Climate change and the skiing industry in southern Ontario (Canada): exploring the importance of snowmaking as a technical adaptation
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Résumé
CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 23:171-181 (2003) - doi:10.3354/cr023171 Climate change and the skiing industry in southern Ontario (Canada): exploring the importance of snowmaking as a technical adaptation Daniel Scott1,*, Geoff McBoyle2, Brian Mills1 1Adaptation and Impacts Research Group, Environment Canada, at the Faculty of Environmental Studies, and 2Department of Geography, Faculty of Environmental Studies, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada *Email: dj2scott@fes.uwaterloo.ca ABSTRACT: The winter tourism industry has been repeatedly identified as potentially vulnerable to global climate change. Climate change impact assessments of ski areas in Australia, Europe and North America all project negative consequences for the industry. An important limitation of earlier studies has been the incomplete consideration of snowmaking as a climate adaptation strategy. Recognising that snowmaking is an integral component of the ski industry, this study examined how current and improved snowmaking capacity affects the vulnerability of the ski industry in southern Ontario (Canada) to climate variability and change. A 17 yr record of daily snow conditions and operations from a primary ski area in the region was used to calibrate a ski season simulation model that included a snowmaking module with climatic thresholds and operational decision rules based on interviews with ski area managers. Climate change scenarios (2020s, 2050s, 2080s) were developed by downscaling climate variables from 4 general circulation models (using both IS92a and SRES emission scenarios) with the LARS weather generator (parameterized to local climate stations) for input into a daily snow depth simulation model. In contrast to earlier studies, the results indicate that ski areas in the region could remain operational in a warmer climate, particularly within existing business planning and investment time horizons (into the 2020s). The economic impact of additional snowmaking requirements remains an important uncertainty. Under climate change scenarios and current snowmaking technology, the average ski season at the case study ski area was projected to reduce by 0-16% in the 2020s, 7-32% in the 2050s and 11-50% in the 2080s. Concurrent with the projected ski season losses, the estimated amount of snowmaking required increased by 36-144% in the scenarios for the 2020s. Required snowmaking amounts increased by 48-187% in the scenarios for the 2020s. The ability of individual ski areas to absorb additional snowmaking costs and remain economically viable in addition to the relative impact of climate change on other nearby ski regions (Québec, Michigan and Vermont) remain important avenues of further research. The findings reveal the importance of examining a wide range of climate change scenarios and the necessity of including snowmaking and other adaptation strategies in future climate change vulnerability assessments of the ski industry and winter tourism in other regions of the world. KEY WORDS: Climate change · Skiing · Adaptation · Snowmaking · Canada Full text in pdf format PreviousExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 23, No. 2. Online publication date: January 31, 2003 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2003 Inter-Research.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».