to Swiss.

The case of inbound tourism to France shows not only the effects of long haul versus short haul

markets, but also the influence of the area chosen for recreation (figure 2). Coastal and mountainous

areas tend to show unfavourable eco-efficiency while rural and urban areas show more favourable

ones. Note that for distant countries, urban tourism is relatively more eco-efficient, whereas it is the

opposite for neighbouring countries; this is a result of the likelihood of short urban stays for the

latter.

Generally, long stays are more eco-efficient than short stays, since the impact of transport to the

destination is distributed over a longer period. One should also notice that, as long as the distances

do not compel tourists to use planes, national habits regarding means of transport have a significant

effect ( for example tourists from Poland, since they use buses, have an unexpectedly favourable

eco-efficiency).

The Rocky Mountain National Park case shows an overall eco-efficiency of 1.04 kg CO2–e/€. Of

the total emissions of 643,300 tons of CO2–e, about 71% was from transport to the RMNP, 17%

from accommodations and 12% from activities (mainly hiking and climbing, including transport

within the destination). Again this confirms the strong role of transport for CO2-e emissions.

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CO 2 -e emissions total O/D-transportation per market

40%

12% 7% 6%

5% 5%

4% 3%

2% 2% 2%

1% 11% United States

Australia/New Zealand United Kingdom Japan

Canada

Other Asia

Israel

Mexico

Spain

ScandinaviaIreland

Italy

Rest world

Total revenue per market (excluding OD-transport revenues)

22%

20%

16%

6%

5%

3%

3%

3%

3%

2%

2%

2% 13%

The Netherlands

United Kingdom

United States

Germany

Spain

Italy

France

Ireland

Scandinavia

Australia/New Zealand

Canada

Belgium/Luxembourg

Rest world

Figure 1: CO2-e emissions and revenue from 2001 inbound tourism to Amsterdam by country of origin

111

0,00

1,00

2,00

3,00

4,00

5,00

6,00

7,00

8,00

Coastalareas

Mountain Rural Urbanareas

Coastalareas

Moutain Rural Urban

kg C

O2-

e/Eu

roDistant countries Neighbouring countries only

Figure 2: Eco-efficiency of travel to different environments, France Table 1 shows the results for all cases in comparison with the world average. The World Gross

Product was €27.4 trillion in 1999, which can be compared to CO2 emissions of 22.9 trillion kg for

fossil fuel burning and cement production (17). Based on data by Houghton et al. (18), Peeters

calculated a global equivalence factor of 1.4. The world average eco-efficiency would thus be in the

order of 1.2 kg CO2-e/€.

Table 1: Eco-efficiencies: tourism and global economy Eco-efficiency (kg CO2-e/€)

Average Min Max

EE above world

average (%)

World 1.2 - - -

Amsterdam (including accommodation

emissions; excl. transport revenues)

1.1 0.1 6.0 30

Amsterdam (incl. transport revenues) 0.9 0.1 3.2 35

France (excluding transport revenues) - <0.1 16.1 -

Seychelles (excluding transport revenues) 7.6 3.2 13.0 100

Val di Merse (including transport revenues

and accommodation emissions)

0.9 0.4 4.0 10

Rocky Mountains 1.04 - - -

The variability is shown by the average and the columns with minimum and maximum values found

for the different market sectors. The last column shows the share of the market with an EE less

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favourable than world average. This share ranges from 10% for Val di Merse to 100% for the

Seychelles. The general trend is towards more long haul tourism and hence less favourable average

EE for tourism as a sector.

CONCLUSIONS AND DISCUSSION

The analysis has shown that the eco-efficiency of tourism, on average, is not necessarily more

favourable than the average world economy eco-efficiency. Overall, the comparably small share of

tourism with a particularly unfavourable EE (e.g. tourism based on long distance air travel) seems

to substantially increase tourism’s world average EE. This article thus underscores earlier findings

that air travel is the largest problem when attempting to conform tourism to sustainability goals (4,

6). Clearly, shorter travel distances are a precondition for sustainability. By increasing the length of

stay and/or expenditure at the destination the eco-efficiency may reach more favourable values.

Overall, we conclude that eco-efficiency is a useful concept to analyse the combined environmental

and economic performance of tourism. The concept can help to assess the relative importance of

different tourism sectors, and provide insights into how to improve its environmental performance

in the most economically feasible way. The concept has also proved to be applicable on very

different levels. It may be used to evaluate the eco-efficiency of destinations/markets, to identify

‘problematic’ aspects of a journey, or to reveal differences between different forms of tourism or

tourist types. Eco-efficiency calculations may even help to make decisions in carbon emission

trading, should the scheme be applied to economic sectors, such as tourism or related sectors (air

transport, etc.).

The case studies show a large variability. As the example of France illustrates, EE can vary by a

factor of 400. Overall, and in order of importance, travel distance, means of transport, average

length of stay, and expenditures per day are the factors influencing eco-efficiency. Developing

countries focusing on international tourism as a source of income rely heavily on long haul tourism

with a very unfavourable eco-efficiency. However, for poor countries such as the Seychelles or

rural areas of industrialized countries such as France, tourism may be one of few options for

economic development. Tourism in France could focus on European arrivals, thus reducing

environmental impact while maintaining the same revenues. This strategy will clearly not be

possible for the Seychelles in the absence of nearby markets. Such countries should seek to explore

alternative economic sectors or try to increase length of stay and total expenditures of the tourist,

while reducing their numbers.

All case studies in this survey allow the identification of beneficial markets with a favourable eco-

efficiency in juxtaposition to markets with an unfavourable eco-efficiency. In combination with the

analysis of the relative overall economic importance of these markets, it becomes clear which

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markets should be promoted or abandoned. Generally long distance markets should be avoided and

short haul ones developed. Overall, the case studies suggest that eco-efficiency can be an advanced

tool to assess some aspects of the combined environmental and economic performance of tourism.

Marketing strategies may help to change eco-efficiency into a favourable direction as shown by

Table 2. The table shows the results for the Amsterdam case, which suggests substituting arrivals

from distant countries for those from more nearby areas. Only large markets should be treated with

care, to not disturb economic continuity.

Table 2: CO2-e emissions and revenues by market, 2002 Large market Small market

Unfavourable

eco-efficiency

Less marketing:

USA

No marketing:

Japan, Australia/New Zealand, Canada and

Asia

Favourable

eco-efficiency

Current marketing:

United Kingdom and The

Netherlands

Strong marketing:

Germany, Belgium, France, Austria and

Switzerland

In order to use eco-efficiency as an assessment tool of sustainability, a benchmark for sustainability

has to be found. According to different sources, sustainable emissions of CO2 need to be some 80%

lower than current emissions (7). Theoretically, a sustainable average world eco-efficiency should

thus have an average of some 0.24 kg CO2-e/€. Under a scenario of growing global economic

turnover, EE ratios will continuously need to decrease, as total emissions need to remain constant.

REFERENCES

1. Ceron, J.-P. and Dubois, G. 2003. Changes in Leisure-tourism Mobility facing the Stake of

Global Warming: The Case of France. Paper presented at the International Geographical

Union Conference Human Mobility in a Globalising World. (Palma de Mallorca, April 3-5,

2003).

2. Becken, S., Simmons, D. and Frampton, C. 2002. Segmenting tourists by their travel pattern

for insights into achieving energy efficiency. Journal of Travel Research. 42(1):48-52.

3. Gössling, S. 2002. Global environmental consequences of tourism. Global Environmental

Change. 12(4):283-302.

4. Gössling, S., Borgström-Hansson, C., Hörstmeier, et al. 2002. Ecological Footprint Analysis

as a Tool to Assess Tourism Sustainability. Ecological Economics. 43(2-3):199-211.

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5. Patterson, M. 2003. Environmental Performance and Ecoefficiency of the New Zealand

Tourism Sector.

On http://eerg.massey.ac.nz/adobefiles/wordAM2.1_Patterson.pdf.

6. Peeters, P. 2003. The tourist, the trip and the earth. In Creating a fascinating world, edited

by NHTV Marketing and Communication Departments (Breda: NHTV), 1-8.

7. Grassl, H., J. Kokott, M. Kulessa, et al. 2003. Climate protection strategies for the first

Century: Kyoto and beyond. Special Report. (Berlin, WBGU, Berlin).

8. Iwand, W. M. 2003. TUI policies, programmes and actions related to climate impact.

Presentation during the 1st International Conference on Climate Change and Tourism, April

9-11, 2003 (Djerba, Tunisia).

9. Cramer, J. 2000. Early Warning: Integrating Eco-Efficiency Aspects into the Product

Development Process. Environmental Quality Management. Winter 2000:1-10.

10. Schumann, U. 2004. Aviation, Atmosphere and Climate - What has been learned?

Proceedings of the AAC-Conference, June 30 to July 3, 2003 (Friedrichshafen, Germany).

11. Essen, H. Van. 2003. To shift or not to shift, that's the question. The environmental

performance of the principal modes of freight and passenger transport in the policy-making

context. (Delft, CE 98).

12. Eurostat. 2000. Transport and environment: Statistics for the transport and environment

reporting mechanism (term) for the european union. Data 1980-1998. (Luxembourg,

Eurostat).

13. Gugele, B., K. Huttunen and M. Ritter. 2003. Annual European Community greenhouse gas

inventory 1990-2001 and inventory report 2003 (Final draft). (Copenhagen, European

Environmental Agency, Technical Report no 95, Copenhagen).

14. International Panel on Climate Change (IPCC). 1999. Aviation and the Global Atmosphere.

A special report of IPCC Working Groups I and III. (Cambridge University Press,

Cambridge, United Kingdom and New York, USA).

15. Becken, S. and Simmons, D. 2002. Understanding Energy Consumption Patterns of Tourist

Attractions and Activities in New Zealand. Tourism Management. 23(4):343-354.

16. Patterson, T. 2004. The ecological economics of sustainable tourism; a case study of local

versus global ecological footprints from Tuscany, Italy. PhD Dissertation University of

Maryland, Department of Marine, Estuarine, Environmental Sciences College Park, MD,

USA.

17. UNEP and Earthscan. 2002. Global environmetal outlook 3, past, present and future

perspectives. (London, Earthscan Publications Ltd).

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18. Houghton, J. T., et al. (eds.) 2001. Climate change 2001: The scientific basis. Working

group i, third assessment report. (Cambridge, International Panel on Climate Change).

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METHODS OF SENSITIVITY ANALYSIS FOR ASSESSING IMPACTS OF

CLIMATE CHANGE ON TOURISM AT THE REGIONAL SCALE

C. R. de Freitas1

1. School of Geography and Environmental Science, University of Auckland, PB 92019, Auckland,

New Zealand.

E-mail address: c.defreitas@auckland.ac.nz

ABSTRACT

Assessment of the impact of climate change on tourism requires knowledge of future climate as

well as methods capable of transforming this knowledge into likely societal effects. There are two

ways of approaching these challenges: from the top down or the bottom up. In the top down or

single scenario approach, a future climate state is identified and its impacts evaluated.

Unfortunately, this method is hampered by the unreliability of global climate models on which

future climate scenario development relies. As output from climate models is particularly unreliable

at the regional level, the likelihood climate models being ‘wrong’ is high. Clearly, this has serious

planning implications. In the bottom up approach, many of these problems are circumnavigated by

using sensitivity assessments. Sensitivity of a tourism activity to climate is assessed, and then the

question is asked: What is the net effect of change on the tourism activity? By identifying the

sensitivity to climate and evaluating it in terms of the adaptive capacity of the tourism-related

exposure unit, vulnerability of tourism to change may be determined and evaluated. With this

information, planning decisions would be possible without knowing precisely the magnitude of climate

change that will occur. Details of the sensitivity approach are presented and specific examples

discussed.

KEYWORDS: Sensitivity to climate change, Response surface, Tourism climate, Regional climate

INTRODUCTION

There are two ways of assessing the impact of climate change on tourism. These are the so called

top down or bottom up approach. The top down method is by far the most common. In this

approach, a future climate state is identified using global climate models and impacts evaluated. But

this method is hampered by the unreliability of climate models. The truth is that there are no

dependable predictions of future climate, especially at the regional scale. Present understanding of

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global atmospheric processes is barely sufficient to predict the weather a week ahead let alone

climate many decades into the future.

Another problem is that there is often an implicit assumption that a specific changed climate

condition is predicted. This is reinforced by the fact that global climate models are limited to

calculating a single equilibrium response condition. Clearly, the consequences of models being

‘wrong’ could have serious planning implications. To make matters worse, there are large

discrepancies between predictions from different global climate models, especially when model

output is transformed into impacts at the regional scale, the very scale at which planners and

policy-makers typically operate.

The alternative bottom up approach, circumnavigates many of these problems. First the sensitivity

of a tourism activity to climate is assessed, and then the question is asked: What is the net effect of

change on the tourism activity or tourism-related socioeconomic exposure unit? By identifying the

sensitivity to climate and evaluating it in terms of the adaptive capacity of the exposure unit,

vulnerability of tourism to change may be determined and assessed. With this information, planning

decisions would be possible without knowing precisely what future climate will be like.

The purpose of this paper is to examine concepts and methods that address some of the above

issues. Concepts of impact sensitivity based on climate type at the regional scale are described

which provide a broad framework that may be useful in climate change impact analysis. A

generalised approach is taken that is climatic-zone or climate-type specific rather than activity

specific. This enables regional rather than point-specific assessment to be made. Scenarios of future

changed climate are used to show the relative effects on tourism, to provide information for use in

local scale impact assessments for operational planning purposes.

SIMPLE APPROACHES

The aim of climate change impact assessment is to determine how the availability of tourism

climate resources will change and which regions will lose or gain from these changes. The impact

potential of a given change in climate is related to the overall sensitivity of a particular tourism

activity to those aspects of climate that do change. Alernatively, it may be related to the particular

climate type or climate regimes in which change occurs. For example, an average 2 oC temperature

rise and 10 % increase in the number of rain days may be of little consequence in an equatorial

climate region where high air temperatures are commonplace and where there are already extended

periods of rainfall throughout the year. On the other hand, a sub-temperate environment already

marginal for tourism may be highly sensitive and thus respond dramatically to even the smallest

decrease in temperature or increase in precipitation in an already short summer beach recreation

season.

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There are a variety of ways of identifying sensitivity. In theory, sensitivity of a region or activity to

changes in climate does vary depending on climate type or regime. Climatic types can be

characterised and assessed on the basis of this sensitivity since a given change will perturb some

climatic regimes more than others (1). But the net effect is not always intuitive. For example, in

coastal and maritime climates, the occurrence of higher average annual air temperatures due to

greenhouse gas induced warming could be moderated by the local moderating influence of the

nearby ocean on air temperatures. However, this could be deceptive since, for any given increase in

annual average air temperature, the sensitivity to that change could be quite different. An example

of the relationship is shown in Figure 1, where a given increase in mean monthly air temperature

results in a greater increase in the length of the tourist season (A-A increased to B-B) at a site with a

maritime climate (Figure 1a) than at a site with a continental climate (Figure 1b).

Figure 1: The effect of a small change in mean monthly air temperature on the length of the tourist season

for a mid-latitude maritime climate (a) and a mid-latitude continental climate (b). Counter intuitively, the

effect (period B-A) will be greater in the mid-latitude maritime climate. Adapted from Ford (2)

The observation that, in some areas, tourism conforms to climate regions that are preferred or are

optimal for a particular type of tourist activity has given rise to labels and climate connotations; for

example, the ‘Sun Belt’ in the United States, the ‘Costa del Sol’ and ‘the Riviera’ in Europe and the

‘Gold Coast’ and ‘Sunshine Coast’ in Queensland, Australia. Building on this conceptually,

imagine how these ‘zones’ may evolve spatially. In theory, neglecting non-climatic constraints, if

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there is a significant change in climate, the size and appeal of the zone will not necessarily change.

Rather, the geographical location of the zone will shift. As climate changes, there is a shift of the

margins or transition zones at the boundaries. This change in location of the zone is a spatial

manifestation of response to changed climatic conditions. Figure 2 shows how the boundaries are

affected. Taking a southern hemisphere example and using a very simple case of air temperature as

an index of climate, an increase in temperature will result in a southerly shift of a hypothetical

vacation-climate ‘zone’. Figure 2 shows that the north is most vulnerable to change since the new

climatic conditions are no longer optimal or as appealing for tourism. The central region is

unaffected, in that there is no change in appeal or suitability. It is therefore labelled a zone largely

‘insensitive’ to the specified change in climate. To the south, there is a zone in which conditions for

tourism improve, assuming that tourists and the tourist industry respond accordingly and exploit the

changed opportunities.

Figure 2: Simulated spatial shift in a climatically determined vacation region (e.g. ‘Sun Belts’) in the

southern hemisphere resulting from climatic warming showing zones of sensitivity to change

Thermal time indices such as degree days, or other indices such as a rain free days or ‘sunshine

days’, can be used as measures of the changing appeal of tourism climate. These indices may be

employed along with climate-change scenarios to approximate possible spatial shifts in boundaries

to identify zones of high risk or vulnerability to change. In analogous examples from agricultural

climatology, Newman (3) and Blasing and Solomon (4) found that climatic warming would displace

the United States Corn Belt approximately 170 km per degree of warming in a roughly northerly

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direction. Williams and Oakes (5) describe a similar northward expansion of the Canadian Small

Grain Belt, neglecting all environmental barriers other than climate.

RESPONSE SURFACES

Climate change impact assessment of the type described above relies on a greatly simplified picture

of the role of climate, mainly because it deals with change in terms of single, secondary climatic

variables that allow for only elementary statistical connections to be made with impacts. This

approach is of limited use since the significance of the impact will depend on the net combined

effect of several changed climatic variables. For example, thermal state of the climatic environment

in terms human comfort is a function of the combined effect of air temperature, humidity, solar

radiation and wind (6). Impact will also depend on the timing as well as the magnitude of change.

For example, increases in the number of raindays in winter may have no consequences for mid

latitude locations geared to summer beach recreation while increases in summer may destroy the

tourism climate amenity value of a place with an already a marginal beach recreational climate. A

response surface analysis allows the decision maker to take these issues into account all at once. An

example of this is shown in Figure 3.

Figure 3: Response surface showing the sensitivity of the tourism climate of a region, expressed as change

in the Climate Index for Tourism (7), to climate change expressed as change in number of rain days (%),

and change in thermal conditions expressed as an integrated thermal comfort index. Climate change

envelopes show incremental change based on hypothetical scenarios

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A response surface is a two-dimensional representation of the sensitivity of a specific response

variable (for example, the Climate Index for Tourism in Figure 3) to change in the two controlling

features of climate (for example, change in rain days and change in thermal comfort index in

Figure 3). The relationship between the response variable and climate is determined from a

pre-tested set or relationships, usually in the form of an empirical model, called a transfer function

(such as is in the case for the Climate Index for Tourism presented by de Freitas et al (7)). The

output from the groups of determinants can be plotted using values relative to a baseline

representing no climate change (Figure 3). The latter representation is a step removed from absolute

input and output but does have the advantage of providing a direct measure of sensitivity. For

example, a 20 % response to a 10 % change in a controlling climate variable is clearly an example

of impact amplification. Response surface isolines are a summary of a matrix of response points

associated with various combinations of changes to the two groups of driving climate variables

(Figure 3). The required data are derived from repeated runs of the transfer function with the

prescribed changes to the input. The slope and closeness of the isolines are an indicator of

sensitivity, and discontinuities an indicator of change in response (Figure 3). Plotting climate

change scenarios on the response surface enables it to be used for impact analysis. A scenario of,

say, a 10 % increase in rain days and a 20 % increase in the thermal comfort index, for example,

can be plotted on the response surface to assess the anticipated impact on the response variable, say,

a change in the Climate Index for Tourism (Figure 3).

CONCLUSION

Given that, for many regions, climate is the main impetus for attracting visitors, it forms an

important part of the natural resource base for tourism. Any change in climate will affect not only

the resource but also demand for the resource. The capacity of society to respond will depend on

tourism’s sensitivity to changing climate. This will vary from region to region. An advantage of the

response surface method is that it is less likely to obscure inherent sensitivities to change that can

occur in top down approach. Another advantage of this method is its flexibility. A wide range of

new or changed scenarios can be easily handled by plotting them on the response surface. This

avoids the need to rerun the transfer function, thus facilitating use by non-climate specialists such as

planners and policy makers wanting to reassess impacts. In the top down approach the impression is

given that a future climate state will occur at a particular time. This may not be particularly useful

since a variety of planning time frames may be required. In contrast, the response surface method

has an additional advantage of allowing, through interpolation, both longer and shorter term impacts

to be assessed by way of response envelopes.

122

REFERENCES

1. De Freitas, C.R. and A.M. Fowler, 1989: Identifying sensitivity to climatic change at the

regional scale: the New Zealand example. Proceedings of 15th Conference New Zealand

Geographical Society, R. Welch (ed.), New Zealand Geographical Society Conference Series,

No. 15, Dunedin, 254-261.

2. Ford, M. J., 1982: The Changing Climate: Response of the Natural Flora and Fauna. George

Allen and Unwin, London.

3. Newman, J.E., 1980: Climate change impacts on the growing season of the North American

Corn Belt. Biometeorology, 7 (2), 128-142.

4. Blasing, T.J. and Solomon, A.M., 1983: Response of North American Corn Belt to climatic

warming. US Department of Energy, DOE/NBB-004, Washington, DC.

5. Williams, G.D.V. and Oakes, W.T., 1978: Climatic resources for maturing barley and wheat in

Canada. In: Hage, K.D. and Reinhelt, E.R. (eds.), Essays on Meteorology and Climatology, in

Honor of Richard W. Longley. Studies in Geography, 3, University of Alberta, Edmonton,

367-385.

6. De Freitas, C.R., 2003: Tourism climatology: evaluating environmental information for

decision making and business planning in the recreation and tourism sector. International

Journal of Biometeorology, 47 (4), 190-208.

7. De Freitas, C.R, Scott, D. and McBoyle, G., 2004. A new generation climate index for

tourism. In: A. Matzarakis, C. R. de Freitas, D. Scott (eds.) Advances in tourism climatology.

Ber. Meteor. Inst. Univ. Freiburg Nr. 12, 19-26.

123

ALTERNATIVE FUTURES FOR COASTAL AND MARINE TOURISM

IN ENGLAND AND WALES

M.C. Simpson 1 and D.Viner 2

1. School of Geography and the Environment, University of Oxford, UK

2. Climatic Research Unit, University of East Anglia, UK

E-mail addresses: murray.simpson@ouce.ox.ac.uk (M.C. Simpson), d.viner@uea.ac.uk (D. Viner)

ABSTRACT

This paper presents the future for coastal and marine tourism in England and Wales under four

different possible scenarios in the context of sustainable development. The scenarios applied in the

paper - World Markets, National Enterprise, Global Responsibility and Local Stewardship - have

been adapted from the SRES scenarios of the IPCC for the Department of Trade and Industry in the

United Kingdom. The paper is divided into five sections and covers the impacts of climate change

on coastal and marine tourism, trends in visitor flows and tourist numbers, future tourist product

and activities, destinations and development, and the socio-economic costs and benefits of change

in tourism and leisure.

KEYWORDS: Coastal tourism, Marine tourism, England, Wales

INTRODUCTION

Britain is made up of 6,100 islands of which 291 are inhabited. England and Wales, including their

islands, have a coastline of 5,214 km. England and Wales have 45 Heritage Coasts measuring 1,520

km, and around 40% of the total coastline of England and Wales is designated for its natural or

scenic beauty (1). International and domestic tourism in the UK contributes approximately £75

billion to the national economy and over 2 million jobs exist in the UK as a result of tourism (there

are more jobs in tourism than in construction or transport). By the year 2020 foreign visitor

numbers to the UK are expected to rise to around 56 million per year.

Seaside resorts, such as Blackpool, which remains Britain’s number one tourist destination with

approximately 7 million visitors per year, generate around £4.5 billion in spending each year. In

2002 Britons took 79.8 million holidays in England and 8.8 million holidays in Wales, and they

spent £13.3 billion in England and £1.2 billion in Wales. The British took 20.7 million seaside

holidays in England in 2002, staying approximately 78 million nights and spending £3.8 billion. In

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Wales the equivalent figures were 3.8 million holidays and £0.5 billion spent (2). In addition to the

staying holidays, there are in excess of 240 million day visits made by the British to the British

coast each year, spending an additional £2 billion (3).

Ocean, marine and coastal tourism is one of the fastest growing areas of the tourism industry (4, 5,

6) and covers a multitude of recreational activities and attractions including: sun and sand, fishing,

surfing, heritage visits, swimming, scuba diving, windsurfing, yachting and coastal hiking.

However the very dynamic nature of the coastal environment leaves it open to interference from

developments, such as climate change, which may result in severe changes affecting its long term

stability.

THE IMPACTS OF CLIMATE CHANGE ON THE COASTAL AND MARINE TOURISM

AND LEISURE INDUSTRIES OF ENGLAND AND WALES

The UK Climate Impacts Programme (UKCIP) has, in its published documents, consistently

identified the tourism sector as under-researched in relation to the potential impacts as a result of

climate change. All scoping studies carried out under the umbrella of UKCIP have advocated

further research into this topic. Many of the studies have identified the tourism sector as an area of

opportunity with climate change. There are however many factors involved in a thorough

assessment of the tourism sector’s opportunities and threats as a result of climate change, and there

will inevitably be some ‘winners’ and some ‘losers’ (7, 8, 9, 10).

The impacts on tourism and attractions in England and Wales, as a result of climate change, are

both direct and indirect and include the following:

increases in temperature and changes in precipitation and humidity - affecting visitor

numbers, season length and type of activities, leading to: potential economic growth, job

creation, SME business opportunities, diversification of regional tourism industries, and

potential pressure on utilities, services and infrastructure such as water supply and treatment,

waste management, emergency services, health care and transportation;

sea level rise and coastal erosion - threatening destinations and attractions in coastal areas,

e.g. beaches, historic buildings, etc;

sea temperature increase - increasing emphasis on water-related activities;

changes in soil moisture - affecting historic gardens and parks;

increase in infectious and vector borne diseases - affecting tourists health and choice of

destination;

changes in biodiversity and species - causing changes in the countryside, potential loss of

recreational sites and affecting tourist choice in destination;

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increase in frequency and magnitude of extreme events e.g. flooding, storm surges and

subsidence - affecting tourism infrastructure, tourist safety, industry risk management

strategies and risk assessment ‘hazard mapping’ by insurance companies.

TRENDS IN VISITOR FLOWS AND TOURIST NUMBERS

The existing research, albeit limited, has postulated that climate change may benefit the tourism

industry in some parts of England and Wales. There will be warmer winters, leading to a more year-

round and extended tourist season. This will result in increased visitor numbers due to an increase

in temperature in Northern Europe, making England and Wales more attractive for holidays. There

will also be changes in the visitation flow and movement of tourists between the UK and Southern

Europe, due to Southern Europe becoming a less desirable destination as the region becomes overly

hot and uncomfortable, particularly in peak summer holiday periods. This will lead to UK travellers

favouring more domestic based holidays.

In the World Markets scenario, due to greater mobility, personal freedoms, and a lack of

constraints on international air travel, international visitation to virtually all global destinations,

including England and Wales, would increase. Domestic travellers would increase their mobility

and be more inclined to travel overseas, and in this way the pre-existing trend of UK residents

travelling overseas in greater numbers than domestically would continue to escalate. The purchase

of second or holiday homes overseas by UK residents would also increase, contributing further to

international travel and detracting from the visitation of travellers originating domestically to the

coastal areas of England and Wales. Conversely, should the World Markets scenario also serve to

perpetuate or increase current threats to international security and safety, issues and concerns

associated with the impacts of increased terrorism will lead to less international travel and therefore

less foreign visitation, resulting in an increase in domestic travel and visitation. Travellers of a

domestic origin tend to be more inclined to visit the English and Welsh coastal areas whereas

international visitors are more inclined to visit heritage sites and inland cities. Hence a decline in

international visitation and an increase in domestic travel will result in an overall increase in

visitation to coastal areas.

Similarities exist when considering the impacts on visitor flow and visitor numbers under the

scenarios of National Enterprise and Local Stewardship. Focus on both national identity and

local communities will result in increased visitation by domestic tourists to destinations in England

and Wales, and thus the trend towards overseas travel would slow or be reversed. Furthermore, the

destinations in receipt of higher visitor numbers will be predominantly coastal, and short breaks and

day visits to these destinations will also increase.

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Table 1: Trends in Visitor Flows and Tourist Numbers by Scenario

SCENARIO VISITOR NUMBERS & FLOW

Domestic Tourists to Overseas Destinations

Domestic Tourists to English and Welsh Destinations

Int’l Tourists to English and Welsh Destinations

PRESENT DAY*

Coastal Tourist Numbers in England & Wales

Domestic Tourists to Overseas Destinations

Domestic Tourists to English and Welsh Destinations

Int’l Tourists to English and Welsh Destinations

WORLD MARKETS

Coastal Tourist Numbers in England & Wales

Domestic Tourists to Overseas Destinations

Domestic Tourists to English and Welsh Destinations

Int’l Tourists to English and Welsh Destinations

NATIONAL ENTERPRISE

Coastal Tourist Numbers in England &

Wales

Domestic Tourists to Overseas Destinations Domestic Tourists to English and Welsh Destinations

Int’l Tourists to English and Welsh Destinations

GLOBAL RESPONSIBILITY

Coastal Tourist Numbers in England & Wales

Domestic Tourists to Overseas Destinations Domestic Tourists to English and Welsh Destinations

Int’l Tourists to English and Welsh Destinations

LOCAL STEWARDSHIP

Coastal Tourist Numbers in England & Wales

*Present Day Trends Sourced from 2

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UK residents will tend to support both the seaside resorts of yesteryear and the smaller towns that

provide a typically English or Welsh regional experience. In the case of a Global Responsibility

scenario taxes on fuel used for air travel would be highly likely and would result in the reduction or

disappearance of cheaper airlines and routing; therefore international visitation to all destinations

would reduce dramatically. Cheaper and closer alternatives for holidays would become more

popular hence visitation to coastal resorts in England and Wales by domestic visitors would

increase.

FUTURE TOURIST PRODUCT AND ACTIVITIES

Under all four scenarios there will be more emphasis on outdoor and water-related activities -

alfresco dining and the consumption of warm weather food and drinks will be more common.

However, product emphasis will change depending on the individual scenario. In a World Markets

scenario there will be more homogeneity across products generally and a prevalence for fuel use

and engine powered activities such as surf-skiing, water-skiing, power boating and cabin-cruising.

The cruise ship industry will continue its current trend of expansion and the use of marinas will

increase. Ownership of tourism product will tend to be more by multi-national companies and

international conglomerates, with less product focus on the local community or uniqueness of the

specific destination.

A Global Responsibility scenario will result in less use of powered activities, and more focus on

nature based product and ‘ecotourism’ activities such as shore/coastal walking, hiking and cycling.

Health and spa tourism will also experience considerable growth. Ownership of products is likely

to be multi-stakeholder based and public / private partnerships will be more common. The National

Enterprise scenario will provide a fertile environment for the growth of more traditional activities

such as sailing, yachting, angling and the use of promenades and piers. Support will be shown by

tourists to product that is nationally owned and nationally focused. The English and Welsh coastal

destinations will strengthen their cultural identity and the protection and re-emergence in popularity

of coastal heritage sites such as castles, holy places, etc. will be seen.

The growth in local small and medium enterprise (SME) owned product will be vast under a Local

Stewardship scenario and overall livelihood benefits to the local community and its individuals

will be more likely. Products throughout England and Wales will display heterogeneous tendencies

and will be characterised by their uniqueness and local flavour. Local produce will be an attraction

in its own right. The availability of new crops and species in certain regions (particularly the

Southern regions) will lead to an increase in local produce markets and farmers markets, enhanced

production and quality of local produce, and new local varieties of fruits and aquaculture produce

(fish and shellfish).

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Figure 1: The Different Uses of the English and Welsh Coastal and Marine Environment by Tourists

(adapted from 11) DESTINATIONS AND DEVELOPMENT

With increased visitation comes increased pressure on infrastructure, transport, utilities, services

and the natural environment in destinations. In three of the four scenarios, see Table 1, increased

visitation to coastal areas will be experienced and it is essential that adequate investment and

preparation is made by local and national governments, and appropriate management systems put in

place. Equally, as a result of climate change coastal destinations, attractions and accommodation

will be more vulnerable to both sea level rise and the increased frequency and magnitude of storms.

Threats exist to coastal flora and fauna, historic gardens and species, and the prospect of

deterioration in water quality is real. Risk management procedures must be developed to minimise

these threats.

The National Enterprise scenario will ensure that a strong emphasis is given to the regeneration of

seaside resorts in England and Wales. Whilst tourism alone will not provide a regenerative solution

to these towns many still have a viable future as a tourist destination (12). Funding and investment

is more likely to be found in this scenario, which will be essential to make strides towards positive

change for these towns. The profile of resorts within government and the role of local authorities

and regional tourist boards will be enhanced in a National Enterprise scenario. The development

of essential transport links, quality assurance programs and best practice schemes, and the

development and diversification of product will all play a vital part in the overall vision for the

regeneration of the resorts of yesteryear (13).

Unique Selling Points (USPs) and the idiosyncrasies of individual destinations will be drawn upon

heavily under a Local Stewardship scenario. Cooperatives and joint ventures will be devised, and

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development will be kept low rise and in-keeping with the existing natural and built environment.

The character, history, and heritage of each destination will be essential to its individual

development, and destinations will provide a more ‘eco-friendly’ experience with sustainable values

reflecting its local environment. Under a World Markets scenario many destinations will become

increasingly homogenised with similarly designed hotels, marinas, beach facilities and coastal

attractions being developed such as artificial wrecks, hotels at sea, etc. Competition between

destinations for development, investment and visitation will become progressively fiercer with

divisions being created between ‘successful’ destinations and other ‘failed’ destinations. Continued

GHG emissions in this scenario will increase the likelihood of losing natural coastal assets such as

wetlands and beaches, reducing the number of attractive destinations. Under a Global

Responsibility scenario concern over water pollution levels will increase and become a major

factor influencing tourist choice of destination. This will lead to an increase in demand for blue flag

beaches and other quality assurance and best practice schemes. Due to temperature increase and

changes in precipitation patterns flora and fauna with pronounced southern distribution will become

more widespread (9), and in a scenario of Global Responsibility integrated land management will

aid nature conservation and attract more ‘eco-tourists’ to those destinations.

SOCIO-ECONOMIC COSTS AND BENEFITS OF CHANGE IN TOURISM AND LEISURE

Growth in the coastal tourism and leisure sector in England and Wales under the National

Enterprise, Global Responsibility and Local Stewardship scenarios will create jobs, encourage

opportunities for small and medium enterprises (SMEs) and national companies, boost the regional

economy, provide opportunities for diversification of the industry in currently less developed areas

and have a knock on effect for secondary and linked industries. However, conversely, some coastal

towns and regions of England and Wales may become progressively more vulnerable as a result of

climate change, thereby potentially threatening the existing tourism and leisure industry in certain

regions and increasing socio-economic instability. For example, beach erosion and degradation to

cultural heritage sites such as ancient settlements and fortifications, holy places, ruined castles etc.

located in coastal or low-lying areas could be irreversibly damaged by a changed climate. The

impacts of the destruction, erosion and degradation of tourist attractions such as these will lead to

lower regional revenues, increased unemployment and ensuing social problems.

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Great Yarmouth

Lowestof t

Whitstable/Herne BaySouthend-on-Sea

Clacton

Folkestone/Hy the

Deal

Thanet

Hastings/Bexhill

Bognor Regis

Eastbourne

Greater Worthing

Greater Brighton

Scarborough

Skegness

Bridlington

Whitley Bay

Whitby

Swanage

Greater Bournemouth

Isle of Wight

ExmouthWeymouthTorbay

Dawlish/Teignmouth

Sidmouth

Greater BlackpoolMorecambe and Heysham

SouthportRhy l/Prestatyn

Llandudno/Colwy n Bay /Conwy

Dunoon

Greater Ay r

St. Iv es

Penzance

Falmouth

Newquay

Weston-super-MareBarry

Porthcawl

MineheadIlf racombe

Burnham-on-Sea

Figure 2: Location of Britain’s 43 Principal Seaside Towns (from 14)

131

The seaside towns in England and Wales began to develop in the eighteenth and nineteenth

centuries, and mainly flourished into the twentieth century. However, with the development of

package holidays in the 1970s to destinations with a more reliable climate and more exotic

experiences, followed by increasingly cheap air travel, many of the coastal towns and regions of

England and Wales fell into decades of decline. This decline has been coupled with a growth in

competition domestically and a lack of ability of resorts to evolve into a product that meets modern

day standards and expectations. As a result many of these towns now share the same socio-

economic problems as other one industry towns (12, 14). In a recent study, ‘The Seaside Economy’,

Beatty and Fothergill with the assistance of the British Resort Association selected key seaside

resorts in Britain. These towns were selected using the criteria of focusing on towns in their own

right and not districts, minimum size (population over 8000) and the exclusion of ports and

residential settlements. Forty-three towns were selected, see Figure 2, and the study made some

significant findings. For example, whilst there has been strong recent employment growth in-

migration is outstripping that local employment growth. The in-migration appears to be driven by

residential preference, particularly by the unemployed, and extensive joblessness in seaside towns

appears to exist beyond the recorded level of unemployment. According to the study the successful

adaptation of individual seaside towns has depended on regional location more than size - the South

West and South East of England have generally fared better than Wales, the North West and the

East (14).

As discussed earlier whilst under a World Markets scenario little improvement is likely to be seen

in coastal towns as visitor flow will be away from England and Wales’ coastal areas, and tourist

numbers to coastal towns will reduce overall. Socio-economic benefits will be more evenly spread

in the National Enterprise, Global Responsibility and Local Stewardship scenarios.

As national, political and cultural institutions strengthen the coastal regions and towns in the North

and East of England and in Wales will benefit from the protection, investment and subsidies

afforded to them and will be brought closer to the level of the South West and South East coastal

towns of England. Similarly public policy, a tendency towards the more equal distribution of

welfare and opportunities and the meeting of social objectives through public provision, will lead to

more parity between the different geographical coastal regions. Additional investment opportunities

will also result in those coastal areas where increased economic activities, resulting from increased

tourism, have occurred. This will lead to a more conducive environment for a diversified and

sustainable economy.

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Figure 3: The Integrated Relationship between Sustainable Tourism and Sustainable Development

(adapted from 11)

As discussed, a longer and more reliable summer season is likely to increase both visitor numbers

and £ spent per head and result in a boost for coastal economies, particularly as there will be more

emphasis on outdoor and water-related recreation. However an increase in visitor numbers to a

given town or region will put greater pressure on transport, utilities and infrastructure. Health

services will also experience additional pressure; visitors will be vulnerable to an increased risk of

food poisoning, sunburn, heat-stroke, infection, heat exhaustion, dehydration and skin cancer. This

increased stress on resources and facilities will not prove beneficial to the economy, should an

increase in visitor numbers not be prepared for or managed efficiently. In addition, under a World

Markets scenario competition will occur for available space between commercial fisheries,

aquaculture and tourism. Also, commercial fishing and aquaculture may impact on tourism with

pollution being a major issue. These conflicts between sectors and the pressures on services as a

result of increased visitation must be dealt with as all scenarios will face challenges of one kind or

another.

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Regardless of the future scenario an integrated approach to sustainable tourism is essential for the

coastal towns and regions of England and Wales to receive net socio-economic and environmental

benefits. Equal consideration must be given to agriculture, infrastructure and services, conservation

and the environment, economic systems, societies and communities. This integrated relationship is

depicted below in Figure 3.

REFERENCES

1. British Resorts Association. 2004. Some Facts. (www.britishresorts.org.uk).

2. VisitBritain. 2003. United Kingdom Tourism Survey 2002. (VisitBritain, London, UK).

3. VisitBriatin. 1998. United Kingdom Day Visits Survey 1998. (VisitBritain, London, UK).

4. Miller, M.L. and Auyong, J. 1991. Coastal zone tourism: A potent force affecting

environment and society. Marine Policy. 15(2):75-99.

5. Pollard, J. 1995. Tourism and the environment. Irish Tourism and Development, edited by

Breathnach, P. (Maynooth: Geographical Society of Ireland), 61-77.

6. Orams, M. 1999. Marine Tourism. (London, Routledge, UK).

7. Wade, S., et al. (eds). 1999. The Impacts of Climate Change in the South East: Technical

Report. (WS Atkins, Epsom, UK).

8. Hulme, M., et al. 2002. Climate Change Scenarios for the United Kingdom: The UKCIP02

Scientific Report. (Tyndall Centre for Climate Change Research, School of Environmental

Sciences, University of East Anglia, Norwich, UK).

9. Metcalf, G. et al. (eds). 2003. Warming to the idea: meeting the challenge of climate change

in the South West, The South West Climate Change Impacts Scoping Study. (Cheltenham,

UK).

10. Willows, R.I. and Connell, R.K. (eds). 2003. Climate adaptation: Risk, uncertainty and

decision-making. (UKCIP Technical Report. UKCIP, Oxford, UK).

11. Swarbrooke, J. 1999. Sustainable Tourism Management. (CABI, Oxford, UK).

12. English Tourism Council (ETC). 2001. Sea Changes: Creating world-class resorts in

England. (ETC, London, UK).

13. European Commission. 2000. Towards quality coastal tourism – Integrated quality

management (IQM) of coastal tourist destinations. (Enterprise Directorate-General,

Luxembourg).

14. Beatty, C. and Fothergill, S. 2003. The Seaside Economy: The final report of the seaside

towns research project (Centre for Regional Economic and Social Research, Sheffield

Hallam University).

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POTENTIAL ECONOMIC IMPACTS OF CLIMATE CHANGE ON CARIBBEAN

TOURISM INDUSTRIES

M.C. Uyarra1, I.M. Côte1, J.A. Gill1,2,3, R.R.T. Tinch2+, D. Viner4, A.R. Watkinson1,2,3

1. Centre for Ecology, Evolution and Conservation, School of Biological Sciences, University of

East Anglia, Norwich NR4 7TJ, UK

2. School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK

3. Tyndall Centre for Climate Change Research, Norwich, NR4 7TJ, UK

4. Climatic Research Unit, University of East Anglia, Norwich NR4 7TJ, UK + Present address : The Macaulay Land Use Research Institute, Aberdeen AB15 8QH, UK

E-mail addresses: mcaluya_2000@yahoo.es (M.C. Uyarra), i.cote@uea.ac.uk (I.M.Cote),

j.gill@uea.ac.uk (J.A.Gill), r.tinch@macaulay.ac.uk (R.R.T. Tinch), d.viner@uea.ac.uk (D. Viner),

a.watkinson@uea.ac.uk (A.R. Watkinson)

ABSTRACT

Climate change may affect important environmental components of holiday destinations, which

may have repercussions for tourism-dependent economies. Changes in snow cover seasonality,

increased frequency of heat waves, coral bleaching events linked to increased sea surface

temperature, and reduction of beach size as a result of sea-level rise are some of the possible

changes; all of these could have negative impacts on regions where the tourism industry depends on

environmental features. This paper’s research, a questionnaire-based study of tourists in two

Caribbean islands, revealed the importance of environmental features for tourists when choosing a

holiday destination. It also highlighted the fact that environmental changes as a consequence of

climate change could significantly alter tourist preference for holiday destinations. Potential

economic impacts may therefore arise in the Caribbean tourism industry due to alterations of the

environmental features that make this region attractive for tourism.

KEYWORDS: Climate change, Tourism, Coral bleaching, Sea level rise

INTRODUCTION

In recent decades climate change has attracted the world-wide attention, and concern, of researchers

from a wide range of scientific disciplines. Economists, conservationists and policy-makers now

agree that the global climate is changing (1), and that this will not only affect the environment and

135

survival of species (2, 3, 4), but it will also have an impact on human systems. Climate change can

potentially affect global patterns of tourism because environmental considerations are important

components in the decision-making process determining holiday destinations (5). Climate-change-

induced changes in the environment are likely to have economic implications, since neither

ecological systems nor humans may be able to adapt to these rapid changes (2, 6, 7, 8).

Potential impacts of climate change on environmental features important to tourism Climate change is not limited to changes in temperature; the environment will also undergo

significant changes (1). During the last century, global mean surface temperature has increased by

0.6 ºC (9), which was accompanied by rises in sea surface temperature from 0.46 ºC to 2.59 ºC (10)

and an increase of 0.2 metres in sea level (11). Due to the high complexity of the Earth’s systems,

such changes have been associated with the flooding of coastal areas (12), increases in coral

bleaching events (with consequent lost of marine wildlife diversity and abundance) (2), possible

changes in frequency and intensity of hurricanes (13), changes in biodiversity distribution (14), and

re-colonisation in some areas of mosquitoes transmitting tropical diseases (15). Regions such as the

Caribbean are likely to experience many of these changes, which may affect their environment-

dependent tourism industry.

Over the past few decades, the Caribbean region has become a very popular holiday destination,

with the number of tourists increasing at a higher rate (5.5%) than the global average (4.2%). As a

consequence, this region has become one of the most tourism-dependent regions of the world (16).

This dependency is particularly marked for those small islands that have few alternatives to tourism

(17).

Bonaire and Barbados are two examples of small, tourism-dependent islands in the Eastern

Caribbean, whose economies may be vulnerable to global warming. The tourism industry is a major

contributor to the economies of both islands, providing 40% of the gross domestic product (GDP) in

Bonaire (18) and 12.3% in Barbados (19). Moreover, the tourism industry is based on

environmental features that are attractive to tourists yet have been previously mentioned as likely to

be affected by global warming (e.g. beach size, pristine coral reefs, diversity of wildlife, absence of

tropical diseases).

In our questionnaire-based survey in Bonaire and Barbados, tourists highlighted the importance of

environmental features for choosing these islands as their holiday destination. Out of the sixteen

attributes we considered in the questionnaire, warm temperatures, clear waters and low risk of

tropical diseases were some of the most attractive environmental features. Tourists also indicated

other features such as attributes related to marine wildlife (e.g. healthy coral, large and abundant

136

fish, etc.), beach structure and landscape characteristics as being relevant to their holiday

destination choice.

Potential impacts of climate change on repeat visits Several studies have examined the extent to which climate change can affect a country’s economy

(tourism industry) through impacts on environmental features. For example, the exposure of low-

lying locations to sea- level rise or hurricane impacts may seriously affect infra-structure and cause

flooding of coastal areas (20). The resurgence of malaria as a consequence of climate change may

involve increases in health expenditures (21), and decreases in visitors to specific locations. In

Scotland and the Alps changes in duration and spatial distribution of snow cover is likely to reduce

the revenue obtained through recreational winter sports (8). For tropical locations like Bonaire and

Barbados, where the tourism industry is highly dependent on environmental features, changes in

key environmental components (e.g. coral reefs and beaches), as a consequence of global warming,

may cause shifts in preferred holiday destinations (22).

Two open-ended questions were included in our survey to gauge the willingness of tourists to return

to Bonaire or Barbados, under the same price conditions, if key environmental attributes, namely

coral reefs and beaches, were negatively affected by climate change. Most tourists felt that such

changes would be likely to alter their future choice of holiday destination. This result should raise

serious economic concern because approximately 40% of the tourists surveyed were revisiting those

two islands. If our results are representative of these two islands, economic impacts arising from the

consequences of climate change on key environmental attributes would need to be considered in

future management strategies.

Inter-island variation in potential impacts of climate change Are climate change impacts on tourism likely to vary across destinations? The effects of climate

change on tourism industries may differ among destinations depending on their location,

characteristics, and the key environmental attributes used in promoting these locations as holiday

destinations. At present, climate change models have only been applied at regional scales, but these

models already indicate differences in how climate change may affect different regions (11);

therefore, it should be expected that climate change may also have variable effects at very local

scales.

For Bonaire and Barbados, two islands similar in size but with markedly different tourism

orientations, the impacts of climate change would have very different tourism-related effects. The

mass-beach tourism in Barbados contrasts with the more environmentally friendly tourism

developed in Bonaire based on pristine coral reefs (23, 21). Although all environmental attributes

137

proposed in the questionnaire were of some importance in the selection of these islands as holiday

destinations, beach structure was an issue of greater concern, and a more important attraction, for

tourists visiting Barbados than those visiting Bonaire. Tourists in Bonaire had a greater interest and

concern for coral reefs. Therefore, island-specific strategies should be developed, based on key

environmental attributes.

DISCUSSION

Environmental attributes affecting holiday destination choice Our results confirm both the general appeal of Caribbean islands based on the attractiveness of

their environmental attributes, and the remarkable divergence of tourism orientation between our

two study islands (24, 25).

Clear waters and warm temperatures were the major criteria for choosing Bonaire and Barbados as

holiday destinations. It is likely that these features are generally important in selecting other

destinations within the Caribbean region. Tourists in Bonaire mainly chose this island based on

marine attributes, some of which have already been described as important features for divers (i.e.

fish abundance, coral diversity, etc.) (26, 27). Indeed, our sample in Bonaire included a large

proportion of divers and snorkellers. By contrast, tourists in Barbados were mainly attracted by

terrestrial features, the most significant being beach characteristics.

Economic impacts of change in key environmental attributes

Environmental features valued by tourists when choosing a holiday destination are not static; they

can change due to natural disasters, the tourism industry itself, human exploitation, and to climate

change. Alterations in environmental attributes, independently of their original cause, may lead to

shifts in holiday destinations (20, 28, 29).

Our study indicated that a high percentage of tourists would be unwilling to return to Bonaire or

Barbados if climate change affected negatively two key environmental attributes (reefs and

beaches). A reduction in tourism revenue for these islands should therefore be expected. Accurate

estimates of economic losses as a consequence of climate changes impacts are difficult to make

because (a) there are a number of other important environmental attributes which may also change,

attracting other visitors, (b) the cost of holidays can vary, and (c) environmental attributes may also

change simultaneously in different world regions. However, our results may be useful if they are

considered as indicators to analyse the trends that islands may follow in the future, and to promote

mitigation activities.

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Island-specific management strategies

Our findings suggest that strategies to mitigate the socio-economic impacts of climate change

should be island-specific, and based on a clear understanding of those environmental features that

determine attractiveness to tourists. Thus, adaptive management for Bonaire should focus largely on

maintaining healthy coral reefs and fish populations, and for Barbados should principally be aimed

at maintaining or enhancing beaches.

The establishment of Marine Protected Areas (MPAs) on islands dependent on reef-based tourism,

such as Bonaire, may be the best strategy to protect reefs. Although elevated sea surface

temperatures will not respect reserve boundaries (14), MPAs in which fishing pressure is absent and

landward development limited will provide the best opportunity for reefs to cope with climate

change (30).

Adequate coastal management plans and protection strategies will be the key tools for islands

dependent on beach-based tourism such as Barbados. These strategies should include enforced set-

back building regulations, limiting the construction of coastal structures (such structures disrupt

natural sand transport patterns and lead to erosion and consequent habitat loss (31)), and preserving

beach vegetation.

ACKNOWLEDGEMENTS We are grateful to the dive shops and hotels that collaborated in this study, and in particular to the

staff at the Bonaire Marine Park, Bonaire Flamingo Airport and Bellairs Research Institute in

Barbados for their substantial help. Thank you to all the tourists who gave 20 minutes of their

holidays to participate in the surveys. This study was funded by the Tyndall Centre for Climate

Change Research. Maria C. Uyarra was supported by a scholarship from the Fundación Alfonso

Martin Escudero.

REFERENCES 1. Hannah, N., and Stanford, E. 1999. The National Geographic traveller: the Caribbean. (The

National Geographic Society, Washington DC).

2. Hoegh-Guldberg, O. 1999. Climate change, coral bleaching and the future of the world’s

coral reefs. Marine and Freshwater Research. (50):839-866.

3. Sillett, T.S., Holmes, R.T., and Sherry, T.W. 2000. Impacts of a global climate cycle on

population dynamics of a migratory songbird. Science. (2888):2040-2042.

4. Intergovernmental Panel on Climate Change (IPCC). 2001. Climate Change 2001: Impacts,

adaptation, and vulnerability. Contribution of Working Group II to the Third Assessment

Report of the Intergovernmental Panel on Climate Change.

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5. Braun, O.L. et al. 1999. Potential impacts of climate change effects on preferences for

tourism destinations. A psychological pilot study. Climate Research. (11):247 – 254.

6. Turner, R.K., Pearce, D., and Bateman, I. 1994. Climate change. Environmental economics,

an elementary introduction. (Harvester Wheatsheaf, London).

7. Nicholls, R.J., Hoozemans, F.M.J., and Marchand, M. 1999. Increasing flooding risk in

wetland losses due to global sea-level rise, regional and global analysis. Global environment

change – human and policy dimensions. (9):69-87.

8. Harrison, S.J., Winterbottom, S.J., and Johnson, R.C. 2001. A preliminary assessment of the

socio-economic and environmental impacts of recent changes in winter snow cover in

Scotland. Scottish Geographical Journal. (117):297-312.

9. Department for Environment, Food & Rural Affairs (DEFRA). 2001. Climate change

science: some results from the Hadley Centre. (Met Office, Bracknell, Berkishire).

10. Hawkins, J.P., and Roberts, CC.M. 1994. The growth of coastal tourism in the Red Sea:

present and future effects on coral reefs. Ambio. (23):503-508.

11. Intergovernmental Panel on Climate Change (IPCC). 2001. Climate Change 2001: The

scientific basis. Contribution of Working Group I to the Third Assessment Report of the

Intergovernmental Panel on Climate Change. (Cambridge, UK: Cambridge University

Press).

12. World Wildlife Fund for Nature (WWF). 1999. Tourism: facing the challenge of climate

change. (WWF-UK, Surrey).

13. Gardner, T.A., et al. 2003. Long-term region-wide declines in Caribbean corals. Sciences.

(301):958-960.

14. Reaser, J.K., Pomerance, R., and Thomas, P.O. 2000. Coral bleaching and global climate

change: scientific findings and policy recommendations. Conservation Biology. (14):1500-

1511.

15. Hopp, M.J., and Foley, J.A. 2001. Global scale relationship between climate and dengue

fever vector, Aedes aegypti. Climate Change. (48):441-463.

16. European Commission. 2002. The Caribbean and the European Union. (Official Publications

of the European Communities, Luxembourg).

17. Beekhuis, J.V. 1981. Tourism in the Caribbean : impacts on the economic, social and natural

environments. Ambio. (10):325-331.

18. Simal, F. 2003. Personal communication (Manager of Washinton Slagbaii National Park).

19. Sealey, N. 2001. Caribbean Certificate Atlas, 3rd edition. (McMillan Caribbean, Brisbane,

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20. Mimura, N. 1999. Vulnerability of island countries in South Pacific to sea level rise and

climate change. Climate Research. (12):137-143.

21. Hay, S.I. et al. 2002. Climate change and the resurgence of malaria in the East African

highands. Nature. (415):905-909.

22. Agnew, D. and Viner.D. 2001. Potential impacts of climate change on international tourism.

Tourism and Hospitality Research. (3):37-59.

23. Bendure, G., and Friary, N. 1998. Lonely Planet: Eastern Caribbean 2nd edition. (Lonely

Planet Publications, London).

24. De Meyer, K. 1997. Bonaire, Netherlands Antilles. Coastal Region and Small Island (CSI).

Environment and development in coastal regions and in small islands. Paper 3 (Available

at http ://www.unesco.org/csi/pub/papers/parker.htm, 2nd Feb 2002).

25. Tourism Corporation Bonaire (TCB). 2001. Bonaire tourism. Annual Report 2001. Tourism

Corporation Bonaire, Kralendijk, Bonaire.

26. Pendleton, L.H. 1994. Environmental quality and recreation demand in a Caribbean coral

reef. Coastal Management. (22):399-404.

27. Williams, I.D. and Polunin, N.C.V. 2000. Differences between protected and unprotected

reefs of the western Caribbean in attributes preferred by dive tourists. Environmental

Conservation. (27):382-391.

28. Braun, O.L. et al. 1999. Potential impacts of climate change effects on preferences for

tourism destinations. A psychological pilot study. Climate Research. (11):247-254.

29. Lise, W. and Tol, R.S.J. 2002. Impact of climate on tourism demand. Climatic Change.

(55):429-449.

30. West, J.M. and Salm, R.V. 2003. Resistance and resilience to coral bleaching: implications

for coral reef conservation and management. Conservation Biology. (17):956-967.

31. Brown, A.C. and McLachlan, A. 2002. Sandy shore ecosystems and the threats facing them:

some predictions for the year 2025. Environmental Conservation. (29):62-77.

141

INTERACTIONS BETWEEN TOURISM, BIODIVERSITY

AND CLIMATE CHANGE IN THE COASTAL ZONE

E. G. Coombes1, A. P. Jones1, W. J. Sutherland2, I. J. Bateman1

1. School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK

2. School of Biological Sciences, University of East Anglia, Norwich, NR4 7TJ, UK

E-mail address: emma.coombes@uea.ac.uk (E. G. Coombes)

ABSTRACT

As climate plays a significant role in influencing tourism and recreation behaviour, it is important to

consider the effect that climate change may have on visitors to the coastal zone. In terms of coastal

environments such change may hold implications for patterns of tourist distribution, including the

numbers and types of visitors to the coast, as well as the locations they visit. It may also affect the

behaviour of tourists at specific beaches, influencing their activities, their habitats, and the lengths

of their visits. This study, not yet complete, investigates the impacts that climate change may have

on tourism and recreation on the coast of Norfolk, UK. At the regional scale, Geographical

Information Systems (GIS) are being employed to assess the time and distance that visitors travel

from population centres to reach the coast. At the local scale, surveys are being undertaken at

Holkham beach, Norfolk, to assess visitor behaviour, and to determine how this behaviour relates to

weather conditions. The results will be used to assess how changes in climate may influence the

frequency and spatial distribution of visitors, and how such changes may impact coastal

biodiversity. These coastal impacts will be studied via the development of models, and will be used

to determine the implications of policy and management options for the coastal zone.

KEYWORDS: Coastal tourism, Coastal biodiversity, Climate change

INTRODUCTION

Coastal tourism is one of the fastest growing areas of contemporary tourism. It includes leisure and

recreational activities that take place in the coastal zone and the offshore coastal waters, such as

swimming, boating, snorkelling and diving. It also includes coastal tourism development and

supporting infrastructure, for example accommodation, restaurants, retail businesses and activity

suppliers (1).

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The consequences that climate change may have on tourism are likely to be particularly marked at

the coast, as this environment is highly susceptible to change. Climate change is likely to influence

local weather patterns, affecting average and extreme conditions. It may also affect the structure of

beaches, and the type and extent of habitats available (2). Changes to local weather patterns and

beach structure hold implications for tourist distribution at the regional scale. They are also likely to

affect the behaviour of visitors at specific beaches. It is important to understand these two possible

changes because the intensity and type of recreational use influences the impact that tourism may

have on biodiversity. Thus the type of habitat to which the use is being applied is particularly

important.

Tourism and recreational activities have significant impacts on biodiversity. These may be negative.

For example recreational use of the intertidal zone can result in losses of flora and fauna due to

trampling, the overturning of rocks and the collection and disturbance of certain species (3).

Tourism may also have positive impacts. Beach users with high environmental awareness value the

natural environment and prefer to visit undeveloped beaches. This may raise conservation

awareness with local bodies and promote the protection of such areas (4).

The coastal zone provides an excellent environment to study the interactions between tourism,

biodiversity and climate change because many coastal areas receive a large number of visitors

annually, which results in a high impact upon a relatively small area. Furthermore, coastlines are

very sensitive to disturbance. In particular, stabilised sand dunes that carry climax vegetation have

been found to respond more strongly to disruption than shifting and semi-stabilised dunes (5). It is

therefore important to understand the interactions between tourism, biodiversity and climate change

so that we can manage the coast appropriately. The results of this study and similar studies may be

useful in conserving coastal habitats, providing appropriate tourist facilities, aiding coastline

protection decisions and adapting to climate change.

PREVIOUS STUDIES

A number of investigations have been undertaken to assess the behaviour of visitors at specific

locations (6-9). These studies have focused on determining the influence of visitors’ socio-

economic background on their behaviour, including the activities they undertake. Relatively little is

known, other than in very general terms, about the effects of climate on tourism (10). Some

investigations have considered visitors’ preferences for ‘highly developed’, ‘less-highly developed’

or ‘undeveloped’ environments (4), but the manner in which visitors interact within these

environments has not been greatly explored (11). Furthermore, studies have tended to focus on

landscapes as a whole (8) and have not considered individual habitats. Information concerning the

manner in which visitors interact with habitats, including the activities they undertake and the

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length of their stay in relation to weather conditions, is required to provide insight into the

interactions between tourism, biodiversity and climate change.

AIMS AND OBJECTIVES

This investigation will be conducted in five stages to assess the interactions between tourism,

biodiversity and climate change. Stage 1 will study the regional distribution of visitors and explore

the factors that influence which locations tourists visit. Stage 2 will study visitor behaviour at a

specific beach. The activities that visitors undertake, the habitats they use and the length of time

they stay will be monitored. This will allow the intensity of use the beach receives to be

investigated. Stage 3 will assess the sensitivity of this beach to visitor impacts. It will be examined

in terms of the types of coastal habitats it comprises, the extent of area these habitats cover, the

maturity of the habitats and the species they support. Stage 4 will investigate the relationships

between tourism and biodiversity by combining the results from Stages 2 and 3. At Stage 5 these

relationships will be extrapolated for several climate change scenarios, to explore how they may

develop. Stages 1 and 2 of the investigation are currently being undertaken and this paper focuses

on their methods.

METHODS

STUDY AREA

The distribution of visitors to the coast will be assessed for the entire Norfolk coastline, which is

approximately 200km in length. In addition, surveys will be undertaken at Holkham beach to obtain

information about visitor behaviour.

The coastline of Norfolk, UK, (Figure 1) is an interesting location to examine the interactions

between tourism, biodiversity and climate change for three reasons. Firstly, tourism is important to

the local economy and a significant proportion of the tourist activity in Norfolk is related to the

coast. Secondly, it comprises a wide range of coastal habitats including mudflats, sandflats, shingle

beaches, saltmarsh, sand dunes and cliffs. Finally, it is anticipated that climate change may have

significant impacts on the Norfolk coastline because it is low-lying, so a relatively small increase in

sea level rise could significantly alter coast morphology. It is also geologically soft and

consequently very susceptible to change.

Holkham National Nature Reserve (NNR), where the visitor surveys are being undertaken, is

situated on the North Norfolk coastline and covers an area of 3,850ha. It was identified as a suitable

location to examine visitor behaviour because it receives 500,000 visitors annually. In addition, a

variety of habitats exist within the reserve including sandflats, saltmarsh, dunes and wooded areas

towards the back of the beach. The area is important in terms of the vegetation it supports, including

144

rare species such as shrubby seablite (Suaeda fruticosa) and Jersey cudweed (Gnaphalium

luteoalbum). It is one of only three dune systems in the UK where the natterjack toad (Bufo

calamita) is present.

Figure 1: The coastline of Norfolk, UK. Holkham is situated in North Norfolk STUDY APPROACHES

GIS will be used to examine the time and distance that visitors travel from populations centres to

reach the coast. The assessment will be based on three data sets. Firstly, aerial video footage of

visitors along the Norfolk coastline is available from a recent study undertaken at the Tyndall

Centre for Climate Change, University of East Anglia, UK. This investigation examined the effect

of tourists on the distribution of ringed plover (Charadrius hiaticula) and oystercatcher

(Haematopus ostralegus) territories, and the impacts of climate change on this interaction. The

coastline was filmed in April, June and August 2002 from an aeroplane. The locations of the

visitors who were filmed have been entered into a GIS to allow their distribution to be examined.

Secondly, National Census (2001) data will provide information on the populations of urban areas

in Norfolk. Finally, Ordnance Survey data of the transport network will allow the time and distance

from population centres to beach access points to be investigated. The time and distance that

visitors travel to reach the coast will be compared to the distribution of visitors illustrated by the

videos.

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ON-SITE SURVEYS

Surveys are being undertaken at Holkham NNR to examine visitor behaviour. Surveys are

conducted at the main access point to the beach, and surveying commenced in January 2004. They

are undertaken one day a week and will continue for 12 months. The surveys include recording

weather conditions and pedestrian counts, and undertaking interviews with visitors. Weather

conditions are recorded hourly. Pedestrian counts are also undertaken hourly. The results from the

weather conditions and pedestrian counts will be compared to examine the influence of weather on

the numbers of people entering and leaving the beach. Questionnaires are undertaken with visitors

as they leave the beach. The questionnaire examines three levels of use. Firstly, visitors are asked

about their use of the coast in general, including how often they visit and their preferences

regarding difference types of beach characteristics. Secondly, they are asked about their use of

Holkham beach, including the number of visits they have made in the last 12 months, and what they

like and dislike about the beach. Thirdly, they are asked about their visit on that particular day. This

includes why they chose to visit, the activities they have undertaken and how long they have stayed.

They are shown a map of Holkham NNR (Figure 2) and are asked to draw a line showing the route

they took and mark any points where they stopped, and write what activities they participated in and

estimate the length of time they stopped. The results from the visitor maps are being entered into a

GIS so that a grid of the intensities of use each habitat receives can be created.

Examining how visitor behaviour relates to weather conditions and coastal habitats will allow the

impacts that climate change may have on behaviour to be explored, both directly (e.g. through

changes in average temperatures), and indirectly (e.g. due to changes in beach structure).

RESULTS

To-date, 50 responses to the visitor survey have been obtained. Preliminary results suggest that

many people visit Holkham for dog walking, other walking or bird watching. Table 1 illustrates the

numbers of people who indicated that one of these activities was the main activity they came to

undertake. The table also includes details of their preferences for various beach characteristics.

Table 1 demonstrates that all coastal user groups at Holkham may be influenced by the impacts of

climate change. Dog walkers and other walkers, who account for 70% of the visitors interviewed,

both stated they have a strong preference for a wide beach. Furthermore, all of the groups indicated

that they have a strong preference for a quiet beach with wooded areas. Sea level rise may result in

a reduction of the enjoyment that these groups experience from visiting Holkham. This may be

directly due to a decrease in beach width. It may also occur indirectly, as a narrower beach may

result in visitors spending a greater proportion of their visit in areas towards the back of the beach,

such as the wooded dunes at Holkham. Increased use of such areas could lead to them becoming

146

degraded. Also, a narrower beach may result in visitors perceiving the beach to be busy, even if

visitor numbers remain unchanged, which may influence tourists’ behaviour. Bird watchers

indicated that they have a strong preference for a beach with a saltmarsh. If the extent of this habitat

was reduced by sea level rise, or it became degraded by increased storminess as a result of climate

change, bird watchers may choose to visit alternative locations.

Figure 2: A monochrome reproduction of the map that is used with visitors at Holkham. The map is

based on Ordnance Survey data

Table 1: The beach characteristic preferences of visitors at Holkham, for different types of coastal

users (where SP = strong preference, WP = weak preference, NP = no preference). The results indicate

the mode responses recorded in questionnaires undertaken at Holkham NNR, January – April 2004

Beach Characteristics Coastal

User Type

Sample

Size Beach width Sand dunes Saltmarsh Woodland Sea defences Quietness

Dog walker 12 SP for wide SP for dunes NP SP for wood NP SP for quiet

Other walker 23 SP for wide SP for dunes NP SP for wood NP SP for quiet

Bird watcher 10 NP SP for dunes SP for marsh SP for wood NP SP for quiet

Other 5 SP for wide SP for dunes SP for marsh SP for wood NP SP for quiet

All users 50 SP for wide SP for dunes NP SP for wood NP SP for quiet

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DISCUSSION

The early results suggest that climate change may influence the behaviour and distribution of

visitors to the Norfolk coastline, and that change is likely to have implications for biodiversity.

Surveys provide an excellent method to obtain detailed information on visitor behaviour. They

allow information to be collected regarding tourists’ and recreational users’ preferences for beaches,

and socio-economic details, which could not be obtained from observations. Maps provide a cost-

effective means of eliciting information concerning visitors’ use of coastal habitats. Thus, both

surveys and maps represent simple methods that may complement other, more traditional

techniques for examining visitor behaviour, such as questionnaires. Finally, the use of GIS provides

an effective tool for examining the spatial distribution of visitors.

The methods presented are also applicable to other environments. Such methods provide a means to

understanding contemporary visitor distribution and behaviour at specific sites, and how this relates

to weather conditions, so that the implications of climate change can be examined and the

consequences for biodiversity may be explored.

ACKNOWLEDGEMENTS

This paper forms part of a Ph.D. that is jointly funded by the Economic and Social Research

Council and the Natural Environment Research Council. Many thanks to Viola Kimmel for

assisting with surveys at Holkham.

REFERENCES

1. Hall, C.M. 2001. Trends in ocean and coastal tourism: the end of the last frontier? Ocean

and Coastal Man. 44:601-618.

2. Davis, R.A. and Fitzgerald, D.M. 2004. Beaches and Coasts. Blackwell Science Ltd. pp.419.

3. Alessa, L., Bennett, S.M. and Kliskey, A.D. 2003. Effects of knowledge, personal attribute

and perception of ecosystem health on depreciative behaviours in the intertidal zone of

Pacific Rim National Park and Reserve. J. of Env. Man. 68:207-218.

4. Tzatzanis, M. and Wrbka,T. 2002. Sun beds vs. sand dunes: a conservation – tourism

conflict. Coastal Env., Env. Problems in Coastal Regions IV, edited by Brebbia, C.A. (WIT

Press, Southampton, Boston), 25-34.

5. Kutiel, P., Zhevelev, H. and Harrison, R. 1999. The effect of recreational impacts on soil

and vegetation of stabilised Coastal Dunes in the Sharon Park, Israel. Ocean and Coastal

Man. 42:1041-1060.

6. Obua, J. 1996. Visitor characteristics and attitudes towards Kibale National Park, Uganda.

Tourism Man. 17(7):495-505.

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7. Carr, N. 2002. A comparative analysis of the behaviour of domestic and international young

tourists. Tourism Man. 23:321-325.

8. DeLucio, J.V. and Múgica, M. 1994. Landscape preferences and behaviour of visitors to

Spanish national parks. Landscape and Urban Plan. 29:145-160.

9. Gimblett, H.R., Richards, M.T. and Itami, R.M. 2001. RBSim: Geographic Simulation of

Wilderness Recreation Behaviour. J. of Forestry. 99(4):36-42.

10. de Freitas, C.R. 2001. Theory, Concepts and Methods in Tourism Climate Research.

Proceedings of the First International Workshop on Climate, Tourism and Recreation, Porto

Carras, Greece, Oct. 2001, edited by Matzarakis, A., and de Freitas, C.R. Commission on

Climate Tourism and Recreation, 3-20.

11. Soini, K. 2001. Exploring human dimensions of multifunctional landscapes through

mapping and map-making. Landscape and Urban Plan. 57:225-239.

149

THE DEVELOPMENT PROSPECTS FOR GREEK HEALTH TOURISM AND THE ROLE

OF THE BIOCLIMATE REGIME IN GREECE

E.A. Didaskalou1, P.Th. Nastos1, A. Matzarakis2

1. Laboratory of Climatology and Atmospheric Environment, University of Athens, Greece

2. Meteorological Institute, University of Freiburg, Germany

E-mail addresses: edidask@unipi.gr (E.A. Didaskalou), nastos@geol.uoa.gr ( P.Th. Nastos), and

andreas.matzarakis@meteo.uni-freiburg.de (A. Matzarakis)

ABSTRACT

The tourism sector has experienced significant growth in recent years, which is expected to

continue into the future. In attempting to control mass tourism so as to preserve the environment

upon which tourism thrives, and to minimize tourism’s negative impact on the environment, new

forms of tourism have been developed, e.g. health tourism, and cultural tourism. The country’s

tourism advantages are enriched by introducing such alternative forms of tourism to the Greek

tourism profile.

The differentiation and enrichment of the composition of the Greek health tourism product can be a

comparative advantage in relation to competitors of neighbouring countries. Also, the climate of

Greece is a natural factor that contributes to the development of a successive and competitive

product. The analysis of existing climate and bioclimate material from point stations and the

generated maps by GIS and other geo-statistical methods provides relevant results. The obtained

and calculated information can be helpful in the quantification of climatic and bioclimatic

conditions for application in health and wellness tourism destinations in Greece

KEYWORDS: Health tourism, Bioclimate regime, Greece

INTRODUCTION

Health tourism is not a new phenomenon. People have used thermal and mineral waters for bathing

and their health for many thousands of years. In Greek mythology miraculous powers of healing

were attributed to many springs, and by the fifth century B.C. this belief in medical waters was well

incorporated in the practice at the “Asclepeia” which were built near mineral based thermal springs

(1). During the Roman period water was used for treatment, fitness and fun. After the fall of the

Roman Empire many of the public baths stopped working. The gradual evolution of spas in Europe

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began in the 19th century. In Greece the evolution of hydrotherapy stops with the end of Byzantine

times. The exploitation of the spas started again in the beginning of the 20th century. The first

balnear stations which were developed were those of Ipati, Aidipsos, Kythnos, Kyllini, Loutraki,

Kaiafas. Many spas, because of their proximity to the sea, became great, fashionable resorts which

attracted not only those who ‘take the waters’, but also many tourists. In recent years the market for

health and wellness tourism has become more sophisticated, as different people are attracted to

different forms of tourism for different reasons. However segmented, such tourism can contribute

towards the diversification and enrichment of the Greek tourism product, and to the reduction of

seasonal demand.

This article aims to discuss the concept of the Greek health tourism product and to identify key

components for success. Also, as climate is one of the top factors that tourists consider when

choosing a tourism destination, the climatic and bioclimatic characteristics of Greece are examined

(Fig. 1).

CRITERIA FOR CHOOSING A TOURISM DESTINATION

0

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Figure1: From Eurobarometer Survey: Europeans on holiday 1997-1998

HEALTH TOURISM PRODUCT

We define health tourism as the attempt on the part of a tourist facility or destination to attract

tourists by deliberately promoting health-care services and facilities, in addition to its regular tourist

amenities. These health-care services may include medical exams, hydrotherapy, special diet, etc

(2). Based on this explanation, there are many countries with health-care tourism facilities, such as

Switzerland, Germany, Austria, Hungary, Britain, and the U.S.A. (3) (4). Many health tourism

151

facilities have been developed around mineral/thermal springs and health spas (5). Today, the health

tourism market in Europe spans two different segments –those visiting spas and health resorts for

primarily medical reasons, and those for purposes more akin to traditional tourism (6).

The factors that most influence a consumer’s choice of a spa are: a) ambience of the destination, b)

location and access, c) spa programs and facilities, d) characteristics of the visitors to this

destination (e.g. average age of the visitors).

Health tourism facilities may function 12 months a year, giving services such as: medical

examinations, hydrotherapy (e.g. bathing, inhalations, and nose rinsings), aquatics, physiotherapy,

exercise or movement, natural therapeutics agents (muds), beauty care, etc. It should be pointed out

that the presence of auxiliary facilities are now having a major impact on the preference of a tourist

destination, and as a result they are very important elements for the future development of

spa/health resorts (7) (8). Treatment facilities can be used not only for spa treatments and cures but

also for programs that refresh and revitalize the body and mind. Those programs refer to: reducing

weight, quitting smoking and drinking, eliminating or reducing stress, skin treatment, muscle

development, etc. If there are constraints in creating and operating a resort that will provide all

services (treatment facilities, accommodation facilities, ancillary facilities) it is preferable to operate

a hydrotherapy center.

Table 1: National Mineral Springs/Spas, Greece

Name of mineral spring Department Kind of mineral spring

Edipsos Evia Saline water spring

Elefteres Kavala Alcaline saline water spring

Ikaria Samos Radioactive super warm spring

Kaiafas Ilia Sulphurated hydrogen saline water spring

Kamena Vourla Fthiotida Radioactive – saline water spring

Kythnos Kyklades Chalybeate acid water spring

Kyllini Ilia Sulphured hydrogen saline water

Lagadas Thesaloniki Akratothermi

Loutraki Korinthia Slightly warm – saline water, hypotonic

Methana Attiki Sulphureous brine water spring, warm

Nigrita Serres Alcaline carbonated water spring

Platystomo Fthiotida Alcaline sulphureous spring

Smokovo Karditsa Alcaline – sulphureous spring

Thermopyles Fthiotida Sulphurated hydrogen saline water spring, warm

Vouliagmeni Attiki Saline water spring, hypertonic

Ypati Fthiotida Sulphurated hydrogen saline water spring

152

The nature of the services provided by spas/health resorts are not only defined by the chemical

characteristics of water, but also by the geographical position of the springs. The chemical

characteristics are closely related to the specific treatment offered, whereas the geographical

position is related to the characteristics of the auxiliary facilities, which should help in the creation

of a special identity for the resort.

In Greece the development possibilities for health tourism are based on different types of health

centers providing a range of services, such as thalassotherapy centers, or spa centers. There are

already two thalassotherapy centers operating in Kriti, and another two are under construction in

other regions. The 16 spa centers operating in Mineral Water Sources of National Importance

(Table 1) are used annually by 90.000 people and provide 1.300.000 curative baths and other cures.

There are also 40 spa centers operating in Mineral Water Sources of Local Importance (Source:

NSSG).

Table 2: Spas of tourist importance, Greece. Individuals using the springs, and bathing or other

hydrotherapy effected within 1978 and 1997

Year Individuals using the springs Bathings etc. effected 1978 112858 1868154

1979 116117 2056993

1980 116376 2018432

1981 109400 1779103

1982 115227 1873877

1983 116063 1752649

1984 124654 1877316

1985 134890 1872369

1986 118449 1781155

1987 104247 1555694

1988 109912 1485751

1989 114787 1555214

1990 111306 1470730

1991 106161 1440780

1992 109164 1395731

1993 113320 1438110

1994 109631 1477159

1995 101676 1398392

1996 95690 1276700

1997 92635 1303446

153

Statistical data on mineral springs are based on individuals using the springs, bathings, or other

hydrotherapy for the period 1978 to 1997. Analysis of the data (Figure 2 and 3) indicates a trend of

decline, and thus a need to tap a new receptive market. To achieve this requires improvements in

facilities, equipment and a need to develop ancillary services that could appeal to other tourists than

those simply seeking a cure for physical disabilities. The development of health tourism contributes

to the improvement and competitiveness of Greek tourism, because it can tap the needs of new

sectors of the tourism market.

1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998

Years

90000

95000

100000

105000

110000

115000

120000

125000

130000

135000

140000

Indi

vidu

als

usin

g th

e sp

rings

y = 2,0315E6-965,9797*x, r= -0.61, p<0.05

Figure 2: Number of Individuals, who used the springs, for the period 1978 to 1997

1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998

Years

1200000

1300000

1400000

1500000

1600000

1700000

1800000

1900000

2000000

2100000

Bat

hing

s

y = 7,7903E7-38374,2233*x, r = -0.93, p<0.05

Figure 3: Bathings or other hydrotherapy for the period to 1978 to 1997

154

Figure 4: Geographical distribution of climate

stations of the Greek Weather Service and mineral

springs

Figure 5: Geographical distribution of the

monthly mean air temperature in January in

Greece

Figure 6: Geographical distribution of the monthly

mean air temperature in July in Greece

Figure 7: Geographical distribution of the

monthly mean physiologically equivalent

temperature in January in Greece

155

Figure 8: Geographical distribution of the monthly

physiologically equivalent temperature in July in

Greece

Figure 9: Geographical distribution of the

frequency of days with a Physiologically

equivalent temperature over 29 ° C in Greece

A major aim of this study was how to add biometeorological information to the classification of

health and spa resorts for Greece. First we examined the possibility of relevant data by checking the

health and spa resorts proximity to synoptical stations, or stations from the Greek climatic network

(Fig. 4). Unfortunately, the number of stations in the Greek climatic network are very limited. A

gap of information also exists in the spatial realm. For this paper it has been attempted to take into

consideration existing climatic and bioclimatic maps (9, 10, 11). In Fig. 5 and 6 are shown the mean

monthly values of air temperature for January and July. Another possibility is the distribution of

human-biometeorological information i.e. the Physiological Equivalent Temperature for January

(Fig 7) and July (Fig 8), which represents the conditions for 12 UTC for the whole area of Greece.

Additionally, Figure 9 shows the geographical distribution of the frequency of days with a PET

value higher than 29 °C, which represents thermal perception in the level of moderate to high heat

stress.

With the existing information it is possible to describe and quantify climatic and bioclimatic

conditions for Greek spas and health resorts without existing measured data from networks. The

differentiation and enrichment of the Greek health tourism product is associated with the offer of

health-care treatments in combination with services which aim for the mental prosperity of their

156

visitors. The good climatic conditions of Greece are a comparative advantage in relation to

competitors of neighbouring countries.

CONCLUSIONS

Alternative forms of tourism are flourishing as more and more people are interested not only in

trying out new places but also in discovering different forms of tourism. They are also placing

greater emphasis on quality products, more environmentally conscious forms of tourism and on

shorter but more frequent trips. One way to meet these new challenges is to consider developing the

more specialised, and increasingly popular, tourism of health and wellness tourism.

This paper has attempted to provide an overview of the Greek spa tourism product. The climatic

and bioclimatic conditions of Greece were also examined, as both have an important effect on the

development of spa tourism. Whatever the motivation to go to a spa and health resort almost

everybody is looking for a personal experience. If the holiday climate is not satisfactory it is not

possible to have the holiday replaced, and thus climate is a crucial variable, regardless of the quality

of the product. The good climatic and bioclimatic conditions of Greece contribute to satisfying the

needs of visitors.

Furthermore, an important method for spa and health resorts to gain more clients is not only by

diversify their product, but also by creating a new image, as most believe that spa and health resorts

are a place for those with various ailments. This image is a critical element which influences

individual’s not to visit a place with hot/mineral springs. Also, it is important to point out that

spa/health resorts must be easily accessible through normal means (e.g. airplane, automobile, etc.).

The development of health tourism contributes to the improvement and competitiveness of Greek

tourism, as it bears in mind the sustainable development of each area, taps the needs of new parts of

the tourism market, and promotes the proper use of natural, and cultural resources. The goal of a

competitive health tourism product requires the study of trends, and the identification of the

characteristics of visitors. Also, study is needed on what the future uses of springs will be, and the

potential of natural supplies combined with the offering of health treatments.

REFERENCES

1. Gilbert, D. and Van De Weert, M. 1991.The Health Care Tourism Product in Western

Europe. Revue de Tourisme. (2):5-10.

2. Goodrich, J. and Goodrich, G. 1991. Health-Care Tourism. Managing Tourism, edited by

Medlik, S. (Oxford: Butterworth-Heinemann), 107-114.

3. Bywater, M. 1990. Spas and Health Resorts in the EC, Travel and Tourist Analyst.

(6):52-67.

157

4. Lund, J. 1996. Balneological use of thermal and mineral waters in the U.S.A.,

Geothermics, 25(1):103-147.

5. Goodrich, J. 1994. Health Tourism:A New Positioning Strategy for Tourist

Destinations. Global Tourist Behavior, edited by Uysal, M. (New York: Haworth Press),

227-238.

6. Cockerell, N. 1996. Spas and Health Resorts in Europe. Travel and Tourist Analyst.

(1):53-77.

7. Meler, M. and Ruzic, D. and Kovacevic, D. 1996. Health service: a part of the tourism

product. Tourism and Hospitality Management. 2(2):265-278.

8. Didaskalou, E and Nastos P., 2003. The Role of Climatic and Bioclimatic Conditions in the

Development of Health Tourism Product. Anatolia, 14(2):107-126.

9. Matzarakis Α (1995) Human-biometeorological Assessment of the climate of Greece.

Thesis, Thesaloniki (in Greek), 231 pp

10. Matzarakis, A. and Mayer, H. Heat stress in Greece, Int. J. Biometeorol 1997, 41:34-39.

11. Matzarakis, A., Balafoutis, Ch., Mayer, H., 1998: Construction of bioclimate and

climate maps of Greece. Proc. 4th Panhellenic Congress Meteorology-Climatology-Physics

of the Atmosphere, Athens September 1998, Volume 3, 477-482.

158

THE IMPACT OF HOT WEATHER CONDITIONS ON TOURISM IN FLORENCE,

ITALY: THE SUMMERS 2002 - 2003 EXPERIENCE

Marco Morabito1, Lorenzo Cecchi1, Pietro Amedeo Modesti1, Alfonso Crisci2,

Simone Orlandini1, Giampiero Maracchi2, Gian Franco Gensini1

1. Interdepartmental Centre of Bioclimatology - University of Florence - Piazzale delle Cascine18

Florence, 50144, Florence, Italy

2. Institute of Biometeorology, CNR, Via Caproni 8, 50145, Florence, Italy

E-mail address: m.morabito@ibimet.cnr.it (M. Morabito)

ABSTRACT

The Italian summers of 2002 and 2003 showed differing weather conditions: the former was very

hot only in June, while the latter was very hot quite consistently. In fact, during the summer of

2003, in the month of August, there was a catastrophic heat-wave, and Italy was the second most

affected country in Europe after France. For this reason, Florence, an important Italian city for

tourism, was chosen to study the influence of weather conditions on tourists, in particular on

emergency room admissions. Admission data were provided by the hospital located in the historical

centre of Florence. The sample was divided into four groups according to nationality and residency.

A biometeorological index based on the human energy balance, the Physiological Equivalent

Temperature (PET), was calculated. Daily minimum, maximum and average PET values were

considered with the aim of evaluating the thermo-physiological discomfort of tourists during hot

weather conditions in the Mediterranean area. The percentages of variation in event rates, according

to PET modifications in both summer 2002 and 2003, were derived from their relative risks by

using a regression model. PET values showed very different patterns, and summer 2003 always

showed higher daily maximum, minimum and average PET values than 2002, except for the third

week of June 2002. The results of this study showed a highly significant linear increase in event

rates of tourists coming from high northern latitudes in Europe and America, especially when the

daily minimum PET was increased. The study of the impact of these weather conditions could

represent the first step towards the development of an operative watch/warning system calibrated

for tourists.

KEYWORDS: Tourism, PET, Biometeorology, Discomfort, Summer

159

INTRODUCTION

Although weather and climate are widely recognised as vitally important for tourism, relative little

is known about their effects (1). For many regions, such as Italy, tourism is a very important source

of income. Since about 40% of tourists come to Italy during summer, the hot weather can play a

very important role in determining the quality of a vacation. Furthermore, the extreme hot

conditions may represent a risk factor to tourists, increasing emergency room visits, expecially

among the elderly and those who are affected by chronic diseases. The Italian summers of 2002 and

2003 showed very different weather conditions: the former was very hot only in June, while during

the latter, very hot conditions frequently occurred. In fact, during August of 2003 there was a

catastrophic heat-wave throughout Europe, and Italy was the second most affected country after

France (2).

Florence is one of the most important cities in Italy from the point of view of tourism: in 2002

2,450,736 tourists visited the city, dropping only slightly to 2,368,044 in 2003. The days spent by

tourists in the city were 6,314,508 in 2002 and 6,049,123 in 2003 (3). Tourists especially come

from Germany, France, United Kingdom and Austria (4).

The aim of this study was to evaluate, from a biometeorological point of view, the impact of hot

weather conditions on emergency room admissions among tourists to the hospital located in the

historical centre of Florence. This impact was evaluated by the integration of physical factors

influencing the body-atmosphere thermal state by using a thermal index based on the energy

balance model for humans. These kinds of indices are reliable indicators of on-site thermal

conditions (5). The difference in the susceptibility of people coming from countries located at

different latitudes were also studied. The identification of thresholds of risk leading to emergency

room admissions could be used to implement a watch/warning system for tourists. This information

could also be used for business planning and decision-making in the field of recreation (1). In these

ways tour operators could change sightseeing plans when extreme weather conditions were forecast

(for example, indoor instead of outdoor activities).

METHODS

Study site

Florence is an Italian city located in the Region of Tuscany (λ = 11°11' E; Φ = 43°47' N). The city is

50 m a.s.l. in a closed valley bottom at the foot of the Apennines, and extends along the plain in a

SE-NW direction. The surface area is about 100 km2, is crossed by the river Arno, and is

surrounded by hills to the South and mountains to the North, which rise to almost 1000 m.

160

The city has a climate which can be defined as Mediterranean semi-continental, with cold winters

and hot summers. The coldest month is January, with an average temperature of about 6°C. The

warmest months are July and August, with an average temperature of 24°C.

Hospital admissions

Data on daily hospital admissions of tourists into the emergency room were provided by the

administration of Santa Maria Nuova Hospital (Azienda Sanitaria 10, Florence). The data covers a

2-year period, 2002-2003, from June 1st to August 31st. Only the admission data of people whom

were residents abroad were considered. Patients from countries where people usually come to Italy

for reasons other than tourism (for example: job, asylum, etc.) were excluded. Only hospital

admissions due to acute events were included in the study, based on the reading of the diagnoses

performed by doctors. The total number of hospital admissions of tourists for all causes was 455.

This sample was divided into four groups according to residence data: Central and Northern

European, mainly coming from Germany, Austria, United Kingdom, France, and the Scandinavian

countries; Mediterranean, mostly coming from Spain and Greece; North American, coming from

the United States and Canada; and Central and South American, prevalently coming from Mexico,

Ecuador, Brazil and Argentina.

Meteorological data and the biometeorological index

Hourly meteorological data of air temperature (°C), relative humidity (%), wind velocity (ms-1),

cloud cover (in eighths) and global radiation (Wm-2) were obtained from the urban weather station

located in the centre of Florence for the summers of 2002 and 2003. This weather station is

managed by the Regional Office for Environmental Protection in Tuscany (ARPAT). The two

summer seasons considered in this study include the months of June, July and August. To evaluate

daily thermo-physiological discomfort conditions for tourists, a thermal index based on the energy

balance model for humans, the Physiological Equivalent Temperature (PET) (6,7), was applied by

using the RayMan model version 2.0 (8). This model integrates physical factors influencing the

body-atmosphere thermal state, listed above, and considers several body characteristics, such as

metabolic rate (80 W), posture (standing) and clothing (0.9 clo). Daily average, maximum and

minimum PET (PET_ave, PET_max, PET_min, respectively) were assessed for the period studied.

Statistical analyses

Statistical analyses were performed assessing the relative risk (RR, or RRs if plural) of event rates

for each daily value of PET. This examination was performed for each group of hospital

admissions of tourists, according to residence data. A RR of 1.0 means that the probability of

admissions of tourists on days with a specific value of PET is equal to the probability observed on

161

days when these specific values are not observed. A regression analysis was carried out on the

values of RRs and the percentages of variations of event rates, according to PET modifications in

both summer 2002 and 2003, and were derived from each RR by using the following expression:

100×(RR-1).

Consecutive days characterized by high biometeorological values, corresponding to the 90th

percentile of daily PET_ave (PET_ave ≥ 31°C), PET_max (PET_max ≥ 47°C) and PET_min

(PET_min ≥ 18°C) were assessed, and the hospital admissions of the last of these consecutive days

were counted. For each group of consecutive days a RR was performed.

RESULTS

The Italian summers of 2002 and 2003 showed very different weather conditions from a

biometeorological point of view. The summer of 2003 showed a shift of daily frequency

distributions of PET_ave, PET_max and PET_min, reaching higher values in comparison to the

summer of 2002 (Fig. 1).

Figure 1: Distribution of daily maximum Physiological Equivalent Temperature during the summers

2002 and 2003

Hospital admissions prevalently occurred during the summer of 2003, representing 72.7% of the

total sample, against 27.3% during the summer of 2002. The maximum frequency of hospital

admissions concerned tourists coming from Central and Northern Europe, with 30.1% in 2003 and

10.1% in 2002. This was followed by tourists coming from North America, with 19.8% in 2003 and

7.7% in 2002, those living in Mediterranean areas, with 19.8% in 2003 and 7.3% in 2002, and the

minimum frequencies where observed for tourists coming from Central and South America, with

8.8% in 2003 and 2.2% in 2002.

162

Considering data for the summers of 2002 and 2003 together, plots of RRs of hospital admissions

versus daily PET values often suggested a linear relationship. This was especially true when taking

into consideration the relationships between daily PET_min and tourists coming to Florence from

different countries, with the only exception being tourists coming from Central and South America.

In particular, for 1°C increase in PET_min the increase in RRs of event rates was 43% for Central

and North European tourists (P<0.01), 27% for Mediterranean tourists (P<0.001) and 18% for

North American tourists (P<0.001) (Fig. 2).

Figure 2: Relative risk of daily event rates versus daily minimum Physiological Equivalent

Temperature during summer 2002 and 2003. Blue, green and red broken lines represent linear

regressions of relative risks for tourist coming from Central and North Europe, Mediterranean area

and North America respectively

Similar linear relationships were also found between daily PET_ave and RRs of admission of

Central and North European tourists (P<0.001) and North American tourists (P<0.001) (Fig. 3).

Daily PET_max were only associated with the RRs of North American tourists (P<0.01). All of

these significant linear relationships were more evident during the summer of 2003 than 2002, and

especially concerned tourists coming from high northern latitudes in Europe and America.

Consecutive days with high daily average and maximum PET showed high RRs of event rates,

especially for Central and North European tourists (Fig. 4). All RRs showed values higher than 1.0,

163

which means a higher probability of admissions than that occurring on non-consecutive days. In

particular a probability of hospitalization of 243% occurred on the seventh consecutive day with a

high PET_max.

Figure 3: Relative risk of daily event rates versus daily average Physiological Equivalent Temperature

during summer 2002 and 2003. Red and blue broken lines represent linear regressions of relative

risks for tourist coming from North America and Central and North Europe respectively

Figure 4: Relative Risks of hospital admissions for Central and North European tourists on

consecutive days with high daily maximum Physiological Equivalent Temperature assessed during

summer 2003

164

DISCUSSION

Although many aspects of the relationship between tourism and climate/weather have been

investigated so far (1), the impact of some extreme events on the emergency room visits of tourists

has been poorly studied. In the present study, the effects of thermo-physiological discomfort due to

hot weather conditions on emergency room admissions of tourists has been found.

Florence is one of the most famous historical cities in the world and more than 2 million tourists

visit its monuments every year. Some of these tourists are suffering from chronic diseases and some

could be at risk for systemic diseases (for example cardiovascular diseases); in both cases, hot

weather conditions could represent trigger factors determining acute events. The results of this

study showed a significant increase in event rates when the daily minimum PET increased. This is

probably caused by the fact that this parameter is perceived by tourists in a negative manner.

PET_min is generally nocturnal, or occurs in the early diurnal hours, and therefore is a better

indicator in comparison to the daily maximum and average PET. This is because during these hours

the body needs physiological rest. These effects were more evident in the summer of 2003,

characterized by a high rate and persistent extreme hot conditions. During the summer of 2002,

extreme hot weather conditions only occurred in the second half of June. The effects of high daily

average and maximum PET on hospital admissions of tourists were more evident when these

discomfort conditions occurred on consecutive days - that is heat-waves. This high relationship

might be the result of the impact of the addition of the time spent outside during the hottest hours of

the day visiting monuments, followed by nights without rest.

The evaluation of the role of acclimatization in the susceptibility to the thermo-physiological

discomfort caused by hot weather conditions showed that people usually living in colder countries

at high northern latitudes of Europe and America were more affected than people coming from

other countries. However, even tourists coming from Mediterranean countries, such as Spain and

Greece, showed a great susceptibility when daily PET_min values increased. On the other hand,

people coming from Central and South America did not show any vulnerability to such thermo-

physiological discomfort conditions. The results of the present study have to be confirmed on a

larger sample, extending this study to other years and seasons. However, they could represent the

first step for the development of a watch/warning system for tourists that might be used by tour

operators for planning sightseeing activities (outdoor or indoor), and to alert those tourists at high

risk. Furthermore, it will be possible to improve hospital assistance when weather discomfort

conditions are forecast. This is particularly necessary because, in the upcoming years, severe heat-

waves, or short periods with extremely hot days, are very likely to increase in frequency in the

Mediterranean area (9).

165

ACKNOWLEDGEMENTS

The authors wish to thank: Dr F. Giovannini of ARPAT-Firenze (Agenzia Regionale per la

Protezione Ambientale della Toscana) for providing meteorological data, and Miss Carol Dorrei

and Mister Simone Aveotti of Azienda Sanitaria 10 Firenze for providing hospital admission data.

REFERENCES

1. de Freitas, C.R. 2003. Tourism climatology: evaluating environmental information for

decision making and business planning in the recreation and tourism sector. Int. J.

Biometeorol. 48:45-54.

2. Grynszpan, D. 2003. Lessons from the French heatwave. Lancet. 362:1169-1170.

3. Provincia di Firenze. 2002. http://www.provincia.firenze.it/istrcult/turismo/xinternet/

firenze2002. htm (last accessed 28 April 2004).

4. Comunicato stampa. 2003. L'Italia e il turismo internazionale nel 2002: Risultati e tendenze

per incoming e outgoing. IV Conferenza CISET-UIC in collaboration with DOXA:,

Venezia, Auditorium Santa Margherita, 11 April 2003.

http://www.doxa.it/italiano/ nuoveindagini/turismointernaz.pdf (last accessed 15 April

2004).

5. de Freitas, C.R. 1999. Recreation climate assessment. Int. J. Climatol. 10:89-103.

6. Höppe, P. 1999. The physiological equivalent temperature - a universal index for the

biometeorological assessment of the thermal environment. Int. J. Biometeorol. 43:71-75.

7. Matzarakis, A., Mayer, H. and Iziomon, M. 1999. Applications of a universal thermal index:

physiological equivalent temperature. Int. J. Biometeorol. 43:76-84.

8. Matzarakis, A., Rutz, F. and Mayer, H. 2000. Estimation and calculation of the mean radiant

temperature within urban structures. Biometeorology and Urban Climatology at the Turn of

the Millennium, edited by R.J. de Dear, J.D. Kalma, T.R. Oke and A. Auliciems. Selected

Papers from the Conference ICB-ICUC'99, Sydney, WCASP-50, WMO/TD No. 1026:273-

278.

9. Perry, A. 2001. More heat and drought – Can Mediterranean tourism survive and prosper?

Proceedings of the First International Workshop on Climate, Tourism and Recreation, edited

by A. Matzarakis and C.R. de Freitas, Porto Carras, Neos Marmaras, Halkidiki, Greece, 5-

10 , October 2001, p35-40.

166

MANAGING WEATHER RISK DURING MAJOR SPORTING EVENTS:

THE USE OF WEATHER DERIVATIVES

S. S. Dawkins1 and H. Stern1

1. Australian Bureau of Meteorology, Melbourne

E-mail addresses: s.dawkins@bom.gov.au (S. S. Dawkins), h.stern@bom.gov.au (H. Stern)

ABSTRACT

Weather influences various activities, including most of the major outdoor sporting events across

many countries. The revenue of major sporting events is influenced by, among other things, the

‘right’ kind of weather during the duration of a sporting event. This is because the ‘right’ kind of

weather could influence the actual occurrence of a sporting event, and also the number of people

attending such an event. Hence, the uncertainty and the unpredictability of the ‘right’ kind of

weather increase the revenue risk, or revenue exposure, of the organisers of major sporting events.

In the State of Victoria in Australia this is relevant in the case of major sporting events such as the

Australian Open (tennis), the Australian Formula 1 Grand Prix, the Australian Motorcycle Grand

Prix, the Australian Rules Football Grand Final, and the Melbourne Cup (horse racing).

This paper conducted a preliminary examination of the relationship between weather and revenue

generated for a number of products associated with the Australian Open, as well as the potential

application of weather derivatives in ameliorating revenue generating risks. Among the

relationships found were that ground pass ticket sales purchased at the gate on the day of the event

(as a proportion of total ground pass ticket sales) were negatively correlated with maximum

temperature, that hat sales were positively correlated with both maximum temperature and sunshine

hours, and that windcheater sales were negatively correlated with both sunshine hours and

maximum temperature. No useful relationships were found in regard to the influence of rainfall due

to the almost complete absence of rain during the period of available sales data.

It was shown that the application of weather derivatives may be a useful strategy in managing the

weather-related risks associated with the generation of revenues at the Australian Open. For the

purpose of illustration, the relationship between Per Capita Windcheater Sales and Sunshine Hours

& Maximum Temperature was examined. It was shown how to determine, supposing that one

wishes to compensate Windcheater marketers for poor sales on every occasion when the per capita

sales are below 0.25%, a "fair value" price of a weather derivative.

KEYWORDS : Weather, Risk, Revenue, Derivative, Insurance, Sport

167

INTRODUCTION

The application of weather derivatives, or weather insurance, has emerged in recent years as a tool

to manage the revenue risks associated with businesses and other activities that are sensitive to the

uncertainty and variation in weather conditions, known as the ‘weather risk’ (1, 2). In most

situations, the term ‘weather risk’ relates to the exposure of earnings or revenues to the effect of

meteorological phenomena such as unseasonable temperatures or rainfall. Weather derivatives are a

form of financial instrument similar in nature to the commodity futures contracts and options, but

their price is tied to some facet of the weather such as temperature, precipitation, wind, or heating

(and cooling) degree-days (3, 4, 5).

The weather insurance market, taken as a whole, is certainly growing. In its 2003 survey, the

Weather Risk Management Association (http://www.wrma.org) noted that, since its previous survey

in 2002, there had been a near tripling of contracts transacted worldwide (to some 12,000 compared

with 4,000 previously), although the notional value of contracts fell slightly ($US4.2 billion

compared with $US4.3 billion previously). The total business generated over the past 6 years was

$US15.8 billion. Most transactions occur in the USA, but there are rapidly growing markets in

Europe and Asia. Most contracts are related to protection against extremes in temperature, but there

is a growing market in rain-related contracts.

Weather-linked securities may be used as channels for weather risk transfer. Their prices are linked

to the historical weather in a region. They then provide returns related to weather observed in the

region subsequent to their purchase. Therefore, they may be used to help businesses hedge against

weather related risk. They also may be used to help speculators monetise their view of likely

weather patterns.

Emerging issues in the weather risk area include quality of weather and climate data, changes in the

characteristics of observation sites, security of data collection processes, privatisation of weather

forecasting services, the value of data, and the issue of climate change.

The Weather Risk Management Association states that “nearly one-third of the USA economy or

$3.5 trillion is at risk due to weather”, and that they are optimistic about world wide growth in the

weather derivatives market. With respect to the potential development of the weather derivative

market in Australia, a survey in 2002 identified 15 contracts valued at $A25 million. The

Australian market is considered relatively small and the use of weather derivatives is developing

slowly. Further growth in the area of temperature derivatives for (energy) utilities and in rainfall

for hydroelectricity power and agribusiness was foreshadowed. The practice is to undertake

contract settlement on the basis of the official observations, partially settling contracts almost

immediately, and then awaiting confirmation (following quality control procedures) for final

settlement. The Weather Risk Management Association designates an official authority in each

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country from which meteorological data can be obtained, typically the national meteorological

service. Employees of the observing authority are not permitted to trade in weather contracts.

METHODS

The purpose of this work is to conduct a preliminary examination of the relationships between

several Australian Open products and the associated weather conditions. The products of interest

are:

• Walkup gate ground pass sales; these are examined as a proportion of total ground pass

ticket sales and analysed as a function of temperature and sunshine hours. The occurrence

of rainfall is too infrequent across the period of available data to draw any useful

conclusions about the influence of that element. One might expect temperature to be an

important influence because of the "Australian Open extreme heat policy", which requires

that no games commence once the Wet Bulb Globe Temperature exceeds 28°C while at the

same time the ambient temperature is greater than 35°C. With this in mind, should a very

hot day be anticipated the public may very well be discouraged from attending, or decide to

take advantage of the heat by, for example, going to the beach.

• Hat sales; one might expect both sunshine hours and temperature to be important influences

because of a consciousness of the risk of sunburn, skin cancer and heat stroke (Australia has

one of the world's highest rates of skin cancer).

• Windcheater sales; one might expect temperature to be the primary influence here on

account of there being no requirement for warm clothing once a particular temperature is

exceeded. However, sunshine hours would also be expected to play a role, because of the

sunshine ameliorating the impact of even very cool weather.

Once these relationships are established, they may be utilised by marketers to predict profits on a

day-to-day basis (determined using the official weather forecasts). This could allow the risk to be

minimised using a weather derivative. An example of such an application will be shown.

DATA

Weather information (2001-2004) was provided by the Australian Bureau of Meteorology.

Maximum daily temperature data and daily rainfall totals were measured at the Melbourne Regional

Office site (World Meteorological Organization (WMO) no. 94868), whilst daily sunshine hours

data was measured at the Melbourne Airport site (WMO no. 94866). Information on ground pass

ticket sales (2001-2004) and merchandise sales (2004) from the Australian Open was provided by

Tennis Australia. Data from days 1-8 of the tournament were chosen to be used for this study. After

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day 8, scheduling on the outside courts reduces significantly. This ensured the validity of any

relationships found.

The ground pass ticket sales information that was of particular interest were the walkup sales. The

raw walkup ground pass sales totals were expressed as a proportion of the total ground pass ticket

sales for analysis. Merchandise data was expressed as sales per total number of people attending the

Open on a particular day (per capita = no. of sales/total attendance).

RESULTS

The weather information was plotted against the sales information (both ground pass ticket and

merchandise) (Figures 1-2). Although the data set was not large, there were some clear

relationships evident. After conducting regression analysis on the data, a series of relationships

were derived, and these, and their significance using the t-test, are presented in Table 1.

Firstly, windcheater sales (per capita) showed a negative correlation with both sunshine hours and

maximum temperature data (Figures 1-2). It was expected that the sales of such an item would be

negatively correlated with both maximum temperature and sunshine hours. The relationship of

windcheater sales and sunshine hours proved to be significant at the 98% level (t-test), whereas the

relationship between maximum temperature and windcheater sales was not as strong. The fact that

sales increased when conditions were cooler and less sunny is not surprising. In addition, as the sun

can ameliorate the impact of lower temperatures, the relationship is not as strong with maximum

temperature. People tend to feel more comfortable on a 20°C sunny day than they would on a 20°C

overcast day. The day of most sales (per capita) had zero sunshine hours and the lowest maximum

temperature recorded during the period of data.

Table 1: Significance of relationships

Significance of

relationship

Maximum

Temperature (T)

Total Sunshine

Hours (S)

Relationship Equation

Windcheater Sales

(W)

56% 98% W = 0.0197-0.000989*S-0.000198*T

Hat Sales (H) 67% 70% H=0.0148+0.000354*S+0.000269*T

Walkup Gate sales

(G)

51% 7% G = +60.189-0.518*T+0.0325*S

Secondly, hat sales (per capita) were positively correlated with both sunshine hours (R

squared=+0.25) and maximum temperature (R squared=+0.23). The sun necessitates people to

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reduce the risk of sunburn and skin cancer by wearing a hat. High temperatures reminds people of

the sun – as it does not need to be particularly sunny for people to get badly burnt, especially if they

are sitting outside for prolonged periods. From the available data set, the lowest level of hat sales

also corresponded to the day with no sunshine and the lowest maximum temperature.

Thirdly, walkup gate sales (as a proportion of total ground pass ticket sales) were negatively

correlated to the maximum temperature. This relationship was highly significant (>95%) (Table 1).

This strong relationship may be, in part, related to the "Australian Open extreme heat policy". With

this in mind, should a very hot day (~38°C/100°F) be anticipated, the public may well be

discouraged from attending. Also, the prospect of sitting for long periods in high temperatures may

discourage people from attending the Australian Open. The relationship between gate ground pass

sales and sunshine hours was not significant.

y = 0.0003x + 0.0161R2 = 0.2296

y = -0.0004x + 0.0161R2 = 0.1453

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

0 5 10 15 20 25 30 35 40

Max Temperature

Per C

apita

Sal

es hats

w indcheaters

Linear (hats)

Linear (w indcheaters)

Figure 1: Merchandise sales vs. Maximum Temperature

APPLICATION

How this approach might be used in application is now addressed. For the purpose of illustration,

the relationship between Per Capita Windcheater Sales and Sunshine Hours & Maximum

Temperature is examined, namely:

Per Capita Windcheater Sales = +0.0197-0.000989(Sunshine Hours)-0.000198(Max Temp)

(R squared=0.74; n=8)

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In general, when estimating the demand for a service you look at the price variable. In this case we

have not taken into account the price variable.

y = 0.0004x + 0.0212R2 = 0.2498

y = -0.001x + 0.015R2 = 0.709

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

0 2 4 6 8 10 12 14

Sunshine Hours

Per C

apita

Sal

es

hatswindcheatersLinear (hats)Linear (windcheaters)

Figure 2: Merchandise sales vs. Sunshine Hours

The relationship has been developed using data from the Australian Open 2004 and will now be

discussed. Suppose that one wishes to compensate Windcheater marketers for poor sales on every

occasion when the per capita sales are below 0.25%. Also suppose that, when the per capita sales

are below 0.25%, $100 compensation (an amount that we have chosen quite arbitrarily) is

attributable for every 0.01% below 0.25%. This is a synthetic Put Option with a "strike" of 0.25%,

that is, a pay-off occurs whenever the per capita sales are below 0.25% on any particular day.

Applying the equation to 24 days of data from earlier years (2001-2003), there were only 3 days

when the per capita sales would have been below the "strike". For example, for Day-5 2001, the

per capita sales predicted, using the relationship derived, were 0.15% - this per capita value is

0.10% below the "strike", leading to a payout of $1000; for Day-6 2002 (0.09% - payout $1600);

and, for Day-7 2003 (0.21% - payout $400). This leads to a total payout of $3,000 over the 24 days,

and suggests a "fair-value" price of the option of $125 per day ($3000/24 days).

DISCUSSION

This paper has presented the results of a preliminary examination of the relationship between

weather and revenue generated for a number of products associated with the Australian Open, and

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the associated potential application of weather derivatives in ameliorating risks associated with the

revenue generated. Among the relationships found were:

- Ground pass ticket sales on the day of the event (as a proportion of total ground pass ticket

sales) were negatively correlated with maximum temperature.

- Per capita hat sales were positively correlated with both maximum temperature and sunshine

hours.

- Per capita windcheater sales were negatively correlated with both sunshine hours and

maximum temperature.

No useful relationships were found in regard to the influence of rainfall due to lack of rain during

the period of available sales data.

A "fair value" price of a synthetic put option with a strike of 0.25% was determined to be $125 (this

may be used to compensate Windcheater marketers for poor sales on every occasion when the per

capita sales are below 0.25%).

CONCLUSION

We have shown that there are relationships between the sales of various products and various

weather parameters associated with the Australian Open. Using a simple illustrative example, we

highlighted the potential of weather derivatives to guarantee the income associated with these

products. Future work may involve the application of some thermal comfort index.

ACKNOWLEDGEMENTS

The authors wish to gratefully acknowledge Tennis Australia's Cameron Pearson and Sarah

Clements for their advice, Dean Collins, Don Gunasekera, and Andrew Watkins for their helpful

comments, and Tennis Australia for making their data available.

REFERENCES

1. Geman, H. 1999. Insurance and weather derivatives. From exotic options to exotic

underlyings. Risk Books, 213.

2. Stern, H. 2001. The application of weather derivatives to mitigate the financial risk of

climate variability and extreme weather events. Aust. Meteor. Mag. Vol 50, September

2001.

3. Dawkins, S. S. and Stern, H. 2003. Cashing in on the weather: how agriculture might protect

against adverse seasons using weather derivatives. Australia New Zealand Climate Forum:

Climate Serving Agriculture, Palmerston North, New Zealand, 19-21 Mar., 2003.

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4. Stern, H. and Dawkins, S. S. 2003. Pricing a financial instrument to guarantee the accuracy

of a weather forecast. Third Conference on Artificial Intelligence Applications to

Environmental Science, Long Beach, California, USA 9-13 Feb., 2003. Refer also (for a

report on the presentation) to At the Annual Meeting. Pricing forecast guarantees. Bulletin

of the American Meteorological Society, March 2003, 84:325-326.

5. Stern, H. and Dawkins, S. S. 2004. Weather derivatives as a vehicle to realise the skill of

seasonal forecasts. 15th Conference on Global Change and Climate Variations & 14th

Conference on Applied Climatology, Seattle, Washington, USA 11-15 Jan. 2004.

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SPORTS TOURISM AND CLIMATE VARIABILITY

Allen Perry1

1. Department of Geography, University of Wales Swansea

E-mail address: A.H.Perry@swan.ac.uk

ABSTRACT

There is evidence that sport and activity holidays are growing in importance and forming an

increasingly important segment of the holiday market. More leisure time and affluence could lead to

an increasing demand for sporting facilities. Provision of facilities, such as golf courses, requires an

estimation of future climate. To date there has been a concentration of research activity on the effect

of climate on winter sports activities, such as skiing, in part because of the importance of winter

sport for mountain areas. However, it can also be shown that environmental change, for example in

river valleys affecting fishing and boating activities, could be highly damaging to local economies.

The purpose of the paper is to review the whole field of sports tourism and climate variability and

suggest where research activities might usefully be concentrated. Developing a research agenda and

identifying priorities is a pre-requisite to making progress in this hitherto neglected area.

KEYWORDS: Sports, Climate, Climate Change, Tourism

INTRODUCTION

It is timely in this year of the Athens Olympic Games, and following the publication of the World

Meteorological Organization study ‘Weather and Sport’ (1) to consider how climate variability

might affect sports tourism. Since most sporting events take place outdoors they are subject to the

possibility of disruption, delay, postponement or cancellation due to adverse weather. Whether it is

participating or going to watch professional sporting events, climatic conditions can affect the

enjoyment and safety of the participants and the commercial viability of an organized event.

Forecasts of bad weather make many less committed fans reluctant to watch an event in an open

stadium or uncovered stands. In addition, quality of play can be diminished by bad weather on the

playing field.

There has been a big increase in the importance of sports tourism, activity holidays and “soft

adventures”. In the UK, John Hall, chairman of Gulliver Sports Travel, suggests that this surge in

interest is indicative of wider changes in the way people travel. Sports tourism is no longer a male

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only preserve, with the number of couples and families travelling on this kind of holiday increasing.

The term ‘sports tourism’ has been defined as “travel away from home to play or watch sport or to

visit a sports attraction and including both competitive and non-competitive activities” (2). It might

be said to comprise two main areas:

1) Participation in individual or team sporting events. Whilst sport is a major element of recreation,

many regular participants of sports seek to enjoy their sport in a different setting while on holiday.

For example, they may seek to play on a different golf course, or to devote more time to their sport.

For many people a holiday is also seen as an excellent opportunity to try out a new sport, perhaps

one that is water-based and not available at home, or to learn a new skill (e.g. deep sea diving). 2) Sport spectator travel. This may be to regular annual events (such as the Dubai World Cup,

Monaco Grand Prix, French Open Tennis, Six Nations Rugby, Ryder Cup, etc.) or to occasional

events (such as the Olympic Games, Rugby World Cup, International Cricket Tours, etc.). Fans

want to see not just the sporting spectacle but also their heroes in action. Audience participation in

sport certainly extends back to the Ancient Greeks.

PREVIOUS RESEARCH

One of the first overviews of the subject was published in Poland and later translated into German

(3). Although it does not cover such popular sports as golf, Table 1 suggests that for activities

ranging from sailing and rowing to skiing and football, wind and temperature are the most

influential climate parameters. Paul (4) in Canada looked at how weather affected the daily use of

outdoor recreation areas and found a series of relationships between participation rates in various

sports and weather parameters. Multiple regression techniques have been used by Illingworth (5) to

predict attendance at premier division football matches in the UK and by Thornes (6) for an open-

air swimming pool. To date there has been a concentration of research activity on the effect of

climate and climate change on winter sports activities, such as skiing; this is because of the obvious

marginality of such activities in areas where they are currently popular. The recent announcement

of the closure of two of the five Scottish ski resorts as a result of financial failure is a reminder that

climate change could already be affecting the financial health of the leisure industry. Because there

have been several reviews of the possible impacts of climate change on the winter sports industry in

various parts of the world, winter sports industries will not be considered in detail in this review.

Many individual sports have attracted a considerable literature, for example climbing and hill

walking (7, 8), sailing (9), and marathon running (10). Thornes (6) recognised that there could be a

division into specialised weather sports. These sports are dependent on certain weather conditions to

take place at all, such as gliding or sailing. Weather interference can also be a major factor in sport,

where weather can afford an advantage to one side of the competition over the other. For example,

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wind can affect ball play in a soccer or rugby match, leading to an unequal advantage. One of the

most comprehensive studies of the impact of adverse weather on a range of sports in a particular

country was Kay and Vamplew’s (11) study for the UK.

Table 1: Subjective Assessment of Influence of Meteorological Parameters on a Range of Sports Sport Air Pressure Temperature Wind Precipitation Fog Sailing 1 4 5 3 4 Ice Sailing 1 5 5 4 3 Rowing 2 4 5 3 1 Canoeing 2 4 5 3 1 Downhill Skiing 1 4 3 5 4 Spring Skiing 1 4 5 5 5 Cross Country Skiing 2 5 4 5 2 Bob-sleighing 1 5 3 4 4 Ice Hockey 2 5 - - - Ice Skating 2 5 3 4 1 Swimming 1 5 4 1 1 Aerial Sports 2 5 5 1 4 Football 1 2 4 5 1 Parachuting 2 3 5 4 4 Cycling 3 3 5 4 1 Athletics (Jumps) 2 4 5 3 1 Athletics (Track) 3 3 5 3 1 Athletics (Field) 3 1 5 2 1 Archery - 3 5 3 4 Shooting 1 1 5 3 4 TOTAL POINTS 33 75 86 65 47 Key to POINTS: 1 – slight; 2 – little; 3 – noticeable; 4 – important; 5 - large A major theme in weather and sports has been player comfort. Most sports have high activity levels

and exercise changes the heat transfer within the body and on the skin surface. An early example of

this research looked at heat production and loss in a man playing squash. In order for an athlete to

perform to their top ability they must be within their thermal comfort zone. Weather indices can

provide information on the effects of single or multiple environmental elements on the human body.

For example, the Heat Index, Windchill index and the principals involved should be extended to

produce more suitable indices relating to sport.

RESEARCH AGENDAS

Much of the existing research has focused on climate change in specific geographical locations or

milieus, such as mountains. A number of further research avenues that could produce useful

information include:

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1) Changes in sports preference. Substitution of activities might occur as climate changes, along

with extension or contraction of the season over which the sport is played. In general it could be

assumed that warmer conditions in summer might favour more water-based sports and recreation.

There could be a need for more outdoor swimming facilities, or for current facilities to remain open

for a longer season. Such warmer summers might also dissuade athletes from using indoor leisure

centres unless there was more widespread air conditioning. The economics of running and operating

leisure centres could thus change. From another perspective, specific threshold weather conditions,

both permissive and prohibitive, are applicable to specific sporting activities. Quite often these

thresholds are merely the subject of value judgements by individuals. Each sport is affected by a

particular level of severity of weather. Even apparently low risk recreational pursuits, such as hill

walking, can be dangerous in certain conditions, and the frequency of occasions when an activity

becomes dangerous may well change over time. Therefore any change in the frequency of events,

which themselves may not be severe, could have implications for safety. For group and team

activities specialist staff, such as grounds crew or referees, may need to make decisions on behalf of

players; weather impacts both on turf and soil, and hence the playability of those surfaces.

2) Cold-weather sports dependent on a grass turf surface could benefit. They could find that less

disruption from snow and ice would result in less postponements and abandonment of fixtures so

that the economics of sports like horse racing could benefit. Lengthy and expensive shut-downs,

abandonment and suspensions can not only play havoc with fixture lists, but also result in huge

financial losses. Often it is the quality of play on the field that is diminished by poor weather. Mud-

soaked fields, blizzards, fog and strong winds affect the style of the game, the degree of athletic

control and the overall performance of the players.

3) Summer sports. Cricket and outdoor tennis are particularly weather sensitive. Rain, poor lighting

and the state of the pitch or court are highly influential. Both sports, when played professionally,

tend to be lengthy. For example Test matches and Wimbledon are spread over several days or

weeks, and thus there is considerable potential for disruption. Heat can upset players and spectators

in crowded stadiums, whilst drought can influence the playing surface. It is an interesting paradox

that while sport is perceived as a healthy activity, more people are killed in developed countries

from severe weather events like lightning, while engaged in outdoor activities such as golf, than in

almost any other activity. If convective summer storms become more severe or more frequent this

toll could rise. Similar storms, leading to flash flooding, could make extreme sports like potholing,

kayaking and rafting more dangerous. How can we provide better forecasts for sports participants

and do we need shelters on courses?

4) Deterioration and destruction of sports infrastructure. Many sports facilities, such as race tracks,

(in the UK at Newton Abbot, Windsor, Worcester and Perth) and athletic stadiums are situated in

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flood prone areas. Climate changes that might include more winter rainfall could mean periods

when flood plain resources are unusable. Coastal golf links are subject to coastal erosion

accelerating as a result of rising sea levels. Perry (12) reported that in the UK the Royal West

Norfolk Golf Course spent £165,000 in 1990 to improve sea defences. Re-alignment of the coast

could impact further on coastal courses. The costs of upkeep of sports facilities could change with

changes in site drainage and irrigation needs, in both winter and summer. Operational decision-

making on course and pitch maintenance will need to be reviewed. Expertise by sports professionals

in this area (e.g. the golf industry) should be passed on to others involved in turf management, such

as park and garden management.

5) The economics of weather-proofing, including stadiums with retractable roofing, and pitch

protection systems, could change. As Taylor (12) suggests, the more sophisticated the weather-

proofing, the more expensive the cost of the proofing. These costs would mirror the scale of the

environmental stress which is being countered. In addition, the economics of using artificial grass

and under-pitch heating systems could also change.

6) Sports gear design. Opportunities exist in the design and use of new materials for sports wear

that helps to maintain body micro-climate. With higher temperatures there will be a need for more

lightweight clothing suitable for exercising in warmer conditions.

7) Narrow-margin sports. Conditions are most critical when the margin of victory or defeat narrows

to hundredths of a second. This would apply to sports such as athletics and motor racing. Changes

in conditions can be extremely influential in such sports and winning may come down to

anticipating the most likely climate conditions when training for an event, and studying the actual

weather when the event takes place. Climate change could affect the future likelihood of breaking

world records in such sports.

8) Fauna and flora. Some sports involve fauna and flora in the natural environment. Climate

change that alters the balance or number of species could have an impact on sport. Examples would

include fishing (a very popular sport in many industrial countries), and hunting. Marine tourism

would also fall into this category if there are changes to the underwater environment.

CONCLUSIONS

Engaging in and watching sporting events is likely to grow further in popularity as the population

ages but becomes more health conscious and wishes to stay fit and trim. Climate change will offer

threats and opportunities to the whole sports provision industry as changes develop in the supply

side. Tastes and preferences of the public are likely to change, and there will be a considerable need

to monitor and to anticipate what facilities are likely to be most in demand.

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Already there is a lot of activity in this area, but it has not been drawn together into a cohesive

picture of how climate change will influence the sports industry as a whole. There is a need for

climatologists to collaborate with the new and developing field of sports science to initiate and to

develop a research agenda. Sports planners and organisers represent an important group of

stakeholders who are often unaware of how changes of climate could impact sport. Sports tourism

can be seen as an important sub-set of tourism; it is growing rapidly, and offers some interesting

research areas.

REFERENCES

1. WMO. 2003. Weather and Sports. (Geneva, Switzerland).

2. Hudson, S. 2003. Sport and adventure tourism.(Haworth Hospitality Press, New York).

3. Lobozewicz, T. 1981. Meteorology in Sport. (Sportverlag, Berlin).

4. Paul A.H. 1972. Weather and the daily use of outdoor recreation area in Canada, edited by

Taylor J.A. (Weather Forecasting for Agriculture and Industry, David and Charles, UK)

132-146.

5. Illingworth, J. 1977. Whether to weather the weather or not. B.A. undergraduate

dissertation, Dept of Geography, University College, London.

6. Thornes J.E. .1977. The effect of weather on sport. Weather. 32:258-268.

7. Pedgley D.1979. Mountain weather:a practical guide for hillwalkers and climbers in the

British Isles. (Milnthorpe, Cumbria UK, Cicerne Press).

8. George, D. 1993. Weather and mountain activities. Weather. 48:404-410.

9. Houghton, D. 1993. Winds for sailors. Weather. 48: 414-19.

10. Spellman, G. 1996. Marathon running-an all weather sport? Weather. 51:118-25.

11. Kay, J. and Vamplew, W. 2002. Weather Beaten: sport in the British climate. (Mainstream

Publishing, Edinburgh).

12. Perry, A.H. 2001. Tourism and Recreation. ACACIA Report chapter 12, University of East

Anglia. 217-226.

13. Taylor, J.A. 1979. Recreation weather and climate. SC/SSRC Panel: State of the Art Review

Sports Council, University of Wales, Aberystwyth.

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DEVELOPING AN OPERATIONAL SYSTEM TO SUPPORT TOURISM ACTIVITIES IN

TUSCANY REGION

D. Grifoni1, G. Messeri3, M. Pasqui1, A. Crisci1, M. Morabito2, B. Gozzini1, C. Tei3, G. Carreras1,

M. Rossi3, F. Pasi3, G. Zipoli1

1. Institute of Biometeorology, CNR, Via Caproni 8, 50145, Florence, Italy

2. Interdepartmental Centre of Bioclimatology – University of Florence

3. Laboratory for Meteorology and Environmental Modelling (LaMMA)

E-mail address: grifoni@lamma.rete.toscana.it (D. Grifoni)

ABSTRACT

The climate of a region, together with its historical and geographical characteristics, is one of main

factors considered by an individual tourist in planning a holiday to a specific region, and weather is

one of the main factors affecting daily activities while on vacation. On the other side, climate data

assumes a very important role in the activity of tourist planner and tour operators.

The Laboratory for Meteorology and Environmental Modelling for the Tuscany region (LaMMA,

operated by the Institute for Biometeorology-CNR; www.lamma.rete.toscana.it) currently issues a

suite of products to provide the regional authorities with notice of environmental conditions: for the

protection of people and property, and more generally to support and plan outside activities

(tourism, agriculture, and so on). Such information varies from detailed classical weather forecasts,

to the presentation of bio-meteorological indices (i.e. empirical indices, such as the Heat Index, the

new Wind Chill Temperature Index, the UV index, and indices based on the energy balance of

human body, such as PMV and PET, to climatological data. In this paper some of these products,

mainly based on elaborations performed by the Regional Atmospheric Modelling System (RAMS),

operational at LaMMA, are shown.

KEYWORDS: Tourism, UV Index, Bioclimatology, Weather forecasts, Bulletin

INTRODUCTION

At global scale, tourism is one of the faster-growing economic sectors today. Six-seven hundred

million people travel yearly throughout the world, and this is expected to reach 1.6 billion by 2020.

The World Tourism Organization has estimated that tourism receipts account for some 25 percent

of total export earnings in the Pacific, and over 35 percent for Caribbean islands. Tourists hope to

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have a period of relaxation, but often they can be stressed if they are unfamiliar with their

destination’s language, traffic patterns, customs and, of course, climate. They often have little

information about climatological conditions at the destination area. In addition, tourists at the

destination location are, generally, not well informed about the probable weather conditions for

their vacation period, making correct activity planning difficult.

The knowledge of climate and weather is also assuming a strong relevance in the field of business

planning and decision making for the tourism industry, as financial returns are directly dependent

on them. In fact, environmental information is widely available, but generally not in a form useful

and relevant for the end user whom must have incentive, technical skill, and intellectual capacity to

use the information effectively (1).

The relevance of the problem has induced authorities to support research activities in the field of

climate, bioclimate and weather products for tourism; recommendations have been made to improve

specific travel information related to climate and human focused tourist needs (2). For example,

some weather services, in addition to their traditional activity of climate analysis and weather

forecasts, have focused their activity on the development of several biometeorological indices to

provide information on human discomfort conditions, such as the Apparent Temperature Index (3,

4), the new Wind Chill temperature Index (5), thermal indices derived from the energy balance of

the human body (6, 7, 8), UV index to classify the potential risk from sun exposure (9), and so on.

The main work of international centers is now to develop simple products for planners, tourist

operators, the tourist sector generally, and for the people making possible the creation of a correct

‘climate image’ of a tourist destination.

The Tuscany region represents a very interesting area from a tourist point of view, and recently it

has been visited by a mean of 38 million tourists per year, half of them coming from all over the

world. The tourists come to Tuscany for sea (15,5 million), for culture and affairs (13 million), for

thermal centers (4 million), for agri-tourism (2,5 million), for mountains (1 million) and for some

other interests (2 million). In this paper the activity developed at LaMMA-IBIMET, in the field of

tourism, is presented both in terms of climate and weather information useful for individual tourists

in planning and experiencing their holiday, and for tourist planners and tour operators in their

operational decision-making, investment decisions, marketing, and so on.

METHODS

Below are described the main tools and scientific approaches used at LaMMA to develop products

useful in the field of tourism activity.

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RAMS (Regional Atmospheric Modeling System)

RAMS is a regional model constructed around the full set of non-hydrostatic, compressible

equations for atmospheric dynamics and thermodynamics (10), plus a large selection of

parameterizations for turbulent diffusion, solar and terrestrial radiation, moist processes, cumulus

convection, and energy exchange between the atmosphere and the surface through vegetation.

In RAMS a grid nesting is used to provide a high horizontal resolution in a selected area. The grids

may be configured as a telescoping sequence with several nested grids.

RAMS includes:

- a surface model (Land Ecosystem Atmosphere Feedback - LEAF) which evaluates fluxes of

energy, water vapour, and momentum between atmosphere and surface, solving heat and water

balance equations for multiple soil layers, multiple snow cover layers, vegetation, and canopy air.

- a cloud microphysics scheme which is a bulk microphysics representation of each hydrometer

category, and it provides the best compromise between accuracy and efficiency for most model

applications.

Recently the availability of Reanalysis data (from 1948 up to the present) made possible the use of

RAMS as a climatological instrument. The RAMS in the operational configuration runs twice every

day, initialized from GFS at 00 UTC and 12 UTC. In this configuration two nested grids are

present:

- the outer coarser grid with a horizontal resolution of 32 km on the Mediterranean Sea ;

- the inner grid with 8 km horizontal resolution on Italy.

Sea surface temperatures are derived from NOAA-AVHRR satellite observations at the resolution

of 4 km. Model outputs are shown on the web page as maps, are used as input data for the

elaboration of additional products, and are used by weather forecasters to develop weather bulletins.

Wave model (WW3 model)

Wind outputs from the RAMS model are used as input data by the wave model WaveWatch 3 (11),

which runs daily over the Mediterranean sea. The model output are shown on the web page, and are

used by weather forecasters to develop sea bulletins.

Biometeorological Indices

Empirical indices

At LaMMA, procedures for assessment of discomfort conditions both during hot periods (by using

the Heat Index, taking into consideration the combined effect of air temperature, relative humidity

and wind speed) and cold periods (by using the new Wind Chill Temperature Index (5)) are applied

to RAMS outputs to forecast human discomfort conditions.

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Thermal indices

A further development at LaMMA will be the forecast of human thermal perception by using more

complex biometeorological indices, based on models on the energy balance of humans, such as the

Predicted Mean Vote (PMV) (12) or the Physiological Equivalent Temperature (PET) (8). These

indices take into consideration both atmospheric and personal variables. These methodologies are

very important because at any given air temperature the thermal conditions experienced will vary,

depending on the relative influence and often offsetting effects of wind, humidity, solar radiation,

clothing and level of a person’s activity (1).

UV Index

The UV Index is recommended by the World Health Organization (WHO), together with other

international organizations, as a vehicle to raise public awareness about the potential detrimental

effects on health from solar UV exposure, and to alert people of the need to adopt protective

measures (9). At LaMMA, UV Index forecasts are presented daily for altitude effect at 1 km x 1 km

resolution, the basic UV Index forecast valid for mean sea level and clear sky conditions produced

by Deutscher Wetterdienst (Large Scale UV Index). UV Index forecasts considering cloud amount

forecasted by the RAMS model will also be developed applying cloud attenuation factors to the

clear sky forecast. In addition, UV radiation is monitored in terms of the UV index by means of two

broadband radiometers (erythemally weighted) located in Florence (Lat. 53°.8 N, Lon. 11°.20 E)

and Livorno (43.55 Lat., Lon.10°.30).

PRODUCTS AND DISCUSSION

Climatology

The Tuscany region’s climatology has been elaborated for selected sub areas and locations using

data collected by a regional network of meteorological stations. Rainfall and temperature spatialized

data (elaborated by means of spatialization methods) are available for all of Tuscany; in addition, an

annual temperature-rainfall diagram is available for the major cities (Figure 1).

One of the main problems in climatology is the number of meteorological station available over the

territory, which generally are not sufficient to reach a good representation of the climate for specific

sub areas or locations. To avoid such problems, recently at LaMMA-IBIMET an activity was

started to obtain climate data by means of the RAMS model using Reanalysis data; such data will

permit more accurate climatology for Tuscany, and for the areas over the sea. Such information,

together with classical observed data, could be used to better meet the needs of the tourism industry:

to promote tourism in locations characterized by particular climate conditions (for example

particularly snowy, windy, mild, and so on, in connection with possible sport activities or wellness

184

centers); to better individuate the length of the tourist season and consequently optimize resources;

to better perform publicity over depliant - not limiting information to average values which are

difficult to translate into real conditions, and generally cause unrealistic tourist expectations; to

evaluate the correct number of snow making systems over a ski resort; and so on. In addition, the

assessment and the forecast of climate change will permit the tourism industry to take more

appropriate policy in terms of investment, to better react to environment change and the planning of

future tourist destinations.

Figure 1: Example of climatological data available for Tuscany: Florence annual temperature-rainfall

diagram; winter mean temperature for Livorno’s area

Weather bulletins

Three type of weather bulletins are routinely developed by weather forecasters at LaMMA: general

bulletins, mountain bulletins, and sea bulletins. General bulletins (Figure 2 a) are elaborated twice a

day, and include a weather forecast for up to the next two days, with textual and graphical

information displayed over a map of the Tuscany region. The general tendency for the next period

is also presented.

Mountain (Figure 2 b) and sea (Figure 3 a) weather bulletins are elaborated once a day, and include

forecasts for the next two days. They are focused, respectively, on selected mountains (mainly ski

or summer resorts) and coastal areas. In addition, automatic weather forecasts are also developed

directly by the RAMS output for several Tuscany sub-areas or locations. UV Index forecasts are

daily produced for the next three days both in terms of maps and in terms of tables containing

185

forecasted UV Index values for the main locations (Figure 3 b). Biometeorological indices are also

provided by daily regional maps, referred to several hours along the day (Figure 4).

Figure 2: Example of bulletins: left) general bulletin; right) mountain bulletin

Figure 3: left) Sea bulletin; right) UV Index forecast: clear sky conditions forecast over central Italy

(map) and UV Index over selected locations (for clear sky and for forecasted cloudy

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Figure 4: Examples of Wind Chill Temperature Index (left) and Heat Index (right) maps. The colors

indicate the discomfort degree

Such information is diffused to the public by means of the internet, radio, television, and e-mail to

make possible the planning of outdoor activities (sports, naturalistic or cultural excursions, travel,

etc.), and, in the case of non-optimal conditions, to find possible alternative activities (which in any

case induces the tourist to a good evaluation of the vacation, particularly if the possibility of such

non-optimal conditions were previously considered by the tourist planner in the selection of the

location). Also, communicating this information permits a good holiday without health problems,

particularly for people who are sensitive to environmental parameters.

CONCLUSIONS

Climate and weather data assume a very important role for individual tourists, for tourist planners,

and for the tourism industry in general – all need to know the ‘Climate Image’ of a vacation

destination. The knowledge of climate, together with some other specific characteristics, will

determine an areas tourist potential, which has generally been assumed to be self-evident and

therefore not requiring elaboration.

The continuous development of tourism, and its relevance in the economy of many countries, makes

necessary a more specific knowledge of the possible impact of weather and climate on the

economic aspect of tourism. It is in this context that at LaMMA-IBIMET researchers are involved

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to improve the RAMS model performance (13, 14), and to develop more appropriate operational

products for tourism activities.

ACKNOWLEDGEMENTS

This work was supported by the project DOCUP founded by the EU. We thank Carlo Brandini for

the WW3 operational configuration, and Francesco Sabatini and Luca Fibbi for the UV radiation

monitoring.

REFERENCES

1. de Freitas G. R. 2003. Tourism climatology: evaluating environmental information for

decision making and business planning in the recreation and tourism sector. Int. J.

Biometeorol. 48:45-54.

2. WMO. 1995. Report from the meeting of experts on climate, tourism and human health (Topes

de Collantes, Cuba, 22-29 January. WMO/TD-No. 682, WMO, Geneva.

3. Steadman, R.G. 1979. The assessment of sultriness. Part I: A temperature-humidity index

based on human physiology and clothing science; Part II: Effect of wind, extra radiation

and barometric pressure on apparent temperature. J. of Appl. Meteorol. 18:863-885.

4. Steadman, R.G. 1984. A universal scale of apparent temperature. J. of Clim. and Appl.

Meteorol. 23:1674-1687.

5. Osczevski, R. and Bluestein, M. 2001. Meeting della Jag/Ti a Toronto, www.nws.noaa.gov

6. Höppe, P. 1999. The physiological equivalent temperature - a universal index for the

biometeorological assessment of the thermal environment. Int. J. of Biometeorol. 43:71-75.

7. Matzarakis, A. and Mayer, H. 1996. Another kind of environmental stress: Thermal stress.

WHO News. 18:7-10.

8. Höppe, P and Mayer, H. 1987. Thermal comfort of man in different urban environments.

Theor. Appl. Clim. 38:43-49.

9. Vanicek, K. et al. 2000. UV-Index fort he public. EUR - COST Action 713, WMO.

10. Pielke, R. A. et al. 1992. A comprehensive meteorological modelling system-RAMS.

Meteor. Atmos. Phys. 49:69– 91.

11. User manual and system documentation of WAVEWATCH-III version 1.15. NOAA / NWS

/NCEP / OMB Technical Note 151, 97 pp

12. Fanger, P.O. 1972. Thermal Comfort Conditions, a new rational basis for the heating and air

conditioning technology, Danfoss News.

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13. Meneguzzo, F. et al. 2004. Sensitivity of meteorological high-resolution numerical

simulations of the biggest floods occurred over the arno river basin, Italy, in the 20th

century. J of Hydrology, 288:37-56.

14. Soderman, D. et al. 2003. Very high resolution precipitation forecasting on low cost high

performance computer systems in support of hydrological modeling. Prepr. 17th Conf. on

Hydrology, AMS, Long Beach.

189

THE EFFECTS OF WEATHER ON FREQUENCIES OF USE BY COMMUTING AND

RECREATION BICYCLISTS

Christiane Brandenburg1, Andreas Matzarakis2 and Arne Arnberger1

1. University of Natural Resources and Applied Life Sciences – BOKU, Department of Spatial-,

Landscape-, and Infrastructure-Sciences, Institute for Landscape Development, Recreation and

Conservation Planning, 1190 Vienna, Austria

2. Meteorological Institute, University of Freiburg, 79085 Freiburg, Germany

E-mail addresses: christiane.brandenburg@boku.ac.at (Christiane Brandenburg),

andreas.matzarakis@meteo.uni-freiburg.de (Andreas Matzarakis)

ABSTRACT

In this paper we investigate the effect of daily weather conditions on the frequency of road

bicycling in recreation areas in Vienna. We also compare the weather effects on recreational biking

and biking for commuting. A series of linear regression analyses, with the daily frequency of

recreation or commuting bicyclists as the dependent variable, and precipitation and the

Physiological Equivalent Temperature (PET) thermal comfort index as independent variables, show

that for both user groups the number of bicyclists is influenced by both independent variables, but

that recreational bicyclists are more sensitive to weather conditions than are commuters.

KEYWORDS: Weather, Bicycling, Recreation, Commuting

INTRODUCTION

Bicycling is a highly sustainable means of transportation and is used for both commuting and

recreation purposes. Over the last two decades, the popularity of bicycling has increased

tremendously among all age groups in most developed economies (1). However, this revival of

bicycling has been mainly associated with recreational uses, in particular new forms of bicycling

such as mountain biking, while commuting to work has not increased at the same rate.

The increasing numbers of bicyclists, their speed, and their spatial and temporal distribution may

create conflicts with other user groups, or may lead to environmental impacts. Consequently,

demands on recreation management as well as transportation planning in general are increasing (2,

3, 1). Well informed managers and planers require high quality investigations and data. However, in

most locations adequate data are scarce (4), and the field can benefit from improved analytical

190

techniques. Fundamental to such improvements are a solid data base of counts and observations

based on surveys.

The relationship between human behaviour and weather and climate conditions is intuitively

obvious and has been acknowledged for a long time (5). Numerous studies exist on wide-scale

climatic evaluation, like the relationship between weather and tourism and on recreation from a

medical point of view (6, 7, 8). Some studies have focused on thermal comfort and human activities

(9, 10, 11, 12, 13, 1). In transportation planning only a handful of studies have investigated the

various factors (i.e. temperature, precipitation, wind, the combination of weather and time of year,)

influencing road bicycling (14, 15, 4, 16, 17, 1). Nankervis (1) concluded that heavy rain is the most

powerful deterrent for bicycling. The most common conclusion has been that recreational cycling is

affected by variations in weather more than commuting by bicycle. One of the major problems with

most of these previous studies on the relationship between weather and bicycling has been that the

underlying data has not been very reliable. Hanson and Hanson observed this in 1975, and the

situation has not improved significantly since.

This paper focuses on the affects of varying weather conditions on leisure-time bicycling and

commuting by bicycle. Variations in the impact of weather can be related empirically to different

user characteristics in order to enable a greater understanding of the determinants of the day-to-day

variations in bicycle use in the areas of investigation.

In particular, the effect of thermal comfort and precipitation will be investigated, as these two

measures represent the seasonal climate variations and the extremes of the day-to-day variability.

Other crucial meteorological parameters (i.e., air temperature, clouds) are included in the models

indirectly via the Physiological Equivalent Temperature (PET), a thermal comfort index.

Data was collected in two recreation areas in the metropolitan area of Vienna: the Wienerberg Park

located in the south of the city, and the Marchfeldkanal to the north-east of the city. The areas are

used for both recreation and commuting to school and work. The respective authorities managing

these parks have observed steadily increasing use levels leading to more evidence that both the

ecological and social carrying capacities might be exceeded.

METHODS

At the entrance-points to the recreation areas, permanent time-lapse video recording systems were

installed to monitor recreational activities (3, 18) from dawn to dusk, over a one year period (the

type of video system installed did not allow the identification of individual persons, thus

guaranteeing anonymity). When analysing the video tapes, the following data were registered: date,

day of the week, time, video station, number of persons in the groups, direction of movement, type

of user-group (bikers, hikers, joggers, ....), and number of dogs. Based on on-site interviews,

191

information about the visitor structure and the motivation for visiting the areas was collected (19,

20).

The two types of users, leisure-time bicycling and commuting by bicycle, were identified from the

hourly use patterns of workdays. In both areas a distinct peak in the mornings of workdays, as

opposed to weekends, was apparent (Fig. 1). Therefore we identified the bikers monitored during

the morning peak time, i.e. seven to nine a.m., on workdays as commuters, and bikers monitored

after nine a.m. on workdays as recreational bikers.

This is of course a crude simplification, but we have further confirmation that very few recreational

bicyclists use the early morning hours from visitor monitoring data in the Viennese part of the

Danube Floodplains National Park, which is a recreation area without any commuting. In the

afternoon an equivalent separation of recreational and commuting bicyclists is impossible because

of the more wide-spread time of returning from school or work and the presence of recreational

bicyclists.

For the analysis the dependent variable was the number of recreational or commuting bicyclists per

day, and the dependent variables were meteorological measures such as precipitation and the

Psychological Equivalent Temperature (PET), which incorporates both meteorological and thermo-

physiological parameters (12, 21). The meteorological data were obtained from the closest

meteorological stations of the Austrian Central Institute for Meteorology and Geodynamics. The

values of the PET thermal index were calculated using the radiation and bio-climate program

RayMan (22). The input values for the RayMan model consist of air temperature (°C), vapour

pressure (hPa), wind speed (m/s), cloud cover (in 1/8), global radiation (W/m2), human activity

(W), and clothing insulation (clo).

RESULTS

In the following, the results of the relationship between the current weather and the behaviour of

recreational bicyclists and commuting bicyclists in the recreational areas Wienerberg and

Marchfeldkanal are shown (Figure 1). We identified a total of approximately 12% commuting

bicyclists in the Wienerberg Park, and around 10% in the Marchfeldkanal recreation area.

The relationship between the PET thermal index and the two bicycling types shows that both can be

encountered when the weather conditions are pleasant (Figure 2). However, it becomes apparent

that bicycling for the purpose of commuting is less sensitive to cooler weather conditions than

bicycling for recreation. The differences between the two purposes during the warmer weather

period may be the result of other external factors, such as school and public holidays.

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0

2

4

6

8

10

12

14

16

18

6 7 8 9 10 11 12 13 14 15 16 17 18 19Time

Num

ber o

f Bic

ylis

ts /

Per

cent

age

WorkdayWienerbergWeekendWienerbergWeekendMarchfeldkanalWorkdayMarchfeldkanal

Figure 1: Daily courses of bicyclists in the recreational areas, differentiated by workdays and

weekends

0

5

10

15

20

25

very

cold

cold

cool

sligh

tly co

ol

comfor

table

sligh

tly w

arm warm hot

very

hot

Categories of the PET

Per

cent

age

shar

e of

the

bicy

clis

ts MarchfeldkanalCommutingMarchfeldkanalRecreationWienerbergCommutingWienerbergRecreation

Figure 2: Connection between the PET thermal index and both bicycling types in the recreation areas

193

Next, we distinguish between the two precipitation categories – with, and without, precipitation.

Following Harrison et al. (23), a day with precipitation is defined as any day receiving at least 1 mm

of precipitation. Investigations on the difference between the influence of precipitation on the

behaviour of both types of bicyclists clearly show that, if it is raining, approximately 10% more

commuting bicyclists are on the move compared with recreational bicyclists (Figure 3).

0

10

20

30

40

50

60

70

80

No precipitation Precipitation

Per

cent

age

shar

e of

the

bicy

clis

ts

Wienerberg CommutingWienerberg RecreationMarchfeldkanal CommutingMarchfeldkanal Recreation

Figure 3 Relationship between precipitation and both bicycling types in the recreation areas

In the next step, the effect of weather on recreation bicyclists and commuting bicyclists was

investigated with a general linear model. With the meteorological parameters precipitation

(categorized in presence/absence of precipitation) and PET thermal index the influence of the

current weather conditions enters the modeling as independent variables. This regression is based

on workdays only. Because of the low significance of the interaction between the PET and the

precipitation for the commuting bicyclists in the Marchfeldkanal recreation area we calculated this

particular model without the mentioned interaction.

The variance explained by the various models is lower for the commuting bicyclists than for

recreation bicyclists for both recreation areas (Table 1). Obviously, commuting bicyclists are less

weather-sensitive. The significance of the independent factors PET and precipitation are high. The

significance of the interaction between the PET and precipitation is much lower, and insignificant in

one situation. The power of influence of the independent factor is indicated by the eta square value.

194

The PET has the highest influence. The presence or absence of precipitation has a very low

influence on the behaviour of bicyclists in the recreation areas. In our investigation, the relationship

between the number of bikers and the PET is, therefore, much more meaningful in contrast to the

results gained by Richardson (24). He observed that rainfall has a more significant effect on

bicycling behaviour than temperature.

Table 1: Effects of weather on participation in bicycling (all models significant at the p < .05 level),

significance levels and eta square of fixed factors – during workdays

Wienerberg Commuting

Wienerberg Recreation

MarchfeldkanalCommuting

Marchfeldkanal Recreation

R2adj .595 .782 .540 .632

Significance of PET .000 .000 .000 .000

Significance of Precipitation .000 .000 .052 .000

Significance of PET * Precipitation .029 .116 ns .198

Eta Square PET .434 .673 .528 .368

Eta Square Precipitation .041 .078 .017 .044

Eta Square PET * Precipitation .072 .055 ns .051

DISCUSSION

The analysis above has produced a strong relationship between current weather conditions and both

recreation bicycling and commuting bicycling. Participation in bicycling depends not only on actual

meteorological variables, such as precipitation, but also on human thermal perception, as expressed

by the PET. Furthermore, the results of the linear models indicate a differentiation between

recreation bicycling and bicycling for commuting. The recreation bicyclists are more affected by the

current weather conditions than are commuting bicyclists. The differences observed for recreation

bicyclists in the two study areas should be interpreted as a function of their different areas of origin.

About 80% of the visitors to the Marchfeldkanal area origin from within a walking distance of 15

minutes, which is equivalent to a distance of less than 5 minutes bicycling. For them it is possible to

return home promptly if the weather turns worse. Therefore, these people visit the recreation areas

even if at the time of decision the weather is doubtful. The short distance to their homes also makes

it possible to stay for a shorter time, even when it is very cold or slightly rainy. On the other hand,

only 60% of visitors to the recreation area Wienerberg live within a walking distance of up to 15

minutes. Therefore, the visit lasts somewhat longer (20) and, as a consequence, the dependence on

the weather is higher.

195

The thermal perception and the implementation of more meteorological parameters of the current

weather and, additionally, the memory or history of thermal perception – perhaps one week – would

explain, with a higher significance level, the correlation between the number of different types of

bicyclists and the combination of bio-climatic conditions. In future studies we will investigate the

relationship between the current bio-climatic and meteorological conditions, and also the effect of

memory of thermal perception for the past days for various other kinds of outdoor activities, such as

jogging and walking.

ACKNOWLEDGEMENTS

The research project in the recreation area Marchfeldkanal was funded by the FWF – Fond zur

Förderung der wissenschaftlichen Forschung in Österreich (Funds for the Support of Scientific

Research in Austria). The data from the recreation area Wienerberg was taken from the visitor

monitoring project funded by the Forest Department of the City of Vienna.

REFERENCES 1. Nankervis, M. 1999. The effect of weather and climate on bicycle commuting.

Transportation Research Report A 33. 417-431.

2. Cessford, G. 2002. Perception and Reality of Conflicts: Walkers and Mountain Bikes on

the Queen Charlotte Track in New Zealand. Monitoring and Management of Visitor

Flows in Recreational and Protected Areas, edited by Arnberger, A., Brandenburg, C.,

Muhar, A. Proc., 7-13; published by Institute for Landscape Architecture and Landscape

Management.

3. Arnberger, A., 2003. Modellierung sozialer Tragfähigkeitsgrenzen von Erholungsgebieten.

Dissertation an der Universität für Bodenkultur, Vienna.

4. Goldsmith, S. A. 1992. National Bicycling and Walking Study, Case Study, No. 1: Reasons

Why Bicycling and Walking Are and Are Not Being Used More Extensively as Travel

Modes. FHWA-PD-92-041: FHWA, U.S. Department of Transportation.

5. Auer, I., Bogner, M., Hammer, N., Koch, E., Rudel, E., Svabik, O., Vielhaber, C. 1990. Das

Bioklima von Gmunden. Zentralanstalt für Meteorologie und Geodynamik, Vienna.

6. Harlfinger, O. 1985: Bioklimatischer Ratgeber für Urlaub und Erholung, Gustav Fischer

Verlag.

7. Rotton J., Shats M., Stander R. 1990. Temperature and Pedestrian Tempo: Walking

Without Awareness, Environment and Behavior, Volume 22, Nr. 5, Sage Periodicals

Press.

196

8. Matzarakis, A., De Freitas, C.R. (eds.) 2001. Proceedings of the first international

workshop on climate, tourism and recreation. International Society of Biometeorology,

Commission on Climate Tourism and Recreation. December 2001.

9. Gerth, W.-P. 1987. Anwendungsorientierte Erstellung großräumiger Klimaeignungskarten

für die Regionalplanung, Berichte des Deutschen Wetterdienstes Nr. 173, Selbstverlag des

Deutschen Wetterdienstes, Offenbach.

10. Hammer, N., Koch, E., Rudel, E. 1986. Die Beurteilung der thermisch-hygrischen

Befindlichkeit des Menschen nach verschiedenen Modellen, Arch. Met. Geoph. Biokl., Ser.

B 36:343-355.

11. Hammer, N., Koch, E., Rudel, E. 1990. Das Bioklima an österreichischen Badeseen und

auf Mallorca, Wetter und Leben 42, Heft 2.

12. Höppe, P. 1999. The physiological equivalent temperature – a universal index for the

biometeorological assessment of the thermal environment. Int. J. Biometeorol. 43:71-75.

13. Jendritzky, G., Menz, G.; Schirmer, H.; Schmidt-Kessen, W. 1990. Methodik zur raum-

bezogenen Bewertung der thermischen Komponente im Bioklima des Menschen.

Beitr. Akad. Raumforsch. Landesplan. Nr. 114.

14. Hanson, S. and Hanson, P. 1977. Evaluating the Impact of Weather on Bicycle Use.

Transportation Research Record 629, pp 43-48.

15. Hepkinson P, Casten and Tight M. 1989. Review of Literature on Pedestrian and Cycle

Route Choice Criteria. Institute for Transport Studies. (University of Leeds, Working

Paper 20).

16. Keay C. 1992. Weather to Cycle. Ausbike 92, Bicycle Federation of Australia, pp152-155.

17. Niemeier, D. 1996. Longitudinal Analysis of Bicycle Count Variability: Results and

Modelling Implications. Journal of Transportation Engineering. 200-206.

18. Lengauer, M. 1995. Quantifizierung der Freizeit- und Erholungsnutzung am

Marchfeldkanalsystem mittels Langzeit- Videobeobachtung, Diplomarbeit, Institut für

Freiraumgestaltung und Landschaftspflege, Universität für Bodenkultur, Wien.

19. Pöll, W. 1994. Erholungs- und Freizeitnutzung am Marchfeldkanal: Nutzungs-ansprüche,

Nutzungskonflikte, Lenkungsmaßnahmen, Diplomarbeit, Universität für Bodenkultur,

Wien.

20. Arnberger, A. 2004. Besuchermonitoring im Erholungsgebiet Wienerberg. Im Auftrag der

Magistratsabteilung 49 der Stadt Wien.

21. Matzarakis, A.; Mayer, H.; Iziomon, M. 1999. Applications of a universal thermal index:

physiological equivalent temperature. Int. J. Biometeorol. 43:76-84.

197

22. Matzarakis, A., Rutz, F.; Mayer, H. 2000. Estimation and calculation of the mean radiant

temperature within urban structures. Biometeorology and Urban Climatology at the

Turn of the Millennium, edited by R.J. de Dear, J.D. Kalma, T.R. Oke and A. Auliciems.

Selected Papers from the Conference ICB-ICUC’99, Sydney. WCASP-50, WMO/TD No.

1026, 273-278.

23. Harrison, S.J., Winterbottom, S.J. and Sheppard, C. 1999. The potential effects of climate

change on the Scottish tourist industry. Tourism Management 20:203-211.

24. Richardson, A.J. 2000. Seasonal and Weather Impacts on Urban Cycling Trips. TUTI

Report 1-2000, 1-21.

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TOURISM STAKEHOLDERS’ PERSPECTIVES

ON CLIMATE CHANGE POLICY IN NEW ZEALAND

S. Becken1 and P. Hart1

1. Landcare Research, Lincoln 8152, New Zealand

E-mail addresses: beckens@landcareresearch.co.nz (S. Becken), hartp@landcareresearch.co.nz (P.

Hart)

ABSTRACT

In response to growing greenhouse gas emissions associated with tourism in New Zealand, 14

possible strategies centering on sustainable tourist types, energy-efficiency and educational

campaigns were developed to reduce emissions. The strategies, along with some information on

greenhouse gas emissions from tourism, were discussed with eight members of an Advisory Group

(private and public sector, and marketing agencies) for a research program on international tourists’

energy use. Each member was interviewed separately to gain individual perspectives on climate

change in general and the proposed strategies in particular. Climate change was not of high priority

to those tourism stakeholders interviewed, but they expressed concern about the planned carbon tax

to be introduced in 2007. Notwithstanding this, the interviewees stressed the importance of

sustainability, triple bottom line reporting, and resource efficiency. They felt that to deal effectively

with tourism’s greenhouse gas emissions some legislation would be required, and that this should

allow for a partnership between Government and industry that provides sufficient room for industry

initiatives, such as voluntary benchmarking and certification. Tourists could also play a role in this

process, for example by participating in carbon-offsetting schemes, supporting new technologies

(e.g. hydrogen cars) and by actively engaging in energy-efficiency measures (e.g. switching off

lights in their hotel). Overall, competition and the economic imperative dominate decision making

and, therefore, climate change strategies need to provide multiple benefits, particularly financial, to

be effective. Existing competition makes it hard for individual companies to take the lead, which

demonstrates the need for concerted initiatives by a critical mass of companies, supported by a

favorable policy framework.

KEYWORDS: Climate change policy, Tourism stakeholders, Greenhouse gas emissions,

Partnerships

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INTRODUCTION

Tourism is a contributor to climate change by its use of fossil fuels and emission of greenhouse

gases, especially carbon dioxide. In New Zealand, tourism contributes 6% of the national carbon

dioxide emissions (exc. international travel), and this share will increase with international arrivals

growing at about 5–6% annually (1). Despite the increasing recognition of tourism’s role in climate

change (2), research on tourism’s greenhouse gas emissions and policies remains limited.

Moreover, the debate mostly takes place at an academic level and rarely in the arena of policy

makers and key tourism stakeholders. It is therefore unclear to what extent tourism stakeholders

recognize the importance of climate change for their industry, and whether they are interested in

particular mitigation actions. It is possible that the specific characteristics of climate change, as a

complex and uncertain environmental phenomenon with large temporal and spatial scales and

irreversible impacts (3), will result in conflict and differing value judgments within public and

private sector decision-making processes. This research explores the level to which tourism

stakeholders in New Zealand perceive a need and potential for climate-change-related strategies

within tourism. To this end, possible strategies for reducing carbon dioxide emissions from tourism

were developed and discussed with selected tourism stakeholders.

METHODS

The current study builds on work undertaken on energy use and tourism in New Zealand over the

last four years, which has been brought together in a summary report (4). The focus of the report

was to better understand international tourists’ travel behavior, energy use, decision-making,

benefits sought and underlying values. From this report, 14 strategies for reducing carbon dioxide

emissions were derived (Table 1). These comprise marketing strategies for sustainable tourist types,

energy-efficient itineraries and transport modes, accommodation and transport energy-efficiency

measures, and educational campaigns for businesses and tourists. The summary report and

strategies were sent to eight tourism stakeholders; interviews were conducted with individuals to

discuss the findings and propositions. Stakeholders were also asked about their concern regarding

climate change, and who should be responsible to deal with it, including the desired level of

government regulation. The members (senior staff or chief executive officers) were chosen to

represent the public, private and marketing sectors (Ministry of Tourism, Energy Efficiency and

Conservation Authority (EECA), Tourism New Zealand, Tourism Industry Association New

Zealand (TIANZ), Christchurch and Canterbury Marketing, Air New Zealand, Tourism Holdings

Limited, and Real Journeys). When necessary, interviewees were asked to specify whether their

answers reflected personal opinions, the position of their institution, or the attitude of the whole

tourism industry. The interviews were conducted between September and November 2003 and took

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Table 1: Strategies to reduce greenhouse gas emissions from tourism in New Zealand based on previous research (4)

Strategy Uncertainties Impact on energy use Implementation 1. Market (target) auto tourists, camping tourists and backpackers in overseas marketing campaigns. They were the most energy-efficient tourist types.

What is the real (sustainable) yield associated with those types?

Potentially significant TNZ and RTOs, tour operators

2. Make marketing less icon-dependent and diversify tourist icons (one in each region). Visiting the major tourist icons in New Zealand results in large travel distances.

What makes an icon? Potentially significant TNZ and RTOs

3. Develop regional itineraries, particularly aimed at campers, backpackers and auto tourists. Regional itineraries involve less travel per day than nation-wide touring itineraries.

Do tourists take up suggested routes? Does it make them drive more or less?

Potentially significant RTOs, operators, wholesalers, ITOs

4. Promote packaged special-interest tourism instead of mainstream coach tours. How big is the market for special-interest tours? Minor ITOs, TNZ and RTOs 5. Market two-week holidays on one island for Australians. This has the potential to reduce the need for transport.

Would TNZ change their marketing strategy for Australia? Minor to medium TNZ, RTOs, wholesalers

6. Monitor fast-growing markets (e.g. China, South Korea) in terms of (a) length of stay, (b) travel distance and transport mode, (c) expenditure. Speed up development process of maturing.

What is the potential to increase length of stay, to reduce dependency on icons, and to encourage FIT travel?

Medium TNZ, TRCNZ?

7. Assess price structures that encourage auto tourists, campers and comfort travellers to travel less distance with rental vehicles.

What is the price-elasticity for rental vehicles? Does travel distance depend on costs?

Minor to medium Rental vehicle companies

8. Improve public transport system for tourists (mainly backpackers and comfort travellers).

How can barriers be overcome so that tourists use public transport?

Minor to medium MoT, EECA, regional councils, RTOs

9. Encourage rental vehicle companies or other businesses to build an energy-efficient fleet.

Are companies interested? Potentially significant EECA, TIANZ, companies

10. Include advice on travel distances in travel agent training programs in Australia and elsewhere.

Would TNZ consider this issue important enough to be included?

Minor TNZ, RTOs

11. Target hotels to increase energy efficiency in accommodation sector. What are the barriers to improving energy efficiency? Minor to medium EECA, TIANZ, hotel associations

12. Review air links into and within New Zealand to avoid unwanted travel through Auckland or other hubs.

What is more energy-efficient overall, the hub-feeding principle or the direct-link principle?

Medium Air New Zealand

13. Educate tourism businesses, visitor centres (VIN), and RTOs about sustainable itineraries.

Do they want to be educated? Minor to medium TNZ, MfE, TMT, TIANZ, RTOs, companies

14. Educate tourists about sustainable itineraries and carbon off-setting opportunities (e.g. planting trees).

Tourists are more interested in nature in general than in greenhouse gas emissions.

Medium MfE, TMT, Air New Zealand, RTOs, companies

Abbreviations: EECA- Energy Efficiency and Conservation Authority, FIT - Free Independent Travel, ITO - Inbound Tour Operator, MfE - Ministry for the Environment, MoT - Ministry of Transport, RTP - Regional Tourism Organisation, TIANZ - Tourism Industry Association New Zealand, TMT - The Ministry of Tourism, TNZ - Tourism New Zealand, TRCNZ - Tourism Research Council New Zealand, VIN - Visitor Information Network.

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about one hour each. The interview was semi-structured to allow the interviewee to elaborate

about their institution’s (business) situation and experience.

RESULTS

CONCERN ABOUT CLIMATE CHANGE

The eight interviews showed that concern about climate change was comparatively low on the

part of stakeholders, who also reported little interest from the tourism sector as a whole (a

number of operators have benchmarked under the GreenGlobe21 standard and are aware of

climate change). Tourism operators have more concrete and immediate concerns (e.g.

investment decisions) and they are simply unaware and uninformed about climate change.

While greenhouse gas emissions and climate change were not of great importance, the need

for a sustainable tourism sector, triple bottom line reporting, and resource efficiency was

stressed in most interviews, often because these provide direct benefits to the businesses. The

statements made by stakeholders indicated that climate change is conceptually not closely

linked to the better-known concept of sustainability, and confirmed Wilbanks’ (5) statement

that ‘sustainable development…is more of an ambiguous integrative slogan than an

operational term’. All interviewees were concerned about the carbon tax, because the

emission charge ($25 per tonne of carbon dioxide) created by the government for 2007 (6)

could put a financial burden on the industry (NZ$36 million per annum, (7)), especially on the

small and medium enterprises (SME). An important part of the government’s policy package

are Negotiated Greenhouse Agreements (NGA) for competitiveness-at-risk firms or sectors.

As tourism consists largely of SMEs that currently do not have the same means to negotiate

NGAs as do the large companies, the Government is currently investigating further policies

for SMEs in cooperation with the Ministry of Tourism.

RESPONSIBILITY FOR GREENHOUSE GAS REDUCTION MEASURES

Despite a general disagreement with the carbon tax and issues around NGAs, stakeholders felt

that some legislation (e.g. for energy efficiency) and policing would be required to deal

effectively with tourism’s greenhouse gas emissions. Industry representatives stressed that a

partnership between government and industry should be envisaged, leaving room and

providing support for industry initiatives, such as voluntary certification through

GreenGlobe21. Industry stakeholders, in particular, pointed out that companies already

addressing climate change should be exempt from the carbon tax, or receive other

compensation for their voluntary efforts. Several interviewees suggested a ‘bottom-up

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strategy’ that encourages early adopters. As a result of peer pressure more operators would be

encouraged to follow such energy champions. However, industry representatives also

admitted that it is hard for individual operators to invest in new technologies, products or

itineraries, or to engage in voluntary self-regulation, because this results in a competitive

disadvantage for the company. Interviewees could not agree on which government agency

should act as lead agency for implementing greenhouse gas reduction strategies (i.e. those in

Table1). The Ministry of Tourism, for example, currently has no formal policy on climate

change. The Climate Change Office within the Ministry for the Environment does not treat

tourism differently from other sectors, and therefore integrates it into their wider programs of

sustainable development and SME policies. As a marketing agency, Tourism New Zealand

does not feel responsible for educating tourists or conveying environmental information.

TIANZ is concerned about an energy-efficient sector, but has a limited member base (about

20% of all tourism businesses) and its primary task is to ensure that the interests of members

are met. Most interviewees believed that tourists could play a role in mitigating climate

change effects; however strategies should increase visitor experience (e.g. learning about

nature), rather than compromising experiences and causing a feeling of guilt. Both public and

private stakeholders saw potential for ‘green marketing’, which would increasingly attract

‘green tourists’ who are more likely to participate in greenhouse gas reduction strategies.

PROMISING STRATEGIES

The interviews showed that a number of the suggested strategies are already taken up to some

level for reasons other than reducing greenhouse gas emissions. For example, the current

national marketing campaign of ‘interactive travelers’ promotes regional travel and ‘going

slow’, which could potentially lead to more energy-efficient itineraries (Strategies 1, 2, 3 and

4). Similarly, regional tourism organizations increasingly promote regional attractions and

seek to increase the length of stay in their particular region (Strategies 2 and 3). There are

opportunities for public sector – private sector partnerships including the administration of

energy efficiency tools developed by EECA (e.g. energy-efficient fleet management, Strategy

9) through TIANZ to its members. Overall, there is a clear need for more and better

information on energy efficiency and climate change that contains concrete suggestions about

how tourism operators could improve their environmental bottom line without compromising

their economic viability (Strategy 11). Similarly, tourists should be informed better about

travel distances in New Zealand and the regional density of attractions to avoid misconception

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and dissatisfaction resulting from long travel days (Strategies 10 and 13). Tourism New

Zealand’s travel agent programs already consider this issue to some degree.

STRATEGIES NOT LIKELY TO BE IMPLEMENTED

Stakeholders agreed that the geographical dispersion of tourist icons in New Zealand and the

desire of tourists to visit many of them may lead to substantial travel, but changing the

dominance of existing, well-established and continuously reinforced (by word-of-mouth and

promotion) icons would be extremely difficult and a long-term process (Strategy 2).

Moreover, there is a perception among some within the industry that ‘packed itineraries

generate high-yield’, although the stakeholder who made this comment agreed that such a

belief lacks foundation because too little is known about yield. Several interviewees noted

that the current promotion of regional itineraries (see above) does not help efforts by EECA to

promote public transport, because public transport typically connects the main centers. Public

transport (except within cities) in New Zealand was not seen as a viable option for tourism,

because it is too expensive, too inconvenient and not flexible enough (Strategy 8). The trend

of increasingly cheaper airfares and convenient electronic booking counteracts both public

transport initiatives and the promotion of regional travel.

Major impediments to reducing carbon dioxide emissions are the high capital costs that

prevent energy efficiency measures for vehicles (Strategy 9). For example, coaches are rarely

adapted to group sizes, because it is too expensive for the operator to have both large 50-

seater coaches and minibuses. The expectation of space and comfort by tourists plays an

additional role in decisions about coach sizes. Several interviewees noted that overseas

wholesalers and national tour operators are major players in the tourism sector focusing on

satisfying market needs and not on sustainable tourism development. Hence, an emerging

market is seen as a business opportunity – irrespective of its sustainability – and products are

developed that are demanded by this market (e.g. package tours for Chinese, Strategies 4 and

6). Because tourism depends on international companies and markets, there is little control

over product supply and demand from within New Zealand, unless severe regulatory

measures were to be taken, for example limitation of visitor visas.

DISCUSSION

Tourism has not yet been specifically featured in New Zealand’s climate change policies and

strategies, and, possibly as a result, there is little concern about climate change itself on the

part of tourism stakeholders. Overall, there is little evidence both of linking climate change

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and sustainability strategies (the New Zealand Tourism Strategy 2010 briefly mentioned

carbon neutrality), and synergies between policies (so-called ancillary benefits) are not

exploited (8). This seems a missed opportunity given the relatively high recognition of

sustainability among stakeholders. Stakeholders are concerned that climate change policy

focuses on large and energy-intensive companies (see also 9), whereas SMEs as part of the

‘general energy users group’ (10) are charged emission taxes. This decreases the profit

margins of SMEs and diminishes competitiveness compared with non-Kyoto tourism

destinations such as Australia. All interviewed stakeholders stated that a successful climate

change initiative must not harm the economic bottom line. Thus the New Zealand tourism

sector shows weak sustainability, i.e. one that places human needs at the center, around which

environmental concerns are accommodated. Existing structures make it hard for individuals to

take the lead, which demonstrates the need for concerted action by a critical mass of

companies, supported by a favorable policy framework.

At this stage no single stakeholder feels it is their responsibility to individually implement

strategies just for the sake of reducing greenhouse gas emissions. Instead, it was evident that a

multi-stakeholder (public and private sector) approach is needed where there is regulation as

well as room for voluntary initiatives. Voluntary agreements, however, require the specific

adaptation to a target group (tourism businesses) with clearly specified targets that are not too

ambitious, but provide incentives to effectively decrease greenhouse gas emissions. Such

agreements have to be embedded in a broader policy mix that guarantees support for the

businesses, as well as compliance with the agreement (9). Partnerships between government

and industry need to accept that policy makers and entrepreneurs have contrasting

perspectives with regard to time frames, degree of individualism and control, flexibility,

innovation and planning, continuity, rationality, and level of risk taking (11). While not

explicitly mentioned in the interviews it appears that participatory approaches as practiced in

the European Commission Climate Policy Process (3) would provide a promising avenue to

distribute knowledge among stakeholders, and thereby derive policies acceptable to all

parties.

New Zealand is one of a few countries that have invested in research on how tourism

contributes to climate change. There is potential to implement some of the strategies resulting

from this research, given the structure and responsiveness of institutions and a general

awareness of New Zealand’s image of being clean and green, but it will not be easy.

Comparing the New Zealand situation against the propositions made in the Djerba

Declaration (2) one can conclude that some progress has been achieved. However, the

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existing initiatives (e.g. energy-efficiency road-shows by TIANZ) have not resulted in

significant reductions in greenhouse gas emissions and a change in tourist behavior seems a

major challenge. For this reason, the notions of ‘encouraging’ and ‘calling upon’, as used in

the Djerba Declaration and as – at first – was practiced in New Zealand, are not strong

enough to achieve significant reductions. It seems necessary that the sector – possibly in the

form of a partnership between the Ministry of Tourism, the TIANZ and the Ministry for the

Environment – work on refining the policy framework, to improve the sector’s energy

efficiency, incorporate renewable energy sources, change tourist behavior and invest in

carbon sinks to offset emissions that can not be reduced.

REFERENCES

1. Patterson, M.G. and McDonald, G. 2002. How Green and Clean is New Zealand

Tourism? Lifecycle and Future Environmental Impacts. Draft 15 March 2002, Massey

University, Palmerston North.

2. World Tourism Organisation. 2003. Djerba Declaration. Available at (20/11/03)

www.world-tourism.org/sustainable/climate/decdjerba-eng.pdf.

3. Hove van den, S. 2000. Participatory approaches to environmental policy-making: the

European Commission Climate Policy Process as a case study. Ecological Economics.

33:457-472.

4. Becken, S. 2003. An integrated approach to travel behaviour with the aim of

developing more sustainable forms of tourism. Landcare Research Internal Report

LC0304/005. Available at (20/02/2004)

http://www.landcareresearch.co.nz/research/sustain_business/tourism.

5. Wilbanks, T.J. 2003. Integrating climate change and sustainable development in a

place-based context. Climate Policy. 3S1:147-154.

6. Ministerial Group on Climate Change 2002. Climate Change I: Confirmation of

Preferred Policy Package. Cabinet Paper (02) 143. Available at (28/01/04):

http://www.climatechange.govt.nz/resources/cabinet/index.html.

7. Turney, I., et al. 2002. Tourism Industry Association New Zealand (TIANZ): Climate

Change response. A report to establish the knowledge required for a TIANZ response

and policy formulation with the Government post Kyoto Protocol ratification.

Landcare Research Contract Report LC0102/107.

Available at: http://www.tianz.org.nz/Files/ClimateChange.pdf.

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8. Michaelis, L. 2003. Sustainable consumption and greenhouse gas mitigation. Climate

Policy. 3, 1:135-146.

9. Krarup, S. and Ramesohl, S. 2002. Voluntary agreements on energy efficiency in

industry – not a golden key, but another contribution to improve climate policy mixes.

Journal of Cleaner Production. 10:109-120.

10. New Zealand Climate Change Project 2002. The Government's Preferred Policy

Package. A Discussion Document. Wellington.

11. Russell, R. and Faulkner, B. 2003. Movers and shakers: chaos makers in tourism

development. Progressing Tourism Research, edited by Faulkner, B. (Clevedon,

Channel View Publications), 220-243.

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CLIMATE AND THE DESTINATION CHOICE OF GERMAN TOURISTS:

A SEGMENTATION APPROACH

Jacqueline M. Hamilton1, David J. Maddison2,3 and Richard S.J. Tol1,4,5

1. Centre for Marine and Climate Research, Hamburg University, Hamburg, Germany

2. Economics Institute, University of Southern Denmark, Odense, Denmark

3. Centre for Cultural Economics and Management, London, UK

4. Institute for Environmental Studies, Vrije Universiteit, Amsterdam, The Netherlands

5. Center for Integrated Study of the Human Dimensions of Global Change, Carnegie Mellon

University, Pittsburgh, PA, USA

E-mail address: hamilton@dkrz.de (Jacqueline M. Hamilton)

ABSTRACT

The purpose of this study was to examine the climate preferences of German tourists in terms

of their demand for various countries. A survey of the holiday travel behaviour of German

citizens during 1997 was segmented using three criteria: phase in the life cycle, holiday

motivation and holiday activities, and region of origin. Statistical regression analysis was used

to estimate 14 different demand equations, which included average monthly temperature,

average monthly precipitation and the monthly frequency of wet days as explanatory

variables. The results showed that there were indeed different preferences across age, activity

groups, and regions. Optimal temperature ranged from 22°C to 24°C, and differences in the

steepness of the climate-demand relationship could be seen. Spain had the highest climate

index values for the month of August for all segments, and the segment swimming and

sunbathing had the highest index values for all segments. This study provides useful

information for the construction of tourism scenarios, which could be used to improve climate

change impact studies.

KEYWORDS: Segmentation, Tourism demand, Climate index

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INTRODUCTION

Climate is an important factor in determining tourism demand. Climate change will therefore

affect tourism by making certain locations more attractive, and others less. Previous papers on

the impact of climate change on tourism are largely restricted to the behaviour of the average

tourist (1,2,3). However, there is little reason to assume that all tourists are a homogenous

group. Scenarios based on the behaviour of the average tourist are likely to be wrong if one

considers that the age structures of populations change, and that new trends are always

developing. This paper investigates the climate-related behaviour of several classifications of

German tourists.

Market segmentation involves defining tourist groups according to certain demographic,

behavioural, or psychographic traits. In the numerous segmentation studies in the literature

the segments are compared according to socio-economic characteristics, holiday

characteristics, or preferences for certain destination characteristics. The objective of this

study was to combine the segmentation approach and the travel cost model approach, used in

the studies by Hamilton, Maddison and Lise and Tol (1,2,3), to examine the climate

preferences of different segments. A survey of the holiday travel behaviour of German

citizens during 1997 was segmented using three different means: phase in the life cycle,

holiday motivation and holiday activities, and region of origin. In the following section, the

methods used to segment the survey data and to analyse the climate-demand relationships of

the segments are presented. The climate optima and, as a means of comparison, the climate

index values for certain destinations are discussed in section three. The fourth section

concludes with a discussion of the results.

METHODS

The original survey used in the study by Hamilton (1) contained the responses of 7780

German citizens in 1998 about the holidays that they took in 1997 (4). The data set was

originally constructed so that, for every destination and month, the total number of visits by

the survey group was calculated. This was used as the dependent variable in the statistical

estimation. In this study fourteen different datasets were created for the segments defined in

Table 1. Firstly, segmentation according to the stage in the life phase of the respondents was

carried out. There were eight possible responses covering three age groups and whether or not

there were dependent children in the household. These were combined to form four clusters.

Secondly, there were eight survey questions on motivation and nine on holiday activities.

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Three segments were produced for both motivation and activities. Thirdly, information was

available on the federal state-of-residence of the respondent. There are 16 federal states in

Germany. These were used to form four segments, which captured broad regions of

topographic and climatic similarity.

Table 1: Segment definitions

Segment name Definition

Life phase segments YOUNG From 14 to 39 years old CHILDREN Families with children under 14 MIDDLE From 40 to 59 years old SENIOR Older than 60 years Regional segments EAST Berlin, Brandenburg, Saxony, Saxony-Anhalt and Thuringia NORTH Bremen, Hamburg, Mecklenburg-Western Pomerania, Lower Saxony and Schleswig-Holstein SOUTH Baden-Württemberg and Bavaria WEST Hessen, North Rhine-Westphalia, Rhineland-Palatinate and Saarland Motivation and activity segments SPORT Taking part in sport HEALTH Doing something for one's health and appearance FAMILY Spending time with one’s partner, friends and family OUTDOOR Walking, hiking, cycling and other outdoor activities SIGHTS Sightseeing and taking part in cultural events SWIMSUN Sunbathing and swimming

In Hamilton (1) the following specification was estimated using panel corrected least squares

regression for the complete data set:

2 3 4 2log( ) PRE + 1 2 3 4 5 6 7 8VISITS X TEMP TEMP TEMP PRE WETD WETD eα β β β β β β β β= + + + + + + + + 2

where X represents all other non –climate characteristics of the countries (see (1) for more

detail on these other variables), TEMP is the average monthly mean temperature, PRE is the

average monthly precipitation (mm) and WETD is the average number of wet days per month

(5). For each segment, it is possible to compare the estimated coefficients. Moreover, the

estimated coefficients can be used to estimate the optimal value of the climate variables (2).

In addition, climate index values for a particular month and destination can be calculated

using the estimated coefficients of the climate variables. In the following section the climate

index values for the month of August are presented and compared for certain European

countries and segments.

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RESULTS

The temperature optima ranged from 22°C to 24°C. There were, however differences in the

steepness of the temperature-demand relationship. The segment CHILDREN had the highest

optimal temperature of 24°C, and the relationship between temperature and demand was

much more peaked for this segment. Those with children prefer destinations that are near this

optimal temperature. A change in the temperature would lead to larger changes in demand

than it would for the YOUNG segment, that had a similar optimal temperature but a less

peaked temperature-demand relationship. The optimal wet day frequency ranged from 11 to

13 wet days per month. Again, the segment CHILDREN had a more peaked wet days-demand

relationship than the other segments. SOUTH and WEST had an optimal temperature of

23°C. The estimated temperature-demand relationship, however, was much steeper for the

segment SOUTH. It is interesting that SOUTH and WEST had significant climate optima, as

the south west of Germany is the warmest and sunniest region of Germany. It would seem

that tourists with a warmer home climate are more particular about their holiday destination

climate. There was little difference in the optima for temperature or for wet days across the

motivation segments. Nevertheless, differences in the steepness of the climate-demand

relationships could be seen.

0

0.5

1

1.5

2

2.5

3

3.5

ALL

SENIORS

YOUNG

MIDDLE

CHILDREN

EAST

NORTH

SOUTHW

EST

SPORT

SIGHTS

OUTDOOR

HEALTH

FAMILY

SWIM

SUN

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Figure 1: Climate index values for Germany in the month of August for all segments

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0

0.5

1

1.5

2

2.5

3

3.5

ALL

SENIORS

YOUNG

MIDDLE

CHILDREN

EAST

NORTHW

EST

SOUTH

SIGHTS

OUTDOOR

SPORT

HEALTH

FAMILY

SWIM

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Figure 2: Climate index values for Greece in the month of August for all segments

0

0.5

1

1.5

2

2.5

3

3.5

ALL

SENIORS

YOUNG

MIDDLE

CHILDREN

EAST

NORTH

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EST

SIGHTS

OUTDOOR

SPORT

HEALTH

FAMILY

SWIM

SUN

CLI

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Figure 3: Climate index values for Spain in the month of August for all segments

The segment FAMILY had the steepest and highest demand relationship for temperature and

wet day frequency. As before, there were differences in the steepness of the temperature-

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demand relationships. SWIMSUN had the steepest relationship, particularly as the

temperature climbed above 23°C.

The climate index values for Germany, Greece, and Spain were calculated for all of the

segments and are shown in Figures 1 to 3. It can be seen that Spain had the highest index

values of these three countries for all of the segments. The index values were highest for the

segments with dependent children, those motivated to spend time with their families, and the

segment of tourists who were swimming and sunbathing on their holiday.

DISCUSSION

For the fourteen segments presented here, the temperature optima ranged from 22°C to 24°C.

There were, however, differences in the steepness of the temperature-demand relationship.

This was markedly so for the segments of tourists with dependent children and for those

whose activities included sunbathing and swimming. Moving away from the optimal

temperature lead to a sharp drop in demand, particularly for temperatures above the optimal.

More than one third of the days in a month with rain may seem high for an optimal holiday

climate. It must be borne in mind that a wet day is one where there is more than 1mm of rain

and such a frequency is normal for a central European summer, or early and late summer in

southern Europe. Moreover, climate is not just a thermal or physical factor it is also an

aesthetic one, in that it affects the appearance and type of flora, the appearance of the built

environment and visibility among other things. Occasional rain is not necessarily detrimental

for tourism demand. There are also some more practical effects of regular rain, such as water

availability, which may be considered by the tourist. The ranking of tourist destinations, using

the climate index developed in Hamilton (1), did not differ significantly across the segments.

Nevertheless, the size of the climate index, on average and for the individual destinations, did

differ. The most preferred destination in August, in terms of climate, was Spain.

The results of this study confirm many of the results of previous segmentation studies. This

can be seen in the differences in preferences across the life cycle or across activities. Like

Mykletun et al. (6), this study examined several kinds of segmentation and used regression

analysis to provide detail on the different preferences of the different groups. Previous studies

that examined destination image preferences did so using a ranking of attributes (7). In this

study, demand for a destination has been estimated with respect to environmental

characteristics, such as climate or beaches. This not only provides information on what is

important for each segment, but also on how this quantitatively affects demand.

For climate change impact studies a segmentation approach can provide useful information.

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Scenarios of population and economic change are only two aspects that will shape the

development of tourism in the future. New trends or structural changes in the population will

also affect demand. This study provides quantitative relationships, for different segments,

such as seniors, health tourists or tourists from different geographic regions. It was shown that

regions with a warmer climate generate tourists that are more particular about their holiday

destination climate. It would seem that home climate is less of a push factor than a pull factor.

Applying this to countries, we would then expect that warmer countries would also produce

tourists that are less tolerant of temperatures away from the optimal. This has implications for

climate change impact studies. Climate change may result in many origin countries having a

climate closer to their optimal temperature, which combined with a preference, ceteris

paribus, for domestic tourism, would result in a reduction in international tourism. Moreover,

as a warmer climate becomes the norm, the tourists may also become more particular about

their holiday destination climates.

This study examined the destination choices of German tourists in a single year. To obtain a

more complete picture it would be useful to repeat this study for different years, and for

different countries/regions at various scales. It is planned to use the results of this study and

subsequent repetitions to generate tourism-climate scenarios to develop impact studies

further, so that other aspects of societal change can be examined.

ACKNOWLEDGEMENTS

The European Commission Research DG I funded project DINAS-COAST (EVK2-2000-

22024) provided welcome financial support. Provision of the tourism data by the

“Zentralarchiv für empirische Sozialforschung” and the “Forschungsgemeinschaft Urlaub und

Reisen e.V” is gratefully acknowledged. All errors and opinions are our own.

REFERENCES

1. Hamilton, J.M. 2003. Climate and the Destination Choice of German Tourists.

Research Unit Sustainability and Global Change Working Paper FNU-15 (revised)

Centre for Marine and Climate Research, Hamburg University, Hamburg.

2. Lise, W., and Tol, R.S.J. 2002. Impact of climate on tourism demand. Climatic

Change. 55(4):429-449.

3. Maddison, D. 2001. In Search of Warmer Climates? The Impact of Climate Change on

Flows of British Tourists. Climatic Change. 4:193-208.

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4. Forschungsgemeinschaft Urlaub und Reisen e.V. (F.U.R) 1998. Die Reiseanalyse RA

98. (Köln: Zentralarchiv für empirische Sozialforschung).

5. Mitchell, T.D., Hulme, M.. and New, M.. 2002 Climate Data for Political Areas. Area.

34:109-112.

6. Mykletun, R.J. Crotts, J.C. and Mykletun, A. 2001. Positioning an island destination in

the peripheral area of the Baltics: a flexible approach to market segmentation. Tourism

Management. 22(5):493-500.

7. Kozak, M. 2002. Comparative analysis of tourist motivations by nationality and

destinations. Tourism Management. 23:221-232.

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KNOWLEDGE MANAGEMENT FOR TOURISM, RECREATION AND

BIOCLIMATOLOGY: Mapping the Interactions (Part II)

Trista Patterson1

1. University of Siena, Department of Science and Technology for Physical Chemistry in

Biosystems, Via Della Diana 2A, 53100 Siena/Italy

ABSTRACT

The fields covered in the workshop of tourism and recreation climatology (biometeorology,

bioclimatology, thermal comfort and heat balance modelling, tourism marketing and planning,

urban and landscape planning, architecture, climate change, emission reduction and climate

change impact assessment) refer to a wide variety of spatial scales and scopes of tourism

policy intervention. To more effectively address these challenges as a cooperative scientific

network, a knowledge management approach can be of assistance in identifying

commonalities, differences, and information gaps among studies. To this end, this paper

reports on the development of a conceptual platform for the meta-analysis of ongoing tourism

and climate change research. This diagram is particularly suited to the Crete conference, given

the mission of the Orthodox Academy of Crete to encourage discourse and dialog among

opposing viewpoints. It was designed to challenge the assumption that the first step to

designing clear policy in tourism climate change interactions is to select and defend a strategy

of either adaptation or mitigation. The purpose of this paper is to benchmark and discuss the

evolution of the ‘State and Change’ diagram (1, 2), thereby advancing tourism and

bioclimatology knowledge management.

KEYWORDS: Systems theory, Tourism, Recreation

THE EVOLVING CONCEPTURAL MODEL FOR TOURISM AND CLIMATE

CHANGE

Optimal use of information generated by tourism and recreation bioclimatology requires

providing information and infrastructure, dealing with conflict, understanding compliance to

social rules, and preparing institutions for change. Policy debate and knowledge can be most

productive precisely when different contributors reveal different interpretations of key issues.

An effectively designed conceptual model will: place the body of research in neutral ground,

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be inclusive to multiple perspectives, be used strategically to neutralize polarizing tendencies

(3, 4, as cited by 5), and promote new collaborations.

The fields covered in the workshop of tourism and recreation climatology refer to a wide

variety of spatial scales and scopes of tourism policy intervention, and present a notable

challenge of knowledge integration. Construction, modification, and use of conceptual models

are participative activities that capitalize on the rare opportunities of face-to-face interaction.

This paper is representative of some of the ongoing discussions which have occurred between

researchers. Reporting on the changing diagram as a benchmark is important because in the

acts of building up, tearing down, and rebuilding again, researchers become fluent in using the

jargon, concepts, and tools of measurements necessary to communicate across disciplines and

case studies. If viewpoints cannot be reconciled within a given conceptual map, this also

draws useful attention to epistemological differences which may otherwise be overlooked.

Active, focused, and participative use of conceptual models can assist a research group such

as the International Society of Biometeorology, Commission on Climate Tourism and

Recreation (ISBCCTR) in reaching its fullest potential.

THEORETICAL BACKGROUND

The construction of the ‘State and Change’ conceptual map (1) was informed by a few

important areas of research: adaptive governance, adaptive management (6), integrated

assessment (7, 8), and general systems theory (9, 10, 11). The common link among these

areas is that they not only accept differences, but they emphasize differences in perspectives,

interests, fundamental philosophies. They also test conditions as a means to spark learning

and change. New to the diagram presented in this paper is the incorporation of two bodies of

literature- the first of which focuses on successful system transition (12, 13), and the second

on successful system tempos (ie. the multiple paces of system change) (14). These concepts

and discussion with members of the ISBCCTR and the èCLAT network, led to a refinement of

the terms and concepts presented in the diagram which follows.

CONCEPTUAL MODEL 1: ADAPTATION OR MITIGATION?

The first conceptual model presented depicts the tourism/climate change system as ‘a two way

street’: climate influencing tourism, and tourism influencing climate (Figure 1).

217

Tourism’s impact on Climate

(Implies need for mitigation)

Climate’s impact on Tourism

(Implies need for adaptation)

Figure 1: The tourism-climate change system is typically illustrated as a two-way street

When attention is focused on climate’s influence on tourism, adaptation to changes in climate

is viewed as the most urgent area of knowledge. When tourism’s influence on climate is a

primary concern, discussions center on mitigation. Thus, when finances, time, or other

resources for problem solving are limited, adaptation and mitigation appear almost as

mutually exclusive options. Concerns for economy and environment appear to be

diametrically opposed. Under this conceptual model, win-win solutions are precluded: to

advance in one direction means that less progress is made in another.

CONCEPTUAL MODEL 2: STATE AND CHANGE

The design of the second map is strategic in two ways. First, it joins, rather than divides, the

two perspectives offered by model 1: that of the tourist and tourism industry’s effect on

climate, and that of climate’s impact on the tourism industry and destinations. Second, it was

designed with the idea that the tourism/climate system is dynamic, has multiple scales and

feedbacks to consider, and that system drivers underlying these dynamics are not discussed in

current research. Advancing the state-of-the-art at the intersection of tourism/climate

knowledge means addressing the challenge of referring separate factions of investigation to

what is ostensibly a broader, self-organizing, non-linear feedback system (Figure 2).

218

D. State of Rules: Adaptation, Mitigation,

Indication Supplied Global

Trans-National

Nation

Destination

Site

Individual

Shifting perceptions bring about 3. Changes in Political and Civil Society

Adaptation and Mitigation

Measures affect

4. Changes in the Market

Demand and Tourist Behavior

Tourism and Non-Tourism Activity and Natural Variability,

Cause

5. Change In Climate Forcing Factors

A. State of Climate

E. State of Tourism Activity

C. State of Perceptions

(valuation: costs and benefits in terms of

worth, fairness, success)

Shifts in climate induce

1. Changes in The

Tourism Product Which is Supplied

B. State of Resources (physical

conditions, cultural, social,

natural)

Variability in Tourism Resources

influence

2. Change in Image and experience

Figure 2: An updated version of the tourism/climate system

TRANSITION

The diversity of topics covered in tourism and recreation bioclimatology underscores the

difficulty that tourism policy makers face when integrating this information and selecting

appropriate interventions. From the standpoint of a tourism policy maker, a bioclimatic

approach to tourism management will not only effectively identify these leverage points, but

will also use interventions from other public entities to support the most consistent and

enduring change. This requires some shift in approach, whether comparing experiences

among destinations, or linking entities which deal with tourism management at multiple

scales, or coping with tourism stakeholders who hold disparate interests with regard to climate

and how tourism should be managed. The goal of integrated knowledge management in

tourism bioclimatology is to bring about more effective transition. Rotmans et al (12) define a

system transition as a gradual, continuous process of structural change within a society or

culture. Rather than being deterministic, transitions adapt, learn, and anticipate new paths

through exposure to time. The ways in which intervention takes place in a system transition

219

can influence scale, speed and direction, but system control should be considered to be limited

and temporary. Tempo is relevant to two distinct dimensions of system transition as reflected

in this diagram: multiple scales in space as defined at a given time, and multiple states

through time as measured in a single space.

TEMPOS IN SCALE

The first of these two dimensions is straightforward. The conceptual model (Figure 2) was

designed to reflect multiple spatial scales of investigation. Within the diagram, a concentric

set of circles allows researchers to specify the applicable spatial scale of their work, from

individual to global measurement/application. Differentiating scales explicitly in this way

draws attention to the fact that, among the scales specified below, timesteps are usually not

congruent. Societal, economic, and ecological changes can occur at any range of time period-

from an immediate agreement among two cooperating individuals, to coordinated movements

among individuals which take decades or more to emerge. Because little attention is called to

this issue in tourism research, interventions are not coordinated well among scales, nor is the

optimal scale for intervention necessarily selected. Increased attention to scale will allow

tourism managers to reach a more optimal balance between quantity and quality of

information supplied.

Table 1: Tourism and recreation bioclimatology scale descriptions

SCALE

Individual Autonomous concerns, perceptions, behavior and decisions

Site Location such as beach, park, hospitality facility, hotel, etc.

Destination A particular region or group of sites with homogenous marketing

characteristics

National National policy or actions

Trans-national Policies which influence two or more nations

Global Global commons as a whole

TEMPOS IN STATE

Another relevant challenge to knowledge management for tourism and recreation

bioclimatology is that the tourism/climate system is continuously dynamic. This means that

bioclimatic researchers are faced with case studies and data collections which are often

220

difficult to separate out from external conditions, or repeat. Therefore, the second tempo

relevant to system transition has to do with this dynamism- tracking information at a given

area through multiple timesteps. The terms ‘stock’ and ‘flow’ in dynamic modeling are useful

to understanding the relationship between ‘state’ and ‘change’. The boxes in figure 2

represent ‘states’. These are the aspects of the tourism/climate change system that change

relatively slowly over time. They can be described in terms of quantity and quality. Between

these stocks lie ‘changes’. The arrows in the diagram represent the flows which adjust

relatively rapidly, and from which the relationships between the stocks can be discerned.

Research in tourism and climate change can be categorized as attempts to reveal the quantity

or quality of these states, or the relationships of change among them.

The changes in syntax and refinement in concepts can be explained as follows. The bottom of

Figure 2, represents work done to establish the State of Climate (A). Moving left on the

diagram, leads to an area which represents research done that relates shifts in climate, and

how they induce Changes in The Tourism Product Which is Supplied (1). The next state

established is the State of Resources (physical, cultural, social and natural conditions) (B).

Next, the upper left sector of the diagram represents work done on how variability in tourism

resources influence Change in Image and Expferience (2). Research work done on

documenting the State of Perceptions (valuation: costs and benefits in terms of worth,

fairness, success) (C) is represented at the top of the diagram. At the upper right corner is an

area representing investigations of how shifting perceptions bring about Changes in

Political and Civil Society (3). To the right of that area is one which represents the State of

Rules: (adaptation, mitigation, and indication supplied) (D). Next, adaptative and mitigation

measures effect Changes in the Market Demand and/or Tourist Behavior (4). This leads to

an area of the diagram representing the body of literature and indicators which document the

State of Tourism Activity (E). The last set of research in this conceptual map is that which

considers tourism and non-tourism activity and natural variability, as they bring about Change

In Climate Forcing Factors (5). This brings us back around to the work which documents how

these climate forcing factors influence the state of the climate (A again).

TEMPO AND TRANSITION

Unexpected bioclimatic conditions can cause an impact on tourism destinations both in the

short and long term. The variety and extent of climate impacts on tourism highlight the

importance of extending the tourism bioclimatology knowledge base beyond interventions

which act at only one spatial scale, at only one point in time, or without the full range of

221

institutions which might otherwise bring about change. Systems theorists are interested

particularly in actions which trigger others, thereby supporting changes “spiraling through a

system” (7): in other words, profound and prolonged shifts leading to new stable states. The

‘success’ of a transition, according to Rotmans et al (12), is one hallmarked by multiple

causality and co-evolution of independent developments. How deeply tourism policy changes

are linked to and reinforced by predictable tendencies of economic, environmental, and social

systems has much to do with how that change will persist through time, and to what extent. A

complete description of system transition in the tourism/climate change system must address

tempos of state and scale.

DISCUSSION

This paper attempted to describe developments to a conceptual map which relates tourism and

climate change as an integral system. As explained in Patterson (1), previous studies relating

tourism and climate change have tended to adopt one of two perspectives: climate’s influence

on tourism, or tourism’s influence on climate. Bioclimatic problem solving for tourism

management requires collective examination of shared concepts and knowledge, drawing out

various assumptions and causal links between areas of research interest, and identifying gaps

in understanding. The conceptual model presented is a good context within which to place

recent tourism and recreation bioclimatology research, particularly because it orients

discussion of the problem solving community away from the academic tendency to depict the

most complex problems as polar opposites (5), away from an idea of short-term optimization

(11), and away from an idea that either adaptation or mitigation can be exclusively successful

strategies.

When discussing the tempos and transition of knowledge management in tourism climate

change systems, two themes are emphasized: first, addressing various spatial scales, and

second using measured time-steps to explicitly examine the causal links between aspects of

supply/demand and climate forcing/intervention. Knowledge about a system can be structural

(the quantity or quality of something about the system that changes relatively slowly over

time), or functional (the relationships between elements of structure, ones which change

relatively rapidly over time). These terms are similar to ‘fast change/slow change’ or

‘stock/flow’ descriptions found in dynamic modeling. In this paper, this aspect of tempo of

system transition is in part reflected by separating out the 6 system states, from the 6 system

changes. This information is complimented by tourism and bioclimatic information about

spatial scales, from individual to global extents.

222

ACKNOWLEDGEMENTS

Thanks to participants of Thinktank LeVaudreuil, (March 2004), Bas Amelung for suggesting

transitions nomenclature and literature, and our generous hosts at the Crete workshop June

2004.

REFERENCES

1. Patterson, T. 2003. Tourism and Climate Change: Mapping the Interactions.

Proceedings of the "NATO Advanced Research Workshop: Tourism and Climate

change: Assessment and Coping Strategies", edited by B. Amelung and D.Viner.

(NATO publications, Warsaw, Poland).

2. Amelung, B. and Viner, D. (eds). 2004. Proceedings of the “NATO Advanced

Research Workshop: Tourism and Climate change: Assessment and Coping

Strategies” (Warsaw, Poland).

3. Costanza, R. 1998. Beyond the argument culture. Ecological Economics 27:113-114.

4. Tannen, D. 1999. The Argument Culture. (Ballantine, New York).

5. Costanza, R. 2003. A vision of the future of science: reintegrating the study of humans

and the rest of nature. Futures 35:651-671.

6. Deitz, T., E.Ostrom, P. Stern. 2003. The Struggle to Govern the Commons. Science

302:1907-1912.

7. Rotmans, J. and M.B.A. van Asselt, 2001. Uncertainty in integrated assessment

modelling: a labyrinthic path. Integrated Assessment, 2001(2):43-57.

8. Kasemir, B., et al. 1999. Integrated assessment: multiple perspectives in interaction.

International Journal of Environment and Pollution. 11(4):407-425.

9. Von Bertanffy, L. 1968. General Systems Theory; Foundations, Deveopment,

Applications. (George Braiziller pub.).

10. Forrester,J.W. 1968. Principles of Systems, (Wright-Allen, Cambridge, USA).

11. Meadows, D. H. 1997. Ways to Intervene in a System. Whole Earth Review.

(Winter).

12. Amelung, B., et al. 2002. Tourism in motion: Is the sky the limit? Transitions in a

globalizing world, edited by P. Martens and J. Rotmans. (Swets & Zeitlinger Press.

Linne, Netherlands), 85-110.

13. Martens, P. and J. Rotmans. 2002. Transitions in a globalising world. (Swets &

Zeitlinger. Press Linne, Netherlands).

14. Tiezzi, E. 2004. The essence of time. (WIT Press, Southhampton, UK).

223

BOAT TOURISM AND GREENHOUSE GAS EMISSIONS:

CONTRIBUTIONS FROM DOWNUNDER

T. A. Byrnes1 and J.Warnken1

1. Centre for Aquatic Processes and Pollution (CAPP), Griffith University, Gold Coast

PMB 50 GCMC Qld 9726

Email address: j.warnken@griffith.edu.au (J. Warnken)

ABSTRACT

Transport to and between destinations by air, road and rail has been identified as tourism’s

major contributor to greenhouse gas (GHG) emissions. Travel by boat has received less

attention, even though this form of transport, particularly at high speed, can be very energy

intensive and could add a significant amount of GHG emissions to the overall energy budget

of a holiday trip. Many destinations, especially in the mass tourism sector, are located in or

near coastal environments, which provide opportunities for exploring adjacent waterways and

marine environments. Recent trends in product diversification have helped to increase the

demand for boat trips. The popularity of whale watching and dive tours are examples of the

growth in this sector. Consequently, this paper tries to estimate the overall and per capita

energy costs and GHG contributions associated with tourboat operations in Australia, a

country with a 35 000 km coastline and world class marine attractions. Using a

comprehensive database of Australian tourboat operators, 45 face-to-face interviews and 100

completed postal survey questionnaires, the overall GHG emissions for this industry sector

was estimated conservatively at 70 000 tons CO2-e or 0.1 percent of the transport sector in

Australia, the fastest growing sector in terms of GHG outputs. On average, this consumption

translated into an extra 61 kg CO2-e per tourist if their travel itineraries included a trip on a

boat with a diesel engine, or 27 kg CO2-e for a trip on a boat with a petrol engine.

Information obtained from Australian tourboat operators, however, indicated a range of

technical and operational opportunities for reducing GHG emissions. Some of the most

promising solutions and their implementation are discussed in this paper.

KEYWORDS: Boats, Tourism, Greenhouse gas, Tourboat operators

224

INTRODUCTION

Marine transport, similar to air transport, has long been recognised as one of the major

industry sectors responsible for producing large quantities of greenhouse gases (GHG) (1,2)

and other air pollutants such as nitrous oxides (NOx) and sulphate oxide (SO2) (3,4). At the

international level, emissions of GHG and pollutants from marine transport are largely

associated with the use of large combustion (mostly diesel) engines operated on vessels

navigating mostly in international waters. The release of air pollutants from these sources is

currently being addressed by the International Maritime Organisation (IMO) under Annex IV

of MARPOL 73/78, which is planned to come into force by 2005. However, GHG emissions

have not yet been incorporated into this Annex.

At a national level in Australia, the increase in GHG emissions associated with transport is of

major concern. Although the majority of these emissions across all types of GHGs (79.2 M

tons CO2-e or 14.4% the national budget) were associated with road transport (88%), marine

transport or navigation contributed over-proportionally high amounts of CH4, CO and SO2 (5).

Marine transport within Australian territorial waters includes shipping of raw materials,

manufactured goods and products between major ports, commercial fishing operations, inner

city public transport (river ferries), recreational activities, tourism, and a range of other minor

activities. The contributions from each of these subsectors have not yet been quantified on a

per sector basis. Of all the marine transport subsectors in Australia, tourism is likely to be one

of the fastest growing⎯fuelled by a strong growth in numbers of both domestic and

international visitors over the past ten years, and an ongoing diversification of Australia’s

tourism product. A good example of the more recently developed markets in the marine

environment is the whale, dolphin and whale shark viewing sector with a growth rate of

23.5% between 2000 and 2003, generating 665 000 visitor nights in 2003 (6,7).

So far, there has been little interest in assessing the GHG emissions of the Australian tourboat

industry. This lack of concern is somewhat surprising given that transport on water,

particularly at high speeds, can be very energy intensive (8) and, therefore, has the potential to

add a significant amount of GHG emissions to the overall energy budget of a holiday trip.

Tourism is one of Australia’s major export industries, and thus coastal and marine

environments along its 35 000 km coastline provide key attractions for its tourism product.

The only subsector of the tourism industry that has so far attracted more detailed attention in

regard to climate change is Australia’s ski industry (9,10). This particular interest has possibly

originated from the fact that several Australian ski resorts were considered marginal in regard

225

to their natural snow cover, and thus would face serious problems under the warmest climate

change scenarios (9).

Another reason for the lack of data and research in regard to the tourboat industry may lie in

the diversity of this sector and the corresponding difficulties associated with obtaining reliable

information at reasonable costs. In theory, marine tours can include anything from hiring a

surfboard to cruising onboard a major ocean liner (11). For this study, surfboards, windsurfers

and kite surfers were excluded because they resemble sporting equipment rather than vessels.

At the other extreme, ferries and ocean liners were excluded because their emissions were

considered similar to large commercial vessels (except for solid wastes and sewage) that often

operate in international waters as part of the major international shipping fleet and are,

therefore, subject to treaties administered by IMO under MARPOL 73/78. Vessels used for

ferry services as part of a regional public transport network were also excluded from the work

presented here, even though many of these vessels are also popular with tourists (e.g. Sydney

Harbour ferries, New York City ferries, Hong Kong ferries, etc.).

The main objectives for this research were: to characterise the types of small to medium size

tourboat operations in Australia’s coastal environment; to estimate the overall and per capita

GHG emissions for this sector; and to ascertain perceptions of tourboat operators (TBOs) in

regard to their contributions to GHG emissions in comparison to other impacts. Based on

previous experiences with other small to medium size tourism enterprises (SMTEs) (12,13),

information for this study was to be collected using a suite of methods ranging from face-to-

face interviews, postal survey questionnaires and in-situ audits. Results from these

investigations were to be used to highlight current problems with addressing GHG emissions

in this industry sector, and to explore how existing policy frameworks could be improved to

increase reductions in GHG emissions in the Australian tourboat industry.

METHODS

The general lack of comprehensive data for the Australian tourboat sector required the work

for this study be broken up into a set of consecutive tasks and phases. The first phase involved

establishing a reliable database for all (or most) TBOs in Australia. This database was used to

identify hotspots of tourboat operations and allowed a random selection of TBOs for

conducting initial face-to-face interviews and in-situ audits to fine tune and test the sampling

instrument. The third and final phase involved the development and distribution of 750 postal

survey questionnaires covering seven hotspot destinations in four Australian states (Qld,

NSW, Vic., WA).

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TBO database

Several online sources (Yellow Pages, White Pages, the Australian Charter Guide, etc.) were

queried for the listing of potential TBO addresses and phone numbers using categories such as

diving, snorkeling, fishing, boat hire, cruises, etc.. All entries in the resulting TBO database

were standardized in regard to type and content of information provided in each field (e.g.

street address, postcode, telephone number, etc.) and cross-checked for duplication of records.

The remaining records were grouped by postcodes, geocoded to a current postcode layer

available from CDATA 96 (14), and visually analysed for hotspots of tourboat activities.

Face-to-face interviews

Following a review of ship-sourced pollution legislation, and literature relating to impacts of

boats and larger vessels in general (11,15), a questionnaire was prepared to establish current

views and practices of Australian TBOs in regard to their business and tour operations,

vessels in use, general environmental impacts, and aspects relating to GHG emissions.

Following an initial small scale trial (16), 48 TBOs from within the seven study locations

were randomly selected and asked to participate in a 20 minute interview to be conducted at a

location dictated by the interviewee. Despite considerable investment in time and resources,

the willingness to participate in this research exercise remained low (17).

Postal survey

The type and style of responses noted during face-to-face interviews were analysed and

incorporated into a postal survey questionnaire reflecting the type of questions asked during

face-to-face interviews. Wherever possible, operators were provided with tick boxes to

facilitate providing information and to limit the time for completing the questionnaire to 25

minutes. The method used for the postal survey closely followed that outlined in Dillman

(18), including the initial mailout (questionnaire, cover letter, contact details slip, etc.), a

follow-up postcard, and (in case of a non-response) a follow-up letter three weeks after initial

mail-out. Copies of the materials used for the survey questionnaire can be requested from the

principal author.

Data compilation and analysis

Information from each business or operator was entered into a set of databases. Entries in

each field had to be standardized in order to reduce the number of categories. For example,

small open tinnies and trailerable half-cabin cruisers or any other similar vessel, according to

227

its hull type and configuration, dimensions, means of propulsion, and engine details

(inboard/outboard, two or four stroke, petrol or diesel) were amalgamated into one category

named “tinnie/half-cabin”. Similar amalgamations or condensations were applied to other

fields such as principal type of activity (fishing, diving, etc.).

RESULTS At first, 2998 business addresses of Australian TBOs were compiled from telephone

directories and other databases. Based on the returns from 750 questionnaires allowing

corrections for agents only or businesses that were no longer in operation, the number of

TBOs operating one or more vessels in 2001 was estimated at 1476. Originally, 1505 TBOs

were listed in the initial database for the seven study areas. Of these, 799 were sampled.

Applying the same correction factor (49.2 % of actual TBOs in operation, see above) left

approximately 400 TBOs that could be surveyed, or could have replied to the questionnaire.

Overall, 145 operators responded to this study, either through participation in face-to-face

interviews or a returned and, at least mostly, completed survey questionnaire. The overall

response rate to the survey questionnaire and the face-to-face interviews was therefore

estimated at 35%, which equated to approximately 10% of all TBOs in Australia.

Business characteristics

The majority (34.5%) of TBOs were, at the time of the study, in business for 5-10 years.

Almost three quarters of operators considered their businesses to be small, employing usually

one to five staff. Accordingly, 76 % of TBOs operated only one or two vessels. As expected,

hire companies had relatively more vessels than other operators.

Data from this research also indicated that some operators preferred a certain vessel type

based on their principal type of activity. For example, more than 65% of fishing tour operators

(28 out of 43) used fly-bridge cruisers or fishing boats/cruisers for their businesses, whereas

62% of hire operators used tinnies/half-cabins or houseboats. The use of purpose-built

tourism vessels, such as dive boats and hi-speed catamarans, was rare (i.e. only seven

percent).

Tour characteristics

Given that purchasing and maintaining a tourism vessel in Australia is quite expensive, it was

surprising to discover that 50% of all operators who provided information stated that their

228

vessel was in operation for only 50 % of the year or less. Cruise/sail operators dominated this

low use group.

The majority of tours conducted were half day (3 to 6 hrs) or full day (6 to12 hrs) trips. The

most notable difference in trip duration for different types of tourboat activities were between

fishing tours and cruises (sail or engine propelled). The majority of fishing trips lasted for

between six to 12 hours, whereas cruises were usually less than 6 hours. The latter were also

available for a more equal spread of passenger group sizes, whereas the number of customers

on fishing trips rarely exceeded ten (χ2 = 47.7, 6 df, p < 0.0001 for primary activities with

‘zero to five’, ‘five to ten’ or ‘more than ten’ guests/trip, ‘others’ excluded) (Table 1).

Table 1: Trip characteristics of Australian tourboat operations

Primary Activity Tour Characteristics Fishing Cruise/sail Hire Dive/ snorkel Other Total

Boat Use Total 43 39 27 27 6 142 (%/yr) Mean 50 43 49.5 70.7 65.8 52.5

4 4.28 5.32 5.35 17.09 2.41 ± 1 SE No. Guests Total 43 38 24 27 6 138 Per Average Mean 8 31.3 7 21.6 59.7 19.2

1 6.28 1.29 3.78 33.47 2.58 Length Trip ± 1 SE Time Average Length Trip (hrs/trip)

1 (2%) 11 (30%) 8 (33%) 7 (25%) 2 (33%) 29 (21%)0–3 16 (39%) 16 (43%) 7 (29%) 8 (29%) 2 (33%) 49 (36%)3– <6 24 (59%) 8 (22%) 5 (21%) 10 (36%) 2 (33%) 49 (36%)6– <12 0 2 (5%) 4 (17%) 3 (11%) 0 9 (7%) ≥12 41 37 24 28 6 136 Total 7 10.3 19.9 18.2 4.1 12.4 Mean 0 5.09 7.26 7.68 1.1 2.48 ± 1 SE

Vessel characteristics

Most of the vessels used by TBOs in Australia were of a single hull configuration, on average

13 years old and 13 m in length overall (LOA) (Table 2 below). Smaller vessels (usually

those less expensive and therefore less costly to replace) were not necessarily younger in age

than the larger vessels. Tinnies and half-cabin cruisers, mostly around 5 m LOA, were on

average 10 years old. Purpose-built tourism vessels, such as dive boats and hi-speed

catamarans, on the other hand, were on average the largest and youngest vessels (Table 2)

Engine configuration on vessels used by Australian TBOs varied considerably. Overall,

operators used engines from small two-stroke petrol outboard designs to large inbuilt,

turbocharged two- and four-stroke diesel engines with claimed outputs of up to 1720 kW.

Table 1 attempts to summarise the more general aspects of engine types fitted to tourism

229

vessels. Most double-hulled vessels had two engines whereas 58% and 40% of all mono-hulls

had a single or double engine configuration, respectively. Close to two thirds of all engines

used by Australian TBOs were inboard four-stroke diesel engines, the remainder being mostly

two- or four-stroke outboards. An additional type of engine, used on larger tourboats, was

generators operated to provide electricity. However, due to low numbers, the emissions from

these combustion engines were not considered any further.

Table 2: Vessel characteristics of Australian tourboat operators

INS

Tw

in-D

eck/

Fly-

Bri

dge

Cru

iser

Sail

Boa

t

Tin

ny/H

alf-

Cab

in

Spee

d B

oat

Fish

ing

Boa

t/Cru

iser

/etc

.

Div

e B

oat

Hou

sebo

at/B

BQ

B

oat Vessel

Characteristic Hi-S

peed

Cat

Oth

er

Tot

al*

%

Hull Configuration INS 4 – – – – – – – – – 4 2.8 Mono-Hull 9 31 15 16 10 12 6 2 – 1 102 70.3 Double-Hull 2 6 9 1 7 1 – 6 4 – 36 24.8 Tri-Hull 1 – – – 2 – – – – – 3 2.1

16 37 24 17 19 13 6 8 4 1 145 Total* 100.0 Length over Total* 13 36 24 17 18 13 6 8 4 1 140 100.0 all (m) Mean 11.5 13.7 14.9 4.9 12.5 15.4 18.8 10.7 27.2 25.0 13.2 –

1.93 0.58 1.11 0.34 1.77 1.83 3.60 1.16 4.44 – 0.57 – ± 1 SE Vessel Total* 13 37 24 15 19 13 6 8 4 1 140 100.0 age (yrs )Mean 16.3 13.3 12.8 9.7 8.1 19.3 8.0 14.4 7.5 70.0 13.0 – ± 1 SE 3.18 1.23 2.30 1.19 1.25 5.61 3.29 2.58 3.28 – 0.96 – Hull Engines Mono-Hull 1 5 14 15 5 15 4 – – – 1 59 41.3 2 4 17 – 5 1 8 2 4 – – 41 28.7 3 – – – – – – – 2 – – 2 1.4 Total* 9 31 15 10 16 12 2 6 – 1 102 71.3 Double-Hull 1 2 – – – – – 5 – – – 7 4.9 2 – 6 8 7 1 1 1 – 4 – 28 19.6 Total* 2 6 8 7 1 1 6 – 4 – 35 24.5 Mean 1.3 1.6 1.3 1.6 1.1 1.7 1.4 2.3 2.0 1.0 1.5 – 0.13 0.08 0.10 0.11 0.08 0.13 0.18 0.21 0.00 – 0.04 – ± 1 SE Diesel 8 36 19 8 1 13 – 4 4 – 93 Petrol 7 – 4 10 16 – 8 2 – – 47 0.7 Total* 15 36 24 18 17 13 8 6 4 1 140 100.0 Engine Total* 13 34 22 19 16 12 8 6 4 1 135 100.0 Age (yrs) Mean 14.4 10.4 9.0 3.7 6.4 12.2 4.1 4.3 3.5 70.0 8.9 – 4.83 1.29 1.56 0.41 0.80 2.58 0.90 0.92 1.19 – 0.85 – ± 1 SE NOTE: * = percentages of totals are given for the total number of responses for each variable

measured

On average, marine engines used for propelling Australian tourism vessels were about 4 years

younger than the hulls (Table 2) The highest turnover appeared to be with two-stroke (and to

230

a lesser degree four-stroke) outboard engines. Many houseboats, tinnies/half-cabins and dive

vessels (mostly semi-rigid inflatables) used outboard engines, which were on average younger

than diesel engines in fly-bridge cruisers and sailboats (yachts). These results were not

surprising as, from a technical and not uncommonly economical point of view, it is much

easier to exchange an outboard engine compared to an inboard diesel engine. Further, changes

to emission regulations for small spark ignition marine engines, under the U.S. Clean Air Act

1990 (as amended) (19), prompted engine manufacturers to develop more fuel efficient two-

stroke and, ultimately, four-stroke outboard designs. According to feedback during

interviews, several operators preferred the modern two-stroke and four-stroke designs, mainly

for their greater reliability.

Calculations of GHG emissions

The results presented above, and further pattern analyses (results not shown here), of the

dataset demonstrated that Australian TBOs could not be grouped into homogenous categories

based on ‘primary activity’ type, vessel type or trip characteristics. Further, the relative

proportion of different types of TBOs of the entire population of operators in Australia is

unknown. Therefore, GHG emissions were estimated based on the following equations:

Equation 1

GHGfuel type (CO2e) = FCfuel type (l h-1) × TDfuel type (h) × 0.6a × V/opfuel type × USEfuel type

(% × 0.01) × 365 × Opsfuel type (% sample × 1476 × 0.01) × CRfuel type (kg CO2e l-1)

where

FC = average fuel consumption stated by operators

TD = average trip duration

V/op = average number of vessels per operator

USE = average percent use per annum

Ops = number of operators

CR = AGO conversion rate

NOTE: a = correction factor for turning off engines while stopped (or moored) at destination

(one or several)

Equation 2

GHG TBOsAustralia = ∑ GHGfuel type

231

Based on these equations, the overall GHG emissions for the Australian tourboat industry

sector was estimated conservatively at 70 000 tons CO2-e or 0.1 percent of the transport sector

in Australia, the fastest growing sector in terms of GHG outputs. Taking the average number

of guests per trip into account, GHG emissions translated into an extra 61 kg CO2-e per tourist

if their travel itineraries included a trip on a boat with a diesel engine, or 27 kg CO2-e for a

boat with a petrol engine – the equivalent of a single person driving 140 km or 300 km,

respectively, in a standard passenger vehicle

Reducing GHG emissions

When asked to consider the impacts of their vessel on coastal environments in an open-ended

question, almost 75 % of operators were of the opinion that their activities caused no impact.

Of those who were aware of their potential impacts, 27 out of 38 mentioned engine emissions,

mostly in the form of small fuel spillages and oil leaks .

A slightly different picture could be obtained when operators where asked to rank the severity

of potential impacts of different aspects of operating a vessel on a scale of one to ten (the

latter representing the highest impact). Under these circumstances, TBOs ranked engine

emissions second highest after impacts associated with the release of, until then, mostly

untreated raw sewage from marine toilets and galleys (Table 3).

Table 3: Ranking of impacts of different boating aspects

Primary Activity Fishing Cruise/ Hire Boating Aspect Sail

Dive/ Snorkel

Other Total

Mean Mean ± 1 SE

Mean ± 1 SE

Mean ± 1 SE

Mean ± 1 SE ± 1 SE

Mean ± 1 SE

Boat Sewage (n = 120) 4.5 6.6 0.51 7.6 0.53 6.1 0.65 3.9 0.53 0.99 5.9 0.29Boat Engine Emissions (n = 122) 5.2 5.8 0.53 6.2 0.56 5.7 0.49 3.6 0.53 1.17 5.6 0.26Antifouling Paints (n = 120) 4.4 6.0 0.48 5.1 0.55 5.7 0.65 3.6 0.49 1.17 5.2 0.27Litter/Garbage (n = 123) 4.5 5.6 5.4 4.8 1.4 4.9 0.64 0.66 0.66 0.64 0.52 0.32

3.7 4.1 3.9 4.9 1.4 4.0 0.42 0.57 0.61 0.66 0.53 0.27Physical Damage to Substrate (n = 121) 2.8 4.4 3.3 3.7 1.3 3.4 0.42 0.60 0.59 0.58 0.41 0.27Wildlife Disturbance (n = 122) 2.6 3.9 3.8 3.0 1.6 3.2 0.34 0.48 0.59 0.41 0.38 0.22Breakdown of Sacrif. Anodes (n = 122)

Both the survey questionnaire and the face-to-face interviews provided TBOs with an

opportunity to comment on any aspect of their operations that they believed was of concern or

should be addressed. None of these comments contained any remarks on GHG emissions or

the potential effects of climate change on the day-to-day operations of TBOs in Australia.

232

DISCUSSION

Accuracy of CO2-e emission estimates

All data obtained for this study were derived from operators, or the supply side of the tourboat

market, and could not be verified against information collected from the demand side, i.e. the

International Visitor Survey (IVS) or National Visitor Survey (NVS) conducted by the

Australian Bureau of Tourism Research (BTR). The data collected by the BTR focus on

activities (e.g. going fishing) and does not differentiate between ways in which this activity

was conducted, for example fishing from the shore or beach versus fishing using a private

vessel or a commercial tour. Another compounding factor associated with the BTR data is that

many operators offered several activities on a single trip. Whale shark operators in Western

Australia usually incorporated a guided dive tour, either after all their customers had been

provided with the opportunity to swim with a whale shark, or, in case no whale sharks were

spotted on that day, as an alternative program. Similar scenarios were observed for operators

on the Great Barrier Reef, who often combined snorkelling and SCUBA dives in one trip.

It must be further acknowledged that the information provided by operators (notably with the

questionnaire) was sometimes inconsistent or occasionally conflicting. For example, some

operators considered themselves a small business and yet they indicated employing three

staff, which was considered to be a medium-sized enterprise by other operators. Another

potential source of error was the number of days or the time period that operators considered

their vessel(s) to be out at sea and in use. Some may have taken adverse weather conditions or

periods for maintenance into consideration, while others may have just reflected on their usual

weekly business activities. A third factor limiting the accuracy of GHG emissions calculated

for this study was the use of two-stroke diesel and petrol engines (no conversion factors could

be obtained for predicting the amount of CO2-e for these types of combustion engines).

Using data from fuel purchases at marinas and boat harbours would have quite possibly

provided estimates with similar (if not greater) levels of uncertainties, as many TBOs share

fuelling stations with other vessel operators (e.g. users of recreational vessels of similar size

and commercial fishermen). TBOs with boats propelled by small outboard engines sometimes

purchase fuel from road petrol stations to avoid additional charges or higher costs for fuel sold

at marine refuelling stations. Again, the degree to which these practices exist among TBOs is

currently unknown and would have caused estimates, based on fuel use, to be equally

speculative.

233

Reduction of GHG emissions

Even though the actual overall amount of GHG emissions of Australian TBOs represents only

0.1 percent of emissions associated with transport activities across Australia, there appears to

be a number of practices that lead to unnecessary wastes of non-renewable fossil fuels and

emissions of GHGs. At the technical level, a comparison of engine specifications and

combinations of hull and engine designs of individual operators revealed that a number of

operators used old, and possibly outdated, engines or combinations of engine/hull designs.

Using a vessel equipped with two 485 kW engines, an average fuel consumption of 300 l/hr

and an average of no more than 11 passengers per trip should become a practice that is

strongly discouraged.

Data from this study also indicated that the majority of Australian TBOs were ignorant

regarding the potential for their operations to cause environmental impacts. Further, the idea

that GHG emissions and climate change could affect day-to-day operations was non-existent

among the operators surveyed for this study. This included larger companies which claimed to

have internal environmental management systems in place and were accredited, or in the

process of becoming accredited, as ecotour operators.

The lack of awareness about GHG emissions and climate change amongst Australian TBOs,

and possibly most of their domestic clients could be the result of Australia’s arid, hot climate

and its history of sometimes drastic climate changes triggered by oscillations of surface water

temperatures in the southern oceans (El Niño Southern Oscillation (ENSO) effects). Although

this might need further research, the colloquial term of ‘another stinking hot summer’

indicates that Australians have learned to accept extremes. This not only includes heat waves

and droughts but also rain events and flooding associated with tropical cyclones (particularly

along the northern part of the continent anywhere between Perth and northern NSW (9)).

Another difficulty with experiencing long-term changes in Australia’s climate is that ENSO

effects can be very localised and, therefore, allow only little or no direct comparison.

The following measures, all coordinated and facilitated by the Australian Greenhouse Office

(AGO), could prevent many of the current ill-informed practices leading to the unnecessary

use of non-renewable fossil fuels:

a) better communication of technical innovations/solutions between TBOs, naval

architects and engineers,

b) introduction of a fuel consumption based excise on operator permits, and

c) small industry grants for upgrades of marine engines.

234

In summary, work from this project has provided some estimates for GHG emissions and

contributions of Australian TBOs to global climate change. These results have also

demonstrated that there is a considerable lack of awareness of impacts in general, and GHG

emissions in particular, and that it would be prudent to assume that this level of information

would be sufficient to design industry-specific measures for reducing fuel consumption and

subsequently GHG emissions.

ACKNOWLEDGEMENTS

This work would have not been possible without financial support provided by the

Cooperative Research Centre for Sustainable Tourism.

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236

A BIBLIOGRAPHY OF THE TOURISM CLIMATOLOGY FIELD TO 2004

Daniel Scott1, Brenda Jones1 and Geoff McBoyle1

1 Department of Geography, University of Waterloo, 200 University Avenue West, Waterloo,

Ontario, Canada, N2L 3G1

E-mail addresses: dj2scott@fes.uwaterloo.ca (Daniel Scott), be2jones@fes.uwaterloo.ca

(Brenda Jones), gmcboyle@fes.uwaterloo.ca (Geoff McBoyle),

INTRODUCTION

Weather and climate have a strong influence on the tourism and recreation sector, which is among the

largest and fastest growing industries in the world. For example, weather and climate influence the

environmental resources that are the foundation for tourism/recreation, the length and quality of

tourism and recreation seasons, the health of tourists, and even the quality of tourism experiences.

In spite of the recognized inter-relationships between climate and tourism, there has been no

comprehensive resource to date of the growing body of research that examines the interactions of

climate and tourism-recreation. ‘A Bibliography of the Tourism Climatology Field to 2004’ provides a

compendium of relevant research that spans more than four decades. The bibliography currently

contains over 330 references from academic journals, books, government and university reports, and

conference proceedings. To make the bibliography as comprehensive and up-to-date as possible, the

authors request other researchers to submit their work or other published studies they are familiar with

to the corresponding author (Dr. Daniel Scott). The basic criterion for inclusion in the bibliography are

the terms ‘climate or weather’ and ‘tourism or recreation’ in the publication’s title or keywords. The

authors will endeavor to update the bibliography periodically and updated versions of the bibliography

can be accessed at the following web sites:

Daniel Scott Home Page: http://www.fes.uwaterloo.ca/u/dj2scott/

ISB-CCTR web site: http://www.mif.uni-freiburg.de/isb/

eCLAT web site: http://www.cru.uea.ac.uk/tourism/publications/publications.html

237

BEFORE 1970

Bar-On, R. 1969. Seasonality and Trends in Israel Tourism. Technical Publication No. 30, Israel CBS, Jerusalem. An earlier study, based on a paper was presented at the 1966 Franco-Israel Colloquium on Operations Research. Technion-Israel Institute of Technology, Haifa. Clausse, R., Guérout, A. 1955. La durée des précipitations: Indice climatique ou élément de climatologie touristique. La Météorologie 37, p.1-9. Davis, N.E. 1968. An Optimum Weather Index. Weather 23, p.305-317. Deasy, G.F. 1949. The tourist industry in a North Woods county. Economic Geography 25, p.240-259. Fergusson, P. 1964. Summer weather at the English seaside. Weather 19, p.144-146. Green, J.S.A. 1967. Holiday meteorology: reflections on weather and outdoor comfort. Weather 22, p.128-131. Heurtier, R. 1968. A study of the summer synoptic tourist climate of Western Europe and the Mediterranean: Part I. La Meteorologie 7, p.71-107.

Heurtier, R. 1968. A study of the summer synoptic tourist climate of Western Europe and the Mediterranean: part II. La Meteorologie 8, p.519-566. Nikerk, J. 1952. Assurance pluie pour tourists. Revue de Tourisme 2(6), p.46-51. Poulter R.M. 1962. The next few summers in London. Weather 17, p.253-257. Rackcliffe P.G. 1965. Summer and winter indices at Armagh. Weather 20, p.38-44. Selke, A.C. 1936. Geographic aspects of the German tourist trade. Economic Geography 12, p.205-216. Terjung, W.H. 1968. Some thoughts on recreation geography in Alaska from a physio-climatic viewpoint. California Geo-grapher 9, p.27-39. Tucker, G.B. 1965. The weather and the holiday maker. In What is Weather Worth? Melbourne, Australia: Bureau of Meteorology.

238

1970 TO 1979

Adams, R.L.A. 1971. Weather, Weather Information, and Outdoor Recreation Decisions: A Case Study of the New England Beach Trip. Unpublished PhD Dissertation. Clark University, Worcester, Maryland. Adams, R.L.A. 1973. Uncertainty in nature, cognitive dissonance, and the perceptual distortion of environmental information: weather forecasts and New England beach trip decisions. Economic Geographer 49, p.287-297. Ahlstrom, B, Pohlman, J. 1976. Travelers Weather: Europe and North Africa. London, United Kingdom: Hale, 250pp. Bar-On, R. 1975. Seasonality in Tourism: A Guide to the Analysis of Seasonality and Trends for Policy Making. London, United Kingdom: The Economist Intelligence Unit. Besancenot, J.P., Mouiner, J., De Lavenne, F. 1978. Les conditions climatiques du tourisme littoral. Norois 99, p.357-382. Cato, J., Gibbs, K. 1973. An Economic Analysis Regarding the Effects of Weather Forecasts on Florida Coastal Recreationists. Economics Report No. 50. Gainesville, Florida: Food and Resource Economics Department, University of Florida. Crapo, D.M. 1970. Recreational Activity Choice and Weather: The Significance of Various Weather Perceptions in Influencing Preferences and for Selected Recreational Activities in Michigan State Parks. Unpublished PhD Dissertation. Michigan State University, East Lansing, Michigan. Crowe, R., McKay, G., Baker, W. 1973. The Tourist and Outdoor Recreation Climate of Ontario, Vol I: Objectives and Definitions of Seasons. Project Report No. REC-1-73. Downsview, Ontario: Atmospheric Environ-ment Service, Environment Canada. Crowe, R.B. 1975. Recreation, Tourism and climate: a Canadian perspective. Weather 30, p.248-253.

Crowe, R.B. 1976. A Climatic Classification of the Northwest Territories for Recreation and Tourism. Project Report No. 25. Downsview, Ontario: Atmospheric Environment Service, Environment Canada, Meteorological Appli-ations Branch, 232pp. Crowe, R., McKay, G. and Baker, W. 1977. The Tourist and Outdoor Recreation Climate of Ontario, Vol II: the Summer Season. Project Report No. REC-1-73. Downsview, Ontario: Atmospheric Environment Service, Environ-ment Canada. Crowe, R., McKay, G. and Baker, W. 1977. The Tourist and Outdoor Recreation Climate of Ontario. Vol III: the Winter Season. Project Report No. REC-1-73. Downsview, Ontario. Atmospheric Environment Service, Environment Canada: D’Allenger, P.K. 1970. Parachuting and the weather. Weather 25, p.188-192. Danilova, N.A. 1974. A Recreational Evaluation of the Climate of the Black Sea Coast. Report No. 25. Downsview, Ontario: Atmospheric Environment Service, Department of the Environment, Meteorological Translations. Day, E.E.D., McCalla, R.J., Millward, H.A., Robinson, B.S. 1977. The Climate of Fundy National Park and Its Implications for Recreation and Parks Management. Atlantic Region Geographical Series No. 1. Halifax, Nova Scotia: Department of Geography, Saint Mary’s University. Dowell, C.D. 1970. The Relationship of Reservoir Pleasure Boating to Selected Meteorological Factors. Unpublished PhD Dissertation. Texas A&M University, Texas. Duffell, J.R. 1972. Further studies in recreational trip generation, Volume 2: recreational indices of use – meteorological factors. Traffic Engineering and Control 14, p.285-288.

239

Durden, G.C., Silberman, J. 1975. The determinants of Florida tourist flows: a gravity model approach. Review of Regional Studies 5(3), p.31-41. Foord, H.V. 1973. Holiday Weather. London, U.K.: William Kimber. Gates, A.D. 1975. The Tourism and Outdoor Recreation Climate of the Maritime Provinces. Project Report No REC-3-73. Downsview, Ontario: Meteorological Applications Branch, Atmospheric Environment Service, Environment Canada, 113pp. Gates, A.D. 1975. The Tourism and Outdoor Recreation Climate of the Prairie Provinces. Downsview, Ontario: Meteorological Appli-cations Branch, Atmospheric Environment Service, Environment Canada. Godin, V.B., Maatz, G.J. 1976. The effect of weather conditions on backcountry overnight facilities usage. Journal of Leisure Research 8(4), p.307-311. Herrisson, Y., Perie, H. 1974. Méthodologie d'étude de l'utilisation des facteurs climatiques à titre préventif. Problème général du sport et des vacances. Bioclimat 10, p.249-255. Jehn, K., Jehn, M. 1979. Beach atmosphere. Weather 34(6), p. 223-232. Landsberg, H.E. 1975. Climate and Recreation. Weather, Climate and Settlements. A Report Prepared on behalf of the World Meteorological Organization for the Secretariat of Habitat: The U.N. Conference on Human Settlements, Geneva, p.33-39. Masterton, J.M. 1976. Bibliography of Climate Applied to Outdoor Recreation and Tourism. Downsview, Ontario: Recreation and Tourism Unit, Atmospheric Environment Service, Environment Canada. Masterton, J.M., Crowe, R.B., Baker, W.M. 1976. The Tourism and Outdoor Recreation Climate of the Prairie Provinces. Project Report No. REC-1-75. Downsview, Ontario: Atmospheric Environment Service, Environ-ment Canada, 221pp.

Matley, I. 1975. The Geography of International Tourism. Resource Paper 76-1. Washington, D.C.: Association of American Geographers.

Maunder, W.J. 1970. The Value of Weather. London, United Kingdom: Methuen and Co., 388pp. Mayo, E. 1976. Tourism and the national parks: a psychographic and attitudinal study. Journal of Travel Research 14, p. 14-18. Miossec, J. 1977. L'image touristique comme introduction à la Géographie du tourisme. Annales de Géographie LXXXVI(473), p.55-70. Murray R. 1972. A simple summer index with an illustration for summer 1971. Weather 27, p.161-169. Paul, A.H. 1971. Relationships of Weather to Summer Attendance at Some Outdoor Recreation Facilities in Canada. PhD Dissertation. University of Alberta, Edmonton. Paul, A.H. 1972. Weather and the daily use of outdoor recreation areas in Canada. In Taylor, J.A. (ed.) Weather Forecasting for Agriculture and Industry. New York City, New York: Newton Abbot, p.132-146. Peach, J.A. 1975. The Tourism and Outdoor Recreation Climate of Newfoundland and Labrador. Downsview, Ontario: Atmospheric Environment Service, Environment Canada, Unpublished Manuscript. Perry, A.H. 1971. Climatic influences on the development of the Scottish skiing industry. Scottish Geographical Magazine 87, p.25-29. Perry, A.H. 1972. The weather forecaster and the tourist – the example of the Scottish skiing industry. In Taylor, J.A. (ed.) Weather Fore-casting for Agriculture and Industry. New York City, New York: Newton Abbot, p.126-131. Perry, A.H. 1972. Weather, climate and tourism. Weather 27, p.199-203.

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Phillips, D.J. 1975. Assessment of recreation and tourism weather services in Canada. Zephyr. Downsview, Ontario: Atmospheric Environment Service, Environment Canada, 13pp. Pigram, J.J.J., Hobbs, J.E. 1975. The weather, outdoor recreation and tourism. Journal of Physical Education and Recreation 46, p.44-45. Reifsnyder, W. 1979. A bioclimatology for outdoor recreation. Münchener Universitäts Schriften 35, p.126-131.

Rense, W.C. 1974. Weather as an Influencing Factor in the Use of Oregon’s Coastal Recreation Areas. Unpublished PhD Dissertation. Oregon State University, Oregon, Washington.

Samuel, G.A. 1972. Some meteorological and other aspects of hot-air ballooning. Meteoro-logical Magazine 101, p.25-29. Schmidt-Kessen, W. 1977. Therapy in natural climates. Progress in Biometeorology (Tromp and Bouma ed.) 1(II), p.258-261. Thornes, J.E. 1977. The effect of weather on sport. Weather 32, p.258-268. Yapp, G.A., McDonald, N.S. 1978. A recreation climate model. Journal of Environmental Management 7, p.235-252.

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Berichte des Meteorologischen Institutes der Universität Freiburg

Nr. 1: Fritsch, J.: Energiebilanz und Verdunstung eines bewaldeten Hanges. Juni

1998.

Nr.2: Gwehenberger, J.: Schadenpotential über den Ausbreitungspfad Atmosphäre

bei Unfällen mit Tankfahrzeugen zum Transport von Benzin, Diesel, Heizöl

oder Flüssiggas. August 1998.

Nr. 3: Thiel, S.: Einfluß von Bewölkung auf die UV-Strahlung an der Erdoberfläche

und ihre ökologische Bedeutung. August 1999.

Nr. 4: Iziomon, M.G.: Characteristic variability, vertical profile and modelling of

surface radiation budget in the southern Upper Rhine valley region. Juli 2000.

Nr. 5: Mayer, H. (Hrsg.): Festschrift „Prof. Dr. Albrecht Kessler zum 70. Geburts-

tag“. Oktober 2000.

Nr. 6: Matzarakis, A.: Die thermische Komponente des Stadtklimas. Juli 2001.

Nr. 7: Kirchgäßner, A.: Phänoklimatologie von Buchenwäldern im Südwesten der

Schwäbischen Alb. Dezember 2001

Nr. 8: Haggagy, M.E.-N.A.: A sodar-based investigation of the atmospheric bound-

ary layer. September 2003

Nr. 9: Rost, J.: Vergleichende Analyse der Energiebilanz zweier Untersuchungsflä-

chen der Landnutzungen “Grasland“ und „Wald“ in der südlichen Oberrhein-

ebene. Januar 2004

Nr. 10: Peck, A.K.: Hydrometeorologische und mikroklimatische Kennzeichen von

Buchenwäldern. Juni 2004

Nr. 11: Schindler, D.: Characteristics of the atmospheric boundary layer over a Scots

pine forest. Juni 2004

Nr. 12: Matzarakis, A., de Freitas, C.R., Scott, D. (Eds.): Advances in Tourism Cli-

matology. November 2004

260