Managing for Climate Change in the Alpine Ski Sector.

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit: https://www.elsevier.com/copyright Author's personal copy Tourism Management 35 (2013) 244e254 Contents lists available at SciVerse ScienceDirect Tourism Management journal homepage: www.elsevier.com/locate/tourman Managing for climate change in the alpine ski sector J. Dawson a, b, *, D. Scott c a Department of Geography & Institute for Science, University of Ottawa, 75 Laurier Ave. East, Ottawa, ON K1N 6N5, Canada Department of Society and Policy, University of Ottawa, 75 Laurier Ave. East, Ottawa, ON K1N 6N5, Canada c Department of Geography and Environmental Management, University of Waterloo, Canada b h i g h l i g h t s < The impact of climate change is modeled for all 103 operating ski areas in the US Northeast. < Modeling techniques are refined through the inclusion of generic lapse rates. < Many ski areas are not expected to be economically viable as early as mid-century. < Ski areas remaining viable could take advantage of a reduction in marketplace competition. < A decision-making flowchart is proposed to assist ski resort managers deal with climate change. a r t i c l e i n f o a b s t r a c t Article history: Received 22 March 2012 Accepted 30 July 2012 The impact climate change will have on the alpine ski sector has received increasing attention from tourism scholars and industry representatives. This paper uses the US Northeast as a case study to examine the integrated regional impacts of climate change by modeling local-level impacts at all 103-ski areas operating in the region. Failing to examine an entire marketplace e as has been done in much of the climate change and ski tourism literature e means it is difficult to understand the regional implications that vulnerability at one ski area could mean for an adjacent ski area, for the regional ski marketplace, or for communities and individuals reliant on the sector generally. This paper presents the results of this comprehensive analysis, provides a discussion of the implications of change, and proposes a decision-making tool intended to help guide ski resort management in light of projected climate change. Ó 2012 Elsevier Ltd. All rights reserved. Keywords: Ski tourism Climate change Vulnerability Adaptation Management 1. Introduction Weather and climate are intrinsic components of the tourism experience, influencing tourist demand, comfort and satisfaction, as well as tourism operations (e.g. water supply, energy costs, insurance costs) and environmental resources critical to the industry (e.g. glaciers, biodiversity, water levels, snow). A changing climate has the potential to significantly influence this economically important and climate-sensitive sector. The winter tourism sector, and in particular the ski industry, has been earmarked as one of the most vulnerable industries to climatic change (UNWTOUNEP, 2003; UNWTO-UNEP-WMO, 2008). Bicknell and McManus (2006) portray the ski sector as a ‘canary in the coalmine’, * Corresponding author. Department of Geography & Institute for Science, University of Ottawa, 75 Laurier Ave. East, Ottawa, ON K1N 6N5, Canada. Tel.: þ1 613 562 5800x1118. E-mail addresses: [email protected], [email protected] (J. Dawson). 0261-5177/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. https://dx.doi.org/10.1016/j.tourman.2012.07.009 suggesting the first signs of a changing climate for any tourism sector is being witnessed directly within the ski industry. Ski resorts have long been dealing with variability in seasonal temperature and natural snowfall leading to early adaptive interventions and investments in research and development aimed at sustaining longer and more reliable snow seasons (Scott, 2005). Among many important business, structural, and technology-based adaptations, the widespread uptake of snowmaking (particularly in North America) has been one of the most important investments ski areas have made in ensuring their economic viability. A number of other adaptive strategies have become popular including, building ski resorts at higher elevations to account for lower temperatures at altitude, investing in all-season resorts, delivering non-snow-based activities, and providing an après ski atmosphere. Despite technological advances in snowmaking machinery and application, and the modernization of business plans, even the most sophisticated adaptation strategies still cannot shelter ski areas from the current and expected impacts of climate change. Importantly, it is not the entire ski market that is necessarily at risk to climate change, but rather at risk are individual ski areas and Author's personal copy J. Dawson, D. Scott / Tourism Management 35 (2013) 244e254 regions that are not able to adapt or afford the increased costs of adapting to projected change. Failing to understand the implication of climate change for an entire ski marketplace means it is difficult to understand the regional implications that vulnerability at one ski area could have for an adjacent ski area, for the regional ski marketplace, or for communities and individuals reliant on the sector generally. The implications of climate change have been shown to vary substantially by market segment and geographic region, and will undoubtedly depend on the impacts experienced by competitors. Understanding how the entire ski marketplace may transform, and may be influenced by changes to individual ski areas, can help ski resort managers as well as municipal, state and federal decision-makers establish sustainable development plans and future management strategies. This paper examines the impact of climate change on ski area operations using the US Northeast as a case study. All 103 operating ski areas in the region were modeled using three Global Climate Models (GCM’s) forced with a series of A1 (high emission) and B1 (low emission) SRES emission scenarios (see IPCC, 2007 e special report on emission scenarios) and combined with the “ski-sim” operations model (see Scott, McBoyle, & Mills, 2003) in order to project changes to ski season length, probability of being operational during the economically important ChristmaseNew Year holiday season, snowmaking requirements, and to determine the economic viability of regional ski areas for the 2020s, 2050s and 2080s time periods. A discussion of the implications of change is provided and a decision-making tool is proposed that is intended to help guide ski resort management in light of projected climate change. 2. Literature review: modeling climate change impacts for ski tourism Many ski areas can, and have, rebounded easily from the impact of a single poor snow season among a series of good snow seasons; this is something ski managers have come to expect. However, changing climatic conditions mean that limited natural snowfall and previously analogous warm temperatures are becoming the ‘new normal’ and the financial impact of single marginal seasons can no longer be buffered around a series of average or above average seasons. Considering modeled projections that global temperature is expected to rise by more than þ2  C by 2020 and by more than þ4  C by 2050, and considering more precipitation is expected to fall as rain instead of snow (IPCC, 2007), ski area operators must adapt for the possibility of continuously marginal snow seasons. König and Abegg (1997) projected that a change in climate by as little as þ2  C could cause the snow line to raise from 1200 m above sea level [masl] to 1500 masl and result in a reduction in the number of ‘snow reliable’ ski resorts in Switzerland to just 63% (i.e. using the thresholds: ski areas with at least 100 day seasons and a minimum of 30 cm snow cover). Elsasser and Bürki (2002) modeled an additional rise in the snow line to 1800 masl, which they project would cause a reduction in the number of ‘snow reliable’ Swiss ski resorts to just 43%. Similarly, Abegg, Agrawala, Crick, and De Montfalcon (2007) examined ‘natural snow reliability’ in Austria, France, Germany, Italy, and again in Switzerland, determining that for 2007 climate conditions, 609 of the 666 operating ski areas (91%) could be considered ‘snow reliable’. However, under a scenario of þ1  C the number of ‘snow reliable’ ski areas drops to 500 (75%); under a þ2  C scenario this drops to 404 (61%), and under a þ4  C scenario the number drops to 202 (30%). Germany is expected to experience the most significant impacts (60% reduction under 1  C scenario) and Switzerland the least (10% reduction under 1  C scenario). 245 Complementing studies that estimate climate change impacts on regional ski resort viability, are a number of studies that have concentrated on understanding the impact climate change has on average ski season lengths. Season length studies have been conducted in Australia, Sweden, Canada and the United States. These studies tend to consistently use the 100-day rule as an indicator of economic viability e meaning ski seasons must be at least 100-days long each year (Dawson & Scott, 2007; Scott et al., 2003; Scott, McBoyle, Mills, & Minogue, 2006; Scott, McBoyle, & Minogue, 2006). Galloway (1988) and Whetton, Haylock, and Galloway (1996) both believe that Australian ski resorts are not likely to maintain 100-day seasons and project a reduction in snow-reliable ski-days to a total of 60e75 days per season in the 2070e99 time period. In the same timeframe, Moen and Fredman (2007) project a reduction in skier days at Sweden’s resorts to between just 64 and 96 days. McBoyle and Wall (1987) used a doubling of atmospheric CO2 scenario (the most sophisticated scenario modeling method at the time that of publication) to model ski season length reductions in Canada, projecting declines of between 30 and 40% for a region on the north shore of Lake Superior and between 80 and 100% for the southern Great Lakes region near Georgian Bay. Using the same methodology, skiable days in southern Quebec, Canada were estimated to decline by 50e70% (Lamothe & Périard Consultants, 1988), by between 40 and 89% in the Lower Laurentian Mountains of Quebec, Canada (see McBoyle & Wall, 1992) and by between 30 and 100% in Michigan, USA (Lipski & McBoyle, 1991). Clearly, the impacts of climate change on the international ski industry could be profound. However, Scott et al. (2003); Scott, McBoyle, Mills, et al. (2006); Scott, Dawson, and Jones (2007) suggest that many of the ‘first generation’ impact modeling studies (i.e. those outlined above) that examine climate change vulnerability for the ski sector likely overestimate the potential future impacts by not considering the widely used adaptation of snowmaking. Scott et al. (2003); Scott, McBoyle, Mills, et al. (2006); Scott, McBoyle, and Minogue (2006) were the first to include a snowmaking module in their analysis of climate change impacts on ski tourism. As a result, their findings suggest ski season lengths in the same Canadian regions modeled by McBoyle and Wall (1987) and McBoyle & Wall (1992) are expected to decrease by only 1e13% in the 2020s and 7e32% in the 2050s. These estimates describe significantly less impact than those suggested in studies that do not account for snowmaking as an adaptation. However, these studies do not account for temperature reductions that occur with elevation (i.e. where ski resorts are often built, and where precipitation falls more often as snow vs. rain) suggesting impacts may in some case still be overestimated especially for higher-elevated ski areas. Recognizing this limitation, studies by Dawson and Scott (2007) and Steiger (2010) have attempted to incorporate the role of temperature change at different elevations. Steiger (2010) has additionally refined modeling techniques to include other influencing factors that occur at elevation such as aspect and slope; features that significantly impact a mountains ability to maintain sufficient snowpack. In addition to the suite of minor limitations that restrict our comprehensive understanding of the impacts of climate change for the ski sector, the single most limiting factor is a lack of consideration of the integrated impacts of change among individual ski resorts and between regional ski areas. Our inability to understand the differential vulnerability among individual ski areas, and even between regional or international ski marketplaces, is in part due to the wide range of methodological approaches that have been used to evaluate climate change impacts for the ski sector. As a result, it is not possible to compare findings between studies despite there now being a useful foundation of over 30 published studies modeling climate change impacts for alpine skiing (see Scott, Hall, Author's personal copy 246 J. Dawson, D. Scott / Tourism Management 35 (2013) 244e254 & Gössling, 2012). This is unfortunate considering it is not the entire ski marketplace that is at risk to climate change, but rather it is the particularly vulnerable ski areas that operate within a region e and thus failing to develop a comprehensive understand of an entire ski marketplace or the global ski marketplace means it is difficult if not impossible to understand the regional or international implications of change. This limitation severely limits the ability of ski area and regional managers, planners and developers to create and implement adaptation plans designed to manage projected climate change. 3. Methods Responding to the acknowledged limitations of previous studies modeling the impact of climate change on the ski sector, this study uses the US Northeast as a case study to model all of the individually operating ski areas in the region. Methodologies applied account for snowmaking technologies available and also utilize generic lapse rates to calibrate projections for temperature change based on elevation differences at each individual ski area. Incorporating these refinements, and modeling every ski area in the region, allows conclusions to be made about which ski areas are more vulnerable than others under different time horizons and under a range of future climate change scenarios. (Fig. 1). There were 103 ski areas operating in the region at the time of this study, including small and large scale resorts located at both low and high elevations (137 m above sea level [masl] to 1353 masl). Just over 8% of the US population (15.5 million people) participates in snow-based recreation (including alpine skiing, Nordic skiing, snowboarding and snowshoeing, but not snowmobiling) and the highest participation rate across the country is in the Northeast region. Over 13 million skier visits are recorded in the US Northeast region annually (NSAA; 2005, 2006a, 2007). The ski tourism sector in the state of Vermont alone contributes over US$1.5 billion to the annual state economy and creates over 13,000 jobs (VSAA, 2004). Since 1900, annual temperature across the US Northeast has increased an average of 0.08  C (0.14  F) per decade and from 1970 to 2002 the region experienced warming at a higher average rate of 0.28  C (0.5  F) per decade (Hayhoe et al., 2006). Warming that has been projected under some climate change scenarios for the 2010e 39 time period has already been realized in some areas of the US Northeast (Hamilton, Brown, & Keim, 2007). Hayhoe et al. (2006) project an increase in average regional temperature between 2.9  C and 5.3  C by the 2070e99 time period relative to the 1961e 90 baseline under a low (B1) and high emissions scenario (A2) respectively. 3.2. Climate change and ski operations modeling 3.1. Case study The US Northeast (as defined by the National Ski Area Association e NSAA) includes the states of Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island, and Vermont Future climate change scenarios and the baseline period (1961e 90) used in this study were derived from gridded climate data (daily temperature and precipitation at 1/8 resolution) supplied by the Northeast Climate Impact Assessment [NECIA] (see Frumhoff Fig. 1. Map of US Northeast ski areas. New York (n ¼ 36): 1-Peek’n Peak, 2-Cockainge, Holiday Valley, Holimount, 3-Kissing Bridge, 4-Brantling, 5-Bristol, 6-Swain, 7-Dryhill, 8-Snowridge, Woods, 9-Song, 10-Four Seasons, Labrador, Toggenburg, 11-Greek Peak, 12-Whiteface, 13-Gore, 14-Oak, 15-Mccauley, Titus, 16-Royal, 17- Hickory, West, Willard, 18- Catamount, Cortina, Hunter, Sawkill, 19-Belleayre, Platekill, Whindam, 20- Mt. Peter, Stirling, 21-Bobcat, Holiday Mountain. Vermont (n ¼ 18): 22-Jay Peak, 23-Smugglers Notch, 24-Bolton Valley, Stowe, 25Cochran, 26-Madriver, 27-Middlebury, 28-Killington, Pico, 29-Sugarbush, 30-Okemo, 31-Ascutney, 32-Bromley, 33-Magic, Stratton, 34-Mt. Snow, 35-Burke, 36-Suicide Six. New Hampshire (n ¼ 18): 37-Balsams, 38-Cannon, Loon, 39-Bretton, 40-Black, Wildcat, Whaleback, 41-Dartmouth, 42-Tenney, 43-Watterville, 44-Attiash, 45-Cranmore, 46-King Pine, 47-Sunapee, 48-Ragged, 49-Gunstock, 50-Pats Peak, 51-Crotched. Maine (n ¼ 14): 52-Saddleback, Sugarloaf, 53-Black, Mt. Abram, Sunday River, 54-Shawnee, 55-Big Rock, 56-Eaton, 57-New Hermon, 58Lost Valley, 59-Titcomb, 60-Big Squaw, 61-Camden, 62-Mt. Jefferson. Massachusetts (n ¼ 12): 63-Berkshire, 64-Blandford, Otis, 65-Jiminy Peak, 66-Butternut, 67-Nashoba, 68-Pine Ridge, Wachusett, 69-Blue Hills, 70-Bousquet, 71-Bradford, 72-Ward. Connecticut (n ¼ 5): 73-Sundown, 74-Mohawk, 75-Woodbury, 76-Southington, 77-Powder Ridge. Author's personal copy J. Dawson, D. Scott / Tourism Management 35 (2013) 244e254 et al., 2008; Hayhoe et al., 2006; NECIA, 2007). Six climate change scenarios were utilized to project impacts for three future time periods (i.e. 2010e39, 2040e69 and 2070e99). Three different Global Climate Models [GCM] (HadCM3, PCM, GFLD) were each run under two IPCC-SRES emission scenarios representing a high emissions future (A1Fie970 ppm) and a relatively low emissions future (B1e550 ppm) (IPCC, 2000). For concise presentation, results from the three GCMs (GFDL, HadCM3, PCM) were averaged for both the lower (B1) and higher emission (A1fi) scenarios for each of the three future time periods (2010e39, 2040e69, 2070e99). These climate change scenarios are consistent with those used in the Northeast Climate Impact Assessment (Union of Concerned Scientists, 2006). Additional information on scenario selection and methodological details of scenario construction are described in Hayhoe et al. (2006) and Frumhoff et al. (2008). The GCMs were specifically chosen for that assessment and also for this study because of their superior performance in reproducing historic climate in the region relative to other GCMs (see Hayhoe et al., 2008). All of the individual ski areas modeled in this study are located at different elevation ranges meaning that average temperatures at different ski areas will vary considerably. This also means that the average grid cell elevation and temperature data extracted from the cell would not represent each ski area equally well. To account for the different temperatures at varying elevations at individual ski areas, vertical adjustments were made to the temperature data extracted from the gridded climate data using a generic lapse rate of þ0.65  C per 100 m of elevation. The elevation of each ski area was represented by its mid-range elevation (summit e base/2), which is consistent with the most comprehensive analysis of the ski industry in the European Alps (OECD, 2007) and specifically chosen to facilitate comparison with that study. The lapse rate adjusted climate data were then input into the ski-sim operations model originally developed by Scott et al. (2003); Scott, McBoyle, Mills, et al. (2006); Scott, McBoyle, and Minogue (2006), which is designed to estimate snow depth using a snow model that is coupled with a snowmaking module and calibrated with the “ski-sim” business-side decision making rules in order to calculate important ski area operations indicators (i.e. season length, snowmaking requirements, operational during holiday seasons). Complete methodological details on the snow, snowmaking, and ‘ski-sim’ ski operations model, including limitations, can be found in Scott et al. (2003) (also see Dawson & Scott, 2007 and Scott et al., 2012 for additional information). 4. Results Three main impact factors are reported here: ski season length, probability of being operational during the economically important ChristmaseNew-Year period, and snowmaking requirements. From these factors an estimation of economic viability is conducted for each of the modeled ski areas. 4.1. Season length The extent to which ski season length is affected is more severe for some ski areas than others. Results from the ski areas located at higher elevations (i.e. many ski areas in New Hampshire and Vermont) show longer season lengths in the baseline period and under all climate change scenarios for all future time periods than those resorts located at lower elevations (i.e. many ski areas in Connecticut, Massachusetts, Maine and southern New York). In the 2010e2039 time period only 55% and 54% of the 103 total ski areas in the US Northeast are projected to be able to maintain 100-day season lengths under the low (B1) and high (A1fi) emissions 247 scenario respectively. These figures drop to 54% and 40% in the 2040e2069 time period and 45% and 29% in the 2070e2099 time period, respectively. At the state level, none of the ski areas in Connecticut or Massachusetts are projected to be able to maintain season lengths of 100 days in the 2010e2039 time period under either the low or high emissions scenario. In the 2040e2069 time period, under low and high emission scenarios respectively, only 57% and 50% of ski areas in Maine and New Hampshire, and 36% and 22% of ski areas in New York have season lengths of more than 100 days. In the 2070e2099 time period, 67% and 33% of ski areas in New Hampshire, and 25% and 6% of ski areas in New York have season lengths of greater than 100 days under the low and high emissions scenario respectively. Ski areas in Vermont are the least vulnerable to climate change, with 100% (B1) and 94% (A1fi) of ski areas maintaining ski season lengths of greater than 100 days even into the 2070e2099 time period (Table 1). 4.2. Snowmaking requirements The amount of snow required for ski areas to remain operational is projected to increase under all scenarios and all time periods for all 103-ski areas. Snowmaking requirements for many lower lying ski areas in Connecticut and New York are expected to increase less dramatically than those located in higher elevated areas in the states of Vermont and New Hampshire. The reason for these differences are twofold: Firstly, ski areas located at lower elevations are already producing more snow under current climate conditions, therefore the percent change in snowmaking does not increase as quickly as for those not currently producing this baseline amount of snow. Secondly, the ski-sim snow module used in this study restricts snowmaking to days when temperatures are at least 5  C (23  F), which is the temperature that current technology allows for efficient snowmaking (Scott et al., 2003). Ski areas located at lower elevations where higher temperature is more prevalent are projected to experience a higher proportion of days warmer than 5  C Table 1 Projected change in ski season length (ability to maintain 100-days). B1 low emissionsa A1 high emissionsb >50% 25e49% <24% n n %D n %D 2020s CT 0 ME 0 MA 0 NH 0 NY 0 VT 0 2050s CT 0 ME 0 MA 0 NH 0 NY 0 VT 0 2080s CT 5 ME 0 MA 0 NH 0 NY 0 VT 0 %D >50% n %D 25e49% <24% n %D n %D 0 0 0 0 0 0 5 0 0 0 2 0 100 0 0 0 6 0 0 14 12 18 34 18 0 100 100 100 94 100 0 0 0 0 0 0 0 0 0 0 0 0 5 0 2 0 2 0 100 0 17 0 6 0 0 14 10 18 34 18 0 100 83 100 94 100 0 0 0 0 0 0 5 0 5 0 5 0 100 0 42 0 14 0 0 14 7 18 31 18 0 100 58 100 86 100 5 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14 12 18 36 18 0 100 100 100 100 100 100 0 0 0 0 0 0 7 12 9 23 1 0 50 100 50 64 6 0 7 0 9 13 17 0 50 0 50 36 94 5 6 5 1 22 0 100 43 42 6 61 0 0 5 7 15 14 18 0 36 58 83 39 100 0 3 0 2 0 0 0 21 0 11 0 0 Note: Connecticut (CT) n ¼ 5, Maine (ME) n ¼ 14, Massachusetts (MA) n ¼ 12, New Hampshire (NH) n ¼ 18, New York (NY) n ¼ 36, Vermont (VT) n ¼ 18. a 30 year average of three scenarios (GFDL- B1, HadCM3-B1, PCM1-B1). b 30 year average of three scenarios (GFDL-A1Fi, HadCM3-A1Fi, PCM1-A1Fi) n ¼ number of ski areas in the state %D ¼ percentage change. Author's personal copy 248 J. Dawson, D. Scott / Tourism Management 35 (2013) 244e254 Table 2 Projected change in snowmaking requirements. B1 low emissionsa n 2020s CT 0 ME 3 MA 0 NH 0 NY 0 VT 0 2050s CT 0 ME 3 MA 0 NH 3 NY 0 VT 8 2080s CT 0 ME 4 MA 0 NH 9 NY 4 VT 16 A1 high emissionsb 25e49% >50% Table 3 Projection of ski areas with at least 75% probability of being operational during the Christmas-New Year holiday. <24% 25e49% >50% %D n %D n %D n 0 21 0 0 0 0 0 4 0 18 8 18 0 29 0 100 22 100 5 7 12 0 28 0 100 50 100 0 78 0 0 3 0 0 0 0 0 21 10 17 0 44 0 5 3 15 10 10 0 36 25 83 28 56 5 6 9 0 26 0 100 43 75 0 72 0 0 29 0 50 11 88 0 7 3 9 6 2 0 50 25 50 17 12 5 3 9 0 26 0 100 21 75 0 72 0 %D 2010-39 <24% n %D n %D 0 21 0 0 0 0 0 6 0 18 7 18 0 43 0 100 19 78 0 5 12 0 29 0 0 36 100 0 81 0 0 7 0 13 4 18 0 50 0 72 11 100 0 7 9 5 11 0 0 50 75 28 31 0 5 0 3 0 21 0 100 0 25 0 58 0 0 10 0 12 8 18 0 62 0 67 22 100 0 3 4 6 5 0 0 21 33 33 14 0 5 1 8 0 23 0 100 7 67 0 64 0 Note: Connecticut (CT) n ¼ 5, Maine (ME) n ¼ 14, Massachusetts (MA) n ¼ 12, New Hampshire (NH) n ¼ 18, New York (NY) n ¼ 36, Vermont (VT) n ¼ 18. a 30 year average of three scenarios (GFDL- B1, HadCM3-B1, PCM1-B1). b 30 year average of three scenarios (GFDL-A1Fi, HadCM3-A1Fi, PCM1-A1Fi) n ¼ number of ski areas in the state %D ¼ percentage change. (23  F) in comparison to higher elevated ski areas and may therefore be restricted in their snowmaking efforts (Table 2). 4.3. Open during holiday periods In the US Northeast ski region, up to 20% of skier visits occur in the twelve-day holiday period between December 23rd and January 3rd (NSAA, 2005) and, as such, being operational during this time period is extremely important. However, because the ChristmaseNew Year holiday falls early in the snow season it is sometimes difficult for ski areas to be fully operational at this time. The probability that ski areas in the region will be fully operational for the duration of the 12-day ChristmaseNew Year holiday was examined finding that projections are fairly constant between time periods, with any significant impacts appearing by the end of the 2010e39 time period. Of the 103 ski areas modeled only 43% (B1 low emission) and 44% (A1fi high emissions) were projected to maintain more than a 75% probability of being operational during the ChristmaseNew Year holiday periods for the 2010e39 timeframe. This projection decreased to 35% and 26% respectively Connecticut Maine Massachusetts New Hampshire New York Vermont Northeast Region 2040-69 2070-99 Total ski areas B1a n (%D) A1fib n (%D) B1a n (%D) A1fib n (%D) B1a n (%D) A1fib n (%D) 5 14 12 18 36 18 103 0(0) 7(50) 0(0) 9(50) 11(31) 17(94) 44(43) 0(0) 7(50) 0(0) 6(33) 6(17) 16(89) 35(34) 0(0) 7 (50) 0(0) 7(39) 6(17) 16(89) 36(35) 0(0) 7(50) 0(0) 3(17) 2(3) 15 (83) 27(26) 0(0) 7(50) 0(0) 6(33) 5(14) 16(89) 34(33) (0)0 3(50) 0(0) 0(0) 0(0) 4(22) 7(7) n ¼ number of ski areas in the state. %D ¼ percentage change. a 30 year average of three scenarios (GFDL- B1, HadCM3-B1, PCM1-B1). b 30 year average of three scenarios (GFDL-A1Fi, HadCM3-A1Fi, PCM1-A1Fi). during the 2040e69 time period and to 33% and 7% in the 2070e99 timeframe (Table 3). 4.4. Economic viability of ski areas Within previous studies, the combination of two indicators has been regularly used to comprehensively explore the future economic viability of individual ski areas including: 1) season length of at least 100-days, and 2) 75% probability of being operational during the ChristmaseNew-Year holiday period (Bürki, 2002; Dawson & Scott, 2007; Erickson, 2005; König & Abegg, 1997; Scott et al., 2007). In this study, only 57 and 56 of the 103 operating ski areas in the US Northeast are projected to be able to maintain 100day season lengths under the low (B1) and high (A1fi) emissions scenario in the 2010e39 time period. These figures drop to 56 and 41 in the 2040e69 time period and 46 and 30 in the 2070e99 time period, respectively. Only 43 and 34 have a 75% (or greater) probability of remaining operational during the ChristmaseNew Year season under the low (B1) and high (A1fi) emissions scenario for the 2010e39 time period. These figures drop to 35 and 26 in the 2040e69 time period and 33 and 7 in the 2070e99 time period, respectively (Table 4). Using the combination of these two economic indicators (100day season þ 75% probability open for ChristmaseNew Year holiday), it is projected that only 41 ski areas will remain economically viable by the 2010e2039 time period under the high (A1fi) emission scenario. The number of operational ski areas drops to 34 for the 2040e2069 time period and 30 for the 2070e2099 time period (Fig. 2). Under a lower (B1) emissions scenario the number of operating ski areas in the 2010e2039 time period is Table 4 Percent of ski areas with 100þ day season length and >75% probability of being operational during the Christmas-New Year holiday. % With 100þ day season 2010-39 CT ME MA NH NY VT Region % With >75% probability of operating during ChristmaseNew Year holiday 2040-69 2070-99 2010-39 2040-69 2070-99 B1a A1b B1a A1b B1a A1b B1a A1b B1a A1b B1a A1b 0 57 8 94 36 100 55 0 57 0 94 36 100 54 0 57 0 94 36 100 54 0 50 0 50 22 94 40 0 50 0 67 25 100 45 0 50 0 33 6 94 29 0 50 0 50 31 94 43 0 50 0 33 17 89 34 0 50 0 39 17 89 35 0 50 0 17 3 83 26 0 50 0 33 14 89 33 0 50 0 0 0 22 7 Note: Connecticut (CT) n ¼ 5, Maine (ME) n ¼ 14, Massachusetts (MA) n ¼ 12, New Hampshire (NH) n ¼ 18, New York (NY) n ¼ 36, Vermont (VT) n ¼ 18. a 30 year average of three scenarios (GFDL- B1, HadCM3-B1, PCM1-B1). b 30 year average of three scenarios (GFDL-A1Fi, HadCM3-A1Fi, PCM1-A1Fi). Author's personal copy J. Dawson, D. Scott / Tourism Management 35 (2013) 244e254 249 Fig. 2. Contraction of the US Northeast ski areas marketplace under the A1fi e high emissions scenario. Economically Sustainable Ski Areas (A1fi) (100-day season and >75% probability of being operational during holiday periods) 2010-39 (n=41) 2040-69 (n=34) 2070-99 (n=30) NY – (12) Whiteface(13) Gore (14) Oak (15) McCauley(16) Royal (17) West, Willard (18) Cortina (21) Bobcat; VT – (22) Jay Peak (23) Smugglers Notch (24) Bolton, Stowe (26) Madriver (27) Middlebury (27) Killington, Pico (29) Sugarbush (30) Okemo (31) Ascutney (32) Bromley (33) Magic, Stratton (34) Mt. Snow (35) Burke (36) Suicide Six; NH – (37) Balsams, Loon (38) Cannon (39) Bretton (40) Wildcat (43) Watterville (47) Sunapee; ME – (52) Saddleback, Sugarloaf, Sunday River (55) Big Rock (56) Eaton (59) Titcomb (60) Big Squaw (62) Mt. Jefferson NY – (12) Whiteface (13) Gore (14) Oak (15) McCauley; VT – (22) Jay Peak (23) Smugglers Notch (24) Bolton, Stowe (26) Madriver (27) Middlebury (27) Killington, Pico (29) Sugarbush (30) Okemo (31) Ascutney (32) Bromley (33) Magic, Stratton (34) Mt. Snow (35) Burke (36) Suicide Six; NH – (38) Cannon (39) Bretton (37) Balsams (38) Loon (40), Wildcat (43) Watterville ME – (52) Saddleback, Sugarloaf (55) Big Rock (56) Eaton (59) Titcomb (60) Big Squaw (62) Mt. Jefferson NY – (12) Whiteface (14) Oak; VT – (22) Jay Peak (23) Smugglers Notch (24) Bolton, Stowe (26) Madriver (27) Middlebury (27) Killington, Pico (29) Sugarbush (30) Okemo (31) Ascutney (32) Bromley (33) Magic, Stratton (34) Mt. Snow (35) Burke; NH – (38) Cannon (39) Bretton (37) Balsams (38) Loon (40) Wildcat (43) Watterville; ME – (52) Saddleback, Sugarloaf (55) Big Rock (56) Eaton (59) Titcomb (60) Big Squaw Note: numbers in brackets correspond to ski area location outlined in figure 1 higher at 42. This number drops to 41 and further to 35 during the 2040e2069 and 2070e2099 time periods, respectively (Fig. 3). Regional distribution of the least and most vulnerable ski areas is clearly delineated from north to south. Ski areas in the southern portion of the US Northeast region, including those in Connecticut, Massachusetts and southern New York, are projected to experience significant challenges making it very difficult to maintain 100-day ski seasons within the next two to three decades. Conversely, ski areas located in more northern latitudes and generally within higher elevated terrain, including many ski areas in Vermont, New Hampshire, Maine and northern New York, are expected to be significantly more resilient to projected climatic change and in some cases will maintain at least 100 day seasons until the end of the twenty-first century even under high emissions scenarios (refer again to Figs. 2 and 3). It is also possible to approximate total revenue loss for the regional ski marketplace by conducting a crude calculation that combines existing revenue data with modeled outputs, such as shortening of ski season length, increased snowmaking requirements and decreased probability of being operational during the economically important ChristmaseNew Year holiday period. For example, within a high emissions scenario in the 2049e60 time period, ski areas in the US Northeast could see an aggregated revenue reduction of US$3.2 billion. Revenue reductions and increased operating costs expected to be experienced by ski areas across the region will need to be offset Author's personal copy 250 J. Dawson, D. Scott / Tourism Management 35 (2013) 244e254 Fig. 3. Contraction of the US Northeast ski area marketplace under the B1 e low emissions scenario. Economically Sustainable Ski Areas (B1) (100-day season and >75% probability of being operational during holiday periods) 2010-39 (n=42) 2040-69 (n=41) 2070-99 (n=35) NY – (7) Dryhill (12) Whiteface(13) Gore (14) Oak (15) McCauley(16) Royal (17) West, Willard (18) Cortina (21) Bobcat; VT – (22) Jay Peak (23) Smugglers Notch (24) Bolton, Stowe (26) Madriver (27) Middlebury (27) Killington, Pico (29) Sugarbush (30) Okemo (31) Ascutney (32) Bromley (33) Magic, Stratton (34) Mt. Snow (35) Burke (36) Suicide Six; NH – (37) Balsams, Loon (38) Cannon (39) Bretton (40) Wildcat (43) Watterville (47) Sunapee; ME – (52) Saddleback, Sugarloaf, Sunday River (55) Big Rock (56) Eaton (59) Titcomb (60) Big Squaw (62) Mt. Jefferson NY – (12) Whiteface (13) Gore (14) Oak (15) McCauley(17) West (21) Bobcat (18) Cortina; VT – (22) Jay Peak (23) Smugglers Notch (24) Bolton, Stowe (25) Cochran (26) Madriver (27) Middlebury (27) Killington, Pico (29) Sugarbush (30) Okemo (31) Ascutney (32) Bromley (33) Magic, Stratton (34) Mt. Snow (35) Burke (36) Suicide Six; NH–(38) Cannon (39) Bretton (37) Balsams (38) Loon (40) Wildcat, (43) (47) Sunapee, Watterville (48) Ragged (49) Gunstock ME – (52) Saddleback, Sugarloaf (55)Big Rock (56) Eaton (59)Titcomb (60) Big Squaw (62) Mt.Jefferson NY – (12) Whiteface (13) Gore (14) Oak (15) McCauley (16) Royal (21) Bobcat; VT – (22) Jay Peak (23) Smugglers Notch (24) Bolton, Stowe (26) Madriver (27) Middlebury (27) Killington, Pico (29) Sugarbush (30) Okemo (31) Ascutney (32) Bromley (33) Magic, Stratton (34) Mt. Snow (35) Burke (36) Suicide Six; NH – (38) Cannon (39) Bretton (37) Balsams (38) Loon (40) Wildcat (43) Watterville; ME – (52) Saddleback, Sugarloaf (55) Big Rock (56)Eaton (59)Titcomb (60)Big Squaw through other revenue streams and innovative businessdecisions (Table 5). 5. Discussion 5.1. Contraction of ski area supply The implications of climate change will likely lead to the closure of highly vulnerable ski areas and will influence a contraction of viable ski resorts that favors climatically advantaged regions. The slow contraction of the ski sector is not a new phenomenon in North America. For example, a series of marginal snow years throughout the late 80s and early 90s is thought to have in part contributed to permanent closures of 592 ski areas in the US Northeast region (NELSAP, 2008). A significant proportion of these ‘lost ski areas’ were small-scale privately owned and operated family business and in some cases small seasonal hobby businesses that were located on family-owned lands. The change in snow conditions necessitated significant investment in snowmaking technology and equipment that many small family businesses could not afford. Considering some of the remaining ski areas in North America remain privately owned, and climate change is likely to influence a further increase in temperature, an exacerbation of the historic contraction of the ski sector, at least in North America, seems very plausible. Because the probable consequence of climate change is a contraction in the number of ski operators in most Author's personal copy J. Dawson, D. Scott / Tourism Management 35 (2013) 244e254 Table 5 Estimated economic impact of climate change on ski area supply. If, under a high emissions scenario for the 2049e60 time period, ski areas in the US Northeast...  Lost the ChristmaseNew Year holidaya, B Revenue reduction ¼ US$183,600,000  Lost shoulder seasons (open e Dec 21 & April e close)b; and B Revenue reduction ¼ US$ 137,700,000  Experienced a 42% increase in snowmaking requirementsc B Expense increase ¼ US$ 1,169,000 The total impact could be ¼ $322,468,000 per season Note: Values are in 2007 US dollars e assessment of future financial impact will require consideration of inflation. a 0% visitation during this time period X $68 revenue per skier visit X average of 13,500,000 visits (NSAA, 2007). b 15% visitation during these time periods X $68 revenue per skier visit X average of 13,500,000 visits (NSAA, 2007). c Average regional increase in snowmaking requirements (Table 3) X seasonal cost of snowmaking (NSAA, 2007). regional markets, determining where the ski industry is most likely to contract at a regional and international scale will be of particular interest to the ski industry, investors, real estate developers, insurers, governments and communities over the next two decades. 5.2. Consideration of the demand-side response An important factor that will play a significant role in determining the net vulnerability and viability of a ski area or a ski area marketplace is the behavioral response of individual skiers to marginal snow conditions and to the closure of local ski areas. Tourists can easily adapt their behavior in response to climate variability, poor snow conditions, and ski resort closures e especially in comparison to the difficulty and expense involved in structural and management-based adaptations currently being used or considered by ski areas (see Scott & McBoyle, 2007; Pickering, Castley, & Burtt, 2010; also see Gössling & Hall, 2006; Gössling, Scott, Hall, Ceron, & Dubois, 2012). A number of studies have begun to examine the behavioral adaptations of skiers to increasingly marginal snow conditions being experienced at ski areas. For example, König (1998) examined how skiers in Australia might respond to hypothetically poor snow conditions expected in the future finding that 25% would continue skiing at the same place and frequency, 31% would ski less often, 38% would ski at another overseas location, and six percent would quit skiing altogether. Pickering et al. (2010) followed up on König’s original study finding that more skiers are now indicating that they will ski less often and fewer would travel overseas to ski at more viable resorts. Similar surveys were also conducted by Behringer, Buerki, and Fuhrer (2000) and Bürki (2002) at resorts in Switzerland. 251 Non-survey based research (modeling & analog) has revealed a less severe outlook for the future of ski area demand under changing climate conditions. For example, Dawson, Scott, and McBoyle (2009) used a climate change analog in the US Northeast to examine the impact of unusually warm winter seasons of the past that are representative of average winter conditions under a mid-range and a high emission scenario (ccsmA1B and ccsmB1) for the 2040e69 time period. Findings reveal a decrease in visitation of just 11 and 12% respectively (also see Scott, 2005; Steiger, 2011). Furthermore, modeling-based studies by Fukushima, Kureha, Ozaki, Fujimori, and Harasawa (2002) and Shih, Nicholls, and Holecek (2009) examined the relationship between snow depth, skier visits and ticket sales in Japan and Michigan, USA respectively, again revealing much less significant changes in skier demand during marginal winters (7e9% reduction). Considering a projected contraction of the ski sector, which would result in the closure of many of the more vulnerable ski areas, it will be important to determine the willingness among skiers to travel longer distances in order to participate at ski areas that remain operational. Unbehaun, Probstl, and Haider (2008) suggest skiers in Austria find a travel distance of 250e500 km acceptable for a ski holiday (i.e. not a day trip). Of course these travel thresholds will vary significantly from region to region and certainly from country to country (i.e. Europe vs. North America where longer driving distances are more commonplace). In response to the realization that the US ski marketplace (and likely other international ski marketplaces) is likely to experience a contraction due to ski resort closures (this study; Scott et al., 2007 and Dawson & Scott, 2010), Vivian (2011) surveyed over 570 skiers directly in the US Northeast and found individuals were not willing to drive more than 3 h for a day trip or more than 5 h for a weekend trip (one-way) in order to participate at a viable ski area. Based on the projected contraction of ski area supply found in the study discussed in this manuscript (also see Dawson & Scott, 2010, Dawson et al., 2011; Scott et al., 2012), individuals living in Boston would have to travel an additional 5 h (one-way) to participate at any viable ski area in the 2040e69 time period (high emission scenario e A1fi), meaning the day-trip market coming from Boston would more or less disappear. Compared to available options today, individuals from New York, NY would have to travel three additional hours (one-way), and those residing in Buffalo would experience a 2-h increase (one-way). Individuals residing in the Canadian city of Montreal would not see any increase in travel distance in the 2040e69 time period as ski areas in the US Northeast that are in close proximity to that city are projected to remain operational at least until the end of the 21st century (Table 6). However, the extent to which individuals from Montreal, Canada will continue to ski in the US Northeast region is uncertain considering popular ski areas on the Canadian side of the border are also likely to be in operation late into the 21st century (see Scott, Table 6 Distance between large population centres and reliable ski areas (2050s). Starting Location Population (2007) Present 2040-69 (A1fi high emissions) Change New York, NY 8,274,527 404 km (251 mi)/4 h 15 min (Mt. Snow, VT)a þ3 h Montreal, QC 1,620,693 82 km (51 mi)/1 h 15 min (Mt. Peter, NY, Stirling Forest, NY) 196 km (122 mi)/2 h 31 min (Whiteface, NY, Bolton, VT) 79 km (48 mi)/1 h 1 min (Nashoba, MA, Bousquet, MA) 153 km (95 mi)/1 h 37 min (Peek’n Peak, NY, Cockainge, NY, Holiday Valley, NY, Holimount, NY) 196 km (122 mi)/2 h 31 min (Whiteface, NY, Bolton, VT) 0 h (no change) 246 km (153 mi)/3 h 5 min (Mt Snow, VT) þ2 h 4 min 544 km (338 mi)/6 h 48 min (Mccauley, NY, Bobcat, NYb, Whiteface, NY) þ5 h 21 min Boston, MA 599,351 Buffalo, NY 272,632 a b According to modeled projections, Bobcat, NY (21 - Fig. 1) would be closest; however it is reportedly closed during the time of this study. According to modeled projections, McCauley, NY (15 - Fig. 1) would be closest; however it is reportedly closed during the time of this study. Author's personal copy 252 J. Dawson, D. Scott / Tourism Management 35 (2013) 244e254 McBoyle, & Minogue, 2006). It is highly possible that Canadian skiers will reduce their trips to the US Northeast and that there will be an increasing number of American skiers traveling to Canada where ski operations are more likely to remain viable e especially in comparison to those in the southern and less elevated areas of the US Northeast. The irony in the projected contraction of ski area supply causing increased travel distances for skiers is the resulting increase in transportation emissions, which contributes further to climate change. Another concern associated with increased travel distances between major urban centers and viable ski areas is a possible overall loss of skier participation. It is unclear how the future demographic of skiers will develop if existing skiers are not willing to travel longer distances to ski or perhaps more importantly if new skiers are not exposed to a skiing culture. Many smaller ski areas currently located in the highly vulnerable states Connecticut and Massachusetts act as ‘nursery hills’ to the larger ski resorts located further north. Currently, individuals from Boston and New York City are exposed to skiing and learn to ski at local resorts. If a skiing culture does not exist within a 500 km radius of these major centers it is possible that fewer individuals will take up the activity. This is evident through a simple spatial analog of skier participants. According to the NSAA (2006b) the total percentage of skiers residing in popular and mountainous ski regions is 64% versus participants from more southern US regions with fewer skiing opportunities, which contained just 31.3% of total skiing participants. When examining which US states create the most skiers it is clear that mountainous regions with pre-existing ski cultures SUPPLY DEMAND Is there reliable natural snow for winter sport activity? Are there adequate winter sports participants? (b) (c) (d) a No Yes Yes Can reliable machine made snow be produced? How have direct competitors in snow based marketplace been affected by climate change? Is current business plan profitable? (a,c,d) No (f) Yes Can reliable machine snow be produced economically? Yes (e) No No Yes No Can alternative business plan be developed for: No Terminate business Ii. Non snowbased activity? i. winter snowbased activity? Yes No Terminate snow-based business Yes Remain in snow -based market place. Adapt to climate change as required a) Marketplace competition is likely to decline according to existing literature. If demand remains stable or dilutes proportionality less than supply, there would be a net transfer of demand throughout the remaining marketplace. b) Are necessary ‘natural’ climate conditions present c) numbers could stabilize or increase if there were increases in travel costs or emission rights d) numbers could decrease because of changing demographics (aging and multi culturalism); social trends; climate variability; and cost e) direct operator costs – capital investments for snowmaking systems and their upgrades; increased operating costs (energy, water, labour) of snowmaking if more snow needed at higher temperatures. Also consider indirect economic changes – changes in skier demand, marketplace, and market share. f) examine alternative marketing plans to increase participation rates Fig. 4. Climate change management decision-making flowchart for ski operators. Author's personal copy J. Dawson, D. Scott / Tourism Management 35 (2013) 244e254 consistently produce more skiers than lower elevated states with less developed skiing cultures. For example, 11% of skiers during the 2006 season were from Colorado, a mountainous state known for good ski conditions, in comparison to states with less developed ski infrastructure like Tennessee, Alabama, or South Dakota which produced between 0% and 0.6% of total skiers (NSAA, 2006b). 5.3. Operational decision-making for supply- and demand-side impacts Because the implications of climate change for the ski sector will partially depend on the impacts experienced by competitors and on the behavioral response of skiers/snowboarders to both environmental and operational changes (among other factors), it is extremely difficult for ski area managers to fully access expected vulnerability and to plan for future change. Fig. 1 outlines a basic decision-making flowchart that could assist ski area managers to at least begin to negotiate some of the climate change-related decisions that require near-term attention. Importantly, ski area managers need to consider both supply-side and demand-side implications of a changing climate, as well as a contracting ski area marketplace. For example, it is important to know if there is reliable snow, if reliable snow is expected in the future, if adequate snow can be produced, if there are adequate participants now, if there will be in the future, and of course what the cost will be of required adaptive strategies. Other relevant questions include: can reliable machine-made snow be produced with available technology? how have direct competitors in snow-based business been impacted by climate change? how have competitors adapted to current changes? is the current business model profitable? can an alternative business-model be developed for either snow-based activities or non-snow-based activities? 6. Conclusion Not only are billions of industry dollars at risk, the communities and individuals that rely on ski tourism will also be significantly impacted under projected warming conditions. Based on this casestudy analysis of 103 individual ski areas operating in the US Northeast, it appears that it is not the entire ski area marketplace that is at risk to climate change but rather at risk is a suite of the individually operating ski businesses located in climatically and geographically disadvantaged regions. Resorts that are least susceptible to a changing climate generally include those at higher elevation where temperatures are lower, those located at more northern latitudes, and those that are located in close proximity to large urban markets. The probable consequence of climate change will be a continuation of the historic contraction and consolidation of the ski industry. Although projected climate change will contribute to the demise of ski businesses in some areas, it could advantage some of the ski operations that remain viable into the future e especially those that are able to take advantage of the misfortunes of others. Assuming skier demand stays relatively stable (see Dawson et al., 2009, 2011), the 30e42 ski areas that, in this study, are projected to remain operational beyond the 21st century, could be in a position to take advantage of a changed business environment whereby they gain market share due to lost competition. Although these ski areas and associated communities are likely to benefit from increased or stable tourism revenue, they will still need to adapt and prepare for the possibility of increased development pressures (e.g. water use for snowmaking, real estate development, slope expansion, congestion), crowding, and infrastructure deficiencies. Community-based impacts could also include increasing 253 real-estate values as well as increased pressure on local services and environmental resources. In turn, the communities that lose ski tourism operations will need to develop economic diversification strategies, due to lost winter tourism revenues and related jobs, and could also see increased pressure on social services and unemployment as well as a drop in real-estate value (see Hamilton et al., 2007; Scott et al., 2007). These more vulnerable ski areas will, at varying points, need to determine if they should invest heavily in adaptations that will aid in the continuation of a snow-based business at least in the short to medium term (i.e. high efficiency snowmaking, renewable energy production), if they should invest in adapting and evolving into a multi-season destination (i.e. four-season resort, spa, conference centre), or if they ultimately need to terminate their business altogether (refer back to Fig. 4). Importantly, climate change is only one of many factors that will influence the future of the ski industry. Other issues such as federal and state tourism policy, economic recessions, demographic change, increasing costs associated with travel, competition with other tourism destinations, as well as social trends that favor particular experiences over others are likely to prove equally, or in some cases even more important than the direct impacts of climate change. The synergistic impacts of these diverse macro-scale influences require a more holistic understanding in order to inform strategic investment, planning and development in the ski industry over the next 10e20 years. Examination of the relative vulnerability of ski resorts within a regional marketplace (i.e. comparison among individually operating ski areas) is just one of the many steps required in to create a more complete understanding of the complexities associated with environmental and economic change occurring within contemporary societies. Acknowledgments The Social Sciences and Humanities Research Council provided support for this research. Additional funds provided by the Global Environmental Change Group at the University of Guelph are gratefully acknowledged. The contribution of Dr. Geoff McBoyle is also appreciatively acknowledged e his contribution to the decision-making flowchart was invaluable, as was his influence to the overall project. 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(2011). Behavioural adaptation of skiers and snowboarders in the US Northeast to climate variability and change. Unpublished thesis. University of Waterloo. VSAA. (2004). About us. Accessed 19.03.07, available at: https://www.skivermont. com. Whetton, P. H., Haylock, M. R., & Galloway, R. (1996). Climate change and snowcover duration in the Australian alps. Climatic Change, 32, 447e479. Jackie Dawson is the Canada Research Chair in Environment, Society, and Policy in the Department of Geography and in the Institute for Science, Society and Policy at the University of Ottawa, Canada Daniel Scott is the Canada Research Chair in Global Change and Tourism in the Department of Geography& Environmental Management at the University of Waterloo, Canada