The climate tourism potential of Alpine destinations using the example of Sonnblick, Rauris and Salzburg (original) (raw)

Abstract

The climate tourism potential of a region can be described by methods used in human biometeorology and applied climatology. Frequency analyses based on complex thermal bioclimatic indices (e.g. physiologically equivalent temperature) and diagrams of precipitation patterns based on thresholds offer new approaches of visualisation. An integral approach for tourism climatologic analyses is provided by the climate-tourism/transfer-information-scheme that also bases on frequency distributions of relevant factors and parameters which are important for a destination. The knowledge about the vertical variability of tourism climatologic factors is of high importance because of the several kinds of tourism activities affected by weather. The same holds for a quantification of extreme events like heat waves because of their possible effects on health and recreation over a year's course. The results show that the vertical gradient of bioclimatic and tourism-related parameters can be of value when developing strategies of adaption to climate change.

Figures (14)

variables as input (air temperature, air humidity, wind speed, and short and long wave radiation fluxes) and they can be calculated with free software packages, e.g. RayMan (Matzarakis et al. 2007b, 2010b). In this study, PET is used due to its widely known unit degrees Celsius as an indicator of thermal stress and/or comfort. This commonly used unit is of particular importance for users such as planners and policymakers, who most likely are unfamiliar with human biometeorological terminology (Matzarakis 2007; Lin and Matzarakis 2008; Zaninovic and Matzarakis 2009). In order to calculate PET for the three stations, the following variables were included in the RayMan model: air temperature, vapour pressure, average wind speed and global radiation.

variables as input (air temperature, air humidity, wind speed, and short and long wave radiation fluxes) and they can be calculated with free software packages, e.g. RayMan (Matzarakis et al. 2007b, 2010b). In this study, PET is used due to its widely known unit degrees Celsius as an indicator of thermal stress and/or comfort. This commonly used unit is of particular importance for users such as planners and policymakers, who most likely are unfamiliar with human biometeorological terminology (Matzarakis 2007; Lin and Matzarakis 2008; Zaninovic and Matzarakis 2009). In order to calculate PET for the three stations, the following variables were included in the RayMan model: air temperature, vapour pressure, average wind speed and global radiation.

Fig. 2. Frequency diagram of PET classes for Salzburg Airport (1961-1990)

Fig. 2. Frequency diagram of PET classes for Salzburg Airport (1961-1990)

Fig. 3. Frequency diagram of precipitation classes for Salzburg Airport (1961-1990)

Fig. 3. Frequency diagram of precipitation classes for Salzburg Airport (1961-1990)

Fig. 4 CTIS for Salzburg Airport (frequency of occurrence in the period 1961—1990 in percent)

Fig. 4 CTIS for Salzburg Airport (frequency of occurrence in the period 1961—1990 in percent)

Fig. 5 Rated CTIS for Salzburg Airport (frequencies put into qualitative classes) The bioclimate diagram for the Sonnblick observatory (Fig. 10) does not surprise very much: It is extremely cold The precipitation diagram for the station Rauris (Fig. 7) generally corresponds to the diagram of Salzburg Airport. The annual course of the RR classes shows again a typical seasonal pattern, only when regarding the selected parameters

Fig. 5 Rated CTIS for Salzburg Airport (frequencies put into qualitative classes) The bioclimate diagram for the Sonnblick observatory (Fig. 10) does not surprise very much: It is extremely cold The precipitation diagram for the station Rauris (Fig. 7) generally corresponds to the diagram of Salzburg Airport. The annual course of the RR classes shows again a typical seasonal pattern, only when regarding the selected parameters

Fig. 6 Frequency diagram of PET classes for Rauris (1961-1990)

Fig. 6 Frequency diagram of PET classes for Rauris (1961-1990)

Fig. 7 Frequency diagram of PET classes for Rauris (1961-1990) The precipitation diagram (Fig. 11) shows an annual course which follows only the seasons marginally. With a

Fig. 7 Frequency diagram of PET classes for Rauris (1961-1990) The precipitation diagram (Fig. 11) shows an annual course which follows only the seasons marginally. With a

Fig. 8 CTIS for Rauris (frequency of occurrence in the period 1961-1990 in percent) high frequency of strong rain events (in this case, it should be mostly snow events) all over the year, the maximum month- ly precipitation sums stretch from April to August, only slightly interrupted in June. The minimum RR sum occurs in October which is followed by another period of relatively high precip- itation all over winter. Almost all over the year, the ground lays under a snow cover. Foggy and cloudy conditions prevail in about 50 and 75 % of the year, respectively, and almost one twelfth of the year has wind velocities of over 8 m/s. Sonnblick observatory (Fig. 13) shows a relatively big set of parameters and factors which are classified as ideal over ong annual periods. Apart from the expected positive rating of the parameters heat stress and sultriness, also the distri- bution of rainy days shows clearly a favourable distribution all over the average year. Even the windy days seem not to concentrate in a certain period which leads also for this parameter to ideal conditions all over the year. Less favour- able but still on the positive side of the scale is the frequency and distribution of foggy days which results in ideal con- ditions especially in winter. Finally, the snow cover is ideal almost anytime with only a short qualitative drop at the beginning of October.

Fig. 8 CTIS for Rauris (frequency of occurrence in the period 1961-1990 in percent) high frequency of strong rain events (in this case, it should be mostly snow events) all over the year, the maximum month- ly precipitation sums stretch from April to August, only slightly interrupted in June. The minimum RR sum occurs in October which is followed by another period of relatively high precip- itation all over winter. Almost all over the year, the ground lays under a snow cover. Foggy and cloudy conditions prevail in about 50 and 75 % of the year, respectively, and almost one twelfth of the year has wind velocities of over 8 m/s. Sonnblick observatory (Fig. 13) shows a relatively big set of parameters and factors which are classified as ideal over ong annual periods. Apart from the expected positive rating of the parameters heat stress and sultriness, also the distri- bution of rainy days shows clearly a favourable distribution all over the average year. Even the windy days seem not to concentrate in a certain period which leads also for this parameter to ideal conditions all over the year. Less favour- able but still on the positive side of the scale is the frequency and distribution of foggy days which results in ideal con- ditions especially in winter. Finally, the snow cover is ideal almost anytime with only a short qualitative drop at the beginning of October.

Fig. 9 Rated CTIS for Rauris (frequencies put into qualitative classes) Despite the impression of a very unfavourable environ- ment in terms of tourism climate, the rated CTIS for the In our analysis, climatic conditions and changes relevant to tourism were analysed for existing data sets. Several studies on tourism climate indicate their importance especially re- garding vulnerable mountain regions (OECD 2007). The tourism sector is more interested in what might happen in the coming years than what the climate will be like in the end of the twenty-first century. In contrast to the model- based regional studies KLIWA, KLARA and KUNTIKUM (KLIWA 2006; Stock 2005; Bartels et al. 2009), our

Fig. 9 Rated CTIS for Rauris (frequencies put into qualitative classes) Despite the impression of a very unfavourable environ- ment in terms of tourism climate, the rated CTIS for the In our analysis, climatic conditions and changes relevant to tourism were analysed for existing data sets. Several studies on tourism climate indicate their importance especially re- garding vulnerable mountain regions (OECD 2007). The tourism sector is more interested in what might happen in the coming years than what the climate will be like in the end of the twenty-first century. In contrast to the model- based regional studies KLIWA, KLARA and KUNTIKUM (KLIWA 2006; Stock 2005; Bartels et al. 2009), our

Fig. 10 Frequency diagram of PET classes for Sonnblick (1961-1990)

Fig. 10 Frequency diagram of PET classes for Sonnblick (1961-1990)

Fig. 11 Frequency diagram of PET classes for Sonnblick (1961-1990) and more aware of those long-term climate changes and thinks about new touristic products and activities. Also, extreme events are important and pose new challenges to destinations. analyses indicate conditions and trends on the basis of measured data. In general, winters will become milder and moister due to an increase in western weather situations (KLIWA 2006). The tourism industry is more

Fig. 11 Frequency diagram of PET classes for Sonnblick (1961-1990) and more aware of those long-term climate changes and thinks about new touristic products and activities. Also, extreme events are important and pose new challenges to destinations. analyses indicate conditions and trends on the basis of measured data. In general, winters will become milder and moister due to an increase in western weather situations (KLIWA 2006). The tourism industry is more

Fig. 13. Rated CTIS for Sonnblick (frequencies put into qualitative classes)

Fig. 13. Rated CTIS for Sonnblick (frequencies put into qualitative classes)

Fig. 12 CTIS for Sonnblick (frequency of occurrence in the period 1961-1990 in percent)

Fig. 12 CTIS for Sonnblick (frequency of occurrence in the period 1961-1990 in percent)

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