New simulations can improve avalanche forecasting

Date:
January 19, 2022
Source:
Simon Fraser University
Summary:
Computer simulations of snow cover can accurately forecast avalanche
hazard, according to a new international study. Currently, avalanche
forecasts in Canada are made by experienced professionals who rely
on data from local weather stations and on-the-ground observations
from ski and backcountry ski operators, avalanche control workers
for transportation and industry, and volunteers who manually test
the snowpack.



FULL STORY ========================================================================== Computer simulations of snow cover can accurately forecast avalanche
hazard, according to a new international study involving researchers
from Simon Fraser University.


========================================================================== Currently, avalanche forecasts in Canada are made by experienced
professionals who rely on data from local weather stations and
on-the-ground observations from ski and backcountry ski operators,
avalanche control workers for transportation and industry, and volunteers
who manually test the snowpack.

But simulated snow cover models developed by a team of researchers
are able detect and track weak layers of snow and identify avalanche
hazard in a completely different way -- and can provide forecasters with another reliable tool when local data is insufficient or not available, according to a new study published in the journal Cold Regions Science
and Technology.

"As far as natural hazards go, avalanches are still one of the leading
causes of fatalities in Canada," says Simon Horton, a post-doctoral fellow
with the SFU Centre for Natural Hazards Research and a forecaster with Avalanche Canada.

"We've had these complex models that simulate the layers in the snowpack
for a few decades now and they're getting more and more accurate, but it's
been difficult to find out how to apply that to actual decision-making
and improving safety." Researchers took 16 years' worth of daily meteorological, snow cover and avalanche data from two sites in Canada (Whistler and Rogers Pass, both in British Columbia) and Weissfluhjoch
in Davos, Switzerland and ran computer simulations that could classify different avalanche situations.

The simulations could determine avalanche risk, for either natural
or artificial release, for problem types such as new snow, wind slab, persistent weak layers and wet snow conditions.

"In the avalanche forecasting world, describing avalanche problems --
the common scenarios that you might expect to find -- are a good way
for forecasters to describe avalanche hazard and communicate it to the
public, so they know what kind of conditions to expect when they head
out," says Horton.

"So that information is already available, except those are all done
through expert assessment based on what they know from available field observations. In a lot of situations, there's a fair bit of uncertainty
about the human assessment of what these types of avalanche problems
will be.

"That's where having more automated tools that can help predict
potential hazards can help forecasters better prepare an accurate,
precise forecast." The results of the study showed the modelling was consistent with the real observed frequencies of avalanches over those 16
years and that the approach has potential to support avalanche forecasting
in the future.

Researchers also believe the modelling might be useful to study the
future impacts of climate change on snow instability.

========================================================================== Story Source: Materials provided by Simon_Fraser_University. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Benjamin Reuter, Le'o Viallon-Galinier, Simon Horton, Alec van
Herwijnen,
Stephanie Mayer, Pascal Hagenmuller, Samuel Morin. Characterizing
snow instability with avalanche problem types derived from snow
cover simulations. Cold Regions Science and Technology, 2022; 194:
103462 DOI: 10.1016/j.coldregions.2021.103462 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/01/220119135042.htm

--- up 6 weeks, 4 days, 7 hours, 13 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1:317/3)