Spatial and Temporal Assessment of China's Skiing Climate Resources

Spatial and Temporal Assessment of China's Skiing Climate Resources | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial and Temporal Assessment of China's Skiing Climate Resources Dandan Yu, Zhanglin Lin, Yan Fang, Weijia Zhang, Juan Guo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3299526/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The primary objective of this study is to analyze the characteristics of ski climate resources and quantitatively assess the suitability for skiing by utilizing a more appropriate Ski Climate Index. Taking China as a case study, this paper collected daily meteorological data from 733 weather stations spanning the period from 1991 to 2020, along with information on 415 ski resorts. Subsequently, GIS 10.5 spatial analysis tools were employed to examine the temporal and spatial variations in ski climate resources across China during this timeframe. In order to illustrate the relationship between skiing climate resources and the development of ski resorts more clearly, a comparison was thus drawn between the distribution of ski climate resources and the vitality of ski resorts in China. The results show that:1) the SCI was developed using fuzzy logic, with a predominant influence from the snow reliability facet on overall performance. Furthermore, the aesthetics and comfort facet, which includes factors such as sunshine, wind, and thermal comfort conditions, contributed to further refinement of the index. 2) Areas with high SCI values are primarily concentrated in the northwestern and northeastern regions of China, as well as certain parts of northern China. Against the backdrop of climate change, there has been a significant increase in ski climate resources in regions like Shaanxi-Gansu-Ningxia regions, southwest Tibet, and Sichuan, and noticeable declines have occurred in southern regions within Northeast China.3) Through comparison with vitality of ski resorts,SCI can partially reflect the development of ski resorts. The suitability evaluation model for skiing based on climate resources provides valuable insights for management decision-making in developing and operating ski resorts. It also offers scientific support for promoting ice-snow economic development. Ski climate index ski resorts’ vitality ski industry Tourism climate Spatial distribution Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Climate conditions play a crucial role in determining the operations of ski resorts and the demand for skiing. The availability of consistent snowfall is vital for ski resorts, with a minimum snow depth requirement of 30cm being deemed necessary to guarantee the safety of skiers (Damma et al.,2014). The patterns of snowfall are significantly influenced by various climatic factors, including temperature, precipitation, and humidity (McClung, 2006; Matzarakis et al.,2012). Additionally, the pace at which snow melts is dictated by fluctuations in temperature (Zhong et al., 2018). Climate conditions also affect ski resort facilities. For instance, T-bar lifts will be closed if wind speeds exceed 30 km/h (Andersen et al., 2004). Various climate factors, including wind speed or force, temperature, extreme weather events, and visibility, have a direct impact on both the safety and skiing experience of skiers (Demiroglu et al., 2016). Optimal skiing conditions prevail when the highest temperature falls within the range of -12°C to 2°C, coupled with wind speeds that are below 2 on the Beaufort scale. Conversely, skiing becomes less viable when the highest temperature plunges below − 16°C or when wind speeds escalate beyond 5 on the Beaufort scale (China Meteorological Administration, 2022). Additionally, extended periods of exceptionally cold weather can lead to the hardening of snow, consequently heightening the potential for skiers to encounter increased risks of falling. Skiing, as an activity closely tied to climatic conditions, confronts a substantial peril due to the escalating impacts of global climate warming (Steiger et al., 2019). The Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) predicts that climate change will result in reduced snowfall and natural snow cover across the majority of regions (Gajić-Čapka, 2011; Steiger et al., 2019; Stocker, 2022). With future climate warming, outdoor ski resorts worldwide will experience varying degrees of shortened skiing seasons and reduced skiing areas, leading to an increased demand for artificial snowmaking (Scott et al., 2020). Consequently, some ski resorts may be forced to close due to decreased revenue and higher operating costs. For instance, in North America alone, 31% of outdoor ski resorts are already at risk of extinction due to climate change (Barber, 2015). Furthermore, skiing demand is highly sensitive to changes in weather patterns. It has been estimated that every one-degree Celsius increase in temperature at ski resorts results in a six percent reduction in lift ticket sales (Demiroglu et al., 2015). With the successful bid and hosting of the Beijing 2022 Winter Olympics and Paralympics, coupled with the continuous introduction of favorable national policies and a growing public enthusiasm for skiing participation, China's skiing industry is experiencing rapid development. According to the White Paper on Ski Industry in China in the Snow Season of 2021–2022 (Wu, 2022), there is a strong momentum in the construction and expansion of ski resorts, attracting a total of 21.54 million skiers. Nevertheless, despite boasting the highest count of ski resorts on a global scale, a mere fraction of these, amounting to less than 10%, adhere to international standards. For instance, only 159 ski resorts are equipped with lifting chairs (Wu, 2022; Tang et al., 2022). Furthermore, China's skiing industry is still in its early stage, with relatively extensive development approaches (Yang et al., 2019). Therefore, promoting high-quality development after the Winter Olympics has become crucial by emphasizing on interplay between industry foundation, consumer demand for skiing activities, and ski resort capacity (Wang et al., 2022). This involves achieving a balance between supply and demand while enhancing service quality through policy refinement, as well as fostering innovation and technological capabilities (Jiang & Li, 2021). The Policy and Action for Climate Change in China emphasizes that addressing climate change is a crucial driver for promoting high-quality economic development in the country. However, there remains a relative lack of research on the ski industry's development in China from a climatic perspective. Although the Skiing Meteorological Index (QX/T386-2017), an industry standard, defines skiing weather suitability and provides meteorological services to the public, it falls short in comprehensively evaluate China's skiing climate. Consequently, there is limited understanding of the temporal and spatial characteristics of skiing climate in China, which hampers effective responses to climate change impacts on the ski industry (Fang et al., 2021) and impedes its sustainable and high-quality development. The primary aim of this study is to develop a comprehensive China Ski Climate Index (SCI), which centers on assessing snow reliability and skiing comfort. This objective is pursued through the utilization of pertinent meteorological variables, including snow water equivalent, temperature, humidity, sunshine duration, and average wind speed. By employing daily observation data from 733 meteorological stations, the study computes the SCI for two distinct periods: 1961–1990 and 1991–2020. Using ArcGIS 10.5 software, the temporal and spatial evolution characteristics of skiing climate resources in China are described. To provide a deeper illustration of the guiding influence of the SCI on the growth of China's skiing industry, this paper undertakes a comparative analysis between the obtained results and the comprehensive skiing resort development indicator - Ski Resort Vitality - formulated by Fang et al. (2023). The research findings provide a scientific basis for coordinating ski climate resources across different regions and optimizing the spatial configuration of ski resorts. 2 Data and model 2.1 Data source and processing This study primarily utilizes the “China Surface Climate Daily Dataset” provided by the China Meteorological Administration Data Service Center (http://data.cma.cn/). The dataset comprises daily meteorological elements from 740 reference and basic meteorological stations across China, encompassing daily average temperature (°C), average wind speed (km/h), atmospheric pressure (0.1hPa), sunshine hours (h), and relative humidity (%). The snow-water equivalent (SWE) data (kg/m 2 ) is obtained from the reanalysis project of the National Environmental Prediction Center (NCEP) and the National Center for Atmospheric Research (NCAR) with a resolution of 2.5° × 2.5°. Bilinear interpolation is employed to standardize the resolution of SWE data and daily meteorological element stations (Zha et al., 2020; Khan et al., 2021). Considering data availability and stability, the study chooses the most recent climate standard period as the reference, encompassing the years 1991 to 2020, while also utilizing a historical reference period ranging from 1961 to 1990. We analyzed and processed data from December to March each year, utilizing available data from 733 meteorological stations during this period for further analysis. Given the uneven spatial distribution of these stations, we conducted a weighted analysis based on Tyson polygon methodology (Fig. 1). To enhance the comparative analysis of the vitality of ski resorts, we collected latitude and longitude data for the same 415 ski resorts using web crawler technology from the Pow Snow Technology App, as outlined by Fang et al. (2023). This dataset comprises 26 indoor ski resorts and 389 outdoor ski resorts (Fig. 2). 2.2 Calculation Model 2.2.1 SCI Model and Standard Optimization The SCI model was initially proposed by Demiroglu et al. (2021) based on the Turkish skiing market. It comprises a snow module, namely Snow Reliability (SR), and a non-snow module, referred to as Aesthetics and Comfort (AC), which encompasses aesthetics and comfort considerations. The measurement indicators encompass snow water equivalent, wet bulb temperature (taking into account temperature and humidity), sunshine duration, average wind speed, and other meteorological factors. The SCI model employs fuzzy logic algorithms to describe and quantitatively analyze ski climate assessment while integrating the effects of various climate factors considered in both the snow module and non-snow module into a single quantitative index. The range of this index is [0,1], where a higher SCI value indicates closer proximity to the maximum fuzzy membership value of 1, signifying more favorable conditions for skiing. This application of fuzzy logic approach is increasingly prevalent in tourism climatology studies (Cai et al., 2010; Olya & Alipour, 2015). The SCI consists of two parts: the snow module and the non-snow module. The specific formula is as follows: In formula (1), SR takes into account the depth of natural snow and the duration of artificial snowmaking during the skiing tourism season from December 1st to March 31st, the following year (referred to as DJFM hereinafter). The symbol "∩" also considers the significant impact caused by certain conditions being absent to some extent. Regarding natural snow, Natural Snow Reliability (NSR) is determined based on the average number of days in DJFM with a water equivalent of snow cover (SWE) greater than or equal to 53 kg/m². However, it should be noted that the initial design of the SCI model was not tailored to the specific context of China. Therefore, adjustments and optimizations were undertaken to ensure its suitability. Considering that China's average snow depth falls below the evaluation standard proposed by the US Bureau of Land Management for ski tourism resources (Li,1985), and that third-class ski resorts in China require a minimum snow depth of 20 cm on ski tracks according to Classification of Quality Grades for Ski Resorts(LB/T 037-2014), SR meets its minimum condition when SWE reaches 53 kg/m² (calculated based on a snow density of 265 kg/m³ and a snow depth of 20 cm) (Demirogl et al.,2016; Sorman & Beser, 2013). As for artificial snowmaking, Wet Bulb Temperature (WBT) is used considering temperature and relative humidity. The specific formulation is as follows (Stull, 2011): Formula (2) comprehensively considers ski reliability, aesthetics, and comfort through AC. In this study, we have selected 0.9 as the gamma coefficient (G), as indicated in Table 1. Table 1 Definitions of the SCI facets and sub-indices Facet Sub-index Definition Snow reliability (SR) Natural snow reliability (NSR) Seasonal (DJFM) average number of days when SWE is larger than 53 kg/m 2 in a 30-year range Snowmaking (SM) Pre- and actual seasonal (DJFM) average number of hours when WBT is <- 7 °C in a 30-year range Aesthetics and comfort (AC) Sunshine duration (SS) Seasonal (DJFM) average number of days when the sunshine duration is more than 6 h in a 30-year range Wind conditions (WC) Seasonal (DJFM) average number of days when the top wind speed is less than 40 km/h in a 30-year range Thermal comfort (TC) Seasonal (DJFM) average number of days when WBT is between- 7 and 2 °C in a 30-year range Note: DJFM indicates December to March of the following year In order to provide a more intuitive representation of the SCI, we have established a simplified 4-level rating standard. This standard draws inspiration from the notion of perceived tolerance width (Yu et al., 2019) and is guided by the classification scheme introduced by Scott et al. (2016), using quartiles as the foundation for classification. The rating criteria are as follows: Excellent ([0.75-1]), Good ([0.5-0.75)), Fair ([0.25-0.5)), and Poor ([0-0.25)). If the SCI score falls below 0.25, it indicates a severe inadequacy in skiing climate resources within that particular area. 2.2.2 Kernel density method The kernel density method assigns varying weights to ski resort locations based on their proximity to the center of the study area. By considering the search radius as the axis, a continuous density distribution map is generated to depict the level of clustering or dispersion of ski resorts in spatial distribution. A higher value of kernel density indicates a greater concentration of ski resorts, while a lower value suggests dispersion. The analysis and visualization of kernel density were conducted using ArcGIS 10.5 software. The specific formula for estimating ski resort kernel density is as follows (Liu et al., 2022): 3 Results It is significant to examine the temporal and spatial distribution of skiing resources from a climatic perspective for the development and management of tourism activities. In this study, we have developed the SCI to evaluate the suitability of skiing climate resources in China based on their spatial distribution characteristics during the research baseline period from 1991 to 2020, as well as their evolving trends through a two-stage difference analysis. Additionally, we have utilized kernel density estimation method to compare SCI with the spatial distribution of outdoor ski resorts in China, providing valuable insights. Moreover, this study has conducted a comparison between China's ski climate resources and the spatial vitality of ski resorts, thus illustrating the significant role of ski climate assessment in representing the development of the industry. 3.1 Spatial pattern of SCI in China The fuzzy spatial representation of sub-indicators for SCI is illustrated in Fig.3. In the SR module (Fig.3A), altitude is observed to have a strong correlation with NSR. Regions with higher values are prominently situated along the Tianshan, Qilian, Hengduan, and Da Hinggan Mountains. Regarding SM, a distinct latitudinal distribution pattern emerges, with the Qinling-Huaihe Line serving as the dividing line between higher values in the north and lower values in the south. Additionally, SM demonstrates elevated values in high latitude and high-altitude areas, reaching its peak in third-tier cities as well as northern Gansu and eastern Inner Mongolia. In the AC pertaining to aesthetics and comfort (Fig. 3B), the SS exhibits a gradual increase from the southeastern coastal regions towards the northwestern inland areas, with higher values observed in the western and northern parts while lower values are found in the eastern and southern regions. The southeastern region is more significantly impacted by a maritime climate, resulting in a higher occurrence of overcast and rainy weather conditions as well as shorter durations of sunshine compared to the northern and western regions. The level of WC is high, with the highest concentration observed in the northeastern region and most parts of the southern region, including certain areas in Gansu and Ningxia as well as the Beijing-Tianjin region. Low values occur on the southwestern slopes of the Tibetan plateau and on the borders of Shanxi and Hebei. TC, on the other hand, represents heat comfort, with high values in Heilongjiang, the northern Inner Mongolia, and the northern part of Xinjiang. Fig.4 displays the spatial distribution of SCI in China from 1991 to 2020. In terms of spatial distribution, a substantial portion of the country's land area (approximately 54%) exhibits a favorable skiing climate, falling into the “Excellent”(≥0.75-0.98) and “Good”(≥0.5-0.75) categories based on weighted analysis by area. Among them, the “Excellent” (≥0.75-0.98) accounts for 40.9%, primarily concentrated in the west of the Hu Huanyong Line, especially in the Northwest, Northeast, and certain areas of North China. Notably, the Xinjiang Uygur Autonomous Region has the highest Ski Climate Index, with its peak values observed in the Bayinbuluke and Altay regions of Xinjiang. Altay has officially been designated as the “Snow City of China” by the China Meteorological Bureau, offering compelling evidence to substantiate this recognition. Moreover, Heilongjiang province closely follows in similar standing as a region of high esteem. Situated within China, this province is renowned for its traditional winter sports and benefits from exceptional geographical advantages. It flourishes through a combination of rich snow and ice culture as well as a flourishing ice-based economy. The regions with low values (≥0-0.25) cover about 36.5% of China's land area, primarily concentrated in East China, Central China, South China, Southwest China (especially southern Sichuan), and Yunnan Province. This distribution can be mainly attributed to the scarcity of natural ice and snow resources in these regions, resulting in fewer and shorter snowfall days. The spatial distribution of China's skiing climate resources dose not aligned well with the current distribution of ski resorts. Generally, the existing ski resorts demonstrate a spatial distribution pattern centered around Beijing and its surrounding areas, with the northeast and northwest regions forming what can be termed as the “two wings”. Upon comparing Fig.4 with Fig.3A in terms of snow reliability module, it becomes evident that the SR module, particularly the NSR (natural snow reliability) indicator, has a significant impact on the overall performance of SCI and exhibits a highly similar spatial pattern. In contrast, AC modules, which includes metrics like sunlight duration, wind speed, and thermal comfort conditions, show relatively weak correlations. 3.2 Evolution characteristics of SCI in China Within the context of climate change, the alteration in the spatial distribution of the 30-year average SCI between the last three decades (1991-2020) and the preceding three decades (1961-1990) is computed and visually represented in Fig.5. Warm colors (i.e., red) indicate an increase in SCI, while cool colors (i.e., green) signify a decline, and white areas denote negligible alteration. Based on this figure, the following observations can be made: The SCI has experienced an increase across 41.8% of the country's land area, with significant rises concentrated in regions such as Shanxi-Gansu-Ningxia regions, and certain parts of Tibet and Sichuan. Among these areas, approximately 34.7% fall within the "Excellent" category (≥0.75-0.98) of SCI. However, approximately 12.5% of the national land area experiences a decline in SCI, primarily concentrated near the Changbai Mountain region. This can be attributed to an observed downward trend in annual precipitation in the southern parts of Northeast China, national land area experiences a decline in SCI, primarily concentrated near the Changbai Mountain region. This can be attributed to an observed downward trend in annual. Additionally, research indicates that there has been a decreasing trend observed for the annual snow-covered period at Changbai Mountain Ski Resort from 1981 to 2018, accompanied by relatively low levels of accumulated precipitation (Wang et al.,2023). Moreover, the most substantial portion (approximately 45.7%) comprises areas that have experienced relatively little change, primarily concentrated in the majority of southern China. Upon assigning weights to the SCI values of each province based on the Tyson polygon area, slight adjustments were observed in the rankings between the research baseline period and the historical reference period. The top three provinces remain relatively consistent, with Xinjiang ascending from second place to the first. Despite a slight drop in ranking, Heilongjiang still maintained its high position. Qinghai closely followed suit by ascending from fourth place to third. These leading three provinces exhibit distinctive advantages in terms of skiing resources. Notably, Jilin Province experienced a significant decline in ranking, dropping from fifth to eleventh place. Conversely, the Shanxi-Gansu-Ningxia regions emerged prominently and holds a noteworthy position among the top provinces. For regions undergoing substantial growth, it is imperative to recalibrate local ski industry development strategies in response and harness ski climate resources to their fullest potential. In areas encountering notable declines, swift actions are requisite to mitigate and address the adverse impacts of change. 3.3 Implications of SCI for ski industry development through comparing with ski resorts’ vitality Ski resorts’ vitality was developed by Fang et al.(2023). This comprehensive evaluation system encompasses the ski resorts’ attractiveness to skiers (“place”), the ski resorts’ capability to support skiing activities (“activity”), and the skiers’ perception of ski resorts (“people”). When evaluating the vitality of a ski resort from a "place" perspective, several critical factors come into play. These encompass accessibility, skiing facilities, supporting amenities, and operational hours. The assessment of “activity” revolves around two key aspects: the sheer number of participants involved in skiing activities within the ski area (aggregation) and the reliability and consistency of participation across different days, including weekdays, weekends, and holiday periods like the Spring Festival (stability). The aspect of "people" finds its embodiment in the subjective evaluations provided by skiers concerning diverse elements related to facilities and services. This includes 26 indoor ski resorts and 389 outdoor ski resorts. The vitality characteristics of China's ski resorts were analyzed using the linear combination method to calculate the combined weights of different evaluation indexes for ski resort’s vitality. Please refer to Table 2 for indicators in each dimension and their corresponding weights. Table 2 Evaluation index system and weight of ski resort’s vitality Dimensions Indicators Descriptions Ski resort attractiveness (0.14) Accessibility (0.01) Overall Accessibility including local and regional accessibility outside these areas Operation time (0.02) The length of a ski season; Daily business hours Skiing facilities (0.08) Ski slopes variety (i.e., green/blue/black runs); The number and combination of ski-lift facilities Supporting facilities (0.03) The number and combination of service infrastructure like hotels, restaurants, parking lots Activity level of ski activity (0.53) Aggregation (0.40) Nuclear density analysis using the number of visits to the ski resort as the weight parameters Stability (0.13) Changes in the skier visitors between the working days, weekend days and the Spring Festival holiday Skiers' perception (0.33) Facilities experience (0.13) Experience evaluation of the perception of traffic, snow slopes, restaurants, and accommodation Service experience (0.20) Ski and snowboard rental and education services Source: Fang et al., 2023 Ski resorts’ vitality offers a comprehensive depiction of the development across different ski resorts. By juxtaposing SCI with the vitality of ski resorts, a deeper understanding can be gained regarding the indicative role of the SCI in guiding the growth of the skiing industry. Fig.6 presents the scores of the three-dimensional evaluation indicators for spatial vitality in 415 ski resorts across China. It also showcases the standardized spatial vitality values assigned to Chinese ski resorts. Moreover, these values correlate with the distribution of SCI in the geographical expanse of China. By employing the natural breakpoint method, five levels are established to classify ski resort attractiveness, ski Activity level, skiers' perception, and spatial vitality: high, relatively high, medium, relatively low, and low. The findings reveal that: 1) The attractiveness of ski resorts in China generally exhibits spatial dispersion, and their alignment with ski climate resources is relatively weak (Fig.6a). The proportion of ski resorts classified as "high" and "relatively high" in terms of attractiveness is limited to only 51, accounting for merely 12.3% of the total number of statistical ski resorts. In contrast, a mere 22 ski resorts are categorized as having "Excellent" and "Good" SCI levels. On the other hand, a total of 145 ski resorts fall under the classification of "low" and "relatively low" attractiveness, with an additional 88 being labeled as having a "Poor" SCI level. Specifically, areas such as Beijing and its surrounding environs, Northeast China, and the Altay region of Xinjiang are prominent ski destinations boasting comprehensive ski facilities and supporting amenities. These areas exhibit good accessibility and long snow seasons, resulting in the high attractiveness and relatively elevated status of many resorts. Meanwhile, despite the natural constraints posed by climate, ski resorts in southern China have witnessed the emergence of highly appealing indoor facilities. An example is the Guangzhou Sunac Snow World, which is situated in a low latitude region. This achievement is made possible through advancements in snowmaking and snow storage technologies driven by market demand. 2) The overall correlation between SCI and the activity level of ski activity is relatively low. However, there is a notably higher correlation observed in the northern region compared to the southern region (Fig.6b). Regions exhibiting a high level of ski activity are primarily situated in the Beijing-Tianjin-Hebei region and the three provinces in Northeast China. Notably, Zhangjiakou in Hebei province stands out prominently in this regard. This area has earned an "excellent" rating based on SIC criteria and is distinguished by its consistent influx of frequent visitors, rendering it a preferred destination for skiing enthusiasts. Despite the emergence of indoor ski resorts in southern provinces such as Guangdong and Yunnan, their availability of ski climate resources remains limited. Furthermore, constraints related to venues and the tourism market have contributed to comparatively lower levels of ski activity. 3) The correlation between skiers' experience and SCI exhibited a weak connection (Fig.6c). Out of the 174 resorts that received "good" or "excellent" SCI scores, only 58 were associated with skiers who reported "high" or "very high" levels of perceived activity. This suggests a lack of alignment between these factors. Meanwhile, the skiers’ perception demonstrates a multi-core clustering pattern, illustrated in Figure 6c. Ski resorts boasting high and relatively high skiing experiences tend to cluster in the Beijing-Zhangjiakou, Northeast China, and Altay regions. These areas benefit from superior natural resource conditions and effective supply-demand coordination, contributing to enhanced skiing experiences. Conversely, ski resorts offering moderate and low skiing experiences are primarily situated in regions like Shandong and Shaanxi. Despite Northeast China's advantageous natural resources and early involvement in the ski industry, numerous ski resorts in this area still provide only moderate or low skiing experiences. This situation is often attributed to intense market competition and inadequate infrastructure development. 4) Figure 6d indicates a limited alignment between the available natural resources for skiing and the vitality of ski resorts. The spatial vitality of ski resorts exhibits distinct regional disparities, primarily characterized by a multi-center distribution pattern with a higher concentration in northern regions and a lower concentration in southern regions. High spatial vitality ski resorts are mainly clustered in and around Beijing, Northeast China, and the Altay region of Xinjiang. In these areas, favorable natural conditions such as terrain and climate, abundant natural snow resources, and the early development of ski resorts with integrated facilities have contributed to the establishment of a stable customer base.The spatial distribution of ski resorts displays significant heterogeneity, with only 28.6% of ski resorts demonstrating high or relatively high activity levels. Moreover, over half of these ski resorts are located in areas characterized by a "poor" SCI rating. 4 Conclusion and discussion 4.1 Conclusion In summary, this study has integrated the SCI specifically tailored for China's unique conditions through local adjustments. The empirical analysis is based on daily meteorological data from 733 representative meteorological stations covering the research period of 1991-2020 and the historical reference period of 1961-2020, along with data from 415 ski resorts in China. ArcGIS 10.5 spatial analysis tools were utilized to conduct the empirical research. Furthermore, this study has compared the ski climate resources in China with the spatial vitality of ski resorts. The primary conclusions are as follows: 1. The snow module holds a predominant sway in shaping the distribution of SCI, where natural snow stands as the foremost decisive factor. Nevertheless, additional non-snow natural elements, like sunshine duration, wind speed, and thermal comfort, wield comparatively less influence over the comprehensive SCI assessment. Further enhancements are necessary to gain a better understanding of their respective roles. 2. The favorable skiing climate regions in China, accounting for approximately 54% of the total, are primarily concentrated to the west of the Hu Huanyong Line under equivalent natural and human conditions, particularly in the northwest, northeast, and parts of North China. Xinjiang and Heilongjiang, endowed with distinct skiing climate advantages, act as significant drivers in this context. The majority of ski resorts are located in regions renowned for their exceptional or favorable skiing conditions. However, the spatial distribution of ski climate resources exhibits misalignment with the current positioning of many ski resorts, underscoring a notable level of incongruity. 3. The skiing climate resources in China have significantly transformations amidst the backdrop of climate change. Regions such as Shanxi-Gansu-Ningxia regions and southwestern Tibet and Sichuan have witnessed remarkable increases, while the southern parts of the three northeastern provinces have encountered noticeable decreases. Particularly noteworthy is the substantial increase in skiing climate resources observed in the Shanxi-Gansu-Ningxia regions, which creates favorable climatic conditions for the progression of the ski industry. However, it is worth noting that only nine ski resorts with aerial cableways are currently available in this region, accounting for a mere 13% of the total number of ski resorts in China. The findings indicate that there is still untapped potential for the development of ski tourism in the Shaanxi-Gansu-Ningxia region. The scarcity of high-quality ski resorts results in a pronounced discrepancy between the available skiing climate resources and the existing ski facilities. 4. The correlation between SCI and spatial vitality is relatively weak, whereas the correlation between SCI and local areas is significantly strong. For instance, as regions abundant in ice and snow tourism resources, ski resorts in Heilongjiang and Xinjiang have relatively high and moderate average spatial vitality, respectively. Ski resorts in Beijing and Hebei demonstrate high levels of attractiveness, activity, and skiing experience. In contrast, Jilin and Liaoning display high activity level and relatively high skiing experience but generally or relatively low overall attractiveness. Guangdong excels in attractiveness and skiing experience but has a relatively low activity level. Moreover, improvements are necessary to enhance their attractiveness and skiing experience. Besides, the spatial vitality of China's ski resorts exhibits apparent differentiation across various evaluation dimensions, including spatial vitality, attractiveness differentiation characteristics, activity level, and skiing experience. 4.2 Discussion Among the various factors influencing skiing climate resources, natural snowfall plays a dominant role in their distribution. Besides, ski resorts that are well-developed with high vitality are primarily situated in areas rich in skiing climate resources. However, some regions with abundant skiing climate resources, such as Inner Mongolia, Tibet, and Shanxi-Gansu-Ningxia, face limited ski market potential and a scarcity of ski resorts. The full realization of economic benefits from ice and snow tourism resources and climatic advantages has been hindered by topography, transportation limitations, inadequate supporting facilities, and market perception. Additionally, policy support and hosting of large-scale ice and snow events have accelerated the development of the ski industry in these regions. Driven by the government's robust promotion of "ice and snow sports" and the impact of the 2022 Beijing Winter Olympics, the ice and snow tourism market has experienced rapid expansion. In regions endowed with abundant skiing climate resources, it is necessary to make a systematic cultivation of the industry is necessary achievable through the formulation of tailor-made development strategies. The impact of climate change on ski resorts in various regions of China has indeed led to a reshaping of the competitiveness of skiing destinations in the country (Fang et al., 2021). It is imperative for these regions to promptly adapt their product offerings and business strategies in response to such changes. The phenomenon of global warming has emerged as a prevailing issue in China over the past few years, resulting in an elevation of winter temperatures and a significant decline in snowfall across regions like Heilongjiang province. Consequently, this poses considerable challenges for the operation of ski resorts. The ongoing warming trend poses new challenges to China's skiing industry, such as reduced operating hours and rapidly escalating skiing expenses. To effectively address this challenge, continuous adjustments in ski equipment are necessary, along with intensified investments in snowmaking facilities. Seizing optimal opportunities for snow production, generating substantial amounts of snow in suitable regions for storage purposes, and being well-prepared for unforeseen circumstances are of utmost importance. In addition, regions with an enhanced ski climate, particularly Shanxi-Gansu-Ningxia regions, and certain parts of Tibet and Sichuan, can fully capitalize on their advantageous ski climate to further explore the potential of climate attractiveness. By innovating "tourism + climate" ski product offerings, the untapped resources of ski climates are transformed into valuable tourism assets, thereby strengthening the tourism climate brand and catering to diverse demands for tourism experiences. This approach better aligns with the objectives of poverty reduction through tourism promotion, revitalization through tourism promotion, and development through tourism promotion. Meanwhile, the ice and snow sports market in China has exhibited a trend of "expanding towards the western regions while advancing into the eastern areas," indicating a discrepancy between available resources and market demand. Targeted development strategies should be formulated for typical regions like Beijing-Tianjin-Hebei, Northeast China, and Xinjiang based on their specific characteristics. We recognize certain limitations to our study. The SCI index is designed to integrate the reliability, aesthetics, and comfort aspects of snow, including factors such as sun exposure, wind conditions, and thermal comfort. Future research recommendations involve incorporating additional components like critical high altitude snow depth into the index and providing technical suggestions for enhancing its application. Declarations Author Contribution declaration ： All authors contributed to the writing of the main manuscript text. Dandan Yu processed and analyzed the data, as well as prepared figures. Yan Fang reviewed and revised the manuscript. Funding Declaration ： The financial support for this study was provided by the National Natural Science Foundation of China [grant number 42001255] and Beijing Winter Olympics Culture and Ice & Snow Sports Development Research Center Fund. Declaration of interests The authors declare that they have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper. References Andersen PA, Buller DB, Scott MD, Walkosz BJ, Voeks JH,Cutter GR, Dignan MB (2004) Prevalence and diffusion of helmet use at ski areas in Western North America in 2001-02. Injury Prevention 10(6):358-362. https://doi.org/10.1136/ip.2004.005967 Barber M (2015). Ski industry expert says 31% of today's ski areas are dying. [Online]. [Accessed 2022-10-22]. Available from: https://ski.curbed.com/2015/1/29/9997450/ski-industry-expert-says-31-of-todays-ski-areas-are-dying. 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Atmosphere7(6):80-96. https://doi.org/10.3390/atmos7060080 Scott D, Steiger R, Knowles, N, Yan F (2020) Regional ski tourism risk to climate change: An inter-comparison of Eastern Canada and US Northeast markets. Journal of Sustainable Tourism 28(4):568-586. https://doi.org/10.1080/09669582.2019.1684932 Snowathome. Wet-bulb temperature chart: Snowmaking Weather Tools [Online]. [Accessed 2022-10-22]. Available from: www.snowathome.com/pdf/wet_bulb_chart_celsius.pdf. Sorman AU, Beser O (2013) Determination of snow water equivalent over the eastern part of Turkey using passive microwave data. Hydrological Processes 27(14):1945-1958. https://doi.org/10.1002/hyp.9267 Steiger R, Scott D, Abegg B, Pons M, Aall C.(2019). A critical review of climate change risk for ski tourism. Current Issues in Tourism 22(11):1343-1379. https://doi.org/10.1080/13683500.2017.1410110 Stocker T. Climate change 2013: the physical science basis. Cambridge University Press, 2022. [Accessed 2022-10-22]. Available from: https://www.docin.com/p-1246852538.html. Stull R (2011) Wet-bulb temperature from relative humidity and air temperature. Journal of Applied Meteorology and Climatology 50(11):2267-2269. https://doi.org/10.1175/JAMC-D-11-0143.1 Tang CC, Zeng R, Yang YY, Xu SY, Wang X (2022) High-quality development paths of ice-snow tourism in China from the perspective of the Winter Olympics.Journal of Resources and Ecology 13(4): 552-563. https://doi.org/10.5814/j.issn.1674-764x.2022.04.002. Wang C, Man J (2022). Power elements, theoretical framework, and driving mode of high-quality development of ski industry from the perspective of configuration: A fuzzy set qualitative comparative analysis. Journal of Shanghai University of Sport (in Chinese) 46(08): 65-76. https://doi.org/10.16099/j.sus.2021.06.17.0003. Wu B. China Ski Industry White Paper 2021 [Online]. [Accessed 2022-10-22]. Available from: https://baijiahao.baidu.com/s?id=1740125839779326644&wfr=spider&for=pc. Yang Y, Luo B, Jin Y (2019) Challenges and strategies for promoting winter sports consumption among Chinese residents. Journal of Sports and Culture (in Chinese) 07:19-24. https://doi.org/10.3969/j.issn.1006-8902.2021.07.043 Yu DD, Li S (2019) Scale of human thermal sensation using seasonal anchor method: A Chinese case study. Journal of Meteorological Research (in Chinese) 34(8):1633-1653. https://doi.org/10.31497/zrzyxb.20190806 Zha J, Wu J, Zhao D, Fan W X(2020) Future projections of the near-surface wind speed over eastern China based on CMIP5 datasets. Climate Dynamics54(3):2361-2385. https://doi.org/10.1007/s00382-020-05118-4 Zhong Z, Li X, Xu X, Liu XP, He ZJ (2018) Analysis of spatial and temporal variations in snow cover in China from 1992 to 2010. Science Bulletin 63(25):2641-2654.https://doi.org/10.1360/N972018-00199 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3299526","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":229523893,"identity":"43e2a56c-f7e4-420c-b065-faf89c56ea77","order_by":0,"name":"Dandan Yu","email":"","orcid":"","institution":"Shanghai University of Sport","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dandan","middleName":"","lastName":"Yu","suffix":""},{"id":229523894,"identity":"68a7a01a-9532-4765-bb49-b93b465907a6","order_by":1,"name":"Zhanglin Lin","email":"","orcid":"","institution":"Shanghai University of 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1","display":"","copyAsset":false,"role":"figure","size":3252504,"visible":true,"origin":"","legend":"\u003cp\u003eTyson polygon division of the meteorological stations in China\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3299526/v1/cd5ae242e005dd701d839fec.png"},{"id":42496326,"identity":"93af3753-3564-4aaa-8ce3-5e3f96c4f53f","added_by":"auto","created_at":"2023-09-01 14:13:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2317035,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial location of 415 ski resorts in China\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3299526/v1/84960a6dc71e92eb8976c0f4.png"},{"id":42496325,"identity":"917b694f-8570-47a4-94de-29bd28795be0","added_by":"auto","created_at":"2023-09-01 14:13:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":227760,"visible":true,"origin":"","legend":"\u003cp\u003eThe fuzzification space display of SCI sub-indexes (1991-2020)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3299526/v1/17b60007c4e7133e89f96640.png"},{"id":42498719,"identity":"7b006345-a937-4c6b-a021-1f2124a5eec8","added_by":"auto","created_at":"2023-09-01 14:29:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3066288,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial pattern of the SCI and ski resorts in China (1991-2020)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3299526/v1/7b03dab795bd0d598babc015.png"},{"id":42496330,"identity":"92429adb-2d35-441c-8889-92fb5da0cbdb","added_by":"auto","created_at":"2023-09-01 14:13:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2867763,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial change pattern of the ski climate resources in China between 1961-1990 and 1991-2020\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3299526/v1/3107a88471660e6ba0743497.png"},{"id":42496327,"identity":"4d9ad7f6-1669-4bb1-aca2-82977fc09105","added_by":"auto","created_at":"2023-09-01 14:13:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":363051,"visible":true,"origin":"","legend":"\u003cp\u003eSCI and ski resorts’ vitality in China\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3299526/v1/79046810c9bc93798380848a.png"},{"id":48772261,"identity":"18afff72-58a1-425b-85bf-e22377466bc4","added_by":"auto","created_at":"2023-12-25 12:37:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2657282,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3299526/v1/85c991fc-af00-464d-8ac9-fa73eaaf51f9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial and Temporal Assessment of China's Skiing Climate Resources","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eClimate conditions play a crucial role in determining the operations of ski resorts and the demand for skiing. The availability of consistent snowfall is vital for ski resorts, with a minimum snow depth requirement of 30cm being deemed necessary to guarantee the safety of skiers (Damma et al.,2014). The patterns of snowfall are significantly influenced by various climatic factors, including temperature, precipitation, and humidity (McClung, 2006; Matzarakis et al.,2012). Additionally, the pace at which snow melts is dictated by fluctuations in temperature (Zhong et al., 2018). Climate conditions also affect ski resort facilities. For instance, T-bar lifts will be closed if wind speeds exceed 30 km/h (Andersen et al., 2004). Various climate factors, including wind speed or force, temperature, extreme weather events, and visibility, have a direct impact on both the safety and skiing experience of skiers (Demiroglu et al., 2016). Optimal skiing conditions prevail when the highest temperature falls within the range of -12\u0026deg;C to 2\u0026deg;C, coupled with wind speeds that are below 2 on the Beaufort scale. Conversely, skiing becomes less viable when the highest temperature plunges below \u0026minus;\u0026thinsp;16\u0026deg;C or when wind speeds escalate beyond 5 on the Beaufort scale (China Meteorological Administration, 2022). Additionally, extended periods of exceptionally cold weather can lead to the hardening of snow, consequently heightening the potential for skiers to encounter increased risks of falling.\u003c/p\u003e \u003cp\u003eSkiing, as an activity closely tied to climatic conditions, confronts a substantial peril due to the escalating impacts of global climate warming (Steiger et al., 2019). The Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) predicts that climate change will result in reduced snowfall and natural snow cover across the majority of regions (Gajić-Čapka, 2011; Steiger et al., 2019; Stocker, 2022). With future climate warming, outdoor ski resorts worldwide will experience varying degrees of shortened skiing seasons and reduced skiing areas, leading to an increased demand for artificial snowmaking (Scott et al., 2020). Consequently, some ski resorts may be forced to close due to decreased revenue and higher operating costs. For instance, in North America alone, 31% of outdoor ski resorts are already at risk of extinction due to climate change (Barber, 2015). Furthermore, skiing demand is highly sensitive to changes in weather patterns. It has been estimated that every one-degree Celsius increase in temperature at ski resorts results in a six percent reduction in lift ticket sales (Demiroglu et al., 2015).\u003c/p\u003e \u003cp\u003eWith the successful bid and hosting of the Beijing 2022 Winter Olympics and Paralympics, coupled with the continuous introduction of favorable national policies and a growing public enthusiasm for skiing participation, China's skiing industry is experiencing rapid development. According to the White Paper on Ski Industry in China in the Snow Season of 2021\u0026ndash;2022 (Wu, 2022), there is a strong momentum in the construction and expansion of ski resorts, attracting a total of 21.54\u0026nbsp;million skiers. Nevertheless, despite boasting the highest count of ski resorts on a global scale, a mere fraction of these, amounting to less than 10%, adhere to international standards. For instance, only 159 ski resorts are equipped with lifting chairs (Wu, 2022; Tang et al., 2022). Furthermore, China's skiing industry is still in its early stage, with relatively extensive development approaches (Yang et al., 2019). Therefore, promoting high-quality development after the Winter Olympics has become crucial by emphasizing on interplay between industry foundation, consumer demand for skiing activities, and ski resort capacity (Wang et al., 2022). This involves achieving a balance between supply and demand while enhancing service quality through policy refinement, as well as fostering innovation and technological capabilities (Jiang \u0026amp; Li, 2021). The Policy and Action for Climate Change in China emphasizes that addressing climate change is a crucial driver for promoting high-quality economic development in the country. However, there remains a relative lack of research on the ski industry's development in China from a climatic perspective. Although the Skiing Meteorological Index (QX/T386-2017), an industry standard, defines skiing weather suitability and provides meteorological services to the public, it falls short in comprehensively evaluate China's skiing climate. Consequently, there is limited understanding of the temporal and spatial characteristics of skiing climate in China, which hampers effective responses to climate change impacts on the ski industry (Fang et al., 2021) and impedes its sustainable and high-quality development.\u003c/p\u003e \u003cp\u003eThe primary aim of this study is to develop a comprehensive China Ski Climate Index (SCI), which centers on assessing snow reliability and skiing comfort. This objective is pursued through the utilization of pertinent meteorological variables, including snow water equivalent, temperature, humidity, sunshine duration, and average wind speed. By employing daily observation data from 733 meteorological stations, the study computes the SCI for two distinct periods: 1961\u0026ndash;1990 and 1991\u0026ndash;2020. Using ArcGIS 10.5 software, the temporal and spatial evolution characteristics of skiing climate resources in China are described. To provide a deeper illustration of the guiding influence of the SCI on the growth of China's skiing industry, this paper undertakes a comparative analysis between the obtained results and the comprehensive skiing resort development indicator - Ski Resort Vitality - formulated by Fang et al. (2023). The research findings provide a scientific basis for coordinating ski climate resources across different regions and optimizing the spatial configuration of ski resorts.\u003c/p\u003e"},{"header":"2 Data and model","content":"\u003cp\u003e\u003cstrong\u003e2.1 Data source and processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study primarily utilizes the \u0026ldquo;China Surface Climate Daily Dataset\u0026rdquo; provided by the China Meteorological Administration Data Service Center (http://data.cma.cn/). The dataset comprises daily meteorological elements from 740 reference and basic meteorological stations across China, encompassing daily average temperature (\u0026deg;C), average wind speed (km/h), atmospheric pressure (0.1hPa), sunshine hours (h), and relative humidity (%). The snow-water equivalent (SWE) data (kg/m\u003csup\u003e2\u003c/sup\u003e) is obtained from the reanalysis project of the National Environmental Prediction Center (NCEP) and the National Center for Atmospheric Research (NCAR) with a resolution of 2.5\u0026deg; \u0026times; 2.5\u0026deg;. Bilinear interpolation is employed to standardize the resolution of SWE data and daily meteorological element stations (Zha et al., 2020; Khan et al., 2021). Considering data availability and stability, the study chooses the most recent climate standard period as the reference, encompassing the years 1991 to 2020, while also utilizing a historical reference period ranging from 1961 to 1990. We analyzed and processed data from December to March each year, utilizing available data from 733 meteorological stations during this period for further analysis. Given the uneven spatial distribution of these stations, we conducted a weighted analysis based on Tyson polygon methodology (Fig. 1).\u003c/p\u003e\n\u003cp\u003eTo enhance the comparative analysis of the vitality of ski resorts, we collected latitude and longitude data for the same 415 ski resorts using web crawler technology from the Pow Snow Technology App, as outlined by Fang et al. (2023). This dataset comprises 26 indoor ski resorts and 389 outdoor ski resorts (Fig. 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Calculation Model\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003e2.2.1 SCI Model and Standard Optimization\u003c/h3\u003e\n\u003cp\u003eThe SCI model was initially proposed by Demiroglu et al. (2021) based on the Turkish skiing market. It comprises a snow module, namely Snow Reliability (SR), and a non-snow module, referred to as Aesthetics and Comfort (AC), which encompasses aesthetics and comfort considerations. The measurement indicators encompass snow water equivalent, wet bulb temperature (taking into account temperature and humidity), sunshine duration, average wind speed, and other meteorological factors. The SCI model employs fuzzy logic algorithms to describe and quantitatively analyze ski climate assessment while integrating the effects of various climate factors considered in both the snow module and non-snow module into a single quantitative index. The range of this index is [0,1], where a higher SCI value indicates closer proximity to the maximum fuzzy membership value of 1, signifying more favorable conditions for skiing. This application of fuzzy logic approach is increasingly prevalent in tourism climatology studies (Cai et al., 2010; Olya \u0026amp; Alipour, 2015). The SCI consists of two parts: the snow module and the non-snow module. The specific formula is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eIn formula (1), SR takes into account the depth of natural snow and the duration of artificial snowmaking during the skiing tourism season from December 1st to March 31st, the following year (referred to as DJFM hereinafter). The symbol \u0026quot;\u0026cap;\u0026quot; also considers the significant impact caused by certain conditions being absent to some extent. Regarding natural snow, Natural Snow Reliability (NSR) is determined based on the average number of days in DJFM with a water equivalent of snow cover (SWE) greater than or equal to 53 kg/m\u0026sup2;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, it should be noted that the initial design of the SCI model was not tailored to the specific context of China. Therefore, adjustments and optimizations were undertaken to ensure its suitability. Considering that China\u0026apos;s average snow depth falls below the evaluation standard proposed by the US Bureau of Land Management for ski tourism resources (Li,1985), and that third-class ski resorts in China require a minimum snow depth of 20 cm on ski tracks according to Classification of Quality Grades for Ski Resorts(LB/T 037-2014), SR meets its minimum condition when SWE reaches 53 kg/m\u0026sup2; (calculated based on a snow density of 265 kg/m\u0026sup3; and a snow depth of 20 cm) (Demirogl et al.,2016; Sorman \u0026amp; Beser, 2013). As for artificial snowmaking, Wet Bulb Temperature (WBT) is used considering temperature and relative humidity. The specific formulation is as follows (Stull, 2011):\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Formula (2) comprehensively considers ski reliability, aesthetics, and comfort through AC. In this study, we have selected 0.9 as the gamma coefficient (G), as indicated in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1 Definitions of the SCI facets and sub-indices\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"583\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.382504288164665%\" valign=\"top\"\u003e\n \u003cp\u003eFacet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.874785591766724%\" valign=\"top\"\u003e\n \u003cp\u003eSub-index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.74271012006861%\" valign=\"top\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.382504288164665%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSnow reliability\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.874785591766724%\" valign=\"top\"\u003e\n \u003cp\u003eNatural snow reliability\u003c/p\u003e\n \u003cp\u003e(NSR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.74271012006861%\" valign=\"top\"\u003e\n \u003cp\u003eSeasonal (DJFM) average number of days when SWE is larger than 53 kg/m\u003csup\u003e2\u003c/sup\u003e in a 30-year range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.297872340425535%\" valign=\"top\"\u003e\n \u003cp\u003eSnowmaking\u003c/p\u003e\n \u003cp\u003e(SM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.702127659574465%\" valign=\"top\"\u003e\n \u003cp\u003ePre- and actual seasonal (DJFM) average number of hours when WBT is \u0026lt;- 7 \u0026deg;C in a 30-year range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.382504288164665%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eAesthetics and comfort\u0026nbsp;(AC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.874785591766724%\" valign=\"top\"\u003e\n \u003cp\u003eSunshine duration\u003c/p\u003e\n \u003cp\u003e(SS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.74271012006861%\" valign=\"top\"\u003e\n \u003cp\u003eSeasonal (DJFM) average number of days when the sunshine duration is more than 6 h in a 30-year range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.297872340425535%\" valign=\"top\"\u003e\n \u003cp\u003eWind conditions\u003c/p\u003e\n \u003cp\u003e(WC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.702127659574465%\" valign=\"top\"\u003e\n \u003cp\u003eSeasonal (DJFM) average number of days when the top wind speed is less than 40 km/h in a 30-year range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.297872340425535%\" valign=\"top\"\u003e\n \u003cp\u003eThermal comfort\u003c/p\u003e\n \u003cp\u003e(TC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.702127659574465%\" valign=\"top\"\u003e\n \u003cp\u003eSeasonal (DJFM) average number of days when WBT is between- 7 and 2 \u0026deg;C in a 30-year range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: DJFM indicates December to March of the following year\u003c/p\u003e\n\u003cp\u003eIn order to provide a more intuitive representation of the SCI, we have established a simplified 4-level rating standard. This standard draws inspiration from the notion of perceived tolerance width (Yu et al., 2019) and is guided by the classification scheme introduced by Scott et al. (2016), using quartiles as the foundation for classification. The rating criteria are as follows: Excellent ([0.75-1]), Good ([0.5-0.75)), Fair ([0.25-0.5)), and Poor ([0-0.25)). If the SCI score falls below 0.25, it indicates a severe inadequacy in skiing climate resources within that particular area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2 Kernel density method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe kernel density method assigns varying weights to ski resort locations based on their proximity to the center of the study area. By considering the search radius as the axis, a continuous density distribution map is generated to depict the level of clustering or dispersion of ski resorts in spatial distribution. A higher value of kernel density indicates a greater concentration of ski resorts, while a lower value suggests dispersion. The analysis and visualization of kernel density were conducted using ArcGIS 10.5 software. The specific formula for estimating ski resort kernel density is as follows (Liu et al., 2022):\u003c/p\u003e\n\u003cp\u003e\u003cimg 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v/71YtmyZekq7LC46sptxaSpegavklqHoPOOO+4oNtxww2K11VZLO4QPfOADxT777NN1g07gSCBK4EiAE+/rjNZk6xBcfeELX0jPtzaB5ztJb1yuulLWXM3kKiX5piGjYRqZaqqBJpbl+eefv/IqK+ny2iAa05rvml5mkqTB0RYGj0lcf/31KfgcFL894YQTUlrdGnOa7wxeJbUOgecFF1yQniEheOUdq3Rc0et21Y2DeALeCFa5ffdtb3tbGt4NV+k23XTT4uqrry6HDIbfk06Tz8+OGuXJ1UxaS2RnOUwjU1wJbKqBpmhVOpAeV92HeT3SpGhymUmShse2mPYVfv7zn6c2OfrFb/itgevMDF4ltRJX7ZYvX57ec/rQQw/19OwHweq22267MsDlXau8suaf/umf0uduaLp+vfXWSzufQfA7pks644hy4vnQTs8Hz4Srgccff3xqZOLwww8vhw6Gq67/8R//kd67GsuSYdwGHFcYuQLLldi6br5cnR12mUmSmsX+jxOK/eI3w+475wuDV0kTg1ZO81fb0MrtVVdd1fNVKQJkGnkaBL8b9LdtwLPCnO3t9nxwN0cddVR6PdA3v/nNlQFnv7hCfskll6RXCXDVNQ/KuBLLyYVYlixbTmzUdXPVuvFsG3aZSZI0bgxeJWme46opV/G4atrt+eBOuJ2bZ30IOAkw666G9tIRmB5zzDErWwjO38FKoJbfMjzfr7wOu8wkSRpHBq+SNEF47pSrl3vuuefKZ3gJdBi29tprp67asjINW/F8Mb/hqulM4+eigaamcUWR24YRtwzzmbxhvl95HWaZSZI0rgxeJWlCcMWRFmi5eklT+zwfSlBzxhlnpAaP7rnnnjQezwbn4jO3n9aN/+Uvfzn1q6KBplHjduJnn302BbG93pJM3rj9mHKgTCbtVTKDLjNJksaZwaskTQiuOMa7TbmdlNtwuRp32mmnpVtLo4GfHIEPryWK8a+55pqu4+fyRrWa7kg3XrVD//e//31fL2yv5m3Y1o/bZJhlJknSODN4laQJwlXKV155pdhxxx2LK6+8MrV+HFcreW4U+bOk+e2n9913X/GRj3zkdeP7epp2cZlJkuYrg1dJmiBxO2m8ViZa5+WWW94jx1XZeN0MGJ8rdU888UTxj//4jzOOP5+tueaaxUYbbVRssMEG5ZC50fZlRqNdu+66a/lJkqTmGLxK0oSI20kJbLgSl98qy6tmaA2Yd8lFsBPj4+9///uM42vuucwkSfOZwaskTYi4nZQrb9UWgOtuJ83HP/fcc8uhK3j7aTu5zCRJ85nBqyRNiGis58gjj1zlylvcTrrLLrsUO++8czl05vG9Zbh9XGaSpPnM4FWSJgRX3gheDjzwwHLICnE7KQ38cNspHWYanwBpwYIFK8fX3HOZSZLmM4NXSZoAceWt7nlHAp6FCxcW66+/fmqBmOdh+x1fc89lJkma7wxeJWkCxJW3bs87/vWvf03vA0WMv/nmm6fPdfLxNfdcZpKk+W7BFG9vlyRJXXHl8wMf+EC6srnllluWQ1V10UUXpf6xxx6b+pIkNcUrr5IkSZKk1jN4lSRJkiS1nsGrJEmSJKn1DF4lSeoR71jdaqut0utoqt3ixYvTc7HzAc+11pUB3XHHHVeOJUlSs2ywSZIkSZLUel55lSRJkiS1nsGrJEmSJKn1DF4lSZIkSa1n8CpJkiRJaj2DV0mSJElS6xm8SpIkSZJaz+BVkiRJktR6Bq+SJEmSpNYzeJUkSZIktZ7BqyRJkiSp9QxeJUmSJEmtZ/AqSZIkSWo9g1dJkiRJUusZvEqSJEmSWs/gVZIkSZLUegavkiRJkqTWM3iVJEmSJLWewaskSZIkqfUMXiVJkiRJrWfwKkmSJElqPYNXSZIkSVLrGbxKkiRJklrP4FWSJEmS1HoGr5IkSZKk1jN4lSRJkiS1nsGrJEmSJKn1DF4lSZIkSa1n8CpJkiRJaj2DV0mSJElS6xm8SpIkSZJaz+BVkiRJktR6Bq+SJEmSpNYzeJUkSZIktZ7BqyRJkiSp9QxeJUmSJEmtN1bB69NPP11stNFGqXvppZfKobODaZ9yyinFWmutVTzwwAPl0Nlzww03FHvssUfqJgXLkPlieZ5//vnl0MGwTPbff/9iwYIFqTvyyCNnvY4MimU6V/VqnMxVOc31uj8uKCfW40ksJ7YlzBvbKrYv9G+//fby27lFvsjTlVdeWQ6ZfP1uCxiPfQLl1Ks2lOtcbnua3D+PUizbUR0bDVrXes2P+5fR8ZhhcjUavP7qV78q1l577bTBX7x4cfHCCy+U34w3Kt8111xTnHPOOcXLL79cDp097Dwvuuii4q677iqHjD92jJQpG/lly5aVQwfDQeQhhxySls/y5cuLrbfeurj88stHtjPT/DHX6/64iHI64YQTJrKcYlvy1FNPFUuWLEnbrL333rs1Aaw6IwC79NJL0z5hnMzltoeD76b2z6NEgMC+vy3Ltt/8uH+ZPC7TWTI1AgcddNDULrvsMvXqq6+WQ8bLo48+OnXeeeeVn14zHRRNUWT3339/OWT2ME2mvfvuu5dDJgPlzHzVlXev1lxzzamTTz65/DQ1NR3ApmXVtrK64oor5qTuqD/77bdf+d9r5nLdHyesc5NWTqy3zBPblTAdwE5kfei07xsndfPAsmN50bVZ27Y9TeyfR41yIY9t2d93y08bjy01PI8ZZl/jtw3/7W9/K/74xz8We+65Z/GmN72pHDpeOt0mtHDhwvI/tQVnuTi7tcYaa5RDViynhx9+uLjzzjvLIe1wwQUXlP+prbhK85e//KX89BrX/fnrO9/5TurndYBHFKb3n8UOO+xQDpkMk3Drcd08jMP667Zn8nlsOXlcb+dG48Hrk08+WTz66KPFJptsUg4ZL4899tjY3V40nz344IPlf+3GTqvNt19pxa3sn//858tP0gqT9LhGN5Ow7xvXeXDbM/k8tpw8rrdzp/Hg9d57701Xwd7xjneUQ8YHFfGwww4rP0nNYKd10kknlZ/UVqeeeqonGDQvTcK+b5znwW3PZPPYcjK53s6dxoPXX//618WGG25YrLbaauWQ5nCLaKcWA2k4Y5tttknf0cIXt3XxuVc0UPDOd76zeOSRR9JnGh4hrbppgYAkWrelNT5uHahDnvNWcGn4g982gbQj3egYFthg5i1kUi7VVgPJS94qGvPB//yGMql+z7C5nm9ES5AsJ+TLi2nF/3R5meTD8wadmC+ujkbLipRdzDcd/9fhd9RJxiFN+nzm96Bevvvd71754P6OO+64yrT5fV6+VaRDevky5DO/yw2a/6bQWNsll1ySHhe4+uqr0zAeIWAYjbjRMc6gupVTvi5G2fK5rjw74TdxVpwrbZFWXRr59Ea9DrAcSZ/fRp2J5UyapB91jenFtOjydZ3f5t/lqulTZyN9hkWe8/lmOVS3JVXkJ7bJpNfplrnqOsS41YaQmHa+/KvbqX6Qdl4e5LFuGcb3IT5HOfWCaVFm8RvKLOYzL9uqXvPIss/X+9DP9oBxZ9r3kZ9h9q/VuknHsLrhefnmZRDzV7ct6GUecvn80K8uh07l2s/62CvSaeO2p4oyjjSjDtXNK+URZUvH/5R3rp/6mWO86rbijjvuKL9doa5OMaxueCxD8H8M71bXcr3kp9+62esyVnfdll1exnQsez7XLeNOxmW9nVgrHn1txvPPPz+1aNGiqQsvvHDl/0wiumEacaKBjCOOOGJlWrl4QD4ejF62bFl6gLo6Xi+6NVAQjZHQgAcPYzNOniemmyPPNCYU+aLPZzoe3O9HzGO1EQCmv3jx4jStvEGRaLSI7/mfLvJKH+SBho4i//xPR3r55/h+FPPdrbx70en3zG/MR+QjxDxFWTIuvyd/DOd7vov5j+HMV4554Tt+SxqIcqGcclF38rzwf+S/+h0ifdKMMmYZMIwuynLQ/DeFBtpiHhYuXDi1dOnStJ4fd9xxU0888UT6zHC2C4PoVk7MO/PH9/E5xq2W50wYn99V1zHE8pvtdT+mF/limnRMP+o3n3Ox7YsyCbfddtvK/AbGiXmL9Pk9w2O6TIc885n6lM83aeYirah3fI58xvBc1HHmE5RjTDfqK+PwuzwNuki3n3pN3ple5JvpRZ6r5Rhiuv1inmJZMI3Ic0yPjrxU606veWR4Xg9ieQ+6PeA3eToh1ouoy0x3kP1rpEOX15uoA3XTBvWB+Wa+SCPySRd5Cp3mAfEb5pvyYJwoV8owdCpXDLI+9iLKhjSrIo+zve0B04p54vfkJeafjv9zMX7ULfIW5RL5GbR+8jumRxfzHPPK+HnZRXnSjaqu9ZMfRFp10+13Gau7bsuO5cYyiuUQ9bE6Xi8Yn99VlzX6XaZNrrfzQf975C7iIPX73/9++kwAu80220wdc8wxUy+88EIaNqxY8DkqBZUjFxW0X1GJo2LnojJSAXNRIfONLRWTYdWVIdKvq+zd1K0kTI/PzGsVeapOI8qkmq+YL3YcYLz8e8qW7zvNNytn6He+u5V3L7r9Puarmpe6sgTzz3DmNy9TypnhHLDl2ClX02D+KeP8YAid8oJO35GPajqgvBm/+l2/+W9SnKziBBXr+plnnrnyRBXbA6Y/aPAa6soplmV1HWDcurLuJtKqLlPEtOdi3efgi99Vd2Ixnep2rts6wXC6XMx3Nf18e0Hdyss4plEtj7yc8vGjnOjId6AOV/OZ5ydPI9LutJ2aSZRjvqxAOuSj7jswnG4Q+bzk28n8IDovw0Hy2Gl597s96JQOn0kjR3rkv1916ws6TRvV/CPqQqd1rC4dhtdNO8o1r5folFa/62Mvop7UbRvydSpXV5ZNb3vidyyDvHyiDlWnH3U616kc+62f5J30q3Wh07yNuq71m59u041p9LKMR4Fj90984hPpRHSI4/l82CiNIg91yy7WtbrlXF3GM4m0qssa/SzTptfb+aDR24bz5125ZfCqq65Kt4bwjtJ11lmnHGs0uCUjvxQ/XeHT5fdROPDAA8v/Vthggw1S/9lnn0198J6n6Z3i61qj3H777VOf2wz6vb0ox60qv/nNb1KLusxrjnS5nYH3jeUYb9ttt03/V29tAbd8gvHyfEf6neb7lVdeSX2Mer5HKVos/vCHP7xKmb7lLW9J/bxFOera9Aan+MxnPlMOWWE6gCv+/Oc/p/dBDoPbrKjT0zvwcshrDj/88GJ6p5mmn9f5fvLftBdffDHVA26LZp3n+Z5obfzmm29O/VE24sazJ7lq3W/KXKz7q6++euqz7m6xxRbpf1DX0NS75Krp59sLtg15nYp54dasOpRTPv5ll12W3r+MW265JfWp49ThfffdN30OUW7M1y9+8Yv0f67Tdmomp512WurvuuuuqR9I5/jjj0//j6rxDcqR9TZQzpQJ8kZcmsxjk9uDpvavsf5ce+21qR+iDpx77rmpH+L23jz/w6rmm9v48Pzzz6f+TGZrfayay+MO6lDMHyjD6eAz/Z8vS6Yd6/lM+qmf3DpJ3plutS7EvFWNsq4Nkp9e9LKMm8bjPGxvvvGNbxS33XZb+hzDZutYbS7yMMnHDPNBY8ErwSoBEc+7cq/2oYceWvzbv/1bseWWW5ZjjA4bIw7mDzjggHSPeDxfEQcHc+GXv/xlOjCLe9ej4+A+PP744+V/vaMCs4G9++67iy9+8Yvl0FVFupRHdfqsBCB/ozCq+W4bThwgDmSa9tOf/jT1Ywdftdtuu6X+fffdl/pzjRNX4ACQk1frrrtu+vzCCy+k5zg48BlFI25s7DlYIgCIZ0lYRzio6CewadJ8WQf6xUEqli5dmvq//e1vU5+ddrWsQowzLOpEPHNWd3C63XbbpT7LrVNA3rQ8QGUdaWMe0eT+lXWS5c1+KJ8HTmhwoo7ALw+Sv/rVr64S9Ku72dz2xEmkhx56KPXBK+roQIDHSfZqkDiIOG6JA/9ejLKuDZKftuIYffny5cWSJUvSNoh9+be//e3iZz/7GZfQi+uuu64cc3RmMw8eM0yGxoLXV199tfjDH/6QDlI//vGPFzfddFN63+tsYJq8ooczgWxU9t5771Qp+3n4ehRYQVjxOnWDrChxUENFZ8fQDWew6qZLN8p3oI5ivttmVMF/mCn9rbbaKvVn8yC2kzhxxQ6Aq63ve9/7ym9WzAd5ZJlHQNs0DpauuOKKdMWbA+yNN944Xf2dS/NhHejXZpttlvrVK34ctNSVEd3nPve5cqzhzLTjz6+g9XoFbljVALWNeUTT+9e4gszVBrDd4ITGOeeckz5zpxbYbvBdPt+a2WxtezbddNPUr15ppl5wgp2GiXbaaafiiCOOKL8ZXJwc7deo6tqg+Wkz9tu77LJL8aUvfal4//vfn/ajs2228uAxw/hrLHiNg1RaGeYMFju8uF1wNnAgwJVIgjo2lvQ5azGXASxn0dkgNo3AkzOKnDnqtsLF1bvZNqr5bhPqN+puv25CpB9XqTp5z3veU/43d+LdzuT5uOOOK4euENuAuOo2KpwxJx/nnXde+vypT31qlVZCZ9t8WAf69b//+7+pX62zdbcGN2299dYr/1txRaibuThIIBBocx6b3L/Gldy4XZrA4qCDDkrbj9133z2tO6TL8GOPPTaNo97N1rbnueeeS32WWeCEOvXijDPOSMcpXM3qdPfQbLCu9Y4Tz29+85vT8uo1aCT4r14t7NTFiYJuBsnDoDxmGG+NBa8cpMbBK1dYuH2YK69clRm1aGod5IHbmbj9AJdeemnqzzbygep99YGgc9CrZhxI3HrrrWmjzAqX3/qCOAhig113EERZjWolHeV8t0mcob3xxhtrNzjsEIc5ccIZa3B7eJ1nnnkm9Yd5vqYprOeUAQcu+dXVuGWYM6k777xzObRZpB+3MbJecKXuxz/+cVo3mrhdbRDzZR3oVzzjEwe7b33rW1Of5zzr1iG2XdVt26BYJpzww89//vPUz8XyyA/ERy3mmTPu1N025hFN71+ZV4IartixLnDssNdee6Xv4rkzbhlk28p46t1sbnvizroPfvCDqc/6yjEHJzdieTYlTnjRjko/RlXXBs1P222++eap7QrasOgFAX/dVcK6rteTA/3moV8eM0yGRoJXAlQ2ZPF+V86e8DwEV2M4s8H3X/nKV0YayMZtIaGpnR4b5LoDq5nEQ9pszMlLBJGkRUX8zne+s7LCDoLg6Xvf+176n6AhD1JJlwMcNti8YzTu6Qfj8dzSqIKeUc93P2IH8+CDD6Y+yAc7q2HFGV2uQLDByesIG8fPfvazXa+OzBTYUnYcyLIM6040ENSyjIe5AkM94IzosAFCnLiqNkwQd2OwLWDdH9X6z50eOdaNaGhoGCzTfL3q1VyvA7FuV29tY9pzhXmnPHjWLE788Mwn6xBnnPNnKcH/H/rQh1Z5LnRYZ599duqfdNJJq6yviOf2RtVgU10DbtFwVX5QN5d5rKLeRh6a3r/Gs4XMZz7/pEudiLoyrHwexgl5bvu2h+0u+yj2hYgTLtWD7DjRGgZZHjEN9redtmOd0h1FXRsmP2HQukn5ctv+WmutNVAd6YSTzW984xvTVc+5ui16tvLgMcMEmGpANGedvwojXo9Bc9A33nhjepXGsKJZarq8ifq8CfJo/jqG9dv0dbyGZHqjltKY3qil4dMbqZXNwOevPADjMHw6mFil+e1oCr7akU6e/15EWtM7i1WmEc1ukyZN+Ic8v9WO34R8PKZRxffxu6bnm9/wW76Lcu4Hv9966xWv8aGf5wF5fSFPdEwnXnUQw8lTnlY1L3mdoDwCdTvS4TvmhTSq84koK75nmtQtMN0o/2r5kkb1O8Zn+VXz0m/+87Khy9PqB+v19Ea1til7hrFdYP0//fTTy6GD6VROMR/Mc3Xeoox7RXlHebCMol6TbqdlFMt1lOt+rOPVdT/Pb77u52VF/igH8pePz/D4TeS1mj7zzTC+q5Yln+M3ed2JvFIXY9vL93yulhHydbHasX6FfBmQ30Hl+Yt8k0/Srks3X8fz/PQqX8+Ydsw/aTFNhlX1m0fKlfHz9Z7p9LM9QD6c5Ru/i2VNP/Ifw2IZD4L8UX+qoj7mecuRB/LIONX1sdM85PUsX/9IK+p4tWzryhWxfKrrS6f1sRf5b8lHrCt5vZ+LbU/Uf34b9Z9pUQbMf55ePg/kjfJnGUc+GZ/fMU+k0W/9zNdF0qHuMSzSoWPe6+rkKOpav/npVDf7Xcax7tHVzdOgrr766vSaO153F/tzPs+mJvPQadmxPBhG2cZyj2GUbT/aut7OB40ErxGoxvtdEQe1vOeV4HZYLOjqQo2KRp/vo7LQsQGp24jNhMoU04rKlW8s8i4qfLXLsRPLN2b5CtOrPO3oyNNM0yfvbFSjXNjQ5StSp/kK/c53Xt69zHf+2+h6XWad8kaXY7yYf8qCMmEalAXfkadu81M3nN8FxsnrJvNZt6FhGNNkHDZSqJsH0sqRv+oy5Pf5Rm+Q/PP72KGTNuMOom7dDxG8fuMb3yiHDKZbOZHvmI/4jv+rO4xexQ6ENFhmddOm61S2uVGu+3mdiy6vO0w7yoThUSdZ1tSn+FxNg470O813p9/k9YcDuNhB01EGDOuEvFTHz9PrlpdBVA8wmXZdsJFPK+/IT6+injA96lanbXFVL3nsVAf7HR7zwzYh6hX92MbwPZ8j73TVZTQI5rGuLFlH8rqcq6sL+bh18xCfq7/plFanckLd8Cif6nCG9aNt257A9Kr7IPIW9SPHMo3xYpp0DIv56pT/TsOZVsjnizT5jt/xP+XXaR5HUdfQT37q6mbdNOg6lQUoQ9KP6Q6DfTQd8xHH6Xzm2P2+++6buvjii9OwURokD4xHbNHpoli3ZUfZUnaxf6SbaXvcTVvX20m3gD/ThSRpnuKWzekN7srbOSU1g8cDaMBm+sBppC28S5p/uHWYFnMHRcNaX/va19Kz6/EowA9+8IP0PPDBBx+cvo/3tY9KG/Kg8WPwKs1jPFNB8Brv5pPUHINXSaPAtoX2OwZ53/K4I6hl/ulG9Qo+tVtjrQ1LGj8XX3xxcfXVV5efJElSm3HSmcD1rLPOKofML0cffXSxzz77GLjOYwav0jxEi4WnnHJK8ZGPfMTbhaURiZbOaW3YVx1IGhZXGznpTODKq17mI1oi5k0amr+8bViSpIbxGqoqni3nvYKSpP7xyr1DDz20OPHEE4stt9yyHKr5xiuvkiQ1jPPC1c7AVZIG9+STT6b+xhtvnPqanwxeJUmSJLUatwzzzO8LL7xQDtF85G3DkiRJkqTW88qrJEmSJKn1DF4lSZIkSa1n8CpJkiRJaj2DV0mSJElS6xm8SpIkSZJaz+BVkiRJktR6Bq+SJEmSpNYzeJUkSZIktZ7BqyRJkiSp9QxeJUmSJEmtZ/AqSZIkSWo9g1dJkiRJUusZvEqSJEmSWs/gVZIkSZLUegavkiRJkqTWM3iVJEmSJLWewaskSZIkqfUMXiVJkiRJrWfwKkmSJElqPYNXSZIkSVLrGbxKkiRJklrP4FWSJEmS1HoGr5IkSZKk1jN4lSRJkiS1nsGrJEmSJKn1DF4lSZIkSa1n8CpJkiRJaj2DV0mSJElS6xm8SpIkSZJaz+BVkiRJktR6Bq+SJEmSpNYzeJUkSZIktZ7BqyRJkiSp9QxeJUmSJEmtZ/AqSZIkSWo9g1dJkiRJUusZvEqSJEmSWs/gVZIkSZLUeiMJXl966aXiyiuvLDbaaKPigQceKIeucP755xcLFixI36t3lOkNN9yQypQylMLTTz9dnHLKKcVaa61VDpk/WCf22GOP1M03t99+e1rm+++/fzlkcrEfOfLIIxtZzqwvbEMpu+r+qe3IO/sAOvYJk4x1e5tttknHCyyrpvd7+XZzXOpBfmzVa3nMpzqj/nQ7Vh83barn47htGTeNB68sqKOOOqr41Kc+VSxbtqwcqmGwIlxzzTXp4M0yVY4DvP32268455xzipdffrkcOj9w8HbRRRcVd911VzlEk4iDgEMOOaS4/PLLyyGDe+yxx9K29IQTTph368s4YZl/97vfLe68887i0UcfTcNYZgxvAscp1INx2m5yHHDLLbcUJ510kscBGtqoj9VfeOGFYvHixenkE30+zwfjuG0ZS1Mjsvvuu0+R/P33318Oac70wXr53/xy3nnnpTKlPwrTBwkjS1ujRb0Y4ercCnXr/W233Zbmm+3NpBqn9fKKK64YyTafNJtczqPcPzVlvu7nqO8sG9btwLA111xz4PWg0zq09dZbt74eVDEf5Nl99eBGtZ0aR6PeFh5zzDFTBx10UPlpMtVtq8dx2zJOxu6ZV84+3njjjeUnNclbudVWndb71Vdfvfxvco3TennBBReU/2kY3FHxl7/8pfw0v8SdFPm6vcUWWxR//vOfi8997nPlkP50WocWLlxY/qf5xO3U7OBqK3dQbL755uWQydNpW+22ZbTGLng9+eSTy//UJG6na+K2PGkU5ut6P07rJQGCtzMOj+e1Pv/5z5ef5p977723/K8Z7tuUczs1e1588cXijW98Y/Ge97ynHDJZ5vu2ei6NVfDKGQ6vujaPFfCwww4rP0ntMl/X+3FaLwkQeBZPwzv11FM9uG6I+zbl3E7NLk5E/b//9/+KddZZpxwyWdxWz51GgldavYxWAeloWIidRhXDOOtFa2DVlvL4jt/ROhdpkB6fYzz6BxxwQPofMa08HdLO88H/5C3HxitvBYzbEWmtk/HJFwfKdRg3xotxq/MQSKOX8hhGnm/mhXnqVObkk/zGuHm+Seed73xn8cgjj6TPNIoR+Wa8+D861A3P08yHU26BaeXLmDxVl0+oljetjLLsAvNFOectzUbdYnx+20+Zc4bwkksuKTbZZJPUCFD429/+Vhx77LEpTfq9IO/kKfJeV1eGyT9pdVqek4Z5m2m9z+Vl06kMGTbKMuy1nufbTcYlv3xGt/UyVOtPoP4x/RiX9SbqY54XyiHPZ912is9sW6Ks6Mhnvi6S3rvf/e6VjVPsuOOOabxqvnrdLlbzxbTvuOOO8tvRi+nn9YPPLJM61eUd41fnjfEou27jUWZxlZDbZxmPLraj1WVbxbKIZU1HeVPuVYyXp0O6sWzo58t3WKSVzzflyvpWLZ/Id9w2HPUo8tivXtahXOST78ljXbmBsorx6Mh3U+WVb5fos4yY1kz4XeQnujBTnekH85kfP1FG/E9e8/Wj1zLqdX57rUPd8sf+tZft1Fxifmba3mLQ9ZffxXh0pFEtw6Zw7MR2e4cddijWXXfdcujodKvn+XxTHyhTPg+DetNtW52L+sv3LNu52LZMnPLZ14EtWbIkPZTMA/BYtmxZeniZYXTxsDLDaWBg8eLFaXi1sQEeGud3y5cvT59Jt66Bhki3Khox4HdgejGtyAONNpx88skr0yDPPFTNb4844oiVw/ltLvISaZPHeBib9HKkw3eRBtNgPIbFvA0q5pFpkB/KLPIR08hFPhmf/+liPunnIu1qecdwujz/sdzpqg+kMx7Do04gGtzI60nkPco1RHlHuvT5TEc6iGXH7ykH5oeO/MZyr85jJ0uXLp1auHBh+g3dokWLpp5//vmpV199dWqXXXZZOZxGBxjWDXnNp01ZxPqQl+2g+WdYtWwiHbpJ1Wn+orx7LUOWR6/rxCB6reeR71iOjBf1JBfrX153wGfmme/oB6YR80PHZ6bP+Pl2mXxGmbENI88Mr27PGCdf7yLfDKPscpGfmKcc0yEfM20XY/nk48b2gPHzeR1Gp7zG8iO/eV4ZlpdDiPEp35iPKH/mIfAd4+XzG8u2Ok9RxtXh1WVbxXdMIxo6Iv8xn3wX+D6vC6TLeHmdYv1pAmmTHmXIfNPFPri67EPkoa4eDSLKmX5VTCu2x4yTl3HUgRB1MfJGn8901brRL6ZNucc0SZs8kcdcp/nht4ybN3Q1U53pB/OXHz/xP11sa5kWei2jXue31zrUa/6arl9NIm95OZFH8sqwmM9B19+8HEG55+k0XR4cQ3EsdeGFF6bPHDvReBPTmg4c0/dNYd461fMow5i/fL6HFWlX6yxiWbRh2zKJhlp6seCqG1FWMgqc72JBBMat+w3D8o0uGKduPLqqmF6u07SoSAzPd+iIihUrN6hgdWmQ12oaUfliIxOiElfT6FfMD/nPK35slOj4P5C36krVadl0KivExr+6ssV8VZcx41WnSxrVtKP+5GUW5d2p3uTpxjLg9/nKHWkwvB9PPPHEymD1+9//ftrQxoa3V7FRzPMf81ktk37zH2VQLZtIn24UCO4/8YlPrNJiYAT8s9WKYKf5i3nvVIbVnXg/68Qgeq3njMN6nIt85GKZV9NEpFudHzCcLt8egHHjN5RRiLTy8qI869KPNKpl1Wl4P9tFhtWNG+VQzcugOuWVZVKtM2CfwPjV7/hczRPlyjzk48bvq8uRYXS5bssVdb+JbUl1eVOO5KP6HcMjnepvYvxhD5iiHKr7WcR2su67TstmUN3WoZhWNR98ZnheNrFNqearqbrJ7wm2ckyrmm7d/JDf6m9zjE/XhCizmB51KcqknzLqZX4HqUPd8of4vprHudbP9rbf9ZffMqy6DpAO5VtNvwkcQ3GMwLECgepxxx2XjrE4pmJ6fN800qXLMc+97GsHEeVaXWaI5daGbcskGuq24UsvvTT1991339QPtLK17bbblp96d9ppp61yC0M13W6mV9hiuoKWn7qLVsAOPPDA1A8bbLBB6r/yyiupj+kVLfUPPfTQ1A977bUXa0hx2WWXlUOKdLspl/wj/RAPq998882pP6wPf/jDxaJFi8pPRZrm9IY6/X/ttdemPuXILQ28HzGXL5teb8X75Cc/mfq8Yy4XaUc9CLzjKp8ut2xMr5yvW57cTgJu4/nFL36R/ue3LMv4Lmy//fapz+0ZUUeiNUrmh9YoQ5RNv+/Y2njjjYujjz46/c8twptuumnPtwoH8j69USzWW2+9ckhn/eb/3HPPrS2b6ucm/epXvyp23XXX4hvf+EYxfYCcPsewUd1uNIhOZUi9C02uE3X6qefgdsb89iHywbrcpGp6sS2in29DIo95ea222mqpLr/jHe8ohwym1+0it0exfteNG+v/KLH8WCbTB8XlkNccfvjhqSwon1hm9Pn8mc98Jn0OlCst4z711FPlkKJYf/31U3+zzTZL/aax7wTrZY5yPP7449P/ecMieflW6wi3teHVV19N/UGxLafOv+997yuHvCaeO2R97HQ79mzqdCzw7LPPpj762TcNivLIbxNkWvl6WsX0uLVwp512Kr74xS+WQ2fHnnvumfrUpSiTfstopvkdpg7V5a/N+tne9rv+Nn2s3ovf//73xRprrFG86U1vKq6++uq0jeIY69e//nWa7pvf/OZyzNGbjX1tJ23ZtkyaoYLXaESl28a1VwRfVDAqN88ysDEi3V6bxn/44YdTBzaG3PvOwf6wIs18Y9EJ+WdDGverR8ezNvH9qMSG+qGHHkr9xx9/PPV5XrCaH1YE/PKXv0z9mcQG76qrrkr9QBPou+++e6oHsfNgBeNzvmH47W9/m/qsnNW8hBiHPHFAWB2P51NCzNsosOOg3nGwOcjGjQMIDlxJgzKhLleDpUHwLAQ78dgxzZYtt9yyWL58ebFkyZK0bGmA4dvf/nbxs5/9LJ28ue6668ox26/JdaJOP/WcdYoDFfLCwSeBE/KTYXONOkxdjoNi8sg6EWXVq163i5Fu7Nxn209/+tPU54Crzm677Zb69913X+r/5je/Sf1eXtcUJzvpsx5xIDXsM1eB9KIM6/ZT2223XeqzXZ3NQPHuu+9O/bry4UQT9R+xz2q7Ue+bOAnCNv7tb397On6JoK7TNmHp0qXpeIn1dLYOxGfSTxn1Mr+TVoe6aWp7W6fJY/Ve5M+7crxAo2kEsQz/4x//mJY5dXc2jMO+dq6Pe8fRwMFr3UPJw2CFvf/++9PD1BzUcADYqRGiTsgTBwT8njORRxxxRPnN4PoNOAnCOUjp1I0KVwnBziDH1bK6fNDdeeed5VjdscHjagQrV6z4lPVWW221MjDjzBHuueee2isXIAiqywddfpKCK+h140Q3yrOoNCxA+o8++mjx5JNPlkP7wwEiOx0OdjkQPvvss8tvBvfggw+W/80NznzvsssuxZe+9KXi/e9//6zteEahiXWim17qOesU9YvtBQcne++9dzox0fR2tQnRkNgPfvCD4qCDDkonrPrVy3ax6Vek9GumExds7xABYL8nOtiXsU+jESECX65GNGGmg5r8joTnn3++/G/0Ztp3xhWf/ApE241y38SJDfaxrF+c7OEAn4PtTiccOFYC4+ZXleZar2XUy/xOYh2aSRPb29xc7FPYt3EMxb6WCwLRYFMM56orwexsGJd97Vwe946jVr0qh4XDrVZc5SF4Peecc4qPfvSj5bfdceaOsxRnnHFGOgAleOh0Br0f5AMRtM0kzhTOtueeey71qxu6uJowrE9/+tOpf/3116c+ARm3UnObGme12PlwcMYtgtVbrEN+y2Q37LD6OWnRJF6qzcEg0x/kYJqDCOoMHXWZ2w3f8pa3lN+OL3Y07HBYp8Y5cEVT60QnvdZzrpJx0o4DOE600Wcb1padKusAJwN5of+tt96azlRzwDmIudou9iOuSnBFq5v3lLc7x/i93GrOVSXWGwJe6gfLPQ8qh5E/opDfgllnNg+AYt/5u9/9LvU7mY1bwpsy6n0TdYrjF07ksy/nYJuD2rppcofCj3/84/Q/V5VmWvazpZ8ymml+J7EOddLk9naucXWV+eGExM4771wOXXGCkuE8+jab2r6vxVwe946jgYPXfCfYxFk/zkgHAk928GzE2JjNtFHme4InKmXTKzt5QARtVdwWGtjQUgE7lUc+j01jY4EPfvCDqR8HNJRLXfmxkuR5nwnLm/nj9hPmj50OGwQ6lhdXfC+++OI0ThzUhbe+9a2pX32mOZC/KLP4Le/PqsNZyVHe+sZO9PTTT0/zxbMZ/eIkCmUQt/40JX/2YZTz383mm2+engfntULjqOl1oqqfes50YhzqPAcqnLRD9RnyucIz7mzPOFE1TKDV63YxgsLq4wmzhbt10CnQfuaZZ1I/1sUoE7aJdcubA6M4OOKOBbaRLGe2LU2i/lDG+PnPf576udheDHsFp19xm3UEWFWc3OPEZ9w11Haj3jflxwfsbwnqOKah3lTbmwjUwdhu8BqYuu3abOqnjHqZ30mrQ900tb2tavpYvRe0Y8B2jkA8v8LKMRV1ZNh2FPoxDvvaUW9bJtFQV17Z0ICGIOp23vjrX/9a/tddXLkLVPxezs5wcBA77OrCjYON0CmP3cQVRw5QqEA5PufTiIaNOAvKChP5oU9Q08SV4E6++tWvpgOYeD6VlYGDFXYE7NTYaMX8s4Pj9pxuZysZp1pe0fAH83Lcccel/8HVRXClnNtcquLqLBvm/JkD8P+HPvShlQ2NxMPt1AeC4tgZkxfK+zvf+c7KFb1pNETEy7Q5iOWMIXWLK7G46aab0vMa3ZBXypvnVvKyi6viYZB6GCcPwO0vnQySdi8ohze+8Y2pDs/17Z0Y5IzpsOvETPqp54hb7QP1fSZ16+WoxAFjfkse086n3y0vsYx63S7Gtouz4tVtbRjlvFP+rGPUj7qTGAS11J84GIxnqcgvBx153pj3z372syvHjYA4v223ur/qNG8Mj+1gJ/FoAo3YVNOJ5wHzBptmQ+wj2HdW11fmnXI78cQTGw/mZzLoOjTqfRNX5avlVG3spQ55Oe+881K95dnCQeatKf2UUS/zOxt1qJpuP8gDt6ByC/dM6+hMht3edtPksfpM4rlWbs+lzYzAMQRlzTaRxqlmOp5q0iD72kFQtoPUg7k87h1bU0Oguemty9fOTO/0U9PPNPU8vaKsbH6b7/Pmuad3/mk4zZznYlx+j+mNUkqT8XORLr+nY/xoYjyGR9PY/M8w0iFPpEkX4+avxEGMzzSZt8Bv4zeRJ/pMIx8PUR7Vrm7cflG+pEUZ8D9Ik/yRn7xpdDCvUV7Vjt/k4lUOjE/5URZVTKvut2D+yEMn8SqHui7mJUwHZ7Xjkbd8HmO5MN28bPP6wHS74VUvvB6H5txPP/30cujUyubcyRvD+b4XUd6UB+VIXcnrD/MWeeo3/wyP9EmX7+ii3tLxf7U8m3D11VdPTe98UlnF63H4PFtivpk/uthORF3ptQz7WScGwbTq0qbLlwt1g2H0I98xLOYN3dbLGL8673ke8vWFcaKu0M9/wzTjNzH9mDYd5UxHvYttXNTtEGnzPeOSvxC/qXYMz/NBGcV3pE1eGJb/nrTzMupXXgeq+4B8HYvvyB95YTi/zeX55fsoH/7Pyz7KhuGUC+kxLKbF5yivvO5GmUcZ5cspTx+kwXCmH/lkfKZBOrk839U6Qn1iePU3g4jpkIdYZuSNPNJV5eXP/DSh0zrUrR7E8srLHpQJw6sd6VSXR7+YFulQZmC6UUciD/QZj2nGfIRYbpRrnpdudaZfeZl1qh+9llEv84t+6lAv+Ytly28ZJ99O9YvfxvxR/sPoZ3vb7/rL8EiH7/k9ZUl6UV58P0xZBI6VFi5c+LrXDPJqHKbDtM8888wZ35nfr071PJYR/ahXMSzq06CYTkwzlhfTaNu2ZRINFbyCws9XABYIBU2fLjZMecXKu8AKFSsXHemxMPOFC9LjO7pIGzGc31I5qDxRgUibPOUbmrzrlLe8YpN+bBRIk3mu5i0wnXzcuvkYFHnKy5vp5CtlVXX5MH51ZQLjsbwYp7pS5UirboWnfMhHNyyDWHHpWN51aYED8Lw+xDINMTzvmH7MQ94xrJN4aTb9fGMaG2Beps27yXrFPMayz+eP/1kGUWfz/EXXS/6rZRj1MP7vVJ6DIEilY1lE8M7nRYsWTd13331TF198cRoWGIfyavLl4yHW70HLMK+bva4Tg+qlnkc+Iw+dxuu0XsZv8o7f1s07w/iuOpyu02+ivGKHSj5jOxbBcZ4f5HWfcatIM77P06vK133G43fkM36Tbwf61akccqRfrR+d8opqGbLsKYscv41x8vpGugyrllcMZ9xIq9tyCqwbUXZ05IXyzNWlwzDSqg6nGxblU10f6ta3unzRMXwYednT53Onee2lfsy0bxoU6VTLIE+7U94YXjc/pFVNj65aZ3rVT/3opYxmmt9cL3Wo1/zNtJ3qB2mxnYh5HVas9/n2sbq9rVumDJtp/vltvl3jN+SfPl3sV4dF0MqxU/WEfwSv1WOtJtSVSdRz+nwf803H8qJONaG6re60HOZy2zKJFvBnupAkaRU0fva1r30tPRcSt9nQAiItTB988MHp+9lqMVCSpLZaa6210iNDkkbP4FVSIwhoeaaFLprGlyRpkrHP432mNAYkafRa9aocSePr6KOPLvbZZx8DV0nSvECjOgSuZ511VjlE0qgZvEpqBK0Q04qvJEmTjiuuvCKQwHW2W82W5jNvG5Y0NJq9P/TQQ9NrC/Lm8SVJkqSmeOVV0tCefPLJ1N94441TX5IkSWqawaukoXHLMM/+8CJySZIkaRS8bViSJEmS1HpeeZUkSZIktZ7BqyRJkiSp9QxeJUmSJEmtZ/AqSZIkSWo9g1dJkiRJUusZvEqSJEmSWs/gVZIkSZLUegavkiRJkqTWM3iVJEmSJLWewaskSZIkqfUMXiVJkiRJrWfwKkmSJElqPYNXSZIkSVLrGbxKkiRJklrP4FWSJEmS1HoGr5IkSZKk1jN4lSRJkiS1nsGrJEmSJKn1DF4lSZIkSa1n8CpJkiRJaj2DV0mSJElS6xm8SpIkSZJaz+BVkiRJktR6Bq+SJEmSpNYzeJUkSZIktZ7BqyRJkiSp9QxeJUmSJEmtZ/AqSZIkSWo9g1dJkiRJUusNFbzefvvtxR577JG6tnr66aeLU045pVhrrbXKIc06//zziwULFhRXXnllOaS7fvOTj//AAw+UQyVJkiRpfhk4eCVoO+2004q77rqrHNI+N9xwQ7HffvsV55xzTvHyyy+XQ+dOv/khWL3mmmtak39JkiRJmisLpqaV//eN4GrHHXcsdt999+LOO+8sh7YPV0YxxKw2qlt+9t9//xTk5rbZZpvikUceKe6///5ihx12KIdKkiRJ0vzhM68tQtD6l7/8pfz0moULF5b/SZIkSdL8ZPDaEi+99FLx+c9/vvwkSZIkScoZvLbEqaeeWixbtqz8JEmSJEnKNRa8cuUwWsXlmU6e3aSl3Cqek6V1Ysah22ijjVLjTznS4hbavCVjWvNl3EibceqQVoxHXqppI6YdXYxTHU4XGCeGRZ7IQ+SrbjroJT+kd/nll6f/aQArplPXuvBjjz2W5p/vSbf6fKwkSZIkTaLGglcCsLvvvrvYdttt0+cbb7yx2HrrrVcJYKOBp0WLFqXGipYvX57GOeGEE1YJ6m655ZbioosuWtmS8ZFHHpmCtk9+8pPF4sWLU9pcqaxivHPPPbe49tprU/rf+973iptvvrn89jVc4SQdnHzyycXnPve59H/kB7QKzOfAOIzL95dddlmaL1oCvuCCCzpeMe01PzR2RWNMoPErxqWrNs70u9/9rjjssMOKrbbaqjjiiCPSdA844IDakwSSJEmSNFGmg6SBTQdcNJc7teaaa6b/w6OPPpqG8d10kFUOnZqaDgjTsHzcSGM6aCuHrHDbbbetTJv0wnTAtnJ47rzzzntd2oj06XJLlixJw/L8oVN+wLjV9GO69HP95qfbdBnGd9W88pnhzIskSZIkTbJGrrxytTW/SrjFFlsU3/rWt9L/cTssuNo5HXQW6623Xjmks9VXXz31SZv0AldtUX3vKVc4Sb96tbLTq2V23XXXlBduu81vQWZ80uGqL1d7A+NwZblTelX95qcXBx54YPnfChtssEHqP/vss6kvSZIkSZNqZA027bXXXuV/K57TxBe/+MXiz3/+cwpAudWVW4UPOeSQ9N0wuB2ZYJZnQHvF62d4dpTf3XPPPeXQFWnFrcM8zxq4Rfj4448vP3U3SH4kSZIkSZ2NtLXhCAJfffXV1AdBK0HjbrvtVqyxxhrF2WefXX4zuAcffLD8rz+HH3546vN8bbj00kuLr3/96+mqKVeN46rsVVddVey7777p/5kMmh9JkiRJUr2RBq/RkFHcKsstugSFdE899VQKHt/ylrek7+YCtyPTQNIjjzySrg5zxZS8cVWWxqHAFdfbb789BdsMlyRJkiTNvpEFr1yx5NZZWu0NtL5LcMjtw03afvvtU5/nVPtteTduW+YWYa4CH3rooelzXGXliuv111+/8iptL4bJjyRJkiTp9UYWvHLFEieddFLqc2WTYJZnXvMGkp577rnyvxXy73oVjSyB19l0Upc2tzBHw008ixsNQtEn8ObqMV3eaNRMhslP4Lu8wah+cKWY54kHKUtJkiRJaqOhgtfVVlstBX5cYcyDJa5i0trukiVLVgZ99BmXAJZ3wjI+/fvuuy99TxqnnHJK8Ytf/CJ9/va3v5363F6cB2F5QEeQFm699daUPu+AJV2+oyM4DUcddVQKUquOOOKIlK/jjjuuHLLCQQcdlPrHHnts6te59957U3/p0qWpH/rND2WJKIePfvSj6ZZqrtw+9NBD6Tve85qLaZKHvIz23nvv9O7cOIEgSZIkSWOvfGXOwHjv6sknnzy1ePHi9M7R6YAtvX+U4VW8rzXG23rrrVe+A5X/+V28r5Tvqx3vTY33neZd/l5U0o93ydKRj+XLl6/8v/rO1UBe696vCvJGGlX5+1rzLtdvfihHvqOM+G28K7bazTRtpkl5dppfSZIkSRo3C/gzHfhIkiRJktRaI21tWJIkSZKkJhi8SpIkSZJaz+BVkiRJktR6Bq+SJEmSpNYzeJUkSZIktZ7BqyRJkiSp9QxeJUmSJEmtZ/AqSZIkSWo9g1dJkiRJUusZvEqSJEmSWs/gVZIkSZLUegavkiRJkqTWM3iVJEmSJLWewaskSZIkqfUMXiVJkiRJrWfwKkmSJElqPYNXSZIkSVLrGbxKkiRJklrP4FWSJEmS1HoGr5IkSZKk1jN4lSRJkiS1nsGrJEmSJKn1DF4lSZIkSa1n8CpJkiRJaj2DV0mSJElS6xm8SpIkSZJaz+BVkiRJktRyRfH/Ab6qZlhBe4/0AAAAAElFTkSuQmCC\"\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003eIt is significant to examine the temporal and spatial distribution of skiing resources from a climatic perspective for the development and management of tourism activities. In this study, we have developed the SCI to evaluate the suitability of skiing climate resources in China based on their spatial distribution characteristics during the research baseline period from 1991 to 2020, as well as their evolving trends through a two-stage difference analysis. Additionally, we have utilized kernel density estimation method to compare SCI with the spatial distribution of outdoor ski resorts in China, providing valuable insights. Moreover, this study has conducted a comparison between China\u0026apos;s ski climate resources and the spatial vitality of ski resorts, thus illustrating the significant role of ski climate assessment in representing the development of the industry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Spatial pattern of SCI in China\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe fuzzy spatial representation of sub-indicators for SCI is illustrated in Fig.3. In the SR\u0026nbsp;module (Fig.3A), altitude is observed to have a strong correlation with NSR. Regions with higher values are prominently situated along the Tianshan, Qilian, Hengduan, and Da Hinggan Mountains. Regarding SM, a distinct latitudinal distribution pattern emerges, with the Qinling-Huaihe Line serving as the dividing line between higher values in the north and lower values in the south. Additionally, SM demonstrates elevated values in high latitude and high-altitude areas, reaching its peak in third-tier cities as well as northern Gansu and eastern Inner Mongolia.\u003c/p\u003e\n\u003cp\u003eIn the AC pertaining to aesthetics and comfort (Fig. 3B), the SS exhibits a gradual increase from the southeastern coastal regions towards the northwestern inland areas, with higher values observed in the western and northern parts while lower values are found in the eastern and southern regions. The southeastern region is more significantly impacted by a maritime climate, resulting in a higher occurrence of overcast and rainy weather conditions as well as shorter durations of sunshine compared to the northern and western regions. The level of WC is high, with the highest concentration observed in the northeastern region and most parts of the southern region, including certain areas in Gansu and Ningxia as well as the Beijing-Tianjin region. Low values occur on the southwestern slopes of the Tibetan plateau and on the borders of Shanxi and Hebei. TC, on the other hand, represents heat comfort, with high values in Heilongjiang, the northern Inner Mongolia, and the northern part of Xinjiang.\u003c/p\u003e\n\u003cp\u003eFig.4 displays the spatial distribution of SCI in China from 1991 to 2020. In terms of spatial distribution, a substantial portion of the country\u0026apos;s land area (approximately 54%) exhibits a favorable skiing climate, falling into the \u0026ldquo;Excellent\u0026rdquo;(\u0026ge;0.75-0.98) and \u0026ldquo;Good\u0026rdquo;(\u0026ge;0.5-0.75) categories based on weighted analysis by area. Among them, the \u0026ldquo;Excellent\u0026rdquo; (\u0026ge;0.75-0.98) accounts for 40.9%, primarily concentrated in the west of the Hu Huanyong Line, especially in the Northwest, Northeast, and certain areas of North China. Notably, the Xinjiang Uygur Autonomous Region has the highest Ski Climate Index, with its peak values observed in the Bayinbuluke and Altay regions of Xinjiang. Altay has officially been designated as the \u0026ldquo;Snow City of China\u0026rdquo; by the China Meteorological Bureau, offering compelling evidence to substantiate this recognition. Moreover, Heilongjiang province closely follows in similar standing as a region of high esteem. Situated within China, this province is renowned for its traditional winter sports and benefits from exceptional geographical advantages. It flourishes through a combination of rich snow and ice culture as well as a flourishing ice-based economy. The regions with low values (\u0026ge;0-0.25) cover about 36.5% of China\u0026apos;s land area, primarily concentrated in East China, Central China, South China, Southwest China (especially southern Sichuan), and Yunnan Province. This distribution can be mainly attributed to the scarcity of natural ice and snow resources in these regions, resulting in fewer and shorter snowfall days.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe spatial distribution of China\u0026apos;s skiing climate resources dose not aligned well with the current distribution of ski resorts. Generally, the existing ski resorts demonstrate a spatial distribution pattern centered around Beijing and its surrounding areas, with the northeast and northwest regions forming what can be termed as the \u0026ldquo;two wings\u0026rdquo;. Upon comparing Fig.4 with Fig.3A in terms of snow reliability module, it becomes evident that the SR module, particularly the NSR (natural snow reliability) indicator, has a significant impact on the overall performance of SCI and exhibits a highly similar spatial pattern. In contrast, AC modules, which includes metrics like sunlight duration, wind speed, and thermal comfort conditions, show relatively weak correlations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Evolution characteristics of SCI in China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWithin the context of climate change, the alteration in the spatial distribution of the 30-year average SCI between the last three decades (1991-2020) and the preceding three decades (1961-1990) is computed and visually represented in Fig.5. Warm colors (i.e., red) indicate an increase in SCI, while cool colors (i.e., green) signify a decline, and white areas denote negligible alteration. Based on this figure, the following observations can be made:\u003c/p\u003e\n\u003cp\u003eThe SCI has experienced an increase across 41.8% of the country\u0026apos;s land area, with significant rises concentrated in regions such as Shanxi-Gansu-Ningxia regions, and certain parts of Tibet and Sichuan. Among these areas, approximately 34.7% fall within the \u0026quot;Excellent\u0026quot; category (\u0026ge;0.75-0.98) of SCI. However, approximately 12.5% of the national land area experiences a decline in SCI, primarily concentrated near the Changbai Mountain region. This can be attributed to an observed downward trend in annual precipitation in the southern parts of Northeast China, national land area experiences a decline in SCI, primarily concentrated near the Changbai Mountain region. This can be attributed to an observed downward trend in annual. Additionally, research indicates that there has been a decreasing trend observed for the annual snow-covered period at Changbai Mountain Ski Resort from 1981 to 2018, accompanied by relatively low levels of accumulated precipitation (Wang et al.,2023). Moreover, the most substantial portion (approximately 45.7%) comprises areas that have experienced relatively little change, primarily concentrated in the majority of southern China.\u003c/p\u003e\n\u003cp\u003eUpon assigning weights to the SCI values of each province based on the Tyson polygon area, slight adjustments were observed in the rankings between the research baseline period and the historical reference period. The top three provinces remain relatively consistent, with Xinjiang ascending from second place to the first. Despite a slight drop in ranking, Heilongjiang still maintained its high position. Qinghai closely followed suit by ascending from fourth place to third. These leading three provinces exhibit distinctive advantages in terms of skiing resources. Notably, Jilin Province experienced a significant decline in ranking, dropping from fifth to eleventh place. Conversely, the Shanxi-Gansu-Ningxia regions emerged prominently and holds a noteworthy position among the top provinces. For regions undergoing substantial growth, it is imperative to recalibrate local ski industry development strategies in response and harness ski climate resources to their fullest potential. In areas encountering notable declines, swift actions are requisite to mitigate and address the adverse impacts of change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Implications of SCI for ski industry development through comparing with ski resorts\u0026rsquo; vitality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSki resorts\u0026rsquo; vitality was developed by Fang et al.(2023). This comprehensive evaluation system encompasses the ski resorts\u0026rsquo; attractiveness to skiers (\u0026ldquo;place\u0026rdquo;), the ski resorts\u0026rsquo; capability to support skiing activities (\u0026ldquo;activity\u0026rdquo;), and the skiers\u0026rsquo; perception of ski resorts (\u0026ldquo;people\u0026rdquo;). When evaluating the vitality of a ski resort from a \u0026quot;place\u0026quot; perspective, several critical factors come into play. These encompass accessibility, skiing facilities, supporting amenities, and operational hours. The assessment of \u0026ldquo;activity\u0026rdquo; revolves around two key aspects: the sheer number of participants involved in skiing activities within the ski area (aggregation) and the reliability and consistency of participation across different days, including weekdays, weekends, and holiday periods like the Spring Festival (stability). The aspect of \u0026quot;people\u0026quot; finds its embodiment in the subjective evaluations provided by skiers concerning diverse elements related to facilities and services. This includes 26 indoor ski resorts and 389 outdoor ski resorts. The vitality\u0026nbsp;characteristics of China\u0026apos;s ski resorts were analyzed using the linear combination method to calculate the combined weights of different evaluation indexes for ski resort\u0026rsquo;s vitality. Please refer to Table 2 for indicators in each dimension and their corresponding weights.\u003c/p\u003e\n\u003cp\u003eTable 2 Evaluation index system and weight of ski resort\u0026rsquo;s vitality\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.462081128747796%\" valign=\"top\"\u003e\n \u003cp\u003eDimensions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.80952380952381%\" valign=\"top\"\u003e\n \u003cp\u003eIndicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.72839506172839%\" valign=\"top\"\u003e\n \u003cp\u003eDescriptions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.462081128747796%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eSki resort attractiveness\u003c/p\u003e\n \u003cp\u003e(0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.80952380952381%\" valign=\"top\"\u003e\n \u003cp\u003eAccessibility (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.72839506172839%\"\u003e\n \u003cp\u003eOverall Accessibility including local and regional accessibility outside these areas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.835051546391753%\" valign=\"top\"\u003e\n \u003cp\u003eOperation time (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.16494845360825%\" valign=\"top\"\u003e\n \u003cp\u003eThe length of a ski season; Daily business hours\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.835051546391753%\" valign=\"top\"\u003e\n \u003cp\u003eSkiing facilities (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.16494845360825%\" valign=\"top\"\u003e\n \u003cp\u003eSki slopes variety (i.e., green/blue/black runs); The number and combination of ski-lift facilities\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.835051546391753%\" valign=\"top\"\u003e\n \u003cp\u003eSupporting facilities (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.16494845360825%\" valign=\"top\"\u003e\n \u003cp\u003eThe number and combination of service infrastructure like hotels, restaurants, parking lots\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.462081128747796%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eActivity level of ski activity\u0026nbsp;(0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.80952380952381%\" valign=\"top\"\u003e\n \u003cp\u003eAggregation (0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.72839506172839%\" valign=\"top\"\u003e\n \u003cp\u003eNuclear density analysis using\u003c/p\u003e\n \u003cp\u003ethe number of visits to the ski resort as the weight parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.835051546391753%\" valign=\"top\"\u003e\n \u003cp\u003eStability (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.16494845360825%\" valign=\"top\"\u003e\n \u003cp\u003eChanges in the skier visitors between the working days, weekend days and the Spring Festival holiday\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.462081128747796%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSkiers\u0026apos; perception\u003c/p\u003e\n \u003cp\u003e(0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.80952380952381%\" valign=\"top\"\u003e\n \u003cp\u003eFacilities experience (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.72839506172839%\" valign=\"top\"\u003e\n \u003cp\u003eExperience evaluation of the perception of traffic, snow slopes, restaurants, and accommodation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.835051546391753%\" valign=\"top\"\u003e\n \u003cp\u003eService experience (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.16494845360825%\" valign=\"top\"\u003e\n \u003cp\u003eSki and snowboard rental and education services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: Fang et al., 2023\u003c/p\u003e\n\u003cp\u003eSki resorts\u0026rsquo; vitality offers a comprehensive depiction of the development across different ski resorts. By juxtaposing SCI with the vitality of ski resorts, a deeper understanding can be gained regarding the indicative role of the SCI in guiding the growth of the skiing industry. Fig.6 presents the scores of the three-dimensional evaluation indicators for spatial vitality in 415 ski resorts across China. It also showcases the standardized spatial vitality values assigned to Chinese ski resorts. Moreover, these values correlate with the distribution of SCI in the geographical expanse of China. By employing the natural breakpoint method, five levels are established to classify ski resort attractiveness, ski Activity level, skiers\u0026apos; perception, and spatial vitality: high, relatively high, medium, relatively low, and low.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe findings reveal that:\u003c/p\u003e\n\u003cp\u003e1) The attractiveness of ski resorts in China generally exhibits spatial dispersion, and their alignment with ski climate resources is relatively weak (Fig.6a). The proportion of ski resorts classified as \u0026quot;high\u0026quot; and \u0026quot;relatively high\u0026quot; in terms of attractiveness is limited to only 51, accounting for merely 12.3% of the total number of statistical ski resorts. In contrast, a mere 22 ski resorts are categorized as having \u0026quot;Excellent\u0026quot; and \u0026quot;Good\u0026quot; SCI levels. On the other hand, a total of 145 ski resorts fall under the classification of \u0026quot;low\u0026quot; and \u0026quot;relatively low\u0026quot; attractiveness, with an additional 88 being labeled as having a \u0026quot;Poor\u0026quot; SCI level. Specifically, areas such as Beijing and its surrounding environs, Northeast China, and the Altay region of Xinjiang are prominent ski destinations boasting comprehensive ski facilities and supporting amenities. These areas exhibit good accessibility and long snow seasons, resulting in the high attractiveness and relatively elevated status of many resorts. Meanwhile, despite the natural constraints posed by climate, ski resorts in southern China have witnessed the emergence of highly appealing indoor facilities. An example is the Guangzhou Sunac Snow World, which is situated in a low latitude region. This achievement is made possible through advancements in snowmaking and snow storage technologies driven by market demand.\u003c/p\u003e\n\u003cp\u003e2) The overall correlation between SCI and the activity level of ski activity is relatively low. However, there is a notably higher correlation observed in the northern region compared to the southern region (Fig.6b). Regions exhibiting a high level of ski activity are primarily situated in the Beijing-Tianjin-Hebei region and the three provinces in Northeast China. Notably, Zhangjiakou in Hebei province stands out prominently in this regard. This area has earned an \u0026quot;excellent\u0026quot; rating based on SIC criteria and is distinguished by its consistent influx of frequent visitors, rendering it a preferred destination for skiing enthusiasts. Despite the emergence of indoor ski resorts in southern provinces such as Guangdong and Yunnan, their availability of ski climate resources remains limited. Furthermore, constraints related to venues and the tourism market have contributed to comparatively lower levels of ski activity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3) The correlation between skiers\u0026apos; experience and SCI exhibited a weak connection (Fig.6c). Out of the 174 resorts that received \u0026quot;good\u0026quot; or \u0026quot;excellent\u0026quot; SCI scores, only 58 were associated with skiers who reported \u0026quot;high\u0026quot; or \u0026quot;very high\u0026quot; levels of perceived activity. This suggests a lack of alignment between these factors. Meanwhile, the skiers\u0026rsquo; perception demonstrates a multi-core clustering pattern, illustrated in Figure 6c. Ski resorts boasting high and relatively high skiing experiences tend to cluster in the Beijing-Zhangjiakou, Northeast China, and Altay regions. These areas benefit from superior natural resource conditions and effective supply-demand coordination, contributing to enhanced skiing experiences. Conversely, ski resorts offering moderate and low skiing experiences are primarily situated in regions like Shandong and Shaanxi. Despite Northeast China\u0026apos;s advantageous natural resources and early involvement in the ski industry, numerous ski resorts in this area still provide only moderate or low skiing experiences. This situation is often attributed to intense market competition and inadequate infrastructure development.\u003c/p\u003e\n\u003cp\u003e4) Figure 6d indicates a limited alignment between the available natural resources for skiing and the vitality of ski resorts. The spatial vitality of ski resorts exhibits distinct regional disparities, primarily characterized by a multi-center distribution pattern with a higher concentration in northern regions and a lower concentration in southern regions. High spatial vitality ski resorts are mainly clustered in and around Beijing, Northeast China, and the Altay region of Xinjiang. In these areas, favorable natural conditions such as terrain and climate, abundant natural snow resources, and the early development of ski resorts with integrated facilities have contributed to the establishment of a stable customer base.The spatial distribution of ski resorts displays significant heterogeneity, with only 28.6% of ski resorts demonstrating high or relatively high activity levels. Moreover, over half of these ski resorts are located in areas characterized by a \u0026quot;poor\u0026quot; SCI rating.\u003c/p\u003e"},{"header":"4 Conclusion and discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1 Conclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn summary, this study has integrated the SCI specifically tailored for China\u0026apos;s unique conditions through local adjustments. The empirical analysis is based on daily meteorological data from 733 representative meteorological stations covering the research period of 1991-2020 and the historical reference period of 1961-2020, along with data from 415 ski resorts in China. ArcGIS 10.5 spatial analysis tools were utilized to conduct the empirical research. Furthermore, this study has compared the ski climate resources in China with the spatial vitality of ski resorts. The primary conclusions are as follows:\u003c/p\u003e\n\u003cp\u003e1. The snow module holds a predominant sway in shaping the distribution of SCI, where natural snow stands as the foremost decisive factor. Nevertheless, additional non-snow natural elements, like sunshine duration, wind speed, and thermal comfort, wield comparatively less influence over the comprehensive SCI assessment. Further enhancements are necessary to gain a better understanding of their respective roles.\u003c/p\u003e\n\u003cp\u003e2. The favorable skiing climate regions in China, accounting for approximately 54% of the total, are primarily concentrated to the west of the Hu Huanyong Line under equivalent natural and human conditions, particularly in the northwest, northeast, and parts of North China. Xinjiang and Heilongjiang, endowed with distinct skiing climate advantages, act as significant drivers in this context. The majority of ski resorts are located in regions renowned for their exceptional or favorable skiing conditions. However, the spatial distribution of ski climate resources exhibits misalignment with the current positioning of many ski resorts, underscoring a notable level of incongruity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. The skiing climate resources in China have significantly transformations amidst the backdrop of climate change. Regions such as Shanxi-Gansu-Ningxia regions and southwestern Tibet and Sichuan have witnessed remarkable increases, while the southern parts of the three northeastern provinces have encountered noticeable decreases. Particularly noteworthy is the substantial increase in skiing climate resources observed in the Shanxi-Gansu-Ningxia regions, which creates favorable climatic conditions for the progression of the ski industry. However, it is worth noting that only nine ski resorts with aerial cableways are currently available in this region, accounting for a mere 13% of the total number of ski resorts in China. The findings indicate that there is still untapped potential for the development of ski tourism in the Shaanxi-Gansu-Ningxia region. The scarcity of high-quality ski resorts results in a pronounced discrepancy between the available skiing climate resources and the existing ski facilities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4. The correlation between SCI and spatial vitality is relatively weak, whereas the correlation between SCI and local areas is significantly strong. For instance, as regions abundant in ice and snow tourism resources, ski resorts in Heilongjiang and Xinjiang have relatively high and moderate average spatial vitality, respectively. Ski resorts in Beijing and Hebei demonstrate high levels of attractiveness, activity, and skiing experience. In contrast, Jilin and Liaoning display high activity level and relatively high skiing experience but generally or relatively low overall attractiveness. Guangdong excels in attractiveness and skiing experience but has a relatively low activity level. Moreover, improvements are necessary to enhance their attractiveness and skiing experience. Besides, the spatial vitality of China\u0026apos;s ski resorts exhibits apparent differentiation across various evaluation dimensions, including spatial vitality, attractiveness differentiation characteristics, activity level, and skiing experience.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Discussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the various factors influencing skiing climate resources, natural snowfall plays a dominant role in their distribution.\u0026nbsp;Besides, ski resorts that are well-developed with high vitality are primarily situated in areas rich in skiing climate resources. However, some regions with abundant skiing climate resources, such as Inner Mongolia, Tibet, and Shanxi-Gansu-Ningxia, face limited ski market potential and a scarcity of ski resorts. The full realization of economic benefits from ice and snow tourism resources and climatic advantages has been hindered by topography, transportation limitations, inadequate supporting facilities, and market perception. Additionally, policy support and hosting of large-scale ice and snow events have accelerated the development of the ski industry in these regions. Driven by the government\u0026apos;s robust promotion of \u0026quot;ice and snow sports\u0026quot; and the impact of the 2022 Beijing Winter Olympics, the ice and snow tourism market has experienced rapid expansion.\u0026nbsp;In regions endowed with abundant skiing climate resources,\u0026nbsp;it is necessary to make a systematic cultivation of the industry is necessary achievable through the formulation of tailor-made development strategies.\u003c/p\u003e\n\u003cp\u003eThe impact of climate change on ski resorts in various regions of China has indeed led to a reshaping of the competitiveness of skiing destinations in the country (Fang et al., 2021). It is imperative for these regions to promptly adapt their product offerings and business strategies in response to such changes. The phenomenon of global warming has emerged as a prevailing issue in China over the past few years, resulting in an elevation of winter temperatures and a significant decline in snowfall across regions like Heilongjiang province. Consequently, this poses considerable challenges for the operation of ski resorts. The ongoing warming trend poses new challenges to China\u0026apos;s skiing industry, such as reduced operating hours and rapidly escalating skiing expenses. To effectively address this challenge, continuous adjustments in ski equipment are necessary, along with intensified investments in snowmaking facilities. Seizing optimal opportunities for snow production, generating substantial amounts of snow in suitable regions for storage purposes, and being well-prepared for unforeseen circumstances are of utmost importance. In addition, regions with an enhanced ski climate, particularly Shanxi-Gansu-Ningxia regions, and certain parts of Tibet and Sichuan, can fully capitalize on their advantageous ski climate to further explore the potential of climate attractiveness. By innovating \u0026quot;tourism + climate\u0026quot; ski product offerings, the untapped resources of ski climates are transformed into valuable tourism assets, thereby strengthening the tourism climate brand and catering to diverse demands for tourism experiences. This approach better aligns with the objectives of poverty reduction through tourism promotion, revitalization through tourism promotion, and development through tourism promotion.\u0026nbsp;Meanwhile, the ice and snow sports market in China has exhibited a trend of \u0026quot;expanding towards the western regions while advancing into the eastern areas,\u0026quot; indicating a discrepancy between available resources and market demand. Targeted development strategies should be formulated for typical regions like Beijing-Tianjin-Hebei, Northeast China, and Xinjiang based on their specific characteristics.\u003c/p\u003e\n\u003cp\u003eWe recognize certain limitations to our study. The\u0026nbsp;SCI\u0026nbsp;index is designed to integrate the reliability, aesthetics, and comfort aspects of snow, including factors such as sun exposure, wind conditions, and thermal comfort. Future research recommendations involve incorporating additional components like critical high altitude snow depth into the index and providing technical suggestions for enhancing its application.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contribution declaration\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the writing of the main manuscript text. Dandan Yu processed and analyzed the data, as well as prepared figures. Yan Fang reviewed and revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe financial support for this study was provided by the National Natural Science Foundation of China [grant number 42001255] and Beijing Winter Olympics Culture and Ice \u0026amp; Snow Sports Development Research Center Fund.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAndersen PA, Buller DB, Scott MD, Walkosz BJ, Voeks JH,Cutter GR, Dignan MB (2004) Prevalence and diffusion of helmet use at ski areas in Western North America in 2001-02. 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Climate Dynamics54(3):2361-2385. https://doi.org/10.1007/s00382-020-05118-4\u003c/li\u003e\n \u003cli\u003eZhong Z, Li X, Xu X, Liu XP, He ZJ (2018) Analysis of spatial and temporal variations in snow cover in China from 1992 to 2010. Science Bulletin 63(25):2641-2654.https://doi.org/10.1360/N972018-00199\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ski climate index, ski resorts’ vitality, ski industry, Tourism climate, Spatial distribution","lastPublishedDoi":"10.21203/rs.3.rs-3299526/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3299526/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe primary objective of this study is to analyze the characteristics of ski climate resources and quantitatively assess the suitability for skiing by utilizing a more appropriate Ski Climate Index. Taking China as a case study, this paper collected daily meteorological data from 733 weather stations spanning the period from 1991 to 2020, along with information on 415 ski resorts. Subsequently, GIS 10.5 spatial analysis tools were employed to examine the temporal and spatial variations in ski climate resources across China during this timeframe. In order to illustrate the relationship between skiing climate resources and the development of ski resorts more clearly, a comparison was thus drawn between the distribution of ski climate resources and the vitality of ski resorts in China. The results show that:1) the SCI was developed using fuzzy logic, with a predominant influence from the snow reliability facet on overall performance. Furthermore, the aesthetics and comfort facet, which includes factors such as sunshine, wind, and thermal comfort conditions, contributed to further refinement of the index. 2) Areas with high SCI values are primarily concentrated in the northwestern and northeastern regions of China, as well as certain parts of northern China. Against the backdrop of climate change, there has been a significant increase in ski climate resources in regions like Shaanxi-Gansu-Ningxia regions, southwest Tibet, and Sichuan, and noticeable declines have occurred in southern regions within Northeast China.3) Through comparison with vitality of ski resorts,SCI can partially reflect the development of ski resorts. The suitability evaluation model for skiing based on climate resources provides valuable insights for management decision-making in developing and operating ski resorts. It also offers scientific support for promoting ice-snow economic development.\u003c/p\u003e","manuscriptTitle":"Spatial and Temporal Assessment of China's Skiing Climate Resources","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-01 14:13:06","doi":"10.21203/rs.3.rs-3299526/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ea5b8568-fa16-4c17-aa35-8e2410bfb0ef","owner":[],"postedDate":"September 1st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-12-25T12:29:13+00:00","versionOfRecord":[],"versionCreatedAt":"2023-09-01 14:13:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3299526","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3299526","identity":"rs-3299526","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}