Show Summary Details

Page of

date: 26 September 2018

# Observation and Spatial Modeling of Snow- and Ice-Related Mass Movement Hazards

## Summary and Keywords

Snow- and ice-related hazardous processes threaten society in tropical to high-latitude mountain areas worldwide and at highly variable time scales. On the one hand, small snow avalanches are recorded in high numbers every winter. On the other hand, glacial lake outburst floods (GLOFs) or large-scale volcano–ice interactions occur less frequently but may evolve into destructive process chains resulting in major disasters. These extreme examples document the huge field of types, magnitudes, and frequencies of snow- and ice-related hazardous processes.

Mountain societies have learned to cope with natural hazards for centuries, guided by personal experiences and oral and written tradition. Historical records are today still important as a basis to mitigate snow- and ice-related hazards. They are complemented by a broad array of observation and modeling techniques. These techniques differ among themselves with regard to (1) the type of process under investigation and (2) the scale and purpose of investigation. Multi-scale monitoring and warning systems for snow avalanches are in operation in densely populated mid-latitude mountain areas. They build on meteorological and snow profile data in combination with a large pool of expert knowledge.

In contrast, ice-related processes such as ice- or rock-ice avalanches, GLOFs, or associated process chains cause damage less frequently in space and time, so that societies are less well adapted. Even though the hazard sources are often far from the society—making field observation challenging—flows travelling for tens of kilometers sometimes impact populated areas. These hazards are strongly influenced by climate change–induced glacier and permafrost dynamics. On the regional or national scale, the evolution of such hazards has to be monitored at short intervals through aerial and satellite imagery and terrain data, employing geographic information systems (GIS). Known hazardous situations have to be monitored in the field.

Physical models—applied either in the laboratory or at real-world sites—are employed to explore the mobility of hazardous processes. Since the 1950s, however, computer models have increasingly gained importance in exploring possible travel distances, impact areas, velocities, and impact forces of events. While simple empirical-statistical approaches are used at broad scales in combination with GIS, advanced numeric models are applied to analyze specific case studies. However, the input parameters for these models are uncertain so that (1) the model results have to be validated with observations and (2) appropriate strategies to deal with the uncertainties have to be applied before using the model results for hazard zoning or dimensioning of protective structures. Due to rapid atmospheric warming and related changes in the cryosphere, hazard situations beyond historical experiences are expected to be increasingly relevant in the future. Scenario-based modeling of complex systems and process chains therefore represents an emerging research direction.

# Introduction

Compared to many types of rocks, which may persist for billions of years, snow and ice—summarized as the cryosphere—are rather short-lived substances undergoing dynamic changes. Even though ice several million years old is found in Antarctica (Sugden et al., 1995), melting of valley glaciers sometimes becomes obvious within a few weeks. Snow may melt as soon as it reaches the earth’s surface, but it may also persist for hours, days, or months or even evolve into glacier ice over a few years.

The cryosphere provides many services to the society—be it natural beauty, the potential for tourism, water storage, or, in times of climate warming, the capacity of permafrost to retain carbon. Lack of understanding of the cryosphere, on the other hand, induces certain risks for society. Such risks may be related to slippery surfaces or thin lake ice, increased carbon emissions from thawing permafrost, jamming of lake and river ice, surging glaciers, snow load hazards, etc.

Not all of these aspects are covered in detail here; the present article instead focuses on various types of cryosphere-related mass movements in mountain areas. Hazards related to the seasonal snow cover are compared to those emanating from a changing glacial and periglacial environment. High-mountain environments are highly sensitive geosystems, in which the spatio-temporal distribution and evolution of snow, glaciers, permafrost, ecosystems, and the water cycle serve as early indicators for a fluctuating or even changing climate (Beniston, 2003; Harris et al., 2009; Huber, Bugmann, & Reasoner, 2005).

The observation and modeling techniques presented are thought to increase the awareness and understanding of ongoing processes, the capacity to foresee their possible consequences, and, therefore, the ability to reduce the related risks. Broad-scale spatial observation and modeling techniques are as important for prioritizing hot spots of hazard and risk as are fine-scale techniques for thoroughly analyzing specific events or situations.

## Snow Avalanche Hazards

Snow is involved in various types of hazardous processes. The rapid melting of snow leads to saturation of the soil and causes landslides or debris flows. Such melting may be related to the climatic conditions (Cardinali, Ardizzone, Galli, Guzzetti, & Reichenbach, 2000; Decaulne, Sæmundsson, & Petursson, 2005) or to volcanic or geothermal activity. Further, snow cover may strongly influence the propagation of mass movements (Preh & Sausgruber, 2015). Here, however, the focus is put on snow avalanches.

Snow avalanche hazards are widespread throughout mid-latitude mountain areas. Various types of snow avalanches are known, depending on the physical characteristics of the snow and on the topography (De Quervain, De Crecy, LaChapelle, Losev, & Shoda, 1973). Major classification criteria include the type of rupture (slab avalanche vs. loose snow avalanche) and the form of movement (Fig. 1; powder avalanche vs. flow avalanche). A detailed account of snow physics and avalanche classifications would go beyond the scope of the present article and can be found in The Avalanche Handbook (McClung & Schaerer, 2006).

Click to view larger

Figure 1. Powder snow avalanche near Innsbruck, Austria (photo courtesy of Jan-Thomas Fischer, Austrian Research Centre for Forests [BFW]).

In Switzerland, the first protective measures for single buildings were implemented in the 17th century, while protective measures in the release areas were first implemented in the early 19th century (SLF, 1999–2015). The first avalanche inventory for Switzerland dates back to the end of the 19th century (Coaz, 1881). The first snow and avalanche guideline was published in the first half of the 20th century (Paulcke, 1938). At the same time, more specialized literature on snow physics (Bader et al., 1939) and avalanche dynamics (Oechslin, 1938) appeared. Two avalanche disasters in the early 1950s (much of the Alps was affected in 1950/51; in 1954 it was part of Vorarlberg in western Austria) induced broad-scale technical protection measures. Voellmy (1955) developed a mathematical model for snow avalanche motion which represents the basis for many of the computer models still used six decades later. Since the 1960s, spatial planning and hazard maps have aided the trend toward modern integrated hazard and risk management strategies for snow avalanches (SLF, 1999–2015).

For more detailed information on avalanches, the related hazards and risks, and their mitigation, see Ancey (2016) and Schweizer, Bartelt, & van Herwijnen, (2014).

## Glacial and Periglacial Hazards

Hazards emanating from winter snow cover are often highly relevant for everyday life of the mountain population. Hazardous processes related to the glacial environment, in contrast, usually start far away from populated areas, and only high-magnitude, low-frequency events cause damage. While snow hazards are often associated with the cold season, glacial hazards are more relevant during summer when some water is available in its more active liquid state and the glacial systems become more dynamic and therefore potentially unstable.

Evidence for a worldwide accelerated retreat of glaciers over the last few decades is overwhelming (WGMS, 2008), involving the tropics (e.g., Kaser, 1999), arid and humid mid-latitudes (e.g., Aizen, Kuzmichenok, Surazakov, & Aizen, 2007; Lambrecht & Kuhn, 2007), and the polar regions (e.g., Cook, Fox, Vaughan, & Ferrigno, 2005). Most of this retreat has been attributed to the evident atmospheric temperature rise (IPCC, 2007, 2013). Even though the rates and characteristics of glacier retreat vary from place to place, the general trend is more than clear (Zemp et al., 2015). Locally, the dynamics of the glacial and periglacial environment disturb the equilibrium of the system, inducing an increased level of hazard (Dussaillant et al., 2010; Evans & Clague, 1994; Haeberli, Clague, Huggel, & Kääb, 2010a; Harris et al., 2009; Huggel, Haeberli, Kääb, Bieri, & Richardson, 2004a; Huggel, Kääb, & Salzmann, 2004b; Kääb et al., 2005; IPCC, 2007; Quincey et al., 2007). Ice hazards mainly include the following types of processes (Clague & O’Connor, 2014; Deline et al., 2014; Evans & Delaney, 2014; Fig. 2):

1. 1. Ice avalanches and rock-ice avalanches, mainly starting from hanging glaciers or steep glacierized rock cliffs (e.g., Alean, 1985; Huggel, Kääb, Haeberli, Teysseire, & Paul, 2002; Noetzli, Huggel, Hoelzle, & Haeberli, 2006). In rare cases they pose a direct threat to people not involved in mountaineering or to infrastructures (Mahboob, Iqbal, & Atif, 2015). Furthermore, they may initiate destructive process chains when impacting a lake (e.g., Haeberli, Portocarrero, & Evans, 2010b) or entraining a large amount of snow, ice, or debris (Evans et al., 2009; Huggel et al., 2005).

2. 2. Volcano–ice interactions, i.e., snow and ice melting due to volcanic eruptions or geothermal activity, possibly resulting in floods or volcanic debris flows (lahars) (e.g., Lowe et al., 1986; Pierson, Janda, Thouret, & Borrero, 1990; Wilson, Smellie, & Head, 2013). Such phenomena are well known in Iceland (Björnsson, 2002), where they are referred to as jökulhlaups (Icelandic “glacier runs”), a term that is also used for glacial lake outburst floods. The most notable disaster related to volcano–ice interactions in recent history occurred in Armero, Colombia (Table 1).

Table 1. Selected Disasters Related to Snow and Ice Hazards

Year, place

Description

Ref.

1941, Palcacocha, Perú

A glacial lake outburst flood (GLOF) from Lake Palcacocha devastated part of the city of Huaraz, claiming approx. 5,000 lives.

Carey (2005)

1954, Blons, Austria

Extraordinarily heavy snowfall caused a huge number of avalanches in the federal state of Vorarlberg. The most disastrous event resulted in 56 fatalities in the village of Blons.

Höller (2007)

1970, Huascarán, Perú

An earthquake-triggered rock/ice mass movement from Nevado Huascarán converted into a high-velocity flow of debris and mud, reaching velocities up to 85 m/s. The flow overtopped a lateral ridge and buried the town of Yungay. The main flow alleviated on a debris cone originating from a similar, but smaller event in 1962, but the debris flood reached the Pacific Ocean at a distance of 180 km. In contrast to earlier reports of up to 30,000 victims, Evans et al. (2009a) estimated a death toll of approx. 6,000.

Evans et al. (2009)

1985, Armero, Colombia

An eruption of the volcano Nevado del Ruiz led to melting of snow and ice, resulting in massive lahar flows destroying the town of Armero. The number of victims is given with >22,000.

Herd (1986); Huggel et al. (2007)

1999, Galtür and Valzur, Austria

The accumulation of more than 2 m of new snow within one month resulted in an extreme avalanche cycle, the most disastrous events leading to 31 deaths in Galtür and 7 deaths in Valzur (federal state of Tyrol).

Höller (2007)

A 100 million m³ rock/ice avalanche, originating at the northern slopes of the Mt. Kazbek massif, mobilized almost the entire Kolka Glacier. The resulting mass flow continued for a total of 35 km, the last 15 km as a distal mudflow. The event caused the death of approx. 140 people along with massive destruction.

Huggel et al. (2005)

2002, Dasht, Tajikistan

A GLOF destroyed the village of Dasht 11 km downstream and killed dozens of people.

Mergili et al. (2012a)

2012, Siachen-Gayari, Pakistan

An ice avalanche buried an army camp along with 148 people.

Mahboob et al. (2015)

2012, Pokhara, Nepal

The Seti River debris flow, with estimated 72 deaths, originated as a rockfall 29 km away from and >3,400 m above the impact area. The actual debris flow evolved through the entrainment of snow, ice, and debris.

Allen & Simmon (2012); Petley & Stark (2012)

(*) Note that this list is not exhaustive.

Click to view larger

Figure 2. Generic high-mountain system with possible ice hazards.

1. 3. Since the beginning of the 21st century research has strongly focused on GLOFs. Glacial lakes (Fig. 3) may be dammed by bedrock, moraines, or by the glaciers themselves. Such lakes often develop in areas influenced by permafrost. Tweed and Russell (1999) distinguished nine types of ice-dammed lakes. Some lakes are prone to sudden outbursts and pose a potential threat to the downstream communities. Studies of GLOFs or GLOF hazard cover most glacierized mountain areas in the world, such as the Himalayas of Nepal and Bhutan (Bajracharya, Mool, & Shrestha, 2007; ICIMOD, 2011; Richardson & Reynolds, 2000; Watanabe & Rothacher, 1996), the Karakorum (Hewitt, 1982; Hewitt & Liu, 2010), the Pamir (Gruber & Mergili, 2013; Mergili & Schneider, 2011; Mergili, Müller, & Schneider, 2013), the Tien Shan (Bolch et al., 2011; Narama, Duichonakunov, Kääb, Daiyrov, & Abrakhmatov, 2010), the Andes (Carey, Huggel, Bury, Portocarrero, & Haeberli, 2012; Emmer & Vilímek, 2014; Haeberli et al., 2010b; Vilímek, Zapata, Klimeš, Patzelt, & Santillán, 2005), the Alps of New Zealand (Allen, Schneider, & Owens, 2009), the North American mountains (Clarke, 1982), the Norwegian mountains (Breien, Blasio, Elverhoi, & Hoeg, 2008), and the European Alps (Emmer, Merkl, & Mergili, 2015; Haeberli, 1983; Huggel, Kääb, Haeberli, & Krummenacher, 2003; Huggel et al., 2002; Tinti, Maramai, & Cerutti, 1999). The anticipation of possible glacial lake development is seen as a first important step in hazard assessment (Frey, Haeberli, Linsbauer, Huggel, & Paul, 2010). GLOFs can evolve in various ways, for example, by any type of rapid mass movement (such as rock/ice avalanches or calving ice fronts) into lakes, rising lake levels leading to overflow, progressive incision, mechanical rupture or retrogressive erosion of a dam, hydrostatic failure or degradation of glacier dams, or ice-cores in moraine dams (Richardson & Reynolds, 2000; Walder & Costa, 1996). Peak discharges are often magnitudes higher than in the case of precipitation- or snow melt–related floods (Cenderelli & Wohl, 2001). Entrainment may considerably increase the magnitude of the event and convert the flood into a destructive debris flow. Another, particular type of outburst flood, often related to big lowland rivers, consists in the release of ice jams (Jasek, 2003).

2. 4. Various types of mass movement triggered or facilitated by the degradation/thawing or fluctuations of permafrost (Haeberli, Schaub, & Huggel, 2016; Harris et al., 2009) or by debuttressing of slopes through glacier retreat. It is now well documented that permafrost dynamics are a prominent preparatory factor for the occurrence of mass movements, but until the 1980s and even 1990s, this fact had not been widely recognized (e.g., Haeberli, 1992). Thawing of permafrost may affect slope stability, for example, by fracturing of rocks during freeze–thaw cycles, by increased availability of physically active water, or by changed topography (e.g., Gruber & Haeberli, 2007; Haeberli, Wegmann, & Von der Muehll, 1997; Huggel, Clague, & Korup, 2012; Huggel et al., 2010; Krautblatter, Funk, & Günzel, 2013; Ravanel & Deline, 2011). Several studies on climate change–induced dynamics of permafrost and related geo-hazards have been conducted. Intensive and detailed research was carried out at selected sites in the Swiss and Italian Alps (e.g., Monte Rosa) including measurements and modeling of permafrost temperatures and related rockfall events, rock-ice avalanches, slides, and debris flows. Harris et al. (2009) provide a comprehensive list of references on permafrost, its changes, and related hazards.

Click to view larger

Figure 3. Glacial lakes evolving in the forefields of retreating glaciers (all photos by Martin Mergili): (A) Upper Godley Valley, South Island, New Zealand, February 8, 2015; (B) Glaciar Grande and Laguna Torre, Patagonia, Argentina, December 2, 2006; (C) Upper Rivakdara catchment, Pamir, Tajikistan, August 18, 2011; (D) Rhonegletscher, Switzerland, September 4, 2013.

The scientific community has paid most attention to GLOFs, phenomena which are—as all other ice-related hazardous processes—not new. Floods originating from the glacial Lake Missoula in the period between 16,000 and 12,000 years ago are considered the largest floods documented in the geological record (Baker, 1973; Baker & Bunker, 1985). Between the 16th and the 19th centuries, some valleys in the Alps were repeatedly devastated by large floods or debris flows, many of them observed during fine weather. This happened in the Saaser Tal (Switzerland), the Ötztal (Austria), and the Martelltal (today Italy). All of these valleys were heavily glacierized. Some glaciers blocked valleys, impounding lakes fed by the water of other glaciers. Failure of these glacier dams caused periodic or episodic GLOFs leading to those flood waves.

The earlier events were not documented in a scientific way, but they were written down in valley chronicles such as by the priest Josef Eberhöfer in the case of the Martelltal. Finsterwalder (1890) reported about a sequence of GLOFs in the Martelltal ( 1887, 1888, 1889, and 1891), causing destructive debris flows extending tens of kilometers down to the Venosta Valley. A dam was constructed in 1892/93 in order to protect the underlying villages from future events, which it successfully did in 1895 (Fig. 4).

Click to view larger

Figure 4. Retention dam constructed in the last decade of the 19th century in order to protect the Martelltal (Italy) from GLOFs.

Due to massive glacier retreat since the late 20th century, ice-dammed lakes and associated GLOFs have become rare in the Alps. Similar processes are observed in other mountain areas such as the Himalayas, the Karakorum, and the Hindukush, as nicely described by Richardson and Reynolds (2000) and Iturrizaga (2011). Other types of glacial lakes, however, have formed in front of the retreating glaciers in many areas worldwide (Tweed & Russell, 1999). Some of them are prone to sudden drainage by the impact of ice avalanches detaching from glaciers retreating over steep rock walls or of rock avalanches from glacially debuttressed lateral slopes and/or slopes with degrading permafrost (Haeberli et al., 2010b). Such process chains reflect the dynamics of the changing high-mountain environments. Other incidents such as the 1985 Nevado del Ruiz disaster (Lowe et al., 1986) illustrate the interaction of glacial environments with volcanic activities.

## Snow and Ice Hazard Mitigation and Disasters

In terms of research and risk mitigation, snow hazards and ice hazards are commonly addressed by different scientific and technical communities. Snow hazards are often covered by physicists and meteorologists, technical protection by forest and civil engineers in some countries. The higher relevance of snow hazards—i.e., avalanches—for the everyday life of mountain people is reflected in the existence of country-level organizations responsible for avalanche research, technical protection, and warning, such as the Torrent and Avalanche Control WLV (Austria, founded in 1884) or the Institute for Snow and Avalanche Research SLF (Switzerland, founded in 1931). While in Switzerland, the SLF also takes over avalanche warning (targeted mainly at making outdoor leisure activities safer), in Austria this is done independently by the avalanche warning services at the provincial level. Other examples of organizations dealing with snow avalanche hazards include AINEVA (Italy), the American Avalanche Association (and avalanches centers in the mostly affected states), Avalanche Canada, and the New Zealand Avalanche Centre.

Research on glacial hazards, in contrast, is very often covered by geoscientists at the level of universities and other research institutions. While the standing group on Glacier and Permafrost Hazards in Mountains by the Commission on Cryospheric Sciences and the International Permafrost Association (GAPHAZ) collects the scientists working in this direction, more formal national-level agencies do not exist. In cases where glacial hazards result in flooding downstream, the water authorities are in charge of mitigation, such as the Autoridad Nacional de Aguas for the Cordillera Blanca (Perú). However, the implementation of mitigation measures often relies on stand-alone engineering projects and, in the case of developing countries, on initiatives launched by intergovernmental centers such as the International Centre for Integrated Mountain Development (ICIMOD, Himalayas) or nongovernmental organizations such as FOCUS Humanitarian Assistance (Pamir).

If the magnitude of the natural process is high enough and society fails to appropriately mitigate the associated risk, events may evolve into disasters. Table 1 summarizes some of the most notable snow/ice disasters in the past 100 years. The international database EM-DAT (Guha-Sapir, Below, & Hovois, 2015) lists 106 snow avalanche disasters with 5,230 fatalities in the period 1910–2015. It is not possible to identify the number of glacial disasters from this database, but the database of glacial hazards events produced within the scope of the Glaciorisk European Project (2001–2003) lists 672 hazard events in European history, including glacier surges (rapid increases in length without mass increase; Harrison et al., 2014; Post & Mayo, 1971).

Spatial observation and analysis techniques may help to anticipate hazardous events, to mitigate the associated risks, and to prevent possible disasters. In this sense, the following sections will focus on (1) remote sensing, i.e., the interpretation and analysis of satellite or aerial imagery and the products derived thereof, and (2) computer modeling.

## Methodological Framework

“Essentially, all models are wrong, but some are useful.” This statement of Box and Draper (1987) is valid also—and particularly—with regard to computer models of geomorphic phenomena and, therefore, snow and ice hazards. While such models may help us to anticipate the occurrence, location, and possible impact of hazardous events in the future, the usefulness of any model strongly depends on the availability of data appropriate to feed or to validate the model. As a consequence, observation and spatial modeling cannot be covered separately (a methodological framework is illustrated in Fig. 5).

Click to view larger

Figure 5. Methodological framework for the observation and spatial modeling of snow and ice hazards. RS = Remote sensing, GB = ground-based observation, R = release, P = propagation, C = consequences.

A particular issue consists in the spatial and temporal scales necessary to appropriately consider specific phenomena of snow and ice hazards. Figure 6 shows the time frames relevant for selected subprocesses. In principle, all those subprocesses may be considered at scales from local to global. While the appropriate observation and modeling methods strongly depend on the scale of investigation, most modern applications rely on geographic information systems (GIS), enabling an efficient workflow of spatial data management and analysis.

Click to view larger

Figure 6. Temporal scales of selected phenomena directly related to snow and ice hazards. Those phenomena relevant for ice hazards are shown in purple, those relevant for snow hazards are shown in blue. Phenomena relevant for both are shown in red. The green arrow indicates the dependency of the shorter-term processes on the longer-term processes.

# Observation, Monitoring, and Documentation of Snow- and Ice-Related Hazardous Processes

The broad spectrum of earth observation techniques summarized under the term remote sensing represents one of the most essential methodological approaches to the observation of snow and ice hazards. Methods of remote sensing are widely used in geo- and environmental sciences, but they are particularly important in the field of snow and ice hazards, often involving high-mountain areas hard or dangerous to access.

Remotely sensed data can be classified according to its spatial, temporal, spectral, and radiometric resolution. Countless applications in the geosciences rely on passive sensors capturing reflected radiation in the visible and near-infrared wave lengths. Passive sensors depend on the radiation reflected from features at the earth’s surface. This strategy largely fails in cloudy conditions and is limited to receiving spectral signals. Active sensors, emitting radiation by themselves, also work in cloudy conditions (with some limitations). They further enable deducing the time needed by the radiation to reach the earth’s surface, and therefore the distance between the sensor and the surface. Light detection and ranging (LiDAR—also referred to as laser scanning) and synthetic aperture radar (SAR) are the most relevant active remote sensing techniques in geosciences.

Most types of sensors can be mounted to satellites (space-borne), aircraft, helicopters, or unmanned aerial vehicles (air-borne) or terrestrial systems. Two types of remotely sensed data are of particular interest (1) satellite and aerial imagery and (2) digital elevation models (DEMs)

Numerous space-borne sensors yield optical and infrared imagery at various levels of spatial and temporal resolution (a non-exhaustive account of such sensors is provided by Mergili, 2015). Aerial images and derived orthophotos are further available for many countries and regions worldwide.

Space-borne, air-borne, and terrestrial stereo-photogrammetry has been used traditionally to produce high-resolution DEMs (Kääb et al., 2005; Mergili, 2007; San & Suzen, 2005; Toutin, 1995). However, there has been rapid progress in the first decade of the 21st century. While Kääb et al. (2005) still referred to LiDAR as a promising technique, this methodology has become the standard for producing high-resolution DEMs in many fields of geosciences, expressed by a large number of publications (French, 2003; Glenn, Streutker, Chadwick, Thackray, & Dorsch, 2006; Höfle & Rutzinger, 2011; Jones, Brewer, Johnstone, & Macklin, 2007; Liu, 2008). As laser radiation has a limited range, LiDAR sensors cannot be mounted to space-borne sensors and are therefore not suitable for producing global-scale DEM databases. Instead, SAR Interferometry (InSAR) is often used for this purpose. The Shuttle Radar Topographic Mission (SRTM; Jarvis, Reuter, Nelson, & Guevara, 2008; Van Zyl, 2001) and TanDEM-X (Krieger et al., 2007; Moreira et al., 2004) are the most notable initiatives in this direction.

Nevertheless, remotely sensed data are applicable to surficial features only, and they are limited by the spatial and temporal resolution of the sensor. Further, radar signals penetrate into snow or ice, so that the information provided may be inaccurate (Rignot, Echelmeyer, & Krabill, 2001). For many applications, remotely sensed data have to be complemented by ground-based methods such as geophysical explorations, digging or drilling, or field mapping.

All types of remotely sensed or directly measured data show the status of a system at a certain point in time (see Fig. 5). However, with regard to possible hazards it is often more important to explore the dynamics of the system. The deduction of changes from the analysis of multi-temporal data is referred to as monitoring. In this context, the temporal resolution—i.e., the frequency of observation—is essential and strongly depends on the relevant temporal scale (see Figs. 5 and 6).

Hazardous events by themselves rapidly modify the system involved. The documentation of events and the comparison of data obtained before and after the event strongly contribute to understand the underlying processes (see Fig. 5). Consequently, detailed analysis of past events plays an essential role in better understanding snow and ice hazards and anticipating future events. Event documentation is further important to feed or to validate computer models. In this context, inventories of various types of processes and landforms are particularly valuable. They represent a collection of observed cases (ideally, covering these cases in their entire spatial extent and attributing to them additional information such as the time of occurrence). Inventories are commonly produced and used in the field of mass movement research as a basis for spatial modeling (Galli, Ardizzone, Cardinali, Guzzetti, & Reichenbach, 2008; Guzzetti et al., 2012; Wieczorek, 1984).

However, questions regarding the quality, potentials, and limitations of such inventories remain a big issue (Pellicani & Spilotro, 2015; Steger, Bell, Petschko, & Glade, 2015). It has to be considered that high-mountain environments in many regions of the world are currently in a stage of transition at a level not experienced and observed in historic times. Consequently, hazard situations have the potential of being beyond historical experience and evidence. Highly dynamic glacial and periglacial environments require frequent updating of inventories of glaciers, glacial lakes, or other objects: new features such as emerging glacial lakes may be highly relevant from a hazard perspective (Emmer, Merkl, & Mergili, 2015). Obviously, such inventories cannot directly be used for assessing future situations, which may be different not only from the past but also from present-day conditions (Frey, Haeberli, Linsbauer, Huggel, & Paul, 2010; Haeberli, Schaub, & Huggel, 2016; Linsbauer, Paul, & Haeberli, 2012; Linsbauer et al., 2015, 2016). Large ice-related events are very often nonrepetitive, i.e., they may reduce or eliminate the hazard permanently (e.g., moraine breach and subsequent lake drainage) or at least temporarily (e.g., the detachment of an ice cliff).

## Observation, Monitoring, and Documentation of Snow Avalanches and Related Hazards

While glaciers, permafrost, and the related geosystems commonly display their dynamics at time scales from months to decades (see next section), snow cover—and therefore also the related avalanche hazards—may change at time scales from hours to days, weeks and months. These dynamics imply a number of challenges with regard to observation: most importantly, status observations of the snow cover often lose their validity within a very short period.

Consequently, remote sensing of the development of the snow cover relies on sensors with at least daily resolution. A thermal infrared sensor has been used for monitoring global snow cover distribution with a frequency of 14 images per day (Armstrong & Brodzik, 1999). However, such a high temporal resolution can only be achieved at the cost of a low spatial resolution, 25 km in this case. Daily 1.1 km resolution snow cover and snow temperature data sets from the MODIS satellite have been available since 1999 (Hall, Riggs, Salomonson, DiGirolamo, & Bayr, 2002). Even though such data sets may be useful for a very broad-scale overview of snow avalanche hazards in terms of identifying snow climatic zones at regional scales (Negi, Thakur, & Mishra, 2007), the identification of snow avalanche hazards at finer scales would require high-frequency LiDAR and SAR application in order to capture snow depth changes. This requires terrestrial techniques, may suffer from mountain shadows or the presence of trees, and is usually only possible for test sites of limited size (Schaffhauser et al., 2008). The same issue arises with terrain data. While the general terrain features can be extracted from ordinary DEMs, high-frequency LiDAR applications are necessary to capture snow depth changes or to produce pre- and post-event DEMs. Examples of LiDAR applications in the field of snow avalanches are manifold (Bühler, Christen, Kowalski, & Bartelt, 2011; Deems, Painter, & Finnegan, 2013; Prokop, 2008; Sailer et al., 2008). Due to the high temporal variability of snow cover, broad-scale before-and-after comparisons are rarely available. Avalanche deposits can be detected by using space-borne SAR images due to the difference in backscatter from undisturbed snow and the rougher surface of avalanche deposits (Malnes et al., 2013).

Not all characteristics of snow can be captured through remotely sensed data. On-site investigations are particularly important in the following cases:

• The vertical structure of snow, which can hardly be captured by remote sensing methods, is a key factor for snow avalanche formation (Schweizer, Bruce Jamieson, & Schneebeli, 2003). Depth and vertical structure of snow, with particular regard to snow avalanches, requires digging of snow pits. This is a standard procedure regarding avalanche hazard analysis (Fig. 7). The temporal development of the properties and structure of the snow pack—also for forecasting purposes—is often explored by computer simulations (e.g., with the SNOWPACK Model; Bartelt & Lehning, 2002).

• Meteorological observations: snow avalanche hazard depends not only on the topography and the depth of the snow cover. Instead, it is also strongly influenced by the meteorological conditions such as precipitation, wind, and temperature (Davis, Elder, Howlett, & Bouzaglou, 1999; Lehning et al., 1999; Mock & Birkeland, 2000; Perla, 1970).

Click to view larger

Figure 7. Snow pits are essential to investigate the vertical structure of the snow cover (photo courtesy of Jan-Thomas Fischer, Austrian Research Centre for Forests [BFW]).

Remote sensing–derived inventories of snow avalanches are available for a number of countries and regions such as Norway (Frauenfelder et al., 2010) or the Catalan Pyrenees (Oller et al., 2006). However, as the traces of snow avalanches usually disappear rapidly, the preparation of inventories is highly challenging, compared to landslide inventories.

A robust basis of snow avalanche data is mainly available for particular test sites such as the Col du Lautaret in France (Prokop et al., 2013; Thibert et al., 2015), the Vallee de la Sionne in Switzerland (Ammann, 1999; Vallet et al., 2001), or the Seehore Peak in Italy (Maggioni et al., 2013). Those test sites are also employed to derive more specialized parameters such as velocities and impact pressures of snow avalanches (e.g., Baroudi, Sovilla, & Thibert, 2011; Gauer et al., 2007; Sovilla, Schaer, Kern, & Bartelt, 2008; Thibert, Baroudi, Limam, & Berthet-Rambaud, 2008). Physical model tests (full-scale experimental avalanches) are used to gain information on those parameters and to build a basis for the validation of mathematical models (Ammann, 1999; Issler et al., 1999; Lied, Moe, Kristensen, & Issler, 2001; Prokop et al., 2013; Sovilla et al., 2008). Impact pressures are particularly important for estimating the vulnerability of structures to an event (Barbolini, Cappabianca, & Sailer, 2004; Fuchs & Bründl, 2005; Grêt-Regamey & Straub, 2006; Keiler et al., 2006) and are part of the legal basis for avalanche hazard zoning, e.g., in Austria (Sauermoser, 2006). Estimates of impact pressures may also be derived from reported damage to structures.

## Observation, Monitoring, and Documentation of Ice-Related Processes and Associated Hazards

A broad spectrum of remote sensing methods is employed for the observation and monitoring of the glacial and periglacial environment. Kääb et al. (2005) provide a comprehensive overview of applications of optical remote sensing for glacial and permafrost hazards. More specialized applications—particularly those where subsurface features are of interest—make use of thermal infrared (LeSchack & Del Grande, 1976), which has been employed to identify the presence and the state of permafrost (Ran, Li, Jin, & Guo, 2015) and the mapping of debris-covered glaciers and buried ice (Lougeay, 1974; Shukla, Arora, & Gupta, 2010). However, such applications are particularly challenging.

Radar remote sensing, an active technique, is a particularly useful but challenging method to detect small surface changes over large areas. It has been used for monitoring changes of glaciers (Akbari, Doulgeris, &Eltoft, 2014; Luckman, Quincey, & Bevan, 2007; Strozzi, Luckman, Murray, Wegmüller, & Werner, 2002; Vachon et al., 1996), in permafrost environments (Chen, Lin, Li, Chen, & Zhou, 2012; Chen, Lin, Zhou, Hong, & Wang, 2013; Delaloye, Strozzi, Lambiel, Perruchoud, & Raetzo, 2008; Grosse et al., 2014; Kenyi & Kaufmann, 2003; Wang & Li, 1999; Wang et al., 2015; Yoshikawa & Hinzman, 2003), for glacial lakes (Strozzi, Wiesmann, Kääb, Joshi, & Mool, 2012), and volcano-glacier interactions (Scharrer, Spieler, Mayer, & Münzer, 2008). LiDAR techniques are commonly used for monitoring changes in the glacier surface (Arnold, Rees, Devereux, & Amable, 2006; Jóhannesson, 2013; Rees & Arnold, 2007) and for high-mountain environments in general (Avian, Kellerer-Pirklbauer, & Bauer, 2009; Deline, Jaillet, Rabatel, & Ravanel, 2008; Fischer, Huggel, Kääb, & Haeberli, 2013; Hubbard et al., 2013). Some purposes require the combination of more than one type of remotely sensed data (Robson et al., 2015).

The glacial environment typically changes at lower rates than the snow cover. Inventories of glaciers, mainly derived from remotely sensed data, are available at a global scale, such as the World Glacier Inventory (WGI; Cogley, 2010), the Global Land Ice Measurement from Space (GLIMS; Armstrong et al., 2005), or recently the Randolph Glacier Inventory (RGI; Pfeffer et al., 2014), and for various countries (Kääb, Paul, Maisch, Hoelzle, & Haeberli, 2002; Lambrecht & Kuhn, 2007). However, those datasets need continuous updating to keep up with the changing glacial environments.

Click to view larger

Figure 8. The retreat of the Fox Glacier, New Zealand, is visible within a time frame of months: (A) February 7, 2014 (photo courtesy of Julia Krenn); (B) February 5, 2015 (photo courtesy of Martin Mergili); (C) March 24, 2015 (photo courtesy of Julia Krenn).

While glacier fluctuations may become evident within time frames of few weeks or months (Fig. 8), detecting long-term trends requires multi-year analyses of remotely sensed data. In accordance with the observation, monitoring, and documentation of snow avalanches and related hazards (see Fig. 5), monitoring of glacial hazards operates in a spatio-temporal field between two extremes:

• Broad-scale low-frequency (multi-annual) monitoring based on regionally available optical satellite imagery or orthophotos, resulting in inventories of relevant features such as glaciers (e.g., Abermann, Lambrecht, Fischer, & Kuhn, 2009; Abermann et al., 2012; Lambrecht & Kuhn, 2007; Stocker-Waldhuber, Wiesenegger, Abermann, Hynek, & Fischer, 2012 for Austria and parts thereof) or glacial lakes (e.g., Mergili, Müller, & Schneider, 2013 for the Pamir; Emmer et al., 2015 for western Austria).

• Fine-scale high-frequency (permanent to annual) monitoring of situations of particular interest by means of photogrammetry, terrestrial LiDAR, or InSAR is required to capture the evolution of hazardous processes. Monitoring initiatives of unstable permafrost rock walls serve as examples of such efforts (Fey et al., 2012; Ravanel, Deline, & Bodin, 2015).

Glacier and permafrost hazards are sometimes strongly influenced by interacting subannual, annual, and multi-annual trends (see Fig. 6). Ice avalanche hazard may increase by the continuous retreat of a glacier over a steep rock cliff, losing its abutment, but the actual triggering of the ice avalanche event may occur due to melting processes in summer or due to an earthquake. Surging glaciers may quickly block valleys and impound lakes (Harrison et al., 2014; Post & Mayo, 1971).

Monitoring of potentially hazardous glacial lakes is of particular concern. Lake fluctuations occur on subannual time scales to decadal ones. Multi-temporal orthophotos, being available back to the 1950s for some areas worldwide, are particularly useful to identify the long-term trends (Emmer et al., 2015; Merkl, 2015). Where long series of orthophotos are not available, declassified CORONA imagery from the 1960 and 1970s is often useful in combination with newer high-resolution satellite images. For broad-scale overviews, medium-resolution images such as ASTER or Landsat can be used, which, however, fail to capture some details and have a number of limitations (Mergili et al., 2013). When it comes to hazard analysis, it is essential to consider not only the lake and its surroundings, but also its catchment. GLOF hazard is influenced not only by the characteristics of the lake and the dam impounding it, but also by the possibility of ice avalanches or other types of mass movement possibly impacting the lake. Particularly the latter type of hazard has been observed to increase in a systematic way (Haeberli et al., 2016).

The glacial retreat during the past decades, in combination with the increased availability of remotely sensed data, has revealed a growth of pro-glacial lakes in many areas throughout the world. In the Pamir of Tajikistan, for example, lakes which did not exist or were very small ponds at the end of the 1960s have evolved to lakes of hundred thousands of square meters, posing a certain threat with regard to GLOFs (Fig. 9A). Similar trends are seen in other mountain areas such as the Himalayas and the Alps.

Glacial lakes, however, do not always evolve at constant rates. Lakes may remain stable or grow slowly for decades and then experience a sudden increase in area and volume within a few years, strongly coupled to the dynamics of the related glacier. Figure 9B illustrates the evolution of a lake in the Pamir of Tajikistan which suddenly appeared in summer 2001, then remained constant in size for approximately one year before suddenly draining in August 2002 without an obvious trigger, causing a major disaster with dozens of fatalities in the village of Dasht 11 km downstream (see Table 1; Mergili, Kopf, Müllebner, & Schneider, 2012a). From its surficial appearance, this lake would have been rated less hazardous than hundreds of other lakes in the same area.

Click to view larger

Figure 9. Contrasting trends of glacial lake evolution in the Pamir (Tajikistan). (A) Continuous development of a lake in the Varshedzdara catchment between 1969 and 2008. (B) Rapid development and sudden drainage of a lake in the Dashtdara catchment.

The example of Dasht illustrates that remote sensing cannot capture glacial environments in their full complexity. A particular issue consists in the internal structure of glaciers, with particular regard to water pockets that might be prone to sudden drainage. Geophysical techniques such as ground-penetrating radar (GPR) are most frequently applied for this purpose (e.g., Murray, Booth, & Rippin, 2007; Vincent et al., 2012). Also, the depth of glacial lakes cannot be directly derived from remotely sensed data. Instead, echo sounding has been applied for this purpose (Cochachin, Huggel, Salazar, Haeberli, & Frey, 2015). Also, the presence and properties of permafrost at a particular site requires further investigation. Even though the presence of permafrost is often indicated by surface features, ground-based investigations are necessary to explore the depth of permafrost or of the active layer or the vertical temperature distribution (Hoelzle, 1992; Krautblatter, Huggel, Deline, & Hasler, 2012). Morris, Hassan, and Vaskinn (2007) have performed physical model tests of dam breaches, which can be relevant for the understanding of GLOFs.

While inventories of glaciers and, partly, also glacial lakes are readily available, inventories of recorded hazardous events exist in a rather fragmented way. GAPHAZ runs a worldwide database on glacier and permafrost hazards (Flubacher, Huggel, Kääb, & Zemp, 2007). The International Disaster Database EM-DAT (Guha-Sapir, Below, & Hoyois, 2015) explicitly lists snow avalanche disasters, but disasters related to ice hazards are assigned to mass movements instead of being listed in an explicit way. The GRIDABASE database (Glaciorisk European Project, 2001–2003) provides information on hazardous events related to European glaciers. Mool, Bajracharya, and Joshi (2001) prepared a GLOF inventory for the Himalaya-Hindukush Region, while the Glacial Lake Outburst Floods database (Vilímek, Emmer, Huggel, Schaub, & Würmli, 2014) aims at compiling information on recorded GLOFs worldwide. Schneider, Huggel, Haeberli, and Kaitna (2011) compiled data about 64 large rock/ice avalanches worldwide. It is, however, important to note that the interpretation of event inventories requires some care as the hazard connected to a slope, a glacier, a lake, etc., may change considerably over time, e.g., glacial lakes may appear or disappear within a relatively short period. Often it is even the event itself that changes the level of hazard.

# Spatial Modeling of the Release of Snow- and Ice-Related Hazardous Processes

Any type of observation allows one to look into the past or (almost) the present. Even though it is widely accepted in geosciences that the past is the key to the future, observations do not necessary allow direct conclusions about what can happen in the future. With the widespread availability and the increasing capacity of computers, mathematical models have become the dominant tool employed to better understand, or even to anticipate, natural hazards processes. A primary goal of hazard modeling is to explore magnitude–frequency relationships, i.e., to predict the probability in space and time of a particular process of a given magnitude. However, lack of detailed information often makes it necessary to express the hazard in a less direct way. A particular challenge in terms of hazard analysis is posed by singular events such as GLOFs through the breach of a moraine. A clear distinction should be made between potentially repetitive events with somewhat stochastic patterns of occurrence (snow avalanches) and nonrepetitive events resulting from cumulative developments such as glacier retreat, permafrost degradation, or moraine breaching. For the latter cases, probabilities cannot easily be defined.

Various types of models do exist, implemented in a broad array of computer programs often relying on GIS. Recalling the statement of Box and Draper (1987), the question arises as to which type of model is useful for which purpose. In principle, simple but evocative models are assumed superior to those characterized by overparameterization (Box, 1976). Mergili (2015) distinguishes between statistical, rule-based, and physically based models, whereby all three types may either be fully deterministic, or may contain stochastic elements (Fig. 10). Each type of model is useful for a certain scale range and for a certain level of process understanding and data availability.

Click to view larger

Figure 10. Types of models (modified after Mergili, 2015).

For practical reasons, two groups of models are distinguished in the present article:

1. 1. Models attempting to delineate those areas tending to release hazardous processes.

2. 2. Models capturing the propagation of hazardous processes in order to anticipate possible impact areas, flow velocities, depths, and impact pressures of hazardous processes.

Both groups may be applied at a broad range of scales, from rough rule-based overviews at regional, national, or even global levels (Gruber & Mergili, 2013; Nadim, Kjekstad, Peduzzi, Herold, & Jaedicke, 2006) to very detailed physically based investigations of particular situations (see Fig. 10).

While in classical landslide research, modeling the release is often given priority over modeling the motion, this trend is not so much observed in snow and ice hazards research. Because snow and ice hazards are diverse, there are—with the exception of snow avalanches—hardly any standard procedures accepted for computing their release: possible release areas of ice-related hazardous processes are often known a priori, e.g., a glacial lake in case of GLOF hazards or a hanging glacier, steep glacier section, or cliff in case of ice avalanche hazards. Modeling of the motion of snow- and ice-related hazardous processes commonly makes use of techniques similar to those used in landslide and debris flow research.

Mass movements start as soon as the destabilizing forces exceed the stabilizing forces in a given system, i.e., when the factor of safety becomes smaller than 1. The physically based concept of the factor of safety (limit equilibrium method) is extensively used in a direct way in local-scale geotechnical engineering (Duncan & Wright, 2005). In contrast, it is less commonly employed at broader scales and for snow and ice hazards. Instead, different types of surrogates are used. The factor of safety—particularly in terms of its spatial patterns—requires much detailed technical information on the properties as well as on the horizontal and vertical structure of the materials involved. At regional scales—but, in the case of complex processes, also at local scales—these parameters are highly uncertain or not known at all.

With the emergence of GIS it has become common to produce susceptibility maps for mass movement processes (generally referred to as landslide susceptibility mapping; Guzzetti, 2006; Van Westen, Van Asch, & Soeters, 2006). Nadim et al. (2006) produced a map of global landslide and avalanche hot spots, relying on a set of globally available spatial datasets and a largely rule-based approach. Usually, susceptibility mapping is applied at regional or catchment scales, whereby those efforts—mainly relying on statistical or physically based approaches—are much more evident for landslides than for snow avalanches.

The purpose of susceptibility maps for mass movement processes is to provide a general overview useful to prioritize areas requiring further investigation. They do not consider the dimension of time. While in landslide research, it is common practice to use physically based or statistical models in order to identify rainfall thresholds for landslide occurrence (Baum & Godt, 2010; Caine, 1980; Glade, Crozier, & Smith, 2000; Guzzetti, Peruccacci, Rossi, & Stark, 2007), in snow avalanche research the highly dynamic interaction of meteorological conditions with the structure of the snow is most relevant (Höller, 2007).

## Spatial Modeling of Snow Avalanche Release

Ghinoi and Chung (2005) introduced a GIS-based statistical model for snow avalanche susceptibility mapping, mainly relying on terrain features. The resulting susceptibility maps are useful for identifying avalanche hot spots.

However, spatial modeling of snow avalanche release is most commonly performed in the context of avalanche warning systems. Avalanche warning systems aim at near-real-time predictions of the hazard level. They often consist in a combination of observations with physically based, statistical, and rule-based modeling approaches. The analysis procedures involved in such systems commonly result in bulletins targeted mainly at out-of-bounds skiers and snowboarders (Brabec, Meister, Stöckli, Stoffel, & Stucki, 2001). They have been established since the 1950s (Höller, 2007; Längle, 1977) and focus on meteorological (Höller, 2007) and—as far as available—snow data in order to estimate the hazard at the regional scale, without considering snow avalanche motion. Near-real-time avalanche warning systems do exist for countries such as Norway (Engeset, 2013) or on a provincial scale such as in Austria (Nairz & Kriz, 2005). Most commonly a five-step ordinal scale is used to describe avalanche hazard at the regional scale (Meister, 1995), which was developed in Europe but also used in North America, where it was later modified (Statham et al., 2010; Table 2). Most work has been published for Switzerland (e.g., Meister, 1995; Russi, Ammann, Brabec, Lehning, & Meister, 2003). The dynamics of the scow cover is computed by physically based models such as SNOWPACK (Bartelt & Lehning, 2002; Lehning, Bartelt, Brown, & Fierz, 2002b; Lehning, Bartelt, Brown, Fierz, & Satyawali, 2002a), attempting to predict snow layering, settlement, mass balance, energy exchange, and metamorphism. This model relies on meteorological and snow measurements. It is an integral part of the Swiss avalanche warning system, where predictions are made as a now-casting system for time horizons of a few hours, based on a dense network of automatic stations (Bartelt & Lehning, 2002; Lehning et al., 1999). The model results are highly valuable for estimating avalanche hazards in situations where direct measurements of the snowpack properties cannot be obtained due to limited accessibility or danger. Local-level avalanche hazard knowledge is complemented by direct snow measurements. The results are compiled into regional-level forecasts published daily during the main winter season (Regional Avalanche Information and Forecasting System RAIFoS; Brabec et al., 2001). The regional-scale maps are produced using a nearest-neighbor interpolation algorithm (NXD-VG; Brabec & Meister, 2001).

Table 2. The North American Public Avalanche Danger Scale

Danger level

Description

5

Extreme

Natural and human-triggered avalanches are certain and may occur in many areas. All avalanche terrain has to be avoided.

4

High

Very dangerous avalanche conditions. Natural avalanches are likely, human-triggered avalanches are very likely. Large avalanches may occur in many areas, very large avalanches in specific areas. Traveling in avalanche terrain is not recommended.

3

Considerable

Dangerous avalanche conditions. Natural avalanches are possible, human-triggered avalanches are likely. Small avalanches occur in many areas, large avalanches in specific areas, and very large avalanches in isolated areas. A careful snow pack evaluation, cautious route-finding, and conservative decision-making are essential.

2

Moderate

Heightened avalanche conditions on specific terrain features. Natural avalanches are unlikely, but human-induced avalanches are possible. Small avalanches occur in specific areas, while large avalanches may still occur in isolated areas. Snow and terrain have to be evaluated carefully, features of concern have to be identified.

1

Low

Generally safe avalanche conditions. Natural and human-triggered avalanches are unlikely, but small avalanches may occur in isolated areas or extreme terrain. Therefore, unstable snow on isolated terrain features requires attention.

Source: Modified after Statham et al. (2010).

## Spatial Modeling of the Release of Ice-Related Hazardous Processes

Ice hazards are usually recorded at low frequencies or even as singular events. As a consequence, hazard maps exploring magnitude-frequency relationships are rarely supported. Spatial modeling of the release of ice-related hazardous processes therefore concentrates on susceptibility mapping. A common way employed to create susceptibility maps of snow and ice hazards is to use rule-based scoring schemes relying on field and remote sensing observations, and on statistical relationships:

1. 1. The susceptibility of a glacier or a portion of a glacier to produce ice avalanching is derived from the statistical analysis of observed ice avalanches (Alean, 1985; Gruber & Mergili, 2013; Huggel, Haeberli, Kääb, Bieri, & Richardson, 2004a). Thereby, the susceptibility scores are determined through a two-dimensional scheme of glacier slope and mean annual air temperature (as a surrogate for the ice temperature). Steeper glaciers in warmer areas receive higher scores than less steep glaciers in colder areas.

2. 2. For glacial lake outburst floods it is common to use rule-based models evaluating the various characteristics of a lake and its catchment in order to derive an index or a score for its susceptibility to sudden drainage. This is done for individual lakes (Emmer et al., 2015), but also at regional or national scales (Emmer & Vilímek, 2014; Gruber & Mergili, 2013; Mergili & Schneider, 2011). Empirical equations have been developed to estimate average lake depth from lake area (Emmer & Vilímek, 2014; Huggel, Kääb, Haeberli, Teysseire, & Paul, 2002) and to derive the expected peak discharge from the dam height or the lake volume (Costa, 1985; Costa & Schuster, 1988; Walder & O’Connor, 1997).

3. 3. Rule-based models have also been used to identify possible release areas of periglacial debris flows (Gruber & Mergili, 2013; Huggel, Kääb, & Salzmann, 2004b).

Ice hazard susceptibility models generally rely on DEMs at a level of detail corresponding to the scale of the study and on inventory maps of the features of interest (glaciers, lakes, permafrost). Information yielded by additional data sets such as seismic hazard (Giardini, 1999) or geological maps is sometimes included in the rule sets. It should be emphasized that the results of this type of models are often highly uncertain and mainly represent first-order analyses directed toward a basis for discussion or the identification of hot spots requiring further attention (Gruber & Mergili, 2013). Figure 11 illustrates some examples of susceptibility—or hazard indicator—maps and derived risk indicator maps for ice-related hazardous processes. Here, also, mass movement propagation is considered.

Click to view larger

Figure 11. Hazard and risk indication maps with regard to ice avalanches and lake outburst floods for the Pamir of Tajikistan (Gruber & Mergili, 2013), building on a framework of rule-based models. A and B show the impact hazard indicators (considering also propagation of the mass movements) for a selected area on the basis of pixels and the associated risk indicators generalized to communities. The possible impact of ice avalanches into lakes is included in the automated generation of the hazard indicators. C and D display for each district the fraction of communities associated to each risk indicator level. This type of information is mainly targeted at facilitating the allocation of resources for risk mitigation.

Also, regional, national, or global permafrost distribution maps are important in this context. Because permafrost is a highly complex phenomenon involving numerous variables (Harris et al., 2009), physically based models are only useful for improving the understanding of local-scale phenomena. The global permafrost zonation map of Gruber (2012), even though it includes different components, is most appropriately characterized as the result of a rule-based model (1 km resolution). Boeckli, Brenning, Gruber, and Noetzli (2012) produced a permafrost distribution map for the European Alps at a cell size of 25 m. Mergili et al. (2012a) describe the development and application of a map of potential permafrost areas for the territory of Tajikistan. A system of rules of thumb of lower permafrost boundaries established by Haeberli (1975) for Switzerland is adapted to the temperature regime in Tajikistan. Such an approach disregards local conditions such as snow cover or irradiation, but it can help to gain a rough idea on permafrost distribution at broad scales. In the study of Mergili et al. (2012a), potential future permafrost distribution maps are created for various assumptions of temperature changes in order to assess the possible response of permafrost distribution to scenarios of temperature increase established by the IPCC (2007, 2013).

Another effort developed recently is the detection of possible future glacial lake formation mainly deduced from the topography of the glacier bed (Frey et al., 2010; Köhler, Geilhausen, & Linsbauer, 2014; Linsbauer et al., 2012, 2015, 2016).

# Spatial Modeling of the Motion of Snow- and Ice-Related Hazardous Processes

## Model Approaches

Gaining an idea about possible source areas of snow and ice hazards is sometimes sufficient as a basis to alert people or to protect objects located close to possible release zones. As soon as the consequences of snow avalanches, ice avalanches, GLOFs, or other types of mass movements for villages, roads, buildings, or humans in the valleys (i.e., the risks associated to these hazards) are of interest, the propagation of the movement has to be considered, be it for dimensioning of protective measures or for hazard and risk zoning. Hazard zoning in many European countries such as Switzerland, Italy, France, or Austria builds on return periods of events with defined impact pressures (Cappabianca, Barbolini, & Natale, 2008; Sauermoser, 2006). Damage records for potentially repetitive events, on the one hand, and models yielding impact pressures, on the other hand, are highly important in this regard.

Starting with the catastrophic winter of 1999 with the Galtuer disaster (see Table 1), substantial effort has been put in quantifying the risk related to snow avalanches, considering also the socioeconomic dimension (Cappabianca et al., 2008; Fuchs & Bründl, 2005; Fuchs, Bründl, & Stötter, 2004; Keiler et al., 2006). Impact pressures were derived from the results of physically based dynamic mass flow models such as AVAL-1D (Fuchs et al., 2005), SAMOS and ELBA+ (Keiler et al., 2006), or VARA1D (Cappabianca et al., 2008) and combined with vulnerability functions to predict the expected degree of damage, e.g., to buildings.

It should be emphasized that mathematical models employed for the propagation of snow- and ice-related hazardous processes are usually developed for a broader scope, i.e., for many types of geophysical mass flows. They can be used for various types of industrial flows, but also for flow-like mass movements such as debris flows or rock avalanches and largely go back to Voellmy (1955), who first formulated a physically based equation for the propagation of mass flows, an initiative resulting from the avalanche disasters in the Alps in 1950/51 and 1954. Powder-snow avalanches, however, require different types of modeling approaches (Norem, 1995; Sampl & Zwinger, 2004).

Two branches of mass flow models have developed out of the Voellmy model:

1. 1. Physically based dynamic models make use of physical laws, assuming specific flow rheologies (Hungr, 1995; Iverson, 1997; McDougall & Hungr, 2004, 2005; Pitman & Le, 2005; Pudasaini, 2012; Pudasaini & Hutter, 2003; Pudasaini, Wang, & Hutter, 2005; Savage & Hutter, 1989; Takahashi, Nakagawa, Harada, & Yamashiki, 1992). A particular issue of importance is the entrainment along the path (Sovilla & Bartelt, 2002). This type of model is useful for the detailed backcalculation or prediction of specific events.

2. 2. Mass point models relate the shear traction to the square of the velocity and assume an additional Coulomb friction effect (Pudasaini & Hutter, 2007). This approach considers only the center of the flowing mass, not its deformation and the spatial distribution of the flow variables (Gamma, 2000; Mergili, Schratz, Ostermann, & Fellin, 2012b; Perla, Cheng, & McClung, 1980; Wichmann & Becht, 2003). Mass point models are applied both at local and at regional scales. Even though they do not capture the processes in full detail, they are sufficient for certain purposes and are more comfortable to employ in combination with GIS than physically based dynamic models for reasons of the conceptual compatibility (i.e., type of coordinate system used) and the computational times.

A third type of model consists in empirical-statistical relationships. Since mass flow processes are often complex in detail, first estimates of travel distance and impact area often make use of statistical models. Threshold values of slope or horizontal and vertical distances (Burton & Bathurst, 1998; Corominas, Copons, Vilaplana, Altamir, & Amigó, 2003; Lied & Bakkehøi, 1980; McClung & Lied, 1987; Vandre, 1985) related to volume (Rickenmann, 1999) are used as criteria. The scatter in the relationships is usually large, and key parameters for design issues, such as impact pressures, are not provided (Hungr, Corominas, & Eberhardt, 2005). Some of the most important published relationships are shown in Table 3. Empirical-statistical relationships are also implemented in simple flow routing models (e.g., Gruber & Mergili, 2013; Mergili, Krenn, & Chu, 2015b; Huggel, Kääb, Haeberli, & Krummenacher, 2003; see Fig. 11).

Table 3. Selected Empirical Relationships Used for Estimating the Travel Distance of Various Types of Relevant Flow-Type Mass Movements

Relationship

Process type

Ref.

$ωT=(0.62−0.28ΔZ⋅y'')β+19ΔZ⋅y''−2.3+0.12⋅θ$

Snow avalanches

Lied & Bakkehøi (1980)

$L=1.9Vd0.16ΔZ0.83$

Debris flows

Rickenmann (1999)

ω‎r = 11

Debris flows from GLOFs or periglacial debris flows, suitable mainly for coarse-grained debris flows

Haeberli (1983); Huggel et al. (2003, 2004a,b); Zimmermann et al. (1997)

$ωr=18Qp−0.07$

Worst case for debris flows from GLOFs

Huggel (2004)

ω‎r≥2

Floods from GLOFs

Haeberli (1983); Huggel et al. (2004a)

$log10ωT=−0.103⋅log10V−0.165$

Rock-ice avalanches

Noetzli et al. (2006)

ω‎r = 17

Ice avalanches

Alean (1985); Huggel et al. (2004a,b)

ω‎r = 7

Fine-grained debris flows

Zimmermann et al. (1997)

Notes: ω‎T = Angle of reach, Δ‎Z = total vertical displacement, y” = second derivative of avalanche slope, β‎ = average gradient of avalanche track, θ‎ = gradient of rupture zone, L = travel distance, V = volume, Qp = peak discharge.

Referring to Figure 10, mass point models contain elements of statistical and rule-based models while empirical-statistical relationships represent rule-based models as soon as applied to areas different from those where they were developed. In contrast to physically based dynamic models, mass point models and empirical-statistical relationships are mainly useful for delineating possible impact areas instead of flow pressures or energies. Their application in combination with GIS implies two problems:

1. 1. While the direction of the flow is implicitly computed in physically based dynamic models, particular strategies of flow routing are necessary when applying mass point models or empirical-statistical relationships in combination with GIS. Using the steepest path through the DEM would disregard the lateral spreading. Therefore, either multiple flow direction algorithms (e.g., Horton, Jaboyedoff, Rudaz, & Zimmermann, 2013; Huggel et al., 2002) or constrained random walk approaches (Gamma, 2000; Mergili et al., 2015b; Pearson, 1905) are applied. This means that flow routing is repeated many times with randomly varied flow paths. Therefore, such computations contain a stochastic element.

2. 2. Curves and bends in the flow path are not considered. It has to be known whether the relationship in question was developed based on true flow paths or on straight distances.

A particular challenge consists in modeling of process chains, i.e., including the impact of a mass movement on a lake. Appropriate modeling of process chains principally requires physically based modeling. Schneider, Huggel, Cochachin, Guillén, and García (2014) presented an effort to backcalculate a rock/ice avalanche–triggered GLOF by applying a model chain of three interacting, physically based models. The simulation of process chains represents an important emerging field of geoscience (Somos-Valenzuela et al., 2014; Worni, Huggel, Clague, Schaub, & Stoffel, 2014; Worni, Huggel, & Stoffel, 2013), with several challenges remaining. The two-phase flow model of Pudasaini (2012), and the—closely related—prospective software tool r.avaflow (Mergili, Fischer, Fellin, Ostermann, & Pudasaini, 2015a) are promising initiatives in this direction (Fig. 12).

Click to view larger

Figure 12. Physically based dynamic modeling of a process chain with a preliminary version of the prospective software r.avaflow (Mergili et al., 2015a). This example illustrates the simulation of a moraine sliding into a glacial lake, causing a flood wave spilling over the bedrock dam. This flood wave impacts a second, lower, lake leading to the erosion of its moraine/debris cone dam and the development of a debris flow downstream. The basis for this simulation is the first part of a process chain that occurred in 2012 in the Artizón and Santa Cruz Valleys, Cordillera Blanca, Perú (Emmer et al., 2016).

In order to apply mathematical models to a specific problem, a software tool allowing appropriate functions for parameter input and output of the results is required. In general, there is still a lack of ready-to-use software applications employing the more advanced physically based models. Most notably, RAMMS (Christen, Bartelt, & Kowalski, 2010a; Christen, Kowalski, & Bartelt, 2010b) is widely used for modeling snow avalanches and other types of mass flows. Other software packages are SAMOS-AT and TITAN-2D. Particularly GLOFs often take an intermediate character between debris flows and floods. Therefore, it may be useful to compare the outcomes of more than one type of modeling software. In this sense, Mergili, Schneider, Worni, and Schneider (2011) have compared RAMMS and the river flow software FLO-2D for GLOFs. Table 4 lists some of the software applications used in the field of snow and ice hazard modeling.

Table 4. Selected Software Applications Suitable for Physically Based Dynamic Modeling of Snow and Ice Hazards

Name

Description

Ref.

SAMOS-AT

2D (depth-averaged) granular flow model for flow component and 3D turbulent mixture model for powder component.

Sampl & Zwinger (2004)

ELBA+

ArcGIS module; shallow water model.

Volk (2005)

RAMMS

Voellmy-fluid friction model, based on 2D shallow water equations.

Christen et al. (2010a,b)

AVAL-1D

Dense-snow model based on Voellmy (1955) and powder snow model based on Norem (1995).

Christen et al. (2002)

VARA1D

Mass and momentum conservation equations combined with probabilistic approach.

Barbolini et al. (2003); Natale (1994)

TITAN-2D

2D (depth-averaged) two-phase flow model for incompressible Coulomb continuum (shallow water model).

Pitman & Le (2005)

FLO-2D

2D finite difference model for routing non-Newtonian flows.

O’Brien et al. (1993)

## Model Validation and Parameter Optimization

Any type of model application relies on a set of input parameters. The type and number of parameters used depends on the type and complexity of the model. Friction parameters are most essential for physically based mass movement models and may significantly vary, depending of the type, physical characteristics, magnitude, and topography of the process under investigation. All spatial models for mass flow propagation rely on a digital elevation model (DEM) as well as the distribution or depth of the released mass. It should be mentioned that different purposes require different levels of DEM resolution. The highest possible resolution can cause unrealistic artefacts and is not a priori the best choice for hazard analysis purposes.

Many parameters are uncertain as they cannot be directly measured in their full spatial distribution. Strategies are needed to deal with those uncertainties. Therefore, modeling requires three main steps:

1. 1. Optimization of the parameters in terms of the capability of the model to reproduce well-documented events in terms of impact area, flow depths, velocities, or pressures.

2. 2. Validation of the model with another set of observed events of the same type and magnitude.

3. 3. Forward calculations of possible future events (e.g., scenario-based modeling).

Traditionally, ad hoc approaches are used to optimize the parameters for well-documented events before applying the model for forward calculations, using the optimized set of parameters (Gruber & Bartelt, 2007; Jamieson et al., 2008). Validation is most commonly performed against observed deposition areas. Where available, other parameters are also used.

One-at-a-time parameter variation is frequently applied for optimization purposes, meaning that one parameter is varied—and optimized—while all the others are kept constant. This approach, however, has been criticized as inappropriate (Saltelli & Annoni, 2010) as it is not capable of considering interdependencies of the parameters. Fischer (2013) has attacked this gap with the tool AIMEC, building on random testing of a large number of parameter combinations, so-called Monte Carlo simulations. However, in the case of many-parameter models, a very high number of model runs is required. Mergili et al. (2015b) suggest considering not definite parameter values but instead combinations of parameter ranges, resulting in an impact indicator index which denotes the fraction of model runs predicting an impact on a given pixel (range 0–1).

# Conclusions and Future Perspectives

The present article focuses on mass movements in mountain areas which are (1) related to snow and ice and (2) represent a potential threat for people, property, and infrastructures. However, modern perspectives prefer the view that damaging events, or even disasters, have social rather than natural reasons/sources, i.e., result from the failure to appropriate adaptation to or mitigation of the natural conditions or processes (Blaikie, Cannon, Davis, & Wisner, 2014; Carey et al., 2014). Even though this perspective may not be applicable to all “natural” disasters to the same extent, it is undisputed that strategies to reduce the losses should consider not only the natural processes but also the socioeconomic dimension, which is actually multi-dimensional. Hewitt (2016) underlines the fact that the vulnerability of a population to mountain hazards strongly depends on the economic capacities, i.e., “poverty seems the main source of vulnerability”. This issue is not further elaborated upon here; Hewitt (2016) provides a comprehensive account of the human ecology of disaster risk in cold mountainous regions. This type of viewpoint complements a more technical understanding of risk as, e.g., nicely outlined for the example of debris flows in Jakob, Holm, and McDougall (2016) or followed by Gruber and Mergili (2013) (see Fig. 11).

With regard to cryosphere-related mass movements, one should point out that risk evaluation is fundamentally different for snow avalanches than for most glacier- or permafrost-related processes. Damaging snow avalanches are potentially repetitive with a more or less stochastic temporal occurrence, while damaging ice-related processes are often nonrepetitive within policy-relevant time scales. As a consequence, probabilities of occurrence are much more difficult—if not impossible—to define for the latter than for the first. Moreover, the latter also often imply low-frequency/high-magnitude phenomena, which are politically difficult to treat from a risk management perspective.

Moreover, the terms “hazard” and “risk” relate to the future. Particularly under the prevailing climate-driven transition of virtually all cold mountain regions worldwide, the future will most likely be different not only from the past but even from present-day conditions. Modern, future-oriented climate change impact and adaptation research therefore relies on scenario-based hazard, vulnerability, and risk modeling in complex, highly interconnected systems and new landscapes. Haeberli et al. (2016) outline such research directions. First, they point to the impact of possible mass movements released from steep areas with degrading permafrost or retreating glaciers on possible future lakes forming on or in front of decaying or retreating glacier tongues and to the anticipation of the consequences of the resulting process chains throughout the 21st century. Second, they underline the importance of an appropriate “dialogue between scientists, engineers, policy makers and the public.” Such a dialogue is considered key for effectively reducing the risks from a changing cryosphere (Carey, Huggel, Bury, Portocarrero, & Haeberli, 2012; Carey et al., 2014).

## References

Abermann, J., Lambrecht, A., Fischer, A., & Kuhn, M. (2009). Quantifying changes and trends in glacier area and volume in the Austrian Ötztal Alps (1969–1997–2006). The Cryosphere, 3, 205–215.Find this resource:

Abermann, J., Seiser, B., Meran, I., Stocker-Waldhuber, M., Goller, M., & Fischer, A. (2012). The third glacier inventory of North Tyrol, Austria, for 2006.. Supplement to: Abermann, J., Seiser, B., Meran, I., Stocker-Waldhuber, M., Goller, M., & Fischer, A. (2012). A new ALS glacier inventory of North Tyrol, Austria, for 2006 AD. Zeitschrift für Gletscherkunde und Glazialgeologie, 43(44), 109–119.Find this resource:

Aizen, V. B., Kuzmichenok, V. A., Surazakov, A. B., & Aizen, E. M. (2007). Glacier changes in the Tien Shan as determined from topographic and remotely sensed data. Global and Planetary Change, 56, 328–340.Find this resource:

Akbari, V., Doulgeris, A. P., & Eltoft, T. (2014). Monitoring glacier changes using multitemporal multipolarization SAR images. IEEE Transactions on Geoscience and Remote Sensing, 52(6), 3729–3741.Find this resource:

Alean, J. (1985). Ice avalanches: Some empirical information about their formation and reach. Journal of Glaciology, 31(109), 324–333.Find this resource:

Allen, J., & Simmon, R. (2012). Aftermath of the Seti River Landslide. Visible earth: A catalog of NASA images and animations of our home planet. Retrieved from http://visibleearth.nasa.gov/view.php?id=78117.Find this resource:

Allen, J., & Simmon, R. (2012).“Aftermath of the Seti River Landslide. NASA image using EO-1 ALI data with caption. Retrieved from http://visibleearth.nasa.gov/view.php?id=78117.Find this resource:

Allen, S. K., Schneider, D., & Owens, I. (2009). First approaches towards modelling glacial hazards in the Mount Cook region of New Zealand’s Southern Alps. Natural Hazards and Earth System Sciences, 9, 481–499.Find this resource:

Ammann, W. J. (1999). A new Swiss test-site for avalanche experiments in the Vallée de la Sionne/Valais. Cold Regions Science and Technology, 30(1), 3–11.Find this resource:

Ancey, C. (2016). Snow avalanches. Natural Hazard Sience: Oxford Research Encyclopedias.Find this resource:

Armstrong, R., Raup, B., Khalsa, S. J. S., Barry, R., Kargel, J., Helm, C., et al. (2005). GLIMS glacier database. Boulder, CO: National Snow and Ice Data Center.Find this resource:

Armstrong, R. L., & Brodzik, M. J. (1999). A twenty year record of global snow cover fluctuations derived from passive microwave remote sensing data. Fifth Conference on Polar Meteorology and Oceanography, Dallas, Texas, USA, January 10–15, 113–117.Find this resource:

Arnold, N. S., Rees, W. G., Devereux, B. J., & Amable, G. S. (2006). Evaluating the potential of high‐resolution airborne LiDAR data in glaciology. International Journal of Remote Sensing, 27(6), 1233–1251.Find this resource:

Avian, M., Kellerer-Pirklbauer, A., & Bauer, A. (2009). LiDAR for monitoring mass movements in permafrost environments at the cirque Hinteres Langtal, Austria, between 2000 and 2008. Natural Hazards and Earth System Sciences, 9(4), 1087–1094.Find this resource:

Bader, H., Neher, J., Eckel, O., Thams, C., Haefeli, R., & Bucher, E. (1939). Der Schnee und seine Metamorphose. Zürich: Kommissionsverlag Kümmerly & Frey.Find this resource:

Bajracharya, S. R., Mool, P. K., & Shrestha, B. R. (2007). Impact of climate change on Himalayan glaciers and glacial lakes: Case studies on GLOF and associated hazards in Nepal and Bhutan. Kathmandu: ICIMOD.Find this resource:

Baker, V. R. (1973). Paleohydrology and sedimentology of Lake Missoula flooding in eastern Washington. Geological Society of America Special Papers, 144, 1–73.Find this resource:

Baker, V. R., & Bunker, R. C. (1985). Cataclysmic late Pleistocene flooding from glacial Lake Missoula: A review. Quaternary Science Reviews, 4(1), 1–41.Find this resource:

Barbolini, M., Cappabianca, F., & Sailer, R. (2004). Empirical estimate of vulnerability relations for use in snow avalanche risk assessment. Atti del convegno Risk Analysis. Rhodos, Greece, September 27–29, 533–542.Find this resource:

Barbolini, M., Cappabianca, F., & Savi, F. (2003). A new method for the estimation of avalanche distance exceeded probabilities. Surveys in Geophysics, 24(5–6), 587–601.Find this resource:

Baroudi, D., Sovilla, B., & Thibert, E. (2011). Effects of flow regime and sensor geometry on snow avalanche impact-pressure measurements. Journal of Glaciology, 57(202), 277–288.Find this resource:

Bartelt, P., & Lehning, M. (2002). A physical SNOWPACK model for the Swiss avalanche warning: Part I: numerical model. Cold Regions Science and Technology, 35(3), 123–145.Find this resource:

Baum, R. L., & Godt, J. W. (2010). Early warning of rainfall-induced shallow landslides and debris flows in the USA. Landslides, 7(3), 259–272.Find this resource:

Beniston, M. (2003). Climatic change in mountain regions: A review of possible impacts. Climatic Change, 59, 5–31.Find this resource:

Björnsson, H. (2002). Subglacial lakes and jökulhlaups in Iceland. Global and Planetary Change, 35(3), 255–271.Find this resource:

Blaikie, P., Cannon, T., Davis, I., & Wisner, B. (2014). At risk: Natural hazards, people's vulnerability and disasters. New York: Routledge.Find this resource:

Boeckli, L., Brenning, A., Gruber, S., & Noetzli, J. (2012). Permafrost distribution in the European Alps: Calculation and evaluation of an index map and summary statistics. The Cryosphere, 6, 807–820.Find this resource:

Bolch, T., Peters, J., Yegorov, A., Prafhan, B., Buchroithner, M., & Blagoveshchensky, V. (2011). Identification of potentially dangerous glacial lakes in the northern Tien Shan. Natural Hazards, 59, 1691–1714.Find this resource:

Box, G. E., & Draper, N. R. (1987). Empirical model-building and response surfaces. New York: John Wiley.Find this resource:

Box, G. E. (1976). Science and statistics. Journal of the American Statistical Association, 71, 791–799.Find this resource:

Brabec, B., & Meister, R. (2001). A nearest-neighbor model for regional avalanche forecasting. Annals of Glaciology, 32(1), 130–134.Find this resource:

Brabec, B., Meister, R., Stöckli, U., Stoffel, A., & Stucki, T. (2001). RAIFoS: Regional avalanche information and forecasting system. Cold Regions Science and Technology, 33(2), 303–311.Find this resource:

Breien, H., De Blasio, F. V., Elverhoi, A., & Hoeg, K. (2008). Erosion and morphology of a debris flow caused by a glacial lake outburst flood, Western Norway. Landslides, 5(3), 271–280.Find this resource:

Bühler, Y., Christen, M., Kowalski, J., & Bartelt, P. (2011). Sensitivity of snow avalanche simulations to digital elevation model quality and resolution. Annals of Glaciology, 52(58), 72–80.Find this resource:

Burton, A., & Bathurst, J. C. (1998). Physically based modeling of shallow landslide sediment yield at a catchment scale. Environmental Geology, 35, 89–99.Find this resource:

Caine, N. (1980). The rainfall intensity: Duration control of shallow landslides and debris flows. Geografiska Annaler Series A (Physical Geography), 62(1–2), 23–27.Find this resource:

Cappabianca, F., Barbolini, M., & Natale, L. (2008). Snow avalanche risk assessment and mapping: A new method based on a combination of statistical analysis, avalanche dynamics simulation and empirically-based vulnerability relations integrated in a GIS platform. Cold Regions Science and Technology, 54(3), 193–205.Find this resource:

Cardinali, M., Ardizzone, F., Galli, M., Guzzetti, F., & Reichenbach, P. (2000). Landslides triggered by rapid snow melting: The December 1996–January 1997 event in Central Italy. Proceedings of the 1st Plinius Conference on Mediterranean Storms, Maratea, Italy, October 14–16, 1999, 439–448.Find this resource:

Carey, M. (2005). Living and dying with glaciers. People’s historical vulnerability to avalanches and outburst floods in Peru. Global and Planetary Change, 47, 122–134.Find this resource:

Carey, M., Huggel, C., Bury, J., Portocarrero, C., & Haeberli, W. (2012). An integrated socio-environmental framework for glacier hazard management and climate change adaptation: Lessons from Lake 513, Cordillera Blanca, Peru. Climatic Change, 112(3), 733–767.Find this resource:

Carey, M., McDowell, G., Huggel, C., Jackson, M., Portocarrero, C., Reynolds, J. M., et al.. (2014). Integrated approaches to adaptation and disaster risk reduction in dynamic socio-cryospheric systems. In W. Haeberli, & C. Whiteman (Eds.), Snow and ice-related hazards, risks and disasters (pp. 219–261). Amsterdam: Elsevier.Find this resource:

Cenderelli, D. A., & Wohl, E. E. (2001). Peak discharge estimates of glacial lake outburst floods and “normal” climatic floods in Mount Everest region, Nepal. Geomorphology, 40, 57–90.Find this resource:

Chen, F., Lin, H., Li, Z., Chen, Q., & Zhou, J. (2012). Interaction between permafrost and infrastructure along the Qinghai–Tibet Railway detected via jointly analysis of C-and L-band small baseline SAR interferometry. Remote Sensing of Environment, 123, 532–540.Find this resource:

Chen, F., Lin, H., Zhou, W., Hong, T., & Wang, G. (2013). Surface deformation detected by ALOS PALSAR small baseline SAR interferometry over permafrost environment of Beiluhe section, Tibet Plateau, China. Remote Sensing of Environment, 138, 10–18.Find this resource:

Christen, C., Bartelt, P., & Kowalski, J. (2010a). Back calculation of the In den Arelen avalanche with RAMMS: Interpretation of model results. Annals of Glaciology, 51, 161–168.Find this resource:

Christen, M., Bartelt, P., & Gruber, U. (2002). AVAL-1D: An avalanche dynamics program for the practice. International Congress Interpraevent, Matsumoto, Japan, 715–725.Find this resource:

Christen, M., Kowalski, J., & Bartelt, B. (2010b). RAMMS: Numerical simulation of dense snow avalanches in three-dimensional terrain. Cold Regions Science and Technology, 63, 1–14.Find this resource:

Clague, J. J., & O’Connor, J. E. (2014). Glacier-related outburst floods. In W. Haeberli & C. Whiteman (Eds.), Snow and ice-related hazards, risks and disasters (pp. 487–519). Amsterdam: Elsevier.Find this resource:

Clarke, G. K. C. (1982). Glacier outburst floods from ‘Hazard Lake,’ Yukon Territory, and the problem of flood magnitude prediction. Journal of Glaciology, 28(98), 3–21.Find this resource:

Coaz, J. (1881). Die Lawinen der Schweizeralpen. Bern: Schweizerisches Handels- und LandwirtschaftsdepartementFind this resource:

Cochachin, A., Huggel, C., Salazar, C., Haeberli, W., & Frey, H. (2015). Assessing the variability of glacier lake bathymetries and potential peak discharge based on large-scale measurements in the Cordillera Blanca, Peru. Geophysical Research Abstracts, 17, 11141.Find this resource:

Cogley, J. G. (2010). A more complete version of the World Glacier Inventory. Annals of Glaciology, 50(53), 32–38.Find this resource:

Cook, A. J., Fox, A. J., Vaughan, D. G., & Ferrigno, J. G. (2005). Retreating glacier fronts on the Antarctic Peninsula over the past half-century. Science, 308(5721), 541–544.Find this resource:

Corominas, J., Copons, R., Vilaplana, J. M., Altamir, J., & Amigó, J. (2003). Integrated landslide susceptibility analysis and hazard assessment in the principality of Andorra. Natural Hazards, 30, 421–435.Find this resource:

Costa, J. E. (1985). Floods from dam failures. Denver: United States Department of the Interior Geological Survey.Find this resource:

Costa, J. E., & Schuster, R. L. (1988). The formation and failure of natural dams. Geological Society of America Bulletin, 100, 1054–1068.Find this resource:

Davis, R. E., Elder, K., Howlett, D., & Bouzaglou, E. (1999). Relating storm and weather factors to dry slab avalanche activity at Alta, Utah, and Mammoth Mountain, California, using classification and regression trees. Cold Regions Science and Technology, 30(1), 79–89.Find this resource:

De Quervain, M. R., De Crecy, L., LaChapelle, E. R., Losev, K., & Shoda, M. (1973). Avalanche classification. Hydrological Sciences Bulletin, 18(4), 391–402.Find this resource:

Decaulne, A., Sæmundsson, Þ., & Petursson, O. (2005). Debris flow triggered by rapid snowmelt: A case study in the Gleiðarhjalli area, northwestern Iceland. Geografiska Annaler Series A (Physical Geography), 87(4), 487–500.Find this resource:

Deems, J. S., Painter, T. H., & Finnegan, D. C. (2013). Lidar measurement of snow depth: A review. Journal of Glaciology, 59(215), 467–479.Find this resource:

Delaloye, R., Strozzi, T., Lambiel, C., Perruchoud, E., & Raetzo, H. (2008). Landslide-like development of rockglaciers detected with ERS-1/2 SAR interferometry. Proceedings of FRINGE 2007 Workshop, Frascati, Italy, November 26–30, 2007.Find this resource:

Deline, P., Gruber, S., Delaloye, R., Fischer, L., Geertsema, M., Giardino, M., et al. (2014). Ice loss and slope stability in high-mountain regions. In W. Haeberli & C. Whiteman (Eds.), Snow and ice-related hazards, risks and disasters (pp. 303–344). Amsterdam: Elsevier.Find this resource:

Deline, P., Jaillet, S., Rabatel, A., & Ravanel, L. (2008). Ground-based LiDAR data on permafrost-related rock fall activity in the Mont-Blanc massif. Proceedings of the 9th International Conference on Permafrost, Fairbanks, Alaska, USA, June 28–July 3, 349–354.Find this resource:

Duncan, J. M., & Wright, S. G. (2005). Soil strength and slope stability. Hoboken, New Jersey: Wiley.Find this resource:

Dussaillant, A., Benito, G., Buytaert, W., Carling, P., Meier, C., & Espinoza, F. (2010). Repeated glacial-lake outburst floods in Patagonia: An increasing hazard? Natural Hazards, 54(2), 469–481.Find this resource:

Emmer, A., Mergili, M., Juřicová, A., Cochachin, A., & Huggel, C. (2016). Insights from analyzing and modeling cascading multi-lake outburst flood events in the Santa Cruz Valley (Cordillera Blanca, Perú). Geophysical Research Abstracts, 18, 2181.Find this resource:

Emmer, A., Merkl, S., & Mergili, M. (2015). Spatiotemporal patterns of high-mountain lakes and related hazards in western Austria. Geomorphology, 246, 602–616.Find this resource:

Emmer, A., & Vilímek, V., (2014). New method for assessing the susceptibility of glacial lakes to outburst floods in the Cordillera Blanca, Peru. Hydrology and Earth System Sciences, 18, 3461–3479.Find this resource:

Engeset, R.V., (2013). “National Avalanche Warning Service for Norway–established 2013.” Proceedings of the International Snow Science Workshop(ISSW), Grenoble, France, October 7–11, 301–310.Find this resource:

Evans, S. G., Bishop, N. F., Fidel Smoll, L., Valderrama Murillo, P., Delaney, K. B., & Oliver-Smith, A. (2009). A re-examination of the mechanism and human impact of catastrophic mass flows originating on Nevado Huascarán, Cordillera Blanca, Peru in 1962 and 1970. Engineering Geology, 108, 96–118.Find this resource:

Evans, S. G., & Clague, J. J. (1994). Recent climatic change and catastrophic geomorphic processes in mountain environments. Geomorphology, 10(1–4), 107–128.Find this resource:

Evans, S. G., & Delaney, K. B. (2014). Catastrophic mass flows in the mountain glacial environment. In W. Haeberli & C. Whiteman (Eds.), Snow and ice-related hazards, risks and disasters (pp. 563–606). Amsterdam: Elsevier.Find this resource:

Fey, C., Zangerl, C., Haas, F., Rutzinger, M., Sailer, R., & Bremer, M. (2012). Rock slide deformation measurements with Terrestrial Laser Scanning in inaccessible high mountain areas. Geophysical Research Abstracts, 14, 11944.Find this resource:

Finsterwalder, S. (1890). Die Gletscherausbrüche des Martellthales. Zeitschrift des Deutschen und Österreichischen Alpenvereins, 21, 21–34.Find this resource:

Fischer, J. T. (2013). A novel approach to evaluate and compare computational snow avalanche simulation. Natural Hazards and Earth System Sciences, 13(6), 1655–1667.Find this resource:

Fischer, L., Huggel, C., Kääb, A., & Haeberli, W. (2013). Slope failures and erosion rates on a glacierized high‐mountain face under climatic changes. Earth Surface Processes and Landforms, 38(8), 836–846.Find this resource:

Flubacher, M., Huggel, C., Kääb, A., & Zemp, M. (2007). Web-based database on worldwide glacier and permafrost disasters. Geophysical Research Abstracts, 9, 04374.Find this resource:

Frauenfelder, R., Kronholm, K., Solberg, R., Larsen, S. Ø., Salberg, A. B., Larsen, J. O., et al. (2010). DUE avalRS: Remote-sensing derived avalanche inventory data for decision support and hind-cast after avalanche events. Proceedings of ESA Living Planet Symposium, Bergen, Norway, June 28–July 2, 2010.Find this resource:

French, J. R. (2003). Airborne LiDAR in support of geomorphological and hydraulic modeling. Earth Surface Processes and Landforms, 28(3), 321–335.Find this resource:

Frey, H., Haeberli, W., Linsbauer, A., Huggel, C., & Paul, F. (2010). A multi level strategy for anticipating future glacier lake formation and associated hazard potentials. Natural Hazards and Earth System Sciences, 10, 339–352.Find this resource:

Fuchs, S., & Bründl, M. (2005). Damage potential and losses resulting from snow avalanches in settlements of the canton of Grisons, Switzerland. Natural Hazards, 34(1), 53–69.Find this resource:

Fuchs, S., Bründl, M., & Stötter, J. (2004). Development of avalanche risk between 1950 and 2000 in the municipality of Davos, Switzerland. Natural Hazards and Earth System Sciences, 4(2), 263–275.Find this resource:

Fuchs, S., Keiler, M., Zischg, A., & Bründl, M. (2005). The long-term development of avalanche risk in settlements considering the temporal variability of damage potential. Natural Hazards and Earth System Sciences, 5(6), 893–901.Find this resource:

Galli, M., Ardizzone, F., Cardinali, M., Guzzetti, F., & Reichenbach, P. (2008). Comparing landslide inventory maps. Geomorphology, 94, 268–289.Find this resource:

Gamma, P. (2000). dfwalk—Ein Murgang-Simulationsprogramm zur Gefahrenzonierung. Geo-graphica Bernensia, G66.Find this resource:

Gauer, P., Issler, D., Lied, K., Kristensen, K., Iwe, H., Lied, E., et al. (2007). On full-scale avalanche measurements at the Ryggfonn test site, Norway. Cold Regions Science and Technology, 49(1), 39–53.Find this resource:

Ghinoi, A., & Chung, C. J. (2005). STARTER: A statistical GIS-based model for the prediction of snow avalanche susceptibility using terrain features—application to Alta Val Badia, Italian Dolomites. Geomorphology, 66(1), 305–325.Find this resource:

Giardini, D., Grünthal, G., Shedlock, K., & Zhang, P. (1999). The GSHAP Global Seismic Hazard Map. Annals of Geophysics, 42(6), 1225–1230.Find this resource:

Glaciorisk European Project. (2001–2003). Survey and prevention of extreme glaciological hazards in European mountainous regions. Retrieved from http://glaciorisk.grenoble.cemagref.fr/projet_glaciorisk.htm.Find this resource:

Glade, T., Crozier, M., & Smith, P. (2000). Applying probability determination to refine landslide-triggering rainfall thresholds using an empirical “Antecedent Daily Rainfall Model.” Pure and Applied Geophysics, 157(6–8), 1059–1079.Find this resource:

Glenn, N. F., Streutker, D. R., Chadwick, D. J., Thackray, G. D., & Dorsch, S. J. (2006). Analysis of LiDAR-derived topographic information for characterizing and differentiating landslide morphology and activity. Geomorphology, 73(1), 131–148.Find this resource:

Grêt-Regamey, A., & Straub, D. (2006). Spatially explicit avalanche risk assessment linking Bayesian networks to a GIS. Natural Hazards and Earth System Sciences, 6(6), 911–926.Find this resource:

Grosse, G., Romanovsky, V., Goetz, S., Bartsch, A., Jones, B., & Heim, B. (2014). New frontiers in remote sensing of periglacial landscapes and permafrost. 4th European Conference on Permafrost, Evora, Portugal, June 18–21, 2014.Find this resource:

Gruber, F. E., & Mergili, M. (2013). Regional-scale analysis of high-mountain multi-hazard and risk indicators in the Pamir (Tajikistan) with GRASS GIS. Natural Hazards and Earth System Sciences, 13(11), 2779–2796.Find this resource:

Gruber, S. (2012). Derivation and analysis of a high-resolution estimate of global permafrost zonation. The Cryosphere, 6, 221–233.Find this resource:

Gruber, S., & Haeberli, W. (2007). Permafrost in steep bedrock slopes and its temperature-related destabilization following climate change. Journal of Geophysical Research, 112, F02S18.Find this resource:

Gruber, U., & Bartelt, P. (2007). Snow avalanche hazard modelling of large areas using shallow water numerical methods and GIS. Environmental Modelling & Software, 22(10), 1472–1481.Find this resource:

Guha-Sapir, D., Below, R., & Hoyois, P. (2015). EM-DAT: International disaster database. Brussels: Université Catholique de Louvain.Find this resource:

Guzzetti, F. (2006). Landslide Hazard and Risk Assessment. PhD Thesis, University of Bonn.Find this resource:

Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., & Chang, K. T. (2012). Landslide inventory maps: New tools for an old problem. Earth-Science Reviews, 112, 42–66.Find this resource:

Guzzetti, F., Peruccacci, S., Rossi, M., & Stark, C. P. (2007). Rainfall thresholds for the initiation of landslides in central and southern Europe. Meteorology and Atmospheric Physics, 98(3–4), 239–267.Find this resource:

Haeberli, W. (1975). Untersuchungen zur Verbreitung von Permafrost zwischen Flüelapass und Piz Grialetsch (Graubünden) (Dissertation). University of Basel, Switzerland.Find this resource:

Haeberli, W. (1983). Frequency and characteristics of glacier floods in the Swiss Alps. Annals of Glaciology, 4, 85–90.Find this resource:

Haeberli, W. (1992). Construction, environmental problems and natural hazards in periglacial mountain belts. Permafrost and Periglacial Processes, 3(2), 111–124.Find this resource:

Haeberli, W., & Whiteman, C. (2014). Snow and Ice-related Hazards, Risks and Disasters. Amsterdam: Elsevier.Find this resource:

Haeberli, W., Clague, J. J., Huggel, C., & Kääb, A. (2010a). Hazards from lakes in high-mountain glacier and permafrost regions: Climate change effects and process interactions. Avances de la Geomorphología en España, 2008–2010, XI Reunión Nacional de Geomorphología, Solsona, Spain, September 20–24, 2010, 439–446.Find this resource:

Haeberli, W., Portocarrero, C., & Evans, S. (2010b). Nevado Hualcán, Laguna 513 y Carhuaz 2010—Observaciones, evaluación y recomendaciones (un corto informe técnico luego de las reuniones y visita de campo en Julio 2010). Unpublished report on behalf of the Comunidad Provincial de Carhuaz.Find this resource:

Haeberli, W., Schaub, Y., & Huggel, C. (2016). Increasing risks related to landslides from degrading permafrost into new lakes in de-glaciating mountain ranges. Geomorphology, in press.Find this resource:

Haeberli, W., Wegmann, M., & Von der Muehll, D. (1997). Slope stability problems related to glacier shrinkage and permafrost degradation in the Alps. Eclogae Geologicae Helveticae, 90, 407–414.Find this resource:

Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. E., & Bayr, K. J. (2002). MODIS snow-cover products. Remote Sensing of Environment, 83(1), 181–194.Find this resource:

Harris, C., Arenson, L. U., Christiansen, H. H., Etzelmüller, B., Frauenfelder, R., Gruber, S., et al. (2009). Permafrost and climate in Europe: Monitoring and modelling thermal, geomorphological and geotechnical responses. Earth-Science Reviews, 92, 117–171.Find this resource:

Harrison, S., Glasser, N., Winchester, V., Haresign, E., Warren, C., & Jansson, K. (2006). A glacial lake outburst flood associated with recent mountain glacier retreat, Patagonian Andes. The Holocene, 16(4), 611–620.Find this resource:

Harrison, W. D., Osipova, G. B., Nosenko, G. A., Espizua, L., Kääb, A., Fischer, L., et al. (2014). Glacier surges. In W. Haeberli & C. Whiteman (Eds.), Snow and ice-related hazards, risks and disasters (pp. 437–485). Amsterdam: Elsevier.Find this resource:

Herd, D. G. (1986). The 1985 Ruiz Volcano disaster. Eos, Transactions American Geophysical Union, 67, 457–460.Find this resource:

Hewitt, K. (1982). Natural dams and outburst floods in the Karakorum Himalaya. In J. W. Glen (Ed.), Hydrological aspects of alpine and high-mountain areas. Proceedings of the Exeter Symposium, July 1982 (pp. 259–269). IAHS Publication.Find this resource:

Hewitt, K. (2016). The human ecology of disaster risk in cold mountainous regions. In Oxford research encyclopedia of natural hazards, in press.Find this resource:

Hewitt, K., & Liu, J. (2010). Ice-dammed lakes and outburst floods, Karakoram Himalaya: Historical perspectives on emerging threats. Physical Geography, 31(6), 528–551.Find this resource:

Hoelzle, M. (1992). Permafrost occurrence from BTS measurements and climatic parameters in the Eastern Swiss Alps. Permafrost and Periglacial Processes, 3(2), 143–147.Find this resource:

Höfle, B., & Rutzinger, M. (2011). Topographic airborne LiDAR in geomorphology: A technological perspective. Zeitschrift für Geomorphologie, 55(2), 1–29.Find this resource:

Höller, P. (2007). Avalanche hazards and mitigation in Austria: A review. Natural Hazards, 43(1), 81–101.Find this resource:

Horton, P., Jaboyedoff, M., Rudaz, B., & Zimmermann, M. (2013). Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale. Natural Hazards and Earth System Sciences, 13(4), 869–885.Find this resource:

Hubbard, S. S., Gangodagamage, C., Dafflon, B., Wainwright, H., Peterson, J., Gusmeroli, A., et al. (2013). Quantifying and relating land-surface and subsurface variability in permafrost environments using LiDAR and surface geophysical datasets. Hydrogeology Journal, 21(1), 149–169.Find this resource:

Huber, U., Bugmann, H., & Reasoner, M. (Eds.). (2005). Global change and mountain regions. An overview of current knowledge. Dordrecht: Springer.Find this resource:

Huggel, C. (2004). Assessment of glacial hazards based on remote sensing and GIS modeling (PhD Diss.). University of Zurich.Find this resource:

Huggel, C., Ceballos, J. L., Pulgarín, B., Ramírez, J., & Thouret, J. C. (2007). Review and reassessment of hazards owing to volcano-glacier interactions in Colombia. Annals of Glaciology, 45, 128–136.Find this resource:

Huggel, C., Clague, J. J., & Korup, O. (2012). Is climate change responsible for changing landslide activity in high mountains? Earth Surface Processes and Landforms, 37, 77–91.Find this resource:

Huggel, C., Haeberli, W., Kääb, A., Bieri, D., & Richardson, S. (2004a). Assessment procedures for glacial hazards in the Swiss Alps. Canadian Geotechnical Journal, 41, 1068–1083.Find this resource:

Huggel, C., Kääb, A., Haeberli, W., & Krummenacher, B. (2003). Regional-scale GIS-models for assessment of hazards from glacier lake outbursts: Evaluation and application in the Swiss Alps. Natural Hazards and Earth System Sciences, 3, 647–662.Find this resource:

Huggel, C., Kääb, A., Haeberli, W., Teysseire, P., & Paul, F. (2002). Remote sensing based assessment of hazards from glacier lake outbursts: A case study in the Swiss Alps. Canadian Geotechnical Journal, 39, 316–330.Find this resource:

Huggel, C., Kääb, A., & Salzmann, N. (2004b). GIS-based modeling of glacial hazards and their interactions using Landsat-TM and IKONOS imagery. Norwegian Journal of Geography, 58, 761–773.Find this resource:

Huggel, C., Salzmann, N., Allen, S., Caplan-Auerbach, J., Fischer, L., Haeberli, W., et al. (2010). Recent and future warm extreme events and high-mountain slope stability. Philosophical Transactions of the Royal Society A: Mathematical, Physical, and Engineering Sciences, 368, 2435–2459.Find this resource:

Huggel, C., Zgraggen-Oswald, S., Haeberli, W., Kääb, A., Polkvoj, A., Galushkin, I., et al. (2005). The 2002 rock/ice avalanche at Kolka/Karmadon, Russian Caucasus: Assessment of extraordinary avalanche formation and mobility, and application of QuickBird satellite imagery. Natural Hazards and Earth System Sciences, 5, 173–187.Find this resource:

Hungr, O. (1995). A model for the runout analysis of rapid flow slides, debris flows, and avalanches. Canadian Geotechnical Journal, 32, 610–623.Find this resource:

Hungr, O., Corominas, J., & Eberhardt, E. (2005). State of the art paper: Estimating landslide motion mechanism, travel distance and velocity. In O. Hungr, R. Fell, R. Couture, & E. Eberhardt (Eds.), “Landslide Risk Management. Proceedings of the International Conference on Landslide Risk Management, Vancouver, Canada, May 31–June 3 (pp. 129–158). Rotterdam: Balkema.Find this resource:

ICIMOD. (2011). Glacial lakes and glacial lake outburst floods in Nepal. Kathmandu: ICIMOD.Find this resource:

IPCC. (2007). Climate change 2007: The physical science basis. Cambridge and New York: Cambridge University Press.Find this resource:

IPCC. (2013). Climate change 2013: The physical science basis. Cambridge and New York: Cambridge University Press.Find this resource:

Issler, D., Lied, K., Rammer, L., Revol, P., Sabot, F., Suriñach Cornet, E., et al. (1999). European avalanche test sites: Overview and analysis in view of coordinated experiments. SAME: Snow Avalanche Mapping, Model Validation and Warning Systems in Europe. Fourth European Framework Programme: Environment and Climate Section. Project report CEMAGREF - 98-0846.Find this resource:

Iturrizaga, L. (2011). Glacier lake outburst floods. In V. P. Singh, P. Singh, & U. K. Haritashya (Eds.), Encyclopedia of snow, ice and glaciers (pp. 381–399). Dordrecht: Springer.Find this resource:

Iverson, R. M. (1997). The physics of debris flows. Reviews of Geophysics, 35, 245–296.Find this resource:

Jakob, M., Holm, K., & McDougall, S. (2016). Debris flow risk assessment. Oxford Research Encyclopedia of Natural Hazards.Find this resource:

Jamieson, B., Margreth, S., & Jones, A. (2008). Application and limitations of dynamic models for snow avalanche hazard mapping. Proceedings of the International Snow Science Workshop (ISSW), Whistler, British Columbia, Canada, September 21–27, 730.Find this resource:

Jarvis, A., Reuter, H. I., Nelson, A., & Guevara, E. (2008). Hole-filled seamless SRTM data V4. International Centre for Tropical Agriculture (CIAT). Retreived from http://srtm.csi.cgiar.org.Find this resource:

Jasek, M. (2003). Ice jam release surges, ice runs, and breaking fronts: Field measurements, physical descriptions, and research needs. Canadian Journal of Civil Engineering, 30(1), 113–127.Find this resource:

Jóhannesson, T., Björnsson, H., Magnusson, E., Guðmundsson, S., Pálsson, F., Sigurðsson, O., et al. (2013). Ice-volume changes, bias estimation of mass-balance measurements and changes in subglacial lakes derived by Lidar mapping of the surface of Icelandic glaciers. Annals of Glaciology, 54(63), 63–74.Find this resource:

Jones, A. F., Brewer, P. A., Johnstone, E., & Macklin, M. G. (2007). High-resolution interpretative geomorphological mapping of river valley environments using airborne LiDAR data. Earth Surface Processes and Landforms, 32(10), 1574–1592.Find this resource:

Kääb, A., Huggel, C., Fischer, L., Guex, S., Paul, F., Roer, I., et al. (2005). Remote sensing of glacier- and permafrost-related hazards in high mountains: An overview. Natural Hazards and Earth System Sciences, 5, 527–554.Find this resource:

Kääb, A., Paul, F., Maisch, M., Hoelzle, M., & Haeberli, W. (2002). The new remote-sensing-derived Swiss glacier inventory: II. First results. Annals of Glaciology, 34(1), 362–366.Find this resource:

Kaser, G. (1999). A review of the modern fluctuations of tropical glaciers. Global and Planetary Change, 22, 93–103.Find this resource:

Keiler, M., Sailer, R., Jörg, P., Weber, C., Fuchs, S., Zischg, A., et al. (2006). Avalanche risk assessment? A multi-temporal approach, results from Galtür, Austria. Natural Hazards and Earth System Sciences, 6(4), 637–651.Find this resource:

Kenyi, L. W., & Kaufmann, V. (2003). Estimation of rock glacier surface deformation using SAR interferometry data. IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1512–1515.Find this resource:

Köhler, D., Geilhausen, M., & Linsbauer, A. (2014). Modelling glacier beds in the Austrian Alps: How many lakes will form in future? Geophysical Research Abstracts, 16, 16386.Find this resource:

Krautblatter, M., Funk, D., & Günzel, F. K. (2013). Why permafrost rocks become unstable: a rock–ice-mechanical model in time and space. Earth Surface Processes and Landforms, 38, 876–887.Find this resource:

Krautblatter, M., Huggel, C., Deline, P., & Hasler, A. (2012). Research perspectives on unstable high‐alpine bedrock permafrost: Measurement, modelling and process understanding. Permafrost and Periglacial Processes, 23(1), 80–88.Find this resource:

Krieger, G., Moreira, A., Fiedler, H., Hajnsek, I., Werner, M., Younis, M., et al. (2007). TanDEM-X: A satellite formation for high-resolution SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 45(11), 3317–3341.Find this resource:

Lambrecht, A., & Kuhn, M. (2007). Glacier changes in the Austrian Alps during the last three decades, derived from the new Austrian glacier inventory. Annals of Glaciology, 46(1), 177–184.Find this resource:

Längle, G. (1977). Der Lawinenwarndienst in Vorarlberg. Für die Sicherheit im Bergland. Jahrbuch 1977 des Österreichischen Kuratoriums für alpine Sicherheit, 91–93.Find this resource:

Lehning, M., Bartelt, P., Brown, B., & Fierz, C. (2002b). A physical SNOWPACK model for the Swiss avalanche warning: Part III: Meteorological forcing, thin layer formation and evaluation. Cold Regions Science and Technology, 35(3), 169–184.Find this resource:

Lehning, M., Bartelt, P., Brown, B., Fierz, C., & Satyawali, P. (2002a). A physical SNOWPACK model for the Swiss avalanche warning: Part II. Snow microstructure. Cold Regions Science and Technology, 35(3), 147–167.Find this resource:

Lehning, M., Bartelt, P., Brown, B., Russi, T., Stöckli, U., & Zimmerli, M. (1999). SNOWPACK model calculations for avalanche warning based upon a new network of weather and snow stations. Cold Regions Science and Technology, 30(1), 145–157.Find this resource:

LeSchack, L. A., & Del Grande, N. K. (1976). A dual-wavelength thermal infrared scanner as a potential airborne geophysical exploration tool. Geophysics, 41(6), 1318–1336.Find this resource:

Lied, K., & Bakkehøi, S., (1980). Empirical calculations of snow-avalanche run-out distance based on topographic parameters. Journal of Glaciology, 26, 165–177.Find this resource:

Lied, K., Moe, A., Kristensen, K., & Issler, D. (2001). Ryggfonn. Full scale avalanche test site and the effect of the catching dam. Proceedings of the Symposium Snow and Avalanche Test Sites, Grenoble, France.Find this resource:

Linsbauer, A., Frey, H., Haeberli, W., Machguth, H., Azam, M. F., & Allen, S. (2015). Modelling glacier-bed overdeepenings and possible future lakes for the glaciers in the Himalaya–Karakoram region. Annals of Glaciology, 57, 119–130.Find this resource:

Linsbauer, A., Frey, H., Haeberli, W., Machguth, H., Azam, M. F., & Allen, S. (2016). Modelling glacier-bed overdeepenings and possible future lakes for the glacier areas in the Himalaya-Karakoram region. Annals of Glaciology, 57, 119–130.Find this resource:

Linsbauer, A., Paul, F., & Haeberli, W. (2012). Modeling glacier thickness distribution and bed topography over entire mountain ranges with GlabTop: Application of a fast and robust approach.. Journal of Geophysical Research, 117, F03007.Find this resource:

Liu, X. (2008). Airborne LiDAR for DEM generation: Some critical issues. Progress in Physical Geography, 32(1), 31–49.Find this resource:

Lougeay, R. (1974). Detection of buried glacial and ground ice with thermal infrared remote sensing. Washington, DC: National Academy of Sciences.Find this resource:

Lowe, D. R., Williams, S. N., Leigh, H., Connort, C. B., Gemmell, J. B., & Stoiber, R. E. (1986). Lahars initiated by the 13 November 1985 eruption of Nevado del Ruiz, Colombia. Nature, 324, 51–53.Find this resource:

Luckman, A., Quincey, D., & Bevan, S. (2007). The potential of satellite radar interferometry and feature tracking for monitoring flow rates of Himalayan glaciers. Remote Sensing of Environment, 111(2), 172–181.Find this resource:

Maggioni, M., Freppaz, M., Ceaglio, E., Godone, D., Viglietti, D., Zanini, E., et al. (2013). A new experimental snow avalanche test site at Seehore Peak in Aosta Valley (NW Italian Alps)—Part I: Conception and logistics. Cold Regions Science and Technology, 85, 175–182.Find this resource:

Mahboob, M. A., Iqbal, J., & Atif, I. (2015). Modeling and simulation of glacier avalanche: A case study of gayari sector glaciers hazards assessment. IEEE Transactions on Geoscience and Remote Sensing, 53(11), 5824–5834.Find this resource:

Malnes, E., Eckerstorfer, M., Larsen, Y., Frauenfelder, R., Jónsson, Á., Jaedicke, C., et al. (2013). Remote sensing of avalanches in northern Norway using synthetic aperture radar. Proceedings of the International Snow Science Workshop (ISSW), Grenoble, France, October 7–11, 2013, 955–959.Find this resource:

McClung, D., & Schaerer, P. A. (2006). The avalanche handbook. Leicester: The Mountaineers Books.Find this resource:

McClung, D. M., & Lied, K. (1987). Statistical and geometrical definition of snow avalanche runout. Cold Regions Science and Technology, 13, 107–119.Find this resource:

McDougall, S., & Hungr, O. (2004). A model for the analysis of rapid landslide motion across three-dimensional terrain. Canadian Geotechnical Journal, 41, 1084–1097.Find this resource:

McDougall, S., & Hungr, O. (2005). Dynamic modeling of entrainment in rapid landslides. Canadian Geotechnical Journal, 42, 1437–1448.Find this resource:

Meister, R. (1995). Country-wide avalanche warning in Switzerland. Proceedings of the International Snow Science Workshop (ISSW), Snowbird, Utah, USA, October 30–November 3, 1994, 58–71.Find this resource:

Mergili, M. (2007). Stereo matching of terrestrial digital photographs—an alternative for the generation of high-resolution DEMs in situations of poor data availability? In A. Car, G. Griesebner, & J. Strobl (Eds.), Geospatial Crossroads @ GI_Forum. Proceedings of the First Geoinformatics Forum, Salzburg, Austria, July 4–6, 2007, 110–119. Heidelberg: Wichmann.Find this resource:

Mergili, M. (2015). Environmental changes and hazardous processes in mountain areas. Challenges in observation and modelling at different spatial scales. Habilitation, University of Innsbruck.Find this resource:

Mergili, M., Fischer, J.-T., Fellin, W., Ostermann, A., & Pudasaini, S. P. (2015a). An advanced open source computational framework for the GIS-based simulation of two-phase mass flows and process chains. Geophysical Research Abstracts, 17, 3886.Find this resource:

Mergili, M., Kopf, C., Müllebner, B., & Schneider, J. F. (2012a). Changes of the cryosphere in the high-mountain areas of Tajikistan and Austria: A comparison. Geografiska Annaler Series A (Physical Geography), 94(1), 79–96.Find this resource:

Mergili, M., Krenn, J., & Chu, H.-J. (2015b). r.randomwalk, a multi-functional conceptual tool for mass movement routing. Geoscientific Model Development Discussions, 8, 8193–8237.Find this resource:

Mergili, M., Müller, J. P., & Schneider, J. F. (2013). Spatio-temporal development of high-mountain lakes in the headwaters of the Amu Darya river (Central Asia). Global and Planetary Change, 107, 13–24.Find this resource:

Mergili, M., Schneider, D., Worni, R., & Schneider, J. F. (2011). Glacial lake outburst floods (GLOFs): Challenges in prediction and modeling. In R. Genevois, D. L. Hamilton, & A. Prestininzi (Eds.), Proceedings of the 5th International Conference on Debris-Flow Hazards Mitigation: Mechanics, Prediction and Assessment, Padova, Italy, June 14–17. Italian Journal of Engineering Geology and Environment—Book, 973–982. Rome: Casa Editrice Università La Sapienza.Find this resource:

Mergili, M., & Schneider, J. F. (2011). Regional-scale analysis of lake outburst hazard in the south-western Pamir, Tajikistan, based on remote sensing and GIS. Natural Hazards and Earth System Sciences, 11, 1447–1462.Find this resource:

Mergili, M., Schratz, K., Ostermann, A., & Fellin, W. (2012b). Physically-based modeling of granular flows with Open Source GIS. Natural Hazards and Earth System Sciences, 12, 187–200.Find this resource:

Merkl, S. (2015). Gefahrenpotential von Gletscherseen in Tirol unter Betrachtnahme deren räumlicher und zeitlicher Entwicklung (Master’s Thesis). University of Natural Resources and Life Sciences (BOKU), Vienna.Find this resource:

Mock, C. J., & Birkeland, K. W. (2000). Snow avalanche climatology of the western United States mountain ranges. Bulletin of the American Meteorological Society, 81(10), 2367–2392.Find this resource:

Mool, P. K., Bajracharya, S. R., & Joshi, S. P. (2001). Inventory of glaciers, glacial lakes and glacial lake outburst floods. Monitoring and early warning systems in the Hindu Kush-Himalayan Region: Nepal. Kathmandu: ICIMOD.Find this resource:

Moreira, A., Krieger, G., Hajnsek, I., Hounam, D., Werner, M., Riegger, S., et al. (2004). TanDEM-X: A TerraSAR-X add-on satellite for single-pass SAR interferometry. 2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings, 2, 1000–1003.Find this resource:

Morris, M. W., Hassan, M. A. A. M., & Vaskinn, K. A. (2007). Breach formation: Field test and laboratory experiments. Journal of Hydraulic Research, 45(Suppl. 1), 9–17.Find this resource:

Murray, T., Booth, A., & Rippin, D. M. (2007). Water-content of glacier-ice: Limitations on estimates from velocity analysis of surface ground-penetrating radar surveys. Journal of Environmental & Engineering Geophysics, 12(1), 87–99.Find this resource:

Nadim, F., Kjekstad, O., Peduzzi, P., Herold, C., & Jaedicke, C. (2006). Global landslide and avalanche hotspots. Landslides, 3, 159–173.Find this resource:

Nairz, P., & Kriz, K. (2005). Online decision support for avalanche risk management. Proceedings of the International Snow Science Workshop (ISSW), Jackson Hole, Wyoming, USA, September 19–24, 2005, 514–519.Find this resource:

Narama, C., Duishonakunov, M., Kääb, A., Daiyrov, M., & Abdrakhmatov, K. (2010). The 24 July 2008 outburst flood at the western Zyndan glacier lake and recent regional changes in glacier lakes of the Teskey Ala-Too range, Tien Shan, Kyrgyzstan. Natural Hazards and Earth System Sciences, 10(4), 647–659.Find this resource:

Natale, L., Nettuno, L., & Savi, F. (1994). Numerical simulation of snow dense avalanche: An hydraulic approach. Proceedings of 24th International Conference on Modelling and Simulations, 94, 2–4.Find this resource:

Negi, H. S., Thakur, N. K., & Mishra, V. D. (2007). Estimation and validation of snow surface temperature using MODIS data for snow-avalanche studies in NW-Himalaya. Journal of the Indian Society of Remote Sensing, 35(4), 287–299.Find this resource:

Noetzli, J., Huggel, C., Hoelzle, M., & Haeberli, W. (2006). GIS-based modeling of rock-ice avalanches from Alpine permafrost areas. Computational Geosciences, 10, 161–178.Find this resource:

Norem, H. (1995). Shear stresses and boundary layers in snow avalanches. Technical Report 581240-3. Oslo, Norway: Norges Geotekniske Institutt.Find this resource:

O’Brien, J. S., Julien, P. Y., & Fullerton, W. T. (1993). Two‐dimensional water flood and mudflow simulation. Journal of Hydraulic Engineering, 19(2), 244–261.Find this resource:

Oechslin, M. (1938). Lawinengeschwindigkeiten und Lawinenluftdruck. Schweizer Zeitschrift für Forstwesen, 6, 153–160.Find this resource:

Oller, P., Muntán, E., Marturià, J., García, C., García, A., & Martínez, P. (2006). The avalanche data in the Catalan Pyrenees. 20 years of avalanche mapping. Proceedings of the International Snow Science Workshop (ISSW), Telluride, Colorado, USA, October 1–6, 2006, 305–313.Find this resource:

Paulcke, W. (1938). Praktische Schnee- und Lawinenkunde. Berlin: Springer.Find this resource:

Pearson, K. (1905). The problem of the random walk. Nature, 72(1865), 294.Find this resource:

Pellicani, R., & Spilotro, G. (2015). Evaluating the quality of landslide inventory maps: Comparison between archive and surveyed inventories for the Daunia region (Apulia, Southern Italy). Bulletin of Engineering Geology and the Environment, 74(2), 357–367.Find this resource:

Perla, R., Cheng, T. T., & McClung, D. M. (1980). A two-parameter model of snow avalanche motion. Journal of Glaciology, 26, 197–207.Find this resource:

Perla, R. I. (1970). On contributory factors in avalanche hazard evaluation. Canadian Geotechnical Journal, 7(4), 414–419.Find this resource:

Petley, D., & Stark, C. (2012). Understanding the Seti River landslide in Nepal. The Landslide Blog. Retrieved from http://blogs.agu.org/landslideblog/2012/05/23/understanding-the-seti-river-landslide-in-nepal/.Find this resource:

Pfeffer, W. T., Arendt, A. A., Bliss, A., Bolch, T., Cogley, J. G., Gardner, A. S., et al. (2014). The Randolph Glacier Inventory: A globally complete inventory of glaciers. Journal of Glaciology, 60, 537–552.Find this resource:

Pierson, T. C., Janda, R. J., Thouret, J. C., & Borrero, C. A. (1990). Perturbation and melting of snow and ice by the 13 November 1985 eruption of Nevado del Ruiz, Colombia, and consequent mobilization, flow and deposition of lahars. Journal of Volcanology and Geothermal Research, 41(1), 17–66.Find this resource:

Pitman, E. B., & Le, L. (2005). A two-fluid model for avalanche and debris flows. Philosophical Transactions of the Royal Society A, 363, 1573–1601.Find this resource:

Post, A., & Mayo, L. R. (1971). Glacier dammed lakes and outburst floods in Alaska. Washington, DC: U.S. Geological Survey.Find this resource:

Preh, A., & Sausgruber, J. T. (2015). The extraordinary rock-snow avalanche of Alpl, Tyrol, Austria. Is it possible to predict the runout by means of single-phase Voellmy-or Coulomb-type models? Engineering Geology for Society and Territory, 2, 1907–1911.Find this resource:

Prokop, A. (2008). Assessing the applicability of terrestrial laser scanning for spatial snow depth measurements. Cold Regions Science and Technology, 54(3), 155–163.Find this resource:

Prokop, A., Schön, P., Singer, F., Pulfer, G., Naaim, M., & Thibert, E. (2013). Determining avalanche modelling input parameters using terrestrial laser scanning technology. Proceedings of the International Snow Science Workshop (ISSW), Grenoble, France, October 7–11, 2013, 770–774.Find this resource:

Pudasaini, S. P. (2012). A general two-phase debris flow model. Journal of Geophysical Research, 117, F03010.Find this resource:

Pudasaini, S. P., & Hutter, K. (2003). Rapid shear flows of dry granular masses down curved and twisted channels. Journal of Fluid Mechanics, 495, 193–208.Find this resource:

Pudasaini, S. P., & Hutter, K. (2007). Avalanche dynamics: Dynamics of rapid flows of dense granular avalanches. Berlin, Heidelberg: Springer.Find this resource:

Pudasaini, S. P., Wang, Y., & Hutter, K. (2005). Rapid motions of free surface avalanches down curved and twisted channels and their numerical simulation. Philosophical Transactions of the Royal Society A, 363, 1551–1571.Find this resource:

Quincey D. J., Richardson S. D., Luckman A., Lucas R. M., Reynolds J. M., Hambrey M. J., et al. (2007). Early recognition of glacial lake hazards in the Himalaya using remote sensing datasets. Global and Planetary Change, 56(1–2), 137–152.Find this resource:

Ran, Y., Li, X., Jin, R., & Guo, J. (2015). Remote sensing of the mean annual surface temperature and surface frost number for mapping permafrost in China. Arctic, Antarctic, and Alpine Research, 47(2), 255–265.Find this resource:

Ravanel, L., & Deline, P. (2011). Climate influence on rockfalls in high-alpine steep rockwalls: The North side of the Aiguilles de Chamonix (Mont Blanc massif) since the end of the Little Ice Age. The Holocene, 21, 357–365.Find this resource:

Ravanel, L., Deline, P., & Bodin, X. (2015). LiDAR-helped recognition and promotion of high-alpine geomorphosites. Engineering Geology for Society and Territory, 8, 249–252.Find this resource:

Rees, W. G., & Arnold, N. S. (2007). Mass balance and dynamics of a valley glacier measured by high-resolution LiDAR. Polar Record, 43(04), 311–319.Find this resource:

Richardson, S. D., & Reynolds, J. M. (2000). An overview of glacial hazards in the Himalayas. Quaternary International, 65/66, 31–47.Find this resource:

Rickenmann, D. (1999). Empirical relationships for debris flows. Natural Hazards, 19, 47–77.Find this resource:

Rignot, E., Echelmeyer, K., & Krabill, W. (2001). Penetration depth of interferometric synthetic‐aperture radar signals in snow and ice. Geophysical Research Letters, 28(18), 3501–3504.Find this resource:

Robson, B. A., Nuth, C., Dahl, S. O., Hölbling, D., Strozzi, T., & Nielsen, P. R. (2015). Automated classification of debris-covered glaciers combining optical, SAR and topographic data in an object-based environment. Remote Sensing of Environment, 170, 372–387.Find this resource:

Russi, T., Ammann, W., Brabec, B., Lehning, M., & Meister, R. (2003). Avalanche warning Switzerland 2000. In Early warning systems for natural disaster reduction (pp. 569–577). Berlin: SpringerFind this resource:

Sailer, R., Fellin, W., Fromm, R., Jörg, P., Rammer, L., Sampl, P., et al. (2008). Snow avalanche mass balance calculation and simulation model verification. Annals of Glaciology, 48, 183–192.Find this resource:

Saltelli, A., & Annoni, P. (2010). How to avoid a perfunctory sensitivity analysis. Environmental Modelling & Software, 25(12), 1508–1517.Find this resource:

Sampl, P., & Zwinger, T. (2004). Avalanche Simulation with SAMOS. Annals of Glaciology, 38, 393–398.Find this resource:

San, B. T., & Suzen, M. L. (2005). Digital elevation model (DEM) generation and accuracy assessment from ASTER stereo data. International Journal of Remote Sensing, 26(22), 5013–5027.Find this resource:

Sauermoser, S. (2006). Avalanche hazard mapping—30 years experience in Austria. Proceedings of the International Snow Science Workshop (ISSW), Telluride, Colorado, USA, October 1–6, 2006, 314–321.Find this resource:

Savage, S. B., & Hutter, K. (1989). The motion of a finite mass of granular material down a rough incline. Journal of Fluid Mechanics, 199, 177–215.Find this resource:

Schaffhauser, A., Adams, M., Fromm, R., Jörg, P., Luzi, G., Noferini, L., et al. (2008). Remote sensing based retrieval of snow cover properties. Cold Regions Science and Technology, 54(3), 164–175.Find this resource:

Scharrer, K., Spieler, O., Mayer, C., & Münzer, U. (2008). Imprints of sub-glacial volcanic activity on a glacier surface—SAR study of Katla volcano, Iceland. Bulletin of Volcanology, 70(4), 495–506.Find this resource:

Schneider, D., Huggel, C., Cochachin, A., Guillén, S., & García, X. (2014). Mapping hazards from glacier lake outburst floods based on modelling of process cascades at Lake 513, Carhuaz, Peru. Advances in Geosciences, 35, 145–155.Find this resource:

Schneider, D., Huggel, C., Haeberli, W., & Kaitna, R. (2011). Unraveling driving factors for large rock–ice avalanche mobility. Earth Surface Processes and Landforms, 36(14), 1948–1966.Find this resource:

Schweizer, J., Bartelt, P., & van Herwijnen, A. (2014). Snow avalanches. In W. Haeberli & C. Whiteman (Eds.), Snow and ice-related hazards, risks and disasters (pp. 395–436). Amsterdam: Elsevier.Find this resource:

Schweizer, J., Bruce Jamieson, J., & Schneebeli, M. (2003). Snow avalanche formation. Reviews of Geophysics, 41(4), 2.1–2.25.Find this resource:

Shukla, A., Arora, M. K., & Gupta, R. P. (2010). Synergistic approach for mapping debris-covered glaciers using optical-thermal remote sensing data with inputs from geomorphometric parameters. Remote Sensing of Environment, 114(7), 1378–1387.Find this resource:

SLF. (1999–2015). Geschichte des Lawinenschutzes in der Schweiz. Retrieved from http://www.slf.ch/ueber/geschichte/lawinenschutz/index_DE.Find this resource:

Somos-Valenzuela, M. A., McKinney, D. C., Byers, A. C., Rounce, D. R., Portocarrero, C., & Lamsal, D. (2014). Assessing downstream flood impacts due to a potential GLOF from Imja Lake in Nepal. Hydrology and Earth System Sciences Discussions, 11, 13019–13053.Find this resource:

Sovilla, B., & Bartelt, P. (2002). Observations and modeling of snow avalanche entrainment. Natural Hazards and Earth System Sciences, 2(3/4), 169–179.Find this resource:

Sovilla, B., Schaer, M., Kern, M., & Bartelt, P. (2008). Impact pressures and flow regimes in dense snow avalanches observed at the Vallée de la Sionne test site. Journal of Geophysical Research: Earth Surface, 113(F1).Find this resource:

Statham, G., Haegeli, P., Birkeland, K. W., Greene, E., Israelson, C., Tremper, B., et al. (2010). The North American public avalanche danger scale. Proceedings of the International Snow Science Workshop(ISSW), Squaw Valley, California, USA, October 17–22, 2010, 117–123.Find this resource:

Steger, S., Bell, R., Petschko, H., & Glade, T. (2015). Evaluating the effect of modelling methods and landslide inventories used for statistical susceptibility modelling. Engineering Geology for Society and Territory, 2, 201–204.Find this resource:

Stocker-Waldhuber, M., Wiesenegger, H., Abermann, J., Hynek, B., & Fischer, A. (2012). The glacier inventory of Salzburg, Austria, 2007/2009. Supplement to: Stocker-Waldhuber, M., Wiesenegger, H., Abermann, J., Hynek, B., & Fischer, A. (2012). A new glacier inventory of the province of Salzburg, Austria 2007/2009. Zeitschrift für Gletscherkunde und Glazialgeologie, 43(44), 121–128.Find this resource:

Strozzi, T., Luckman, A., Murray, T., Wegmüller, U., & Werner, C. L. (2002). Glacier motion estimation using SAR offset-tracking procedures. IEEE Transactions on Geoscience and Remote Sensing, 40(11), 2384–2391.Find this resource:

Strozzi, T., Wiesmann, A., Kääb, A., Joshi, S., & Mool, P. (2012). Glacial lake mapping with very high resolution satellite SAR data. Natural Hazards and Earth System Sciences, 12, 2487–2498.Find this resource:

Sugden, D. E., Marchant, D. R., Potter Jr., N., Souchez, R. A., Denton, G. H., Swisher III, C. C., et al. (1995). Preservation of Miocene glacier ice in East Antarctica. Nature, 376(6539), 412–414.Find this resource:

Takahashi, T., Nakagawa, H., Harada, T., & Yamashiki, Y. (1992). Routing debris flows with particle segregation. Journal of Hydraulic Research, 118, 1490–1507.Find this resource:

Thibert, E., Baroudi, D., Limam, A., & Berthet-Rambaud, P. (2008). Avalanche impact pressure on an instrumented structure. Cold Regions Science and Technology, 54(3), 206–215.Find this resource:

Thibert, E., Bellot, H., Ravanat, X., Ousset, F., Pulfer, G., Naaim, M., et al. (2015). The full-scale avalanche test-site at Lautaret Pass (French Alps). Cold Regions Science and Technology, 115, 30–41.Find this resource:

Tinti, S., Maramai, A., & Cerutti, A. V. (1999). The Miage Glacier in the Valley of Aosta (Western Alps, Italy) and the extraordinary detachment which occurred on August 9, 1996. Physics and Chemistry of the Earth (A), 24(2), 157–161.Find this resource:

Toutin, T. (1995). Generating DEM from stereo images with a photogrammetric approach: Examples with VIR and SAR data. EARSeL Advances in Remote Sensing, 4(2), 110–117.Find this resource:

Tweed, F. S., & Russell, A. J. (1999). Controls on the formation and sudden drainage of glacier-impounded lakes: implications for jökulhlaup characteristics. Progress in Physical Geography, 23, 79–110.Find this resource:

Vachon, P. W., Geudtner, D., Mattar, K., Gray, A. L., Brugman, M., & Cumming, I. (1996). Differential SAR interferometry measurements of Athabasca and Saskatchewan glacier flow rate. Canadian Journal of Remote Sensing, 22(3), 287–296.Find this resource:

Vallet, J., Gruber, U., & Dufour, F. (2001). Photogrammetric avalanche volume measurements at Vallée de la Sionne, Switzerland. Annals of Glaciology, 32(1), 141–146.Find this resource:

Van Westen, C. J., Van Asch, T. W. J., & Soeters, R. (2006). Landslide hazard and risk zonation—Why is it still so difficult? Bulletin of Engineering Geology and the Environment, 65, 167–184.Find this resource:

Van Zyl, J. J. (2001). The shuttle radar topography mission (SRTM): A breakthrough in remote sensing of topography. Acta Astronautica, 48(5), 559–565.Find this resource:

Vandre, B. C. (1985). Rudd Creek debris flow. In D. S. Bowles (Ed.), Delineation of landslide, flash flood and debris flow hazards in Utah (pp. 117–131). Logan: Utah Water Research Laboratory, Utah State University.Find this resource:

Vilímek, V., Emmer, A., Huggel, C., Schaub, Y., & Würmli, S. (2014). Database of glacial lake outburst floods (GLOFs)—IPL Project No. 179. Landslides, 11, 161–165.Find this resource:

Vilímek, V., Zapata, M. L., Klimeš, J., Patzelt, Z., & Santillán, N. (2005). Influence of glacial retreat on natural hazards of the Palcacocha Lake area, Peru. Landslides, 2, 107–115.Find this resource:

Vincent, C., Descloitres, M., Garambois, S., Legchenko, A., Guyard, H., Lefebvre, E., et al. (2012). Detection of a subglacial lake in Glacier de Tête Rousse (Mont Blanc area). Journal of Glaciology, 58(211), 866–878.Find this resource:

Voellmy, A. (1955). Über die Zerstörungskraft von Lawinen. Schweizerische Bauzeitung, 73, 159–162, 212–217, 246–249, 280–285.Find this resource:

Volk, G. (2005). Handbuch ELBA+. Pressbaum: NiT Technisches Büro GmbH.Find this resource:

Walder J. S., & Costa J. E. (1996). Outburst floods from glacier-dammed lakes: The effect of mode of lake drainage on flood magnitude. Earth Surface Processes and Landforms, 21, 701–723.Find this resource:

Walder, J. S., & O’Connor, J. E. (1997). Methods for predicting peak discharge of floods caused by failure of natural and constructed earth dams. Water Resources Research, 33(10), 2337–2348.Find this resource:

Wang, C., Zhang, H., Zhang, B., Tang, Y., Zhang, Z., Liu, M., et al. (2015). New mode TerraSAR-X interferometry for railway monitoring in the permafrost region of the Tibet Plateau. Geoscience and Remote Sensing Symposium (IGARSS) 2015, Milan, Italy, July 26–31, 2015, 1634–1637.Find this resource:

Wang, Z., & Li, S. (1999). Detection of winter frost heaving of the active layer of Arctic permafrost using SAR differential interferograms. Geoscience and Remote Sensing Symposium 1999 (IGARSS’99) Proceedings, 4, 1946–1948.Find this resource:

Watanabe, T., & Rothacher, D. (1996). The 1994 Lugge Tsho glacial lake outburst flood, Bhutan Himalaya. Mountain Research and Development, 16, 77–81.Find this resource:

WGMS. (2008). Global glacier changes: Facts and figures. UNEP, World Glacier Monitoring Service, University of Zurich, Switzerland.Find this resource:

Wichmann, V., & Becht, M. (2003). Modelling of geomorphic processes in an alpine catchment. In D. Martin (Ed.), Proceedings of the 7th International Conference on GeoComputation, Southampton, September 8–10.Find this resource:

Wieczorek, G. F. (1984). Preparing a detailed landslide-inventory map for hazard evaluation and reduction. Bulletin of the Association of Engineering Geologists, 21(3), 337–342.Find this resource:

Wilson, L., Smellie, J. L., & Head, J. W. (2013). Volcano-ice interactions. In S. A. Fagents, T. K. P. Gregg, & R. M. C. Lopes (Eds.), Modeling volcanic processes: The physics and mathematics of volcanism (pp. 275–299). Cambridge, U.K.: Cambridge University Press.Find this resource:

Worni, R., Huggel, C., Clague, J. J., Schaub, Y., & Stoffel, M. (2014). Coupling glacial lake impact, dam breach, and flood processes: A modeling perspective. Geomorphology, 224, 161–176.Find this resource:

Worni, R., Huggel, C., & Stoffel, M. (2013). Glacial lakes in the Indian Himalayas from an areawide glacial lake inventory to on-site and modeling based risk assessment of critical glacial lakes. Science of the Total Environment, 468–469, 71–84.Find this resource:

Yoshikawa, K., & Hinzman, L. D. (2003). Shrinking thermokarst ponds and groundwater dynamics in discontinuous permafrost near Council, Alaska. Permafrost and Periglacial Processes, 14(2), 151–160.Find this resource:

Zemp, M., Frey, H., Gärtner-Roer, I., Nussbaumer. S. U., Hoelzle, M., Paul, F., et al. (2015). Historically unprecedented global glacier decline in the early 21st century. Journal of Glaciology, 61(228), 745–762.Find this resource:

Zimmermann, M., Mani, P. & Gamma, P. (1997). Murganggefahr und Klimaänderung—ein GIS basierter Ansatz. Zürich: NFP 31 Schlussbericht, Hochschulverlag an der ETH.Find this resource: