Snow management and climate change

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Snow management and climate change :
A synthesis of current physical and economic
knowledge
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Pierre Spandre
PhD student, under the co-supervision of
Samuel Morin
CNRM/GAME UMR 3589 CNRS/Météo-France, 38400, Saint Martin d’Héres, France
Emmanuelle George-Marcelpoil
Irstea, UR DTM, 2 rue de la Papeterie, 38400, Saint Martin d’Héres, France
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For any details, please contact by e-mail : [email protected]
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Draft 2014-01-28
Abstract
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This draft is a preliminary report of the PhD projet titled GANESH for "analyse et simula-
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tion de la Gestion de lA NEige dans les Stations de sports d’Hiver", or in english "analysis and
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modelisation of snow management in winter sports areas". This report aims to introduce the
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main points that will be coped with all along GANESH’s project. We shall draw a picture of
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current knowledge about snow management in general and how we expect to treat this question.
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Index Terms
Snow management, skifields, climate change, skiing economy, grooming, snowmaking, snowpack modeling.
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Table des matières
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Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Index Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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1 Introduction
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2 The ski industry
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History of skiing industry : a short review . . . . . . . . . . . . . . . . . . . . .
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2.1.1
From summer tourism to winter recreation : the early XXth century . . .
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2.1.2
Post WWII : consumption society and growing of the skiing industry . .
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2.1.3
Winter tourism as a current global market . . . . . . . . . . . . . . . . .
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Managing snow for industrial success . . . . . . . . . . . . . . . . . . . . . . . .
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2.2.1
Assessing operators’ and customers’ expectations . . . . . . . . . . . . .
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2.2.2
Carrying solutions : the first snow management methods . . . . . . . . .
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2.1
2.2
3 Climate change and snow management
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3.1
Climate change : general overview . . . . . . . . . . . . . . . . . . . . . . . . . .
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3.2
Downscaling issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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3.3
Snow reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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3.3.1
Assessing snow reliability, the so-called "100 days - rule" . . . . . . . . .
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3.3.2
Snow reliability vs. Climate Change . . . . . . . . . . . . . . . . . . . . .
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3.4
Skiers/riders demand vs. Climate Change . . . . . . . . . . . . . . . . . . . . . .
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3.5
The Climate Change : a catalyst for structural change . . . . . . . . . . . . . . .
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4 Ski resorts management : a wide range of goals and options
Technical approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4.1.1
Grooming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4.1.1.1
Snow compaction . . . . . . . . . . . . . . . . . . . . . . . . . .
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4.1.1.2
Snow structure modifications . . . . . . . . . . . . . . . . . . .
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4.1.1.3
Penetration and shear resistance . . . . . . . . . . . . . . . . .
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Snowmaking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4.1.2.1
History and development of snowmaking . . . . . . . . . . . . .
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4.1.2.2
How does it work ? . . . . . . . . . . . . . . . . . . . . . . . . .
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4.1.2.3
Machine made snow properties . . . . . . . . . . . . . . . . . .
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4.1
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4.1.2
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4.1.2.4
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How much does it cost ? . . . . . . . . . . . . . . . . . . . . . .
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4.1.3
Work timing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4.1.4
Slopes modelling and smoothing . . . . . . . . . . . . . . . . . . . . . . .
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4.1.5
Reaching higher elevations and glaciers areas . . . . . . . . . . . . . . . .
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4.1.6
Textile protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Complementary approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4.2.1
Conglomerates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4.2.2
Diversification of winter tourism offer . . . . . . . . . . . . . . . . . . . .
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4.2.3
4-seasons tourism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4.2.4
Financial products, weather related insurances . . . . . . . . . . . . . . .
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4.2.5
Giving up snow dependent activities : activities conversion (Francois, 2009) 39
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4.2
5 GANESH : objectives and strategies
5.1
Creating synergy in building a work community . . . . . . . . . . . . . . . . . .
5.1.1
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Assessing the impact of current methods on snow physical properties :
fields’ campaigns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Understanding operators’ management strategies . . . . . . . . . . . . .
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5.1.2.1
Profiling resorts . . . . . . . . . . . . . . . . . . . . . . . . . . .
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5.1.2.2
Surveying practices and converting data into a systematic ap-
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5.1.2
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proach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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5.1.3
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5.2
Developping and running a new Crocus-based snowpack model : Crocus
RESORT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Will there still be snow for coming winter holidays ? . . . . . . . . . . . . . . . .
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5.2.1
Coupling Crocus with ski lifts database : a diagnostic of resorts reliability 45
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5.2.2
2050 winter holidays landscapes :"winners and losers" . . . . . . . . . . .
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5.2.3
Diagnostic of potential limits of adaptation strategies . . . . . . . . . . .
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6 Conclusions
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Acknowledgement
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References
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1
Introduction
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From the very first day skiing became a leisure activity the snow in skifields is managed
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in some way. Skifields operators moved, removed, densified, produced, hardened, smoothed the
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snow to match their expectations and their consumers’ones.
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In 2013, France was the leader of the global skiing market. A rank France and USA have
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been competing for in the last years. 57.9 millions day tickets were sold (DSF, 2013). The french
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skiing market is definitely an industry on its own. While the market was growing, climate change
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appeared as a concern for all outdoor activities, wether they are a leisure or not. Regarding the
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passed decades and the prospectives that were recently displayed, climate change should be an
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important input for managing snow today.
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Snow management is based on both economic and climatic (environmental) aspects. That
makes it a complex topic of research regarding that :
— climate change is a trend we are experiencing but its evolution and impacts are only
uncertain prospectives.
— strategies and decisions taken by all skifields managers may highly differ.
Hard to say which one is the most important factor.
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I finally decided to introduce snow management as a solution to an economic issue. Cli-
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mate change shall be considered as the main (by far) secondary factor that influences snow
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management today. I will argue this choice later.
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The first section of the report will be dedicated to the birth of skiing as a leisure activity in
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the early XXth century in France and its evolution, particularly after the Second World War.
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Snow management will shortly appear as a tool that was set up by skifields operators to achieve
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their goals :
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— Satisfy their clients’ expectations
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— Ensure an industry based on a natural, brittle and quickly changing material : the snow.
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These industrial constraints can explain why and how the skifields operators started to manage
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the snow.
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In a second section i will describe the climate change and the commonly admitted prospec-
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tives. Their expected impacts on natural snow cover (quantity, duration) will be presented. It
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should be obvious then that climate change has a major role in the orientations and the quick
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development of some of the methods both in the past and the future decades (snowmaking in
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particular).
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Once put together, these two sections will lead us to go deeper into the different methods
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that are used today (third section). I shall give an idea as accurate as possible of the current
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scientific knowledge about these methods and their impacts on snow properties.
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The final goals of GANESH will be presented as well as the potential paths we may walk
through regarding all these aspects.
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2
The ski industry
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2.1
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2.1.1
History of skiing industry : a short review
From summer tourism to winter recreation : the early XXth century
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Figure 1 – Summer tourism on the "Mer de Glace" above Chamonix. Only advantaged social
classes travelled at that time, among who were many british tourists.
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In 1860, Nice and the Savoy were annexed by France. Many alpine territories actually
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became french on that occasion. In the meantime, alpinism developped in the Alps, particularly
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in relation to british tourism. Edward Whymper, William Coolidge are part of mountaineering
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history. Summer activities took off in the late XIXth century. Many associations were created
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such as the British Alpine Club (1857), the "Club Vosgien" (1872) and the French Alpine Club
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(1874).
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New infrastructures were built to give access to these isolated areas. Chamonix was connected to the train network in 1901.
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In the early XXth century, scandinavian skiing technics arrived in alpine villages and qui-
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ckly spread out all around. The french army played a central role in giving value to the skis as
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efficient tools for winter travels. The Briançon military school taught to 5000 soldiers how to
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Figure 2 – Flyer for 1924 winter olympics in Chamonix
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ski from 1900 to 1914. In the few years before the first world war, ski competitions were organi-
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sed by mountaineering associations in Montgenèvre (Briançon area), Mont Revard (Chambéry
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area), Chamonix. Straight after the war, further competitions were organised and the 1924
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Chamonix winter olympic games actually represent the kick-off for international visibility of
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skiing activities and for the Alps as a leader among mountain ranges.
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Ski lifts quickly appear afterwards : PlanPraz was built in 1927, the Brévent in 1930. The
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Rochebrune skilift in Megève is the first to be specifically built for alpine skiing in 1933. Val
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d’Isère is on its way for the "success story" when building its first button lift in 1936.
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Until WWII breaks that run-up, skiing spots are based in mountain villages (Megève, Val
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d’Isére, Chamonix) in which lifts are settled. So far, very few healthy people are enjoying skiing
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activities.
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2.1.2
Post WWII : consumption society and growing of the skiing industry
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After WWII, Europe is knocked-out and bloodless. Now is time to look forward. The U.S.A
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definitely display a leader status and industrial developments are on the way. Modernity knocks
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at Europe’s door. The society changed. Firms turned to a new production model originally used
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by Ford : massive production. All fields are impacted. Agriculture turned to engineering and
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intense use of lands. This is an important aspect since mountains areas were poorly adap-
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ted to mechanization and large surfaces growing. On top of natural difficulties due to shorter
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summer seasons and poor weather, mountain lands could not make it and catch agriculture
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metamorphosis evolution (Francois and George-Marcelpoil, 2012).
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Altitude villages cleared out in favour of urban areas down in the valleys and flatlands.
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That new model of massive production needed two main factors for success : efficiency and
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consumption. Financial success is based on production efficiency and of course selling products.
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In the meantime, efficiency involves specialization of workers and thus specific actions infini-
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tely repeated. To compensate the dazing effect of repetition and improve consumption, leisure
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activities appeared.
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Mountain areas found here an opportunity to be attractive again. The first winter sports
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villages experienced a real success (Val d’Isére in particular) and the snow industry appeared
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as the alpine golden future (Francois and George-Marcelpoil, 2012).
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An original concept will start from that point in the district of St Bon en Tarentaise. Maurice
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Michaud as a civil engineer and a director of the first mountain planning mission will experiment
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and grow that concept. Laurent Chappis who is an architect and a friend of Michaud will take
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part in this project which will appear as fundamental for french skiing industry.
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St Bon is a village of the tarentaise valley, at an altitude of 1100m.a.s.l. Above the village,
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at an altitude of about 1850m is an intermediate plateau named "Les Tovets". For the first
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time, Michaud and Chappis will imagine a ski-field "from scratch" on the Tovets’ plateau :
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Courchevel. The new born village had to be as autonomous as possible, handy, close to the
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ski tracks. Above an altitude of about 1800m lands belong to the local community. This was
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supposed to mitigate competition with original (agriculture, farming) activities and to reduce
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by the maximum the initial invesments in lands. On top of that, high altitude should be a
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guarantee for high quality snow.
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The financial gain from real estate business would be used for lifts investments. Courchevel
1850 appeared from a virgin site in 1946 and knew a very quick and important success.
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Based on that experimental site, Michaud and Chappis spread that concept all over the
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french Alps. Ski resorts were built in high altitude, virgin mountain areas. The lands originally
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Figure 3 – The 2Alpes skifield (Isére) in 1950 3(a) and today 3(b). Pictures from chalet-skiles-2-alpes.com
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Figure 4 – Day tickets sold in 2013. Skiing market is definitely global. Figures from DSF
(2013)
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belonged either to local community or to private owner and were taken for "public interest".
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Real estate operations benefited from very interesting loans. The french government was a
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major actor in both aspects. Major skifields were built such as Les Arcs, La Plagne, Tignes,
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Super Dévoluy, Isola 2000 on that model known as the "third generation". That trend took
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place between the 1960’s and the 1980’s. It is often referred to as the "Plan Neige".
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In the middle of the 80’s, the "Plan Neige" had reached the initial purpose : in the mountain
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areas, the snow industry had overcome the previous industries. (Badre, 2009)
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2.1.3
Winter tourism as a current global market
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As a major effect, skiing became a common activity shared by many people, from middle
to uppest social classes.
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From the 80’s, the number of skiers is roughly steady. The industrial golden era "Trente
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glorieuses" is over and financial crisis showed in the last few years, affecting both the resorts’
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operators and the customers.
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On top of that, sustainability is a rising demand of citizens. National parcs were created
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from the 60’s to protect wilderness (the Vanoise national park, surrounded by the Tarentaise
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Figure 5 – Overall revenue breakdown (DSF, 2013)
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and the Maurienne valleys was the first in 1963). The "Plan Neige" was criticized because of
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its obvious misinterest for nature and sustainability (Badre, 2009).
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Anyway, snow industry is a central activity for most alpine regions. Every year, 400 millions
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of day tickets are sold in the world, in over 80 countries. Almost half of them in Europe : 45%.
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In 2013, France showed at the first rank : 57.9 millions of day tickets were sold. The U.S.A
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and Austria are sharing the top ranks with France. 3900 lifts exist in France, which is roughly
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18% of the global figure. 18 000 people are directly employed by french ski resorts. The national
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revenue from all ski resorts (only lifts operators) is about 1.350 billionse.
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75% of customers are french (20% are locals, 55% come from the rest of France) and 25%
are foreigners.
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According to DSF (2013), for 1e spent in ski lifts, customers spend 6e in the rest of the
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economy (accomodation, bars and restaurants, extra activities). Thus the skiing industry is
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estimated as generating about 7 billionse every year and employing 120 000 people in France
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(18% of the total french tourism industry).
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2.2
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2.2.1
Managing snow for industrial success
Assessing operators’ and customers’ expectations
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While skiing was developping as a leisure activity both customers and operators expected
specific and steady properties of snow.
According to Fauve (2002), customers and operators have different expectations of snow
quality. Some of them are falling into agreement, some others seem contradictory.
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For customers (skiers/riders) :
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— "Good grip" for skiers
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— Uniform roughness (no ice, no humps)
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— Safety (no rocks, no surprises)
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— Entertaining slopes (diversity)
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— Attractive slopes (visual)
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For operators :
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— Satisfaction and safety of customers
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— Resistance
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— Durability
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— Flow (lifts efficiency)
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— Ecological impact
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Guily (1991) assessed that snow management had to build safe, comfortable and enjoyable
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tracks for skiers with the guarantee of resistance and durability for operators.
Badre (2009) completed by explaining that tracks quality had to be matched to ensure lifts
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cost-effectiveness : opening and closing dates, flow of skiers, etc.
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2.2.2
Carrying solutions : the first snow management methods
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Originally, the very first action that was undertaken to manage snow was avalanche trigge-
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ring. One may think this is not the topic but i guess it deserves to be outlined. As explained
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before, the main goal of skifields operators is skiers’ safety. Before WWI, ski patrollers compa-
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nies were created to carry first aid assistance to skiers and assess snowpack stability. Avalanches
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were first triggered by skis.
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Figure 6 – Snowmaking diffusion factors from Steiger (2008). I guess one can extent that
diagram to snow management diffusion.
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After avalanche threat, the second method to reduce injuries and carry comfort was groo-
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ming. The natural snow surface has a roughness and smoothness that can be very irregular and
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sensitive to weather : erosion by wind, crusts, no base to step on, etc. To solve that issue, ski
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patrollers started to groom tracks with skis. Emile Allais took part into technological impro-
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vements of grooming. The first dedicated machines for gromming (SR5, Ratrac) appeared in
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1965 (Guily, 1991).
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Steiger (2008) used a synthetical diagram to explain snowmaking diffusion. One can see
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that diagram as more general and representative of snow management. Actually, the two steps
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"General conditions" and "Explanatory context" are definitely true for all snow management
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methods. Main factors are based on global warming, variability of precipitations, competitive
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economic pressure, global trend in tourism and particularly in ski tourism.
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3
Climate change and snow management
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3.1
Climate change : general overview
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First of all, one has to keep in mind a fundamental difference between climate and weather.
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While the weather may experience important variability, climate is a stable phenomenon, an
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average pattern over a long period of time. In a recent usage, climate change is more referred
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to as the change of the modern climate, including the rise in surface temperature. As so one
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is sometimes using "global warming" instead of climate change. The current change of climate
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may be partially due to human activities but it must be clear that this is not my purpose here.
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The main expected change of the climate is related to the global radiative balance of the
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system, in relation with the concentration rise of Greenhouse Gases (GHG, Lafaysse (2011)).
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GHG are naturally constituting the earth’s atmosphere. The main one is the water vapor.
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Without them, the earth’s surface temperature would be approximately -17˚C. Their presence
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brings a gain in energy for the surface and thus a higher surface’s balance temperature (see fig.
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7(a)).
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Higher concentrations of GHG (see fig. 7(b)) will lead to an increased gain for the surface
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and thus a rise of the surface’s temperature. All commonly presented scenarii agree with a
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rise of that surface’s temperature. A1B scenario shows a temperature’s rise between +2˚C and
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+3.5˚C in 2100 with respect to 1981-1999 period. Precipitations are expected to increase by 1
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to 6% (Lafaysse, 2011). Many parameters’ evolution and impact are poorly or even unknown
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which explains the uncertainties of forecasted variables. Among them a central uncertainty is
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related to downscaling the general scenarii to local situations.
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3.2
Downscaling issues
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The recent warming in the Alps seems to be more sensitive than the global average, parti-
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cularly during summer season. In the swiss Alps, temperatures may be +1˚C to +5˚C higher
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in summer and +1˚C to +3˚C higher in winter in 2050 with respect to 1990. In the meantime,
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precipitations may increase by approximately 5 to 25% in winter and decrease by 5 to 40% in
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summer during the same period (Agrawala, 2007). The main impact of these prospectives is
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the evolution of precipitations’ phase. The latest OECD report on climate change in the Alps
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suggests that due to global warming, the altitude of snow precipitations will rise by about 150m
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per 1˚C of warming (Agrawala, 2007).
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(a)
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Figure 7 – Earth surface energy balance and the role of Green house gases 7(a) and the likely
rise of CO2 concentration by 2100 7(b)
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In France, assuming a temperature rise of +1.8˚C, Etchevers (2002) showed that both du-
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ration and snow depth would be impacted. At 1500m.a.s.l the natural snow cover duration
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may decrease by 45 days (+/- 15days), while at 3000m.a.s.l it would decrease by 35 days (+/-
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10days). The average snow depth may be 20 to 30cm thinner (+/-10cm) while the maximum
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depth might be reduced by 30 to 50% (Isere district, France).
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It is very delicate to apply these prospectives to very local areas and no one can be sure of
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how a certain skifield could be affected in a near future. Anyway, this will affect for sure the
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snow reliability of most alpine skifields. However, all of them will not be identically impacted.
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3.3
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3.3.1
Snow reliability
Assessing snow reliability, the so-called "100 days - rule"
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The presence and the amount of snow is the key ingredient of the complex recipe for winter
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holidays success. That point is shared by anyone concerned in winter holidays. Other factors
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are probably less sensitive and might not be felt the same way by all (weather conditions,
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skifields’frequentation, snow quality, tickets’ prices, weather forecasts etc.). Anyway, as a natural
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and fragile material the snow is by far the weakest spot of the ski’s industry. Of course that looks
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obvious and here is coming the next and harder wonder : what conditions are ideal conditions
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for the ski’s indutry ?
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Most authors are using the "100-days rule" to assess wether a skifield is snow reliable even if
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they often defend from considering that criteria as mandatory for operators’ success. According
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to Koenig, different criteria have been discussed to assess snow reliability. The "100-days rule"
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apparently summarises most of these studies and was used in the latest OECD report (Agrawala,
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2007).
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It states that "to operate a ski area with profit, snow cover sufficient for skiing (i.e. 30cm)
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should last at least 100 days per season (between the first of December and the end of April)"
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(Koenig, 1997). That rule can be found in a different form in literature. Little difference in
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words but potentially a huge difference in results with more restrictive terms. Steiger (2008)
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and Elsasser (2002) consider a ski-resort as snow reliable if "in 7 out of 10 winters a sufficient
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snow covering of at least 30 to 50 cm is available for ski sport on at least 100 days between
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December 1 and April 15". Even if in both cases authors are referring to the "100-days rule",
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it is not perfectly clear that they are using the exact same conditions. Steiger (2008) explains
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he used the same methodology as the OECD. So far most authors considered natural snow
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conditions and not actual ski slopes snow.
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The altitude at which snow is sufficient for skiing with respect to the "100-days rule" was
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assessed for different alpine areas (Agrawala, 2007). Three main groups were distinguished. Re-
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sults are displayed in table 1.
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Snow reliability line
France
Switzerland
1050m.a.s.l
Austria
Italy
Salzbourg
Styrie
1200m.a.s.l
Isere
Vaud district
Tyrol
Savoie
Valais
Vorarlberg
Hte-Savoie
Oberland
Central Swit.
Eastern Swit.
1500m.a.s.l
Drome
Tessin
Piemont
Htes-Alpes
Lombardia
Alpes de Hte Provence
Adige
Alpes Maritimes
Frioul - Venetia
Trente
Table 1 – Snow reliability line for main alpine areas
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Figure 8 shows the high spatial variability of snow cover in a same alpine region (Tyrol,
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calculated snow relaibility altitude 1200m.a.s.l, table 1). One can notice that most climate
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stations are not reliable according to the "100 days rule", even if they are above 1200m.a.s.l.
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Snow reliability at 1200m.a.s.l spreads from 0 to 65%. Between 1600 and 1700m.a.s.l, climate
320
stations are from 20 to 75% reliable. Just to remind that snow conditions in a specific area
321
is a highly sensitive factor that is very delicate to treat with general rules and conditions like
322
altitude. In the OECD report Agrawala (2007), sensitivity of ski-resorts to snow conditions is
323
only based on that altitude criteria no matters the other characteristics (resort main orientation,
324
local precipitations etc.). A skifield is taken as snow reliable if its mid range altitude is above
325
the threshold altitude. The OECD figure (9) is very likely to be optimistic.
326
For example, according to OECD report 97% of swiss ski-resorts are snow reliable at present
327
(159 out of 164 resorts). A few years earlier, Koenig (1997) and Elsasser (2002) agree with 85%
328
of snow reliable swiss ski resorts (195 out of 230 resorts). They splitted skifields into ski areas.
329
A ski area is snow reliable if its transport facilities are above the threshold altitude and can be
330
reached by snow reliable paths. All three are using the same "100-days rule".
331
16
Figure 8 – Relative number of winters with a snow depth above 30cm for 100 days or more
(52 climate stations in Tyrol, period 1981-2001) (Steiger, 2010)
332
3.3.2
Snow reliability vs. Climate Change
333
334
Figure 9 from OECD report displays the reliability of ski resorts under current conditions
335
and +1˚C/+2˚C/+4˚C warmings. Under +2˚C warming only 79% of ski resorts would be snow
336
reliable (-18% with respect to current situation). Koenig (1997) and Elsasser (2002) agree with
337
63% of snow reliable swiss ski resorts (-22%) in the same conditions.
338
3.4
Skiers/riders demand vs. Climate Change
339
Scott (2003) raised the question : will there still be some demand for skiing under changed
340
climatic conditions ? What will be the impact of climate change on skiing demand ? So far, this
341
is the only publication i found which treated that question. A central one i guess.
342
In a survey by Bürki in 2000 (in german only), the following question was asked to skiers/riders
343
from five swiss skifields : "Where and how often would you ski, if you knew the next five winters
344
would have very little natural snow ?".
345
346
— 58% indicated they would ski with the same frequency (30% in the same resort and 28%
at a more snow reliable resort)
347
— 32% indicated they would ski less often
348
— 4% indicated they would stop skiing
349
Overall, more than the third of sampled skiers indicated they would ski less often or even
17
Figure 9 – Snow reliable ski resorts in alpine countries (%) (Agrawala, 2007)
Dark blue column is the the total existing ski areas. Then comes the results today / under
+1˚C/+2˚C/+4˚C warming.
Figure 10 – Projected ski season length under natural snow (dark grey) and snowmaking
conditions (light grey) in three different ski areas in Tyrol (Steiger, 2010).A1B scenario was
assumed here
18
350
quit. The implications of climate change for skiing demand in Switzerland might be potentially
351
significant.
352
353
In a second survey on australian ski areas, asking the same question, the replies are even
more pessimistic :
354
— Only 25% indicated they would ski with the same frequency
355
— 31% indicated they would ski less often, still in Australia
356
— 38% indicated they would substitute destinations and ski overseas
357
— 6% indicated they would stop skiing
358
These are more than 44% of potential losses and 38% skiing less often. This is potentially
359
irreversible.
360
361
For more details about these surveys, read Scott (2003)
3.5
The Climate Change : a catalyst for structural change
362
All representatives involved in winter tourism are familiar with the potential impacts of
363
climate change. Anyway, several authors outline the fact that climatic change is seen as being of
364
little importance (Elsasser, 2002). Scott (2003) also notices that "a climate change adaptation
365
strategy has not been developed by the US ski industry". He expects that "the more likely
366
scenario is a continuation of the existing competitive business environment and the unplanned
367
contraction of the US ski industry".
368
Actually, the relationship between ski operators and climate change was revealed as very
369
ambivalent. On the one hand, they strongly distrust the information. On the other hand, they
370
use it to legitimate forward strategies (Elsasser, 2002).
371
Finally, the climate change will highlight the capacity of resorts to adapt. It is very likely it
372
will reinforce and accelerate structural change in the tourist industry. As so, some of the resorts
373
will not be able to go through it and can be considered as the "losers" of climate challenge. On
374
the contrary, some of the resorts might benefit from disappearance of some of the resorts by
375
catching their part of the market. Koenig showed that during 1989/1990 and 1990/1991 seasons
376
known as relatively poor in terms of natural snow, the number of people transported in glaciers
377
resorts was extremely high (See fig. 11).
378
It is clear that resorts will "survive" if they can guarantee snow reliability. This might be
379
thanks to natural snow only (if local conditions match the needs) or thanks to their capacity to
380
correctly manage the snowpack and to invest into mitigation tools (snowmaking in particular).
19
Figure 11 – Comparison of frequentation between a lift sitting on a glacier (white squares) and
another not on a glacier (black squares).Engelberg area (central Switzerland) (Koenig, 1997)
Figure 12 – Snowmaking costs (investments+production) and day-tickets’ price, ODIT France
report (Berlioz, 2008)
20
381
On both points, the competition is particularly unbalanced. Smaller skifields are usually at lower
382
altitudes. They have to cope with a more sensitive snowpack to climate change with reduced
383
capacities of investing either in snowmaking ressources or higher altitudes areas, topography
384
layout, etc.
385
Figure 12 displays the snowmaking costs and the day ticket’s price (costs and ticket per
386
person) in the case of a small, medium size and large resort. The weight is far heavier for small
387
resorts (ODIT France, (Berlioz, 2008)).
388
Facing the climate challenge, there will be winners and losers. Resorts’ strengths are de-
389
pendent on many factors. To guess which one will be strong enough is a hard question to
390
answer today.
391
392
Scott (2007) displays a synthetic diagram of current options undertaken by ski resorts to
cope with climate change impacts : figure 13
21
Figure 13 – Mitigation and adaptation strategies of skiing industry facing climate change
(Scott, 2007)
22
393
4
Ski resorts management : a wide range of goals and options
394
395
4.1
396
4.1.1
Technical approach
Grooming
397
398
Grooming machines are caterpillars about 3m wide and 4 to 5m long. They range between
399
200 to 450 horsepower (catalog from a well known french/german company Kassbohrer) and
400
consume about 20 to 25 l/h of gas. Grooming engines realise very different tasks, depending on
401
the tool one is using when work is done (Fauve, 2002).
402
— Snow tiller : the main tool. It is a rotating abrasive axe applied on snow when groomer
403
is moving ahead. Main parameters are the pressure applied on snow, the rotating speed
404
and depth of penetration.
405
— Front blade : moving snow from one point to another
406
— Finisher : smoothing the snow surface to achieve a good grip and nice visual aspect
407
— Winch : used on steep tracks. It improves drivers safety by reducing sliding risks. On
408
snow properties, it disminished the snow erosion when grooming steep slopes by allowing
409
uphill work. It also favours densification of snow since it must be used at low speed.
410
These are the main tools used with grooming machines. The grooming machines can be twice
411
longer when the front blade and tiller are used at the same time.
412
413
When grooming one has several actions on snowpack :
4.1.1.1 Snow compaction
414
Unless detailled, all data from Guily (1991)
415
— Impact of chosen tool
416
The ground pressure applied by the caterpillar itself is about 3.5 to 5kPa. The compaction
417
effect can be twice more important if using the tiller. See table 2.
418
— Impact of initial density
419
The impact of grooming decreases with initial density. Above an initial density of 0,500,
420
the densification factor is almost 1 (density is no longer affected). See figures 14 and 16.
421
— Impact of snow depth
23
Density
Initial density
Density
after grooming once after grooming once
(no tool)
(Tiller)
0.089
0.160
0.300
0.120
0.160
0.350
Table 2 – Snow compaction by grooming machines
Figure 14 – Densification decreases with the number of grooming session i.e. with initial
density. Here is shown the combined effect of engine weight+ tiller tool. Data from Guily
(1991)
422
The densification factor is constant on the 40 first cm below the surface. Then it decreases
423
linearly up to 75cm below the snow surface. Then the grooming effect can be neglected.
424
As far as one can criticize results from Guily (1991), that point deserves to be checked.
425
426
427
— Impact of engine speed
The slower, the denser, see figure 15.
— Uphill or downhill ?
428
Compaction proved to be more efficient when groomers moved upward (figure 16). Is
429
that because they are slower when going upward compared to downward ? In that case,
430
there might not be any relation between compaction and direction. Another reason for
24
Figure 15 – Densification decreases with engine speed (m/s).
431
upward grooming is snow erosion. Gravity already carries snow downward when skiers
432
erode the surface. When moving upward, grooming engines tend to reduce natural and
433
skiers’ erosion instead of worsening it otherwise.
Figure 16 – Compaction is more important when moving upward. Final density (after one
grooming session) is approximately +13% higher.
25
434
4.1.1.2 Snow structure modifications
435
Unless detailled, all data from Guily (1991)
436
Grooming has a different impact on snow structure depending on initial state.
437
— Precipitation particles
438
When grooming fresh snow, crystals are broken down by compaction and tiller action.
439
Precipitation particles get fragmented and finally change to angular rounded grains.
440
The size changes to reach approximately 0.25mm rounded grains after several sessions.
441
Multiple contact points are created, which relates to the effect of grooming on snow
442
resistance (see further). Once rounded grains shapes are reached, grains start changing
443
under natural metamorphosis again. Grooming has no more effect on their structure.
444
— Faceted crystals, depth hoar and melt forms
445
If faceted crystals and depth hoar crystals already exist, Guily (1991) showed that groo-
446
ming can not break them down, whatever the tool. The only action grooming may have
447
is avoiding these crystals from getting created. This is related with another effect of groo-
448
ming : compaction highly increases thermal conductivity. Thermal gradient is reduced,
449
freezing temperatures can reach the ground.
450
Melt forms can not be fragmented by grooming either.
451
Guily (1991) showed that regularly groomed snowpack are composed of fragmented particles,
452
angular rounded grains and melt forms. The rounded shape of grains proved to be steady along
453
the season.
454
4.1.1.3 Penetration and shear resistance
455
— Penetration resistance
456
According to Guily (1991), when grooming the snow resistance increases a lot in the 20
457
first cm of the snowpack (by a factor of approximately 2.3). Deeper, the effect decreases
458
until it becomes very small close to the ground. A second grooming session does not
459
affect much the penetration resistance. Overall, resistance becomes more homogeneous
460
in the snowpack.
461
— Shear resistance
462
Shear resistance was measured by Guily (1991) thanks to shear vanes. Grooming imme-
463
diatly increases shear resitance by a factor of 2. A second session looks does not carry
464
any better results, the resistance does not improve much (18(a). The major parameter
26
Figure 17 – Penetration resistance at the Col de Porte (1325m, Chartreuse Mountain range).
Yellow and red curves are natural snowpack measurements (thanks to SMP and snow beating
probe). Green curve is a groomed snowpack. SMP stopped when reaching an icy layer that
overpassed its maximum penetration force.
465
to achieve much higher resistance values is resting time. As shown on figure 18(b), re-
466
sistance increases very quickly in the first hours after grooming. This is an important
467
point we will developp in a further section "Work timing".
27
(a)
(b)
Figure 18 – Shear resistance vs. grooming sessions 18(a) and resting time 18(b)
468
4.1.2
Snowmaking
469
470
Snowmaking is based on many fields one can investigate ! Paccard (2010) built a scheme of
471
relations between snowmaking and water resources, climate and socio-economic issues (figure
472
25). Thus, this section aims to present a general overview of that method.
28
473
4.1.2.1 History and development of snowmaking
474
Snowmaking appeared in the 60’s in U.S.A. Flaine was the first large french resort to get
475
equipped with snowmakers. Previous tests had been undertaken in the Vosges and the Morvan
476
mountain ranges. Snowmaking looked at first as a negative equipment. One thought it was
477
similar to poor quality resort. For a deeper historical review, please read Paccard (2010).
478
After poor snow conditions winters in the early 90’s, snowmaking started spreading very
479
quickly. In 1990, 20% of resorts were equipped (<1000ha). Ten years later, 85% were equipped
480
(about 3000ha) (Berlioz, 2008). 3 snowmakers are installed per hectare on average.
Figure 19 – Evolution of equipped surface with snowmakers (Berlioz, 2008)
481
In 2008 in France, according to Badre (2009),
482
— 330 resorts and
483
— 5300ha were equipped which is 20% of the total surface of resorts ski tracks
484
— 16 000 snowmakers were installed (15 000 high pressure and 1100 fans)
485
— 17 millions of m3 were collected for snowmaking use
486
Usually 7000m3 of snow per hectare are produced which means 70cm deep snow.
487
According to Badre (2009), the 70cm deep snow produced on the 20% surface carries as
488
much water as natural precipitations (about 250kg/m2 or mm of equivalent water). Under that
489
point of view, snowmaking is not just sprinkling.
29
Figure 20 – Equivalent snow height produced (Berlioz, 2008)
490
4.1.2.2 How does it work ?
491
DSF created a website www.lamontagneenmouvement.com to communicate about snowma-
492
king to the public. The main idea is to give value to snowmaking by replying to clichés (figure
21). Air and water are the two components of machine made snow. From 2005, all french resorts
Figure 21 – Flyer from DSF campaign "Machine made snow is air and water, that’s all !"
493
494
decided to stop using any extra products in water to improve efficiency (?). Anyway, there is
495
no law in France about additives and other european countries and U.S.A are still using these
496
products.
497
Since air and water are the only components, Lafeuille (1988) showed that specific atmos-
498
pheric conditions had to be found to achieve the balance of the thermal process of snowmaking.
30
499
Four physical processes occur when producing snow :
500
— cold atmospheric air absorbs sensible heat
501
— water releases sensible heat to reach 0˚C
502
— water absorbs latent heat to evaporate until neighbouring atmospheric air reaches satu-
503
rated vapor pressure
504
— water (at 0˚C) that did not evaporate releases latent heat by freezing
505
The result is a mix between frozen water particles and vapor saturated air. Pure water would
506
experience undercooling (until -36˚C) if no freezing seeds were to be found in air/water mixture.
507
This aspect is important since it leads operators to avoid producing snow in borderline tempe-
508
rature and humidity conditions. Usually, a maximum -4˚C temperature matches conditions for
snow production.
Figure 22 – Theoretical snowmaking conditions : air/water ratio depending on temperature
and humidity (Lafeuille, 1988).
509
510
If atmospheric conditions are not sufficient or if the snowmaker flow is too important, liquid
31
511
water may still exist when reaching the ground. In that case the produced snow gets wet,
512
heavier and may even turn to ice. Resorts operators try to avoid that case as much as possible.
513
They usually tend to produce the driest possible snow. Anyway, in some cases they may have
514
to produce a maximum snow with borderline conditions and flow.
515
Machine made snow grains look very similar to melt forms. They formed from liquid water
516
and take an almost perfect spherical shape. Usual diameter is about 0.3mm. See pictures 23(a)
517
and 23(b).
(a)
(b)
Figure 23 – Machine made snow grains. Pictures taken at the Col de Porte lab, Centre d’Etude
de la Neige, february 2014.
518
4.1.2.3 Machine made snow properties
519
Guily (1991) did an extensive work about snowmaking. In the last two decades, snowmakers
520
really experienced a huge improvement. Thus results from his work may not be exact. The same
521
for previous works undertaken by scientists from the Centre d’Etude de la Neige in the 90’s :
522
they probably need to be checked.
523
Anyway, some aspects did probably not change much :
524
— Density
525
Since machine made snow looks very similar to melt forms, its density is quite close as
526
well. According to Guily (1991), density goes from 0.300 to 0.500.
527
— Resistance
528
Resistance depends a lot on liquid water content (LWC) of machine made snow and the
529
potential freezing. When LWC is 0 to 2.5% (dry to little wet snow) the shear resistance
32
(a)
(b)
Figure 24 – Main morphological grain shape classes 24(a) and specific machine made snow
symbols 24(b) (Fierz et al., 2009)
530
goes from 5 to 10kg/dm2 . Measures by Konig (1987) looked higher with 5 to 25kg/dm2
531
shear resistance. These values were measured in the few minutes after production but
532
this is sometimes unclear. This is an important factor in case of freezing snow.
533
Penetration resistance was measured by Konig (1987) using the snow beating probe.
534
Values over 250kgf were measured. The measurement conditions (on a ski track, in spring)
535
lead to be very careful with that value : probably the measured snow had evolved since
536
it was produced.
33
Figure 25 – Snowmaking relations with water ressources, climate and socio-enconomic issues
(Paccard, 2010)
34
537
4.1.2.4 How much does it cost ?
538
According to Badre (2009), 1m3 of snow costs 2 to 2.5e.
539
One can breakdown that price between :
540
— Production : 0.8e/m3
energy+maintaining+employees
541
542
— Amortisation : 1e/m3
equipment 150 to 200keper hectar paid off in 20 to 30 years
543
544
— Artificial lakes : 0.25e/m3
545
20 to 30e/m3 of water paid off in 20 years. The average artificial lake is about 60 000m3
546
(1.8millioneto invest). In 2008, the huge resort Les Arcs built an artificial lake of 400
547
000m3 named l’adret des Tuffes. As far as we know, it the largest artificial lake built for
548
snowmaking purposes only.
549
Snowmaking would be responsible for 5 to 10% of the total day ticket price. The annual
550
revenue of snowmakers constructors company was about 100millionsein 2008.
551
4.1.3
Work timing
552
553
Snow initial conditions might be highly different in a single day. To achieve a specific goal,
554
one has to take care to the best period of time in the day to prepare snow tracks. As shown
555
in figure 26(a) (winter conditions) snow grooming first decreases the shear resistance. Then it
556
increases again. If the resting time between snow working and tracks opening is too short, the
557
resistance is much disminished. On the same figure for example, snow resistance when opening
558
tracks is twice higher if undertaken at 5pm the previous evening than at 3am in the morning
559
(+6kg/dm2 ). In the case snow working is done at 8am, the snow resistance is lower when
560
opening tracks than initial state (-9kg/dm2 with respect to 5pm work)
561
Here are some general rules that should be followed for ideal snow conditions (Fauve, 2002) :
562
— Resting time should be as long as possible. Apart from night snow falls, snow works
563
564
565
566
567
should be udertaken in the previous evening.
— When preparing snow tracks, the snow temperature should be as close as possible to
0˚C. Reaching higher resistance values is much quicker.
— If snow density is very low, one should wait a few hours that natural densification starts.
Snow grooming of this sort of snow is little efficient.
35
(a)
(b)
Figure 26 – Snow shear resistance vs. snow grooming timing 26(a) : winter conditions (Fauve,
2002) 26(b) : Spring, wet conditions (Guily, 1991)
568
— During snowfalls, one should wait until a 20cm thick layer is reached before starting
grooming.
569
570
4.1.4
Slopes modelling and smoothing
571
572
Topography has a major role to play in snow management.
573
First of all the minimum snow height suitable for skiing highly depends on surface roughness.
574
On a smooth and flat area, grooming can be done from 20 to 30cm deep snow (Guily, 1991).
575
That minimum depth can reach up to 1m in case of a very rough ground (rocky areas). Thus
576
existing ski slopes can be smoothed to reduce the minimum requirements of snow (start earlier
577
with natural snow and produce less snow). Grass is usually grown on slopes to prevent soil
578
erosion and disminish landscape default due to ski tracks. Furthermore, on a smooth slope, one
579
can expect to keep a homogeneous surface as late as possible. This can prevent rocks to appear
580
in spring, catch solar radiation and finally increase melting. Ski operators we could meet so far
581
confirmed that smoothing slopes was a major action in summer.
582
On top of smoothing, slopes orientation is also taken into account. This is linked with solar
583
radiation absorption by snowpack. For a given snowpack (same albedo in particular), slopes
584
angle and orientation affect the energy that is absorbed as shown on figure 28. In winter, a
585
30˚angle southern slope can absorb up to 3 times more energy than a flat area while a northern
36
(a)
(b)
(c)
(d)
Figure 27 – All figures from (Fauve, 2002) Adapted working time in case of 27(a) : Dry snow
and cold, dry weather 27(b) : Dry, very cold snow and warming conditions to come in the
evening 27(c) : Dry snow and warming conditions to come in the night 27(d) : Wet snow in
spring conditions
Figure 28 – Solar energy absorption vs. slope orientation (Fauve, 2002)
37
586
slope would not absorb any solar radiation at all (Guily, 1991). In some occasions it can be
587
worth modifying slopes orientation for a few˚of angle to decrease solar absorption. These works
588
are also realised in summer and based both on operators’ observations and external studies by
589
private consultants.
590
Modelling and smoothing slopes raise a difficulty regarding the environmental impact of
591
snow management (Agrawala, 2007) :
592
— soil vegetation is modified,
593
— erosion and local hydrology are impacted,
594
— construction engines are working in summer, competing with tourism (among other
activities) in that period.
595
596
4.1.5
Reaching higher elevations and glaciers areas
597
598
As exposed in the section about climate change, a warming of +2˚C would be equivalent to a
599
rise of snow reliability line of 300m. Try to reach higher elevations can be a solution to the lack
600
of snow. It takes part in the overall research of climatically advantaged areas, with northern
601
slopes and glacier areas. This would even be the third favorite adaptation strategy of austrian
602
ski resort managers after snowmaking and sharing snowmaking costs with the accomodation
603
industry (Scott, 2003).
604
Some alpine areas are already in this situation. In Chamonix area, snowmaking is by far less
605
developped with respect to average european standards. In an interview with B. Fayolle from
606
La Flegere skifield, we were told that the way back from skifield to the valley by skis was not
607
a priority. Hence, the lower section (between 1900m.a.s.l and 1000m.a.s.l in the valley) is not
608
equipped with snowmaking canons. Meanwhile, they equipped a part of the slopes at around
609
2000m.a.s.l.
610
Reaching higher elevations has a few defaults anyway (Agrawala, 2007) :
611
— Mountains have a finite elevation, thus this option is limited
612
— High altitude areas are more exposed to winds, which may involve interruptions or
613
discomfort
614
— Building transport facilities at these altitudes is very expensive
615
— High altitudes ecosystems are very specific and rare. There is a real environmental issue
616
in going higher, on top of visual aspect of high altitude facilities.
38
617
Glaciers skiing was at first a summer option. As shown in figure 11, during winters when
618
snow was missing, glaciers ski areas were popular. In Austria (Tyrol), the rules for glaciers
619
protection were changed to allow the skifields to expand on glaciers areas Agrawala (2007).
620
Anyway, expanding on glaciers does not appear as a robust option either. The retreat of glaciers
621
is important at the moment. By 2050, 75% of alpine glaciers may have disappeared. On top
622
of that point, environmental groups as well as local residents are a majority ot be opposed to
623
expand on glaciers as explained byAgrawala (2007) and Scott (2003).
624
4.1.6
Textile protection
625
626
Textile protections are an interesting tool to help reducing summer snow and ice ablation
627
on glaciers. They may play a complementary role with snow trapping (fences, furrows, etc.),
628
densification and redistribution of snow and snowmaking.
629
Olefs (2008, 2010) conducted experimental campaigns in 2005 and 2006. Textile sheets are
630
4.5mm thick, made of polypropylene fibers and have a high shortwave reflectivity. Radiation
631
effects of textile sheets are mainly confined to shortwave reflected fluxes. As a result of that
632
campaign, he concluded after two consecutive ablation periods, the total ice ablation observed
633
in undisturbed areas was 3.4m while it was only 0.4m beneath sheets. See picture 29. This
634
corresponds to a 90% ablation reduction.
635
636
— Cover albedo is responsible for approx. 45% of the total conservation effect in terms of
snow height
637
— Thermal insulation of the cover itself is responsible for 20 to 25%
638
— Air thermal insulation (air layer between sheets and snow) is responsible for an extra 30
639
to 35% of the conservation
640
Simulations have been undertaken as well using the snow model SNOWPACK. It tends
641
to show that the method is particularly sensitive to air temperature and wind speed. Textile
642
sheets do not actually reduce snow ablation below an altitude of about 2200m.a.s.l (or a mean
643
temperature of 6˚C or more).
644
After the success of these experiments, all five Tyrolean glacier resorts started to use textile
645
sheets during ablation period. They cover 0.3km2 which represents 2 to 3% of the glacier area
646
used for skiing, particularly strategic areas : ski lift tracks, rock outcrops, etc.
647
39
Figure 29 – The experimental site, close to the end of ablation period (september 2006). A
3.1m difference between undisturbed and protected areas ! (Olefs, 2010)
648
4.2
649
4.2.1
Complementary approaches
Conglomerates
650
651
A promising approach to any sort of event (including climate change) is business conglome-
652
rates. Merging businesses may prove to be one of the most efficient way to face challenges. It
653
enhances adaptive capacity and reduces vulnerability. Companies like American Skiing Com-
654
pany, Intrawest in the U.S.A as well as the Compagnie des Alpes (CDA) in France acquired
655
many ski areas all around their own mountain range Scott (2007).
656
The CDA owns ski fields that are attractive, settled at high altitudes, which guarantees a
657
satisfying quality and quantity of snow. It is structured and became a quasi monopolistic opera-
658
tor facing local authorities. Each operator still has some margin to deal with local specificities,
659
offer newer options or ideas and benefits from the group’s strength, transversal services (for
660
example within the "Compagnie du Mont Blanc", a transverse team advises operators about
661
snowmaking and takes care of maintaining grooming machines). Finally, the CDA is listed on
662
the stock exchange, its financial results are a priority. In the last few years, the CDA develop-
663
ped into snow independent activities to be even safer. They invested into entertainment parks
664
(George-Marcelpoil and Francois, 2012).
40
Figure 30 – The 6 largest conglomerates of ski resorts in France (Leader, 2013). The CDA
weights 31.19% of the total revenue of ski operators !
665
4.2.2
Diversification of winter tourism offer
666
667
Beyond traditional ski activities, ski resorts developped extra activities (snowmobiling, ska-
668
ting, indoor pools, fitness centers, squash and tennis areas, restaurant, retail stores, etc.). Ac-
669
cording to Agrawala (2007), 48% of tourists in italian ski resorts do not practice any skiing
670
activities. Scott (2007) comes with 20 to 30% of resorts’ visitors who do not ski in Canada.
671
Scott (2007) completes with the evolution of revenue sources in canadian ski resorts. Among
41
672
the total revenue,
673
— 2.8% were spent in food and beverages in 1975 with respect to 14.1% in 2002.
674
— 2.8% in ski lessons (1975) and 9.8% in 2002
675
— 1.8% in accomodation (1975) and 9.4% in 2002
676
In the meantime, the lift tickets moved from 79.4% of the revenue in 1975 to 47.4% in 2002.
677
This is related to a quote from a large ski resort manager : "if Tignes’s revenue is improving,
678
it’s only because of the increase of lifts ticket’s price. Increasing that price does not matter :
679
most of our customers come from far away, lifts’ ticket is like peanuts for them". Today, the
680
day ticket is worth 50.5ein Tignes.
681
The french conglomerate Labellemontagne applied that strategy in middle size resorts. They
682
tend to consider that these resorts have an important margin for progress. They offer "all
683
included" formulas : accomodation, ski rental, lifts ticket. The economic balance is made over
684
all activities and not only lifts’ cost-effectiveness (George-Marcelpoil and Francois, 2012). That
685
balance highly reduces the financial impact of a poor season (in terms of snow conditions).
686
Anyway, over a long period of time, extra activities can not replace the central attractiveness
687
of skiing. If skiing is no longer possible that option will not carry any improvements any more
688
(Agrawala, 2007).
689
4.2.3
4-seasons tourism
690
691
A number of resorts operate ski lifts in summer to offer a wider range of possibilities.
692
Downhill mountain bike, hiking, paragliding, summer luge benefit from reaching high altitude
693
in a minimized effort. Other activities are also developped that do not need ski lifts such as
694
golf, boating, horse riding, etc. So far, the summer revenue is very weak : 5% maximum of
695
the annual revenue. Charges and incomes would hardly balance in summer (DSF, 2013). Some
696
resorts may highly benefit from balancing investments between winter and summer activities.
697
Over a larger scale (say national) the main question is "could alternate activities balance the
698
economic revenue generated by winter sports ?"
699
4.2.4
Financial products, weather related insurances
700
701
After poor winter seasons in the 90’s, some resorts started to offer financial incentives to
42
702
overcome skiers’ reluctance to book a ski holiday because of snow/weather uncertainties. This
703
was an extra reason to choose a skifield or another. In the case the resort could not match a
704
certain percentage of opened ski tracks, a discount (up to 25%) was given to customers.
705
To compensate the losses involved by these discounts (and extra operations costs), the resorts
706
contracted weather related insurances. Companies like Société Générale ( !) and Goldman Sachs
707
( ! !) made available derivative products to the ski industry (Scott, 2007).
708
In France, the national federation of skifields operators (DSF) created NIVALLIANCE in
709
2001. That insurance was based on solidarity between resorts operators. Fees depended on
710
resorts’ revenues but the average was about 10% of annual revenue. A franchise of about 20%
711
of the annual revenue would apply in case of damages payment.
712
For the first season, 93% of ski resorts signed up for that insurance. About 2.8Mewere
713
collected and 2.4Mewere refunded to needy resorts (according to initially defined criterias)
714
(DSF, 2001).
715
4.2.5
Giving up snow dependent activities : activities conversion (Francois, 2009)
716
717
Small to medium size resorts are the most impacted by climate variability and potential
718
change. Even if the definition of snowline reliability can (should ?) be discussed it helps to
719
explain an important aspect. Resorts with altitude ranges that include the snowline would be
720
highly impacted if it was to rise by about 300m (+2˚C rise of temperatures). On top of that,
721
insurances and snowmaking investments have a financial weight much more important for them
722
than for large resorts. These points were discussed in previous sections. One can think here is a
723
double punishment for these small to medium size resorts. For some of them, one can actually
724
wonder wether pushing ahead is worth it.
725
Giving up snow dependent activities relates to previous chapter "4 seasons tourism" : is
726
it possible to convert the mountain rural activity from skiing to alternate activities ? Laurent
727
Reynaud is the president of the ski lifts operators federation DSF. In 2008 he said "100% ski is
728
over. Whitout the ski, all is over". After the ski, what else ?
729
Here is propbably the point where community’s behavior has the strongest impact. Regar-
730
ding the local life and developpement, how should the local authorities react ? Should they
731
help the resorts to go ahead, invest in snowmaking facilities or assist them into changing their
732
activity ? Authorities (government, districts, mayors) have an important role to play here.
43
733
5
GANESH : objectives and strategies
734
5.1
Creating synergy in building a work community
735
GANESH is rather a synthetical project than a creative one. Probably all we need to achieve
736
is linking existing themes and teams. I do not mean here this is not challenging. Coupling
737
existing frameworks may be particularly tough. But i would say we are very close to daily
738
engineering issues.
739
Until a larger project starts that PhD project is the common workbench for snow phy-
740
sic experts, mountain areas economy and development scientists, ski lifts operators and their
741
federation (DSF) (See figure 31).
742
Hopefully all together will build an efficient and interesting workplace.
Figure 31 – GANESH simplified scheme of main actors and goals
743
744
5.1.1
Assessing the impact of current methods on snow physical properties : fields’
campaigns
745
746
The Centre d’Etude la Neige owns a range of instruments to measure snow properties.
44
747
— Snow height, Density, and Snow Water Equivalent(SWE) can be measured by
scaling and weighing snow samples
748
749
— Snow Specific Surface Area (SSA) can be measured thanks to DUFISSS instrument
(Gallet et al., 2009).
750
751
— Penetration strength thanks to the Snow Micro Pen (SMP) (Löwe and van Herwijnen,
2012).
752
753
— Shear resistance thanks to shear vanes. Even if that method’s repeatability is dependent on who realises tests. Much less objective than the SMP.
754
755
— Liquid water content thanks to a dedicated instrument.
756
— Snow albedo can be measured by the spectroradiometer Solalb (under development at
the Laboratoire de Glaciologie et de Géophysique de l’Environnement, LGGE).
757
758
Both 2014-2015 and 2015-2016 winters should be useful to collect a sufficient database to
759
take part into the development and the evaluation of the new snowpack model Crocus RESORT,
760
figure 34. All properties should not be paid an equal attention since some of them were already
761
fairly observed, in particular thanks to Guily (1991). A protocol still has to be prepared and
762
the partners resorts should be precisely defined during next summer.
763
5.1.2
Understanding operators’ management strategies
764
765
One can guess that this section may be quick to sum up but quite tough to achieve. The
766
high altitude, giant and far away resort of Tignes does not exactly have the same requirements,
767
capacities and goals as the Grenoble’s tiny neighbour Lans-en-Vercors. This looks obvious. But
768
then which direction should we step towards ?
769
5.1.2.1 Profiling resorts
770
Under which criterias ?
771
— Urban centers’ proximity ?
772
— Customers : locals, french, foreigners ?
773
— Size : resorts’ total ski lift power (km.skiers/hr) ?
774
— Altitude range ?
775
— Annual revenue or net profit ?
776
— Territory concern and potential help ?
45
Number
Type
Ski Lift Power (SLP)
km.skiers/hr
130
Small resorts
SLP < 2500
37
Middle size resorts
2500 < SLP < 5000
40
Large resorts
5000 < SLP < 15 000
13
Very large resorts
SLP > 15 000
Table 3 – French resorts profiles and numbers as defined by the federation DSF (2013)
777
— Isolated resort or member of a conglomerate ?
778
— Investment capacity ?
779
That point still has to be cleared. There is a lot to do in here. We shall probably give weight
780
to a limited number of criteria with the goal to have a maximum of 4 to 5 different profiles.
781
The deeper we will go the more complicated. When looking with enough details, each resort is
782
a special case. As a first approach, we might just consider the size (ski lift power) which is the
783
DSF (2013) federation criteria.
784
5.1.2.2 Surveying practices and converting data into a systematic approach
785
A range of resorts are opened to partnerships :
786
— Tignes (73)
787
— Les 2Alpes (38)
788
— Chamrousse (38)
789
— La Flégére (74)
790
— Les 7Laux (38)
791
— L’Alpe d’Huez (38)
792
— Samoens (74)
793
— Villard de Lans (38)
794
Assisted production softwares exist from year 2000 approximately and in recent years groo-
795
ming machines were equipped with positionning systems (GPS). Some are even monitoring
796
snow height (such as in Les 2Alpes resort from 2013/2014 winter). Thus data is avalaible and
797
should allow us to use statistical approaches to build working "rules" and habits.
798
799
This should be completed with collecting expert approach of grooming engines drivers,
snowmakers, ski resort managers.
46
800
There might be a gap between ideal working frame (e.g. ideal working timetable regarding
801
snow properties) and actual practices (e.g. drivers availability, labour’s laws). This gap can
802
potentially be important and differ from a resort to another.
803
GANESH’s goal is to model actual practices wether they are ideal for snow preparation or
804
not.
805
5.1.3
806
Developping and running a new Crocus-based snowpack model : Crocus
RESORT
807
Figure 32 – A future extent for the snowpack model Crocus
808
Crocus is a snowpack model developped from the 90’s by the french national weather forecast
809
operator "Météto-France". A review of Crocus structure is given by Brun et al. (2012) and
810
Vionnet et al. (2012). Crocus is part of a modelling chain which includes Safran and Mepra
811
models. Safran fills Crocus with meteorological data in the closest layers of the atmosphere to the
812
ground (Quintana Segui et al., 2008). According to atmospherical conditions and topography
813
(slope, orientation, altitude), Crocus simulates snowpack properties. Stability conditions for
814
avalanche risk forecasting is treated by Mepra model (Giraud, 1992).
815
That chain is based on natural precipitations and evolution of the snowpack. Yet when
816
skiing in resorts one is not skiing on natural snowpack. GANESH aims to create a new branch
817
of Crocus to model resorts snowpacks : Crocus RESORT.
47
818
Preliminary results from Crocus RESORT evolution are displayed on figures 34 and 33.
Figure 33 – Preliminary results for snowmaking by Crocus RESORT. Base run is a natural
snowpack. One can notice productions periods and related snowfall properties : snow density at
350kg/m3 , light green on b) figure and sphere diameter at about 0.3mm on figure d) Belledonne
mountain range - 1800m.a.s.l - flat terrain
Figure 34 – Preliminary results for grooming by Crocus RESORT. Stars show field measurements by A. Guerrand. 2Alpes glacier - Oisans mountain range - 3200 to 3600m.a.s.l
48
819
5.2
820
5.2.1
Will there still be snow for coming winter holidays ?
Coupling Crocus with ski lifts database : a diagnostic of resorts reliability
821
Figure 35 – Coupling snow simulation by Crocus and geographic and economic database from
Irstea (Francois et al., 2013).
822
Francois et al. (2013) showed that snowpack conditions can be coupled with geographical and
823
economic data (location, altitude range, ski lift power, etc.). Local snow conditions depending
824
on slope, orientation, altitude were simulated by Crocus and applied on resorts surfaces. The
825
"100 days rule" was used and the resort’s reliability was assessed (figure 35). That method was
826
applied with natural snowpack properties. Thanks to GANESH results we hope to be shortly
827
able to re-assess these diagnostics with more accuracy.
828
5.2.2
2050 winter holidays landscapes :"winners and losers"
829
830
Previous research was made at the Centre d’Etudes de la Neige, investigating potential
831
climate conditions in the next decades (Rousselot et al., 2012). That project was titled SCAM-
832
PEI and generated forcing data files to run the snowpack model Crocus under changed climate
49
Figure 36 – Natural snow height forecasting from SCAMPEI project (ALD1 run, Rousselot
et al. (2012))
833
conditions. Results from Crocus runs with SCAMPEI data (figure 36) can be visualized on the
834
web www.cnrm.meteo.fr/scampei
835
As far as we know, all diagnostics of resorts reliability in the next decades were made by
836
taking into account natural snow conditions. As shown before, adaptive capacities highly differ
837
from a resort to another. We are convinced that all resorts will not be identically impacted.
838
Some of them may even be positively affected by climate change over an economic point of
839
view. If a significative proportion of resorts can not work any longer, customers will turn to
840
still existing resorts. As shown in figure 11, even if the overall market decreases with poor snow
841
conditions, larger and higher resorts benefit from smallers’ ruin.
842
5.2.3
Diagnostic of potential limits of adaptation strategies
843
844
Hopefully, when Crocus RESORT will correctly model current skifields’ snowpack, we should
845
be able investigate resorts reliability in future climate conditions. We may investigate adapta-
846
tions strategies (among which is snowmaking) and assess wether they are relevant or not to face
847
climate change. Scott (2007) wondered if snowmaking is actually a relevant method : will the
848
weather be cold enough to procude snow ? Wll there be enough water to do so ? How expensive
849
will that be ?
50
Figure 37 – Somehow that diagnostic map should be impacted by climate change. (Francois
et al., 2013)
850
851
6
Conclusions
[...]
852
Acknowledgement
853
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854
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