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BY 4.0 license Open Access Published by De Gruyter Open Access April 28, 2022

Assessment of climatic conditions for tourism in Xinjiang, China

  • Kaijun Cao EMAIL logo and Jun Gao
From the journal Open Geosciences


Tourism is a highly climate-sensitive sector, and the suitability of climate and weather can be a central motivation for travel. Through the tourism climate index (TCI), this study uses daily data from the period of 1980 to 2014 to identify the spatial and temporal distribution of climatic conditions in Xinjiang of China with regard to its climatic attractiveness for general tourism. The analytical results indicate that the climatic conditions of the most suitable months for tourism in Xinjiang are April, May, September, and October. The best climatic condition (TCI > 80) for tourism activities is found in September in most parts of Xinjiang, except for central Tianshan and Turpan Basins. The climate is not attractive for tourism in Xinjiang during the winter months. The annual average number of climatically suitable days (TCI > 70) are the highest in northwestern Aksu, most parts of Kashgar, and western Hotan. Most parts of northern Xinjiang are favorable for summer travel, with high TCI scores and a high average number of climatically suitable days. By contrast, tourist areas in southern and eastern Xinjiang have favorable climatic conditions during Spring and Autumn. These results can provide useful information to both tourists and the tourism industry.

1 Introduction

The tourism industry is a highly climate-sensitive economic sector [1,2,3]. The climate during different seasons affects the tour schedules of travel agencies and tourists [4]. Studies have found that weather and climatic conditions influence tourist destination selection [5], tourist experience, and the seasonality of tourism [6]. This is largely because meteorological parameters affect tourist comfort and health issues [7,8]; for instance, De Freitas indicated that tourists respond to the integrated effects of weather [9] (e.g., air temperature, humidity, wind, rain, and sunshine). Hamilton and Lau confirmed that climate is at least the third most popular attribute in decision making [10]. In general, the availability of real-time weather information via the internet might influence the behavior of tourists in selecting destinations and making travel plans [11]. From the perspective of the supply side (i.e., planners, decision-makers, stakeholders, etc.), variations in the climate across time and space need to be matched with the patterns of tourist activities and tourism planning projects, as favorable weather can increase the number of tourists [4,12].

For tourism, a “favorable climate” can be regarded as a resource [13], therefore, tourism climatic conditions should be measured and evaluated. A climate index approach is a method to illustrate the multifaceted nature of the climatic conditions by integrating several tourism-related climatic elements [2,6,14]. Over the last three decades or so, researchers have sought to assess the suitability of a destination’s climate for tourism using numerical climate indices [15]. Tourism climate indices are mostly based on the biophysical principles of human comfort [10]. For example, the Universal Thermal Climate Index is used only to evaluate the thermal component (thermal comfort) [16], while the Climate Index for Tourism and Climate Tourism Information Scheme evaluates thermal, aesthetic, and physical components [17,18]. Among various tourism climate indices, the Tourism Climatic Index (TCI) developed by Mieczkowski [19]is the most widely applied index to assess climate suitability for general tourist activities across a range of geographical scales [15]. The advantages of TCI lie in two aspects. First, TCI is a comprehensive index, as it includes all three facets of climate considered relevant for tourism, namely thermal comfort, physical aspects such as rain and wind, and the aesthetical facet of sunshine/cloudiness. Second, it is designed to use readily available weather data, making easy-to-operate calculations, which also reflect the destination climate suitability for “average” tourists engaged in outdoor activities [20,21,22]. However, TCI is not without limitation, as it is based on expert judgment and does not take tourists’ perceptions and preferences into consideration [13,15]. It should also be noted that TCI cannot be applied well to assess climatic suitability for specific types of tourism activities such as skiing and mountaineering. Yet, modifications to TCI have been made to specifically assess climatic comfort for specific tourism activities such as the Beach Comfort Index [23].

The comprehensive nature of the TCI allows for its applicability to the quality of the tourism experience for general tourism activities [2,24] (e.g., sightseeing, shopping, and relaxing;). Utilizing TCI, Scott et al., for instance, explored the spatial and temporal patterns of the tourism climate resources in North America [2]. Likewise, Amelung and Moreno applied TCI to simulate the effect of future climate change on outdoor international tourism expenditures within Europe [1]. Other TCI analyses also exist for DPR Korea [25], Zimbabwe [26], Indonesia [27], Iran [28], Puerto Rico [11], Central Europe [20], etc. There have been few specific studies about the current spatial and temporal distribution of climatic conditions for seasonal tourism in Xinjiang using TCI. In this study, we chose different climatic variables on each day in the period 1980–2014 from 105 stations. This allows us to determine the number of favorable days per month and allows the temporal scale to achieve daily resolution. The purpose of this study is to determine the climatic conditions of areas suitable for tourism and provide insight into the climatic attractiveness of Xinjiang. The structure of this study is as follows: In Section 2, the concept of TCI is presented, including factors and weighting. In Section 3 three determines the current tourist temporal-spatial distribution of climatic conditions each month. In Section 4, the number of climatically suitable days per season and year are shown. Finally, a discussion of the results is combined with the main conclusions.

2 Materials and methods

2.1 Study area

Xinjiang Uygur Autonomous Region covers an area of approximately 166.04 × 104 km2, accounting for more the one-sixth of China’s total territory. Located in the center of Eurasia, far away from oceans and surrounded by mountains (see Figure 1), it encompasses both semi-arid and arid areas with various landscapes [29,30]. Xinjiang is characterized by landforms that include the Junggar Basin and the Tarim Basin, which lie among three mountain ranges from north to south; these consist of the Altai, Tianshan, and the Kunlun Mountains. The Taklamakan Desert is the largest desert in China and the second-largest shifting sand desert in the world, with a total area of 32.44 km2 × 104 km2. Xinjiang comprises defined northern, eastern, and southern regions. Northern Xinjiang includes Altai, Karamay, Tacheng, Ili, Bortala, Changji, and Urumqi. Eastern Xinjiang includes Turpan and Hami. Southern Xinjiang includes Bayingolin, Aksu, Kizilsu, Kashgar, and Hotan.

Figure 1 
                  Distribution of meteorological stations and topography of Xinjiang.
Figure 1

Distribution of meteorological stations and topography of Xinjiang.

The climate of Xinjiang is typical of inner-continental landmasses [30]. The annual precipitation and the annual average temperature in northern Xinjiang range from 100 to 500 mm, and from 4 to 8°C, respectively. In southern Xinjiang, annual precipitation ranges from 20 to 100 mm, and the average temperature ranges from 10 to 13°C [31]. Xinjiang is a special geographical area located between the oasis and desert and over 90% of its population is distributed in the oases [32,33]. More than 50% of the land area is covered by deserts, with a low percentage (about 9%) of desert vegetation coverage; about 29% of the area is covered by alpine meadows and dry steppe, while only about 1% is covered by forests [34].

2.2 Data

To determine TCI categories of Xinjiang, we considered daily surface meteorological datasets (1951–2014) obtained from the China Meteorological Data Sharing Service System ( In the process of collection, the dataset has been imposed strict quality controls to ensure its accuracy. Continuously observed meteorological data for maximum and average air temperature, precipitation, minimum and average relative humidity, wind speed, and sunshine hours from 105 meteorological observation stations were selected (see Figure 1). Stations with large amounts of incomplete data records were excluded. Because the minimum relative humidity record started in 1980, the year was chosen as the starting point for the investigation. It was also noted that at the time of writing this study (in 2021), 2014 was the most recent year with complete available data. Second, and more importantly, the climatic conditions of Xinjiang have not changed significantly over the last few years [35,36]. Finally, in our study, the daily data of more than 30 years were used to calculate the average number of good days (TCI > 70) in each season as well as the monthly average TCI scores. The calculation on the daily scale further reduced the impact of changes in climate variables within a short period on the robustness of our findings. To this end, the findings continue to be highly applicable today. Minor data gaps (i.e., <5% of data missing) were filled by averaging the data from the periods immediately preceding and following the gap.

2.3 TCI

The TCI used in this study was derived from the original formula developed by Mieczkowski [19], it refers only to the common and general tourism activities of sight-seeing and similar light outdoor activities [13]. The TCI can be an effective tool to assess the suitability of climatic conditions for tourism [37]. The suitability was based on establishing the physiologically comfortable/acceptable ranges of the human body as a standard and applying them to climate data to determine climatic comfort for outdoor activities. It is based on the notion of “human comfort,” and consists of five sub-indices, each represented by one or two daily climate variables, as listed in Table 1. The five sub-indices and their constituent variables are as follows: (1) daytime comfort index (CID; calculated from the daily maximum temperature (°C) and daily minimum humidity (%)), (2) daily comfort index (CIA; calculated from daily mean temperature (°C) and humidity (%)), (3) precipitation (daily total precipitation, in mm), (4) sunshine (daily total hours of sunshine), and (5) wind (daily average wind speed, in m/s). The weights used in the equation are ultimately subjective, although they do have a basis in scientific knowledge [11,14]. In the Equation (1), the highest weight is given to CID to reflect the fact that tourists are generally most active during the day. However, the amount of sunshine and precipitation was given the second-highest weight, followed by daily thermal comfort and wind speed.

Table 1

Components of the tourism climate index

Sub-index Daily climate variables Influence on TCI Weight (%)
CID Maximum daily temperature; Minimum daily relative humidity Represents thermal comfort when maximum tourist activity occurs. 40
CIA Mean daily temperature; Mean daily relative humidity Represents thermal comfort over the total 24 h period 10
Precipitation (R) Total precipitation Reflects the negative impact that this element has on outdoor activities and holiday enjoyment 20
Sunshine (S) Total hours of sunshine Positive impact on tourism (can be negative because of the risk of sunburn and added discomfort on hot days) 20
Wind (W) Average wind speed Variable effect depending on temperature (evaporative cooling effect in hot climates rated positively, whereas windchill in cold climates rated negatively) 10

[Source: 2].

Mieczkowski proposed the following equation for calculating the TCI for outdoor recreational activities:

(1) TCI = 8 × CID + 2 × CIA + 4 × R + 4 × S + 2 × W

In the equation, CID is the daytime comfort index; CIA is the daily comfort index; R is the precipitation; S is sunshine, and W is wind speed. All variables are calculated in their specific units and then rated on a scale from −3 to 5 (or 0 to 5 for precipitation, sunshine, and wind speed); the maximum value of TCI is 100. The precipitation in Mieczkowski’s study is measured by monthly average precipitation. Therefore, the scheme was changed by simply dividing the monthly precipitation values by 30 to obtain a rating scheme based on daily values.

Both daytime and daily comfort indices were based on effective temperature, which is a measure of temperature that takes the effect of relative humidity into account. In both cases, the maximum rating of 5 was given for a new effective temperature between 20 and 27°C. If the effective temperature deviates from this temperature zone, CID or CIA fails. Less rain and more solar insolation were considered more comfortable. Mieczkowski took light outdoor activities as the point of reference for his rating system, and the rating scheme is detailed in Table 2. For a more detailed account of the set of variables, see Mieczkowski [19].

Table 2

Rating categories of the output score of the TCI

TCI score Category
90–100 Ideal
80–89 Excellent
70–79 Very good
60–69 Good
50–59 Acceptable
40–49 Marginal
30–39 Unfavorable
20–29 Very unfavorable
10–19 Extremely unfavorable
−30 to 9 Impossible

[Source: 13].

The calculated TCI values of Xinjiang ranged from -20 (impossible) to 100 (ideal). Acceptable scores are considered greater than 50; good scores should be above 60, and excellent scores are to be above 80 (see Table 2). In this study, based on the work of Amelung, Nicholls, and Viner [38], a TCI value of 70 or higher was considered attractive to a “typical” tourist engaged in relatively light outdoor activities.

2.4 Spatial analysis

The ArcGIS software was adopted for spatial analysis. As TCI calculation involved a large amunt of daily resolution data over 30 years, we utilized R language to program batch calculation. Through inverse distance weighted interpolation, we obtained each raster map (1 km × 1 km scale) from the calculated results. Monthly and seasonal TCI values from 1980 to 2014 were “joined” to the base map to display the spatial and temporal distributions. The effects of elevation were not incorporated in this interpolation process. For all the mentioned maps, coordinate systems of Universal Trasverse Mercator and ellipsoid reference of World Geodetic System were utilized. This region is located in the northern hemisphere zone 45.

3 Results

3.1 Climatic conditions distribution for tourism

Xinjiang is in an arid zone in Central Asia – one of the regions farthest from the ocean. The maps in Figure 2 are the spatial distributions of mean monthly values of TCI over a year, indicating that the climatic conditions in Xinjiang feature large differences across regions and seasons.

Figure 2 
                  Comparison of TCI scores for the twelve months distribution of climatic conditions for tourism (TCI categories of “Extremely unfavorable” to “Excellent”) in Xinjiang. The same map legend is used in each case to allow for a better comparison of months.
Figure 2

Comparison of TCI scores for the twelve months distribution of climatic conditions for tourism (TCI categories of “Extremely unfavorable” to “Excellent”) in Xinjiang. The same map legend is used in each case to allow for a better comparison of months.

During the spring, “very good” conditions (TCI > 70) for general tourism activities were observed in the south, and a decline to “good” and then “acceptable” conditions with northward travel in March (Figure 2a). In April, ‘good’ conditions (TCI > 60) were found in most regions of Xinjiang. “Excellent” conditions (TCI > 80) existed over Southern Xinjiang and in the Turpan Basin (Figure 2b). In May, “excellent” conditions appeared to decline in southern Xinjiang, whereas the conditions in northern and eastern Xinjiang improved (Figure 2c); excellent conditions were found in most parts of northern Xinjiang, the midwestern part of southern Xinjiang, and the southeastern part of eastern Xinjiang.

In the summer season, prolonged comfort conditions occurred across most of Xinjiang. The TCI distribution was similar during June and August. Areas with “good” conditions were in the southeast of Xinjiang. “Excellent” conditions were in the Altai region of northern Xinjiang, which was largely attributed to the area’s high latitude. Most other areas experienced “very good” conditions. In July, “very good” and “excellent” conditions occurred in the Pamirs and along the Tianshan and Altai Mountains. Compared with the spring in April and May, the region of “excellent” conditions exhibits a relative decrease. This decline is primarily due to the maximum daily air temperature increasing above comfort levels for sightseeing activities.

In September, except for the middle of the Taklamakan Desert, Turpan Basin, and the central part of the Tianshan Mountains, most regions of Xinjiang could be classified as having “excellent” climatic conditions for travel. September was thus the best time for general outdoor activities in Xinjiang. In October, the zone of “excellent” climatic attractiveness was considerably reduced, though the entire southern Xinjiang did continue to enjoy these conditions; by contrast, compared with September, the Turpan Basin showed an improving trend. For a long stretch of the Tianshan Mountain range, northern Xinjiang and eastern Xinjiang were lowered to “good” and “very good” conditions. In November, the climatic conditions for tourism across the region showed three zones along with a latitudinal distribution; TCI > 60 was only found in southern Xinjiang and the Turpan Basin.

In winter, a region of “good” conditions was only observed in southern Xinjiang (i.e., a small area of Hotan Prefecture in February). Hence, the winter season in Xinjiang is not a suitable time for tourism.

The percentage of surface area in each TCI class each month is shown in Figure 3. The months with a large amount of surface area “excellent” for general tourism activities (TCI > 80) were April, May, September, and October, with 57.7, 47.2, 88.2, and 52.1% of Xinjiang’s surface area, respectively. The months showing the highest proportion of “unfavorable” conditions (TCI < 30) were January, February, and December, with the highest percentage observed in December (approximately 19.5%).

Figure 3 
                  Frequency diagram of monthly TCI classes (percentages of surface area).
Figure 3

Frequency diagram of monthly TCI classes (percentages of surface area).

3.2 Number of good days (TCI > 70)

In this study, TCI values higher than 70 were considered suitable for general tourism activities such as sightseeing. Figure 4a–c show the spatial distribution of the average number of good days (TCI > 70) in Xinjiang in spring, summer, and autumn, respectively. In winter, because the average number of good days for the selected meteorological station was less than one, winter distribution was not considered on its own. Winter values however were included to identify the annual spatial distribution (Figure 4d).

Figure 4 
                  Number of good days (TCI > 70) for Xinjiang: spring (a), summer (b), autumn (c), and mean annual (d, including winter values).
Figure 4

Number of good days (TCI > 70) for Xinjiang: spring (a), summer (b), autumn (c), and mean annual (d, including winter values).

In terms of seasonal variations, the average number of good days increased with decreasing latitude and elevated in spring. As shown in Figure 4a, central Tianshan and the northern part of Pamir had the lowest number of good days (0–30 days) compared to other regions, including the Junggar Basin. The average number of good days generally increased to 30–60 days in northern Xinjiang and exceeded 70 days in the Hotan Prefecture. In summer (see Figure 4b), the regions with the lowest number of good days (<30 days) included the Turpan-Hami Basin and most parts of southern Xinjiang (e.g., central and eastern Tarim Basin). With a typically arid continental climate, both regions have high air temperatures throughout the summer because of their low elevation and locations far from the sea and in the desert, resulting in low CID and CIA scores. Less precipitation and long sunshine duration lead to “acceptable” and “good” conditions during summer. In addition, areas with more than 60 good days were mainly distributed in Altai, western Tianshan, and the western Kunlun Mountains. Similar to spring, the distribution of the average number of good days during autumn increased southward, and the overall distribution was transformed into four lateral zones, with a reduction in the overall number of good days. Similar to spring, the central Tianshan and northern part of Pamir had the lowest number of good days (0–30 days); 30–40 good days were observed in the northern Junggar Basin, 40–50 good days were observed in the Ili Valley area, eastern Xinjiang and Junggar Basin, and more than 50 good days were observed in southern Xinjiang.

Figure 4d presents the annual average number of good days (TCI > 70). Tourism destinations that have a favorable climate for a long period are likely to attract more tourists. The least attractive areas, that is, those with less than 120 good days per year included northern Pamirs, central Tianshan, and Turpan Basin. The rest of Xinjiang enjoyed the number of good days ranging from 120 to 200, among which the most climatically attractive areas (with 180–200 good days of the year) were northwestern Aksu, most parts of Kashgar, and western Hotan.

3.3 Frequencies of TCI categories (TCI > 50) per month for Xinjiang

Figure 5 presents the average number of acceptable, good, and excellent days per month for the whole of Xinjiang. In southern and eastern Xinjiang, nearly all days were with at least “acceptable” climatic conditions for travel (TCI > 50) from March to November, but the number of days declined slightly in eastern Xinjiang during summer. Similar results were obtained for northern Xinjiang from May to October. With regards to the distributions of good days (TCI > 60), there was a significant temporal variability. For example, in eastern Xinjiang, more than 20 days were observed in spring and autumn, whereas for northern Xinjiang, this occurred from April to October; in southern Xinjiang, this occurred from March to October, and the summer months were accompanied by a slight decrease in the good days. In terms of excellent days (TCI > 80), the three regions exhibited a high degree of similarity, with a maximum average number of good days occurring in spring and autumn. In southern and eastern Xinjiang, the maximum number of good days in a month occurred in April and September, whereas in the northern area, this occurred in May and September. Furthermore, the numbers of acceptable, good, and excellent days were all greater than ten in northern Xinjiang during the summer months. In general, the spring and autumn of Xinjiang displayed high suitability for tourism.

Figure 5 
                  Comparison of the present number of acceptable, good, and excellent (top, middle, and bottom line) days per month for Eastern Xinjiang (a), Northern Xinjiang (b), Southern Xinjiang (c), and all of Xinjiang (d), Acceptable, good, and excellent days are defined as having a TCI above 50, 60, and 80, respectively.
Figure 5

Comparison of the present number of acceptable, good, and excellent (top, middle, and bottom line) days per month for Eastern Xinjiang (a), Northern Xinjiang (b), Southern Xinjiang (c), and all of Xinjiang (d), Acceptable, good, and excellent days are defined as having a TCI above 50, 60, and 80, respectively.

In comparison with the regional variation reveals that the impact of air temperature on the TCI category varied as latitude and topography changed. In the Turpan-Hami Basin of eastern Xinjiang, due to its low altitude, it became too hot to travel in summer. Hence, with an increase in sunshine and a decrease in precipitation, the air temperature effect was amplified in this region in summer. By contrast, in northern Xinjiang, due to the increase in latitude, the air temperature stayed at a comfortable level in summer. As a result, northern Xinjiang obtained a high TCI score from April to September.

4 Discussion

This research is the first study to use the TCI to estimate the spatial and temporal distribution of climatic conditions for tourism in Xinjiang. The study showed that southern and eastern Xinjiang have favorable climatic conditions for general tourism activities during spring and autumn. By contrast, most parts of northern Xinjiang are only suitable for summer travel with high TCI scores and a high average number of good days. However, tourism in southern and eastern Xinjiang is much less developed than in northern Xinjiang. For example, in 2014, southern and eastern Xinjiang's domestic tourism revenue accounted for 13 and 10% of Xinjiang’s total tourism revenue, respectively, which was far lower than that of northern Xinjiang (i.e., 76%).

Seasonality strongly influences tourism destinations [13]. It causes tourism facilities to be crowded in peak seasons and underutilized in off-seasons [39]. For example, an increase in tourism activity may result in additional demands on tourism resources by tourists, with implications for planning, development, and management of new accommodations and transportation [40]. Also, uncertainties regarding the atmospheric factors (e.g., dust storms in desert areas and air pollution in some locations) have a significant potential impact on the environmental resources for tourism. Thus, climatic conditions should be integrated with other tourism resources in the design of tourism products [41].

It is noteworthy that the climatic conditions of Xinjiang are by no means the only attraction in the region. Attractive landscapes, geographic location, topography, vegetation and fauna, nature, and cultural heritage are among the other factors influencing successful tourism destinations [42]. Therefore, aesthetic and physical conditions are also important [9]. For instance, southern and eastern Xinjiang are widely distributed in the Gobi Desert, surrounded by high mountains blocking warm and humid air, where strong winds can blow gusts of sand and dirt. This might be a key reason why current tourism receipts in eastern and southern Xinjiang do not match the TCI distribution. Thus, a high TCI score does not necessarily translate to a high level of visitation.

An interrogation of TCI-based studies of other regions, such as DPR Korea [25], Zimbabwe [26], Iran [28], and the Mediterranean region [43], showed that researchers tend to use observed and projected climate data to calculate climatic conditions attractive for tourism. This, however, can be insufficient for tourism purposes because they do not offer a high temporal resolution as the variables used are monthly averages. In this regard, our calculations were based on daily climate data, which allowed us to determine the number of good days in different seasons, thus offering a seasonal assessment of the climate with high temporal resolution. In addition, using TCI as the primary analysis tool, our study only refers to general tourism (e.g., sightseeing). Some climate-dependent tourism seasons are, however, the opposite of those considered favorably in this study, such as the peak season of ski tourism in northern Xinjiang is the winter. In this regard, future studies may modify the TCI index to assess the tourism comfort for specific tourism activities (e.g., ice and snow tourism). Moreover, for tourists, climate and weather conditions are important factors affecting travel-related decision-making and experiences. This study used the TCI formula (including climate variables such as temperature, precipitation, relative humidity, and wind speed) to calculate the tourism climatic suitability of Xinjiang. However, the index does not include snow, vegetation, bare land (Gobi Desert), and variables alike that can also affect climatic conditions in the earth's climate system. Hence, future research may also take these variables into consideration to assess the tourism potential of Xinjiang more accurately across different regions and seasons.

5 Conclusions

The results demonstrated the distribution of the climatic conditions for tourism over space and time, which clearly showed the suitable or unfavorable periods for general tourism activities in given places or the comparability of places in a given period. The findings reveal that overall, the most suitable months for general tourism activities in Xinjiang are April, May, September, and October, while the winter months are the least favorable. In terms of seasonal variations, the average number of good days increased with decreasing latitude and elevation in spring and autumn. The spring and autumn of southern and eastern Xinjiang exhibit high suitability and attraction for tourism, whereas northern Xinjiang is most suitable for tourism in summer. The analysis also showed that latitude and topography play an important role in the TCI distribution.

A detailed analysis of the climate for tourism purposes should draw on detailed data and include important parameters. Thus, the use of daily climatic variables allowed for a seasonal assessment of the climate with a high temporal resolution, offering detailed information for tourists. The results of this study can be helpful for tourists, travel agencies, and tourism authorities to assess and compare the climatic conditions of destinations, with implications for tourism planning, development, and management.

  1. Funding information: This research was funded by the Natural Science Foundation of Xinjiang Uygur Autonomous Region, grant number 2019D01C051; This work was also partially supported by the Key Laboratory of the Sustainable Development of Xinjiang’s Historical and Cultural Tourism, grant number LY2020-08; Start-up Fund for Doctors of Xinjiang University, grant number BS180280.

  2. Author contributions: K.C. managed the entire research project and also analyzed and considered the research materials. J.G. performed the review and editing.

  3. Conflict of interest: Authors state no conflict of interest.


[1] Amelung B, Moreno A. Costing the impact of climate change on tourism in Europe: results of the PESETA project. Climatic Change. 2012;112:83–100.10.1007/s10584-011-0341-0Search in Google Scholar

[2] Scott D, McBoyle G, Schwartzentruber M. Climate change and the distribution of climatic resources for tourism in North America. Clim Res. 2004;27:105–17.10.3354/cr027105Search in Google Scholar

[3] Daneshvar M, Bagherzadeh A, Tavousi T. Assessment of bioclimatic comfort conditions based on Physiologically Equivalent Temperature (PET) using the RayMan Model in Iran. Open Geosci. 2013;5(1):53–60.10.2478/s13533-012-0118-7Search in Google Scholar

[4] Lin T-P, Matzarakis A. Tourism climate information based on human thermal perception in Taiwan and Eastern China. Tour Manag. 2011;32:492–500.10.1016/j.tourman.2010.03.017Search in Google Scholar

[5] Kovačić S, Jovanović T, Miljković Ð, Lukić T, Marković S, Vasiljević D, et al. Are Serbian tourists worried? The effect of psychological factors on tourists’ behavior based on the perceived risk. Open Geosci. 2019;11(1):273–87.10.1515/geo-2019-0022Search in Google Scholar

[6] Bigano A, Hamilton J, Maddison D, Tol RJ. Predicting tourism flows under climate change. Climatic Change. 2006;79:175–80.10.1007/s10584-006-9190-7Search in Google Scholar

[7] Lin T-P, Matzarakis A. Tourism climate and thermal comfort in Sun Moon Lake, Taiwan. Int J Biometeorol. 2008;52:281–90.10.1007/s00484-007-0122-7Search in Google Scholar PubMed

[8] Matzarakis A. Transferring climate information for application and planning: the climate-tourism/transfer-information-scheme. In: Helmis CG, Nastos PT, editors. Advances in meteorology, climatology and atmospheric physics. Berlin Heidelberg: Springer; 2013. p. 591–7.10.1007/978-3-642-29172-2_84Search in Google Scholar

[9] de Freitas CR. Tourism climatology: evaluating environmental information for decision making and business planning in the recreation and tourism sector. Int J Biometeorol. 2003;48:45–54.10.1007/s00484-003-0177-zSearch in Google Scholar PubMed

[10] Hamilton J, Tol RJ. The impact of climate change on tourism in Germany, the UK and Ireland: a simulation study. Reg Env Change. 2007;7:161–72.10.1007/s10113-007-0036-2Search in Google Scholar

[11] Mendez-Lazaro PA, Terrasa-Soler JJ, Torres-Pena C, Guzman-Gonzalez P, Rodriguez S, Aleman M, et al. Tourism and climate conditions in San Juan, Puerto Rico, 2000–2010. Ecol Soc. 2014;19(2):11.10.5751/ES-06380-190211Search in Google Scholar

[12] Olya HGT, Alipour H. Risk assessment of precipitation and the tourism climate index. Tour Manag. 2015;50:73–80.10.1016/j.tourman.2015.01.010Search in Google Scholar

[13] Perch-Nielsen S, Amelung B, Knutti R. Future climate resources for tourism in Europe based on the daily Tourism Climatic Index. Climatic Change. 2010;103:363–81.10.1007/s10584-009-9772-2Search in Google Scholar

[14] Farajzadeh H, Matzarakis A. Quantification of climate for tourism in the northwest of Iran. Meteorolog Appl. 2009;16:545–55.10.1002/met.155Search in Google Scholar

[15] Yu DD, Rutty M, Scott D, Li S. A comparison of the holiday climate index:beach and the tourism climate index across coastal destinations in China. Int J Biometeorol. 2021;65:741–8.10.1007/s00484-020-01979-wSearch in Google Scholar PubMed PubMed Central

[16] Bröde P, Fiala D, Błażejczyk K, Holmér I, Jendritzky G, Kampmann D, et al. Deriving the operational procedure for the universal thermal climate index (UTCI). Int J Biometeorol. 2012;56:481–94.10.1007/s00484-011-0454-1Search in Google Scholar PubMed

[17] De Freitas CR, Scott D, McBoyle G. A second generation climate index for tourism (CIT): specification and verification. Int J Biometeorol. 2008;52:399–407.10.1007/s00484-007-0134-3Search in Google Scholar PubMed

[18] Zaninović K, Matzarakis A. The bioclimatological leaflet as a means conveying climatological information to tourists and the tourism industry. Int J Biometeorol. 2009;53:369–74.10.1007/s00484-009-0219-2Search in Google Scholar PubMed

[19] Mieczkowski Z. The tourism climatic index: a method of evaluating world climates for tourism. Can Geograp/Le Géograp Canadien. 1985;29:220–33.10.1111/j.1541-0064.1985.tb00365.xSearch in Google Scholar

[20] Kovacs A, Unger J. Modification of the tourism climatic index to Central European climatic conditions – examples. Idojaras. 2014;118:147–65.Search in Google Scholar

[21] Limb M, Spellman G. Evaluating domestic tourists’ attitudes to British weather. A qualitative approach. In: Matzarakis A, de Freitas CR, editors, Proceedings of the First International Workshop on Climate, Tourism and Recreation. Halkidiki, Greece: Report of a Workshop Held at Porto Carras, Neos Marmaras; 2001. p. 5–10.Search in Google Scholar

[22] Németh Á. Estimation of tourism climate in the lake balaton region, Hungary. J Environ Geogr. 2013;6(1–2):49–55.10.2478/v10326-012-0006-0Search in Google Scholar

[23] Morgan R, Gatell E, Junyent R, Micallef A, Özhan E, Williams AT. An improved user-based beach climate index. J Coast Conserv. 2000;6:41–50.10.1007/BF02730466Search in Google Scholar

[24] Hein L, Metzger MJ, Moreno A. Potential impacts of climate change on tourism; a case study for Spain. Curr Opin Environ. 2009;1:170–8.10.1016/j.cosust.2009.10.011Search in Google Scholar

[25] Om K-C, Ren G, Jong S-I, O K-C, Ryu H-C. Tourism climatic condition over PDR Korea. Asia Pac J Tour Res. 2018;23:1173–83.10.1080/10941665.2018.1529686Search in Google Scholar

[26] Mushawemhuka WJ, Fitchett JM, Hoogendoorn G. Towards quantifying climate suitability for Zimbabwean nature-based tourism. South Afr Geograp J. 2021;103(4):443–63.10.1080/03736245.2020.1835703Search in Google Scholar

[27] Hasanah NAI, Maryetnowati D, Edelweis FN, Indriyani F, Nugrahayu Q. The climate comfort assessment for tourism purposes in Borobudur Temple Indonesia. Heliyon. 2020;6:e05828.10.1016/j.heliyon.2020.e05828Search in Google Scholar PubMed PubMed Central

[28] Masoudi M. Estimation of the spatial climate comfort distribution using tourism climate index (TCI) and inverse distance weighting (IDW) (case study: Fars Province, Iran). Arab J Geosci. 2021;14:363.10.1007/s12517-021-06605-6Search in Google Scholar

[29] Baodi M, Tianqi N, Xiangfang X. Tourist resources and tourism development in North Xinjiang. GeoJournal. 1990;21:161–5.10.1007/BF00645323Search in Google Scholar

[30] Wu Z, Zhang H, Krause C, Cobb N. Climate change and human activities: a case study in Xinjiang, China. Climatic Change. 2010;99:457–72.10.1007/s10584-009-9760-6Search in Google Scholar

[31] Du J, Shu J, Yin J, et al. Analysis on spatio-temporal trends and drivers in vegetation growth during recent decades in Xinjiang, China. Int J Appl Earth Observ Geoinf. 2015;38:216–28.10.1016/j.jag.2015.01.006Search in Google Scholar

[32] Zhang Y, Yao Y, Lin Y, Xiang L. Satellite characterization of terrestrial drought over Xinjiang Uygur Autonomous Region of China over past three decades. Env Earth Sci. 2016;75:1–13.10.1007/s12665-016-5315-0Search in Google Scholar

[33] Fang S, Yan J, Che M, et al. Climate change and the ecological responses in Xinjiang, China: Model simulations and data analyses. Quater Int. 2013;311:108–16.10.1016/j.quaint.2013.08.032Search in Google Scholar

[34] Li C, Zhang C, Luo G, Chen X. Modeling the carbon dynamics of the dryland ecosystems in Xinjiang, China from 1981 to 2007 – The spatiotemporal patterns and climate controls. Ecol Model. 2013;267:148–57.10.1016/j.ecolmodel.2013.06.007Search in Google Scholar

[35] Li M, Yao J, Guan J, Zheng J. Observed changes in vapor pressure deficit suggest a systematic drying of the atmosphere in Xinjiang of China. Atmos Res. 2021;248:105199.10.1016/j.atmosres.2020.105199Search in Google Scholar

[36] Yao J, Mao W, Chen J, Dilinuer T. Signal and impact of wet-to-dry shift over Xinjiang. China Acta Geograph Sin (Chin). 2021;76:57–72.Search in Google Scholar

[37] Lewis-Bynoe D, Howard S, Moore W Climate change and tourism features in the Caribbean. Tenth annual Sir Arthur Lewis institute of social and economic studies conference: navigating risks and building resilience in small states, Cave Hill, Barbados[online]; 2009. http://mpraubuni-muenchende/21470.Search in Google Scholar

[38] Amelung B, Nicholls S, Viner D. Implications of global climate change for tourism flows and seasonality. J Travel Res. 2007;45:285–96.10.1177/0047287506295937Search in Google Scholar

[39] Getz D, Nilsson PA. Responses of family businesses to extreme seasonality in demand: the case of Bornholm, Denmark. Tour Manag. 2004;25:17–30.10.1016/S0261-5177(03)00067-0Search in Google Scholar

[40] Amelung B, Nicholls S. Implications of climate change for tourism in Australia. Tour Manag. 2014;41:228–44.10.1016/j.tourman.2013.10.002Search in Google Scholar

[41] Ge Q, Kong Q, Xi J, Zheng J. Application of UTCI in China from tourism perspective. Theor Appl Clim. 2017;128:551–61.10.1007/s00704-016-1731-zSearch in Google Scholar

[42] Matzarakis A, Endler C, Nastos PT. Quantification of climate-tourism potential for Athens, Greece – recent and future climate simulations. Glob Nest J. 2014;16:43–51.10.30955/gnj.001264Search in Google Scholar

[43] Amelung B, Viner D. Mediterranean tourism: exploring the future with the tourism climatic index. J Sustain Tour. 2006;14:349–66.10.2167/jost549.0Search in Google Scholar

Received: 2021-07-28
Revised: 2022-02-05
Accepted: 2022-03-14
Published Online: 2022-04-28

© 2022 Kaijun Cao and Jun Gao, published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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