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Landslide Runout Analysis - Current Practice and Challenges

Journal: Canadian Geotechnical Journal

Manuscript ID cgj-2016-0104.R1

Manuscript Type: Review

Date Submitted by the Author: 05-Oct-2016

Complete List of Authors: McDougall, Scott; University of British Columbia, Earth, Ocean and Atmospheric Sciences

Keyword: landslides, runout analysis, modelling, risk assessment

https://mc06.manuscriptcentral.com/cgj-pubs

Canadian Geotechnical Journal

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Landslide Runout Analysis – Current Practice and Challenges 1

2014 Canadian Geotechnical Society Colloquium Lecture 2

Scott McDougall 3

University of British Columbia Department of Earth, Ocean and Atmospheric Sciences 4

Abstract 5

Flow-like landslides such as debris flows and rock avalanches travel at extremely rapid velocities and 6

can impact large areas far from their source. When hazards like these are identified, runout analyses 7

are often needed to delineate potential inundation areas, estimate risks and design mitigation 8

structures. A variety of tools and methods have been developed for these purposes, ranging from 9

simple empirical-statistical correlations to advanced three-dimensional computer models. This paper 10

provides an overview of the tools and methods that are currently available and discusses some of the 11

main challenges that are currently being addressed by researchers, including: the need for better 12

guidance in the selection of model input parameter values, the challenge of translating model results 13

into vulnerability estimates, the problem with too much initial spreading in the simulation of certain 14

types of landslides, the challenge of accounting for sudden channel obstructions in the simulation of 15

debris flows, and the sensitivity of models to topographic resolution and filtering methods. 16

Keywords 17

landslides, runout analysis, modelling, risk assessment 18

Introduction 19

Landslide runout analysis is used to simulate the motion of past landslides and to predict the motion 20

of potential future landslides. It is often a key step in landside risk assessment and mitigation design, 21

especially in cases involving extremely rapid, flow-like landslides, such as debris flows and rock 22

avalanches (cf. Hungr et al. 2014 for landslide type definitions). 23

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The high mobility and destructive potential of these types of landslides was demonstrated by the 24

recent (2010) Mount Meager rock slide-debris flow in southwestern British Columbia (Figure 1). 25

Approximately 50 million m3 of weak volcanic rock failed on the flank of Mount Meager and travelled 26

down Capricorn Creek, reaching estimated velocities of over 80 m/s (290 km/hr), before temporarily 27

damming Meager Creek and then Lillooet River more than 12 km from the landslide source (Guthrie 28

et al. 2012). The risk of a sudden landslide dam outburst flood on Meager Creek forced the 29

temporary evacuation of 1,500 residents from the lower Lillooet River valley (Guthrie et al. 2012); 30

however, the dam breached calmly within a couple of hours. 31

In spite of substantial direct economic costs of about $10 million CAD (Guthrie et al. 2012), the 32

outcome of the Mount Meager event was very fortunate in the sense that no lives were lost, in part 33

because of its relatively remote location (although the landslide did narrowly miss an occupied 34

campground located next to Lillooet River). However, worldwide, landslides cause thousands of 35

fatalities every year (Petley 2012). Significantly, most landslide-related deaths occur on relatively flat 36

land in the distal runout zone (D. Petley pers. comm.), where human development is generally 37

concentrated and high velocity landslide impacts can still occur, often with little or no advanced 38

warning. The landslide that occurred near Oso, Washington in March 2014, which travelled more 39

than one kilometre across the valley bottom and caused 43 fatalities in the community of Steelhead 40

Haven (Keaton et al. 2014; Iverson et al. 2015; Hibert et al. 2016; Wartman et al. 2016) is a recent 41

example of this widespread problem. In some cases, agricultural development on the flat land in the 42

runout zone may also contribute to the mobility of landslides and exacerbate the consequences (e.g. 43

Evans et al. 2007). Because stabilization of landslide source areas is not always possible, tools and 44

methods are needed to predict landslide runout behaviour and help manage land use and/or design 45

protection in the runout zone. 46

The main objectives of this paper are to provide an overview of the runout analysis tools and 47

methods that are currently available and to discuss some of the main challenges that are currently 48

being addressed by researchers. Some ideas for future research are also briefly discussed. The 49

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material described in this paper was presented during the annual Colloquium Lecture at the 2014 50

Canadian Geotechnical Society conference in Regina, but includes several updates. 51

Landslide Runout Analysis 52

Landslide runout analysis is the analysis of post-mobilization landslide motion. It can involve both the 53

forensic-style back-analysis (simulation) of previous events and the forward-analysis (prediction or 54

forecasting) of potential future events. Runout prediction is often required in the context of a landslide 55

hazard or risk assessment (e.g. Willenberg et al. 2009; Froese et al. 2012; Jakob et al. 2013; Loew et 56

al. 2016), in which case it is desirable to be able to assign conditional probabilities to a range of 57

potential mobility outcomes. 58

Figure 2 illustrates the concept of probabilistic runout mapping in the context of Turtle Mountain 59

in southwestern Alberta, site of the 1903 Frank Slide (McConnell and Brock 1904). The coloured 60

dashed lines shown in Figure 2 represent conceptual runout exceedance probability isolines for a 61

potential failure of the South Peak of Turtle Mountain, where various unstable rock masses have 62

been identified and are currently being monitored (Froese et al. 2012; Froese and Moreno 2014). 63

Figure 2 is not an actual hazard map but is intended to illustrate the concept that the runout 64

exceedance probability (i.e. the probability that a future event of a given size will travel past each 65

isoline) decreases with distance from the source slope. The analyses on which Figure 2 is based are 66

described in Hungr (2007). 67

Runout analysis is also used to design mitigation structures, including debris barriers, berms and 68

nets (Mancarella and Hungr 2009; Ashwood 2014). Runup heights and impact loads on such 69

structures can be modelled directly or estimated indirectly based on estimated flow depths and 70

velocities at specific points of interest (e.g. Hübl et al. 2009; Kwan 2011). 71

Runout analysis is also used to help assess the potential secondary effects of landslides, 72

including landslide-generated waves (Pastor et al. 2009a; BGC 2012; Wang et al. 2015; Yavari-73

Ramshe and Ataie-Ashtiani 2015) and flooding caused by landslide dams (both upstream flooding 74

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behind a dam and downstream flooding following a dam breach) (Schneider et al. 2014; Worni et al. 75

2014). Other secondary effects, such as air blasts and dust cloud cover, can also be delineated on 76

the basis of estimated runout limits. 77

Unfortunately, limited guidance is currently provided to practitioners carrying out runout analysis. 78

Landslide guidelines published by the Association of Professional Engineers and Geoscientists of 79

British Columbia (APEGBC 2010) describe runout analysis and the associated design of control 80

structures as ‘specialty services’ that may be beyond the scope of typical landslide assessments or 81

may require expert help. Some guidance on the selection of appropriate runout analysis tools and 82

methods is provided in the national landslide guidelines that are being published online by the 83

Geological Survey of Canada (Lato et al. 2016). In contrast, relatively prescriptive guidance is 84

provided to practitioners in Hong Kong by the Geotechnical Engineering Office (GEO 2011). 85

Overview of Methods 86

Runout analysis methods can be grouped into two broad categories (Figure 3): 1) empirical-statistical 87

methods that rely on statistical geometric correlations, and 2) analytical methods that rely on process-88

based modelling. Numerical models, including both continuum and discontinuum models, fall into the 89

second category. Within this sub-category, hybrid ‘semi-empirical’ numerical models that rely on 90

some form of parameter calibration are more common than pure mechanistic models that rely on 91

independent material property estimates. 92

Landslide modellers tend to rely heavily on empiricism because there are no universal 93

constitutive laws governing landslides that are straightforward to incorporate into numerical models 94

(Pastor et al. 2012). Iverson and George (2014) recently formulated a two-phase model that is 95

capable of simulating the effects of dilatancy on evolving pore pressure response, which is a step 96

towards more purely mechanistic modelling of debris flows. However, landslides as a collective 97

phenomenon are extremely diverse and complex. There is still debate in the landslide community 98

about the mechanisms of long runout behaviour, which includes pore pressure response (Heim 1932; 99

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Abele 1997; Iverson 1997; Hungr and Evans 2004; Legros 2006; Iverson et al. 2011; Iverson 2012), 100

but could also involve more exotic behaviour, including: lubrication by snow or ice (Evans and Clague 101

1988; Delaney and Evans 2014); fluidization by trapped air, vapour or dust (Kent 1966; Hsu 1975; 102

Manzanal et al. 2016); mechanical or acoustic fluidization of particles (Melosh 1979; Johnson et al. 103

2016); frictional weakening by flash heating (Lucas et al. 2014); and/or forces generated by dynamic 104

rock fragmentation (Bowman et al. 2012; Davies and McSaveney 2012). Such theories are very hard 105

to test, and the physical properties that go along with them are very hard to measure, because shear 106

rates, pressures and temperatures at the field-scale are challenging to replicate in the lab. On the 107

other hand, numerical models that rely heavily on empirical calibration have been criticized for their 108

ability to simulate the bulk behaviour of landslides simply through ‘tuning’ of parameters that may 109

have questionable physical significance (Iverson 2003). 110

Empirical-Statistical Methods 111

The most practical empirical methods are based on simple geometric correlations. Two well-112

established examples are shown schematically in Figure 4. Figure 4a illustrates an inverse 113

correlation between landslide volume and angle of reach or ‘fahrböschung’ (the angle of the line 114

connecting the crest of the source with the toe of the deposit), which has been documented by 115

several workers for a variety of landslide types (e.g. Scheidegger 1973; Li 1983; Nicoletti and 116

Sorriso-Valvo 1991; Corominas 1996; Hunter and Fell 2003). Figure 4b illustrates a similar simple 117

correlation, based on Galileo scaling laws, between landslide volume and the area covered by the 118

deposit, which has been documented for rock avalanches and lahars (e.g. Li 1983; Hungr 1990; 119

Iverson et al. 1998; Griswold 2004). The latter correlation is the basis for the GIS-based computer 120

program LAHARZ (Iverson et al. 1998), which is used by the U.S. Geological Survey to map lahar 121

hazards around U.S. volcanoes. Modifications were made to LAHARZ by Berti and Simoni (2014) to 122

develop the program DFLOWZ for unconfined flow conditions. Other empirical methods have been 123

presented by Hsu (1975), Davies (1982) and Fannin and Wise (2001). 124

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Whittall (2015) demonstrated that the empirical methods described above are also applicable to 125

open pit slope failures, for which the mobility of events depends largely on the nature of the source 126

material (with failures of weathered or altered rock masses being more mobile than similarly-sized 127

failures of massive crystalline rock masses). Whittall (2015) proposed decision making methods 128

based on these correlations to help mine operators improve their trigger action response plans and 129

reduce the risk to workers and equipment if an imminent pit wall failure is detected. 130

These types of empirical-statistical methods are simple, but extremely powerful, because the 131

inherent data scatter shown schematically in Figure 4 can be expressed in quantitative statistical 132

terms. The statistical results can be used to establish limits of confidence for prediction (Hungr et al. 133

2005; Iverson 2008; Schilling et al. 2008; Berti and Simoni 2014), which can then be used for 134

quantitative risk assessment. For example, using the angle of reach method shown in Figure 4a, if 135

the volume of a potential failure can be estimated, a range of travel angles can be estimated that 136

bound the data points in that magnitude range, and those uncertainties can be translated into 137

estimates of runout exceedance probability. An example of this probabilistic approach is illustrated in 138

Figure 5. Using a dataset of case histories that are similar to the case in question, runout estimates 139

based on the best-fit (orange line) could be associated with an exceedance probability of 50% (i.e. a 140

50% chance that future landslides of this type and size will travel farther), while runout estimates 141

based on the lower 10th percentile prediction interval (yellow line) could be associated with an 142

exceedance probability of 10% (i.e. a 10% chance that future landslides of this type and size will 143

travel farther). Such an approach provides useful context for decision makers and is consistent with 144

evolving professional practice guidelines for landslide assessments in Canada and around the world 145

(APEGBC 2010; Porter and Morgenstern 2013; Corominas et al. 2013). 146

This probabilistic framework is not new for natural hazards. Similar approaches are used for 147

weather forecasting (DeMaria et al. 2009), flood mapping (APEGBC 2012) and snow avalanche 148

mapping (CAA 2016). A good example is the probabilistic forecasts that are provided for hurricanes 149

and tropical storms by the U.S. National Hurricane Center, which provides real-time predictions of 150

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hurricane tracks based on the probability of tropical storm wind speeds. These maps are used 151

routinely by various agencies to make life-saving decisions, and users of the maps are used to 152

working within the inherent uncertainty. 153

Numerical Models 154

The empirical methods described above are very useful for estimating runout distances and 155

inundation areas, but numerical models have the potential to provide more information because they 156

can also be used to estimate relevant landslide intensity parameters, such as flow depths, flow 157

velocities and impact pressures, within these limits. Animations or time lapse images generated from 158

numerical model output are also useful visualization and communication tools. An example of a time 159

lapse image based on a back-analysis of the 2010 Mount Meager rock slide-debris flow (Guthrie et al. 160

2012) using the numerical model DAN3D (McDougall and Hungr 2004) is shown in Figure 6. 161

At least 20 different numerical runout models have been developed over the past two decades, 162

the majority of which are continuum models that are based on established hydrodynamic modelling 163

methods, but with some landslide-specific modifications to account for the effects of entrainment, 164

internal stresses and spatial variations in rheology. A unique model benchmarking workshop was 165

held in Hong Kong in 2007 to compare the performance of 17 different models that were in 166

development at the time using a series of validation tests, laboratory experiments and full-scale case 167

studies (Hungr et al. 2007). A more recent overview of selected numerical runout models was 168

provided by Pastor et al. (2012). Newer models have recently been introduced by Mergili et al. (2012 169

and 2016), Horton et al. (2013) and Iverson and George (2014). 170

A consolidated list of selected numerical models that are currently available or at an advanced 171

stage of development is provided in Table 1. Note that, for simplicity, models denoted as 2D or 3D in 172

Table 1 are capable of simulating motion along a 2D path or 3D surface, respectively, regardless of 173

the numerical integration scheme that is used. 174

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With the exception of TOCHNOG (Roddeman 2002), all of the continuum models listed in 175

Table 1 are based on depth-averaged shallow flow equations that have been adapted to simulate the 176

flow of earth materials, as described in the pioneering work of Savage and Hutter (1989). The 177

resultant forces acting on each computational element in these types of models look very similar to 178

the forces acting on the columns of soil in a limit equilibrium slope stability analysis (Figure 7). 179

Gravity (W) is the main driver of motion. There are also internal stress gradients (∆P and ∆S) that 180

arise because of the sloping free surface; these forces influence how the flow spreads out. There 181

may also be some inertial resistance if the flow is entraining new material from the path (E) because 182

momentum needs to be transferred from the moving mass to accelerate that material up to speed. 183

However, most of the resistance to motion typically comes from basal shear stress (T), which may be 184

moderated by pore pressure and/or other possible mechanisms described earlier. 185

In continuum models, the mass and momentum balance equations are solved at each time step 186

at several locations within the landslide mass. In depth-averaged 2D models, reference slices are 187

used and the flow direction and path width need to be pre-defined by the user. In depth-averaged 3D 188

models, reference columns are used that allow for lateral movement, so that the flow direction and 189

path width do not need to be pre-defined and instead become key outputs of the model. Different 190

computational methods are available to solve the equations of motion, including Eulerian (fixed frame 191

of reference) and Lagrangian (moving frame of reference) approaches. 192

Since the early 2000s, the author has been closely involved in the development of the model 193

DAN3D (McDougall and Hungr 2004 and 2005; McDougall 2006), which is a 3D extension of the 2D 194

model DAN-W (Hungr 1995). The computational method used in DAN3D is based on the meshless 195

Lagrangian numerical technique known as Smoothed Particle Hydrodynamics (SPH), which was 196

originally developed in the 1970s for the simulation of astrophysical phenomena like galaxy collisions 197

(Lucy 1977; Gingold and Monaghan 1977). Using this approach, the landslide mass is divided up into 198

a collection of so-called ‘smooth particles’. In the depth-averaged context, these particles can be 199

visualized as bell-shaped objects moving across the sliding surface (Figure 8). The free surface of 200

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the landslide, which defines the flow depths and depth gradients that are used in the equations of 201

motion, is constructed by superposition of the particles (i.e. the depth of the slide mass at any given 202

location is the sum of the contributing depths of each individual particle at that location). In effect, 203

each particle pushes on all of its neighbours, so that in areas with a denser concentration of particles, 204

the flow depth and depth gradients will be greater and, in general, so will the spreading forces. The 205

equations of motion are solved at each particle location and their positions are advanced in time. A 206

major advantage of this method is that the particles are free to split apart from each other to move 207

around obstacles in the path without causing mesh distortion problems. 208

This ability to handle large deformations and flow splitting can be very important when dealing 209

with steep, complex terrain. For example, in the 2012 Johnson’s Landing landslide at Kootenay Lake, 210

B.C. (Figure 9), an approximately 380,000 m3 debris avalanche-debris flow, most of the damage was 211

caused by a large lobe of debris that left the main creek channel, while approximately half of the 212

debris stayed in the creek and eventually flowed into the lake (Nicol et al. 2013). The 1970 rock/ice 213

fall-debris flow at Nevado Huascarán in Peru, in which the town of Yungay was buried by a large lobe 214

of material that separated from the main flow, is another striking example of this behaviour (Plafker 215

and Ericksen 1978; Evans et al. 2009). 216

Landslide models also need to be able to account for non-hydrostatic internal pressures that can 217

develop within deforming earth materials (Savage and Hutter 1989), similar to the passive and active 218

earth pressures that develop next to a deflecting retaining wall. These non-hydrostatic pressures 219

develop because of internal shear strength, which resists internal deformation. This resistance to 220

deformation means that landslides do not spread out as readily as water, which has no internal shear 221

strength. 222

The majority of continuum landslide runout models listed in Table 1 use methods to estimate 223

internal pressure distributions that are based on Rankine earth pressure theory, following methods 224

that were originally developed by Savage and Hutter (1989). In DAN3D, internal strains and stresses 225

are tracked at each time step based on the relative change in position of the moving particles, which 226

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allows the simulation of anisotropic pressure distributions that can develop, for example, if the flow is 227

converging in one principal direction and diverging in the other. This capability can have a significant 228

influence on the extent and shape of the modelled inundation area. To illustrate this effect, Figure 10 229

shows DAN3D simulations of hypothetical experiments involving idealized material flowing down a 230

ramp onto a flat surface (after McDougall 2006). The material on the left (Figure 10a) has zero 231

internal shear strength (like water), while the material on the right (Figure 10b) is a frictional material 232

with an internal friction angle of 40°. Both materials have the same basal friction angle (25°). In both 233

cases, the centre of mass travels the same distance; however, with the material that has internal 234

strength, high passive pressures that develop during converging movement through the slope 235

transition zone result in more longitudinal spreading and therefore longer runout of the flow front. 236

The simulation of entrainment of material along the flow path is also an important model 237

capability. This process involves volume change and momentum transfer from the moving mass to 238

the stationary path material, which gives rise to the momentum flux component, E, shown in Figure 7. 239

Debris flows, in particular, can sometimes gather most of their material through entrainment 240

(Takahashi 1991; Revellino et al. 2004; Hungr et al. 2005; Iverson 2012). But the process of 241

entrainment and plowing of path material can also be critical to the behaviour of large rock 242

avalanches (Hungr and Evans 2004; Evans et al. 2009). In the case of the 1903 Frank Slide, most of 243

the damage in the town of Frank was actually caused by alluvium that was mobilized when the rock 244

avalanche impacted the valley floor (McConnell and Brock 1904; Cruden and Hungr 1986). 245

Different approaches to simulating material entrainment have been proposed, ranging from 246

empirical methods that require the input of user-prescribed volume growth rates (e.g. McDougall and 247

Hungr 2005; Chen et al. 2006) to process-based methods that simulate entrainment as a function of 248

basal shear stress conditions (e.g. Crosta et al. 2009; Iverson 2012). Rheology changes along the 249

path can accompany entrainment and are also important to consider. Undrained loading of weak, wet 250

path material has been recognized as a long runout mechanism for well over a century, dating back 251

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to interpretations of the 1881 Elm Slide in Switzerland by Albert Heim (Heim 1932; Abele 1997; Hungr 252

and Evans 2004; Legros 2006; Iverson 2011). 253

Current Challenges 254

Model Calibration 255

All of the landslide-specific features described above present modelling challenges and have been 256

the focus of the model development work that has been carried out over the past two decades. But 257

now that we have models that incorporate these key features, how do we select the input parameter 258

values and actually use these models for reliable landslide runout forecasting? In the author’s 259

opinion, this is the biggest current challenge for researchers and practitioners involved in this type of 260

work. 261

One modelling approach is to base the input parameter values on physical material properties 262

that are measured in the field or laboratory (e.g. Iverson and George 2014 and 2016). This approach 263

typically involves complex constitutive relationships with a relatively large number of input parameters 264

and requires the use of material sampling and testing methods that are appropriate for the scale and 265

velocity of real landslides, which can be a significant challenge. A variation of this approach using 266

parameter values based on a combination of laboratory experiments and field-scale stress field 267

observations has been proposed by Pellegrino et al. (2015). 268

An alternative modelling approach, which has been adopted previously with the majority of the 269

models listed in Table 1, is to base the selected parameter values on calibrated values obtained 270

through numerical back-analysis of past landslides. This approach can be used with relatively simple 271

rheological models that do not necessarily capture the complex micro-mechanics of real landslides, 272

but are still able to simulate their bulk behaviour (e.g. flow velocities, inundation area, distribution of 273

deposits), which is the main goal in runout forecasting. In some cases, this approach may also have 274

expediency and cost advantages in practice because specialized material testing is not required. 275

Furthermore, as discussed later in this paper, models that are calibrated to groups of events are also 276

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potentially well-suited to probabilistic analysis. Analogous parameter calibration is carried out 277

routinely in geotechnical practice with limit equilibrium slope stability analyses (e.g. back-analyzing a 278

failed slope to help constrain shear strength values). However, in contrast to landslide runout 279

modelling, the selection of the parameter values in slope stability modelling can be more readily 280

informed by the results of conventional field and/or laboratory strength tests. 281

The focus of the calibration-based approach is on the main external aspects of landslide 282

behaviour (i.e. how fast and how far do they travel?). The landslide mass is treated as an ‘equivalent 283

fluid’ (Hungr 1995), a material governed by simple basal resistance relationships with a limited 284

number of adjustable parameters. The resistance parameter values in an equivalent fluid model are 285

not necessarily real material properties that can be measured; instead, they are adjusted (calibrated) 286

by the user to produce the best possible simulation of a given real event. Calibration trends amongst 287

groups of similar landslides are then sought that can be used for prediction. 288

A variety of simple rheological models can be used for this purpose. The selection of the most 289

appropriate rheological model depends on the type of landslide in question and, often, the nature of 290

the materials along the path. Two rheological models that are referred to later in this paper, the 291

frictional model and the Voellmy model, are shown in Figure 11. With the frictional model 292

(Figure 11a), the basal resistance is controlled by a single parameter, the bulk basal friction angle, φb, 293

which accounts for pore pressure implicitly. The Voellmy model (Figure 11b) also includes a frictional 294

component (again with implicit pore pressure effects), but adds a turbulence term to account for 295

velocity-dependent resistance. Voellmy (1955) originally developed this model for snow avalanches, 296

but it has since been adopted by landslide modellers (Körner 1976) because it is able to simulate the 297

range of velocities and shape of deposits that are observed in many real landslides, as described 298

below. 299

The frictional model produces forward-tapering deposits and relatively high peak velocities. In 300

comparison, the Voellmy model produces relatively uniform deposits. The turbulence component of 301

the Voellmy model can also limit the peak velocities, in the same way that air resistance limits the 302

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freefall speed of a skydiver. This effect can be visualized using the energy grade line (EGL) concept 303

shown in Figure 11. The EGL in this case connects the centres of mass of the stationary source and 304

deposit material. During motion, the vertical distance between the centre of mass (which can be 305

closely approximated by the elevation of the sliding surface) and the EGL approximates the velocity 306

head, v2/2g, of the centre of mass. In the frictional case (Figure 11a), the EGL slopes uniformly 307

downward at the same angle as the bulk basal friction angle; the velocity head would therefore 308

increase along the path as long as the friction angle is lower than the slope angle. In contrast, in the 309

Voellmy case (Figure 11b), the EGL bends towards the sliding surface as the flow velocity increases. 310

One approach to model calibration is to visually compare simulation results with observations 311

and adjust the parameter values by trial-and-error to achieve a satisfactory match in terms of the 312

simulated runout distance, deposit distribution and velocities (Hungr 1995). This subjective approach 313

is simple to implement using 2D models with one or two adjustable parameters that dominate 314

different characteristics of the simulation and can therefore be adjusted relatively independently of 315

each other. For example, using a 2D runout model with the two parameter Voellmy rheology 316

(Figure 11b), the friction coefficient, which governs the slope angle on which material begins to 317

deposit, can be adjusted to achieve a satisfactory visual match of the observed runout distance, while 318

the turbulence parameter, which limits flow velocities as described above, can be adjusted 319

simultaneously to achieve a satisfactory visual match of independent velocity estimates along the 320

path. 321

With 3D models, this visual approach tends to require more interpretation, and is therefore even 322

more subjective. An example of subjective visual calibration using a 3D model (DAN3D) with the two-323

parameter Voellmy rheology (Figure 11b) is shown in Figure 12. The results of a series of model 324

calibration runs are presented as two visual matrices, one showing simulated deposit depths 325

(Figure 12a) and the other showing simulated flow velocities (Figure 12b). In each matrix, the friction 326

coefficient increases (and therefore reduces the simulated runout distances) from left to right and the 327

turbulence parameter increases (and therefore increases the simulated velocities) from top to bottom. 328

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The range of best match (subjectively-visually) parameter combinations, in terms of the simulated 329

deposit and velocity distributions, are indicated in each case. The best overall match occurs where 330

those two independent results intersect, in this case, at f = 0.05 and ξ = 500 m/s2. 331

A more objective and efficient calibration method has been proposed by Aaron et al. (2016a) 332

using DAN3D automated batch runs and the parameter estimation package PEST (Watermark 333

Numerical Computing 2010). PEST uses a systematic inverse analysis algorithm to determine the 334

combination of model input parameters that best minimize the error variance between model outputs 335

and observed data. In the example shown in Figure 13, DAN3D model results using the two-336

parameter Voellmy rheology are being judged by PEST based on how well they simulate the extent of 337

the actual flow trimline in a series of simulations of the 1903 Frank Slide. The red line highlighted in 338

Figure 13 identifies the parameter combinations that resulted in quantitatively comparable best fits. 339

Trimline fitness can be judged using automated methods that can be coded directly into models. 340

Figure 14 shows one method based on maximization of the ratio of the intersection and union of the 341

simulated and observed inundation areas, as proposed by Galas et al. (2007). The intersection is 342

represented by the purple area where the simulated and observed trimlines overlap, while the union 343

is represented by the whole area covered by both. A perfect fit would occur when the intersection to 344

union ratio is exactly 1. A caveat of automated methods like this is that they can sometimes produce 345

non-unique results (e.g. two loci of possible best-fit parameter combinations). Subjective judgment is 346

therefore still required when interpreting the results. 347

Cepeda et al. (2010) proposed an alternative, multi-criteria calibration method based on a 348

technique known as receiver operator characteristic analysis (ROC). This method permits the 349

evaluation of results against multiple calibration criteria simultaneously and allows the user to 350

subjectively assign weights to the criteria to reflect their relative importance and/or reliability. For 351

example, more weight may be placed on an accurate estimate of the total runout distance than on a 352

point velocity estimate that was back-calculated from relatively unreliable flow superelevation 353

measurements. 354

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In addition to the calibration methods described above, geomorphic clues can also be used to 355

help constrain model input parameters. For example, the friction parameters in both the frictional and 356

Voellmy rheologies (Figure 11) control the slope angles on which material decelerates and deposits in 357

runout models. Therefore, if one expects material to deposit in a certain area (for example, 358

downslope of the fan apex on a well-defined debris flow fan), the field-observed local slope angles 359

can be used to constrain the friction input. 360

The methods described above can be used to produce very good simulations of past events on a 361

case-by-case basis, and many examples of successful case-specific landslide back-analyses have 362

been documented. A thorough recent compilation of over 300 documented back-analyses was 363

presented by Quan Luna (2012). Although one successful runout prediction based on a case-specific 364

calibration was recently documented (Loew et al. 2016), case-specific calibration parameters have 365

limited use in the prediction of future events. Model calibration is more powerful when groups of 366

similar events are back-analyzed together because the resulting patterns are more broadly applicable 367

and can be used in a statistically-justifiable probabilistic way. 368

An early attempt at group calibration was carried out by Hungr and Evans (1996). Using the 2D 369

model DAN-W to back-analyze 23 rock avalanches, they found that the total runout distance in 70% 370

of the cases could be simulated within an error of approximately 10% using the Voellmy rheology with 371

a single combination of input parameters. Similar group calibration exercises were carried out by 372

Ayotte and Hungr (2000), Revellino et al. (2004) and Pirulli (2005). The explicit error bounds reported 373

in all of these studies provide extremely useful information for probabilistic prediction. Like the data 374

scatter of the empirical-statistical methods described earlier, such error bounds can be translated into 375

estimates of runout exceedance probability. 376

An extension of this calibration approach was studied by McKinnon (2010). Forty flow-like 377

landslides were back-analyzed using DAN-W with the same wide range of input parameter 378

combinations. The results for each parameter combination were plotted as histograms showing the 379

number of cases that simulated the observed runout distances within certain error range bins, with 380

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the goal of identifying the input parameter combinations that minimized the error and variance of the 381

results. McKinnon’s results were comparable to those of Hungr and Evans (1996). 382

A similar calibration study was carried out by Quan Luna (2012), who fitted 2D probability density 383

functions to groups of calibrated parameter values (based on back-analyses using various landslide 384

runout models). Such probability density functions can be used directly in Monte-Carlo style 385

probabilistic analysis, similar to the routine methods that are built into several existing rockfall 386

modelling programs. The same probabilistic approach to parameter value selection can also be 387

applied to landslide runout models that require the input of measured material properties, which have 388

inherent variability that can be quantified during material testing; this approach is used in slope 389

stability modelling to predict the probability of failure (Nadim, 2007). Unfortunately, landslide runout 390

models are still limited by relatively long model run times, which can make the Monte-Carlo approach 391

time-prohibitive in practice (Dalbey et al. 2008). 392

Despite the significant advancements described above, more work is still needed to expand the 393

record of calibrated case studies to provide better guidance to practitioners on the selection of model 394

input parameters. An emphasis should be placed on seeking calibration patterns for different types of 395

landslides that can be applied in a probabilistic framework. Most of the existing numerical models 396

listed in Table 1 can be used in this context. 397

Estimating the Vulnerability of Elements at Risk 398

Besides being used to estimate inundation limits and associated spatial impact probabilities, runout 399

models can also be used to estimate the vulnerability of elements at risk within the impact area. One 400

approach to estimating vulnerability that appears promising is based on a parameter called the debris 401

flow intensity index, IDF, defined by Jakob et al. (2012) as the product of the square of the flow velocity 402

and the flow depth (Figure 15). The intensity index represents a simple proxy for dynamic impact 403

pressure. Jakob et al. (2012) noted a good correlation between the intensity index and the degree of 404

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damage to buildings that have been impacted by debris flows, and defined four building damage 405

classes ranging from ‘some sedimentation’ to ‘complete destruction’. 406

Kang and Kim (2016) carried out a similar study of the vulnerability of both reinforced concrete 407

and non-reinforced concrete buildings to a series of debris flows in South Korea in 2011, and 408

proposed three different vulnerability curves based on estimated flow depths, flow velocities and 409

impact pressures. Similar approaches to developing vulnerability curves were presented by Quan 410

Luna et al. (2011) using data from a series of damaging debris flows in Italy in 2008 and Eidsvig et al. 411

(2014) using data from a debris flow event in Italy in 1987. 412

Using one of the approaches described above to estimate building damage, the vulnerability of 413

building occupants can then be estimated. However, because historical fatalities and their 414

relationship to building damage are not well-documented, fairly wide vulnerability uncertainty bounds 415

need to be carried through the risk assessment calculations. More work to compile historical fatality 416

records and correlate them with building damage estimates should be carried out. 417

Limiting Initial Spreading 418

Another modelling issue that researchers are currently working on is how to limit the initial spreading 419

of the slide mass. Continuum models based on shallow flow theory assume that the landslide 420

fluidizes instantaneously upon failure, but in reality this process can be progressive. The result is that 421

continuum models tend to overestimate the amount of spreading during the early stages of motion. A 422

DAN3D simulation of the early stages of the Mount Meager landslide described by Guthrie et al. 423

(2012) is shown in Figure 16, which demonstrates overestimation of initial spreading by a continuum 424

model. 425

Aaron and Hungr (2016) developed a modified version of DAN3D that allows the user to delay 426

fluidization. The method treats the landslide mass as a coherent body until it reaches a certain user-427

specified distance, at which point the original DAN3D algorithm takes over and spreading is allowed 428

to begin. The coherent motion stage is simulated using the method of columns, similar to the 429

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approach used in 3D limit equilibrium slope stability analysis (e.g. Hungr 1987). The individual forces 430

and torques on each column are calculated at each time step and then combined to determine the 431

total force and torque acting on the whole column assembly. This total force and torque are then 432

used to determine the translational and rotational accelerations of the landslide for that time step. 433

The ability to delay fluidization can be very important when analyzing rock slides, which can 434

travel hundreds of metres before they fragment enough to be treated as a fluid body. The 435

translational rock slide that occurred in Goldau, Switzerland in 1806 (Heim 1932) is a good example 436

of this behaviour. A comparative simulation of the Goldau rock slide using Aaron and Hungr’s (2016) 437

flexible block version of DAN3D is shown in Figure 17. A detailed description of this analysis is 438

presented in Aaron and Hungr (2016). As shown in Figure 17, the modified model produces a better 439

simulation of the actual flow trimline. The only extra parameter that needs to be specified is the 440

location where fluidization starts. Aaron and Hungr (2016) suggest that this parameter can be 441

selected based on examination of the pre-slide topography, to identify topographic obstacles or 442

sudden changes in slope (e.g. at the point where a rock slide leaves its planar source area) that could 443

cause the mass to fragment. 444

Simulating Obstructions and Avulsions 445

Another big challenge practitioners face is predicting where debris flows might jump out of their 446

channel, as occurred in the Nevado Huascarán (Evans et al. 2009) and Johnson’s Landing (Nichol et 447

al. 2013) cases described earlier. Three-dimensional runout models can simulate superelevation and 448

runup around channel bends, which can help indicate the most likely avulsion locations, but avulsions 449

can also happen if the channel becomes blocked by woody debris or coarse deposits. The presence 450

of wood itself in the flow can influence where this type of mass deposition occurs, particularly around 451

channel bends (Lancaster and Hayes 2003). At present, this kind of behaviour requires makeshift 452

modelling assumptions to simulate. 453

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The Johnson’s Landing case study shown in Figure 7 is a good example of a major flow avulsion 454

that would have been very difficult to predict in advance. Nicol et al. (2013) hypothesized that the 455

deeply incised channel at this location was temporarily choked with woody debris, which forced more 456

material than expected to jump the bank. They simulated this behaviour by manually modifying the 457

local topographic surface to force an avulsion in their DAN3D model. A subsequent re-examination of 458

this event by Aaron et al. (2016b) using different rheological assumptions also required manual 459

modifications to the topography to adequately simulate the observed channel avulsion. 460

Bouldery debris flow surges can also deposit in the channel and cause avulsions. This coarse 461

material can also be bypassed by muddy afterflows, which may not be as life-threatening as coarse 462

debris flow surges but can still cause considerable property damage. 463

Figure 18 shows a shaded slope LiDAR image of a relatively active debris flow fan. Abandoned 464

paleochannels are visible on both sides of the current active channel, indicating that avulsions are 465

common on this fan. Standard desktop GIS tools can be used to map drainage pathways on the fan 466

and help identify potential avulsion locations. However, judgment is then needed to select the 467

locations where avulsions are most likely to occur during future debris flow events. In a risk 468

assessment, the number of simulated avulsion scenarios needs to be representative enough to 469

capture the spectrum of potential outcomes without making the risk event tree unnecessarily 470

complicated and/or unmanageable. Significant judgment is also needed to assign reasonable 471

conditional probabilities to these sub-scenarios. 472

Sensitivity to Topographic Input 473

Numerical models are also sensitive to the roughness of the topographic input. All other factors being 474

equal, the rougher the surface, the higher the simulated momentum losses and the shorter the 475

modelled runout distance. A very rough sliding surface can also destabilize depth-averaged flow 476

models. 477

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To address this issue, in the 2D model DAN-W, the user builds the sliding surface by adding 478

points along the path and the model fits a smooth spline function to the points. In DAN3D, this 479

process must be mimicked by filtering or smoothing the input DEM, which is analogous to draping a 480

blanket over the surface to smooth out small-scale roughness. Figure 19 shows the visual effect of 481

filtering on a bare earth LiDAR data sample. In this case, most of the important (large-scale) 482

topographic details were preserved. 483

Figure 20 compares DAN3D simulation results from three different model runs using three 484

different degrees of smoothing and demonstrates that modelled landslides travel farther over 485

smoother topography, all other things being equal. With DAN3D, the filtering method used in 486

Figure 20b tends to produce results that are similar to the spline interpolation method used in the 2D 487

model DAN-W. Standardization of this model setup approach is desirable so that calibration results 488

can be directly compared for landslides of different types and scales. The optimum approach may be 489

scale-dependent (e.g. the optimum number of filtering passes could be a function of the characteristic 490

flow depth, or the spacing of grid nodes in the original digital elevation data). With the increasing 491

availability of high resolution bare earth topographic models, there is a strong temptation to use the 492

high resolution data directly in runout models to get a more accurate solution, but such results may 493

not be comparable with, for example, model results based on more widely available SRTM data. 494

Summary and Conclusions 495

Runout analysis is a key step in landslide risk assessment and mitigation design. This paper has 496

provided an overview of the tools and methods that are currently available to practitioners. Although 497

significant advancements in this field have been made over the past decade, particularly with respect 498

to the development of numerical models, several key challenges remain, including: the need for better 499

guidance in the selection of model input parameter values, the challenge of translating model results 500

into vulnerability estimates, the problem with too much initial spreading in the simulation of certain 501

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types of landslides, the challenge of accounting for sudden channel obstructions in the simulation of 502

debris flows, and the sensitivity of models to topographic resolution and filtering methods. 503

In addition to these main current challenges, other emerging topics that warrant more attention 504

from researchers and practitioners include: 505

• Improved model efficiency and user-friendliness, including shorter model setup, run and 506

processing times. For example, the next generation of landslide runout models could 507

potentially make use of the physical realism, high efficiency and intuitiveness of advanced 508

3D video game engines, as has been favourably demonstrated for rockfall applications 509

recently by Ondercin et al. (2015). 510

• Improved model availability and cost. Many models are being developed non-511

commercially for research purposes and are therefore difficult for practitioners to obtain, 512

while models that are commercially-available tend to be expensive. 513

• Improved simulation of mitigation elements. For example, models could include built-in 514

berms and barriers that can be easily adjusted to test sensitivity and optimize their 515

effectiveness. Reliability-based design of mitigation structures may also be possible in a 516

probabilistic analysis framework (e.g. the probability of a deflection berm being 517

overtopped could be estimated based on the probability distribution of the model input 518

parameters). 519

• Integration of model results directly into risk assessment calculations. For example, 520

automated hazard mapping and risk estimates could be developed using batch model 521

runs. 522

• Coupling of landslide and landslide-generated wave models, or development of stand-523

alone models that can simulate both processes equally well. 524

Of all of the challenges summarized above, the selection of model input parameters within a 525

framework that is suited to quantitative risk assessment remains the biggest challenge for 526

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practitioners. Work should therefore continue to focus on the collection of case history data and the 527

probabilistic calibration of runout models for a variety of landslide types. Researchers and 528

practitioners carrying out this work should recognize that calibrated parameter values can depend 529

strongly on the roughness of the input topography; therefore, until a standard approach to model 530

setup is widely adopted, calibration results documented by different workers using different models 531

may not be directly comparable. 532

Acknowledgements 533

This paper was prepared for the 2014 Canadian Geotechnical Society Colloquium Lecture. The 534

author would like to thank the Canadian Geotechnical Society and the Canadian Foundation for 535

Geotechnique for this opportunity. The author would also like to acknowledge the following 536

individuals for their contributions to the paper: Oldrich Hungr, Jordan Aaron, Matthias Jakob, Richard 537

Guthrie, John Clague, Peter Jordan, Dwain Boyer and Dave Southam. Finally, the author would like 538

to thank Stephen Evans and an anonymous reviewer for providing constructive feedback that 539

substantially improved the paper. 540

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Table Captions

Table 1. List of selected numerical landslide runout models that are currently available or in

development.

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Figure Captions

Figure 1. View looking up Capricorn Creek towards the source of the 2010 Mount Meager rock slide-

debris flow. Note the dramatic superelevation in channel bends and the complete stripping of

vegetation along the path. Photograph courtesy of Prof. John Clague, Simon Fraser University.

Figure 2. View from the top of the South Peak of Turtle Mountain showing the 1903 Frank Slide

deposit on the left and conceptual runout exceedance probability isolines for a potential failure

of South Peak on the right. The exceedance probabilities decrease with distance from the

source.

Figure 3. Available runout analysis methods fall into two broad categories: empirical-statistical or

analytical. The red dashed line indicates a sub-category of hybrid ‘semi-empirical’ numerical

models that require parameter calibration.

Figure 4. Schematic illustrations of two landslide geometric correlations: (a) volume, V, vs. angle of

reach, α, and (b) volume, V, vs. deposit area, A.

Figure 5. A probabilistic runout prediction framework based on volume vs. angle of reach data.

Figure 6. A time lapse simulation of the 2010 Mount Meager landslide using the program DAN3D.

The images are shown at 1 minute intervals. See Figure 1 for a photo of the event and

Figure 12 for a summary of the numerical analysis. The digital elevation model that was used

for the simulation was provided by Dr. Richard Guthrie, Stantec.

Figure 7. Simplified depth-averaged forces acting on a column of flowing material. W = gravity, T =

basal shear, ∆P = differential earth pressure, ∆S = differential transverse shear and E =

momentum flux due to entrainment.

Figure 8. Schematic illustration of the Smoothed Particle Hydrodynamics method used in the model

DAN3D. The landslide mass is discretized into a collection of ‘smooth particles’, which can be

visualized as bell-shaped objects. The local flow depths and depth gradients are constructed

by superposition of the particles.

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Figure 9. An example of significant flow splitting during the 2012 fatal landslide at Johnson’s Landing,

B.C. The inset shows the view looking downstream above the point where part of the flow

jumped out of the creek channel. Photographs courtesy of Dr. Peter Jordan and Dwain Boyer,

BC Ministry of Forests, Lands and Natural Resource Operations.

Figure 10. DAN3D simulations of idealized material flowing down a ramp onto a flat surface: (a)

material with zero internal shear strength (like water), and (b) material with an internal friction

angle of 40°. After McDougall (2006).

Figure 11. Two simple rheological models that can be used in an equivalent fluid framework: (a)

frictional, and (b) Voellmy. The adjustable parameters are highlighted in red. φb = bulk basal

friction angle, f = friction coefficient, ξ = turbulence parameter, σ = basal normal stress, ρ = bulk

density, v = velocity, g = gravitational acceleration and EGL = energy grade line.

Figure 12. An example of subjective visual calibration using a 3D model (DAN3D) with the two

parameter Voellmy rheology to simulate the 2010 Mount Meager landslide described by Guthrie

et al. (2012). The best overall match was achieved using f = 0.05 and ξ = 500 m/s2, where the

best match deposit depth (a) and flow velocity (b) results intersect (green box). The digital

elevation model that was used for the simulations was provided by Dr. Richard Guthrie,

Stantec.

Figure 13. An example of a trimline fitness test using PEST with output from the model DAN3D to

simulate the 1903 Frank Slide: (a) trimline fitness test for all parameter combinations that were

run, and (b) output from the simulation using f = 0.1 and ξ = 500 m/s2, which falls on the locus

of best-fit combinations (yellow star in (a)). Images courtesy of Jordan Aaron, University of

British Columbia.

Figure 14. A simple calibration method proposed by Galas et al. (2007) based on maximization of the

ratio of the intersection (purple area) and union (blue + red + purple areas) of the simulated and

observed inundation areas. A perfect fit would correspond with a ratio of 1.

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Figure 15. A sample runout analysis showing modelled debris flow intensity and associated building

damage classes based on definitions proposed by Jakob et al. (2012). The digital elevation

model that was used for the simulation was provided by Dave Southam, BC Ministry of Forests,

Lands and Natural Resource Operations.

Figure 16. A DAN3D simulation of the early stages of the Mount Meager landslide demonstrating

overestimation of initial spreading by a continuum model. The digital elevation model that was

used for the simulations was provided by Dr. Richard Guthrie, Stantec.

Figure 17. Simulations of the Goldau rock slide using (a) the original DAN3D model and (b) the

flexible block version of DAN3D. The actual flow trimline is shown by the dashed line. Images

courtesy of Jordan Aaron, University of British Columbia, modified from Figures 3 and 10 of

Aaron and Hungr (2016) with permission of Elsevier.

Figure 18. A shaded slope LiDAR image of a relatively active debris flow fan showing abandoned

paleochannels that indicate high avulsion potential.

Figure 19. Shaded slope images showing the effect of filtering on bare earth LiDAR data from a

location in the Coast Mountains, B.C.: (a) raw LiDAR data at 1 m grid spacing; (b) LiDAR data

resampled at 5 m grid spacing and filtered 3 times using a Gaussian algorithm.

Figure 20. Simulations of a hypothetical 10,000 m3 debris avalanche-debris flow showing the

influence of surface roughness on runout models. The bare earth LiDAR data was resampled

at 5 m grid spacing with (a) no additional filtering; (b) 3x Gaussian filtering; (c) 10x Gaussian

filtering. The source area is at the upper right in each image. The scale markers on the

horizontal and vertical axes are in metres. The digital elevation model that was used for the

simulations was provided by Dave Southam, BC Ministry of Forests, Lands and Natural

Resource Operations.

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Table 1. List of selected numerical landslide runout models that are currently available

or in development.

Model Type Selected Reference

3dDMM 3D, continuum Kwan and Sun (2007)

DAN 2D, continuum Hungr (1995)

DAN3D 3D, continuum McDougall (2006)

FLATModel 3D, continuum Medina et al. (2008)

FLO-2D 3D, continuum FLO-2D Software Inc. (2007)

Flow-R 3D, spreading algorithm Horton et al. (2013)

GeoFlow-SPH 3D, continuum Pastor et al. (2009b)

D-Claw 3D, continuum Iverson and George (2014)

MADFLOW 3D, continuum Chen and Lee (2000)

MassMov2D 3D, continuum Begueria et al. (2009)

PFC 3D, discontinuum Poisel and Preh (2008)

RAMMS 3D, continuum Christen et al. (2010)

RASH3D 3D, continuum Pirulli (2005)

r.avalanche 3D, continuum Mergili et al. (2012)

r.avaflow 3D, continuum Mergili et al. (2016)

Sassa-Wang 3D, continuum Wang and Sassa (2002)

SCIDDICA S3-hex 3D, cellular automata D’Ambrosio et al. (2003)

SHALTOP-2D 3D, continuum Mangeney-Castelnau et al. (2003)

TITAN2D 3D, continuum Pitman et al. (2003)

TOCHNOG 3D, continuum Roddeman (2002)

VolcFlow 3D, continuum Kelfoun and Druitt (2005)

Wang 2D, continuum Wang (2008)

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