Developing Climate Change Maps for Tourism

Abstract

Climate change adaptation is a pressing need. However, local level stakeholders often find themselves overwhelmed with climate change information presented at both small temporal and spatial scales. To address this gap, and using a case study from New Zealand’s Southern Lakes region, this research links tourism operators’ information requirements with climate change projections. Interviews with 42 stakeholders provided exemplary storylines and insights into the climate parameters that would be useful for their planning (mean precipitation, extreme wind conditions, mean temperature, and frost days). These findings were then used to generate sector-relevant maps. Climate change maps were produced based on global and regional models to generate detailed climate projection information for the A2 emission scenario in the form of regional scale, color-coded maps. A final stakeholder workshop confirmed the usefulness of the maps as a planning tool but also highlighted a number of future challenges for climate change communication.

Keywords

regional climate information downscaling destinations climate change projections maps

Introduction

There is increasing confidence that climate change is occurring and that this can be attributed to anthropogenic inputs (IPCC Assessment Report 2007, 2013). Monitoring shows that climatic changes have been observed in the mean conditions, but also in both the observed frequency and intensity of climate extremes (e.g., WMO 2011) at the upper levels of the IPCC predictions. The decade 2001–2010 was the warmest on record, with average temperatures being 0.46°C above the 1961–1990 mean and 0.21°C warmer than the previous record decade 1991–2000 (WMO 2011). In its latest Assessment Report, the IPCC (2013) summarizes major changes in atmospheric temperature, with each of the last three decades having been warmer than any preceding decade in the last 150 years. Warming will continue and is likely to be over 1.5 degrees (for the period 2081–2100 relative to 1986–2005) up to 4.8 degrees, depending on the emissions scenario. An observed increase in extreme events across the globe is reported as well.

Tourism is an important economic sector in many countries that is intricately linked with society and communities. Climate change poses a major challenge for tourism, and adaptation measures are increasingly discussed (e.g., Amelung, Nicholls, and Viner 2007; Scott and Becken 2010; Shih, Nicholls, and Holecek 2009). Tourist destinations have begun to recognize the complexities of destination competitiveness and climate change (Hendrikx et al. 2013) and develop strategic plans for the future (Rodríguez-Díaz and Espino-Rodríguez 2008). However, at the destination and business levels, there is still widespread scepticism and uncertainty around the existence and implications of climate change (Nicholls and Holecek 2008; Weaver 2011). Even the ski industry, a “canary in the coalmine” (Bicknell and McManus 2006), was found to see little urgency in responding to climate change, because of short investment cycles and successful “mal-adaptation” to existing climate variability, for example through artificial snowmaking (Hennessy et al. 2008). Lack of proactive climate change adaptation by tourism stakeholders (Scott and Becken 2010) also relates to the way climate change projections are typically presented. Participants in climate change workshops at four Australian tourist destinations put forward that regional-scale projections would greatly assist their planning and response process (Turton et al. 2009). However, the application of climate change projections for tourism adaptation has been quite limited to date (Scott and Lemieux 2010).

Research on Californian coastal managers’ engagement with climate change also indicated that locally specific projections of particular changes in climate would be useful, despite the scientific uncertainty associated with downscaling of climate models (Tribbia and Moser 2008). Producing relevant local climate information benefits from a participatory approach that ensures that climate experts and local users jointly generate knowledge about what climate (change) information might be useful to end users (Goebbert et al. 2012). Such “knowledge co-production” also yields information on how to translate relevant changes into impacts (Jagtap et al. 2002), which is critical for successful adaptation (Mastrandrea et al. 2010). In New Zealand, for example, the National Institute for Water and Atmospheric Research (NIWA) provides seasonal climate and pasture updates for farmers in the Otago region (NIWA 2012). Information relevant for the agricultural sector includes the traditional parameters of temperature and precipitation, as well as end user–specific parameters such as soil moisture deficit and river flows presented at a regional scale. Similarly, in terms of longer term projections (e.g., for 5, 10, or 25 years), coastal managers recommended that visualization of changes in flood zones under different sea level rise scenarios by means of maps would be beneficial (Tribbia and Moser 2008).

Maps are indeed a commonly used tool for planners, alongside the use of Geographic Information Systems (Tribbia and Moser 2008), and represent a common visualization of scientific data, alongside diagrams, graphics, and computer simulations (Sheppard 2005). Maps could be an important first step in the visualization of climate change data to tourism stakeholders for the purpose of better communication of impacts and promotion of behavioural change. The production of localized and specific climate change maps could therefore be helpful in “bridging the gap between formalised analytical models” (Sheppard et al. 2011, p. 401) and affected stakeholders. With particular respect to tourism, Scott and Lemieux (2010) argued that the current availability of climate data for tourist destinations is hampering successful adaptation, and that the refinement of near-term climate change predictions that are most relevant to business and policy decisions should be encouraged.

Against this background, and considering that tourism constitutes an important economic sector in many countries and destinations around the world, this research explores the feasibility of producing high resolution climate change maps at a regional scale for the purpose of tourism adaptation, both at business and destination level. Based on the tourist destination of “Queenstown and Wanaka” in the South Island of New Zealand, three key research questions are being explored: (1) what climatic parameters are relevant for tourism operators at a destination, (2) can climate change projections be “downscaled” to tourism-relevant scales with scientific integrity, and (3) will such maps be understandable and suitable for tourism end users?

Method

The overall approach involved three research phases. The first phase, addressing research question 1, collected information from tourism stakeholders in Queenstown and Wanaka via interviews. Data were analyzed in terms of key day-to-day weather-tourism impacts and parameters that are of greatest relevance to tourism stakeholders. This approach assumes that a tourism operator’s coping capacity with current weather as the manifestations of climate variability is an antecedent to future oriented (longer-term) climate change adaptation (Berman, Quinn, and Paavola 2012). In other words, a strategic integration of climate change into tourism decision making could successfully build on stakeholders’ present understanding of how tourism responds to the weather, and their attempts to improve operational efficiency under present day conditions (Busch 2011). Thus, the second phase (research question 2) used existing climate change models to downscale (i.e., move from a global to local / regional scale) climate data for those parameters that emerged as useful from the interviews. These were visualized in the form of sector-relevant climate change maps. Thirdly, in response to research question 3, a stakeholder workshop was undertaken to obtain feedback on these maps to assess their utility to the tourism sector.

Study Area and Stakeholder Interviews

The Queenstown–Wanaka region is a popular all-year tourism destination in New Zealand for both domestic and international visitors. Both Queenstown and Wanaka are situated at lakes and they draw on their mountainous environments for diverse tourist activities. Queenstown received more than 2.3 million visitor-nights in 2009, and the smaller town of Wanaka accommodated about 600,000 visitor-nights. The region’s climate is suitable for a range of tourism activities, with mild summers and cool winters, and annual average high and low temperatures of 16° and 6° C, respectively. Average annual precipitation is 900 mm. However, the mountainous surroundings mean that conditions are often cooler and wetter toward the west and at increased elevations, and drier in the east (Sturman and Tapper 1996). The area is affected by extreme weather, for example, heavy rain events in summer or snowstorms in winter. Queenstown and Wanaka can also suffer from lack of snow during dry or warm winters.

The websites of the Regional Tourism Organisations of Queenstown (www.queenstownnz.co.nz/) and Wanaka (www.lakewanaka.co.nz) helped to identify a broad range of potential interviewees from tourism businesses, including accommodation, transport, and attraction providers. Selected managers were contacted and asked if they were willing to provide information on their business in relation to the weather in a 45-minute interview. More specifically, interviewees were asked about which weather events affected their tourism businesses and the destination as a whole, and whether they can provide any examples of severe weather events in the past that impacted on their tourism operation.

Altogether, 34 tourism business interviews across several sectors were conducted to gain a broad cross section of how tourism stakeholders at the destination interact with the weather (Table 1). In addition, eight interviews were undertaken with the following tourism stakeholders from the Queenstown Lakes District Council (QLDC), Destination Queenstown, Queenstown Information Centre, Harbour Master, Department of Conservation, and a former employee of the New Zealand Transport Authority. Interviews were conducted by four different researchers, tape recorded and partially transcribed for analysis. The findings from the interviews informed the choice of climate parameters for the projection maps.

Table 1.

Participants in the Tourism Stakeholder Interviews.

Stakeholder Categories	Subcategories	Number
Activity	Ski field, heliskiing, fishing, rafting, jet boating, kayaking, scenic boat trips, scenic flights, sky diving, air show, events, guided walks, horse riding, wine trails, 4-wheel driving, entertainment, museum, bungee, gondola/luge, golf course	25
Accommodation	Backpacker hostel, hotel, camping ground, luxury lodge	5
Transport	Airport, shuttle services, scenic tours, local bus	6
Information/marketing	Visitor centres and regional tourism organization	2
Asset management	Transport, conservation	1
Planning and other	Council, harbor master	3
Total		42

Generating Tourism-Relevant Climate Maps

Most climate change scenarios are derived from Global Climate Models (GCMs), which are able to simulate past, current, and future climate scenarios under different emission scenarios. These “SRES scenarios” (after the IPCC Special Report on Emissions Scenarios; Nakicenovic and Swart 2000) describe distinctly different future developments of economic growth, global population, and technological change. Analysis of the climate change impact for the different emission scenarios for New Zealand (MfE 2008) indicates that the so-called A1FI and A2 scenarios are likely to produce the highest surface temperature increase by 2090. This study will focus on the A2 emission scenario¹ as a mid-range to high emission development path.

As the most relevant climate change prediction to tourism business and policy decisions are short term (Scott and Lemieux 2010), this study will focus on the period 2030–2049 (or abbreviated as 2040 period) for the parameters rainfall, temperature, and number of days experiencing frost. A frost day is defined as a day when the minimum air temperature is negative. This is important, as snowmaking capability is a key issue for some tourism stakeholders in this case study area. The operational setting of snowmaking potential for a particular location requires the establishment of a negative threshold wet bulb temperature. For the purpose of this study, it is assumed that snow making will potentially occur if the wet bulb temperature is negative, which is acknowledged to likely overestimate snowmaking potentials. As a result, in this study the number of frost days functions as a surrogate for the maximum number of potential snow-making days (for a more sophisticated approach, see Hendrikx and Hreinsson 2012; Hendrikx et al. 2013).

The climate change impacts for wind are only available for the period 2070–2099 (Mullan et al. 2010) and the results are therefore presented for this later period. Because of the diversity of the characteristic of the wind information (e.g., change in prevailing winds and weather patterns, storm frequency and intensity, and extreme winds), it is common to use indices to represent the occurrence of severe weather (see Mullan et al. 2010 for more detail). Of particular interest is the CAPE index,²¹ which is used in this study as an indicator of the stability of the atmosphere during large events. CAPE is an indicator of particularly windy events, and is relevant for all those tourism operations that are directly sensitive to wind (e.g., scenic flights, skydiving, heliskiing), but it is also of importance to the wider destination as storms are likely to affect tourism negatively across most operations (Wilson and Becken 2011).

This present study is based on the average result from the 12 global climate models that have been shown to perform acceptably in simulating the past climate of New Zealand and the South Pacific (MfE 2008). It is acknowledged that providing mean projections masks the variability and uncertainty indicated in the range of GCMs; however, the addition of these elements in a mapped product further complicates the message to the end user and in itself is problematic. Global models cannot be directly used in more local climate change analyses as their spatial resolution is not fine enough to make informed decisions at a regional scale. Research undertaken by NIWA in New Zealand that downscaled global climate projections to a 0.05-degree grid² (approx. 5 km × 5 km) was available to this research team (Tait et al. 2006; Tait 2008; MfE 2008) and has also successfully been used for site-specific snow modeling applications (Hendrikx and Hreinsson 2012).

There are several ways to downscale GCM results. A simple statistical downscaling method (so-called delta-change method) was used to define the changes to temperature, rainfall, and frost days as described in MfE (2008). Monthly changes, determined through the application of GCM, are specified for the 2040 time period relative to the climate of 1990 (1980–1999) (MfE 2010). A more complex method was used for wind. Here, a Regional Climate Model (RCM)⁴³ was embedded into a GCM (Durman et al. 2001; Drost et al. 2007; Sood 2012), which allowed to better represent the physical processes of interest at the regional scale (Durman et al. 2001). This is important for wind, as wind can vary over short distances and time periods. Surface winds are mainly driven by large-scale circulation, which is generally captured by GCMs. However, interaction with surface roughness and larger-scale topography generally result in spatial and temporal wind fluctuations that GCMs cannot resolve because of their large grid size (Webster et al. 2008).

Stakeholder Workshop

Following the development of the maps in Phase 2 of this research, tourism businesses were invited to participate in a workshop in Queenstown on October 24, 2012, to discuss the research and provide feedback on the usefulness of the maps. Eighteen stakeholders attended the two-hour workshop, including some of those who had participated in the initial interviews. The workshop involved presentations from the principal author and other researchers about the regional climate change maps for 2040 for Queenstown, snow modeling, and vulnerability perceptions. A discussion around how to link the present with the future for tourism operators was held, serving as useful feedback on the research and, in particular, the climate change maps.

Results

Weather and Climate Impacts on Tourism

The business interviews were analyzed systematically for individual weather-tourism impacts, whereas the stakeholder interviews were analyzed more holistically to identify potential applications of downscaled climate maps. The business interviews generated 224 different impacts, of which 27% related to rain, 19% to wind, 18% to snow, and 10% to temperature) (Figure 1). “Bad weather” was mainly a combination of rain and wind. The remaining impacts included “severe weather,” “good weather,” and cloud.

Figure 1.

Summary of weather/climate impacts on tourism businesses in Queenstown and Wanaka.

Clearly, the impacts of the weather on tourism were complex. Rain, for example, was directly detrimental to tourism as some activities or events are unable to proceed in the case of rain. Rain also resulted in issues of access (e.g., because of flooded rivers), higher operational costs (e.g., leakage in buildings), structural damage to infrastructure (e.g., bridges and tracks), and increased snowmelt. At the same time, rain increased the business of indoor attractions and resulted in a shift of guests from campgrounds to other accommodation. Some operators mentioned the lack of rain as an issue, for example, in relation to low river flows that reduced the opportunity to operate river-based activities. Wind was a concern because of safety issues (e.g., falling branches), disruption of air-borne activities, closure of ski lifts, lack of participation in outdoor activities, and movements of structures (e.g., bungy bridge). Snow provided the greatest diversity of impacts, as some operations depended on the existence of snow (although differentiated by timing and intensity of snowfall), whereas others were negatively affected during or after snowfall, either directly or indirectly through road closures. Temperature in Queenstown–Wanaka was mainly discussed in relation to cold temperatures (e.g., ice in the river preventing jetboat operations), although warm temperatures in winter are a challenge for snow-based activities. Analyzing the business interviews assisted in identifying useful climate parameters for tourism purposes in this region. These included the standard parameters of temperature and precipitation, and more specific ones of frost days and wind. Probability of intense rainfall events was also identified as an important parameter, but this was beyond the scope of this current study.

The interviews with tourism stakeholders brought a number of issues for the wider destination to the forefront), generating exemplary storylines or contexts for which detailed climate change maps could be used. Heavy rain in the Queenstown–Wanaka area, especially in the headlands of the rivers, is a major concern as it leads to flooding of the lakes or rivers. The different types of floods cause different impacts: “Lake flooding is a business cost, but something like a flash flood in a river is more dangerous” (Queenstown Lakes District Council Planner). Another key concern was the condition of roads, especially in winter. The Crown Range road, which links Queenstown and Wanaka via a 1076 m (New Zealand’s highest sealed highway) windy pass, is an important transport link but “highly susceptible to interference by weather” (interviewee New Zealand Transport Authority). The availability of snow, especially early in the season in June, is critical for the operation of ski fields and the destination that depends on them. Increasingly warmer temperatures will likely reduce the window of opportunity for both natural snowfall and artificial snowmaking. However, while outside the scope of this current study, the occurrence of “well-timed” (as perceived by stakeholders) storms in winter could mitigate some of the risk associated with warmer temperatures. Warmer temperatures in summer increase the risk of fire, especially in combination with conditions of drought, as mentioned by a number of interviewees.

While most tourism stakeholders reported that they monitor the weather every day as it forms a critical part of their operational decisions, longer term engagement with climate was not apparent. Most reported that they had not observed changes in climatic parameters, although one noted, “The seasons aren’t as easy to define as they used to be, sometimes things happen out of seasons more often than they used to” (QLDC, Emergency Manager). Climate change was of little concern, with the QLDC Planner stating, “We have not identified anything that would be harshly affected, all the advice we get is that apart from a few extreme events, the district won’t be too badly impacted. We obviously don’t have coastal issues, we are not too dry, and heatwaves won’t be an issue. If anything it will be good for tourism, there are opportunities.” The lack of certainty and detail of climate projections was perceived as a key barrier to proactive adaptation (“very hard to put plans in place when it is still woolly”).

Climatic Maps

The climate analysis for all parameters was performed at annual and seasonal levels; however, because of lack of space and to demonstrate the relevance of the approach in principle, only selected maps are presented here. Analysis of the climate change impact on the average annual precipitation shows that the expected changes differ substantially across the Queenstown–Wanaka area. The resulting spatial distribution is organized in bands that are parallel to the orientation of the Main Divide (the New Zealand Southern Alps). The increase in mean annual precipitation is minimal in the east (+4%−6%), increases to +6%−8% around Lake Dunstan and south of the township of Queenstown, and increases to up to +9% for Lake Hawea, Lake Wanaka, and the northern part of Lake Wakatipu, where many of the tourism businesses are located (Figure 2). This result is expected, as headwaters are subject to large projected increases in precipitation during winter and spring, because of the combined effect of an increased westerly windflow resulting in increased spillover from orographic precipitation, and an average annual temperature increase resulting in an increase of atmospheric humidity. It is important to note that the range of changes provided here are relatively narrow because of the approach of averaging the 12 GCMs.

Figure 2.

Southern Lakes projected change in average annual precipitation between 1980–1999 and 2030–2049.

Projected changes in wind also show a differentiated pattern. In this study, wind is presented in terms of occurrence of a current climate 99th percentile event in a future climate, which is the number of times an extreme CAPE event occurs in a future scenario relative to the current climate. Analysis of the results indicates that extreme CAPE events are about three to four times more likely to occur in future climate scenarios across all seasons. In winter, large CAPE events are up to five times more likely (Figure 3). Both Lake Wakatipu, including Queenstown as the major tourist area, and Lake Wanaka, are likely to see four to five times more CAPE events in winters in 2090.

Figure 3.

Southern Lakes projected occurrence of 99th percentile event between 1970–1999 and 2070–2099 of convective available potential energy during winter. Colors symbolize an increase in occurrence.

Analysis of the climate change impact on the average annual temperature across the Queenstown–Wanaka area indicates that temperature is expected to increase by about 1° C over the whole area. No clear seasonal pattern of temperature change has been identified for the A2 emission scenario for the 2040 time period (MfE 2008). Thus, temperature will not be a differentiating factor across different locations and seasons. Our analysis of the potential impact of climate change on the number of frost days per year is more relevant. Here, the results indicate that the average number of frost days will decrease by 20–49 days per year in the Queenstown–Wanaka area. This represents a reduction of between 13% and 79% of the average number of days experiencing frost, with the highest reduction expected to occur around the centers of tourism activity at Lake Wakatipu, Lake Wanaka, and Lake Hawea (Figure 4). The lowest reduction in the percentage of frost days is expected to be experienced at the top of the various surrounding ranges, which is where the ski resorts are located. The map indicates that climate change will not have a uniform impact on snow-making capabilities across the Queenstown–Wanaka area.

Figure 4.

Southern Lakes projected change in ratio of days experiencing frost between 1980–1999 and 2030–2049.

Stakeholder Feedback

Participants at the workshop considered the maps to be useful for understanding potential climatic changes in their particular area of operation. The color coding made the maps easy to read, and the plotting of key tourism businesses (e.g., the skifields) helped to identify particular geographic areas. Despite the positive feedback from the workshop, there appears to be a considerable amount of uncertainty around climate change impacts in general, and the maps in particular. The time frame of 2040 was still perceived to be well beyond the stakeholders’ planning horizon, although the ski field representatives noted that they were taking climate change projections into account in their longer term business development. The question of whether one can interpolate between now and 2040 in a linear fashion, or whether one should assume an exponential change in climate variables, was raised by participants. Threshold effects were discussed as well. While scientific uncertainty (e.g., in the sense of confidence intervals around the projected mean changes) was not raised as an issue, the limited ability to predict extreme conditions—rather than means—was criticized. The workshop highlighted that climate change maps can serve as a useful catalyst for detailed discussions about future planning; however, in isolation they may not provide enough (and specific) information to inform tourism stakeholders’ decision making. For this reason, while we promote the utility of these maps for a given sector to highlight the regional impacts, their adoption for direct business planning would require further collaboration between researchers and businesses.

Discussion

Our research shows that tourism is very sensitive to the weather and therefore likely to be exposed to climate change. The interviews brought forward a large number of distinct weather-tourism impacts that require coping capacities (see also Becken 2013) by tourism businesses. Most of the impacts are related to rain, wind, or a combination of these in the form of “bad weather.” While impacts are typically negative, a number of positive impacts for some businesses associated with these climate variables were identified, for example, a shift of guests from campground to motels in rainy conditions. This indicates that some weather conditions produce not only “losers” but also “winners,” both within the region and outside of it (Hopkins, Higham, and Becken 2013). Overall, the interviews with both businesses and other destination stakeholders highlighted the complexity of the tourism–weather nexus in terms of activity, place, timing, and climate-variable specific impacts. The two standard variables of temperature and precipitation emerged as highly relevant, in addition to more tourism-specific variables of frost days and strong wind events.

The maps generated in this research show some interesting and highly relevant climatic change, especially when analyzed in terms of their spatial and temporal differentiation and plotted against the location of major tourism businesses. Projected increases in precipitation, for example, highlight the growing risk of rainy conditions and potential flooding, both of which constitute issues for tourism under present-day climatic conditions. The maps show that the areas where tourism activities occur will suffer particularly high increases of up to +9% more precipitation by 2040. This raises the question whether there is a threshold beyond which coping is not sufficient any longer to maintain business viability. For example, tourism operations on the Dart River (e.g., jet boating and fishing) may reach a point where cancellations due to adverse weather of river flooding reach an untenable level. The tourist resort of Queenstown has suffered major flooding from Lake Wakatipu in 1999 (Forsyth, Clark, and Becker 2005), and the projections of an increase in precipitation upstream is likely to exacerbate this risk. The Council’s current policy “Living with the Flood” may then not be sufficient to cope with such change, with important implications for future business and infrastructure developments and insurance premiums.

The results for extreme wind events show that most areas in which tourism businesses are located are likely to experience four to five times such wind events by 2090. The increases were particularly pronounced in the summer and winter seasons, which coincides with the two peak seasons for the tourist destinations. Both skiing operations and many summer activities are highly sensitive to wind, and about a fifth of all identified tourism-weather impacts of the present climate can be attributed to high wind. The airport, which is an important asset for the facilitation of tourist travel, but also functions as critical infrastructure for the wider community, is sensitive to high winds and already experiences occasional closure as a result. The index CAPE used in this research indicates the propensity of big “weather events,” which is significant for tourist destinations beyond the effect on single types of activities. Storms and their consequences are most likely to affect travel to a destination, given that a significant number of tourists adjust their itineraries to avoid adverse weather (Becken and Wilson 2013).

The projected change in temperature was found to be uniform for the whole tourist destination (about 1° C), which is likely to be seen as a positive change, considering that New Zealand is often perceived as a cold or wet destination (Becken, Reisinger, and Wilson 2010). Warmer temperatures could enable tourism businesses to lengthen the summer season into both autumn and spring, thus reducing the negative impacts of tourism seasonality with low occupancy rates. However, warmer temperatures, and especially periods of extreme heat, are likely to increase the risk of fire. This was already identified by some stakeholders as a major concern, especially in the backcountry where tourists recreate in the form of bushwalking. Higher temperatures are also likely to have implications for the wine industry, which is closely interlinked with tourism (Getz and Brown 2006). Finally, warmer temperatures in winter are a major concern for all snow-based activities, because precipitation might fall in the form of rain instead of snow. For ski fields, warmer temperatures also reduce the window for snow making, an important adaptation strategy to climate variability and change (Töglhofer, Eigner, and Prettenthaler 2011; Hendrikx et al. 2012; Hendrikx and Hreinsson 2012).

Warmer temperatures closely relate to a reduction in frost days, although the geography (especially altitudes) has a moderating effect. Overall, the number of frost days will reduce substantially by 2040, with a lesser change at higher elevation. This will likely have a positive effect on road safety and management. A number of tourist activities reported that their operations in winter are constrained by frost events (e.g., jetboating) and less frost at these lower altitude activities would result in larger operating windows. The fact that frost days reduce relatively more in the low-lying areas (i.e., where the tourist centers are) and relatively less at higher altitudes where ski fields are located, is positive news for the destination. Of particular interest with regard to the change in frost days at the ski area locations is that the percentage change presented here is remarkably close to the percentage change in snow-making hours as presented by Hendrikx and Hreinsson (2012) for the 2040 period, using a much more involved methodology. While the scenario used by Hendrikx and Hreinsson (2012) was the A1FI, the amount of warming is expected to be similar to that in the A2 emissions scenario. These results, while only providing one data point, provide some confidence in our results as they relate to this industry and also suggest that basic climate change assessments for changes in snow-making potential may be feasible using more simple methods than those applied in the past.

Despite the pronounced changes emerging from the downscaled regional climate maps, a longer term concern about how the climate might change the operating environment was not acutely evident among stakeholders, with only the ski field operators considering integration of climate change into their decision making strategically. This is remarkable considering that the proactive reduction in exposure to the disruptions from climate change would lead to a competitive advantage in a changing environment (Busch 2011), and as a result add to destination competitiveness (Rodríguez-Díaz and Espino-Rodríguez 2008). Considering the lack of a perceived need for climate change information, maps such as produced in this research are unlikely to function as information tools, although in contrast operators who attended the stakeholder workshop at the end of this research explicitly stated that they found the maps very informative. In fact, the maps generated more questions than answers by participants, which can be interpreted as a positive sign in terms of stimulating debate. Thus, the maps help to raise awareness of the potential impacts of climate change on the destination and, more specifically, on those areas on which tourism businesses depend for their operation. This need seems to exist in other tourist destinations, for example, the mountain areas of Australia (Hennessy et al. 2008), and may reflect the higher relative vulnerability when compared to New Zealand (Hendrikx et al. 2013).

The framing of climate change as a phenomenon that manifests in local impacts in people’s own backyard rather than in far-away countries could generate greater interest or action (Shaw et al. 2009), although in some cases this may actually lead to a reduced concern, as people feel more in control (Spence and Pidgeon 2010). Overall, the maps could assume an important role as education tools and catalysts for further future thinking among operators, as was evidenced in the Queenstown stakeholder workshop. In addition, it might be useful to test the usefulness and appetite for seasonal climate forecasts (e.g., Jagtap et al. 2002) tailored for tourism. This approach might raise awareness and build capacity among tourism stakeholders to think longer term and use climate information beyond standard weather forecasts (Scott and Lemieux 2010).

This paper therefore provides a novel approach to initiating more effective climate science communication with the tourism sector. The maps produced in this research can act as a much needed bridge between complex climate models and climate change adaptation (McNie 2013). The research process of consulting with tourism stakeholders first before producing the climate change maps was useful, but generated some lessons learned. Clearly, there is a trade-off between producing useful information, ideally in short periods of 10 to 20 years, and scientific integrity. The results also depend on both the average of the various selected GCMs and the particular downscaling method, and the climate change scenario selected. As discussed previously, we have employed a relatively simple methodology for the changes in precipitation and temperature. This limits our analysis, as changes in storm track or duration are not explicitly considered. By contrast, the wind analysis section has, to some extent, accounted for these likely changes. The final results are all associated with the uncertainty that is inherent in climate projections; however, the maps do not portray this. They show a single average value estimate for the chosen parameter for each location and portray a sense of certainty that is possibly not warranted. Thus, neither uncertainty nor variability are presented or documented in these maps. Both are thought to be important components of climate change that end users should consider to assist in their decision making. However, research in California to evaluate the importance of uncertainty to end users indicates that this may not actually be the case (Tribbia and Moser 2008). The Queenstown stakeholder workshop confirmed this but highlighted that users would be interested in more information on climate variability (especially extreme events outside mean changes). We consider that both uncertainty and variability are even more difficult to communicate in map form and should be a focus of future climate impact mapping research.

The final production of maps is subjective in that presentation of results, for example, absolute versus relative change, the size of the increments, color, and disaggregation into seasons, are all likely to influence the impact the maps can have. While we argue that the maps are useful and the approach presented in this paper could be replicated for other areas and sectors, there are some important limitations. First, while the maps are an attempt to “simplify” climate impacts through visualization, the actual parameters (especially CAPE) may still be complex and difficult to understand for lay people. At the same time, they may not be complex enough to capture the reality of what impacts matter to users. Often, the combination of more than one climate parameter is of relevance; for example, for aviation the combination of wind and visibility might be important, and for ski fields the combined effect of precipitation and temperature is of interest. The identification of thresholds as perceived by local stakeholders would be another important aspect to consider (Mastrandrea et al. 2010). The horizon of 2040 might still be too long, and the presentation of means rather than extreme values is insufficient to inform specific planning such as flood protection. To understand the importance of these limitations, further research should involve evaluation of the maps with the tourism stakeholders who were involved in the first phase of this research.

Conclusion

This research addressed the need for local and sector-specific climate change information for the case of the tourism sector in Queenstown–Wanaka, New Zealand. Information on weather-tourism impacts was collected from tourism stakeholders to identify key issues and relevant parameters. These were precipitation, rain, temperature, and frost days. Accordingly, a simple downscaling method (and a more complex method for wind) was applied to generate climate change maps for these parameters at a high resolution that would allow tourism operators and decision-makers to find their business and area of operation on a map, associated with an expected change in climatic parameter. Feedback from stakeholders indicated that the maps highlighted interesting seasonal and spatial patterns of change that while having some limitations could be useful for tourism planning and investment.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Susanne Becken is a Professor of Sustainable Tourism at Griffith University, Australia, and an Adjunct Professor at Lincoln University, New Zealand. Susanne is on the editorial boards of Annals of Tourism Research, Journal of Travel Research, Journal of Sustainable Tourism, Journal of Policy Research in Tourism, Leisure and Events, and the Tourism Review. She is as a contributing author to the Fourth and the Fifth IPCC Assessment Reports and is actively involved in debates on climate change, resilience, risk management and tourism.

Christian Zammit is a hydrology scientist at the National Institute for Water and Atmospheric Research, New Zealand. He leads research objectives related to downstream impact of projected climate change across the hydrological cycle and NIWA water resource program. His specialties include modeling of hydrological processes across spatial and temporal scales, impact assessment and product development for policy advice.

Jordy Hendrikx is a snow and avalanche scientist with experience in North America, Europe, Antarctica, the Arctic and New Zealand. He is interested in snow as a resource and as a hazard. His work spans from technical snow and avalanche risk assessments, snow monitoring and modeling to applied climate change assessments with snow and hydro industries. Dr Hendrikx has also worked as a Snow and Ice Scientist at the National Institute for Water and Atmospheric Research in Christchurch, NZ, as an avalanche forecaster and scientist in NZ, and as a researcher at the Swiss Federal Institute for Snow and Avalanche Research in Davos, Switzerland. He is now the Director of the Snow & Avalanche Laboratory at Montana State University in Bozeman, Montana, USA.

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