Populations and infrastructure in high mountain regions are exposed to a wide range of natural hazards, the frequency, magnitude, and location of which are extremely sensitive to climate change. In cases where several hazards can occur simultaneously or where the occurrence of one event will change the disposition of another, assessments need to account for complex process chains. While process chains are widely recognized as a major threat, no systematic analysis has hitherto been undertaken. We therefore establish new understanding on the factors that directly trigger or alter the disposition for subsequent events in the chain and derive a novel classification scheme and parameters to aid natural hazard assessment. Process chains in high mountains are commonly associated with glacier retreat or permafrost degradation. Regional differences exist in the nature and rate of sequencing—some process chains are almost instantaneous, while other linkages are delayed. Process chains involving rapid sequences are difficult to predict, and impacts are often devastating. We demonstrate that process chains are triggered most frequently by progressive failures, being the result of gradual landscape weakening and not due to the occurrence of a distinct process. If fluvial processes are part of the process chain the reach (or mobility) of process chains is increased. Increased mobility can also occur if sediment deposition areas along river channels are activated. As climate changes causes glacial environments to transform into sediment-rich paraglacial and fluvial landscapes, it is expected that the mobility of process chains will increase in the future.

Key Points

High mountain hazard process chains commonly are associated with glacier retreat or permafrost degradation
Process chains are triggered most frequently by progressive failure, which is a result of long-lasting landscape weakening
An important implication of climate change is that we can expect an increase in the reach (horizontal length along the flow path) of process chain events in the future

Plain Language Summary

Communities and their infrastructure located in mountain regions of the world are particularly susceptible to natural hazards, such as floods and landslides. While these events on their own can be bad enough, when such events occur at the same time or cause a chain-reaction of different processes, consequences can be worse and extend far downstream. In view of this, we have undertaken a first comprehensive review of 51 relevant events occurring across different mountain regions globally to better understand how these process chains develop, and to identify which conditions increase the likelihood and or size of these events. A key finding is that process chains most frequently begin as the result of a mountain slope gradually becoming unstable over time, and in these cases, no other trigger such as an earthquake or unusually wet or hot weather are necessary. Far-reaching process chains occur when rivers are involved and when additional gravel and debris sources are added to the mix. As a consequence of climate warming, icy mountain landscapes are thawing and become more unstable, increasing the risk of such process chains in the future.

1 Introduction

1.1 Mountain Regions and Mountain Hazards

Mountain regions make up 24% of global land area (35.8 million km²; Kapos et al., 2000) and are home to 12% of the world's population (Hock et al., 2019) as well as of 15%–20% of worldwide tourism with an annual turnover estimated to US$ 70–90 billion (UNEP, 2007). Mountains are also home to vital infrastructure including that related to water supply and the generation of hydroelectric power (Farinotti et al., 2019). They are characterized by sharp topographic gradients, resulting in a high local elevation range, distinctive meteorological conditions and—under most circumstances—glacial, paraglacial and periglacial environments which define the mountain cryosphere. Often in combination with enhanced tectonic activity (heaving, earthquakes), mountain regions are home to a wide range of natural hazard processes (Stäubli et al., 2018), including various types of gravitational processes, (rock/debris avalanches, rockfalls, landslides and debris flows), massive glacial detachments, snow avalanches and a range of types of floods generated by heavy rains or lake outburst. As a result, almost 1 billion people who inhabit mountain regions are potentially exposed to or affected by the occurrence of catastrophic processes and the severe impacts that accompany them including fatalities, damage to settlements, and destruction of infrastructure (Hock et al., 2019).

Over the past decade (2012–2022), a number of major disasters with numerous fatalities and/or severe damage on local infrastructure have occurred in the world's mountain regions. Examples include (a) India (2013) where unusually heavy, very early monsoon rainfall in the northern part of India triggered floods and landslides to result in the June 2013 Uttarakhand disaster, claiming more than 5,700 lives (Nair & Singh, 2014); (b) Nepal (2015) where the M7.8 Gorkha earthquake in Nepal triggered about 25,000 landslides, rockfalls and ice avalanches (Fujita et al., 2017; Kargel et al., 2016; Lacroix, 2016). In this event, about 8,200 people died and several villages as well as important infrastructure were destroyed by mass movements (Roback et al., 2018; Zhao, 2016); (c) Salkantay, Peru (2020) where an outburst flood triggered by a rock/ice avalanche caused fatalities in the valley downstream (Vilca et al., 2021); (d) the European Alps (2020) where abnormally heavy rainfall in early October 2020 caused widespread devastation of settlements and infrastructure in the southern French and Italian Alps, with more than 20 killed or reported missing (Carrega & Michelot, 2021; Payrastre & Nicolle, 2021); or (e) Chamoli, India (2021) where a huge rock/ice avalanche rapidly transformed into an extraordinarily large and mobile debris flow (Figure 1). More than 200 people were killed or are missing (Shugar et al., 2021). In addition, a number of large-scale catastrophic events have occurred in the world's mountain regions with less fatalities and/or damage to infrastructure, for example, the twin glacier collapse in Aru Mountains, Tibet in 2016 (Gilbert et al., 2018), the 2020 landslide at Elliot Creek, British Columbia (Geertsema et al., 2022) or a glacier lake outburst flood in 2020 at Jinwuco (Tibet) (Zheng, Mergili, et al., 2021).

Details are in the caption following the image — **Figure 1**
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A real-time perspective of the Chamoli (India) process chain that occurred on 7 February 2021 (a), showing origin of the rockslide and the path of the debris flow captured on a satellite image at 10:30 Indian Standard Time, just before the front of the flow reached the Tapovan Hydropower Project site (CubeSat image reused with permission from Shrestha et al. (2021)). The dust cloud accompanying the flow is clearly visible. The dotted black line shows the sediment and dust deposited in the river valley. The release zone of the rock/ice avalanche is shown in the enlarged image (b) (CNES and Airbus DS, Pléiades image processed by E. Berthier and S. Gascoin and reused with permission). Inset image (c) is a screenshot taken from a video, showing the debris flood around 2 km below the Tapovan hydropower site (shown in a) (reused with permission from Anand Bahuguna).

Detailed knowledge and assessment of natural hazard processes is therefore key when it comes to sustaining livelihoods in mountain regions, limiting exposure, reducing disaster risk and, enhancing adaptive capacity of humans and their infrastructure in mountain ranges worldwide where ongoing and future climate change will likely increase negative impacts (Hock et al., 2019).

Our understanding and the assessment of natural hazard processes in mountain regions has improved substantially over recent decades. An important step was the Sendai Framework for Disaster Risk Reduction 2015–2030 (United Nations Office for Disaster Risk Reduction, 2015). In the Sendai document, the “understanding of disaster risk” is given highest priority. To achieve this aim, it is necessary “to promote comprehensive surveys on multi-hazard disaster risks and the development of regional disaster risk assessments and maps, including climate change scenarios.” These efforts resulted in the development of a suite of assessment methods for catastrophic hazard processes (e.g., Alcántara-Ayala & Sassa, 2021; Sassa et al., 2017), whereupon the focus was more on the individual processes and less on the interactions between different processes. Specifically for the mountain cryosphere, guidelines for the assessment of glacial and permafrost hazards have been produced by a Scientific Standing Group of the International Association of Cryospheric Sciences IACS and the International Permafrost Association IPA (GAPHAZ, 2017). However, major challenges still persist, especially in cases where interactions between these different processes occur, thereby changing the nature, magnitude, and reach (horizontal length along the flow path) of a catastrophic event. Such interactions can be manifold but can be summarized under the term “multi-hazard” (e.g., Kappes et al., 2012; van Westen et al., 2014). In general, a multi-hazard event is characterized by a close spatial and/or temporal proximity of different processes that contribute to a single event and involves the occurrence of multiple processes in a single hazard event. This occurrence of multiple processes in a single hazard event may lead to a greater impact on communities and infrastructure than individual hazards and may reduce resilience and recovery (Liu et al., 2016).

1.2 Definitions of Multi-Hazard

In general, a multi-hazard event is characterized by a close spatial and temporal proximity and a cause-effect relation of multiple processes within a single hazard event. The term “multi-hazard” has been defined in various ways. Kappes et al. (2012) presents 16 different terms for the relations within multi-hazard processes. Following Schauwecker et al. (2019), this term can be assigned to two main types of interactions between single processes:

A single overall driver triggers a similar process in a cluster of events that are independent of each other, yet are nearly coincident in space and/or time. For instance, a heavy rain event may cause flooding and landslides (including debris flows) in a mountain region. An example is the case in August 2005 in the Bernese Oberland (Switzerland), where a large area underwent intense slope and channel processes (Rickenmann et al., 2016). This type of multi-hazard can be termed a compound event (Schauwecker et al., 2019; Zscheischler et al., 2018) (Figure 2).
A hazard process triggers a sequence of processes that are spatially linked in rapid sequence or delayed time. A first example (rapid sequence) involves a rock avalanche entering a lake, which in turn generates a tsunami displacing water downstream to generate a debris flood/debris flow. A second example (delayed time) can be when a landslide blocks a river, which leads to the formation of a lake. A delayed collapse of the dam following overtopping results in a flood wave, which can trigger further landslides due to bank destabilization. For this type of interaction, the terms “cascade effect” or “process chain” are used (e.g., Gilbert et al., 2018; Kirschbaum et al., 2019; Mergili et al., 2020; Schauwecker et al., 2019) (Figure 2).

Gill and Malamud (2016) likewise emphasize that a multi-hazard event can be more than a spatial or temporal overlap of different hazard processes (multi-layer single hazard) because it can also involve the interaction between the different hazard processes.

Liu et al. (2016) present a systematic classification of hazard interactions, differentiating independent, mutual, parallel and serial relationships (Figure 2). Their description of serial relationship coincides with the description of process chains or process cascade in other publications. Pescaroli and Alexander (2015) for instance, give a definition of cascading disasters and cascading effects in which cascades of disasters related to technical systems are described, some of them triggered by natural processes (e.g., tsunamis, floods, storm surges). They distinguish between linear sequences of events and non-linear paths of cascades. While linear interactions can still be analyzed relatively easily, non-linear interactions can trigger network effects that result in complex system behavior, such as feedbacks or bifurcations.

Kappes et al. (2010) distinguish two types of relations between hazard interactions that are crucial in natural hazards management, namely (a) those in which one process triggers near-instantaneously the next (cascades, domino effects) and (b) those in which the disposition of one hazard is altered by another (delayed) (Figure 2). In the case of altered disposition, a longer period normally elapses between the two processes, leaving some time for intervention planning. In situations where one process triggers another process (in a cascading manner or a domino effect), processes follow one another almost instantaneously without leaving time for intervention or emergency measures (Baer et al., 2017; Walter et al., 2020). From a disaster risk reduction perspective, the time sequencing either giving or denying options to reduce harm is key. Is there adequate time for an intervention, such as a self-evacuation or a rescue of threatened people, or an engineered disaster mitigation? In the case of a rapidly progressing event, such as a glacial lake outburst flood (GLOF), if a real-time early warning system exists (quite rare) and the resident population has been trained to respond, then evacuations from a small village may be possible in minutes. Lacking hazard early warning or the required response training, a GLOF or landslide dam-induced flood may effectively be nearly instantaneous. In the case of a landslide blockage of a river, a rescue may take days, and an engineered solution may take place in days to months. Hence, whether a process chain is viewed as near instantaneous or delayed depends on the specific circumstances and what is at risk. However, we avoid a rigid definition of the timeframes of events and responses, as terms like “near instantaneous” and “delayed” may be readily understandable.

1.3 Process Chains and Climate Change in Mountain Regions

We focus on disastrous natural hazards in mountain regions and examine process chains resulting from geomorphic cascades (cf., Schauwecker et al., 2019), where a hazard process triggers a sequence of other processes in a single overall event. We also include linear and non-linear interactions following the concept provided by Pescaroli and Alexander (2015) as well as direct triggering and disposition change according to Kappes et al. (2010). Interactions with technical and/or social systems are not part of our analysis. Therefore, in brief, we define “process chains in mountain regions” as a sequence of natural hazard processes in a single event, where one process transforms in one or more subsequent processes that are spatially linked, and where the transformation occurs either nearly instantaneously or is delayed by changing the disposition of the subsequent processes. Process chains can amplify the impact of the initial event and increase the area affected (e.g., Evans et al., 2021).

Ongoing and accelerating anthropogenic warming (Arias et al., 2021) has led to substantial changes in high-mountain environments (Beniston et al., 2018; Gobiet et al., 2014; IPCC, 2019). Climate change impacts on natural hazards can occur through changes in the amount or peak intensity of precipitation, changing balances of the physical state of precipitation (snow-to-rain ratio), by increased melting rates of ice and snow, initiation of basal melting of glaciers or introduction of surface melt or rainwater to glacier beds, development of glacial lakes, thawing of permafrost with melting of ground ice, or by changes in vegetation. Since these individual climate change effects normally do not occur in isolation, multiple climate change effects can be active. The hazard responses can involve a change in hazard intensity or frequency (increasing or decreasing), a shift in their location to higher elevation or a different aspect on a mountain range, a shift from one kind of hazard to another, or a change in the linkages within hazard process chains. The impacts of these climate change related influences on hazard activity are further modulated by changing exposure to hazards, including increases in human populations and infrastructure in mountain regions (Cremen et al., 2022; Rimal et al., 2015; Tellman et al., 2021; Thornton et al., 2022).

Atmospheric warming has well-documented impacts on snow, ice and permafrost, and this is altering the frequency, magnitude and location of related natural hazards in the mountain cryosphere (Hock et al., 2019) and thus, is expected to have increased the probability for process chains to occur (Shugar et al., 2021) and to increase their complexity. The number and area of glacier lakes has increased in most mountain regions of the globe (Shugar et al., 2020), and changes in the decadal occurrences of GLOFs has oscillated (Harrison et al., 2018), but it remains uncertain whether the frequency of GLOFs has increased over the longer term as is widely supposed (Harrison et al., 2018; Veh et al., 2019, 2022). Snow avalanches involving wet snow have increased in several regions and mostly at higher elevations (Ballesteros-Cánovas et al., 2018; Stoffel & Corona, 2018), whereas snow avalanches have become much less frequent at lower altitudes (Castebrunet et al., 2014; Giacona et al., 2021). As a consequence of increases in temperature and snowfall limits, rain-on-snow floods have increased in winter but have started to decrease in spring (Freudiger et al., 2014). With ongoing climate change rain-induced snowmelt floods will likely become more frequent at high elevations in winter over decades to come (Beniston & Stoffel, 2016; Morán-Tejeda et al., 2016). Such floods may cease to be a hazard in some lower elevation regions, though thunderstorm cloudburst type runoff floods may increase in those areas.

Evans and Clague (1994) provided examples from the Canadian Cordillera and the Andes where climate change has likely altered the disposition (readiness or susceptibility (S. Allen et al., 2022)) of natural hazard processes in the periglacial and glacial domain. In their Special Report on EXtreme events and disasters (SREX; Field et al., 2012), the IPCC did not take process chains explicitly into account, whereas the IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (IPCC, 2019) discussed process chains explicitly, including the effect of climate change on GLOF processes (Harrison et al., 2018; Huggel et al., 2020; Shugar et al., 2020; Zheng, Mergili, et al., 2021) and increasing landslide hazards (Gruber & Haeberli, 2007; Stoffel et al., 2014).

1.4 Objective of the Study

Despite the recent inclusion of process chains in IPCC's climate change assessments, progress made in the field as well as the substantial body of literature existing on individual case studies of process chains, no attempt has been made so far to analyze process chains systematically with the aim to provide an overview of the characteristics of process chains in the high mountain environment. As such, approaches to hazard assessment still primarily focus on individual processes, and there is no consolidated understanding of the factors that promote the development of far-reaching process chains, and how such events may evolve as icy mountain landscapes continue to warm. The objective of this study, therefore, is to fill this gap by (a) assembling a broad list of process chains resulting from geomorphic cascades from high mountain regions across the world. This is based on a broad literature search. We then (b) typify events by applying a classification scheme containing both characteristics and parameters of process chains. For this the information from literature and additional sources is stored in a database. Based on the classification, we (c) derive a series of parameters that are relevant for natural hazard assessment, and (d) highlight the relevance of climate change for the different process chains discussed. Finally, (e) we show which further efforts are necessary to increase awareness of process chains. As high mountain environment we consider the area that is defined as “high mountains” in the K3 landform classification (Karagulle et al., 2017).

2 Classification Approach

2.1 Classification Scheme

Based on information from the broad literature search, a classification scheme for the characterization of process chain was defined. It consists of a set of 26 parameters that are grouped into 8 classes (Table 1). The parameters are selected in terms of their relevance for natural hazard assessment, their availability in the literature, or the possibility of deriving them from globally available spatial datasets. The classes are described in more detail in the following chapters. The classification scheme is the basis for the event database that is part of the supplementary material.

Table 1. Classification Scheme for the Characterization of Process Chain Events

Class	Parameters
Spatial context	Region (glacier region from GTN-G)
	Location (mountain, valley, region)
	Coordinates (lat/lon)
Processes involved	Triggering process that initiates the process chain
	Initial process of the process chain
	Sequence of subsequent processes (cf., box with relevant processes)
Process interaction	Direct triggering of secondary processes (yes/no)
Process interaction	Increasing disposition for secondary processes (yes/no)
Starting zone parameters	Elevation of starting zone (meters above sea level (m asl))
	Local elevation range (meters radius)
	Seismicity (peak Ground Acceleration (PGA))
	Lithology (lithological classification)
	Glacier (on glacier/glacier just below/glacier below within 0.5 km/glacier below within 2.0 km/no glacier)
	Permafrost (classes of permafrost zonation index (PZI))
	Stream order of basin
Amplifying effects	Snow entrainment (yes/no)
	Ice entrainment (yes/no)
	Fine grained loose material (yes/no)
	Fluvial processes involved (yes/no)
	Lakes involved (yes/no)
Volume	Initial volume (m³ (order of magnitude))Total volume mobilized (m³ (order of magnitude))
Trajectory parameters	Average slope (degree)
	Reach (kilometers)
	Large retention areas (yes/no)
Potential impact of climate change	Existing effect on the event (amplifying/indifferent/diminishing)
Potential impact of climate change	Future influence (by 2,100) (amplifying/indifferent/diminishing)

Note. For the different aspects (classes) of process chains, distinct characteristics (parameters) are defined.

2.2 Spatial Context

The location where a process chain occurs is essential information. Beside the precise location given by the geographic coordinates, and the local name of the location, the events are assigned to one of the GTN-G Glacier Regions (Global Terrestrial Network for Glaciers, 2017). The aim of the GTN-G is to make glacier data easily accessible to a broader audience (Gärtner-Roer et al., 2019). As GNT-G covers all high mountain regions of the whole Earth and is digitally available it is used here for the geographic classification of the events. The elevation can be used within a regional classification.

2.3 Processes Involved in Process Chains

Related to process chains three types of processes are distinguished here: (a) triggering processes initiating a process chain but are not part of the process chain, (b) initial processes representing the first process in a process chain, and (c) subsequent processes in a process chain. Whereas geomorphic phenomena operating on hillslopes and in channels constitute the most common initial and subsequent processes, triggering processes can also include tectonic and hydro-meteorological phenomena. The nature of the processes involved determines under which conditions a process can occur and—accordingly—a process chain can develop. The boundary of the system under consideration is the Earth surface and atmosphere without the oceans. As such, we do not consider tsunamis caused by landslides into fjords. A more detailed description of the involved processes is given in Text S1 of Supporting Information S1.

For the definition and visualization of the process chains considered in this study we utilize the matrix proposed by Gill and Malamud (2014). As the focus of this review is on mountain regions, the selection of processes is limited to seven gravitational and hydrological processes, that can be part of a process chain (Figure 3). In addition, four tectonic and hydro-meteorological processes are defined as external triggering processes that are not part of a process chain. However, not all process chains are triggered by distinct processes, but are the result of long-term landscape weakening processes that lead to progressive failure.

2.4 Process Interactions

The timespan between the initial or antecedent and the subsequent process covers a large range. In some cases, as for example, in the Pizzo Cengalo-Val Bondasca (Swiss Alps) event 2017, one process (the debris flow) in the process chain is directly triggered by the antecedent process (the rockslide). In other cases, the initial process creates conditions for secondary processes in the chain. The delay until the subsequent process occurs can be days, months, or years. In the Hattian Bala case (Kashmir), for example, a landslide dam that was emplaced by a rock avalanche which was triggered by an earthquake in 2005, partly breached in 2010 (Dunning et al., 2007; Konagai & Sattar, 2012).

The relation between single processes constituting a process chain is fundamental for natural hazards management as it will change the nature, magnitude, probability of occurrence and threat of a natural hazard process in areas at risk. In case of direct triggering of the subsequent process in a chain, very little time remains for administration and/or first responders to put (organizational) protective measures into place. On the other hand, where the initial process does not directly trigger a subsequent process, but its disposition is changed, this can be addressed with the implementation of protective measures or other response strategies.

In this study we define a timeframe from seconds to 1 day for direct triggering. A disposition change or delayed response is presumed if the subsequent process in a chain starts within days to a few years. We do not consider processes in a chain occurring over more than 10 years.

2.5 Starting Zone Characteristics

The disposition of the processes involved in process chains depends on a multitude of environmental properties. The key properties listed in Table 2 are used for the characterization of the starting zones, provided that this information is deducible from the literature.

Table 2. Spatial Distribution of Process Chain Events in the Database

Continent	Glacier region	Events	With detailed description
Asia	Himalaya	11	3
	Hissar Alay	1	1
	Karakoram	1	0
	S and E Tibet	2	0
	Tien Shan	4	0
Europe	Alps	10	3
Europe	Caucasus	3	1
North America	Coast Ranges	9	1
South America	Andes	9	3
Oceania	New Zealand	2	0
Total		51	12

While the elevation of the starting zone itself is not a determining factor for the formation of process chains, it does allow to derive the relevant altitude range for the origin of process chains occurring in a given region. The relative relief or local elevation range (LER) calculated between the highest and the lowest point within a predefined radius around the starting point is an indication of the gravitational forces and thus for the gain of energy in the starting zone. Where LER is large, high potential energy transforms beside kinetic energy to heat, due to friction between components. In the case of rock/ice avalanches this can melt the ice and cause high mobility of the mobilized material (Shugar et al., 2021). Furthermore, high gravitational forces can cause a massive shattering of rock which generates loose debris that can be easily mobilized. LER values are calculated from the SRTM 30 m DTM with a vertical accuracy of 4.31 m (±14.09 m) in mountain regions (Uuemaa et al., 2020). For each starting point of a process chain, the range between the highest and the lowest point within a radius of 0.5 km, 1.0 and 5.0 km is derived.

The lithology of rock and sediments is, in combination with the types and orientations of discontinuities, a determining factor for slope stability. In this study, the basis for lithological characterization is the global lithological map database GLiM (Hartmann & Moosdorf, 2012b), provided in the Living Atlas of ESRI. Meanwhile, the effect of seismicity is twofold. On the one hand, it can trigger processes or even process chains (Evans et al., 2009; Kargel et al., 2016; Lacroix, 2016; Roback et al., 2018), on the other hand, seismicity can gradually destabilize rock bodies and thus increase the disposition for failure over time (Evans et al., 2009; Gischig et al., 2016; Kargel et al., 2016). Seismic characterization is based on the GSHAP global seismic hazard map available in the Global Seismic Hazard Assessment Program (Shedlock et al., 2000). The map shows peak ground acceleration with a probability of 10% to be exceeded in 50 years. Values are classified with regard to the perceived shaking; for this the Modified Mercalli scale is applied (Petersen et al., 2018).

Regarding the cryosphere, glacier retreat and permafrost degradation reduce the stability of rock walls, colluvial deposits, and sediments. Debuttressing effects on rock slope stability, glacial deposits and sediment supply, induced by glacier thinning, are described by Stoffel and Huggel (2012), Evans et al. (2021), Cody et al. (2020) or Mancini and Lane (2020). Steep glacier tongues can lose their stability due to a decrease in basal friction, promoted by increased meltwater supply (Faillettaz et al., 2012). In addition, glaciers can have an amplifying effect on rock and rock/ice avalanches (see Section 2.6). In permafrost, ice-filled joints are common in fractured bedrock. In this way ice acts as cement and contributes to rock mass strength. If ice warms, this results in a higher disposition for rock falls and rock avalanches (Draebing et al., 2014; Gruber & Haeberli, 2007; Hasler et al., 2012). Beside the disposition, permafrost degradation also contributes to the magnitude of mobilized material in rock walls (Krautblatter et al., 2013; Tapia Baldis & Trombotto Liaudat, 2019). In colluvial and glacial deposits, permafrost degradation results in an increased thickness of the active layer. This in turn augments the availability of loose material for debris flows (Damm & Felderer 2013; Stoffel et al., 2014). Data on permafrost occurrence rely on the permafrost zonation index (PZI) Map (Gruber, 2012) and index values between 0 (permafrost exists only in the most favorable conditions) and 1 (permafrost nearly everywhere) are classified following Boeckli et al. (2012). The spatial resolution of one km² is quite coarse compared to relief variability in mountain areas, but it is the most detailed permafrost map available, with worldwide coverage.

From a hydrological perspective, the stream order, according to Strahler (1957), describes the position of a stream channel within the hierarchy of a river network. Associated with the stream order are specific patterns of geomorphic and hydrological processes. The stream order is calculated from a river network derived from a hydrologically corrected flow directions data set based on SRTM digital elevation models. The flow direction data are part of the HydroSHEDS product (Lehner et al., 2008).

2.6 Amplifying Effects

The reach (or horizontal length along the flow path) of process chains can be increased owing to the presence of favorable cryosphere and geomorphic conditions. Both snow and ice reduce the basal friction of rock avalanches and thus enlarge their reach (Deline, 2009; Schneider et al., 2011; Sosio et al., 2012). Entrained snow and ice can also reduce the friction within the moving mass (Sosio, 2015). This may also be the case for debris flows, where ice and snow entrained in the debris flow melts due to the heat that develops (Iverson, 1997). Thus, the shear stress exceeds the threshold given by the resistance to erosion. A larger volume can lead to an increased reach and to a higher impact (Evans et al., 2021; Huggel et al., 2005; McSaveney, 2002; Schneider et al., 2011; Sosio, 2015).

Likewise, the availability of fine-grained loose material in a travel path leads to process chains that exhibit a greater travel distance and higher velocity. This is due to (a) higher erodibility and as a result to (b) higher volume and beside that to (c) lower internal friction during movement (Pierson, 2005). Sources of this material are glacial, lacustrine and fluvial deposits, disaggregated rock, generated by seismic or volcanic impact as well as shattered rock in cases of grate fall height of rock falls or rock avalanches.

If slope processes such as slides, rock avalanches or debris flows are connected to river channels, fluvial processes can displace big volumes of sediments over large distances, as water as a transport medium can transport loose material over flatter areas, than is the case with granular slope processes. The distance depends on the gradient of the receiving river channel (Rickenmann et al., 2016). This leads to an enhanced reach of process chains if they include fluvial transport. High fluvial discharge can destabilize the riverbed and so mobilize large volumes of sediment thus increasing the length of the process chain (Cook et al., 2018). The presence of lakes can boost but also dampen the reach of process chains. Lakes dammed by moraines or landslides can release high volumes of water if the dams break, (a) caused by volume reduction by massive but gradual material input into the lake or (b) by displacement waves, triggered instantaneously by slides or rock avalanches (Byers et al., 2019; Emmer et al., 2020; Konagai & Sattar, 2012). In bedrock dammed lakes, flood waves triggered by landslides can still trigger a GLOF, but full drainage of the lake is less likely. On the other hand, if the surface of a lake is large compared to the sediment input from debris flows or fluvial transport, and if dam freeboards are sufficient, this will dampen or even arrest a process chain.

2.7 Volume

The volume involved is an important parameter to assess the potential impact of a process chain. For the classification the initial volume and the total volume mobilized of a process chain are collated. Both the initial volume and the total volume can cover a large range from less than 1 million (e.g., ice or rockfall) to several tens of million cubic meters (e.g., massive ice or rock avalanche). The ratio of the total relocated volume to the start volume or entrainment ratio within a process chain can also be very different between process chains and is indicative of the amplification processes occurring (Hungr & Evans, 2004).

2.8 Trajectory Characteristics

Trajectory characteristics are a further class of parameters for the classification of process chains. Relief controls the potential energy that is inherent in hazard processes. Hereinafter the parameters used in the classification are described.

In this study, reach is defined as the horizontal distance of a process chain along the trajectory. Depending on the processes involved in a process chain and the relief characteristic, the reach may have a large range (from a few to hundreds of km) and is therefore an important parameter to estimate potential impacts.

The affected area can further be defined by the Fahrböschung or average slope. This parameter means the average slope between the highest point of the starting area and the distal limit of the effects of a process chain. The horizontal distance is measured along the cascade trajectory. This parameter is widely used for the characterization of rock and/or ice avalanches (Alean, 1985; Deline et al., 2011; Geertsema et al., 2006; Schneider et al., 2011), but can also be used to characterize a process chain as a whole (Guthrie et al., 2012).

Finally, extensive low gradient areas along the path of a process chain can function as retention areas, where material is deposited temporarily or indefinitely (Rickenmann et al., 2016). Due to the low gradient, remobilization of the deposited material is limited; substantial loss of material in low gradient areas can lead to a reduced length of a process chain. As far as available, we derived the information from literature. Otherwise, we analyzed the situation using GoogleEarth imagery.

2.9 Climate Change Impacts

The impact of climate change on the current and future development of natural hazards and related process chains is essential for natural hazard assessment. Over recent decades, temperature has increased in mountain regions, while precipitation changes are less well quantified (M. R. Allen et al., 2018). Glacier, snow and permafrost decline have altered the frequency, magnitude and location of most related natural hazards and are projected to change further (Hock et al., 2019). The impact of climate change on process chains can be manifold. It can affect both the individual processes and the interaction between the processes. The effect can enhance or diminish the occurrence and scale of process chains. For example, atmospheric warming can increase the potential for ice avalanches due to the basal warming of hanging glaciers (Faillettaz et al., 2011; Huggel, 2009). However, if hanging glaciers have melted away this potential no longer exists (Evans et al., 2021). On the other hand new starting zones for rock avalanches are exposed (S. K. Allen et al., 2011; Fischer et al., 2006). Overall, as glaciers retreat and permafrost thaws, an increasing potential for process-chains involving rock or ice avalanches into expanding and more numerous glacial lakes is anticipated (Haeberli et al., 2017; Zheng, Allen, et al., 2021). Furthermore, changes in precipitation patterns can alter the disposition and triggering of slope processes (Stoffel et al., 2011). Paranunzio et al. (2016) and S. Allen and Huggel (2013) have shown that rockfall events can in many cases be associated with temperature anomalies, although deeper and larger volume failures are complicated by delayed responses to changes in atmospheric conditions (Evans et al., 2021). Two aspects are addressed in this study: (a) The existing effect of climate change on the event (i.e., due to effects of warming over the past century), and (b) the possible future influence of climate change over the next up to the end of the century. As event descriptions in the literature typically provide only limited information regarding climate change impact on the respective process chain, we estimated the further development (glacier, glacier lakes) based on Google Earth imagery. Main criteria are the existence and shape of glaciers and the existence of glacier lakes. Climate projections for the end of this century are derived from the IPCC WG I Interactive Atlas (Iturbide et al., 2021).

3 Description of Process Chains

To characterize process chain events, we conducted a comprehensive literature review. This resulted in 51 events that occurred in high mountain regions and for which the majority of the parameters for characterization could be derived. The parameters according to Table 2 for these events are stored in the database, which is available in the SI. The events cover all continents of the world except Africa and Antarctica (Table 2) and the latitude from 43.4°S to 54.4°N. In the database most events originate from the Alps, the Himalaya, the Andes and the Coast Ranges. This not only represents the occurrence of process chains in the regions but the availability of literature on these events that provides sufficient information to complete the database.

Of these 51 events, we select and describe 12 events in greater detail in the following paragraphs. These are examples from various regions of the World and illustrative of the characteristics of different types of process chains. For some of these events, publications exist to which we refer readers to. Where this is not the case, we provide a more extensive description.

The examples cover all triggering processes, progressive failure as well as all geomorphic processes either as initial or subsequent processes, as shown in Figure 4. Examples cover all GTN-Regions which are represented in the database except Karakoram, Tibet and New Zealand. The starting zones cover altitudinal ranges comprised between 900 and 7,000 m asl. Eight process chains link one initial with a subsequent process, while four have two or more subsequent processes. Thereby, fluvial processes comprise multiple processes such as hyperconcentrated flow, debris flood or fluvial sediment transport. Ten of the process chains described in detail show a direct linking of the initial and the subsequent processes while three show a delayed reaction. Six of the events claimed victims, and in four cases more than 100 fatalities, therefore underlining the threat posed by such events in the absence of any significant warning time.

3.1 Earthquake as Triggering Process

3.1.1 Huascaran 1970

The Cordillera Blanca in Peru is the most heavily glacierized tropical mountain range worldwide. It stands above the densely populated valley of the Callejón de Huaylas, the vertical drop exceeding 4,000 m over a horizontal distance of approx. 20 km in some places. The risk of glacial disasters is therefore high, with most documented events attributed to GLOFs (Carey et al., 2012; Mergili et al., 2018; Schneider et al., 2014). Two of the most notable disasters in recent history, however, evolved from rock-ice falls at the west face of Huascarán Norte (6,654 m asl.) in 1962 and 1970. Both events have been extensively described and analyzed (e.g., Evans et al., 2009; Ghiglino Antunez, 1971; Mergili et al., 2018). There was no obvious trigger for the 1962 event, whereas the 1970 event was directly caused by an earthquake that was also highly destructive by itself, creating a multi-hazard situation (Evans et al., 2009).

The 1970 earthquake-triggered fall dropped over a vertical distance of approximately 2 km before impacting a steep glacier. Glacier ice, firn and snow, and huge amounts of paraglacial debris were entrained, resulting in high-energy debris-ice avalanches (Figure 5). An initial volume of 7.5 × 10⁶ m³ (80% rock, 20% ice) increased to approx. 66 × 10⁶ m³, according to estimates derived by Evans et al. (2009).

The 1970 event, more energetic due to its larger volume compared to 1962, developed extremely dynamic behavior, including ballistic travel of particles over distances of kilometers and an estimated maximum velocity in exceedance of 100 m/s (Evans et al., 2009) (Figure 4a). The average velocity was in the range 50–85 m/s. Back-calculating this event with numerical simulation software required the decrease of friction parameters to almost zero in order to achieve empirically adequate results (Mergili et al., 2018). Most of the mass passed through the Shacsha Valley and spread over the debris fan of Ranrahirca. It converted into a distal mud flow/debris flood propagating all the way down to the Pacific Ocean at a distance of 180 km. The most notable feature, however, was the fact that part of the mass (an estimated volume of 3.6 × 10⁶ m³) left the Shacsha Valley by overtopping a more than 100 m high ridge, the Cerro de Aira. The town of Yungay at the other side of this ridge was completely destroyed and buried in debris and ice. The number of fatalities was initially given with >20,000, a number which turned out to be unrealistically high in the re-analysis of Evans et al. (2009). The real number of fatalities might have been around 6,000 in this event. Few inhabitants of Yungay escaped the event, some of them by climbing on Cemetery Hill (Figure 5b).

The 1962 and 1970 events at Nevado Huascarán demonstrated the importance of topographic sensitivities in high-mountain process chains: overtopping of Cerro de Aira in the 1970 event led to an increase in the number of fatalities by approximately one order of magnitude, compared to 1962. This phenomenon highlights the challenges of simulation-based hazard mapping, as relatively minor misestimates of initial conditions or uncertain flow parameters might lead to completely wrong predictions (Mergili et al., 2018).

3.1.2 Langtang 2015

On 25 April 2015, the Mw 7.8 Gorkha earthquake struck Nepal, causing thousands of landslides over a broad swath of the Lesser and Greater Himalaya in Nepal and Tibet (Collins & Jibson, 2015; Kargel et al., 2016; Roback et al., 2018; Shrestha et al., 2016). The most devastating mass movements hit Langtang Valley, north of Kathmandu. The valley had several small villages whose economic mainstay was logistical support of trekkers and mountaineers. About 350 people were killed or left missing in Langtang Valley. The worst event impacted directly on the village of Langtang (Kargel et al., 2016). Other mass movements also affected the valley (Kargel et al., 2016; Lacroix, 2016; Roback et al., 2018).

A careful study of the Langtang Valley landslides by Lacroix (2016) used the best available satellite imaging and derived topography. Those results show 160 landslides and avalanches in the Langtang Valley in the year between before/after SPOT stereo image sets. Nearly all of the mass movements are thought to have been co-seismic. Most were small and inconsequential, and most originated at low elevations just above the valley floor. However, the five largest mass movements clustered along the Langtang ridgeline (above 6,800 m elevation) up to Langtang Lirung Peak (7,200 m elevation); they coalesced and together destroyed Langtang village (Lacroix, 2016). Four of the five large events were likely co-seismic with the main shock. Digital Globe (now Maxar) images indicate that the final of these largest mass movements happened several weeks after the main shock (Kargel et al., 2016).

The largest events each started as snow and ice avalanches, with most of the initial mass comprised of snow derived from anomalously thick snowpack at the time of the earthquake (Fujita et al., 2017). The avalanches picked up rock mass and then went airborne over a cliff, freefalling more than a kilometer before crashing on the valley floor, producing powerful snow-ice-and-debris-laden air blasts (Figure 6). The air blast leveled and scoured almost every structure in Langtang that was not buried under the avalanche. Thus, part of the village was buried; part was blown away. Burial depths, according to Lacroix (2016) reached 60 m, but most areas had a thinner snow-ice-rock avalanche cover of just a few meters, and many areas were scoured but not covered at all, as field photos show (Kargel et al., 2016). Blown-away buildings were generally constructed of stone slab. The landslide air blast also flattened a small forest and stripped branches and leaves on the opposing valley wall, suggesting wind speeds comparable to an Enhanced Fujita Scale EF5 tornado (>322 km hr⁻¹), according to Kargel et al. (2016). Three nearby settlements, Singdum, Mundu, and Kyangjin, were also damaged by avalanche air blasts.

“Before” and “after” photos of Langtang village and the valley taken by mountaineer and cinematographer David Breashears in 2012 and 2015, reported in Kargel et al. (2016), illustrate the destruction of Langtang. Just one hotel, though damaged, escaped complete destruction due to its unique location under a protective bedrock overhang (Figure 6).

Breashears's photos showed that the deposit initially contained snow and ice in addition to rock debris. Landsat 8 thermal imaging on 30 April 2015 showed depressed temperatures of the debris surface apparently caused by melting ice (Kargel et al., 2016). The ice-rock avalanche reached the Langtang River, but the stream soon incised it, so impoundment was minor.

Devastation in the air-blast zones indicates the huge energy involved. Kargel et al. (2016) made a guess at the Langtang landslide mass using an assumed thickness and estimated the energy release from the final freefall phase of descent as ≥3.2 × 10¹³ J (7.6-kiloton TNT equivalent), which begins to explain the devastation. However, Lacroix (2016) produced a measured thickness distribution and made a better volume estimation—about four times greater. Hence, the energy involved in the final freefall phase was roughly twice that of the Hiroshima atomic bomb explosion.

The conditions needed for a repetition of the Langtang event still prevail. The extreme depth of the snow cover prior to the 2015 disaster has been estimated to exceed a 100-year return period (Fujita et al., 2017) although such events may become more frequent at high elevation under a wetter future climate. Nonetheless the simultaneity and large sizes of multiple avalanches probably (but not definitively) require a seismic trigger, while weakened peri- and para-glacial slopes provide an abundant source of rock and debris that can be entrained into a mass of snow and ice.

3.2 Volcanic Eruption as Triggering Process

3.2.1 Nevado del Ruiz, Cordillera Central, Colombia 1985

Nevado del Ruiz is the northernmost glaciated volcano of the Andean chain. It reaches an elevation of 5,320 m asl and is a composite stratovolcano with a flat summit and a glacial ice cap. At 9:09 p.m. on 13 November 1985, Nevado del Ruiz ejected volcanic particles more than 30 km into the atmosphere (Pierson et al., 1990; Voight, 1990). The total mass of the erupted material was 35 × 10⁶ tonnes (i.e., only 3% of the amount that erupted from Mount St. Helens in 1980). The eruption reached 3 on the 8-stage Volcanic Explosivity Index, giving it a classification of “moderate.” Despite the relatively modest size and short duration of the eruption, the devastating lahar of water, mud and debris traveling more than 100 km downstream led to one of the deadliest volcanic disasters in history (Huggel et al., 2007).

The key to this event was the interaction between volcanic activity and the snow and ice of the glaciated upper slopes of Nevado del Ruiz (Pierson et al., 1990) (Figure 7). While the 1985 eruption lasted only 20–90 min, it contributed to a reduction in the glaciated area of the summit by 16% from around 25 to around 20.8 square kilometers (Thouret et al., 2007). In terms of volume, the overall loss of snow and ice was estimated to be 60 × 10⁶ m³, which was about 10% of the pre-eruption total (Thouret et al., 2007). The most significant loss of snow and ice occurred where high-energy pyroclastic flows violently scoured or removed the snow and fractured ice that detached from steep-sided glaciers. In contrast, the loss was lower on glaciers with gentle slopes, where tephra was passively deposited, even though the temperature of the deposit exceeded 500°C initially. This has been seen also on other glaciated volcanoes, with melting resulting from violent pyroclastic flows estimated to be at least 10 times greater than melting under static depositions (Seynova et al., 2017). Other eruption-induced effects observed at Nevado del Ruiz included tunnel formation at the margins of the glacier. A striking pattern of parallel gullies emerging from these tunnels was the result of internal drainage within the glacier, which, once established, enhanced the transfer of heat and meltwater. During the eruption, these drainage tunnels collected pyroclastic material and increased in size, allowing hotter meltwater to be drained toward the margins of the glacier. It is thought that this influx of warm meltwater generated by the eruption enhanced melting at the sides of the glacier and probably led to the detachment of glacier ice from the surrounding bedrock, leading to further instability and ice avalanches that contributed additional mass to the downstream lahars.

Watery flows and avalanches originating from the glaciated summit area of the volcano rapidly transformed into devastating lahars following entrainment of both volcanic and glacial sediment within the downstream river valleys (Pierson et al., 1990) (Figure 7). Some of this sediment would have contained water, and surface water from rain that fell throughout the evening of the eruptions would have further added to the volume of the lahars. Two to three main waves of mud and debris were reported, with depths of up to 30 m (Voight, 1990). The total volume of lahars reaching downstream areas was around 90 × 10⁶ m³. Peak velocities ranged from 5 to 15 m per second, and a peak discharge of 48,000 m³/s was recorded at a distance of 9 km downstream from the source. It took slightly over 1 hr for the lahars to travel 50 km downstream wiping out several villages, while the town of Armero was reached in a little over 2 hr. Most of the devastation was focused in Armero, where up to 75% of the residents were killed following the arrival of the lahars shortly before midnight (Figure 7). The most distant areas affected by the lahars, more than 100 km away from the source, were reached in about 4 hr (Huggel et al., 2007; Pierson et al., 1990).

3.3 Falls as Initial Processes

3.3.1 Cengalo-Bondasca, Switzerland, 2011 and 2017

At Pizzo Cengalo, a 3,369 m high summit located at the upper end of the Bondasca Valley (Canton of Grisons, Switzerland), two massive rock slope failures occurred on its NE face. A first event was recorded in December 2011, a second event took place in August 2017 (Walter et al., 2020), with both developing into rock avalanches. Assessment after the first event showed that the start zone was largely covered by a thick permafrost ice layer (Wilhelm et al., 2019).

Despite the quite comparable initial conditions and comparable starting zone elevations, distinct differences exist between the process chains that developed as a result of these two rock slope failures. According to Baer et al. (2017), the 2011 rock slope failure mobilized 1.5–1.7 × 10⁶ m³ of rock material at an altitude of 3,100 m asl; the rock masses traveled down to 2,200 m asl in the form of a rock avalanche and were deposited in the upper part of the Bondasca Valley. As the event occurred in winter, the rock avalanche entrained substantial amounts of snow with an estimated water equivalent of the entrained snow of 7–15 × 10⁴ m³. In this case, snowmelt in spring increased the water saturation of the rock avalanche deposits, leading to the triggering of four debris flows in summer 2012 with the biggest event in August 2012 reaching the village of Bondo (Baer et al., 2017).

Ice slabs in the detachment zone provide clear evidence of the critical role of permafrost in the starting zone of the process chain. Also, debris flows were not triggered directly by the rock avalanche, but the rock avalanche increased the availability of material in the starting zone of debris flows and thus increased the disposition for them to occur. The disposition was amplified further by the snow that was entrained by the rock avalanche.

During the second event in August 2017, c. 3 × 10⁶ m³ of rock detached below the starting zone of the 2011 rock avalanche (Mergili et al., 2020). On its way down the rock avalanche entrained c. 0.6 × 10⁶ m³ of glacier ice. The sudden addition of melted ice favored the immediate conversion of part of the rock avalanche into a debris flow (Figure 8) which reached the village of Bondo. Six additional surges were observed the same day and obviously developed out of the freshly deposited, saturated rock avalanche deposit. Two more debris flows developed in the days following the initial process chain, leaving a total of 5–8 × 10⁵ m³ of material in the area of Bondo. In addition to the liquefaction of parts of the rock avalanche material by the melted ice, Walter et al. (2020) suggested that much water was also stored in the deposits of the 2011 rock avalanche that were overrun by the 2017 event and that the dynamic loading of sediments could have resulted in excessive pore pressure, therefore triggering the debris flows immediately after the rock avalanche (Mergili et al., 2020; Walter et al., 2020).

By contrast to the December 2011 event, the August 2017 rock avalanche resulted in an immediate triggering of debris flows. Both events had starting zones in permafrost, even if only small ice patches were visible in the starting zone of the 2017 rock slope failure, with many wet patches in the back scarp. In both cases, a majority of the entrained rock avalanche material was retained in the upper part of the Bondasca Valley, whereas the narrow gorge in the lower part of the valley system promoted sediment transport by debris flows.

Climate change will likely enhance the occurrence of such process chains as a result of the anticipated degradation of permafrost. By contrast, the gradual disappearance of glaciers is not expected to alter the situation and/or process dynamics in this specific case. Even if opinions diverge between experts, it is possible that the entrained glacier ice neither led to an increased runout distance nor was it responsible for the immediate triggering of debris flows (Walter et al., 2020).

3.3.2 Kolka, Republic of North Ossetia–Alania, 2002

In the evening of 20 September 2002, a complex catastrophic mass movement occurred in the Genaldon Valley, Caucasus Mountains, Republic of North Ossetia-Alania, Russian Federation. The Kolka glacier snout with a volume of more than 100 × 10⁶ m³ (Kääb et al., 2021), detached from the bed and traveled 19 km downstream. A narrow gorge stopped the ice mass but a debris flow with a volume of 3–5 × 10⁶ m³ (Evans et al., 2009) traveled 17 km farther down to stop just 2 km above the settlement of Gizel with a population of c. 5,000 people. A total of 135 people perished (Kortiev et al., 2009). The event has been described and analyzed in many papers, although processes involved are debated (Drobyshev, 2006; Evans et al., 2009; Haeberli et al., 2004; Huggel et al., 2005; Kääb et al., 2021; Kotlyakov et al., 2004).

According to initial studies, a massive rock slope failure occurred on the slope of Mt. Dzhimarai-Khokh, falling onto the surface of Kolka glacier, leading to its instantaneous detachment (e.g., Haeberli et al., 2004; Huggel et al., 2005). However, later assessments of satellite images and eyewitness reports evidenced that abnormal slope failures already started to occur from the northern flank of Mt. Dzhimarai-Khokh during summer 2002 and were probably triggered by a low-magnitude earthquake on 14 July 2002 with an epicenter near Mt. Dzhimarai-Khokh (Drobyshev, 2006; Evans et al., 2009). The volume of rock and ice that were falling onto the glacier were estimated at about 15 × 10⁶ m³. During this period of mass-movement activity, the glacier bulged and became heavily crevassed, developing a differential vertical displacement and partial dislocation of the Kolka Glacier ice mass indicating its response to ice and rock debris loading (Evans et al., 2009; Kääb et al., 2021). It is possible that this overloading of the Kolka glacier might have disrupted internal drainage of the glacier, thereby leading to the development of excess water pressures at its base (Evans et al., 2009). High geothermal fluxes, the impact energy of rock and ice falls, as well as a positive precipitation anomaly might have further contributed to excess water underneath the glacier as well (Evans et al., 2009; Kotlyakov et al., 2004). The basal shear stress is hypothesized to have increased until it exceeded a frictional threshold given by the glacier bed material, topography, and hydraulic conditions (Kääb et al., 2021).

The flow velocity of the mass reached 65–80 m/s during the first 19 km and reduced significantly thereafter (up to 10–20 m/s) when the mass was transformed into a debris flow. In addition to immediate partial transformation of the glacier detachment into a debris flow, the ice masses that were deposited in the Karmadon depression dammed a tributary river. A number of lakes formed with 3 × 10⁶ m³ of water stored in the largest lake (Popovnin et al., 2003). Saniba village was partially flooded and the threat of an outburst flood was significant. Luckily, lake drainage was slow, also thanks to the relatively low temperatures at the end of October.

3.3.3 Seti River, Nepal, 2012

On 5 May 2012, a flash flood crashed out of an intermontane basin, the Sabche Cirque (Figure 9), nestled between the Annapurna peaks north of Pokhara (Nepal's second largest city). Conditions were partly cloudy and dry (Dwivedi & Neupane, 2013). Flowing from this high basin, the flood reached small villages and Pokhara's northern outskirts, where damages and casualties occurred on the lowest terraces and in the Seti riverbed (Figure 10). More than 70 people were killed (Nibanupudi et al., 2015); homes, bridges, and agricultural land were destroyed. Casualties included children recreating on a nearly dry riverbed, gravel workers, tourists, and villagers in their homes, shops, and farms on the lowest terrace. Location details determined life or death (Figure 10e).

Satellite imagery, resident videos, and post-event air- and ground-based inspections constrain the process chain, while leaving some elements unclear. An air tour plane's pilot and video camera caught early glimpses of a turbulent “brown cloud” within the Sabche Cirque. Simultaneous seismic data (Dwivedi & Neupane, 2013) indicated that a large avalanche of ice and rock began while the brown cloud was billowing and collapsing, then continued for 70 min. Half an hour later, the mass movement at Kharapani (20 km from the source) had transformed to a flood-like debris flow, then further downstream, it became a hyperconcentrated flow. Satellite and airborne images revealed that the debris field (Figure 10b) traces toward the Annapurna ridgeline—seen also in an airborne photo by Dixit (2014). A missing ridgeline cornice disappeared, narrowly bracketed around the time of the disaster, suggesting that an ice avalanche initiated near 6,900 m (Figure 10a). The process chain left an ice-rock avalanche field ∼2,500 m lower (Figure 10b); layered above that—emplaced by the same overall mass movement—a darker, maybe rockier landslide is visible in a Landsat 7 image (Figure 10b).

Maxar images (Google Earth) reveal stripped alpine vegetation, and field photos show a downed forest of large trees blown over with root balls ripped out. Maxar also shows movement of some large boulders—up to 30 m across—during a 1-year interval between “before” and “after” images (Figures 10e and 10f); and formation of erosional rills (2–5 m wide) likely due to prompt mud runoff on landslide-impacted surfaces (Figure 10f). The mass movement involved somewhere around 15 × 10⁶ m³ of rock and ice (Petley, 2012).

Evolving flow vectors are difficult to trace. In Figure 9b the flowlines are conceptual over different phases of the mass movement. To obtain the distribution of rock, ice, and mud, and the directions of downed trees and pattern of mud-plastering of boulders, much material must have collapsed from a debris cloud during the mass movement until it transitioned to a surface-based debris-flow and slurry flood.

Residents' and travelers' mobile-phone videos, some still widely available on the internet, give important constraints on the avalanche/debris flow processes and properties. Where the mass flow reached the Seti barrage (north side of Pokhara), the dimensions of built structures and flow tracing of trees and other floating debris seen in an eyewitness video allowed a flow discharge estimation of 2.5 × 10⁵ m³ of water and suspended or floating debris and ice during the first couple of minutes after the flood reached that site (Kargel, 2014). The leading edge of the flood transported huge masses of large floating tree trunks and branches, which apparently were derived from the avalanche-blown forest (Figure 10c). The violence of the event in the lower forested part of the Sabche Cirque is evident from the upturning of tree root balls, complete defoliation, and removal of most branches from 1-m-diameter tree trunks (Figure 10d). Below roughly 3,500 m elevation, protruding objects such as boulders and vegetation, where not displaced, were plastered on one side by a calcium carbonate and phyllosilicate-rich mud. The mud plaster—typically 2 cm thick, plus and minus, may total ∼4 × 10⁵ m³ over the plastered area of the Sabche Cirque. Hence, at that elevation, there already was abundant liquid water. The debris flow flood starting from Kharapani (elevation 1,250 m asl) and lower was made of similar mud, but millions of cubic meters of it with more water, and some large pieces of ice, according to eyewitnesses.

The first news reports (The Hindu, 2012) labeled this event a GLOF. Satellite imagery made immediately clear that no significant glacial lake existed in the Seti basin. The lack of a big lake required a search for liquid water sources: possibly wet snow, supraglacial ponds, landslide-dammed lakes hidden in gorges, water in subglacial lakes or caverns, and water produced by conversion of gravitational potential energy to kinetic energy and then heat to melt ice. Preconditioning by filling of a landslide-impounded lake in a deep glacier-fed river gorge may have been important. Conceptually, all of these sources might have contributed to transform the event from an initial avalanche of ice and rock, into a far-reaching flood (Kargel et al., 2014).

3.4 Slides as Initial Processes

3.4.1 Phuktal River, Zanskar Himalaya, India, 2014

On 31 December 2014, a large landslide (2.4–2.6 × 10⁶ m³) blocked the Phuktal river (also known as Tsarap river) in what was then the state of Jammu and Kashmir, Northern India, leading to the formation of a 17.2 km long lake (Martha et al., 2017). Subsequent breaching of the lake dam 127 days later on 7 May 2015, resulted in a debris-rich flood, but transitioning to pure water flood that then extended over a much larger distance down to the confluence with the Indus, leading to extensive damage to the bridges, culverts, buildings and land downstream. While the evacuation of 3,000 people downstream of the lake prevented loss of life, the event is representative of the type of cascading process chain associated with the formation and breaching of a landslide dammed lake (LLOF). The description below primarily draws on the remote sensing based analyses undertaken by Martha et al. (2017), while some direct field observations have been reported by the Indian National Disaster Management Authority (NMDA).

The landslide into the Phuktal river originated as a translational movement from within the tectonically active Zanskar synclinorium, which comprises of nappes and several imbricate thrusts (Fuchs & Linner, 1995). The lithology of the area consists of a weak sedimentary sequence (carbonate, limestone, shale, and sandstone) resting on top of the Higher Himalayan Crystalline rocks. At least three older landslide dammed lakes are observed within 10 km of the 2014 site, highlighting the propensity for these processes to occur in the region (Martha et al., 2017).

The 2014 debris landslide initiated from an elevation of around 4,100–4,400 m asl, sliding a length of around 600–800 m into the river valley below where the flow was obstructed by the adjacent valley wall. Distinct tension cracks were observed above the crown of the landslide, where maximum slope angles are up to 45°. Pre- and post-landslide terrain modeling revealed the landslide deposit extended over a width of 630 m across the valley, had an area of around 9 × 10⁴ m², with a maximum height of the dam being 69 m (Martha et al., 2017). The landslide dam was classified as being of type III (after Costa & Schuster, 1988), whereby the depositing material was located both upstream and downstream of the initial point of impact into the valley. Analyses of two distinct depositional lobes on the downstream side of the dam were interpreted as consisting of a fine-grained initial slide of talus and scree, overlaid by a secondary, near-simultaneous slide of larger blocky material originating from further up the slope. Given the lack of significant rainfall or any seismic activity, there seems no obvious trigger for this event. The long-term undercutting by the Phuktal river likely played a role in destabilizing the steep, and geologically unfavorably structured slopes.

Following the damming of the river, the width of the lake near the blockage increased to a maximum of 160 m by March 2015, with the lake volume estimated at up to 30 × 10⁶ m³. Subsequent blasting and dredging efforts coordinated by NDMA led to the creation of a 2 × 2 m channel through the dam, allowing surface water to flow. However, the channel proved insufficient and unstable, and with the increase in outflow resulting from snow and ice-melt in early May, the dam catastrophically breached (Martha et al., 2017).

3.4.2 Meager Creek

Long complex process chains are also initiated on rock slopes in glacierized dissected Quaternary volcanic centers, such as the Mount Meager Volcanic Complex (Garibaldi Volcanic Belt) in southwestern British Columbia (Friele et al., 2008; Jordan & Slaymaker, 1991). Here we describe three examples from within the Meager Creek watershed that occurred over an approximate elevation range of 2,250 m (2,625–375 m asl). On 22 July 1975 a complex series of landslide events took place at Devastation Glacier near Pemberton, British Columbia when approximately 13 × 10⁶ m³ of altered Quaternary volcanic rock and glacier ice slid from the west flank of Pylon Peak within the Mount Meager volcanic complex (Figure 11; Petrakov et al., 2020). The rockslide formed a long complex process chain as it quickly transformed into a high-velocity debris flow, consisting of glacial ice and disaggregated pyroclastic rocks, which continued down Devastation Creek valley. The overall length of the flow path was 7 km and its vertical height was 1,220 m, yielding a Fahrböschung of 10°. Stability analysis of the initial 1975 failure suggests that the 1975 rockslide was the result of a complex history of glacial erosion, loading and unloading of the toe of the slide mass caused by the Little Ice Age advance and subsequent retreat of Devastation Glacier (Figure 11). In early August 2010 a mass flow occurred in Capricorn Creek, a tributary to the main Meager Creek (Guthrie et al., 2012). It involved initial failure of the western flank of Mount Meager in Pleistocene rhyodacitic volcanic rock which mobilized additional volcanic material below; rapid evacuation of the entire flank ensued and the initial rock slope failure was transformed into a massive high-velocity ice-rock debris flow (Figures 12a and 12b; Guthrie et al., 2012; Roberti et al., 2017, 2018). The disintegrating mass traveled down Capricorn Creek at an average velocity of 64 m/s, exhibiting marked super-elevation in bends, to the intersection of Meager Creek, 7.8 km from the source. At Meager Creek the debris impacted the south side of Meager valley causing a runup of 270 m above the valley floor (Iverson et al., 2016) and the deflection of the landslide debris both upstream (for 3.7 km), and downstream into the Lillooet River valley (for 4.9 km) where it blocked the Lillooet River (Figure 12b) for some hours, ca. 13 km from the landslide source (Figure 12b). Deposition at the Capricorn-Meager confluence also dammed Meager Creek for about 19 hr creating a lake 1.5 km long. The volume of the initial displaced mass from the flank of Mount Meager was estimated to be 53 × 10⁶ m³ (Roberti et al., 2018), the height of the path (H) to be 2,183 m and the total length of the path (L) to be 12.7 km. This yields H/L = 0.17 and a Fahrböschung of 9.8° (Guthrie et al., 2012). Initial failure involved a slope that had been debuttressed by twentieth century glacial retreat (Holm et al., 2004; Roberti et al., 2018).

The two examples from the Quaternary Mount Meager Volcanic Complex show that in glacierized dissected volcanic centers steep topographic gradients combine with geomechanically weak materials and glacier retreat to create conditions for large-scale mass failure that frequently form long complex process chains. Initial failure is driven by glacier ice-loss and pre-failure slope deformation (Bovis, 1990; Holm et al., 2004) that progressively weakens the moving rock mass until catastrophic failure ensues (Roberti et al., 2018). During motion the initial failure volume is increased by entrainment of material in the path of the flow. Mass flows in this complex can dam rivers and create transient debris-dammed lakes which represent temporary stored energy fundamental to understanding complex process chains in this geomorphic environment.

3.5 Flows as Initial Processes

3.5.1 Gastern Valley, Switzerland, 2011

The Gastern Valley is the uppermost part of the Kander River catchment (Bernese Oberland, Switzerland). It is affected by steep relief and permafrost degradation, resulting in a high intensity of hazard processes. On 10 October 2011, an extraordinary event hit this region involving several debris flow channels in which about 8 × 10⁵ m³ of material was eroded and deposited in the valley bottom. It is noteworthy that the main part of the material was eroded from the higher parts of the cones (Figure 13b), something that obviously did not occur in the last few centuries or even millennia. The material from these channels blocked the receiving Kander River at several places (Figure 13a). Just below the uppermost blockage the Kander River shows massive widening and degradation. The next flatter stretch was blocked by deposits of a debris flow from the Märbegggrabe (about 3 × 10⁵ m³). Behind this, most of the eroded material was deposited. Large deposits of fine material show that for some time a lake existed (Figure 13c). The vast cone of the Märbegggrabe was not eroded but overflown. Relieved from sediment the Kander degraded its riverbed in the next steeper stretch again (Figure 13d). The eroded material was then deposited in the following flatter stretch. Here, meandering led to lateral erosion (Figure 13e). The sum of these processes led to a reshaping of the landscape of the upper Gastern valley.

Not only the changes in landscape but also the triggering of these processes was extraordinary. During 3 days before the event, heavy snowfall far down into the valleys occurred. At 2,000 m asl, about 50 cm snow depth was measured, at 2,500 m asl about 100 cm. In the afternoon of October 9, the temperature rose rapidly. This was caused by an atmospheric river that brought very warm air and a lot of humidity into the area. Together with strong winds this led to fast moisture penetration into the snowpack (Badoux et al., 2013). There are indications that as a result, snow slides were released from the steep rock faces and that a lot of snow accumulated in the area of the steep gullies in the cirques. This is confirmed by modeling the possible snow slides applying the GIS based mass conserving approach proposed by Gruber (2007).

Shortly before midnight rainfall started whereupon the zero-degree level was at about 3,000 m asl. Simulation results from a study carried out for this event indicate that for about 5 hr the liquid water was almost completely retained in the thick cold snowpack (Mani et al., 2014). Würzer et al. (2016) show that in cases where initial snow cover conditions are mostly cold and dry, water retention in snow cover is considerably higher than in conditions where snow cover is isothermal as is the case in spring. Increasing saturation of the snow cover leads to compaction and to destruction of the initial snow matrix with the result that storage capacity decreases (Wever et al., 2014). The consequence of this is that even more water is released from snow cover. Figure 14a shows this effect: from 4 a.m. the curve of water available for runoff (WAR) becomes steeper and increasingly faster than the precipitation at the IMIS (Intercantonal Measurement and Information System) station Fisi. Simulation results show that roughly 10 mm of water was released every hour from the snow cover between 7 a.m. and 1 p.m. (Mani et al., 2014).

Eyewitnesses say that the first debris flow mixed with snow in the Märbegggrabe occurred at about 9 a.m., followed by many more till about 1 p.m. (Figure 14b). Within this time roughly 3 × 10⁵ m³ of material were deposited on the cone (Figure 14c). In the Märbegggrabe catchment the WAR sum over this period is 5 × 10⁴ m³ what results in an extremely high bulk density of 2.4 kN/m³. This suggests that the surges should have stopped on the steep terrain. However, the surges extended beyond the existing cone. The slope of the cone diminished from 15.3° to 14.8°. The hypothesis for this is that the debris flows not only consisted of debris and water but also entrained snow. This can lead to a twofold effect: (a) the incorporated snow reduces the friction and, as a result, leads to a greater reach; and (b) thermal effects in the debris flow as described by Iverson (1997) add additional water to the debris flow which means that the power of erosion is kept high (Hungr et al., 2005) down to the apex where erosion cross section of 600 m² were observed and half of the total debris flow volume was eroded (Figure 14b).

Badoux et al. (2013) show that rain-on-snow events in fall, as seen in this case, are not common at higher elevation. Such events show a different, more complex behavior compared to rain-on-snow events in spring. With increasing temperatures related to global warming such events may occur more frequently as a deep snowpack will still exist at higher elevations, while rainfall up to high elevations may become more frequent (CH2018, 2018).

Further effects of climate warming that were relevant are the glacier retreat and the permafrost degradation. Most of the starting zone of the debris flows in the Gastern valley were either until recently covered by firn or glacier ice or are in the region of permafrost degradation. Comparable situations are widespread in the Alps (Figure 14d).

3.5.2 Giétro, Switzerland, 1818

On 16 June 1818 in the Bagnes Valley (Valais, Switzerland) the most significant GLOF in Switzerland during the last centuries occurred. It is also one of the world's most documented GLOFs (Ancey et al., 2019). Already in the sixteenth and seventeenth century at least three flood events occurred, two of them very likely related to GLOFs (Raymond et al., 2003).

The climate cooling provoked by the Tambora eruption in Indonesia in April 1815 (Brönnimann & Krämer, 2016) accelerated the glacial advance. In the Bagnes Valley this had the consequence that the glacier front of the Giétro glacier came to lie on the escarpment above the valley and the activity of ice avalanches increased. Subsequently a regenerated glacier developed in the valley. Initially, the Drance River found its path underneath the ice dam, but creeping ice caused the passage to shrink progressively (Ancey et al., 2019). In spring 1818 the regenerated glacier grew rapidly, reaching a volume of about 10 × 10⁶ m³ (Lambiel et al., 2020). The ice dam was 600 ± 100 m wide at its base and 90 m high on the lowest part, opposite of the Giétro glacier (Ancey et al., 2019). This resulted in a blockage of the Drance River and a lake developed behind the ice dam (Figure 15). By early May 1818 the estimated volume of the lake reached 5 × 10⁶ m³ and it was continuing to fill quickly with water resulting from snowmelt. From 10 May to 13 June 1818, the lake's depth increased by 22 m (Ancey et al., 2019).

This was perceived by the local population as the runoff in the Drance River became vanishing small in late April 1818. They informed the authority of the Canton Valais which evaluated the risk posed by the lake and to propose countermeasures. Authorities feared that the lake surface would reach the top of the ice dam and spill over it, so it was decided to dig a tunnel 23 m below the top of the ice dam through which the water would flow, when it reaches the tunnel. Thermal and mechanical erosion would then lower down the tunnel floor which would gradually empty the lake. The tunnel was finished on June 13 after about 1 month of work (Ancey et al., 2019).

In the following days, about 10 × 10⁶ m³ were released through the tunnel and the water level decreased by c. 14.6 m. However, strong erosion at the base of the ice dam as well as water infiltration through the ice and sediment below the dam provoked a dam failure during which 18 × 10⁶ m³ of water were released within only 30 min.

The resulting flood was reconstructed by compiling both historical sources and geomorphological observations (Lambiel et al., 2020). Ancey et al. (2019) used numerical models to analyze the drainage of the lake and the propagation of the flood down the Drance Valley. The calculated peak discharge was 14,500 m³/s and the frontal velocity 5–6 m/s (Ancey et al., 2019). 1.5 hr after the dam break the flood reached the town Martigny, 33 km away from Giétro, at the confluence with the Rhone River. Here the peak discharge was about 3,500 to 5,000 m³/s. Five hours later the flood reached Lake Geneva, about 40 km away from Martigny.

In the uppermost part of the course the water level was about 8 m above the upper edge of the 30 and 25 m deep gorges (Lambiel et al., 2020). At Champsec, 11 km below the ice dam and Le Chable, 15 km below the ice dam, the flood simulation of Ancey et al. (2019) results in a flow depth of 13 m while the geomorphological analysis resulted in 6 m flow depth (Lambiel et al., 2020). A flow depth of 2–3 m was calculated for Martigny (Ancey et al., 2019). The 1818 Giétro flood ranks among the most intense recorded floods for this range of volume, regardless of the failure scenario (Ancey et al., 2019).

The extreme flood led to massive material displacements along the flow path. The DEM-analysis of Lambiel et al. (2020) shows large deposition areas at three wider sections along the Drance River. In narrower sections, lateral erosion was dominant. Large boulders were transported about 11 km downwards (Lambiel et al., 2020). The high intensity of these processes led to extensive damage. Hundreds of buildings were destroyed and 34 people died (Lambiel et al., 2020).

This event occurred during a period when glaciers advanced. Thereby the snout of the Giétro glacier became unstable as it became positioned on a steep slope. This led to an ice dammed lake that consisted of a regenerated glacier produced by avalanching ice (Lambiel et al., 2020), what is rather uncommon today, but was more likely during the Little Ice Age, when glaciers advanced. Nevertheless, unstable glacier tongues are not limited to advancing glaciers. Glacial recession can cause glaciers to recede to terminate on steep relief and their tongues become unstable. This was the case, for example, with the Allalin glacier catastrophe that occurred in 1965 (Faillettaz et al., 2012). So, climate warming may create new situations where glacier tongues become unstable, resulting in new ice dammed lake—at least as long as glaciers still exist.

3.6 GLOFS as Initial Processes

3.6.1 Tronador, Argentina, 2009

Mount Tronador (3,480 m asl) is the highest peak in Nahuel Huapi National Park and straddles the border between Chile and Argentina in northern Patagonia. Its summit is covered by an ice cap from which 11 outlet glaciers (64 km²) flow into several valleys and down to 950 m asl. On the south-eastern flank of Mount Tronador, Ventisquero Negro—meaning black glacier due to a thick debris layer covering large portions of its ice—flow into Rio Manso valley (Figure 16) (Villalba et al., 1997). Ventisquero Negro is constrained by a massive terminal moraine with a fork-like shape, forming the Rio Manso outlet at its lowermost point (Figure 16).

On 21 May 2009, the Ventisquero Negro terminal moraine breached to produce a lake outburst flood devastating the Rio Manso Valley (Figure 16). Over a period of 6 days prior to the GLOF, the nearby (16 km) Lake Mascardi meteorological station recorded unusually heavy rainfall (170 mm), whereas daily temperatures remained at their long-term average (Ø 7°C). Both the length and the intensity (50 mm in the 48 hr preceding the GLOF) of the rainfall event were among the highest reported for the station (Worni et al., 2012). According to that study, these rainfalls would have increased the level and outflow of the glacier lake. As a further consequence, ice blocks originating from glacier calving were uplifted and subsequently floated toward the outlet. The increasing lake level and lake outflow most likely induced the moraine failure, as other triggering mechanisms could be excluded due to the absence of traces of mass-wasting events impacting the lake. Worni et al. (2012) also presented alternative scenarios that could have led to dam failure and breach formation at Ventisquero Negro: According to the authors it is also possible (a) the unusually high lake level could have helped stranded ice blocks to drift toward the outlet where they plugged the outlet, ponding the lake to an even higher level and increasing water pressure further to abruptly wash away the ice blocks and exceed the critical shear stress for erosion. As an alternative trigger, Worni et al. (2012) consider that (b) the rising water level of the lake could have resulted in increased hydrostatic pressure on the dam, which ultimately could have led to instability of the ice core abutting the moraine through an uplift caused by the hydrostatic gradient, by partially breaking apart, or by gradual melting as a result of water infiltration.

Using field evidence and the dynamic dam-break model BASEMENT (Faeh et al., 2011), Worni et al. (2012) modeled the breach trigger mechanism, breaching and lake emptying processes as well as the dynamics of the outburst flood. They demonstrate that the lake volume of c. 10 × 10⁶ m³ was released within 3 hr, resulting in high-discharge (c. 4,100 m³/s) debris flows and hyperconcentrated flows as the escaping water entrained large volumes of clastic material.

Levees with boulders of up to 6 m in diameter indicate that the outflowing water entrained moraine sediment to form a debris flow over a distance of c. 350 m downstream of the breach mark, from where the GLOF flowed for 150 m as a sediment-laden flow. The surge again evolved into a debris flow to deposit material on relatively flat terrain (slope c. 2°). Maximum grain sizes and levee heights decrease with increasing flow distance and terminal lobes are found c. 1,000 m below the breach. Total approximate debris-flow volume was assessed at c. 25 × 10⁴ m³, corresponding roughly with the volume of eroded moraine material. The GLOF propagated downstream as a hyperconcentrated flow to become a stream flow before entering Mascardi Lake. Over a distance of 10 km the outburst flood changed the flood plain morphology significantly by eroding entire forest stands and destroyed the bridge and parts of the road to Pampa Linda, while a camp ground and houses were partly flooded. Downstream of Pampa Linda the channel of Rio Manso was able to accommodate most of the released lake water.

3.6.2 Shakhimardan, Kyrgyzstan, 1998

On 8 July 1998, the deadliest GLOF in Central Asia for at least the last 100 years occurred in the Shakhimardan catchment, Kyrgyzstan. Most of the >100 victims were, however, killed in the Uzbek enclave of Shakhimardan, that is, in the downsteam part of this transboundary catchment. Due to political tensions, access to the site was impossible and a detailed assessment of the disaster was realized just in recent years. There were no published reports on the event, so almost all data is taken from Petrakov et al. (2020).

The lake at the origin of the Shakhimardan event appeared in the 1960s, reached its maximum area with 32.2 ± 0.7 × 10³ m² in 1976 and drained periodically, without causing damage to downstream areas. The total area of the lake before the 1998 disaster was estimated at 20 ± 1.2 × 10³ m², the volume of the lake was assessed as 120–133 × 10³ m³.

The Shakhimardan event started with a GLOF which then triggered a suite of chain reactions. The GLOF was triggered by a heat wave after a period of relatively cold weather. Most probably, the englacial drainage system did not adapt to rapidly increasing ablation and the lake has been overfilled. The GLOF transformed quickly into a debris flow dominated by coarse-grained components and traveled for approximately 17 km down the valley before the flow transformed into a debris flood between 17 and 37 km downstream. Farther downstream, and over ∼60 km away from the source, field evidence points to a transformation of the debris flood into a flash flood.

The rapid transformation of the GLOF into a debris flow started at the lower end of the moraine complex where the GLOF was forced to pass through a narrow river section due to the presence of two rock glacier lobes; as a result, intense erosion roughly estimated to 100 m³/m occurred at this segment of the channel. Erosion zones are mostly located in the headwaters where bed and bank erosion was favored by steeper slope angles in the main channel with a mean weighted channel angle in the erosion zones of 6.7°. This value is significantly lower comparatively to international literature where debris-flow erosion is described to be important for cases in which channel gradients exceed 8° (e.g., Huggel et al., 2004; O'Connor et al., 2001). Due to considerable entrainment of material along its path, the resulting volume of accumulated debris was estimated as 0.80 × 10⁶ m³. This value does not include sediments transported out of the study zone by the flash flood. Water was added to the flow by numerous tributaries with significant meltwater discharge as well as by entrainment of water-saturated soils. Debris flow discharge at a distance ca. 10 km from the source was assessed as 600 m³/s.

Transformation of the debris flow to a debris flood happened within a very flat (1.2°) and long (about 2 km) section of the valley bottom, after the confluence with Ikedavan and Akbek rivers. Mean channel angle along the debris flood path was 2.2°. Flood discharge reduced due to sediment deposition and wave flattening. In Shakhimardan it has been assessed as 150–200 m³/s. Further downstream the debris flood transformed into a flash flood, and no significant depositions were found in the lower Shakhimardan settlement.

Transformation of a GLOF to debris flow or debris floods is typical for Central Asia including Kazakhstan (Medeu et al., 2016; Yafyazova, 2007), Kyrgyzstan (Erokhin et al., 2018; Narama et al., 2010, 2018; Zaginaev et al., 2019), and Tajikistan (Mergili & Schneider, 2011) as they move down steep terrain covered with abundant loose sediments. The volume of initial outbursts could increase up to 17 times during the stage of debris flow as happened in July 1973 during the GLOF and disatrous consequent debris flow in the Malaya Almatinka river near city of Almaty (Yafyazova, 2011). The average slope from the GLOF source to the Medeo dam where debris flow has been stopped was 8.8° which is 1.5 times higher than for the Shakhimardan event with an entrainment ratio of 8. Typical regional entrainment ratio for chain GLOF—debris flow is significantly smaller (Erokhin et al., 2018; Medeu et al., 2016).

Shakhimardan event thus might be considered as a “worst-case,” especially taking into account the extremely low channel angle of the debris flow path and the whole process chain.

3.7 Toward Idealized Process Chains

Based on the case studies presented in Section 3, two idealized process chain models emerge.

Catastrophic water release events (floods, lahars, GLOFs) commonly associated with an external trigger (heavy rainfall, snowmelt, volcanic eruption), that directly transform into debris flows and mudflows following immediate entrainment of loose debris and sediment (Sections 3.2.1, 3.5.1, 3.6.1, and 3.6.2).
Catastrophic mass movement events (typically rock-/ice avalanches), triggered by earthquakes (Sections 3.1.1 and 3.1.2) or where there is often no discernible trigger (Sections 3.3.1, 3.3.3, 3.4.1, and 3.4.2), and numerous phase changes occur downstream from debris flows through to floods. GLOFS may be initiated as a direct secondary process, while delayed secondary events can be associated with valley blocking and lake formation.

To further illustrate the legitimacy of these two models of idealized process chains, the well-documented Kedarnath (Figure 17) and Chamoli events occurring within the Indian Himalayan state of Uttarakhand are analyzed below. The intention here is to demonstrate how in a similar geotechnical context, two very different process-chains can develop in line with the aforementioned idealized models, with a particular focus on the reported role climate change could have had in enhancing the likelihood or magnitude of these events.

Following the idealized process chain model (1), the catastrophic water release from a small lake (0.4 × 10⁶ m³) dammed at the side of Chorabari Glacier, just 1.2 km upstream from the village of Kedarnath, was triggered by extremely heavy rainfall which overwhelmed the limited capacity of the seasonal lake (Das et al., 2015; Singh et al., 2014). Unusually rapid snowmelt during the month preceding the early onset of the monsoon rainfall, and a limited amount of debris eroded from the surrounding slopes likely also contribute to high water levels in the lake (S. K. Allen et al., 2015). Early morning on June 17, the combined volume of melt-water, runoff and debris accumulating in the lake led to catastrophic breaching and erosion of the lateral moraine of the glacier, producing a devastating debris flow (Figure 17a). Debris from the surface of the glacier and within steep channels (>20°) upstream of the villages provided an additional source of volume (S. K. Allen et al., 2015). Kedarnath was destroyed, and as the debris flow transformed downstream into a hyperconcentrated flow and flood along the Mandakini river, further villages were damaged, and access into the valley was cut off (Figures 17b and 17c). Unlike typical proglacial lakes that are in contact with their glacier, the size of Charobari lake dammed at the side of the glacier had remained essentially stable over many decades (Dobhal, Gupta, et al., 2013). However, climate warming and associated lowering of the adjacent glacier has increased the height of the moraine dam, and removed lateral support, with 15 m of vertical ice loss observed from 2003 to 2010 alone, and up to 0.072 km² of loose, steep moraine has been uncovered by glacial retreat over the past half-century (Dobhal, Mehta, et al., 2013). In addition, climate change was assessed to have increased the probability of the rainfall event that ultimately triggered the process chain (Singh et al., 2014). Hence, climate change had both increased the likelihood of the lake breaching over time, and amplified the volume of erodible debris within the downstream path (S. K. Allen et al., 2015). Given the full depth of the breach that occurred, the lake cannot refill, meaning a repeat event from this source cannot occur. However, climate change is increasing the threat of a similar cascade process initiating from supraglacial lakes that are enlarging on the tongue of Chorabari glacier.

In line with idealized process chain model (2), the catastrophic mass movement from the 5,500 m Ronti Peak, in Chamoli district of Uttarakhand, 7 February 2021, initiated as a 26.9 × 10⁶ m³ failure of mostly rock (80%) and glacial ice (20%) (Bhardwaj & Sam, 2022). Melting of this ice from frictional heating as the flow descended over a local elevation range of >3,000 m (to the hydropower dam at Tapovan) was essential to the downstream evolution of the flow, transforming the rock and ice avalanche into a highly mobile debris flow. Along the valley floor, within what Zhang et al. (2022) refer to as the dynamic entrainment zone, colluvium (likely saturated), fluvial sediments, and fresh snow would have added to the initial mass of the flow. At the confluence of the Ronti Gad and Rishiganga rivers, significant deposition of sediment formed a 46 m high dam, leading to formation of an 800 m long lake (Kropáček et al., 2021). Although this was initially a concern for authorities, lake outflow stabilized and began to exceed inflow with a few weeks, such that a secondary LLOF was avoided. Upon reaching the wider and lower gradient Dhauliganga river, the sediment load of the debris flow reduced, and a devastating flood continued downstream to destroy the Tapovan hydropower station, where most of the 204 fatalities and missing persons are located. The flood attenuated further downstream, but increased turbidity observed at Kanpur on the Ganges River some 2.5 weeks later, around 900 km from the source, are testimony to the extreme scale of this event (Shugar et al., 2021).

While several studies have focused on meteorological conditions prior to the rock and ice collapse (e.g., Siddique et al., 2022; Srivastava et al., 2022), there is no compelling evidence to suggest any direct external triggering. Rather, analyses suggest that long term regional climate and related cryospheric change combined with unfavorable geological structure and extremely steep topography were the main predisposing factors (Shugar et al., 2021). Retrospective analyses of optical satellite imagery has revealed that the failure block moved 20–30 m horizontally between 2016 and 2021, opening up a large fracture with a depth of up to 150 m, but with no acceleration in velocity immediately prior to collapse (van Wyk de Vries et al., 2021). While the north facing failure zone is expected to contain cold permafrost, the presence of a glacier at the surface, and heat fluxes from the adjacent ice-free south face, likely led to local thermal anomalies (Shugar et al., 2021). Increasing ground temperatures and associated permafrost degradation over time, enhanced by meltwater entering the large fracture, could have gradually reduced the strength of the frozen rock mass until a critical threshold was reached. Additional weight associated with the loading of winter snow into the widened fracture is a possible, but highly speculative final contributing factor (van Wyk de Vries et al., 2021).

Importantly, for both idealized process chain models illustrated above, potential would have existed for monitoring and early warning, and hence, reduction in societal impacts could have been possible. In the case of a catastrophic water release as occurred at Kedarnath, early warning systems would typically be two-fold, monitoring the water source (e.g., lake level), and prediction or forecasts based on the external triggering factors (e.g., heavy rainfall). In combination, this can enable a warning time in the order of days to hours. As has been demonstrated in the case of Chamoli, process chains initiating as a catastrophic mass movement are typically more difficult to predict temporally, given the lack of any direct trigger in many cases (threshold failure), and challenges in real time monitoring of slope movement on high mountain faces. However, satellite-based monitoring to detect slope movement years to months before a critical threshold is reached enables problem zones to be identified, and instrumentation can then be positioned downstream to signal the initiation of any secondary hazards. Given a considerably reduced warning time, estimated at 10–20 min in the case of Chamoli (Shugar et al., 2021) effective risk management in such cases critically depends on downstream hazard mapping, and well-planned response strategies.

4 Characteristics and Key Parameters of Process Chains: Toward a Classification Scheme

Characteristics and parameters of process chains were analyzed, based on the classification concept described in Section 2, for a total of 51 events for which information was either available in the literature or derived from globally available datasets. The information is stored in a database that is available as supplementary material.

4.1 Processes Involved and Process Interaction

When analyzing the full data set of 51 case studies and looking first at the reason why process chains occurred, we realize that initial processes most frequently result from progressive failure and not due to the occurrence of a distinct triggering process (Table 3). This means that the long lasting, gradual loss of stability of rock or loose material—induced by for example, weathering, permafrost degradation or seismicity - is more frequent than any other factor. As could be expected, heavy precipitation is a crucial trigger of process chains and the second most common trigger.

Table 3. Frequency of Triggering and Initial Processes

Initial process	Triggering process
Initial process	Volcano	Earthquake	Precipitation	Snow-/icemelt	Progressive failure^a	No information	Total
Debris flow	0	0	1	0	1	0	2
Glacial surge	0	0	0	0	2	0	2
GLOF	0	0	5	4	2	3	14
Ice avalanche	0	1	0	0	3	1	5
Lahar	1	0	0	0	0	0	1
Slide	1	0	1	1	2	2	7
Rock avalanche	0	1	0	1	9	1	12
Rock-/ice avalanche	0	0	0	0	5	1	6
Rock-/ice fall	0	1	0	0	1	0	2
Rockfall	0	0	1	0	0	0	1
Rockslide	0	0	1	1	1	0	3
Total	2	3	9	7	26	8	55

Note. The difference in total of 51 respectively 55 events is caused by cases with a combination of precipitation and snow/icemelt as triggering process.
^a Progressive failure is not a triggering process, but rather the absence of a distinct process.

If all 51 events in the database are taken into account, rock avalanches are the most frequent initial process, followed by GLOFs (Table 3). Rock avalances are the most frequent initial process in the Alps and in the Coast Range, while GLOFs are most frequent in Tien Shan. For slides as well as rock-/ice-avalanches, no distinct hot spots exist. The Andes and the Himalaya show the highest variety of initial processes.

From the 51 case studies analyzed, debris flows are the most frequent subsequent process, followed by debris floods (Figure 18a). From a regional perspective, most debris flows occur in the Alps, in the Coast Range, the Andes and the Himalayas. The distribution of the debris floods is comparable. GLOFs as a subsequent process occur most frequently in the Andes. The blocking of a receiving river is most documented in the Alps, the Coast Range and in the Himalaya.

Process chains initiated by rock avalanches show the largest number of subsequent processes. Debris flows, debris floods and blockings of receiving rivers are the most frequent subsequent processes (Figure 18a). GLOFs initiate the second largest number of subsequent processes. Here, debris flows and debris floods are the most common subsequent processes. Slides as initial processes are mainly followed by GLOFs and debris floods, rock-/ice-avalanches by GLOFs, debris flows and debris floods. Process chains initialized by ice-avalanches mostly involve GLOFs.

The process linking of initial processes with the first subsequent process is summarized in Figure 18b. Direct interactions occur in most cases listed in the database—the only exceptions found are glacial surges and some rockfall events. By contrast, all GLOF events show a direct triggering of the subsequent process, mostly as a result of the high discharge of GLOFs compared to rainfall induced floods, abundance of loose unconsolidated moraine in the outflow area, and their considerable erosive forces (Cenderelli & Wohl, 2001; Cook et al., 2018). Ice avalanches and rock-/ice-avalanches show a high proportion of direct triggering of subsequent processes as well; here, we ascribe the direct triggering to the melting of ice, which in turn leads to the liquefaction of the mass, and far reach of these initial processes.

A delayed linking is much less frequent. Most often it occurs in the case of rock avalanches/rockslides. A reason for this is that deposits of rock avalanches consist of coarse debris and thus show higher stability, thereby preventing direct failure of the valley blocking dam (Dunning & Armitage, 2011; Dunning et al., 2005; Fan et al., 2021). However, rockslide dam failure most often occurs within days of formation (Evans et al., 2011) and forms an important part of rockslide process chains.

4.2 Starting Zone Parameters

More than 50% of the events in the database have their starting zone in the range where permafrost is probable (mostly in the PZI classes “cold” or “in nearly all conditions,” Table 4). In the Tien Shan region, where all starting zones are in probable permafrost area, median and maximum elevations of the starting zones are located at about 3,500 m and 3,900 m, respectively, and therefore well above the lower limit of present-day permafrost in this region (about 2,800 m according to Gruber (2012)). This means that even with continued global warming and rising of the permafrost boundary, permafrost will still be an important factor in the starting zones of process chains by the end of the century and beyond. This is also true for the Himalaya and the Karakoram whereupon for the latter only one event is represented in the database. On the other hand, smaller differences between local maximum elevation and elevation of the starting zones are seen for the Andes, the Alps, the Caucasus and for New Zealand.

Table 4. Permafrost Occurrence According to a Global Model (Gruber, 2012), Classified According to Boeckli et al. (2012) and Elevation of Starting Zones by Region

Region	Permafrost (count)				Elevation (m asl)
Region	No	Only in very cold cond.	Mostly in cold cond.	In nearly all cond.	Maximum	Median starting zone	Maximum starting zone
Himalaya	3	1	4	2	8,849	4,675	7,000
Hissar Alay			1		4,643	3,800	3,800
Karakoram			1		8,611	4,800	4,800
S and E Tibet	1		1		7,253	4,122	4,444
Tien Shan			3	1	7,439	3,400	3,600
Alps	2	4	3	1	4,809	2,840	3,870
Caucasus			1	2	5,642	4,400	4,780
Coast Range	2	1	6		4,019	2,300	2,620
Andes	5	3	1		6,962	5,230	6,600
New Zealand	2				3,754	2,295	2,460
Total	15	9	21	6

Note. The large grid cell (ca. 1 km) of the permafrost model may not capture local presence of permafrost in some situations.

A striking feature is the high proportion of rock- and rock-/ice-avalanches starting zones outside or in the lowest category of permafrost (55%) (Figure 19b). Smaller rock instabilities that respond more directly to warming of the active layer of permafrost (e.g., Gruber et al., 2004) are unlikely to involve sufficient volumes to generate process chains. Looking at progressive failure, the most important reason for landslide-generated process chains, roughly 60% of the starting zones of all initial processes are within the zone where permafrost is probable, which means that also in the starting zones of other initial processes such as slides or GLOFs, permafrost is present (Figure 19a). Almost all of the starting zones with progressive failure as the triggering process are also in the range of strong to violent seismicity, according to the Modified Mercalli scale (Petersen et al., 2018).

Regarding glaciers, Figure 20 shows that debuttressing by thinning glaciers may have played an important role in triggering initial processes. About 30% of the events with progressive failure as the triggering process have their starting zones on glaciers or just above them (Figure 20a). In terms of initial processes (Figure 20b), ice avalanches and all rock-/ice falls dominate here as they have their starting zones on glaciers. About 50% of the rock and rock-/ice avalanches and landslides have their starting zone just above glaciers, what makes debuttressing effects very likely.

Regarding lithology, acid plutonic rocks are most frequently (15) present in the starting zone of the initial processes (not considering ice avalanches and glacial surges). They make up the majority in the case studies with rock avalanches, rock-/ice falls and GLOFs. With 35%, their occurrence is far out of proportion with the global distribution of 5.6% or the distribution in the GTN-T regions with events (8%). Since acid plutonic rocks are more likely to be stable and weathering-resistant (Moosdorf et al., 2018), seismicity and permafrost is rather the reason for an increased disposition for slope failure. Another explanation could be that this lithology produces steeper slopes, more prone to large catastrophic failures than a weaker lithology from which smaller, more frequent instabilities may originate.

The local elevation range (LER) within a radius of 500 and 1,000 m of the starting zones is shown in Figure 20 (LER within a radius of 5,000 m shows no clear pattern and fades into background). Rock- and/or ice-falls and rock- and/or ice avalanches are associated with the highest LER500 median, with values over 600 m, while the lowest values are associated with glacial surges. In between are the sliding processes, lahars, GLOFs and debris flows. This is in line with the process characteristics but for some processes (e.g., lahars or rockfalls) there are only few entries in the database. Rock-/ice-avalanches show by far the largest range between minimum and maximum LER500, with about 1,200 m. The LER1000 median values show a comparable pattern (Figure 21b). Again rock-/ice-avalanches, rock-/ice-falls, rockfalls show the highest values with 1,000 and more meters. In addition, rockslides and debris flows show comparable values. On the other hand, rock avalanches show a median value that is slightly lower. Again, glacial surges and lahars have the lowest median values. Rock-/ice-avalanches show the largest range of LER1000 values with about 1,800 m, followed by GLOFs with about 1,100 m.

4.3 Amplification Effects

Snow entrainment or the reduction of friction caused by snow is only a relevant factor in 11 cases, mostly in the Alps and the Coast Range and most cases are related to rock avalanches and rockslides (Table 5). Ice entrainment plays a role in 17 events, mainly for rock avalanches and rock-/ice-avalanches but also for slides. However, in 11 cases, information on snow entrainment is not available. For ice entrainment for 5 events the information is missing.

Table 5. Amplification Effects Related to Initial Processes

Process	Yes	No	ND	Yes	No	ND	Yes	No	ND	Yes	No	ND	Yes		No	ND
	Snow entrainment			Ice entrainment			Fine grained material			Fluvial processes			Lakes involved
Debris flow	1	1	0	0	2	0	2	0	0	2	0	0	1		1	0
Glacial surge	0	1	1	0	1	1	2	0	0	1	0	1	2		0	0
GLOF	0	10	0	0	10	0	9	2	0	5	5	1	11		0	0
Ice avalanche	0	4	1	0	4	1	3	2	0	4	1	0	4		1	0
Lahar	1	0	0	1	0	0	1	0	0	0	0	1	0		1	0
Slide	1	6	0	3	4	0	6	1	0	5	2	0	3		4	0
Rock avalanche	5	6	1	5	6	1	6	6	0	4	7	1	3		9	0
Rock-/ice avalanche	0	0	6	4	0	2	6	0	0	4	0	2	4		2	0
Rock-/ice fall	0	0	2	2	0	0	2	0	0	2	0	0	0		2	0
Rockfall	1	0	0	0	1	0	1	0	0	1	0	0	0		1	0
Rockslide	2	0	0	2	0	0	2	0	0	0	2	0	0		2	0
Total	11	28	11	17	28	5	40	11	0	28	17	6	28	23	0

Note. ND, no data available.

For the availability of fine-grained loose material, which contains a relevant ratio of grain size less than or equal to sand and what is relevant for the reach of debris flows and debris floods (Pierson, 2005), three sub-parameters are combined in this study, namely (a) fine grained material along the path trajectory (visual detection of fluvial or lacustrine sediments from satellite images), (b) highly fractured deposits of rock avalanches and rock-/ice-avalanches if LER500 exceeds 600 m, assuming that high fall energy pulverizes part of the mass; and (c) disaggregated rock caused by seismicity if seismicity classified as severe or violent. If at least one of these sub-parameters is true, the existence of fine-grained loose material is assumed. This is the case for 40 events and occurs most frequently in the case of GLOFs, slides and rock-ice avalanches.

Fluvial processes have an amplification effect in 27 events as they can transport high volume of sediments over long distance. Notably, however, only 4 of 10 GLOF events included fluvial processes in their process chain. Much more frequent are debris flows as subsequent process. Again, this amplification effect applies to less than half of the process chains that are triggered by rock avalanches, while it is important for slides and rock-/ice avalanches. Lakes formed by natural dams and bedrock play a role along the process chain trajectory in 27 cases. This includes obviously all GLOF events as well as all glacial surges, most ice avalanches and 3 out of 7 slides. In 25 cases, lakes have amplifying effects as the failure or overtopping of their dams lead to flood waves downstream from the dam site. For the other cases lakes can have a damping effect, if sediments of debris floods or debris flows are trapped.

4.4 Volume

Volume data are not available for all events in the database. Information is missing for 8 events for initial volume, and almost for half of the events for total volume. Moreover, the uncertainty regarding the volumes is very high. The largest initial volume for the analyzed events is for lahars (Nevado del Ruiz) with 90 × 10⁶ m³ (Figure 22a). This is followed by rock-/ice-avalanches with a maximum value of 50 × 10⁶ m³ and a median value of about 18 × 10⁶ m³. For the other processes the initial volume lies below 10 × 10⁶ m³. Lowest values are for debris flows, GLOFs and rockfalls. For the events with values for the total volume (including the volume of entrained material), rock avalanches show the highest value (135 × 10⁶ m³, Kolka event, Figure 22b). Except for lahars all other total volumes are below 20 × 10⁶ m³. Where both initial and total volumes are available, the ratio between the two values can be calculated. Here, the highest values result for GLOFs. For the event in the Klattasine Creek (Coast Range) the ratio is 750, for the Zharsay Issyk 180. For the other events the ratio is below 20.

In order to look for possible controls of volumes involved in process chains, we analyzed the respective role of triggering processes and thermal state of the ground on the size of events. As shown in Figure 23a, volcanic processes and earthquakes tend to trigger the largest initial volumes, both in terms of maximum and median values. A high maximum value can also be observed in the case of progressive failures, while their median value is in the range of the remaining triggering processes with rather low initial volumes. Concerning the role of permafrost, the largest overall initial volume has been recorded from a zone without permafrost (lahar, Nevado del Ruiz) whilst median and 75% values of volumes increase as the likelihood of permafrost increases (Figure 23b). This could potentially be indicative of deep permafrost thaw destabilizing large rock volumes, as, for example, was recently suggested for the Chamoli disaster in Northern India, that falls in the “mostly in cold conditions” class (Shugar et al., 2021).

4.5 Trajectory Characteristics

We used reach and the average slope for the characterization of the path of the process chains, in 35 of the 51 events in the database for which this information is available. Typically, the reported reach is defined based on the extent of societal or major geomorphological impacts, although this is not homogeneous across studies, involves a degree of subjectivity, and cannot easily be reconstructed for older events. Particularly where there is no distinct endpoint, such as for pure floods, and the process rather gradually attenuates downstream, the reach could not be reliably determined.

Figure 24 shows the expected relation whereby high values of average slope are achieved for short reaches. By contrast, long reaches lead to low values of average slope, which is self-evident from topographic limits. For better readability, the Yarlung Tsangpo event with a reach of 460 km is omitted.

The distinction between process chains with and without snow or ice entrainment shows no difference in the average slope (Figure 24a). Surprisingly, the maximum reach of process chains with snow or ice entrainment seems to be shorter when compared to the maximum reach of these processes in the absence of entrainment. This is not in correspondence with events and processes described for example, by Evans and Clague (1999), McSaveney (2002), Sosio (2015), and Evans et al. (2021). Even if process chains with fluvial processes are excluded there is no clear evidence of larger reaches for process chains with snow or ice entrainment. This is also the case if we look only at fall processes without subsequent fluvial processes. But here the reason for this is that all cases except for one include entrainment of ice and/or snow.

The presence of fine-grained material along a process chain can lead to long reaches (Figure 24b). The reach of events with no relevant sources of fine-grained material is smaller than 20 km in all cases except for the event with the largest reach (Nostetuko Lake, Coast Range; 115 km). The reach of six events with fine grained material involved lies between 30 and 80 km.

Process chains that include fluvial processes (Figure 25a) or lakes (Figure 25b) show larger reaches and tend to result in lower average slope (Figure 25). The average reach of process chains with fluvial processes is 36.4 km, compared to 14.3 km without fluvial processes. Comparable values result for process chains with and without lakes involved (36.1 and 13.1 km, respectively). Hence, fluvial processes, lakes, and associated outburst events have the potential to transform mountain hazards into far-reaching downstream events where societal impacts may be larger. The process chains with or without large retention areas do not show a distinct difference in reach (see Figure S1 in Supporting Information S1). Likewise, we do not observe any significant trends between initial and total volume or reach of process chains (Figure 26a).

The Yarlung Tsangpo rock-/ice-avalanche event in Tibet (2018) shows an exceptional reach of 460 km, owing to the subsequent outburst of the temporary dammed lake. All other values involving lakes in this study are below 120 km. Most events with a reach of more than 50 km include fluvial processes as (one of) the subsequent process (es). This suggests that initial processes do not exert a key control on the reach of a process chain, and more important is the potential for the initial event to intersect with large fluvial systems and/or lead to secondary lake formation. This is supported by the fact that reaches show a negative trend related to the LER values for all radius (500, 1,000 and 5,000 m) (Figure 26b), thereby suggesting that LER is an indication for potential energy of the initial process, but not for the whole process chain.

4.6 Potential Climate Change Impact

Only sparse and often qualitative information is usually available in the literature on the role of climate change on past process chains, and even less information exists on how changing climatic conditions and the related glacier retreat and thawing of permafrost could change the frequency of occurrence, magnitude and/or triggers of process chains by the end of the 21st century. Therefore, we combined here different rules to derive the potential role of climate change in these cases and to assess their future occurrence. These rules are specific to the triggers and initial processes affected by climate change and thus consider the PZI, occurrence of glaciers along the process trajectories, observed changes in precipitation (Sun et al., 2021) and projected changes by the end of this century (Iturbide et al., 2021). For the latter, we analyzed changes of annual precipitation sums, changes in the highest 1-day (Rx1d) and 5-day (RX5d) precipitation totals for the Representative Concentration Pathway 8.5. The applied rules are shown in Table S3 of Supporting Information S1 together with their likely impacts on triggering, initial and subsequent processes. Using these rules and existing information from the literature, we assessed the impacts of climate change on triggering and initial processes, both for the time the event occurred in reality and for a future situation, that is, toward the end of the century.

Most process chains in the database are related to progressive failures. In most of these cases (21), climate change has had an amplifying effect; the fact that a majority of the progressive failures had their starting zones in permafrost areas also supposes that a direct impact of climate change on slope stability and up with that on the triggering process can be assumed as likely, as shown for example, by Evans and Delaney (2015), Fischer et al. (2006), McColl and Draebing (2019) or S. Allen and Huggel (2013). Due to more intense precipitation, 7 process chain events, mainly with GLOFs as initial process, show an amplifying effect. Five of these are in regions where, according to Sun et al. (2021), more than 70% of the stations show an increase in Rx1d and Rx5d. For two events in the Andes, data is missing, in two cases other cases, snowmelt also played a role. In total, 26 triggering processes show an amplifying effect of climate change, while 18 events show an indifferent effect (Figure 27a).

In the future, for 30 events an amplifying effect is assumed. Again, progressive failure is crucial and most of the events are related to permafrost degradation. For four events the effect is diminishing, for 10 events it is considered indifferent (Figure 26b). The diminishing cases are related to glacier retreat and/or a decrease of meltwater, respectively, as well as to a decrease in heavy rainfall in the southern part of the Andes, where Rx5d is expected to decrease by 5%–15% (Iturbide et al., 2021).

The impact of climate change on initial processes is shown in Figure 28, indicating that more than 80% of these processes that occurred show an amplifying effect from climate change (Figure 28a). In the case of GLOF events, most important with 12 events, an amplifying effect can be assumed to apply in all cases, except in the case of GLOFs relating to glacial surges. GLOFs from moraine dammed lakes as an initial process are per se typically closely associated with climate change because lake development is intimately coupled with glacial retreat (Harrison et al., 2018; Otto, 2019). Amplifying effects are also strong for ice avalanches, rock avalanches and rock/ice avalanches, in line with findings number of previous work (e.g., S. Allen & Huggel, 2013; Bessette-Kirton & Coe, 2020; Fischer et al., 2013; Gruber & Haeberli, 2007; Stoffel & Huggel, 2012). Only in 8 out of the 51 process chain events analyzed, climate change may have played an indifferent role.

When looking at the development until the end of the 21st century, the picture becomes something different (Figure 28b). Whereas in 70% of all cases, further climate change is anticipated to have an amplifying effect on future processes, we observe some changes with respect to future GLOFs at the sites at which such events have occurred in the past. Indeed, only in 3 out of the 11 cases we project an amplifying effect of climate change. The diminishing effect can be attributed to the reduction of the water volume stored in a dam that was incised by the event or even eroded down to the bedrock so that only small impact waves can occur in the future. There are other cases where the lake was emptied during a GLOF and no further lake can develop. At the same time, however, ample evidence exists that the ongoing climate warming and glacier wasting will lead to the formation of myriads of new lakes across all mountain chains of the globe, which could eventually become sources of new GLOFs (e.g., Ahmed et al., 2021; Emmer et al., 2022; Mal et al., 2021; Zheng, Allen, et al., 2021). These new lakes may be more exposed to ice-rock avalanches, as they grow closer toward steep mountain headwalls characterized by degrading permafrost (Haeberli et al., 2017). Downwasting of glaciers and expansion of lakes can also directly destabilize adjacent lateral moraines, and expose the lake to new threats. This was illustrated, for example, in the 2021 GLOF from Jinwuco in Tibet, where the triggering slide from the side hit the terminal part of the lake, in an area that was covered by the glacier only a few years before (Zheng, Mergili, et al., 2021).

Ice avalanches may diminish as a result of climate change in four out of five cases because many hanging glaciers will significantly retreat or completely disappear due to global warming, with the result that ice avalanches will no longer be possible. However, in the meantime, we may not rule out the possibility of a temporarily increasing likelihood for such events to occur as the warming could still destabilize the increasingly smaller, yet still-existing hanging glacier (e.g., Faillettaz et al., 2015).

The evaluation of the database shows that slides, rock and rock/ice avalanches are again strongly associated with amplifying effects of climate change (23 events), due to the possible role of permafrost both in gradual weakening, and in enhancing the initial process. Moreover, the drastic glacier melting projected by the end of the century will result in large destabilization of slopes due to debuttressing (S. K. Allen et al., 2017; Evans et al., 2021). According to Rounce et al. (2023), climate change will result in a loss of about 40% of their ice mass by the end of 21st century relative to 2015 and more than 80% of the glaciers will disappear, assuming the RCP8.5 scenario.

As a result of glacier retreat, more sediment could be mobilized and fluvial processes become more important, as examples from Asia (Li et al., 2021) or the Alps (Lane et al., 2017) show. Due to the increased availability of fine-grained glacial deposits and the greater importance of fluvial processes can be assumed that the reach of process chains will increase.

For the process chains initiated by lahars, future climate change is considered to be indifferent. On the one hand a reduced ice volume on glacier-clad volcanoes means that water generated by the melting of ice during an eruption will be less and the potential for large-scale lahars will decrease. On the other hand, more intense rainfall will increase the potential for large-scale lahars and due to the glacier retreat more loose material is exposed.

5 Conclusions

This first comprehensive compilation of geomorphic process chain events in high-mountain regions shows the diversity and complexity of such phenomena, but also their widespread occurrence across all major mountainous regions worldwide. The 51 process chain events assembled in the database cover High Mountain Asia, the European Alps and the Caucasus, North and South America and New Zealand. From the analysis of the broad characterization of the events the following key findings can be derived:

Process chains show a big diversity of processes involved but initialize as slope instability or water release events. Most slope processes according to the classification in Chapter 2 are associated with process chains, with the most frequent being rock and ice avalanches or a combination of both as well as slides. In addition, many events associated with water release are listed in the database. They occur in most mountain regions of the Earth.
Falls and slides have the highest relevance as initial process in process chains. As subsequent processes flow processes are most represented.
There is often no discernible trigger of a process chain, and the initial process is most frequently classified as a progressive failure. This is especially true for rock- and rock-/ice-avalanches. We ascribe this to intense weathering, glacial debuttressing, and permafrost degradation in cryosphere environments and to the seismic weakening in areas with high seismic activity. But we also understand that process chains triggered by volcanic eruptions may be underrepresented in this database where the focus was clearly on high mountain regions.
A look at the starting areas of the process chains shows in two ways clear regional differences: (a) In the Asian mountains, the median elevation is well below the maximum elevation of the mountains (<3,000 m). In the other regions the difference is less than 2,000 m. (b) Seismic activity is obviously lower in the Alps and in the Coast Range than in tectonically active Asian, South American and New Zealand mountains.
We distinguish initial volume and total volume of process chains. In most cases, the initial zone of process chains lies in an area where permafrost occurs, and the total volume of events typically is larger from zones where permafrost is more extensive. The largest initial volumes are related to volcanic and earthquake triggering. For the initial processes the largest volumes are observed for lahars, rock or rock ice avalanches, rockfalls and slides. Surprisingly, our findings suggest that the initial volume does not influence the total volume or travel distance.
High relative relief is an important precondition for the occurrence of landslide-generated process chains, whereupon the range of LER values remains very wide. High relative relief is generally associated with first order watersheds.
An instantaneous linking of initial and subsequent processes occurs much more frequently than a delayed linking, making these processes difficult to predict, fast and often devastating. However, and exception to this is the occurrence of river damming by landslides and the formation of landslide dammed lakes which may fail after some time forming delayed distal components of landslide-generated process chains. Indeed, the blocking of receiving rivers with subsequent breaching and debris flows and/or debris floods is an important element of process chains.
We do not find a significant effect of ice and/or snow entrainment on larger travel reach to the process chain as a whole; the assumption of such an effect cannot be confirmed through the evaluation of the events in the database. A reason for this may be that this effect is masked by other factors or that this effect only influences the initial process but not the entire process chain. Nevertheless, this effect can be crucial as a higher reach of the initial process makes the triggering of a subsequent process more likely. However, we have to consider uncertainties in the definition of the reach. Length of process chains rarely exceed 120 km.
Longer process chains occur if fluvial processes are part of a process chain or when lakes exist along the trajectory of a process chain giving rise to instantaneous or delayed water release. But in some cases, lakes can reduce the reach if they retain sediments or high volume of water and thus cause flood waves to flatten out.
The presence of fine-grained material along the path leads to a larger reach of process chains. This may be due to a change in rheology (buoyancy effect), which causes the sediments to be transported over longer distance. Thereby it must be taken into account that it is difficult to determine the reach of process chains in the case of pure water discharge if direct observations are lacking.
Climate change has had an amplification effect on the occurrence, scale, travel distance and velocity in the majority of the analyzed process chains mainly by the melting of mountain permafrost, glacier retreat, and changes in rainfall patterns. Amplifying can be effective on the triggering process as well as initial and subsequent processes. The impact of future climate change is not one-directional. In some cases, it can be still amplifying, in other cases the effect is to diminish process chains. An important implication of climate change is that we can expect an increase in the reach of events in the future, as glacial environments transform into paraglacial and fluvial environments. The impact can also change over time as shown for the ice avalanches.

The results of this study provide comprehensive information about the determining factors of the processes involved in mountain process chains. They therefore represent an important contribution to natural hazard assessments of process chains. Recent events like the Chamoli (Shugar et al., 2021) or Melamchi (Maharjan et al., 2021) disasters show that process chains have a high destructive potential on settlements and infrastructure in mountain regions. Despite their devastating nature, we realize that very limited information often exists on these events and that insights remain limited or established by proxy, presumably as their starting zones are often located in remote areas to which access is difficult. In view of the potentially high destruction caused by these process chains and the risk that they pose to mountain communities, it would be extremely valuable if future process chain events were documented in greater detail, and in line with the parameters described in this study. Notes on data and methods used to derive the parameters for the documentation are given in Text S2 of Supporting Information S1.

Acknowledgments

Joel Fiddes for providing the Google Earth Engine script to calculate local elevation ranges (LER). Open access funding provided by Universite de Geneve.

Open Research

Data Availability Statement

GTN-G glacier regions, used for geographic classification, are available under https://www.gtn-g.ch/data_catalogue_glacreg/, (Gärtner-Roer et al., 2019; Global Terrestrial Network for Glaciers, 2017). The Global Lithological Map Database is accessible on https://www.arcgis.com/home/item.html?id=53c82af69cae4c1f99902c0e0d456bf8 (Hartmann & Moosdorf, 2012a, 2012b). Seismic characterization is based on the GSHAP global seismic hazard map available in the Global Seismic Hazard Assessment Program were obtained from http://static.seismo.ethz.ch/GSHAP/global/ (Shedlock et al., 2000). The PZI Map is available on https://www.geo.uzh.ch/microsite/cryodata/pf_global/ (Gruber, 2012). The flowdirection is part of the HydroSHEDS product https://www.hydrosheds.org/hydrosheds-core-downloads (Lehner et al., 2008). Figures were made with GrapherTM (Version 21) from Golden Software, LLC. (www.goldensoftware.com) and the maps were created using ArcGIS Pro, Version 2.9. Redlands, CA: Environmental Systems Research Institute (www.esri.com).

Supporting Information

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Citing Literature

Volume61, Issue4

December 2023

e2022RG000791

Filename	Description
2022RG000791-sup-0001-Supporting Information SI-S01.docx284.9 KB	Supporting Information S1
2022RG000791-sup-0002-Data Set SI-S01.xlsx51.6 KB	Data Set S1

Geomorphic Process Chains in High-Mountain Regions—A Review and Classification Approach for Natural Hazards Assessment

Abstract

Key Points

Plain Language Summary

1 Introduction

1.1 Mountain Regions and Mountain Hazards

1.2 Definitions of Multi-Hazard

1.3 Process Chains and Climate Change in Mountain Regions

1.4 Objective of the Study

2 Classification Approach

2.1 Classification Scheme

2.2 Spatial Context

2.3 Processes Involved in Process Chains

2.4 Process Interactions

2.5 Starting Zone Characteristics

2.6 Amplifying Effects

2.7 Volume

2.8 Trajectory Characteristics

2.9 Climate Change Impacts

3 Description of Process Chains

3.1 Earthquake as Triggering Process

3.1.1 Huascaran 1970

3.1.2 Langtang 2015

3.2 Volcanic Eruption as Triggering Process

3.2.1 Nevado del Ruiz, Cordillera Central, Colombia 1985

3.3 Falls as Initial Processes

3.3.1 Cengalo-Bondasca, Switzerland, 2011 and 2017

3.3.2 Kolka, Republic of North Ossetia–Alania, 2002

3.3.3 Seti River, Nepal, 2012

3.4 Slides as Initial Processes

3.4.1 Phuktal River, Zanskar Himalaya, India, 2014

3.4.2 Meager Creek

3.5 Flows as Initial Processes

3.5.1 Gastern Valley, Switzerland, 2011

3.5.2 Giétro, Switzerland, 1818

3.6 GLOFS as Initial Processes

3.6.1 Tronador, Argentina, 2009

3.6.2 Shakhimardan, Kyrgyzstan, 1998

3.7 Toward Idealized Process Chains

4 Characteristics and Key Parameters of Process Chains: Toward a Classification Scheme

4.1 Processes Involved and Process Interaction

4.2 Starting Zone Parameters

4.3 Amplification Effects

4.4 Volume

4.5 Trajectory Characteristics

4.6 Potential Climate Change Impact

5 Conclusions

Acknowledgments

Open Research

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