Theoretical Background: 2.1 Landslide Processes 2.1.1 Definitions and Classifications

Chapter 2
Theoretical Background
2.1 Landslide Processes
2.1.1 Definitions and Classifications
A very basic but widely accepted and used definition for landslide was established
by Cruden (1991) and Cruden and Varnes (1996) and defines a landslide as
‘‘the movement of a mass of rock, debris or earth down a slope’’. However, the
term can be confusing if the parts of the word are considered. Cruden and Varnes
(1996) note that it describes all kinds of mass movements and is not limited to
granular soil (as land might suggest) or a sliding movement process. The term
landslide is well established in the research community and will therefore also be
used in this thesis as an overarching term referring to all movement types and
material properties. Further on, the term mass movement is used interchangeably
with landslide.
The most common classification for landslides is based on material properties
and process types (Table 2.1). Besides the main types of movement processes
there is one complex class which contains movement processes with two or more
different processes acting together along with downslope movement of the land-
slide mass.
A second widely acknowledged classification of landslides is based on move-
ment velocity (Cruden and Varnes 1996), which ranges from extremely fast to
extremely slow (Table 2.2). Moreover, landslides can be distinguished regarding
their state of activity. Cruden and Varnes (1996) established eight groups, namely
active, suspended, reactivated, inactive, dormant, abandoned, stabilized and relict
mass movements. Further on, single, multiple and successive movements are
distinguished. Other differentiations can be based on, for example, the water
content of involved materials (Cruden and Varnes 1996).
The term creep, which was used to describe continuous and imperceptible slow
movements of the ground (e.g., Terzaghi 1950, 1961) was omitted due to various
definitions and interpretations. Cruden and Varnes (1996) propose to not use the
B. Thiebes, Landslide Analysis and Early Warning Systems, Springer Theses, 7

DOI: 10.1007/978-3-642-27526-5_2, Springer-Verlag Berlin Heidelberg 2012
8 2 Theoretical Background
Table 2.1 Mass movement classification based on process type and material (Cruden and Varnes
1996; Dikau et al. 1996)
Process type Type of material
Rock Debris Earth
Topple Rock topple Debris topple Earth topple
Fall Rock fall Debris fall Earth fall
Slide Translational Rock slide Debris slide Earth slide
Rotational
Flow Rock flow Debris flow Earth flow
Spread Rock spread Debris spread Earth spread
Complex e.g., rock avalanche e.g., flow slide e.g., slump-earthflow
Table 2.2 Mass movement classification based on velocity of displacement (Australian

Geomechanics Society 2002 after Cruden and Varnes 1996)
Class Description Typical Expected damages and population reaction
velocity
1 Extremely [5 m/sec Disaster of major violence; buildings destroyed by
rapid impact of displaced material; many deaths;
escape unlikely
2 Very rapid [3 m/min Some lives lost; velocity too great to permit all
persons to escape
3 Rapid [1.8 m/h Escape evacuation possible; structures destroyed
4 Moderate [13 m/month Some temporary and insensitive structures can be
temporarily maintained
5 Slow [1.6 m/year Remedial constructions can be undertaken during
movement; insensitive structures can be
maintained
with frequent maintenance work if total movement
is not large during a particular acceleration phase
6 Very slow [15 mm/year Some permanent structures undamaged by movement
7 Extremely slow \15 mm/year Imperceptible without instruments; construction
possible with precautions
term creep and to replace it with the appropriate descriptors of their classification.
However, the term creep may still be applied in a simple mechanical way to
describe deformation that continues under constant stress (Cruden and Varnes
1996; Terzaghi et al. 1996).
2.1.2 Principles of Slope Stability
Landslides are a sign of slope instability which is defined as the ‘‘propensity for a
slope to undergo morphologically and structurally disruptive landslide processes’’
(Glade and Crozier 2005b, p. 43). Glade and Crozier (2005b) visualise slope
stability as a dynamic spectrum (Fig. 2.1). On one end, there is a stable slope
2.1 Landslide Processes 9
Fig. 2.1 Stability states and destabilising factors (after Crozier 1989; Glade and Crozier 2005b)
Fig. 2.2 Stress vectors

within a slope (after Ahnert
2003)
which is subject to preparatory factors which convert the slope to a marginally

stable state. At this point, dynamic triggering factors exceeding certain thresholds
can alter the state of the slope to actively unstable which leads to continuous or
intermittent movement. During the described transformation from stable to
actively unstable slope conditions the margin of stability is continuously
decreasing. Precondition or pre-disposing factors are thought as static factors that
influence the margin of stability and allow dynamic factors. Preparatory factors are
dynamic which change the stability margin over time without initiating slope
failure. Typical examples for preparatory factors are weathering, deforestation,
tectonic uplift or environmental change. Triggering factors actively shift the state
of stability to an unstable condition. Common triggers for landslides are intense
rainstorms, seismic shaking or slope undercutting (Glade and Crozier 2005b).
Inherent in this concept is the theory of extrinsic and intrinsic thresholds (Schumm
1979). Sustaining factors control the behaviour of the actively unstable state and
therefore dictate the duration of movement, form and run out distance of slope
failure.
A similar concept is described by Leroueil (2004) who distinguishes four stages
of landslide movement: a pre-failure stage including deformation process leading
to failure, the onset of failure characterized by the formation of a continuous shear

surface through the entire soil mass, a post-failure stage starting from failure until
the mass stops, and a reactivation phase when sliding occurs on a pre-existing
shear surface.
Stresses acting within a slope can be illustrated by vectors (Fig. 2.2), where a
mass (m) is subject to acceleration of gravity (g) which can be differentiated into a
downslope component (s) and a force acting perpendicular to slope surface (r).
Distribution of stresses depends on slope angle (b) and downslope force increases
with higher slope angles.
The potentially destructive effects of slope instability led to early research in
prediction of slope failures. Calculation of slope stability dates back to Coulomb
(1776) and his work on stability of retaining walls and determination of the most
likely shear surfaces with a wedge method, which are still valuable today (Ahnert
2003). Another important advance of slope stability calculation was made by
Terzaghi (1925), who established the fundamental concept of effective stress.
Therein, the effects of pore water pressure in slope stability are acknowledged.
Pore water pressure is the pressure of water in the voids between solid particles of
the soil (Casagli et al. 1999). As water cannot sustain shear stress, only the
skeleton of solid particles at their contact points can, slope stability decreases with
a higher pore water pressure. The stability of slope can be assessed by calculating
the Factor of Safety (FoS), which is the ratio of driving and resisting forces within
a slope (Crozier 1989):
W
shear strength c þ ðr uÞ tan u0 A cos B u tan u0
FoS ¼ ¼ ¼cþ W ð2:1Þ
shear stress s A sin B
where s = shear stress

c = cohesion with respect effective normal stress
r = total normal stress
u = pore water pressure
r = total normal stress
r0 = ru
u0 = angle of internal friction with respect to effective normal stress
W = weight of the material; that is c = bulk density multiplied by V
B = angle of shear surface
In theory, a slope is stable as long as the FoS is greater than one and slope
movement commences if the FoS is 1.0 or smaller. However, Glade and Crozier
(2005b) stress the point that the FoS is only a relative measure of stability as it
gives no information on the magnitude of destabilisation that is needed until slope
failure occurs. Moreover, some authors describe the onset of movements even
before the FoS becomes lower than 1.0 (Petley et al. 2002, 2005b, c) which they
Fig. 2.3 Idealized stress–strain curves for brittle (a) and ductile (b) deformation (Petley and
Allison 1997)
attribute to the development of micro-cracks which progressively form a complete

shear surface.
The Mohr-Coulomb equation given above is the basis for limit equilibrium
analyses and has been widely applied to calculate the stability of potential slip
surfaces. However, in this form it applies to drained failures, where no excess pore
pressure is generated during shearing. However, undrained conditions can involve
the development of significant excess pore pressure and cause liquefaction, like in
the case of low density, fine-grained, saturated soils (e.g., quick-clay) (Bovis
2004). Moreover, shear strength of rock is largely affected by geological bedding
and stratification, the properties of involved materials, and the morphology and
complex interactions along discontinuities like cracks and joints during shearing
(Prinz and Strauß 2006).
Examination of shear parameters and the stress–strain behaviour of materials
are primarily experimental, because of the technical difficulties to study the pro-
cesses in nature. Shear parameters are generally determined in the laboratory by
undertaking uniaxial or triaxial shear tests (Wu 1996). A relatively undisturbed
soil sample is placed into a shear box and stress is applied until the material fails.
Applied loads and subsequent strains are recorded. Idealized stress-strain curves
for brittle and ductile failure regimes are given in Fig. 2.3.
Most geological materials and engineering soils can display both brittle and
ductile failure modes depending on their confining pressure (Cristescu 1989).
However, brittle failure is dominant at low confining pressures representative for
shallow failures (Petley and Allison 1997). As stress or load is applied soil
materials generally display an initial phase of elastic and recoverable strain. The
applied stress is loaded on the grain-bonds within the material which deform but
do not break. An increase of stress causes the material’s weakest bonds to break
and an elastic–plastic phase can be observed which is characterised by increasing
strain rates. As more and more bonds break, peak strength is exceeded and shear
strength is significantly reduced. The shear surface fully develops in the strain
weakening phase in which shear strength steadily reduces to a residual value.
Fig. 2.4 Idealized strain

curves for the three stages of
creep (after Petley et al.
2008)
During this phase shear zone contraction or dilation may occur which affects pore
pressures and therefore strain rates (Iverson 2005). Thereafter, strains primarily
occur as displacement along the shear surface.
Ductile behaviour can be observed at high effective stresses prevalent in very
deep-seated landslides and in materials with little or no inter-particle bonding like
weathered clays (Petley and Allison 1997). The initial phases of elastic and
elastic–plastic strain are similar to the brittle failure regime. However, due to the
high confining stress no shear surface can develop. Increased load results in purely
plastic deformation at constant stresses as the material reforms. Moreover, a
transition between ductile and brittle behaviour was observed by Petley and
Allison (1997) at very high pressures, which are present in very deep-seated
landslides.
As mentioned above the term creep does not describe a certain landslide type but
refers to the mechanical behaviour of geological materials to constant stress. Some
creep takes place in almost all steep earth and rock slopes and may concentrate along
pre-existing or potential slip surfaces or distribute evenly across the landslide profile
(Fang 1990). Creep movements in landslide can be continuous or may vary sea-
sonally with hydrological conditions (Petley and Allison 1997). Creep can be
maintained for long periods, however, creep gradually decreases shear strength and a
slope’s margin of stability (Fang 1990) and eventually the slope may fail.
A widely acknowledged concept of creep distinguishes between the phases of
creep movement (Okamoto et al. 2004; Petley et al. 2005b, c, 2008). When con-
stant stress lower than peak strength is applied to a soil mass subsequent strains are
time-dependant and can be visualised as displacement versus time plot (Fig. 2.4).
In the primary creep stage strains are initially high due to elastic deformation but
decrease with time. During the secondary creep phase the material suffers diffuse
damage but strains are generally slow or almost steady (Okamoto et al. 2004), or
may even stop altogether (Petley et al. 2008). When diffuse micro-cracks start to
interact to form a shear surface, the critical point into the tertiary phase is reached
(Reches and Lockner 1994; Main 2000). This phase is characterized by a rapid
acceleration of displacement until final failure.
The increasing displacement rates associated with rupture growth and micro-
crack interactions during the tertiary creep stage have been subject to research for
a long time in order to predict final failure (Saito 1965; Bjerrum 1967; Saito 1969;
Voight 1989; Fukuzono 1990) and volcanic eruptions (Voight 1988). The concept
is frequently termed progressive failure analysis and usually employs examination
of movement patterns by plotting movement in K t space, where K ¼ 1=v (v is
velocity and t is time) (Petley et al. 2002).
It has been observed in many shear experiments and real landslides that linear
trends in acceleration occur if failure is imminent. This was the case for first-time
failures and for failures in which brittle behaviour was dominant in the basal shear
zone. However, reactivated landslides and failures where ductile deformation is
dominant display asymptotic trend in K t space which has been observed in several
landslides, e.g., in Italy, New Zealand, California, Japan and the UK (Petley et al.
2002; Carey et al. 2007).
The potential for prediction and early warning of landslide failures has been
showed by several case studies. Kilburn and Petley (2003) and Petley and Petley
(2006) analysed displacement data from the famous Vaiont reservoir rockslide in
Northern Italy, which caused a flood wave that killed around 2000 people in 1963.
The result of the analysis was that at 30 days before final failure a transition to a
linear trend in movement acceleration was visible and final failure was therefore
predictable. Moreover, in the case of the artificial landslide experiment at the
Selford slide (Selford Cutting Slope Experiment) final failure could be predicted
50 days in advance (Petley et al. 2002).
Despite its potential, progressive failure analysis has not been integrated into an
early warning system yet. A test application to the slope under investigation in this
study failed because of slow movement rates and insufficient acceleration phases
(Thiebes et al. 2010).
Slow active landslides are widespread in many geomorphological contexts and
materials, and can display steady movements over long periods of time, often
along completely developed shear zones (Picarelli and Russo 2004). Changes in
displacement rates of slow or extremely slow landslides is in many cases related to
varying pore water pressures (Leroueil 2004) and movements can be continuous or
intermittent. Especially in landslides of moderate depths pore pressures primarily
drive displacements, while in deeper landslides creep and erosion, as other phe-
nomena of stress relief, are the main influential factors (Picarelli and Russo 2004).
While pore pressures control landslide movement on short and medium time-
scales, erosion, weathering, progressive weakening due to strain are influencing on
a larger time-scale (Picarelli and Russo 2004).
Seasonal variations of pore pressures close to surface are not necessarily
reflected by deeper layers if materials are rich in clay (Leroueil 2004). Moreover,
clays also influence infiltration and slope stability by their swelling and drying
behaviour. Very dry clay may develop cracks which allow for quick percolation
into depth along preferential flow paths. Preferential flow paths can have a
positive effect on slope stability by allowing quick drainage of potentially
unstable areas, but can also have an adverse effect by contributing additional
water to areas where shear surfaces may develop (Uchida et al. 2001). Infiltra-
tion in unsaturated materials is a complex process (Leroueil 2004) and strongly
Fig. 2.5 Effects of external perturbations on a geomorphological system (after Bull 1991)
dependant on initial conditions such as antecedent soil water conditions, degree

of saturation, pore pressure field, hydraulic conductivity and amount of water
required for saturation. As a result, it is extremely difficult to relate rainfall
conditions to pore water pressures and to the occurrence of landslides. Moreover,
transferring one threshold to an entire landslide is extremely difficult (Picarelli
and Russo 2004).
Slow moving slopes often interact with infrastructure as movement rates are
generally low, so that permanent avoidance or evacuation is not necessary
(Picarelli and Russo 2004). Still there is a danger of acceleration, as many cata-
strophic slope failures are preceded by long periods of slow creep (Petley and
Allison 1997). Geotechnical stabilisation on the other hand would in many cases
be too expensive or non-effective.
2.1.3 Systems Theory Considerations
Landslides are the results of complex interaction within the natural environment, and
if human intervention is present, the interactions and feedbacks become even more
complex (Armbruster 2002). A widely acknowledged approach in physical geog-
raphy was laid out by Chorley and Kennedy (1971) and aimed to provide a theoretical
framework which allows for analysis of form, material, and processes, as well as
interaction and feedbacks (Dikau 2005). Moreover, the conceptual approach com-
prises variable space and time-scales of system evolution and external system con-
trol, as well as early approaches to non-linear system response (Slaymaker 1991).
Four types of systems can be distinguished: morphological systems, cascading sys-
tems, process-response systems and control systems (Chorley and Kennedy 1971).
Following Glade (1997) morphological systems can be used to describe the interac-
tion between landslide-prone regions and potentially landslide-triggering rainfall
events. Bell (2007) notes, that if research focuses on, for example, landsliding of
periglacial strata cascading systems may be more appropriate. Research on factors
controlling landslide behaviour can benefit from a process-response system point of
view, while control systems are important in geomorphological hazard research
where direct human manipulation of material parameters aims to decrease risks
(Dikau 2005). The effects of external perturbation on a geomorphological system are
exemplified for alluvial deposits in Fig. 2.5.
Reaction time is important within landslide research and illustrates how fast a
slope reacts to external perturbation, such as rainfall, snow melting or earthquakes.
Relaxation time describes the velocity of movement until all energy is depleted
and may range from slow creeping movements to sudden failure. During persis-
tence time a slope is stable until further perturbation impacts trigger further system
response.
The classic systems approach by Chorley and Kennedy (1971) is essentially
based on the concept of thermo-dynamic equilibrium which means that a system
will return to a steady-state by negative feeback effects after external perturbations
(Dikau 2006). In recent years, however, research shifted more to the analyis of
non-equilibrium systems and non-linear relationships (Dikau 2005). Nonlinearity
implies that ‘‘outputs or responses of a system are not proportional to inputs or
forcings across the entire range of the latter’’ (Phillips 2006, p. 110), which is
dominant in geomorphic systems. Sources of nonlinearity in nature are summa-
rized by Phillips (2003) and comprise thresholds, storage effects, saturation and
depletion, self-reinforcing positive feedbacks, self-limitation, competitive rela-
tionships, multiple modes of adjustment, self-organisation and hysteresis. Non-
linear system analysis provides, according to Dearing (2004), new insights and
aids to understand system behaviour. Novel concepts developed in this area of
research include complexity, self-organisation, deterministic chaos and are
reviewed and discussed in detail elsewhere (Phillips 1992a, b, 2003; Richards
2002; Favis-Mortlock and De Boer 2003; Dikau 2006).
There is no single, precise definition of complexity (Favis-Mortlock and De
Boer 2003). However, complexity may loosely be delineated as the fact that
systems cannot be described by the properties of its parts (Gallagher and
Appenzeller 1999). According to Phillips (1992a) complexity can arise from
cumulative process-response mechanisms which are far too numerous to be
accounted for in individual details, or due to multiple controls over process–response
relationships that operate over a range of spatial and temporal scales.
The studies of Bak et al. (1988) on sand-pile models provided some insights on
complex systems. In these models grains were dropped onto a sand pyramid. This
resulted either in no changes, or in landslides of various sizes. The landslide sizes
were found to follow a power-law distribution, but it was, however, not possible to
predict the size of the next landslide. The fact that the system drove itself to a
critical state was referred to as self-organised criticality, a concept that has been
been widely applied to geomorphological processes (Favis-Mortlock 1998;

Phillips et al. 1999; Fonstad and Marcus 2003; Favis-Mortlock 2004).
Deterministic chaos describes the sensitivity of a system to initial conditions
and small perturbations, whereby initial differences or effects of minor perturba-
tions tend to persist and grow over time and may have unpredictable and appar-
ently random consequences (Phillips 2003). The basic principle of deterministic
chaos has popularly been described by the butterfly effect, where the flap of
butterfly wings in one part of the world may cause a hurricane at another place.
Chaotic behavior was first described by Lorenz (1963) who simulated meteoro-
logical processes and found drastically differing model results depending on
minimal changes to small decimal places. Prediction of model states was only
possible for short time-spans.
These aspects of nonlinearity have drastic consequences for the predictability of
natural phenomena such as landslides (Von Elverfeldt 2010). However, this does not
mean that prediction is not possible as Dikau (2005) notes: nonlinear systems can be
simple and predictable, but this is not necessarily the case for complex systems. Most
landslide simulation programs, such as the models presented in Sect 1.3, cannot
accomodate nonlinear system behaviour, such as complexity, self-organisation or
deterministic chaos. Some landslide related research projects, however, exploited for
example self-organised criticality for prediction of landslides and forest fires
(Turcotte and Malamud 2004), characterisation of landslide evolution (Huang et al.
2008) or analyis of triggering conditions (Stähli and Bartelt 2007). Moreover cellular
automata have widely been used to simulate self-organising complex systems in
geomorphic research (Smith 1991; Avolio et al. 2000; D’Ambrosio et al. 2003;
Iovine et al. 2005; Fonstad 2006).
2.1.4 Landslide Triggering
Even though landslides can occur without the impact of external factors, generally
their occurrence is connected to some kind of triggering event. Many factors can
act as triggers for landslides. The most common natural triggers are either related
to geological events, such as seismic shaking due to volcanic eruptions or earth-
quakes, or hydrological events such as intense rainfall, rapid snow melt or water
level changes in rivers or lakes at the foot of slopes (Wieczorek 1996). Moreover,
human interaction in the form of loading or slope cutting can trigger landslide
events. The most important trigger, however, in both shallow and deep-seated
landslides is intense rainfall (Crosta and Frattini 2008). Infiltrating rain percolates
within the soil, thus increasing pore pressures at hydrologic boundaries, which
subsequently decreases shear strength. Positive pore water pressure may occur
directly caused by infiltration and percolation (saturation from above), or may be
the result of perched groundwater tables (saturation from below) (Terlien 1998).
Important factors determining the evolution of saturation are soil permeability and
stratification, preferential flow paths, as well as mechanical characteristics (Berardi

et al. 2005).
In the following landslide triggering, the efforts made to predict landslide
occurrences with respect to hydrological thresholds will briefly be described. More
general reviews on landslide triggering (Wieczorek 1996; Schuster and Wieczorek
2002; Wieczorek and Glade 2005) and rainfall threshold determination (Terlien
1998; Wieczorek and Guzzetti 1999; Polemio and Petrucci 2000; Aleotti 2004;
Wieczorek and Glade 2005; Guzzetti et al. 2007; Guzzetti et al. 2008; Brunetti
et al. 2010) can be found in the respective literature.
Prediction of landslide triggering thresholds is one of the key issues in landslide
research (Berardi et al. 2005), and established thresholds have an important role in
early warning (Terlien 1998). One of the most influential works in this field was
published by Caine (1980) who worked on landslide triggering rainfall thresholds
based on rainfall intensity and duration analyses. Since then, many research pro-
jects worked on defining rainfall thresholds which trigger landslides. Triggering
thresholds are predominantly expressed as rainfall intensity and duration, or
cumulative and antecedent rainfall, and can be defined as the line fitting the
minimum intensity of rainfall associated with the occurrence of landslide in dif-
ferent areas (Caine 1980). However, Terlien (1998) notes that rainfall events that
did not cause landslides also should be recognised. Therefore, minimum and
maximum thresholds should be acquired, where rainstorms below the minimum
threshold never cause landslides, and storms above maximum threshold always
lead to landslides (Glade 1998; Crozier 1999). Between these thresholds landslides
may occur under certain conditions.
Landslide triggering thresholds differ from one region to another based on
hydro-climatological and geophysical properties, such as regional and local rain-
fall characteristics and patterns, slope morphometry, soil characteristics, lithology,
morphology, climate and geological history (Crosta 1998). Further on, landslide
triggering thresholds may also vary with time (Crozier 1999), for example due to
seasonal changes of vegetation (Wieczorek and Glade 2005). Moreover, Crozier
and Preston (1999) note that after movements have occurred resistance to further
events may occur, if for example., all material for future debris flows has already
been transported.
Guzzetti et al. (2007) distinguish between rainfall thresholds on three spatial
scales, for example, global, regional and local scale. A further distinction between
landslide triggering rainfall thresholds can be made between statistical or empir-
ical and deterministic thresholds (Guzzetti et al. 2007). When sufficient data on
landslide occurrences and rainfall conditions are available, thresholds can be
determined in a statistical way. With limited data deterministic models have to be
applied to predict landslide behaviour under certain hydrological conditions
(Terlien 1998).
Most case studies of rainfall thresholds relate to shallow landslides or debris
flows. Triggering of these landslide types generally refers to short and intense rain
storms, while the occurrence of deep-seated landslides is more affected by long-
term rainfall trends (Terranova et al. 2007). Deep-seated landslides share a more
complex hydrology compared to shallow landslides and simple correlations

between rainfall and deep-seated landslide triggering cannot be determined
(Terlien 1998). To establish triggering rainfall thresholds in deep-seated landslides
it is necessary to include rainfall, water infiltration and percolation, generally by
means of modelling subsurface hydrology (Ekanayake and Phillips 1999), and to
determine the location of shear surface, as well as the hydrological triggering
mechanism (Terlien 1998).
The intensity-duration method first proposed by Caine (1980) has been applied
in many other studies (Pasuto and Silvano 1998; Jakob et al. 2006; Matsushi and
Matsukura 2007; Crosta and Frattini 2008; Guzzetti et al. 2008; Capparelli et al.
2009; Saito et al. 2010). Rainfall duration generally refers to periods between
10 min and 35 days (Guzzetti et al. 2008), but some authors extended the time
span analysed to several months (Terranova et al. 2007; Marques et al. 2008).
Long-term rainfall trends have been integrated into threshold determination by
relating intensity and duration of rainstorms to mean annual precipitation (MAP)
(Giannecchini 2006; Giannecchini et al. 2007; Guzzetti et al. 2007; Sengupta et al.
2009).
Crozier and Eyles (1980) developed the Antecedent Daily Rainfall method to
determine rainfall thresholds based on antecedent and daily rainfall. A decay factor
derived from discharge hydrographs controls the influence of antecedent soil
water. Several other case studies applied this methodology as well (e.g., Crozier
1999; Glade 2000; Glade et al. 2000; Strenger 2009).
Although rain is regarded as the prime factor of landslides triggering, infiltra-
tion and the development of positive pore water pressures at potential shear sur-
faces are initiating landslide processes (Reichenbach et al. 1998; Ekanayake and
Phillips 1999; Leroueil 2004). There is, however, no established standard proce-
dure for calculation of pore pressure in relation to rainfall events (Persson et al.
2007). A common procedure is to calculate pore pressures conditions required for
slope instability which are then compared to observed pore pressures and checked
for reasonability. Based on multiple regression analysis of piezometric measure-
ments Matsushi and Matsukura (2007) established rainfall intensity duration
thresholds. Godt et al. (2006) applied a similar approach and derived rainfall
thresholds by comparing rainfall data with measurements of volumetric water
content. Other authors utilise models to predict pore pressure in response to
rainfall events. Wilson (1989) presented a simple numerical model to investigate
the build up of saturation and establish rainfall thresholds. The model represents
soils as leaky barrels, where additional water is added at one rate, while water is
lost by another rate. Wilson and Wieczorek (1995) combined the model with
measurements of piezometric levels and data on antecedent rainfall to derive
rainfall thresholds. A related approach has been performed by Terranova et al.
(2007) who derive critical rainfall situation for landslide triggering based on
modelled infiltration and comparison with piezometer data. Moreover, several
other case studies also applied hydrological models to predict pore pressure
evolution in response to rainfall events to establish landslide triggering rainfall
thresholds (Reid 1994; Crosta 1998; Terlien 1998; Ekanayake and Phillips 1999;
Iverson 2000; Frattini et al. 2009).
Coupled hydrology and stability models have been widely applied to predict the
effects of rain storms, and to define critical situations. Examples for local scale
(Buma 2000; Brooks et al. 2004; Berardi et al. 2005; Pagano et al. 2008), and
regional scale (Dhakal et al. 2002; Crosta and Frattini 2003) approaches can be
found in the respective literature.
2.2 Landslide Investigation and Monitoring
The occurrence of landslides is sometimes surprising for humans as they seem to

occur without previous warning signs. However, (Terzaghi 1950) noted that if a
landslide comes as a surprise to eyewitnesses, it would be more accurate to say that
the observers failed to detect the phenomena which preceded the slide. Therefore,
dedicated landslide analyses and monitoring methods have to be applied to be able
to recognise potential slope failures.
Many methodological approaches have been developed to reveal the occurrence
of landslides in space and time, to investigate processes acting within mass
movements and to monitor ground displacements. The wide range of possible
methodological approaches for landslide research stretch from field or desk-based
mapping, to measurements of surface and subsurface movement in field or by
remotely acquired data sources, recordings of triggering factors like rainfall or
hydrological parameters, and the use of simulation models.
Landslides can be assessed on various spatial scales (Glade and Crozier 2005a),
but a general distinction between local and regional approaches can be made. The
initial step of regional approaches is to define the spatial occurrence of landslides,
commonly by preparing landslide inventories (Wieczorek et al. 2005). For local
analyses Nakamura (2004) argues that one of the first steps to understand the
landslides under investigation are field investigation s and boreholes to define the
slip surface. Regarding rockslides, but also applicable to other landslide phe-
nomena, Glawe and Lotter (1996) stated that when instabilities can be expected
geotechnical investigation s, displacement monitoring and modelling techniques
are generally applied. Following Cornforth and Mikkelsen (1996) ideal features of
a landslide monitoring system are continuous measurements of pore water pressure
in the shear zone by automated sensors in order to correlate these with rainfall
data.
In the following a review of methods for landslide detection, surface and sub-
surface investigations and monitoring techniques is given. The aim of this chapter
is not to provide a complete summary of all methods available for landslide
research, but rather to give a comprehensive overview on the methods most used in
research practice and to highlight their advantages and disadvantages for moni-
toring. More information on the methodological approaches to landslide investi-
gation and monitoring methods can be found in the sources given or in the general
reviews (Franklin 1984; Keaton and DeGraff 1996; McGuffey et al. 1996;
Mikkelsen 1996; Soeters and Van Westen 1996; Turner and McGuffey 1996;
Olalla 2004; Van Westen 2007; Liu and Wang 2008). No self-contained review on
existing monitoring systems will be given as many examples of monitoring sys-
tems are presented in the review on early warning systems (Sect 1.4). It is however
important to mention that many landslide monitoring systems employ several
different techniques, such as methods for measuring landslide movement and
hydrology.
Regarding monitoring it should be made clear, that there is no obvious
threshold that determines what time intervals between repeated measurements are
necessary for it to be classed as monitoring. Olalla (2004) points out that moni-
toring can range from, for example, inclinometer measurements carried out once in
a year, or automatic measurements in intervals of seconds. Therefore, every
repeated measurement could be defined as monitoring. Automatic monitoring
systems are however more convenient than manual measurements as they do not
require humans to regularly go to study sites which may be remote or difficult to
access. Another advantage of automated monitoring systems is the ability to
control measures by time intervals, thresholds or user input. Besides this, questions
of data storage, transmission and security arise with such automatic systems.
Moreover, data should automatically be processed and checked to prevent
inconsistencies (Olalla 2004). However, issues of managing automatic monitoring
systems will not be discussed here.
2.2.1 Mapping and Inventory Approaches
A basic method to detect landslides in space and to prepare landslide inventories is

geomorphological mapping in the field. Geomorphological mapping requires
expert knowledge and experience of landslides and the study area. Results can
vary drastically depending on the specialists who prepared the map, the knowledge
on the study area and the processes present (Guzzetti et al. 2000; Ardizzone et al.
2002). Repeated mapping campaigns in the field without further measurements
give rather qualitative information on how processes have evolved over time but
are essential for process understanding. Important information could include
cracks that open up due to ground displacements, or damage to existing
infrastructure.
Landslide maps and inventories are frequently prepared based on the analysis
of remote sensing data like stereographic aerial or space-borne images, and digital
terrain models (DTM) from, for example, LiDAR (Light Detection And Ranging )
data. General reviews on landslide mapping and inventories can be found in
various literature sources (e.g., Soeters and Van Westen 1996; Malamud et al.
2004; Guzzetti et al. 2006; Van Westen 2007).
The use of aerial photography is well established in landslide research (Soeters
and Van Westen 1996). Interpretation of aerial images is primarily qualitative;
2.2 Landslide Investigation and Monitoring 21
however photogrammetric methods can be used to extract quantitative information.

While qualitative interpretation is common, quantitative studies are more rare,
probably due to limited availability of good quality photographs, adequately fixed
control points and cost (Morgenstern and Martin 2008). However, several landslide
related quantitative photogrammetric studies have been described (Maria et al. 2004;
Mills et al. 2005; Hu et al. 2008; Liu and Wang 2008; Smith et al. 2009).
Medium resolution satellite imagery, such as LANDSAT, SPOT, ASTER,
IRIS-D etc., is today used routinely to create landuse maps and inventories of
landslides (Van Westen 2007). He also notes that Google Earth and other providers
of high resolution satellite data greatly facilitate mapping by offering capabilities
of creating polygons and exporting them to Geographic Information Systems (GIS).
In several case studies landslide inventories were created based on satellite imagery
(Mondini et al. 2009; Fiorucci et al. 2010; Santurri et al. 2010; Yang and Chen 2010)
or Google Earth (Sato and Harp 2009; Chigira et al. 2010).
In recent years the interpretation of Airborne Laser Scanning (ALS) DTM data
has been frequently applied for creation of landslide inventories (Van Westen
2007). By removing vegetation and other objects from the DTM, Digital Surface
Models (DSM) can be created (Schulz 2004). This together with different modi-
fiable angles of shading enable very detailed mapping of landslides and other
geomorphological features (Haneberg 2004; Thiebes 2006). Examples of
ALS-based landslide maps are provided by several authors (Chigira et al. 2004;
Sekiguchi and Sato 2004; Ardizzone et al. 2007; Eeckhaut et al. 2007).
By using multi-temporal remote sensing data sets landslides can be dated, and
activity and evolution quantitatively investigated. Examples for multi-temporal
landslide inventories are provided by several authors (e.g., Cardinali et al. 2002; Dai
and Lee 2003; Brennecke 2006; Imaizumi et al. 2008; Chiang and Chang 2009).
Several automatic landslide detection and mapping approaches have been
developed and are comprehensively reviewed by Van Westen (2007). These
approaches utilise DTM subtraction analysis or multi-spectral analysis of satellite
imagery. Fairly recent applications of an automated landslide mapping system are
presented by Tarantino et al. (2004) and Booth et al. (2009).
Another remote-sensing method for mapping landslides and for detection rates
and extents of ground deformations is Synthetic Aperture Radar (SAR). SAR and
the related methods of Interferometric SAR (InSAR), and their use for landslide
research are extensively reviewed by Rosen et al. (2002), Froese et al. (2004) and
Morgenstern and Martin (2008). SAR is based on microwave signals which are
emitted by a satellite or airplane and the back-scattered signals, which represent
distance measurements, are recorded. By processing two slightly offset images
from the same flight paths InSAR images can be created which can be used to
create pixel-based images which form a DTM. The benefits of this method are that
radar measurements can be performed at almost every weather condition and at
day and night. Furthermore, large areas can be analysed in a short period of time.
Determination of displacements can be achieved by analysing images from dif-
ferent flights. To further increase the accuracy of displacement measurements
Permanent or Persistent Scatterer Interferometric Synthetic Aperture Radar
(PS-InSAR) was introduced to landslide research (Morgenstern and Martin 2008).

Permanently fixed ground-points, in many cases buildings or other constructions,
are determined in multiple InSAR images and relative movements of these points
can be measured. Best results, with accuracy of measurements in the sub-centi-
metre range, can be obtained for movements along the line of sight (Rosen et al.
2002; Luzi et al. 2005). Several case studies have been performed utilising
satellite-based InSAR technology to detect and monitor ground movements
(Colesanti and Wasowski 2004; Ferretti et al. 2005; Meisina et al. 2006; Calcaterra
et al. 2008; Vallone et al. 2008; Yin et al. 2010b).
Also historic data such as newspaper articles, eyewitness records, road con-
struction office reports, city archives, old photographs and many more sources can
be utilised for landslide mapping. In many cases spatial and temporal information
regarding landslide occurrence can be found which are exceptionally useful for
understanding the magnitude-frequency characteristics of mass movements (Glade
2001). Examples of landslide inventories based on historic data can be found in,
for example, Calcaterra et al. (2003), Carrara et al. (2003), Tropeano and Turconi
(2004) and Kohn (2006).
2.2.2 Displacement Measurements
Many field methods exist to measure ground displacements due to landslide

movements. A simple but convenient field method is the use of quadrilaterals
(Keaton and DeGraff 1996) which consist of four stakes that are fixed inside and
outside the landslide body. Distances between the stakes can then be measured
manually by tape. Quadrilaterals have been applied within several research
applications (Baum and Fleming 1991; Bogaard 2000; Giraud 2002; Fernandez
Merodo et al. 2004; Keaton and Gailing 2004).
Standard theodolite geodetic measurements are frequently applied to measure
and monitor ground displacements (Reyes and Fernandez 1996; Walstra et al.
2004; Wasowski et al. 2004; Burghaus et al. 2009) and available automatic sys-
tems have often been used (Oboni 2005; Heincke et al. 2010). However, theodolite
measurements require pre-defined ground points or prisms. Manual measurements
are also cost and time intensive.
The Global Positioning System (GPS) is another method that is often used to
monitor landslide movements (Bonnard et al. 1996; Wasowski et al. 2004; Mills
et al. 2005; Webster and Dias 2006; Yin et al. 2008; Zhang et al. 2008). Precision
of measurements is in the range of cm to mm. However, the applicability of this
method depends on the visibility of satellites which may not be the case in narrow
valleys, densely forested areas or on steep cliffs.
The methods described above are applicable to measure and monitor ground
deformation at the surface. In order to understand landslide behaviour subsurface
measurements are necessary. Basic approaches are pits and trenches which can be
used to investigate e.g., the depth of a landslide and the position of shear surfaces
or to take undisturbed material samples. Generally, pits and trenches can only be
established on shallow movements. Examples are given by Bromhead et al.
(2000), Clark et al. (2000) and Topal and Akin (2008). Penetration tests can also
be performed to investigate stiffness of subsurface materials. More often, drillings
are utilised to investigate landslide bodies. An ample variety of drilling devices is
available on the market from simple handheld sounding poles to truck-sized rotary
drilling machines. Besides the advantage of directly probing the landslide body
and having the opportunity to take core samples, sensors can be applied within the
boreholes to further investigate subsurface movement and hydrological processes.
In many cases inclinometers are used to determine subsurface movement of
landslides (Borgatti et al. 2006; Bonnard et al. 2008; Bressani et al. 2008;
Jongmans et al. 2008; Mihalinec and Ortolan 2008; Yin et al. 2008). General remarks
on the use of inclinometers for landslides research are provided by Stark and Choi
(2008). Inclinometers consist of a flexible drilled pipe which is placed vertically into
a drilled borehole. A high-precision probe is inserted and the inclination of the pipe is
measured in even distances, for example, every e.g., 50 cm. Repeated measurements
give information of the occurred inclination changes in downslope and horizontal
direction for the entire length of the pipe. However, it is important that inclinometers
are fixed into the stable ground beneath the shear surface to prevent data bias.
Automated inclinometer s are commercially available and usually consist of several
inclinometer probes connected to each other to a chain, or automatic systems where
the probe automatically moves within the pipe. Within landslide monitoring the use
of automatic inclinometer and inclinometer chains has been described by for
example, Lollino et al. (2002) and Olalla (2004), Volkmann and Schubert (2005) and
Wienhöfer and Lindenmaier (2009). However, inclinometers can only withstand a
certain amount of displacement before pipes break. This makes them especially
applicable for monitoring of slow moving landslides, but also for detection of shear
processes in faster moving landslides.
A more recent method for the detection of subsurface movements and defor-
mation is Time Domain Reflectometry (TDR). Barendse and Machan (2009) note
that inclinometers can determine the magnitude and direction of ground deforma-
tion, while TDR is primarily used to identify depths of active shearing. The TDR
method has initially been developed in the 1950s for locating discontinuities in
power transmission cables (Pasuto et al. 2000). TDR has first been used within
landslide research in the 1980s in underground coal mine monitoring (Olalla 2004)
and since then applied to several other case studies (Pasuto et al. 2000; Barendse
and Machan 2009; Singer et al. 2009; Yin et al. 2010a). The principle of TDR is
based on an electric signal sent through a coaxial cable. Shear movements deform
the cable which creates a spike in cable signature and depth can be detected from
the signal. Laboratory tests of TDR method for detection of shear processes have
been performed by Baek et al. (2004) and Blackburn and Dowding (2004). Pasuto
et al. (2000) compared TDR cables to inclinometer measurements and extensom-
eters. Their result was that TDR cables are less sensitive to deformations but can
withstand a larger displacement than usual inclinometers. The higher stability of
TDR cables make them a good choice for monitoring faster moving processes like
the Gschliefgraben flowslide in Austria (Marschallinger et al. 2009).
Wire or rod extensometers are used to monitor the distance between two points
and are frequently utilised in surface movement investigations (Furuya et al. 2000;
Angerer et al. 2004; Barla et al. 2004; Willenberg et al. 2004; Wu et al. 2008).
Extensometers are in most cases applied to investigate surface movements but can
also be installed within boreholes (Bloyet et al. 1989; Krauter et al. 2007).
Accuracy of extensometers depends on the length measured and usually is in the
sub-mm range.
Tiltmeters are able to give high resolution information on inclination and have
been applied to several landslide monitoring systems (Clark et al. 1996; Meidal
and Moore 1996; Barton and McCosker 2000; Blikra 2008; García et al. 2010).
Crackmeters are used to monitor displacements in the sub-mm range at joints
and cracks in rocks, buildings and other structures. The application of crackmeters
has been described by several authors (e.g., Keaton and DeGraff 1996; Greif et al.
2004; Olalla 2004; Vlcko 2004; Moore et al. 2010).
The mentioned field based methods only give information on ground dis-
placements for points or along lines. However, spatial methods are also available
that give information on displacement for entire slopes.
In recent years many studies utilised Terrestrial Laser Scanning (TLS) for
monitoring of geomorphological processes. The technique is similar to LiDAR,
but ground-based. In contrast to LiDAR it is appropriate for steep cliffs and rock
faces as the scanner can be placed in front of it. TLS scans are used to create three
dimensional DTM which can further be analysed quantitatively within GIS or
CAD environments to assess e.g., the volume of displaced material between
measurements. Precision of TLS is heavily dependent on distance to the target and
ranges from centimetres to mm, as well as environmental conditions such as rain
or vegetation. General remarks on TLS and its usage for monitoring geomor-
phological processes are provided by Prokop and Panholzer (2009) and Schaefer
and Inkpen (2010). Many case studies applied TLS for landslide monitoring (e.g.,
Mikoš et al. 2005; Rosser et al. 2005; Rosser and Petley 2008; Avian et al. 2009;
Baldo et al. 2009; Oppikofer et al. 2009; Abellán et al. 2010).
SAR methods can also be applied in ground-based studies, which are frequently
termed Slope Stability Radar (SSR) (Van Westen 2007). The major advantages of
this method are that they provide high precision data in sub-millimetre range
without being affected by weather conditions and without the need to install
reflectors or ground marks. However, vegetation drastically decreases accuracy.
Luzi et al. (2005) used a ground-based DinSAR system to monitor displacement on
the Italian Tessina landslide and compared the measurements to regular theodolite
surveys. Based on comparable displacement results by both methods they conclude
that InSAR is also applicable for landslide early warning systems. Several other
research projects installed SSR systems to monitor displacements of landslides
(Canuti et al. 2002; Antonello et al. 2004; Casagali et al. 2004; Eberhardt et al.
2008; Bozzano et al. 2010; Casagli et al. 2010).
Another method that has increasingly been used in recent years is Brillouin
Optical Time-Domain Reflectometry (BOTDR). These optical fibres can be used
for measurement of ground deformations along profiles. The principle of this
method is based on an interaction of pulsed beam and photons that are thermally
excited within the light propagation medium (Wang et al. 2008a), which are
affected by temperature and strains. Laboratory simulations to test the applicability
of BOTDR (Dai et al. 2008; Wang et al. 2008a), as well as field applications
(Higuchi et al. 2007; Dai et al. 2008; Shi et al. 2008a, b; Moore et al. 2010) have
been described.
2.2.3 Hydrological Measurements
Given the great importance of rainfall and slope hydrology for landslide triggering,
these factors are frequently analysed and monitored within landslide research.
Climatic factors such as rain, snowfall, temperature and wind are usually measured
at climate stations, which are commercially available or in many cases provided by
meteorological agencies. Measurement of ground-water conditions such as pore
pressures and soil water suction is usually accomplished by using piezometers and
tensiometers. An overview on different types of these sensors can be found in
Kneale (1987). Piezometers are probably the most common hydrological sensor
utilised for landslide research (e.g., Wu et al. 2008; Yin et al. 2008; Calvello et al.
2008; Ching-Chuan et al. 2009; Yin et al. 2010a) and come as simple standpipe or
more advanced vibrating wire piezometers. Piezometers measure the pressure of
water in saturated soils and therefore give information on the height of the
groundwater table within a soil. Tensiometers measure matrix potentials and are
frequently utilised to assess the soil suction in the vadose zone (Li et al. 2004;
Rinaldi et al. 2004; Montrasio and Valentino 2007; Greco et al. 2010). Piezometers
and tensiometers are usually installed within boreholes or directly into the soil at
trenches.
In recent years Time Domain Reflectometry (TDR) has also been applied to
measure volumetric soil water contents. Greco et al. (2010) compared TDR sen-
sors with tensiometers and concluded that TDR might be more useful for landslide
monitoring and early warning since TDR measurements of soil water content
change smoothly, while soil suction showed abrupt steep fronts. More examples of
TDR application for assessing soil water are presented by e.g., Hennrich (2000),
Tohari et al. (2004) and Kim (2008).
The chemical properties of ground and pore water have a widely acknowledged
effect on shear strength and affect slope stability (Di Maio and Onorati 2000;
Angeli et al. 2004) by for example, influencing the mechanical behaviour of clays
(Leroueil 2004). However, monitoring of ground water composition is only rarely
included in landslide monitoring systems (Sakai and Tarumi 2000; Montety et al.
2007; Sakai 2008).
2.2.4 Geophysical Measurements
Several methods from geophysics have been utilised for landslide research, mainly
for prospection of landslide bodies and for investigation of hydrological processes
acting within landslides. However, wider application of geophysics in landslide
research have been hindered for two reasons (Jongmans and Garambois 2007):
geophysical methods provide images of geophysical parameters which are not
directly linked to geological parameters required by geotechnical engineers and
geomorphologists; and the overestimation of the quality and reliability of results
among some geophysicists. The main advantages of geophysical methods com-
pared to standard geotechnical approaches are that they are non-invasive and can
be applied to large areas for a low cost. However, the main disadvantages are the
decrease of resolution with depth, the non-uniqueness of solutions for data
inversion and interpretation, and the in-direct information (Jongmans and
Garambois 2007). Generally, geophysical methods are used for prospection of
landslide bodies, detection of discontinuities and shear surfaces, as well as for
investigation of hydrological regimes. Measurements are usually short-term and
only a few long-term monitoring exist (Supper and Römer 2003; Lebourg et al.
2005). Geophysical methods will only briefly be presented here, more detailed
reviews on geophysical application are provided by many introductive textbooks
(Telford et al. 1990; McGuffey et al. 1996; Parasnis 1997; Reynolds 1997; Kearey
et al. 2002; Milsom 2003; Schrott et al. 2003; Knödel et al. 2005).
Seismic methods are based on the velocity measurements of seismic waves in
subsurface materials. Generally, denser material causes faster wave propagation.
At layer interfaces waves are partly reflected, but also partly transferred into depth
due to refraction. In geomorphological applications seismic signal is usually
induced by a sledge hammer that is pounded on a steel plate. Penetration depths of
more than 30 m can be reached by more powerful sources e.g., drop weights or
explosives) (Schrott and Sass 2008). Measurements are taken by geophones which
are located at even distances along a profile. A number of different seismic
techniques have been established, of which seismic reflection, seismic refraction
and seismic tomography are the most common. Although seismic methods proved
to be suitable for many geomorphological studies which require definition of
subsurface properties (Hecht 2001), such as determination of active layer in per-
mafrost or volumes of sediment bodies, problems may occur if low velocity layers
are sandwiched in high velocity layers (Schrott and Sass 2008). Examples of
dedicated landslide subsurface characterisation applying seismic methods are
provided by several authors (Schmutz et al. 2000; Willenberg et al. 2002; Meric
et al. 2004; Meric et al. 2005; Heincke et al. 2006, 2010).
A variation of seismic method is applied to record fracture signals produced by
deformation within landslides, and to locate fracture both in space and time. These
methods can be distinguished as micro-seismic, nano-seismic and passive seismic
(Joswig 2008). Instead of creating a seismic signal by e.g., a sledge hammer, these
methods use the acoustic signals emitted by deformation processes by ‘‘listening’’
to ruptures. This method yields information on depth and mode of slope

deformation (Bláha 1996). An increasing number of studies employ such
approaches in laboratory tests (Dixon and Spriggs 2007; Ishida et al. 2010) and
field applications (Merrien-Soukatchoff et al. 2005; Amitrano et al. 2007; Meric
et al. 2007; Häge and Joswig 2009; Walter and Joswig 2009).
Ground penetrating radar (GPR) has increasingly been used in recent years in
many geomorphological research projects to investigate subsurface (Sass and
Krautblatter 2007; Gomez et al. 2009). Advantages of the method include high
resolution and wide range of penetration depth (Jongmans and Garambois 2007).
In landslide research, GPR is generally used to determine landslide boundaries and
discontinuities, such as shear surfaces and cracks in rock, but GPR is also sensitive
to groundwater. High-frequency electromagnetic waves are emitted by an antenna
and wave propagation is determined by dielectric properties of the subsurface
materials (Schrott and Sass 2008). Emitted pulses are reflected by inhomogeneities
and received by a second antenna. The antennas are usually moved along a profile,
and travel times of pulses are measured and subsequently inverted into 2D images.
Penetration depth depends on material properties and frequencies used, but can be
up to 60 m if conditions are favourable (Schrott and Sass 2008). Landslide related
research applying GPR are provided by (Bichler et al. 2004; Avila-Olivera and
Garduño-Monroy 2008; Sass et al. 2008; Willenberg et al. 2008; Pueyo-Anchuela
et al. 2009). However, GPR is generally only applied for short-term measurement
campaigns, and has not been employed in monitoring projects. Still, Roch et al.
(2006) propose repeated GPR monitoring for rockfall monitoring.
Electrical resistivity and spontaneous potential are both geoelectrical methods.
Self-potential measurements investigate natural electrical potential by assessing
potential differences between pairs of electrodes. If electrochemical processes are
absent within a slope changes reflect changes of fluid flows, i.e. ground water.
Spontaneous potential measure can more easily be deployed and monitored
compared to the resistivity method (Jongmans and Garambois 2007); however,
electrical resistivity measurements are more common (Telford et al. 1990). This
method is based on the measurement of electrical potentials between an electrode
pair while a direct current is transmitted by another electrode pair (Schrott and
Sass 2008). Several different procedures and array setups exist for electrical
resistivity measurements which have to be chosen depending on the research
question, study site properties and desired penetration depth. Electrical resistivity
is affected by the nature of material, in particular clay content, water content, as
well as rock weathering and fracturing (Jongmans and Garambois 2007).
Geoelectric geophysical methods have frequently been used for landslide prospec-
tion and determination of internal structure, such as the location of shear surfaces and
boundaries of landslide bodies (Jomard et al. 2007; Deparis et al. 2008; Sass et al.
2008; Heincke et al. 2010). However, sensitivity of resistivity to water content also
allows detection of groundwater tables or preferential flow (Hiura et al. 2000;
Kusumi et al. 2000; Lebourg et al. 2005). Geoelectrical methods are usually applied
for short-term measurement campaigns, and only a few case studies describe con-
tinuous monitoring (Supper and Römer 2003; Lebourg et al. 2004).
2.3 Landslide Modelling
Landslide modelling is, along with experimental subsoil exploration and

experience driven safety assessment, one of the main tasks of slope stability practice
(Janbu 1996). Models are applied to analyse current stability status and to predict
slope behaviour under certain conditions such as rainfall events or scenarios for
environmental change. Moreover, models are used for the back-analyses of already
failed slopes and for assessment of effectiveness of geotechnical stabilisation mea-
sures (Barla et al. 2004). However, when dealing with modelling it is important to
keep in mind, that all models are necessarily simplified generalisation and approx-
imations of processes which are occurring in nature (Favis-Mortlock and De Boer
2003).
Modelling of landslide failures can be either qualitative or quantitative (Carrara
et al. 1999). Qualitative approaches integrate descriptive prediction and the
opinion of experts, while quantitative applications are based on numerical simu-
lations. Landslide modelling approaches can broadly be separated into models that
are focussing on single landslide processes (i.e. local models), and models with
greater spatial extent (i.e. regional models) (Crozier and Glade 2005).
Local approaches to landslides have a long tradition within geotechnical
engineering slope stability practice, while regional applications have increasingly
emerged since the wide availability of powerful computers and GIS. A brief
summary on regional and local approaches to model landslides in order to predict
slope failures is given in the following sub-chapters.
2.3.1 Regional Models
Regional landslide modelling methods generally focus on either landslide sus-

ceptibility or hazard, or eventually landslide risk. Landslide susceptibility is
defined as the ‘‘probability of spatial occurrence of slope failures, given a set of
geo-environmental conditions’’ (Guzzetti et al. 2005, p. 113). Therefore landslide
susceptibility modelling seeks to delineate the terrains’ potential for landslide
processes. In contrast, landslide hazard is defined as ‘‘the probability of occurrence
within a specified period of time and within a given area of potentially damaging
phenomenon’’ (Varnes 1984). The term hazard therefore also requires definition of
magnitude of potential events, that is, affected area, volume and velocity of
expected landslide events (Reichenbach et al. 2005). The term landslide risk refers
to the outcomes of landslide events and is defined as ‘‘the expected degree of loss
due to a landslides and the expected number of lives lost, people injured, damage
to property and disruption of economic activity’’ (Varnes 1984). While landslide
susceptibility and hazard concentrate on the causes and properties of landslides,
risk also refers to the consequences and outcomes of such processes, which are
strongly dependant on the vulnerability of the effected people and infrastructure.
2.3 Landslide Modelling 29
Soeters and Van Westen (1996) distinguish between four distinct approaches
for regional landslide hazard analysis, i.e. inventory-based, heuristic, statistic and
deterministic approaches.
Landslide inventories allow for detailed analyses of landslide distribution and
in case of multi-temporal inventories activity patterns and form the basis for
regional modelling of landslide susceptibility, hazard and risk.
Heuristic methods integrate the knowledge and experience of geomorphological
and geotechnical experts to derive a regional map of landslide susceptibility and
hazard. Soeters and Van Westen (1996) distinguish between geomorphological
analysis (Kienholz et al. 1984; Cardinali et al. 2002; Reichenbach et al. 2005) and
weighted combination of thematic maps (Pachauri et al. 1998; Nagarajan et al.
2000; Dikau and Glade 2003; Moreiras 2005; Petley et al. 2005a).
Statistical methods are the most frequently applied method to model regional
landslide susceptibility and hazard, and to predict future slope failures (Armbruster
2002). Herein, a statistical relationship between possible landslide causative fac-
tors and the presence of existing landslides is established, and used for prediction
of future landslide by spatial interpolation. A vast range of different methods has
been developed. Bell (2007) provides a extensive list of statistical methods, of
which the most frequently applied are bivariate regression (Ayalew et al. 2004;
Süzen and Doyuran 2004), multiple regression (Carrara 1983; Chung et al. 1995),
discriminant analyses (Ardizzone et al. 2002; Carrara et al. 2003; Guzzetti et al.
2006), logistic regression (Atkinson et al. 1998; Ohlmacher and Davis 2003; Süzen
and Doyuran 2004; Brenning 2005), neural networks (Fernández-Steeger et al.
2002; Lee et al. 2003; Catani et al. 2005), support vector (Brenning 2005),
bayesian statistic (Chung and Fabbri 1999; Lee et al. 2002; Neuhäuser 2005),
fuzzy logic (Tangestani 2003; Dewitte et al. 2006; Lee 2006) and likelihood ratio
(Chung et al. 1995; Chung 2006; Demoulin and Chung 2007).
Regional deterministic models apply physically-based simulations to assess
landslide susceptibility expressed chiefly as FoS, and provide useful insights into
landslide causes (Carrara et al. 1992). The most frequently applied methodology for
regional deterministic modelling is based on distributed hydrological modelling and
stability calculation using a simplified approach, i.e. the infinite-slope model.
Hydrological modelling is essentially based on topographical flow routing and the
simulated development of soil saturation above an impermeable layer (O’Callaghan
and Mark 1984; Fairfield and Leymarie 1991; Freeman 1991; Quinn et al. 1991; Lea
1992; Costa-Cabral and Burges 1994; Terlien et al. 1995; Tarboton 1997). Calcu-
lation of slope stability utilises geotechnical parameters such as cohesion and internal
friction, which can be measured in the field or laboratory (Soeters and Van Westen
1996; van Westen et al. 1997). The infinite-slope model estimates stability for single
grid-cells of a DTM and neglects any effects of neighbouring areas. Moreover,
deterministic methods are only applicable when geomorphic and geologic conditions
are fairly homogenous over the entire study area and landslide types are simple
(Soeters and Van Westen 1996). Due to these limitations, regional deterministic
models are only suitable for simple landslide processes, such as shallow translational
landslides. The most widely used models for regional deterministic analyses are
Fig. 2.6 Simplified illustration of method of slices (after Conolly 1997)
TOPMODEL (Montgomery and Dietrich 1994; Casadei et al. 2003; Meisina and
Scarabelli 2007), SHALSTAB (Dietrich et al. 1998; Morrissey et al. 2001; Huang Jr
et al. 2006), and SINMAP (Pack et al. 1998, 2001, 2005; Zaitchik and van Es 2003;
Pack and Tarboton 2004; Kreja and Terhorst 2005; Thiebes 2006; Deb and El-Kadi
2009), but similar studies have also been performed by other authors (Hammond
et al. 1992; Terlien et al. 1995; Wu and Sidle 1995; van Westen et al. 1997; Wu and
Sidle 1997; Sidle and Wu 1999; Xie et al. 2004; Claessens et al. 2005).
2.3.2 Local Models
Models for the analysis of single slope failures, i.e. local models, have a long
tradition in geotechnical slope stability practice. These models have frequently
been applied to assess the stability of human-made or natural slopes, and the
design of slopes, such as embankments, road cuts, open-pit mines etc. Moreover,
physically-based models for single slopes allow detailed investigation of failure
processes, assessment effects of triggering events, and assessment of the effec-
tiveness of remedial measures and stabilisation works.
Today, a wide range of computer calculation programs are available for
numerical slope stability assessment. Despite the development of more sophisti-
cated numerical models, limit-equilibrium methodology is still widely applied
(Abramson 2002). In the following a short overview of local landslide modelling
methods and techniques is presented. It is beyond the scope to review the theo-
retical background and mathematical and mechanical derivation of local stability
calculation. These can be found in the literature sources provided or in various
textbooks (Chandler 1991; Bromhead 1998; Abramson 2002; Aysen 2002;
Eberhardt 2003a; Ortigão and Sayao 2004; Duncan and Wright 2005; Gitirana Jr
2005; Cheng and Lau 2008).
Limit-equilibrium methods provide a mathematical procedure to determine the
forces within a slope that drive and resist movement. The factors included in the
calculation have been presented in Sect. 2.1.2. Limit-equilibrium analysis usually

calculates stability for discrete two-dimensional slices of a slope and for assumed
or known potential shear surfaces (Fig. 2.6), but three-dimensional approaches
have also been developed. Shear strength of materials along shear surface is
assumed to be governed by linear or nonlinear relationship between shear strength
and normal stress (US Army Corps of Engineers 2003). The result of limit-equi-
librium analysis is a global FoS for shear surface, which provides a snapshot on
stresses and resisting forces relationship.
Several numerical methods are available today which assist in locating critical
shear surfaces, this is, where the lowest FoS is prevalent. The most widely applied
methods are Bishop’s simplified approach, which accounts for circular slip sur-
faces, and Janbu’s method for con-circular, i.e. polygonal shear surfaces. Other
methods like the infinite-slope wedge method, ordinary slice method, general slice
method, Spencer’s method, Morgenstern and Price’s method, and some others are
reviewed elsewhere (Graham 1984; Anderson and Richards 1987; Nash 1987).
Given the wide use of numerical limit-equilibrium methods for slope stability
analysis in geotechnical practice, most available models allow assessment of
effects of e.g., external loads or remedial stabilisation structures, such as soil nails
or other reinforcements.
However, limit-equilibrium methods also comprise some drawbacks. The
resulting FoS represents a global value for a two-dimensional slope profile, where
movement occurs if the FoS \ 1.0. However, (Bonnard 2008, p. 46) notes that
limit-equilibrium methods only provide an approximation of force balance within
landslides and that in reality displacements may occur with a FoS between 1.0 and
1.1–1.15. Moreover, the FoS is an average value for an assumed critical failure
surface and provides no information about the actual distributions of stresses or the
progressive development of unstable state (Eberhardt et al. 2004).
A second family of stability models on a local scale is concerned with con-
tinuum modelling. The entire slope mass is divided into a finite number of ele-
ments and represented as a mesh. Continuum approaches include finite-difference
and finite-element methods. Finite-difference methods provide numerical
approximations of differential equations of equilibrium, strain–displacement
relations or the stress–strain equations (Eberhardt 2003a). In contrast, finite-
element procedures exploit approximations to the connectivity of elements, and
continuity of displacements and stresses between elements (Eberhardt 2003a).
However, in both methods the problem domain is discretised into a set of sub-
domains or elements. In contrast to limit-equilibrium analysis, continuum mod-
elling software allows for complex time-dependant landslide analysis by including
constitutive models such as elasticity, elasto-plasticity and strain softening.
A third family of local landslides models are discontinuum methods, where
slopes are represented by distinct blocks which dynamically interact during
movement or deformation. The underlying concept of these methods is that limit-
equilibrium is repeatedly computed for each block, so that complex non-linear
interaction can be accounted for (Eberhardt 2003a).
Three variations of discrete-element variation can be distinguished.

Distinct-element methods are based on a force–displacement law to describe
interaction between deformable elements, and a law of motion to numerically
simulate displacements. Discrete element methods are computationally intensive
as many case studies involve a very high number of interacting discrete objects.
More detailed information is provided by (Hart 1993) and (Jing 1998). Discon-
tinuous deformation analysis simulates interaction of independent blocks along
discontinuities, such as fractures and joints. In contrast to distinct-elements
methods, discontinuous deformation analysis accounts for displacement instead of
simulating forces (Cundall and Strack 1979). Particle flow methods represent
slopes with spherical particles that interact through frictional sliding contacts
(Eberhardt 2003a).
Below, an overview of commercially available local landslide analysis and
simulation models is presented. This is, however, only a selection of models
frequently applied in landslide research as a complete summary is far beyond the
scope of this study.
GGU is a CAD-based (Computer Aided Design) stability model which allows
for predicting stability based on Bishop’s and Janbu’s, as well as general wedge
and vertical slice method. Moreover, stabilising factors such as anchors, soil nails
and geosynthetics can be included in the modelling process. However, the GGU
stability software does not account for dynamic hydrological modelling. Several
applications utilising the GGU stability software are available (Chok et al. 2004;
Schneider-Muntau and Zangerl 2005; Kupka et al. 2009; Hu et al. 2010).
Galena is another numerical software solution available which includes limit-
equilibrium analysis. It includes Bishop’s and Spencer-Wright method and Sarma
method, which utilises non-vertical slices for slope stability analysis. However, the
Galena software was developed mainly for slope design in open-pit mines, and
only few project applied it to analyse landslides (Kumar and Sanoujam 2006).
The program Xslope is capable of calculating slope stability based on Bishop’s
or Morgenstern and Price’s method, and is available through University of Sydney.
It does not include hydrological modelling, but pore water pressure from an
external finite element steady-state seepage model can be integrated. Several case
studies are available that describe the application of Xslope (Lee et al. 2001;
Hubble 2004; Cássia de Brito Galvão et al. 2007).
Another model for limit-equilibrium analysis of soil and rock slopes is provided
by SLOPE/W which includes several methods (Morgenstern-Price, Spencer,
Bishop, Ordinary, Janbu and more). Also, several soil strength models are available.
Stability analysis may be performed using deterministic or probabilistic input
parameters. However, dynamic hydrological modelling of pore pressures is not
included in SLOPE/W, but can be imported from SEEP/W, a finite-element soft-
ware by the same company. Moreover, external stresses by earthquakes for exam-
ple, and also the effects of reinforcements can be analysed. The use of SLOPE/W,
also in combination with SEEP/W is fairly widely acknowledged in recent landslide
research (Anderson et al. 2000; Rahardjo et al. 2007; Cascini et al. 2008; Heng 2008;
Yagoda-Biran et al. 2010; Rahimi et al. 2010; Navarro et al. 2010).
SVSlope is a slope stability software with a similar complexity and range of

available methods as SLOPE/W. However, three-dimensional limit-equilibrium
analyses can also be performed for which slopes are represented as columns to
analyse stability by a series of two-dimensional calculations. Moreover, a finite-
element stability analysis tools have been added to compute stresses and strains.
Several authors presented case studies applying SVSlope (Gitirana Jr 2005;
Fredlund 2007; Gitirana Jr. et al. 2008).
A similar program is Clara-W, which performs two and three-dimensional
stability limit-equilibrium analysis (Eberhardt 2003b; Stead et al. 2006; Cotza
2009; Montgomery et al. 2009).
CHASM (Combined Hydrology And Stability Model) is a coupled hydrology
and slope stability model for limit-equilibrium analysis. The software program
integrates simulation of saturated and unsaturated hydrological processes to cal-
culate pore water pressures, which are then incorporated into stability computa-
tion. CHASM is essentially two-dimensional but hydrological simulations can be
extended to account for flow concentration at topographic hollows. Moreover,
vegetation and stabilisation measures can be integrated into Janbu and Bishop
stability simulations. Furthermore, a simple empirical-based run-out simulation is
integrated into the model. CHASM was also employed within this study and a
more detailed review on CHASM methodology is presented in Sect. 5.1.3.2.
The CHASM model has been applied by several research projects, for example
in New Zealand (Wilkinson et al. 2000), Malaysia (Collison and Anderson 1996;
Lateh et al. 2008), Hong Kong (Wilkinson et al. 2002b), the Caribbean (Anderson
et al. 2008), Kuala Lumpur (Wilkinson et al. 2000; Wilkinson et al. 2002a), and
Greece (Matziaris et al. 2005; Ferentinou et al. 2006; Sakellariou et al. 2006).
Common applications of CHASM include investigations of effects of rainfall on
slope hydrology and subsequently on slope stability (Matziaris et al. 2005;
Ferentinou et al. 2006). Other studies compared CHASM to other slope stability
model and carried out sensitivity analyses for rainfall, groundwater conditions and
slope geometry (Lloyd et al. 2004).
Sensitivity analysis of CHASM has been carried out for geotechnical and
hydrological parameters (Hamm et al. 2006), hydraulic conductivity (Ibraim and
Anderson 2003), vegetation (Wilkinson et al. 2002b) and slope stability methods
(Wilkinson et al. 2000). Comparisons of field measurements of pore water pressures
with modelled results are provided by Anderson and Thallapally (1996) and
Hennrich (2000). Other applications of CHASM include studies to improve criteria
for geotechnical slope design (Anderson et al. 1996), and validation of rainfall
thresholds to enhance the performance of a landslide early warning system in Kuala
Lumpur (Lloyd et al. 2001). Later works of Wilkinson et al. (2002a) implemented a
slope information system in which pre-defined slopes can be modelled in CHASM
using a variety of input parameter constellations. Recent applications of CHASM
include the work of Karnawati et al. (2005) in which the aim was to provide guidance
to local population in Java, Indonesia on how to judge stability and hazardousness of
slopes. Back analysis of recent landslides within CHASM was applied by Anderson
et al. (2008) in the Caribbean. Based on modelling results critical hydrological

situations were determined and an effective slope drainage system was designed.
Several models are available from RocScience for calculation of stability,
stresses and displacement for rock and soil. Phase2 offers two-dimensional elasto-
plastic finite-element stress analysis for underground or surface excavations with
integrated groundwater seepage modelling (Hammah et al. 2008, 2009). The
program Slide utilises two-dimensional limit-equilibrium analysis with built-in
finite-element hydrological modelling. Generally, capabilities of Slide are similar
to other limit-equilibrium programs described above. Case studies utilising Slide
are described by several authors (Kjelland et al. 2004; Hadjigeorgiou et al. 2006;
Hammah et al. 2006; Brandon et al. 2008; Topal and Akin 2008).
Plaxis is a collection of finite-element methods for numerical analysis of
deformation and stability in geotechnical engineering in two and three dimensions.
Moreover, unsaturated groundwater flow and pore pressures, as well as their
effects on slope stability can be simulated. Static loads and dynamic loads and
stability in response to earthquakes, as well as non-linear, time-dependent and
anisotropic behaviour of soils and/or rock are more features of this complex
software. Plaxis-aided research concerned with landslides has been provided by
several authors (e.g., Spickermann et al. 2003; Ausilio et al. 2004; Comegna et al.
2004; Kellezi et al. 2005; Keersmaekers et al. 2008; Majidi and Choobbasti 2008;
Chang et al. 2010).
FLAC (Fast Lagrangian Analysis of Continua) is a two-dimensional explicit
finite difference program widely acknowledged in analysis of plastic deformation
(Cala et al. 2004; Maffei et al. 2004; Chugh and Stark 2005; Petley et al. 2005d;
Jian et al. 2009). Slopes are represented by elements which form a grid and behave
according to a prescribed linear or nonlinear stress and strain law. Deformation of
the grid allows for flowing and plastic-deformation, and large strain can be sim-
ulated. A simplified of this model is FLAC/Slope, which is sold by the same
company. Moreover, a three-dimensional version, FLAC3D, is available and has
frequently been applied within research (Teoman et al. 2004; Pasculli and Sciarra
2006; Sitharam et al. 2007; Poisel et al. 2009).
DAN, and its related version DAN-W and DAN3D, are software tools used for
dynamic run-out analysis of rapid landslides processes such as rock avalanches
(Hungr 1995). The core of the models is a Lagrangian solution of the integrated
equations of motion which is implemented for thin elements of the flowing mass.
The model simulates travel distance, velocity and flow depth and volumes in
iterative time-steps. Many case studies acknowledge the use of DAN codes (Hungr
1995; Sosio et al. 2008; Hungr and McDougall 2009; Pirulli 2009).
UDEC (Universal Distinct Element Code) simulates the response of discon-
tinuous media such as jointed rock which subject to loading. The two-dimensional
analysis allows for rigid or deformable blocks. However, the related 3DEC model
offers similar modelling capabilities but in three dimensions. Several case studies
applied UDEC on rock slopes (Bandis et al. 2000; Bozzano et al. 2000; Chen et al.
2000; Gunzberger et al. 2004; Watson et al. 2004; Gong et al. 2005; Zhao et al.
Fig. 2.7 General risk

management cycle (after
Alexander 2000, 2002)
2008), and 3DEC (Cheng et al. 2006; Ming-Gao et al. 2006; Lato et al. 2007; Bai
et al. 2008).
2.4 Landslide Early Warning Systems
In the following an introduction to early warning and the challenges of early

warning systems is presented. Therein, general aspects on social and technical
aspects of early warning are demonstrated. The focus, however, is put on landslide
early warning. More information regarding early warning systems for natural
hazards in general is provided by several other authors (e.g., Zschau et al. 2001;
Zschau and Küppers 2003; Dikau and Weichselgärtner 2005; Hall 2007; Schuster
and Highland 2007; Felgentreff and Glade 2007; Glantz 2009). In addition, a
review of existing landslide early warning systems worldwide is presented. It is
beyond the scope to give a complete summary of all existent systems, but to
illustrate a wide range of technical applications and to highlight integration of
social components into early warning process.
Early warning can broadly be defined as the timely advice before a potentially
hazardous phenomenon occurs. Dikau and Weichselgärtner (2005) add that the
effective use of information for early warning is an important element of general
risk management which includes activities such as hazard zoning and prediction,
warning communication, disaster prevention and evacuation planning (Fig. 2.7).
Good early warning systems therefore comprise identification and estimation of
hazardous processes, communication of warnings and adapted reaction of local
population. Moreover, early warning systems have to be embedded into local

communities to ensure effectiveness of the entire system.
A more pin-pointed definition is used by UNISDR (2009) where early warning
system are described as ‘‘the set of capacities needed to generate and disseminate
timely and meaningful warning information to enable individuals, communities
and organizations threatened by a hazard to prepare and to act appropriately and
in sufficient time to reduce the possibility of harm or loss’’.
Early warning systems have been developed for a wide range of natural hazards
of which extreme weather events, floods and tsunamis are maybe the best known.
However, also for processes such as volcanic eruptions, droughts, snow ava-
lanches, earthquakes and landslides early warning systems have been installed.
Extensive information on applied early warning systems is presented by several
publications in the United Nations International Strategy for Disaster Reduction
(UNISDR 2004a, b, 2006a, b). However, specialized landslide early warning
systems are not described therein.
Within the UNISDR a Platform for the Promotion of Early Warning Systems
(UNISDR-PPEW) has been founded to stimulate the advance of early warning.
UNISDR puts an emphasis on social aspects of early warning and promotes the
development of people-centred early warning systems (UNISDR 2006b). Four
essential key parts for effective early warning can be defined (UNISDR 2006b):
1. Knowledge about the risks that threaten a community
2. Monitoring and warning service for these risks
3. Dissemination and communication of warning messages in a way that is
understood by the local population
4. Response capability of involved people, who need to know how to react
appropriately in case of a warning.
These four segments are also reflected in a general distinction of early warning
proposed by Zschau et al. (2001), who distinguish the elements of prediction,
warning and reaction. These components constitute the early warning chain. The
segments of this chain have to be tightly connected and interlinked in order to
provide an effective measure for risk reduction.
Prediction is strongly influenced by a natural science and technological per-
spective and aims to improve knowledge on the hazardous process itself and its
timing, size, extent, severity, duration etc. The time-span between warning and the
occurrence of the hazardous event is another important issue of prediction, and can
last from seconds (for e.g. earthquakes) to months (for e.g., droughts) (Zschau
et al. 2001).
The second element, warning, can be regarded as the critical element within
the early warning chain (Dikau and Weichselgärtner 2005). Prediction has to be
transferred into an adequate warning message and distributed to the target popula-
tion. Several communication channels (e.g., SMS, Fax, Email, sirens) can be used to
distribute warning messages. However, effective warning is not only a technical
problem, but is also dependant on the social and political decisions and the legal
framework (Zschau et al. 2001). Moreover, it is important that communication
2.4 Landslide Early Warning Systems 37
of warning is carefully planned (Mayer and Pohl 2010). According to Kunz-Plapp

(2007) warning messages should be believable, clearly formulated, adapted to the
context of the target group, and should contain clear instructions on appropriate
protection action.
Reaction is the third component of early warning, in which warning messages
should lead to appropriate action such as evacuation of hazardous areas. Decision
makers have to initiate protection actions based on the warning message. Effective
reaction to warning messages primarily depends on the administrative and
organisational circumstances (Zschau et al. 2001).
Given the complex conditions of early warning it is obvious that purely tech-
nical approaches cannot provide effective early warning. It has already been noted
by the fathers of hazard research that it is important to know how technological
advances in early warning systems can be used to more efficiently trigger
appropriate reactions of populations to prevent losses from natural hazards (White
and Haas 1975). Still, for a long time advances in early warning systems were
primarily related to the use of more sophisticated monitoring equipment while
social aspects of early warning were neglected (Zschau et al. 2001). This is
problematic, as many communities cannot afford expensive high-tech warning
systems. Sorensen (2000, p. 214) states that ‘‘better local management and deci-
sion making about the warning process are more critical than promoting more
advanced technologies, although both would help’’. Many important social aspects
are not accounted for in technical approaches to early warning. However, the
importance of the hazard awareness can be illustrated by recent disaster events,
such as devastating the tsunami in the Indian Ocean in 2004 which caused more
than 200,000 fatalities. Even though no early warning system was installed, this
disaster illustrates a major problem in early warning. As many people did not
know that a sudden decrease in sea-level precedes the occurrence of a tsunami, no
appropriate reaction could be initialised. If a threatened population is not informed
about potential hazards, their consequences and suitable protective actions, early
warning cannot be effective.
Although an early warning system could have enabled many people to evacuate
coastal areas, early warning systems cannot provide full security from hazardous
events. An example of this took place in April 2010, when a train derailed in the
Etsch Valley, South Tyrol, Italy, because of a rockfall occurring above the track.
Even though an automatic early warning system existed to close the track in case
of blockages, it failed in this event because the rockfall took place right above the
passing train (Murmelter 2010).
Moreover, it is important to keep in mind that only people and moveable
objects can benefit from early warnings and not stationary objects such as infra-
structure. An alarm can motivate people to escape from potentially dangerous
situations, but it does not stop the hazardous event itself (Hübl 2000). Therefore,
early warning systems do not substantially decrease property damage (National
Research Council 2004).
Although early warning systems can be an effective tool for risk reduction
(Dikau and Weichselgärtner 2005) they can also increase the risk. Increased risk
may be due to a false sense of security and building of higher value infrastructure
in potentially hazardous areas.
Uncertainties always prevail in hazard prediction and are also a major challenge
for early warning (UNISDR 2004b). Storms may change their track, or lose their
strength over time, earthquakes may be expected for a large area, but no exact
location can be determined. For many hazard events, only statistical forecasts,
such as an El Niño event probability for the next year of 60% can be made. In
addition, uncertainties within the social components complicate the prediction of
the hazard consequences. These include the reaction of the population to warnings
and hazardous events, and the functioning of evacuation plans and general disaster
management. Baum and Godt (2009) provide interesting examples where people
were moving into warning areas on purpose to secure their homes or save pets.
Others misunderstand the warning and believe that if a warning is issued by for
example the Department of Forestry it only relates to areas with actual logging
activities.
Moreover, the costs of unnecessary evacuations due to false alarms are a major
concern for decision makers. False alarms are a problem of early warning systems
as they can substantially compromise the credibility of early warning systems
(Larsen 2008). In 1982 the United States Geological Survey (USGS) issued a
warning for the Mammoth Lakes Area because of an expected volcanic eruption
potentially threatening a ski resort on the slopes of the volcano. After the eruption
did not occur the USGS was mocked as the US Guessing Society (Die Zeit 2010).
However, Sorensen (2000) argues, that false alarms do not necessarily diminish
the trust in early warning systems if the reason for the false alarm is understood.
The number of false alarms can be reduced by pursuing a conservative strategy and
by issuing generalised warnings. However, the use of generalised warnings
decreases with the size of the geographic area (Larsen 2008).
An interesting example of consequences of false warning took place in Italy in
2009 were a scientist had been measuring the emissions of radon gases which are
associated with earthquakes. Based on his measurements he was expecting a major
earthquake for the city of Sulmona two days before the devastating L’Aquila
earthquake (5.8 magnitude on Richter scale) which is located 70 km north-west.
As his prediction did not turn out to be accurate he was accused for creating panic
but later absolved (Die Zeit 2010).
On the other hand a group of seven Italian earthquake scientists who were
assessing the seismic activity in the L’Aquila region were accused of gross neg-
ligent manslaughter as they failed to predict the disaster. Only days before the
earthquake they had stated at a meeting with city officials that there were no
grounds for believing a major quake was on the way despite some smaller quakes
in the previous days (Cartlidge 2010). The allegations gained much attention in the
scientific community as well as from general public, and a petition to end the
investigations had been signed by over 5000 scientists. In this open letter it was
stated that at the moment there are no scientific method to predict earthquake
timing and that therefore, there is no ground for the allegations (Die Zeit 2010).
Warner Marzocchi, chief scientist at the Italian National Institute for Geophysics
and Vulcanology commented that ‘‘as scientists, we have to focus on giving the
best kind of scientific information’’ and that the decisions of what actions need to
be taken ‘‘is down to others to decide’’ (Cartlidge 2010). Thomas Jordan, earth
scientists who had also been working in the L’Aquila region added that the costs of
false alarms are too high compared to the low probabilities of an earthquake
occurring, so that there was no basis to initiate actions such as mass evacuations
(Cartlidge 2010).
A similar case happened in the Italian community of Sarno, which was hit by
devastating landslides and a debris flood in 1998. Before the disaster event the
mayor had told the people to stay calm and to stay at home even though there were
already heavy rainfall and landslides occurring in the vicinity of the town (Die Zeit
2010). After the event he was accused for negligent manslaughter but later
absolved because the event could not have been foreseen.
The previous examples clearly illustrate some of the problems and challenges
of early warning systems, arising from both natural and social components.
Besides technical difficulties of natural hazard prediction, legal, social and polit-
ical dimensions add to the complexity of early warning. Effective early warning
systems must therefore be carefully planned. Resulting from the work of the
Integrative Landslide Early Warning Systems (ILEWS) project, issues have been
identified that need to be addressed when early warning systems are to be installed
(Bell et al. 2010). Important factors to be accounted for include the process (flood,
volcanic eruptions, landslides), time (slowly developing or rapidly initiating
hazards), forewarn time needed to provide useful warning, financial aspects (pri-
vate or public investments), communication of warning (unidirectional, bidirec-
tional), threatened human lives and infrastructure (cost-effectiveness) and
stakeholders to be warned (governmental agencies, emergency services). Thus,
early warning systems have to be demand-orientated and adapted to local condi-
tions (Twigg 2003). In addition, it is important that early warning systems are
embedded into the local community to increase acceptance of warnings (Mileti
1999; Greiving and Glade 2011).
Given the variety of hazards for which early warning systems have been
installed it is difficult to define clear categories. Some basic distinction, however,
can be made (Bell et al. 2010):
– Monitoring systems are primarily installed to increase the understanding of
natural processes but can also be utilised to plan further actions. These moni-
toring systems differ by technologies applied, time intervals between mea-
surements and degree of automation.
– Expert- or control systems provide information on potentially hazardous events
and are chiefly implemented to gain information on critical developments and
with the aim to guide scientists and decision makers.
– Alarm systems are based on monitoring systems and provoke an automatic
warning if, for example, a predefined threshold is exceeded. Further differen-
tiation of alarm systems can be made between pre- and post event systems and
the forewarn time provided by the system. Moreover, these systems differ in
their degree of integration of social aspects between purely technical

applications and integrative early warning systems.
Early warning systems have also been applied for landslide processes, e.g.
rotational and translational slides, debris flows and rock slides. Landslide early
warning systems can be installed for single slopes, but also for entire regions. Also
global landslide early warning systems have been proposed by applying methods
such as rainfall intensity and duration thresholds (Guzzetti et al. 2008) or satellite-
based InSAR monitoring and progressive failure analysis (Petley et al. 2002).
Regional landslide early warning systems can only issue warnings, such as a
70% probability of debris occurrence for a certain region; single slopes cannot be
identified (Wieczorek and Glade 2005). However, local or site-specific landslide
early warning systems provide another quality of information. Exceedance of
critical thresholds may automatically lead to protective actions, such as alarms,
road and bridge closures, evacuation and further disaster management actions.
Local landslide early warning systems have been frequently applied, partly
because they can sometimes replace structural measures of slope stabilisation
while providing sufficient protection (Palm et al. 2003). Site-specific systems
generally apply monitoring systems for slope movement or landslide triggering
factors such as rainfall and pore water pressure (see Sect. 2.2) as the early basis of
warning.
One of the first modern landslide early warning systems was installed in 1984 in
Utah, USA, after significant damage by debris flows initiated by snow melt (Baum
2007). Initially, monitoring of precipitation, temperature and slope movement on
potential landslides were used to alert local officials and issue a regional debris
flow early warning. Later works of (Ashland 2003) established groundwater
thresholds for instrumented potential landslides. Regional thresholds were deter-
mined based on annual cumulative rainfall. Recent developments include snow
monitoring to account for landslide triggering by snow-melt (Baum 2007).
Based on the works of Campbell (1975), who established landslide and debris flow
triggering rainfall thresholds by intensity and duration analysis, a regional debris flow
early warning system was set up for the San Francisco Bay Area, USA in 1986 (Keefer
et al. 1987; Wilson 2005). The system was developed and implemented as cooperation
between USGS and National Weather Service (NWS). Quantitative weather forecasts
issued by NWS two times a day for the upcoming 24 h were provided to the Landslide
Initiation and Warning Project of USGS. Rainfall forecasts were combined with data
from automatic rainfall gauges and consequently checked against pre-defined
thresholds. Estimation of hazard level and final decision upon warning was assessed
cooperatively by experts from USGS and NWS. Initially, triggering thresholds based
on rainfall intensity in relation to annual rainfall (Cannon and Ellen 1988) were uti-
lised. Later thresholds were adjusted to account for water storage capacity (Wieczorek
1987) and minimum thresholds for debris initiation (Wilson et al. 1993). Below the
minimum rainfall threshold debris flow occurrences are unlikely while above the upper
threshold significant debris initiation in the region can be expected. In 1992 it was
attempted to integrate radar data into the early warning system to improve the spatial
resolution of rainfall measurement. However, integration failed because no reliable

relations between radar reflectivity and ground based measurement could be estab-
lished. Along with technical problems the social aspects were a major challenge for the
early warning system. For instance, NWS and USGS had different expectations to the
technical system. While USGS interpreted the system as an experimental prototype of
which warnings are by-products, NWS demanded reliable predictions and warnings.
Warning communication was another challenge for the early warning system. USGS
considered early warning as an entirely technical system which ended with issuing an
alarm. The population was expected to react appropriately by avoiding dangerous
areas. Yet, the population and also emergency services were mostly unaware of debris
hazards and their damage potential. In reality, many people intentionally drove into
hazardous areas in severe storm conditions trying to get home to save the house or feed
a pet (Wilson 2005). Eventually, the system was shut down in 1995 because USGS
could not afford to continue the service.
Based on the experiences in the San Francisco Bay Area, USGS and National
Oceanic and Atmospheric Administration (NOAA) cooperatively initiated a regio-
nal landslide early warning system for burned areas in south California, which are
prone to debris flow initiation (NOAA-USGS Debris Flow Task Force 2005). An
assessment of potential end-users and their demands towards landslide early warning
were clarified before the system was set-up. Alert level terminology was overtaken
from NWS severe weather forecasts to increase the acceptance of the population.
Dissemination of information and warning communication were adjusted to end-
users needs. USGS also developed an education program for involved meteorologists
and interested public to explain hydrological characteristics of debris flow initiation
in areas with burned vegetation (e.g., California Geological Survey 2003). Technical
advances compared to the previous system comprise improved quantitative rainfall
forecasts, and implementation of test areas to improve the understanding of trig-
gering groundwater conditions. In addition, empirical and physically-based models
were applied to assess susceptibility to debris flows, their potential volume and run-
out distance (NOAA-USGS Debris Flow Task Force 2005).
Another regional debris flow early warning system for Oregon, USA, was
developed and implemented as a cooperation between Oregon Departments of
Forestry (ODF), Transportation (ODOT) and Geology and Mineral Industry
(DOGAMI) with Oregon Emergency Management (OEM) (Baum 2007). During
periods of intense rainfall meteorologists of ODF monitor measured rainfall and
forecasts and assess current hazard level together with geotechnicians which are
available 24 h a day. Warning messages are issued if thresholds are almost reached
or exceeded. Thresholds used within the system account for rainfall intensity and
duration, but are modified in case of significant antecedent rainfall or snow
melting. Warning is spread via the emergency channel of the National Weather
Service. Within the developed system information and education on debris flow
hazards and early warning are also addressed by for example, warning signs along
the highway, advise to homeowners and information on websites (Burns et al.
2008; Oregon Department of Geology and Mineral Industry 2010).
A regional early warning system for shallow landslides is implemented for the
Seattle Area, USA since 2002 (Baum et al. 2005a; Baum and Godt 2009) and is
jointly managed by NWS, USGS and the city of Seattle. The technical system
comprises a total number of 17 automatic rain gauges with an average distance of
2–5 km between them and quantitative weather forecasts. In addition, a test slope
was instrumented to improve understanding of pore water pressure development
and landslide triggering. Furthermore, landslide mapping and probabilistic regio-
nal hazard modelling were performed (Baum et al. 2005b). Detailed examination
of rainfall data led to the establishment of a minimum threshold for landslide
triggering based on intensity and duration analysis and an antecedent water index
calculated from cumulative rainfall of 3 days, and rain within the previous 15 days
(Chleborad 2000, 2003, 2006). For the assignment of alert levels thresholds are
used for both, intensity-duration and antecedent water index. Rainfall exceeding
intensity-duration thresholds triggers a warning status at high antecedent water
status. Watch level is issued, in the occurrence of medium antecedent rainfall
index values and observed or forecasted rainfalls above thresholds. Outlook level
is activated if any of the rainfall threshold is exceeded. In other cases the alert is
null. In addition, warnings are only provided if thresholds are exceeded for at least
three gauges relating to an expected number of three or more landslide events
(Baum and Godt 2009). Warning thresholds performed satisfactory in back-anal-
ysis with data from the 1978 to 2003 period; only eight storms caused landslides
without previous threshold exceedance (Godt et al. 2006). Forty per cent of all
warnings were followed by landslides events and 85% off all landslides were
triggered by rainfall above thresholds values. To increase the acceptance of the
early warning system and improve risk awareness of the local residents, USGS
provides educational material and information (USGS 2006).
In the USA, USGS is responsible for allocation of warnings related to geo-
logical events, including landslides. To increase interoperability of warning sys-
tems and ensure smooth warning communication a common alerting protocol
(CAP) was created (Highland and Gori 2008). This data format is the same for
many different kinds of warnings including also man-made hazards and terrorism.
Today it is widely used by state agencies in the USA. Landslide related CAP
warning have been adopted for all study areas, for which reliable rainfall thresh-
olds have been established, i.e. Seattle, San Francisco Bay Area and burned areas
and parts of the Appalachian mountain areas of the eastern US. All alerts,
including archived warnings, are presented on USGS website. To increase the
populations’ awareness of landslide hazards and the potential outcomes of land-
slide events, a documentary movie was produced, which is also planned for school
education (Highland and Gori 2008). Moreover, a wide range of fact sheets, reports
and other information on landslide hazards, consequences and warnings is pro-
duced by USGS.
The most advanced and successful landslide early warning system may be that
installed in Hong Kong, China (Schuster and Highland 2007). Hong Kong is
densely populated by seven million inhabitants and very prone to landslides
occurrences and damage consequences. The terrain is rugged, with hills rising up
Fig. 2.8 Number of landslide fatalities in Hong Kong (after Wong and Ho 2000)
steeply, and less than 30% of the densely populated areas are flat (0–5) (Brand
et al. 1984). The limited availability of favourable land means that geotechnical
slope construction works including design of cut and fill slopes and slope stabi-
lisation are frequently required. Moreover, strong rainfalls with hourly intensities
exceeding 150 mm occur along with tropical cyclones and low pressure. After two
catastrophic landslide events in 1972 and 1976 which together caused more than
100 fatalities, the Geotechnical Control Office was established to reduce landslide
consequences (Malone 1997). Later, the agency was renamed the Geotechnical
Engineering Office (GEO). GEO has many responsibilities, such as establishing
instructions and guidelines for slope design, slope stabilisation, quantitative risk
management and early warning (Chan 2007). Moreover, education programs for
the general population and homeowners aim to raise awareness of landslides and
related risks. Radio and television features, a telephone hotline and a website
provide a wide range of information and advice to local residents (Massey et al.
2001). Detailed information on integrative landslide risk management strategies in
Hong Kong and the efforts and experiences of GEO is available in a collection of
scientific papers published for the 30th anniversary of GEO (Geotechnical Engi-
neering Office 2007). The great success of slope safety in Hong Kong is also
illustrated by significantly lower fatalities due to landslides after the establishment
of GEO’s predecessor in 1977 (Fig. 2.8).
The Hong Kong regional landslide early warning system was launched in 1977
and is managed cooperatively by GEO and Hong Kong Observatory. More than
100 automatic rain gauges built the technical base for early warning. Rainfall
thresholds triggering landslides in Hong Kong were first established by Lumb
(1975), but were modified several times afterwards when improved real time
rainfall and landslide data became available. Initially, warning thresholds
accounted for cumulative 24 h rainfall in relation to rainfall of the preceding
15 days. Warnings were issued if measured rainfall of the last 20 h and the
forecasted rain for the next 4 h exceed 175 mm (Chan et al. 2003). In the 1980s an
hourly rainfall threshold of 70 mm was added to the warning scheme. Progressive
analysis of landslide initiation and related rainfall events led to prediction of the
number of landslides expected for certain storm events. Warnings were only issued
if 15 or more landslides were expected to occur (Chan et al. 2003). Since 2003 a
GIS-based approach has been used for landslide prediction (Yu et al. 2004).
Therein, the entire area of Hong Kong is represented as grid cells accounting for
number of properties contained on the slopes. The number of expected landslides
is then modelled according to a spatially variable susceptibility to slope failure.
A recent development of the landslide early warning system includes the inte-
gration of radar data from the SWIRLS system (Short-range Warning of Intense
Rainstorms in Localised Systems) to track storm cells and improve quantitative
prediction of localised storms (Cheung et al. 2006). Warning dissemination utilises
TV, radio and internet to inform the public. In addition, emergency forces and
hospitals are contacted if large numbers of landslide are expected. In early years
warning messages were mostly aimed at slum dwellers because they lived in most
hazardous areas. However, social and geotechnical developments since the 1980s
changed the focus. Today, the intention of early warning is to inform the entire
population about potentially hazardous events, thus to provoke cautious behaviour.
Mainland China is probably experiencing the highest landslide damage and
number of fatalities in the world (Tianchi 1994). China has begun to address the
landslide problem in the 1990s by starting a nationwide investigation program
including landslide mapping, susceptibility zoning, risk analysis, rainfall threshold
analysis, prevention planning and engineering counter measures (Yin 2009) and
early warning (Zhou and Chen 2005). Since 2003 landslide warning based on
rainfall forecasts are issued after general weather reports on prime time TV shows
(Yin 2009).
A regional landslide early warning system based on susceptibility maps and
rainfall thresholds was installed for Zhejiang Province (Kunlong et al. 2007; Eng
et al. 2009). The system is based on rainfall forecasts and works as a WebGIS.
Warnings are issued if rainfall predictions exceed one of the two defined thresh-
olds and near real-time warnings are spread through various communication
channels (internet, telephone, etc.). The warning system is also combined with an
assessment of economical risks which aim to extend the system to landslide risk
warning (Wu et al. 2009).
Zhong et al. (2009) provide detailed information of the precipitation based early
warning system for Hubei Province. The landslide early warning system was
installed in 2006 and represents a WebGIS. Critical rainfall thresholds have been
determined by analysing the statistic relationship between spatial distribution of
occurred landslides and rainfall data. Warning generation is based on 2 and 15 day
antecedent rainfall, which is compared to 24 h rainfall forecast. If thresholds are
exceeded in any of the 82 divisions in which Hubei is differentiated, the Meteo-
rological Survey of Hubei Province issues a warning on the internet. More
information on the current situation is freely accessible in the form of maps on the
internet. In three years of operation the system issued 11 warnings, of which 6

were followed by landslide events (Zhong et al. 2009).
The Geotechnical Engineering Office of Rio de Janeiro, Brazil, implemented a
regional landslide early warning based on rainfall thresholds and rainfall moni-
toring in 1996 (Ortigao et al. 2002; Ortigao and Justi 2004). In early years the early
warning system was not entirely automated which resulted in a lack of warnings on
for example, weekends and holidays, as no experts were available (D’Orsi et al.
2004). Instead, automated fax messages were sent without proper data analysis.
However, due to wide acceptance of the early warning system it was expanded in
1998, and since then provides continuous service. At the same time a rainfall radar
was included into the technical input to increase forewarn time. In addition, a test
slope was equipped with piezometers and inclinometers to gain more insights into
the failure processes. However, it was decided not to establish more site-specific
monitoring systems as the costs were too high. Two critical rainfall thresholds
were determined which relate hourly rainfall intensity to accumulated rainfall for
24 and 96 h (D’Orsi 2006). Four warning levels are used, e.g., low (landslides
could happen), medium (occasional landslides), high (scattered landslides) and
very high (generalized landsliding). Current information on warning levels is
broadcasted by media and is also available online. In addition, emergency services
are informed by fax to prepare for potential landslide events. In addition to the
landslide early warning system, flash floods were later integrated into forecasting
activities (D’Orsi 2006).
A regional debris flow early warning system was installed in Combeima-Tolima
Region in Colombia (Huggel et al. 2008; Huggel et al. 2009). The project was
initiated by the Swiss Agency for Development and Cooperation (SDC) which
promoted investment into early warning, risk awareness education and disaster
prevention training instead of solely focussing on reconstruction. The automatic
technical monitoring system includes three rainfall stations and a series of geo-
phones. At the Regional Emergency Committee Centre data is collected and
analysed 24 h a day and is available online. Rainfall thresholds initiating debris
flows were calculated based on intensity and duration, and with respect to ante-
cedent rainfall up to 30 days. If thresholds are exceeded an emergency plan
determines actions to be taken. Interestingly, Huggel et al. (2009) analyse the
performance of the developed system by a cost-effectiveness calculation based on
historic records. Therein, the costs of false alarms are compared to losses in case of
hazard events which can be used to adjust rainfall thresholds. However, this
approach is ethically questionable as it requires definition of monetary costs of lost
human lives.
The combined hydrology and slope stability model CHASM, which is applied
within this study, has also been used in another landslide early warning system.
However, instead of applying CHASM for continuous modelling of slope stability,
rainfall thresholds previously calculated were validated by detailed analyses for
single slopes. The system is located along the Kuala Lumpur Highway, Malaysia,
and is in service since 1996 (Lloyd et al. 2001). An automated monitoring system
measures rainfall intensity which is compared to pre-defined threshold values also
Fig. 2.9 Comparison of warning curve and warning line thresholds and subsequent time spans
for evacuation (after Aleotti 2004)
accounting for antecedent rainfall conditions. A series of single slope CHASM

simulations illustrated the effect of high soil permeability on slope stability.
In conclusion, rainfall longer than 6 days ago did not significantly influence slope
stability and could be neglected in rainfall threshold determination in the study
area.
An extensive early warning system for debris flows in Indonesia is presented by
Apip et al. (2009, 2010). Based on local rainfall threshold analyses accounting for
rainfall intensity and duration, as well as antecedent rainfall, design charts were
created which provide three warning levels (safe, watch, danger). Moreover, it is
tried to widen predictive power of the warning system by integrating spatially
distributed modelling of slope stability with physically-based hydrology and sta-
bility model. Therein, quantitative rainfall forecasts by National Oceanic and
Atmospheric Administration (NOAA) are utilised for real-time modelling of
potential landslide initiation. In addition, the model is planned to be further
developed to provide forecasts of river flows, sediment transport and debris flow
run out.
A similar approach to early warning is provided by Liao et al. (2010), who also
worked on regional landslide early warning in Indonesia. The proposed system
includes a physically-based hydrology and slope stability model, as well as inte-
gration of quantitative rainfall forecasts. Modelling is performed in two steps.
Initial calculations provide hot spots, which can then be modelled using data with
higher resolution. However, the technical system represents a prototype devel-
opment and further research has to be accomplished in order to increase its pre-
dictive capacities.
Schmidt et al. (2008) present an innovative landslide early warning system for
New Zealand. Therein, probability of landslide failures is computed by combi-
nation of a regional physical-based hydrology and stability model with quantitative
weather forecasts. However, landslide prediction is subject to large uncertainties
which are assessed by probabilistic methods. Unfortunately, the early warning
system was only a prototypic development and is currently not active.
Aleotti (2004) presents a prototype of a regional landslide early warning system
for shallow landslides in the Piedmont Region of north-western Italy. Rainfall
thresholds were determined by analysing intensity-duration, antecedent rainfall
and mean annual precipitation. Warning thresholds, however, were established
lower than triggering thresholds to provide more safety. Instead of a warning
threshold parallel to the triggering threshold (Aleotti 2004) applied a curve to
account for different rain paths (Fig. 2.9). By doing so time spans until landslide
triggering threshold exceedance are integrated, which are important for initiation
of evacuations. The technical warning system includes rainfall forecasts and sta-
tion measurements. The system remains in an ordinary attention state until
thresholds are exceeded by rainfall forecasts. A preceding warning procedure is
launched if landslide prone areas are affected by threshold exceedance. Rainfall
paths are then plotted according to antecedent and real-time rainfall and an alert is
issued according to the pre-defined thresholds.
Recent developments of the regional landslide early warning system concen-
trated on the improvement of thresholds by including local properties, such as
topography and geological properties (Tiranti and Rabuffetti 2010). Consequently,
three thresholds were established, i.e. regional, sub-regional and a pragmatic
threshold, which accounts for multiple occurrences of landslide in single rain
events. All thresholds were tested for their performance in a back-analysis in terms
of correct, false and missed alarms. In addition, a technical system named SMART
was developed which analyses rainfall time series for each rain gauge in real time
and identifies where thresholds are exceeded. The current early warning system
utilises rainfall forecast and real-time measurements and applies the pragmatic
threshold (Tiranti and Rabuffetti 2010).
The Åknes rockslide in Norway is one of the most intensely investigated and
monitored landslides worldwide (e.g., Derron et al. 2005; Ganerød et al. 2008;
Kveldsvik et al. 2008, 2009; Eidsvig et al. 2011; Heincke et al. 2010; Grøneng
et al. 2010). The rockslide itself does not pose direct threat to a community,
however, slope failure is supposed to trigger a tsunami affecting ships and towns
along the fjord. The rockslide mass has a volume of 30–40 million m3 and dis-
placements vary with seasons and reach 3–10 cm/year with daily movements up to
1 mm (Blikra 2008). The technical monitoring system includes extensometers,
inclinometers, crackmeters, tiltmeters, geophones, piezometers, automated mea-
surements by theodolites, laser and GPS and ground based radar, and a climate
station. All data is available in a web-based database and supervised by experts
24 h a day. Threshold values for displacement velocity have been established
which relate to five alert levels colour-coded from green to red. In case of
imminent slope failure sirens warn the population in potentially affected towns.
Fig. 2.10 Early warning system at Winkelgrat landslide equipped with automatic extensometers
(left) and traffic light for road closure (right)
Other topics of the project include development and implementation of warning

routines and evacuation planning (Blikra 2008).
Another example of an intensely monitored rock slide is the Frank Slide at
Turtle Mountain, Canada, which exhibited a catastrophic failure in 1903 causing
70 fatalities (Froese et al. 2005). A landslide monitoring and early warning pro-
gram was launched in 2003 and commenced with detailed site investigations using
InSAR, microseismic surveys, ground penetrating radar, drilling and core sample
analysis. Within the actual early warning system tiltmeters, extensometers and
crack meters are used as primary sensors as they provide high detail displacement
data (Froese et al. 2006). Secondary sensors include differential GPS and auto-
matic theodolite measurements which have higher fluctuations but improve
understanding of the overall situation. Background information is gained by ter-
tiary sensors, i.e. climate station data and microseismic monitoring. The early
warning system comprises four elements (Froese et al. 2005). A monitoring pro-
cedure was established to determine responsibilities for measurements and their
frequencies, which might change in response to trends and anomalies in the data.
Within the threshold development procedure value-based and velocity-based
thresholds two standard deviations above noise level were established. Develop-
ment of alert levels and notification protocols comprise the third element of the
early warning system. A standardised terminology and appropriate response to
trends in monitoring data were determined. Moreover, action advice was devel-
oped for emergencies including procedures for communication and evacuation.
The current alert level is accessible on the internet and is presented in four colours.
The green alert level indicates normal situations where measurements are in the
range of background noise but may exhibit seasonal fluctuation. The watch level is
active if multiple sensors display unusual trends and leads to direct communication
between technical experts and local decision makers and municipal officials). The
warning level is initiated if multiple sensors demonstrate acceleration trends
exceeding pre-defined thresholds. If several sensors indicate accelerations and final
failure is imminent (one to three days to failure) then the alarm level is issued and
the emergency response procedure is executed. More detailed information on

monitoring (Read et al. 2005), modelling (Froese et al. 2009) and the information
platform (Froese et al. 2006) is given in the respective literature.
An early warning system for rockfall is installed at the Winkelgrat, also located at
the Swabian Alb in Germany (Fig. 2.10). The technical system consists of nine
automatic extensometers installed in 2002 (Ruch 2009). An automatic alarm mes-
sage is sent to the local emergency service if pre-defined thresholds are exceeded.
The road below the unstable rock mass is then closed by setting two traffic lights to
red to prevent cars from entering the hazardous area. At the same time road main-
tenance service, police, rescue forces and the regional geological department are
informed via SMS and fax. Following field investigations by experts of the regional
geological department it is decided to initiate further protective measures, or in case
of false alarms, to reopen the road (GEOSENS 2009; Krause 2009).
Another landslide early warning system in Germany is described by Lauterbach
et al. (2002) and Krauter et al. (2007). This system relies primarily on technical
solutions, for example, GPS displacement measurements, and is installed at an
autobahn in south-west Germany. The slope under investigation is known to cause
deformations to the road surface since the 1960s. The landslide mass is calculated
to be 700,000 m3 with average annual movement rates of 1–2 cm. As structural
measures were considered uneconomical a GPS based warning system was
installed. The system consists of 5 measured points of which the main station is
based outside the landslide mass. Accuracy of the system is about 1 mm in
location, and 2–3 mm in height. Two kinds of alarms are implemented: one occurs
when obviously false measurements are being taken or maintenance works are
necessary, the other if pre-defined thresholds of movement rates are exceeded. If
warning thresholds are exceeded the experts operating the system are informed via
automatic telephone calls and immediately check the situation in the field, which
then can lead to emergency actions like, for example, road closure.
Several slope monitoring and early warning systems have been installed in the
United Kingdom. An extensive technical monitoring and early warning was installed
for the coastal landslides on the Isle of Wight (Clark et al. 1996) where the first
tiltmeter slope early warning started in 1981 (Barton and McCosker 2000). Today,
the technical system comprises monitoring of surface and subsurface movements by
theodolites, GPS, inclinometers, tiltmeters and crackmeters. Weather stations and
piezometers record also rainfall and its effect on landslide triggering. Automatic
alarms are issued if pre-defined displacement thresholds are exceeded. Automatic
monitoring systems were preferred over manual systems even though initial costs are
considerably higher. Similar technical monitoring systems have also been installed
in Lyme Regis, Scarborough and Cromer (Clark et al. 1996).
In Lyme Regis a series of inclinometers, piezometers and GPS ground markers
are continuously monitored and provide an alarm to experts and decision makers if
an imminent threat is given (Clark et al. 2000). In addition, an increased moni-
toring frequency can be initiated and emergency response is prepared.
At Cromer, automatic readings of field sensors activate an alarm by sending a

message by pager. Warning thresholds are based on a pre-determined movement
velocity of 3 mm per hour or 10 mm in 6 h.
Landslide early warning on the Isle of Wight is part of an extensive general
coastal management scheme by the Isle of Wight Centre for Coastal Environment
which promotes a holistic approach. Coastal management comprises for example,
allocation of planning guidance maps, building codes, engineering measures,
monitoring, forecasting and early warning. A wide range of information is
available on the websites (Isle of Wight Centre for the Coastal Environment 2010),
such as a best practice guide (McInnes 2000) providing detailed descriptions of
monitoring and warning schemes, as well as advice to homeowners on how to
reduce risk of coastal erosion and instability. In addition, the local management
and information centre arranges workshops and educational field trips.
An extensive technical monitoring system is installed along the slopes of Clyde
Dam Reservoir, New Zealand (Macfarlane et al. 1996). More than 5,500 theodolite
observation points were installed to monitor displacements during dam construc-
tion and reservoir filling. After filling was accomplished the number of observation
points was reduced. Further monitoring equipment includes borehole extensome-
ters and inclinometers for subsurface movements and piezometers for slope
hydrology. All data is automatically stored in a database and alarms are raised
automatically if pre-defined thresholds are exceeded.
Given the great number of engineering works in China many applications of
monitoring and early warning systems are described in the literature. Since the
1990s much attention has been paid to the landslide hazards along the Three
Gorges Dam Reservoir, China, and many investigations have been performed by
researchers on this emerging topic (Fourniadis et al. 2007a, b; Li et al. 2008; Wang
et al. 2008c; Jian et al. 2009; Li et al. 2009a, b; Yin 2009). Several landslide
monitoring and early warning systems are located along the lake created by the
Three Gorges Dam. One of these was installed for the Shuping landslide, a
reactivated mass movement which accelerated after the impoundment of the lake.
Several extensometers are used to measure the landslide (Wang et al. 2008b; Wang
et al. 2009). Dai et al. (2008) describe a monitoring and warning system based on
high resolution optical fibres for the Yuhuangge landslide in the Three Gorges
Area. Four alert levels were determined which account for changes in velocity and
pore water pressure. Local decision makers are informed about current alert levels
and are obliged to issue final warnings and initiate evacuation. Since 2004 the alert
level was once set to yellow level due to significant acceleration and damage on
infrastructure (Yin et al. 2010a). Later however, displacement rates decreased to
former values and the alert level was subsequently lowered.
Moreover, several more interesting papers on applied landslide early warning
systems in China have been published, unfortunately, many are only available in
Chinese language with English abstracts (Jiang et al. 2009; Xu and Zeng 2009; Ye
et al. 2009).
The Illgraben catchment (9.5 km2) in Switzerland has some one of the highest
debris-flow activity in the Alps. A monitoring and early warning system for debris
flows was installed and described in detail by Badoux et al. (2009). The
overarching early warning concepts includes ongoing education and allocation of
information for the local population regarding debris flows and possible conse-
quences, a monitoring system, repeated field surveys to assess changes in the
catchment, and integration of meteorological measurements to increase forewarn
time (Graf et al. 2006). Several education campaigns were performed to inform
local population about potential hazards and the early warning systems. In addi-
tion, children at elementary level learn about debris flows in school. Tourists are
provided information at local tourist information centre. Along the debris flow
channel warning signs were put up every 200 m explaining the threat of debris
flow occurrences in five languages. Moreover, warning lights and loud speakers
were installed at three spots where hiking trails cross the debris flow channel. The
early warning system is managed and maintained by Illgraben Security Com-
mission and contacts local emergency task forces if potentially dangerous situa-
tions emerge. The technical system includes several geophones located at check
dams, which can automatically trigger warning lights and speakers further down
the debris channel if a seismic signal lasts for more than five seconds. At the same
time SMS and emails are sent to local decision makers. A forewarn time of 5 to
15 min between measurement of debris flow by geophones and a debris flow
reaching settlement areas in the valley is provided by the system. Alarms can be
cancelled if geophones further downslope do not detect seismic signals 10 min
after the first signal. This is done to decrease chances of false alarms due to other
potential geophone triggers, e.g., rock fall, thunderstorms or earthquakes. Further
technical equipment of the Illgraben monitoring systems includes measurement of
discharge by ultrasonic sensors, laser and radar. Based on their experiences
Badoux et al. (2009) propose radar as the most suitable method for early warning,
as it provides smooth and reliable data on discharge even in situations of rapidly
fluctuating discharge amounts. The catchment is visited and mapped regularly to
detect changes in the debris flow source area, such as landslides that provide
material for further debris flow occurrences. Including meteorological forecasts
into the early warning systems and defining rainfall thresholds was also trialled.
However, integration failed because local thunderstorms in alpine areas are diffi-
cult to predict. The debris flow early warning system at the Illgraben can be
regarded successful. Since its implementation 20 alarms were issued, of which
only one was a false alarms, and in only three cases a warning was cancelled even
though the debris flow had not stopped. The Illgraben catchment is also part of the
national IFKIS-Hydro early warning and information platform, which provides
monitoring data and event documentation (Romang et al. 2010).
In the North Italian community of Nals a local debris flow early warning system
was installed after devastating debris flow events in 2000 (Egger and Mair 2009).
The aim of the early warning system was to be an addition to structural protection
measures. Debris flow material is supplied by landslide processes in the upper
catchment. However, due to the high activity it was decided not to install an
automatic system there, but to place a series of geophones into the debris flow
channel to detect already initiated events. Still, a forewarn time of 20 to 60 min
between a geophone alarm and the debris reaching settled areas is accomplished.
Further technical equipment includes a piezometer, rainfall stations and a remote
controlled video camera with flood lights.
Another Italian case study on a debris flow warning system utilising geophones
is presented by Arattano (1999), however, it only was active for one summer.
A prototype of a mudflow early warning system for the Italian city Sarno is
described by Sirangelo and Braca (2004). Therein, the probabilistic hydrology
model FLaIR (Forecasting of Landslides Induced by Rainfall) was applied, which
correlates rainfalls with landslide occurrence. Warning thresholds were established
by back analysis and included the large 1998 event. According to these thresholds
three warning levels were determined, i.e. attention, alert and alarm.
The same model has been applied to Lanzo Valley of the Piedmont, Italy
(Capparelli and Tiranti 2010). Promising performance led to the current imple-
mentation of an automatic early warning system.
Hübl (2000) describes the application of a prototypic early warning system for
the Wartschenbach catchment in Austria, which frequently experiences debris
flows and flash flood events. The developed early warning system is thought as a
passive protection measure. The technical system is based on measurement of
rainfall and flow discharge by ultrasonic sensors in the upper catchment. If mea-
surements exceed pre-defined thresholds it is up to experts to decide whether to
close lower lying roads to prevent cars being hit by debris flows. By developing
adequate response plans for hazardous events they have tried to address the
response of the local community. The implemented system was planned and
implemented as a prototype and should be installed in other catchments which
could potentially produce debris flows after a test period.
A novel a landslide early warning system is described by Sakai (2008). Earlier
research (Sakai and Tarumi 2000) indicated that concentrations of sodium, cal-
cium and, sulphate ions in groundwater changed before phases of landslide
activity. Landslide failures could be predicted up to 90 days in advance. Therefore,
a prototypic landslide early warning system was set up which utilises automated
ion-selective electrodes to provide early warning to railroads in Japan. Data is
transmitted from the sensors in the field to train dispatchers and track maintenance
engineers via mobile phone networks. Measurements are taken every 1 to 3 days,
but frequency can be increased in the case of unusual sensor readings.
Another example of an early warning system in Japan is presented by Chiba
(2009). The warning system strongly emphasises warning communication and is
regarded as an integral element of the local disaster prevention program against
sediment-related processes (debris flows and debris floods). Earlier research on
local hazards provided information on potential hazardous zones and return
periods, which was utilised to allocate warning and evacuation zones in the
occurrence of debris flow events. Several people were employed to carry out
education programs in which local residents were informed about potential hazards
and appropriate reactions in case of a warning. If exceedance of pre-defined river
flow thresholds occurs a disaster management headquarter is assigned in which all
information about the current hazard status is collected. Information is gathered
primarily by the employees by calling local residents by cell phone. Moreover, all
data is updated to a GIS platform which is available online. Chiba (2009) illus-
trates the effectiveness of the warning system by comparing its performance to a
neighbouring town, in which no early warning system was installed, and no
detailed hazard maps and evacuation plans were available. During a debris flow
event local disaster managers were overwhelmed by incoming information and no
efficient evacuation could be initialised. In contrast, disaster managers of the town
with a warning system were able to quickly determine where debris flows had
occurred and to initialise evacuation according to the pre-determined schemes.
Flentje et al. (2005) present a real-time monitoring network for time pore water
pressure, slope movement and rainfall in Wollongong, Australia, which aims to
enhance understanding of landslide triggering process and improve quantitative
assessment of landslide hazards. All data is automatically sent via a cell phone
network to a web-based database available online. Threshold values have been
determined and current measurements are colour-coded to allow for easy inter-
pretation. However, the described system is essentially technical and does not aim
to provide warning messages or initiate counter measures or evacuation.
Besides the ILEWS project several other research programs focussing on early
warning systems for natural hazards were funded within the Geotechnologien
framework. Three of these projects also concentrated on landslide processes, and
will briefly be described in the following.
The SLEWS (Sensor-based Early Warning System) project focused on three
sensor types measuring acceleration, inclination and pressure to monitor landslide
initiation (Fernandez-Steeger et al. 2009). The project emphasized technological
developments accounted for sensor development and laboratory testing. A large
proportion of the accomplished work concentrated on wireless sensor networks to
ensure smooth data transmission. Developed sensors were applied to several real
case studies on the Barcolonette earthflow in France, and rockfall warning in Rathen,
Germany. However, besides the technical research, the integration of early warning
in social decision making process was another topic of the SLEWS project.
The main goal of the alpEWAS project was sensor-based monitoring and early
warning in the Bavarian Alps (Singer et al. 2009; Thuro et al. 2009). Thereto, three
main methods were utilised to detect displacements, i.e. TDR measurements,
prism-less tachymetrie and low-cost GPS measurements. In addition, an infor-
mation platform was set up to collect all data and inform involved experts by email
and SMS if pre-defined thresholds were exceeded.
The EGIFF project adopted a technical approach and concentrated on the devel-
opment of new methods applicable within landslide early warning. A wide range of
geotechnical data for a test slope south of Munich, Germany, was compiled and
modelled by a finite element model (Breunig et al. 2009). In addition, 3D/4 D databases
were developed for effective data visualisation. Moreover, the project implemented an
automated system to interpret media news and extract landslide related information.
From the examples of landslide early warning systems illustrated above some
conclusions can be drawn. Regional landslide early warning systems that focus on
shallow landslides or debris flows generally rely on rainfall thresholds derived by
Fig. 2.11 General structure of ILEWS project (Bell et al. 2009)
empirical or physically-based methods. In some cases local test slopes are

equipped with monitoring systems to improve understanding of landslide trig-
gering and consequently modify rainfall thresholds. Innovative approaches try to
integrate sophisticated slope stability models and real time modelling of landslide
initiation Local landslide early warning systems however are installed for a wider
range of processes. For debris flows technical early warning systems can be fairly
simple because it may take several minutes until an initiated debris flow reaches
settled areas. Forewarn time for other landslide processes is often shorter and early
warnings are more complex. For deep-seated landslides and failures in rock more
complex monitoring is applied to measure displacements and processes related to
movement triggering. Warning thresholds may account for displacement or critical
parameter values of triggering factors. Modelling of slope behaviour by complex
slope stability models is an important part of landslide prediction and is frequently
applied for landslide early warning. Most landslide early warning system are not
fully automated but leave judgment of the current situation and final warning to
experts. Existing landslide early warning systems differ substantially regarding the
integration of social aspects. In some cases landslide early warning is integrated
into larger schemes for slope safety, risk management, disaster prevention and
hazard awareness, while other systems constitute simple technical approaches.
2.5 The ILEWS Project
The work described within this thesis is embedded into the ILEWS project (Inte-
grative Landslide Early Warning Systems) which will be briefly introduced in the
following. Integrativity therein does not only refer to a strong interdisciplinary and
cooperative work between the project partners, but also to involvement of social
sciences aiming to cooperatively embed early warning into the prevalent political
2.5 The ILEWS Project 55
structures. Some results are presented in Chap. 7; more detailed information is

provided in Bell et al. (2010). The ILEWS project was funded by the German Federal
Ministry of Education and Research (BMBF) and integrated into the Geotechnolo-
gien research program. The project started in 2007 and ran for three years.
It is important to note that the research carried out in the ILEWS project partly
overlaps with the topics of this thesis; however, several aspects are investigated in
more detail in this work while other aspects are not covered. While the analysis of
slope movement and hydrological monitoring data, and subsequent development
of landslide early warning models are main goals of this thesis, the ILEWS project
had a wider scope. The overall goal of the ILEWS project was to develop and
implement a transferable early warning concept starting with sensors in the field
and modelling of early warning, and ending with user-optimized action advice
embedded in a holistic risk management strategy. To address the multiple issues
arising from such a comprehensive approach project partners from various sci-
entific backgrounds participated in the project, i.e. sensor technology, geoinfor-
mation, geomorphology, geodesy, history, social geography and spatial planning.
Altogether, 10 research partners cooperated within the ILEWS project, of which
five were private companies, and five university research groups. The general
structure of the ILEWS project is illustrated in Fig. 2.11.
The project can be distinguished in three clusters, i.e. monitoring, modelling and
implementation. However, due to the complex mission of developing and imple-
menting integrative landslide early warning systems and the interlinked workflow of
the involved project partners, several tasks were carried out cooperatively.
Within the monitoring cluster the main goals were the prospection, and
installation and operation of an adapted monitoring system for hydrology and
slope movement on a landslide at the Swabian Alb, South-West Germany.
Important milestones of the monitoring cluster include:
• geophysical prospection and development of a technical monitoring system
accounting for local geomorphology
• installation of hydrological sensors, inclinometers and geodetic network
• continuous and periodic measurements of hydrology and slope movement
• automated data storage, transmission and web-based visualisation
• development of web-based data management platform allowing for analysis and
interpretation
• archival research for analysis of magnitude -frequency characteristics of land-
slides in the study areas.
The overall goal of the modelling cluster was to analyse the data, and provide
reliable information on future slope behaviour based on a range of modelling
approaches. Some of the main aims of this cluster were:
• data analysis and validation
• application of empirical-based, physically-based and movement-based models
• integration of models into an early warning system
• modelling of early warning in real-time for both local and regional study areas.
Within the cluster implementation cooperative risk management and end-user

optimised warning communication were the main objectives. Other goals include:
• clarification of local and regional demands towards landslide early warning
• integration of warning into the respective social processes of decision making
• cooperative definition of protection goals
• development of alternative risk management strategies.
The objective of ILEWS, to develop and implement an adaptive and integrative
landslide early warning concept, was examined by a transfer to two studies of
which one is already equipped with a technical system.
References
Abellán A, Calvet J, Vilaplana JM, Blanchard J (2010) Detection and spatial prediction of rock
falls by means of terrestrial laser scanner monitoring. Geomorphology 119(3–4):162–171
Abramson LW (2002) Slope stability and stabilization methods. Wiley, New York
Ahnert F (2003) Einführung in die Geomorphologie. Ulmer, Stuttgart
Aleotti P (2004) A warning system for rainfall-induced shallow failures. Eng Geol
73(3–4):247–265
Alexander DE (2000) Confronting catastrophe: new perspectives on natural disasters. Oxford
University Press, Oxford
Alexander D (2002) Principles of emergency planning and management, 1st edn. Oxford
University Press, Oxford
Amitrano D, Gaffet S, Malet J.-P, Maquaire O (2007) Understanding mudslides through micro-
seismic monitoring: the Super-Sauze (South French Alps) case study. Bulletin de la Société
Géologique de France 178(2):149–157. Accessed 15 Oct 2010
Anderson MG, Richards K (1987) Modelling slope stability: the complementary nature of
geotechnical and geomorphological approaches. In: Anderson MG (ed) Slope stability:
geotechnical engineering and geomorphology. Wiley, New York, pp 1–9
Anderson SA, Thallapally LK (1996) Hydrologic response of a steep tropical slope to heavy
rainfall. In: Senneset K (ed) Landslides. Seventh international symposium on landslides.
Balkema, Rotterdam, pp 1489–1495
Anderson MG, Lloyd DM, Park A, Hartshorne J, Hargraves S, Othman A (1996) Establishing
new design dynamic modelling criteria for tropical cut slopes. In: Senneset K (ed) Landslides.
Seventh international symposium on landslides. Balkema, Rotterdam, pp 1067–1072
Anderson H, Bengtsson P-E, Berglund C, Larsson C, Larsson R, Sällfors G, Öberg-Högsta A-L
(2000) The landslide at Vagnharad in Sweden. In: Bromhead E, Dixon N, Ibsen M-L (eds)
Landslides in research, theory and practice. Eighth international symposium on landslides.
T. Telford, London, pp 65–70
Anderson M, Holcombe L, Flory R, Renaud J-P (2008) Implementing low-cost landslide risk
reduction: a pilot study in unplanned housing areas of the Caribbean. Nat Hazards
47(3):297–315
Angeli M-G, Gasparetto P, Bromhead E (2004) Strength-regain mechanisms in intermittently
moving slides. In: Lacerda W, Ehrlich M, Fontoura SAB, Sayao AS (eds) Landslides:
evaluation and stabilization. Ninth international symposium on landslides. A.A. Balkema
Publishers, Leiden, pp 689–696
Angerer H, Hermann SW, Kittl H, Poisel R, Roth W (2004) Monitoring, mechanics and risk
assessrnent of the landslide Lärchberg Galgenwald (Austria). In: Lacerda W, Ehrlich M,
References 57
Fontoura SAB, Sayao AS (eds) Landslides: evaluation and stabilization. Ninth international
symposium on landslides. A.A. Balkema Publishers, Leiden, pp 821–826
Antonello G, Casagli N, Farina P, Leva D, Nico G, Sieber AJ, Tarchi D (2004) Ground-based
SAR interferometry for monitoring mass movements. Landslides 1(1):21–28
Apip K, Takara K, Yamashina S, Ibrahim AB (2009) Study on early warning systems for shallow
landslide in the upper Citarum River catchment, Indonesia. In: Annuals of disaster prevention
research institute, Kyoto University, Japan (52B), pp 9–17
Apip K, Takara K, Yamashiki Y, Sassa K, Ibrahim AB, Fukuoka H (2010) A distributed
hydrological–geotechnical model using satellite-derived rainfall estimates for shallow
landslide prediction system at a catchment scale. Landslides 7(3):237–258
Arattano M (1999) On the use of seismic detectors as monitoring and warning systems for debris
flows. Nat Hazards 20(2):197–213
Ardizzone F, Cardinali M, Carrara A, Guzzetti F, Reichenbach P (2002) Impact of mapping
errors on the reliability of landslide hazard maps. Nat Hazards Earth Syst Sci 2:3–14
Ardizzone F, Cardinali M, Galli M, Guzzetti F, Reichenbach P (2007) Identification and mapping
of recent rainfall-induced landslides using elevation data collected by airborne Lidar. Nat
Hazards Earth Syst Sci 7:637–650
Armbruster V (2002) Grundwasserneubildung in Baden-Württemberg. University of Tübingen,
Germany
Ashland FX (2003) Characteristics, causes, and implications of the 1998 Wasatch Front
landslides. Utah Geological Survey Special Study, Utah, p 105
Atkinson P, Jiskoot H, Massari R, Murray T (1998) Generalized linear modelling in
geomorphology. Earth Surf Proc Land 23(13):1185–1195
Ausilio E, Cairo R, Dente G (2004) Role of viscous soil properties on landslide movement
triggered by pore water fluctuations. In: Lacerda W, Ehrlich M, Fontoura SAB, Sayao AS
(eds) Landslides: evaluation and stabilization. Ninth international symposium on landslides.
A.A. Balkema Publishers, Leiden, pp 1195–1200
Australian Geomechanics Society (2002) Landslide risk management concepts and guidelines.
Australian Geomechanics Society sub-committee on landslide risk management, pp 51–70
Avian M, Kellerer-Pirklbauer A, Bauer A (2009) LiDAR for monitoring mass movements in
permafrost environments at the cirque Hinteres Langtal, Austria, between 2000 and 2008. Nat
Avila-Olivera JA, Garduño-Monroy VH (2008) A GPR study of subsidence-creep-fault processes
in Morelia, Michoacán, Mexico. Eng Geol 100(1–2):69–81
Avolio MV, Di Gregorio S, Mantovani F, Pasuto A, Rongo R, Silvano S, Spataro W (2000)
Simulation of the 1992 Tessina landslide by a cellular automata model and future hazard
scenarios. Int J Appl Earth Observ Geoinform 2(1):41–50
Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based
weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture,
Japan. Landslides 1(1):73–81
Aysen A (2002) Soil mechanics: basic concepts and engineering applications. Taylor & Francis,
Vancouver
Badoux A, Graf C, Rhyner J, Kuntner R, McArdell BW (2009) A debris-flow alarm system for
the Alpine Illgraben catchment: design and performance. Nat Hazards 49(3):517–539
Baek Y, Koo H, Bae GJ (2004) Study on development monitoring system of slope using the
optical fibre sensor. In: Lacerda W, Ehrlich M, Fontoura SAB, Sayao AS (eds) Landslides:
Bai Y, Huang R, Ju N, Zhao J, Huo Y (2008) 3DEC stability analysis of high and steep rock
slope. J Eng Geol 16(5):592–597
Bak P, Tang C, Wiesenfeld K (1988) Self-organized criticality. Phys Rev A 38(1):364. Accessed
2 Nov 2010
Baldo M, Bicocchi C, Chiocchini U, Giordan D, Lollino G (2009) LIDAR monitoring of mass

wasting processes: The Radicofani landslide, Province of Siena, Central Italy. Geomorphol-
ogy 105(3–4):193–201
Bandis S, Colombini V, Delmonaco G, Margottini C (2000) New typology of low environmental
impact consolidation for rockfall prone cliffs through interventions from the underground. In:
Bromhead E, Dixon N, Ibsen M-L (eds) Landslides in research, theory and practice. Eighth
international symposium on landslides. T. Telford, London, p 107
Barendse MB, Machan G (2009) In-place microelectromechanical system inclinometer strings:
evaluation of an evolving technology—Publications index. In: TRB 88th annual meeting
compendium of papers DVD. Washington, p 12
Barla G, Amici R, Vai L, Vanni A (2004) Investigation, monitoring and modelling of a landslide
in porphyry in a public safety perspective. In: Lacerda W, Ehrlich M, Fontoura SAB,
Sayao AS (eds) Landslides: evaluation and stabilization. Ninth international symposium on
landslides. A.A. Balkema Publishers, Leiden, pp 623–628
Barton ME, McCosker M (2000) Inclinometer and tiltmeter monitoring of a high chalk cliff. In:
Bromhead E, Dixon N, Ibsen M-L (eds) Landslides in research, theory and practice. Eighth
international symposium on landslides. T. Telford, London, pp 127–132
Baum RL (2007) Landslide warning capabilities in the United States—2006. In: Proceedings of
the first North America landslide conference. Association of Engineering Geologists Special
Publication, Vail Colorado, USA, vol 23, pp 1–14
Baum RL, Fleming RW (1991) Use of longitudinal strain in identifying driving and resisting
elements of landslides. Geol Soc Amer Bullet 103(8):1121–1132. Accessed 20 Sep 2010
Baum RL, Godt JW (2009) Early warning of rainfall-induced shallow landslides and debris flows
in the USA. Landslides 7(3):259–272
Baum RL, Coe JA, Godt JW, Harp EL, Reid ME, Savage WZ, Schulz WH, Brien DL,
Chleborad AF, McKenna JP, Michael JA (2005a) Regional landslide-hazard assessment for
Seattle, Washington, USA. Landslides 2(4):266–279
Baum RL, Godt JW, Harp EL, McKenna JP, McMullen SR (2005b) Early warning of landslide
for rail traffic between Seattle and Washington. In: Hungr O, Fell R, Couture R, Eberhardt E
(eds) International conference on landslide risk management. Taylor & Francis Ltd,
Vancouver, p 731
Bell R (2007) Lokale und regionale Gefahren- und Risikoanalyse gravitativer Massenbeweg-
ungen an der Schwäbischen Alb. University of Bonn, Germany
Bell R, Glade T, Thiebes B, Jäger S, Krummel H, Janik M, Holland R (2009) Modelling and web
processing of early warning. In: Malet J-P, Remaître A, Bogaard T (eds) Landslide processes,
From geomorphic mapping to dynamic modelling. CERG Editions, Strasbourg, pp 249–252
Bell R, Mayer J, Pohl J, Greiving S, Glade T (2010) Integrative Frühwarnsysteme für Gravitative
Massenbewegungen (ILEWS)—Monitoring, Modellierung, Implementierung. Klartext, Essen
Berardi R, Mercurio G, Bartolini P, Cordano E (2005) Dynamics of saturation phenomena and
landslide triggering by rain infiltration in a slope. In: Hungr O, Fell R, Couture R, Eberhardt E
(eds) International conference on landslide risk management. Taylor & Francis Ltd.,
Vancouver, pp 212–219
Bichler A, Bobrowsky P, Best M, Douma M, Hunter J, Calvert T, Burns R (2004) Three-
dimensional mapping of a landslide using a multi-geophysical approach: the Quesnel Forks
landslide. Landslides 1(1):29–40
Bjerrum L (1967) Progressive failure in slopes of over consolidated plastic clay and clay shales.
J Soil Mech Found Div Amer Soc Civil Eng 93:1–49
Blackburn JT, Dowding CH (2004) Finite-element analysis of time domain reflectometry cable-
grout-soil interaction. J Geotech Geoenviron Eng 130(3):231–239
Bláha P (1996) Geoacoustic method and slope deformations. In: Senneset K (ed) Landslides.
Seventh international symposium on landslides. Balkema, Rotterdam, pp 1521–1524
Blikra LH (2008) The Aknes rockslide; monitoring, threshold values and early-warning. In:
Landslides and engineered slopes. From the past to the future proceedings of the tenth
international symposium on landslides and engineered slopes. pp 1089–1094
References 59
Bloyet J, Beghoul N, Ricard Y, Froidevaux C (1989) In situ test of a borehole extensometer. Rock
Mech Rock Eng 22(4):289–297. Accessed 11 Oct 2010
Bogaard T (2000) The slope movements within the Mondorès graben (Drôme, France); the
interaction between geology, hydrology and typology. Eng Geol 55(4):297–312. Accessed 20
Sep 2010
Bonnard C (2008) Introduction to Lanslide: mechanisms of landslides and specificities of large
landslides. Presentation at laram summer school, Ravello, Italy, 2008
Bonnard C, Noverraz F, Dupraz H (1996) Long-term movement of substabilized versants and
climatic changes in the Swiss Alps. In: Senneset K (ed) Landslides. Seventh international
symposium on landslides. Balkema, Rotterdam, pp 1525–1530
Bonnard C, Tacher L, Beniston M (2008) Prediction of landslide movements caused by climate
change: modelling the behaviour of a mean elevation large slide in the Alps and assessing its
uncertainties. In: Proceedings of the tenth international symposium on landslides and
engineered slopes (Volume 1)
Booth AM, Roering JJ, Perron JT (2009) Automated landslide mapping using spectral analysis
and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills,
Oregon. Geomorphology 109(3–4):132–147
Borgatti L, Corsini A, Barbieri M, Sartini G, Truffelli G, Caputo G, Puglisi C (2006) Large
reactivated landslides in weak rock masses: a case study from the Northern Apennines (Italy).
Landslides 3(2):115–124
Bovis MJ (2004) Failure. In: Goudie A (ed) Encyclopedia of geomorphology. Taylor & Francis,
Routledge, pp 360–361
Bozzano F, Gaeta M, Scarascia GM, Valentini G (2000) Numerical modelling of slopes
surrounding ignimbrite plateaux. In: Bromhead E, Dixon N, Ibsen M-L (eds) Landslides in
research, theory and practice. Eighth international symposium on landslides. T. Telford,
London, p 177
Bozzano F, Mazzanti P, Prestininzi A, Scarascia Mugnozza G (2010) Research and development
of advanced technologies for landslide hazard analysis in Italy. Landslides 7(3):381–385
Brand EW, Premchitt J, Philipson HB (1984) Relationship between rainfall and landslides in
Hong Kong. In: Proceedings of the fourth international symposium on landslides. Toronto,
pp 377–384
Brandon TL, Wright SG, Duncan JM (2008) Analysis of the stability of I-Walls with Gaps
between the I-Wall and the Levee Fill. J Geotech Geoenviron Eng 134(5):692
Brennecke M (2006) Erstellung einer Inventarkarte gravitativer Massenbewegungen an der
Schwäbischen Alb—Kartierung aus Luftbildern und einem digitalen Höhenmodell. Univer-
sity of Bonn, Germany
Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and
evaluation. Nat Hazards Earth Syst Sci 5(6):853–862
Bressani LA, Pinheiro RJB, Eisenberger CN, Soares JMD (2008) Movements of a large urban
slope in the town of Santa Cruz do Sul (RGS), Brazil. Landslides Eng Slopes. From the Past to
the Future 1:293–298
Breunig M, Schilberg B, Kupfer PV, Jahn M, Reinhardt W, Nuhn E, Boley C, Trauner F-X,
Wiesel J, Richter D, Abecker A, Gallus D, Kazakos W, Bartels A (2009) EGIFF—Developing
advanced GI methods for early warning in mass movement scenarios. In: Stroink L (ed)
Geotechnologien science report 13. Early warning system in earth management. pp 49–72
Bromhead EN (1998) Stability of slopes. Taylor & Francis, Routledge
Bromhead E, Huggins M, Ibsen M-L (2000) Shallow landslides in Wadhurst clay at
Robertsbridge, Sussex, UK. In: Bromhead E, Dixon N, Ibsen M-L (eds) Landslides in
London, pp 183–188
Brooks SM, Crozier MJ, Glade TW, Anderson MG (2004) Towards establishing climatic
thresholds for slope instability: use of a physically-based combined soil hydrology-slope
stability model. Pure Appl Geophys 161(4):881–905
Brunetti MT, Peruccacci S, Rossi M, Luciani S, Valigi D, Guzzetti F (2010) Rainfall thresholds
for the possible occurrence of landslides in Italy. Nat Hazards Earth Syst Sci 10:447–458
Bull WB (1991) Geomorphic responses to climatic change. Oxford University Press, Oxford
Buma J (2000) Finding the most suitable slope stability model for the assessment of the impact of
climate change on a landslide in southeast France. Earth Surf Proc Land 25(6):565–582
Burghaus S, Bell R, Kuhlmann H (2009) Improvement of a terrestric network for movement
analysis of a complex landslide. Presentation at FIG conference, Eilat, Israel, 2009
Burns SF, Harden TM, Andrew CJ (eds) (2008) Homeowner’s guide to landslides—Recognition,
prevention, control and mitigation. Portland State University, Portland
Caine N (1980) The rainfall intensity: duration control of shallow landslides and debris flows.
Geografiska Annaler. Series A, Phys Geog 62(1/2):23–27. Accessed 18 Oct 2010
Cala M, Flisiak J, Tajdus A (2004) Slopc stability analysis with modified shear strength reduction
technique. In: Lacerda W, Ehrlich M, Fontoura SAB, Sayao AS (eds) Landslides: evaluation
and stabilization. Ninth international symposium on landslides. A.A. Balkema Publishers,
Leiden, pp 1085–1090
Calcaterra D, Parise M, Palma B (2003) Combining historical and geological data for the
assessment of the landslide hazard: a case study from Campania, Italy. Nat Hazards Earth Syst
Sci 3:3–16
Calcaterra D, Ramondini M, Calò F, Longobardi V, Parise M, Galzerano CM (2008) DInSAR
techniques for monitoring slow moving-landslides. In: Chen Z, Zhang J-M, Ho K, Wu F-Q,
Li Z-K (eds) Landslides and engineered slopes. From the Past to the Future Proceedings of the
tenth international symposium on landslides and engineered slopes. pp 1089–1094
California Geological Survey (2003) Hazards from ‘‘Mudslides’’…Debris Avalanches and Debris
flows in Hillslide and Wildfire Areas. U.S. Geological Survey Open File Report 2006-1064
Calvello M, Cascini L, Sorbino G (2008) A numerical procedure for predicting rainfall-induced
movements of active landslides along pre-existing slip surfaces. Int J Numer Anal Meth
Geomech 32(4):327–351
Campbell RH (1975) Soil slips, debris flows, and rainstorms in the Santa Monica Mountains and
vicinity, Southern California. US Geological Survey Professional Paper 851
Cannon SH, Ellen SD (1988) Rainfall that resulted in abundant debris flow activity during the
storm. In: Ellen SD, Wieczorek GF (eds) Landslides, floods, and marine effects of the storm of
January 3–5, 1982. US Geological Survey Professional Paper, San Francisco Bay region,
California, pp 27–33
Canuti P, Casagali N, Moretti S, Leva D, Sieber AJ, Tarchi D (2002) Landslide monitoring by
using ground-based radar differential interferometry. In: Rybár J, Stemberk J, Wagner P (eds)
First European conference on landslides. Balkema Publishers, Czech Republic, pp 523–528
Capparelli G, Tiranti D (2010) Application of the MoniFLaIR early warning system for rainfall-
induced landslides in Piedmont region (Italy). Landslides 4(7):401–410
Capparelli G, Biondi D, De Luca DL, Versace P (2009) Hydrological and complete models for
forecasting landslides triggered by rainfalls. In: Rainfall–induced landslides. Mechanisms
monitoring techniques and nowcasting models for early warning systems. Proceedings of the
first Italian workshop on landslides. pp 8–10
Cardinali M, Reichenbach P, Guzzetti F, Ardizzone F, Antonini G, Galli M, Cacciano M,
Castellani M, Salvati P (2002) A geomorphological approach to the estimation of landslide
hazards and risks in Umbria, Central Italy. Nat Hazards Earth Syst Sci 2(1/2):57–72
Carey JM, Moore R, Petley DN, Siddle HJ (2007) Pre-failure behaviour of slope materials and their
significance in the progressive failure of landslides. In: McInnes R, Jakeways J, Fairbank H,
Mathie E (eds) Landslide and climate change—Challenges and solutions. Taylor & Francis,
Routledge, pp 217–226
Carrara A (1983) Multivariate models for landslide hazard evaluation. Math Geol 15(3):403–426
Carrara A, Cardinali M, Guzzetti F (1992) Uncertainty in accessing landslide hazard risk. ITC J
2:172–183
Carrara A, Guzzetti F, Cardinali M, Reichenbach P (1999) Use of GIS technology in the
prediction and monitoring of landslide hazard. Nat Hazards 20(2):117–135
References 61
Carrara A, Crosta G, Frattini P (2003) Geomorphological and historical data in assessing

landslide hazard. Earth Surf Proc Land 28(10):1125–1142
Cartlidge E (2010) Scientists face trial over L’Aquila quake. http://physicsworld.com/cws/article/
news/43001. Accessed 13 Sep 2010
Casadei M, Dietrich WE, Miller NL (2003) Testing a model for predicting the timing and
location of shallow landslide initiation in soil-mantled landscapes. Earth Surf Proc Land
28(9):925–950
Casagali N, Farina P, Leva D, Tarchi D (2004) Landslide monitoring on the Stromboli volcano
through SAR interferometry. In: Lacerda W, Ehrlich M, Fontoura SAB, Sayao AS (eds)
Landslides: evaluation and stabilization. Ninth international symposium on landslides. A.A.
Balkema Publishers, Leiden, pp 803–808
Casagli N, Rinaldi M, Gargini A, Curini A (1999) Pore water pressure and stream bank stability:
results from a monitoring site on the Sieve River, Italy. Earth Surf Proc Land
24(12):1095–1114
Casagli N, Catani F, Del Ventisette C, Luzi G (2010) Monitoring, prediction, and early warning
using ground-based radar interferometry. Landslides 7(3):291–301
Cascini L, Cuomo S, Pastor M (2008) The role played by mountain tracks on rainfall-induced
shallow landslides: a case study. In: Proceedings of the iEMSs fourth biennial meeting:
International congress on environmental modelling and software (iEMSs 2008). 7–10 July
2008, Barcelona, Spain, pp 1484–1491
Cássia de Brito Galvão T, Parizzi MG, Sobreira FG, Elmiro T, Beirigo EA (2007) Landslide in
Belo Horizonte, Brazil
Catani F, Casagli N, Ermini L, Righini G, Menduni G (2005) Landslide hazard and risk mapping
at catchment scale in the Arno River basin. Landslides 2(4):329–342
Chan RKS (2007) Challenges in slope engineering in Hong Kong. In: Proceedings of the
sixteenth southeast Asian geotechnical conference, Southeast Asian Geotechnical Society,
Malaysia. pp 137–151
Chan RKS, Pang PLR, Pun WK (2003) Recent developments in the landslip warning system in
Hong Kong. In: Proceedings of the fourteenth Southeast Asian geotechnical conference,
Southeast Asian Geotechnical Society, Hong Kong, pp 219–224
Chandler RJ (1991): Slope stability engineering: developments and applications? In: Proceedings
of the international conference on slope stability. Thomas Telford, London
Chang K-T, Wan S, Lei T-C (2010) Development of a spatial decision-support system for
monitoring earthquake-induced landslides based on aerial photographs and the finite element
method. Int J Appl Earth Observ Geoinform 12(6):448–456. Accessed 27 Oct 2010
Chen SG, Cai JG, Zhao J, Zhou YX (2000) Discrete element modelling of an underground
explosion in a jointed rock mass. Geotech Geol Eng 18(2):59–78
Cheng YM, Lau CK (2008) Slope stability analysis and stabilization: new methods and insight.
Taylor & Francis, Routledge
Cheng DX, Pan W, Liu DA, Feng SR, Guo HF, Ding EB (2006) 3 DEC modeling of equivalent
mechanical parameters in anchored jointed rock mass. Yantu Lixue (Rock and Soil
Mechanics) 27(12):2127–2132
Cheung PY, Wong MC, Yeung HY (2006) Application of rainstorm nowcast to real-time warning
of landslide hazards in Hong Kong. In: WMO PWS workshop on warnings of real-time
hazards by using nowcasting technology. pp 9–13
Chiang S, Chang K (2009) Application of radar data to modeling rainfall-induced landslides.
Geomorphology 103(3):299–309
Chiba M (2009) Warning and evacuation in response to sediment-related disasters. Nat Hazards
2(56):499–507
Chigira M, Duan F, Yagi H, Furuya T (2004) Using an airborne laser scanner for the
identification of shallow landslides and susceptibility assessment in an area of ignimbrite
overlain by permeable pyroclastics. Landslides 1(3):203–209
Chigira M, Wu X, Inokuchi T, Wang G (2010) Landslides induced by the 2008 Wenchuan
earthquake, Sichuan, China. Geomorphology 3–4(118):225–238
Ching-Chuan H, Yih-Jang J, Lih-Kang H, Jin-Long L (2009) Internal soil moisture and

piezometric responses to rainfall-induced shallow slope failures. J Hydrol 370(1–4):39–51
Chleborad AF (2000) Preliminary method for anticipating the occurrence of precipitation-
induced landslides in Seattle, Washington. U.S. Geological Survey Open-File Report 03-463
Chleborad AF (2003) Preliminary evaluation of a precipitation threshold for anticipating the
occurrence of landslides in the Seattle, Washington, area. U.S Department of the Interior, U.S.
Geological Survey, Denver
Chleborad AF, Baum RL, Godt JW (2006) Rainfall thresholds for forecasting landslide in the
seattle. Washington, Area—Exceedance and probability. U.S. Geological Survey Open File
Report 2006-1064
Chok YH, Kaggwa W, Jaksa MB, Griffiths DV (2004) Modelling the effects of vegetation on
stability of slopes. In: Proceedings of the ninth Australia New Zealand conference on
geomechanics. Auckland, New Zealand, pp 391–397
Chorley RJ, Kennedy BA (1971) Physical geography—A systems approach. Prentice-Hall,
London
Chugh AK, Stark TD (2005) Permanent seismic deformation analysis of a landslide. Landslides
3(1):2–12
Chung CJ (2006) Using likelihood ratio functions for modeling the conditional probability of
occurrence of future landslides for risk assessment. Comput Geosci 32(8):1052–1068
Chung CJ, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping.
Photogrammetric Eng Remote Sensing 65(12):1389–1399
Chung C, Fabbri AG, Van Westen CJ (1995) Multivariate regression analysis for landslide hazard
zonation. In: Carrara A, Guzzetti F (eds) Geographical information systems in assessing
natural hazards. Kluwer Academic Publishers, Dordrecht, pp 107–134
Claessens L, Heuvelink GBM, Schoorl MJ, Veldkamp A (2005) DEM resolution effects on
shallow landslide hazard and soil redistribution modelling. Earth Surf Proc Land 30:461–477
Clark AR, Moore R, Palmer JS (1996) Slope monitoring and early warning systems: Application
to coastal landslide on the south and east coast of England, UK. In: Senneset K (ed)
Landslides. Seventh international symposium on landslides. Balkema, Rotterdam,
pp 1531–1538
Clark AR, Fort D, Davis GM (2000) The strategy, management and investigation of coastal
landslides at Lyme Regis, Dorset. In: Bromhead E, Dixon N, Ibsen M-L (eds) Landslides in
Colesanti C, Wasowski J (2004) Satellite SAR interferometry for wide-area slope hazard
detection and site-specific monitoring of sloe landslides. In: Lacerda W, Ehrlich M, Fontoura
SAB, Sayao AS (eds) Landslides: evaluation and stabilization. Ninth international symposium
on landslides. A.A. Balkema Publishers, Leiden, pp 795–802
Collison AJC, Anderson MG (1996) Using a combined slope hydrology/stability model to
identify suitable conditions for landslide prevention by vegetation in the humid tropics. Earth
Surf Proc Land 21(8):737–747
Comegna L, Urciuoli G, Picarelli L (2004) The role of pore pressures on the mechanics of
mudslides. In: Lacerda W, Ehrlich M, Fontoura SAB, Sayao AS (eds) Landslides: evaluation
and stabilization. Ninth international symposium on landslides. A.A. Balkema Publishers,
Leiden, pp 1183–1188
Conolly H (1997) World wide web pages for slope design. School of Engineering, University of
Durham, UK
Cornforth DH, Mikkelsen PE (1996) Continuous monitoring of the slope above an excavation
within a marginally stable landslide. In: Senneset K (ed) Landslides. Seventh international
symposium on landslides. Balkema, Rotterdam, pp 1539–1544
Costa-Cabral MC, Burges SJ (1994) Digital elevation model networks (DEMON): a model of
flow over hill slopes for computation of contributing and dispersal areas. Water Resour Res
30(6):1681–1692. Accessed 26 Oct 2010
References 63
Cotza G (2009) Geologische und geotechnische Verhältnisse der Massenbewegungen bei

Pontives (Grödnertal, Südtirol). University of Vienna, Austria
Coulomb CA (1776) Essai sur une application des regles des maximis et minimis a quelques
problemes de statique relatifs a l’architecture. Memoires de l’Academie Royale pres Divers
Savants 7:343–387
Cristescu N (1989) Rock rheology. Springer, Berlin
Crosta G (1998) Regionalization of rainfall thresholds: an aid to landslide hazard evaluation. Env
Geol 35(2):131–145
Crosta GB, Frattini P (2003) Distributed modelling of shallow landslides triggered by intense
rainfall. Nat Hazards Earth Syst Sci 3:81–93
Crosta GB, Frattini P (2008) Rainfall-induced landslides and debris flows. Hydrol Process
22(4):473–477
Crozier MJ (1989) Landslides: causes, consequences and environment. Taylor & Francis,
Routledge
Crozier MJ (1999) Prediction of rainfall-triggered landslides: a test of the antecedent water status
model. Earth Surf Proc Land 24(9):825–833
Crozier MJ, Eyles RJ (1980) Assessing the probability of rapid mass movement. In: Proceedings
of the third Australia and New Zealand conference on Geomechanics. New Zealand Institute
of Engineers, pp 247–251
Crozier MJ, Glade T (2005) Landslide hazard and risk: issues, concepts, and approach. In: Glade T,
Anderson M, Crozier MJ (eds) Landslide hazard and risk. Wiley, New York, pp 1–40
Crozier MJ, Preston NJ (1999) Modelling changes in terrain resistance as a component of
landform evolution in unstable hill country. In: Hergarten S, Neugebauer HJ (eds) Process
modelling and landform evolution. Springer, Berlin, pp 267–284
Cruden DM (1991) A very simple definition for a landslide. IAEG Bulletin, pp 27–29
Cruden DM, Varnes DJ (1996) Landslide types and processes. In: Turner AK, Schuster RL (eds)
Landslides: investigation and mitigation (Special Report). Washington, DC, USA: National
Research Council, Transportation and Research Board Special Report 247, pp 36–75
Cundall PA, Strack ODL (1979) A discrete numerical model for granular assemblies.
Géotechnique 29(1):47–65
D’Ambrosio D, Di Gregorio S, Iovine G, Lupiano V, Rongo R, Spataro W (2003) First
simulations of the Sarno debris flows through cellular automata modelling. Geomorphology
54(1–2):91–117
D’Orsi R (2006) Alerta Rio—The Rio de Janeiro warning system against severe weather and
mass movements. Presentation at laram summer school, Ravello, Italy, 2006
D’Orsi R, Feijo RL, Paes NM (2004) 2,500 operational days of Alerta Rio System—History. In:
Lacerda W, Ehrlich M, Fontoura SAB, Sayao AS (eds) Landslides: evaluation and
stabilization. Ninth international symposium on landslides. A.A. Balkema Publishers, Leiden,
pp 549–555
Dai FC, Lee CF (2003) A spatiotemporal probabilistic modelling of storm-induced shallow
landsliding using aerial photographs and logistic regression. Earth Surf Proc Land
28(5):527–545
Dai Z-Y, Liu Y, Zhang L-X, Ou Z-H, Zhou C, Liu Y-Z (2008) Landslide monitoring based on
high-resolution distributed fiber optic stress sensor. J Electron Sci Technol China
6(4):416–419
Dearing J (2004) Non-linear dynamics. In: Goudie A (ed) Encyclopedia of Geomorphology.
Taylor & Francis, Routledge, pp 721–725
Deb SK, El-Kadi AI (2009) Susceptibility assessment of shallow landslides on Oahu, Hawaii,
under extreme-rainfall events. Geomorphology 108(3–4):219–233. Accessed 10 Sep 2010
Demoulin A, Chung CJ (2007) Mapping landslide susceptibility from small datasets: A case
study in the Pays de Herve (E Belgium). Geomorphology 89(3–4):391–404
Deparis J, Fricout B, Jongmans D, Villemin T, Effendiantz L, Mathy A (2008) Combined use of
geophysical methods and remote techniques for characterizing the fracture network of a
potentially unstable cliff site (the ‘Roche du Midi’, Vercors massif, France). J Geophys Eng 5:147
Derron MH, Blikra LH, Jaboyedoff M (2005) High resolution digital elevation model analysis for
landslide hazard assessment (Åkerneset, Norway). In: Senneset K, Flaate K, Larsen JO (eds)
Landslide and Avalanches. ICFL 2005, Norway. Taylor & Francis Group, London,
pp 101–106
Dewitte O, Chung C, Demoulin A (2006) Reactivation hazard mapping for ancient landslides in
West Belgium. Nat Hazards Earth Syst Sci 6(4):653–662
Dhakal G, Yoneda T, Kato M, Kaneko K (2002) Slake durability and mineralogical properties of
some pyroclastic and sedimentary rocks. Eng Geol 65(1):31–45
Di Maio C, Onorati R (2000) Influence of pore liquid composition on the shear strength of an
active clay. In: Bromhead E, Dixon N, Ibsen M-L (eds) Landslides in research, theory and
practice. Eighth international symposium on landslides. T. Telford, London, pp 463–468
Die Zeit H (2010) Erdbeben in Italien: ‘‘Trinken Sie lieber einen!’’. http://www.zeit.de/2010/37/
T-Erdbeben. Accessed 13 Sep 2010
Dietrich WE, Real de Asua R, Coyle1 J, Orr B, Trso M (1998) A validation study of the shallow
slope stability model, SHALSTAB, in forested lands of Northern California. Stillwater
Ecosystem, Watershed & Riverine Sciences, Berkley, USA
Dikau R (2005) Geomorphologische Perspektiven integrativer Forschungsansätze in Physischer
Geographie und Humangeographie. In: Wardenga U, Müller-Mahn D (eds) Möglichkeiten
und Grenzen integrativer Forschungsansätze in Physischer Geographie und Humangeogra-
phie. forum ifl. Leibniz-Institut für Länderkunde, Leipzig, pp 91–108
Dikau R (2006) Komplexe Systeme in der Geomorphologie. Mitteilungen der Österreichischen
Geographischen Gesellschaft 148:125–150
Dikau R, Glade T (2003) Nationale Gefahrenhinweiskarte gravitativer Massenbewegungen. In:
Liedtke H, Mäusbacher R, Schmidt K-H (eds) Relief, Boden und Wasser. Nationalatlas
Bundesrepublik Deutschland. Spektrum Akademischer Verlag, Heidelberg, pp 98–99
Dikau R, Weichselgärtner J (2005) Der unruhige Planet. Der Mensch und die Naturgewalten. 1st
ed. Primus Verlag, Darmstadt
Dikau R, Brunsden D, Schrott L (1996) Landslide recognition: identification, movement and
causes: identification, movement and courses. Wiley, New York
Dixon N, Spriggs M (2007) Quantification of slope displacement rates using acoustic emission
monitoring. Canad Geotech J 44(8):966–976. Accessed 15 Oct 2010
Duncan JM, Wright SG (2005) Soil strength and slope stability. Wiley, New York
Eberhardt E (2003a) Rock slope stability analysis—Utilization of advanced numerical
techniques. University of British Columbia, Vancouver
Eberhardt E (2003b) Rock slope stability analysis—Utilization of advanced numerical
techniques. University of British Columbia, Vancouver
Eberhardt E, Spillmann T, Maurer H, Willenberg H, Loew S, Stead D (2004) The Randa
Rockslide Laboratory: establishing brittle and ductile instability mechanisms using numerical
modelling and microseismicity. In: Lacerda W, Ehrlich M, Fontoura SAB, Sayao AS (eds)
Eberhardt E, Watson AD, Loew S (2008) Improving the interpretation of slope monitoring and
early warning data through better understanding of complex deep-seated landslide failure
mechanisms. In: Chen Z, Zhang J-M, Ho K, Wu F-Q, Li Z-K (eds) Landslides and engineered
slopes: From the past to the future. Proceedings of the tenth international symposium on
landslides and engineered slopes. Taylor & Francis, Xi’an, pp 39–51
Eeckhaut MVD, Poesen J, Verstraeten G, Vanacker V, Nyssen J, Moeyersons J, Beek LPH,
Vandekerckhove L (2007) Use of LIDAR-derived images for mapping old landslides under
forest. Earth Surf Proc Land 32(5):754–769
Egger P, Mair V (2009) Innovative Maßnahmen zur gefahrenreducktion am beispiel
Grissianerbach. J für Wildbach-, Lawinen-, Erosion- und Steinschlagschutz 161:1–15
Eidsvig UM, Medina-Cetina Z, Kveldsvik V, Glimsdal S, Harbitz CB, Sandersen F (2011) Risk
assessment of a tsunamigenic rockslide at Åknes. Nat Hazards 56(2):529–545
References 65
Ekanayake JC, Phillips CJ (1999) A model for determining thresholds for initiation of shallow
landslides under near-saturated conditions in the East Coast region, New Zealand. J Hydrol
(NZ) 38(1):1–28
Eng HJ, Li W, Ma TH, Li CJ (2009) Geological disasters early warning and forecast information-
releasing system: A new generation of releasing system based on ann and gis. J Nat Disasters
18(1):187–193
Fairfield J, Leymarie P (1991) Drainage networks from grid digital elevation models. Water
Resour Res 27(5):709–717. Accessed 26 Oct 2010
Fang H-Y (1990) Foundation engineering handbook. Springer, Berlin
Favis-Mortlock D (1998) A self-organizing dynamic systems approach to the simulation of rill
initiation and development on hill slopes. Comput Geosci 24(4):353–372
Favis-Mortlock D (2004) Self-organization and cellular automata models. In: Wainwright J,
Mulligan M (eds) Environmental modelling: finding simplicity in complexity. Wiley, New
York, pp 349–370
Favis-Mortlock D, De Boer D (2003) Simple at heart? Landscape as a self-organizing complex
system. In: Trudgill S, Roy A (eds) Contemporary meanings in physical geography. Oxford
University Press, Oxford, pp 127–172
Felgentreff C, Glade T (2007) Naturrisiken und Sozialkatastrophen. 1st ed. Spektrum
Akademischer Verlag, Heidelberg
Ferentinou MD, Sakellariou M, Matziaris V, Charalambous S (2006) An introduced methodology
for estimating landslide hazard for seismic and rainfall induced landslides in a geographical
information system environment. In: Nadim F, Pöttler R, Einstein H, Klapperich H, Kramer S
(eds) Geohazards Proceedings of the ECI Conference on Geohazards. Lillehammer, Norway,
pp 1–8
Fernandez Merodo JA, Pastor M, Mira P, Tonni L, Herreros MI, Gonzalez E, Tamagnini R (2004)
Modelling of diffuse failure mechanisms of catastrophic landslides. Comput Methods Appl
Mech Eng 193(27–29):2911–2939
Fernandez-Steeger TM, Arnhardt C, Hass S, Niemayer F, Nakaten B, Homfeld SD, Asch K,
Azzam R, Bill R, Ritter H (2009) SLEWS—A prototype system for flexible real time
monitoring of landslide using on open spatial data infrastructure and wireless sensor networks.
In: Geotechnologien Science Report 13. Early Warning System in Earth Management.
pp 3–15
Fernández-Steeger TM, Rohn J, Czurda K (2002) Identification of landslide areas with neural
nets for hazard analysis. In: Rybár J, Stemberk J, Wagner P (eds) Landslides: Proceedings of
the first European conference on landslides, June 24–26, 2002. Taylor & Francis, Czech
Republic, pp 163–168
Ferretti A, Prati C, Rocca F, Casagli N, Farina P, Young B (2005) Permanent Scatterers
technology: a powerful state of the art tool for historic and future monitoring of landslides and
other terrain instability phenomena. In: Hungr O, Fell R, Couture R, Eberhardt E (eds)
International conference on landslide risk management. Taylor & Francis Ltd., Vancouver,
pp 389–397
Fiorucci F, Cardinali M, Carlà R, Mondini A, Santurri L, Guzzetti F (2010) Comparison of event-
based landslide inventory maps obtained interpreting satellite images and aerial photographs.
Geophys Res Abstracts 12
Flentje PN, Chowdhury RN, Tobin P, Brizga V (2005) Towards real-time landslide risk
management in an urban area. In: Hungr O, Fell R, Couture R, Eberhardt E (eds) International
conference on landslide risk management. Taylor & Francis Ltd., Vancouver, pp 741–751
Fonstad MA (2006) Cellular automata as analysis and synthesis engines at the geomorphology-
ecology interface. Geomorphology 77(3–4):217–234
Fonstad M, Marcus WA (2003) Self-organized criticality in riverbank systems. Ann Assoc Amer
Geographers 93(2):281–296. Accessed 4 Nov 2010
Fourniadis IG, Liu JG, Mason PJ (2007a) Landslide hazard assessment in the Three Gorges area,
China, using ASTER imagery: Wushan-Badong. Geomorphology 84(1–2):126–144
Fourniadis IG, Liu JG, Mason PJ (2007b) Regional assessment of landslide impact in the Three
Gorges area, China, using ASTER data: Wushan-Zigui. Landslides 4(3):267–278
Franklin JA (1984) Slope instrumentation and monitoring. In: Brunsden D, Prior DB (eds) Slope
instability. Wiley, New York, pp 143–170
Frattini P, Crosta G, Sosio R (2009) Approaches for defining thresholds and return periods for
rainfall-triggered shallow landslides. Hydrol Process 23(10):1444–1460
Fredlund DG (2007) Slope stability hazard management systems. J Zhejiang Univ Sci A
8(11):1695–1711. Accessed 28 Oct 2010
Freeman T (1991) Calculating catchment area with divergent flow based on a regular grid.
Comput Geosci 17(3):413–422
Froese CR, Van der Kooij M, Kosar K (2004) Advances in the application of inSAR to complex,
slowly moving landslides in dry and vegetated terrain. In: Lacerda W, Ehrlich M, Fontoura
Froese CR, Murray C, Cavers DS, Anderson WS, Bidwell AK, Read RS, Cruden DM,
Langenberg W (2005) Development and implementation of a warning system for the South
Peak of Turtle Mountain. In: Hungr O, Fell R, Couture R, Eberhardt E (eds) International
Froese CR, Carter G, Langenberg W, Morena F (2006) Emergency response planning for a
second catastrophic rock slide at Turtle Mountain, Alberta. In: First specialty conference on
Disaster Mitigation. Calgary, Canada, pp 1–10
Froese CR, Jaboyedoff M, Pedrazzini A, Hungr O, Moreno F (2009) Hazard mapping for the
eastern face of Turtle Mountain, adjacent to the Frank Slide, Alberta, Canada. Landslide
Processes, pp 283–289
Fukuzono T (1990) Recent studies on time prediction of slope failure. Landslide News 4:9–12
Furuya G, Sassa K, Fukuoka H, Hiura H, Wang J, Yang Q (2000) Monitoring of slope
deformation in Lishan Landsiide, Xi’an, China. In: Bromhead E, Dixon N, Ibsen M-L (eds)
Landslides in research, theory and practice. Eighth international symposium on landslides.
T. Telford, London, pp 591–596
Gallagher R, Appenzeller T (1999) Beyond reductionism. Science 284(5411):79
Ganerød GV, Grøneng G, Rønning JS, Dalsegg E, Elvebakk H, Tønnesen JF, Kveldsvik V, Eiken
T, Blikra LH, Braathen A (2008) Geological model of the Åknes rockslide, Western Norway.
Eng Geol 102(1–2):1–18
García A, Hördt A, Fabian M (2010) Landslide monitoring with high resolution tilt measurements
at the Dollendorfer Hardt landslide, Germany. Geomorphology 120(1–2):16–25
GEOSE NS (2009) Felsmonitor Winkelgrat. Alarm- und Überwachungssystem zur Sicherung der
Kreisstraße 7145 gegen Felssturz. Messsystem- und Softwareentwicklung, Ebringen
Geotechnical Engineering Office ed. (2007) Thirty years of slope safety practice in Hong Kong.
Hong Kong, China
Giannecchini R (2006) Relationship between rainfall and shallow landslides in the southern
Apuan Alps (Italy). Nat Hazards Earth Syst Sci 6:357–364
Giannecchini R, Naldini D, D’Amato Avanzi G, Puccinelli A (2007) Modelling of the initiation
of rainfall-induced debris flows in the Cardoso basin (Apuan Alps, Italy). Quaternary
International 171-172, pp 108–117. Accessed 18 Oct 2010
Giraud RE (2002) Movement history and prelimanary hazard assessment of the Heather Drive
Landslides, Layton, Davis County, Utah. Utah Geological Survey, Utah Department of
Natural Resources, Utah
Gitirana Jr, G (2005) Weather-related geo-hazard assessment model for railway embankment
stability. Department of Civil and Geological Engineering, University of Saskatchewan,
Saskatoon
Gitirana Jr, G, Santos MA, Fredlund MD (2008) Three-dimensional analysis or the Lodalen
Landslide. In: Geosustainability and geohazard mitigation. Proceedings of Geocongress 2008.
New Orleans, Louisiana, USA, pp 1–5
References 67
Glade T (1997) The temporal and spatial occurrence of rainstorm-triggered landslide events in
New Zealand. School of Earth Science Institute of Geography, Victoria University of
Wellington, New Zealand
Glade T (1998) Establishing the frequency and magnitude of landslide-triggering rainstorm
events in New Zealand. Env Geol 35(2):160–174
Glade T (2000) Modelling landslide-triggering rainfalls in different regions of New Zealand—the
soil water status model. Zeitschrift für Geomorphologie Suppl.-Bd. 122:63–84
Glade T (2001) Landslide hazard assessment and historical landslide data—an inseparable
couple? In: Glade T, Albini P, Francés F (eds) The use of historical data in natural hazard
assessments. Springer, Berlin, pp 153–167
Glade T, Crozier MJ (2005a) A review of scale dependency in landslide hazard and risk analysis.
In: Glade T, Anderson M, Crozier MJ (eds) Landslide hazard and risk. Wiley, New York,
pp 75–138
Glade T, Crozier MJ (2005b) The nature of landslide hazard impact. In: Glade T, Anderson M,
Crozier MJ (eds) Landslide hazard and risk. Wiley, New York, pp 43–74
Glade T, Crozier M, Smith P (2000) Applying probability determination to refine landslide-
triggering rainfall thresholds using an empirical ‘‘Antecedent Daily Rainfall Model’’. Pure
Appl Geophys 157(6):1059–1079
Glantz MH (2009) Heads Up!: Early Warning Systems for Climate-, Water- And Weather-
Related Hazards. United Nations University Press, Tokyo
Glawe U, Lotter M (1996) Time prediction of rocl slope failures based on monitoring results. In:
Senneset K (ed) Landslides. Seventh international symposium on landslides. Balkema,
Rotterdam, pp 1551–1555
Godt JW, Baum RL, Chleborad AF (2006) Rainfall characteristics for shallow landsliding in
Seattle, Washington, USA. Earth Surf Proc Land 31(1):97–110
Gomez C, Lavigne F, Hadmoko DS, Lespinasse N, Wassmer P (2009) Block-and-ash flow
deposition: A conceptual model from a GPR survey on pyroclastic-flow deposits at Merapi
Volcano, Indonesia. Geomorphology 110(3–4):118–127
Gong QM, Zhao J, Jiao YY (2005) Numerical modeling of the effects of joint orientation on rock
fragmentation by TBM cutters. Tunn Undergr Space Technol 20(2):183–191
Graf C, Badoux A, MacArdell B, Dufour F, Rhyner J, Kuntner R (2006) A warning system for
natural hazards in summer at the Illgraben. In: Proceedings of the fourth Swiss geoscience
meeting. Bern, Switzerland
Graham J (1984) Methods of slope stability analysis. In: Brunsden D, Prior DB (eds) Slope
instability. Wiley, New York, pp 171–215
Greco R, Guida A, Damiano E, Olivares L (2010) Soil water content and suction monitoring in
model slopes for shallow flowslides early warning applications. Phys Chem Earth
35(3–5):127–136
Greif V, Sassa K, Fukuoka H (2004) Monitoring of rock displacement at Bitchu-Matsuyama
Rock Slope in Japan using Linear Variable Differential Transformer (LVDT) Sensors. In:
pp 773–779
Greiving S, Glade T (2011) Risk governance. In: Bobrowsky P (ed) Encyclopedia of natural
hazards. Springer, Berlin
Grøneng G, Christiansen HH, Nilsen B, Blikra LH (2010) Meteorological effects on seasonal
displacements of the Åknes rockslide, western Norway. Landslides 1(8):1–15
Gunzberger Y, Merrien-Soukatchoff V, Senfaute G, Piguet J-P (2004) Field investigations,
monitoring and modeling in the identification of rock fall causes. In: Lacerda W, Ehrlich M,
Guzzetti F, Cardinali M, Reichenbach P, Carrara A (2000) Comparing landslide maps: A case
study in the Upper Tiber River Basin, Central Italy. Environ Manag 25(3):247–263. Accessed
7 Oct 2010
Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Probabilistic landslide

hazard assessment at the basin scale. Geomorphology 72(1–4):272–299. Accessed 25 Oct
2010
Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of
landslide susceptibility models. Geomorphology 81(1–2):166–184
Guzzetti F, Peruccacci S, Rossi M, Stark CP (2007) Rainfall thresholds for the initiation of
landslides in central and southern Europe. Meteorol Atmos Phys 98(3–4):239–267
Guzzetti F, Peruccacci S, Rossi M, Stark CP (2008) The rainfall intensity–duration control of
shallow landslides and debris flows: an update. Landslides 5(1):3–17
Hadjigeorgiou J, Kyriakou E, Papanastasiou P (2006) A road embankment failure near Pentalia in
southwest Cyprus. In: International conference on the stability of Rock Slopes in open pit
mining and civil engineering. The South African Institute of Mining and Metallurgy, Cape
Town, South Africa, pp 343–352
Häge M, Joswig M (2009) Microseismic study using small arrays in the swarm area of Nový
Kostel: increased detectability during an inter-swarm period. Studia Geophysica et
Geodaetica 52(4):651–660. Accessed 15 Oct 2010
Hall P (2007) Early warning systems: reframing the discussion. The Australian J Emerg Manag
22(2):32–36. Accessed 12 Oct 2010
Hamm NAS, Hall JW, Anderson MG (2006) Variance-based sensitivity analysis of the
probability of hydrologically induced slope instability. Comput Geosci 32(6):803–817
Hammah R, Yacoub T, Curran J (2006) Investigating the performance of the shear strength
reduction (SSR) method on the analysis of reinforced slopes. In: Proceedings of the 59th
canadian geotechnical and seventh joint CGS/IAH-CNC groundwater specialty conference.
Vancouver, British Columbia, Canada
Hammah RE, Yacoub TE, Curran JH (2008) Probabilistic slope analysis with the finite element
method. In: Proceedings of the 41st U.S. symposium on rock mechanics and the fourth U.S.-
Canada rock mechanics symposium. Asheville, North Carolina, USA
Hammah RE, Yacoub TE, Curran JH (2009) Numerical modelling of slope uncertainty due to
rock mass jointing. In: Proceedings of the international conference on rock joints and jointed
rock masses. pp 7–8
Hammond C, Hall D, Miller S, Swetik P (1992) Level I stability analysis (LISA). U.S.
Department of Agriculture, Forest Service, Intermountain Research Station
Haneberg WC (2004) The ins and outs of airborne LIDAR: An introduction for practicing
engineering geologists. AEG News 48(1):16–19
Hart RD (1993) An introduction to distinct element modelling for rock engineering. In:
Proceedings of the seventh international congress on rock mechanics. Pergamon Press,
Aachen, pp 1881–1892
Hecht S (2001) Anwendung refraktionsseismischer Methoden zur Erkundung des oberflächen-
nahen Untergrundes: Mit acht Fallbeispielen aus Südwestdeutschland. University of Stuttgart,
Germany
Heincke B, Maurer H, Green AG, Willenberg H, Spillmann T, Burlini L (2006) Characterizing an
unstable mountain slope using shallow 2D and 3D seismic tomography. Geophysics
71(6):241–256
Heincke B, Günther T, Dalsegg E, Ronning JS, Ganerod GV, Elvebakk H (2010) Combined
three-dimensional electric and seismic tomography study on the Aknes rockslide in western
Norway. J Appl Geophys 70:292–306
Heng M (2008) Comparative study of rock slope stability analysis based on SLOPE/W and fuzzy
evaluation. Express Information of Mining Industry 476(9):23–26
Hennrich K (2000) Modelling critical water contents for slope stability and associated rainfall
thresholds using computer simulations. In: Bromhead E, Dixon N, Ibsen M-L (eds) Landslides
in research, theory and practice. Eighth international symposium on landslides. T. Telford,
Highland LM, Gori PL (2008) Two approaches for public landslide awareness in the United
States—U.S. geological survey warning system and a landslide film documentary. In: Chen Z,
References 69
Zhang J-M, Ho K, Wu F-Q, Li Z-K (eds) Landslides and engineered slopes. From the past to
the future. Proceedings of the tenth international symposium on landslides and engineered
slopes. pp 1173–1176
Higuchi K, Fujisawa K, Asai K, Pasuto A, Marcato G (2007) Application of new landslide
monitoring technique using optical fiber sensor at Takisaka Landslide, Japan. In: AEG Special
Publication. Proceedings of the first North American landslide conference. Vail, Colorado,
pp 1074–1083
Hiura H, Furuya G, Fukuoka H, Sassa K (2000) Investigation of the groundwater distribution in a
crystalline Schist Landslide Zentoku, Shikoku Island, Japan. In: Bromhead E, Dixon N,
Ibsen M-L (eds) Landslides in research, theory and practice. Eighth international symposium
on landslides. T. Telford, London, pp 719–724
Hu XW, Tang HM, Li JS (2008) General digital camera-based experiments for large-scale
landslide physical model measurement. In: Chen Z, Zhang J-M, Ho K, Wu F-Q, Li Z-K (eds)
Landslides and engineered slopes: from the past to the future proceedings of the tenth
international symposium on landslides and engineered slopes, pp 249–255
Hu H, Fernandez-Steeger TM, Dong M, Nguyen HT, Azzam R (2010) 3D modeling using
LiDAR data and its geological and geotechnical applications. In: Geoinformatics. 18th
international conference on geoinformatics. Beijing, China, pp 1–6
Huang Jr C, Kao SJ, Hsu ML, Lin JC (2006) Stochastic procedure to extract and to integrate
landslide susceptibility maps: an example of mountainous watershed in Taiwan. Nat Hazards
Earth Syst Sci 6:803–815
Huang Z, Law KT, Liu H, Jiang T (2008) The chaotic characteristics of landslide evolution: a
case study of Xintan landslide. Environ Geol, 56:1585–1591
Hubble TCT (2004) Slope stability analysis of potential bank failure as a result of toe erosion on
weir-impounded lakes: an example from the Nepean River, New South Wales, Australia.
Marine Freshwater Res 55(1):57–65. Accessed 27 Oct 2010
Hübl H (2000) Frühwarnsysteme als passive Schutzmaßnahmen in Wildbacheinzugsgebieten. In:
Wildbach und Lawinenverbauung (ed) Jahresbericht 2000 des Bundesministeriums für
Landwirtschaft, Forst, Wasser und Umwelt. p 46
Huggel C, Ramírez JM, Calvache M, González H, Gutierrez C, Krebs R (2008) A landslide early
warning system within an integral risk management strategy for the Combeima-Tolima
Region, Colombia. In: The international disaster and risk conferences. IDRC Davos,
Switzerland, pp 273–276
Huggel C, Khabarov N, Obersteiner M, Ramírez JM (2009) Implementation and integrated
numerical modeling of a landslide early warning system: a pilot study in Colombia. Nat
Hazards 52(2):501–518
Hungr O (1995) A model for the run out analysis of rapid flow slides, debris flows and
avalanches. Can Geotech J 32(4):610–623
Hungr O, McDougall S (2009) Two numerical models for landslide dynamic analysis. Comput
Geosci 35(5):978–992
Ibraim I, Anderson MG (2003) A new approach to soil characterisation for hydrology-stability
analysis models. Geomorphology 49(3–4):269–279
Imaizumi F, Sidle RC, Kamei R (2008) Effects of forest harvesting on the occurrence of
landslides and debris flows in steep terrain of central Japan. Earth Surf Proc Land
33(6):827–840
Iovine G, D’Ambrosio D, Di Gregorio S (2005) Applying genetic algorithms for calibrating a
hexagonal cellular automata model for the simulation of debris flows characterised by strong
inertial effects. Geomorphology 66(1–4):287–303
Ishida T, Kanagawa T, Kanaori Y (2010) Source distribution of acoustic emissions during an in
situ direct shear test: Implications for an analog model of seismogenic faulting in an
inhomogeneous rock mass. Eng Geol 110(3–4):66–76
Isle of Wight Centre for the Coastal Environment (2010) Isle of Wight Centre for the Coastal
Environment. http://www.coastalwight.gov.uk/index.html. Accessed 2 Dec 2010
Iverson RM (2000) Landslide triggering by rain infiltration. Water Resour Res 36(7):1897–1910
Iverson RM (2005) Regulation of landslide motion by dilatancy and pore pressure feedback.
J Geophys Res 110:1–17
Jakob M, Holm K, Lange O, Schwab JW (2006) Hydrometeorological thresholds for landslide
initiation and forest operation shutdowns on the north coast of British Columbia. Landslides
3(3):228–238
Janbu N (1996) Slope stability evaluations in engineering practice. In: Senneset K (ed)
Landslides. Seventh international symposium on landslides. Balkema, Rotterdam, pp 17–34
Jian W, Wang Z, Yin K (2009) Mechanism of the Anlesi landslide in the Three Gorges Reservoir,
China. Eng Geol 108(1–2):86–95
Jiang T, Wang W, Cui JL, Chen XT (2009) Landslide forecast based on state vector method.
Yantu Lixue/Rock Soil Mech 30(6):1747–1752
Jing L (1998) Formulation of discontinuous deformation analysis (DDA)–an implicit discrete
element model for block systems. Eng Geol 49(3–4):371–381
Jomard H, Lebourg T, Binet S, Tric E, Hernandez M (2007) Characterization of an internal slope
movement structure by hydrogeophysical surveying. Terra Nova 19(1):48–57
Jongmans D, Garambois S (2007) Geophysical investigation of landslides: a review. Bulletin de
la Société Géologique de France 178(2):101–112
Jongmans D, Renalier F, Kniess U, Bièvre G, Schwartz S, Pathier E, Orengo Y, Villemin T,
Delacourt C (2008) Characterisation of the Avignonnet landslide (French Alps) using seismic
techniques. In: Chen Z, Zhang J-M, Ho K, Wu F-Q, Li Z-K (eds) Landslides and engineered
slopes: from the past to the future. Proceedings of the tenth international symposium on
Joswig M (2008) Nanoseismic monitoring fills the gap between microseismic networks and
passive seismic. First Break 26:81–88
Karnawati D, Ibriam I, Anderson MG, Holcombe EA, Mummery GT, Renaud J-P, Wang Y
(2005) An initial approach to identifying slope stability controls in Southern Java and to
providing community-based landslide warning information. In: Glade T, Anderson M,
Crozier MJ (eds) Landslide hazard and risk. Wiley, New York, pp 733–763
Kearey P, Brooks M, Hill I (2002) An introduction to geophysical exploration. Wiley, Blackwell
Keaton JR, and DeGraff JV (1996) Surface observation and geologic mapping. In: Turner AK,
Schuster RL (eds) Landslides: investigation and mitigation (Special Report). Washington, DC,
USA: National Research Council, Transportation and Research Board Special Report 247,
pp 178–230
Keaton JR, Gailing RW (2004) Monitoring slope deformation with quadrilaterals for pipeline risk
management. In: ASME conference proceedings. Proceedings of the 2004 international
pipeline conference (IPC). Calgary, Canada, pp 269–274
Keefer DK, Wilson RC, Mark RK, Brabb EE, BROWN III WM, Ellen SD, Harp EL,
Wieczorek GF, Alger CS, Zatkin RS (1987) Real-time landslide warning during heavy
rainfall. Science 238(4829):921
Keersmaekers R, Maertens J, Van Gemert D, Haelterman K (2008) Modeling landslide triggering
in layered soils. In: Chen Z-Y, Zhang J-M, Ho K (eds) Landslides and engineered slopes:
From the past to the future. Proceedings of the 10th international symposium on landslides
and engineered slopes. Taylor & Francis, Xi’an, pp 761–767
Kellezi L, Allkja S, Hansen PB (2005) Landslide FE stability analysis. In: Proceedings of the
IACMAG. Italy, pp 545–553
Kienholz H, Hafner H, Schneider G, Zimmermann M (1984) Methods for the assessment of
mountain hazards and slope stability in Nepal. Erdwissenschaftliche Forschung 18:147–160
Kilburn CR, Petley DN (2003) Forecasting giant, catastrophic slope collapse: lessons from
Vajont, Northern Italy. Geomorphology 54(1–2):21–32
Kim HW (2008) Development of wireless sensor node for landslide detection. In: Chen Z,
Zhang J-M, Ho K, Wu F-Q, Li Z-K (eds) Landslides and engineered slopes: from the past to
the future. Proceedings of the 10th international symposium on landslides and engineered
slopes. Taylor & Francis, Xi’an, pp 1183–1187
References 71
Kjelland NH, Hutchinson DJ, Diederichs MS, Lawrence MS, Harrap R (2004) Development of
GIS-based decision-support systems for stability analysis of slow moving, active landslides
using geotechnical modeling. In: Lacerda W, Ehrlich M, Fontoura SAB, Sayao AS (eds)
Kneale P (1987) Instrumentation of pore pressure and soil water suction. In: Anderson MG (ed)
Slope stability: geotechnical engineering and geomorphology. Wiley, New York, pp 77–112
Knödel K, Krummel H, Lange G (2005) Handbuch zur Erkundung des Untergrundes von
Deponien: Geophysik. Springer, Berlin
Kohn J-C (2006) Potenzial der Auswertung des Archivs der Straßenbauverwaltung für die
Risikoforschung - Nutzung des Archivs der Baustoff und Bodenprüfstelle Ludwigsburg als
historische Quelle. University of Bonn, Germany
Krause R (2009) Felsmonitor Winkelgrat - Erfahrungen mit einem sensorbasierten Frühwarn-
system zum Schutz vor Bergsturz. Presentation at SLEWS and ILEWS Workshop on Warn-
und Risikomanagement bei Massenbewegungen, Hannover, Germany, 2009
Krauter E, Lauterbach M, Feuerbach J (2007) Hangdeformationen–Beobachtungsmethoden und
Risikoanalyse. geo-international & Forschungsstelle Rutschungen, pp 1–6
Kreja R, Terhorst B (2005) GIS-gestützte Ermittlung rutschungsgefährdeter Gebiete am
Schönberger Kapf bei Öschingen (Schwäbische Alb). Die Erde 136(4):395–412
Kumar A, Sanoujam M (2006) Landslide studies along the national highway (NH 39) in Manipur.
Nat Hazards 40(3):603–614
Kunlong YIN, Lixia C, Guirong Z (2007) Regional landslide hazard warning and risk assessment.
Earth Sci Frontiers 14(6):85–93
Kunz-Plapp T (2007) Vorwarnung, Vohersage und Frühwarnung. In: Felgentreff C, Glade T (eds)
Naturrisiken und Sozialkatastrophen. 1st ed. Spektrum Akademischer Verlag, Heidelberg,
pp 213–223
Kupka M, Herle I, Arnold M (2009) Advanced calculations of safety factors for slope stability.
Int J Geotech Eng 3(4):509–515. Accessed 27 Oct 2010
Kusumi H, Nakamura M, Nishida K (2000) Monitoring of groundwater behaviour caused by
rainfall in fracture zone of rock slope using electric resistivity method. In: Bromhead E,
Dixon N, Ibsen M-L (eds) Landslides in research, theory and practice. Eighth international
symposium on landslides. T. Telford, London, pp 871–876
Kveldsvik V, Einstein HH, Nilsen B, Blikra LH (2008) Numerical analysis of the 650,000 m2
Åknes rock slope based on measured displacements and geotechnical data. Rock Mech Rock
Eng 42(5):689–728
Kveldsvik V, Kaynia AM, Nadim F, Bhasin R, Nilsen B, Einstein HH (2009) Dynamic distinct-
element analysis of the 800 m high Aknes rock slope. Int J Rock Mech Mining Sci
46(4):686–698
Larsen MC (2008) Rainfall-triggered landslides, anthropogenic hazards, and mitigation
strategies. Adv Geosci 14:147–153
Lateh H, Anderson MG, Ahmad F (2008) CHASM—The model to predict stability of Gully
Walls along the East-West highway in Malaysia: A case study. In: Proceeding of the first
world landslide forum. ISDR, Tokyo, pp 340–343
Lato M, Hutchinson J, Diederichs M, Kalenchuk K (2007) Evaluating block shape and block
volume distributions of rock faces using LiDAR and 3DEC. In: Geophysical research
abstracts
Lauterbach M, Krauter E, Feuerbach J (2002) Satellitengestütztes Monitoring einer Großruts-
chung im Bereich eines Autobahndammes bei Landstuhl/Pfalz. Geotechnik 25(2):97–100
Lea NL (1992) An aspect driven kinematic routine algorithm. In: Parsons AJ (ed) Overland flow:
hydraulics and erosion mechanics. Taylor & Francis, Routledge, pp 374–388
Lebourg T, Binet S, Jomard HET, El Bedoui S (2004) 3D geophysical survey of the ‘‘La
Clapiere’’ landslide, southeastern France. In: Lacerda W, Ehrlich M, Fontoura SAB, Sayao
AS (eds) Landslides: evaluation and stabilization. Ninth international symposium on
Lebourg T, Binet S, Tric E, Jomard H, El Bedoui S (2005) Geophysical survey to estimate the 3D
sliding surface and the 4D evolution of the water pressure on part of a deep seated landslide.
Terra Nova 17(5):399–406. Accessed 15 Oct 2010
Lee S (2006) Application and verification of fuzzy algebraic operators to landslide susceptibility
mapping. Environ Geol 52(4):615–623
Lee CF, Kwong AKL, Ghazavi M, Emami N (eds) (2001) Analysis of failed slope in saturated
soft soils: A case study. In: Soft soil engineering. Taylor & Francis, Routledge, pp 11–116
Lee S, Choi J, Min K (2002) Landslide susceptibility analysis and verification using the Bayesian
probability model. Environ Geol 43(1–2):120–131. Accessed 25 Oct 2010
Lee S, Ryu JH, Lee MJ, Won JS (2003) Use of an artificial neural network for analysis of the
susceptibility to landslides at Boun, Korea. Environ Geol 44(7):820–833
Leroueil S (2004) Geotechnics of slopes before failure. In: Lacerda W, Ehrlich M, Fontoura SAB,
Li AG, Tham LG, Lee CF, Yue QZ, Law KT, Deng JH (2004) Field instrumentation for a
saprolite cut slope. In: Lacerda W, Ehrlich M, Fontoura SAB, Sayao AS (eds) Landslides:
Li C, Knappett J, Feng X (2008) Centrifuge modelling of reservoir landslides in three. In:
Proceedings of the international conference on landslide risk management. Vancouver,
Canada, pp 732–735
Li D, Yin K, Leo C (2009a) Analysis of Baishuihe landslide influenced by the effects of reservoir
water and rainfall. Environ Earth Sci 60(4):677–687
Li D, Yin K, Gao H, Liu C (2009b) Design and application analysis of prediction system of geo-
hazards based on GIS in the three gorges reservoir. In: Liu Y, Tang X (eds) International
symposium on spatial analysis, spatial-temporal data modeling, and data mining. Proceedings
of SPIE—The international society for optical engineering. Wuhan, Hubei, China
Liao Z, Hong Y, Wang J, Fukuoka H, Sassa K, Karnawati D, Fathani F (2010) Prototyping an
experimental early warning system for rainfall-induced landslides in Indonesia using satellite
remote sensing and geospatial datasets. Landslides 7(3):317–324
Liu S, Wang Z (2008) Choice of surveying methods for landslides monitoring. In: Chen Z,
slopes. Taylor & Francis, Xi’an
Lloyd DM, Wilkinson PL, Othmann MA, Anderson MG (2001) Predicting landslides: assessment
of an automated rainfall based landslide warnings systems. In: Ho KKS, Li KS (eds)
Geotechnical engineering—Meeting society’s needs. Taylor & Francis, Routledge,
pp 135–139
Lloyd DM, Anderson MG, Renaud JP, Wilkinson P, Brooks SM (2004) On the need to determine
appropriate domains for hydrology-slope stability models. Adv Environ Res 8(3–4):379–386
Lollino G, Arattano M, Cuccureddu M (2002) The use of the automatic inclinometric system for
landslide early warning: the case of Cabella Ligure (North-Western Italy). Phys Chem Earth
27(36):1545–1550
Lorenz EN (1963) Deterministic non-periodic flow. J Atmos Sci 20:130–141
Lumb P (1975) Slope failures in Hong Kong. Quart J Eng Geol Hydrogeol 8(1):31
Luzi G, Pieraccini M, Macaluso G, Mecatti D, Noferini L, Atzeni C, Galgaro A, Teza G (2005)
Ground based microwave interferometry for estimating the movement of landslides.
In: Hungr O, Fell R, Couture R, Eberhardt E (eds) International conference on landslide risk
management. Taylor & Francis Ltd, Vancouver, pp 309–314
Macfarlane DF, Silvester PK, Benck JM, Whiford ND (1996) Monitoring strategy and
performance of instrumentation int the clyde power project landslide, New Zealand. In:
References 73
Maffei A, Martino S, Prestininzi A, Scarascia Mugnozza G, Pellegriono A (2004) The impact of

alteration on DSGSD in crystalline-metamorphic rocks: the case of Mt. Granieri-Salineriti
(Calabria - Italy). In: Lacerda W, Ehrlich M, Fontoura SAB, Sayao AS (eds) Landslides:
Main IG (2000) A damage mechanics model for power-law creep and earthquake aftershock and
foreshock sequences. Geophys J Int 142(1):151–161. Accessed 6 Oct 2010
Majidi A, Choobbasti AJ (2008) Numerical analysis of Hollar landslide. Electron J Geotech Eng
13(B):1–10
Malamud BD, Turcotte DL, Guzzetti F, Reichenbach P (2004) Landslide inventories and their
statistical properties. Earth Surf Proc Land 29(6):687–711
Malone AW (1997) Risk management and slope safety in Hong Kong. Trans Hong Kong Inst Eng
4(2–3):12–21
Maria FA, Gianfranco F, Hélène VI (2004) Rock slope stability analysis based on
photogrammetric surveys. In: Lacerda W, Ehrlich M, Fontoura SAB, Sayao AS (eds)
Marques R, Zêzere J, Trigo R, Gaspar J, Trigo I (2008) Rainfall patterns and critical values
associated with landslides in Povoação County (São Miguel Island, Azores): relationships
with the North Atlantic Oscillation. Hydrol Process 22(4):478–494
Marschallinger R, Eichkitz C, Gruber H, Heibl K, Hofmann R, Schmid K (2009) The
Gschliefgraben Landslide (Austria): A remediation approach involving Torrent and avalanche
control, geology, geophysics, geotechnics and geoinformatics. Austrian J Earth Sci
102(2):36–51
Massey JB, Mak SH, Yim KP (2001) Community based approach to landslide risk reduction. In:
Proceedings of the fourteenth Southeast Asian geotechnical conference. Hong Kong, China,
pp 141–147
Matsushi Y, Matsukura Y (2007) Rainfall thresholds for shallow landsliding derived from
pressure-head monitoring: cases with permeable and impermeable bedrocks in Boso
Peninsula, Japan. Earth Surf Proc Land 32(9):1308–1322
Matziaris VG, Ferentinou M, Sakellariou MG (2005) Slope stability assessment in unsaturated
soils under rainfall conditions In: Agioutantis Z, Komnitsas K (eds) Ovidius University
Annals Series: Civil Engineering 1 (7), pp 103–110
Mayer J, Pohl W (2010) Risikokommunikation. In: Bell R, Pohl J, Glade T, Mayer J, Greiving S
(eds) Integrative Frühwarnsysteme für gravitative Massenbewegungen (ILEWS) Monitoring,
Modellierung, Implementierung. Klartext, Germany, pp 180–202
McGuffey V, Modeer J, Victor A, Turner AK (1996) Subsurface exploration. In: Turner AK,
Schuster RL (eds) Landslides: investigation and mitigation (Special Report). National
Research Council, Transportation and Research Board Special Report 247, Washington, DC,
USA, pp 231–277
McInnes RG (2000) Managing ground instability in urban areas—A guide to best practice. Isle of
Wight Centre for the Coastal Environment, UK
Meidal KM, Moore DP (1996) Long-term performance of instrumentation at Dutchman’s Ridge.
In: Senneset K (ed) Landslides. Seventh international symposium on landslides. Balkema,
Meisina C, Scarabelli S (2007) A comparative analysis of terrain stability models for predicting
shallow landslides in colluvial soils. Geomorphology 87(3):207–223
Meisina C, Zucca F, Fossati D, Ceriani M, Allievi J (2006) Ground deformation monitoring by
using the permanent scatterers technique: the example of the Oltrepo Pavese (Lombardia
Italy). Eng Geol 87:240–259
Meric O, Jongmans D, Garambois S, Giraud A, Vengeon J-M (2004) Investigation of the
gravitational movement of Séchilienne by geophysical methods. In: Lacerda W, Ehrlich M,
Meric O, Garambois S, Jongmans D, Wathelet M, Chatelain JL, Vengeon JM (2005) Application

of geophysical methods for the investigation of the large gravitational mass movement of
Séchilienne, France. Canad Geotech J 42(4):1105–1115. Accessed 15 Oct 2010
Meric O, Garambois S, Malet J-P, Cadet H, Gueguen P, Jongmans D (2007) Seismic noise-based
methods for soft-rock landslide characterization. Bulletin de la Societe Geologique de France
178(2):137–148. Accessed 15 Oct 2010
Merrien-Soukatchoff V, Clément C, Senfaute G, Gunzburger Y (2005) Monitoring of a potential
rockfall zone: The case of ‘‘Rochers de Valabres’’ site. In: Hungr O, Fell R, Couture R,
Eberhardt E (eds) International conference on landslide risk management. Taylor & Francis
Ltd., Vancouver, pp 416–422
Mihalinec Z, Ortolan Ž (2008) Landslide ‘‘Granice’’in Zagreb (Croatia). In: Chen Z, Zhang J-M,
Ho K, Wu F-Q, Li Z-K (eds) Landslides and engineered slopes: From the past to the future.
Proceedings of the tenth international symposium on landslides and engineered slopes. Taylor
& Francis, Xi’an, pp 1587–1593
Mikkelsen PE (1996) Field instrumentation. In: Turner AK, Schuster RL (eds) Landslides:
investigation and mitigation (Special Report). National Research Council, Transportation and
Research Board Special Report 247, Washington DC, USA, pp 278–316
Mikoš M, Vidmar A, Brilly M (2005) Using a laser measurement system for monitoring
morphological changes on the Strug rock fall, Slovenia. Nat Hazards Earth Syst Sci
5:143–153
Mileti DS (1999) Disasters by design: A reassessment of natural hazards in the United States.
Joseph Henry Press, Washington
Mills JP, Buckley SJ, Mitchell HL, Clarke PJ, Edwards SJ (2005) A geomatics data integration
technique for coastal change monitoring. Earth Surf Proc Land 30(6):651–664
Milsom J (2003) Field geophysics. Wiley, New York
Ming-Gao T, Qiang XU, Huang RQ, Ming YAN (2006) 3DEC analysis on 6# high rock slope
with joints in Xiaowan Hydropower Project. Hydrogeol Eng Geol (3)
Mondini A, Carlà R, Reichenbach P, Cardinali M, Guzzetti F (2009) Use of remote sensing
approach to detect landslide thermal behaviour. Geophys Res Abstracts (11)
Montety V, de Marc V, Emblanch C, Malet J-P, Bertrand C, Maquaire O, Bogaard TA (2007)
Identifying the origin of groundwater and flow processes in complex landslides affecting
black marls: insights from a hydrochemical survey. Earth Surf Proc Land 32(1):32–48
Montgomery DR, Dietrich WE (1994) A physically based model for the Topographic control on
shallow landsliding. Water Resour Res 30(4):1153–1171
Montgomery DR, Schmidt KM, Dietrich WE, McKean J (2009) Instrumental record of debris
flow initiation during natural rainfall: implications for modeling slope stability. J Geophys
Res 114:1–16. Accessed 28 Oct 2010
Montrasio L, Valentino R (2007) Experimental analysis and modelling of shallow landslides.
Moore JR, Gischig V, Button E, Loew S (2010) Rockslide deformation monitoring with fiber
optic strain sensors. Nat Hazards Earth Syst Sci 10:191–201
Moreiras SM (2005) Landslide susceptibility zonation in the Rio Mendoza Valley, Argentina.
Geomorphology 66(1–4):345–357. Accessed 25 Oct 2010
Morgenstern NR, Martin CD (2008) Landslides: seeing the ground. In: Chen Z, Zhang J-M, Ho
K, Wu F-Q, Li Z-K (eds) Landslides and engineered slopes: From the past to the future.
Proceedings of the tenth international symposium on landslides and engineered slopes. Taylor
& Francis, Xi’an, pp 3–24
Morrissey MM, Wieczorek GF, Morgan BA (2001) A comparative analysis of hazard models for
predicting debris flows in Madison County, Virginia. USGS (ed) U.S. Department of the
Interior, U.S. Geological Survey, Washington
Murmelter G (2010) Erdrutsch löste Zugunglück im Vinschgau aus. Der Standard, 13.04.2010,
p6
References 75
Nagarajan R, Roy A, Vinod Kumar R, Mukherjee A, Khire MV (2000) Landslide hazard

susceptibility mapping based on terrain and climatic factors for tropical monsoon regions.
Bull Eng Geol Environ 58(4):275–287. Accessed 25 Oct 2010
Nakamura H (2004) Field instrumentation and laboratory investigation. In: Lacerda W, Ehrlich
M, Fontoura SAB, Sayao AS (eds) Landslides: evaluation and stabilization. Ninth
international symposium on landslides. A.A. Balkema Publishers, Leiden, pp 541–548
Nash DFT (1987) A comparative review of limit equilibrium methods of stability analysis. In:
Anderson MG (ed) Slope stability: geotechnical engineering and geomorphology. Wiley, New
York, pp 11–75
National Research Council (2004) Partnerships for reducing landslide risk: assessment of the
national landslide hazards mitigation strategy. The National Academies Press, Washington
Navarro V, Yustres A, Candel M, López J, Castillo E (2010) Sensitivity analysis applied to slope
stabilization at failure. Comput Geotech 37(7–8):837–845
Neuhäuser B (2005) Probabilistische Beurteilung der Rutschanfälligkeit zur Einschätzung der
Gefährdung durch Hangrutschungen am Beispiel Schwäbische Alb. In: Angewandte
Geoinformatik. Beiträge zum 17. AGIT-Symposium. Salzburg, Austria, pp 129–137
NOAA-USGS Debris Flow Task Force (2005) NOAA-USGS Debris-Flow Warning
System—Final Report. US Geological Survey Open File Report 2006-1064
O’Callaghan JF, Mark DM (1984) The extraction of drainage networks from digital elevation
data. Comput Vis Graph Image Process 28(3):323–344
Oboni F (2005) Velocity-rain relationship at the Cassas Landslide. In: Hungr O, Fell R, Couture
R, Eberhardt E (eds) International conference on landslide risk management. Taylor & Francis
Ltd., Vancouver, pp 280–284
Ohlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict
landslide hazard in northeast Kansas, USA. Eng Geol 69(3–4):331–343
Okamoto T, Larsen JO, Matsuura S, Asano S, Takeuchi Y, Grande L (2004) Displacement
properties of landslide masses at the initiation of failure in quick clay deposits and the effects
of meteorological and hydrological factors. Eng Geol 72(3–4):233–251
Olalla C (2004) Recent developments in landslide monitoring. In: Lacerda W, Ehrlich M,
Oppikofer T, Jaboyedoff M, Blikra L, Derron MH, Metzger R (2009) Characterization and
monitoring of the Aknes rockslide using terrestrial laser scanning. Nat Hazards Earth Syst Sci
9:1003–1019
Oregon Department of Geology and Mineral Industry (2010) Debris flow warnings. http://
www.oregongeology.com/sub/Landslide/debrisflow.htm. Accessed 3 Dec 2010
Ortigao B, Justi MG (2004) Geotechnical instrumentation news. Geotech News 22(3):28–31
Ortigão JAR, Sayao ASFJ (2004) Handbook of slope stabilisation. Birkhäuser, London
Ortigao JAR, Justi MG, D’Orsi R, Brito H (2002) Rio-Watch 2001: the Rio de Janeiro landslide
alarm system. In: Ho KKS, Li KS (eds) Proceedings, 14th Southeast Asian geotechnics
conference—Geotechnical engineering: Meeting Society’s Needs. pp 237–241
Pachauri AK, Gupta PV, Chander R (1998) Landslide zoning in a part of the Garhwal Himalayas.
Environ Geol 36(3–4):325–334. Accessed 25 Oct 2010
Pack R, Tarboton D (2004) Stability index mapping (SINMAP) applied to the prediction of
shallow translational landsliding. Geophys Res Abstracts (6)
Pack RT, Tarboton DG, Goodwin CN (1998) SINMAP—A stability index approach to terrain
stability hazard mapping. http://www.neng.usu.edu/cee/faculty/dtarb/sinmap.pdf. Accessed
26 Oct 2010
Pack RT, Tarboton DG, Goodwin CN (2001) Assessing terrain stability in a GIS using SINMAP.
In: GIS 2001. 15th annual GIS conference. British Columbia, Vancouver, pp 1–9
Pack RT, Tarboton DG, Goodwin CN, Prasad A (2005) SINMAP 2—A stability index approach
to terrain stability hazard mapping. http://www.hydrology.neng.usu.edu/sinmap2/sinmap2.pdf.
Accessed 26 Oct 2010
Pagano L, Rianna G, Zingariello MC, Urciuoli G, Vinale F (2008) An early warning system to
predict flowslides in pyroclastic deposits. In: Chen Z, Zhang J-M, Ho K, Wu F-Q, Li Z-K
(eds) Landslides and engineered slopes: from the past to the future. Proceedings of the tenth
international symposium on landslides and engineered slopes. Taylor & Francis, Xi‘an,
pp 1259–1264
Palm H, Staab S, Schmitz R (2003) Verkehrsicherheiten an klassifizierten Straßen im Hinblick
auf Steinschlag- und Felssturzgefahr. In: Rutschungen in Rheinland-Pfalz. Erkennen,
Erkunden und Sanieren. Felssicherung und Sanierung von Stützmauern. Weiterbildungsse-
minar III. Mainz, Germany, pp 16–22
Parasnis DS (1997) Principles of applied geophysics. Springer, Berlin
Pasculli A, Sciarra N (2006) A 3D landslide analyses with constant mechanical parameters
compared with the results of a probabilistic approach assuming selected heterogeneities at
different spatial scales. Giornale di Geologia Applicata 3:269–280
Pasuto A, Silvano S (1998) Rainfall as a trigger of shallow mass movements. A case study in the
Dolomites, Italy. Env Geol 35(2):184–189
Pasuto A, Silvano S, Berlasso G (2000) Application of time domain reflectometry (TDR) technique
in monitoring the Pramollo Pass Landslide (Province of Udine, Italy). In: Bromhead E, Dixon N,
Ibsen M-L (eds) Landslides in research, theory and practice. Eighth international symposium on
landslides. T. Telford, London, pp 1189–1194
Persson H, Alén C, Lind BB (2007) Development of a pore pressure prediction model. In:
McInnes R, Jakeways J, Fairbanks J, Mathie E (eds) Landslides and climate change.
Challenges and solutions. Proceedings of the international conference on landslides and
climate change. Taylor & Francis, Isle of Wight, UK, pp 21–24
Petley DN, Allison RJ (1997) The mechanics of deep-seated landslides. Earth Surf Proc Land
22(8):747–758
Petley DN, Petley DJ (2006) On the initiation of large rockslides: perspectives from a new
analysis of the Vaiont movement record. Earth Environ Sci 49(2):77–84
Petley DN, Bulmer MH, Murphy W (2002) Patterns of movement in rotational and translational
landslides. Geology 30(8):719
Petley DN, Hearn GJ, Hart A (2005a) Towards the development of a landslide risk assessment for
rural roads in Nepal. In: Glade T, Anderson M, Crozier MJ (eds) Landslide hazard and risk.
Wiley, New York, pp 597–619
Petley DN, Higuchi T, Dunning S, Rosser NJ, Petley DJ, Bulmer MH, Carey J (2005b) A new
model for the development of movement in progressive landslides. In: Hungr O, Fell R,
Couture R, Eberhardt E (eds) International conference on landslide risk management. Taylor
& Francis Ltd., Vancouver, pp 350–358
Petley DN, Higuchi T, Petley DJ, Bulmer MH, Carey J (2005c) Development of progressive
landslide failure in cohesive materials. Geology 33(3):201–204
Petley DN, Mantovani F, Bulmer MH, Zannoni A (2005d) The use of surface monitoring data for
the interpretation of landslide movement patterns. Geomorphology 66(1–4):133–147
Petley DN, Petley DJ, Allison RJ (2008) Temporal prediction in landslides—Understanding the
Saito effect. In: Chen Z, Zhang J-M, Ho K, Wu F-Q, Li Z-K (eds) Landslides and engineered
slopes: from the past to the future. Taylor & Francis, Xi’an, pp. 794–800
Phillips J (1992a) Nonlinear dynamical systems in geomorphology: revolution or evolution?
Geomorphology 5(3–5):219–229. Accessed 16 Sep 2010
Phillips J (1992b) The end of equilibrium? Geomorphology 5(3–5):195–201. Accessed 16 Sep
2010
Phillips JD (2003) Sources of nonlinearity and complexity in geomorphic systems. Progr Phys
Geog 27(1):1
Phillips JD (2006) Deterministic chaos and historical geomorphology: a review and look forward.
Geomorphology 76(1–2):109–121
Phillips JD, Golden H, Cappiella K, Andrews B, Middleton T, Downer D, Kelli D, Padrick L
(1999) Soil redistribution and pedologic transformations in coastal plain croplands. Earth Surf
Proc Land 24(1):23–39
References 77
Picarelli L, Russo C (2004) Remarks on the mechanics of slow active landslides and the
interaction with man-made works. In: Lacerda W, Ehrlich M, Fontoura SAB, Sayao AS (eds)
Pirulli M (2009) The Thurwieser rock avalanche (Italian Alps): Description and dynamic
analysis. Eng Geol 109(1–2):80–92
Poisel R, Angerer H, Pöllinger M, Kalcher T, Kittl H (2009) Mechanics and velocity of the
Lärchberg-Galgenwald landslide (Austria). Eng Geol 109(1–2):57–66
Polemio M, Petrucci O (2000) Rainfall as a landslide triggering factor: An ovewiew of recent
international research. In: Bromhead E, Dixon N, Ibsen M-L (eds) Landslides in research,
theory and practice. Eighth international symposium on landslides. T. Telford, London,
pp 1219–1226
Prinz H, Strauß R (2006) Abriss der Ingenieurgeologie. Spektrum Akademischer Verlag,
Heidelberg
Prokop A, Panholzer H (2009) Assessing the capability of terrestrial laser scanning for
monitoring slow moving landslides. Nat Hazards Earth Syst Sci 9:1921–1928
Pueyo-Anchuela Ó, Pocoví Juan A, Soriano MA, Casas-Sainz AM (2009) Characterization of
karst hazards from the perspective of the doline triangle using GPR–Examples from Central
Ebro Basin (Spain). Eng Geol 108(3–4):225–236
Quinn P, Beven K, Chevallier P, Planchon O (1991) The prediction of hillslope flow paths for
distributed hydrological modelling using digital terrain models. Hydrol Process 5(1):59–79
Rahardjo H, Ong TH, Rezaur RB, Leong EC (2007) Factors controlling instability of
homogeneous soil slopes under rainfall. J Geotech Geoenviron Eng 133(12):1532–1543.
Accessed 28 Oct 2010
Rahimi A, Rahardjo H, Leong E-C (2010) Effect of hydraulic properties of soil on rainfall-
induced slope failure. Eng Geol 114(3–4):135–143. Accessed 28 Oct 2010
Read RS, Langenberg W, Cruden D, Field M, Stewart R, Bland H, Chen Z, Froese CR, Cavers DS,
Bidwell AK, Murray C, Anderson WS, Jones A, Chen J, McIntyre D, Kenway D, Bingham DK,
Weir-Jones I, Seraphim J, Freeman J, Spratt D, Lamb M, Herd E, Martin D, McLellan P, Pana D
(2005) Frank slide a century later: The Turtle mountain monitoring project. In: Hungr O, Fell R,
Couture R, Eberhardt E (eds) International conference on landslide risk management. Balkema
Publishers, Rotterdam, pp 713–723
Reches Z, Lockner DA (1994) Nucleation and growth of faults in brittle rocks. J Geophys Res
99(B9):18159–18173. Accessed 6 Oct 2010
Reichenbach P, Cardinali M, De Vita P, Guzzetti F (1998) Regional hydrological thresholds for
landslides and floods in the Tiber River Basin (central Italy). Env Geol 35(2):146–159
Reichenbach P, Galli MJ, Cardinali M, Guzzetti F, Ardizzone F (2005) Geomorphological
mapping to assess landslide risk: concepts, methods and applications in the Umbria Region of
Central Italy. In: Glade T, Anderson M, Crozier MJ (eds) Landslide hazard and risk. Wiley,
New York, pp 429–468
Reid ME (1994) A pore-pressure diffusion model for estimating landslide-inducing rainfall.
J Geol 102(6):709–717. Accessed 20 Oct 2010
Reyes CA, Fernandez LC (1996) Monitoring of surface movements in excavated slopes. In:
Reynolds JM (1997) An introduction to applied and environmental geophysics. Wiley, New York
Richards A (2002) Complexity in physical geography. Geography 87(2):99–107
Rinaldi M, Casagli N, Dapporto S, Gargini A (2004) Monitoring and modelling of pore water
pressure changes and riverbank stability during flow events. Earth Surf Proc Land
29(2):237–254
Roch K-H, Chwatal W, Brückl E (2006) Potentials of monitoring rock fall hazards by GPR:
considering as example the results of Salzburg. Landslides 3(2):87–94
Romang H, Zappa M, Hilker N, Gerber M, Dufour F, Frede V, Bérod D, Oplatka M, Hegg C,

Rhyner J (2010) IFKIS-Hydro: an early warning and information system for floods and debris
flows. Nat Hazards 2(56):509–527
Rosen PA, Hensley S, Joughin IR, Li FK, Madsen SN, Rodriguez E, Goldstein RM (2002)
Synthetic aperture radar interferometry. Proc IEEE 88(3):333–382
Rosser NJ, Petley DN (2008) Monitoring and modeling of slope movement on rock cliffs prior to
failure. In: Chen Z, Zhang J-M, Ho K, Wu F-Q, Li Z-K (eds) Landslides and engineered
Rosser NJ, Dunning SA, Lim M, Petley DN (2005) Terrestrial laser scanning for quantitative
rockfall hazard assessment. In: Hungr O, Fell R, Couture R, Eberhardt E (eds) International
Ruch C (2009) Georisiken. Aktive Massenbewegungen am Albtrauf. LGRB-Nachrichten
8(2):1–2
Saito M (1965) Forecasting the time of occurence of a slope failure. In: Proceedings of the sixth
international conference on soil mechanics and foundation engineering. Montreal, Canada,
pp 537–541
Saito M (1969) Forecasting time of slope failure by tertiary creep. In: Seventh international
conference on soil mechanics and foundation engineering. pp 677–683
Saito H, Nakayama D, Matsuyama H (2010) Relationship between the initiation of a shallow
landslide and rainfall intensity–duration thresholds in Japan. Geomorphology
118(1–2):167–175. Accessed 18 Oct 2010
Sakai H (2008) A warning system using chemical sensors and telecommunicationtechnologies to
protect railroad operation from landslide disaster. In: Chen Z, Zhang J-M, Ho K, Wu F-Q,
Li Z-K (eds) Landslides and engineered slopes: from the past to the future. Proceedings of the
tenth international symposium on landslides and engineered slopes. Taylor & Francis, Xi’an,
pp 1277–1281
Sakai H, Tarumi H (2000) Estimation of the next happening of a landslide by observing the
change in groundwater composition. In: Bromhead E, Dixon N, Ibsen M-L (eds) Landslides in
Sakellariou M, Ferentinou M, Charalambous S (2006) An integrated tool for seismic induced
landslide hazards mapping. In: Agioutantis Z, Komnitsas K (eds) First European conference
on earthquake engineering and seismology. Proceedings of the joint event of 13th ECEE &
30th general Assembly of the ESC. Geneva, Switzerland, pp 1365–1375
Santurri L, Carlà R, Fiorucci F, Aiazzi B, Baronti S (2010) Assessment of very high resolution
satellite data fusion techniques for landslide recognition. In: Wagner W, Székely B (eds)
ISPRS TC VII Symposium—100 years if ISPRS. Vienna, Austria, pp 493–497
Sass O, Krautblatter M (2007) Debris flow-dominated and rockfall-dominated talus slopes:
Genetic models derived from GPR measurements. Geomorphology 86(1–2):176–192
Sass O, Bell R, Glade T (2008) Comparison of GPR, 2D-resistivity and traditional techniques for
the subsurface exploration of the Öschingen landslide, Swabian Alb (Germany). Geomor-
phology 93(1–2):89–103
Sato HP, Harp EL (2009) Interpretation of earthquake-induced landslides triggered by the 12 May
2008, M7.9 Wenchuan earthquake in the Beichuan area, Sichuan Province, China using
satellite imagery and Google Earth. Landslides 6(2):153–159
Schaefer M, Inkpen R (2010) Towards a protocol for laser scanning of rock surfaces. Earth Surf
Process Landforms 35:417–423
Schmidt J, Turek G, Clark MP, Uddstrom M, Dymond JR (2008) Probabilistic forecasting of
shallow, rainfall-triggered landslides using real-time numerical weather predictions. Nat
Schmutz M, Guerin R, Schott JJ, Maquaire O, Descloitres M, Albouy Y (2000) Geophysical
method contribution to the Super Sauze (South France) flowslide knowledge. In: Bromhead E,
References 79
Dixon N, Ibsen M-L (eds) Landslides in research, theory and oractice. Eighth international
symposium on landslides. T. Telford, London, pp 1321–1326
Schneider-Muntau B, Zangerl C (2005) Numerical modelling of a slowly creeping landslide in
crystalline rock—a case study. In: Impact of the human activity on the geological
environment. Proceedings of the fifth ISRM regional symposium Eurock. Brno, International
Society for Rock Mechanics, Czech Republic, pp 535–540
Schrott L, Sass O (2008) Application of field geophysics in geomorphology: advances and
limitations exemplified by case studies. Geomorphology 93(1–2):55–73
Schrott L, Hördt A, Dikau R (2003) Geophysical applications in geomorphology. Borntraeger,
Berlin
Schulz WH (2004) Landslides mapped using LIDAR imagery, Seattle, Washington. U.S.
Department of the Interior, U.S. Geological Survey
Schumm SA (1979) Geomorphic thresholds: The concept and its applications. Trans Inst Brit
Geogr NS4:485–515
Schuster RL, Highland LM (2007) The third Hans Cloos Lecture. Urban landslides:
socioeconomic impacts and overview of mitigative strategies. Bull Eng Geol Environ
66(1):1–27
Schuster RL, Wieczorek GF (2002) Landslide triggers and types. In: Rybár J, Stemberk J,
Wagner P (eds) Landslides: proceedings of the first European conference on landslides,
Taylor & Francis, Prague, June 24–26, 2002, pp 59–78
Sekiguchi T, Sato HP (2004) Mapping of micro topography using airborne laser scanning.
Sengupta A, Gupta S, Anbarasu K (2009) Rainfall thresholds for the initiation of landslide at
Lanta Khola in north Sikkim, India. Nat Hazards 52(1):31–42
Shi B, Wang B, Li K, Haibo H, Wei G, Piao C (2008a) Distributive monitoring of the slope
engineering. In: Chen Z, Zhang J-M, Ho K, Wu F-Q, Li Z-K (eds) Landslides and engineered
Shi YX, Zhang Q, Meng XW (2008b) Optical fiber sensing technology used in landslide
monitoring. In: Chen Z, Zhang J-M, Ho K, Wu F-Q, Li Z-K (eds) Landslides and engineered
Sidle RC, Wu W (1999) Simulating effects of timber harvesting on the temporal and spatial
distribution of shallow landslides. Zeitschrift für Geomorphologie 43:15–201
Singer J, Schuhbäck S, Wasmeier P, Thuro K, Heunecke O, Wunderlich T, Glabsch J, Festl J
(2009) Monitoring the Aggenalm landslide using economic de-formation measurement
techniques. Austrian J Earth Sci 102(2):20–34
Sirangelo B, Braca G (2004) Identification of hazard conditions for mudflow occurrence by
hydrological model: application of FLaIR model to Sarno warning system. Eng Geol
73(3–4):267–276
Sitharam TG, Maji VB, Verma AK (2007) Practical equivalent continuum model for simulation
of jointed rock mass using FLAC3D. Int J Geomech 7:389
Slaymaker O (1991) Mountain geomorphology: A theoretical framework for measurement
programmes. Catena 18(5):427–437. Accessed 2 Nov 2010
Smith R (1991) The application of cellular automata to the erosion of landforms. Earth Surf Proc
Land 16(3):273–281
Smith MJ, Chandler J, Rose J (2009) High spatial resolution data acquisition for the geosciences:
kite aerial photography. Earth Surf Proc Land 34(1):155–161
Soeters R, Van Westen CJ (1996) Slope instability recognition, analysis, and zonation. In:
Turner AK, Schuster RL (eds) Landslides: investigation and mitigation (Special Report).
National Research Council, Transportation and Research Board Special Report 247,
Washington, DC, USA, pp 129–177
Sorensen JH (2000) Hazard warning systems: review of 20 years of progress. Nat Hazards Rev
1(2):119–125
Sosio R, Crosta GB, Hungr O (2008) Complete dynamic modeling calibration for the Thurwieser
rock avalanche (Italian Central Alps). Eng Geol 100(1–2):11–26. Accessed 28 Oct 2010
Spickermann A, Schanz T, Datcheva M (2003) Numerical study of a potential landslide in the
Swiss Alps. In: Aifantis EC (ed) Fifth euromech solid mechanics conference. Thessaloniki,
Greece, pp 17–22
Stähli M, Bartelt P (2007) Von der Auslösung zur Massenbewegung. In: Hegg C, Rhyner J (eds)
Warnung bei aussergewöhnlichen Naturereignissen. Forum für Wissen. pp 33–38
Stark TD, Choi H (2008) Slope inclinometers for landslides. Landslides 5(3):339–350
Stead D, Eberhardt E, Coggan JS (2006) Developments in the characterization of complex rock
slope deformation and failure using numerical modelling techniques. Eng Geol
83(1–3):217–235. Accessed 28 Oct 2010
Strenger MP (2009) Niederschlagsschwellenwerte bei der Auslösung von Muren. University
Vienna, Austria
Supper R, Römer A (2003) New achievments in development of a high-speed geoelectrical
monitoring system for landslide monitoring. In: Proceedings of the environmental and
engineering geophysical society. Prague, Czech Republic, pp 1–6
Süzen ML, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment
methods: multivariate versus bivariate. Env Geol 45(5):665–679
Tangestani MH (2003) Landslide susceptibility mapping using the fuzzy gamma operation in a
GIS, Kakan catchment area, Iran. In: Disaster Management. Proceeding of the Map India
2003 conference. New Delhi, India, pp 107–134
Tarantino C, Blonda P, Pasquariello G (2004) Application of change detection techniques for
monitoring man-induced landslide causal factors. In: Geoscience and remote sensing.
Proceedings of the IGARSS symposium. pp 1103–1106
Tarboton D (1997) A new method for the determination of flow directions and upslope areas in
grid digital elevation models. Water Resour Res 33(2):11
Telford WM, Geldart LP, Sheriff RE (1990) Applied geophysics. Cambridge University Press,
Cambridge
Teoman MB, Topal T, Isik NS (2004) Assessment of slope stability in Ankara clay: a case study
along E90 highway. Env Geol 45(7):963–977
Terlien MT (1998) The determination of statistical and deterministic hydrological landslide-
triggering thresholds. Env Geol 35(2):124–130
Terlien MTJ, Van Westen CJ, Van Asch T (1995) Deterministic modelling in GIS-based
landslide hazard assessment. In: Carrara A, Guzzetti F (eds) Geographical information
systems in assessing natural hazards. Kluwer, Dordrecht, pp 57–77
Terranova O, Antronico L, Gulla G (2007) Landslide triggering scenarios in homogeneous
geological contexts: The area surrounding Acri (Calabria, Italy). Geomorphology
87(4):250–267
Terzaghi K (1925) Erdbaumechanik auf bodenphysikalischer Grundlage. F. Deuticke, Vienna
Terzaghi K (1950) Mechanisms of landslides. In: Paige S (ed) Application of geology to
engineering practice. Geological Society of America, Berkley, pp 83–123
Terzaghi K (1961) Die Bodenmechanik in der Baupraxis. Springer, Berlin
Terzaghi K, Peck RB, Mesri G (1996) Soil mechanics in engineering practice. Wiley, New York
Thiebes B (2006) Räumliche Gefährdungsmodellierung flachgründiger Hangrutschungen—GIS
gestützte Analyse an der Schwäbischen Alb. University of Bonn, Germany
Thiebes B, Bell R, Glade T (2010) Bewegungsanalyse-Frühwarnmodell. In: Bell R, Pohl J, Glade
T, Mayer J, Greiving S (eds) Integrative Frühwarnsysteme für gravitative Massenbewegungen
(ILEWS) Monitoring, Modellierung, Implementierung. Klartext, Essen, pp 150–151
Thuro K, Wunderlich T, Heunecke O, Singer J, Schuhbäck S, Wasmeier P, Glabsch J, Festl J
(2009) Low cost 3D early warning system for unstable alpine slopes—the Aggenalm
Landslide monitoring system. Geomech Tunn 2(3):221–237
Tianchi L (1994) Landslide disasters and human responses in China. Mountain Res Develop
4(14):341–346. Accessed 16 Sep 2010
References 81
Tiranti D, Rabuffetti D (2010) Estimation of rainfall thresholds triggering shallow landslides for
an operational warning system implementation. Landslides 4(7):471–481
Tohari A, Nishigaki M, Komatsu M, Kankam-Yeboah K, Daimuru S (2004) Field monitoring of
hydrological response of a residual soil slope to rainfall. In: Lacerda W, Ehrlich M, Fontoura
Topal T, Akin M (2008) Investigation of a landslide along a natural gas pipeline (Karacabey-
Turkey). In: Chen Z, Zhang J-M, Ho K, Wu F-Q, Li Z-K (eds) Landslides and engineered
Tropeano D, Turconi L (2004) Using historical documents for landslide, debris flow and stream
flood prevention. Applications in Northern Italy. Nat Hazards 31(3):663–679
Turcotte DL, Malamud BD (2004) Landslides, forest fires, and earthquakes: examples of self-
organized critical behavior. Phys A Statist Mech Appl 340(4):580–589
Turner AK, McGuffey VC (1996) Organization of investigation process. In: Turner AK, Schuster
RL (eds) Landslides: investigation and mitigation (Special Report). National Research
Council, Transportation and Research Board Special Report 247, Washington, pp 121–128
Twigg J (2003) The human factor in early warning: risk perception and appropriate
communications. In: Zschau J, Küppers AN (eds) Early warning systems for natural disaster
reduction. Springer, Berlin, pp 19–27
Uchida T, Kosugi K, Mizuyama T (2001) Effects of pipeflow on hydrological process and its
relation to landslide: a review of pipeflow studies in forested headwater catchments. Hydrol
Process 15(11):2151–2174
UNISDR (2004a) Early Warning Systems. In: Living with Risk. UN/ISDR, Geneva, Switzerland,
pp 358–383
UNISDR (2004b) ISDR: Platform for the Promotion of Early Warning. http://www.unisdr.org/
ppew/whats-ew/ew-made.htm. Accessed 19 Nov 2010
UNISDR (2006a) Compendium of early warning systems. UN/ISDR, Bonn, Germany
UNISDR (2006b) Global survey of early warning systems. UN/ISDR
UNISDR (2009) ISDR?: Terminology. http://www.unisdr.org/eng/library/lib-terminology-
eng%20home.htm. Accessed 15 Sep 2010
US Army Corps of Engineers (2003) Slope stabiliy. Washington, D.C., USA
USGS (2006) Landslide Hazards in the Seattle, Washington, Area. Department of the Interior and
U.S. Geological Survey
Vallone P, Giammarinaro MS, Crosetto M, Agudo M, Biescas E (2008) Ground motion
phenomena in Caltanissetta (Italy) investigated by InSAR and geological data integration.
Eng Geol 98(3–4):144–155
Van Westen CJ (2007) Mapping landslides: recent developments in the use of digital information.
In: Turner A, Schuster RL (eds) Landslides and society? Proceedings of the first North
American conference on landslides, Vail, Colorado, USA, June 3–8, 2007. Association of
Environmental and Engineering Geologists, Vail Colorado, USA, pp 221–238
Van Westen CJ, Rengers N, Terlien MTJ, Soeters R (1997) Prediction of the occurrence of slope
instability phenomenal through GIS-based hazard zonation. Geologische Rundschau
86(2):404–414
Varnes DJ (1984) Landslide hazard zonation: A review of principles and practice. UNESCP
Press, Paris
Vlcko J (2004) Extremely slow slope movements influencing the stability of Spis Castle,
UNESCO site. Landslides 1(1):67–71
Voight B (1988) A method for prediction of volcanic eruptions. Nature 332(6160):125–130.
Accessed 6 Oct 2010
Voight B (1989) A relation to describe rate-dependent material failure. Science 243(4888):200
Volkmann G, Schubert W (2005) The use of horizontal inclinometers for the optimization of the
rock mass-support interaction. In: Erdem Y, Solak T (eds) Underground space use-analysis of
the past and lessons for the future. Taylor & Francis, Routledge, pp 967–972
Von Elverfeldt K (2010) Systemtheorie in der Geomorphologie. Problemfelder, erkenntnistheor-

etische Konsequenzen und praktische Implikationen. University of Vienna, Austria
Walstra J, Chandler JH, Dixon N, Dijkstra TA (2004) Extracting landslide movements from
historical aerial photographs. In: Lacerda W, Ehrlich M, Fontoura SAB, Sayao AS (eds)
Landslides: Evaluation and stabilization. Ninth international symposium on landslides. A.A.
Walter M, Joswig M (2009) Seismic characterization of slope dynamics caused by soft rock-
landslides: The Super-Sauze case study. In: Malet J-P, Remaître A, Bogaard T (eds) Landslide
processes: from geomorphic mapping to dynamic modelling. Proceedings of the landslide
processes conference. CERG Editions, Strasbourg, France, pp 215–220
Wang B-J, Li K, Shi B, Wei G-Q (2008a) Test on application of distributed fiber optic sensing
technique into soil slope monitoring. Landslides 6(1):61–68
Wang FW, Wang G, Zhang YM, Huo ZT, Peng XM, Araiba K, Tekeuchi A (2008b)
Displacement monitoring on Shuping landslide in the three Gorges Dam Reservoir area,
China from August 2004 to July 2007. In: Chen Z, Zhang J-M, Ho K, Wu F-Q, Li Z-K (eds)
Landslides and engineered slopes: from the past to the future. Proceedings of the tenth
international symposium on landslides and engineered slopes. Taylor & Francis, Xi’an,
pp 1321–1327
Wang H, Harvey AM, Xie S, Kuang M, Chen Z (2008c) Tributary-junction fans of China’s
Yangtze Three-Gorges valley: Morphological implications. Geomorphology
100(1–2):131–139
Wang F, Zhang Y, Huo Z, Peng X (2009) Monitoring on shuping landslide in the three Gorges
Dam Reservoir, China. In: Wang F, Li T (eds) Landslide disaster mitigation in three gorges
reservoir, China. Springer, Berlin, pp 257–273
Wasowski J, Lollino P, Limoni PP, Del Gaudio V, Lollino G, Gostelow P (2004) Towards an
integrated field and EO-based approach for monitoring peri-urban slope instability. In:
pp 809–816
Watson AD, Moore DP, Stewart TW (2004) Temperature influence on rock slope movements at
Checkerboard Creek. In: Lacerda W, Ehrlich M, Fontoura SAB, Sayao AS (eds) Landslides:
Webster TL, Dias G (2006) An automated GIS procedure for comparing GPS and proximal
LiDAR elevations. Comput Geosci 32(6):713–726
White G, Haas JE (1975) Assessment of research on natural hazards. MIT Press, Cambridge
Wieczorek GF (1987) Effect of rainfall intensity and duration on debris flows on the central Santa
Cruz mountains, California. In: Costa JE, Wieczorek GF (eds) Debris flows/avalanches:
process, recognition, and mitigation. Reviews in Engineering Geology 7. Geological Society
of America, Boulder, pp 93–104
Wieczorek GF (1996) Landslide triggering mechanisms. In: Turner AK, Schuster RL (eds)
Landslides: investigation and mitigation (Special Report). National Research Council,
Transportation and Research Board Special Report 247, Washington, D.C., USA, pp 76–90
Wieczorek GF, Glade T (2005) Climatic factors influencing occurrence of debris flows. In: Jakob
M, Hungr O (eds) Debris-flow hazards and related phenomena. Springer, Berlin, pp 325–362
Wieczorek GF, Guzzetti F (1999) A review of rainfall thresholds for triggering landslides. In:
Claps P, Siccardi F (eds) Mediterranean storms. Proceedings Plinius conference ’99. Maratea,
Italy, pp 407–414
Wieczorek GF, Gori PL, Highland LM (2005) Reducing landslide hazards and risk in the United
States: The role of the US geological survey. In: Glade T, Anderson M, Crozier MJ (eds)
Landslide hazard and risk. Wiley, New York, pp 351–375
Wienhöfer J, Lindenmaier (2009) Temporal variability of a slow-moving landslide: the Heumöser
case study in Vorarlberg, Austria. In: Malet JP, Remaître A, Bogaard T (eds) Landslide
References 83
processes: from geomorphological mapping to dynamic modelling. Proceedings of the

international conference on landslide processes. CERG Editions, Strasbourg, pp 221–226
Wilkinson PL, Brooks SM, Anderson MG (2000) Design and application of an automated non-
circular slip surface search within a combined hydrology and stability model (CHASM).
Hydrol Process 14(11–12):2003–2017
Wilkinson PL, Anderson MG, Lloyd DM (2002a) An integrated hydrological model for rain-
induced landslide prediction. Earth Surf Proc Land 27(12):1285–1297
Wilkinson PL, Anderson MG, Lloyd DM, Renaud JP (2002b) Landslide hazard and
bioengineering: towards providing improved decision support through integrated numerical
model development. Environ Model Softw 17(4):333–344
Willenberg H, Spillmann T, Eberhardt E, Evans KF, Loew H, Maurer H (2002) Multidisciplinary
monitoring of progressive failure process in brittle rock slopes—concepts and system design.
In: Rybár J, Stemberk J, Wagner P (eds) First European conference on landslides. Balkema
Publishers, Prague, pp 477–483
Willenberg H, Evans KF, Eberhardt E, Loew S, Spillmann T, Maurer HR (2004) Geological,
geophysical and geotechnical investigations into the internal structure and kinematics of an
unstable, complex sliding mass in crystalline rock. In: Lacerda W, Ehrlich M, Fontoura SAB,
Willenberg H, Loew S, Eberhardt E, Evans K, Spillmann T, Heincke B, Maurer H, Green A
(2008) Internal structure and deformation of an unstable crystalline rock mass above Randa
(Switzerland): Part I—Internal structure from integrated geological and geophysical
investigations. Eng Geol 101(1–2):1–14
Wilson RC (1989) Rainstorms, pore pressures, and debris flows: A theoretical framework. In:
Sadler PM, Morton DM (eds) Landslides in semi-arid environment. Inland Geological
Society, Riverside, pp 101–117
Wilson RC (2005) The rise and fall of a Debris-Flow warning system for the San Francisco Bay
Region, California. In: Glade T, Anderson M, Crozier MJ (eds) Landslide hazard and risk.
Wiley, New York, pp 493–516
Wilson RC, Wieczorek GF (1995) Rainfall thresholds for the initiation of debris flows at La
Honda, California. Environ Eng Geosci 1(1):11–27. Accessed 18 Oct 2010
Wilson RC, Mark RK, Barbato G (1993) Operation of a real-time warning system for debris flows
in the San Francisco Bay area, California. In: Shen HW, Su ST, Wen F (eds) Proceedings of
the 1993 conference on hydraulic engineering, pp 1908–1913
Wong HN, Ho KKS (2000) Learning from slope failures in Hong Kong. In: Bromhead E, Dixon N,
Ibsen M-L (eds) Landslides in research, theory and practice. Proceedings of the eighth
international symposium on landslides. Thomas Telford, London
Wu TH (1996) Soil strength properties and their measurement. In: Turner AK, Schuster RL (eds)
Landslides: investigation and mitigation (Special Report). National Research Council,
Transportation and Research Board Special Report 247, Washington, D.C., USA, pp 319–336
Wu W, Sidle RC (1995) A distributed slope stability model for steep forested basins. Water
Resour Res 31(8):2097–2110. Accessed 26 Oct 2010
Wu W, Sidle RC (1997) Application of a distributed shallow landslide analysis model (dSLAM)
to managed forested catchments in Oregon, USA. In: Human impact on erosion and
sedimentation. Proceedings of the Rabat Symposium. IAHS Publication 245, pp 213–222
Wu MJ, Li ZC, Yuan PJ, Jiang YH (2008) Twenty years of safety monitoring for the landslide of
Hancheng PowerStation. In: Chen Z, Zhang J-M, Ho K, Wu F-Q, Li Z-K (eds) Landslides and
engineered slopes: from the past to the future. Proceedings of the tenth international
symposium on landslides and engineered slopes. Taylor & Francis, Xi’an, pp 1335–1341
Wu YP, Yin KL, Jiang W (2009) Early warning of landslide risk in Yongjia County, Zhejiang
Province. J Nat Disast 18(2):124–130
Xie M, Esaki T, Zhou G (2004) GIS-based probabilistic mapping of landslide hazard using a
three-dimensional deterministic model. Nat Hazards 33(2):265–282. Accessed 26 Oct 2010
Xu Q, Zeng Y (2009) Research on acceleration variation characteristics of creep landslide and

early-warning prediction indicator of critical sliding. Yanshilixue Yu Gongcheng Xuebao/
Chinese J Rock Mech Eng 28(6):1099–1106
Yagoda-Biran G, Hatzor YH, Amit R, Katz O (2010) Constraining regional paleo peak ground
acceleration from back analysis of prehistoric landslides: example from Sea of Galilee, Dead
Sea transform. Tectonophysics 490(1–2):81–92. Accessed 28 Oct 2010
Yang X, Chen L (2010) Using multi-temporal remote sensor imagery to detect earthquake-
triggered landslides. Int J Appl Earth Observ Geoinform 6(12):487–495. Accessed 16 Sep
2010
Ye Y, Mu Q, Zhang C (2009) Tunnel construction multivariate information forewarning and
safety management system research. Yanshilixue Yu Gongcheng Xuebao/Chinese J Rock
Mech Eng 28(5):900–907
Yin Y (2009) Landslide mitigation strategy and implementation in China. In: Sassa K, Canuti P
(eds) Landslides-disaster risk reduction. Springer, Berlin, pp 482–484
Yin JH, Zhu HH, Jin W (2008) Monitoring of soil nailed slopes and dams using innovative
technologies. In: Chen Z, Zhang J-M, Ho K, Wu F-Q, Li Z-K (eds) Landslides and engineered
Yin Y, Wang H, Gao Y, Li X (2010a) Real-time monitoring and early warning of landslides at
relocated Wushan Town, the Three Gorges Reservoir, China. Landslides 7(3):339–349
Yin Y, Zheng W, Liu Y, Zhang J, Li X (2010b) Integration of GPS with InSAR to monitoring of
the Jiaju landslide in Sichuan, China. Landslides 7(3):359–365
Yu YF, Lam J, Siu CK, Pun WK (2004) Recent advance in landslip warning system. In: Recent
advances in geotechnical engineering. Proceedings of the twenty-fourth geotechnical division
Annual Seminar. Institution of Engineers, Hong Kong, pp 139–147
Zaitchik BF, van Es HM (2003) Applying a GIS slope-stability model to site-specific landslide
prevention in Honduras. J Soil Water Conserv 58(1):45–53
Zhang Q, Wang L, Zhang XY, Huang GW, Ding XL, Dai WJ, Yang WT (2008) Application of
multi-antenna GPS technique in the stability monitoring of roadside slopes. In: Chen Z,
slopes. Taylor & Francis, Xi’an, pp 1367–1372
Zhao XB, Zhao J, Cai JG, Hefny AM (2008) UDEC modelling on wave propagation across
fractured rock masses. Comput Geotech 35(1):97–104
Zhong L, Xiao S, Zhou Y (2009) Research on the early warning and forecast system of geologic
hazards in Hubei Province based on WEBGIS. In: First international workshop on education
technology and computer science. Wuhan, Hubei, China, pp 602–606
Zhou PG, Chen HQ (2005) Research on geologic hazard risk management in china based on
geologic hazard survey and zoning. In: Hungr O, Fell R, Couture R, Eberhardt E (eds)
International conference on landslide risk management. Taylor & Francis, Vancouver,
pp 54–60
Zschau J, Küppers AN (2003) Early warning systems for natural disaster reduction. Springer,
Berlin
Zschau J, Merz B, Plate EJ, Goldammer JG (2001) Vorhersage und Frühwarnung. In: Plate EJ,
Merz B (eds) Naturkatastrophen. Schweizerbart’Sche Verlagsbuchhandlung, pp 273–350
http://www.springer.com/978-3-642-27525-8

Theoretical Background: 2.1 Landslide Processes 2.1.1 Definitions and Classifications

Uploaded by

Document Informationclick to expand document information

Copyright:

Available Formats

Theoretical Background: 2.1 Landslide Processes 2.1.1 Definitions and Classifications

Uploaded by

Document Information

Original Description:

Original Title

Copyright

Available Formats

Share this document

Share or Embed Document

Sharing Options

Did you find this document useful?

Is this content inappropriate?

Copyright:

Available Formats

Theoretical Background: 2.1 Landslide Processes 2.1.1 Definitions and Classifications

Uploaded by

Copyright:

Available Formats

Chapter 2

2.1 Landslide Processes

2.1.1 Definitions and Classifications

B. Thiebes, Landslide Analysis and Early Warning Systems, Springer Theses, 7

Table 2.2 Mass movement classification based on velocity of displacement (Australian

2.1.2 Principles of Slope Stability

Fig. 2.2 Stress vectors

which is subject to preparatory factors which convert the slope to a marginally

to failure, the onset of failure characterized by the formation of a continuous shear

where s = shear stress

attribute to the development of micro-cracks which progressively form a complete

Fig. 2.4 Idealized strain

dependant on initial conditions such as antecedent soil water conditions, degree

2.1.3 Systems Theory Considerations

been widely applied to geomorphological processes (Favis-Mortlock 1998;

2.1.4 Landslide Triggering

stratification, preferential flow paths, as well as mechanical characteristics (Berardi

complex hydrology compared to shallow landslides and simple correlations

2.2 Landslide Investigation and Monitoring

The occurrence of landslides is sometimes surprising for humans as they seem to

2.2.1 Mapping and Inventory Approaches

A basic method to detect landslides in space and to prepare landslide inventories is

however photogrammetric methods can be used to extract quantitative information.

(PS-InSAR) was introduced to landslide research (Morgenstern and Martin 2008).

2.2.2 Displacement Measurements

Many field methods exist to measure ground displacements due to landslide

2.2.3 Hydrological Measurements

2.2.4 Geophysical Measurements

to ruptures. This method yields information on depth and mode of slope

2.3 Landslide Modelling

Landslide modelling is, along with experimental subsoil exploration and

2.3.1 Regional Models

Regional landslide modelling methods generally focus on either landslide sus-

Fig. 2.6 Simplified illustration of method of slices (after Conolly 1997)

2.3.2 Local Models

calculation have been presented in Sect. 2.1.2. Limit-equilibrium analysis usually

Three variations of discrete-element variation can be distinguished.

SVSlope is a slope stability software with a similar complexity and range of

et al. (2008) in the Caribbean. Based on modelling results critical hydrological

Fig. 2.7 General risk

2.4 Landslide Early Warning Systems

In the following an introduction to early warning and the challenges of early

population. Moreover, early warning systems have to be embedded into local

of warning is carefully planned (Mayer and Pohl 2010). According to Kunz-Plapp

their degree of integration of social aspects between purely technical

resolution of rainfall measurement. However, integration failed because no reliable

internet. In three years of operation the system issued 11 warnings, of which 6

accounting for antecedent rainfall conditions. A series of single slope CHASM

Other topics of the project include development and implementation of warning

the emergency response procedure is executed. More detailed information on

At Cromer, automatic readings of field sensors activate an alarm by sending a

Fig. 2.11 General structure of ILEWS project (Bell et al. 2009)

empirical or physically-based methods. In some cases local test slopes are

2.5 The ILEWS Project

structures. Some results are presented in Chap. 7; more detailed information is

Within the cluster implementation cooperative risk management and end-user

Baldo M, Bicocchi C, Chiocchini U, Giordan D, Lollino G (2009) LIDAR monitoring of mass