This project analyses the impact of climate change on the financial situation of Ski resorts.
In total six ski resortes were selcted for this study:
- https://www.sattel-hochstuckli.ch/en (800m to 1200) - Central Switzerland
- https://www.hoch-ybrig.ch/en/winter/service/brochure/ (900-1900) Central Switzerland
- https://www.atzmaennig.ch/en/winter/ (800-1200) Zurich Region
- https://www.villars-diablerets.ch/en/ (1200-3000)
- https://www.champex.info/ (1486-2194)
- https://www.davos.ch/winter/berge/jakobshorn/ (1500 - 2500)
CH2018 link
T1 - Climate Data
CH2018
In this task, the data of located climate grids inside each Ski resorts are extracted from CH2018 datasets.
CH2018 "Switzerland CH2018 climate scenarios" here
T2 - Snow Model
Snow model Ablation Accumulation
In this task, a modular grid-based snow model was developed. The current model consists of Ablation, and Accumulation modules, with the possibility of adding new modules in the future. The main
Marty (2017) "How much can we save? Impact of different emission scenarios on future snow cover in the Alps" link
Farinotti (2012) "Runoff evolution in the Swiss Alps: projections for selected high-alpine catchments based on ENSEMBLES scenarios" link
Huss (2008a) "Determination of the seasonal mass balance of four Alpine glaciers since 1865" link
Huss (2008b) "Modelling runoff from highly glacierized alpine drainage basins in a changing climate" link
Hock (2005)"Glacier melt: a review of processes and their modelling" link
No1. snowModel version 2
T3 - Snow Model post processing
Visualization of snow model results Visualization of tipping points
T4 - Deep uncertainty in climate scenarios
Deep Uncertainy
In this task a paython code was developed to produce new climate scenarios based on CH2018 dataset
van Ginkel et al (2020), "Climate change induced socio-economic tipping points" link
Kwakkel (2017), "The Exploratory Modeling Workbench: An open source toolkit for exploratory modeling, scenario discovery, and (multi-objective) robust decision making" link
Damm et al (2014), "Does artificial snow production pay under future climate conditions?"link
No4. Randomness notebook
No5. Visualization of snow model outputs
No6. Visualization of snow model outpts with elevation bands
T5 - Decision Making under Deep Uncertainty
Deep Uncertainy Decision Making
In this task, a python code will be developed to connect our existing notebooks (No.1, No.4) to the Exploratory Modelling and Analysis (EMA) Workbench here
van Ginkel et al (2020), "Climate change induced socio-economic tipping points" link
Kwakkel (2017), "The Exploratory Modeling Workbench: An open source toolkit for exploratory modeling, scenario discovery, and (multi-objective) robust decision making" link
Damm et al (2014), "Does artificial snow production pay under future climate conditions?"link
No1. snowModel version 1
No2. Visualization of snow model
No3. Visualization of snow model with elevation bands
No4. Randomness notebook
No5. Visualization of snow model outputs
No6. Visualization of snow model outpts with elevation bands
No7. snowModel version 2_EMA-workbench
The results of the snow model (csv format) for the case studies are collected here. In these analyses, no elevation bands were considered for Villars-Diablerets and Jakobshorn case studies.
After considering elevation bands (500m) for the case study No.4 (Villars-Diableret) and No.6 ( Jakobshorn), new results were generated and stored here, and here.
After considering the uncertainty of climate scenarios by producing 68 new climate scenarios here and runing the snow model, the new results were stored here, here, and here.
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