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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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
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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
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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
