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Effects of Climate Change on the Season of Botanical Tourism: A Case Study in Beijing

Effects of Climate Change on the Season of Botanical Tourism: A Case Study in Beijing Hindawi Advances in Meteorology Volume 2020, Article ID 8527860, 11 pages https://doi.org/10.1155/2020/8527860 Research Article Effects of Climate Change on the Season of Botanical Tourism: A Case Study in Beijing 1,2 1 1 1 1 Yaqiong Zang, Junhu Dai, Zexing Tao, Huanjiong Wang , and Quansheng Ge Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 10010, China University of Chinese Academy of Sciences, Beijing 10049, China Correspondence should be addressed to Huanjiong Wang; wanghj@igsnrr.ac.cn and Quansheng Ge; geqs@igsnrr.ac.cn Received 24 October 2019; Revised 14 March 2020; Accepted 30 June 2020; Published 17 July 2020 Academic Editor: Budong Qian Copyright © 2020Yaqiong Zang et al. 0is is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Climate change could affect botanical tourism by altering the plant phenology (e.g., flowering and leaf coloring date) and the physical comfort of tourists. To date, few studies have simultaneously considered the influence of plant phenology and physical comfort on the travel suitability of botanical tourism. Taking Beijing as an example, this study used phenological data of 73 species from 1963 to 2017 to construct a phenological ornamental index (POI) according to the flowering and leaf coloring date of ornamental plant. 0e climate comfort index (CCI) of tourism was calculated by using meteorological data of the corresponding periods. Finally, the travel suitability index (TSI) was constructed by integrating the two indices (POI and CCI). 0e POI showed that the best period for spring flower viewing was from April 4 to May 10, while the best period for autumn leaves viewing was fromOctober 11toNovember6on average.AccordingtothevariationoftheCCI withintheyear,themostcomfortableperiodfor spring tourism was matched with the best period for spring flower viewing (April 4 to June 1), but the most comfortable period for autumn tourism (September 4 to October 19) was earlier than the best period for autumn leaves viewing. 0e TSI indicated that the best periods for spring and autumn botanical tourism were April 7 to May 10 and October 10 to November 7, respectively. Based on the climate data under different scenarios (representative concentration pathways 4.5 and 8.5), we simulated the climate and phenological suitability for botanical tourism in the next thirty years. 0e results showed that the best period for spring botanical tourism during 2040–2050 was earlier and the period for autumn botanical tourism was later than that in the past 55 years. Meanwhile, the duration would shorten by 2–7 days for both seasons. 0is study provided a reference for assessing the impact of global climate change on the best season of botanical tourism. sunshine. 0us, climate change could affect botanical 1. Introduction tourism by simultaneously changing plant phenology and 0e plants provide an opportunity to serve as botanical tourists’ physical comfort [3, 4]. tourism in which many different forms of tourism activities Over the past several decades, the best time for botanical can be carried out, such as the cherry blossom festivals in tourism, such as spring flower viewing and autumn leaves Japan, the rose festival in Bulgaria, tulip festival in Holland, viewing, was altered under the background of climate change. and the maple syrup festivals in Canada. 0ese tourism For example, based on phenological data of 232 plant species activities are profoundly affected by climate change [1]. On from 1985 to 2011 on the island of Guernsey in the English the one hand, the landscape of botanical tourism is related to Channel, the significantly earlier beginning of flowering and plant phenophases (e.g., flowering and leaf coloring date), shorter flowering duration was found [5]. Another study which responded sensitively to climate change [2]. On the based on a 73-year long data series of first flowering dates for other hand, climate change exerts a direct influence on 25 species from north-temperate Sweden showed that most human comfort by altering meteorological factors such as time-series of first flowering dates exhibited tendencies to- temperature, humidity, precipitation, wind speed, and wardsearlierflowering[6].Aclimate-associatedshorteningof 2 Advances in Meteorology were detected between UTCI and previous indices of climate the flowering season was also significant in high-Arctic Greenland[7].Regardingtheautumnseason,thetemperature comfort [27], indicating that the earlier indices of climate comfort were still valid. rise led to a later beginning of leaf coloring from 1978 to 2016 in Japan, which made the number of visitors increase by more In recent years, a large number of studies chose one of than 3% [8]. 0e leaf senescence of deciduous trees in the the indices or combine several indices to evaluate the local northern hemisphere became later over time, especially in the climate comfort according to the research purpose and study low-latitude area [9]. area. For instance, Mihail ˘ a˘ et al. [28] used an hourly database For assessing the potential impacts of climate change on for the period 1961–2015 to calculate the PET index and botanical tourism, many studies focused on predicting the outline a series of changes that were likely to intervene in the relationship between climate and tourism in north-eastern spring flower phenology. For instance, a process-based model was developed to predict peak bloom dates of Romania in the immediate future. Cheng and Zhong [29] investigated tourism climate conditions in Grand Shangri- flowering cherry trees in the Tidal Basin, Washington, DC [10]. Hur et al. [11] used the seasonal prediction from the La from 1980 to 2016 using a tourism climate index (TCI) and found that the number of annual and monthly good- global and regional climate models to evaluate the forecast capability of the first flowering date of cherry, peach, and weather days increased over most of the area under the pear over South Korea. Recently, several studies noticed the influence of climate warming. In Beijing, China, the level of impact of phenological change on botanical tourism. For tourism comfort showed an increasing trend for all seasons, example, Sakurai et al. [12] explored the perceptions of which was favorable for the development of tourism [30]. Japanese residents regarding climate change impacts on 0ese studies provided examples for assessing the impact of culturally significant events and found that most (92%) climate comfort on tourism in specific tourism destinations. To date, few studies have considered the effect of plant managers of festival-dependent businesses were concerned about global warming because it affected the flowering phenology and climate comfort on botanical tourism si- multaneously. In order to fill in this gap, we chose Beijing as timing of cherry trees and income of cherry blossom fes- tivals. In Beijing, the administrator of Jingshan Park the research area where botanical tourism was well developed and analyzed the impact of climate change on the phenology scheduled the peony blossom festival date following the variation of peony FFD [13]. and physical comfort of tourists. First, we developed a Besides phenology, the degree of climate comfort could phenological ornamental index (POI) based on phenological directly affect botanical tourism [14]. 0e early evaluation of data of 73 species from 1963 to 2017. Second, using mete- climate comfort was usually based on the empirical model of orological data, we calculated annual changes in the climate human comfort. 0e empirical models reflected the subjective comfort index (CCI). Combining the POI and CCI, the travel feeling or physiological reaction of people and were built by suitability index (TSI) of botanical tourism was constructed. Finally, by using the climate data simulated under different experience or statistical methods. 0e typical empirical models included effective temperature (ET) [15], wet bulb representative concentration pathways (RCP), we predicted the changes in POI, CCI, and TSI from 2020 to 2050. 0is globe temperature (WBGT) [16], temperature-humidity In- dex (THI) [17], and Wind Chill Index (WCI) [18], which study aimed to reveal the impact of climate change on the considered the impact of air temperature, relative humidity, season of botanical tourism and provide references for the wind speed, and solar radiation. Empirical indices were tourism administrators to choose the appropriate time to simple to be calculated and easy to be understood by the arrange activities of botanical tourism. public.Inthelate1960s,advancesinbiometricsandcomputer technology led to the rapid development of climate comfort 2. Materials and Methods indices based on human heat exchange models [19, 20], such as perceived temperature (PT) [21], standard effective tem- 2.1. Study Area. Beijing, located in northern China, has a perature(SET)[22],andphysiologicalequivalenttemperature temperate semihumid climate. In the urban area of Beijing, (PET) [23], which comprehensively considered the meteo- ° the mean annual temperature was 12.53 C (averaged from rological factors (air temperature, relative humidity, wind 1963 to 2017). 0e monthly maximum and minimum speed, and solar radiation), human metabolic rate, clothing, ° ° temperatures were 25.46 C in July and −3.41 C in January, and individual parameters. However, human heat exchange respectively. 0e annual total precipitation was 560.87mm, was a very complex process and difficult to accurately cal- and the annual mean wind speed was 2.44m/s. In Beijing, culate.0us,theaboveindicesbasedonhumanheatexchange there were many tourism activities relating to botanical were not widely used. Recently, scientists developed a uni- tourism, such as peach blossom festival at Beijing Botanical versal thermal climate index (UTCI), which was regarded as Garden, peony festival at Jingshan Park, cherry blossom one of the most comprehensive indices for calculating heat festival at Yuyuantan Park, and red leaf festival at Fragrant stress in outdoor space [24, 25]. 0e input data for calculating Hills Park. 0erefore, Beijing was an ideal place to study the UTCI included meteorological and nonmeteorological impact of climate change on botanical tourism. (metabolic rate and clothing thermal resistance) data [26]. 0e parameters that were taken into account for calculating UTCI involve dry temperature, mean radiation temperature, 2.2. Data Source. All phenological data were derived from the pressure of water vapor or relative humidity, and wind China Phenology Observation Network (CPON). 0ese data ° ° speed. A recent study showed that the significant correlations were observed at the Summer Palace (39.992 N, 116.266 E), Advances in Meteorology 3 which was located in northwestern Beijing (Figure 1). Within each year, two peaks appeared in the daily POI Considering that the urban area of Beijing has a similar curve. 0e first peak occurred in spring because most of the plants flowered in this season. In the first 6 months of the elevation, we believed that the phenological data at one site could generally represent the whole urban area (although year, the period when POI was larger than half of the difference existed among individuals). Four phenophases of maximum was defined as the best period for spring flower 73 woody plants from 1963 to 2017in Beijing were inves- viewing. Similarly, in the last six months of the year, the tigated (Table S1). Most of these species were representative period when POI was larger than half of the maximum was ornamental plants with relatively complete observation data. defined as the best period for autumn leaves viewing. We investigated the phenophases which were closely related to the botanical tourism, including the date of first flowering 2.3.2. Climate Comfort Index. As mentioned in the Intro- (FFD), end of flowering (EFD), beginning of leaf coloring duction section, a variety of climate comfort indices had (BLC), and end of leaf coloring (ELC). been proposed [33]. Among them, the temperature-hu- 0e daily meteorological data in Beijing from 1963 to midity index (THI) and the wind chill index (WCI) were 2017 were downloaded from China meteorological data frequently used in previous studies. According to the en- service center (http://data.cma.cn/). We used the data from vironmental background conditions on which they were ° ° the Haidian meteorological station (39.987 N, 116.290 E), built, THI was more applicable to the warm environment, including mean, maximum, and minimum air temperature, while WCI was more suitable to the cold environment. relative humidity, and wind speed. Future climate data were 0erefore, in this study, we combined these two indices to derived from the Coupled Model Intercomparison Project make it more applicable to a temperate climate. Phase 5 (CMIP5) of the World Climate Research Pro- THI is a measure of the reaction of the human body to a gramme (WCRP). We obtained future climatic datasets combination of heat and humidity [34], and the equation for generated by the Community Climate System Model version THI is 4 (CCSM4) under the representative concentration pathway (RCP) 4.5 and RCP 8.5 [31]. 0e dataset covered the period THI � (1.8 · t + 32) − 0.55(1 − f) × (1.8 · t − 26), (1) from 2020 to 2050, including daily mean, maximum, and where t is air temperature ( C) and f is relative humidity (%). minimum air temperature, relative humidity, and wind ° ° speed with a spatial resolution of 1.4 WCI could reflect the effect of wind on the temperature ×1.4 . According to which humans perceive [35]. 0e equation for WCI is Beijing’s geographic coordinates, we extracted the meteo- √� rological factors of the corresponding pixel in the future WCI � (10 v − v + 10.5) × (33 − t), (2) climate data, and the statistical downscaling method was used to convert the data to the local scale [32]. where t is air temperature ( C) and v is wind speed (m/s). According to the values of THI and WCI, we gave dif- ferent scores to quantify the level of climate comfort. Higher 2.3. Constructionof Travel SuitabilityIndex. Plants were very scores suggested a more comfortable climate (Table 1). 0e attractive to tourists when they were in flowering or leaf CCI was defined as the average of THI and WCI score: coloring. 0erefore, we first constructed a new phenological S + S ornamental index (POI) to describe the number of species in THI WCI (3) CCI � , flowering or leaf coloring at each day of the year. 0e POI 2 could reflect whether it was an appropriate time to travel for where CCI is the climate comfort index and S and S THI WCI spring flower viewing or autumn leaves viewing. Further- are the scores of THI and WCI, respectively. Similarly, more, climate conditions could affect the physical comfort of within a year, twopeaks appeared inthe daily CCIcurve. 0e tourists. 0us, we calculated the daily climate comfort index period when CCI was larger than 7 was defined as the most (CCI) based on two previously proposed indexes to describe comfortable period. whether the climate was comfortable for traveling. Finally, the travel suitability index (TSI) was constructed by con- sidering both POI and CCI. 2.3.3. Travel Suitability Index. We first calculated the product of POI and CCI (X) to measure the overall impact of phenology and climate on the suitability of botanical 2.3.1. Phenological Ornamental Index. To date, there was no tourism ((4)). Subsequently, we normalized the value of X to index to measure the impact of phenology on botanical 0–1 range using (5). 0e normalized value could be defined tourism. 0erefore, we introduced a POI in this study. First, as TSI. wecountedthe numberofspeciesinflowering(fromthe first flowering date to the end of the flowering date) or in leaf X � POI · CCI, (4) coloring (from the beginning date of leaf coloring to the end date of leaf coloring) for each day of the year. Subsequently, X − X min TSI � , (5) the daily changes in the number of species in flowering or X − X max min leaf coloring were smoothed by using the 5-day moving average method. 0e POI at each date was defined as the where X represents the product of POI and CCI. X and max number of species in flowering and leaf coloring (after X are the maximum and minimum values of TSI within a min smoothing). year, respectively. 4 Advances in Meteorology 116°12′E 116°15′E 116°18′E 116°21′E 116°24′E 116°27′E 116°30′E The Summer Palace 40°0′N 39°57′N 39°54′N Haidian meteorological station 39°51′N 39°48′N Figure 1: 0e locations of phenological observation and meteorological stations in Beijing. 0e phenological data were collected in the Summer Palace, and the meteorological data was derived from the Haidian meteorological station. 0e image of the urban area of Beijing is Landsat 8 image observed on May 18, 2015, courtesy of the US Geological Survey. 0e subplots of each station were downloaded from Sogou Map (http://map.sogou.com/). Table 1: Classification and score of the temperature-humidity Based on the phenological and meteorological data from index (THI) and wind chill index (WCI). 1963 to 2017, we calibrated the Unified model and tem- perature-photoperiod model for each species and pheno- THI 0e level of climate comfort WCI value Score phase. We fitted the optimal parameters for each model by value using the simulated annealing method based on the least- Extremely coldand uncomfortable <40 >1000 1 squares principle, that is, the parameters with the minimum Cold and uncomfortable 40∼45 800∼1000 3 sum of the squares of the residuals [38]. 0e goodness of fit Slightly cold and uncomfortable 45∼55 600∼800 5 (R ) and root mean square error (RMSE) were calculated to Cool and comfortable 55∼60 300∼600 7 assess the error of the phenological models. By using the Pleasantly cool and very 60∼65 200∼300 9 comfortable temperature data under two climate scenarios and the Warm and comfortable 65∼70 50∼200 7 calibrated phenological models, we simulated the FFD, EFD, A little hot and comfortable 70∼75 −80∼50 5 BLC, and ELC of each plant from 2020 to 2050. Subse- Hot and uncomfortable 75∼80 −160∼−80 3 quently, future POI could be calculated by using the method Extremely hot and uncomfortable >80 <−160 1 described in Section 2.3.1 Future changes in CCI could be simulated by using future Following the above steps, we calculated the daily TSI climatedataandmethodsdescribedinSection2.3.2.Atlast,the from 1963 to 2017. For a certain year, two peaks appeared in yearly changes in TSI (2020–2050) could be calculated. the TSI curve. 0us, there were two periods during which the TSI was larger than 0.5. 0e first and second periods were 3. Results defined as the best periods for spring and autumn botanical tourism, respectively. 0e linear trend in the start date, end 3.1. Changes in the Phenological Ornamental Index. Figure 2 shows the POI averaged from 1963 to 2017. 0e POI date, and duration of the best period was calculated by regression of these dates against the year. curve showed that the best period for spring flower viewing ranged from April 4 to May 10, which lasted 36 days. 0e best period for autumn leaves viewing was between October 2.4. Simulation of Travel Suitability Index from 2020 to 2050. 11 and November 6, which lasted 27 days. From 1963 to In order to predict the POI in the future, we first used 2017, the start and end date of the best period for spring phenological models to predict the flowering and leaf col- flower viewing advanced by 0.23 days/year (P<0.01) and oring phenology of each plant. 0e Unified model was 0.08 days/year (P>0.05), respectively (Figure 3(a)). 0e chosen for simulating future FFD and EFD, and the tem- duration of the best period for spring flower viewing was perature-photoperiod model was chosen for simulating BLC extended by 0.15 days/year (P>0.05). Regarding the autumn and ELC. 0e detailed information on the model formulas leaves viewing, the start and end date of the best period could be found in previous studies [36, 37]. exhibited significantly delayed trends of 0.35 days/year and Advances in Meteorology 5 0 50 100 150 200 250 300 350 Day of year Phenology ornamental index Spring 50% threshold Autumn 50% threshold Figure 2: Multiyear mean phenological ornamental index (1963–2017) in Beijing. 120 300 1960 1980 2000 2020 1960 1980 2000 2020 Year Year Start End Start End Duration Duration (a) (b) Figure 3: Interannual variation in the start and end date of the best period for spring flower viewing (a) and autumn leaves viewing (b) from 1963 to 2017. 0.22 days/year (P<0.01), respectively (Figure 3(b)). Due to From the perspective of interannual changes the more considerable delay in the start date than the end (Figure 4(b)), the CCI value during the most comfortable date, the duration of the best period for autumn leaves period in spring showed a smaller interannual variation than viewing shortened by 0.13 days/year over the past 55years that in autumn, ranging from 7.46 (1976) to 7.85 (1970). 0e (P<0.01). amplitude of change in mean CCI of the most comfortable period in autumn was larger, ranging between 7.36 (2017) and 8.13 (1990). 0e mean CCI value of the most com- 3.2. Changes in the Climate Comfort Index. 0e mean CCI in fortable period did not show significant linear trends in both Beijing over the past 55 years was shown in Figure 4(a). spring and autumn (P>0.05). Similar to the POI, the curve of CCI within a year was bimodal. 0e most comfortable periods (CCI >7) in spring and autumn were April 4 to June 1 and September 4 to 3.3. Changes in the Travel Suitability Index in the Past. October 19, respectively. 0e most comfortable period in Figure 5 shows the multiyear mean of TSI in Beijing. 0ere spring was highly coincident with the best period for spring were two best periods for botanical tourism. 0e best period flower viewing, but, in autumn, the most comfortable period for spring botanical tourism started on April 7 and ended on was earlier than the best period for autumn leaves viewing. May 10, with a duration of 33 days. 0e best period for Day of year Index Day of year 6 Advances in Meteorology 8.25 y = –0.0009x + 9.38 8.00 R = 0.013 7.75 7.50 y = 0.0004x + 7.14 R = 0.017 7.25 0 100 200 300 1960 1980 2000 2020 Day of year Year CCI Spring Linear fit of spring 30 days moving average Autumn Linear fit of autumn Threshold (a) (b) Figure 4: Multiyear mean and interannual variation of climate comfort index (CCI). autumn botanical tourism was from October 10 to November future were earlier, and the duration was shorter than the past 55 years (April 7 to May 10). 0e best periods for 7, with a duration of 28 days. 0e interannual changes in the best periods for spring autumn botanical tourism during 2040–2050 were from and autumn botanical tourism were illustrated in Figure 6. October 16 to November 11 under RCP 4.5 and October 19 In spring, the start and end of the best period became to November 11 under RCP 8.5. Compared to the past 55 significantly earlier from 1963 to 2017 with a rate of 0.18 years (from October 10 to November 7), the start and end days/year and 0.26 days/year (P<0.01), respectively. Be- dates of the best period for autumn botanical tourism would cause the end date advanced more than the start date, the be predicted tobe 6–9 days and 4days later, and the duration duration was shortened by 0.08 days/year (P>0.05). In would be 2–5 days shorter during 2040–2050. autumn, the start and end dates of the best period were Figure 8 shows the interannual variation in the start and significantly delayed by 0.35 and 0.22 days/year (P<0.01), end date of the best periods for botanical tourism from 2020 respectively. As a result, the duration was shortened sig- to 2050. In spring, the trends in the start and end dates were- nificantly by 0.13 days/year (P<0.01). 0.06 days/year (P>0.05) and −0.15 days/year (P<0.05) under RCP 4.5, respectively. Under RCP 8.5, the start date and end date advanced by 0.41 days/year (P<0.01) and by 3.4. Changes in Travel Suitability Index in the Future. 0e 0.31 days/year (P<0.01), respectively. During the period phenological models were used to simulate the phenophases 2020–2050, the duration of the best period for spring bo- of each species from 2020 to 2050. 0e results of model tanical tourism was shortened by 0.09 days/year (P>0.05) validity were shown in Table S2. For FFD, the RMSE of the and 0.10 days/year (P>0.05) under RCP 4.5 and RCP 8.5, cross-validation was 5.98 days, and the average R was 0.61. respectively. Regarding EFD, the mean RMSE was 8.89 days, and the R 0e best period for autumn botanical tourism under two was 0.52. For BLC and ELC, the mean RMSE of the cross- scenarios is shown in Figures 8(b) and 8(d). Under RCP 4.5, validation was 10.45 days and 9.33 days with R of 0.34 and both start and end date were significantly delayed by 0.31 0.31, respectively. 0us, the phenological models could ac- days/year (P<0.01), and the duration exhibited no obvious curately simulate the phenophases, especially for FFD and trend from 2020 to 2050. Under RCP 8.5, the start date was EFD. Using the climate data under RCP 4.5 and RCP 8.5 and delayed with a rate of 0.33 days/year (P<0.05), and the delay the phenophases simulated by phenological models, the POI in the end date was 0.27 days/year (P<0.05), resulting in a (shown in Figure S1), CCI (shown in Figure S2), and TSI shortened duration (0.06 days/year, P>0.05). (shown in Figure 6) for the next 30 years were calculated. Based on the simulated TSI (Figure 7), the best periods 4. Discussion for spring botanical tourism during 2040–2050 were similar betweenRCP 4.5(March 31toApril26)and RCP 8.5(March From the perspective of phenology, we constructed a POI to 29 to April 25). However, the start date and end date in the describe the best period for spring flower viewing and CCI CCI Advances in Meteorology 7 1.0 0.8 0.6 0.4 0.2 0.0 0 50 100 150 200 250 300 350 Day of year Travel suitability index 50% threshold Figure 5: Multiyear (1963–2017) mean travel suitability index in Beijing. 120 300 1960 1980 2000 2020 1960 1980 2000 2020 Year Year Start End Start End Duration Duration (a) (b) Figure 6: Interannual variation in the best periods for spring and autumn botanical tourism during 1963–2017. (a) Spring. (b) Autumn. 1.0 0.8 0.6 0.4 0.2 0.0 0 100 200 300 Day of year 2040-2050 RCP4.5 1963-2017 2040-2050 RCP8.5 50% threshold Figure 7: Multiyear mean travel suitability index (2040–2050) under two climate scenarios. autumn leaves viewing. 0e results showed that the best previous studies focusing on the changes in the start of the period for spring flower viewing became earlier with climate growing season in the United States, Europe, and Australia warming, which was consistent with the results from [39–42]. 0e advance in spring flowering season may affect Day of year Index Travel suitability index Day of year 8 Advances in Meteorology 140 340 120 320 100 300 80 280 60 260 2020 2030 2040 2050 2020 2030 2040 2050 Year Year Start End Start End Duration Duration (a) (b) 140 340 120 320 100 300 80 280 2020 2030 2040 2050 2020 2030 2040 2050 Year Year Start End Start End Duration Duration (c) (d) Figure8:Interannual variationsinthebestperiod forspringandautumnbotanicaltourismundertwoclimatescenarios.(a)RCP 4.5spring. (b) RCP 4.5 autumn. (c) RCP 8.5 spring. (d) RCP 8.5 autumn. administrators of scenic spots to arrange the date to hold a dates has been advantageous to the Japanese maple tourism blossom festival and the tourists to choose an appropriate industry [8]. time to travel [13]. Since the THI (temperature and relative humidity) and 0e best period for autumn leaves viewing derived from WCI (temperature and wind speed) took different meteo- the POI was delayed in Beijing. In the study area, September rological factors into account when evaluating climate air temperature was decisive for the annual timing of au- comfort, the degree of comfort could be evaluated more comprehensively by the CCI, which synthesized the two tumn leaves coloring, and warming of 1 C led to a delayed beginning and the end date of autumn leaves coloring of 5.3 indices. 0e CCI showed that there were two comfortable days and 3.7 days, respectively [43]. In Europe, leaf coloring periods within a year in Beijing. 0e mean CCI of the spring also was delayed by 1.3 days/decade (1970 to 2000) when the and autumn comfortable periods showed strong interannual average trend per country was examined [2]. In the northern variation, and no significant linear trends were found from hemisphere, autumn phenophases of deciduous trees were 1963 to 2017. A previous study found that the yearly mean overall delayed [9]. 0e change in leaf coloring phenology tourism climate index of Beijing urban areas continued to exerted an essential influence on autumn botanical tourism. increase from 1951 to 2014 [30]. 0is result suggested that For instance, the change in the timing of maple leaf coloring although the level of comfort averaged from each year Day of year Day of year Day of year Day of year Advances in Meteorology 9 impact public health [50]. 0erefore, future research was increased significantly, the level of climate comfort in spring and autumn did not show obvious change. needed to clarify the impact of haze on botanical tourism. In this study, we combined the previously proposed 0e TSI established in this study considered both changes in plant phenology and climate comfort, which indices (THI and WCI) to construct CCI. 0us, we only could comprehensively reflect tourist experience when considered three climatic factors (temperature, humidity, they attended activities of botanical tourism. If the ad- and wind speed) and did not involve precipitation. 0is ministrators of the scenic spots did not adjust the date of was because THI and WCI were the indices that were spring blossom or autumn leaves festival according to the recommended by the China Meteorological Administra- phenophases and climate of the current year, it would tion due to their applicability to the climate of China [51]. Furthermore, the air temperature and relative humidity cause the mismatch between the travel dates of tourists and the best periods for botanical tourism. Too early start of the could to some extent reflect the influence of precipitation on climate comfort, since the temperature was low and festival may lead to the situation that tourists have fewer flowers to view, which may reduce tourist satisfaction. On relative humidity was high on rainy days. 0e most im- portant reason was that the uncertainty involved in the contrary, too late start or too early end of the festival would waste the tourism resources and reduce the income forecasting precipitation was larger than temperature [52]. of tourist attractions. For example, the cherry blossom Including precipitation in the climate comfort index festival of Yuyuantan Park, Beijing, received 170,000 would cause considerable uncertainty when predicting tourists in 2000 [43], which was 15% less than the previous future degrees of climate comfort. Even so, several studies year due to the mismatch between the start date and take precipitation into account when calculating the flowering period of cherries, as well as the influence of tourism climate index [29, 30, 53]. 0erefore, we needed to compare the different indices of climate comfort in the frequent sandstorms [44]. 0erefore, we suggested that the administrator of scenic spots and the organizers of the future. activities related to botanical tourism should consider the best period derived from TSI when determining the time to 5. Conclusion hold the festival. A perfect match between the date of spring blossom (or autumn leaves festival) and the best In this study, we constructed a travel suitability index season of botanical tourism could bring better tourism (TSI) by combining the phenological ornamental index experience to tourists, which would help attract more (POI) and climate comfort index (CCI) to describe the visitors and improve the economic benefits of scenic spots. suitability of different dates for botanical tourism. Sub- In the future, the best period for spring and autumn sequently, we selected Beijing as the study area and cal- botanical tourism would become shorter compared to the culated the best period for spring and autumn botanical past several decades, which may hurt the development of tourism from 1963 to 2017 based on the TSI. 0e results botanical tourism in Beijing. 0e reduction in the duration showed that the best period for spring botanical tourism of the best period for botanical tourism would make the time in Beijing was between April 7 and May 10, while the best to attend the related tourism activities more concentrated, period for autumn botanical tourism was from October 10 which may lead to the traffic congestion around the tourist to November 7. In 2040–2050, the best period for spring attractions and the crowdedness inside the tourist attrac- botanical tourism would start 7–9 days earlier, end 14–15 tions. 0us, the shortened duration of the best period for days earlier, and last 6–7 days shorter than that in the past botanical tourism would hurt the tourism industry. 55 years. 0e best period for autumn botanical tourism In additiontoplantphenology,windspeed,temperature, would start 6–9 days later, 4 days later, and last 2–5 days and humidity, other factors also affected tourism, especially shorter than that in the past 55 years. 0e potential de- air quality [45]. Regional haze (PM 2.5) has been one of the crease in the duration of the best period for botanical most disastrous weather events in China in recent years [46]. tourism may have a negative impact on the development Haze had many adverse effects on botanical tourism [47]. of botanical tourism in Beijing. Haze could block the sunlight and thus affect the phenology 0erefore, it is necessary to take corresponding measures of ornamental plants and disrupt the arrangement of to deal with the impact of climate change on botanical blossom festivals. For example, the haze reduced the quality tourism. First, the scenic spots should improve tourism of flowers (freshness, vibrancy, and color) for peony infrastructure to enhance their reception capacity and add (Paeonia suffruticosa) and resulted in the disorder of the types of ornamental plants to prolong the flowering flowering season (especially for varieties sensitive to sun- season. Second, the administrators of the scenic spots should light), which affected the economic and social benefits of adjust the date for holding the blossom (or leaf coloring) botanical tourism destinations such as Luoyang and Heze, festivals according to the phenophases predicted by the China [48]. Furthermore, haze often led to widespread flight phenological models. 0ird, establishing a real-time infor- delays or cancellations and temporarily closed highways, mation distribution platform for the public is to report the which may affect tourists’ travel to botanical tourism des- daily situations of flowering and leaf coloring of different tinations. Haze was also prone to causing traffic accidents, species. 0is could guide the public to choose the proper increasing the risk of self-driving travel [49]. 0e haze also time to travel. At last, the government could develop flexible influenced tourist behavioral intention during the flowering vacation arrangements, allowing visitors to enjoy the ac- or leaf coloring period since it has the potential to adversely tivities of botanical tourism at the best time. 10 Advances in Meteorology responses using a lifelong study of first flowering dates,” Data Availability International Journal of Biometeorology, vol. 57, no. 3, pp. 367–375, 2013. 0e phenological data could be downloaded from the Na- [7] T. T. Hoye, E. Post, N. M. Schmidt et al., “Shorter flowering tional Earth System Science Data Center (http://www. seasons and declining abundance of flower visitors in a geodata.cn, in Chinese). 0e meteorological data were warmer Arctic,” Nature Climate Change, vol. 3, pp. 759–763, available from the National Meteorological Information Center (http://data.cma.cn/en). 0e future climatic data [8] J. Liu, H. Cheng, D. 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Effects of Climate Change on the Season of Botanical Tourism: A Case Study in Beijing

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Copyright © 2020 Yaqiong Zang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Hindawi Advances in Meteorology Volume 2020, Article ID 8527860, 11 pages https://doi.org/10.1155/2020/8527860 Research Article Effects of Climate Change on the Season of Botanical Tourism: A Case Study in Beijing 1,2 1 1 1 1 Yaqiong Zang, Junhu Dai, Zexing Tao, Huanjiong Wang , and Quansheng Ge Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 10010, China University of Chinese Academy of Sciences, Beijing 10049, China Correspondence should be addressed to Huanjiong Wang; wanghj@igsnrr.ac.cn and Quansheng Ge; geqs@igsnrr.ac.cn Received 24 October 2019; Revised 14 March 2020; Accepted 30 June 2020; Published 17 July 2020 Academic Editor: Budong Qian Copyright © 2020Yaqiong Zang et al. 0is is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Climate change could affect botanical tourism by altering the plant phenology (e.g., flowering and leaf coloring date) and the physical comfort of tourists. To date, few studies have simultaneously considered the influence of plant phenology and physical comfort on the travel suitability of botanical tourism. Taking Beijing as an example, this study used phenological data of 73 species from 1963 to 2017 to construct a phenological ornamental index (POI) according to the flowering and leaf coloring date of ornamental plant. 0e climate comfort index (CCI) of tourism was calculated by using meteorological data of the corresponding periods. Finally, the travel suitability index (TSI) was constructed by integrating the two indices (POI and CCI). 0e POI showed that the best period for spring flower viewing was from April 4 to May 10, while the best period for autumn leaves viewing was fromOctober 11toNovember6on average.AccordingtothevariationoftheCCI withintheyear,themostcomfortableperiodfor spring tourism was matched with the best period for spring flower viewing (April 4 to June 1), but the most comfortable period for autumn tourism (September 4 to October 19) was earlier than the best period for autumn leaves viewing. 0e TSI indicated that the best periods for spring and autumn botanical tourism were April 7 to May 10 and October 10 to November 7, respectively. Based on the climate data under different scenarios (representative concentration pathways 4.5 and 8.5), we simulated the climate and phenological suitability for botanical tourism in the next thirty years. 0e results showed that the best period for spring botanical tourism during 2040–2050 was earlier and the period for autumn botanical tourism was later than that in the past 55 years. Meanwhile, the duration would shorten by 2–7 days for both seasons. 0is study provided a reference for assessing the impact of global climate change on the best season of botanical tourism. sunshine. 0us, climate change could affect botanical 1. Introduction tourism by simultaneously changing plant phenology and 0e plants provide an opportunity to serve as botanical tourists’ physical comfort [3, 4]. tourism in which many different forms of tourism activities Over the past several decades, the best time for botanical can be carried out, such as the cherry blossom festivals in tourism, such as spring flower viewing and autumn leaves Japan, the rose festival in Bulgaria, tulip festival in Holland, viewing, was altered under the background of climate change. and the maple syrup festivals in Canada. 0ese tourism For example, based on phenological data of 232 plant species activities are profoundly affected by climate change [1]. On from 1985 to 2011 on the island of Guernsey in the English the one hand, the landscape of botanical tourism is related to Channel, the significantly earlier beginning of flowering and plant phenophases (e.g., flowering and leaf coloring date), shorter flowering duration was found [5]. Another study which responded sensitively to climate change [2]. On the based on a 73-year long data series of first flowering dates for other hand, climate change exerts a direct influence on 25 species from north-temperate Sweden showed that most human comfort by altering meteorological factors such as time-series of first flowering dates exhibited tendencies to- temperature, humidity, precipitation, wind speed, and wardsearlierflowering[6].Aclimate-associatedshorteningof 2 Advances in Meteorology were detected between UTCI and previous indices of climate the flowering season was also significant in high-Arctic Greenland[7].Regardingtheautumnseason,thetemperature comfort [27], indicating that the earlier indices of climate comfort were still valid. rise led to a later beginning of leaf coloring from 1978 to 2016 in Japan, which made the number of visitors increase by more In recent years, a large number of studies chose one of than 3% [8]. 0e leaf senescence of deciduous trees in the the indices or combine several indices to evaluate the local northern hemisphere became later over time, especially in the climate comfort according to the research purpose and study low-latitude area [9]. area. For instance, Mihail ˘ a˘ et al. [28] used an hourly database For assessing the potential impacts of climate change on for the period 1961–2015 to calculate the PET index and botanical tourism, many studies focused on predicting the outline a series of changes that were likely to intervene in the relationship between climate and tourism in north-eastern spring flower phenology. For instance, a process-based model was developed to predict peak bloom dates of Romania in the immediate future. Cheng and Zhong [29] investigated tourism climate conditions in Grand Shangri- flowering cherry trees in the Tidal Basin, Washington, DC [10]. Hur et al. [11] used the seasonal prediction from the La from 1980 to 2016 using a tourism climate index (TCI) and found that the number of annual and monthly good- global and regional climate models to evaluate the forecast capability of the first flowering date of cherry, peach, and weather days increased over most of the area under the pear over South Korea. Recently, several studies noticed the influence of climate warming. In Beijing, China, the level of impact of phenological change on botanical tourism. For tourism comfort showed an increasing trend for all seasons, example, Sakurai et al. [12] explored the perceptions of which was favorable for the development of tourism [30]. Japanese residents regarding climate change impacts on 0ese studies provided examples for assessing the impact of culturally significant events and found that most (92%) climate comfort on tourism in specific tourism destinations. To date, few studies have considered the effect of plant managers of festival-dependent businesses were concerned about global warming because it affected the flowering phenology and climate comfort on botanical tourism si- multaneously. In order to fill in this gap, we chose Beijing as timing of cherry trees and income of cherry blossom fes- tivals. In Beijing, the administrator of Jingshan Park the research area where botanical tourism was well developed and analyzed the impact of climate change on the phenology scheduled the peony blossom festival date following the variation of peony FFD [13]. and physical comfort of tourists. First, we developed a Besides phenology, the degree of climate comfort could phenological ornamental index (POI) based on phenological directly affect botanical tourism [14]. 0e early evaluation of data of 73 species from 1963 to 2017. Second, using mete- climate comfort was usually based on the empirical model of orological data, we calculated annual changes in the climate human comfort. 0e empirical models reflected the subjective comfort index (CCI). Combining the POI and CCI, the travel feeling or physiological reaction of people and were built by suitability index (TSI) of botanical tourism was constructed. Finally, by using the climate data simulated under different experience or statistical methods. 0e typical empirical models included effective temperature (ET) [15], wet bulb representative concentration pathways (RCP), we predicted the changes in POI, CCI, and TSI from 2020 to 2050. 0is globe temperature (WBGT) [16], temperature-humidity In- dex (THI) [17], and Wind Chill Index (WCI) [18], which study aimed to reveal the impact of climate change on the considered the impact of air temperature, relative humidity, season of botanical tourism and provide references for the wind speed, and solar radiation. Empirical indices were tourism administrators to choose the appropriate time to simple to be calculated and easy to be understood by the arrange activities of botanical tourism. public.Inthelate1960s,advancesinbiometricsandcomputer technology led to the rapid development of climate comfort 2. Materials and Methods indices based on human heat exchange models [19, 20], such as perceived temperature (PT) [21], standard effective tem- 2.1. Study Area. Beijing, located in northern China, has a perature(SET)[22],andphysiologicalequivalenttemperature temperate semihumid climate. In the urban area of Beijing, (PET) [23], which comprehensively considered the meteo- ° the mean annual temperature was 12.53 C (averaged from rological factors (air temperature, relative humidity, wind 1963 to 2017). 0e monthly maximum and minimum speed, and solar radiation), human metabolic rate, clothing, ° ° temperatures were 25.46 C in July and −3.41 C in January, and individual parameters. However, human heat exchange respectively. 0e annual total precipitation was 560.87mm, was a very complex process and difficult to accurately cal- and the annual mean wind speed was 2.44m/s. In Beijing, culate.0us,theaboveindicesbasedonhumanheatexchange there were many tourism activities relating to botanical were not widely used. Recently, scientists developed a uni- tourism, such as peach blossom festival at Beijing Botanical versal thermal climate index (UTCI), which was regarded as Garden, peony festival at Jingshan Park, cherry blossom one of the most comprehensive indices for calculating heat festival at Yuyuantan Park, and red leaf festival at Fragrant stress in outdoor space [24, 25]. 0e input data for calculating Hills Park. 0erefore, Beijing was an ideal place to study the UTCI included meteorological and nonmeteorological impact of climate change on botanical tourism. (metabolic rate and clothing thermal resistance) data [26]. 0e parameters that were taken into account for calculating UTCI involve dry temperature, mean radiation temperature, 2.2. Data Source. All phenological data were derived from the pressure of water vapor or relative humidity, and wind China Phenology Observation Network (CPON). 0ese data ° ° speed. A recent study showed that the significant correlations were observed at the Summer Palace (39.992 N, 116.266 E), Advances in Meteorology 3 which was located in northwestern Beijing (Figure 1). Within each year, two peaks appeared in the daily POI Considering that the urban area of Beijing has a similar curve. 0e first peak occurred in spring because most of the plants flowered in this season. In the first 6 months of the elevation, we believed that the phenological data at one site could generally represent the whole urban area (although year, the period when POI was larger than half of the difference existed among individuals). Four phenophases of maximum was defined as the best period for spring flower 73 woody plants from 1963 to 2017in Beijing were inves- viewing. Similarly, in the last six months of the year, the tigated (Table S1). Most of these species were representative period when POI was larger than half of the maximum was ornamental plants with relatively complete observation data. defined as the best period for autumn leaves viewing. We investigated the phenophases which were closely related to the botanical tourism, including the date of first flowering 2.3.2. Climate Comfort Index. As mentioned in the Intro- (FFD), end of flowering (EFD), beginning of leaf coloring duction section, a variety of climate comfort indices had (BLC), and end of leaf coloring (ELC). been proposed [33]. Among them, the temperature-hu- 0e daily meteorological data in Beijing from 1963 to midity index (THI) and the wind chill index (WCI) were 2017 were downloaded from China meteorological data frequently used in previous studies. According to the en- service center (http://data.cma.cn/). We used the data from vironmental background conditions on which they were ° ° the Haidian meteorological station (39.987 N, 116.290 E), built, THI was more applicable to the warm environment, including mean, maximum, and minimum air temperature, while WCI was more suitable to the cold environment. relative humidity, and wind speed. Future climate data were 0erefore, in this study, we combined these two indices to derived from the Coupled Model Intercomparison Project make it more applicable to a temperate climate. Phase 5 (CMIP5) of the World Climate Research Pro- THI is a measure of the reaction of the human body to a gramme (WCRP). We obtained future climatic datasets combination of heat and humidity [34], and the equation for generated by the Community Climate System Model version THI is 4 (CCSM4) under the representative concentration pathway (RCP) 4.5 and RCP 8.5 [31]. 0e dataset covered the period THI � (1.8 · t + 32) − 0.55(1 − f) × (1.8 · t − 26), (1) from 2020 to 2050, including daily mean, maximum, and where t is air temperature ( C) and f is relative humidity (%). minimum air temperature, relative humidity, and wind ° ° speed with a spatial resolution of 1.4 WCI could reflect the effect of wind on the temperature ×1.4 . According to which humans perceive [35]. 0e equation for WCI is Beijing’s geographic coordinates, we extracted the meteo- √� rological factors of the corresponding pixel in the future WCI � (10 v − v + 10.5) × (33 − t), (2) climate data, and the statistical downscaling method was used to convert the data to the local scale [32]. where t is air temperature ( C) and v is wind speed (m/s). According to the values of THI and WCI, we gave dif- ferent scores to quantify the level of climate comfort. Higher 2.3. Constructionof Travel SuitabilityIndex. Plants were very scores suggested a more comfortable climate (Table 1). 0e attractive to tourists when they were in flowering or leaf CCI was defined as the average of THI and WCI score: coloring. 0erefore, we first constructed a new phenological S + S ornamental index (POI) to describe the number of species in THI WCI (3) CCI � , flowering or leaf coloring at each day of the year. 0e POI 2 could reflect whether it was an appropriate time to travel for where CCI is the climate comfort index and S and S THI WCI spring flower viewing or autumn leaves viewing. Further- are the scores of THI and WCI, respectively. Similarly, more, climate conditions could affect the physical comfort of within a year, twopeaks appeared inthe daily CCIcurve. 0e tourists. 0us, we calculated the daily climate comfort index period when CCI was larger than 7 was defined as the most (CCI) based on two previously proposed indexes to describe comfortable period. whether the climate was comfortable for traveling. Finally, the travel suitability index (TSI) was constructed by con- sidering both POI and CCI. 2.3.3. Travel Suitability Index. We first calculated the product of POI and CCI (X) to measure the overall impact of phenology and climate on the suitability of botanical 2.3.1. Phenological Ornamental Index. To date, there was no tourism ((4)). Subsequently, we normalized the value of X to index to measure the impact of phenology on botanical 0–1 range using (5). 0e normalized value could be defined tourism. 0erefore, we introduced a POI in this study. First, as TSI. wecountedthe numberofspeciesinflowering(fromthe first flowering date to the end of the flowering date) or in leaf X � POI · CCI, (4) coloring (from the beginning date of leaf coloring to the end date of leaf coloring) for each day of the year. Subsequently, X − X min TSI � , (5) the daily changes in the number of species in flowering or X − X max min leaf coloring were smoothed by using the 5-day moving average method. 0e POI at each date was defined as the where X represents the product of POI and CCI. X and max number of species in flowering and leaf coloring (after X are the maximum and minimum values of TSI within a min smoothing). year, respectively. 4 Advances in Meteorology 116°12′E 116°15′E 116°18′E 116°21′E 116°24′E 116°27′E 116°30′E The Summer Palace 40°0′N 39°57′N 39°54′N Haidian meteorological station 39°51′N 39°48′N Figure 1: 0e locations of phenological observation and meteorological stations in Beijing. 0e phenological data were collected in the Summer Palace, and the meteorological data was derived from the Haidian meteorological station. 0e image of the urban area of Beijing is Landsat 8 image observed on May 18, 2015, courtesy of the US Geological Survey. 0e subplots of each station were downloaded from Sogou Map (http://map.sogou.com/). Table 1: Classification and score of the temperature-humidity Based on the phenological and meteorological data from index (THI) and wind chill index (WCI). 1963 to 2017, we calibrated the Unified model and tem- perature-photoperiod model for each species and pheno- THI 0e level of climate comfort WCI value Score phase. We fitted the optimal parameters for each model by value using the simulated annealing method based on the least- Extremely coldand uncomfortable <40 >1000 1 squares principle, that is, the parameters with the minimum Cold and uncomfortable 40∼45 800∼1000 3 sum of the squares of the residuals [38]. 0e goodness of fit Slightly cold and uncomfortable 45∼55 600∼800 5 (R ) and root mean square error (RMSE) were calculated to Cool and comfortable 55∼60 300∼600 7 assess the error of the phenological models. By using the Pleasantly cool and very 60∼65 200∼300 9 comfortable temperature data under two climate scenarios and the Warm and comfortable 65∼70 50∼200 7 calibrated phenological models, we simulated the FFD, EFD, A little hot and comfortable 70∼75 −80∼50 5 BLC, and ELC of each plant from 2020 to 2050. Subse- Hot and uncomfortable 75∼80 −160∼−80 3 quently, future POI could be calculated by using the method Extremely hot and uncomfortable >80 <−160 1 described in Section 2.3.1 Future changes in CCI could be simulated by using future Following the above steps, we calculated the daily TSI climatedataandmethodsdescribedinSection2.3.2.Atlast,the from 1963 to 2017. For a certain year, two peaks appeared in yearly changes in TSI (2020–2050) could be calculated. the TSI curve. 0us, there were two periods during which the TSI was larger than 0.5. 0e first and second periods were 3. Results defined as the best periods for spring and autumn botanical tourism, respectively. 0e linear trend in the start date, end 3.1. Changes in the Phenological Ornamental Index. Figure 2 shows the POI averaged from 1963 to 2017. 0e POI date, and duration of the best period was calculated by regression of these dates against the year. curve showed that the best period for spring flower viewing ranged from April 4 to May 10, which lasted 36 days. 0e best period for autumn leaves viewing was between October 2.4. Simulation of Travel Suitability Index from 2020 to 2050. 11 and November 6, which lasted 27 days. From 1963 to In order to predict the POI in the future, we first used 2017, the start and end date of the best period for spring phenological models to predict the flowering and leaf col- flower viewing advanced by 0.23 days/year (P<0.01) and oring phenology of each plant. 0e Unified model was 0.08 days/year (P>0.05), respectively (Figure 3(a)). 0e chosen for simulating future FFD and EFD, and the tem- duration of the best period for spring flower viewing was perature-photoperiod model was chosen for simulating BLC extended by 0.15 days/year (P>0.05). Regarding the autumn and ELC. 0e detailed information on the model formulas leaves viewing, the start and end date of the best period could be found in previous studies [36, 37]. exhibited significantly delayed trends of 0.35 days/year and Advances in Meteorology 5 0 50 100 150 200 250 300 350 Day of year Phenology ornamental index Spring 50% threshold Autumn 50% threshold Figure 2: Multiyear mean phenological ornamental index (1963–2017) in Beijing. 120 300 1960 1980 2000 2020 1960 1980 2000 2020 Year Year Start End Start End Duration Duration (a) (b) Figure 3: Interannual variation in the start and end date of the best period for spring flower viewing (a) and autumn leaves viewing (b) from 1963 to 2017. 0.22 days/year (P<0.01), respectively (Figure 3(b)). Due to From the perspective of interannual changes the more considerable delay in the start date than the end (Figure 4(b)), the CCI value during the most comfortable date, the duration of the best period for autumn leaves period in spring showed a smaller interannual variation than viewing shortened by 0.13 days/year over the past 55years that in autumn, ranging from 7.46 (1976) to 7.85 (1970). 0e (P<0.01). amplitude of change in mean CCI of the most comfortable period in autumn was larger, ranging between 7.36 (2017) and 8.13 (1990). 0e mean CCI value of the most com- 3.2. Changes in the Climate Comfort Index. 0e mean CCI in fortable period did not show significant linear trends in both Beijing over the past 55 years was shown in Figure 4(a). spring and autumn (P>0.05). Similar to the POI, the curve of CCI within a year was bimodal. 0e most comfortable periods (CCI >7) in spring and autumn were April 4 to June 1 and September 4 to 3.3. Changes in the Travel Suitability Index in the Past. October 19, respectively. 0e most comfortable period in Figure 5 shows the multiyear mean of TSI in Beijing. 0ere spring was highly coincident with the best period for spring were two best periods for botanical tourism. 0e best period flower viewing, but, in autumn, the most comfortable period for spring botanical tourism started on April 7 and ended on was earlier than the best period for autumn leaves viewing. May 10, with a duration of 33 days. 0e best period for Day of year Index Day of year 6 Advances in Meteorology 8.25 y = –0.0009x + 9.38 8.00 R = 0.013 7.75 7.50 y = 0.0004x + 7.14 R = 0.017 7.25 0 100 200 300 1960 1980 2000 2020 Day of year Year CCI Spring Linear fit of spring 30 days moving average Autumn Linear fit of autumn Threshold (a) (b) Figure 4: Multiyear mean and interannual variation of climate comfort index (CCI). autumn botanical tourism was from October 10 to November future were earlier, and the duration was shorter than the past 55 years (April 7 to May 10). 0e best periods for 7, with a duration of 28 days. 0e interannual changes in the best periods for spring autumn botanical tourism during 2040–2050 were from and autumn botanical tourism were illustrated in Figure 6. October 16 to November 11 under RCP 4.5 and October 19 In spring, the start and end of the best period became to November 11 under RCP 8.5. Compared to the past 55 significantly earlier from 1963 to 2017 with a rate of 0.18 years (from October 10 to November 7), the start and end days/year and 0.26 days/year (P<0.01), respectively. Be- dates of the best period for autumn botanical tourism would cause the end date advanced more than the start date, the be predicted tobe 6–9 days and 4days later, and the duration duration was shortened by 0.08 days/year (P>0.05). In would be 2–5 days shorter during 2040–2050. autumn, the start and end dates of the best period were Figure 8 shows the interannual variation in the start and significantly delayed by 0.35 and 0.22 days/year (P<0.01), end date of the best periods for botanical tourism from 2020 respectively. As a result, the duration was shortened sig- to 2050. In spring, the trends in the start and end dates were- nificantly by 0.13 days/year (P<0.01). 0.06 days/year (P>0.05) and −0.15 days/year (P<0.05) under RCP 4.5, respectively. Under RCP 8.5, the start date and end date advanced by 0.41 days/year (P<0.01) and by 3.4. Changes in Travel Suitability Index in the Future. 0e 0.31 days/year (P<0.01), respectively. During the period phenological models were used to simulate the phenophases 2020–2050, the duration of the best period for spring bo- of each species from 2020 to 2050. 0e results of model tanical tourism was shortened by 0.09 days/year (P>0.05) validity were shown in Table S2. For FFD, the RMSE of the and 0.10 days/year (P>0.05) under RCP 4.5 and RCP 8.5, cross-validation was 5.98 days, and the average R was 0.61. respectively. Regarding EFD, the mean RMSE was 8.89 days, and the R 0e best period for autumn botanical tourism under two was 0.52. For BLC and ELC, the mean RMSE of the cross- scenarios is shown in Figures 8(b) and 8(d). Under RCP 4.5, validation was 10.45 days and 9.33 days with R of 0.34 and both start and end date were significantly delayed by 0.31 0.31, respectively. 0us, the phenological models could ac- days/year (P<0.01), and the duration exhibited no obvious curately simulate the phenophases, especially for FFD and trend from 2020 to 2050. Under RCP 8.5, the start date was EFD. Using the climate data under RCP 4.5 and RCP 8.5 and delayed with a rate of 0.33 days/year (P<0.05), and the delay the phenophases simulated by phenological models, the POI in the end date was 0.27 days/year (P<0.05), resulting in a (shown in Figure S1), CCI (shown in Figure S2), and TSI shortened duration (0.06 days/year, P>0.05). (shown in Figure 6) for the next 30 years were calculated. Based on the simulated TSI (Figure 7), the best periods 4. Discussion for spring botanical tourism during 2040–2050 were similar betweenRCP 4.5(March 31toApril26)and RCP 8.5(March From the perspective of phenology, we constructed a POI to 29 to April 25). However, the start date and end date in the describe the best period for spring flower viewing and CCI CCI Advances in Meteorology 7 1.0 0.8 0.6 0.4 0.2 0.0 0 50 100 150 200 250 300 350 Day of year Travel suitability index 50% threshold Figure 5: Multiyear (1963–2017) mean travel suitability index in Beijing. 120 300 1960 1980 2000 2020 1960 1980 2000 2020 Year Year Start End Start End Duration Duration (a) (b) Figure 6: Interannual variation in the best periods for spring and autumn botanical tourism during 1963–2017. (a) Spring. (b) Autumn. 1.0 0.8 0.6 0.4 0.2 0.0 0 100 200 300 Day of year 2040-2050 RCP4.5 1963-2017 2040-2050 RCP8.5 50% threshold Figure 7: Multiyear mean travel suitability index (2040–2050) under two climate scenarios. autumn leaves viewing. 0e results showed that the best previous studies focusing on the changes in the start of the period for spring flower viewing became earlier with climate growing season in the United States, Europe, and Australia warming, which was consistent with the results from [39–42]. 0e advance in spring flowering season may affect Day of year Index Travel suitability index Day of year 8 Advances in Meteorology 140 340 120 320 100 300 80 280 60 260 2020 2030 2040 2050 2020 2030 2040 2050 Year Year Start End Start End Duration Duration (a) (b) 140 340 120 320 100 300 80 280 2020 2030 2040 2050 2020 2030 2040 2050 Year Year Start End Start End Duration Duration (c) (d) Figure8:Interannual variationsinthebestperiod forspringandautumnbotanicaltourismundertwoclimatescenarios.(a)RCP 4.5spring. (b) RCP 4.5 autumn. (c) RCP 8.5 spring. (d) RCP 8.5 autumn. administrators of scenic spots to arrange the date to hold a dates has been advantageous to the Japanese maple tourism blossom festival and the tourists to choose an appropriate industry [8]. time to travel [13]. Since the THI (temperature and relative humidity) and 0e best period for autumn leaves viewing derived from WCI (temperature and wind speed) took different meteo- the POI was delayed in Beijing. In the study area, September rological factors into account when evaluating climate air temperature was decisive for the annual timing of au- comfort, the degree of comfort could be evaluated more comprehensively by the CCI, which synthesized the two tumn leaves coloring, and warming of 1 C led to a delayed beginning and the end date of autumn leaves coloring of 5.3 indices. 0e CCI showed that there were two comfortable days and 3.7 days, respectively [43]. In Europe, leaf coloring periods within a year in Beijing. 0e mean CCI of the spring also was delayed by 1.3 days/decade (1970 to 2000) when the and autumn comfortable periods showed strong interannual average trend per country was examined [2]. In the northern variation, and no significant linear trends were found from hemisphere, autumn phenophases of deciduous trees were 1963 to 2017. A previous study found that the yearly mean overall delayed [9]. 0e change in leaf coloring phenology tourism climate index of Beijing urban areas continued to exerted an essential influence on autumn botanical tourism. increase from 1951 to 2014 [30]. 0is result suggested that For instance, the change in the timing of maple leaf coloring although the level of comfort averaged from each year Day of year Day of year Day of year Day of year Advances in Meteorology 9 impact public health [50]. 0erefore, future research was increased significantly, the level of climate comfort in spring and autumn did not show obvious change. needed to clarify the impact of haze on botanical tourism. In this study, we combined the previously proposed 0e TSI established in this study considered both changes in plant phenology and climate comfort, which indices (THI and WCI) to construct CCI. 0us, we only could comprehensively reflect tourist experience when considered three climatic factors (temperature, humidity, they attended activities of botanical tourism. If the ad- and wind speed) and did not involve precipitation. 0is ministrators of the scenic spots did not adjust the date of was because THI and WCI were the indices that were spring blossom or autumn leaves festival according to the recommended by the China Meteorological Administra- phenophases and climate of the current year, it would tion due to their applicability to the climate of China [51]. Furthermore, the air temperature and relative humidity cause the mismatch between the travel dates of tourists and the best periods for botanical tourism. Too early start of the could to some extent reflect the influence of precipitation on climate comfort, since the temperature was low and festival may lead to the situation that tourists have fewer flowers to view, which may reduce tourist satisfaction. On relative humidity was high on rainy days. 0e most im- portant reason was that the uncertainty involved in the contrary, too late start or too early end of the festival would waste the tourism resources and reduce the income forecasting precipitation was larger than temperature [52]. of tourist attractions. For example, the cherry blossom Including precipitation in the climate comfort index festival of Yuyuantan Park, Beijing, received 170,000 would cause considerable uncertainty when predicting tourists in 2000 [43], which was 15% less than the previous future degrees of climate comfort. Even so, several studies year due to the mismatch between the start date and take precipitation into account when calculating the flowering period of cherries, as well as the influence of tourism climate index [29, 30, 53]. 0erefore, we needed to compare the different indices of climate comfort in the frequent sandstorms [44]. 0erefore, we suggested that the administrator of scenic spots and the organizers of the future. activities related to botanical tourism should consider the best period derived from TSI when determining the time to 5. Conclusion hold the festival. A perfect match between the date of spring blossom (or autumn leaves festival) and the best In this study, we constructed a travel suitability index season of botanical tourism could bring better tourism (TSI) by combining the phenological ornamental index experience to tourists, which would help attract more (POI) and climate comfort index (CCI) to describe the visitors and improve the economic benefits of scenic spots. suitability of different dates for botanical tourism. Sub- In the future, the best period for spring and autumn sequently, we selected Beijing as the study area and cal- botanical tourism would become shorter compared to the culated the best period for spring and autumn botanical past several decades, which may hurt the development of tourism from 1963 to 2017 based on the TSI. 0e results botanical tourism in Beijing. 0e reduction in the duration showed that the best period for spring botanical tourism of the best period for botanical tourism would make the time in Beijing was between April 7 and May 10, while the best to attend the related tourism activities more concentrated, period for autumn botanical tourism was from October 10 which may lead to the traffic congestion around the tourist to November 7. In 2040–2050, the best period for spring attractions and the crowdedness inside the tourist attrac- botanical tourism would start 7–9 days earlier, end 14–15 tions. 0us, the shortened duration of the best period for days earlier, and last 6–7 days shorter than that in the past botanical tourism would hurt the tourism industry. 55 years. 0e best period for autumn botanical tourism In additiontoplantphenology,windspeed,temperature, would start 6–9 days later, 4 days later, and last 2–5 days and humidity, other factors also affected tourism, especially shorter than that in the past 55 years. 0e potential de- air quality [45]. Regional haze (PM 2.5) has been one of the crease in the duration of the best period for botanical most disastrous weather events in China in recent years [46]. tourism may have a negative impact on the development Haze had many adverse effects on botanical tourism [47]. of botanical tourism in Beijing. Haze could block the sunlight and thus affect the phenology 0erefore, it is necessary to take corresponding measures of ornamental plants and disrupt the arrangement of to deal with the impact of climate change on botanical blossom festivals. For example, the haze reduced the quality tourism. First, the scenic spots should improve tourism of flowers (freshness, vibrancy, and color) for peony infrastructure to enhance their reception capacity and add (Paeonia suffruticosa) and resulted in the disorder of the types of ornamental plants to prolong the flowering flowering season (especially for varieties sensitive to sun- season. Second, the administrators of the scenic spots should light), which affected the economic and social benefits of adjust the date for holding the blossom (or leaf coloring) botanical tourism destinations such as Luoyang and Heze, festivals according to the phenophases predicted by the China [48]. Furthermore, haze often led to widespread flight phenological models. 0ird, establishing a real-time infor- delays or cancellations and temporarily closed highways, mation distribution platform for the public is to report the which may affect tourists’ travel to botanical tourism des- daily situations of flowering and leaf coloring of different tinations. Haze was also prone to causing traffic accidents, species. 0is could guide the public to choose the proper increasing the risk of self-driving travel [49]. 0e haze also time to travel. 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Published: Jul 17, 2020

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