Indian Journal of Spatial Science Autumn Issue,2021:12(2) pp.63 - 71 Indian Journal of Spatial Science Peer Reviewed and UGC Approved (Sl No. 7617) Homepage: www.indiansss.org EISSN: 2249 - 4316 ISSN: 2249 - 3921 Understanding Sustainability of Tourism validated by selected Climate Indices: A Case Study on the City of Kolkata, India 1# Trisha Chakraborty , and Dr. Debashish Das 2 1 Junior Research Fellow: Department of Architecture, Jadavpur University, West Bengal, India Associate Professor: Department of Architecture, Jadavpur University, West Bengal, India 2 # Corresponding Author Article Info Article History Received on: 15 December, 2020 Accepted in Revised Form on: 31 July, 2021 Available Online on and from: 23 September, 2021 Keywords Climate, HCI, TCI, Tourism, Tourists. Abstract Climate is one of the characteristics that distinguish one type of tourism from another. Climate data should be evaluated as an important resource for the tourism industry, and because climate data is multifaceted, quantitative tools are required to identify climate suitability in tourism. The current study looks into whether or not tourists base their decision to visit a destination, particularly a metropolitan city, on the weather. Mieczkwski's (1985) Tourism Climate Index (TCI) and Tang's (2013) Holiday Climate Index (HCI) were used to assess the effects of climate on tourism in Kolkata, a metropolitan city. With its rich cultural heritage, breathtaking leisure parks, and enthralling architecture, Kolkata, the city of joy, attracts visitors from all over the world. The primary focus of this research is the compatibility of the final tourist report (2014-2015) (latest report of West Bengal Tourism), which provides domestic and foreign tourist data, and monthly TCI and HCI scores (April 2014-March 2015), which range from ideal to extremely unfavourable. According to the findings of the current study, tourists who chose a metropolitan city as a tourist destination did so for reasons other than climate suitability. © 2021 ISSS. All Rights Reserved Introduction Kolkata, the capital of West Bengal, is a metropolitan city known for its intellect, culture, and magnificent performance in literature, arts, science, social reforms, and socio-political movements throughout history. The iconic landmark Howrah Bridge, lively Princep Ghat, majestic Victoria Memorial, historic South Park Street Cemetery, Shobhabajar Rajbari, St Paul's Cathedral, Indian Museum, Marble Palace, Jorasako Thakurbari, Kalighat Temple, Kumartuli, Birla Planetarium, Alipur Zoo, and other attractions attract tourists from around the world (Annual Final Report of Tourism Survey for the State of West Bengal (April 2014-March 2015))[1]. Kolkata is India's cultural capital, a city brimming with talent and passion (Sofique, Ghosh, 2011) [2] has a tropical wet and dry climate (Köppen climate classification: Aw) (Weatherbase entry for Kolkata, 2006)[3], resulting in year-round warm to hot weather with typical torrential monsoonal rain from June to September (NOAA)[4]. Despite its enormous potential as a tourist destination, Kolkata has seen a relatively low level of foreign visitors (Chakraborty, Debnath, 2008)[5], althoughit has the potential to become a heritage tourism hotspot (Clarke, 2014)[6]. Geographic locations, topography, landscape, flora and fauna, and climate and weather variables all influence the attractiveness of a tourist destination (Matzarakis, de Freitas, 2001)[7]. Because of the multifaceted nature of climate variables, the relationship between climate and tourism is complex (deFreitas, Scott, McBoyle, 2004)[8]. Depending on tourist preferences and sensitivities, ASI-Score:3.00 climatic conditions may be perceived as a resource or a constraint for tourism (Smith, 1993)[9]. Many tourist activities can be seen to be related to specific weather conditions (Schrter de et al., 2005)[10]. Tourists usually learn about the climate of their preferred destination before choosing it or booking tickets; they also like to check the weather before their trip (Hamilton, Lau, 2005)[11]. Though business, education, and cultural tourism are less reliant on ideal climate, they can be reliant on seasonal optimal climate (Amelung, Nicholls, Viner, 2007)[12]. There are several indices for quantifying a tourist destination's climate suitability to tourists (Fichett, Hoogendoorn, Robinson, 2016)[13]. One of the suitable metrics for identifying climate suitability for sightseeing is Mieczkowski's Tourism Climate Index (TCI) (1985)[14], which combines seven climate variables. TCI does not explain tourist activities; rather, it indicates how suitable a location's climate is for sightseeing and shopping. It is one of the most popular indices because it incorporates the thermal facet (comfort), the physical facet (precipitation and wind), and the aesthetic facet (sunshine) (Perch-Nielsen, Amelung, Knutti, 2010)[15]. Tang's (2013) Holiday Climate Index (HCI), which incorporates the thermal facet (thermal comfort), physical facet (precipitation and wind), and aesthetic facet (cloudiness rate), is a more accurate tool for determining the climate suitability of urban tourism (Tang, 2013[16]; Scott, Rutty, Amelung, Tang, 2016[17]; Mahtabi, Taran; 2018[18]). IF: 0.97 IF::2.080 Indian Journal of Spatial Science Autumn Issue,2021:12(1) pp.63 - 71 Research Objectives The major objectives of this research: 1) To understand climate importance on tourism in Kolkata. 2) To analyse the compatibility of the Tourism Climate Index (TCI) on Tourism in Kolkata. 3) To study the compatibility of the Holiday Climate Index (HCI) on Tourism in Kolkata. Material and Methods 1) Weather data was collected from World Weather Online[19] which is a private source for weather data collection which works on the latest holiday weather reports, ski conditions, marine conditions and day to day forecasts for worldwide towns and cities. 2) Annual Final Report of Tourism Survey for the State of West Bengal (April 2014 - March2015) was used to calculate domestic and foreign tourists in Kolkata as it is the latest government document on tourism in West Bengal. Two indices were used to validate climate suitability on tourists in Kolkata. Tourism Climate Index and Urban Holiday Climate Index (HCI) were used to calculate monthly TCI scores and HCI scores respectively which ranged from April 2014 to March 2015 based on the TCI rating categories (Mieczkowski 1985) and HCI rating scheme. Literature Review Tourism, one of the most important sectors of the global economy, is heavily influenced by climate (Scott, McBoyle, Schwartzentruber 2004; Amiranashvili, Matzarakis, Kartvelishvili; 2008[25]). As climate plays an important role in tourism, there is a large body of literature on tourism-related climate indices. Mieczkowski developed one of these indices, the Tourism Climate Index (TCI) in 1985 (Perch-Nielsen, Amelung, Knutti, 2010). TCI, according to Mieczkowski (1985), is a "quantitative evaluation" of the global climate for the benefits of global tourism. He created a significant rating system to evaluate meteorological variables that affect people's tourism experiences. Mieczkowski computed TCI for 453 meteorological stations around the world. Amelung, Nicholls, and Viner (2007) used TCI to identify climatically and seasonally suitable tourist destinations for their study, which is based on the likely impact of climate change on the global tourism industry. Amiranashvili, Matzarakis, and Kartvelishvili (2008) also conducted a small study on Tbilisi, Georgia's capital, using TCI to determine the climate potentiality on tourism. Furthermore, Kubokawa, Inoue, and Satoh (2013)[26] used TCI to evaluate climate factors over Japanese tourism, as well as estimated TCI based on Japan's future climate data projections. Cengiz, Akbulak, Alişkan, and Kelkit (2015) used TCI to assess meteorological activity in relation to tourism activity in Anakkale, Northwest Turkey. In another study, Roshan, Yousefi, and Fichett (2015) computed TCI for 40 Iranian cities over a 50-year period (1961-2010) to investigate the trend of climate variability in tourism in Iran. According to Fichett, Hoogendorn, and Robinson (2016), TCI is a well-known and widely used quantitative tool for measuring climate suitability for specific tourism destinations. However, they found some limitations due to limited recording of sunshine data in their study area of South Africa, so they used a mathematical adaptation of TCI to facilitate more desired use of TCI. Later, Mantao (2013) developed the Holiday Climate Index (HCI) to fill a research gap by modifying the TCI as it has some inadequacies in the rating system and component weightage. He compared HCI ratings to TCI ratings in the past (1961-1990) as well as future climate projections (2010-2039, 2040-2069, and 2070-2099) to investigate the climate suitability of tourism in Europe's top 15 tourist destinations. Scott, Rutty, Amelung, and Tang (2016) examined the climate suitability of European cities by comparing TCI and HCI scores. They found HCI to be a more precise quantitative tool than TCI. Mahtabi and Taran (2018) used HCI to determine the best months for tourism in two Iranian cities: Isfahan and Rasht. Climate is a decision-making factor for tourism-related businesses, according to another study ztürk, Göral (2018)[27]. They used HCI to analyse climate potentiality on tourism destination marketing in Izmir Provence, a Turkish port city. ASI-Score:3.00 1. Tourism Climate Index(TCI): TCI is a combination of seven parameters, among them three are independent and two are in a bioclimatic combination (thermal comfort components) (Table -2). TCI= 8CLD+2CLA+4R+4S+2W — Eqn.1 Where, CLD = the daytime comfort index (combination of mean maximum temperature in
C and the mean minimum relative humidity (%), CLA = the daily comfort index (combination of mean air temperature in
C and the mean relative humidity (%), R = precipitation, S = daily sunshine duration, W = mean wind speed. Here W, R and are rated on a scale from 0 (unfavourable) to 5 (optimal) while CLD and CLA are scaled from -3 to 5 (Table - 3) (Mieczkowski, 1985; Perch-Nielsen et al.,2010; Kovacs & Unger, 2014[20]; Roshan, Yousefi, Fitchett, 2015[21]; Fichett, Hoogendoorn, Robinson, 2016). The thermal comfort components (CLD & CLA) were calculated by Missenard (1933) equation (Eqn.-2). TCI provides the effective temperature (ET) which determines the thermal exchange between organism and environmental variables. Assuming normal body temperature at 37°C and normal atmospheric pressure, the equation of ET: ---Eqn. - 2 Where, T = temperature (°C), Rh= relative humidity (%), and V = wind speed ( m/s). Eqn.-2 estimates the CLD (mean maximum temperature (
C) & mean minimum relative humidity (%) is needed) and CLA (mean air temperature (
C) & mean relative humidity (%) is needed). For R, mean precipitation in mm, for S, mean monthly sunshine duration and for W, mean monthly wind speed in m/s is needed. For calculating CLD, mean maximum temperature (
C) & mean minimum relative humidity (%) and for CLA, mean air temperature (
C) & mean relative humidity (%) were provided in Eqn.-2 64 IF: 0.97 IF::2.080 Indian Journal of Spatial Science Autumn Issue,2021:12(1) pp.63 - 71 (Grillakis, Koutoulis, Tsanis, 2015)[22]. The total range of TCI value is rated -30(impossible) to 100(ideal) (Table 4) (Scott, McBoyle, Schwartzentruber, 2004[23]; Amelung, Nicholls, 2007; Cengiz, Akbulak, Çalişkan, Kelkit, 2015[24]). correlation coefficient (0.70) shows some extent of climatedependent tourism in the case of foreign tourists. 2. Urban Holiday Climate Index (HCI) HCI is a combination of five parameters among them two are in a bioclimatic combination three are independent parameter (Table 6) HCI= 4T + 2A+(3R+W) ---- Eqn.- 3 Where, T = Thermal Comfort, A = Cloud cover, R = Precipitation, and W = Wind. (Tang, 2013). The effective temperature was used to measure 'Thermal Comfort (T)', which means the same Missenard (1933) equation (Equation 2)calculates Thermal Comfort(T) which is previously used to calculate CLD & CLA in TCI. The rating scheme of HCI ranges from 10 to -10 (Table - 7). Finally, the Pearson’s Product Moment correlation coefficient was computed to find out the compatibility of tourist data and TCI scores and HCI scores. Fig.1: TCI Scores showing “Winter Peak” Season favourable for Tourism in Kolkata (Source:www.worldweatheronline.com/kolkata-weatherhistory/west-bengal/in.aspx) Results & Discussions: TCI and Tourism in Kolkata: a) TCI was calculated using climatic variables which were rated based on the rating scheme of Mieczkowski (Table - 3). Table - 9, 10 &11 show month-wise climate variables and their rating scheme for the year 2014 and 2015 in Kolkata. b) Scott &McBoyle (2001)[28] model explained there are six annual distributions (poor, optimal, winter peak, summer peak, dry season peak and bimodal-shoulder peak) based on natural resources of climate. Kolkata has a winter peak distribution (Fig.1) which represents that winter seasons are preferably suitable to visit Kolkata, is explained by monthly TCI scores of 2014 and 2015 (Table - 12). c) Where the monthwise TCI scores (2014 and 2015) show November to January is 'excellent' to 'ideal' for tourism as excessive heat and rainfall caused the monsoon season June to September unfavourable for tourism activities in Kolkata. d) The present study evaluates the compatibility of TCI scores and tourist data, as there was only the last “Final Report of Tourism Survey for the State of West Bengal (April 2014March 2015)”, Ministry of Tourism, Government of India, TCI was implemented to find out the climate suitability of the months April 2014 to March 2015. e) TCI scores of April 2014 - March 2015 shows November to February (TCI scores (82 to 94) was the most suitable time to visit and July (TCI scores 26) was very uncomfortable for tourism in Kolkata (Fig.2). The tourist data represents the scenario that there is a huge difference in the number of leisure domestic tourists and the number of leisure foreign tourists (Table - 13). f) Domestic Tourists in Kolkata, 2014-2015 (Table -10, Fig. 3) differ from the TCI scenario as tourists visit Kolkata in July (TCI category = very unfavourable) were more than in November (TCI categ ory = excellent). g) The correlation coefficient was +0.49 between TCI scores and leisure domestic tourists in 2014-2015. Though the ASI-Score:3.00 Fig.2: Tourism Climate Index showing suitable Months for Tourism in Kolkata, 2014-2015 (Data Source: same in Fig.1) Fig.3: Leisure Domestic Tourists in Kolkata, 2014-2015 (Data Source: Final Report of Tourism Survey for the State of West Bengal (April 2014-March 2015), Ministry of Tourism, Government of India Fig.4: Leisure Foreign Tourists in Kolkata, 2014-2015 (Data Source: same in Fig.3) HCI and Tourism in Kolkata a) Holiday Climate Index (HCI) shows the month-wise distribution of HCI scores for the year 2014-2015 (Fig.4). HCI have been calculated using the rating on climate variables of Kolkata (Table - 14). The rating scheme is based on Tang's rating scheme (Table - 7). b) The HCI shows July is extremely unfavourable and December is ideal for tourism in Kolkata. Monsoon season (June- 65 IF: 0.97 IF::2.080 Indian Journal of Spatial Science Autumn Issue,2021:12(1) pp.63 - 71 September) is not a climate suitable season where the winter season (November- February) is the most favourable season for tourism in Kolkata (Table -15, Fig. 5). c) The correlation coefficient of HCI scores and leisure domestic tourist data is low (0.34) which denotes domestic tourist are not at all bothered by the climate though the correlation coefficient is high (0.82) in the case of leisure foreign tourists and HCI scores. System for Tourism in Kolkata. Journal of Hospitality Application and Research, BIT Mesra, Ranchi. Vol. 3, Iss. 2, pp126- 144. [6]. Clarke, A. (2014). Scotland in Kolkata: Transnational heritage, cultural diplomacy and city image. pp87-97 https://search.informit.com.au/browseJournalTitle;res=IE LHSS;issn=0726-6715 . [7]. Matzarakis, A., de Freitas, C. R. (2001) eds. Proceedings of the First International Workshop on Climate, Tourism and recreation. report of a Workshop held 510 October 2001, Porto Carras, Neosmarmaras, Halkidiki, Greece.pp171-183. [8]. de Freitas, C. R., Scott, D., McBoyle, G. (2004). A new generation climate index for tourism and recreation. Proceedings of the International Society of Biometeorology, Commission on Climate, Tourism and recreation, 912 June, Kolimbari, Greece. Pp19-22 [9]. Smith, K. (1993). The influence of weather and climate on recreation and tourism. Weather, 48, pp 398-404. [10].Schrӧter, D., Cramer, W., Leemans, R., Prentice, IC., Arau´ jo. M.B., Arnell, N.W., Bondeau, A., Bugmann, H., Carter, T.R., Garcia, C.A. et al. (2005). Ecosystem service supply and human vulnerability to global change in Europe. Science 2005, 310:pp1333-1337. [11].Hamilton, J. M., Lau, M. A. (2005). The role of climate information in tourist destination choice decision making. In: Gössling S, Hall CM (eds) Tourism and global environmental change. Routledge, London pp1-21 [12].Amelung, B., Nicholls, S., Viner, D. (2007). Implications of global climate change for tourism flows and seasonality. J Travel Res 45:pp285-296 [13].Fichett, J. M., Hoogerdoorn, G., Robinson, D. (2016). Data challenges and solutions in the calculation of Tourism Climate Index (TCI) scores in South Africa, Vol. 64, No. 4, pp359 - 370 UDC: 338.48:551.58(68) [14].Mieczkowski, Z. (1985). The Tourism Climatic Index: A method of evaluating world climates for tourism. Canadian Geographer, 29; pp220-233 [15].Perch-Nielsen, S. L., Amelung, B. &Knutti, R. (2010). Future climate resources for tourism in Europe based on the daily Tourism Climate Index. Climate Change, 103, pp363-381. [16].Tang, M. (2013) Comparing the “Tourism Climate Index” and “Holiday Climate Index” in a major European destination. A thesis of Master of Environmental Studies in Geography-Tourism Policy and Planning University of Waterloo, Ontario, Canada. Pp1-106 [17].Scott, D., Rutty, M., Amelung, B., Tang, M. (2016). An Inter-Comparison of the Holiday Climate Index (HCI) and the Tourism Climate Index (TCI) in Europe. Atmosphere, 7; pp1-17. [18].Mahtabi, Gh., Taran, F. (2018). Comparing the effect of climate condition on the tourism calendar in arid and humid cities using Holiday Climate Index (HCI) (Case Study: Isfahan and Rasht). Desert Online at http://desert.ut.ac.ir. Desert 23-1 pp63-73. [19].W o r l d W e a t h e r O n l i n e , https://www.worldweatheronline.com/kolkata-weatherhistory/west-bengal/in.aspx [20].Kovács, A., Unger, J. (2014). Analysis of Tourism Climatic Conditions in Hungary Considering The Subjective Thermal Sensation Characteristics of The South-Hungarian Residents. Fig.5: Holiday Climate Index showing suitable Months for To u r i s m i n K o l k a t a , 2 0 1 4 - 2 0 1 5 ( D a t a S o u r c e : https://www.worldweatheronline.com/kolkata-weather-history/westbengal/in.aspx) Findings: 1) The present study shows the winter season is the most preferable season to visit Kolkata. 2) Monsoon season is unfavourable for sightseeing in Kolkata. 3) TCI and HCI scores are more compatible with foreign tourist flow than domestic tourist flow. 4) There was relatively less foreign tourist flow than domestic tourist flow in case of numbers in the latest government tourist data. 5) The highest foreign tourist flow was seen in 2014-2015 in December. Conclusion With its rich cultural heritage, breathtaking leisure parks, and enthralling architecture, Kolkata, the city of joy, attracts visitors from all over the world. The main focus of this research is the compatibility of the final tourist report (2014-2015) (latest West Bengal Tourism report), which provides domestic and foreign tourist data, and monthly TCI and HCI scores (April 2014-March 2015), which range from ideal to extremely unfavourable. According to the findings of the current study, tourists who chose a metropolitan city as a tourist destination did so for reasons other than climate suitability. References [1]. Annual Final Report of Tourism Survey for the State of West Bengal (April 2014 March 2015). Ministry of Tourism (Market Research Division), Government of India.pp13-209 [2]. Sofique, M. A., Ghosh, P. (2011). Tourist Satisfaction with Cultural Heritage destinations in India: with special reference to Kolkata, West Bengal. researchgate.net pp1-4 [3]. W e a t h e r b a s e e n t r y f o r K o l k a t a , 2 0 0 6 . Http://www.weatherbase.com/weather/weatherall.php3?s = 090824&refer=&units=metric [4]. NOAA. Https://library.noaa.gov/Collections/DigitalDocs/Foreign-Climate-Data/India-Climate-Data [5]. Chakraborty, S., Debnath, J. (2008). An Expert Advisory ASI-Score:3.00 66 IF: 0.97 IF::2.080 Indian Journal of Spatial Science Autumn Issue,2021:12(1) pp.63 - 71 Department of Climatology and Landscape Ecology, University of Szeged. ACTA CLIMATOLOGICA ET CHOROLOGICA Universitatis Szegediensis, Tomus 47-48, pp77-84 [21].Roshan, G., Yousefi, R., Fitchett, J. M. (2015). Long-term trends in tourism climate index scores for 40 stations across Iran: the role of climate change and influence on tourism sustainability. Springer. Int J Biometeorol, 60:pp33-52 DOI10.1007/s00484-0 15-1003-0 [22].Grillakis, M. G., Koutroulis, A. G., Tsanis, I. K. (2015). The 2
C global warming effect on summer European tourism through different indices. Int J Biometeorol60:pp1205-1215 DOI 10.1007/s00484-015-1115-6. [23].Scott, D., McBoyle, G. &Schwartzentruber, M. (2004). Climate Change and the Distribution of Climatic Resources for Tourism in North America. Climate Research, 27(2), pp105117. [24].Cengiz, T., Akbulak, C., Çalişkan, V., Kelkit A. (2015). Climate Comfortable for Tourism: A Case Study of C a n a k k a l e . h t t p s : / / w w w. r e s e a r c h g a t e . n e t / publication/266091577 [25].Amiranashvili, A., Matzarakis, A., Kartvelishvili, L. (2008). Tourism Climate Index in Tbilisi. Transactions of the Georgian Institute of Hydrometeorology, vol. 115, pp27-29 [26].Kubokawa, H., Inoue, T., Satoh, M. (2013). Evaluation of Tourism Climate Index over Japan in a Future Climate Using a Statistical Downscaling Method. Journal of the Meteorological Society of Japan, Vol. 92, No. 1, 2014 DOI:10.2151/jmsj.2014103,pp37−54 [27].Öztürk, A., Göral, R. (2018). Climatic Suitability in Destination Marketing and Holiday Climate Index. Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-3170) Vol: 4 Issue: 1pp619-629 [28].Scott D, McBoyle G (2001) Using a 'tourism climate index' to examine the implications of climate change for climate as a natural resource for tourism. In: Matzarakis A, de Freitas CR (eds) Proc. First Int. Workshop on Climate, Tourism and Recreation. International Society of Biometeorology, Commission on Climate, Tourism and Recreation, Halkidiki, Greece. pp69-88 Table -1: Application of TCI on some existing studies Author Mieczkowski Year 1985 Scott, McBoyle, Schwartzentruber 2004 Amelung, Nicholls, Viner Perch-Nielsen et al 2007 Kovacs & Unger 2014 Roshan, Yousefi, Fitchett 2015 Cengiz, Akbulak, Çalişkan, Kelkit Grillakis, Koutoulis, Tsanis 2015 Fichett, Hoogendoorn, Robinson 2016 2010 2015 Paper “The tourism climatic index: A method of evaluating world climates for tourism” “An Inter-Comparison of the Holiday Climate Index (HCI) and the Tourism Climate Index (TCI) in Europe” “Implications of global climate change for tourism flows and seasonality” “Future climate resources for tourism in Europe based on the daily Tourism Climate Index” “Analysis of Tourism Climatic Conditions in Hungary Considering The Subjective Thermal Sensation Characteristics of The South-Hungarian Residents” “Long-term trends in tourism climate index scores for 40 stations across Iran: the role of climate change and influence on tourism sustainability” “Climate Comfortable for Tourism: A Case Study of Canakkale” “The 2? C global warming effect on summer European tourism through different indices” “Data challenges and solutions in the calculation of Tourism Climate Index (TCI) scores in South Africa” Study Region Global Climate Data Type Monthly Europe Monthly Global Monthly Europe Monthly Southern Great Hungarian Plain Monthly Iran Monthly Canakkale (Northwest Turkey) Europe Monthly Monthly South Africa Monthly Table – 2: Sub-indices of TCI (Mieczkowski, 1985) Sub index Daytime comfort index (CLD) Daly comfort index (CLA) Precipitation (R) Monthly climate variables Mean maximum temperature (?C) & mean minimum relative humidity (%) Mean air temperature (?C) & mean relative humidity (%) Total Precipitation Sunshine(S) Wind (W) Total hour of Sunshine Average wind speed Influence on TCI Represent thermal comfort when maximum tourist activity occur. Represent thermal comfort over 24 hours period Negative impact on tourist activity Influence on tourists Influence on tourist Total Weighting on TCI 40% 10% 20% 20% 10% 100 Source: Mieczkowski, 1985 ASI-Score:3.00 67 IF: 0.97 IF::2.080 Indian Journal of Spatial Science Autumn Issue,2021:12(1) pp.63 - 71 Table -3: Rating Scheme of each Climatic Variable of TCI Rating Effective Temperature (°C) 5.0 4.5 Mean Monthly Precipitation (mm) 0.0 - 14.9 15.0 - 29.9 Mean Monthly Sunshine (hrs/day) 10 9 30.0 - 44.9 17 29 16 30 10-15 31 5-9 32 0-4 33 -5- -1 34 35 20 - 26 19 27 18 28 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 Wind Speed (km/h) Normal Trade Wind <2.88 2.88 - 5.75 12.24-19.97 8 5.76 - 9.03 9.04 - 12.23 19.80 24.29 45.0 - 59.9 7 9.04 - 12.23 60.0 - 74.9 6 12.24 19.79 75.0 - 89.9 5 19.8 - 24.29 90.0- 104.9 4 105.0 119.9 120.0 134.9 135.0 149.9 >150.0 3 24.30 28.79 28.8 - 38.52 >36 -10- -6 -1.0 -15- -11 -2.0 -20- -16 -3.0 <-20 Source: Mieczkowski (1985) Hot Climate 5.76 - 9.03 24.30 28.79 2.88 - 5.75 <2.88 28.80 - 38.52 <2.88 2.88 - 5.75 2 5.76 - 9.03 1 9.04 - 12.23 <1 >38.52 >38.52 >12.24 Table – 4: TCI Rating Categories TCI Scores 90 to 100 80 to 89 70 to 79 60 to 69 50 to 59 40 to 49 30 to 39 20 to 29 10 to 19 -30 to 9 Categories Ideal Excellent Very good Good Acceptable Marginal Unfavourable Very unfavourable Extremely unfavourable Impossible Source: Mieczkowski, 1985 Author Year Tang 2013 Scott, Rutty, Amelung, Tang 2016 Mahtabi, Taran 2018 Öztürk, Göral 2018 Sub index Thermal comfort (T) Cloud cover (A) Precipitation (R) Wind (W) Table -5: Application of HCI on some existing studies Paper Study Region “Comparing the “Tourism Climate Index” and 15 “Holiday Climate Index” in major European European destination” cities “An Inter-Comparison of the Holiday Climate Europe Index (HCI) and the Tourism Climate Index (TCI) in Europe” “Comparing the effect of climate condition on Isfahan, tourism calendar in arid and humid cities using Rasht (Iran) Holiday Climate Index (HCI) (Case Study: Isfahan and Rasht” “Climate suitability in destination marketing and İzmir Holiday Climate Index” Provence (Turkey) Table – 6: Sub-indices of HCI Daily climate variables Influence on HCI Maximum temperature (?C) & Represent thermal comfort mean relative humidity (%) when maximum tourist activity occur Cloudiness rate (%) Influence on tourists Total Precipitation(mm) Negative impact on tourist activity Average wind speed(km/h) Negative impact on tourist activity Climate Data Type Daily Daily Daily Daily Weighting on HCI 40% 20% 30% 10% Source: Tang, 2013 ASI-Score:3.00 68 IF: 0.97 IF::2.080 Indian Journal of Spatial Science Autumn Issue,2021:12(1) pp.63 - 71 Rating 10 Table -7: Rating Scheme of each Climatic Variable of HCI Effective Temperature M ean Daily Precipitation Daily Cloud Cover (°C) (mm) (%) 23 - 25 0 11 - 20 9 20 - 22 26 27 - 28 8 7 <3 3-5 18 - 19 29 - 30 15 - 17 31 - 32 11 - 14 33 - 34 7 - 10 35 - 36 0-6 6 5 4 3 2 6-8 W indspeed (km/h) 1-9 1 - 10 21 - 30 0 31 - 40 41 - 50 10 - 19 51 - 60 30 - 39 0 20 - 29 61 - 70 71 - 80 81 - 90 9-12 1 -5 - -1 37 - 39 <-5 0 >39 >12 -1 40 - 49 >90 50 - 70 >25 -10 >70 Source: Tang,2013 Table – 8: HCI Rating Categories HCI Scores 90 to 100 80 to 89 70 to 79 60 to 69 50 to 59 40 to 49 30 to 39 20 to 29 10 to 19 9 to -9 -10 to -20 Categories Ideal Excellent Very good Good Acceptable Marginal Unfavourable Very unfavourable Extremely unfavourable Impossible Impossible Source: Tang, 2013 Table – 9: Rating Scheme of Daytime Comfort Index (CLD) & Daily Comfort Index (CLA) of TCI (Kolkata, 2014 & 2015) 2014 Months January Rating (CLD) 5.0 CLD February 3.0 March 1.0 34.47 April 0 37.35 May 0 41.36 June 0 July 0 August September 2015 CLA Months 26.59 Rating (CLA) 5.00 January Rating (CLD) 5.0 22.07 30.13 5.00 25.11 February 3.50 3.00 30.61 March 0.50 34.96 April 0 38.84 40.99 0 38.94 0 0 38.14 0 October November December 25.58 Rating (CLA) 5.00 22.10 29.21 5.00 24.84 2.0 32.17 3.50 29.55 0 38.29 0 36.29 May 0 42.20 0 40.20 39.28 June 0 41.79 0 40.26 37.82 July 0 39.08 0 37.72 0.25 35.96 August 0 39.55 0 37.60 37.50 1.00 34.47 September 0 37.61 0.25 35.63 1.5 32.61 2.50 30.57 October 2.0 32.17 3.00 30.44 3.5 29.03 5.00 24.94 November 3.5 29.66 5.00 26.10 5.0 25.73 5.00 21.39 December 4.5 26.76 5.00 23.56 ASI-Score:3.00 69 CLD IF: 0.97 CLA IF::2.080 Indian Journal of Spatial Science Autumn Issue,2021:12(1) pp.63 - 71 M onths January February M arch A pril M ay June July A ugust Septem ber O ctober N ovem ber D ecem ber M onths January February M arch A pril M ay June July A ugust Septem ber O ctober N ovem ber D ecem ber M onths January February M arch A pril M ay June July A ugust Septem ber O ctober N ovem ber D ecem ber T able – 10: R ating schem e o f P recipitation, Sunshine, W ind (K olkata 2014) R ating M ean R ating M ean R ating (M ean M onthly (M ean M onthly (W ind Speed M onthly P recipitation M onthly Sunshine (km /h) P recipitation) (m m ) Sunshine) (hrs/day) 5.0 0.10 3.5 7.45 3.5 5.0 7.81 3.5 7.59 4.0 5.0 8.67 4.5 9.66 3.5 5.0 0.02 5.0 10.53 3.5 5.0 6.50 5.0 12.08 3.0 3.0 63.66 5.0 10.78 3.0 0 153.42 4.0 7.82 3.0 2.0 103.16 4.5 8.90 3.5 3.0 74.91 4.5 8.55 3.5 4.5 23.61 4.5 9.03 3.5 5.0 0.29 4.5 9.48 3.5 5.0 0.14 3.5 7.42 3.5 T able – 11: R ating schem e o f P recipitation, Sunshine, W ind (K olkata 2015) R ating M ean R ating M ean R ating (M ean M onthly (M ean M onthly (W ind Speed M onthly P recipitation M onthly Sunshine (km /h) P recipitation) (m m ) Sunshine) (hrs/day) 5.0 10.22 3.5 7.20 3.5 5.0 4.43 3.5 7.53 4.0 5.0 8.03 4.5 9.52 3.5 4.0 42.31 4.5 9.58 3.0 5.0 13.00 5.0 11.92 3.0 2.0 92.69 4.5 9.70 2.5 0 190.37 3.5 7.87 3.0 2.5 80.10 4.5 9.25 3.0 3.5 50.10 3.5 7.85 3.5 4.5 28.06 4.5 9.22 4.0 5.0 0.65 4.5 9.58 4.0 5.0 1.76 3.5 7.03 4.0 T able – 12: M onthw ise T C I S cores in K olkata (2014-2015) T C I Score 2014 C ategories (2014) T C I Score 2015 91 Ideal 91 76 V ery good 80 59 A cceptable 68 48 M arginal 40 46 M arginal 46 38 U nfavo urable 31 22 V ery unfavo urable 20 33.5 U nfavo urable 34 39 U nfavo urable 35.5 61 G ood 66 84 E xcellent 84 90 Ideal 88 W ind Speed (km /h) 10.2 8.8 9.5 10.3 16.1 12.9 13.0 10.4 10.2 7.3 8.1 9.5 W ind Speed (km /h) 9.5 9.0 10.0 14.5 17.7 20.0 13.6 13.3 10.4 6.6 7.3 7.8 C ategories (2015) Ideal E xcellent G ood M arginal M arginal U nfavo urable V ery unfavo urable U nfavo urable U nfavo urable G ood E xcellent Ideal T able – 13: M onthw ise Leisure T ourist D ata in K olkata, 2014-2015 M onths (A pril 2014- M arch 2015) A pril M ay June July A ugust Septem ber O ctober N ovem ber D ecem ber January February M arch ASI-Score:3.00 Leisure D o m estic T ourists 522713 516523 377517 555864 604185 791755 866769 544096 841479 791666 699114 629561 70 Leisure Foreign T ourist 115554 91636 66249 67796 80300 85964 95892 110732 116296 99819 88979 93742 IF: 0.97 IF::2.080 Indian Journal of Spatial Science Autumn Issue,2021:12(1) pp.63 - 71 Table -14: Rating Scheme of Climate Variables of HCI in Kolkata (2014-2015) Months Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Rating (Effective Temperature) 0 0 0 0 0 0 4 6 8 8 6 4 Months April May June July August September October November December January February March Effective Temperature (ºC) 43.44 47.08 45.23 41.59 40.91 40.63 35.1 31.18 27.49 27.37 32.09 35.88 Rating (Mean Precipitation) 10 5 -1 -1 -1 -1 0 10 10 2 8 5 Mean Precipitation (mm) 0.02 6.5 63.66 153.42 103.16 74.91 23.61 0.29 0.14 10.22 4.43 8.03 Rating (Cloud Cover) 10 10 8 6 7 8 10 10 10 10 10 9 Cloud Cover (%) 6 19 35 55 49 35 20 5 8 9 7 14 Rating (Windspeed) Windspeed (km/h) 9 9 9 9 9 9 10 10 10 10 10 9 10.3 16.1 12.9 13 10.4 10.2 7.3 8.1 9.5 9.5 9 10 Table – 15: Monthwise HCI scores in Kolkata (2014-2015) HCI Scores Categories 59 Acceptable 44 At the limit 22 Unacceptable 18 Dangerous 20 Unacceptable 22 Unacceptable 46 Unacceptable 84 Very Good 92 Perfect 68 Good 78 Very Good 58 At the limit Trisha Chakraborty Junior Research Fellow: Department of Architecture, Jadavpur University, Kolkata, West Bengal Email: chtrisha@gmail.com ASI-Score:3.00 Dr. Debashish Das Associate Professor: Department of Architecture, Jadavpur University, Kolkata, West Bengal Email: ddasju@gmail.com / ddasju.arch@jadavpuruniversity.in 71 IF: 0.97 IF::2.080