Nature is often more complicated than it seems. In order to study it, scientists often develop models that represent reality in a simplified manner. Working with models has become a significant part of our research at the WSL.

Thanks to the ever-increasing power of computers, modelling has been gaining enormously in importance over the last few years. However, models per se are nothing new: the first scientific model probably had its origins with the Greek philosopher Aristotle, who developed a model of the solar system that was supposed to explain the collected observations of the sky and predict planetary positions.

Although models are often substantial simplifications of reality, they have become indispensable to science, with applications ranging from meteorology (weather forecasting) to healthcare (e.g. spread of epidemics) to chemistry (e.g. air pollution) to environmental science. At the WSL, we develop and use models to simulate the habitats of plants and animals, for example, or to predict rockfalls.

Models allow us to generate many results in a short time and to run different scenarios by varying the baseline conditions. Moreover, researchers can use models to carry out experiments that cause no harm to the environment, for example in rockfall simulations (LINK to RAMMS:Rockfall). Models can also reveal possible relationships in nature which would not be apparent by mere observation.

What are the different types of models?

To develop a model, e.g. for an avalanche, it is essential to first choose the right degree of simplification and representation for each sub-section and characteristic of the avalanche. Following this, laws are established to link these components together and describe the formation of the avalanches. These laws are usually expressed in the form of mathematical equations.

There are various categories of models depending on the modelling approach, e.g.:

  • Physical models: A physical model is based on a mathematical equation which, in turn, is based on a physical process, e.g. diffusion, melting processes or heat conduction. At the SLF, we model the snowpack to better understand avalanches. The SNOWPACK snow cover model was designed to account for physical processes such as the temperature exchange between the snow and the atmosphere, or phase transformations between snow and ice or snow and water.
  • Statistical models: In a statistical model, simple mathematical functions are derived from a great volume of collected data, which is derived from observations of the factual relationships. Based on a huge volume of data from automatic snow and weather stations, we can, for example, model snow melt or water discharge into the soil, which enables us to determine the generation of run-off – a decisive factor in flood forecasting.

Modeling at the SLF

At the SLF too models are becoming more and more important and represent now a very significant part of the research performed at the institute.
The SLF uses models in order to

  • study the development of the snow cover and its exchanges its the surroundings
  • quantify the changes in the permafrost
  • better understand the processes of natural hazards
  • predict the runout of avalanches
  • evaluate the water resources contained in the snowpack