User:Mafue/Avalanche Forecasting

Introduction
With snow avalanches, there is little that it will not take with it. Rocks, soil, trees, ice and the debris of anything that gets in its way. Avalanches interrupt transportation, destroy buildings, pylons and transmission towers and can ruin the reputation and economy of otherwise desirable settlements. The economic cost of prevention and reaction to all these impacts is considerable, but more importantly the human cost is often high and many people are injured or killed in avalanche related accidents every year.

One of the largest avalanche disasters in recent history occurred in February 1999 when a single avalanche in Galtür cost thirty-eight people their lives (Thompson 1999). The heavily populated mountain ranges of Europe are often where the effects of avalanches are most serious. In the Alps approximately 120 people are killed every year. In some years the death toll is even higher. For example, in 1951, 98 people were killed in Switzerland and, in 1954, 143 people were killed in Austria alone (McClung and Schaerer 1993). Little was known about the physics of avalanches or how to predict them back then in the fifties so these disasters were very difficult to forecast. Fifty million people now go skiing every year (Holdsworth 1998) so it is clear that we need to know when avalanches are going to occur so we can act proactively and save lives and money rather than waiting for the avalanches to happen and then act.

To know when an avalanche may happen we need to know under what conditions they are likely to occur and which of these conditions are most important. Once these conditions are known, processes can be set up to judge whether or not an avalanche is likely. This is the science of avalanche forecasting, a science in which there are no second chances. An unforeseen avalanche could result in loss of life, disruption and high clean up and repair costs; whereas almost as seriously, a false alarm may result in unnecessary panic and evacuation. The net result of both these occurrences being loss of faith in the forecaster and in the science as a whole. If people do not trust the warnings or assurances of an avalanche forecaster they become ineffective no matter how accurate they are next time. Williams (1980) states that to be effective, an Avalanche warning program must inspire public confidence. It is therefore in everyone’s interest for the processes involved in avalanche forecasting to be a fool–proof and trustworthy as possible.

According to LaChapelle (1977), avalanche forecasting has been done ever since human beings first began to live on mountains, even by resorting to witchcraft, he suggests. Since the 1950s methods of avalanche forecasting have become more scientific, although depending on empirical experience and intuition. Recent work has concentrated on the use of computers and models with some exceptions, notably Professor Dent of Montana State University who places an observation hut in the path of an artificially induced avalanche. From there he can make close observations of the way the snow travels and the velocities, densities and pressures thus involved; in the hope of learning more about how an avalanche is initiated. He hopes to develop new models and tools to remove the educated guesswork, replacing it with ‘hard science’ (Dent in Holdsworth 1998). The Swiss Snow and Avalanche Institute has recently initiated similar research led by Dr. Issler. Some of their findings were critical in solving the mystery of the catastrophic and unexpected Galtür avalanche in February 1999. Before these scientists braved real avalanches, studies relied upon the simulation of avalanches using powdered glass in water tanks (Thompson 1999, Hopfinger et al 1977).