Introduction to Hydrology - Solutions Manual, 5th Edition Warren Viessman Jr., Gary L. Lewis

Landslide time‐forecast methods A literature review towards reliable prediction of time of time to failure

Version:

April 2009

Author:

Matthias Busslinger [email protected] HSR University of Applied of Applied Sciences Institut für Bau und Umwelt Rapperswil, Switzerland

Landslide Forecast Methods

Contents Abstract ................................................................................................................................................... ...................................................................................................................................................3 3 Introduction............................................................................................................................................. Introduction.............................................................................................................................................4 4 Characterizing a landslide ....................................................................................................................... .......................................................................................................................5 5 Lead time and accuracy of prediction of prediction ................................................................................................. 6 Long‐term forecast methods................................................................................................................... methods ...................................................................................................................7 7 Mid‐ and short‐term forecasting............................................................................................................. forecasting .............................................................................................................8 8 Forecasts based on in‐situ measurements.......................................................................................... measurements .......................................................................................... 8 Deformation based forecasts .......................................................................................................... ..........................................................................................................8 8 Pore‐water pressure based forecasts ........................................................................................... 15 Water content based forecasts ..................................................................................................... 15 Micro seismic based forecasts ...................................................................................................... 16 Forecasts based on climatic conditions............................................................................................. conditions ............................................................................................. 17 Rainfall threshold based forecasts ................................................................................................ 17 State‐of ‐the‐art and future trends ........................................................................................................ ........................................................................................................20 20 Measuring devices............................................................................................................................. devices .............................................................................................................................20 20 Multi‐parameter based forecasts...................................................................................................... forecasts...................................................................................................... 20 Wireless Sensor Networks WSN.................................................................................................... WSN .................................................................................................... 21 Wireless Underground Sensor Networks WUSN........................................................................... WUSN........................................................................... 24 Conclusion and future needs........................... needs.......................................................................... ...................................................................................... ....................................... 25 Role of Geotechnical of Geotechnical Experts ............................................................................................................ ............................................................................................................25 25 Selection of appropriate of appropriate monitoring parameter............................................................................... parameter ............................................................................... 25 Next steps towards reliable forecasts ............................................................................................... 26 References............................................................................................................................................. References .............................................................................................................................................27 27

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Contents Abstract ................................................................................................................................................... ...................................................................................................................................................3 3 Introduction............................................................................................................................................. Introduction.............................................................................................................................................4 4 Characterizing a landslide ....................................................................................................................... .......................................................................................................................5 5 Lead time and accuracy of prediction of prediction ................................................................................................. 6 Long‐term forecast methods................................................................................................................... methods ...................................................................................................................7 7 Mid‐ and short‐term forecasting............................................................................................................. forecasting .............................................................................................................8 8 Forecasts based on in‐situ measurements.......................................................................................... measurements .......................................................................................... 8 Deformation based forecasts .......................................................................................................... ..........................................................................................................8 8 Pore‐water pressure based forecasts ........................................................................................... 15 Water content based forecasts ..................................................................................................... 15 Micro seismic based forecasts ...................................................................................................... 16 Forecasts based on climatic conditions............................................................................................. conditions ............................................................................................. 17 Rainfall threshold based forecasts ................................................................................................ 17 State‐of ‐the‐art and future trends ........................................................................................................ ........................................................................................................20 20 Measuring devices............................................................................................................................. devices .............................................................................................................................20 20 Multi‐parameter based forecasts...................................................................................................... forecasts...................................................................................................... 20 Wireless Sensor Networks WSN.................................................................................................... WSN .................................................................................................... 21 Wireless Underground Sensor Networks WUSN........................................................................... WUSN........................................................................... 24 Conclusion and future needs........................... needs.......................................................................... ...................................................................................... ....................................... 25 Role of Geotechnical of Geotechnical Experts ............................................................................................................ ............................................................................................................25 25 Selection of appropriate of appropriate monitoring parameter............................................................................... parameter ............................................................................... 25 Next steps towards reliable forecasts ............................................................................................... 26 References............................................................................................................................................. References .............................................................................................................................................27 27

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Abstract The following article is the result of an of an extensive literature review about time forecasts of landslides. of landslides. A short overview about long‐term forecasts is given, but the focus is on mid‐ and short‐term forecasts. The methods are classified by the parameters required to make a prediction (i.e. in‐situ measurements and climatic conditions). The methods are summarized and references to detailed articles are given for each method. The aim of this of this review is to give an overview of previously made attempts to predict landslides and future research needs and challenges are addressed. This review has shown a strong need for low‐cost warning systems for landslides. Therefore, current research activities in the field of wireless of wireless sensor networks are presented as well. In addition to this article, a summary of the presented methods is given in a separate schema called “Landslide Forecast Toolbox”. For the future it is planned to program a wiki, to make this literature review accessible via Internet.

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Introduction The term landslide denotes “the movement of a mass of rock, debris or earth down a slope”. Landslides are a regional and site‐specific problem. The occurrence of landslides depends on topography, geology, groundwater, weather, vibrations and human causes. Landslides range in many orders of magnitude in size, from small boulders to several cubic kilometers of mass. Speeds vary from extremely slow (mm/y) to extremely rapid movements (several 100km/h) (Cruden and Varnes 1996). Landslide processes are very complex and present challenges in the development of early warning systems. Nevertheless, several successful attempts have been made to forecast landslides. This literature review gives an overview about landslide forecast methods with a focus on real‐time warning. An outlook about future needs and research trends is given as well. Besides rigorous avoiding of landslide prone areas, successful early warning is one of the most cost‐ effective ways of disaster prevention. Real‐time warning systems are suitable for communication routes and life lines, such as railroads and highways where hazard zones can not be bypassed. The United Nation International Strategy for Disaster Reduction ISDR divides an early warning system into four elements (web ISDR):

The Four Elements of Effective Early Warning Systems

Risk knowledge

Monitoring and warning service

Dissemination and communication

Response capability

Systematically collect data and undertake risk assessments

Develop hazard monitoring and early warning services

Communicate risk information and early warnings

Build national and community response capabilities

Are the hazards and the vulnerabilities well known? What are the patterns and trends in these factors? Are risk maps and data widely available?

Are the right parameters being monitored? Is there a sound scientific basis for making forecasts? Can accurate and timely warnings be generated?

Do warnings reach all of those at risk? Are the risks and the warnings understood? Is the warning information clear and useable

Are response plans up to date and tested? Are local capacities and knowledge made use of? Are people prepared and ready to react to warnings?

nd

Fig. 1 The Four Elements of Effective Early Warning Systems. This literature review focuses mainly on the 2 element of monitoring and warning service (Graph: web ISDR)

“Risk knowledge” in Fig. 1, is primarily collected by geologists and regional planners and results in maps indicating landslide prone areas. The second element “Monitoring and early warning service” is strongly related to geotechnical engineering and therefore the main objective of this review. The last two elements are essential for the success of an early warning system. Collaboration with authorities as well as communication, public knowledge and participation are crucial, but exceed the general tasks of geotechnical engineers. 4/31


A general early warning system for landslides is not given up to this day. The types of movement (i.e. fall, topple, glide…) are very different processes and therefore it is difficult to develop a general landslide warning system (Baehr, 2004). However, it is possible to monitor instable slopes according to their local properties. This literature review gives an overview about the methods developed so far and intends to inspire engineers to develop new techniques and combine different methods.

Characterizing a landslide Cruden and Varnes 1996 reviewed the range of landslide processes and provided a vocabulary for describing the features of landslides relevant to their classification. A nomenclature for the observable landslide features is illustrated in Fig. 2 below.

Fig. 2 Block diagram of idealized complex earth slide‐earth flow (Cruden and Varnes 1996)

Any landslide can be classified and described by two nouns: the first describes the material and the second describes the type of movement. The material can be divided into either rock, a hard or firm mass that was intact in its natural place before the initiation of movement, or soil, an aggregate of solid particles, generally of minerals and rocks, that either was transported or was formed by the weathering of rock in place. Soil is divided into earth and debris (Tab. 1). Earth describes material in which 80 percent or more of the particles are smaller than 2 mm, the upper limit of sand‐size particles recognized by most geologists. Debris contains a significant proportion of coarse material; 20 to 80 percent of the particles are larger than 2 mm, and the remainder are less than 2 mm. The five kinematically distinct types of landslide movement are, fall, topple, slide, spread and flow.

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Abbreviated Classification of Slope Movements

(After: Cruden and Varnes 1996) Type of Material Engineering Soils Type of Movement

Bedrock

Coarse

Fine

Fall

Rock fall

Debris fall

Earth fall

Topple

Rock topple

Debris topple

Earth topple

Slide

Rock slide

Debris slide

Earth slide

Spread

Rock spread

Debris spread

Earth spread

Flow

Rock flow

Debris flow

Earth flow

Tab. 1 Names to describe landslides are listed (e.g., rock fall, debris flow). After (Cruden and Varnes 1996)

Lead time versus accuracy of prediction In terms of time, forecasts can be roughly divided into three classes of lead time. Long‐term forecasts mostly indicate a potential hazard within a certain region, years before they actually occur. Mid‐term forecasts predict failures several months ahead. And finally short‐term predictions have a lead time of months to days. As a rule of thumb we can say: “Longer lead time allows more preventive actions against landslide disasters. But on the other hand longer lead time comes often with less accuracy, in terms of time and location.” This review focuses mainly on mid‐ and short‐term predictions. Nevertheless, a short overview about long‐term forecasting is given in the following section.

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Long‐term forecast methods The first step towards a landslide forecast is often a systematic collection of data in a landslide hazard zonation. An ideal map of slope instability hazard should provide information on the spatial probability, type, magnitude, velocity, runout distance and retrogression limit of the mass movements predicted in a certain area (Soeters and van Westen 1996). These landslide inventories are mainly made by geologists. The maps can be interpreted as a first long‐term forecast. Although, they can not predict the exact time of an event and cover a region, rather than a specific slope, they indicate a potential hazard several years in advance of an event. The last few decades have shown very rapid development of the application of digital tools such as Geographic Information Systems (GIS), Digital Image Processing, Digital Photogrammetry and Global Positioning Systems (GPS). In landslide risk assessment at scales of 1:10’000 or smaller, GIS has become the standard tool. Much progress has been made in the generation of Digital Elevation Models (DEM) obtained from different sources like Synthetic Aperture Radar (SAR) or Light Detection and Ranging (LIDAR). DEM are used to generate landslide inventories. Landslide inventories can now make use of a variety of approaches, ranging from digital stereo image interpretation to automatic classification, based either on spectral or altitude differences, or a combination of both. Landslide inventory databases become available to more countries and several are now also available through the Internet. A comprehensive landslide inventory is a basic requirement in order to be able to quantify both landslide hazard and risk (van Westen 2007). Soeters and van Westen (1996) as well as Guzzetti et al. (1999) presented very good and structured reviews of current techniques to obtain landslide hazard maps. The results of these assessments result in hazard maps and are used by regional planners to avoid endangered zones in public planning or take according measures.

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Mid‐ and short‐term forecasting Routine surveys of landslide prone areas or slopes provide information about the progress of instable masses. Usually, unstable locations or even specific slopes can be identified. Depending on the method, a failure forecast can be made. Routine surveys allow monitoring with lead times of years to months for mid‐term forecasts. Real‐time monitoring allows the most accurate prediction of landslides, within several months to days. For example Xiaoping et al. (1996) have forecasted a slope failure at Yellow River in Gansu Province, China on the 30. January 1995 at with an accuracy of one day! The following chapter gives an overview about different mid‐ and short‐term forecast methods. The methods are structured into two groups. The first group contains forecast methods based on in‐situ measurements of different parameters in the slope. In the second group, methods based on climatic conditions, are presented. All the following methods require a careful assessment of the instable slope in order to place the measuring devices at characteristic points.

Forecasts based on in‐situ measurements Deformation based forecasts For deformation based forecasts the soil must have a plastic behavior in order to observe any deformations prior fracture. Deformations of soil under a constant load can be plotted in a time vs. strain diagram (Fig. 3). The strain rate ε & is defined as the derivative of strain ε , with respect to time. In the initial stage, known as primary creep, the strain rate is relatively high, but slows with increasing strain. The strain rate eventually reaches a minimum and becomes near‐constant. This is known as secondary or steady‐ state creep and depends on creep mechanism and soil properties. In tertiary creep, the strain‐rate increases exponentially and ends with fracture (web wiki1).

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Strain e

Fracture primary creep secondary strain rate tertiary creep

secondary creep Initial Load

time t

Strain rate ÿ e Fracture

minimun secondary creep rate time t

Fig. 3 In the secondary creep stage the strain‐rate is constant and thereafter increases until failure.

Based on measurements in the secondary creep range, Saito (1965) proposed an empirical formula to predict the time of slope failure. The relationship between constant strain rate and rupture life (time to failure) was successfully applied to forecast landslides:

log 10 t r

=

2.33 − 0.916 log10 ε & ± 0.59 Eq. 1

tr: creep rupture life (min), i.e. total time from the beginning of movement until failure

& : constant strain rate (in 10 4mm)

ε

‐

The relationship seems to be independent of the type of soil or testing method. Measurements of relative displacement should be taken continuously. Forecasting the time of slope failure is done by the following procedure: 1. Measurement of the relative displacements of a slope across tension cracks or along the centre line, depending on field conditions. 2. Determination of the beginning of the unstable state of the slope through the relative displacement curve. 3. Calculation of the constant strain rate from the relative displacement curve. 9/31


4. Estimation of creep rupture life corresponding to the strain rate, using the relationship between strain rate and creep rupture life. Saito (1969) extended his theory to the tertiary creep range in order to obtain more accurate forecasts for the time close to failure. The above mentioned relationship was adapted to the transient strain rate. He presented a graphical and a numerical solution. The empirical formula for

the tertiary creep range relates the time left before failure (tr‐t) to the displacement as follows:

Δl =

l 0 a log

t r − t 0 t r

− t

Eq. 2 Δl:

relative displacement between two measured points

l0: initial distance between two measured points a: constant tr: creep rupture life (min), i.e. total time from the beginning of movement until failure t0: time when movement begins, Δl=0 t: optional time The equation contains three unknown: a or l0∙a, tr and t0. The remaining time to failure (tr‐t) can be obtained with three or more points properly selected on the creep curve. The best way to have a good estimation is to begin displacement measurements as early as possible. The nearer failure comes the more reliable the forecast. It is advisable to roughly estimate time of failure with steady state strain rate in the secondary creep range (Saito 1965) and predict precisely with data from the third creep range (Saito 1969). Hayashi et al. (1988) improved the Saito (1969) prediction in the tertiary creep range. Based on large scale laboratory experiments Fukuzono (1985) presented a new method for predicting the failure time using the inverse number of surface displacement velocity (1/v). If the displacement velocity v at a slope surface increases over time, its inverse number (1/v) decreases. When (1/v) approaches zero, failure occurs.

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1/(a-1)

v / 1 y t i c o l e v f o r e b m u n e s r e v n I

1 = {a (a-1)} v

1/(a-1)

ÿ (tr -t)

a

> 2 ( c o n v e x )

a

= 2 ( l i n e a r ) 1 < a < 2 ( c o n c a v e )

Failure

t

time t

tr

Fig. 4 Typical figures for changes of the Inverse number of velocity in surface displacement just before the failure. After (Fukuzono 1985)

The curve is described by following equation:

1 v

=

{a(

α

}

− 1)

1

1 1

α −

⋅ (t r − t )

1

α −

Eq. 3

v: velocity of surface displacement a: constant α:

constant

tr: creep rupture life (min), i.e. total time from the beginning of movement until failure t: optional time Fukuzono distinguishes three cases, depending on the shape of the graph. For a linear graph α=2 and the failure time tr can be predicted using two points. This time is equal to the time predicted by Saito (1965):

t r

=

t 2 (1 / v )1

(1 / v )1

− t 1 (1 / v ) 2 − (1 / v) 2

Eq. 4

In the case of α≠2, the failure time can be determined by differentiating Eq. 3 by t and solving for the case for 1/v=0.

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d (1 / v) dt

=

1/ v − (α − 1)(t r − t )

Eq. 5

A rough estimation can also be made by the point at which the tangent line of the curve crosses the axis of abscissa. Therefore two graphical methods are presented in Fukuzono (1985). Azimi et al. (1988) proposed a graphical method that is equivalent to the linear case α=2 in Fukuzono (1985) and similar to Saito (1969) (see Fukuzono 1990). Rose and Hungr (2007) successfully applied the inverse velocity method in open pit mines to predict accurately three large failures ranging in size from 1 to 18 million cubic metres. They provide a nice discussion about the limitations of the inverse velocity method and suggest following general rules: •

The method must not be applied in isolation, and displacement monitoring is only one component of a complex process that comprises slope stability management.

•

The method cannot be used for rock slides dominated by brittle failure, and particular care should be exercised when dealing with relatively small failures in strong rock.

•

The monitoring data must be processed to remove the effects if instrument error and eliminate records distorted by local movements.

•

Failure forecasting relies on the identification of consistent trends. The possibility of trend changes, driven by observable or unknown factors, must always be kept in mind. Monitoring must be continued as long as possible prior to failure. The results must be constantly re‐ evaluated and any established best‐fit functions must be revised in view of the latest data.

•

The treatment of cyclic changes depends on the magnitude of their amplitude, relative to the distance from the horizontal axis on the inverse‐velocity plot. If the ratio between these two quantities is high, it may be necessary to assume that the low point of any given cycle may produce sudden rupture.

•

Data fitting using non‐linear inverse‐velocity trend lines may provide a more accurate assessment of some longer‐term trends, but is more complex, which may limit practical use. The authors recommend the use of linear fits, updated on an ongoing basis to identify trend curvature or to signal the onset of trend changes.

Voight (1988) found a material law to predict failure under constant stress and temperature. Beside velocity, other parameters like longitudinal or shear strain, geodetic length, angle change or cumulative seismic (or acoustic) energy release can also be used as input in his model. At any arbitrary time t * , the time to failure ( t f − t * ) can be predicted using following equation: 1−α

t f

− t * =

& Ω *

& −Ω f

1−α

A(α − 1) Eq. 6

where: 12/31


t f :

Time at failure

t * :

Optional time

& Ω * & Ω f α

Displacement rate at t *

: :

Displacement rate at

t f

, A : Constants of experience (commonly

α

= 2 ± 0.3)

Voight considers an estimated displacement rate at failure. Practical estimates are made under consideration of slope geometry, materials and groundwater conditions. Failure occurs when the

& is reached; this is not precisely the point of intersection with the time inverse rate value of Ω f abscissa in Fukuzono (1985). In contrast, Fukuzono assumes the displacement at failure to be infinite. Therewith, the predicted time to failure using Voigths equation is shorter and more conservative. Voights model is more ‘‘physically based’’ with respect to the empirical models proposed by Saito (1965, 1969) and provides, in suitable conditions, a very good forecast of time to failure. As a drawback, the model is only applicable to data characterised by continuous acceleration and constant external conditions. The model fails, or becomes less accurate, when external conditions are not time invariant and deviations induced by seasonal variations in temperature and rainfall regime take place (Crosta and Agliardi 2002). Kawamura (1985) presented a methodology, not only to predict when a landslide occurs, but also to characterize the type of movement. The technique indicates whether a moving slope is going to fail or reaching a stable condition. Another attempt to survey an unstable slope is to define alert velocity thresholds. Crosta and Agliardi (2002) proposed a method to obtain alert velocity thresholds for large rockslides. The method is based on Voights (1988) accelerating creep theory, where failure is assumed to occur at the time corresponding to a particular rate of displacement. Velocity thresholds can be obtained and incorporated in an emergency concept. Like (Voight 1988), this method is only applicable to cases with invariant external conditions. Xiaoping et al. (1996) proposed a very interesting approach. Instead of performing a classical stability analysis with force or moment equilibrium, their method analyzes the deformation power required for a slope failure. The deformation power of a landslide at any time is never larger than at failure state, the so called failure power. Unfortunately, the authors do not give detailed information on how to obtain the failure power threshold. Nevertheless, their concept can be summarized as follows: 1. Arrange the geological survey reasonably to master the geological background and the environmental conditions for a landslide. 2. Monitor systematically with various measures to obtain the real and feasible data for the landslide deformation, abstract the valid information to control the law of active deformation for a landslide. 13/31


3. Calculate the deformation power and the failure power of a landslide, and check the relative level between them. Meantime, consider the acoustic emission reaction state and

the macro traces of landslide deformation to justify the deformation phases for a landslide comprehensively. 4. During the monitoring of landslide deformations, either the monitoring network or the monitoring line consists of monitoring points, which are able to react and feel the deformation and active principle for the local masses of a landslide respectively. Therefore, it is necessary to analyse individual points by using the regressing method based on their own deformation information. 5. Because of the local active traits in the individual points, the results from analyzing individual points might be divergent and do not represent the deformation state of the whole landslide. So the individual measurements have to be adapted to calculate and determine the failure time of the whole landslide. 6. According to the two methods presented in the paper “analysing with individual points” and “predicting with all points” failure time can be forecasted using polynomial regression.

A large landslide along Yellow River in Gansu Province of China occurred on 30 January 1995. Using the approach of Xiaoping et al. (1996), the landslide was predicted with only one day between forecast and effective failure.

TDR based deformation monitoring Time Domain Reflectometry TDR was originally developed during the 1950 to locate and identify cable faults in the telecommunication and power industries. Later on, the application was extended to measurements in geomaterials and is now well recognized for soil moisture measurement in soils. With few modifications TDR can also be used for the monitoring of localized deformation in rock and soils. The comparably low installation costs and the possibility to perform continuous measurements make TDR an interesting alternative to inclinometers. TDR is capable of determining the exact depth of the observed deformation zone. Some semi‐quantitative statement can be made of the amount of movement. The orientation can not be determined at all. A landslide warning system based on TDR deformation detection is not known so far. Nevertheless, a few promising examples of soil deformation monitoring, performed by the California Department of Transportation (CALTRANS), are presented in O’Connor and Dowding (1999). Thuro et al. (2007) are currently developing a Landslide Warning System and make use of TDR for deformation detection.

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Fig. 5 Basic setup of a TDR measuring site. The coaxial cable is installed into an instable slope and connected to the TDR cable tester. As soon as the coaxial cable is deformed by the mass movement a peak can be been in the reflected signal. Its amplitude is dependant to the amount of deformation taking place (Thuro et al. 2007)

Pore‐water pressure based forecasts Beside deformations, pore‐water pressures can be very helpful parameters to monitor unstable slopes. Yagi and others carried out several laboratory experiments. For sandy slopes they’ve found measuring pore‐water pressures to be more effective, than monitoring deformation or strain of the slope surface (Yagi et al. 1985) (Yagi and Yatabe 1987).

Water content based forecasts In order to develop a predictive warning system for rainfall‐induced slope failures, a number of studies have focused on monitoring the pore‐water pressures. However, present day sensors like tensiometers and piezometers have some limitations due to their maintenance, performance and reliability. Pore‐water pressure changes in unsaturated soils go hand in hand with changes in water content (Soil‐Water Characteristic Curve). Tohari et al. (2007) carried out several laboratory tests and found monitoring of moisture content in the soil to be very useful for prediction of slope failures. Moister content measuring devices like, ThetaProbe, TDR waveguides and others overcome the disadvantages of pore‐water pressure sensors. Tohari et al. (2007) proposed a warning algorithm based on volumetric water content measurements (Fig 6).

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Fig. 6 The conceptual prediction methodology for rainfall induced slope failures based on moisture content measurements (Tohari et al. 2007)

1. It is crucial to understand the failure mechanism in order to install an appropriate monitoring system. The use of a numerical analysis of saturated–unsaturated flow can help to find the most appropriate monitoring locations to monitor the development of the seepage area, which are often close to the face of the slope. 2. Prior installation, it is necessary to determine the in situ soil porosity at each monitoring point in order to calculate the values of saturated volumetric moisture content, which are required to determine the degree of saturation. 3. The critical time for evacuation can be defined as the time required for the wetting front to travel from the sensor head to the impervious layer or initial groundwater level t1 to t2. 4. The initiation of the second stage of increase in the moisture content of the near surface soil represents the hydrologic condition in which the groundwater table starts to rise towards the slope surface. 5. Thus, the corresponding time t2 can be designated to trigger a final warning against the slope failure hazard. The extent of the time for evacuation will depend on the depth of the sensor, with respect to the impervious layer or initial groundwater, as well as on the antecedent soil moisture condition. Consequently, the depth of the impermeable layer or groundwater level must also be determined through adequate subsurface investigations to determine an optimum depth for installation of the sensor for the effective prediction of failure initiation.

Micro seismic based forecasts When soil is stressed, it responds with reorganizing its particles and consequently friction generates micro seismic stress waves. This has the advantage to make pre‐movement stresses detectable. Soil particles in close contact to a wave guide generate an elastic wave, the so called acoustic emission AE, which can be detected by high sensitivity sensors. AE have also been called microseismics, microsonics, and subaudible rock noise (SARN). These phenomena have been defined as "the transient elastic waves generated by the rapid release of energy within a material. Jurich and Miller (1987) successfully conducted a study that demonstrated the ability of a properly installed AE monitoring system to detect pre‐movement stresses in soil slopes. Increases in AE activity were recorded at least 30 days in advance of movement. Therefore AE can be used as an 16/31


early warning system. The location of AE observation stations at a study site is critical for recording representative data because acoustic emissions monitoring systems are extremely sensitive. A typical acoustic emission system consists of a backfilled wave guide in a borehole with a transducer fixed to the free end, amplifiers, data acquisition and processing equipment (Fig. 7). Fine grained soils (i.e. clay and silt) generate low AE. Even coarse grained soils that are considered as relatively noisy, have a very high attenuation due to high energy loss as AE is transmitted from one particle to another.

Transducer Pre-amplifier Filter Amplifier

Wave guide

Backfill

Cable

Borehole Slip plane

Data capture and processing

Fig. 7 Typical devices of an acoustic emission system. After (Kousteni et al. 1999)

Depending on the backfill material two possible systems can be formed: Passive systems have to be backfilled with low AE activity material (i.e. clay) so the installation does not introduce any additional sources of AE into the waveguide. Active wave‐guide systems are installed when the monitoring site consists of cohesive material. The borehole is then filed with granular material which produces high AE. Acoustic emission wave guides in dry sands work in a range of 500Hz up to 16kHz (Kousteni et al 1999). Dixon and Spriggs (2007) developed a real‐time slope monitoring system using AE to quantify pre‐ failure slope deformation rates. Beside these buried systems there are surface micro seismic monitoring systems in use to detect AE in rock slopes with geophones. Movements in the slope are located by the geophones in a frequency spectrum of about 10‐650Hz (Stewart et al. 2004).

Forecasts based on climatic conditions Rainfall threshold based forecasts Compared with other methods, rainfall can be monitored with relatively low costs and therefore rainfall thresholds are a cost effective method to forecast landslides. Guzzetti et al. 2005 presented a worldwide literature review of landslide triggering rainfall thresholds. They launched an informative homepage where the thresholds can be viewed (web Guzzetti). Following their work is summarized: Landslide‐triggering rainfall thresholds separate events that resulted, from those which failed to trigger landslides. First of all it must be stated, that rainfall threshold based forecasts only make sense where rainfall is a triggering factor. Most of the proposed thresholds perform reasonably well in the region where they were developed, and cannot be exported to neighboring areas. Also, their 17/31


temporal accuracy remains largely untested. Below different rainfall thresholds are classified and some comments are given: •

Empirical thresholds are obtained studying rainfall conditions which resulted in slope

failures. The main advantage of empirical rainfall thresholds lays in the fact that rainfall is relatively simple and inexpensive to measure over large areas. •

Physically based thresholds attempt to link regional or local rainfall measurements to local

terrain characteristics. For example the time needed for the wetting front to reach the slip surface, is related to the depth of the slip surface and the permeability. Physically based thresholds are calibrated using rainfall events for which rainfall measurements and the location and time of slope failures are known. •

Intensity‐duration thresholds might be very helpful, but have also some disadvantages. They

do not consider the antecedent moisture conditions. For this reason, they are less suited to predict the occurrence of deep‐seated landslides, or of slope failures triggered by low‐ intensity rainfall events. Further, intensity‐duration thresholds do not consider that landslides can occur several hours after the end of the rainfall event, and do not take into account site specific rainfall conditions. •

Normalized intensity‐duration thresholds implicitly consider climatic characteristic of the

area for which they are defined, and emphasise the regionalization of the thresholds, since the calculation takes into account the climatic regimes of the study area. •

Thresholds considering the antecedent rainfall conditions are mostly suited for deep‐seated

landslides. The antecedent rainfall condition thresholds require data of lower resolution (daily rainfall data) which are available for longer periods (up to 120 years in Italy). The forecasting phase of meteorological forecasts has improved considerably in recent years. Thanks to improving weather forecasts, landslide‐triggering rainfall thresholds will play a more significant role in disaster management and become an important tool fore decision makers.

Landslide Warning Systems based on Rainfall Thresholds In a few places of the world rainfall thresholds are part of operational landslide warning systems, in which real‐time rainfall measurements are compared with established thresholds, and when pre‐ established values are exceeded alarm messages are issued. San Francisco Bay region, California

A real time system for issuing warning of landslides during major storms was developed for the San Francisco Bay region, California (Keefer et al., 1987). The warnings issued by the system accurately predicted the times of actual debris flow events during storms and were used by local governments as a basis for planning emergency response and for recommending temporary evacuation of hazardous areas. The warning system was based upon empirical and analytical relations between rainfall and landslide initiation, real time regional monitoring of rainfall data from telemeter rain gauges, National Weather Service precipitation forecasts, and delineation of debris flow hazard areas. Hong Kong

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Landslides in Hong Kong are heavily dependent on the magnitude of the short period rainfall intensity, with a threshold value of about 70 mm/hr. This relationship was adopted as the basis of the Government’s Landslide Warning System that has been in operation since 1984. Japan

The occurrence of debris flows in Japan is closely related to rainfall characteristics, such as the amount, intensity and duration. To predict the standard amount of rainfall for warning and evacuation from debris disaster, a model for the occurrence of debris flow was developed, based on the infiltration process to surface flow. An information system for warning and evacuation was necessary between governmental offices and inhabitants. This system was organized into three sections: observation, analyses and management. Rainfall was measured by rain gauges and the information to a local station where it was analyzed using the standard rainfall programs based on the static slope stability model. To improve the accuracy of rainfall prediction, combinations of radar rain gauge systems (MP‐X) and telemetering were used in order to analyze data in real‐time. Landslide disaster prediction support system LAPSUS (Fukuzono et al.) is a software used by the National Research Institute for Earth Science and Disaster Prevention (NIED) in Japan to provide information about the potential for shallow‐landsides and the evaluation of landslide risk. The information is open to the public. So far this is the one most sophisticated landslide prediction system found in this literature review. Yangtze River, China

A regional warning system to monitor landslides and mudslides was built up and extended along the upper reaches of the Yangtze River in China. The system was set up in 1991 to monitor landslides by using 70 stations and employing over 300 professionals. The network protected a population of 300,000 people and had forecasted 217 landslides avoiding estimated economic losses of US$ 27 million. Rio de Janeiro, Brazil

1996 the Rio de Janeiro Geotechnical Engineering Office (GeoRio) installed a system for warning against landslides triggered by severe rainfall. The watch system was based on landslide‐rainfall correlation and depended on confirmation from three sources of information: (i) a short term (4‐ hour) weather forecast, obtained by meteorological radars, (ii) an automated rain gauge network of 30 rain gauges, and (iii) records of failures reported by the Civil Defence Brigade. Alarms were issued when: (i) the rain gauge network software indicated that the hourly or daily rainfall alarm level were reached at least in three rain gauges, and the weather forecast predicted rain in the successive hours, and (ii) when the hourly or daily rainfall thresholds levels were reached in three rain gauges, and the short term weather report indicated that heavy rains was expected. Once these conditions were reached, GeoRio contacted the Civil Defence Board of the Rio Government to assess the situation and implement action (Ortigao 2000) (Ortigao et al.).

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State‐of ‐the‐art and future trends Despite remarkable progress, substantial research deficits still exist in the development and use of early warning systems for landslides. Therefore, future research and development projects aim on advancement of early warning capacities by improvement of scientific bases, development of new technologies and prototypical implementation of early warning systems (Baehr 2004).

Measuring devices Nowadays and in future satellites play an important role in landslide assessments. New techniques like differential Global Positioning System (dGPS) improved the accuracy of displacement monitoring up to a few millimeters. Aguado et al. (2006) designed a Low‐Cost, Low‐Power Galileo/GPS Positioning System for monitoring landslides. Their research focuses mainly on electro technical and software specific concerns. The system has an accuracy of horizontally ± (5 mm + 0.5 ppm × baseline length) RMS and vertically ± (5 mm + 1 ppm × baseline length) RMS (TBC). Unfortunately, no detailed field application is mentioned so far. Synthetic Aperture Radar (SAR) are usually space‐borne instruments that emit electromagnetic radiation and then record the strength and time delay of the returning signal to produce images of the ground. The elevation of the ground can be estimated using two antennas and applying interferometry (InSAR). Consecutive couples of SAR images can be cross‐correlated to form interferograms representing phase variations which can be directly related to ground displacement within millimeter‐level accuracy. Clear weather is necessary to operate space‐borne SAR. Therefore, Antonello et al. (2004) developed a ground‐based InSAR system, called LISA, and applied it successfully to monitor unstable slopes. Light Detection and Ranging (LIDAR) is an analog method to Radiowave Detection and Ranging (Radar). Similar to the radar technology, light (laser pulses) are used instead of radiowaves, the range to an object is determined by measuring the time delay between transmission of a pulse and detection of the reflected signal. LIDAR can also be used to monitor displacements of instable slopes. Beside these new methods to observe deformations, other measuring techniques like micro seismics have the potential to be used as indicators for landslide warning systems.

Multi ‐ parameter based forecasts The future trend for warning systems goes to multi‐parameter based forecasts. The combination of a few independent parameters has the advantage to make predictions more reliable. The Turtle Mountain Warning System consists of many different measuring devices like tiltmeters, surface wire extensometers, differential GPS hubs, series of prisms with a theodolite set up at the Frank Slide Interpretive Centre (FSIC), crack gauges, surface microseismic sensors, downhole microseismic sensors, a borehole thermistor string and vibrating wire piezometer, a meteorological station and Synthetic Aperture Radar Interferometry (InSAR). Thresholds have been defined for the monitoring devices. Specific values that are typically set two to three standard deviations above the instrument noise and/or known seasonal thermal fluctuations of the sensors and rock mass. As ongoing movement occurs, it may be necessary to reset the absolute thresholds. According to the observations, three alert levels were defined and an emergency plan is in use (Froese et al. 2005, 2006, Read et al. 2005). 20/31


Baum et al. (2005) proposed a multi‐parameter based landslide warning system for the rail traffic between Seattle and Everett in Washington USA. The system is based on continuous monitoring of precipitation, soil water content and pore pressure, combined with an empirical rainfall threshold. Three levels of warning, with successively shorter lead times were projected. 

“Advisory” is the lowest level of warning and given, if field observations indicate a high degree of soil saturation (>60‐80%), and therewith the material is wet enough to be susceptible to landslide activity. In the event of prolonged, intense rainfall, shallow landslides are likely during this lowest level of warning.



“Watch” is given for an "Advisory" combined with a forecast of rainfall that is sufficient to exceed the empirical intensity‐duration threshold.



“Warning” is the highest level of warning, given in case the near real‐time field data indicate that actual rainfall conditions are approaching the threshold and that soil wetness and pressure head are high, so that landslides are likely at any time.

Other multi‐parameter based warning systems are in operation at the south and east coast of England and described in Clark et al. (1996).

Wireless Sensor Networks WSN Many of the warning systems described above are costly. There is a strong need for mobile, low‐cost equipment that is quickly ready for use. A combination of different low cost sensors and monitoring methods allows making warning systems affordable. Wireless Sensor Networks WSN match these requirements quite well. WSN consist of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations. In addition to one or more sensors, each node in a sensor network is typically equipped with a radio transceiver or other wireless communications device, a small microcontroller, and an energy source, usually a battery. The envisaged size of a single sensor node can vary from shoebox‐ sized nodes down to the size of grain of dust, although functioning 'motes' of genuine microscopic dimensions have yet to be created. The cost of sensor nodes is similarly variable, ranging from hundreds of dollars to a few cents, depending on the size of the sensor network and the complexity required of individual sensor nodes. A sensor network normally constitutes a wireless ad‐hoc network, meaning that each sensor supports a multi‐hop routing algorithm (several nodes may forward data packets to the base station) (web wiki 2).

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Fig. 8 Schema of a Wireless Sensor based Landslide Early Warning System (Arnhardt et al. 2007)

SLEWS The SLEWS project (Arnhardt et al. 2007) investigates the complete information chain, starting from data gathering using wireless sensor networks via information processing and analysis to information retrieval. This is demonstrated for landslides and mass movements. The proposed approach pays special attention to mobile, cost‐reduced and easy deployable measurement systems, as well as the modern information systems under consideration of interoperability and service orientated architecture concepts. The SLEWS project is still in the stage of development.

Senslide Senslide is a low cost Landslide Prediction System based on Wireless Sensor Network WSN technique (Sheth et al.2007). The system consists of single‐axis strain gauges connected to cheap nodes, each with a CPU, battery and a wireless transmitter. Measurements are propagated to a set of “base stations”. Sensors (600 to 900 sensors/km2) make point measurements at various parts of the rock, but make no attempt at measuring the relative motion between rocks. Laboratory tests on rock samples from the field site show linear stress‐strain behaviour of the rock until there is fracture. Based on this material relationship two prediction algorithms were developed (threshold based prediction and distributed statistical detection algorithm). So far Senslide was tested in laboratory environment and field applications are planned.

Wireless Sensor Columns coupled with FEM Model Terzis et al. (2006) proposed a network of sensor columns deployed at hills with landslide potential with the purpose of detecting the early signals preceding a catastrophic event. Each column includes four types of sensors: geophones, strain gages, pore pressure transducers and reflectometers as well as seismic sources placed at regular intervals over the length of the column.

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Fig. 9 The Landslide Warning System is based on measurements in the sensor columns. Terzis et al. (2006)

Detection is performed through a three‐stage algorithm: First, sensors collectively detect small movements consistent with the formation of a slip surface separating the sliding part of hill from the static one. Once the sensors agree on the presence of such a surface, they conduct a distributed voting algorithm to separate the subset of sensors that moved from the static ones. In the second phase, moved sensors self ‐localize through a trilateration mechanism and their displacements are calculated. Finally, the directions of the displacements, as well as the locations of the moved nodes are used to estimate the position of the slip surface. This information along with collected soil measurements (e.g. soil‐pore pressures) are subsequently passed to a Finite Element Model that predicts whether and when a landslide will occur. Initial results from simulated landslides indicate that accuracy in the order of cm in the localization as well as the slip surface estimation seems to be possible. In a next step, the system will be tested on a variety of hill configurations.

alpEWAS Thuro et al. (2007) are currently developing an integrative 3D early warning system for alpine instable slopes (alpEWAS). Surface movements will be detected punctiform and highly precise with the Global Navigation Satellite System (GNSS) as well as extensively in a large part of the landslide area through reflector less tacheometry. The final aim of reflector less tachoemetry is to develop a system, able to autonomously find and select suitable natural targets for deformation monitoring. Movements in the depth alongside boreholes will be detected by using newly adapted TDR. Hydrostatic pore pressures and climatic conditions will be measured as well. The researchers expect a relatively short time (6 to 9 months) to be necessary for calibration and definition of critical thresholds. Wireless Local Area Network WLAN is used for communication.

ILEWS The research project Integrative Landslides Early Warning Systems (ILEWS) aims to develop a system to predict landslides and debris flows (Glade et al. 2007). The project is split into subprojects, wherefrom a few aspects are indicated here. Measurements are taken using several different sensors 23/31


in a wireless sensor network (web ScatterWeb). Geoelectrical survey systems are applied to examine the landslide body. In order to make predictions, the monitoring data are fed into three different models: 

The Movement Analysis Model performs trend analysis of reciprocal landslide movement rates (inclinometer, inclinometer chain, tachymetry, GPS). Depending on the way how the movement rates change, the catastrophic failure of a slope can be predicted. For example, linear trends give an early indication of time to a catastrophic event.



The continuously measured field parameters are modeled in a permanent slope stability analysis in the “Near Real‐Time” Physically based Model. For example soil moisture and rainfall will be integrated in equations to calculate slope stability. If the factor of safety gets lower than a specified threshold value, preliminary warnings will are provided through WebGIS and SMS are released.



In the Real‐Time Model for Debris Flows forecasts are made under consideration of weather forecasts.

Data will be visualized in a web based geographical information system WebGIS.

RockNet An interdisciplinary team of geotechnical and electrical engineers at HSR University of Applied Science in Rapperswil, Switzerland developed a rockfall detection system called RockNet. A self ‐ organizing wireless sensor network is used for rockfall surveillance and provides real‐time warning. Wireless sensor nodes are equipped with accelerometers, temperature and other sensors. The nodes are distributed in a rockfall prone area. If a specified number of nodes recognize vibrations an alert is released and for traffic can be stopped outside the danger zone.

Wireless Underground Sensor Networks WUSN Akyildiz and Stuntebeck (2006) presented a good overview of Wireless Underground Sensor Networks WUSN. The current research challenges are addressed and examples of applications of landslide monitoring (soil movement) and mining (high –sensitivity microphone for rescue purposes). WUSN have a potential to be used for landslide monitoring in the future. WUSN are buried and therewith solutions for power conservation, charging methods as well as communication between nodes have to be found first.

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Conclusion and future needs This extensive literature review leads to the following main conclusions: 

There is a lack of appropriate warning systems. More warning system prototypes have to be developed and implemented in practice.



In the future, landslide warning systems will play an important role to avoid loss and damages.



Several attempts have been made to predict landslides by monitoring different parameters. These methods are summarized in this review. The inverse velocity method (Fukuzono 1985, Rose and Hungr 2007) has proven to be applicable in practice and is a relative simple approach to predict time to failure.



There is a trend towards multi‐parameter based warning forecasts. The combination of a few independent indicators has the advantage to make more reliable predictions.



Wireless Sensor Networks are expected to be the technological innovation towards low‐cost landslide warning systems.



Landslides are very complex. In order to make forecasts, a careful assessment of the specific site is essential. The selection of appropriate monitoring parameters is crucial for a reliable forecast. Appropriate parameters are site‐ and trigger‐specific. The selection has to be done by geological and geotechnical experts.

Role of Geotechnical Experts Landslides are a very complex phenomenon and controlled by many variable site specific conditions. This makes it impossible to have an overall technique to predict landslides. Therefore, warning systems have to be adapted to local conditions. Geotechnical experts are required to select the appropriate indicators. To make the systems applicable to different sites, the sensors should be scalable and alerting algorithms have to be adjustable.

Selection of appropriate monitoring parameter Deformation measurements as indicator for an emerging landslide can be very useful. However, they require knowledge of the deformation behavior of the material. Depending on the material properties (e.g. rock or soil, pore‐pressures, water content, permeability, temperature…), deformations for a given stress state can vary by several magnitudes. Soils with a plastic behavior can be conveniently surveyed by displacement observation. Stiff materials experience relatively small deformations compared to the load. According deformation measurements are challenging. Special attention has to be paid to brittle materials, where deformation prior fracture is very small. For brittle materials other indicators (e.g. pore‐water pressure, water content, acoustic emission…) might be more useful. Intensive rainfall has a more significant influence near the slope surface. Monitoring rainfall for landslide prediction makes often more sense, if the expected landslide is shallow rather than for deep seated slip surfaces. Nevertheless, karsts can act as a direct link between surface conditions and deeper layers.

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Next steps towards reliable forecasts A major outcome of the International Decade for Natural Disaster Reduction was the recommendation of early warning systems for loss reduction. As mentioned in the Report on Early Warning Capabilities for Geological Hazards of the United Nations (Hamilton 1997) and reiterated in the UN Global Survey of Early Warning Systems (UN 2006), there is still a lack of appropriate warning systems for landslides. Warning systems should be affordable, especially for third world countries who suffer the most under these hazards. Until today, such systems do not exist (Baehr 2004). Several attempts have been made to predict landslides. Currently research teams develop complete landslide warning systems from measurement to alarm release. Many attempts make use of Wireless Sensor Networks WSN (Arnhardt et al. 2007, Sheth et al.2007, Terzis et al. 2006, Thuro et al. 2007, Glade et al. 2007). Most of these systems are still under development and need to be tested in the field. Nevertheless, there is a strong need in implementation and building more warning system prototypes in different parts of the world to make landslide hazards predictable and help us to live with natural hazards.

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