process-based simulation of snow cover evolution …...and optimizationof snowin alpine ski resorts...

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Provision of a prediction system allowing for management

and optimization of snow in Alpine ski resorts

Process-based simulation of snow cover evolution in ski

resorts using the AMUNDSEN model: results and validation

Florian Hanzer, Daniel Günther, Ulrich Strasser, Valentina Premier, Mattia Callegari, Carlo Marin, Claudia Notarnicola

EGU 2020, 7 May 2020

PROSNOW

This project has received funding from the European Union’s Horizon 2020

research and innovation programme under grant agreement No730203

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Snow management modeling

§ Physically based snowpack models

allow simulation of the (natural) snow

cover

§ For the application in ski resorts, snow

management processes (snowmaking

and grooming) must be considered

§ Physical component: description of the snowmaking and grooming processes

§ Socioeconomic component: when, where,

and how much to produce

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Simulated

Observed

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PROSNOW

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§ H2020 project aiming at

developing an operational

forecasting system for snow

conditions in ski resorts

§ Time scales from days until

several months ahead

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Snow models in PROSNOW

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Pilot ski resorts

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Snow production parameters

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Snow demand

§ Is there a need for producing?

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Ambient conditions

§ Air temperature

§ Humidity

§ Wind speed

Adapted from Hofstätter (2008)

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Infrastructure

§ Snow guns

§ Water availability

§ Pumping capacity

�14 �12 �10 �8 �6 �4 �2

Wet-bulb temperature [�C]

0

5

10

15

20

25

30

Wat

erfl

ow

[m3

h�

1]

Brand A

Brand B

Hanzer et al. (2014)

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Tw < TT?

Wind speed < WT?

Calculate PR Distribute snowReduce accor-

ding to FT

WA > 0?

Reduce accor-ding to WL

Update WA

Current date and time

within PPb and PT b?

Current date and time

within PPr and PT r?

Cum. production< CT?

Snow depth < STr?Snow depth < ST

b?

Yes

Yes

Yes

No

Yes(AMUNDSEN / Crocus)

Yes

Yes

Yes

Yes(SNOWPACK/Alpine3D)

(AM

UN

DSE

N /

SNO

WPA

CK

/Alp

ine3

D)

(AMUNDSEN / SNOWPACK/Alpine3D) (AMUNDSEN / SNOWPACK/Alpine3D)

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Grooming parameters

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§ Explicit simulation of densification

§ Implicit consideration of (re-)distributionSpandre et al. (2016)

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Snow management configurations

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Use a range of

configurations

(parameter sets) to

account for different

snow management strategies during the

season

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Validation data

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Type Temporal resolution Spatial resolution

Snow depth from groomers daily 3 m

Sentinel-2 snow cover maps up to 5-daily 10 m

Recordings from snow guns (water

consumption, energy consumption, wet-

bulb temperature)

(sub-)hourly point-based

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Results: snow depth

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Results: snow depth (temporal evolution)

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Oct Nov Dec Jan2019

Feb Mar Apr May

0.0

0.5

1.0

1.5

Snowdepth(m)

measured

simulated (natural snow + grooming)

simulated (natural snow + grooming + snowmaking)

Obergurgl, 2770 m a.s.l.

Oct Jan2017

Apr Jul Oct Jan2018

Apr

0.00

0.25

0.50

0.75

1.00

1.25

Snowdepth(m)

measured

simulated (natural snow + grooming)

simulated (natural snow + grooming + snowmaking)

Colfosco, 1660 m a.s.l.

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Results: snow cover maps

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(red = clouds / no data)

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2016-01 2016-05 2016-09 2017-01 2017-05 2017-09 2018-01 2018-05 2018-09 2019-01 2019-05

0.2

0.4

0.6

0.8

1.0

Snow-covered slope area (obs)

Accuracy

Evaluation for slope pixels only

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Results: water consumption

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Nov Dec Jan2017

Feb Mar Apr

0

5000

10000

15000

20000

25000

Totalwaterconsumption(m3d−1)

measured

simulated

(blue shading = range of snow management configurations; solid line = mean)

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Conclusions

§ Model implementation represents state of the art of the integration of

snow management processes in physically based snowpack models

§ “Generic” snow management configurations produce robust results for

all considered ski resorts even without further tuning

§ Operational applications require assimilation of local measurements

(snow depth and water consumption)

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Thank you!

Further information:

www.prosnow.org

Hanzer, F., Carmagnola, C. M., Ebner, P. P., Koch, F., Monti, F., Bavay, M., Bernhardt, M.,

Lafaysse, M., Lehning, M., Strasser, U., François, H., Morin, S.: Simulation of snow

management in Alpine ski resorts using three different snow models. Cold Regions

Science and Technology, 172, 102995. https://doi.org/10.1016/j.coldregions.2020.102995.

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https://doi.org/10.1016/j.coldregions.2020.102995

process-based simulation of snow cover evolution …...and optimizationof snowin alpine ski resorts...

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