Understanding avalanche terrain: The development of a GIS-based danger zoning model for Tyrol, Austria

Transcription

1 Understanding avalanche terrain: The development of a GIS-based danger zoning model for Tyrol, Austria Max Gerritsen Supervisor: dr. ir. Arend Ligtenberg (Wageningen University & Research) -

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3 Understanding avalanche terrain: the development of a GIS-based danger zoning model for Tyrol, Austria Masterthesis by Max Gerritsen Supervised by dr. ir. Arend Ligtenberg (Wageningen University & Research) Geographical Information Management & Applications Utrecht University University of Twente Delft University of Technology Wageningen University & Research September

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5 Preface All my life, I have been fascinated by the mountains in wintry seasons. At the age of 7, I saw a snowy mountainous landscape for the first time in my life when my parents took me and my sister on a winter s sports vacation to Gerlos, Austria. Together, we started to discover what grew out to be our passion; being out in such beautiful mountainous terrain to ride our snowboards as fast as possible. Years later, I started to focus more on backcountry off-piste snowboarding with my friends on our own travels. With the joy that we got from exploring challenging and more remote terrain, concerns about our safety came as well. Every meter that we went further away from prepared slopes meant increased avalanche danger. This did not hold us back from continuing exploring; we started delving deeper into avalanche prevention information presented by fellow snowboarders, websites and magazines. We discovered that there was a lot to be learned. In 2013, when I started thinking about possible master s thesis subjects, avalanche research constantly kept going round in my head. Instead of only reading magazines for my own knowledge, I wanted to contribute to academic avalanche research. I realized that location is crucial in avalanche forecasting, zoning and rescuing. The success rate of these activities depends on the ability to know where avalanches are most likely to occur. This knowledge has to do with snowpack conditions, meteorological factors and of course, terrain characteristics. And is there a better way to investigate spatial patterns than using geographical information systems? Therefore, I started to develop clear goals and GISmethods and it turned out to be challenging, but doable. Fortunately, my supervisor, Arend Ligtenberg, agreed with the plan. Now, almost a year later, my zoning model of avalanche danger is finished and I learned a lot on the way. Planning issues, conceptualization issues, data issues, modeling issues, writing issues; they all have their share in the process that I went through from the begin to the end. During this process, Arend Ligtenberg was there to keep me on track, which I would like to thank him for. I would also like to thank my parents and my sister for their support and enthusiasm, my Vlieg m Erin friends for the adventures, my GIMA-buddies for technical advice and the Poederpiraten for making me more fanatic every year. Max Gerritsen September

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7 Contents Preface... 5 Abstract Introduction Theoretical framework Introduction Snow avalanches, an underestimated hazard? Types of avalanches and their danger Avalanche attention: a broad range of perspectives Avalanche management in the Alps A closer look at avalanche zoning in Tyrol Avalanche danger: contributing factors Elevation Slope steepness Aspect Human triggering Ski resort characteristics Land cover type Slope shape Predictability expectations and interpretations of results Methodology Introduction Study area: Tyrol Construction of the model Historical avalanche data The factors values Introduction Elevation Slope steepness Aspect Human triggering Ski resort characteristics Land cover Slope shape The factors weights The use of logistic regression Towards weight scores for each factor Calibrating and validating the model Calibrating the model for Landeck-Imst Validating the model Data quality Switching between measurement levels

8 3.5.2 The avalanche database s potential Focus on release points Modeling results Introduction Understanding of avalanche-causing factors Introduction Elevation Slope steepness Aspect Human triggering Ski resort characteristics Land cover type Slope shape Calibrating the model for Landeck-Imst Introduction Calculating the danger Assessing the model s quality Validating the model The validation area Assessing the model s success Conclusions Answering the questions Discussion, reflection and recommendations Resources Information from interview with Lawinenwarndienst Tirol Literature Appendices Appendix 1: Value table Elevation Appendix 2: Value table Slope steepness Appendix 3: Value table Aspect Appendix 4: Value table Human triggering Appendix 5: Characteristics scores per ski resort Appendix 6: Value table Ski resort characteristics Appendix 7: Meaning of CORINE numeric codes Appendix 8: Value table Land cover Appendix 9: Value table Slope shape Appendix 10: SPSS outputs logistic regression for the first six factors Appendix 11: SPSS outputs logistic regression for ski resort characteristics

9 Abstract Avalanches are among the most frightening hazards that people face in mountainous regions of the Alps, killing around 100 Alpine residents and tourists each year. Austria welcomes the most tourists in wintry months, making itself the most avalanche-sensible country in the region and Tirol, - Tyrol in English is the leading province when it comes to winter tourism. Therefore, the Lawinenwarndienst Tirol zones the danger for avalanches for warning purposes. Its warning system relies mostly on temporary weather and snow conditions and is thus updated daily. However, large areas tend to be given the same danger level while they vary heavily in geographic terrain. Therefore, this research sets out to zone avalanche danger leaving the temporary conditions empty, so that an all-year-valid template with more geographical detail can be developed. Tyrol is obviously a place that could benefit from this research and the model is thus designed for a part of Tyrol, the area of Landeck-Imst. The rest of Tyrol is used to validate the model; to what extent is it applicable to other, similar areas? The key to success for this model to understand the role of avalanche-causing factors. These are derived from theoretical sources and tested on dangerous situations and weight. Elevation and slope steepness turned out to be the most influential ones, followed by the presence of skiers and other winter s sports tourists. Less important are the slope shape (convex, concave or planar), a slope s direction to the sun, the type of land cover and the characteristics of a ski resort if nearby. The most dangerous situations per factor are the highest located areas, slopes with a steepness around 38 degrees, areas within or nearby ski resorts, north-facing slopes and terrain of bare rocks and glaciers. The model is constructed by discovering under which circumstances the factors are getting dangerous. All situations are reclassified to new values, depending on how dangerous they are. This is done by assessing historical avalanche data; which terrain produces the most avalanches? Together with each factor s importance, these values are making up a place s danger. When comparing with the real avalanche locations, the model turns out to be quite successful. Almost every avalanche from the past five years took place in an area that is marked by the model as dangerous above average. Fortunately, the model s success does not stop at Landeck-Imst s boundaries. Applying it to the rest of Tyrol results in meaningful outcomes, although success rates are obviously higher in the original development area. To conclude, it is reasonable to believe that this model could be a useful contribution to the forecasting of the Lawinenwarndienst. 9

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11 1. Introduction During the year s colder months, millions of people move to the Austrian Alps for a week or two. They enjoy something that is missing in their home countries: the snowy mountains which offer many possibilities for winter s sports activities. These activities as a business are a huge contribution to tourism being Austria s largest economic industry. The province of Tyrol accounts for most of the wintry visitors, welcoming almost 10 million tourists each winter (Bundesland Tirol, 2013). However, more tourism does not only mean more money, it causes more problems as well. Apart from the ecological damage that this industry causes, it can also be dangerous for the people themselves. The problem is that winter s sports activities and avalanches go hand in hand, since the most attractive terrain and conditions also produce the most avalanches. More people get trapped in avalanches in Tyrol than in any other Austrian province (Bergrettung Tirol, 2008). In the entire Alps region, an average of 106 people per year gets killed by avalanches. And these victims are not only tourists that visit the dangerous areas voluntarily; these can be residents of mountainous settlements as well was the year in which the Tyrolean village of Galtür, generally known for its good snow conditions, was partly destroyed by a massive avalanche that came down and killed 31 people (Keiler et al., 2005). Avalanches are thus a serious threat in Tyrol, and there are lots of activities to reduce the human and economic damage caused by these natural hazards. There are rescue services that try to save human lives when people are buried under the snow. And in last decades, there has been a growing market for all kinds of portable devices that make people easier to find when trapped in an avalanche, such as backpack airbags and transceivers. However, these measures only deal with the consequences of an avalanche that has already occurred and caught people. The most important goal always remains to be able to predict them. And being a natural hazard, precise avalanche prediction is almost impossible (Schweizer, 2008). Probably no one could ever make danger calculations such as the chance of a place to host an avalanche. However, it is possible though to zone the danger. This does not deal with the question how likely is it that an avalanche takes place here?, but it is more like where are potential avalanches most likely to take place?. Pointing out the most dangerous places, that is what this research is about. One important remark has to be made; there is a clear distinction between avalanche risk and avalanche danger. The concept of risk the potential for damage requires the presence of real danger (slopes susceptible to avalanching) and people and property in potential harm s way. If an avalanche may occur in a remote mountain valley, without trees, settlements, or private property, then there is danger for avalanches, but no risk. However, if a potential avalanche is within reach of settles areas, avalanche danger and high risk for destruction simultaneously exist (Kriz, 2001, pp.78). The choice to only focus on danger means that the potential damage to people is not included. This could be filled in in following research by incorporating population density and other demographic factors. 11

12 The relevance for society in general is to contribute to the existing avalanche zoning system that is maintained by the Lawinenwarndienst Tirol. As will be described in the theoretical chapter, this system heavily relies on temporal snow and weather conditions that determine the danger at the moment. But these maps change constantly and have little attention for terrain s geographical detail. The main goal is to add this detail by constructing a model, rather focusing on terrain than on snow and weather conditions since it should be valid all year long. This model should be seen as a geographical template which could be filled with temporal conditions by specialized parties such as the Lawinenwarndienst to make it up to date. It strives to be at least useful for the area of Landeck-Imst (paragraph 3.2), which it is developed for, but its applicability is tested beyond this area s borders as well. Most of the detailed studies are done on local level and this research investigates whether detailed zoning can still be valid for larger areas. The scientific relevance is to be found deeper inside the avalanche research in general. First of all, after decades of avalanche research, there is a pretty clear understanding of which factors lead to the occurrence of avalanches. For most of the factors, there is theoretical knowledge, or at least expectations, of the conditions that make factors dangerous. But not every factor is agreed on, as explained in paragraph 2.7. This research is innovative by heavily focusing on the presence of skiers and other avalanche-causing tourists. Also, there is little to be found about the contribution of each particular factor individually; how important is one factor compared to another? That is what this research focuses on as well by analyzing them in a GIS-based environment. Analyzing relevant geographical factors combined with aiming to develop a detailed model for a larger area is thus the scientific goal of this study. To make the aims of this research more concrete, it is divided into five goals. Gain insight in the current situation of avalanche-zoning in Tyrol Make clear what the exact role of avalanche-causing factors is Use these factors to develop a model that zones avalanche danger without depending on knowledge of temporal snow and weather conditions Validate the model for further use in areas with similar terrain These goals are translated to questions that can be answered in the concluding chapter. These are: What is the current situation of avalanche-zoning in Tyrol and how could it be improved? Which factors contribute to increased avalanche risk? and what is their influence? To what extent could these factors be used for the development of a model that zones avalanche danger in the Landeck-Imst? To what extent is this model able to zone avalanche danger in similar areas? After all, these questions are pieced together into one central question that summarizes this research in one sentence: How do avalanche-causing factors influence the danger for avalanches and how could this knowledge be used for developing a model that zones avalanche danger for the province of Tyrol? 12

13 The following chapters are splitting the research into a classical division. First, the theoretical foundation discusses avalanches in general, the need for location knowledge, the current situation of avalanche zoning in Tyrol and in the end, the crucial factors that are the basis of this research. Then, the methodological chapter is about the study area and how it is divided into a calibration and a validation area. It also explains the construction of the model in general, the need for factors importance statistics and the entire processing from factor layers to danger values that make up the model. The chapter about results presents the outcomes of the modeling, not only for the Landeck-Imst area but also for the validation area of the rest of Tyrol. And last, the concluding chapter answers the research questions and discusses the research afterwards. 13

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15 2. Theoretical framework 2.1 Introduction This chapter is all about literature; it provides a theoretical foundation for the methodological steps taken to complete this research. Research on avalanches has been around for decades. Reviewing the literature about avalanches learns that research done on this topic does not outdate very fast, since the release of an avalanche is caused by multiple factors that hardly change over time. On the other hand, the quality and accuracy of avalanche research has improved throughout the years as a benefit of technical developments such as the increasing use of geographical information systems that enable researchers to process and analyze large datasets at a time (Gruber & Bartelt, 2007; Gruber & Haefner, 1995; Ghinoi & Chung, 2004; Margreth & Funk, 1999). This chapter uses the literature, both older and more recent, to explain why location is so important in avalanche research and to investigate which factors make up snow avalanche danger in mountainous regions. First, this chapter describes where avalanches are frequent hazards and why they are as deadly and dangerous as they are. Second, a short overview from the broad spectrum of existing literature is presented; what do they mostly focus on and what do they have in common? Third, avalanche management in general is discussed; which measures are taken in the Alps for prevention? Fourth, the current situation of avalanche zoning in Tyrol is zoomed in at. Fifth, the factors that contribute to avalanche danger are listed, according to those studies that focus on how avalanches occur. Sixth, it is explained how these factors seem to cause avalanches. And last, some remarks about predictability of avalanches in general are made. These are the issues this chapter deals with. 2.2 Snow avalanches, an underestimated hazard? In mountainous regions, snow avalanches are one of the major natural hazards for people, infrastructure and the ecological environment. Although typical mountainous natural hazards include rock avalanches, rock fall, landslides, debris torrents and ice avalanches, snow avalanches are the most serious event in this category (Lied, 2004). These mountainous regions account for 20 percent of the earth s continental land mass. The mountain ranges that are the habitat of snow avalanches are those located at sufficiently cold latitudes and those reaching high enough to gather thick enough snow packs (Pudasaini & Hutter, 2007). Although the world s largest mountains, mainly located in the Himalayas, produce the largest avalanches, no region suffers more than the Alps of Austria, France, Italy and Switzerland. Other European regions suffer significantly but less from avalanches and so are parts of the Rocky Mountains, in which the many avalanches that take place have less impact due to low population density (Pudasaini & Hutter, 2007). The European countries are heading the lists of economic suffering as well, since no single region in the world uses its mountainous character more in an economic way than the Alps (McClung & Schaerer, 2006). The lack of economic activity in the world s mountainous regions outside 15

16 the Alps, along with a low population density in general, is more or less why snow avalanches are missing in lists of deadliest or economically most devastating natural hazards; the fact that mountainous regions tend to be sparsely populated makes avalanches less deadly than the big five. These include earthquakes, floods, tropical storms, drought and volcanic hazards. Compared to those hazards, snow avalanches do not create much damage. In fact, one large earthquake or flood can account for more human or economic damage than all of the world s avalanches in human history together (McClung & Schaerer, 2006; CBC News, 2010). However, for inhabitants of Alpine regions and for travelers in these areas, snow avalanches are a serious hazard. For them, the big five are less important when it comes to personal safety. Avalanches, meanwhile, are part of their everyday life; the snowpack that grows in wintry months can change from innocent to a dangerous threat when it decides to crack off and slide down a slope. Each year, around 150 people get killed in an avalanche in the seventeen countries that are members of the International Commission for Alpine Rescue (ICAR) (National Geographic, 2014a; The Guardian, 2012; CBC News, 2012; Canadian Avalanche Center, 2014a) and another 100 are estimated to get trapped in avalanches in non-icar countries (Schweizer et al., 2003). Now, it may be no surprise that the Alps account for the majority of avalanche casualties; a yearly average of 106 people die in avalanches in these countries. Numbers can vary from 60 in good years to 180 in bad years (Österreichisches Kuratorium für alpine Sicherheit, 2003). Apart from their deadly character, avalanches can have effects on all kinds of environment; buildings, infrastructure and the ecological environment are all threatened by the hazard that is a snow avalanche. The effects of an avalanche on the habitat of animals and on soil and slope structure are devastating, and even manmade constructions are not considered safe when a large and powerful avalanche hits them. Although precise measurements in terms of economic loss are difficult to make, even the most conservative estimates agree that only in the United States, these losses amount to millions of dollars when accounting for property damage, snow removal and avalanches rescues (Mock & Birkeland, 2000). Large-scale avalanches can cause these amounts of damage on their own; in February 1999, a few avalanches in Switzerland claimed 17 lives and caused damage of over 600 million Swiss Francs (Bründl et al., 2004; WSL-Institut für Schnee- und Lawinenforschung SLF, 2013). And there is always the infamous example of a large scale avalanche that took place in the Austrian village of Galtür in February 1999 and killed 31 people. The town was partly destroyed and inhabitants and travelers were evacuated, causing for millions of Euros on damages (Keiler et al., 2005; BBC, 2013). The destroying force that avalanches are affects the economy in several industries such as the transportation, construction and tourism branches. However, many studies agree that the large amount of human casualties is the main problem that avalanches cause. 2.3 Types of avalanches and their danger So, what is it that makes these events so deadly? When answering this question, it is important to distinguish three types of snow avalanches. Each type has its own character and its own level of danger. 16

17 Almost every avalanche, of any type, starts when a snow pack layer is not well bonded to the slope itself or to other snow pack layers underneath. When triggered, the avalanches rushes downhill and grows on its way. The types of avalanches differ mutually at starting points and especially at the type of snow. Loose snow or sluff avalanches (figure 2.1) start when unattached snow crystals slide down a slope. It releases at one single point and widens from there as it descends, forming an inverted V-shape. These avalanches consist of cold, powdery snow that has been unable to attach to its underlying layer. Most of the avalanches of this type occur at high areas, and are most frequent on steep slopes. This type of avalanche is very common, but creates relatively little damage (National Geographic, 2014b; Northwest Weather & Avalanche Center, date unknown; Forest Service National Avalanche Center, 2013a, Forest Service National Avalanche Center, 2013b). Wet snow avalanches (figure 2.2) occur when the snow pack layers are warmed by high temperatures. Figure 2.1: Sluff avalanche Source: Cooke City Chronicle Blogspot, 2010) Higher concentrations of water that percolate through the layers weaken the bonds between them. These avalanches move much slower than other types since they are heavier and mostly found on relatively flat slopes, and are harder for a person to trigger. Since they are not as frequent as other types and because of the characteristics named above, these avalanches do not account for most of the casualties, but they can be dangerous because of their heaviness (Forest Figure 2.2: Wet snow avalanche Source: Sayer,

18 Service National Avalanche Center, 2013a; Forest Service Avalanche Center, 2013b). Slab avalanches (figure 2.3) are avalanche type number one when it comes to killing; this type account for nearly all avalanche deaths. Usually, they occur at medium-steep slopes (paragraph 2.7.2). Regularly referred to as the White Death, these avalanches occur when the weakest snow pack layer lies deeper than the top layer. The snow cover ruptures or breaks away all at once, leaving behind a clear fracture line. The weakest layer slides down the slope, along with all layers on top of it. Or, in other words: Picture tipping the living room table up on edge and a magazine sliding off the table. Now picture you standing in the middle of the magazine (Forest Service National Avalanche Center, 2013b). These avalanches are not only killing the most people, they are the most devastating for nature and manmade structures as well (National Geographic, 2014b; Northwest Weather & Avalanche Center, date unknown; Forest Service National Avalanche Center, 2013a; Forest Service National Avalanche Center, 2013b). Figure 2.3: Slab avalanche Source: New Zealand Avalanche Centre (2014b) As stated, the occurrence of each type of avalanches is attended with weakness of the snow pack. Therefore, the temporal snow conditions are always an important factor in the release of avalanches. Although this research intends to omit these conditions because it concerns long-term zoning, it is important to understand what snowpack weakness means. In every snow avalanche that takes place, there are mutual cohesion problems between snow layers or between snow and soil. These problems can have multiple causes. Additionally, snow storms always destabilize the snowpack and fast temperature changes do not build well-connected layers either (National Research Council, 1990). However, different avalanche types have to do with different kinds of snowpack failure. 18

19 2.4 Avalanche attention: a broad range of perspectives Being an ever-present natural hazard which research does generally not outdate, the collection of articles, scientific studies, books and news bulletins on avalanches is very extensive. A broad range of scopes is covered; some studies are focusing on dynamic, mathematical models to understand the physical process of avalanche movement (Gruber & Bartelt, 2007; Gruber & Haefner, 1995; Ghinoi & Chung, 2004; Margreth & Funk, 1999; Naaim et al., 2004), some deal with issues concerned with the technical optimization of avalanche rescue services (Tschirky et al., 2000; Brugger et al., 2007; Christie, 2011), some are concerned with the nature of avalanche occurrence in general (McClung & Schaerer, 2006; Forest Service National Avalanche Center, 2013a), others investigate the human mentality in snow safety (Atkins, 2000; McCammon, 2004; Fredston & Fesler, 1994) while others can hardly be called research because they only aim to bring news on avalanches just in time (de Volkskrant, 2014; CBC News, 2014). As we will see, all of these research types have their own scope but they have one important goal in common. Many studies seem to see the development of a mathematical, physical model that understands the growth and movement of an avalanche as the ultimate goal in avalanche science. In many cases, authors take an infamous area which has hosted many or major avalanches as the basis of their study. The Galtür avalanche of February 1999 is an example of an area that has been chosen frequently. From there, they try to understand the physical complexity of the growth of an avalanche. Increasing velocity, runout zones, deposition areas and mass accumulation are important in these studies (Gruber & Bartelt, 2007; Naaim et al., 2004). The focus on technical rescue applications can be seen as the result of the annual death toll; (technological) measures taken to reduce the damage caused by avalanches have become more common throughout the years. This chapter describes measures taken by governments later. In the studies that focus on these, the efficiently of rescue services is mainly discussed (Tschirky et al., 2000). But last decennia, there has been an increase in the use of technological applications by the potential victims themselves as well. More people that find themselves in open mountainous terrain, mostly tourists, begin to carry avalanche protection devices with them. There are two commonly used types of these devices. First, victims that carry avalanche transceivers, which first appeared in the United States in 1968, might get rescued faster because they transmit and receive electromagnetic signals. These transceivers can make localization of a buried person much easier. And second, the backpack airbags that entered the international market in 1991 aims to prevent a person from being completely buried, which increases survival chances (Brugger et al., 2007). Many studies research the nature of avalanches in general. These studies vary in scale, from books that aim to cover avalanche science as extensive as possible (McClung & Schaerer, 2006) to short publications about the origin of avalanches in general (Österreichisches Kuratorium für alpine Sicherheit, 2003; National Geographic, 2014b; Northwest Weather & Avalanche Center, date unknown; 19

20 Forest Service National Avalanche Center, 2013a). These are often the studies that focus on the factors that cause an avalanche. Remarkably, governmental agencies provide these publications. Every country with avalanche risk has governmental websites that inform the world about the factors that can cause avalanches. The studies that aim to explain people s mental considerations and social structures in backcountry winter s sports activities often focus on why people that enter risky areas believe it is safe enough to go. Is it inexperience? Or maybe peer pressure within a group? Or does the terrain provoke false senses of security? These are questions that these authors try to answer (Atkins, 2000; McCammon, 2004; Fredston & Fesler, 1994). Of course, the categories named above often overlap and there are numerous studies that have scopes apart from all these categories, but this overview of perspectives gives a rough idea about the structure of avalanche research. In the beginning, it was stated that these research types have one important common interest: the ability to predict where avalanches are most likely to occur. They use it for a wide variety of purposes, but the success of all these researches is heavily dependent on avalanche location knowledge. This means that avalanche science in general always benefits from this knowledge. In paragraph 2.6, some studies that focus on the question what causes an avalanche? are used to collect information about the factors that the methodological part of this research relies on. 2.5 Avalanche management in the Alps Since avalanches are a major hazard in mountainous areas, both the private and especially the public sector strive to reduce the impact of avalanches. This is done in several ways. First, the countries suffering most from avalanches have governmental agencies that map, zone and predict avalanches and these agencies deal with avalanche safety when they occur. Different countries with different systems deal with avalanches differently. In Switzerland for example, decentralization gave the cantons, of which 12 out of 26 are mountainous, a high level of responsibility in risk zoning of avalanches. In this, municipalities are authorized to review the maps and to adjust them when necessary. France on the other hand, has a more centralized system in which the national government handles the management of avalanches. In fact, the Minister of Agriculture is directly responsible for the mapping and forecasting of avalanches (National Research Council, 1990; Küchli & Blaser, 2005; Komendantova & Scolobig, 2013). In Austria, avalanche mapping is organized somewhere between France and Switzerland when it comes to centrality. The ministry of Agriculture, Forestry, Environment and Water Management is responsible for the mapping and handling of avalanches, but most of the concrete management tasks are done by the provincial governments (Federal Ministry of Agriculture, Forestry, Environment and Water Management, 2007). Since this research focuses primarily on Austria, the Austrian system is further explained. This is not done to stress the extreme importance of juridical and legislative issues, but rather in order to discover the measures taken by governmental agencies to reduce the avalanches impact. 20

21 These measures are covering the entire avalanche suffering process; they strive to serve the people and the environment before, during and after an avalanche. In other words, the governmental measures against avalanche suffering consist of three phases : precautions, disaster management and regeneration. Together, these phases aim to prevent avalanches from happening, to bring preparedness among rescue services and the people, to intervene and repair when avalanches happen nonetheless, and to take care afterwards (figure 2.4). These phases are to be harmonized by the government in order to work as efficiently as possible. Figure 2.4: Risk cycle of Austrian natural hazard management Source: Federal Ministry of Agriculture, Forestry, Environment and Water Management, 2007 Now that the organizational structure of avalanche control in Austria has been made clear roughly, the question is which concrete avalanche protection activities are the result of this management. First, of course, there is the zone mapping of avalanche danger. The national and provincial government are working together to produce danger zone maps that are technically only an expert opinion, but are strictly followed in local land-use planning. The local governments use these maps to estimate the risk for each individual parcel so that high-risk zones can be made apt for development. In some cases, the government refrains from developing such sites. A second measure are organizational measures and civil disaster control, both governmental service tasks for the moments in which an avalanche already took place. These activities include emergency alerts, alarms, the rescue activities and the evacuation of people (Federal Ministry of Agriculture, Forestry, Environment and Water Management, 2007). The third type of measures taken against avalanches is the one that are best visible in the landscape: Figure 2.5: Wooden Lawinenschutzbauten Source: Österreichisches Kuratorium für Alpine Sicherheit,

22 preventive control measures that have intervened in the geographic landscape in order to protect inhabited valleys from natural hazards. One of the oldest activities in this category is a very old example of bioengineering: using living construction material to prevent landslides, torrents and avalanches from happening and from growing. Nowadays, the plantation of forests in risky areas (Schutzwald) is still a common measure. Since the 1950 s, the Austrian government uses other structures to hold natural hazards back from happening. In the case of snow avalanches, wood, steel and concrete is used to construct Lawinenschutzbauten (Figure 2.5). For these constructions, wood is the material that is used the most, due to environmental conservation issues (Federal Ministry of Agriculture, Forestry, Environment and Water Management, 2007; National Research Council, 1990). Apart from passive measures, there are active visible measures that intervene in the geographic landscape as well. Many countries that encounter the risk of avalanches in inhabited areas use artificial release techniques to create non-destructing avalanches in a controlled setting. This is done so that no people are around at the moment of release, and most of the times they are started before danger has chance to increase. To do so, explosives are detonated either in or above the snowpack. Most of the situations that ask for these measures have to do with high avalanche risk, thus areas with serious danger combined with the presence of people. There is often a reason to believe that the risk will increase rapidly within a short amount of time; this is why the avalanche has to be released as soon as possible (National Research Council, 1990). Remarkably, all measures and activities named above have something in common: their success depends on knowledge where avalanches are most likely to occur. And as we will see in the next paragraph, neither passive nor active measures against avalanches can hold them back entirely. This is why avalanche forecasting, management and rescuing are a big issue in Tyrol. Again, these activities success is highly dependent on the ability to know where avalanches are most likely to occur. This is why the influence of the factors that have been found in this chapter is further investigated. 2.6 A closer look at avalanche zoning in Tyrol Avalanche research, information, prediction and rescuing has been around for decades in an avalanchesensitive area as Tyrol; for years, the national and local governments have been developing prediction models and they provide up-to-date information for all that are in the mountains, as we saw in previous paragraph. Basically, two types of zoning exist in Tyrol; static avalanche-zoning maps on municipal level and up-to-date avalanche forecasting on provincial level. First, most mountainous municipalities have detailed planning reports in which avalanche zoning is included. The role that potential avalanche damage plays is about building restrictions; on local scale level, possible avalanche tracks and catchment zones are calculated (figure 2.6) and as a result of this, municipality maps are made that zone suitability for building from green to red areas (2.7). Greens zones are generally safe, which means that no damage 22

23 from natural mountainous hazards, such as Figure 2.6: Hazard zoning map of Starkenbach avalanches, landslides and debris torrents are to be expected. Yellow zones are those of mediocre danger; these areas can get under pressure in exceptional dangerous situations. Buildings in these areas have to meet certain safety requirements, such as reinforced concrete foundations in walls that face the mountain. In red zones, building prohibitions are dictated since these are the areas that are at risk in any kind of danger (Gemeinde Galtür, date unknown). Since they do not outdate quickly, these maps are useful in spatial planning. Their scale is so small that they cannot serve larger areas at once, but that is outside the scope of such maps. However, these Source: Heumader, date unknown spatial planning maps cover such small geographic zones that they are hardly useful for avalanche management and reporting. Therefore, the Lawinenwarndienst Tirol works on avalanche reporting constantly. Figure 2.7: Building risk map based on avalanche danger Source: Gemeinde Galtür (date unknown) The Lawinenwarndienst, which is responsible for the avalanche warning system in the entire province is a self-governing department of the Lawinenwarndienst Österreich. This is the agency which reporting is aimed to be improved by this research. In the introduction, the main reason for creating this model has 23

24 been that avalanche warning systems in Tyrol rely heavily on the temporal factors about snow and weather. This can be recognized by the fact that such reports and warnings are changing every day; on a daily basis, these predictions are provided on a 1 to 5 range (Lawinenwarndienst Tirol, 2014). The score a place gets relies on practical evaluation of measured snow profile and on meteorological data. This is of huge value for people that (temporally) reside in the mountains, since it can be extremely dangerous to plan outdoors activities when relying on outdated information. These predictions come in two forms: a small danger map (figure 2.8) of the entire province (and including Osttirol), and textual description of the situation at its website. Figure 2.8: Daily avalanche danger maps from the Lawinenwarndienst Tirol Source: Lawinenwarndienst Tirol (2014) An example of a text, which changes when the conditions ask for updates, is written below. Avalanche activity has risen markedly: at high altitudes and in high alpine regions the danger level is frequently considerable (3). Below about 1800 m, as well as in northwestern regions, the danger level is frequently low. In case of intense, diffuse solar radiation or rainfall (which makes the advancing wetness of the snowpack drastically increase) even a "tense" level 3 could be reached up to high altitudes. Increasingly frequent avalanches during the last 3 days corroborate this heightened alarm level. Generally, wet avalanches have released immediately following the snowfall on extremely steep slopes, to some extent medium-sized, in isolated cases even large-sized slab avalanches, particularly on W to N to E facing slopes above approximately 2500 m. Large additional loading is generally necessary to trigger large-sized slab avalanches, e.g. the impulse of a loose avalanche or small, superficial slab releases (Lawinenwarndienst Tirol, 2014). The text is quite detailed and gives slight insight in which slopes are to be avoided best, but it is interesting to see that the map of figure 2.8 is not sufficiently detailed so that one can actually base spatial behavior on. Looking at the map, only real conclusion is that the southern, mountainous part of Tyrol is more dangerous than the northern part and the valleys. This is not the geographical nuance that a hazard this serious asks for. It asks for efficient zoning on large geographical scale, but for geographical detail at the same time. It seems that the current situation has too little attention for geographical differences in terrain; a bit more detail would be valuable in determining the most 24

25 extremely dangerous places that should get maximum attention. This information can be used by avalanche-management agencies to provide more detailed avalanche reporting, especially because these agencies, other than this research, have constant access to information about the snow and weather conditions. The information on these factors are crucial in avalanche predicting, as described multiple times in this report. Therefore, avalanche zoning in Tyrol can be improved by injecting more geographic detail in forecasts using snow and weather conditions. To produce a model that provides this detail, the avalanche-causing factors as listed by the literature are assessed thoroughly. 2.7 Avalanche danger: contributing factors Avalanches are natural hazards that do hardly follow natural laws, which make them not easy to predict. The factors that can cause an avalanche, or that can at least contribute to its occurrence, are complex. However, there are some factors that are agreed on by most authors. Although the exact influence of each factor is hard to investigate, and there is some discussion on the question whether a factor contributes or not, most articles, books and websites list more or less the same factors. It will become clear that there is only one factor which none of the sources is sure on and one factor is included without theoretical foundation. The factors that are found are used as the basis of the entire research. There is one major requirement about the factors before they can be modeled at this desired scale level; their dangerous condition range should be equal for the entire research area. Factors conditions that are dangerous in certain areas in the world might not be in other regions. As described below, the treeline is very important in elevation being a factor, but the treeline is not to be found on the same height in the entire world, for climatological issues. Some other factors have the same, this is why the research area cannot be too large. However, staying on provincial scale level is low enough to be safe Elevation Elevation is important in avalanche occurrence for several reasons and generally speaking, the higher an area is located, the more danger there is for avalanches (New Zealand Avalanche Center, 2014a; Canadian Avalanche Centre, 2014b; National Snow & Ice Data Center, 2014; Canadian Avalanche Centre, 2014a). These sub-factors are somehow interacting with each other, and are discussed below. First of all, the absence of trees is the most important sub-factor, as listed by most sources. Although many of them admit that the danger for avalanches increases with every ascending meter, the influence of the treeline is significant. Fewer trees or none at all means that there are fewer anchors that can hold back avalanches from growing. Although rocks, depending on their size, can be quite effective as well, a forest cover is the main natural avalanche stopper (Tremper, 2001). The denser the forest cover is, the better it protects a mountain from larger avalanches, as clarified in figure

26 Figure 2.9: The thicker the tree cover, the more effective it is against avalanches Source: Tremper (2001), p. 72 In mountainous areas, higher areas have lower air and ground temperatures. Therefore, fewer trees can survive in higher located areas. But in most regions there is no smoothly thinning tree cover as one ascends; the treeline can be clearly visible. The treeline (figure 2.10) is defined best as the highest elevation that allows trees to grow; above the line, the air and ground are too cold for trees to survive (Körner & Riedl, 2012). In many areas, the difference between terrain that is fully or at least half covered with forest and terrain that is free of tree vegetation is just a matter of meters, although this is not the case in every region (Butler et al., 2009). Figure 2.10: Clearly visible treeline in Serfaus, Tyrol Source: author s own, 2012 This means that avalanches that decide to come off the mountain get a chance to grow, since the terrain is smooth enough to allow them to grow. It is also easier for a larger part of the snowpack to break off at once since nothing divides the snowpack in smaller pieces (Tremper, 2001). In open terrain without vegetation, several other sub-factors play a role in the release of avalanches. Elevations matters in wind issues; where no trees are to be found, the wind is better able to destabilize the snowpack. This is the case, simply because wind blowing tends to be stronger in higher located areas (Iowa Energy Center, 2014). Elevation is also an important proxy for average snowfall. The amount of annual snowfall, and thus average snowpack thickness, increases with height since the air is colder in higher areas. At some point of height, the rainfall will turn into snowfall because of lower temperatures. Those areas that are 26

27 always covered with snow because of ever-low temperatures; these are considered the snowiest areas (Singh et al., 2011; New Zealand Avalanche Center, 2014a). To summarize, high areas containing fewer trees, more open terrain, more wind and more snow are more vulnerable for avalanches. It must be noticed that the treeline is not on the same height level worldwide since. Since some places tend to be warmer than others in general or are more for example exposed to wind from colder areas, large treeline differences exist between regions; in several mountainous Bolivian areas the treeline is above 5000 meters, while arctic regions in Norway trees only survive below 500 meters. In Tyrol, the average treeline is on 2000 meters (Nairz, 2014). It varies between 1800 and 2200 meters, due to sun exposure (aspect), and the location of an area in relation to the Alpenhauptkamm, a long range of higher peaks running from southern France to eastern Austria that creates the distinction between northern- and southern Alps Slope steepness The steepness of a slope is definitely a factor that all avalanche-related articles, books and websites agree on (UNIS Health, Safety and Environment, date unknown; Forest Service National Avalanche Center, 2013c; Pudasaini & Hutter, 2007; Tremper, 2001; Canadian Avalanche Centre, 2014a; Northwest Weather & Avalanche Center, date unknown; United States Department of Agriculture, 2013a; McClung & Schaerer, 2006). Well, at least they all agree that slope steepness is of great importance. And although they tend to name the same slope angles as being the most dangerous, minor differences exist in opinions. Most of them illustrate their work with clarifying figures, with varying level of detail that can easily be compared. Some illustrations are shown in figure 2.11 and Figure 2.11: Various steepness angles have various levels of danger Source: Tremper (2001), p. 63 & Northwest Weather & Avalanche Center, date unknown 27

28 Figure 2.12: Various steepness angles have Some studies link the steepness of a slope various levels of danger to avalanche types, which have been discussed earlier in the theoretical chapter. Although this research does not make a distinction between avalanches types in calculating avalanche danger, the factor that slope steepness is makes clear that each angle accounts for an own way to destabilize the snowpack. The slow, heavy, wet snow avalanches that happen when the snowpack is heated through rapid temperature changes mostly occur on relatively flat hills while sluff Source: United States Department of Agriculture (2013a) avalanches tend to be triggered on the slopes that are too steep for the snowpack to stabilize. Last, the slab avalanches, which are the most deadly category, are often released on slopes with medium steepness angles. Since the slab avalanches account for most of the avalanche danger, the slopes of medium steepness are expected to be the most dangerous. Some websites (UNIS Health, Safety and Environment, date unknown; Forest Service National Avalanche Center, 2013c) state that danger increases with every steepness degree, but forget that slopes can also be too steep for high levels of avalanche danger. These extremely steep slopes are thus the ones that host sluff avalanches, which are not scarce but are of less importance because the amounts of snow coming off the hill are relatively small. Slab avalanches usually do not happen on slopes that are too steep, because the gravity holds new snow back from forming a thick, uniformly packed layer. The studies on avalanches agree that the most important zone is to be found on slopes between angles of 30 and 45, with occasional extensions to the range between 25 and 50. According to the New Zealand Avalanche Centre (2014c) and Tremper (2001, p. 63), the bulls-eye is to be found at 38. Moreover, slopes flatter than 25 are generally safe (Pudasaini & Hutter, 2007; Tremper, 2001; Canadian Avalanche Centre, 2014a; Northwest Weather & Avalanche Center, date unknown; United States Department of Agriculture, 2013a; McClung & Schaerer, 2006). The danger criteria concerning slope steepness are the same worldwide Aspect The direction that a slope faces ( aspect ) is a factor that most resources recognize to be important. The direction that slopes face with respect to the sun has profound influence on snowpack stability, making certain slopes more vulnerable than others. That is, as long as one focuses on regions that are at midlatitudes range from 30 to around 55 (approximately from Cairo to Stockholm and thus in the Alps too). In equatorial areas, the sun shines (almost) straight from above, spreading its heating equally over all slopes, so in these areas there is no influence of slope directions (figure 2.13). Neither is this 28

29 important in Figure 2.13: Different levels of sun influence on snowpack stability worldwide wintery periods in arctic regions, when the has not enough radiation power to heat the slopes significantly. However, the 47 latitude that Tyrol has makes this province snowpack highly sensitive for sun exposure differences. The Source: Tremper (2001), p. 77 influence of the sun can easily be explained. In general, north- and east-facing slopes are considered the most dangerous, for two reasons that are somehow related to each other. First, the cold side of a mountain tends to develop the most persistent weak snowpack layers (Northwest Weather & Avalanche Center, date unknown). In the northern hemisphere, the north-facing slopes receive the least sunlight and are thus the coldest. These are the parts of the mountain that have the most snow since melting tends to get faster on south-facing part. Since more snow means generally means more avalanches, these areas are dangerous (Pudasaini & Hutter, 2007). In south- and west-facing slopes, the sun has a stabilizing effect on the snowpack since its heat glues the snow layers together (Tremper, 2001). The contrast of east versus west is the more subtle version of north and south; east-facing slopes receive their sunlight in colder mornings, while the west-facing ones are heated in warmer afternoons. This means that the snowpack remains thicker on northern- and eastern-facing hills. Only in springtime, when solar heating is very powerful, south-facing slopes can be dangerous for heavy wet snow avalanches (United States Department of Agriculture, 2013b). This is linked to the second reason, which has to do with the presence of skiers which is discussed in the next paragraph. Skiers, especially advanced, enjoy off-piste skiing more at northern and eastern sides of the mountain since these areas have the most snow. People are notorious for triggering avalanches and this happens more frequently at the colder sides of the mountain (Tremper, 2001) Human triggering As winter s sports have grown fast in the past decennia, changes are observed in avalanches causes. Socalled skiers-avalanches avalanches that are caused by winter s sports practitioners are becoming more common throughout the years (Kriz, 2001). This means that the influence of the victims themselves is growing. Atkins (2000, p.46), states that it is well established that avalanche victims are generally their own worst enemy and that nine-in-ten avalanche victims trigger their own avalanche. 29

30 Although this has more to do with the people-side of avalanche risk than with avalanche danger since most of the victims dig their own grave but not all avalanches are necessarily caused by people, it shows that human triggering seems to be a serious factor to pay attention to. According to Schweizer & Lütschg (2001, p.154), the vast majority of human triggering is done by skiing, followed by snowboarders. Climbers play a marginal role. It thus seems that nearly all human triggering is to be found in places where skiers and snowboarders are. Therefore, the distance of ski resorts to a place is assessed as a factor Ski resort characteristics As many resources, mostly about skiing or other winter s sports, state, the ski resorts are very heterogeneous. Therefore, the distance to the nearest ski resort as described in paragraph may not fully cover the human triggering as a factor. Huge variety exists in capacity for people, piste-kilometers and skiing difficulty, to name a few. Should resorts that have more human avalanche danger produce higher avalanche risk scores? Now, what should be made clear is why certain resorts are considered more dangerous than others. The basis of measuring the influence of skiers on avalanche triggering to know whether avalanches tend to occur more in or close to ski resorts. But there are two other human danger contributors, which are discussed in this paragraph. The question is whether the occurring of avalanches that took place in or near ski resorts is influenced by the type of ski resort. In other words; are certain types of resorts more dangerous than others? This is unsure. It must be noticed that this hypothesis has no solid theoretical support at all, since sources on avalanches, if they even mention skiers, hardly make distinctions between ski resorts. This suspicion is thus vague and lacking all theoretical support. However, this research tries to find out to what extent it contributes. Nothing ventured, nothing gained is the principle that fits this suspicion best. In other words; you never know. If its influence is uncertain, we can at least try and see what happens. If the characteristics turn out to play a role, the research is extended by one factor and if not, they are excluded in the final equation anyway. First, the capacity of ski resorts is supposed to be boosting avalanche danger. Theoretically, each skier or snowboarder that decides to switch from on-piste to off-piste, even if it is only for a minute, can cause avalanches. Anyone could be an avalanche causer. Now, the largest resorts attract the most people. Since even the smallest off-piste adventures can lead to avalanches, the overall capacity of resorts seems to be of importance as well. Tyrol hosts many ski resorts, which sizes vary from several to hundreds of kilometers of prepared slopes. Second, not every winter s sports practitioner is the same; different resorts attract different tourists. Some resorts attract many skiers and snowboarders whose main goal is to enjoy lots of deep powder snow in off-piste terrain that is as challenging as possible. This is usually the group of tourists that causes avalanches. Some Austrian resorts that are frequently included in these lists are presented below 30

31 (PurePowder.com, 2014; World Snowboarding Guide, 2014; Skiinfo.nl, 2014; Snowplaza.nl, 2014; The Telegraph, 2010). Ski Arlberg (St. Anton, Lech & Zürs) Ischgl Obergurgl-Hochgurgl Kaunertal Sölden Mayrhofen So, what is it that makes these resorts so interesting for off-piste skiing? What type of terrain attracts these people that cause avalanche frequently? Many websites and magazines for winter s sports addicts list three characteristics of popular off-piste destinations. These resorts tend to have large total descents, high snowfall levels and steep terrain, to name few. Although overall capacity has its share in off-piste attractiveness more kilometers create more differences in terrain there are exceptions. The Kaunertaler Glacier for example has only 36 kilometers of prepared terrain but has considerably challenging off-piste terrain, while large-scale resorts like Kitzbühel-Kirchberg and the SkiWelt Wilder Kaiser Brixental are nowhere to be found on these lists. It is thus expected that large resorts and those attractive for off-piste skiing have more avalanches than ski resorts that do not meet these characteristics Land cover type Land cover as contributing factor to avalanche danger is present in many resources (Seres, 2013; Tremper, 2001; Federal Ministry of Agriculture, Forestry, Environment and Water Management, 2007; National Research Council, 1990). Has anyone ever seen an avalanche being released in urban areas? Or in a lake? These statements are self-evident; some land cover types are more dangerous than others. However, there is something interesting about land cover as a factor; it can easily be linked to other factors. For example, it is directly linked to elevation since surfaces without dense tree cover are more dangerous and it is the elevation that determines whether tree growth is possible or not, as stated by (Tremper, 2001). Some other land cover types, such as built-up area and water, are only to be found on flat surfaces and this has to do with slope steepness as a factor. This could make land cover type itself not so important, but other factors (elevation, slope steepness, etc.) even more since they can be determinants for the land cover type. As discussed in some paragraphs below, governments and other institutions often decide to construct forests on certain slopes to prevent avalanches from happening (Federal Ministry of Agriculture, Forestry, Environment and Water Management, 2007; National Research Council, 1990). The most important reason for land cover in having influence on avalanche danger is the anchoring effect that it could have. Mainly, it is all about the distinction between forested and non-forested areas. Vegetated areas have larger chance to stop avalanches from happening or to slow them down when it is already too late (Seres, 2013). Mountainous areas, especially those where 31

32 slopes are steep enough to produce notable avalanches and where the majority of the land is covered with forest, grassland, bare rocks or perpetual snow are notorious. Built-up areas or other areas where human intervention is present are relatively scarce, leaving nature s influences to their maximum Slope shape In previous paragraphs, it has become clear that steepness is very important in the question whether a slope is dangerous or not. However, it is perhaps not only its downward steepness angle that is determining avalanche danger. Some sources state that a place s position compared to the rest of the slope is of importance as well. In other words, it could make a difference if a cell is located at the top or at the bottom of a slope. Tremper (2001, p.82-83) states that a concave, convex or planar shape of a slope makes some difference in avalanche danger, although usually not a significant difference. Avalanches happen on any steep slope without thick anchors despite the shape of the slope. The Canadian Avalanche Centre (2006) and McClung & Schaerer (2006, p.54) do also mention slope shape as being a role-playing factor, but remain vague about its actual influence. However, the level of importance may not be totally clear among these authors and agencies, but if there is any influence the planar and especially the convex parts of a slope seem to be the most dangerous. The main reason for this is that convex slopes have less compressive support from gravity at the bottom than other slopes. Another reason is that if it is a skiers or another form of human triggering, these slopes are more difficult to navigate on. They are tricky to descend because each step or turn you take adds another degree of steepness until suddenly you find yourself on terrain that s too steep (Tremper, 2001). Anyhow, the fact that most resources do not include the shape of a slope in their research makes its importance doubtful, but it is included anyway just to be sure. 2.8 Predictability expectations and interpretations of results This entire research is about the predictability of avalanches and some statements must be made on beforehand to defend this model s outcomes and to make sure that expectations are realistic. In this paragraph, there are two things about avalanche danger that must be explained. The first is the overall predictability of the natural disaster that is an avalanche, and the second is the influence of the snowpack thickness. Starting with the overall predictability, the outcome of the model is not a value that directly corresponds with concrete and tangible danger chances. It is impossible to predict the exact chance of an avalanche to happen in a certain place in percentages or so. Neither is it possible to say: if one avalanche is going to occur in Tyrol right now, it will definitely take place right here. Avalanche zoning is possible though and so is indicating where avalanches are most likely to occur, but how likely it is remains unknown. Fact is that no matter how detailed the prediction is and how many factors are included, coincidence will always be crucial in avalanches occurring or not; even if all temporal conditions and the terrain are right, the chances of an avalanche not to happen would always be larger (Seres, 2008; Schweizer, 2008). 32

33 When it comes to predictability, avalanches are absolutely no random events that could occur anywhere. That would make avalanche research completely useless. It is not like throwing a dice. Neither are they fully predictable, as stated above. It is thus somewhere in between. The release probability in a place will never be above 50 percent and is more likely to be between 1 and 10 percent (Schweizer, 2008). It is wise to speak about the enormous amount of determining factors that make up avalanche danger, rather than about coincidence. As there are too many variables that should be known to predict avalanches in detail, the phenomenon that is a snow avalanche is too complex for a deterministic approach. It is not possible to precisely forecast the size, the location and the time of occurrence. Whereas less extreme and more frequent events are often prevented by countermeasures, it is too costly to try to mitigate extreme events. The only option is usually forecasting and based on it temporary protection measures such as preventive evacuation are taken (Schweizer, 2008, p.688). As the theoretical chapter explained, this is exactly what happens in avalanche management. It seems that agencies need to know the most dangerous places, so that they can both create proactive measures against avalanches and make sure that rescue and evacuation services are extra vigilant when conditions are getting worse. That brings us to the second feature that has to be explained: the influence of the snowpack. No snow means no avalanches and the more snow, the more danger for avalanches. This model can be a basic indicator for vulnerable areas, but the thickness of the snowpack, especially the newest layer, creates the danger. To concretize its impact, Schweizer (2008, p.690) designed the following danger indications for infrastructure when new snow depth accumulates: Table 3.1: Influence of snowpack thickness on avalanche danger Sum of new snow depth Consequences for infrastructure < 30 cm Almost no danger cm Occasionally, some isolated objects and exposed transportation lines might be endangered cm A few large avalanches to the valley bottom are possible, some objects and transportation lines might be endangered cm Several, large avalanches to the valley bottom have to be expected, some objects and transportation lines, as well as exposed parts of residential areas are endangered > 120 cm Disastrous situation, even rarely or so far never observed avalanches to the valley bottom are possible, highest danger for exposed residential areas and transportation lines The integration of these two statements learns that this research must be aware of its strengths and weaknesses to set clear and achievable goals. It is impossible to predict avalanches when time as a 33

34 constraint is excluded. Time enables the research to include temporal meteorological and snowpack conditions, but limits its applicability as well since it may not be valid for other parts in the year. For example, imagine a month in which rapid temperature changes make the top layer of the snowpack soaking wet, thick and heavy while being one meter thick. Developing a model with these assumptions makes results more realistic but in other seasons, while conditions are different, it would not be valid anymore. Therefore, the results of this model can be understood best as the places where avalanches are most likely to occur when snow conditions are getting dangerous. The actual levels of danger that correspondent with the infamous danger warnings are highly dependent on actual snow levels that are excluded in this research. At the same time, this danger is considered equal for too large areas, for instance for entire valleys or even at regional level (Seres, 2008; Lawinenwarndienst Tirol, 2014). In other words; danger warning lacks the use of nuancing geographical differences caused by the factors that are the basis of this very research. That is why this research is complementary to avalanche management in Tyrol, as described in the introduction. The integration of overall predictability and snowpack influence may not result in a chance of avalanches per cell, but the snowpack thickness opens doors for very basic - predicting the amount of avalanches that will release themselves. On the scale of areas of 100km², about 100 cm of new snow (meaning high avalanche danger according to Schweizer (2008)) makes that several large avalanches are expected to come down as long as the conditions stay the same. This is the most concrete prediction that can come out of forecasting on this geographical scale and it is up to this research to develop a zoning model that can assess where these are most likely to take place. To conclude, it seems that avalanche danger consists of three variables: temporal conditions of snowpack and weather, characteristics of terrain, and coincidence. The text above discussed the overall predictability and concluded that avalanches can only partly be predicted. It more or less stated that avalanche research has to accept that no concrete statistics and stochastic expectations could be provided to anyone who benefits from these calculations. However, this does not mean that results are meaningless. The only thing is that the desire of outcomes to be of ratio scale cannot be fulfilled. When an area scores twice as high on danger as another area, this does not necessarily mean that the chance of producing an avalanche is twice as high. This is because the outcomes are of ordinal measurement scale, listing areas from safest to riskiest. The results of this model thus present the most risky places in Tyrol during dangerous periods, which can be useful for the purposes described in the introduction. 34

35 3. Methodology 3.1 Introduction This chapter operationalizes the methodological steps that are needed to answer the main research questions and thus to accomplish the goals that have been set on beforehand. First, the study area of Tyrol and its part of Landeck-Imst is clarified to make further methodological steps easier to understand. Second, the construction of the model is described, along with the methodology for all factors. Third, the concepts of calibration and validation are introduced and fourth, some data quality issues are discussed. 3.2 Study area: Tyrol As mentioned in the theoretical chapter, suffering from avalanches comes with the number of people that reside in a region. Therefore, densely populated mountainous regions and those mountainous places that are welcoming the most visitors are the most at risk. Since Austria attract millions of visitors each winter, the potential number of avalanche victims is among Europe s highest and Tyrol is Austria s leading province in tourist amounts. In the winters seasons of and , Tyrol counted 9,3 and 9,6 million arrivals (Bundesland Tirol, 2013). Therefore, it may be no surprise that Tyrol is on top of the list of avalanche accidents per year (Bergrettung Tirol, 2008). This is the demand-side of the choice for Tyrol as research area. The supply-side is important as well; without proper data, the research success would not be sufficient. Luckily, the government of Tyrol strives to share its data as much as possible to maintain Transparenz, Innovation und Partizipation (Bundesland Tirol, 2014) and this research thankfully makes use of the data provided. Figure 3.1: Tyrol as a part of Austria This data consists of datasets such as a digital elevation model that can be used to model most danger factors, and a shapefile of all skiing areas. Subsequently, the Lawinenwarndienst Tirol makes the avalanches that occurred in Tyrol freely available for its website. 35

36 Figure 3.2: Landeck-Imst as a part of Tyrol, and major tourist destinations The model that helps to answer the research questions is validated by calibrating it for the entire province of Tyrol, but is originally designed based on information from two districts (Bezirke) within Tyrol: Landeck and Imst (black and white in figure 3.1). The location of the capital city Innsbruck (yellow) and the main ski resorts (blue) are highlighted as well. The highlighted ski resorts are listed by name in order from left to right to give an overview of the geographical position of these major places: - Sankt Anton am Arlberg - Ischgl - Serfaus Fiss Ladis - Sölden - Hochgurgl Obergurgl - Zillertal 3000 (Mayrhofen) - Zillertal Arena - SkiWelt Wilder Kaiser - Kitzbühel Kirchberg Designing it for a smaller area is the calibration and applying it to the rest of province is the validation of the model. If the model was designed for the same area as it was developed for, it remains unknown whether the model is actually able to zone avalanches correctly for larger areas. In that case, the model s results do obviously match the avalanche events as recorded over time, simply because the avalanchecausing factors and other contributors are analyzed for the same development area as the calibration area. That is why a smaller area is chosen to make space for validation. This area thus consists of the Bezirke Landeck and Imst. Together, they roughly account for 31 percent of Tyrol s surface. These two are selected because they have all kinds of terrain that are expected to be causing avalanches. They have 36

37 many differences in terrain; there are numerous ski resorts that vary in capacity, they have noticeable towns, they have large differences in elevation and so on. Together, they have hosted 88 avalanches in the last five years, which is considered sufficient to do some analysis on. 3.3 Construction of the model One of the goals of this research is to develop an own zoning map for avalanche danger that is purely based on terrain properties. Based on a model that include the factors mentioned in chapter 2 scores are calculated for every cell, depending on the dangerousness that it scores according to the factors. The model assigns a weight to each factor indicating its contribution to the danger-score of a specific location. These danger scores (D) are the product of a mathematical equation that adds up all the factors danger. Therefore, a cell s danger score can be seen as an index of relative danger, compared to the other cells. A factor s D consist of danger values (V) and importance weights (w), which are the output of analyses that are discussed in the next paragraphs. Simply stated, the danger of a factor s conditions (e.g. how dangerous is a slope of 40 degrees?) is multiplied by the influence that this particular factor has on avalanche danger as a whole (how important is the steepness of a slope?). To make things more clear, the avalanche danger in a cell could mathematically be written as: Each factor (1, 2,, n) gets a weight (w) and a value (V). The calculation for each of these is described in the paragraph 3.2.2; first, some more information about model s input and outcomes is needed. In this paragraph, the design of the entire model is described. All data preparation, generalizations, conversions, analyses and visualizations are done with ERSI s ArcGIS. To make the model s construction more concrete, its design is schematically displayed below in figure 3.3. It must be noticed that blue rectangle boxes are deliverables, tangible products as a result of activities that are displayed as green round boxes. Green lines are influenced by an activity; blue lines need no processing to continue to the next box. 37

38 Figure 3.3: The model s methodology schematically Historical avalanche data The first thing to do is to map historical avalanche data, since documenting avalanche-hosting terrain requires knowledge of where avalanches have occurred in the past. To do so, a database that lists all avalanche events each year is made use of. The Lawinenwarndienst Tirol provides yearly databases with information about occurred avalanches. For this research, 220 avalanches that took place in the last five years ( ) are assessed. The database lists them with several characteristics, such as name of the Alpine region, the name of the mountain s peak, elevation, aspect and if so, the number of people trapped inside. Not all included characteristics are taken into account, only the ones that describe where the avalanche took place. In table 3.1, three avalanches are listed to see what the database looks like. By knowing their peak name, elevation and the slope direction, they can be mapped with sufficient level of detail. After mapping, the distribution of avalanches in Tyrol is made clear in figure 3.4 (area is rotated 90 degrees). Some areas seem to be more dangerous than others, which will be tested more extensively. Table 3.1: Some examples of avalanches from the database Date Peak name Alpine region Elevation Aspect/orientation Schaufelspitze Südliche Otztaler- und Stubaier Alpen 3170 meter East Sassgalunkar Silvretta Samnaun 2500 meter Northwest Hohe Warte Zillertaler Alpen 2380 meter Northeast 38

39 Figure 3.4: Avalanches from 2008 until 2013 as observed by the Lawinenwarndienst Tyrol 39

40 3.3.2 The factors values Introduction The key to assess the danger for avalanches is to know when certain factors are getting dangerous and how much influence they have. We already selected the factors that contribute to avalanche danger besides temporal meteorological and snowpack conditions and discussed why they are of importance. Now, we have to know the conditions under which they become dangerous. Of course, as we have seen, the literature has clear statements about most factors but remain a bit vague. For example, some say that the steeper a slope, the more dangerous it is, while others state that hills can be too steep to host avalanches frequently. Also, for certain factors, for instance a slope s exposure to the sun, the dangerous conditions are unequal worldwide since north-facing slopes get way more sun in the southern hemisphere than in the northern one. Because of these reasons and because of general validity issues, this research sets out to investigate the dangerous conditions of factors itself, specifically for Tyrol and maybe further. To do so, the characteristics of avalanche-hosting terrain raster cells in Landeck-Imst are compared to Landeck-Imst cells in general. What does the terrain look like in cells that hosted avalanches? And how does it look compared to the overall terrain? Now, this is the most time-consuming activity in the entire research. In the end, each raster is supposed to be translated to a raster in which the values are the scores that cells have on this factor. For example, when a cell scores 34 in the slope layer, its slope steepness is 34 percent. When all layers are placed on top of each other, they can be intersected with the avalanche points from the Lawinenwarndienst. Now, it can become clear what avalanche-hosting terrain looks like. How many of the avalanches did occur in areas with dangerous factor conditions, according to literature? Is it really true that avalanches mostly happen at northern-facing slopes? To answer these questions, two principles that have been mentioned to be very important return at this moment: the factors values and their weights. Information about both can be directly derived from the intersection of the point data with the factors layers. Below, the steps are described to get to this information. The danger values were one of the first things mentioned being representing avalanche danger. But what exactly are danger values? Danger values are reclassified values that factors can have, representing the level of danger for avalanches. To explain, we must first go back to the equation that makes up the entire danger. Simply, D is the sum of all factors. For example, a place that is located on only 800 meter height, far away from ski areas, on a west-facing slope, etcetera, may not score high on total avalanche danger, while others do. The factors should therefore somehow be totaled up. However, totaling up 800 meters, 15 kilometers from the nearest ski resort, on a slope that has an aspect of degrees (west), would make no sense at all since these entities are of course not the same. Therefore, reclassification to standardized values is needed. This is done as follows. The total number of avalanches in Landeck-Imst is 88. For each factor, the values are divided into several classes, depending on the breadth of the value range. For example, aspect is generally divided into eight wind directions (N, NE, E, ), while other 40

41 factors allow the classification to be using sixteen classes. The classification method is equal interval, meaning that all classes are of the same size. Then, for every class, the relative representation in the total of avalanches is calculated. The percentage of all avalanches that happened in a certain class becomes that class new value. So, if 10 percent of the avalanches occurred on northeast-facing slopes, the new score of the value class northeast is 10. But there is one more step in reclassifying; it can be the case that 10 percent of the avalanches occurred in the northeast class, while the northeastern slopes in general are accounting for only 5 percent of the cells. That makes the northeastern slopes even more dangerous. Likewise, a class that seemingly hosts many avalanches could also be by far the largest class, which neutralizes its impact dramatically. There should thus be made a correction for this. Therefore, dividing the representation score in avalanche-hosting cells by the representation score in the overall terrain results in a new value that is one step ahead in standardizing. The thing with these new values is that adding them up gives different totals for each factor. Therefore, these values are again divided by their total, so that results are comparable for each factor. The tables included in each factor s explanation clarify the way the calculation is done. Below, the exact mapping is explained for each factor Elevation As described in the theoretical chapter, higher located areas are expected to be more dangerous. This is because more open terrain exists at these heights, where more wind and snow can be found. But, more importantly, in terrain above the treeline, fewer natural anchors are present that can stop avalanches from growing. Elevation is the easiest factor to map, since it requires the least data preparation. The government of Tyrol offers free downloadable digital elevation models (DEM) for each Bezirk, which are the districts of which the province consists. Mosaicking them together results in a DEM that is a proper basis for most of the factors (figure 3.5). The coordinate system that is used is MGI Austria Lambert; this is set as standard for the rest of the research. For the entire area of Tyrol, the range of elevation values starts at 462 meters and ends at 3731 meters. This means that the level of measurement is ratio. This, and not the range that only applies to Landeck-Imst, is the range that should be used for the reclassification to danger values. This is, because Tyrol has lower points than Landeck-Imst s lowest, and the highest point of Tyrol is not to be found in Landeck-Imst either. Since every cell must get danger values in the end, it has to be sure that every cell in Tyrol belongs to a certain class. The total range of elevation values has a width of = To avoid data loss, and because the range of values is broad enough to allow the usage of many classes, sixteen classes are used. Now, each class has a width of 3269/16 = ±205. This makes that the elevation factor can be classified as shown in appendix 1 and in the chart of figure

42 Figure 3.5: Elevation map of Landeck-Imst Looking at the map, chances are significant that the hypothesis of more avalanches in higher located area is true. As stated in the introduction, for each class, the representation in avalanche-hosting terrain (AHT), corrected for the overall representation in the Landeck-Imst area (LI), is the basis for reclassification. This correction is shown in the column AHT/LI. The share that each class has in the total of this new column is the final danger value for the factor. Together, these values should make 1, which is the case for each factor. This makes them comparable. However, elevation is the only factor that has experienced artificial intervention of results. As to be seen in the graphs, no avalanches have been detected in the highest class, ranging up to 3742 meters. However, looking at the graph, learns that the theory is right; the higher, the more dangerous. In this case, the relatively small dataset makes that this class is naturally omitted from danger scores. Therefore, a very rare but crucial exception is made by equaling the highest and second highest danger values. Creating these danger values (figure 3.7) for each class means that there is some loss of data quality since every value within a class is from now treated the same, but this is inevitable for later reclassifying. The representation is calculated using total area surface, since cell sizes vary as a result of switching between raster en vector formats. This means that cells with the same elevation value (e.g. a lake) 42

43 Percentage of terrain Percentage of terrain becomes one polygon in converting. By calculating with surface instead of number of cells, the precise representation can be used. The avalanches are counted as single events. Figure 3.6: Representation of elevation classes in the study area and in avalanche- hosting terrain Value class Figure 3.7: Danger values per elevation class Value class Now we see the necessity of correcting; a situation in which most of the avalanches occur at heights between 2308 and 2512 meters would suggest a high danger value for this class when not taking overall statistics into account. However, when correcting these numbers for the entire Landeck-Imst area, it could be the case that other classes will get much higher danger values because of their smaller overall representation. In other words, more accidents in a class does not automatically mean that this class is more dangerous. The values that are on the right side of the table are the ones that are multiplied by the factor s weights. 43

44 Slope steepness Looking back at the literature, it seems likely that steeper hills have more chance to host an avalanche, although slopes can be too steep as well because at those angles, the snow might not be able to attach to the ground or to other snow layers. Slope steepness as a factor is relatively simple to model; the DEM used for the elevation layer has been set as input for a Slope analysis that generates the slope angle for each cell. The result is to be discovered in figure 3.8, where a slight idea could be formed about the slopes that produce avalanches. It is hard to propose clear conclusions using this high level of detail, but one thing is clear: the valleys, in which very flat slopes make up the majority of terrain, not many avalanches occur. Figure 3.8: Slope steepness map of Landeck-Imst To assess this in more detail, the same way of classification is maintained as in elevation although twelve classes are used; the range of angles that slopes in Tyrol have are values between 0 and 72 percent. To stay consistent in the high amount of classes, sixteen classes are used in total. Reclassifying the classes to new danger values causes the same loss of data quality as in elevation. The same absolute and relative 44

45 Percentage of terrain Percentage of terrain representation columns as in elevation are used to correct over- and underrepresentation. The result is to be discovered in appendix 2 and in the matching charts of figure 3.9 and Figure 3.9: Representation of slope steepness classes in the study area and in avalanche- hosting terrain Value class Figure 3.10: Danger values per slope steepness class Value class Now that the danger distribution of slope steepness angles has become clear, the expectations as derived from the literature seem to be true. The flatter slopes, that have a prominent share in the total study area, are missing in avalanche-hosting terrain. In other words, avalanches only start at angles above 20/25 degrees. The most dangerous angles are between 37 and 48 degrees, which fits the bunch of danger radars as indicated in figure 2.10 and Subsequently, no avalanches have been detected on slopes that are steeper than 55 degrees. This does not automatically mean that these hills are too steep; their overall representation in Landeck-Imst is marginal as well, so there are simply very few slopes 45

46 fitting these criteria that can host avalanches. An interesting link to the theory can be made by looking up to the types of avalanche as listed by several studies; the most dangerous slopes seem to produce the most deadly avalanches. Knowing that sluff avalanches can have long runout zones but are regularly not so powerful and that wet snow avalanches are less frequent and tend to move slowly, slabs avalanches that can produce broad fracture lines and are able to move as one, vast piece are to be found the steepness degrees that host the most avalanches Aspect The next factor, aspect, has clear expectations; north- and east-facing slopes are the ones that should account for the majority of avalanche events due to their lower temperatures and thus higher snow levels. Thicker snowpacks of course host more avalanches, but attract more skiers and other potential avalanche-causers as well. The aspect of a slope can simply be calculated by ArcGIS tool Aspect that uses the slope steepness layer for its analysis. It calculates the orientation to the sun in degrees resulting in the following eight classes; North ,5 (0 degrees is the purest north) Northeast ,5 East 67,5 112,5 Southeast 112,5 157,5 South 157,5 202,5 Southwest 202,5 247,5 West 247,5 292,5 Northwest 292,5 360 These classes are the aspect values that cells could have. Geographically, the distribution of these values is displayed in figure As to be seen on the map, it is hard to understand on which slope directions the most avalanches occur, so the statistics must further be assessed. As for now, the terrain is too scattered to get clear ideas about the danger of values. 46

47 Figure 3.11: Aspect map of Landeck-Imst Aspect is the first factor that is not measured at ratio level; it is a nominal variable. Transforming them to danger values means upgrading to ratio. For the danger distribution, these eight classes are used, which leads to the scores are to be seen in appendix 3 and figure Figure 3.12: Representation of aspect classes in the study area and in avalanche- hosting terrain Now, it seems that the data partly meets the expectations; especially the north-facing slopes seem to be the most dangerous ones and the eastern side of the hills has significant scores as well. Meanwhile, their western equivalent has not hosted a single avalanche in the past five years and thus gets a danger value 47

48 of 0. North accounts for nearly one-third of all the danger value, the east scores Surprisingly, the south-facing slopes have had relatively many avalanches. Many of these are released in spring time, as indicated by the Lawinenwarndienst. In spring, when the sun has enough power to cause instability in the snowpack by its heating, avalanches tend to occur more frequently on south-facing slopes Human triggering As indicated in the theoretical chapter, it seems that avalanches have significant chances to be released in or near areas in which human leisure activities are practiced frequently. To test whether this is true or not, more information is needed about the areas in which tourists and other adventurers are most likely to reside. Therefore, Tyrol s ski areas are mapped as a layer in ArcGIS. The shapefiles that are used are downloaded directly from the government of Tyrol. The file contains all ski resorts in Austria as of 2008, of which 91 are located in Tyrol (data.gv.at, 2012). Assuming that the most potential human avalanche causers are to be found in or around ski resorts, this is a pretty safe choice of data to make use of. Now, for all cells that make up the area of Landeck-Imst, the Euclidean distance to the nearest ski resort is calculated to know how far away it is from potential skiers. The output is a raster (figure 3.13) similar to the rasters that are used for other factors; the score for each cell means how far, in meters, the nearest ski resort is. The cells that are located within the ski area of course get a score of 0, and the most remote cell of Tyrol is almost twenty kilometers (19856 meters) away. Now, as the map makes clear, this is one of those factors that give away some geographical patterns in the mapping phase. This opens doors for some modest conclusions. What becomes clear is that many white areas, those that are within or close to ski resorts, produce most of the avalanches, while the more remote areas only host a minority of these events. Below, it is time for a closer look at the statistics. 48

49 Percentage of terrain Figure 3.13: Map of a cell s distance to the nearest ski resort, Landeck-Imst This factor has a very broad range of ratio values, which makes it attractive to use a large amount of classes. Therefore, the distance to ski resorts is divided into sixteen classes in total, of which the distribution of scores and new danger values are displayed in appendix 4 and figure 3.14 and Figure 3.14: Representation of distance classes in the study area and in avalanche- hosting terrain Value class 49

50 Percentage of terrain Figure 3.15: Danger values per distance class Value class Looking at these numbers clarifies one thing; in Landeck-Imst, almost half of the avalanches occurred within 1250 meters of a ski resort s boundaries. It seems that the influence of tourists is at most as high as the theory supposes. Also, if a place is further away from ski resorts than approximately 1250 meters, the distance does not seem to matter as long as it is below meters. From there, no avalanches are counted, which is not very surprising since there are no cells with those distances anyway in Landeck- Imst. A critical keynote must be set in this factor and to do so, the data must be reviewed once again. The database from the Lawinenwarndienst includes every single avalanche event that has been detected over the past years. It includes also avalanches that did not manage to catch a human being, so human suffering is dealt with. However, there is a significant chance that there have been avalanches that remained undetected because of their remoteness. With other words: the fact that so many of the avalanches have been observed in or near to ski resorts is because most observing people are to be found in these areas. This does not necessarily mean that the information found above is false; the responsible agencies in dangerous, popular and relatively densely populated areas as Tyrol are highly experienced and are equipped with professional detecting systems. However, in the worst situation, in which the influence of ski resort vicinity is a bit overestimated, the overall risk is given a helping hand by stressing the densest populated areas even more Ski resort characteristics In the literature, it has been announced that a distinction is to be made among ski resorts. They are pretty heterogeneous and when analyzing the danger for avalanches, some characteristics play significant roles. Therefore, two characteristics are highlighted: capacity and off-piste attractiveness. In the human triggering factor the geographical polygons of ski resorts were used and these return in this paragraph. As we have seen, an impressive share of the avalanches more than half of them happened 50

51 in or near a ski resort. This class ranges from 0 to 1241 meters. Now, the question is: what is the influence of the two characteristics on the danger for avalanches? Therefore, scores are assigned to all ski resorts within 1241 meters which finally determines to what extent a resort would be marked as dangerous. Subsequently, among avalanches that happened in or near ski these resorts, it is researched whether they occurred in areas that are considered by be dangerous. This is again compared to overall representation, as we have seen in previous paragraphs. Dependent on these scores, new danger values are assigned to each class. But first; how are scores on capacity and off-piste attractiveness assigned? The answer partly comes from the tourism-serving website from Tyrol (Tyrol.com, 2014), that lists all ski resorts that Tyrol has, along with some characteristics of which one is the capacity. The website uses the following classification, based on kilometers of prepared slope: - Small (<50 kilometers) - Medium ( kilometers) - Large (>100 kilometers) This classification is used for this factor. Danger for avalanches does not come and go per kilometer, that is why rough knowledge is sufficient to mark resorts as dangerous or not. Off-piste attractiveness can simply be either yes or no. Some resorts are known for their off-piste, some are not. Since the literature does not seem to be completely sure about the exact influence that skiers have, and does not mention the characteristics of ski resorts at all, both capacity and off-piste attractiveness are given the same weight. First, it has to be noticed that this is the only factor that does not apply to every cell in the model. Areas that are more than 1241 meters away from ski resort boundaries are simply excluded from this layer and get score 0. Now, since off-piste attractiveness is ambiguous, scores are set at 1 (not attractive) and 2 (attractive). It is important to understand that areas within resorts that are not known for their off-piste are still more dangerous than areas with score 0, because they will always host more tourists than those areas. Overall capacity has three classes, but should have the same danger to be allocated as offpiste. A 1,2,3 -allocation would make capacity more important, so the highest value should be 2 as well. This makes the distribution of danger for both characteristics as follows: - Capacity: o Small = 1 o Medium = 1.5 o Large = 2 - Off-piste attractiveness o Non-attractive = 1 o Attractive = 2 51

52 These values are added up for all avalanche-hosting cells and for the overall Landeck-Imst area. Except for those areas that are too far away from resorts (which have got value 0 ), the lowest value a cell could get is now 2 and the highest possible value is 4. In appendix 5, the score that each ski resort gets is revealed. The score that the terrain gets is displayed below in figure Figure 3.16: Map of ski resort characteristics scores for Landeck-Imst It is remarkable that from all resorts that have been given the highest score (left to right: St. Anton am Arlberg, Ischgl, Sölden, Hochgurgl-Obergurgl), only Ischgl has been somehow immune for avalanches these years. The rest of them have significant avalanche numbers. Another interesting fact is that Serfaus-Fiss-Ladis, the largest 3 -scoring area, is not classified as very attractive for off-piste skiing, but it looks like one of the 4 -scoring areas. The results are presented in appendix 6 and figure It seems that the influence of the characteristics is significant; avalanche occurrence increases as the expected danger rises. 52

53 Percentage of terrain Figure 3.17: Representation of characteristics classes in the study area and in avalanche- hosting terrain Value class Value class Land cover The nominal variable of land cover is expected to have significant influence on avalanche danger. As the theory assumed, the anchoring function of a tree cover can prevent avalanches from growing. In mountainous areas, this would mean that grassy slopes, bare rocky terrain and other smooth surfaces are expected to host the most avalanches. The data that is used to model this factor comes from the CORINE land cover database. CORINE stands for coordination of information on the environment, developed by the European Environment Agency in order to work on spatial environmental issues. Its information is presented as a cartographic product, resulting in a raster (figure 3.18) with 44 land cover classes (European Environment Agency, 2014) of which the code meanings are included in appendix 7. The most recent version is of 2006, which is used in this research. The data is freely available for downloading, irrespective of purpose. Looking at the map, the main finding that avalanches do rarely occur in valleys is confirmed even more. The outlines of the valleys are clearly visible, and so are the higher mountainous areas. These are the areas where most avalanches happen, and also where certain land cover types are dominant. Remarkable as well is the confirmation that avalanches do not happen in forested areas. This CORINE type (24, darker green) is dominantly represented in the areas, but it does not seem that it produces many avalanches. This might be because of the anchoring influence that trees could have. 53

54 Percentage of terrain Figure 3.18: Map of CORINE land cover types, Landeck-Imst As this factor is a nominal variable, reclassifying it to danger values means upscaling in measurement level. Again, the overall representation of a certain land cover type is compared to its representation in areas that have hosted avalanches. This, and the distribution of danger, is tabular visualized in appendix 8 and figure 3.19 and Since it is a nominal variable, the charts should not be interpreted in a sense of the higher the land cover score, the more dangerous. It only gives an overview of which types are under- and overrepresented. Figure 3.19: Representation of land cover types in the study area and in avalanche- hosting terrain Value class 54

55 Percentage of terrain Figure 3.20: Danger values per land cover type Value class Looking at the results, it becomes clear that there must be certain influence of this factor; differences in under- and overrepresentation seem to be large. For example, the theory seems to be right that avalanches do not tend to occur in forested areas. In general, this land cover type is well-represented; one out of four cells is covered with forest (CORINE type 24). CORINE type 26, natural grasslands, is an interesting one as well; there is much grassland in the study area, and these areas produce many avalanches. However, since both numbers are high this type is fairly represented. Overrepresentation is mainly to be found in the typical mountainous types; bare rocks (31), sparsely vegetated areas (32) and glaciers and perpetual snow (34) are accounting for the majority of the avalanches, way more than their overall representation. Especially glaciers and perpetual snow are the most dangerous Slope shape Can a cell be located best on the convex or concave part of the slope? Or just in the middle, the so called planar zone? And does it actually matter? Literature may not be sure whether this factor is contributing but if so, it must be the convex part that accounts for the most avalanche danger. Now, let s see if that is true by looking at the distribution of slope shape among avalanche-hosting cells. The relative position of a cell as part of a slope can be calculated using ArcGIS Curvature tool, which is explained by ESRI as the slope-of-the-slope. The digital elevation model is used as input and its output can be either positive or negative. The closer the values are to 0, the flatter the terrain is. Hilly terrain accounts for a maximum of -0.5 to 0.5, while extremely steep mountains have values between -4 and 4. A positive score means that the surface is convex, negative values mean that cells are at the concave part of the slope. Totally planar zones score 0. In Tyrol, the ratio scores range from to 5.22, this makes the province unsurprisingly classifiable as very mountainous. The influence of this factor can be hardly be 55

56 assessed in figure 3.21, since the majority of the cells scores the same. High and low scores are exceptions that are not form clear patterns in the map, which makes serious assessing kind of a problem. Figure 3.21: Curvature map of Landeck-Imst Looking at statistics, using many classes is preferable to avoid loss of data quality and class boundaries do not need be smoothened, the use of 16 classes is legit. The distribution of the classes among overall and avalanche-hosting terrain is to be found in appendix 9. In figure 3.22 and 3.23, these results are visualized even better. 56

57 Percentage of terrain Percentage of terrain Figure 3.22: Representation of curvature classes in the study area and in avalanche- hosting terrain Value class Figure 3.23: Danger values per curvature class Value class When assessing the results, it does not seem that there is any consistency to be discovered. The expectation of convex cells to be the most dangerous part of slopes is not likely to be true; negative scores are represented more than positive ones. In the total amount of cells, it could be expected that there are more concave than convex cells, since runout zones (areas that consist of concave cells) are usually larger than the convex cells that form mountain peaks and sub-peaks. The fact that statistics fluctuate around 0 confirms that most of the avalanches are released in the middle part of a slope, which is of course also the largest category in general. Assessing the weights of this factor will clarify its actual influence. 57

58 3.3.3 The factors weights The use of logistic regression Now that the conditions under which factors are becoming dangerous are known, it is time to assess each factor s importance. As stated in the introduction of this chapter, statistical regression analysis is used to do this. Performing correlation analysis is useful to discover whether and to what extent certain variables are related to each other (Field, 2013). The correlation coefficient that is a major outcome measure of this analysis indicates how strong this relationship is. Although this coefficient can be useful in statistical analysis, they do not tell us anything about either causality or predictability (Field, 2013). To present findings on these, performing regression analysis is required. The difference of regression with respect to correlation is that it makes clear to what extent a phenomenon can be explained by one or multiple factors. Moreover, it could tell whether relationships are positive or negative. Since this research deals with a phenomenon (avalanche occurrence) that is to a certain degree caused by some factors, causality and predictability are of importance. The factors that have been listed and researched are the independent variables that try to explain the dependent variable of avalanche occurrence. Now, it is explained why reclassification of factors to danger values is done; the presence of nominal variables makes it impossible to add them to the regression logit (the equation that makes up the score on the dependent variable). For example, one could speak of higher chances of avalanche occurrence when slope steepness increases with 1 degree, or when a cell is 100 meters closer to a ski resort. This cannot be done with nominal variables; certain cells could not have higher scores on aspect or land cover types than others. Therefore, all factors are reclassified to danger values, measured on the same scale to make them comparable. As a result of this, for every factor a higher score means more danger. This makes that we do not have to worry about positive or negative relationships anymore. This makes the overall danger equation return: 58

59 The weights are dealt with in this section and it is indeed multiple regression that is going to reveal them. There is, however, one specialty about this case that makes it different from linear multiple regression. Not any cell the entire analysis has hosted more than one avalanche. In normal, linear regression analyses, a continuous dependent variable must be explained. Along with other researches that deal with disasters (Ohlmacher & Davis, 2003; Yilmaz, 2010; Lee, 2005), in this study the dependent variable is binary. Cells are classified into two mutually exclusive groups: they have either hosted an avalanche (1) or not (0). This asks for a binominal logistic regression analysis that is designed for analyses with a binary dependent variable. Since we have more than one independent variable, the executed analysis will be a multiple logistic regression (de Vocht, 2013; Lammers, 2007). Using this, the binary dependent is explained by multiple independent variables that may be measured on categorical, binary or ratio scale. The outcome of the regression equation is not any possible continuous value that the dependent variable could get, but the chance, under certain circumstances (at 3000 meters, with 30 percent slope steepness, etc.), that the dependent variable s score turn into 1 instead of 0. The variables used are the same as those that came out of the theory and thus those that are making up the avalanche danger (D) in the equation. The name of the variable as used in SPSS is written between brackets. In appendix 10, all SPSS outputs can be viewed. Dependent variable: - Avalanche occurrence (grid_code_) Independent variables: - Elevation (elev) - Slope (slope) - Aspect (aspect) - Distance to ski resorts (dist) - Land cover type (landcover) - Slope shape (curv) It must be noticed that the ski resort characteristics are not included in this regression analysis. This is, because this factor is the only one that is not applicable to every cell. After all, it is about all the avalanches that have occurred within 1250 meters of a ski resort. Is it of great importance whether resorts excel in size and off-piste attractiveness? Since the majority of cells score 0 on variable offcap because they are too far away, its influence is likely to be underestimated. The fact that the variable offcap is only designed for a specific part of the data, its regression should be done on this exact same part of the data. Therefore, this factor gets its own logistic regression analysis to find out its importance. 59

60 Towards weight scores for each factor Before performing a binary logistic regression analysis there are two ways to check whether the model fits the data. First, the Omnibus Test of Model Coefficient looks at the model quality; will this model, with these variables, be useful to predict the dependent variable? The null hypothesis is positive, meaning that it goes by the assumption that the model is significant. The Omnibus Test is in this case significant, scoring.000. Second, Hosmer & Lemeshow Goodness-of-Fit Test can be executed to check whether the logistic regression model is suitable for this dataset or not. Is it large enough, and is its variance sufficient? In this model, the negative null hypothesis states that the model does not fit the data. Luckily, the score on this test is o.336, so there is no reason to worry about the model s fit to the data. The classification table, in which the dataset is divided into 10 groups to see whether the amount of expected yes dependent variable scores meet the amount of observed ones (Sieben, 2002). Another manner to assess the model s success is Nagelkerke s R-square, a measure similar but not equal to the R² in linear regression. A score of means that more than 12 percent of the chance of a cell hosting an avalanche or not can be explained by the theory. Now this in an interesting statistic because it directly answers one of the main questions; to what extent is the occurrence of avalanches predictable on beforehand without researching temporal factors? Or in other words, can ever-valid avalanche zoning be done realistically? The Nagelkerke coefficient learns that most of the occurrence of an avalanche is caused by temporal factors or by coincidence. Of course, nothing can be concluded about the overall predictability of avalanches, because we do not know the exact influence of the temporal conditions. What this Nagelkerke coefficient means for the overall conclusions is discussed in the next chapters. Next, the factors actual influence on the dependent variable is assessed. A multiple logistic regression is performed with the listed factors as independents. The result is appendix 10, a table that lists the regression scores for each factor. Since we are looking for a specific entity that explains the influence of one independent variable on the chance of a cell to host avalanches, the coefficient that comes out of regular regression analyses is our measure. This coefficient is to be found in B (Sieben, 2002; Gray & Kinnear, 2012), making elevation and slope steepness the largest determinants of avalanche danger. They are followed by the distance to ski resorts and land cover seems to be the least important of all factors. With the exemption of land cover type, all factors are reported to be significant predictors for avalanche occurrence. In this case, it is decided to include this factor anyway. After all, paragraph made clear that there are major differences on danger among land cover types; some are more likely to host avalanches than others. Therefore, it is assumed that the model zones avalanche danger better with than without this factor. As made clear before, the characteristics of ski resorts have their own regression analysis. The outcome is a very low Nagelkerke R-Square (0.006, appendix 11), which is not too surprising. The B coefficient is 60

61 1.784, making the ski resorts characteristics the least influential factor. Altogether, this research can only cover approximately 13 percent of the entire danger for avalanches. The rest is caused by excluded factors or coincidence. Since the cells that do not have anything to do with the characteristics of ski resorts have scores of 0, they are immune for getting scores on this factor. All factors weights are summarized in table 3.3. Table 3.3: Factors with their weights after logistic regression analysis Factor Regression coefficient Elevation Slope steepness Aspect Distance to ski resorts Land cover type Slope shape Ski resort characteristics The outcomes of the logistic regression model make that the equation is ready to be filled in. It looks like this: 3.4 Calibrating and validating the model Now that the model is fully designed, it is time to check if it is able to indicate the right areas as being dangerous. Therefore, the model is calibrated by filling in the danger equation for every cell of the study areas. This is also the most important validation method; to what extent is the model able to produce the right results? In here, the main question is whether the danger map is matching the geographical distribution of avalanches sufficiently. This is done in two steps. First, the model is calibrated for Landeck-Imst. Since the factors values and weights are calculated for this area specifically, the outcomes should match the avalanche points in Landeck-Imst quite well. But it is the next step that is scientifically more interesting; is the model, with its pros and cons, included and excluded factors applicable for other areas with a slightly different geographic character? Is it able to zone avalanche sensitivity correctly? Calibrating the model for Landeck-Imst Calibrating the model for Landeck-Imst is nothing more than filling in the equation and checking whether the avalanche data meets the zoning. There are, however, some discussion points which make this a bit more complicated. The main question is what success level the modeling output should have to be sufficient. How many percent of the occurred avalanches have to be placed indeed in the highest 61

62 danger classes? Or, what should be the minimum average score of D in cells that have hosted an avalanche? First, it must be noticed that most of the places that are indicated as being extremely dangerous will actually never host an avalanche. Does this make the model uncertain or even insufficient? The answer is no, because this is exactly the influence of the excluded temporary factors and more importantly, coincidence. An avalanche could have occurred in those empty areas just as well, but hopefully rather in reddish areas than in others. It is thus not very important that most of the dangerous areas have seen avalanches actually happening on their ground. More important is that most avalanches that took place were in those higher-level danger zones. What if too many events happened in the zones that are marked as relatively innocent? That would be more of a problem. People would then not expect avalanches when only looking at the outcomes of this model. Of course, as stated, avalanches can occur almost anywhere and zoning an area green cannot guarantee any safety. Coincidence can make an avalanche happen in a relatively safe place, but when these fortuitous events are too frequent, the quality of the model is at risk. In that case, the seven factors that are included do not map avalanche danger sufficiently. Therefore, results of avalanche-hosting terrain are again compared to overall results of the same area as we have seen before. In this case, we are for example interested in the average danger score (D) that avalanche-hosting cells have. If the model is working correctly, this would be significantly higher than the average D that the overall terrain of Landeck-Imst has Validating the model One important aspect of research should be taken into account: reproducibility. Although it is a major goal to make the model work for the Landeck-Imst area, this research would be even more valuable for avalanche research if more areas could profit from its zoning power. In that case, other provinces or even countries could use the model as long as their terrain and other characteristics are similar to those of Landeck-Imst. This means for example that all factors should have more or less the same range of values. Values are classified into danger values over their total range. If the elevation of a validation area ranges between 2500 and 4500 meters instead of the of Tyrol, one could understand that this has some serious consequences for the factor elevation because class breadths are smaller and the overall situation of the area is higher. Due to rising elevation, more avalanches are likely to occur. Another example is slope steepness; in areas with relatively flatter slopes, the factor of slope steepness would rather lower the total avalanche danger than strengthen it. But not only should the factors have more or less the same range of values; these values should have the same danger levels as in Tyrol. For example, chapter 2 stated that the danger distribution of aspect values varies worldwide. Also, we have seen that many of the avalanches that occurred in Landeck-Imst took place in or near a ski resort. This is because Tyrol scores high on sports tourism and population 62

63 density, which make the influence of people increase. Areas with fewer, or smaller, ski resorts may be less sensitive for the distance to ski resorts factor. These issues must be considered seriously before applying this model to other areas. As described in paragraph 4.4, the model s results are compared to the rest of Tyrol to assess the model s reproducibility. Therefore, validating the model is a very important step in the research. Of course, it would be nice if the model works for the study area of Landeck-Imst and thus zones avalanche danger correctly as described in previous paragraph. In this case, the goal of being valuable for avalanche mitigation in this area would be achieved, and we would know that knowledge gathered about the release factors of avalanches is at least applicable to this area. However, would it not be nicer if the model would be proved to be applicable to similar areas as well? In that case, other provinces or even countries could use this model to zone avalanche danger themselves as well. To discover whether the model zones avalanche danger as well as for Landeck-Imst, the exact same equation is filled in for the rest of Tyrol. This thus means the entire province of Tyrol except the area of Landeck-Imst to avoid overlapping. The same comparing is done as in calibrating the model. 3.5 Data quality Switching between measurement levels This research includes a very extensive methodological chapter, since much has to be done before any results can be presented. As soon as we know whether avalanches are predictable under specific constraints of time and scale, this research has done what it is supposed to do. Like almost every product in the world, a lot of processing steps are to be followed in order to finalize it. Data is collected, prepared, processed, classified, aggregated, calculated and visualized in order to let the model work. These activities make that some switching happens between levels of measurement, which has consequences for the quality of data. The classical order of measurement levels distinguishes nominal, ordinal, interval and ratio. Some factors start at nominal, some at ratio. And in the end, all of them are being processed and presented as interval variables. The distance between values is meaningful, but there is no absolute zero-point (figure 3.24). This means that some factors will lose data quality, while others will face improvements. Elevation for example is measured on ratio level (meters above sea level) and processing to danger values means that the Figure 3.24: Order of measurement scales Source: Web Center for Social Research Methods,

64 single scores are now treated as a class, meaning lower precision and thus lower quality. For a factor as land cover type, in which no type is better or more than another (nominal), reclassification means an improvement since now, certain types are more dangerous than others The avalanche database s potential The historical avalanche database that is used comes from the Lawinenwarndienst Tirol. Each avalanche is provided with characteristics that describe their location and date, which are used to map them correctly. A few remarks have to be made on this database, influencing the data quality of this research. First, it can be assumed that an agency as the Lawinenwarndienst is the most reliable party one can imagine, since it is the only one whose core business is avalanches. Using a third-party s data, the Lawinenwarndienst secures the best data quality when it comes to accuracy en completeness. But, speaking of completeness, these records are all avalanches worth mentioning that people have observed in Tyrol in the last five years. Second, however, one critical note has to be made on this; observations are made by people. So, avalanches have larger chances to be observed in areas where there are more people. This makes avalanches within or nearby ski resorts more likely to be detected than more remote avalanches. Is it likely that many remote avalanches remain undetected because no one sees them, or are most avalanches taking place in inhabited areas anyway? This is hard to prove. The question is whether this threatens the research validity or not. This is unsure. However, in the worst case scenario there is put more emphasis on inhabited areas than necessary. Since these areas are expected to be the most endangered anyway, this would not threat the research quality. Third, the most important remark about the avalanche database is that many avalanche characteristics remain unused. The database allows one to explore the subject in more detail because it includes information about avalanche size, type and victims. These details are outside the scope and time span of this research and are therefore not included. There was not made any distinction between large or small, deadly or non-deadly and wet or slab avalanches. It could thus be stated that the outstanding quality of this database is not yet used to its full potential. Other researches with a larger time span and a broader scope can build further on this research to include these characteristics Focus on release points Most of the data used to map the factors comes from the digital elevation model that was described earlier. ArcGIS hydrology toolset enables one to create drainage models, to calculate flow lengths and flow accumulations. It is outside this research scope and there is too much restriction on time to include these. This means that the outcome per cell in this research is only about the avalanche danger in that particular cell. However, including these tools would open doors for an even more realistic approach; not only that particular cell is at danger, but the entire area that the avalanche flows through, growing and growing until it stops when the flow velocity goes down. This is another type of modeling, but can be of huge contribution to this research. 64

65 4. Modeling results 4.1 Introduction In this chapter, the outcomes of this research are discussed. Since understanding of avalanche-causing factors is crucial in this research, the first step is to test the literature s assumptions and suspicions. Which are those factors, under which conditions are they contributing the most to avalanche danger and how big is their influence? Those questions are answered in the first paragraph of this chapter. The second paragraph is meant to use these findings for the actual zoning; filling in the D equation generates a danger map in which all factors have influence. After this, the third paragraph discusses the model s quality. To what extent is it capable to zone avalanche danger correctly? Did most avalanches that have been detected indeed take place in areas that have been marked by the model as dangerous? And last, the reproducibility is assessed; to what extent is the model applicable to areas with similar terrain and other characteristics? 4.2 Understanding of avalanche-causing factors Introduction In this section, all factors will be assessed that are included in this research. Two questions, which should be known by now, are relevant: To what extent are the conditions under which the factors are getting dangerous matching the literature s expectations? To what extent are the factors importances in avalanche danger matching the literature s expectations? First of all, although large differences exist, all factors have been proved to be somehow contributing. Now, it must be stated that some, or even most factors have very vague theoretical expectations. Especially for the importance of factors, very little literature aims to fully discover their exact influence. Most of the sources list certain variables that would make up the danger together. As discussed in the introduction chapter, this is one of this research relevance keystones. Therefore, by pioneering itself into clear importance weights, this research cannot really be called testing the theory but this makes the relevance even bigger. As explained, the dangerousness of certain conditions is expressed in the danger values and the logistic regression analysis is executed to assess the relative importance of each factor. These two measures are together making up one factor, one factor after another. The scores on each factor are reclassified to danger values that are displayed in new maps. It should be noticed that the values have been multiplied by 10,000 because ArcGIS cannot reclassify raster cells into decimals. The cells are divided back by 10,000 before the final equation is filled in. 65

66 4.2.2 Elevation From the beginning, elevation faced high expectations in the literature; it was considered to be one of the major variables in the total danger equation. As became clear in the previous chapter, the hypothesis of the higher, the more dangerous was true. In Tyrol, avalanches did not occur below an elevation of 1700 meters above sea level and they hardly took place below 2000 meters. A classification based on the danger values that have been calculated results in the elevation danger map as displayed in figure 4.1. A clear pattern can be recognized; the lowest classes barely have avalanches, while the higher-scoring classes host the most of them. Figure 4.1: Reclassified elevation map of Landeck-Imst The importance of elevation as a factor turns out to be the highest of all variables in the logistic regression analysis. Its B of outnumbers every other factor and this basically follows the literature; none of the sources that have been used forgot to name elevation as being important. 66

67 4.2.3 Slope steepness Most literature sources were clear about the conditions under which the steepness of a slope could be crucial in avalanche release. 38 degrees was the bulls-eye, and most avalanches occurred on slopes between 30 and 45 degrees. Basically, they are not to be found below 22.5 degrees and slopes steeper than 50 degrees have not produced avalanche either. This means that the theory was true about the assumption that avalanche danger does not always increase with every degree that the slope gets steeper; some slopes are indeed too steep for serious avalanche production. The reclassified data is displayed in figure 4.2. Figure 4.2: Reclassified slope map of Landeck-Imst The importance of slope steepness, which was expected to be large, indeed was. None of the theoretical sources forgot to name it as a factor. A B score of means that it is indeed one of the most influential variables in the equation. 67

68 4.2.4 Aspect When it comes to aspect, the theory expects the northern-to-eastern zone to be the most dangerous for an area as Tyrol, especially the north because it receives the least sunlight. But this is not a foolproof prediction, since avalanches can always occur on other directions. Basically, there are no aspect values that slopes could have that make the occurrence of avalanches impossible. Whereas no avalanche can occur on slopes that are too flat, every wind direction slope can host these. There are also conditions imaginable when south-facing slopes can produce many avalanches. This can be recognized in the outcomes; the north is by far the most dangerous zone. This is followed by the east, and indeed, the south. This means that the danger distribution is a bit scattered, with no clear pattern besides the extreme overrepresentation of the north. The danger map of aspect with reclassified values is shown in figure 4.3. Since patterns can hardly be distinguished in aspect, the map is somewhat scattered as well. Figure 4.3: Reclassified aspect map of Landeck-Imst The influence of aspect is doubtful. While most literature sources name it as a factor, the influence of all directions besides north remains somewhat the same. Northwestern and northeastern slopes have the disadvantages of being north, but still lose against the south. Southeast is a bit more influential than 68

69 southwest, probably because of being semi-east and west hosted no avalanche at all. The fact that its B from the logistic regression analysis is only makes aspect a relatively weak contributor to D Human triggering Human triggering as described in the theoretical chapter is a special variable, which is relatively unknown. Remarkable is that this factor has become more influential over the years, since numbers of dangerous tourists have increased rapidly in the last decades. Classic avalanche studies do hardly mention this group of tourists as seriously role-playing, but its growth is recognized by newer sources. The question is to what extent the presence of people is of significant importance in avalanche occurrence. Do people, mainly visitors, actually trigger avalanches? It seems that way. The location of ski resorts is used as a proxy for the presence of the most dangerous people that are likely to reside in snowy areas. This brought some useful information; almost an impressive half of the avalanches recorded in the Landeck-Imst area took place within 1250 meters from a ski resort. Going further flattens the danger; apparently, most skiers that can be dangerous seem to be found directly next to the resorts boundaries. This makes the variable almost binary, causing a very high danger value for the class meters. This is not to be seen in figure 4.4, since every class has got its own color. However, it is indeed clear that most of the avalanches occurred in zones that are at least colored orange. Figure 4.4: Reclassified distance to resorts map of Landeck-Imst 69

70 Although the class of accounts for half of the danger that is to distribute, the rest of the classes do not seem to matter much. The distance to ski resorts gets a B score of 4.936, which is not too high. Due to its high danger value, the most dangerous class has significant influence on D, whereas the other classes stay behind Ski resort characteristics Researching the highlighting of ski resorts that are to certain extent different than others has no theoretical foundation at all, as described in the second chapter. It is more like an educated guess; not every ski resort has the same size and not every resort attracts the same visitors. Although every visitor that resides in the mountains can be a potential avalanche-causer, certain groups are considered more dangerous than others. The tourists that this research looks in in more detail are off-piste skiers and snowboarders; do some resorts attract them in particular? And a second question is, are larger resorts, with more tourists, producing more avalanches than smaller ones? Although there is hardly any reason to make clear presumptions, this is researched anyway. Now, we can see that of all avalanches that happened in or near (maximum of 1250 meters) a ski resort, most of them happened in a red colored area (figure 4.5). These areas are the ones that score the highest possible score on both size (amount of piste kilometers) and off-piste attractiveness (yes or no). However, many events took place in smaller and less attractive areas as well. This makes its influence questionable. Looking at the logistic regression, the factor s B of is very low. The characteristics of ski resorts will thus barely be seriously influential in the total D calculation. Figure 4.5: Reclassified ski resort characteristics map of Landeck-Imst 70

71 4.2.7 Land cover type As the theory describes, there has to be some influence of land cover type. After all, some types can impossibly host avalanches, such as built up area or lakes. Some are more likely to produce them, such as grassy hills, bare rocks and glaciers. Looking at the results, it is true that most avalanches take place at places of certain types of land cover. Forests are, as foreseen, the type that loses the most when comparing representation in avalanche-hosting terrain with overall characteristics of Landeck-Imst. This is indeed compensated by mountainous terrain that is to be seen in figure 4.6. Figure 4.6: Reclassified land cover type map of Landeck-Imst When it comes to influence that land cover type has on D, a disappointing B score of is the result of the logistic regression. This is the lowest score around, and this hard to explain. By all means, it makes that land cover type will not prominently contribute to avalanche danger. 71

72 4.2.8 Slope shape Slope shape is the variable that most theoretical sources discuss about. A significant number of them name it, but none of them is sure whether, and to what extent, it contributes as a factor for avalanche danger. Looking at the results, it is remarkable that outstanding values, being positive or negative, are neither to be found in avalanche-hosting terrain nor in Landeck-Imst in general. Most of the values are to be found in the middle, which results in quite a homogeneous danger map. The center of gravity is for both also to be found in the middle. This means that most of the avalanches occur in the planar part of a slope that is neither convex nor concave. The reclassified map is to be viewed in figure 4.7. Figure 4.7: Reclassified curvature map of Landeck-Imst With not many classes being significantly different from the overall representation, this factor does not seem to be very influential in the total danger for avalanches. This is confirmed by its B score; is not very high. It does thus not have much power in the equation. 72

73 4.3 Calibrating the model for Landeck-Imst Introduction In the previous paragraphs, both ingredients for the calculation of D are derived from the information that the factors provided. Now, the danger values and the factors weights are within reach. This asks for the return of the equation that calculates D: Filling in this equation for all of the 548,988 cells that make up the area of Landeck-Imst results in figure 4.9, that forms the danger zoning map that is one of the main goals of this research. The classification of values is discussed along with the outcomes of the model in general. Then, it is important to interpret its success before validation can be done Calculating the danger Not that the equation has been filled in, all cells have their D scores statically. Now, the question is how to present them, classification-wise. There are several methods to present the knowledge that has been gained and this has all to do with the way class boundaries are organized. Two widely used examples of classification methods are equal interval and quantiles (figure 4.8). The first means that all classes have the same breadth. Basically, this is the way all values have been classified so far. There is always the risk, especially when many records have more or less the same value that certain classes are packed with records while others remain empty. Using quantiles means that every class has the same amount of records, and thus that class breadths vary. Figure 4.8: The classification methods equal interval and quantiles Looking at the histogram of values (figure 4.8), using equal interval would mean that most of the cells would be colored yellowish and using quantiles would make all colors equally be distributed over the map. 73

74 Now, there is some sort of dilemma in choosing the right way to classify. There are two risks: 1. The risk of marking too few areas as risky 2. The risk of marking too many areas as risky Of course, both scenarios are inevitable; there will always be some kind of discrepancy between predicted and observed cell outcomes (either 0 or 1 ; avalanche or no avalanche). But classification can make a difference. The histograms make clear that the equal interval method would mark fewer cells as dangerous than quantiles. Quantiles in its turn has the risk of marking too many areas as dangerous, since it has to fill its classes. In this case, it is very important to remember that this research deals with natural hazards that are proved to be deadly and a big threat for humans. Therefore, it is more desirable for people to be wrongly vigilant in places that turn out to be not so dangerous than to forget to highlight dangerous areas that may produce avalanches. In other words: we prefer wrongly yes rather than wrongly no. That is why quantiles are used; the result is shown below in figure 4.9. Apart from the classification method, the number of classes is of great importance. Using two classes would highlight too many areas being dangerous, while using twenty would mean that hardly any avalanches deserve to be in the most dangerous class. However, as long as there are not too many, a higher amount of classes brings the desired level of detail. Although fewer cells are among the most dangerous, it is still possible to distinguish above, around and below the average. Therefore, the maximum amount of classes is used. According to Ormeling & van der Schans (1997), classifying polygons in different colors allows the use of eight classes in cartography, so this advice is followed. Figure 4.9: Avalanche danger map of Landeck-Imst after filling in the equation for D 74

75 After all, it is the combination of all different factors that determines the score that a cell gets. To clarify, two examples are given of the outcome of the equation, both for innocent and dangerous cells (table 4.1). Both real scores and matching danger values are shown. Table 4.1: Two examples of cells with their attributes Factor Score cell 1 Danger value cell 1 Score cell 2 Danger value cell 2 Elevation 1850 meters Slope 23 degrees degrees 0.22 Aspect Southwest 0.05 North 0.31 Human triggering 6500 meters meters 0.48 Resort characteristics 0 (too far away) Land cover type 24 (forest) Slope shape 1.10 (weak convex) And thus is the calculation done as displayed in table 4.2. Table 4.2: Two examples of calculating a cell s danger Cell Equation (D) 1 D 1 = (12.811*0.02)+(10.536*0.10)+(3.815*0.05)+(4.936*0.03)+(1.784*0)+(1.856*0.01)+(3.865*0.05) 2 D 2 = (12.811*0.29)+(10.536*0.22)+(3.815*0.31)+(4.936*0.48)+(1.784*0.29)+(1.856*0.20)+(3.865*0.21) Cell 1 scores a D of 1.86; Cell 2 has a D of Although D can theoretically be even higher than 11.29, in reality no cell scores higher than The lowest score is Now it becomes clear what the largest contributors to avalanches danger are for these cells. A factor can be important in two ways, both in danger value and in weight. Weights are static; for every danger value that a factor could have, its weight is always the same. For the weights, elevation and slope steepness are the two main contributors. When looking at danger values, factors which have one or more classes that are extremely dangerous compared to other classes can have large influences on D. The distance to ski resorts (0.48) and the land cover type of glaciers and perpetual snow (0.43) are examples of classes that can raise the danger significantly on their own. Of course, a combination of both danger values and weights has the most influence. This can be confirmed when looking at the map. First, the emergence of red zones is mainly caused by the factors that have shown a clear geographical pattern in paragraph 4.2, which especially are elevation and the distance to ski resorts. This makes that the most dangerous areas are to be found around large ski resorts in high areas such as Sölden, Hochgurgl-Obergurgl and Sankt Anton am Arlberg. The clearly visible valleys or mountainous areas without ski resorts nearby are the least dangerous. Land cover type 75

76 is one factor that comes with a clear geographical pattern, but its influence is too weak put its stamp upon D. Meanwhile, slope does not have the clearest pattern distributed over large solid polygons, but is in such way important in the equation that its influence is visible on the map. Therefore, areas in or close to ski resorts are colored red, along with higher located areas and small steeper-sloped zones. The other factors are more of a maker or breaker ; they will not make a cell dangerous on their own, but can correct its score in a positive or negative way Assessing the model s quality Now that the equation is filled in and classification decisions are made, the degree to which the model zones avalanche danger correctly can be judged. One result is already published in the methodological chapter; not the entire danger for avalanches can be explained by the factors that are included in this research. The goal of this research to assess whether avalanches can be predicted without having any knowledge of temporal variables is accomplished. The logistic regression pointed out that the seven variables that have been chosen predict the variance of avalanches happening or not in a specific place for only 13 percent. It is thus hardly possible to predict whether a certain place will produce an avalanche or not. However, it is more important to assess whether the danger is ascribed to the right areas. Therefore, the two risks that have been introduced in the previous paragraph must be assessed. As mentioned, both scenarios are inevitable. There are thus always high-danger zoned areas that did not produce avalanches and nondangerous zones that still hosted an avalanche. First, some impressions from the map itself are discussed and afterwards, statistics will help to base statements on. Looking at the danger map, it becomes clear that most of the observed avalanche points are indeed located in a zone that is marked as more dangerous than the average. But some points seem lost; they appear in widespread greenish zones. Some of them are placed on small red and orange endings of mountain ridges, but most are in large red zones that are dotted with avalanches (figure 4.10). 76

77 Figure 4.10: Three examples of the surrounding area of avalanche-hosting cells in Landeck-Imst About the first risk; as mentioned, avalanches cannot always be predicted due to the influence of local, temporal conditions of weather and snow, and to coincidence. The exact percentages of these two factors filling in this gap are unknown and are not likely to be unveiled easily. Little has to be told about the second risk; the fact that a place is zoned to be dangerous does not directly mean that it actually will produce avalanches. Of course, assessing the model s success is about the distribution of D over overall Landeck-Imst cells on the one hand, and of avalanche-hosting cells on the other hand. Are they similar to each other or are there significant differences? If the model is working correctly, it could at least be expected that avalanche points have a higher average D than other cells. Therefore, these two are compared in figure 4.11, where relative representation (percentages) of each class is displayed on the vertical axis and the classes (equal interval) of D observed values are on the horizontal axis. Now a clear pattern is visible, the historic avalanche points appear in places that have much higher average danger values. It should be noted that figure 4.11 is the histogram of the same dataset as the chart that is produced by ArcGIS (figure 4.8), although they do not look the same. This is because the ArcGIS histogram is about the amount of cells, and it is used to provide a quick insight in the difference between classification methods. However, large differences exist between cell sizes, since polygons are simplified by ArcGIS during conversion from raster to vector. Therefore, calculating statistics is done by using the geometrical surface of all cells 77

78 Percentage of terrain that fall within a class instead of the just the number of cells. This is the same as the method used in chapter 3. A very low number of 5 avalanches took place in zones that score below the average D (3.54), so a vast majority of them scores higher. In fact, even more than 50 percent of the cases took place in the 33.3 percent most dangerous areas. This makes the average D for avalanche-hosting terrain (5.98) much higher than the overall average. Returning to the risks; it can be concluded that the model neither zones too few areas as dangerous and nor does it mark the wrong areas. Of course, it highlights dangerous areas that did not produce avalanches, but this does not mean that it marked too many areas as dangerous. This conclusion makes that it is concluded that the model works sufficiently. Figure 4.11: Comparison of danger values between Landeck-Imst in general and in avalanche-hosting cells 4.4 Validating the model Value class Now that it is concluded that the model works sufficiently for the area it is designed for, the question is to what extent it is applicable to similar areas. If so, the model s usefulness can be significantly extended The validation area As stated in previous chapters, an area similar to the calibration area is used to validate the model. Obviously, a nearby situated validation area is chosen since these areas have the best chances of being similar. However, similar is a vague classification; possible differences in success rates can also be caused by too much differences in terrain. Before the validation is executed, it is thus wise to compare the two areas. This is done per factor Assessing the model s success The same procedure is followed; the same classification method is used. The result is a danger map with the same parameters (figure 4.12). 78

79 Figure 4.12: Avalanche danger map of the rest of Tyrol after filling in the equation for D The first remarkable observation is that ski resorts seem to have a less prominent role in avalanche occurrence in the validation area than in Landeck-Imst. An obvious reason for this is to be found in their characteristics; four out of five resorts that have been given the highest score on off-piste attractiveness and capacity, and thus are the most dangerous, are located in Landeck-Imst. It is therefore not surprising that they are occupying a larger share of the avalanches than in the rest of Tyrol. This can be recognized by looking at the map above; more avalanches have been detected in areas that are not part of a larger dangerous zone. For instance, the north(west)ern part is not known for its danger since its elevation is lower and there are thus less dangerous ski resorts, but it still hosts a significant part of the avalanches. The avalanches that did not take place in large-scale dangerous zones often still happen in smaller, but still dangerous, islands as to be seen in figure

80 Figure 4.13: Three examples of the surrounding area of avalanche-hosting cells in the rest of Tyrol Of course, some major ski resorts (Zillertal 3000, Innsbruck s surroundings) account for relatively many avalanches. However, there are also zones that are far away from any skiing activities that still produce many avalanches, such as the southwestern part of the validation area. In these areas, the avalanches did still occur in places that are made dangerous by other factors. As done when calibrating the model for Landeck- Imst, statistics are used to assess its success in the validation procedure. Again, the representation of each class of D scores as part of the total area and of the avalanche-hosting terrain is displayed in If the model works, the graph should look the same as 4.11; in avalanche-hosting cells, the average D should be much higher compared to all other cells. The model s success is expected to be lower than in the calibration area, since the model is designed for Landeck-Imst and not for the rest of Tyrol. Looking at the charts, it can be concluded that the expected gap of D scores between the two measurements is present. These measurements (Area total and AHT) are closer to each other than we have seen in Landeck-Imst, meaning that the expectation that the model works better in the calibration area is met. Still, it seems that many avalanches took place in the areas that are indicated to be more dangerous than the average, which is confirmed by statistics; in Landeck-Imst the averages of area total and AHT were 3.54 and 5.98, in the validation 80

81 Percentage of terrain area these are 3.21 and Although calibrating the model generates a larger difference, the second does not stay too far behind. Figure 4.14: Comparison of danger values between the rest of Tyrol in general and in avalanche-hosting cells Value class Subsequently, only 12 out of 132 avalanches took place in areas that are marked by the model as relatively innocent, meaning that its danger value is lower than the average. This means that approximately 10 out of 11 (91 percent) avalanches are correctly zoned. More strictly, 17.5 percent of the avalanches happened in the 33.3 most dangerous zones. This is significantly lower than in Landeck-Imst, meaning that the majority of the records are between the average and the two-third-break. However, considering the clearly visible gap in the graph it is concluded that it makes sense to use this model for other areas. When one does this, the application success can be assessed by creating a graph similar to those used above. This is a clear achievement of the model. Now, the last step is to assess to what extent this research proved to be of contribution to avalanche zoning in Tyrol. To do so, the zoning maps that were introduced in the theoretical chapter return. The two different types of existing zoning maps have different purposes, and the goal of this research was to develop new maps that have a level of detail that is just between those two. Additionally, the map should still cover the entire province of Tyrol. Both goals are accomplished; Tyrol is covered and when it comes to detail, the model cannot keep up with the detailed municipal maps but they are much more detailed than the daily zoning maps that the Lawinenwarndienst produces. 81

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83 5. Conclusions 5.1 Answering the questions After developing the model that was constructed following the methodology resulting from theoretical expectations, it is time to look back and present the most important conclusions. The central question from the introduction returns: How do avalanche-causing factors influence the danger for avalanches and how could this knowledge be used for developing a model that zones avalanche danger for the province of Tyrol? This question results from the goals that are set on beforehand. These goals are translated in subquestions that are the foundation of this concluding chapter. The central question is answered by the goals are: Gain insight in the current situation of avalanche-zoning in Tyrol Make clear what the exact role of avalanche-causing factors is Use these factors to develop a model that zones avalanche danger without having knowledge of temporal snow and weather conditions Validate the model for further use in areas with similar terrain First, some returning remarks are made on the scope of this research. Looking at these goals directly reveals that some important factors are excluded from the entire study. This is for a reason, which has been made clear in the previous chapters; this research should result in an avalanche zoning map that is valid permanently. It can contribute to avalanche forecasting and reporting by overlaying it with up-todate snowpack information. Including these temporal factors would mean that the model would only be valid for moments when the snow and weather conditions are the same as the model s original parameters. However, this does not mean that snow and weather conditions are completely excluded; some factors that are included can be used as a rough proxy that represents them indirectly. For example, theoretical sources state that the factor of elevation has a close relationship with average snow depth, since higher located areas are generally colder and receive more snow. The type of land cover also correspondents with expected snowpack thickness; bare rocks, glaciers and perpetual snow are typical landscapes in higher alpine areas. About weather conditions; rapid changes in temperature are impossible to predict on long-term. However, the amount of sunlight that a place receives (weather conditions) can be an indicator for both snowpack depth (snow conditions) and temperature changes. These amounts of solar radiation can be approached by assessing a slope s aspect. 83

84 Below, each sub-question is answered before the original problem statement is discussed. What is the current situation of avalanche-zoning in Tyrol and how could it be improved? Tyrol has a long history in avalanche zoning and forecasting. Therefore, they have a self-governing agency that is responsible for the entire avalanche management. It is thus the responsibility of the Lawinenwarndienst Tirol to forecast, zone and report avalanche danger for everyone that is potentially threatened by avalanches. This results in two types of avalanche zoning that vary in scale and update frequency. Almost every municipality has its Wildbach- und Lawinenverbauung, a detailed map that zones the suitability for building with respect to natural hazards like avalanches. These are compulsory; it is forbidden to build in red zones. These maps are not updated frequently, as situations hardly change over time. When it comes to up-to-date avalanche mapping, much activity radiates from the Lawinenwarndienst; as soon as conditions change, practically daily, the forecasts are updated. This results in a map that provides the danger level (scale 1 to 5) so that local governments and individuals can make their decisions. This zoning and its fellow danger mapping relies heavily on up-to-date snow and weather statistics and forecasts, which has its pros and contras. The main advantage of this is that avalanche danger is after all mostly caused by snow depths and less often extreme meteorological conditions. However, this way of predicting is unattended to geographical diversity that is very useful in specifying an area s danger and that is why this type of avalanche zoning is aimed to be improved by this research. By combining this research (a static danger map based on geographical diversity) with the information of the Lawinenwarndienst (up-to-date data on snow and weather, but not so detailed in geography), Tyrolean avalanche zoning in general could benefit. Which factors contribute to avalanche occurrence and what is their influence? Avalanche literature is quite unanimous on a few factors. Some factors are named as being definitely important by some sources, but are forgotten by others, and other factors are a big question mark for some studies. It is remarkable that the most obvious factors are the ones that turned out to be the most important, while those that were questionable do hardly play significant roles. For every factor, it is discussed how much influence they have and which of their conditions are the most dangerous, since these two are the main ingredients of the final equation that calculates an area s avalanche danger. - Elevation is proved to be the most prominent avalanche-causing factor. This was expected on beforehand, being named by every source. Higher, colder locations receive more snow and have no thick tree cover, due to lacking oxygen and to lower ground temperatures. Wind reigns in this open terrain, often causing serious destabilization of the snowpack. For the model, this means that the importance weight of elevation is the highest of all factors. For the danger that is to be distributed among all possible elevation values ( meters), this means that the highest elevated areas receive the highest danger values. 84

85 - Slope steepness is elevation s runner-up, meeting the theoretical expectations of being of great importance. A slope perfect for avalanches should not neither be too flat nor to steep. The highest danger values are to be found at degrees. This matches the theory s prospect of a bull s eye at 38 degrees. The model is highly influenced by slope as a factor, leading to a very high importance weight. - Aspect is quite a special factor, which has kind of surprising results. On beforehand, it was expected to be very important; in every avalanche, aspect plays a significant role. But the problem with aspect is that most of the eight slope directions have an own reason for being dangerous. As the theory states, north-facing slopes are the most dangerous since they tend to have more snow since the lack of sunlight makes them the coldest sides of the mountain. This opposition is also to be found, although to a lesser extent, between east (colder) and west (warmer). However, southern slopes can also be dangerous in spring time. Every slope direction can thus dangerous in its own way, which makes aspect as a factor not very important in the final equation. - Human triggering turned out to be only important in the areas closest to ski resorts. Once the nearest ski resort is further away than 1250 meters, the danger that this factor causes drops immediately. This means that half of the distributed danger is to be found in regions in or within 1250 meters from a ski resort. After 1250, it does not seem to matter much. The distance to ski resorts thus fills the gap between elevation and slope steepness and the lower-scoring factors. - Ski resort characteristics are the only factor that has no theoretical reasons to be included. The inclusion is the result of the vague assumption that the diversity of ski resorts lead to differences in avalanche danger within these areas. The characteristics that influence these differences are overall size and off-piste attractiveness. All resorts and their surroundings (up to 1250) get a score on these and they are totaled up. The highest scoring resorts turned out to be the most dangerous so that assumption is confirmed, but the overall importance is very low. In fact, there is no strong relationship between the diversity and avalanche danger, making it the weakest contributor in the equation. - Land cover type turned out to be one of the weakest factors, which was not expected on beforehand. Despite its low importance, the danger distribution is very clear: typical mountainous land cover types, such as bare rocks, glaciers and perpetual snow, are raking in most of the danger. - Slope shape s influence has been unsure from the beginning. Although others forget to, some sources name the slope s shape as being of possible influence. A slope is convex, concave and the planar zones are in between. No source is sure about its influence, so it is included just to be sure. After all, there was hardly a need to include it as a factor, there was no clear visible pattern and its influence is very low. 85

86 These factors try to explain where avalanche danger is generally high and what it is that causes this danger. This has everything to do with terrain that is dangerous itself or enables weather and snow to contribute to local danger. Looking at a picture by the New Zealand Avalanche Centre that breaks down the dangerous terrain, it can more or less be concluded that this research succeeded to include every factor (figure 5.1). Figure 5.1: Avalanche-causing terrain features Source: New Zealand Avalanche Centre, 2014b To what extent could these factors be used for the development of a model that zones avalanche danger in the Landeck-Imst? As discussed in the methodological chapter, the improvement to avalanche zoning methods comes in the shape of a model. The input are the factors, the output is the danger map. For every cell, a danger factor (D) is calculated which lead to the mapping by adding up the danger that each factor causes. This danger consists of the extent to which the factor has dangerous situations danger values V (steep slopes, north-facing, close to ski resorts) and of the importance weights of that factor in the total avalanche danger (e.g. elevation is the most important one). The danger values are reclassifications of real scores (from 3500 meters elevation to danger value 0.29), while the importance weights are the result of a multiple logistic regression that can be found in chapter 3. The following equation is meant to clarify these steps: 86