Prepared in cooperation with the U.S. Department of Transportation, 

Pipeline Research Council International, and DGH Consulting Inc. 

Landslide and Land Subsidence Hazards to 

Pipelines 

By Rex L. Baum, Devin L. Galloway, and Edwin L. Harp 

Open-File Report 2008–1164 

U.S. Department of the Interior 

U.S. Geological Survey 
COVER NOTE. Natural gas pipeline crossing an active landslide in Archuleta County, 

Colorado. The 10-cm diameter, flexible steel pipeline is a temporary replacement for a 

20-cm diameter pipeline that ruptured on May 2, 2008, when the landslide reactivated as 

a result of snowmelt. 

ii
Landslide and Land Subsidence Hazards to 

Pipelines 

By Rex L. Baum, Devin L. Galloway, and Edwin L. Harp 

Prepared in cooperation with the U.S. Department of Transportation, Pipeline Research Council 

International, and DGH Consulting Inc. 

This report consists of three chapters: 

1. Landslide hazards to pipelines—Regional hazard mapping 

2. Land subsidence hazards 

3. Landslide investigation methods 

Open-File Report 2008–1164 

U.S. Department of the Interior 

U.S. Geological Survey 

iii
U.S. Department of the Interior 

DIRK KEMPTHORNE, Secretary 

U.S. Geological Survey 

Mark D. Myers, Director 

U.S. Geological Survey, Reston, Virginia: 2008 

For product and ordering information: 

World Wide Web: http://www.usgs.gov/pubprod 

Telephone: 1-888-ASK-USGS 

For more information on the USGS—the Federal source for science about the Earth, 

its natural and living resources, natural hazards, and the environment: 

World Wide Web: http://www.usgs.gov 

Telephone: 1-888-ASK-USGS 

Suggested citation: 

Baum, R.L., Galloway, D.L., and Harp, E.L., 2008, Landslide and land subsidence 

hazards to pipelines: U.S. Geological Survey Open-File Report 2008-1164, 192 p. 

Any use of trade, product, or firm names is for descriptive purposes only and does not 

imply endorsement by the U.S. Government. 

Although this report is in the public domain, permission must be secured from the 

individual copyright owners to reproduce any copyrighted material contained within this 

report. 

iv
Contents 

Chapter 1. Landslide hazards to pipelines—Regional hazard mapping, by E.L. Harp.............. vii 

Chapter 2. Land subsidence hazards, by D.L. Galloway, G.W. Bawden, S.A. Leake, 

and D.G. Honegger ...............................................................................................33 

Chapter 3. Landslide investigation methods, by R.L. Baum and G.F. Wieczorek...................107 

v
vi
Prepared in cooperation with the U.S. Department of Transportation, 

Pipeline Research Council International, and DGH Consulting Inc. 

Landslide Hazards to Pipelines–Regional 

Hazard Mapping 

By Edwin L. Harp 

Chapter 1 of 

Landslide and Land Subsidence Hazards to Pipelines 

Open-File Report 2008–1164 

U.S. Department of the Interior 

U.S. Geological Survey 

vii
Contents 

Contents...... ..............................................................................................................................viii

Abstract........................................................................................................................................1

Introduction...................................................................................................................................1

Regional Landslide Hazard Analysis.............................................................................................2

Documentation...........................................................................................................................2

Methods of Analysis...................................................................................................................2

Qualitative Methods ................................................................................................................3

Geomorphic Analysis ...........................................................................................................3

Weighted Parameter Analysis..............................................................................................3

Quantitative Methods ..............................................................................................................4

Statistical Analyses ..............................................................................................................4

Geotechnical Models............................................................................................................4

Shallow Landslide Analyses..............................................................................................5

Deep-Seated Landslide Analyses .....................................................................................7

Utility of the Various Susceptibility or Hazard Assessment Methods .........................................9

Qualitative Methods ................................................................................................................9

Quantitative Methods ..............................................................................................................9

Statistical..............................................................................................................................9

Deterministic/Probabilistic....................................................................................................9

Estimation of Displacement or Runout.....................................................................................10

References Cited ........................................................................................................................12

Glossary of Terms.......................................................................................................................16

Figures 

1A. Deep-seated rotational slump in San Benito County, California, that has broken a 

natural gas pipeline.....................................................................................................................17

1B. Toe of landslide in figure 1A showing broken pipeline (brown-colored pipe) and the 

temporary replacement. ..............................................................................................................18

2. Portion of landslide inventory map for landslides triggered by the 1994 Northridge, 

California, earthquake.................................................................................................................19

3. Map of Anchorage, Alaska, depicting qualitative relative seismic susceptibility based 

on judgment of authors. ..............................................................................................................20

4. Rock-fall hazard map of Little Mill Campground, American Fork Canyon, Utah.....................21

5. Landslide susceptibility map produced from ratio grid based on elevation and slope. ...........22

6. Map of probability of debris flow occurrence for basins burned by the Hot Creek Fire, 

Idaho, in response to a 1-hour, 10-year recurrence storm. .........................................................23

7A. Shaded relief map of Seattle, Washington, study area and location of area shown in 

figure 7B......................................................................................................................................24

7B. Shallow landslide hazard map of portion of Seattle, Washington. .......................................25

8A. Location of metropolitan area of Tegucigalpa, Honduras. ...................................................26

viii
8B. Inventory of landslides triggered by Hurricane Mitch in Tegucigalpa, Honduras .................27

8C. Landslide hazard map for central part of Tegucigalpa, Honduras. ......................................28

9A. Location maps of Chuuk State in the Federated States of Micronesia ................................29

9B. Map of Chuuk islands within Chuuk (Truk) Lagoon .............................................................30

9C. Debris-flow hazard map of islands of Tonoas and Etten based on landslides 

triggered by Typhoon Chata’an...................................................................................................31

Tables 

Table 1-1. Advantages and disadvantages of different methods of landslide hazard 

assessment.................................................................................................................................11

ix
Conversion Factors 

Inch/Pound to SI 

Multiply By To obtain 

Length 

foot (ft) 0.3048 meter (m) 

mile (mi) 1.609 kilometer (km) 

Volume 

cubic foot (ft3) 0.02832 cubic meter (m3) 

cubic yard (yd3) 0.7646 cubic meter (m3) 

Hydraulic conductivity 

foot per second (ft/s) 0.3048 meter per second (m/s) 

SI to Inch/Pound 

Multiply By To obtain 

Length 

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi) 

Volume 

cubic meter (m3) 35.31 cubic foot (ft3) 

cubic meter (m3) 1.308 cubic yard (yd3) 

Hydraulic conductivity 

meter per second (m/s) 3.281 foot per second (ft/s) 

x
Landslide Hazards to Pipelines–Regional 

Hazard Mapping 

By Edwin L. Harp 

Abstract 

Because of the long, linear nature of pipeline corridors, they often cross areas that are 

highly susceptible to landslides. Techniques to assess the hazard posed by the presence of 

landslides can be applied to these corridors provided certain minimum data requirements, such as 

slope and material properties, can be met within these areas. The level and sophistication of maps 

designed to portray landslide hazard or attributes that relate to landslide hazard vary widely. They 

range from maps that depict the qualitative judgment of the geologist, technician, or engineer to 

maps that display the percent chance (probability) of a landslide occurring in the future with respect 

to time, area, or both. Quantitative methods of depicting probability of landslides vary from the 

purely statistical correlation of various factors to physical process-based models that attempt to 

replicate the failure and, in some cases, the movement of slope materials. Depending on the type of 

landslides being considered, the type of analysis employed, and the accuracy of the data available, 

landslide-hazard maps are subject to considerable variation in their uncertainty. Due to the fact that 

datasets of the variables required for hazard assessments are often incomplete and include 

approximations, it is often difficult to quantify the uncertainty that exists in estimates of hazard 

levels. 

Introduction 

In addition to earthquake shaking, ground displacement, construction disturbance, ground 

settlement, and movement due to freeze and thaw processes, pipelines often are threatened by 

impact and displacement from landslides. Landslides that can affect pipeline corridors vary widely 

in type and in size. Landslide material ranging from bedrock with high intact compressive and 

shear strengths to soil with low cohesion, and failures ranging from a few cubic meters to millions 

of cubic meters volume can and have occurred in pipeline corridors throughout the world (Schuster 

and others, 1998; Porter and others, 2006; Çevik and Topal, 2003). Much of the hazard to pipelines 

from landslides derives from the long, linear nature of the corridors. With widths of up to one 

kilometer, pipeline corridors extend great distances through topography with wide varieties of 

susceptibility to landslides. Almost every pipeline that traverses areas in mountainous terrain has 

some vulnerability to landslide hazards (fig. 1-1). 

Fortunately, not all cases of high landslide susceptibility translate into high hazard to the 

pipeline. Except for some surface pipelines, most pipelines are buried to depths of about one 

meter, only those landslides that penetrate to those depths, or induce damaging stresses at those 

depths, will pose a real hazard to the pipe. Therefore, those failures that tend to be shallow and 

non-eroding to the surrounding terrain pose little hazard to pipelines. In fact, a large portion of 

commonly occurring landslides (falls and slides in soil and rock and debris flows) that tend to be 

triggered in great numbers in seismic or extreme precipitation events are negligible threats to 

1
pipelines except in cases where (1) large enough rock fragments from rock falls and rock slides 

exist to penetrate or transmit damaging stresses to pipeline-burial depths, (2) established rock-fall 

and rock-slide chutes erode to such depths, or (3) debris flows commonly scour deep enough to 

affect the pipeline integrity. Otherwise the problem of defining landslide hazards to pipelines 

reduces to documenting and analyzing the hazard posed by the various types of deep-seated 

landslides, that is landslides that penetrate to pipeline-burial depths. 

It also follows that there is a size, or volume limit of landslides that would result in damage 

to a pipeline. Those landslide masses that are below a certain limit may not generate sufficient 

stress upon failure to deform a pipeline to rupture. Such landslide volume limits would depend 

upon the size and strength of the particular pipe used in a given corridor. 

Regional Landslide Hazard Analysis 

The common types of landslides whose distributions can be estimated using regional hazard 

analysis techniques are those whose failure depths are relatively shallow, for example average 

depths of no more than a few meters. These types of failures are falls and slides in rock and soil 

caused by earthquakes and severe precipitation events, and slumps and translational slides in soil 

and weathered bedrock that can form debris flows, usually triggered by intense rainfall 

(infrequently caused by seismic or volcanic events). 

Documentation 

The accurate documentation of landslides is a key element of any analysis. A landslide 

inventory map at scales of 1:24,000 or larger (1:12,000 scale is larger than 1:24,000 scale) is 

necessary to accurately conduct a landslide-hazard analysis using slope, material properties (such 

as shear strength), and hydrologic data as input. Such an inventory is compiled most effectively by 

the combination of field investigation and aerial photography at scales of 1:20,000 or larger (fig. 1-

2). 

Satellite imagery is increasingly available for many parts of the Earth and at larger scales. 

However, most satellite imagery still has a resolution of 30 m or greater. Some of the more recent 

satellites offer resolutions of less than one meter. The larger landslides (those with long 

dimensions of greater than approximately 100 m) could be mapped at the scales of 30-meter 

resolution, but many of the landslides triggered by an event (severe rainfall or earthquake shaking) 

would be too small to be seen at these scales. Even though some imagery is of sufficiently large 

scale to detect failures of one meter or less, satellite imagery is rarely acquired with the necessary 

overlap so that three-dimensional viewing can be achieved. This makes it extremely difficult to 

interpret and map slope failures in their correct (that is within approximately 30 m) locations. In 

remote areas where pipeline corridors are often located, imagery is often sparse or nonexistent. 

Without suitable imagery to construct a representative and accurate landslide inventory, a landslide 

hazard analysis and/or map cannot be constructed. 

Methods of Analysis 

Methods to assess the susceptibility or hazard posed by landslides fall into two general 

categories: qualitative and quantitative. The qualitative methods can be separated into two 

subcategories: (1) those that evolve from field and/or aerial photographic investigations based 

largely on experience and judgement, and (2) those that are determined based on comparisons of 

index factors or weighted parameters, such as slope aspect, vegetation type, geology, or soil type. 

Quantitative methods can also be separated into two general categories: (1) statistical methods and 

2
(2) methods that rely on geotechnical probabilistic models to evaluate susceptibility or hazard 

(Aleotti and Chowdury, 1999). The above categories are extremely broad. A great degree of 

variation exists in both qualitative and quantitative methods, and, in many instances, qualitative and 

quantitative methods have been merged to introduce a significant degree of judgment and intuition 

into numerical categorization. 

Methods that involve the use of neural networks fall into the category of non-physically 

based methods where a computer algorithm compares assigned weights for selected parameters 

with an expert pre-evaluation of the hazard. The comparisons and weight adjustment continue until 

a certain level of convergence is attained (Aleotti and Chowdury, 1999). In a general sense, the 

automated adjustments of neural networks are similar to the adjustments of parameters in statistical 

or deterministic methods made by the investigator to obtain a better fit to the data. 

Qualitative Methods 

Geomorphic Analysis 

The first of the qualitative methods base landslide susceptibility or hazard on implicit 

determinations made by field observation or by use of aerial-based imagery. The assessment of 

susceptibility or hazard is derived from the evaluator’s experience and recognition of 

morphological patterns that are similar to other situations (Aleotti and Chowdury, 1999). Hazard 

criteria are generally implicit rather than explicit (that is, susceptibility or hazard levels are 

specified but not the criteria by which they were assigned), and assessments produced by this 

process, although relatively rapid and including a large number of factors, are difficult to compare 

with those generated by other investigators (fig. 1-3). 

Weighted Parameter Analysis 

A second general type of qualitative analysis is that based on a combination of weighted 

parameter maps. The evaluator selects and maps parameters (such as slope, geology, and drainage 

density) that influence the stability of slopes based on personal experience and assigns a weight to 

each parameter in accordance with its relative contribution to slope failure. The weighted 

parameter maps are overlain on one another to generate the final hazard map. This type of analysis 

has the advantage of specifying the parameters affecting slope instability and their relative 

contributions. It also allows automation of the process with the use of a Geographic Information 

Systems (GIS) platform. However, it still retains subjectivity in establishing relative weights to 

assign to the various parameters. 

An example of a weighted parameter criteria is that used by Harp and Noble (1993) to assign 

numerical scores to various fracture characteristics of rock slopes to assess rock-fall susceptibility 

and to use the criteria as a means to estimate hazard. The method has since been employed to assess 

susceptibility along corridors (Wieczorek and Harp, 2000) and in subregional areas (Harp and 

Jibson, 2002; Coe and others, 2005). Coe and others (2007; 2005) have used the rock-mass-quality 

criteria from Harp and Noble (1993) in combination with other weighted parameters such as distance 

from base of slope and active talus deposits to construct a hazard map for Little Mill Campground 

(U.S. Forest Service) in American Fork Canyon near Provo, Utah (fig. 1-4). 

3
Quantitative Methods 

Statistical Analyses 

The main limitation of the above methods is the subjective weighting of various parameters 

that influence the landslide process. The advantages of statistical techniques are that they allow the 

weights of the different mapped geological and topographical parameters to be determined by 

direct comparison with a landslide map. This allows the determination of landslide density 

captured by each parameter. This process can be accomplished with a bivariate analysis where 

each parameter is compared separately to a landslide distribution or by multivariate analyses where 

the various parameters are evaluated simultaneously to determine coefficients of regression. A 

bivariate analysis by Coe and others (2004) was used to evaluate the influence of terrain parameters 

(slope and elevation) in triggering landslides from Hurricane Mitch (October 1998) in Guatemala. 

On a GIS platform, ratios of slope and elevation for landslide sources versus slope and elevation for 

nonlandslide sources were computed and used to prepare a susceptibility map for the area affected 

(fig. 1-5). After obtaining the numerical ratios (percentages of slope and elevation parameters with 

respect to landslide locations versus percentages of these parameters with respect to the total area), 

numbers from 1 to 5 were assigned to reflect the relative susceptibilities. 

Multivariate analysis methods determine parameter weights or parameter coefficients that 

numerically describe their respective influences on, or correlations with, the landslide distribution 

or density (Gartner and others, 2007; Cannon and others, 2007; DeGraff and others, 2007; fig. 1-6). 

The main advantage of these methods is that the weights or coefficients are now mathematically 

calculated by a multivariate or step-wise regression instead of being assigned by subjective 

judgment or experience. Correlation coefficients are then statistically evaluated to determine the 

degree of significance of each of the various factors considered in the analysis. 

Geotechnical Models 

The deterministic analysis method refers to standard engineering slope-stability analyses 

that are done for specific sites. Physical properties of materials are quantified and serve as input in 

specific mathematical models, and factor of safety (FS) is calculated. FS is the ratio of the forces 

resisting slope movement to the forces driving it. Thus, FS values greater than 1.0 indicate stability 

while those less than 1.0 indicate instability. Therefore, the greater the FS value the more stable the 

slope. These methods are discussed in a later section of this paper. 

Probabilistic geotechnical models refer to models that use standard geotechnical analyses 

that are coupled with a probabilistic evaluation, usually on a GIS platform, to estimate FS or a 

similar index of performance over a regional area. Most geotechnical models adapted to a GIS 

employ some form of probabilistic input in the calculation of FS (El-Ramly and others, 2002; 

Miller and Sias, 1998; Aleotti and Chowdury, 1999; Xie and others, 2004; Franciss, 2004) or in a 

comparison of FS values with a landslide distribution to estimate probability of failure (fig.7; Harp 

and others, 2006; Harp and others, 2004; Harp and others, 2002; Montgomery and Dietrich, 1994; 

Montgomery and others, 2001). 

Models that employ probabilistic input do so for variables in the FS equation for cohesion 

and internal angle of friction (shear-strength parameters), pore pressure or degree of saturation or 

water-table depth, and unit weight of soil or weathered bedrock. The ideal is to have enough 

measured data for these variables to know their respective value ranges and statistical distributions. 

Most often this is not the case, and a Poisson or normal distribution for each of these variables is 

assumed. Sometimes some other distribution (triangular or uniform) is assumed (Aleotti and 

Chowdury, 1999). The output from the FS calculations using randomly selected values for these 

4
variables is a distribution of FS values that serves as a probability density function in terms of FS. 

This function estimates the probability (percent chance) that FS • 1.0. An additional approximation 

for these models includes the geometry of the failure surface (usually a simple shape such as 

planar, spherical, or elliptical). 

The use of probabilistic models usually employs the assumption that FS values are normally 

or lognormally distributed. This allows the estimation of the standard deviation and the coefficient 

of variation of FS or the individual variables that go into the FS calculation. Most engineers have a 

reasonably good idea of average values of parameters such as shear strength, unit weights, and 

other standard geotechnical properties. If a highest and lowest conceivable value (HCV, LCV) of 

these parameters can be reliably estimated, the “Three-Sigma Rule” can then be invoked to 

estimate the standard deviation of the parameter (Duncan, 2000). Since 99.73 percent of all values 

of a normally distributed parameter fall within three standard deviations of the average value, the 

standard deviation can then be estimated by the formula: σ = (HCV-LCV)/6. The coefficient of 

variation (V) is then V = σ/average value of parameter. Duncan (2000) states that although most 

geotechnical engineers are good at estimating average values of parameters, they have a tendency 

to estimate a range of values between HCV and LCV that is too small. (Duncan, 2000, p. 308) also 

comments that “There is no proof that factors of safety are lognormally distributed, but the writer 

believes that it is a reasonable approximation.” 

Models that employ discrete values (point estimates) as input, calculate FS values and 

compare the resulting spatial FS values on a GIS layer with the landslide distribution to determine 

the probability or percent chance of a landslide occurring at a particular FS value. This method is 

used with a landslide inventory map to allow the input variables to be adjusted to obtain the best fit, 

or “back calculation,” with respect to the landslide data. Such a process estimates a spatial 

probability of landslide occurrence or conditional probability that, given the occurrence of a 

triggering event of some level of rainfall or seismic shaking, the model predicts the probabilities of 

landslides for different FS values and then apportions them into different hazard categories (fig 

7B). 

The methods described above are used for both shallow and deep-seated landslides. While 

there is no reason that regional models cannot conceptually describe deep-seated landslide hazard, 

there are several practical reasons (greater uncertainties of depth, geometry, hydrologic factors, 

etc.) why hazard analyses that employ geotechnical models over a regional scale are not nearly as 

reliable on a regional basis as geotechnical models used for shallow landslides. 

Shallow Landslide Analyses 

Numerous similar methods to estimate shallow landslide hazard exist that include the 

hazard posed by falls and slides in rock and soil and the shallow slumps and translational failures 

that form debris flows. All of these methods employ the infinite slope analysis that models slope 

segments as rigid friction blocks that are considered to be infinitely long in all directions. 

There are a number of methods commonly used in GIS analyses to estimate the stability of 

slopes that are divided into grid cells. Many of these methods calculate the FS of each cell. 

SINMAP (Pack and others, 1999) and SHALSTAB (Montgomery and Dietrich, 1994) are two 

similar analyses that predict slope stability using an infinite-slope analysis. SINMAP uses ranges of 

rainfall and material properties expressed as uniform probability distributions. Both models use the 

same equation to calculate the factor of safety (FS) for each grid cell in a GIS layer: 

5
[ ( ) ( ) ]

( ) D

CC DD D FS

s

sr s wwsw

γαα

γα φγγ

cossin

cos tan 2 ++ −+− = (1) 

Where Cr and Cs

 are root strength and soil cohesion, respectively, D is the vertical soil-depth 

thickness, Dw is the vertical thickness of the saturated layer, and γ is the unit weight of soil (s) and 

water (w). Both methods assume ground-water flow parallel to existing slopes. The variables α

and φ are the slope and friction angles, respectively. 

Level I Stability Analysis (LISA; Hammond and others, 1992) is another infinite slope 

analysis developed by the USDA Forest Service that calculates a probability for failure of slope 

cells from different combinations of variables within the infinite-slope equation, each with their 

own probability distribution. This analysis was one of several models used by Morrissey and 

others (2001) to predict debris flows in Madison County, Virginia. This model uses essentially the 

same equation for factor-of-safety as SINMAP and SHALSTAB except that a term for tree 

surcharge (weight) is introduced. 

Yet another method is Iverson’s transient-response model (Iverson, 2000), which links a 

pore-pressure response function with the governing factor-of-safety equation. The pore-pressure 

response function is determined by applying a fixed rainfall intensity for a specified period of time 

into a one-dimensional infiltration equation using an estimate of soil hydraulic diffusivity. A 

modification of this method (TRIGRS) has been produced by Baum and others (2002) for tensionsaturated soils using varying rainfall intensities (for example, real storm rainfall intensities and 

durations) and by Savage and others (2004) for soils having partially saturated zones. These 

models allow calculation of factor-of-safety at different depths in the soil column and at different 

times in the rainfall period. These models require an estimate of the hydraulic properties of the 

existing soils (which can vary three to four orders of magnitude even within materials of uniform 

texture; Reid, 1997; Freeze and Cherry, 1979) and the initial pore-pressure distribution, parameters 

that are not commonly available for most slopes. The uncertainties related to properties such as 

hydraulic conductivity introduce uncertainties into both the FS of a given slope segment and to the 

timing of the FS response to the infiltrating rainfall. For example, a silty sand can commonly vary 

from 10-7 to 10-3 m/s (Freeze and Cherry, 1979). This leads to standard deviations (estimated by the 

“three sigma rule” above; Duncan, 2000) in the hydraulic conductivity of 1.7 x 10-4 m/s and 

coefficients of variation of 17. As some hydraulic conductivities will be lower than the intensities 

of rainfall, surface runoff may occur that is not predicted with the result of FS remaining higher 

than predicted as well as decreases of FS with rainfall being delayed from that predicted. So, in 

some cases, models that require material properties whose actual values are highly uncertain, may 

produce FS values that have such large degrees of uncertainty that they are not useful. However, 

calibration against a landslide dataset can greatly reduce the uncertainties. 

Harp and others (2006) have used a simpler version of the infinite slope equation to 

construct a shallow landslide hazard map for the city of Seattle, Washington (fig. 1-7 A and B). 

This version assigns each geologic unit an average shear strength in terms of cohesion ( c′) and 

internal angle of friction (φ′) based on actual test data. This equation takes the form: 

' tan ' tan '

sin tan tan

m w c FS

t

φ γ φ

γ α α γα

= +− (2) 

6
where FS, α, and γw, are the same as in eq. 1, γ is the unit weight of slope material, c′ is the 

effective cohesion of the slope material, φ′ is the effective friction angle of the slope material, t is 

the slope-normal thickness of the potential landslide block, and m is the proportion of the slope 

thickness that is saturated (Jibson and others, 2000; Harp and others, 2002; Harp and others, 2004). 

This analysis assumes that ground-water flow is parallel to the ground surface and that the stability 

of each cell is independent of the cells surrounding it. This model was used in conjunction with a 

dataset of landslide locations within the city for over 100 years to compare with the FS values and 

compile a hazard map based on correlations with the dataset. 

The above model has also been used in areas of the world where little test data are available. 

A landslide hazard map was constructed for the city of Tegucigalpa, Honduras, based on the 

landslides triggered by Hurricane Mitch in October 1998 (Harp and others, 2002; fig. 1-8) and for 

the islands of Chuuk State, Federated states of Micronesia based on landslides triggered by 

Typhoon Chata’an in July 2002 (Harp and others, 2004; fig. 1-9). The TRIGRS model has recently 

been used by Salciarini and others (2006) in the Umbria region of central Italy where landslide 

inventories allowed calibration of the model. In all these applications, estimated shear-strengths 

have been calibrated to ensure that no cells fail under dry conditions. 

The above methods of regional analysis have been applied to most common types of 

shallow landslides (falls, slides, and slumps in rock and soil). They have been successfully used to 

portray landslide hazard to communities where these types of failures have caused considerable 

damage to people and property. As mentioned above, the shallow landslide types pose minimum 

hazard to pipelines that are buried unless their movement results in penetration or erosion to 

pipeline-burial depths. Some pipelines, however, are not buried but are carried above ground on 

supports and bridges, and buried pipelines have locations where there are pumping stations, valves, 

and connections above ground and would therefore be vulnerable to shallow-landslide movement 

or impact. 

Deep-Seated Landslide Analyses 

The primary threat to pipelines from slope failures arise from the movement of landslides 

whose failure surface or surfaces are several meters or greater in depth. Landslides with rotational 

and/or translational movement, and in rock or soil, all pose potential threats to pipeline integrity 

from displacements large enough to damage or rupture the pipe. 

As for the shallow landslides discussed above, both qualitative and quantitative methods of 

hazard analysis are used for deep-seated landslides. The field geomorphic, weighted parameters, 

and statistical methods described in an earlier section are applied to deep-seated landslide 

susceptibility or hazard in the same manner as shallow landslide susceptibility or hazard. 

Deterministic geotechnical analyses coupled with a GIS on a regional scale are necessarily different 

than those used for shallow landslides. First, the use of the infinite slope analysis is not appropriate 

for most deeper landslides. Secondly, there are more differences, structurally and geometrically, 

between one deep-seated landslide and another than between shallow landslides. And finally, 

deep-seated landslides tend to be less related to a single triggering event or group of events than 

populations of shallow landslides. Deep-seated landslides are more spread out over time than 

shallow failures, and their populations are generally not as high density as shallow landslides 

leaving them less robust for statistical or probabilistic analysis. 

Methods to evaluate the stability of specific deep-seated landslides are numerous and rely 

on adequate sampling and testing of the material properties of the landslide material to formulate a 

reliable estimate of the stability of the landslide usually described as FS. These methods applied to 

individual landslides are discussed and evaluated in a later section. Here, we will examine the 

7
methods of coupling slope-stability analysis for deep-seated landslide with GIS techniques to 

evaluate slope stability on a regional basis. 

Despite the tendency of deep-seated landslides to have much greater differences in 

geometries and mechanisms of failure than shallow landslides, methods to merge GIS analyses and 

slope-stability calculations for deep-seated landslides have begun to be employed on a regional 

basis. Miller (1995) and Miller and Sias (1997) have used conventional two-dimensional (2-D) 

moment equilibrium analyses coupled with ground-water models to estimate factor-of safety (FS) 

throughout landslide terrain in watersheds in northwestern Washington. For these analyses, 

circular or elliptical slip surfaces along a regional grid parallel to slope were analyzed to select 

circles with minimum FS for each grid point. 

Other investigators such as El-Ramly and others (2002) have employed similar 2-D 

conventional stability analyses coupled with statistical techniques to evaluate and minimize the 

variance of the various input parameters (such as shear strength, unit weight, and pore pressure) so 

that probabilities of failure or “unsatisfactory performance” could be quantified. With these 

methods, results are highly dependent on the degree to which the input parameters can be specified 

and the variance-reduction techniques achieve effective averaging. In fact Aleotti and Chowdury 

(1999, p.34) indicate that when FS is determined as a probability density function based on input 

parameters which are themselves probability distributions, ...”the data must be more detailed or 

numerous than those for deterministic analyses.” 

In addition to deficiencies in data for input parameters, the lack of accounting for threedimensional (3-D) effects on slope stability often introduces errors and hampers 2-D analytical 

accuracy. For this reason, many scientists have chosen to evaluate regional hazards from deepseated landslides by 3-D analyses. Xie and others (2004) and Franciss (2004) have used 3-D 

slope-stability analyses coupled with ground-water models to assess FS on a regional basis for 

slopes in Japan and Brazil respectively. One of the most commonly used models to assess 3-D 

stability employs the method of columns in which a surface grid element and its underlying column 

is examined together with adjacent columns within a slope/drainage area for all the possible 

combinations of columns to determine the least stable of these combinations. Usually a basal 

failure surface of spherical or elliptical shape is adopted for the analysis. Brien and Reid (2007) 

have constructed a deterministic deep-seated landslide-hazard analysis for part of the city of Seattle 

using a hydrologic model and a method of columns to calculate most likely failure surfaces (fig. 1-

10). In some cases, other geometries such as wedges have been employed to match known 

geometries of local failure (Franciss, 2004). 

Input for the various mathematical models in the above deterministic methods are acquired 

from actual measured data where possible and estimated where not. Most geotechnical data 

gathered even from a site-specific slope-stability investigation would still be insufficient to use for 

application to evaluating slope stability over a much wider area. For this purpose, the most 

common techniques of estimating the distributions of properties such as shear-strength parameters 

(c’

 and ’), unit weight ( ), and pore pressure, are assumed to have a normal distribution about an 

expected mean value. In most cases, the ranges of values and the mean are selected from existing 

data and from judgment based on experience. With these selected estimates, the distribution 

(probability density function (PDF)) is then approximated by first order second moment (FOSM) 

methods (Wu and others, 1996) to determine the mean and variance of the parameter data. Using 

these estimates a probability density function of a performance indicator, in many cases, FS is 

calculated. The PDF of FS is then another distribution of FS values with its own mean and 

variance. This PDF describes the probability of FS values being • 1.0. 

8
Utility of the Various Susceptibility or Hazard Assessment Methods 

Qualitative Methods 

The main advantages of the methods of qualitative hazard assessment are that they are 

relatively rapid. The susceptibility or hazard is assigned based solely on the investigator’s 

judgment and is either unspecified or is based on a weighting of specified factors that affect slope 

stability. In either case, the assignment of weights or simply hazard itself is based on judgment of 

the investigator and cannot be replicated by others. Although subjective, once the weights have 

been established, the process of overlapping weighted maps and developing a hazard map can be 

automated and performed on a GIS platform. 

Quantitative Methods 

Statistical 

Of the statistical methods of assessing landslide susceptibility and hazard, bivariate methods 

are the most straightforward. Simply comparing sets of mapped factors to a landslide map and 

determining the weight factors based on the density of landslides captured by the separate factors is 

reproducible, especially if the weighting factors are directly proportional to the densities of 

landslides for the respective factors. The final overlay of factor maps to calculate the resulting 

hazard or susceptibility is easily accomplished within a GIS. 

Multivariate methods of modeling landslide hazards gained popularity from their ability to 

assess the effect of numerous factors on the susceptibility of slopes to landslides either 

simultaneously or stepwise. All considered factors could be regressed against a mapped landslide 

distribution and correlation coefficients could be determined and susceptibility assigned based on 

the individual correlations. Carrera (1983) did much of the early work in using these methods to 

compare with landslide distributions in various drainage basins in Italy in which landslides and all 

considered factors had been carefully mapped and quantified. Carrera and others (1991) used 15 

slope-related variables to regress against mapped landslides in the Tescia Basin in Umbria, Italy, 

and was able to predict both stable and unstable slopes with approximately 80 percent accuracy. 

The advantage of the various statistical methods is that they are extremely systematic and 

reproducible once the different slope-related stability factors are defined and the data collected. 

The actual mapping of these factors and analysis of the data concerning the factors is often timeconsuming and cumbersome (Aleotti and Chowdury, 1999). Carrera and others (1991) remarked 

that the gathering of data and encoding of the various factors for a multivariate statistical study of a 

basin in Calabria, Italy (Carrera, 1983, 1989), required a great deal of time. They also stated that 

“black box” models such as their discriminant analysis of the Tescia Basin “do not unravel the 

internal structure of the process involved,” because even with all of the variables included, the 

analysis is too simple. It also is basin specific and cannot be transferred to other basins with 

different geology and morphology (Carrera and others, 1991, p. 443). Use of these methods 

requires that there is a reliable landslide inventory for comparison with the various factors. 

Deterministic/Probabilistic 

The main advantage of these types of analyses is that uncertainties in the variables that 

affect slope stability can be taken into consideration. These analyses can incorporate modifications 

of slope geometry or other changes that affect the stability of the slopes due to construction 

activities that might occur in the development of a pipeline corridor. If sets of comprehensive data 

exist for variables such as shear strength, material unit weights, and levels of pore pressure, then a 

9
distribution of performance factors such as FS can be reliably estimated. If not, which is usually 

the case, values of the means and variances of these variables are estimated as previously discussed 

in the section on shallow landslide analyses. Variables such as shear strength may not be normally 

or lognormally distributed, especially when considering formations that have interbedded layers of 

differing properties. They may be bimodal or multimodal in their distribution. So, when normal 

distributions are assumed for variables that may not have normal distributions, the results, although 

having what seem to be sophisticated estimates of mean, variance, and numerical probabilities, in 

reality may have no more basis than the hazard map based solely on the implicit judgment and 

experience of the investigator. The results can be misleading and give a false sense of quantitative 

assessment. This is especially true for the analysis of slopes with deep-seated landslides. Not only 

are material properties poorly known for most of these cases, but the failure geometries are also 

poorly known and are highly variable from one to another and are difficult, if not impossible, to 

generalize. Therefore, landslide hazard analyses for deep-seated landslides are inherently fraught 

with high (and often unknown) degrees of uncertainty and are of limited use. A summary of 

advantages and disadvantages of the various methods of landslide hazard analysis is presented by 

Aleotti and Chowdury (1999; reproduced here in modified form as table 1-1). Baum and others 

(2005) review the application of several different methods of hazard and susceptibility assessment 

in Seattle, Washington, and provide insight into some of the relative advantages and disadvantages 

of the different methods. 

Estimation of Displacement or Runout 

Precise prediction or estimation of displacement of coherent landslides or the runout of 

mobilized fluid landslides is not within the current capabilities of modeling methods. Models of 

granular or particle flow have been used to attempt to match the distances and paths of debris flows 

(Hungr and Morgenstern, 1984; Denlinger and Iverson, 2004; Iverson and others, 2004). However, 

no current models accurately model runout distances except in uniform materials that contain few 

irregular particles. Trees and other types of vegetation that commonly become incorporated in 

debris flows are irregularities that cannot be modeled successfully by these methods but can impart 

considerable influence on runout distances and flow paths. The three-dimensional modeling of the 

movement of large landslide masses and mixtures of water and earth materials is useful however, to 

obtain order-of-magnitude estimates of displacement and runout for risk assessment considerations 

(Denlinger and others, 2006). 

Despite the advances being made in the modeling of landslide displacement and runout, it is 

still beyond reliable modeling capabilities to precisely estimate the velocity or the total 

displacement of dislocated earth materials. The most reliable methods of estimating runout still 

rely on the presence of previous deposits. Preexisting debris flows, rock falls, rock slides, slumps, 

and other types of landslides provide “ground-truth” data (Baum and others, 2000) from which 

estimates of average, maximum, and minimum future landslide runouts can be based with the 

confidence that these estimates included in-place conditions and irregularities of the real slopes 

under consideration. 

10
Table 1-1. Advantages and disadvantages of different methods of landslide hazard 

assessment (after Aleotti and Chowdury, 1999). 

Methods Advantage Disadvantage Role of GIS 

Field geomorphic 

analyses 

Allow a rapid assessment taking 

into account a large number of 

factors. 

Totally subjective; 

methodology uses implicit rules 

that hinder the critical analysis of 

the results. 

Only as a 

drawing tool 

Combination of index 

maps 

Solves the problem of hidden rules. 

Total automation of steps. 

Standardization of data 

management. 

Subjective in attributing 

weighted values to each 

parameter. 

Overlay of 

different maps 

Statistical analyses 

(bivariate, multivariate, 

etc.) 

Objective in methodology. 

Total automation of steps. 

Standardization of data 

management. 

Systematic collection and 

analysis of data regarding 

different factors is cumbersome. 

Analysis and 

map overlay 

Probabilistic approaches 

Allows consideration of different 

uncertainties. 

Quantitative in scope. 

Objective in scope and 

methodology. 

Provides insight not possible in 

deterministic methods. 

Requires comprehensive data. 

Otherwise subjective 

probabilities required. 

Probability distributions 

difficult, especially for low level 

of hazard. 

Analysis and 

map overlay 

11
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B.J., and Roque, Y.M., 2007, Emergency assessment of debris-flow hazards from basins burned 

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techniques and statistical models in evaluating landslide hazard: Earth Surface Processes and 

Landforms, v. 16, p. 427–445. 

Carrera, A., 1989, Landslide hazard mapping by statistical methods: a “black-box” model 

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15, no. 3, p. 403–426. 

Çevik, E., and Topal, T., 2003, GIS-based landslide susceptibility mapping for a problematic 

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Coe, J.A., Godt, J.W., Baum, R.L., Buckram, R.C., and Michael, J.A., 2004, Landslide 

susceptibility from topography in Guatemala, in Lacerda, W.A., Erlich, M., Fonterra, S.A.B., and 

Sayao, A.S.F., eds., Landslides—evaluation and stabilization, Proceedings of the 9th 

International Symposium on Landslides: London, A.A. Balkema Publishers, v. 1, p. 69–78. 

Coe, J.A., Harp, E.L., Tarr, A.T., and Michael, J.A., 2005, Rock-fall hazard assessment of Little 

Mill Campground, American Fork Canyon, Uinta National Forest, Utah: U.S. Geological Survey 

Open-File Report 2005-1229, 48 p., 2 pls. 

Coe, J.A., Harp, E.L., Tarr, A.T., and Michael, J.A., 2007, Rockfall hazard at Little Mill 

Campground, Uinta National Forest: Part 1. Geologic hazard assessment: in Schaefer, V.R., 

Schuster, R.L., and Turner, A.K., eds., Conference Presentations: 1st North American Landslide 

Conference, Vail, Colorado, AEG Special Publication 23, Association of Environmental & 

Engineering Geologists, CD-ROM, 14 p. 

DeGraff, J.V., Cannon, S.H., Gartner, J.E., and Gallegos, A.J., 2007, Reducing post-wildfire debris 

flow risk through the burned area emergency response (BAER) process, in Schaefer, V.R., 

Schuster, R.L., and Turner, A.K., eds., Conference Presentations: 1st North American Landslide 

12
Conference, Vail, Colorado, AEG Special Publication 23, Association of Environmental & 

Engineering Geologists, CD-ROM, 10 p. 

Denlinger, R.P., O’Connell, R.H., and Jones, M., 2006, Summary of preliminary 2-D inundation 

modeling for three Hattian landslide dam breaches: U.S. Geological Survey Open-File Report 

2006-1094, 47 p. 

Denlinger, R.P., and Iverson, R.M., 2004, Granular avalanches across irregular three-dimensional 

terrain: 1. Theory and computation: Journal of Geophysical Research, v. 109, F01014. 

Duncan, J.M., 2000, Factors of safety and reliability in geotechnical engineering: Journal of 

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El-Ramly, H., Morgenstern, N.R., and Cruden, D.M., 2002, Probabilistic slope stability analysis for 

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Franciss, F.O., 2004, Landslide hazard assessment on hilly terrain, in Lacerda, W.A., Ehrlich, M., 

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the volumes of debris flows generated by recently burned basins in the Western U.S.: 

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Godt, J.W., Schulz, W.H., Baum, R.L., and Savage, W.Z., in press, Modeling rainfall conditions for 

shallow landsliding in Seattle , Washington, in Baum, R.L., Godt, J. W., and Highland, L.M., 

eds., Landslide and engineering geology of the Seattle, Washington area: Boulder, Colo., 

Geological Society of America, Reviews in Engineering Geology: 

Hamlin, S.N., and Takasaki, K.J., 1984–85, Water-quality reconnaissance of ground water in the 

inhabited outer atoll islands of Chuuk State, Federated States of Micronesia: U.S. Geological 

Survey Water-Resources Investigations Report 96-4180, 77 p. 

Hammond, C., Hall, D., Miller, S., and Swetik, P., 1992, Level I stability analysis (LISA) 

documentation for version 2: USDA Forest Service Intermountain Research Station, General 

Technical Report INT-285, 121 p. 

Harding–Lawson Associates, 1979, Seismically induced ground-failure susceptibility, Anchorage, 

Alaska: Miscellaneous Publication 32, Digitally recompiled by Weems, S.M., and Combellick, 

R.A., 1997, Alaska Division of Geological and Geophysical Surveys. 

Harp, E.L, Michael, J.A., and Laprade, W.T., 2006, Shallow landslide hazard map of Seattle, 

Washington: U.S. Geological Survey Open-File Report 2006-1139, 20 p., 2 pls. 

Harp, E.L., Reid, M.E., and Michael, J.A., 2004, Hazard analysis of landslides triggered by 

Typhoon Chata’an on July 2, 2002, in Chuuk State, Federated States of Micronesia: U.S. 

Geological Survey Open-File Report 2004-1348, 24 p., 2 pls. 

Harp, E.L., and Jibson, R.W., 2002, Anomalous concentrations of seismically triggered rock falls in 

Pacoima Canyon: are they caused by highly susceptible slopes or local amplification of seismic 

shaking: Bulletin of the Seismological Society of America, v. 92, no. 8, p. 3,180–3,189. 

Harp, E.L., and Jibson, R.W., 1995, Inventory of landslides triggered by the 1994 Northridge, 

California, earthquake: U.S. Geological Survey Open-File Report 95-213, 17 p., 2 pls. 

Harp, E.L., Held, M.D., Castañeda, M.R., McKenna, J.P., and Jibson. R.W., 2002, Landslide 

hazard map of Tegucigalpa, Honduras: U.S. Geological Survey Open-File Report 2002-219, 9 p., 

2 pls. 

Harp, E.L., and Noble, M.A., 1993, An engineering rock classification to evaluate seismic rock-fall 

susceptibility and its application to the Wasatch Front: Bulletin of the Association of Engineering 

Geologists, v. 30, p. 293–319. 

13
Hungr, O., and Morgenstern, N.R., 1984, High velocity ring shear tests on sand: Geotechnique, v. 

34, p. 415–421. 

Iverson, R.M., Logan, M., and Denlinger, R.P., 2004, Granular avalanches across irregular threedimensional terrain: 2. Experimental tests: Jornal of Geophysical Research, v. 109, F01015. 

Iverson, R.M., 2000, Landslide triggering by rain infiltration: Water Resources Research, v. 36, no. 

7, p. 1,897–1,910. 

Jibson, R.W., Harp, E.L., and Michael, J.A., 2000, A method for producing digital probabilistic 

seismic landslide hazard maps: Engineering Geology, v. 58, p. 271–289. 

Miller, D.J., and Sias, J., 1998, Deciphering large landslides: linking hydrological groundwater and 

slope stability models: Hydrological Processes, v. 12, p. 923–941. 

Miller, D.J., 1995, Coupling GIS with physical models to assess deep-seated landslide hazards: 

Environmental and Engineering Geoscience, v.1, no. 3, p. 263–276. 

Montgomery, D.R., and Dietrich, W.E., 1994, A physically based model for the topographic control 

on shallow landsliding: Water Resources Research, v. 30, no. 4, p. 1,153–1,171. 

Montgomery, D.R., Greenberg, H.M, Laprade, W.T., and Nashem, W.D., 2001, Sliding in Seattle: 

Test of a model of shallow landsliding potential in an urban environment, in Wigmosta, M.S. and 

Burges, S.J., eds., Land use and watersheds: Human influence on hydrology and geomorphology 

in urban and forest areas, Water Science and Application 2, American Geophysical Union, p. 59–

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Morrissey, M.M., Wieczorek, G.F., and Morgan, B.A., 2001, A comparative analysis of hazard 

models for predicting debris flows in Madison County, Virginia: U.S. Geological Survey OpenFile Report 01-67, 27 p. 

Pack, R.T., Tarboton, D.G., and Goodwin, C.N., 1999, GIS-based landslide susceptibility mapping 

with SINMAP, in Bay, J.A., ed.: Proceedings of the 34th Symposium on Engineering Geology 

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Porter, M., Marcuz, G., Reale, R., and Savigny, K.W., 2006, Geohazard risk management for the 

Nor Andino gas pipeline: Proceedings of the 6th International Pipeline Conference, Calgary, 

Alberta, Canada, p. 1–9. 

Reid, M.E., 1997, Slope instability caused by small variations in hydraulic conductivity: Journal of 

Geotechnical and Geoenvironmental Engineering, v. 123, no. 8, p. 717–725. 

Salciarini, D., Godt, J.W., Savage, W.Z., Conversini, P., Baum, R.L., and Michael, J.A., 2006, 

Modeling regional initiation of rainfall-induced shallow landslides in the eastern Umbria Region 

of central Italy: Landslides, v. 3, p. 181–194. 

Savage, W.Z., Godt, J.W., and Baum, R.L., 2004, Modeling time-dependent areal slope stability, in

Lacerda, W.A., Ehrlich, M., Fontoura, S.A.B., and Sayão, A.S.F., eds., Landslides–evaluation 

and stabilization: Proceedings of the 9th International Symposium on Landslides, Rio de Janeiro, 

Brazil, v. 1, p. 23–36. 

Schuster, R.L., Baum, R.L., and Highland, L.M., 1998, The April 1998 El Niño-triggered Anzar 

road landslide, San Benito County, California: Association of Engineering Geologists News, v. 

41, no. 4, p. 5–7. 

van Westen, C.J., van Asch, T.W.J., and Soeters, R., 2006, Landslide hazard and risk 

zonation−why is it still so difficult?: Bulletin of Engineering Geology and the Environment, v. 

65, p. 167–184. 

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14
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Landslides, Rio de Janeiro, Brazil, v. 1, p. 97–102. 

15
Glossary of Terms 

Hazard-Probability of occurrence within a reference period (van Westen and others, 2006). 

Susceptibility (of slopes)-Tendency to fail. Often expressed as factor-of-safety. 

Vulnerability-The characteristics of a person or group in terms of their capacity to anticipate, 

cope with, resist, and recover from the impact of a natural hazard (Blaikie and others, 1994). 

16
Figure 1A. Deep-seated rotational slump in San Benito County, California, that has 

broken a natural gas pipeline. Landslide was triggered by rainfall from El Niño conditions 

April 22, 1998 (Photo by Robert Schuster, USGS).

17
Figure 1B. Toe of landslide in figure 1A showing broken pipeline (brown-colored pipe) 

and the temporary replacement (white-colored pipe; photo by Lynn Highland, USGS). 

18
Figure 2. Portion of landslide inventory map for landslides triggered by the 1994 

Northridge, California, earthquake (after Jibson and others, 2000). 

19
Figure 3. Map of Anchorage, Alaska, depicting qualitative relative seismic susceptibility 

based on judgment of authors (from Harding and Lawson, 1979; Weems and Combellick, 

1997). 

20
Figure 4. Rock-fall hazard map of Little Mill Campground, American Fork Canyon, Utah 

(from Coe and others, 2007). 

21
Figure 5. Landslide susceptibility map produced from ratio grid based on elevation and 

slope (from Coe and others, 2004). 

22
Figure 6. Map of probability of debris flow occurrence for basins burned by the Hot Creek 

Fire, Idaho, in response to a 1-hour, 10-year recurrence storm (from DeGraff and others, 

2007). 

23
Figure 7A. Shaded relief map of Seattle, Washington, study area and location of area 

shown in figure 7B. 

24
Figure 7B. Shallow landslide hazard map of portion of Seattle, Washington (from Harp 

and others, 2006). 

25
Figure 8A. Location of metropolitan area of Tegucigalpa, Honduras. 

26
Figure 8B. Inventory of landslides triggered by Hurricane Mitch in Tegucigalpa, Honduras 

(from Harp and others, 2002). Dashed rectangle denotes area shown in figure 8C. 

27
0 0.5 1 0.25 Km

High

Moderately High

Moderate

Low

Roads

Landslides

 Debris-Flow Hazard

Under Saturated Conditions

Probability Category

~1.03%

~0.31%

~0.13%

~0.07%

N

~0.03%

Very High

87°12'0"W

87°12'0"W

14°6'0"N 14°6'0"N

Figure 8C. Landslide hazard map for central part of Tegucigalpa, Honduras (from Harp 

and others, 2002). 

28
Figure 9A. Location maps of Chuuk State in the Federated States of Micronesia (from 

Harp and others, 2004). 

29
Figure 9B. Map of Chuuk islands within Chuuk (Truk) Lagoon (after Hamlin and 

Takasaki, 1984-85). Dashed rectangle denotes area shown in figure 9C. 

30
Figure 9C. Debris-flow hazard map of islands of Tonoas and Etten based on landslides 

triggered by Typhoon Chata’an (from Harp and others, 2004). 

31
32
Prepared in cooperation with the U.S. Department of Transportation, 

Pipeline Research Council International, and DGH Consulting, Inc. 

Land Subsidence Hazards 

By D.L. Galloway, G.W. Bawden, S.A. Leake, and D.G. Honegger 

Chapter 2 of 

Landslide and Land Subsidence Hazards to Pipelines 

Open-File Report 2008–1164 

U.S. Department of the Interior 

U.S. Geological Survey 

33
Contents 

List of Figures .............................................................................................................................35

List of Tables ..............................................................................................................................37

Abstract....................................................................................................................................40

Introduction ..............................................................................................................................40

Purpose.................................................................................................................................41

Scope....................................................................................................................................44

Acknowledgments.................................................................................................................44

Recognition ..............................................................................................................................45

Known Subsidence Areas.....................................................................................................46

Subsurface Fluid Withdrawal .............................................................................................46

Drainage of Organic Soils ..................................................................................................50

Sinkholes ...........................................................................................................................52

Underground Mining...........................................................................................................55

Hydrocompaction ...............................................................................................................55

Thawing Permafrost...........................................................................................................56

Natural Consolidation.........................................................................................................59

Subsidence Susceptible Areas .............................................................................................60

Ancillary/Anecdotal Information..........................................................................................60

Increased incidence of damaged wells ...........................................................................60

History of repeated adjustments to local geodetic controls .............................................62

Increasing incidence of local riverine or coastal flooding ................................................62

Local conveyance and drainage problems......................................................................62

Ground failures ...............................................................................................................62

Preliminary Mapping ..........................................................................................................67

Assessment .............................................................................................................................67

Measurement, Mapping, and Monitoring...............................................................................67

Ground-Based Geodetic Surveys ......................................................................................69

Extensometry ..................................................................................................................69

Tripod LiDAR ..................................................................................................................71

Airborne Geodetic Surveys ................................................................................................73

Data Density ...................................................................................................................74

Geodetic Control .............................................................................................................74

Vendor Qualifications......................................................................................................74

QA/QC ............................................................................................................................74

Space-based Geodetic Surveys.........................................................................................74

Global Positioning System ..............................................................................................75

Synthetic Aperture Radar (SAR) Interferometry..............................................................77

Analysis and Simulation........................................................................................................78

Subsurface Fluid Withdrawal .............................................................................................79

Drainage of Organic Soils ..................................................................................................82

Sinkholes ...........................................................................................................................83

Underground Mining...........................................................................................................84

Hydrocompaction ...............................................................................................................90

34
Thawing Permafrost...........................................................................................................90

Natural Consolidation.........................................................................................................91

Summary..................................................................................................................................92

References Cited .....................................................................................................................93

List of Figures 

 2.1. Selected deformation factors (vertical displacement, slope [tilt] and axial strain) for 

hypothetical pipelines traversing an idealized linear subsidence feature. ...................................41

 2.2. Selected deformation features for an idealized nonlinear subsidence feature. A. 

Subsidence surface. B. Deformation profiles. .............................................................................42

 2.3.A. Cover-collapse sinkhole, Winter Park, Florida (1981). B. Approximate location of 

maximum subsidence measured in the San Joaquin Valley, California (1977)...........................45

 2.4. Selected, known areas of permanent land subsidence owing principally or 

secondarily to ground-water or oil and gas extractions (see table 2.1) in the 48 

conterminous United States and associated aquifer systems (modified from Galloway and 

others, 1999)...............................................................................................................................48

 2.5.Locations of organic-soil areas in the U.S. (from Galloway and others, 1999).....................50

 2.6.Subsidence in the Florida Everglades measured on a concrete marker shown in 1998 

photograph (from Galloway and others, 1999)............................................................................52

 2.7. Sinkholes: A. The Meade sink overlying gypsum and salt beds in western Kansas 

(Photo from Kansas Geological Survey); B. Drilling induced sinkhole in carbonates near 

Tampa, Florida (Photo by Tom Scott). C. Cover-collapse sinkhole in mantled carbonate 

karst near Ocala, Florida (Photo by Tom Scott). .........................................................................53

 2.8.General locations of A. salt and gypsum (evaporite) deposits (from Martinez and 

others, 1998), and B. karst from evaporite and carbonate rocks (from Davies and 

LeGrand, 1972) in the 48 conterminous United States. ..............................................................54

 2.9. Map showing distribution of permafrost in Alaska (modified from U.S. Geological 

Survey, 1996)..............................................................................................................................57

 2.10. Trans-Alaska Pipeline near Glennallen, Alaska (July 2007) showing thermal 

syphons on vertical support members and adjustable “shoes” to accommodate horizontal 

and vertical movements. (Photos by Devin Galloway, USGS). ...................................................58

 2.11. Abandoned railroad tracks warped by thermokarst near Valdez, Alaska. During 

construction of the roadbed the thermal equilibrium of the permafrost was disrupted 

causing differential thawing.........................................................................................................59

 2.12. Damaged wells: A. Photographs showing progressively protruding well in Las 

Vegas, Nevada, in 1964 and 1997; B. Map showing distribution of damaged wells in 

Sacramento Valley, California, and correlation to subsidence along section A-A′ (modified 

from Borchers and others, 1998); C. Photograph from borehole camera showing 

collapsed, spiraled well screen in damaged well.........................................................................61

 2.13. Flooding in subsidence affected areas near Houston, Texas: A. Permanently 

submerged lands at the San Jacinto Battleground State Historical Park near the shores of 

Galveston Bay; B. Homes near Greens Bayou flooded during a storm in June 1989 

(Photograph courtesy of Harris-Galveston Subsidence District). ................................................63

 2.14. Surface faults associated with extraction of subsurface fluids: A. Photograph (circa 

1926) of surface fault near Houston, Texas, about one-half mile north of the Goose Creek 

35
oil field; B. Surface faults in Las Vegas Valley, Nevada, shown in relation to measured 

subsidence caused by ground-water pumping, 1963–2000 (modified from Bell and others, 

2002); C. Subsidence rates measured along Line 1 (shown in B.) showing differential 

subsidence across the Eglington fault (modified from Amelung and others, 1999).....................64

 2.15. Earth fissures, south-central Arizona (modified from Carpenter, 1999): A. Picacho 

earth fissure, pictured in October 1967 (inset) and June 1989. By 1989 the fissure had 

developed into a system of multiple parallel cracks with a scarp of as much as 0.6 m of 

vertical offset. B. The Central Main Lateral Canal, part of the Central Arizona Project, (also 

pictured in A., upper left of 1989 photo) was damaged by a fissure (circled) where it 

crosses the Picacho earth fissure. C. Natural-gas pipeline undercut by erosional opening 

of an earth fissure near the Picacho Mountains. .........................................................................66

 2.16. Extensometers: A. Schematic, and B. photo of counter-weighted, two-stage, 

borehole pipe extensometer, Lancaster, California. The extensometer measures 

compaction simultaneously in two depth intervals in the aquifer system. The compaction 

(vertical displacement) is measured as movement of the pipe relative to the reference 

table and reflects shortening (subsidence) or lengthening (uplift) of the distance between 

the shallow-seated piers and the anchor depths of the pipe extensometers. C. Buried 

horizontal quartz-tube extensometer, near Apache Junction, Arizona. A displacement 

sensor is placed between a post and the quartz tube to measure small displacements in 

the interval spanned by the coupled post-sensor tube post-instrument configuration. D. 

Tape extensometer, near Apache Junction, Arizona. The tape is attached at both ends to 

a specially equipped geodetic monument. ..................................................................................70

 2.17. T-LiDAR imagery of a geothermal transmission line, Mammoth Lakes, Calif. A. One 

T-LiDAR setup imaged about 500 m of continuous pipeline. B. Best-fit primitives 

(idealized geometries) can be fit to the data to simulate the pipeline, as well as, measure 

sub-centimeter displacements. C. Detailed imagery shows a telemetered USGS well and 

adjacent pipeline. ........................................................................................................................72

 2.18. Horizontal GPS displacement vectors superimposed on InSAR imagery for the San 

Gabriel Valley, southern California. A. Ground-water levels in the San Gabriel Valley 

declined about 3.5 m between May and October 1999 corresponding with approximately 

12 mm of equivalent (range change) land subsidence measured using InSAR (Bawden, 

2002). The neighboring continuous GPS sites (solid white circles) vyas and lphs are 

pulled inward towards the zone of maximum subsidence which generally corresponds with 

the drawdown cone. The generally southwest-trending horizontal motion of the other GPS 

sites during this period is attributed to regional tectonic processes (Bawden and others, 

2001). B. Record rainfall in the winter-spring of 2005 produced about 40 mm of uplift 

measured using InSAR January to July 2005, and radial outward motion of continuous 

GPS sites sghs, wnra, rhcl, vyas, lphs, wchs, cvhs, and azu1 January through May 

2005 (King and others, 2007)......................................................................................................76

 2.19. Typical types of subsidence associated with underground coal mining: A. Pit 

subsidence, and B. Sag subsidence (modified from Bauer and Hunt, 1982). .............................86

 2.20. Typical profiles and parameters for analyzing deformation associated with 

underground coal mining: A. Subsidence, and B. Horizontal strain. (W is width of minedout panel; h is depth; Smax is maximum subsidence; Emax is maximum extension).....................87

 2.21. Deformation profiles for idealized subsidence feature above underground coal mine: 

A. Subsidence. B. Slope. C. Curvature. D. Horizontal displacement. E. Horizontal strain. .........88

36
 2.22. Schematic of: A. Zone of influence above a mined-out element, and B. Subjacent 

support between two mined-out elements separated by an intact element.................................89

List of Tables 

2.1 Locations of selected known subsidence in the USA attributed to ground-water and oiland-gas extractions.....................................................................................................................49

2.2. Select methods of measuring aquifer-system compaction and land subsidence 

(modified from Galloway and others, 2000). ...............................................................................68

2.3. Geotechnical index properties of collapsible soils near El Llano, New Mexico 

(modified from Haneberg and others, 1992). ..............................................................................90

Conversion Factors 

SI to Inch/Pound 

Multiply By To obtain 

Length 

centimeter (cm) 0.3937 inch (in.) 

millimeter (mm) 0.03937 inch (in.) 

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi) 

Area 

square meter (m2) 0.0002471 acre 

square kilometer (km2) 247.1 acre 

square meter (m2

) 10.76 square foot (ft2) 

square kilometer (km2

) 0.3861 square mile (mi2) 

Volume 

cubic meter (m3) 264.2 gallon (gal) 

cubic meter (m3) 0.0002642 million gallons (Mgal) 

cubic meter (m3

) 35.31 cubic foot (ft3) 

cubic meter (m3

) 0.0008107 acre-foot (acre-ft) 

Flow rate 

meter per year (m/yr) 3.281 foot per year (ft/yr) 

centimeter per year (cm/yr) 0.3937 inch per year (in/yr) 

millimeter per year (mm/yr) 0.03937 inch per year (in/yr) 

Density 

gram per cubic centimeter (g/cm3) 62.4220 pound per cubic foot (lb/ft3) 

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows: 

°F=(1.8×°C)+32 

37
Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows: 

°C=(°F-32)/1.8 

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 (NAVD 88) 

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83) 

Altitude, as used in this report, refers to distance above the vertical datum. 

*Transmissivity: The standard unit for transmissivity is cubic foot per day per square foot times foot of 

aquifer thickness [(ft3

/d)/ft2]ft. In this report, the mathematically reduced form, foot squared per day (ft2/d), is 

used for convenience. 

Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius (μS/cm at 25°C). 

Concentrations of chemical constituents in water are given either in milligrams per liter (mg/L) or micrograms 

per liter (μg/L). 

38
39
Land Subsidence Hazards 

By D.L. Galloway, G.W. Bawden, S.A. Leake, and D.G. Honegger 

Abstract 

Land subsidence poses a hazard to pipelines. The information presented in this chapter is 

intended to aid recognition and assessment of the principal land subsidence hazards to pipelines. 

Subsidence is a global problem and, in the United States, more than 44,000 km2 in 45 States have 

been directly affected by subsidence. The principal causes are subsurface fluid withdrawal, 

drainage of organic soils, sinkholes, underground mining, hydrocompaction, thawing permafrost, 

and natural consolidation. Many subsidence areas have been identified, mapped, and documented. 

Most anthropogenic land subsidence in the United States is caused by the withdrawal of subsurface 

fluids from porous granular media; humans also have caused widespread and significant subsidence 

by other processes. Mining of coal and minerals, and drainage of organic soils are the most 

significant of these as measured by area affected. Various ancillary and anecdotal factors aid 

identifying unrecognized, actively subsiding regions and other regions prone to subsidence, such as 

increased incidences of damaged or protruding wells, a history of adjustments to local geodetic 

controls, increasing incidences of coastal or riverine flooding, local conveyance and drainage 

problems, and ground failures—surface faulting and earth fissuring. Measuring, mapping and 

monitoring subsidence are necessary to assess subsidence hazards. Analysis and simulation of 

subsidence processes, constrained by the available data, often are used to assess present and 

potential future hazards. 

Introduction 

Land subsidence is a gradual settling or sudden sinking of the Earth’s surface owing to 

subsurface movement of earth materials. Subsidence is a global problem and, in the United States, 

more than 44,000 km2 in 45 States, an area roughly the size of New Hampshire and Vermont 

combined, have been directly affected by subsidence. The principal causes are subsurface fluid 

withdrawal, drainage of organic soils, sinkholes, underground mining, hydrocompaction, thawing 

permafrost, and natural consolidation (National Research Council, 1991). More than 80 percent of 

the identified subsidence in the Nation is a consequence of our exploitation of underground water, 

and the increasing development of land and water resources threatens to exacerbate existing land 

subsidence problems and initiate new ones (Galloway and others, 1999). In many areas of the arid 

Southwest, and in more humid areas underlain by soluble rocks such as limestone, gypsum, or salt, 

land subsidence is an often-overlooked consequence of our land- and water-use practices. Some 

subsidence also is associated with tectonic and volcanic processes; however this type of subsidence 

is beyond the scope of this report. 

Subsidence contributes to permanent inundation of coastal lands as the land settles with 

respect to sea level, aggravates riverine flooding, alters topographic gradients, and ruptures the land 

surface in addition to causing other hazards related to deterioration of land and water resources, and 

to disruption of cultural infrastructure. The National Research Council (1991) estimated that annual 

costs in the United States from flooding and structural damage caused by land subsidence exceeded 

$125 million. The assessment of other costs related to land subsidence is complicated by 

difficulties in identifying and mapping the affected areas, establishing cause-and-effect relations, 

40
assigning economic value to environmental resources, and by inherent conflicts in the legal system 

regarding the recovery of damages under established land and water rights. Thus, the total cost of 

subsidence is probably significantly larger than the current best estimate. 

Purpose 

The information presented in this chapter is intended to aid recognition and assessment of 

the principal land subsidence hazards to pipelines. Though subsidence generally refers to the 

downward motion of land surface, lateral ground movements accompany the subsidence. Both 

kinds of surface displacement affect pipelines to varying degrees. The lateral movements at land 

surface can be attributed to relatively deep-seated poroelastic deformation, to flexures or bending of 

the land surface and to ground failures such as those associated with the collapse of surficial 

material into underground voids (for example, sinkholes) and with differential subsidence and (or) 

tensional stresses in the subsurface materials (for example, earth fissures). 

Regional subsidence features (such as aquifer-system or reservoir compaction 

accompanying ground-water and (or) oil and gas extraction) generally create relatively small lateral 

(sub-horizontal) strains1 at the land surface owing in part to poroelastic deformation of the aquifer 

system, and in part to the flexure of land surface. Poroelastic deformation refers to the coupled 

interaction between fluid flow and deformation of the skeletal-matrix of the host rock (Biot, 1941; 

Verruijt, 1969; Detournay and Cheng, 1993; Wang, 2000). Poroelastic deformation is relevant to 

subsidence caused by the extraction of subsurface fluids and is discussed further in the Analysis 

and Simulation section of this report. Flexure characterizes the motion of the land surface subjected 

to the relative vertical displacement of some portion of the surface, such as the small component of 

lateral movement associated with the rotation or tilt (slope) of the land surface between two sites 

with differing amounts of subsidence. 

Meters of vertical displacement may occur locally regardless of the type of process causing 

the subsidence. Figures 2.1 and 2.2 illustrate various deformation factors important to pipelines 

traversing subsidence features. Consider a hypothetical pipeline mechanically coupled to the land 

surface and originally laid on a flat surface. Figure 2.1 shows the computed axial (approximately 

horizontal) displacements and strains (0.125 ppm, extension) resolved (a) in the axis of a 

hypothetical pipeline traversing the center of an idealized linear subsidence feature with a 

maximum 50-mm vertical displacement in a radial displacement field with linear displacement to 0 

mm at a radius of 100 m; and (b) perpendicular to the axis of a hypothetical pipeline traversing a 

chord AB of the circle defined by the same subsidence pattern. In this example, the axial strain 

results from the flexure of the pipeline owing to differential subsidence. Figure 2.2 illustrates an 

idealized nonlinear radial subsidence surface and selected deformation profiles computed using 

 where is subsidence and is the radius from the center of the feature. The 

maximum subsidence is 1 m at the center and approaches zero at a radius of 100 m. Consider the 

200-m-long cross section A-A′ (fig. 2.2A), and profiles of deformation features derived from the 

trace of cross section A-A′ through the center of the subsidence feature (fig. 2.2B). The slope of the 

surface is a maximum of about 0.85° at a radius of about 41 m. At this point, curvature is zero and 

defines the inflection point on the surface. Curvature is approximated as the second derivative of 

the subsidence surface and reaches a maximum of about 6 x 10-4 at the center of the feature. Axial 

displacement computed at 1-m intervals over the profile reaches a maximum value of about 1.1 x 

2 4 3 10 r S e− = − × S r

 

1

 The strains referred to here are computed as the dimensionless ratio of the relative component displacement measured 

in an interval, area or volume and the original length, area or volume, respectively. A uniaxial strain of 1 part per 

million (ppm) is 1 x 10-6 strain, and is equivalent to a change in length of 1 mm over a linear length of 1 km. 

41
10-4 m extension at the inflection point. The cumulative axial displacement (extension) along the 

profile from the end, up to the inflection point, is about 3.2 x 10-3 m, which represents a lengthening 

or extensional strain of about 54 ppm. Between the inflection point and the center of subsidence, 

the profile lengthens an additional about 2.3 x 10-3 m. Accounting for the remaining distance from 

the center of subsidence to the other end of the profile, the profile lengthens about 1.1 x 10-2 m over 

its original 200-m length for a cumulative axial strain of about 54 ppm. The profile lengthens 

unevenly over its extent because of curvature. The inflection points on the profile define a 

transition from relative extension to relative compression along the profile. 

-80 -60 -40 -20 0 20 40 60 80

0.085

0.095

0.105

0.115

0.125

DISTANCE ALONG AB, IN METERS

AXIAL STRAIN PERPENDICULARTO AB, IN PPM

A C B

AZIMUTH, θr ,IN DEGREES

 40

 50

 30

 20

 10

 0

30

20

20

40

40

60

60

80

RADIUS, IN METERS

RADIUS, IN METERS

80

100

100

60

90

0

0

A

C

B

Vertical exaggeration = 2000 X

Tilt, θv=0.0286 degrees, or

 ~100 arcseconds, or

 ~ 500 microradians

0.125 PPM AXIAL STRAIN

θv

RADIUS VERTICAL DISPLACEMENT, IN MM

0 100 M

0 

50

MM 

DISPLACEMENT

Figure 2.1. Selected deformation factors (vertical displacement, slope [tilt] and axial 

strain) for hypothetical pipelines traversing an idealized linear subsidence feature. 

The strains illustrated in figures 2.1 and 2.2 may not be large enough to cause failure of 

most pipelines, but could cause problems in gravity-driven flow in a pipeline or other conveyance 

constructed on the land surface. Pressurized pipelines are less susceptible to flow impedance owing 

to small changes in the vertical gradient of the pipeline. Large local subsidence features (ground 

42
failures such as sinkholes and earth fissures) can be associated with large near-field lateral strains 

(>100 ppm) and can cause structural failure of a nearby pipeline. 

100

80

60

40

20

0

-20

-40

-60

-80

-100

100 80 60 40 20 0

−1

−0.8

−0.6

−0.4

−0.2

0

−1

−0.9

−0.8

−0.7

−0.6

−0.5

−0.4

−0.3

−0.2

−0.1

0

SUBSIDENCE, 

IN METERS

SUBSIDENCE, IN METERS

DISTANCE, IN METERS

DISTANCE, IN METERS

A

A’

Profiles A-A’

−100 −80 −60 −40 −20 0 20 40 60 80 100

−1

−0.5

0

−100 −80 −60 −40 −20 0 20 40 60 80 100

−1

0

1

−100 −80 −60 −40 −20 0 20 40 60 80 100

−2

−1

0 x 10-4

−100 −80 −60 −40 −20 0 20 40 60 80 100

−1

0

1

x 10-3

DISTANCE, IN METERS

CURVATURE SLOPE SUBSIDENCE

AXIAL DISPLACEMENT

IN METERS

IN DEGREES

IN METERS

A.

B.

Inflection point Inflection point

Zone of relative extension Zone of relative compression Zone of relative extension

Figure 2.2. Selected deformation features for an idealized nonlinear subsidence feature. 

A. Subsidence surface. B. Deformation profiles. 

43
Scope 

For practical applications, means to identify locations of future subsidence hazards relies 

upon identifying (a) areas that have subsided recently, (b) areas that are actively subsiding, (c) 

areas where activities leading to subsidence will occur, or (d) areas that are very similar in terms of 

topography, geomorphology, hydrology, and soil properties to areas where evidence of past 

subsidence and subsidence-related hazards are observed. 

This chapter of the report focuses on the means listed above to recognize and assess land 

subsidence hazards and covers the following types of subsidence hazards: 

1. Subsurface fluid withdrawal: Deep-seated deformation of the porous skeletal matrix of the 

saturated rocks, owing to fluid-pressure declines principally caused by ground-water and 

hydrocarbon discharge and recharge. Two types of ground motion are discussed: 

a. Aquifer-system compaction—principal hazard associated with ground-water mining in 

susceptible alluvial, basin-fill deposits; 

b. Ground failures—earth fissures and surface faults associated with areas of differential 

ground displacements. 

2. Drainage of organic soils: Primarily oxidation of peat, muck, bog, fen, moor, and muskeg 

deposits associated with desiccation after water tables have been lowered to enable agricultural 

and other land uses. 

3. Sinkholes: Typically localized collapse of the overburden into underlying cavities that form in 

relatively soluble deposits such as salt, gypsum, and carbonate rocks (for example, limestone 

and dolomite). Two general types of sinkholes are discussed: 

a. Natural—karst terrain; 

b. Anthropogenic—accelerated dissolution and collapse related to water-use, petroleum 

extraction and mining practices. 

4. Underground mining: Often gradual downwarping, sometime sudden collapse over mine 

footprints; typically associated with coal mines. 

5. Hydrocompaction: Shallow subsidence associated with rewetting of dry, low-density sediments. 

6. Thawing permafrost: Subsidence associated with the development of thermokarst terrain. 

7. Natural consolidation: Gradual reduction in volume and increase in density of a soil mass in 

response to increased depositional load. 

Piping and internal erosion is often associated with wetting or modification of natural or 

prior drainage conditions within erodible sediments such as loess, fine sands and silts, and plays a 

role in exposing earth fissures through erosion to fissure gulleys. Piping and internal erosion within 

erodible sediments is not covered in this document. Earth fissures are covered in this document as 

indicated in item 1b above. 

Acknowledgments 

Robert Butler (Golder Associates Ltd.), Thomas Holzer and Keith Prince (both U.S. 

Geological Survey), and Moness Rizkalla (Visitless Integrity Assessment Ltd.) provided valuable 

review comments and suggestions that improved this manuscript. 

44
Recognition 

The occurrence of land subsidence is most obvious in the case of catastrophic sinkholes 

such as those in the mantled karst of Winter Park, Florida (fig. 2.3A). Where ground-water mining 

or drainage of organic soils is involved, the subsidence is typically gradual and widespread, and its 

discovery usually follows increased flooding. Gazing out over the San Joaquin Valley, California, 

one would be hard-pressed to recognize that more than 9 m of subsidence has occurred in some 

locations as a result of ground-water pumping in the valley (fig. 2.3B). However, the discovery of 

land subsidence in the southern San Joaquin Valley has been attributed to a visual observation in 

which a farmer reported that the mail truck traversing his mile-long driveway began to fade from 

view (Thomas Holzer, USGS, written commun., 2007). In the absence of obvious clues such as 

protruding wells, failed well casings, broken pipelines, drainage reversals, and reduced freeboard in 

canals and aqueducts, repeat measurements of land-surface elevation are needed to reveal the 

subsidence. 

A B

Figure 2.3. A. Cover-collapse sinkhole, Winter Park, Florida (1981). B. Approximate 

location of maximum subsidence measured in the San Joaquin Valley, California (1977). 

45
The problem of detection in regional land subsidence is compounded by the large areal 

scale of the elevation changes and the requirement for vertically stable reference marks—bench 

marks—located outside the area affected by subsidence. Where stable bench marks exist and repeat 

surveys are made, subsidence is easily measured using professional surveying instruments and 

methods. 

Known Subsidence Areas 

Many subsidence areas have been identified, mapped, and documented. Most anthropogenic 

land subsidence in the United States is caused by the withdrawal of subsurface fluids from porous 

granular media; humans also have caused widespread and significant subsidence by other 

processes. Underground mining of coal and minerals, and drainage of organic soils are the most 

significant of the other processes as measured by area affected. Collectively, the impacts from these 

processes rival those from withdrawal of subsurface fluids. The National Research Council (1991) 

estimates that about 8,000 and 9,400 km2of land, respectively, have subsided because of mining 

and drainage of organic soils. Though mining subsidence is widespread and mostly associated with 

coal extraction, organic soil subsidence is concentrated in two areas, the Florida Everglades and the 

San Joaquin–Sacramento River Delta, California (National Research Council, 1991; Galloway and 

others, 1999). 

Subsurface Fluid Withdrawal 

Withdrawal of subsurface fluids from clastic sediments has permanently lowered the 

elevation of about 26,000 km2of land in the 48 conterminous United States (fig. 2.4, table 2.1)—an 

area of similar extent to the State of Massachusetts (Holzer and Galloway, 2005). Permanent 

subsidence can occur when fluids (primarily water and hydrocarbons) stored beneath the Earth’s 

surface are removed by pumpage or drainage. The reduction of fluid pressure in the pores and 

cracks of aquifer systems and petroleum reservoirs, especially in unconsolidated clastic rocks, is 

inevitably accompanied by some deformation of the aquifer system or reservoir. Because the 

granular structure—the “skeleton”—of the fluid-bearing and -storing rocks is not rigid, but more or 

less compliant, a shift in the balance of support for the overlying material causes the skeleton to 

deform slightly. Both the aquifers and aquitards that constitute the aquifer systems, and their 

equivalents in petroleum reservoirs, undergo deformation, but to different degrees. Almost all the 

permanent subsidence in aquifer systems is attributable to the compaction2

 of aquitards during the 

typically slow process of aquitard drainage (Tolman and Poland, 1940). Because most of the 

subsidence attributed to subsurface fluid withdrawal is caused by ground-water extractions the 

following discussions will focus on the development of ground-water resources in aquifer systems, 

though the subsidence process is similar for hydrothermal and hydrocarbon resource development. 

In alluvial aquifer systems, especially those that include semiconsolidated, low-permeability 

silt and clay layers (aquitards) of sufficient aggregate thickness, long-term ground-water-level 

declines can result in a vast one-time release of “water of compaction” from compacting aquitards, 

which manifests itself as land subsidence. Accompanying this release of water is a largely 

nonrecoverable reduction in the pore volume of the compacted aquitards, and thus an overall 

reduction in the total storage capacity of the aquifer system. This “water of compaction” cannot be 

reinstated by allowing water levels to recover to their predevelopment status. The extraction of this 

resource for economic gain constitutes ground-water mining in the truest sense of the term. 

 

2

 In this report, the term "compaction" refers to a decrease in thickness of sediments as a result of increase in vertical 

compressive stress. The identical physical process is referred to as "consolidation" by soils engineers. 

46
In compacting aquifer systems, the subsidence is generally regional and spread over a large 

area so that regional-scale lateral (sub-horizontal) strains are rarely as large as 2 ppm and resulting 

regional-scale tilts are < 240 arcseconds. Locally, however, lateral strains may be large, such as 

near pumping wells where hydraulic gradients are large, where the aquifer system thins abruptly 

above inflections in the basement topography of the aquifer system, and near the boundaries of 

hydrogeologic units with contrasting hydraulic and (or) mechanical properties. 

The extraction of oil, gas, and associated water from petroleum reservoirs also causes 

subsidence. Land subsidence caused by hydrocarbon production has been documented in many 

basins of the world (Poland and Davis, 1969; Yerkes and Castle, 1969; Martin and Serdengecti, 

1984; Van Hasselt, 1992; Chilingarian and others, 1995; Nagel, 2001). Subsidence of the Goose 

Creek oil field on Galveston Bay near Houston, Texas was the first subsidence attributed to 

subsurface-fluid withdrawal to be described in the literature (Pratt and Johnson, 1926). The 

subsided volume amounted to about 20 percent of the produced volume of oil, gas, water, and sand. 

Subsidence at the Wilmington (California, USA) and Ekofisk (North Sea, Norwegian sector) oil 

fields are well known examples due both to the magnitude of subsidence as well as the cost of 

remediation. 

Parts of the city and port of Long Beach, California, suffered major problems owing to 

rapid (as much as 0.75 m/yr) land subsidence during 1937–62 related to extraction of oil, gas, and 

associated water from the underlying Wilmington oil field (Harris and Harlow, 1947; Gilluly and 

Grant, 1949; Mayuga and Allen, 1969; Poland and Davis, 1969). Problems principally were caused 

by flooding, but also were caused by structural damages to infrastructure, including pipelines, 

attributed to horizontal strains on the sides of the subsidence bowl. Total subsidence in Long 

Beach reached as much as 9 m before the land surface was stabilized by an integrated program of 

fluid injection to balance the extraction. The amount of subsidence at Long Beach was nearly 

proportional to (about 39 percent) the amount of oil and associated water extracted (Poland and 

Davis, 1969). 

47
? ?

?

?

?

? ?

?

?

?

?

?

?

?

?

?

?

? ?

?

?

?

? ? ?

?

?

?

?

?

?

?

? ?

??

?

?

? ?

?

?

?

?

?

?

?

?? ?

?

0

0 500 MILES

500 KILOMETERS

EXPLANATION

? Known subsidence locations— Adjacent number is indexed

 to location in Table 2.1

18

TEXAS

COLORADO

CALIFORNIA

ARIZONA

NEVADA

UTAH

IDAHO

NEW

MEXICO

GEORGIA

VIRGINIA

DELAWARE

NEW

JERSEY

LOUISIANA

Central Valley aquifer syste

m

Denver Basin

aquifer system

Basin and Range

basin-fill aquifers

Basin and Range basin-fill aquifers

California Coastal Basin aquifers

Coastal lowlands aquifer system

Surficial aquifer system

Northern Atlantic Coastal Plain aquifer system

Rio Grande aquifer system

Snake River Plain basin-fill aquifers

24

30

25 18

21 16 11

20 27

23

32 291331, 33

12 28

15 2617 22 10

14

19

41

38

50

42 43

4 8 2 6 3

1

7 9 5

46

49

47

48

39 40

37

51

52

35

36 44

45

34

Figure 2.4. Selected, known areas of permanent land subsidence owing principally or 

secondarily to ground-water or oil and gas extractions (see table 2.1) in the 48 

conterminous United States and associated aquifer systems (modified from Galloway and 

others, 1999).

48
Table 2.1 Locations of selected known subsidence in the USA attributed to ground-water 

and oil-and-gas extractions.

[Map ID—Map identification number on figure 2.4; Latitude-Longitude—approximate in decimal degrees, reference 

datum is NAD83; Fluid—W is ground water, O&G is oil and gas; Aquifer Name (U.S. Geological Survey, 2003)—na 

is not applicable] 

Map ID State Location Name Latitude Longitude Fluid Aquifer Name

1 Arizona Avra Valley 32.4372 -111.3158 W Basin and Range basin-fill aquifers

2 Arizona East Salt River Basin 33.2525 -111.6461 W Basin and Range basin-fill aquifers

3 Arizona Eloy, Picacho Basin 32.7525 -111.5556 W Basin and Range basin-fill aquifers

4 Arizona Harquahala Plain 33.4147 -113.1558 W Basin and Range basin-fill aquifers

5 Arizona San Simon Basin 32.2344 -109.1475 W Basin and Range basin-fill aquifers

6 Arizona Stanfield Basin 32.8786 -111.9636 W Basin and Range basin-fill aquifers

7 Arizona Tucson Basin 32.2217 -110.9697 W Basin and Range basin-fill aquifers

8 Arizona West Salt River Basin 33.5422 -112.3733 W Basin and Range basin-fill aquifers

9 Arizona Wilcox Basin 31.9814 -109.8814 W Basin and Range basin-fill aquifers

10 California Antelope Valley 34.7344 -118.1333 W Basin and Range basin-fill aquifers

11 California Bakersfield (San Joaquin Valley) 35.3669 -119.0189 W, O&G Central Valley aquifer system

12 California Coachella Valley 33.6858 -116.1833 W Basin and Range basin-fill aquifers

13 California Elsinore Valley 33.6919 -117.4646 W Other rocks

14 California Fremont Valley 35.2172 -117.8494 W Basin and Range basin-fill aquifers

15 California La Verne 34.1133 -117.765 W California Coastal Basin aquifers

16 California Lost Hills (San Joaquin Valley) 35.6161 -119.6603 O&G na

17 California Lucerne Valley 34.4444 -116.95 W Basin and Range basin-fill aquifers

18 California Mendota (San Joaquin Valley) 36.7539 -120.3783 W Central Valley aquifer system

19 California Mojave River Basin 34.9381 -116.6114 W Basin and Range basin-fill aquifers

20 California Oxnard Plain 34.1919 -119.1769 W California Coastal Basin aquifers

21 California Paso Robles 35.6978 -120.6217 W California Coastal Basin aquifers

22 California Pomona 34.5186 -117.75 W Basin and Range basin-fill aquifers

23 California Redondo Beach 33.8444 -118.3881 W California Coastal Basin aquifers

24 California Sacramento Valley 39.0233 -121.9581 W Central Valley aquifer system

25 California Salinas Valley 36.6933 -121.7156 W California Coastal Basin aquifers

26 California San Bernardino 34.1053 -117.2942 W Basin and Range basin-fill aquifers

27 California San Gabriel Valley 34.0522 -118.2433 W California Coastal Basin aquifers

28 California San Jacinto Basin 33.7839 -116.9572 W California Coastal Basin aquifers

29 California Santa Ana Basin 33.7481 -117.8744 W California Coastal Basin aquifers

30 California Santa Clara Valley 37.3531 -121.9047 W California Coastal Basin aquifers

31 California Temecula 33.4906 -117.1494 W California Coastal Basin aquifers

32 California Wilmington 33.7892 -118.2632 O&G na

33 California Wolf Valley 33.4647 -117.1025 W California Coastal Basin aquifers

34 Colorado Denver Area 39.74 -104.9922 W Denver Basin aquifer system

35 Delaware Bowers Area 39.0594 -75.4022 W Northern Atlantic Coastal Plain aquifer system

36 Delaware Dover Area 39.1561 -75.5264 W Northern Atlantic Coastal Plain aquifer system

37 Georgia Savannah Area 32.0808 -81.0908 W Surficial aquifer system

38 Idaho Raft River Area 42.5989 -113.2292 W Snake River Plain basin-fill aquifers

39 Louisiana Baton Rouge 30.4436 -91.1869 W Central lowlands aquifer system

40 Louisiana New Orleans 30.0346 -90.0249 W, O&G Central lowlands aquifer system

41 Nevada Humboldt Valley 40.8589 -117.1175 W Basin and Range basin-fill aquifers

42 Nevada Las Vegas Valley 36.1719 -115.1397 W Basin and Range basin-fill aquifers

43 Nevada Pahrump Valley 36.1583 -115.9633 W Basin and Range basin-fill aquifers

44 New Jersey Atlantic City-Oceanside 39.3808 -74.4514 W Northern Atlantic Coastal Plain aquifer system

45 New Jersey Baranagat Bay-New York Coastal Area 40.3108 -74.035 W Northern Atlantic Coastal Plain aquifer system

46 New Mexico Rio Rancho, Albuquerque Basin 35.2417 -106.66 W Rio Grande aquifer system

47 Texas Corpus Christi 27.7964 -97.4036 O&G na

48 Texas Houston-Galveston Area 29.7606 -95.37 W, O&G Central lowlands aquifer system

49 Texas Hueco-Bolson, El Paso 31.7664 -106.4961 W Rio Grande aquifer system

50 Utah Salt Lake City 40.7596 -111.8883 W Basin and Range basin-fill aquifers

51 Virginia Franklin-Suffolk 36.7367 -76.9769 W Northern Atlantic Coastal Plain aquifer system

52 Virginia Williamsburg-West Point Area 37.2753 -76.5392 W Northern Atlantic Coastal Plain aquifer system

Subsidence is a challenge for a number of other petroleum and natural gas reservoirs. In 

Venezuela, subsidence due to reservoir depletion has led to severe flooding along the coast of Lake 

Maracaibo. In the Netherlands, subsidence at the large Groningen gas field, though only on the 

order of tens of centimeters, poses significant challenges because large portions of the Netherlands 

are below sea level and protected by dikes. The Lost Hills and Belridge oil fields in the San Joaquin 

Valley, California subsided about 400 mm/yr during 1995–96 attributed to compaction of the 

49
petroleum reservoirs (Fielding and others, 1998). Some of the subsidence contributing to relative 

sea-level rise and loss of wetlands along the Texas and Louisiana Gulf Coast has been attributed to 

hydrocarbon production (Morton and others, 2006). 

Drainage of Organic Soils 

In the U.S. system of soil taxonomy, organic soils or histosols are 1 of 10 soil orders. They 

are formally defined as having more than 50 percent organic matter in the upper 12 cm, but may be 

of lesser thickness if they overlie fragmental rock permeated by organic remains. Organic soil is 

commonly termed “peat,” if fibrous plant remains are still visible, or “muck” where plant remains 

are more fully decomposed. Other common names for areas with accumulations of organic soil 

include “bog,” “fen,” “moor,” and “muskeg.” Organic soils form in wetlands where plant litter 

(roots, stems, leaves) accumulates faster than it can fully decompose. Fibrous peats typically 

include the remains of sedges and reeds that grew in shallow water. “Woody” peats form in swamp 

forests. In northerly latitudes with cool, moist climates, many peats comprise chiefly sphagnum 

moss and associated species. The total area of organic soils in the United States is about 210,000 

km2

, about half of which is “moss peat” located in Alaska (Lucas, 1982; fig. 2.5). About 70 percent 

of the organic-soil area in the contiguous 48 States occurs in northern, formerly glaciated areas, 

where moss peats also are common (Stephens and others, 1984). In Canada, the term “muskeg” 

(MacFarlane, 1969) is typically used to describe highly organic soils, which are often subdivided 

into fibrous peat, amorphous peat, silty peat, and organic silt to differentiate the sequence of 

decreasing organic and intact fiber content, natural water content, and compressibility of these 

materials. Similar descriptive terms and ranges of properties of the various organic or peat soils are 

presented in U.S. Department of the Navy (1982, p. 7.1-20–21, table 5).

VIRGINIA

NORTH CAROLINA

GEORGIA

FLORIDA

LOUISIANA

MICHIGAN

MINNESOTA

WASHINGTON

CALIFORNIA

ALASKA

0

0 500 MILES

500 KILOMETERS

Figure 2.5. Locations of organic-soil areas in the U.S. (from Galloway and others, 1999). 

Land subsidence invariably occurs when organic soils are drained for agriculture or other 

purposes. Causes include compaction, desiccation, erosion by wind and water, and, in some cases, 

prescribed or accidental burning. The effects of compaction and desiccation after initial draining 

50
can be dramatic, because organic soils have very low density caused by their water content (as 

much as 80–90 percent). 

The most prevalent mechanism of organic-soil subsidence, however, is oxidation. The 

balance between accumulation and decomposition of organic material shifts dramatically where 

peat wetlands are drained. Under undrained conditions, anaerobic microbial decomposition of plant 

litter cannot keep pace with the rate of accumulation. Lignin, an important cell-wall component of 

all vascular plants, is much more vulnerable to decomposition under aerobic conditions. Oxidation 

under aerobic conditions converts the organic carbon in the plant tissue to carbon dioxide gas and 

water. 

The biochemical origin of much organic-soil subsidence was established through laboratory 

experiments with Florida peat in the early 20th century (Waksman and Stevens, 1929; Waksman 

and Purvis, 1932) that balanced the loss of dry soil weight with rates of carbon dioxide production 

(CO2). This early laboratory work also suggested optimal temperature ranges and moisture contents 

for microbial decomposition. Later field studies and observations have confirmed oxidation as the 

dominant subsidence process in many instances. For example, in the Florida Everglades, sod fields, 

and residential areas—where causal mechanisms such as erosion, burning, and compaction are 

minimized or absent—have sunk as rapidly as the cultivated land (Stephens and others, 1984). It is 

believed that oxidation-related soil loss can be halted only by complete resaturation of the soil or 

complete consumption of its organic carbon content (Wosten and others, 1997). 

Whereas natural rates of accumulation of organic soil are on the order of 10 cm per 100 

years, the rate of loss of drained organic soil can be 100 times greater, as much as 10 cm/yr in 

extreme cases. Thus, deposits that have accumulated over many millennia can disappear over time 

scales that are very relevant to human activity. 

Human experience with subsiding organic soils dates back nearly 1,000 years in The 

Netherlands and several hundred years in the English fen country. The old polders in the western 

Netherlands were reclaimed for agriculture between the 9th and 14th centuries, and by the 16th 

century the land had subsided to such an extent that windmills were needed to discharge water 

artificially to the sea (Schothorst, 1977). Because ground-water levels beneath the polders were still 

relatively high, the rate of subsidence was relatively low—about 1.52 mm/yr, over a roughly 1,000-

year period in which progressively more sophisticated drainage systems were developed 

(Nieuwenhuis and Schokking, 1997). Greatly improved drainage in the 20th century increased the 

thickness of the drained zone above the water table. As a result, subsidence rates rose to about 5 

mm/yr between the late 1920s and late 1960s, and current rates are more than 7.5 mm/yr. 

The organic-soil subsidence rates in the Netherlands are still unusually low in a global 

context. This is due in part to the relatively cool climate, where temperatures are generally below 

the optimal range for microbial decomposition, and in part to a thin layer of marine clay that caps 

much of the peat. Larger average rates have been observed elsewhere: up to 100 mm/yr over the 

last 100 years in the Sacramento-San Joaquin Delta, California (slowing over time to as much as 

10–30 mm/yr in the early 1990s [Rojstaczer and Deverel, 1993; Deverel and Rojstaczer, 1996]); 

about 20–30 mm/yr over the past 100 years in the English fens and the past 70 years in the Florida 

Everglades (about 14 mm/yr during the 1980s and 90s near Belle Glade, Florida [Shih and others, 

1998]). 

In the English fens and the Everglades, long-term subsidence rates have been monitored 

using stone or concrete columns driven into the underlying solid substrate (fig. 2.6). The history of 

both areas has been marked by alternate cycles of improved drainage followed by accelerated 

subsidence and, consequently, inadequate drainage (Stephens and others, 1984), so that the 

achievements of one generation become the problems of the next (Darby, 1956). 

51
V-III

V-II

V-I

V

Subsiding

ground

surface

1924

1954

1968

1976

1978

Marker at Everglades Experiment

Station, Belle Glade, Florida

Roman numerals on marker 

indicate feet above the 

limestone substrate.

Figure 2.6. Subsidence in the Florida Everglades measured on a concrete marker 

shown in 1998 photograph (from Galloway and others, 1999). 

Sinkholes 

A sinkhole is a closed depression in a karst or pseudokarst area, commonly with a circular 

or ellipsoidal pattern. Its size generally is measured in meters or tens of meters, rarely in hundreds 

of meters; and it is commonly funnel-shaped and associated with subsurface drainage (fig. 2.7). 

Sudden and unexpected collapse of the land surface into subsurface cavities is arguably the most 

hazardous type of subsidence. The typically localized collapse features form naturally in relatively 

soluble evaporite (salt and gypsum) deposits and carbonate (limestone and dolomite) rocks. Human 

activities often facilitate the formation of sinkholes in these susceptible materials and trigger their 

collapse, as well as the collapse of preexisting subsurface cavities. Such catastrophic subsidence is 

commonly triggered by ground-water-level declines caused by pumping and (or) by purposeful or 

inadvertent diversion of surface runoff enhancing ground-water flow through susceptible rocks. 

Salt and gypsum are, respectively, almost 7,500 and 150 times more soluble in water than 

limestone, underlie about 35–40 percent of the contiguous United States (fig. 2.8). Bedded or 

domal salt deposits underlie 25 States and constitute about one-half of the area underlain by salt 

and gypsum. Natural solution-related subsidence has occurred in each of the major salt basins (Ege, 

1984; Johnson, 2005). Although evaporites underlie most of the Michigan-Appalachian and Gulf 

Coast basins, naturally forming collapse features are much less common in these areas. Humaninduced collapse cavities are relatively uncommon in gypsum deposits, and more likely to develop 

above salt deposits, where they primarily are associated with purposeful and accidental dissolution 

through mining, or drilling into, salt deposits. 

52
A

B C

Figure 2.7. Sinkholes: A. The Meade sink overlying gypsum and salt beds in western 

Kansas (Photo from Kansas Geological Survey); B. Drilling induced sinkhole in 

carbonates near Tampa, Florida (Photo by Tom Scott). C. Cover-collapse sinkhole in 

mantled carbonate karst near Ocala, Florida (Photo by Tom Scott). 

53
Evaporite rock

Karst from evaporite rock

Karst from carbonate rock

EXPLANATION

EXPLANATION

0

0 500 MILES

500 KILOMETERS

0

0 500 MILES

500 KILOMETERS

Permian Basin

Gulf Coast Basin

Michigan-Appalachian

Holbrook

Basin

Tulsa

Basin

A

B

Figure 2.8. General locations of A. salt and gypsum (evaporite) deposits (from Martinez 

and others, 1998), and B. karst from evaporite and carbonate rocks (from Davies and 

LeGrand, 1972) in the 48 conterminous United States. 

The high solubilities of salt and gypsum permit cavities to form in days to years, whereas 

cavity formation in carbonate rocks is a relatively slow process that generally occurs over centuries 

to millennia. The slow dissolution of carbonate rocks favors the stability and persistence of the 

distinctively weathered landforms known as karst. Both dissolution and erosional processes play 

roles in the maturation of karst in carbonates; if not for a balance between mechanical erosion and 

dissolution, the distinctive landscapes could not persist. The potential for dissolution is controlled 

by the amount of water available and also by the level of saturation of that water with respect to 

calcium carbonate. Where the potential for dissolution is low, mechanical erosion dominates the 

morphology of carbonates. For example, in the arid Southwest, carbonate exposures tend to erode 

as cliffs rather than form karst. 

Carbonate karst landscapes constitute about 40 percent of the contiguous United States east 

of the longitude of Tulsa, Oklahoma (White and others, 1995; fig. 2.8B). In these more humid 

54
landscapes, surface and subsurface drainage pathways converge in discrete conduits formed in the 

carbonate bedrock. Sinkholes, swallows (where streams disappear into the subsurface), and springs 

are linked to form an interconnected surface and subsurface drainage network. Thus, karst aquifer 

systems are directly affected by variability in timing and magnitude of surface runoff. 

Underground Mining 

In terms of land area affected, underground mining accounts for about 20 percent of the 

total land subsidence in the United States, and most of this fraction is associated with underground 

mining for coal (National Research Council, 1991). Subsidence attributed to underground coal 

mining, generally classified as pit subsidence or sag/trough subsidence, had affected about one 

quarter of the area undermined or 2 million acres in the United States by the 1970s with the 

eventual area undermined projected to increase 5-fold in the future (HRB Singer, Inc., 1977; 

Johnson and Miller, 1979). Most of the mining and subsidence has taken place in the eastern half 

(east of 100°W longitude) of the United States, mostly in Pennsylvania, Illinois and West Virginia 

(Gray and Bruhn, 1984). In the western half of the United States in the 1980s, underground coal 

mines occupied about 0.28 percent of the 15-percent portion of western land area underlain by coal 

deposits, and subsidence has occurred locally above many of these mined areas (Dunrud, 1984). 

The subsidence is time-dependent with vertical (generally largest) and horizontal components of 

movement, depending on the type and extent of mining. Early underground mining was less 

efficient than more recent underground mining. Subsidence over early mines can occur tens to 

hundreds of years after mining has ceased, whereas subsidence over more recent mines where 

virtually total extraction is practiced tends to occur contemporaneously with mining. Subsidence 

over underground coal workings develops as a gradual downwarping of the overburden into mine 

voids and is generally unrelated to subsurface water conditions. Mine voids and other subsurface 

voids can be discovered or located using gravimetric or ground-penetrating radar surveys.

 Abandoned tunnels and underground mining for metallic ores, limestone, gypsum, and salt 

contribute a small percentage of the subsidence attributed to underground mining. These mined 

areas are subject to downwarping of the overburden, but limestone, salt and gypsum whether 

undermined or not are susceptible to extensive dissolution by water, frequently leading to sinkholes 

and in some cases catastrophic collapses (Galloway and others, 1999, p. 107-140). 

Hydrocompaction 

Preceding construction of the California Aqueduct in the San Joaquin Valley, California, 

combined field and laboratory studies of the mechanisms and requisite conditions for 

hydrocompaction, also known as hydroconsolidation and “near-surface subsidence,” were done 

(Bull, 1964; California Department of Water Resources, 1964). By means of laboratory tests on 

soil cores from depths to 30 or more meters, and by continuously flooded test plots equipped with 

subsurface benchmarks at various depths and, in some cases, with soil-moisture probes, these 

studies demonstrated that hydrocompaction occurred in the alluvial-fan sediments above the 

highest prehistoric water table and in areas where sparse rainfall and ephemeral runoff had never 

penetrated below the zone subject to summer desiccation by evaporation and transpiration. Under 

these circumstances the initial high porosity of the sediments (often enhanced by numerous bubble 

cavities and desiccation cracks) is sun-baked into the deposits and preserved by their high dry 

strength, even as they are subjected to the increasing load of 30 or more meters of accumulating 

overburden. In the San Joaquin Valley, such conditions are associated with areas of very low 

average rainfall and infrequent, flashy, sediment-laden runoff from small, relatively steep upland 

watersheds that are underlain by easily eroded shales and mudstones. The resulting muddy debris 

55
flows and poorly sorted stream sediments typically contain montmorillonite clay in proportions that 

cause it to act, when dry, as a strong interparticulate bonding agent. Eolian soils or loess deposits 

within arid or semiarid terrain also frequently have low in-place densities as a result of cementation 

and (or) negative pore-suction particle bonding. Similarly, there are numerous cases in which finegrained soils have been placed under dry conditions and without effective watering during 

compaction, producing settlement or collapse of susceptible fills of as much as 30 m thickness. 

These natural soils or fills are often described as being metastable. When water is first 

applied in quantities sufficient to penetrate below the root zone the clay bonds are drastically 

weakened by wetting, and the weight of the overburden crushes out the excess porosity. The 

process of densifying to achieve the strength required to support the existing overburden may 

reduce the bulk volume by as much as 10 percent, the amounts increasing with increasing depth 

and overburden load. 

Localized compaction beneath a water-filled pond or ditch often leads to vertical shear 

failure at depth between the water-weakened sediments and the surrounding dry material. At the 

surface this process surrounds the subsiding flooded area with an expanding series of concentric 

tensional fissures having considerable vertical offset—a severely destructive event when it occurs 

beneath an engineered structure. 

Most of the potential hydrocompaction latent in anomalously dry, low-density sediments is 

realized as rapidly as the sediments are thoroughly wetted. The process goes rapidly to completion 

with the initial thorough wetting, and is not subject to reactivation through subsequent cycles of 

decreasing and increasing moisture content. However, an increase in the surface load such as a 

bridge footing or a canal full of water can cause additional compaction in prewetted sediments. 

Thus the progression of a hydrocompaction event is controlled largely by the rate at which the 

wetting front of percolating water can move downward through the sediments. A site underlain by a 

thick sequence of poorly permeable sediments may continue to subside for months or years as the 

slowly descending wetting front weakens progressively deeper deposits. If the surface-water source 

is seasonal or intermittent, the progression is further delayed. 

Studies undertaken in the mid-1950s led to a better understanding of hydrocompaction and 

to the identification of long reaches of the California Aqueduct route through the San Joaquin 

Valley, California that were underlain by deposits susceptible to hydrocompaction. Construction of 

the aqueduct through these reaches was preceded by prewetting the full thickness of susceptible 

deposits beneath the aqueduct alignment, and thus compacting deposits to a nearly stable state. 

These measures added more than two years and millions of dollars to the cost of the project. 

Thawing Permafrost 

Thawing of permafrost is one of the key issues identified as potential consequences of 

climate variability and change for Alaska (Parson and others, 2001). Permafrost underlies about 85 

percent of Alaska and varies widely in depth, continuity, and ice content (fig. 2.9). About 50 

percent of Canada’s land surface lies in the permafrost region, either in the continuous zone where 

permafrost extends to great depths or in the discontinuous zone where the permafrost is thinner and 

there are areas of unfrozen ground (Canadian Geotechnical Society, 2006). Thawing permafrost 

creates thermokarst terrain—uneven surface topography that includes pits, troughs, mounds, and 

depressions, which can fill with water. 

Particular care often is required in the design and construction of pipelines or other 

infrastructure to prevent changes to the natural site cover and drainage conditions that increase the 

rate of thermal degradation and (or) the depth and extent of the active zone. Stripping of highly 

organic muskeg or tundra surficial soils, which provide a natural insulation layer, and placement of 

granular roadbed or trench backfill materials to facilitate access and construction can result in a 

56
significant increase in the seasonal active zone, and resulting subsidence or instability. Thawing 

will speed organic-decomposition reactions, increase ground-water mobility, increase susceptibility 

to erosion and landslides, and can lead to further subsidence owing to the oxidation of drained, 

exposed organic material. 

170°

170°

150°

160°

180°

170°

160°

150°

140°

130°

120°

110°

45°

50°

55°

60°

65°

70°

Thick

Discontinuous

EXPLANATION

Permafrost 

Discontinuous

Continuous

Isolated Masses

Moderately Thick To Thin

Numerous Isolated Masses

Isolated Masses

Free

Mountainous Area Lowland and Upland Area

0 250 500 KILOMETERS

0 250 500 MILES

PACIFIC OCEAN

ARCTIC OCEAN

BERING SEA

Figure 2.9. Map showing distribution of permafrost in Alaska (modified from U.S. 

Geological Survey, 1996).

Continuous permafrost on the North Slope of Alaska has warmed 2–4 °C since the late 

1800s and more rapidly over the past couple of decades (Osterkamp and Romanovsky, 1996; 

Hinzman and others, 2005). Because temperatures at the upper surface of continuous permafrost 

are still low, no significant loss of continuous permafrost is projected over the 21st century. The 

discontinuous permafrost to the south is warmer, and increased warming suggests that much of the 

discontinuous permafrost south of the Yukon River and on the south side of the Seward Peninsula 

could be thawing. 

Building on permafrost requires that structures be stabilized in permanently frozen ground 

below the active layer, and that they limit their heat transfer to the ground, usually by elevating 

them on piles. For example, to prevent thawing of permafrost from transport of heated oil in the 

57
Trans-Alaska Pipeline, about 640 km of pipeline were elevated on thermosyphon piles (fig. 2.10), 

at an additional cost of $800 million. The pipeline was completed at a cost of $8 billion in 1977, 

about eight times the estimated cost of installing the conventional in-ground pipeline. 

Figure 2.10. Trans-Alaska Pipeline near Glennallen, Alaska (July 2007) showing thermal 

syphons on vertical support members and adjustable “shoes” to accommodate horizontal 

and vertical movements. (Photos by Devin Galloway, USGS). 

The near-term risk of disruption to operations of the Trans-Alaska pipeline owing to 

thawing permafrost is judged to be small. Breaks in the pipeline and other repair costs caused by 

melting permafrost could become significant in the future. The pipeline's support structures are 

designed for specific ranges of ground temperatures, and heaving or collapse if the permafrost 

thaws. Replacing them, if required, would cost about $1.25 million/km. 

Where permafrost has high ice content, typically in about half the area of discontinuous 

permafrost, thawing can lead to the development of thermokarst terrain with subsidence, observed 

in some cases to exceed 4.8 m. Thawing of ice-rich discontinuous permafrost has damaged houses, 

roads, airports, pipelines, military installations, and railroad tracks (fig. 2.11); required costly road 

replacements and increased maintenance expenditures for pipelines and other infrastructure; and 

increased landscape erosion, slope instabilities, and landslides. Present costs of thaw-related 

damage to structures and infrastructure in Alaska have been estimated at about $35 million per 

58
year, of which repair of permafrost-damaged roads is the largest component. Longer seasonal thaw 

of the active layer could disrupt petroleum exploration and extraction and increase associated 

environmental damage in the tundra, by shortening the season for minimal-impact operations on ice 

roads and drilling pads. 

Figure 2.11. Abandoned railroad tracks warped by thermokarst near Valdez, Alaska. 

During construction of the roadbed the thermal equilibrium of the permafrost was 

disrupted causing differential thawing. 

Natural Consolidation 

Rates of natural consolidation generally are low and not likely to pose hazards to pipelines 

during their operational lifetime. The process of natural consolidation is included here in part for 

completeness, but also in part owing to the problem of differentiating the various regional and local 

processes that contribute to the total subsidence in an area. Soils engineers and geologists define 

consolidation in different terms. Geologic consolidation is the process whereby loosely aggregated, 

soft or liquid earth materials become firm and coherent rock—for example, the lithification of loose 

sediment to form sedimentary rock. This process largely is driven by diagenesis accompanying the 

aggradation of sediment over geologic time that successively buries older sediment under 

increasing loads of more recent deposits. Diagenesis refers to the chemical, physical, and biological 

changes undergone by sediment after its initial deposition and during and after lithification, 

exclusive of weathering and metamorphism (Neuendorf and others, 2005). Soils engineers refer to 

consolidation as the gradual reduction in volume and increase in density of a soil mass in response 

to increased load or effective compressive stress; for example, the squeezing of fluids from pore 

spaces. This report refers to “natural consolidation” in accordance with the soils engineer’s 

definition of consolidation. In areas of rapid sedimentary deposition, such as the deltas of large 

river systems or in topographic basins adjacent to zones of rapid tectonic uplift, long-term average 

deposition rates may be as large as 1 mm/yr (Ingebritsen and others, 2006, p. 357). Fluid pressures 

59
greater than hydrostatic (geopressures or overpressures) are associated with areas of active 

deposition, and occur when fluid cannot drain rapidly enough to keep pace with increasing 

overburden loads caused by deposition—a condition known as compaction disequilibrium. 

Sediments that drain freely during deposition maintain fluid pressures near hydrostatic. In the Gulf 

of Mexico basin, overpressures largely have been attributed to compaction disequilibrium 

(Bredehoeft and Hanshaw, 1968; Sharp and Domenico, 1976; Harrison and Summa, 1991; Hart and 

others, 1995), and compressional strain is almost certainly the dominant process contributing to 

overpressures in most active basins (Ingebritsen and others, 2006). 

Subsidence rates of geologically young deposits are expected to be high initially and decline 

exponentially or logarithmically with burial (Magara, 1978) as pore water is expelled as the 

sediments compact under increasing loads. As expected, regional subsidence rates attributed to 

natural consolidation are higher in the Louisiana coastal plain where Holocene sediments derived 

from the Mississippi flood plain and delta are relatively thick as compared to the Texas coastal 

plain where the Holocene sediments are relatively thin. However, anthropogenic factors in the 

developed coastal plains also contribute to subsidence and complicate the determination of natural 

consolidation rates. 

For the Mississippi Delta estimated average subsidence rates vary widely and are 

confounded by estimates of the relative (subsidence and uplift) contributions from natural 

consolidation, from eustasy (global change in sea level), from tectonic uplift and downwarping, and 

glacio-isostasy (continental rebound from glacial retreats), and from local and regional land 

subsidence caused by anthropogenic factors (for example, see González and Törnqvist, 2006): 

subsurface fluid extraction (oil, gas, and ground water), oxidation of artificially drained organic 

soils, and reduced sediment deposition owing to diversion of sediments that would have 

compensated for some of the subsidence. During the latter part of the 20th century much of the 

deltaic plain has subsided more than 10 mm/yr relative to sea level (Penland and Ramsey, 1990). 

Estimates of eustasy range from a rise of 1 to 2 mm/yr (IPCC, 2001). Estimates of natural 

consolidation rates in the Mississippi Delta over the past few thousand years range from 2 to 4 

mm/yr (Penland and others, 1988; Roberts and others, 1994). By contrast, in the Copano Bay area 

of the Texas coastal plain estimated rates of natural consolidation over the past 100,000 years are 

about two orders of magnitude smaller, 0.02-0.05 mm/yr (Paine, 1993). 

Subsidence Susceptible Areas 

The soils characteristics and the geology of the surficial and subsurface rocks can be used to 

help identify subsidence prone areas for each of the types of subsidence listed above. Where this 

information is unavailable, some reconnaissance soils and (or) geologic mapping may be needed. In 

the absence of other obvious features, such as sinkholes (karst terrane) and pit or sag/trough 

subsidence (associated with underground coal mines), other ancillary or anecdotal information that 

suggests subsidence may be occurring is often useful. Ancillary information is pertinent to 

regional-scale subsidence processes where the subsidence may be subtle and difficult to detect, 

therefore much of the emphasis in the following section is on those processes, such as subsidence 

accompanying the withdrawal of subsurface fluids. 

Ancillary/Anecdotal Information 

Increased incidence of damaged wells : Protruding well casings are common in 

agricultural areas and some urban areas where ground water has been extracted from alluvial 

aquifer systems (fig. 2.12). The land surface and aquifer system are displaced downward relative to 

the well casing, which is generally anchored at a depth where there is less compaction. Generally, 

60
for shallow wells, casings protrude because the friction created between the casing and the 

compacting earth materials is small and the casing is not compressed. For deep wells the friction is 

high and the stressed casings are subject to failure through collapse and dislocation. Submersible 

pumps, pump columns, and the well itself may be damaged or require rehabilitation. Deep wells are 

most vulnerable and also are the most expensive to repair and replace. Where the frequency of 

well-casing failures is high, land subsidence is often suspected and is often the cause. For example, 

when drought conditions during 1976–77 in the Sacramento Valley, California, led to decreased 

surface-water deliveries and increased ground-water pumping for agricultural irrigation many wells 

were damaged by vertical compression of the casing attributed to compaction of the aquifer system 

(Borchers and others, 1998). 

Davis

Sacramento

0 10 MILES

0 10 KILOMETERS

CALIFORNIA

Map

area

Subsidence

Casing

compression

0

1.2

0.6

0 8 16

METERS

KILOMETERS

Some corelation was 

observed between 

subsidence and the 

compression of nearby 

well casings

Sacramento River

S

A

C

R

A

M

E

N

T

O

V

A

L

L

E

Y

C O

A S T R

A

N

G

E S

Intact well

Damaged well

A A'

A A'

Well head

Well head

1964 1997

A

B C

Figure 2.12. Damaged wells: A. Photographs showing progressively protruding well in 

Las Vegas, Nevada, in 1964 and 1997; B. Map showing distribution of damaged wells in 

Sacramento Valley, California, and correlation to subsidence along section A-A′ (modified 

from Borchers and others, 1998); C. Photograph from borehole camera showing 

collapsed, spiraled well screen in damaged well. 

61
History of repeated adjustments to local geodetic controls : Detection of 

regional land subsidence may be thwarted by the assumption that the reference marks—bench 

marks—used to establish local geodetic control are stable, that is, located outside the area affected 

by subsidence. Often, public agencies or private contractors discover that key local bench marks 

have moved only after repeat surveys that span several years or longer. Prior to the discovery, when 

the cumulative subsidence magnitude is small, the apparent errors in the surveys may be adjusted 

throughout the geodetic network under the assumption that the discrepancies reflect random errors 

of the particular survey. The subsidence may then go undetected until later routine surveys, or until 

suspicions arise and steps are taken to confirm the current elevations of the affected bench marks. 

For example, comparison of repeat leveling surveys led to the confirmed discovery of land 

subsidence in Antelope Valley (Mojave Desert), California (McMillan, 1973), Las Vegas, Nevada 

(Maxey and Jameson, 1948), the San Joaquin Valley, California (Ingerson, 1941), and south-central 

Arizona (Robinson and Peterson, 1962). 

Increasing incidence of local riverine or coastal flooding: Flooding caused 

by loss of elevation and changes of topographic gradients are the most costly impacts of land 

subsidence (Holzer and Galloway, 2005). Flooding is most severe where land subsides adjacent to 

water bodies, particularly in coastal regions subject to tidal surges. This causes either permanent 

submergence or more frequent flooding. Some of the more conspicuous examples of subsidencerelated coastal flooding are in the greater Houston, Texas metropolitan area (fig. 2.13), Long Beach 

and Santa Clara Valley, California, New Orleans, Louisiana, and Venice, Italy. Approximately 

4,900 km2

 of coastal lands, mostly tidal wetlands, have been lost from the 25,000 km2 Mississippi 

deltaic plain due to water-logging since the 1930s (Barras and others, 2003). In New Orleans and 

Santa Clara Valley, riverine-flood-control levees have been built in subsidence-affected areas. In 

the land below high-tide level, local storm discharge must be captured and pumped over the levees 

to prevent widespread flooding. 

Local conveyance and drainage problems : Changes of topographic gradients 

occur where loss of elevation is not uniform. This may result in stagnation or reversals of streams, 

aqueducts, storm drainages, or sewer lines; failure, overtopping or reduction in freeboard along 

reaches of levees, canals, and flood-conveyance structures; and, more generally, cracks and (or) 

changes in the gradient of linear engineered structures such as pipelines and roadways. Differential 

subsidence contributes to local flood hazards at Luke Air Force Base, Arizona (Schumann, 1995), 

and to conveyance losses in the California Aqueduct (Swanson, 1998) and the Central Arizona 

Project (http://www.cap-az.com/static/index.cfm?contentID=90, accessed September 14, 2007). 

Ground failures : Two types of ground failures—earth fissure formation and movement 

on pre-existing surface faults—commonly are recognized in association with surface deformation 

caused by the extraction of subsurface fluids (fig. 2.14). Ground-water pumping in the greater 

Houston area has caused offsets on more than 86 faults at the land surface with a cumulative length 

of more than 240 km. These faults, which grow by aseismic creep, have wracked and destroyed 

many houses, buildings, and buried utilities. Today, surface faults are associated with land 

subsidence in at least five areas in the United States (south-central Arizona; Fremont Valley, 

California; Houston-Galveston area, Texas; Las Vegas Valley, Nevada; San Joaquin Valley, 

California). The density of faults varies greatly from area to area, but perhaps is highest in the 

greater Houston, Texas, and Fremont Valley, California, subsidence areas (for example, see Holzer, 

1984). 

62
A

B

Figure 2.13. Flooding in subsidence affected areas near Houston, Texas: A. Permanently 

submerged lands at the San Jacinto Battleground State Historical Park near the shores of 

Galveston Bay; B. Homes near Greens Bayou flooded during a storm in June 1989 

(Photograph courtesy of Harris-Galveston Subsidence District). 

63
0

20

40

60

80

DISTANCE, IN KILOMETERS

LINE 1

SUBSIDENCE RATE, IN MM/YR

InSAR

Leveling

1980–1982

4/92–11/93

1/96–12/97 11/93–2/96 Eglington fault

Eglington fault

NW SE

Line 1 

0123

0 2 4 6

0

8 10 KILOMETERS

2468 10 MILES

0

0

0.15

0.3 0.6

0.6

0.3

0.3

0.6

0.3

0.15

0.6

0.15

1.7

1.5

1.25

1.0

Quaternary faults

EXPLANATION

Line of equal subsidence,

in meters—Interval is variable 

Level line

Major highways McCULLOUG H

RANGE

NEVADA

Las Vegas

Valley

95

95

93

93

95

15

15

LAS VEGAS RANGE

S PRING MOUNTAINS 

LAS VEGAS VALLEY

15

FRENCHMAN

MOUNTAIN

B. C.

A.

Figure 2.14. Surface faults associated with extraction of subsurface fluids: A. Photograph 

(circa 1926) of surface fault near Houston, Texas, about one-half mile north of the Goose 

Creek oil field; B. Surface faults in Las Vegas Valley, Nevada, shown in relation to 

measured subsidence caused by ground-water pumping, 1963–2000 (modified from Bell 

and others, 2002); C. Subsidence rates measured along Line 1 (shown in B.) showing 

differential subsidence across the Eglington fault (modified from Amelung and others, 

1999). 

64
Scarps formed by these faults resemble those caused by tectonism, and the two can be 

confused, particularly because both typically form along preexisting geologic faults. Scarps 

commonly are more than 1 km long and 0.5 m high. The longest reported scarp is 16.7 km 

(Verbeek and others, 1979). Scarps range from discrete shear failures to narrow, visually detectable 

flexures. They grow in height by creep. Observed creep rates in the Houston area range from 4 to 

27 mm/yr, which is typical of these faults. The fastest observed creep rate is 60 mm/yr on the 

Picacho fault in central Arizona (Holzer, 1984). Neither sudden offset nor seismicity is observed on 

these faults. Detailed monitoring of differential vertical displacements across a few faults reveals 

that creep rates of individual faults vary with seasonal fluctuations of ground-water level. In 

addition, long-term changes in creep rate, including its cessation when water-level declines stop, 

have been reported (Holzer and Gabrysch, 1987). Only dip-slip displacements have been observed. 

The sense of faulting is high-angle and normal on the basis of measured ratios of horizontal to 

vertical displacement and field evidence. 

Earth fissures occur in at least 18 unconsolidated sedimentary basins in 12 areas in the 

western United States (Holzer and Galloway, 2005). The density of fissures varies greatly between 

areas. In some places only a few isolated fissures have formed, whereas elsewhere, many fissures 

occur. Four distinct hazards are posed by fissures: (1) ground displacements associated with their 

formation, (2) deep, steep-walled gullies caused by post-fissure erosion, (3) interception of surface 

runoff, and (4) erosion of land near the fissure. Although horizontal displacements across fissures 

during their formation are small, they are sufficient to damage rigid engineered structures. In 

addition, differential vertical displacements in narrow zones near fissures may affect structures 

sensitive to small tilts. Gullies associated with fissures are commonly large enough to trap and 

injure livestock and other animals as well as pose a potential hazard to people. Fissures also serve 

as conduits for large quantities of water. Consequently, they are potential hazards to waterconveyance structures such as canals (fig. 2.15). Because of their depth, fissures also can serve as 

conduits or preferential flow paths for contaminants from the surface into shallow aquifers. Fissures 

can be sinks for a large volume of sediments. Their formation may locally trigger severe erosion 

and create badlands topography near the fissure. 

65
A.

B. C.

Figure 2.15. Earth fissures, south-central Arizona (modified from Carpenter, 1999): A. 

Picacho earth fissure, pictured in October 1967 (inset) and June 1989. By 1989 the fissure 

had developed into a system of multiple parallel cracks with a scarp of as much as 0.6 m 

of vertical offset. B. The Central Main Lateral Canal, part of the Central Arizona Project, 

(also pictured in A., upper left of 1989 photo) was damaged by a fissure (circled) where it 

crosses the Picacho earth fissure. C. Natural-gas pipeline undercut by erosional opening 

of an earth fissure near the Picacho Mountains. 

66
Perhaps the most spectacular and costly single incident associated with the extraction of 

subsurface fluids was caused by petroleum production from the Inglewood oil field. On December 

14, 1963, the dam of the Baldwin Hills Reservoir in Los Angeles, California, failed by piping along 

a fault on which movement had been induced by high-pressure fluid injection associated with the 

secondary recovery of petroleum. The catastrophic release of about 900,000 m3 of water took 5 

lives, damaged or destroyed 277 homes, and caused property damage of $12 million in addition to 

the loss of the reservoir (Hamilton and Meehan, 1971). 

Preliminary Mapping 

Compilations of the available geodetic, geologic, hydrogeologic, mining, and cultural 

information are useful. This information may be available from a variety of sources. Sources of 

geodetic information and data include local land surveyors, and municipal, State, such as State 

Transportation Departments, and Federal agencies such as the National Geodetic Survey (National 

Oceanic and Atmospheric Administration). Sources of geologic, hydrogeologic, and mining 

information pertinent to subsidence hazards may include local well drillers, hydrologic and 

engineering consulting firms, State agencies responsible for natural, geologic and water resources, 

and regulating mining activities, and Federal agencies such as the Office of Surface Mining 

(responsible for regulating the surface impacts of underground mining), and the USGS. For 

abandoned mines, maps and other information on subsurface conditions frequently are unavailable. 

The Association of American State Geologists (http://www.stategeologists.org/) is a good place to 

find information on how to access information resources for each of the State Geological Surveys 

or their equivalents. 

Geologic maps may be obtained to assess geologic attributes associated with the various 

types of subsidence. Ground-water levels are available online from many States (such as 

Departments of Natural Resources, Environmental Quality, and Water Resources), and the USGS 

National Water Information System ground-water database (http://waterdata.usgs.gov/nwis/gw). 

Soils maps and other general soils information and specific information pertinent to organic soils 

and soils susceptible to hydrocompaction can be obtained from the U.S. Department of Agriculture, 

Natural Resources Conservation Service (http://soils.usda.gov/). Historical conditions may be 

evaluated using available aerial photography and satellite remote sensing data. Baseline geodetic 

monitoring may be needed to establish current conditions. Interferometric Synthetic Aperture Radar 

(InSAR) displacement maps may be generated to detect land-surface deformation since 1992. 

Assessment 

Subsidence assessments typically address the spatial (magnitude and direction) and 

temporal changes in the position of land surface, and the process causing the subsidence. 

Measuring and monitoring subsidence is critical to constrain analyses of the causative mechanisms 

and forecasts of future subsidence. Computer models of the particular subsidence process, 

constrained by the available data, often are used to assess present and potential future hazards. 

Measurement, Mapping, and Monitoring 

Various methods are used for measuring and mapping spatial gradients and temporal rates 

of regional and local subsidence and horizontal ground motion (table 2.2). The methods generally 

measure relative changes in the position of the land surface. The observable position typically is a 

geodetic reference mark that has been established so that any movement can be attributed to deepseated ground movement and not to surficial effects such as frost heave. Any vertical or horizontal 

movement of a reference mark is measured in relation to other observation points or tied to a global 

67
reference frame. When the reference mark can be assumed to be stable or its movement is 

otherwise known and measurable, it can be used as a control point, and the absolute position of the 

observation point can be determined. By this method, land subsidence has been measured using 

repeat surveys of bench marks referenced to some known, and presumed stable, reference frame. 

Access to a stable reference frame is essential for the measurements needed to map land 

subsidence. In many areas where subsidence has been recognized, and other areas where 

subsidence has not yet been well documented, accurate assessment has been hindered or delayed by 

the lack of a sufficiently stable vertical reference frame (control). 

Table 2.2. Select methods of measuring aquifer-system compaction and land subsidence 

(modified from Galloway and others, 2000). 

[GPS—Global Positioning System; InSAR—Interferometric Synthetic Aperture Radar; LiDAR—Light Detection and 

Ranging; PSI—Permanent Scatterer Interferometry; SAR—Synthetic Aperture Radar] 

Component 

displacement 

Resolution1

(mm) 

Spatial density2

(samples/survey) Method Spatial scale 

Spirit level vertical 0.1-1 10-100 line-network 

Geodimeter horizontal 1 10-100 line-network 

Borehole 

extensometer3 vertical 0.01-0.1 1-3 point 

Horizontal 

extensometer 

Tape horizontal 0.3 1-10 line-array 

horizontal 10-4 Invar wire 1 line 

horizontal 10-5 Quartz tube 1 line 

vertical 

horizontal 

20 

5 GPS 10-100 network 

Satellite SAR 

interferometry 

InSAR range 1-10 105-107 map pixel5

PSI range 1 variable4map pixel5

LiDAR 

vertical 

horizontal 

10 

10 106

-108Tripod 3D point cloud 

vertical 

horizontal 

300 

360 variable map pixel6

Airborne 

1

Measurement resolution obtained under optimum conditions. 2Number of measurements generally 

necessary to define the distribution and magnitude of land subsidence at the scale of the survey. 3

Counterweighted pipe extensometer (Riley, 1969). 4

Depends on presence of permanent scatterers. 5A pixel (picture 

element) on an InSAR/PSI displacement map based on existing spaceborne sensors is typically 40-80 

meters resolution. 6

A pixel on an airborne LiDAR image typically varies from 0.25 to 2m.

68
“Sufficiently stable” is a somewhat relative term that has meaning in the context of a 

particular time-frame of interest and magnitude of differential movement. Because of continuous 

and episodic crustal motions caused mostly by postglacial rebound, tectonism, volcanism, and 

anthropogenic alteration of the Earth’s surface, it is occasionally necessary to remeasure geodetic 

control on a national scale. Networks of geodetic control consist of known positions that are 

determined relative to a horizontal or vertical datum or both. 

Two reference networks are used for horizontal and vertical geodetic control for the United 

States, the North American Datum of 1983 (NAD83) and the North American Vertical Datum of 

1988 (NAVD88). NAD83 replaces the older North American Datum of 1927 (NAD27) and is the 

current geodetic reference system for horizontal control in the United States, Canada, Mexico, and 

Central America. It is the legally recognized horizontal control datum for the Federal government 

of the United States and for 44 of the 50 individual States. NAVD88 replaces the National Geodetic 

Vertical Datum of 1929 (NGVD 1929), which was based on local mean sea levels determined at 26 

tidal gages. NAVD88 is based on the Earth’s geoid—a measurable and calculable surface that is 

equivalent to mean sea level. 

In partnership with other public and private parties, the National Geodetic Survey (NGS) 

has implemented High Accuracy Reference Networks (HARNs) in every State. HARN observation 

campaigns resulted in the establishment of some 16,000 survey stations. These updated reference 

networks facilitate the early and accurate detection and measurement of land subsidence. 

Ground-Based Geodetic Surveys 

Before the advent of the satellite-based Global Positioning System (GPS) in the 1980s, the 

most common means of conducting land surveys involved either the theodolite or, since the 1950s, 

the geodimeter (an electronic distance measuring device, or EDM). If only vertical position were 

sought, the spirit level has been the instrument of choice. The technique of differential leveling 

allows the surveyor to carry an elevation from a known reference point to other points by use of a 

precisely leveled telescope and graduated or bar-coded vertical rods. Despite its simplicity, this 

method can be very accurate. When surveying to meet the standards set for even the lower orders of 

accuracy in geodetic leveling, 10–15 mm changes in elevation can be routinely measured over 

distances of kilometers and 1–2 mm of elevation change over a few kilometers can be obtained 

with the highest quality (order) of leveling. Over long distances leveling and EDM measurement 

errors increase. When the length of the survey is small (on the order of 10 km or less) spirit leveling 

is still commonly used because it is accurate and relatively inexpensive. Large regional networks 

may warrant use of more efficient airborne, and space-based geodetic surveys. If more precise and 

accurate measurements of change are needed on a local scale, extensometers may be used, or if 

more spatial detail is required over short distances, tripod-mounted LiDAR (Light Detection and 

Ranging) may be used. 

Extensometry 

Vertical borehole extensometers can be used to measure the continuous change in vertical 

distance between the land surface and a reference point or “subsurface bench mark” at the bottom 

of a deep borehole (Riley, 1986). If the subsurface bench mark is established below the base of the 

compacting aquifer system or beneath an oxidizing or compaction organic soil, the extensometer 

can be used as the stable reference or starting point for local geodetic surveys. Designs that 

incorporate multiple-stage borehole extensometers in a single instrument are being used to measure 

aquifer-system compaction simultaneously in different depth intervals (figs. 2.16A,B). 

69
When used in conjunction with good well logs and water-level data, the deformation history 

generated by an extensometer can provide the basis for stress-strain analysis (Riley, 1969) and 

modeling that constrains the average compressibility and vertical hydraulic conductivity of the 

aquitards (Helm, 1975; Sneed and Galloway, 2000; Pavelko, 2003). This capability derives from 

the fact that the compaction measured by the extensometer is directly related to the volume of water 

produced by the aquitards. Reliable estimates of aquitard properties are necessary for constraining 

predictive modeling, whether the objective is the prevention or mitigation of land subsidence or 

simply the optimal use of the storage capacity of the aquifer system. 

Level

instrument

table

SHALLOW EXTENSOMETER DEEP EXTENSOMETER

Displacement

transducer

Dial gage Shallow-seated 

pier 

Analog chart

recorder

Pulley cable

Counterweight

Transducer cables to

electronic recording

digital data logger

Extensometer pipe

Concrete slab

Casing

Deep aquifer

Interbed

Shallow aquifer

Not to scale

Reference surface

Fulcrum arm

Counterweights

Pedestal

Dial gage

Pulley cable

Fulcrum arm

Extensometer pipe

Reference surface

Counterweight 

Pedestal

Pipe Housing Post Quartz tube

Monument Dial gage for measurement along tape Tape tension adjustment Dial gage for tape tension . Tape

about 10 m

A.

D.

B. C.

Figure 2.16. Extensometers: A. Schematic, and B. photo of counter-weighted, two-stage, 

borehole pipe extensometer, Lancaster, California. The extensometer measures 

compaction simultaneously in two depth intervals in the aquifer system. The compaction 

(vertical displacement) is measured as movement of the pipe relative to the reference 

table and reflects shortening (subsidence) or lengthening (uplift) of the distance between 

the shallow-seated piers and the anchor depths of the pipe extensometers. C. Buried 

horizontal quartz-tube extensometer, near Apache Junction, Arizona. A displacement 

sensor is placed between a post and the quartz tube to measure small displacements in 

the interval spanned by the coupled post-sensor tube post-instrument configuration. D. 

Tape extensometer, near Apache Junction, Arizona. The tape is attached at both ends to 

a specially equipped geodetic monument. 

70
Several kinds of horizontal extensometers are used to measure differential horizontal 

ground motion at earth fissures caused by changes in ground-water levels (Carpenter, 1993). Buried 

horizontal extensometers constructed of quartz tubes (fig. 2.16C) or invar wires are useful when 

precise, continuous measurements are required on a scale of 3–30 m. Tape extensometers (fig. 

2.16D) measure changes across intermonument distances up to 30 m with repeatability of 

approximately 0.3 mm. The tape extensometer is used in conjunction with geodetic monuments 

specially equipped with ball-bearing instrument mounts, which can serve as both horizontal and 

vertical control points. Arrays or lines of monuments can be extended for arbitrary distances, 

usually in the range of 60–180 m. 

Tripod LiDAR 

Ground-based tripod (terrestrial) LiDAR (shortened to T-LiDAR) is a portable remote 

sensing instrument that uses an infrared laser to scan the landscape and generate very detailed 

(centimeter to subcentimeter) and accurate (±4 mm) digital models of the scanned target at 

distances from 3 to 800 m (fig. 2.17). A three-dimensional image of a scanned target is obtained by 

measuring the two-way travel time of each laser pulse for distance and the look angle for angular 

positioning with respect to the center of the instrument, and by measuring the intensity of the 

reflected signal. The point positions are combined to generate a comprehensive image map of the 

target. More than 7 million point-position measurements per hour can be made depending upon the 

particular T-LiDAR system; scan rates, data densities, and point positional errors vary among the 

different systems. T-LiDAR is an active source technology that collects measurements independent 

of most weather conditions, sky view, and time of day, but is limited to line of sight measurements. 

A full three-dimensional image is obtained by scanning a target from multiple directions to 

characterize all sides of the target and to minimize shadowing. T-LiDAR scans obtained from 

different vantage points are aligned and combined through an algorithm that computes a best-fit 

surface through the individual points in each scan and then minimizes the misfit between common 

surfaces in each scan. 

71
B

C

A

Best-fit cylinder 

Radius=0.272 m

Best-fit cylinder 

Pipeline

Pipeline

Pipeline

Pipeline

Figure 2.17. T-LiDAR imagery of a geothermal transmission line, Mammoth Lakes, Calif. 

A. One T-LiDAR setup imaged about 500 m of continuous pipeline. B. Best-fit primitives 

(idealized geometries) can be fit to the data to simulate the pipeline, as well as, measure 

sub-centimeter displacements. C. Detailed imagery shows a telemetered USGS well and 

adjacent pipeline. 

72
Changes in the position of the land surface or structural features such as exposed pipelines 

can be obtained through differencing of precisely aligned T-LiDAR images collected at different 

times, known as Differential LiDAR (Dif-LiDAR). There are two approaches for Dif-LiDAR 

analysis: absolute and relative. Absolute Dif-LiDAR measures land-surface change by differencing 

two LiDAR datasets that are georeferenced with GPS. This approach is ideal for resolving very 

detailed spatial changes within a larger deformation field. Examples of absolute Dif-LiDAR 

include imaging land-surface change associated with earthquakes, volcanic activity, fluid extraction 

induced land subsidence, large-scale landslides, and areas that have undergone a significant change 

to their geomorphology. Technically, this approach produces the best information of land-surface 

change, but requires additional field equipment and time for GPS data collection and processing 

and may be unnecessary for all scientific applications. Alternatively, relative Dif-LiDAR applies 

the same best-fit surface-matching algorithm used in the alignment of an individual scan to 

common ‘stable’ regions outside of the area of interest that is changing. This approach is ideal for 

resolving very detailed spatial changes within a well-defined deformation zone or imaging change 

in a subset of a larger dataset where absolute positioning is not required. A few examples of 

relative Dif-LiDAR include measuring hill-slope change on moderate and small landslides, 

measuring three-dimensional movement of infrastructure across deforming boundaries, calculating 

volume change associated with debris flows and engineering projects, and characterizing how 

streams respond to floods. Dif-LiDAR can resolve the relative three-dimensional displacement 

field, rotation, localized tilt, and translation within the scanned region, but requires GPS ground 

control to uniquely measure these parameters for the data block as a whole and to reference the 

measurements to a global reference frame. 

To further measure subtle displacement patterns across an actively deforming pipeline, 

individual objects such as poles, support structures, and exposed pipeline can be individually 

selected and mathematically fit to best-fit primitives (idealized geometric shapes) and tracked with 

time to image temporal deformation patterns. Primitive fitting takes advantage of the thousands of 

point measurements and the small (±4 mm) measurement error (random scatter) to fit a 

mathematically defined surface of the feature. For instance, vectors fit to fence posts on an active 

landslide can be used to monitor the change in the intersection of the fence post vector with the 

ground plane measured from one time period to the next and thereby track the movement of the 

individual post in space and time. Similarly, primitives can be fit to signs (planes), bridge support 

pillars (cylinders) and building (planes, polygons) and used to generate a detailed displacement 

map of the deformation or a time series of deformation maps. Because pipelines have artificial 

features with a number of surfaces that can be described mathematically, T-LiDAR is an ideal 

technique for measuring spatial and temporal changes in regions that are actively deforming, but 

the technique may be too labor intensive for characterizing pipelines at linear scales greater than 

about 5 km. 

Airborne Geodetic Surveys 

Airborne LiDAR is capable of rapidly and accurately collecting high-resolution elevation 

data along an infrastructure corridor with 15-cm vertical and 20-cm horizontal uncertainties (one 

standard deviation) for most commercial grade, and with 30 percent lower uncertainties for 

research-grade airborne LiDAR data. The LiDAR imagery can be used to map the pre- and postemplacement topography of a pipeline corridor, to map the three-dimensional character of the 

pipeline, to assess possible hazards along the pipeline, to produce digital blueprints of older 

pipelines through primitive fitting (see Tripod LiDAR section above), and to develop large-scale 

deformation maps using repeat airborne LiDAR imagery. Movements greater than 0.6 m are 

needed between LiDAR surveys to resolve land-surface motion with most commercial grade 

73
airborne LiDAR. There are four primary factors to consider for airborne-LiDAR mapping and 

monitoring of pipeline subsidence hazards: (1) ground sample distance (data density), (2) geodetic 

control, (3) vendor qualifications, and (4) data quality assurance/quality control (QA/QC): 

Data Density : Ground sample distance is the average number of laser shots per square 

meter and controls the spatial resolution of the imagery. A general rule of thumb is that the ground 

sample distance should be a minimum of one-twelfth to one-eighth of the effective length of the 

smallest feature to be resolved. Lower resolution imagery may not have the data density necessary 

for characterizing the infrastructure or target area. One of the primary applications of airborne 

LiDAR is the generation of bare earth Digital Elevation Models (DEMs). DEMS are produced from 

the LiDAR point cloud by digitally removing vegetation and artificial structures (such as buildings, 

bridges, and dams) from the imagery. The resulting product is a bare earth representation of the 

land surface free of vegetation and structures. In regions with heavy vegetation, if the ground 

sample distance is too coarse, then the resulting DEM will lack the desired data density. 

Geodetic Control: Currently, the largest error source in airborne LiDAR is uncertainty 

in the position of the aircraft in flight. The position of the aircraft needs to be determined or 

estimated at the same rate as the laser scanner; a rate of 10,000 samples per second is common for 

many laser scanners. The aircraft position is obtained through GPS positioning measurements on 

the ground and aircraft at 5–10 samples per second and interpolated with an onboard Inertial 

Measurement Unit (IMU) to match the laser scanner rate. Ground-based GPS base stations at 

known locations (controls) are combined with the onboard aircraft GPS to calculate the exact 

latitude, longitude, and elevation of the aircraft and then are transferred to each of the laser returns 

through the angular relationship of the scan angle and the aircraft and the two-way travel time of 

the laser pulse. Error in the GPS position of the aircraft is propagated to the land-surface 

measurements. Increasing the number of ground-based GPS control base stations minimizes the 

aircraft-position error, and a higher quality airborne LiDAR dataset. GPS surveys are discussed in 

more detail below in the Global Positioning System section of this chapter. 

Vendor Qualifications : The capability and (or) experience to collect airborne LiDAR 

imagery with high measurement resolution using accurate and precise geodetic control varies 

widely. Most vendors can produce quality 2-m products, but the quality of the imagery for submeter LiDAR imagery varies among vendors. 

QA/QC: Quality assurance and quality control procedures are necessary to verify that the 

dataset collected meets or exceeds the agreed specifications. The QA/QC component of the 

LiDAR survey is acquired by survey grade ground-based GPS systems (typically Real Time 

Kinematic rovers) collecting hundreds of point positions in a variety of characteristic geologic 

environments, ecosystems, and urban settings. Because the GPS measurements will have a lower 

uncertainty (typically about 1 cm horizontal and 3 cm vertical) than the airborne LiDAR 

measurements, they are considered the ground truth. By collecting QA/QC points in a variety of 

settings, it is possible to evaluate systematic biases in the dataset for a given rock unit, vegetation 

type, or adjacent laser swaths. 

Space-based Geodetic Surveys 

Space-based geodetic techniques that can measure changes in the land-surface position have 

significantly advanced over the past two decades with the development of GPS and interferometric 

synthetic aperture radar (InSAR). Continuously operating reference GPS sites or CORS can 

automatically determine three-dimensional ground position multiple times a second, and averaged 

over a day can be used to obtain long-term time series with millimeter-level resolution of horizontal 

position and sub-centimeter level resolution of vertical positioning at the CORS site. The InSAR 

techniques can measure sub-centimeter ground displacements at high spatial detail (10–100 m 

74
resolution) over regions spanning 100 by 100 km, but are limited to measuring changes in the lineof-sight distance between the ground and satellite, which are most sensitive to vertical motion. 

Global Positioning System 

A revolution in surveying and measurement of crustal motion occurred in the early 1980s 

when tests of the satellite-based NAVSTAR GPS showed that it was possible to obtain 1 part in 1 

million precision between points spaced from 8 to more than 40 km apart. GPS uses Earth-orbiting 

satellites to trilaterate positions based on the time required for radio signals transmitted from 

satellites to reach a receiving antenna. An accurate three-dimensional position can be determined 

from trilateration of the range distances between the receiver and at least four satellites. Since July 

17, 1995, NAVSTAR has been operational with a full constellation of 24 satellites, and in North 

America provides essentially continuous coverage with nominally at least 6 satellites in view at all 

times. Guidelines have been formulated for establishing GPS-derived ellipsoid heights with 

accuracy standards at either the 20-mm or the 50-mm level (Zilkoski and others, 1997). 

In land-subsidence and other crustal-motion surveys, the relative and absolute threedimensional positions of two points can be determined when two GPS receivers, one at each 

observation point, receive signals simultaneously from the same set of four or more satellites. 

When the same points are reoccupied following some time interval, any motion between the points 

that occurred during the time interval can be measured. Geodetic networks of points can be 

surveyed in this fashion. Such a network, one of the first of its kind designed specifically to 

monitor land subsidence, was established in the Antelope Valley, Mojave Desert, California in 

1992 (Ikehara and Phillips, 1994). It was designed to determine the subsidence of previously 

leveled benchmarks and enable precise measurement of points separated by tens of kilometers for 

future subsidence monitoring. Other large GPS-based geodetic networks for subsidence monitoring 

have been established in Albuquerque, New Mexico; the Avra Valley, Arizona; Houston-Galveston 

area, Texas; Las Vegas, Nevada; the Lower Coachella Valley, California; the Sacramento-San 

Joaquin Delta, California; and the Tucson basin, Arizona. GPS surveying also is a versatile 

exploratory tool that can be used in a rapid mode (kinematic or rapid static) to quickly (on the order 

of minutes to tens of minutes observation time per baseline) but coarsely (network of stations and 

baselines) define subsidence regions, in order to site more precise, site-specific and timecontinuous measurement devices such as recording extensometers and tiltmeters, and real-time 

GPS stations. 

One of the strengths of GPS is the ability to measure the three-dimensional deformation 

field—both horizontal and vertical movements. Uplift and subsidence associated with managed 

subsurface fluid production (injection and extraction) is accompanied by measurable horizontal 

movements in the Earth’s crust. As the land surface subsides, points around the subsidence feature 

are pulled inward towards the region with the greatest subsidence and conversely, as a region 

inflates, points will move upward and away from the region of maximum inflation. If the points 

are directly on the margin of the subsidence/uplift feature, then the ratio of vertical to horizontal 

motion may be nearly 1:1 (Bawden and others, 2001). For example, in the San Gabriel Valley in 

southern California, ongoing ground-water pumping pulled nearby continuous GPS stations inward 

toward the region of maximum drawdown (fig. 2.18A). Record rainfall in the same region in the 

winter-spring 2005 produced more than 4 cm of uplift with greater than 1 cm of radial outward 

motion of the nearby GPS sites (fig. 2.18B). 

75
azu1

azu1

long

cit1

sghs

gvrs

vyas

rhcl

cvhs

wchs

lphs

wnra

+10 0 -40-30-20-10

Subsidence Uplift

Relative vertical 

displacement in mm

25 mm/yr

Horizontal motion

95 m 55 m

70 m 110 m

Uplift Subsidence 0 +28

5 mm/yr

2σ error ellipse

-28

Relative range 

change in mm

Horizontal motion

Fig. 2.18

rhcl

azu1

long

vyas

cvhs

wchs

lphs

azu1

5 KILOMETERS

5 MILES

0

0

EXPLANATION

Ground-water level altitude (meters 

above sea level), July 1999—Contour 

interval is 3 meters

Mapped faults—Dashed where 

inferred

Area of shared coverage on images 

(maps)

A

B

Continuously operating

GPS station

Figure 2.18. Horizontal GPS displacement vectors superimposed on InSAR imagery for 

the San Gabriel Valley, southern California. A. Ground-water levels in the San Gabriel 

Valley declined about 3.5 m between May and October 1999 corresponding with 

approximately 12 mm of equivalent (range change) land subsidence measured using 

InSAR (Bawden, 2002). The neighboring continuous GPS sites (solid white circles) vyas

and lphs are pulled inward towards the zone of maximum subsidence which generally 

corresponds with the drawdown cone. The generally southwest-trending horizontal 

motion of the other GPS sites during this period is attributed to regional tectonic processes 

(Bawden and others, 2001). B. Record rainfall in the winter-spring of 2005 produced about 

40 mm of uplift measured using InSAR January to July 2005, and radial outward motion of 

continuous GPS sites sghs, wnra, rhcl, vyas, lphs, wchs, cvhs, and azu1 January 

through May 2005 (King and others, 2007). 

76
Synthetic Aperture Radar (SAR) Interferometry

Satellite SAR interferometry is ideally suited to measure the spatial extent and magnitude of 

surface deformation associated with aquifer-system compaction as well as other types of 

subsidence. SAR interferometry techniques—principally coherent (InSAR) and to a lesser extent, 

persistent-scatterer (PSI)—frequently are used to map, monitor, and analyze subsidence. By 

identifying specific areas of deformation within broader regions of interest, SAR interferometry 

also can be used to site and coordinate local and regional-scale subsidence monitoring (for 

example, borehole extensometers, GPS networks, and leveling lines; Bawden, 2002). These 

attributes of SAR interferometry address each of the information needs identified by the National 

Research Council (1991). Another important attribute of SAR interferometry is the increasing 

historical SAR data archive. For many areas, a substantial data archive exists for the period from 

1992 to the present, enabling measurements of historic surface displacements in this time period. In 

addition, new acquisitions can be ordered as needed. The detailed procedure and cost depends on 

the sensor. 

InSAR uses radar signals to measure deformation of the Earth’s crust in spatial detail and 

high measurement resolution. InSAR can provide millions of data points in a large region (100 by 

100 km/scene) and is often less expensive than obtaining sparse point measurements from laborintensive spirit-leveling and GPS surveys. Geophysical applications of InSAR take advantage of 

the phase component of reflected radar signals to measure apparent changes in the range distance of 

the land surface (Gabriel and others, 1989; Massonnet and Feigl, 1998). The size of a picture 

element (pixel or posting) on a typical InSAR image (interferogram) made from satellite-borne 

SAR may be as small as 100 m2

 or as large as 10,000 m2, depending how the interferogram is 

processed. 

For landscapes with relatively stable radar reflectors (such as buildings or other engineered 

structures, or undisturbed rocks and ground surfaces) over a period of time, it is possible to make 

high-precision measurements of the change in the position of the reflectors by subtracting or 

“interfering” two radar scans made of the same area at different times; the resulting InSAR image is 

called an interferogram. This is the principle behind InSAR. 

Under ideal conditions, it is possible to resolve georeferenced changes in range—the “lineof-sight” distance between the ground and satellite, on the order of 10 mm or less at the scale of 1 

pixel. The component of displacement measured using InSAR depends on the look angle of the 

sensor and for currently available sensor data (for example, for ERS-1 and -2 satellites the look 

angle for California is about 23° subvertical) is primarily vertical (Galloway and Hoffmann, 2007). 

InSAR has been used to map spatially-detailed ground-surface deformations associated with 

earthquakes (for example, Massonnet and others, 1993; Zebker and others, 1994), volcanoes (for 

example, Massonnet and others, 1995; Rosen and others, 1996; Wicks and others, 1998, 2006), 

geothermal production (Massonnet and others, 1997; Thatcher and Massonnet, 1997; Vadon and 

Sigmundsson, 1997; Fialko and Simons, 2000), oil and gas extraction (for example, Fielding and 

others, 1998), ground-water pumping (for example, Galloway and others, 1998; Amelung and 

others, 1999; Bawden and others, 2001; Hoffmann and others, 2001; Bell and others, 2002; 

Hoffmann and others, 2003a; Schmidt and Bürgmann, 2003; Galloway and Hoffmann, 2007), and 

underground mining (for example, Jarosz and Wanke, 2004; Chang and others, 2005). 

Persistent (also known as permanent) scatterer interferometry (PSI) uses a different 

approach than InSAR for processing SAR imagery, and has been shown to overcome some of the 

limitations of the InSAR technique. PSI involves the processing of numerous, typically more than 

30, interferograms to identify a network of persistent, temporally stable, highly reflective ground 

features—permanent scatterers (Ferretti and others, 2000, 2001; Werner and others, 2003). These 

77
scatterers typically are cultural features of the developed landscape such as buildings, utility poles, 

and roadways. The phase history of each scatterer is extracted by estimating a predefined 

displacement model (typically a linear, constant-rate model) to provide interpolated maps of 

average annual displacements, or the displacement history, up to the length of a SAR data archive, 

of each individual scatterer, thus providing a “virtual” GPS network with “instant” history. By 

focusing on temporally stable targets in the image, temporal decorrelation is avoided or strongly 

reduced. Furthermore, most of the strong and stable reflectors identified represent small individual 

scattering elements. For this type of scatterer though, a larger fraction of the reflected energy 

remains coherent for larger interferometric baselines, allowing a larger set of SAR scenes to be 

used in the analysis. The large number of observations available in a typical SAR data set used in a 

PSI analysis supports a statistical analysis of the observed phase histories in space and time, and 

depending on the characteristics of the displacements, it is often possible to separate the phase 

differences caused by atmospheric variations and uncompensated topography from those due to 

surface displacements. 

PSI has been applied primarily in urban environments, where the density of stable scatterers 

typically is quite high (as many as a few hundred per square kilometer). Over natural terrain, the 

scarcity of stable targets severely limits PSI’s successful application. A small number of 

investigations have demonstrated a successful application of PSI in “rural” terrain (Usai, 2001; 

Kircher, 2004). However, the investigations in the Netherlands and western Germany, used stable 

targets such as houses and other man-made features that were present in sufficient numbers. Hooper 

and others (2004) have proposed a modified algorithm for natural terrain, but this has been 

demonstrated for relatively dry conditions and it is questionable whether their approach will work 

over agricultural areas prone to temporal decorrelation owing to variable moisture and crop 

conditions. 

A potentially severe limitation of PSI, particularly where the density of permanent scatterers 

is small and displacement magnitudes are large, is the necessity to determine a motion model a 

priori, which is used in resolving phase ambiguities. Another limitation of PSI is the difficulty of 

identifying stable targets in rural and agricultural areas. Consequently, the majority of PSI 

applications have focused on urban areas—for example, Paris, France (Fruneau and Sarti, 2000); 

San Francisco Bay area, USA (Ferretti and others, 2004); Bangkok, Thailand 

(Worawattanamateekul and others, 2004); Phoenix, USA (Beaver and others, 2005); Arno River 

Basin-Florence, Italy (Canuti and others, 2005); Berlin, Germany (Kampes, 2005) ; London, 

England (NPA, http://www.npagroup.com/insar/apps/london_psi.htm, accessed October 7, 2007) 

and Las Vegas, USA (Kampes, 2005; Bell and others, 2008). 

Analysis and Simulation 

The analysis of subsidence-prone areas generally involves evaluation of the material 

surface- and subsurface-geologic properties, and the potential physical and chemical processes 

causing land subsidence. The analyses typically include one or more of the following: laboratory 

and field measurements, and analytical and numerical modeling of subsidence processes. 

Analytical and numerical models have been used to simulate physical processes governing various 

types of subsidence. Broadly defined, “A model is a tool designed to represent a simplified version 

of reality.” (Wang and Anderson, 1982). Generally, the more sophisticated models are formulated 

from basic principles of physics and involve some conceptual coupling of mechanical, hydraulic, 

and (or) thermal processes. The governing equations used to describe the various coupled processes 

range from fully decoupled, to weakly coupled, to rigorously coupled. Numerous numerical models 

have been developed and applied to simulate subsidence owing to aquifer-system compaction 

78
caused by ground-water extractions. Fewer models have been specifically developed to simulate 

subsidence attributed to other mechanisms. Some general purpose hydraulic-mechanical-thermal 

models have been used to simulate some of the various other processes. 

Calibrated aquifer-system compaction models have been used to forecast subsidence in a 

number of study areas for specified future ground-water extraction and recharge scenarios. The 

calibrated models generally are constrained by historical subsidence and ground-water levels and 

some knowledge of the hydraulic and mechanical parameters governing aquifer-system 

compaction. The use of models to simulate future conditions is widely applied to manage groundwater resources, and the use of coupled simulation/optimization models (Ahlfeld and Mulligan, 

2000; Ahlfeld and others, 2005) is being applied to a limited extent to maximize future beneficial 

use of the water resource while minimizing deleterious consequences, such as subsidence (for 

example, Danskin and others, 2003; Phillips and others, 2003). 

Subsurface Fluid Withdrawal 

The analysis and simulation of regional subsidence caused by the withdrawal of ground 

water has been addressed primarily using conventional ground-water flow theory (Jacob, 1940) and 

secondarily using linear poroelastic theory (Biot, 1941). The chief difference between the two 

theories is the treatment of the deformation of the skeletal-matrix. Conventional ground-water flow 

theory describes only the vertical deformation of the matrix (assumes no horizontal deformation), 

and poroelastic theory describes the three-dimensional deformation of the matrix. Both approaches 

describe a relation between fluid flow and deformation of the aquifer system, but conventional 

ground-water flow theory essentially decouples fluid flow and matrix deformation, whereas 

poroelastic theory couples fluid flow and matrix deformation, and is more general and more 

complex. 

It is well known that the extraction of subsurface fluids causes three-dimensional 

deformation of a pumped aquifer system, and there are several quantitative analyses of the 

phenomenon (Verrjuit, 1969; Sandhu, 1979; Hsieh, 1996; Burbey and Helm, 1999; Burbey, 2001). 

As Hsieh (1996) notes, “Analysis of realistic aquifer settings generally requires a numerical 

poroelascticity model. This type of model is not well-known to most ground-water hydrologists.” 

And, as Burbey (2001) showed, the conventional ground-water theory and the linear poroelasticity 

approaches yield nearly identical head and volume-strain distributions and therefore, nearly 

identical volumes of water released from storage owing to subsidence. These factors, coupled with 

the paucity of regional horizontal-displacement measurements in developed ground-water basins 

has led to the wide application of analytical and numerical models based on conventional groundwater flow theory to address regional land subsidence problems. This approach is described below. 

Almost all the permanent subsidence in aquifer systems is attributable to the compaction of 

aquitards during the typically slow process of aquitard drainage (Tolman and Poland, 1940). This 

concept, known as the aquitard drainage model, has formed the theoretical basis of many 

successful subsidence investigations associated with depressuring of porous media (see Helm, 

1984; Holzer, 1998). The aquitard drainage model is based on conventional ground-water flow 

theory and two principles describing the relations between fluid pressure, intergranular stress, and 

fluid flow. 

The relation between changes in ground-water levels and deformation of the aquifer system 

is based on the principle of effective stress first proposed by Terzaghi (1925). According to the 

Terzaghi relation, 

ij ij ij σ′ = σ δ − u , (2.1a) 

where 

79
σ ij′ is a component of the effective stress tensor, 

σ ij is a component of the geostatic (total) stress tensor, 

ij δ is the Kronecker delta function, and 

u is the fluid pore pressure or hydrostatic stress. 

Equation 2.1a shows that changes in effective stress can result from changes in geostatic stress or 

changes in pore pressure. The geostatic stress is the load of the overlying saturated and unsaturated 

sediments and tectonic stresses. Neglecting tectonic stresses, if the aquitards are assumed to be 

horizontal and laterally extensive with respect to their thickness, changes in pore-pressure gradients 

within the aquitards will be primarily vertical. Assuming that the resulting strains also are primarily 

vertical, a one-dimensional form of equation 2.1a can be expressed as 

σ′ = σ − u (2.1b) 

where σ′ and σ are the vertical components of effective and geostatic stress, respectively. 

 By this principle, when the support provided by fluid pressure is reduced, such as when 

ground-water levels are lowered, support previously provided by the pore-fluid pressure is 

transferred to the skeleton of the aquifer system, which compresses to a degree. Conversely, when 

the pore-fluid pressure is increased, such as when ground water recharges the aquifer system, 

support previously provided by the skeleton is transferred to the fluid and the skeleton expands. In 

this way, the skeleton alternately undergoes compression and expansion as the pore-fluid pressure 

fluctuates with ground-water discharge and recharge. When the load on the skeleton remains less 

than any previous maximum load, the fluctuations create only a small elastic deformation of the 

aquifer system and small displacement of land surface. This fully recoverable deformation occurs 

in all aquifer systems, commonly resulting in seasonal, reversible displacements in land surface of 

as much as 1–3 cm or more in susceptible aquifer systems in response to the seasonal changes in 

ground-water pumpage. The elastic deformation of aquifer systems likely poses little hazard to 

pipelines except in areas where heterogeneities in the aquifer systems (such as near faults or 

changes in depositional facies with contrasting hydraulic and mechanical properties) create 

differential subsidence and amplified lateral deformation. 

When the load on the aquitard skeleton exceeds the maximum previous stress on a skeletal 

element—the preconsolidation stress—the aquitard skeleton may undergo significant, permanent 

rearrangement, resulting in irreversible compaction. Because the skeleton defines the pore structure 

of the aquitard, there is a permanent reduction of pore volume as the pore fluid is “squeezed” out of 

the aquitards into the aquifers. In confined aquifer systems subject to large-scale overdraft, the 

volume of water derived from irreversible aquitard compaction is essentially equal to the volume of 

subsidence and typically can range from 5 to 40 percent of the total volume of water pumped. This 

represents a one-time mining of stored ground water and a small permanent reduction in the storage 

capacity of the aquifer system. Holzer (1981) showed that alluvial aquifer systems in subsiding 

areas of Arizona, California, and Texas are naturally overconsolidated by the equivalent of about 

16–63 m of water-level decline, and that many values of the natural preconsolidation stress fall in 

the range from 25 to 40 m. 

Because aquitards are by definition much less permeable than aquifers, the vertical drainage 

of aquitards into adjacent pumped aquifers may proceed very slowly, and thus lag far behind the 

changing water levels in adjacent aquifers. The duration of a typical irrigation season may allow 

only a modest fraction of the potential yield from aquitard storage to enter the aquifer system, 

before pumping ceases for the season and ground-water levels recover in the aquifers. Typically, 

for thick aquitards, the next cycle of pumping begins before the fluid pressures in the aquitards 

have equilibrated with the previous cycle. The lagged response within the inner portions of a thick 

80
aquitard may be largely isolated from the higher frequency seasonal fluctuations and more 

influenced by lower frequency, longer-term trends in ground-water levels. Because the migration of 

increased internal stress into the aquitard accompanies its drainage, as more fluid is squeezed from 

the interior of the aquitard, larger and larger internal intergranular stresses (effective stresses) 

propagate farther into the aquitard. 

When the internal effective stresses exceed the preconsolidation stress, the skeletal 

compressibility increases dramatically, typically by a factor of 20 to 100 times (Riley, 1998), and 

the resulting compaction is largely nonrecoverable. At stresses greater than the preconsolidation 

stress, the lag in aquitard drainage increases by comparable factors, and concomitant compaction 

may require decades or centuries to approach completion. The theory of hydrodynamic 

consolidation (Terzaghi, 1925)—an essential element of the aquitard drainage model—describes 

the delay involved in draining aquitards when heads are lowered in adjacent aquifers, as well as the 

residual compaction that may continue long after drawdowns in the aquifers have essentially 

stabilized. Numerical modeling based on Terzaghi’s theory has successfully simulated complex 

histories of compaction observed in response to measured water-level fluctuations (Helm, 1978). 

Methods to simulate aquifer-system compaction were developed by Gambolati (1970, 1972a,b), 

Gambolati and Freeze (1973), Helm (1975, 1976), Narasimhan and Witherspoon (1977), and Neuman and 

others (1982). The one-dimensional (vertical) model presented by Helm (1975) computes compaction 

caused by specified ground-water level changes. This approach is used to analyze compaction at borehole 

extensometer sites for which there are detailed records of compaction and water-level changes (Epstein, 

1987; Hanson, 1989; Pope and Burbey, 2003, 2004). Other approaches have focused on incorporating 

subsidence calculations in widely used two- or three-dimensional models of ground-water flow. Meyer and 

Carr (1979), Williamson and others (1989), and Morgan and Dettinger (1996) used modified finitedifference models to simulate ground-water flow and subsidence in the area of Houston, Texas; the Central 

Valley, California; and Las Vegas Valley, Nevada, respectively. Teatini and others (2006) used a finiteelement model to simulate historical and future subsidence owing to ground-water pumping in the EmiliaRomagna coastland, Italy. 

Leake and Prudic (1991) developed the Interbed Storage Package, version 1 (IBS1), to simulate 

regional-scale compaction of interbeds within aquifers using the ground-water model program, MODFLOW 

(McDonald and Harbaugh, 1988). IBS1 also can be used to simulate compaction of confining units if these 

units can be discretized into one or more model layers (Larson and others, 2001; Nishikawa and others, 

2001). MODFLOW and the IBS1 Package also have been used to simulate regional ground-water flow and 

land subsidence (for example, Hanson and others, 1990; Hanson and Benedict, 1994; Galloway and others, 

1998; Nishikawa and others, 2001; Kasmarek and Strom, 2002; Leighton and Phillips, 2003; Hanson and 

others, 2003, 2004), and one-dimensional ground-water flow and compaction measured at borehole 

extensometer sites (Sneed and Galloway, 2000; Pavelko, 2003). 

The IBS1 formulation assumes that during one model time step, head changes in aquifer material are 

propagated throughout the entire thickness of compressible interbeds. Thus, the release of water from, or 

uptake of water into, interbed storage during this time step represents the full volume specified by the 

interbed storage coefficients (a function of the skeletal compressibility) and the change in aquifer hydraulic 

head. To relax this assumption, Leake (1990) developed the Interbed Storage Package, version 2 (IBS2). 

IBS2 allows the user to designate arbitrary systems of interbeds for which delay in release of water will be 

calculated. Previous studies used this approach to investigate the potential effects of land subsidence in the 

presence of delay interbeds (for example, Leake, 1990, 1991; Wilson and Gorelick, 1996; Hoffmann and 

others, 2003a). The SUB Package (Hoffmann and others, 2003b) for MODFLOW-2000 (Harbaugh and 

others, 2000) documents the IBS2 Package and retains the full functionality of the IBS1 Package. 

The SUB Package formulations for delay and no-delay interbeds assume that the total load or 

geostatic stresses, and the vertical hydraulic conductivities and compressibilities that govern compaction of 

the deforming aquifer system remain constant. The SUB-WT Package (Leake and Galloway, 2007) for 

MODFLOW relaxes these assumptions and can be used to simulate changing stresses and compaction in 

unconfined (water table) and underlying confined aquifers owing to ground-water level variations in the 

81
water table. The SUB-WT Package does not simulate a delay in the release of water from, or uptake of 

water into, interbed storage during a single model time step, similar to the IBS1 Package. 

Simulations of subsidence attributed to oil, gas, and associated water extractions have used 

techniques based on physical principles similar to those described above for ground-water pumping 

(for example, Gambolati and Freeze, 1973; Geertsma, 1973; Gambolati and others, 1974a,b; 

Kosloff and others, 1980a,b; Sharp and Hill, 1995; Gambolati and others, 1999). Models using 

formulations similar to the Helm model were used to show that subsidence owing to deep 

petroleum production in the northeast coastal plain of Texas, could explain the additional 

subsidence that is occurring beyond that attributed to eustasy along the Texas coast (Sharp and Hill, 

1995). 

The conventional ground-water theory approach to evaluating aquifer-system compaction 

and land subsidence may be suitable for evaluating regional ground-water resources, but it has 

limitations in evaluating the hazards associated with ground displacements on local scales. The 

limiting assumption of purely vertical strain tends to overestimate the amount of vertical 

displacement (subsidence and uplift) and neglect the lateral displacements that may be important 

near production wells, and other areas where normally small lateral strains may be amplified such 

as near the margins of alluvial, basin-fill ground-water basins, and internally to the basins in areas 

susceptible to earth fissures and near pre-existing surface faults (Burbey, 2001; Burbey, 2002). 

With respect to evaluating hazards to pipelines, these factors should guide the evaluation and where 

relevant, the application of poroelastic models should be considered. Burbey (2002) used a granular 

displacement model (Burbey and Helm, 1999) based on Biot’s consolidation theory and found that 

(1) earth fissures in the vicinity of faults that behave as either flow barriers, or a combination of 

flow and mechanical barriers, may be significantly influenced by horizontal deformation in 

aquifers; and (2) fissures in the vicinity of faults that do not behave as horizontal barriers to flow 

are probably more inclined to be caused by differential subsidence. Therefore, the presence of a 

surface fault and contrasting aquifer-hydraulic heads on either side of the fault would suggest that 

horizontal deformation could be important and perhaps should be evaluated prior to pipeline 

emplacement in these areas of developed ground-water basins. 

Drainage of Organic Soils 

Widespread drainage of organic soil-wetlands for agriculture has significantly altered the 

global carbon balance largely through oxidation, facilitated by aerobic microbial activity, of stored 

carbon and its release to the atmosphere as CO2. Organic soils subside when they are drained 

continuously, and the phenomenon is controlled chiefly by the water and organic content, and the 

temperature of the soil. 

Stephens and Stewart (1976) developed an empirical mathematical model to estimate the 

biochemical subsidence rate for low-moor organic soils: 

 (2.2) 0 ( )/1

10 ( ) T TT S a bD Q − = + 0

where is the subsidence rate (cm/yr) at temperature T , and are constants, T S a b D is the depth 

of the water table, is the change in reaction rate for each 10°C rise in temperature, and is the 

threshold soil temperature where biochemical action becomes perceptible. Based on laboratory 

studies and field data, Stephens and Stewart (1976) found that the subsidence rate at the Everglades 

Experiment Station 

Q10 T0

x S could be expressed as 

, (2.3) ( 5)/10 ( 0.1035 0.0169 )(2) Tx

x S D − =− +

where Tx is the average annual soil temperature (°C) at the 10-cm depth. This relation was 

developed from organic soils with a mineral content of less than 15 percent, and bulk density of 

82
approximately 0.22 g/cm3

. Muck soils with larger mineral contents and bulk densities would yield 

smaller subsidence rates under similar conditions. 

A simple model for computing subsidence based on the measured CO2 flux and specific 

organic soil properties was applied successfully in the Sacramento-San Joaquin Delta (Deverel and 

Rojstaczer, 1996) and in the Venice Lagoon catchment, Italy (Camporese and others, 2005): 

c c

o

f p

p

η

ρ = , (2.4) 

where 

η is the subsidence rate (LT-1), 

c f is the CO2

 flux (in terms of carbon) resulting from oxidation of organic matter (ML-2T-1), 

ρ is the soil bulk density (ML-3), 

c p is the percentage of carbon in the soil organic matter, and 

o p is the percentage of organic matter content in the soil. 

Van der Linden and others (2005) developed a preliminary numerical method that incorporates soil 

compaction and oxidation processes to simulate subsidence. This method is useful where the 

organic soils have significant mineral/clay content and compaction constitutes a significant fraction 

of the total subsidence. 

Sinkholes 

Because sinkholes result from a combination of many factors, forecasting their spatial and 

temporal occurrence is difficult. However, if relations between sinkholes and factors associated 

with their occurrence can be determined, it is possible to assess geologic hazards associated with 

pre-existing sinkholes in karst terranes and risks of new sinkhole formation. 

Hazards and risk assessment provide a scientific basis for planning the development and 

construction of transportation and transmission infrastructure. Geologic mapping can identify karst 

areas and thus areas prone to sinkholes. Aerial photography and airborne LiDAR are useful in 

identifying specific karst features (such as sinkholes, sinks, and sinkhole lakes/ponds) on the 

landscape and frequently are used in the design and routing of pipelines through karst. Buried karst 

features such as paleosinkholes and mantled voids in the mantled karst region of Florida may not 

have identifiable surface expressions at local scales. Proposed pipeline routes through karst terrane 

where surface features are evident and where buried, concealed karst features are suspected can be 

evaluated using a combination of surface geophysical techniques (for example, seismic refraction 

tomography, seismic shear wave analysis, electrical resistivity, gravimetry, and ground-penetrating 

radar), test drilling and geotechnical methods (for example, cone penetrometer technology [CPT]). 

Information gleaned from the geohazards analysis can be used in engineering analyses to determine 

factors of safety for various pipeline spans and surface-loading conditions. The critical factors are 

the diameter of the sinkhole or buried cavity and the roof thickness or burial depth of the feature. 

In regions prone to sinkholes, one identified and often overlooked risk factor for activating 

sinkhole formation is ground-water level differences related to the development of ground-water 

resources. Whitman and others (1999) positively correlated sinkholes with ground-water level 

differences between surficial aquifers and the underlying Floridan aquifer near Orlando, Florida 

using remote-sensing data, ground-water levels, and GIS. 

In China, in recent decades about 70 percent of the documented sinkhole collapses are 

anthropogenic, and about 70 percent of those are attributed to ground-water pumping effects (Lei 

and others, 2002). Hu and others (2001) identified ground-water pumping and earthquakes as 

83
principal risk factors for sinkhole formation in Tangshan, China, and developed a sinkhole riskassessment model. Based on the results of the risk assessment, the risk of sinkhole formation in 

Tangshan is serious. High risk areas were identified in important sections of Tangshan, wh

exists a serious danger to life and property. To mitigate the hazard, Hu and others (2001) 

recommended that (1) some high-risk areas be protected or strengthened, and that the northern part

of the city should be a focus of future development because of its relatively low risk of sinkholes; 

(2) ground-water pumping should be limited, generally, and prohibited in specific areas of the city; 

and (3) further investigations should be carried out to understand the development and distribution

of hidden karst featur

ere there 

es, such as caves and their related disturbed zones, and to develop effective 

mitigation methods.

Underg

 coal 

 mines 

mre competent overburden. 

Trough

ned. 

 rock mass and bulking occurs until the collapsed rock supports 

the ove

 trough 

l 

 to 

urface affected by the mined panel is 

estimat

at 

le). 

 the 

round Mining 

This section focuses on the analysis of subsidence associated with underground coal 

mining, but applies to underground mining in general. Surface subsidence due to underground

mining is generally classified as pit subsidence or sag/trough subsidence. Pit subsidence is a 

circular hole in the ground with essentially vertical to belled-outward sidewalls (fig. 2.19A). The 

diameter of subsidence pits ranges from about 1 to 12 m and generally occurs over shallow

(depths less than about 50 m) with incompetent bedrock overburden. Sag subsidence is a 

rectangular depression with gently sloping sides (fig. 2.19B) and is typically developed over roo

and-pillar mines at greater depths (20 to 100 m or more) and with mo

 subsidence is similar in surface geometry to sag subsidence. 

Pit and sag subsidence occurs more or less randomly and unexpectedly when pillars of coal 

collapse under overburden loading, typically long after an underground mine has been abando

Pit subsidence may involve collapse of only a few pillars, while sag subsidence may involve 

progressive failure of many pillars. Trough subsidence typically occurs in conjunction with

longwall mining. The longwall mining technique involves use of moveable hydraulic roof 

supports, which make it possible to excavate blocks of coal on the order of 300 m wide and about 

1,500–3,000 m long. Hydraulic roof supports are advanced behind the excavation so the mine roof

and overlying rock fracture and collapse into the void behind the supports. Caving and fracturing 

propagate up through the overlying

rlying strata (fig. 2.20A). 

The overlying rock mass subsides and the ground surface ultimately deforms into a trough 

as shown along the axis of the mined-out panel in figure 2.20B. Around the margins of the

the surface slopes downward over the edges of the longwall panel to a point of maximum 

subsidence displacement, which is usually about 50 to 80 percent of the mined thickness of coal. 

As mining continues, the subsidence bowl at the surface elongates in concert with the mined pane

beneath the surface, and the transition zone over the advancing face of the longwall panel moves

along as a waveform. The sloped zone over the advancing face of the mined panel will tend

flatten as the wave advances. Along the left and right sides of the mined panel, the sloped 

transitions will remain stationary as the panel extends in length and bounds the width of the 

subsidence bowl. A measure of the extent of ground s

ed by the angle of draw shown in figure 2.20. 

Sag and trough subsidence takes the form of a reasonably predictable trough depression th

will subject a buried pipeline to bending deformation and axial strain (compressive and tensi

Sag subsidence affects a larger area than pit subsidence, but much less than would occur in 

conjunction with trough subsidence due to longwall mining, and generally with less surface 

displacement. Surface subsidence and deformation profiles for longwall mining (figs. 2.20 and 

2.21) are analogous to a bending beam. The difference in slope between points along a profile is

84
curvature. Horizontal displacement is proportional to curvature and, therefore, slope (tilt) by a 

constant, c, that is analogous to the distance from the neutral axis to the outer fiber of a bending 

beam. The alu v e of c varies from 0.30 to 0.4 times the ratio of depth ( ) to the tangent of angle of 

influence (

h

β ): 

( 0.35 0.05)

tan β

h

c − ± = . (2.5)

d 

uires 

ically 

atterns. 

ntal 

 numerical model for any hypothetical pipeline 

alignm

). 

lanes 

d the numerous input parameters can make it 

much more difficult to calibrate to case histories. 

Limited data on horizontal displacement accompanying subsidence over active mines has

been used to constrain simulation of ground movements. Empirical (for example, National Coal 

Board, 1966; Karmis and others, 1987) and analytical (for example, Ren and others, 1987; Lin an

others, 1992) models, and distinct-element models (for example, O’Connor and Dowding, 1992; 

Exadaktylos and others, 2007) have been used to simulate subsidence. The approach to estimating 

realistic values of horizontal and vertical ground displacements from subsidence typically req

calibration to case histories in which vertical displacements were monitored, for example, in 

underground coal mining—over longwall coal mine panels. Horizontal displacements are typ

not available from these case histories and need to be estimated based upon analyses using a 

numerical model capable of computing both vertical and horizontal ground displacement p

The computed horizontal ground displacements for a case history are assumed to be those 

computed with a set of parameters that result in computed vertical displacements consistent with

actual measured vertical displacements. Once calibrated in this manner, vertical and horizo

displacements can be determined using the

ent through the subsidence zone. 

Prediction of surface subsidence involves the use of mathematical models to compute the 

subsidence profile. Mathematical models based on the influence function approach assume that 

extracting a tiny element of an underground seam, for example a coal seam, will cause the ground

surface to subside into a predefined shape such as a normal probability distribution. The ground 

surface directly above the extracted element receives the greatest amount of influence (fig. 2.22A). 

Coal seam elements offset from this location have less influence on that surface point (fig. 2.22B

The final subsidence at this point is the summation of the influence of each mined-out element. 

Explicit techniques that employ finite-element or distinct-element models are capable of accounting 

for the engineering characteristics of various rock strata as well as the orientation of bedding p

and pre-existing faults. However, the more explicit modeling techniques require much more 

information than the influence function models, an

85
Loess

glacial material

Water table

Subsided 

earth

and

Bedrock

Underclay

Coal

0

3

6

9

12

15

18

DEPTH, IN METERS

Road

Original land surface

Subsided

 earth

A

B

Figure 2.19. Typical types of subsidence associated with underground coal mining: A. Pit 

subsidence, and B. Sag subsidence (modified from Bauer and Hunt, 1982). 

86
Original surface

Coal

W

m

Compression

Smax

Extension

Extension

h

Angle of draw (Y)

Original surface

Coal

W

m

0.06 Smax

Emax

h

Angle of influence (β)

Inflection point

Angle of draw (Y)

A

B

Figure 2.20. Typical profiles and parameters for analyzing deformation associated with 

underground coal mining: A. Subsidence, and B. Horizontal strain. (W is width of minedout panel; h is depth; Smax is maximum subsidence; Emax is maximum extension) 

87
NORMALIZED DISTANCE, FROM CENTER OF MINED PANEL

RELATIVE VALUE OF DEFORMATION PARAMETER

0.0 0.25 0.50 0.75 1.0

0.0 0.25 0.50 0.75 1.0

0.0 0.25 0.50 0.75 1.0

0.0 0.25 0.50 0.75 1.0

0.0 0.25 0.50 0.75 1.0

0.0

0.0

0.0

0.0

0.0

SUBSIDENCE SLOPE

HORIZONTAL STRAIN CURVATURE

HORIZONTAL DISPLACEMENT

A

B

C

D

E

Figure 2.21. Deformation profiles for idealized subsidence feature above underground 

coal mine: A. Subsidence. B. Slope. C. Curvature. D. Horizontal displacement. E.

Horizontal strain. 

88
Angle of influence

Angle of draw

Zone of influence

Mined element

Mined elements

A

B

Coal

Coal

Figure 2.22. Schematic of: A. Zone of influence above a mined-out element, and B.

Subjacent support between two mined-out elements separated by an intact element. 

89
Hydrocompaction 

Hydrocompactive or collapsible soils lose a significant amount of volume through reduction 

of porosity when wetted, and have caused tens of millions of dollars in damage to private and 

public infrastructure in the USA, including Arizona (Soil Conservation Service, 1975), California 

(Bull, 1964; Lofgren, 1969), Iowa (Handy, 1973), New Mexico (Love, 2001) and Utah (Kaliser, 

1978). For example, on the west side of the San Joaquin Valley, California, hydrocompaction 

caused $8 million in damage and added an extra $15 million to the cost of constructing canals 

(National Research Council, 1991). 

The susceptibility to subsidence hazards owing to hydrocompaction is difficult to identify 

and infrequently is incorporated into pipeline-design guidelines. The low-density soils can be 

detected in boreholes using geophysical density logs, nuclear density gages, or by collecting 

undisturbed samples and testing for relative density. Hydrocompaction susceptibility largely has 

been determined by combining soils maps with laboratory data on soil properties to develop maps 

of “collapse probabilities.” Research and geotechnical investigations in New Mexico (for example, 

Lovelace and others, 1982; Johnpeer and others, 1985; Shaw and Johnpeer, 1985a,b; Haneberg and 

others, 1992) have produced a large body of information concerning the identification and 

mitigation of hydrocompactive deposits and associated engineering problems. Experience in 

British Columbia indicates that soils or fills having in-place densities less than 90 percent of 

standard Proctor maximum dry density are susceptible to sudden collapse or settlement (Richard 

Butler, Golder and Associates Ltd., written commun., 2007). Extensive studies of loess or loesslike soils over a period of many years in China, have developed criteria to differentiate the 

susceptibility of various deposits in differing geographic or climatic regions to sudden collapse or 

subsidence as a result of wetting (Lin and Liang, 1982). 

Reimers (1986) conducted a geotechnical study of hydrocompactive deposits at El Llano, 

New Mexico, including stepwise multiple regression and discriminant function analyses, in an 

attempt to delineate index properties that might be used to identify potentially hydrocompactive 

soils. Most collapsible soils have dry densities ranging from 1.20 to 1.52 gm/cm3, low Atterberg 

limits, low moisture content (1–l0 percent), and are poorly graded to silty sands with only minor 

amounts of clay (1–3 percent). Their soil structure is characterized by delicate grain-point contacts, 

clay and silt aggregates that bond the larger sand grains together, clay-coated sand grains (cutans), 

and high porosities, 30–40 percent. Some typical values of geotechnical index properties of 

collapsible soils near El Llano, New Mexico, are shown in table 2.3. 

Table 2.3. Geotechnical index properties of collapsible soils near El Llano, New Mexico 

(modified from Haneberg and others, 1992). 

[Textures are Unified Soil Classification System group symbols: SW—well graded, fine to coarse sand, SP—poorly 

graded sand, SM—silty sand, and SC—clayey sand] 

Index Property Typical Values 

Texture SW, SP, SM, or SC 

1.20-1.52 gm/cm3Dry Density 

Void Ratio 0.5-1.0 

Moisture Content 4-10% by weight 

Saturation <60% 

Liquid Limit 0-40 

Plastic Limit 0-20 

Total Consolidation Upon Wetting > 5% 

Specific Gravity, Granular 2.50-2.65 

Clay Mineralogy smectite, illite, mixed-layer, kaolinite 

Blow Count (N value) <18 

P-wave Velocity <300 m/sec 

90
Thawing Permafrost 

During the past two decades, a number of permafrost numerical models have been 

developed to evaluate spatial and temporal changes in permafrost related to global climate change. 

Despite the importance of permafrost in the climate-change sciences, modeling of permafrost has 

remained highly diverse and uncertain regarding appropriate methods, their accuracy, and their 

applicability to different scales and climatic conditions (Shiklomanov and others, 2004). Typically, 

permafrost models employ numerical multi-layer one-dimensional models of ground-heat transfer, 

accounting for phase transitions of moisture as well as snow and vegetation covers. A wide range 

of numerical models has been developed (Goodrich, 1978; Guymon and others, 1984; Romanovsky 

and others, 1997; Romanovsky and Osterkamp, 2000; Malevsky-Malevich and others, 2001; 

Machul’skaya and Lykosov, 2002; Ling and Zhang, 2003; Molkentin and others, 2003; Sergueev 

and others, 2003). Typical input parameters of a permafrost model include skin temperature at the 

upper boundary of snow or vegetation cover, the thickness of snow and vegetation covers, and 

physical properties of soils. At the lower boundary of the domain, the geothermal heat flux 

generally is prescribed. The principal observational parameters usually are permafrost temperature 

and the thickness of the active layer or frost zone—the top layer of soil that seasonally freezes and 

thaws. 

Many permafrost-related processes are two- or three-dimensional (such as soil settlement 

upon thawing, thermokarst development, and differential frost heave). Therefore, even on the local 

scale, two- and three-dimensional permafrost models are better suited to simulate permafrost 

dynamics. Prevalent local and regional-scale heterogeneity of permafrost properties raises concerns 

about the ability of deterministic models, regardless of their dimensionality or scale, to make 

accurate estimates of the volume of thawed soil. An alternative approach is to consider near-surface 

permafrost parameters as randomly, spatially distributed variables consisting of both deterministic 

and stochastic components and to use their probability distribution functions as the metric for 

evaluation (Anisimov and others, 2002). 

Natural Consolidation 

Analytical and numerical modeling approaches have been used to simulate overpressures, 

compaction (porosity reduction), and long-term subsidence rates in geologic basins undergoing 

natural consolidation. Partially-coupled models using simplifying assumptions (for example, 

Bredehoeft and Hanshaw, 1968; Bethke, 1989) simulate poroelastic ground-water flow and 

compaction using formulations for standard ground-water flow with geologic forcing treated as 

source/sink term (Γ ): 

, s

h S Kh

t

∂ = ∇⋅ ∇ +Γ

∂ (2.6) 

where 

s S is specific storage (L-1), 

h is hydraulic head (L), 

K is the hydraulic conductivity tensor (LT-1); 

 and where compaction, the geologic forcing, is simulated using 

, s

L

l C

t

∂ Γ =

∂ (2.7) 

 where 

CL is a loading coefficient (dimensionless), and 

sl is the elevation of the sedimentary surface (L). 

91
This approach has been used to demonstrate that compressional strain from loading, and the 

accompanying subsidence, is capable of generating overpressures in rapidly accreting sedimentary 

basins (Ingebritsen and others, 2006). 

Summary 

Land subsidence poses hazards to pipelines. The principal causes of land subsidence in the 

United States are subsurface fluid withdrawal, drainage of organic soils, sinkholes, underground 

mining, hydrocompaction, thawing permafrost, and natural consolidation. Many subsidence areas 

have been identified, mapped, and documented. Most anthropogenic land subsidence in the United 

States is caused by the withdrawal of subsurface fluids from unconsolidated porous granular media; 

humans also have caused widespread and significant subsidence by other processes. As measured 

by area affected, mining of coal and minerals, and drainage of organic soils are the most significant 

of the other processes. 

 An area’s susceptibility to subsidence can be evaluated through compilation of available 

geologic, hydrogeologic, and geodetic information as well as other ancillary and anecdotal 

information such as increased incidences of damaged or protruding wells, a history of adjustments 

to local geodetic controls, increasing incidences of coastal or riverine flooding, local conveyance 

and drainage problems, and ground failures—surface faulting and earth fissuring. Measuring, 

mapping, and monitoring subsidence is needed to adequately assess subsidence hazards. Analysis 

and simulation of subsidence processes, constrained by the available data, often are used to refine 

assessments of present and future hazards. 

Various ground-based, airborne and space-based methods are used for mapping and 

measuring spatial gradients and temporal rates of regional and local subsidence and horizontal 

ground motion. These include conventional geodetic techniques such as spirit leveling and GPS, 

extensometric methods using horizontal and borehole extensometers, satellite synthetic aperture 

radar techniques such as interferometric synthetic aperture radar (InSAR) and permanent scatterer 

interferometry (PSI), and tripod-mounted and airborne LiDAR. 

Analyses of subsidence-prone areas typically address material surface- and subsurfacegeologic properties, and the potential physical and chemical processes causing land subsidence. 

The types of analyses generally include laboratory and field measurements, and analytical and 

numerical modeling of subsidence processes. Numerous numerical models have been developed 

and applied to simulate subsidence owing to aquifer-system compaction caused by ground-water 

extractions. Few models have been specifically developed to simulate subsidence attributed to other 

mechanisms. Some general purpose hydraulic-mechanical-thermal models have been used to 

simulate some of the various other processes. 

Calibrated, numerical aquifer-system compaction models have been used to forecast 

subsidence in a number of study areas for specified future ground-water extraction and recharge 

scenarios. Two subsidence-simulation modules, the SUB and SUB-WT Packages, have been 

developed for the widely used MODFLOW ground-water flow model. The SUB package is used to 

simulate either the instantaneous or delayed compaction of aquitards under conditions of constant geostatic 

stress, and is applicable to confined aquifer systems with a static overlying water table. The SUB-WT 

Package is used to simulate the instantaneous compaction of aquitards under conditions of changing 

geostatic stresses and stress-dependent hydraulic and mechanical properties. The SUB-WT Package is 

applicable to confined and unconfined aquifer systems with a dynamic overlying water table. 

Empirical and analytical models, and to a limited extent, numerical models are used to 

simulate subsidence accompanying (1) the extraction of subsurface oil and gas, (2) the drainage and 

subsequent compaction and oxidation of organic soils, (3) sinkholes in carbonate and evaporite 

rocks, (4) underground mining, (5) thawing permafrost, and (6) natural consolidation.

92
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106
Prepared in cooperation with the U.S. Department of Transportation, 

Pipeline Research Council International, and DGH Consulting Inc. 

Landslide Investigation Methods 

By Rex L. Baum and Gerald F. Wieczorek 

Chapter 3 of 

Landslide and Land Subsidence Hazards to Pipelines 

Open-File Report 2008–1164 

U.S. Department of the Interior 

U.S. Geological Survey 

107
Contents 

Abstract....................................................................................................................................113

Introduction...............................................................................................................................114

Landslide Movement Rates.......................................................................................................115

Inferring Probable Rate of Movement ....................................................................................117

Engineering Geologic Mapping and Related Field Studies .......................................................117

Small-Scale Mapping .............................................................................................................118

Large-Scale Mapping.............................................................................................................119

Methods of Estimating Displacement.....................................................................................120

Landslide History ...................................................................................................................120

Subsurface Exploration.............................................................................................................121

Sampling................................................................................................................................123

Testing....................................................................................................................................124

Shear Strength.......................................................................................................................124

Recent Improvements in Test Procedures..........................................................................127

Monitoring and Instrumentation of Landslides...........................................................................127

Kinds of Measurements to Be Made ......................................................................................128

Styles and Techniques of Monitoring .....................................................................................128

Campaign Monitoring..........................................................................................................128

Displacement and Deformation........................................................................................129

Point and Line Surveys .................................................................................................129

Terrestrial Laser Scanning............................................................................................129

Aerial and Satellite Remote Sensing.............................................................................130

Landslide Depth ...............................................................................................................131

Pore Pressure and Water Level .......................................................................................131

Continuous and Real-Time Monitoring................................................................................132

Displacement and Deformation........................................................................................132

Real-Time GPS.............................................................................................................132

Terrestrial Radar Interferometry....................................................................................133

Pore Pressure, Matric Suction, and Water Content .........................................................133

Landslide Depth ...............................................................................................................134

Internal Forces and Pressures .........................................................................................134

Seismoacoustic Emissions...............................................................................................134

Environmental Factors .....................................................................................................135

Measurement Locations.........................................................................................................135

Costs and Reliability...............................................................................................................136

Costs...................................................................................................................................136

Reliability.............................................................................................................................137

Methods of Stability, Stress, and Deformation Analysis ............................................................138

Methods and Capabilities.......................................................................................................139

Empirical and Statistical Methods .......................................................................................139

Prediction of Time to Failure ............................................................................................139

108
Movement Thresholds......................................................................................................139

Travel Distance ................................................................................................................139

Limit-Equilibrium Analysis ...................................................................................................140

Three-Dimensional Methods ............................................................................................141

Ground-Water Flow Modeling .............................................................................................142

Continuum Stress and Deformation Analysis......................................................................142

Displacement Predictions.................................................................................................143

Discrete-Element Analysis ..................................................................................................144

Data Requirements ................................................................................................................145

Challenges, Dilemmas, and Reliability...................................................................................145

Cost.....................................................................................................................................146

Reliability.............................................................................................................................146

Conclusions.. ............................................................................................................................147

Acknowledgments.....................................................................................................................147

References... ............................................................................................................................148

Figures 

3-1. Toe of the Anzar Road landslide, San Benito County, Califorina, that severed two 25-

cm diameter gas pipelines on April 22, 1998. .........................................................................162

3-2. Landslide velocity scale ....................................................................................................163

3-3. Block diagram showing the main parts of a landslide .......................................................164

3-4. Examples of engineering geologic maps ..........................................................................165

3-5. Use of trenches in landslide subsurface exploration.........................................................169

3-6. Sketches of soil test apparatus .........................................................................................170

3-7. Stress-strain curves for drained triaxial tests on loose (contractive) and dense 

(dilatant) medium-fine sand.......................................................................................................171

3-8. The Mohr-Coulomb failure criterion and effective stress...................................................172

3-9. Example images of landslide area obtained by laser scanning.........................................174

3-10. Map of landslide displacements obtained using aerial photogrammetry.........................175

3-11. Plots of displacements versus depth obtained using a slope inclinometer......................176

3-12. Typical monitoring installation.........................................................................................176

3-13. Plot of rainfall measured at15-minute intervals by a tipping-bucket rain gauge and 

subsurface pore-pressure variation obtained using vibrating-wire piezometers........................178

3-14. Map showing the location of boreholes and instrumentation in the Alani-Paty 

landslide, Honolulu, Hawaii.......................................................................................................179

3-15. The method of slices used in limit-equilibrium slope-stability analysis............................180

3-16. Example of results from finite-element analysis..............................................................181

3-17. Probability chart for factor of safety, illustrating the relationship between data 

uncertainty, factor of safety, and likelihood of failure ................................................................182

109
Tables 

Table 3-1. Comparison of equipment and resolution for repeat ground surveys to 

determine surface displacement. ....................................................................................183

Table 3-2. Aerial and satellite remote sensing methods for landslide displacement......184

Table 3-3. Techniques and sensors for continuous measurement of landslide 

displacement and deformation. .......................................................................................185

Table 3-4. Subsurface water measurement techniques.................................................189

Table 3-5. Preferred locations for landslide exploration and instrumentation. ................191

Table 3-6. Data types used in various numerical and analytical methods of landslide 

analysis...........................................................................................................................192

Conversion Factors 

Inch/Pound to SI 

Multiply By To obtain 

Length 

inch (in.) 2.54 centimeter (cm) 

inch (in.) 25.4 millimeter (mm) 

foot (ft) 0.3048 meter (m) 

mile (mi) 1.609 kilometer (km) 

yard (yd) 0.9144 meter (m) 

Area 

square foot (ft2) 929.0 square centimeter (cm2) 

square foot (ft2

) 0.09290 square meter (m2) 

square inch (in2

) 6.452 square centimeter (cm2) 

Volume 

cubic inch (in3) 16.39 cubic centimeter (cm3) 

cubic foot (ft3

) 0.02832 cubic meter (m3) 

cubic yard (yd3

) 0.7646 cubic meter (m3) 

Flow rate 

foot per second (ft/s) 0.3048 meter per second (m/s) 

cubic foot per second (ft3

/s) 0.02832 cubic meter per second (m3/s) 

110
inch per hour (in/h) 0 .0254 meter per hour (m/h) 

mile per hour (mi/h) 1.609 kilometer per hour (km/h) 

Mass 

pound, avoirdupois (lb) 0.4536 kilogram (kg) 

Pressure 

pound per square foot (lb/ft2) 0.04788 kilopascal (kPa) 

pound per square inch (lb/in2

) 6.895 kilopascal (kPa) 

Density 

pound per cubic foot (lb/ft3

) 16.02 kilogram per cubic meter 

(kg/m3

) 

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows: 

°F=(1.8×°C)+32 

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows: 

°C=(°F-32)/1.8 

111
SI to Inch/Pound 

Multiply By To obtain 

Length 

centimeter (cm) 0.3937 inch (in.) 

millimeter (mm) 0.03937 inch (in.) 

meter (m) 3.281 foot (ft) 

meter (m) 1.094 yard (yd) 

Area 

square centimeter (cm2) 0.001076 square foot (ft2) 

square meter (m2

) 10.76 square foot (ft2) 

square centimeter (cm2

) 0.1550 square inch (ft2) 

Volume 

cubic centimeter (cm3) 0.06102 cubic inch (in3) 

cubic meter (m3

) 35.31 cubic foot (ft3) 

cubic meter (m3

) 1.308 cubic yard (yd3) 

Flow rate 

meter per second (m/s) 3.281 foot per second (ft/s) 

cubic meter per second (m3

/s) 35.31 cubic foot per second (ft3/s) 

kilometer per hour (km/h) 0.6214 mile per hour (mi/h) 

Mass 

gram (g) 0.03527 ounce, avoirdupois (oz) 

kilogram (kg) 2.205 pound avoirdupois (lb) 

Pressure 

20.88 pound per square foot (lb/ft2 kilopascal (kPa) ) 

0.1450 pound per square inch (lb/ft2 kilopascal (kPa) ) 

Density 

0.06242 pound per cubic foot (lb/ft3 kilogram per cubic meter ) 

(kg/m3

) 

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows: 

°F=(1.8×°C)+32 

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows: 

°C=(°F-32)/1.8 

112
Landslide Investigation Methods 

By Rex L. Baum and Gerald F. Wieczorek 

Abstract 

As land development increases, more and more pipelines are being forced onto hillsides 

with the result that avoidance of landslide areas is becoming increasingly difficult. Once regional 

studies have identified landslides or potential landslides along the path of a proposed pipeline or 

pipeline corridor, detailed investigations may be needed in locations where landslides cannot be 

avoided. Width, depth, level of activity or potential for reactivation, and expected displacement are 

some of the primary objectives of landslide investigations for pipelines. Future landslide 

displacement (rate, amount, and direction) is one of the most important considerations in planning 

pipelines across landslide-prone areas. However, estimating the rate and amount of displacement is 

one of the most difficult aspects of landslide investigation, because long-term prediction of 

precipitation, ground-water levels, and other factors that affect slope stability is highly uncertain. 

Heterogeneity, anisotropy, discontinuities, and other natural subsurface features of bedrock and 

surficial deposits further complicate the problem. 

Rates of landslide movement range from imperceptibly slow (millimeters per year) to 

extremely rapid (many meters per second). Real or potential pipeline damage resulting from 

rapidly moving landslides is serious and must be avoided, but is much less common than damage 

resulting from slow landslides and reactivation (whether slow or rapid) of landslide deposits. Even 

though movement of many slow landslides appears to be relatively steady, detailed monitoring has 

shown that movement may be episodic or that movement rates may vary greatly over timescales 

ranging from hours to years. Empirical correlations have been made between movement rate and 

either rainfall or pore pressure. Movement rates vary nonlinearly with changes in pore pressure. 

Periods of relatively rapid movement, known as surges, have been observed in many slow 

landslides. Surges result from pore-pressure increases, accumulating strain, and rapid external 

loading acting together or separately. The surges may last several hours or days, and commonly 

result in displacements of several decimeters to several meters. Instrumental monitoring is the 

most reliable way to detect the onset of surges. 

Many techniques are available for landslide investigation, including mapping, subsurface 

exploration, monitoring, and analysis. Methods of engineering geologic mapping are highly 

developed and make it possible to define the boundaries and major internal structures of existing 

landslides, estimate past displacement, and reconstruct history of movement. Engineering geologic 

mapping also aids the detailed characterization of potential landslide areas. Recently laser scanners 

have become available to accelerate and improve the accuracy of detailed geologic mapping. 

Traditional methods of drilling and sampling can be used to estimate the probable depth and 

identify the range of materials within a landslide. Recent innovations show promise for improved 

sample recovery during drilling. Field and laboratory tests are available for determining the 

deformation, strength, and hydraulic properties of landslide materials. Improved computer control 

of test apparatus makes it possible to simulate realistic stress paths during testing, which represents 

a major advancement over traditional strain-controlled testing. 

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A large variety of instrumentation is available for monitoring landslide movement, 

deformation, and subsurface water pressures, and for defining the depth of landslide movement. 

Most landslide monitoring projects rely on a combination of campaign-style surveys (a connected 

series of measurements to characterize a landslide at discrete points in time) and continuous or realtime monitoring of electronic instruments. Recent advances in surveying equipment and methods 

such as GPS, laser scanning, and radar methods have improved remote measurement of landslide 

movement and deformation. Synthetic aperture radar is capable of detecting and measuring small 

displacements and has recently been applied to landslide monitoring. Real-time landslide 

monitoring is becoming more common and makes it possible to track changes in rate of 

displacement at remote locations. 

Available methods for numerical modeling of slope stability and landslides can provide 

time-independent estimates of likelihood of failure, and analyze stress, deformation, and mode of 

failure. Limit-equilibrium slope-stability analyses provide an estimate of the factor of safety 

against failure and are useful for evaluating how changes in slope geometry, loading, or pore 

pressure may affect slope stability. Probabilistic and/or reliability analyses help quantify the 

amount of uncertainty in factor-of-safety calculations; however, they typically do not estimate the 

annual probability of failure. In addition to factor-of-safety, finite-element and finite-difference 

analyses compute deformation, which aids in confirming or predicting failure mechanisms and 

eliminates the need to guess the depth and mode of failure in potential landslides as required for 

limit-equilibrium slope-stability analyses. Computed deformation has approximately reproduced 

observed amount and direction of displacement for a few specific case studies. Discrete-element 

methods have been developed to account for the discontinuous nature of rock slopes and some soil 

slopes. Discrete-element methods have been used to study landslide mechanisms, including 

simulation of historical rock avalanches and to estimate displacement (runout) of potential rock 

avalanches. Recent advances in continuum and discrete-element modeling include the ability to 

combine ground-water flow and slope deformation as coupled processes. The accuracy of stress 

and deformation computed by numerical methods depends on how accurately the landslide and its 

material properties have been characterized during the field and laboratory investigations. 

Modeling technique, choice of constitutive equations, and other details also affect the accuracy of 

model results. 

Computing annual probability of landslide movement or slope failure requires information 

about landslide recurrence obtained either by historical or geochronological methods. Historical 

observations of landslide displacement in the same region that have similar materials and 

geometries may provide the best estimates of future displacements. However, even among 

landslides occurring in the same geologic materials, levels of activity and rates of movement 

commonly differ. 

Introduction 

Although avoidance remains the preferred method for reducing or eliminating landslide 

damage to pipelines, occasionally existing landslides or landslide-prone areas may be unavoidable 

in planning and constructing new pipelines. Damage to pipelines caused by landslide movement is 

costly and disruptive. Notable examples include repeated rupturing of a 66-cm-diameter natural 

gas pipeline that crosses the Cascades landslide complex in the Columbia River Gorge (Braun and 

others, 1998). In February 1999 movement of a small landslide in the complex resulted in an 

explosion that dug a 60-m-wide crater (Schuster and Pringle, 2002). Reactivation of the Manti, 

Utah, landslide during the spring of 1974 destroyed a 20-cm-diameter water supply line, thereby 

disrupting the municipal water supply and hydroelectric power system of the city of Manti 

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(Fleming and others, 1988). Reactivation of a deep-seated landslide in the spring of 1998 

destroyed two gas lines that supply the city of Santa Cruz, California, leaving 60,000 customers 

without gas for several days (fig. 3-1; Schuster and others, 1998). 

Methods and techniques described in this chapter are applicable where landslides pose a 

significant threat to pipelines or related facilities. Estimates of amount and rate of potential 

displacement and assessments of the likelihood of movement are needed where pipelines must 

cross known landslide areas or highly suspect (potential landslide) areas. Field investigations 

attempt to provide these estimates and assessments as well as information that might be used in 

remedial works to reduce potential landslide movement. Landslide investigations usually start with 

a review of existing literature for the area and desk study of existing geologic maps, aerial 

photographs, and similar information. Landslide field investigations generally begin with surface 

exploration, including mapping of landslide structures and features as well as geologic materials 

involved in the landslide. Subsurface exploration of existing landslides helps define the depth of 

the slip surface, ground-water levels, and subsurface materials. Soil and rock samples collected 

from landslides may be tested in the laboratory or the field to determine strength and hydraulic 

properties. Instrumental monitoring is commonly used to observe variation in ground-water levels 

and the rate, amount, and direction of landslide movement. Various numerical models are used to 

estimate the likelihood of landslide movement or to determine how an existing or potential 

landslide will respond to external forces or changes such as grading or drainage. Sometimes 

historical studies are used to estimate the age of landslide deposits, recurrence of landslide 

movement, or the amount of movement that might occur during a future episode. 

This chapter provides a summary review of available methods for investigating individual 

landslides and landslide-prone sites. Because of the importance of characterizing displacement 

where pipelines cross landslides, we also provide a summary discussion of landslide rates and 

displacements as background for describing existing approaches to and the difficulty of predicting 

rates and net displacements. A companion chapter in this report describes applicable methods for 

identifying regional landslide hazard to pipelines (Harp, 2007). Once regional methods have 

identified specific landslides or landslide-prone areas that pose a major threat to facilities, sitespecific methods outlined in this report can be used to further characterize the hazard. A thorough 

review of site-specific methods available up through 1996 is provided in Turner and Schuster 

(1996). Due to the large volume of material already available there and the quality of treatment of 

the subject, emphasis here is on methods that have advanced significantly since that publication. 

However for completeness, we have also provided a brief description and discussion of older 

techniques that are particularly relevant to pipelines. Seismic hazards were beyond the scope of 

this project; therefore, methods specific to seismically induced landslides have not been included in 

this report. 

Landslide Movement Rates 

Rates of landslide movement range from imperceptibly slow (millimeters per year) to 

extremely rapid (many meters per second). Based on previous work by Varnes (1978), Cruden and 

Varnes (1996) proposed a landslide velocity scale, as illustrated in figure 3-2. The scale is divided 

into seven velocity classes, and the divisions increase by multiples of 100. Rapidly moving 

landslides (velocity classes 5, 6, and 7, figure 3-2) include rock and debris avalanches, debris flows, 

rapid earth flows in sensitive clays, rock falls, and some rockslides. Although small rock falls and 

shallow debris flows are unlikely to damage buried pipelines, avoiding large rapidly moving 

landslides and their effects is critical in preventing damage to pipelines. Nearly all types of 

landslides may display long-lasting slow movements. However, slow landslides (velocity classes 

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1, 2, and 3) most commonly include translational landslides in stiff clays and other fine-grained 

deposits (earth slides and earth flows) as well as many deep-seated landslides and spreads, complex 

movements in rock masses, and some slides in granular soils (Picarelli and Russo, 2004). 

Slow movements deserve further attention because of their potential to damage pipelines 

over time as cumulative displacement gradually increases. Even though movement of many slow 

landslides appears to be relatively steady, detailed monitoring has shown that movement may be 

episodic or that movement rates may vary greatly over timescales ranging from hours to years 

(Keefer and Johnson, 1983; Kalaugher and others, 2000; Coe, Ellis, and others, 2003; Petley, 2004; 

Picarelli and Russo, 2004). These changes in rate of movement result from external factors such as 

precipitation and erosion as well as internal changes in the landslide mass that result from 

deformation. Various workers have made empirical correlations between movement rate and either 

rainfall (Grivas and others, 1996, 1998; O'Neil and others, 1996) or pore pressure. Movement rates 

vary nonlinearly with changes in pore pressure (Picarelli and Russo, 2004). The net long-term 

effect of this variation in the instantaneous rate of movement is that cumulative displacement 

usually varies from season to season and year to year, thus making it difficult to forecast long-term 

displacements based on data from a brief (1–2 year) period of observation. 

In new landslides (first time failures), a period of slow but gradually accelerating 

movements typically precedes failure. Once failure occurs, movement accelerates rapidly, and the 

newly released landslide mass moves abruptly (Picarelli and Russo, 2004). Subsequent movements 

tend to be slow, but periods of relatively rapid movement, known as surges, have been observed in 

many slow landslides. Pore-pressure increases and changes in external loading acting together or 

separately have been identified as causing surges (Keefer and Johnson, 1983; Kalaugher and others, 

2000). The surges may last several hours or days, and commonly result in displacements of several 

decimeters to several meters. Major shear distortions have been observed during the early stages of 

displacement surges (Kalaugher and others, 2000). Instrumental monitoring of strain and pore 

pressure is the most reliable way to detect the onset of surges. 

Reactivated landslides commonly follow a similar pattern. An example is the spring 1974 

reactivation of the Manti, Utah, landslide that resulted from loading at the head by debris flows 

(Fleming and others, 1988). The landslide had moved small amounts during the previous 35 years 

and began accelerating soon after deposition of the debris flows. Movement slowed or stopped 

following winter and began again in the spring of 1975. Displacements of many meters occurred 

during 1975 and the 3-km-long landslide was fully developed by November 1975. Movement 

stopped during the winter and began again in spring 1976; however movements were much smaller 

than during 1974 and 1975. The average slope of this landslide was only 8°, yet it moved many 

tens of meters during 1974 and 1975 and destroyed a water pipeline that supplied the city of Manti, 

as noted previously. 

Duration of movement episodes is another factor (besides variable rates) that determines net 

displacement of a landslide. For example, annual displacements at a landslide in central Utah 

varied over a 4-year period (1983–1986) in response to varying amounts of water, derived mainly 

from snowmelt (Baum and others, 1993). Daily rates following spring snowmelt were variable and 

the cumulative displacement at one particular point ranged from 58 cm in 1983 to 280 cm in 1984 

(250 cm in 1985 and 95 cm in 1986). The number of days the slide was active ranged from about 

45 days in 1984 to 116 days in 1986. Similarly, landslides in the US Highway 50 corridor east of 

Sacramento, California, move longer and farther in years when a wet winter season is followed by a 

wet spring than in years when a wet winter season is followed by a dry spring (Mark Reid, USGS, 

oral commun., 2007). 

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Inferring Probable Rate of Movement 

Differentiating between potentially slow and rapid landslides is a key consideration for 

pipelines as it is more critical that large, rapid landslides, or their impacts, need to be avoided than 

slow landslides. Where pipelines must cross a slow landslide, the probable rate of movement may 

determine the preferred approach for avoiding pipeline damage. 

Form and nature of the deposits provide a general indication of rate of movement of past 

landslides. In other words, it is often (but not always) possible to distinguish deposits of debris 

flows, debris avalanches, and other rapidly moving landslides from deposits of slow landslides or 

other processes. Debris flows usually have lateral levees and the main deposits have characteristic 

fan-shaped morphology; the deposits typically have large clasts supported by fine-grained matrix 

(Cruden and Varnes, 1996; Pierson, 2005). Slope and materials also give some indication of 

potential rate of movement. For example, most landslides in high-plasticity clay on slopes flatter 

than 12°–15° usually move at slow to moderate rates (velocity classes 1, 2, 3, and 4); however as 

noted previously, even slow earth flows in plastic soils are noted for occasional surges to rates of 

several meters per minute (Keefer and Johnson, 1983; Baum, 2003) or reactivation with sustained 

slow movement that results in many meters of displacement (Fleming and others, 1988). On the 

other hand, quick-clay landslides usually move rapidly and enlarge retrogressively a distance that is 

many times the slope height. Landslides on steep slopes (>25°–30°) always have the potential for 

rapid movement. 

Despite some general dependence of potential rate on slope, morphology, and earth 

materials, experience also indicates that rate of movement of landslides can be quite unpredictable. 

A recent tragic example is the La Conchita landslide in Southern California, which moved at rates 

of meters per day when it was active in 1995 and moved catastrophically at rates of meters or tens 

of meters per second in 2005 (Jibson, 2005). It has also been observed that debris flows commonly 

form on the toes of large landslide deposits (Mark Reid, USGS, oral commun., 2003). Thus, 

material from a large slow-moving landslide may become part of a smaller rapidly moving 

landslide. A classic example of the difficulty of predicting rate of movement is the 1963 Vaiont 

landslide, which moved catastrophically after creeping slowly for about 3 years (Kiersch, 1964). 

Although such catastrophic movements may be relatively uncommon, nearly all slow landslides 

have the potential for abrupt or sustained relatively rapid movements ranging from a few 

decimeters to several meters or more. We are unaware of any set of circumstances, conditions, or 

characteristics that positively ensure that a slow-moving or inactive landslide will not undergo 

future displacements large and rapid enough to damage or rupture a pipeline. 

Engineering Geologic Mapping and Related Field Studies 

The purpose of engineering geologic mapping in landslide investigations is to determine the 

dimensions and to identify and locate boundaries and other surface features (fig. 3-3) and geologic 

materials of the landslide. Mapping and related field studies also help to unravel the geological 

history of landslide, which may result in estimates of magnitude and frequency of past movements. 

Engineering geologic mapping at various scales serves different purposes. Large-scale (1:50–

1:1,000) mapping shows the geologic (lithology, structure, geomorphology) and hydrologic 

(springs, sag ponds) details needed for study of individual landslides and landslide-prone sites. 

Mapping at small (1:25,000–1:100,000) and intermediate scales show landslides and landslideprone areas in the context of the regional and local geology and terrain. 

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Small-Scale Mapping 

Small-scale regional mapping was discussed in the companion paper by Harp (2008, 

chapter 1 of this report), but some of the data collected in connection with regional mapping can 

contribute to an understanding of specific landslides. Although showing existing landslides on a 

map does not necessarily represent exactly where future landslides may occur, such mapping does 

help bound the ranges of several landslide characteristics for a particular area. For example, ranges 

in size and travel distance of landslides in a pipeline corridor can be determined from small-tomedium-scale mapping (landslide inventory maps, figure 3-4A). The observed size ranges provide 

some constraints or guidance for estimating the potential widths of future landslides. 

Detailed geologic maps showing a history of different periods of previous and recent 

landslides, sometimes referred to as multitemporal maps because they show landslides from 

multiple events spread over a period of years or decades, have been prepared within several areas 

of California, for example, Brabb and Pampeyan (1972), Campbell (1975), Wieczorek (1982, 1984) 

Wieczorek and others (1999), and Coe, Godt, and others (2004). Similar maps exist for other areas, 

but coverage tends to be spotty. Carrara and others (1995), using GIS technology in mapping 

landslides, include landslide typology, degree of activity, relative age, estimated depth, estimated or 

observed velocity, and degree of certainty in mapping and classification. Information needed to 

ascertain landslide hazard can be obtained from analysis of a multitemporal landslide-inventory 

map that portrays the distribution, type, and pattern of landslides and their changes in time. The 

multitemporal map may be compiled from landslide-inventory maps prepared through the analysis 

of stereoscopic aerial photographs of different ages and by use of field surveys (Reichenbach and 

others, 2004). Although uncommon prior to about the 1960s, series of relatively high-quality, 

medium-scale (1:10,000–1:25,000) stereo air photographs taken at 5- to 10-year periods, or more 

frequently, can be utilized to obtain information on the extent, progression, and annual probability 

of occurrence of landslides along or adjacent to pipeline corridors. An estimate of the annual 

probability of landslide occurrence, even if approximate, can provide valuable input in the selection 

of alternative pipeline alignments, as well as in decision making with respect to acceptance of 

existing risks or the need for, and extent of, mitigative measures. Depiction of prehistoric, 

historical, and recent landslides on detailed maps can depict the regional potential future landslide 

hazard (Wieczorek and others, 1999). 

Recent applications of Light Detection and Ranging (LIDAR) to landslide mapping are also 

very useful. LIDAR is a technique based on airborne scanning with a laser rangefinder and GPS 

ground control to produce high-resolution topographic data. Using algorithms for virtual 

deforestation (Haugerud and others, 2003), LIDAR data acquired during the leaf-off season are 

capable of producing detailed bare-earth digital-elevation models. The quality of LIDAR mapping 

has steadily improved over the last several years as point densities of LIDAR surveys have steadily 

increased and postprocessing has become more sophisticated. LIDAR topographic data are 

becoming available for more and more areas of the United States as public and private entities 

commission increasing numbers of LIDAR surveys. In some areas these data are in the public 

domain and freely available; in areas where LIDAR has been acquired with private funding, the 

data may be available for purchase from a vendor. 

Use of LIDAR-derived topography is particularly effective where pipeline alignments and 

adjacent landforms, including landslide features, are masked by extensive tree or vegetation cover 

or where excessively steep and/or otherwise inaccessible or dangerous terrain limits or precludes 

effective ground-based mapping. LIDAR topography has been utilized extensively along the 

Vancouver to Whistler highway, British Columbia, and utility corridor to map steep rock bluffs and 

slopes, and to establish accurate topography and assess potential slide features within heavily tree 

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covered slopes elsewhere within coastal British Columbia (Richard Butler, written commun., 

2007). Schulz (2004, 2005) used LIDAR-derived imagery to map landforms in Seattle, 

Washington, that were created primarily by landslides. These landforms included landslide 

deposits, head scarps, and denuded slopes that were created by prehistoric landslides that have 

occurred since the retreat of the last glacier. Over 93 percent of about 1,300 reported historical 

landslides are located within the LIDAR-mapped landform boundaries. The spatial densities of 

reported historical landslides within the LIDAR-mapped landforms provide the relative 

susceptibilities of the landforms (particularly landslide head scarps and deposits) to landslide 

activity in the recent past. The spatial densities also provide reasonable estimates of future 

landslide susceptibility. The mapped landforms and susceptibilities provide useful tools for 

landslide hazard reduction in Seattle. 

Although buried pipelines are not usually subject to damage by rock fall, aboveground 

facilities such as pumping stations and valves may be exposed to rock falls. Techniques of 

engineering geologic mapping and related field studies characterize the potential for rock fall in 

these areas (fig. 3-4B, Coe and others, 2005). Three-dimensional analysis of rock-slope stability 

has been developed using joint directions, slope orientations, and friction angles with a stereo net to 

create Markland plots (Markland, 1972). A rock-slope stability analysis in Navajo National 

Monument, Arizona, was used to assess the potential for planar or wedge sliding along 

discontinuities at various stations along a steep rocky trail (Wieczorek and Harp, 2000). Using 

Markland plots shows the discontinuities (bedding and joints) in relation to potential wedge and 

planar sliding surfaces on a lower hemisphere stereonet projection in the rock-slide region 

(Wieczorek and Harp, 2000, their figs. 4 and 5). The slope face is shown as a great circle and 

friction is represented by an interior circle. 

Large-Scale Mapping 

Detailed observation of bedrock, field-developed cross sections, classes of slope stability, 

and surface-water features are useful for surface observation and geologic mapping of landslides 

and landslide-prone areas (Keaton and DeGraff, 1996). Details to be included in a large-scale 

engineering geologic map of a landslide depend somewhat on the landslide types and processes 

involved. Several landslide classification schemes are used worldwide; one of the most widely 

used is the Varnes classification, which is based on material and process (Varnes, 1978; Cruden 

and Varnes, 1996). The different types have different three-dimensional forms, but the types that 

most commonly damage pipelines have features similar to those depicted in figure 3-3. 

Complex landslides can be depicted on detailed scale maps (Bogaard and others, 2000; 

Chelli and others, 2005). Fleming and Johnson (1989) described, mapped, and interpreted the 

various structures that commonly occur on the surface of landslides. Their methodology for 

detailed mapping of large individual landslides is useful for constructing conceptual and physical 

models and forms a basis for analyzing landslide movement (fig. 3-4C). Major structures and 

features of the landslide emerge from mapping individual cracks, scarps, lateral shear zones, and 

other deformational features. Examples include the Slumgullion, Colorado, landslide (Baum and 

Fleming, 1996; Fleming and others, 1999) and the Alani-Paty, Hawaii, landslide (Baum and Reid, 

1995; Baum and others, 1998). Areas of active or potential enlargement are identified by mapping 

small fractures outside the main body of the active landslide. 

Similar levels of detail can be portrayed in engineering-geologic mapping of a potential 

landslide area. Although landslide features may not be present, detailed mapping can be used to 

show geologic structures (faults, folds, joints, foliation, and other discontinuities), variations in 

lithology, zones of weathering or alteration, depth to bedrock, seepage zones, and other features 

119
relevant to slope stability, most notably any evidence of recent or ongoing ground deformation. 

Locations of boreholes, trenches, test pits, measurements, geophysical surveys, and instruments can 

also be shown on large-scale engineering geologic maps of landslide and potential landslide areas. 

Methods of Estimating Displacement 

When mapping landslides, it is usually desirable to estimate total past displacement. Such 

estimates often provide an upper bound on possible future displacements (Skempton and others, 

1989). Major reactivations of large old landslide deposits, such as the Manti, Utah, landslide 

(Fleming and others 1988) and the Thistle, Utah, landslide (Schuster and Fleming, 1986), which 

have resulted in major movement and enlargement beyond the previous boundaries of these 

landslides, are notable exceptions. The most direct method is to measure offsets at the boundaries. 

For example, where a fence or road crosses a landslide, the offset across the boundary gives an 

estimate of the total displacement. Displacement varies from point to point; so, where possible, 

multiple measurements of offset should be gathered. Measurements of displaced volume usually 

do not provide reliable estimates of net displacement, because they are based on vertical changes 

and do not track material points on the surface. Point-tracking methods (Smith, 1996; Coe, Ellis, 

and others, 2003) are described in the section on monitoring. 

Landslide History 

Information on landslide history or recurrence of movement constitutes the basis for most 

estimates of temporal landslide probability. Absolute or relative ages of landslides (or rather, the 

ages of their last movements) are also used to make a preliminary assessment of their stability. 

Landslides that have not moved in hundreds or thousands of years are commonly assumed to be 

more stable than those that have moved more recently. However, this approach must be used with 

caution because climate extremes, increased erosion rates, and human activities such as irrigation, 

grading, excavation, or other changes to the land surface can invalidate this assumption. 

Several techniques are available for partially reconstructing history of movement in 

landslide areas. These include use of crosscutting relationships and features (such as scarps and 

hummocks), scarp degradation to define relative ages of deposits (McCalpin, 1984), as well as 

methods for obtaining "absolute" ages. Radiometric ages of buried soils (Madole, 1996) and 

datable materials (wood, bone, or charcoal) embedded in landslide deposits (Chleborad, 1996) 

provide approximate ages of past movement. Ages of organic-rich deposits that have accumulated 

in sag ponds and depressions that have formed on the surface of a landslide (Alexandrowicz and 

Alexandrowicz, 1999) or in the lacustrine sediments that have accumulated upstream of a landslide 

that has dammed a valley (Schuster and Pringle, 2002) can also be determined by radiometric 

techniques. Dendrochronology (Stoffel, 2006), lichenometry (Bull and others, 1994), pollen 

analysis (Adam, 1975; Baron and others, 2004), and similar techniques have also been used for 

estimating ages of landslides. Dendrochronology is capable of giving more precise ages than other 

methods, but corrections are needed when determining ages of young surfaces (Pierson, 2007). It 

should be kept in mind that ages determined by any of these methods are approximate and subject 

to various limitations. Selection of sites for collecting datable materials requires a clear 

understanding of the morphology and internal structure of a particular landslide, as well as an 

understanding of which locations will give minimum ages and an awareness of other potential 

difficulties (Van Den Eeckhaut and others, 2007). The cost of obtaining radiocarbon ages is about 

$300–$600 (U.S. dollars, 2007) per sample, depending on sample size. In areas where historical 

data on landslide occurrence is unavailable, the value of a landslide history constrained by 

radiocarbon or other ages may far exceed the cost. 

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Historical records are also useful for identifying major episodes of past movement, 

particularly in areas that have been occupied for long periods of time (Bisci and others, 1996; 

Castelli and others, 2004; Coe, Michael, and others, 2004). Unfortunately long historical records of 

landslide activity are relatively rare. 

Regardless of the techniques used for determining ages of landslides, an important question 

is how wide an area should be studied to develop a landslide history. At a minimum, a landslide 

history would be needed for the entire length of the pipeline transect or corridor. A more 

conservative approach would be to develop the history for all drainage basins that contribute 

directly to hazard along the pipeline. For example, Coe, Godt, and others (2003) used the basin 

approach in assessing debris flow probability for a major transportation corridor in central 

Colorado. This approach results in a study area that has variable width along the pipeline, but it 

ensures that potential landslide sources are not overlooked by setting an arbitrary fixed width for 

the study area. As noted previously in the case of the Manti landslide, natural processes occurring 

within the drainage basin, but hundreds of meters away from the actual pipeline, can affect slope 

stability. The same can be said for human activities (excavation, grading, irrigation) occurring 

upslope or downslope of a pipeline. 

Subsurface Exploration 

Drilling and trenching are the most commonly used methods for subsurface exploration of 

landslides. Geophysical techniques are sometimes used where drilling is not feasible or to aid 

extrapolating measurements from a single borehole or between boreholes. The most commonly 

used geophysical techniques include seismic reflection, seismic refraction, ground-penetrating 

radar, and methods based on electrical resistivity. In this section, we provide a brief summary of 

commonly used techniques and highlight recent developments. McGuffey and others (1996) 

provide a complete detailed description of subsurface exploration techniques and methods of data 

presentation. 

Drilling of deep-seated landslides is the most useful and most widely applicable method for 

subsurface exploration of landslides. Boring logs for existing water wells or oil and gas wells on or 

near the landslide can be useful sources of supplemental information. At many sites, drilling is 

necessary to determine the depth of the slip surface and the geometry of the landslide mass. For 

documenting landslides, drilling and sampling can determine types of subsurface geologic 

materials, locations, and orientations of joints, landslide shear zones, and ground-water levels. 

Although boring methods have changed little from those described by McGuffey and others (1996), 

a few new techniques and improvements to old ones are worth noting. For example, new types of 

drilling fluid have been developed for use with diamond drilling to help reduce sample disturbance 

and improve sample recovery. Nakamura (2004) described a new technique known as Jet Foam 

Boring that uses stiff foam to help protect the core. 

For major projects, such as a large landslide affecting an important pipeline corridor, largediameter (60–90 cm) boreholes can also be used by geologists or engineers in the same way as test 

pits and trenches to make detailed subsurface logs (maps of borehole walls) and photography for 

documentation deep within the landslide mass and to collect relatively undisturbed samples of the 

basal shear surface. Deep drilling of landslides has often revealed the detailed mechanisms of 

movement, such as that of the October 2, 1978, Bluebird Canyon Landslide in Laguna Beach, 

California, that destroyed 24 homes (Sydnor, 1979). In April 2005, another large, translational 

landslide about 60 m east of the Bluebird Canyon landslide moved destroying 19 homes and 

resulting in evacuation of more than 345 homes. Detailed geologic logs of large-diameter 

boreholes in this landslide revealed details including the presence of ancient landslide debris, the 

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depth, thickness, and orientation of shear zones, fractures, and bedding as well as presence or 

absence of seepage zones to depths of nearly 30 m (Gary Stoney, Stoney-Miller Consultants, Inc., 

written commun., 2007). 

Borehole logging methods including electric and nuclear logging have been described by 

McGuffey and others (1996, fig. 10-2). These logging techniques help to characterize lithology 

and rock or soil density throughout the depth of borehole. Large diameter boreholes and downhole 

logging methods have been proved to be valuable in landslide investigations, for example, in Napa 

County, California, by Johnson and Cole (2001). Borehole televiewers have recently been 

developed to help with logging of smaller diameter boreholes (Borchers, 1994; Nakamura, 2004). 

For landslides involving rock masses, rock-strength properties, including friction angle of 

rock surfaces, the roughness of natural rock surfaces, fracture infilling, and recently displaced 

fractures, are significant (Wyllie and Norrish, 1996). Much of this information can be obtained 

from boreholes. For example, detailed subsurface geologic drilling and examination using 

geophysical logging and borehole television revealed details of the morphology and structure of 

granitic bedrock near Wawona within Yosemite National Park, California (Borchers, 1994; 1996). 

Use of oriented-core sampling methods, with or without complementary borehole video or 

photographic examination provides high-quality information on orientation of rock structure or 

bedding. 

Direct-push (Geoprobe®) techniques have been developed for advancing small-diameter 

holes (up to 75 mm) in soil and soft rock by direct push or driving a sampler into the ground. This 

method returns high-quality samples for logging and some types of testing. Probing can reach 

depths of 30 m, which is adequate for many landslide investigations. Probing can be used as a 

primary method for subsurface exploration on smaller landslides or as a rapid method of creating 

additional holes for installing piezometers or other instruments (Bianchi and Farrington, 2001). At 

steep or difficult sites, probing can be performed by a two-or-three-man crew using a gas- or 

electric-powered breaker hammer; a manual jack can be used to extract the sampler. Hydraulic 

probing rigs that mount on the back of a pickup truck as well as self-contained track-mounted rigs 

are also available. Tools for cone-penetration testing (CPT) as well as electrical and hydraulic 

logging are available for these rigs. 

CPT has been available for some time on larger truck- and track-mounted rigs and has 

evolved into a highly developed technique for geotechnical subsurface exploration, with many 

variants as described by McGuffey and others (1996). A steel rod with a conical tip is forced into 

the ground while the required force is recorded continuously. Within soft-to-stiff or loose-tocompact soils, electronic Cone Penetration Test probes can be used to obtain continuous profiles of 

subsurface characteristics to depths of 30 m or more within a short time period. Since CPT 

methods typically record variations in subsurface conditions over penetration distances of 50 mm 

or less, CPT profiling is effective in detecting the presence of weak or sheared zones that need 

further investigation by more direct methods, as well as the boundaries between or within various 

soil strata. Cone penetrometers can be equipped for geophysical and piezometric measurements. 

Thus, electronic CPT equipment is also typically capable of determining the variations in transient 

pore-water pressures with depth, as well as pore-water-pressure dissipation properties. It can also 

be utilized to determine uphole or downhole shear wave measurements, or electrical resistivity 

profiles. CPT is invaluable as a supplemental exploration technique for landslides. 

Trenches and test pits permit direct observation and sample collection in relatively inactive 

landslides to depths of 3–6 m with adequate shoring or stepped excavation in the case of trenches. 

Consequently, trenches and pits are useful in landslides of moderate depth, and near the edges of 

deep landslides. Trenches can be excavated in a few hours by backhoe at relatively low cost and 

provide a three-dimensional view that is unachievable by any other method (James P. McCalpin, 

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oral commun., 2007). Information obtained by careful logging of trenches can aid in accurate 

interpretation of samples and cuttings from boreholes. Trenches near the toes of landslides 

commonly expose the basal shear zone, which may be quite different in composition and 

appearance from most of the landslide debris, so that the shear-zone materials can be adequately 

sampled and tested (fig. 3-5). 

Use of test pits or test trenches and large-diameter boreholes is often limited by the presence 

of seepage or ground-water levels, or materials having minimal stand-up time close to the ground 

surface. Use of sonic drilling techniques can be used to obtain near continuous, although disturbed 

cores of soils, ranging from fine sands and silts or clays to cobbles and boulders, and weak-tomoderate-strength rock. Recent investigations in British Columbia indicate that such soils and rock 

can be penetrated to depths of the order of 40 m and possibly more (Richard Butler, written 

commun., 2007). Sonic drilling methods can also be used as a relatively rapid means to permit 

installation of slope inclinometers and conventional or rapid-response piezometers. 

The use of geophysical techniques in subsurface exploration is based on attempts to relate 

changes in physical properties, such as the elastic modulus or electrical resistivity, to changes in 

lithology, the location of the water table, or other subsurface features of interest in landslide 

investigations. Ground-penetrating radar (GPR) can be used to detect the soil or rock strata and 

bedding features, and, in particular, zones of disturbance to depths of the order of 5 to 15 m in 

granular soils or rock, although it may have limited penetration within fine-grained soils. Use of 

these indirect methods requires considerable skill in interpretation of the results. Example 

applications include locating the base of a landslide deposit by seismic reflection and refraction 

methods (Williams and Pratt, 1996; Corsini and others, 2006), or by a combination of seismic and 

electrical methods (Bogaard and others, 2000; Chelli and others, 2005). Electrical methods have 

also been used to investigate ground-water distribution in landslides (Hiura and others, 2000). 

Geophysical techniques are often best combined with direct investigation methods to permit 

correlation and corrections to the inferred depths and properties of soil or rock strata while assisting 

in interpreting the variations in conditions between the direct investigation sites. 

Sampling 

Samples obtained from the ground surface or from subsurface exploration can be used to 

determine the types and strengths of the geologic materials involved in landslides. In some cases, 

samples can be used to determine the geologic age of the materials and possibly the previous age of 

landslide movement. For example as noted previously, radiometric analysis of wood or charcoal 

fragments found beneath a landslide can be used to determine the approximate age of previous 

historic/prehistoric landslide movement. Regardless of the specific test(s) or analysis(es) planned 

for a particular sample or suite of samples, the object of sampling is to obtain materials that 

represent the properties or range of properties relevant to understanding past, present, and future 

behavior of the landslide. The heterogeneous nature and complex history of most landslides and 

landslide-prone areas make it imperative that the relationship of samples and sample locations to 

the overall geometry and structure of the landslide or potential landslide be well understood. 

Without adequate understanding it is very likely that irrelevant materials will be sampled and 

tested. For example, it has been observed in many relatively slow-moving landslides of the type 

that are common in areas crossed by pipelines that the basal and lateral shear zones consist of much 

weaker materials than those that make up the main body of the landslide (Baum and Reid, 2000). 

Drilling can be used to obtain detailed samples of landslide materials at many different 

depths. McGuffey and others (1996) describe in detail the various types and applications of 

sampling available with modern drill rigs. We are unaware of any major advances in sampling 

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technique in the last decade. Sample recovery is typically less than 100 percent but careful 

examination of recovered samples makes it possible to approximately reconstruct the distribution of 

materials in the subsurface. In cohesive clay soils, it is sometimes possible to recover materials 

from the basal shear zone for shear-strength testing; however it is usually necessary to use 

inclinometer observations to confirm the depth of sliding. In cases where the basal shear zone can 

be sampled directly, either in a trench or large-diameter borehole, it is possible to obtain relatively 

undisturbed block samples for oriented (shearing parallel to the movement direction) direct-shear 

tests of the slip surface. Again, we refer the reader to McGuffey and others (1996) for a detailed 

discussion of sampling methods. 

Testing 

The main purpose of testing landslide materials is to determine strength and hydraulic 

properties (Wu, 1996; Lambe and Whitman, 1969). Determination of shear strength of materials at 

the landslide slip surface is relevant to stability analysis, estimates of landslide movement and 

understanding failure mechanisms of slopes (Leroueil, 2001). Landslide materials are often 

inhomogeneous, and the strength parameters can vary over an order of magnitude between different 

materials. Therefore correctly identifying slip-surface material is critical to obtaining 

representative test results. In the case of potential landslide areas, a detailed exploration sampling 

program is needed to identify and sample materials from potential slip surfaces. Hydraulic 

properties are used in predicting effects of rainfall, subsurface drainage and other factors on 

subsurface water pressures (Baum and Reid, 1995; Iverson, 2000; Hungr and others 2005). Field 

tests usually provide the most meaningful values of hydraulic properties for landslide modeling 

purposes. Methods that have been used effectively to determine the ground-water and piezometric 

conditions, including “perched” or nonhydrostatic conditions, at depth within soil and rock include 

multi-port piezometers, CPT dissipation testing, and conventional falling-/rising-head testing 

(Richard Butler, written communication, 2007; McGuffey and others, 1996). 

Shear Strength 

Once relevant materials have been obtained for testing, consideration must be given to the 

type of test. Strength testing attempts to duplicate field conditions as closely as possible. These 

conditions include the stress state, stress path, rate of shear, drainage, whether the material has 

previously been sheared, and whether displacement is concentrated along a discrete plane or 

distributed. A complete understanding of soil behavior in the context of slopes is needed to plan a 

soil-testing program and interpret test results (Lerouiel, 2001). The stress-strain and strength 

properties of soils are typically determined in the laboratory by direct-shear and triaxial tests. 

Some soils, such as coarse granular materials, sensitive silty or organic soils present at depths of 10 

m or more below the ground surface, and fractured or friable rocks may be difficult or impossible 

to sample without excessive disturbance. In such cases, use of field testing methods, such as the 

Menard Pressuremeter, dilatometer, and strain-controlled field-vane methods, may be desirable or 

necessary to permit determination of strength values (McGuffey and others, 1996; Wu, 1996). 

Wu (1996) provides a complete description of laboratory and field test procedures; the 

following paragraphs contain a brief summary of laboratory test procedures. Figures 6A and 6B 

respectively show the general configurations of a laboratory direct-shear test apparatus and a 

triaxial test apparatus. 

In a direct-shear test, the soil is placed in a steel or bronze box that is split along the line of 

the shear plane as depicted in figure 3-6A. A normal force, , is applied along the top of the box Fn

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and a shear force, , is applied to the side of the upper, moveable block to cause shearing in the 

enclosed soil. Shear and normal stress magnitudes are obtained by dividing the shear and normal 

force by the original horizontal cross-sectional area of the soil sample. These stresses and the 

displacements of the upper block are recorded and the results are given in terms of shear and 

normal stress on the shear plane and the horizontal and vertical displacements of the upper, 

moveable block. In addition, changes in pore-water pressure in partially saturated or saturated soils 

can be recorded. A ring-shear or torsional-shear device is similar to direct shear in that it forces 

shear to occur on a plane, but the ring-shaped sample is sheared about its central axis, allowing 

large displacements to occur. 

Fs

In the triaxial test apparatus shown in figure 3-6B, the soil sample, enclosed in a flexible 

membrane, is placed between a moveable upper platen and a fixed lower platen and confining 

pressure is applied. This is followed by application of an axial load. The axial load is converted to 

axial stress by dividing the axial load by the original horizontal cross-sectional area of the soil 

sample. The axial stress is the major (most compressive) principal stress and the confining pressure 

is the minor (least compressive) principal stress in a compression test. A porous stone disc 

connected to an outlet tube allows drainage of fluid from the bottom of the sample. The valve on 

the outlet tube is left open in a drained test. An undrained test, considered relevant to rapid 

loading, is achieved by shutting the valve on the outlet tube. The stresses and the displacements of 

the upper platen are recorded and, in addition, changes in water pressure in an undrained test for 

wet or saturated soils are usually recorded. Further details on direct shear and triaxial tests on soils 

are found in Lambe and Whitman (1969), Bishop and Henkel (1957), and Wu (1996). 

Depending on their initial porosity relative to the applied normal stress, soils initially 

exhibit either contractive or dilatant behavior during shearing deformation. Loose soils tend to 

compact or contract as they deform under load. Dense soils tend to dilate or expand as they deform 

under a normal load. Both tend toward a “steady” or “critical” state as deformation increases under 

constant normal load (Lambe and Whitman, 1969). Soil particles deform very little at the low 

normal stresses present in shallow soils on steep slopes. Rather, soil deformation at low normal 

stresses mainly involves rearrangement of soil particles and changes in pore space. 

Figure 3-7 shows the results of triaxial tests on granular soil in loose and dense states. The 

dense soil failed in a relatively brittle manner, as indicated by the steep slope of the initial (rising) 

part of the force-displacement curve. It reached its peak strength after an axial strain of a few 

percent. After reaching the peak, the strength of the dense soil gradually declined toward the 

ultimate or residual shear strength. As shown by the change in void ratio, the dense soil began 

dilating after a very small axial strain and continued dilating throughout most of the test (fig. 3-7). 

The initial slope of the force-displacement curve of the loose, contractive soil is much 

flatter than that of the dense soil, indicating ductile failure (fig. 3-7). The shearing resistance of the 

loose soil gradually builds to its ultimate strength, which is attained after significant axial strain. 

Change in void ratio indicates that the soil compacted slightly at the beginning of the test and then 

dilated to approximately its original void ratio. After large axial strain (about 30 percent), the 

“critical state” is reached where the shear strength and void ratio of the dense and loose specimens 

are approximately equal. 

Shear strength varies with displacement and soil porosity. Dense and cemented soils 

display a peak strength that is developed within the first few millimeters of displacement. Upon 

further shearing, the soil weakens toward the so-called residual strength (fig. 3-7). Peak strength is 

usually considered relevant to first-time slides in natural normally consolidated clay and intact 

rock. The fully softened strength is relevant to first-time failure of stiff-fissured clays and 

claystones (Skempton, 1985; Wu, 1996). Residual strength is generally considered relevant to 

reactivation of landslides (Skempton, 1985), but desiccation or precipitation of minerals from pore 

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water can cause strength to regain between episodes of movement (Bromhead, 2004). In some 

cases, residual strength also appears to be relevant to analyzing progressive failure (Dixon and 

Bromhead, 2002). 

The results of direct shear and triaxial tests of soil show that at the point of incipient shear 

failure there can occur planes along which the shear stress is given by 

τ = c +σ tanφ (1) 

where τ is the shear stress on a potential failure plane, σ is the normal stress at failure on this 

plane, c is cohesion, and φ is the angle of internal friction of the soil. Equation 1, first proposed by 

Coulomb in 1773, is known as the Coulomb failure criterion. Although other failure criteria have 

been proposed and used in some modeling exercises, the Coulomb criterion remains the most 

widely used and easily understood failure criterion for soils. It plots as a straight line in twodimensional Mohr stress space (Terzaghi, 1943) as shown in figure 3-8A. This line separates the 

Mohr stress space into stable and unstable parts. If a Mohr circle constructed from the major and 

minor principal stresses, σ 1 and σ 3 , lies below the Coulomb failure line, the soil behaves 

elastically. If, however, a Mohr circle becomes tangent to these lines, failure will ensue. No Mohr 

circle can lie beyond this limiting line because the shear stress cannot exceed the yield strength of 

the soil. 

The strength parameters, cohesion, c, and the angle of internal friction, φ, of a soil are 

determined as illustrated in figures 8A and 8B. Mohr’s circles representing the state of stress at 

failure for three different tests are plotted and a line tangent to the circles is constructed (fig. 3-8B). 

This line is the failure envelope. Commonly, the failure envelope can be treated as a linear, twoparameter model, the Mohr-Coulomb failure criterion (Equation 1), over the range of stresses that 

apply in shallow landslides. Figure 3-8C shows Mohr-Coulomb strength parameters determined by 

drained direct-shear testing of two soils from the Alani-Paty landslide in Hawaii (Baum and Reid, 

1995). The expansive clay is similar to material from the basal failure surface of the landslide and 

the sandy clay represents some of the material in the body of the landslide above the failure surface. 

Primes indicate that the strength parameters were determined under effective-stress conditions, 

conditions that we now review. 

For water-saturated soils, effective stress is defined by subtracting pore-water pressure 

(interstitial fluid pressure), p, from the total normal-stress components (Terzaghi, 1943). In 

Cartesian xyz coordinates the total normal-stress components are σ x , σ y , and σ z and the effective 

stresses are given by p σ ′ σ xx −= , σ ′ = σ yy − p , and p σ ′ = σ zz − . Static pore pressure does not 

affect shear stress; however, ground-water flow fields can affect shear stresses as well as normal 

stresses (Iverson and Reid, 1992). When pore pressure is present, the Mohr-Coulomb failure 

criterion becomes 

τ =+ − c p ′ (σ )tanφ′ (2) 

where c′ and φ′ are, respectively, the cohesion and the angle of internal friction measured under 

effective-stress conditions. Thus pore pressure reduces the normal stress on potential failure planes 

and, in effect, reduces the internal friction that resists failure on these planes. Thus increasing pore 

pressure reduces the shear strength of a soil mass. This effect is illustrated in figure 3-8D, where 

we see that the addition of positive pore pressure shifts the Mohr circle to the left toward tangency 

with the failure envelope. 

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Recent Improvements in Test Procedures 

The main advancements in laboratory testing during the last decade have been development 

and improvement in stress-controlled testing and improved methods for unsaturated soils 

(Jotisankasa and others, 2007), whereas most traditional methods were strain-controlled and 

restricted to saturated soils. Standardized procedures for tests based on direct shear, torsional 

shear, and triaxial methods have long been codified in standards of the American Society for 

Testing and Materials (ASTM) and national institutes of standards of other countries. Perhaps the 

most notable recent improvement in these procedures is that automated equipment for conducting 

these tests is readily available from commercial sources; however, the tests themselves have 

changed little over the years. Systems for static, dynamic, and controlled stress triaxial tests are 

also available commercially. Recently Sassa and others (2004) developed a new torsional-shear 

testing device that allows stress or strain control for static and dynamic testing. The primary 

advantage of stress-controlled tests is their ability to mimic stress conditions within different parts 

of the landslide. For example, in the traditional strain-controlled direct-shear test, normal stress is 

held constant, and the sample is sheared at a constant rate, which results in variable shear stress 

throughout the test. In a modern stress-controlled direct-shear or ring-shear test, normal stress can 

be varied to represent changing pore pressures and shear stress can be held constant to represent the 

static gravity loading on a hillside (Bromhead, 2004). More sophisticated stress-controlled tests 

can simulate earthquake loading or other field situations (Sassa and others, 2004). Research has 

also continued to develop true triaxial test systems in which stresses on all three principal axes 

differ independently (Alshibli and Williams, 2005; AnhDan and others, 2006), unlike traditional 

triaxial systems in which only the lateral confining stress and axial stress can differ. 

Improvements for testing unsaturated soils include methods for determining soilwater 

characteristics that are more relevant to landslides than the traditional agricultural tests, improved 

methods for testing the shear strength of unsaturated soils, and a new framework for applying the 

effective stress concept to unsaturated soils. The soil-water-characteristic curves define the 

relationships between water content, hydraulic conductivity, and matric suction. Recently, 

capillary-rise experiments have been used to define the soil-water characteristics for wetting, which 

is relevant to characterizing the effects of rainfall infiltration on landslides (Godt, 2004; Lu and 

Likos, 2004). Research and development have continued on ways to improve laboratory systems 

for measuring shear strengths of unsaturated soils (Jotisankasa and others, 2007; Miller and Hamid, 

2007). Recently Lu and Likos (2006) introduced the suction-stress characteristic curve as a 

framework for extending the effective-stress concept to unsaturated soils. This framework 

overcomes many of the limitations of previous attempts to describe the mechanical behavior of 

unsaturated soils (Bishop, 1959; Fredlund and Rahardjo, 1993). 

Monitoring and Instrumentation of Landslides 

Monitoring and instrumentation have several applications in the assessment of deep-seated 

landslides: (1) to obtain parameters and dimensions for stability and deformation analysis, (2) to 

observe the performance or stability of a slope, (3) to help identify the extent of movement (for 

example, what are the limits of the area that is moving?), and (4) to provide notification of renewed 

or accelerated movement. The cost and complexity of instrumentation limits the practical use of 

long-term monitoring and instrumentation to investigations of large, complex landslides that pose a 

serious threat to facilities. In this section we describe three different monitoring styles and various 

measurements, techniques, and kinds of instruments available for each. 

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Kinds of Measurements to Be Made 

The applications of monitoring and instrumentation mentioned above rely on several kinds 

of measurements. Instrumentation to obtain parameters needed for slope stability analysis and 

numerical modeling is concerned primarily with determination of landslide depth or thickness and 

pore-water pressure in the landslide, particularly at its basal slip surface when the landslide is 

active. Most other instrumental monitoring of landslides is concerned with observing and 

characterizing the displacement and deformation of the landslide mass. Precipitation is commonly 

recorded as well to observe any connection between precipitation and landslide movement 

(Mikkelsen, 1996; Baum and Reid, 1995). 

Styles and Techniques of Monitoring 

For the purposes of this report, landslide monitoring can be classified into three different 

styles or types based on the frequency and mode of measurement: (1) campaign, (2) continuous, 

and (3) real-time. Campaign-style monitoring consists of a series of repeated surveys and 

measurements at established monitoring points or repeated acquisition of remotely sensed imagery. 

Consequently, this style of monitoring is generally the least frequent and is amenable to simple 

measuring devices as well as sophisticated instruments. Campaign monitoring shows progressive 

changes in a landslide and is best suited to slow-moving landslides. With the exception of remote 

sensing methods, campaign-style monitoring requires regular visits to the field site. Continuous 

monitoring relies on instruments and equipment to record measurements continuously or at regular 

closely spaced intervals and to save the measurements at the site for later retrieval. Real-time 

(more correctly, near-real-time) monitoring combines continuous monitoring with some form of 

automated telemetry and data processing so that monitoring results from a remote site are available 

to project engineers, emergency-response personnel, or others within a short time after the actual 

measurements occur. Real-time data processing may occur on site or at the project office. 

Continuous and real-time monitoring both require periodic visits to the field site for instrument 

maintenance or repairs; continuous monitoring also requires regular visits to collect the stored data. 

Different landslide-monitoring techniques provide measurements of rate and amount of 

movement or deformation, landslide depth, landslide extent (plan-view dimensions), subsurface 

water conditions, and earth pressures. Each of the three monitoring styles includes a range of 

available techniques for making different kinds of measurements. Most landslide-monitoring 

projects require a combination of different styles of monitoring to adequately characterize 

movement and conditions that induce landslide movement. For completeness, brief reference is 

made in the following sections to long-established (pre-1996) monitoring techniques. Techniques 

that have appeared since the publication of Transportation Research Board Special Report 247 

(Turner and Schuster, 1996) are described briefly. Olalla (2004) provides additional descriptions of 

some of the more recent monitoring techniques. 

Campaign Monitoring 

Campaign monitoring includes the classical methods of landslide monitoring such as 

repeated surveys for landslide movement, depth, and water levels and new methods that use laser 

scanners or satellite remote sensing. The primary strength of campaign monitoring is its ability to 

determine the spatial variability of conditions, such as movement or water level, in landslides. 

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Displacement and Deformation 

Several styles of campaign monitoring exist for determining displacement and deformation 

as described briefly in the following paragraphs. Tables 1 and 2 provide additional description and 

comparison of the methods. 

Point and Line Surveys 

Time-lapse surveys for displacement or deformation continue to serve an important function 

in characterizing the spatial distribution of movement and have been applied along pipelines that 

cross landslides (Braun and others, 1998). Regardless of technique, these surveys attempt to 

determine the change in position of points on the ground surface of the landslide. Originally these 

surveys were performed using a steel measuring tape, tape extensometer, or conventional surveying 

instruments to determine the positions of known points on the landslide relative to fixed points on 

stationary ground (Keaton and DeGraff, 1996). More recently the surveys have been performed 

using total station or Global Positioning System (GPS) survey equipment (Bogaard and others, 

2000; Coe, Ellis, and others, 2003; Tagliavini and others, 2007). Table 3-1 summarizes techniques 

used for these surveys. Keaton and DeGraff (1996) have compiled a more detailed evaluation of 

various conventional and modern surveying techniques for use in geologic mapping and monitoring 

of landslides. 

Terrestrial Laser Scanning 

The recent advent of laser scanning, also referred to as terrestrial LiDAR or tripod LiDAR, 

opens new avenues for time-lapse surveys (Galloway and others, 2008, Chapter 2 this report). 

Laser scanners are capable of rapidly measuring and recording locations of millions of closely 

spaced points on the ground surface. Laser scans make it possible to image the surfaces of 

landslides and unstable hillsides (Rowlands and others, 2003; Jones, 2006; Collins and others, 

2007). Differencing scans taken on different dates reveals changes that result from landslide 

deformation and redistribution of materials (fig. 3-9). Displacement can also be computed for 

features or markers that are identifiable in imagery from successive scans. Distance accuracy 

ranges from ±1–5 cm for rapid, long-range scanners that are suited to topographic surveying to a 

few millimeters for slower, short-range scanners that are designed for detailed scanning. Despite 

the exciting possibilities offered by laser scanning, certain disadvantages hinder widespread 

application to routine landslide monitoring. These are high equipment and software costs, a steep 

learning curve, and the large amount of time required to process data after acquisition. Equipment 

costs are likely to decline as these systems become more widely adopted in the construction 

industry. Processing time is likely to decrease in the near future as personal-computer (PC) 

operating systems and software become capable of accessing larger amounts of memory and of 

parallel processing as computers with multiple processors/cores become more widely available. 

Application of laser scanning to continuous or real-time monitoring is limited to repeatedly 

scanning a small area of several square meters from a fixed point (G. Bawden, oral commun., 

2007). Although this might be adequate for a small landslide or small area of particular concern, 

repeated scanning of selected more widely scattered points using a robotic (automated) total station 

might be more effective on a larger landslide. A robotic total station can be programmed to 

automatically re-survey a series of targets using either standard prism reflectors or reflectorless 

technology. 

129
Aerial and Satellite Remote Sensing 

Much recent work has been devoted to detecting landslides and determining landslide 

displacement from remotely sensed data (Van Westen, 2004; Farina and others, 2006;). Highresolution aerial photography and Synthetic-Aperture-Radar (SAR) imagery have been the most 

widely used for determining displacement (table 3-2). Use of remotely sensed imagery for 

mapping landslides was discussed previously in the section on engineering-geologic mapping and 

in the companion report by Harp. All of these techniques use imagery acquired on different dates 

to determine landslide displacement. 

Displacement measurements based on high-resolution aerial photography use analytical 

photogrammetric techniques to determine x-y-z coordinates of photo-identifiable points (fig. 3-10). 

Differencing these coordinates determines the net three-dimensional displacement of points on the 

surface of the landslide (Fraser and Gruendig, 1985). Accuracy and reliability of photogrammetric 

displacement measurements is mainly a function of image scale and quality and the use of precise 

targets on the landslide surface and adjacent non-moving ground (Fraser and Gruendig, 1985). 

Photo-identifiable points, such as boulders, shrubs, and urban features (such as corners, manhole 

covers) usually provide less-precise measurements than targets. However, when using archival 

photography, such points are often the only basis for controlling the photography and determining 

displacement (Baum and others, 1998; Bruckl and others, 2006). Despite these limitations, analysis 

of photogrammetrically derived displacements and changes in elevation yielded information on 

surface geometry and depth (Baum and others, 1998; Casson and others, 2005). The development 

and increasing availability of digital photogrammetric equipment and software, as well as imageprocessing software, are making these techniques more readily available, but reliable identification 

of points still requires operator judgment (Kääb, 2002; Brückl and others, 2006). 

Satellite Synthetic Aperture Radar interferometry (InSAR) uses two satellite images taken 

from roughly the same point in space on different dates to determine displacement (Van Westen, 

2004; Froese and others, 2005; Colesanti and Wasowski, 2006; Galloway and others, 2008, Chapter 

2 of this report). Wavelengths of radar satellite signals are about 5–6 cm, and displacement is 

determined from the phase shift in the radar signal between the two measurements when compared 

to a reference signal. Consequently, displacement of less than one wavelength can be determined. 

The angle of incidence for radar signals is usually very steep, and so the observed displacement 

(parallel to that line of sight from the radar satellite to the Earth's surface) is mainly the vertical 

component, except on very steep slopes. As a result of these characteristics of radar imagery from 

currently available radar satellites, InSAR is only capable of reliably detecting and measuring very 

slow ground-surface displacements. Farina and others (2006) found that the use of InSAR for the 

monitoring of single slow landslides threatening built-up areas provided satisfactory results, 

allowing the measurement of superficial deformations with high accuracy on landslide sectors 

characterized by good radar reflectivity and coherence. Vegetation and ground disruption, due to 

grading or landslide deformation, degrade image coherence and prevent accurate measurements 

(Froese and others, 2005). Recent analysis of archival L-band radar data, which is planned for some 

future satellites, showed that the L-band radar is capable of detecting higher rates of movement and 

is less affected by vegetation than currently available radar bands (Strozzi and others, 2005). 

A technique known as persistent or permanent scatterers (PS) has been developed to 

overcome several limitations of conventional SAR differential-interferometry (DInSAR) 

applications in landslide studies (Van Westen, 2004; Colesanti and Wasowski, 2006). PS makes 

use of stable reflectors to help identify pixels on radar images and uses long time series of 

interferometric data. However, it has the drawbacks that it requires a large number of SAR scenes 

and measurements can be made for only a limited number of points (Van Westen, 2004). Colesanti 

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and Wasowski (2006) report that, under favorable environmental conditions, PS is suitable for 

monitoring slope deformations with millimeter precision. Environmental conditions that limit the 

application of PS include atmospheric effects, vegetation or ground disruption that degrades image 

coherence, and inadequate distribution of natural permanent scatterers. The PS technique combines 

the wide-area coverage typical of satellite imagery with the capability of providing displacement 

data relative to individual image pixels. However, PS is subject to the same reliability limitations 

mentioned previously for InSAR. Future satellites with new sensors and different acquisition 

geometries, combined with the improvements to radar data processing, are expected to allow a full 

3D reconstruction of deformation data and help to further reduce the current limitations of the PS 

and similar DInSAR approaches (Colesanti and Wasowski, 2006). Froese and others (2005) and 

Farina and others (2006) reported detecting previously unknown landslides using differential and 

PS InSAR techniques; however, independent ground checking is required to determine whether the 

observed deformation is due to a landslide, subsidence, or other process. 

Landslide Depth 

Determination of landslide depth is critical to conducting stability analysis on existing 

landslides or other numerical modeling of a landslide and to planning remedial measures. 

Hutchinson (1983) described a number of techniques for determining or estimating landslide depth 

and emphasized that multiple techniques ought to be used, starting first with the more readily 

accessible observations. Multiple slip surfaces often exist and it is important to find the deepest. 

The more reliable methods depend on direct observation of offset in a borehole following landslide 

movement. The simplest and least expensive of these is the borehole probe pipe, usually a 25-mmdiameter semi-rigid plastic tube (commonly the riser pipe of a piezometer or observation well) that 

is inserted into a borehole. Metal rods of increasing length can be lowered down the tube in turn 

and the rod length that is unable to pass a certain point indicates an increased curvature of the pipe 

at that depth. A section of rod can be hung on a thin wire and left at the bottom of the tube; after 

movement has occurred, the rod can be raised to determine the lower limit of movement. The 

practical application of this technique in landslide investigations is to provide landslide depth 

information from deep piezometers that are located some distance away from the nearest 

inclinometer hole (Baum and Reid, 1995). 

Probe inclinometers, first developed in the 1950s, continue to be the preferred method to 

determine landslide depth (Mikkelsen, 1996). An inclinometer casing, consisting of an internally 

grooved, round, rigid plastic or metal pipe, is inserted into a vertical borehole and grouted into place 

to provide an oriented track for the probe to travel down the boring. Alternately some inclinometer 

probes use a casing of square cross section (Mikkelsen, 1996). Repeated measurements of biaxial 

borehole inclination at fixed depth increments reveal depth, amount, and plan-view directions of 

borehole tilt, which are integrated to determine displacement. If the casing is correctly installed, 

probe inclinometers provide millimeter resolution of displacement amount. Landslide depth can be 

determined to within about one probe length, usually about 0.5 m (fig. 3-11). 

Pore Pressure and Water Level 

Wells and open-tube piezometers have long been used to determine water-table depth and 

pore pressure in landslides (Mikkelsen, 1996). Measurements can be made by manually probing 

the well or electronically by installing a pressure transducer. Pore pressure at the basal slip surface 

is needed for slope-stability analysis. In the absence of more detailed data about subsurface water, 

water-table depth indicates the approximate pore pressure for stability analysis. A well that is 

screened or has a slotted casing over its entire depth indicates water-table depth if the landslide 

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behaves like an unconfined aquifer. Open-tube piezometers, which consist of semi-rigid plastic 

pipe with a porous tip, measure pore pressure at a specific depth (Lambe and Whitman, 1969). The 

height of the column of water in an open-tube piezometer provides an accurate indication of static 

or slowly changing water pressure surrounding the piezometer tip, but the time lag required for 

water levels to respond to changing pressures prevents wells and open-tube piezometers from 

accurately indicating transient changes in pore pressure in response to intense rainfall, earthquakes, 

or landslide movement (Mikkelsen, 1996). Pneumatic piezometers use a mechanical pressure 

transducer that consists of a chamber filled with nitrogen and a small diaphragm that senses 

pressure changes. Small-diameter pneumatic tubes connect the piezometer to the ground surface, 

where the pressure is read by a pneumatic gage. Although these gages are more accurate than 

open-tube piezometers (practically no time lag) due to the small amount of water displaced, they 

are not easily automated (Mikkelsen, 1996). Various types of electronic pressure transducers used 

in continuous and real-time monitoring can also be used for campaign-style monitoring. 

Continuous and Real-Time Monitoring 

Continuous and real-time monitoring both have the advantage of providing time-series 

measurements, but the cost of instrument acquisition and installation often limits continuous 

measurements to relatively few discrete points. Continuous monitoring is used mainly in larger 

projects and in performance monitoring of major engineered works for landslide remediation. 

Real-time monitoring is effective when continuous monitoring is needed at remote sites and in 

cases where construction of engineered works is either in progress or considered too costly or 

dangerous and monitoring would provide adequate warning to take the actions needed to prevent 

loss (Fukuoka and others, 2005; Read and others, 2005). Techniques and measurements performed 

by continuous and real-time monitoring are essentially the same, because both rely on electronic 

sensors and digital recording devices (dataloggers, fig. 3-12). 

Displacement and Deformation 

A large range of instruments and techniques exist for making time-series measurements of 

landslide displacement and deformation (table 3-3). Although the majority of these techniques are 

designed for monitoring points on the ground surface, at least one remote-sensing technique for 

monitoring changes over an area is adapted to continuous monitoring, and several subsurface 

techniques of strain and displacement monitoring are available as well. Each technique listed in 

table 3-3 has certain advantages and drawbacks. For example, wire extensometers are relatively 

inexpensive and reliable, but wind, animals, and other environmental factors can cause false 

readings unless the extensometer cable is adequately protected. Most devices for continuous 

displacement monitoring must occasionally be reset or realigned after an amount of movement that 

varies with the nature and measurement range of the instrument and the rate of movement of the 

landslide. Others, such as borehole inclinometers and coaxial cables for TDR, are destroyed when 

displacement exceeds the range of the instrument. Most available techniques have been described 

and evaluated elsewhere (Mikkelsen, 1996; Keaton and DeGraff, 1996). However a few new 

techniques are worth describing further. 

Real-Time GPS 

“Real-time” GPS has the advantage of being able to measure three-dimensional landslide 

displacement without the constraints of cables or targets required by many other forms of 

displacement monitoring. LaHusen and Reid (2000) developed and tested an automated real-time 

GPS system for monitoring landslide displacement. The modular design uses a low-power 

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controller to store and forward raw data from a variety of single- or dual-frequency GPS receivers 

to a Windows-based PC that controls the remote stations and intermittently calculates fixed static 

solutions. Application of real-time GPS using a short baseline (<10 km) were configured using 

single-channel receivers. Individual solutions obtained twice per hour from 5–20 minutes of 10-

second data showed repeatability of 1 cm horizontal and 2 cm vertical. LaHusen and Reid were 

able to filter noise and clearly discriminate sub-centimeter movements by using the median of 5 

successive solutions to observe changes over 2-hour intervals and 48 successive solutions to 

observe changes over 24-hour intervals. By installing multiple field stations on an active landslide, 

it is possible to monitor displacement at several critical locations on a landslide. Recently such a 

system was installed at the Ferguson rock slide near Yosemite National Park, California. Due to 

the dangerous nature of this slide, the field stations were installed as “spiders,” self-contained, selfleveling instrument enclosures that can be transported and lowered to the site by helicopter (Mark 

Reid, oral commun., 2006). Continuous GPS systems have also been tested in France (Bogaard and 

others, 2000). 

Terrestrial Radar Interferometry 

Radar interferometry implemented using ground-based instrumentation has been tested in 

Europe for monitoring landslides (Tarchi and others, 2003; Luzi and others, 2004; Duranthon, 

2004; Tarchi and others, 2005). Researchers have used the technique to derive accurate, highresolution, multitemporal surface-deformation maps of the entire depletion zone of a landslide 

(Tarchi and others, 2003). The portable device used by Tarchi and others (2003) is known as 

Linear SAR (LISA), and it uses radar waves in various frequency ranges with a synthetic aperture 

of up to 2.8 m. The recorded pixel displacements compared closely with independent 

measurements carried out by a motorized theodolite and Electronic Distance Meter (EDM) on two 

benchmarks. Tarchi and others (2003) reported measuring displacement rates up to about 1 m/day 

with millimeter accuracy and a pixel resolution of approximately 2×2 m on the ground. Scans can 

be made at intervals of about 15 minutes. A contractor working for the California Department of 

Transportation has recently deployed such a system for real-time monitoring of the Ferguson rock 

slide, just west of Yosemite National Park, California (Mark Reid, oral commun., 2007). Although 

terrestrial radar overcomes many of the challenges of satellite radar techniques, factors that degrade 

image coherence (quality), such as atmospheric effects, vegetation, and ground disruption, remain a 

challenge. 

Pore Pressure, Matric Suction, and Water Content 

Several types of sensors exist for observing subsurface water conditions (table 3-4). Figure 

3-13 shows a time-series plot of pore pressure near the base of a landslide. Measurement of 

positive pore pressure is the most common requirement for deep-seated landslides (Mikkelsen, 

1996), but measurements of negative pore pressure (suction) or soil-water content may be needed 

in the tropics (Beneveli and others, 2004) and for shallow landslides in temperate regions (Baum 

and others, 2005; Tan and others, 2007). The main differences in types of instruments for 

measuring pore pressure involve barometric correction, response times, and the ability to measure 

only positive pressure or a combination of positive and negative pressure. Vibrating-wire 

piezometers are the most commonly used type in geotechnical applications, especially where longterm monitoring is planned. As their name suggests, vibrating-wire sensors are based on measuring 

the frequency of a tensioned wire that is attached to a diaphragm. The diaphragm is in contact with 

the pore water; changes in water pressure move the diaphragm, which changes the frequency of the 

wire. Vibrating-wire sensors tend to be stable, accurate, and reliable, and they can be read at 

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intervals as short as 5 seconds. Long-term landslide monitoring usually requires readings at 

intervals of 10–60 minutes. Electrical-resistance pressure transducers have rapid response times 

allowing multiple readings per second for dynamic applications in seismically active areas, but 

most commercially available models have a shorter lifespan than vibrating wire sensors. 

Landslide Depth 

Remote measurement of landslide depth has some definite advantages and offers cost 

savings over regular field visits to make instrument readings. For this reason, time-domain 

reflectometry (TDR) is gaining wider acceptance as an alternative to using probe inclinometers for 

determining landslide depth (Mikkelsen, 1996). A length of coaxial cable may be fastened to the 

outside of an inclinometer casing or other rigid tubing before installing the casing in a borehole, 

which is then filled with grout. As movement occurs, the coaxial cable kinks and may eventually 

break. A cable tester can determine the location and amount of strain or location of a break in the 

cable (Kane and Beck, 1994). The time delay after a transmitted pulse and the reflection from a 

cable deformity determines its location. Unlike probe inclinometers, time-domain reflectometry 

can be readily automated for continuous or real-time monitoring. As with other instruments that 

are monitored in real time, the data-collection system can be programmed to issue an alarm when 

critical amount of strain is detected. Landslide depths determined by TDR are comparable to those 

determined by inclinometers. 

Internal Forces and Pressures 

Vibrating-wire earth-pressure cells and load cells have been available since the 1980s or 

perhaps earlier for monitoring lateral pressures in landslides or forces applied by a landslide mass 

on a wall or on tieback systems. We found few published papers on use of these instruments in 

landslides; most applications seem to be for monitoring dams, embankments, and other earth 

works. Li and others (2004) installed earth-pressure sensors in a landslide, but published no results 

related to the earth-pressure measurement. Load cells have been used to monitor tension in tiebacks (Nichol and Graham, 2001), and increasing load was related to landslide movement at one 

end of a tie-back wall, but could not be explained at the opposite end. 

The distribution of forces within a landslide is related to its stability. Internal forces are 

likely to change in response to progressive and retrogressive failure, movement, and external 

changes (applied loads, major erosion). One potential application of earth-pressure measurements 

to landslides would be for comparing lateral earth pressures in zones of longitudinal shortening or 

extension with results of limit-equilibrium and finite-difference or finite-element slope-stability 

analyses (Baum and Fleming, 1991; Picarelli and Russo, 2004). Application of earth-pressure 

measurements to landslide investigation or warning seems to be an area ripe for additional research. 

However, practical use of earth-pressure measurements in landslide monitoring and investigation 

appears to be quite limited at present. 

Seismoacoustic Emissions 

Since the 1960s, a small body of research has been conducted on detecting landslide 

movement using microseismicity (Goodman and Blake, 1965; Kolesnikov and others, 2003). 

Fracturing of rocks or soil during the formation, reactivation, and movement of landslides emits 

distinctive acoustic and seismic signals that can be detected by seismic monitoring at the site. 

Microseismic monitoring has potential application to detection and early warning of movement, 

rather than monitoring the amount of displacement. Although microseismicity has found 

application in other fields such as hydraulic fracturing and structural monitoring, very few 

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applications have been made to landslides (Gomberg and others, 1996; Gaertner and others, 2000; 

Amitrano and others, 2007). 

Environmental Factors 

Precipitation is the most commonly measured environmental variable in landslide 

investigations and monitoring. Tipping-bucket rain gages are commonly used in these studies. The 

gages can be calibrated for millimeter or inch measurements with reported accuracy of ±1 mm or 

±0.01 inch. Strong correlation usually exists between precipitation and pore pressure at depth, even 

in landslides that are many meters deep (Iversen, 2000; Lollino and others, 2006). Measurements 

of barometric pressure at the site are needed to make corrections to measurements by sealed 

piezometers. Barometer measurements should occur on the same schedule as piezometer 

measurements. Evidence also exists that changes in barometric pressure may affect pore pressure 

in low-permeability clays and thereby induce landslide movement (Köhler and Schulze, 2000). 

Various types of recording barometers are available; however, the corrections can also be made by 

monitoring a pressure transducer of the same type as used for measuring subsurface water 

pressures. Soil temperature can also be measured to observe time and depth of soil freezing. Most 

pressure transducers used in electronic piezometers and tensiometers are temperature-compensated 

and have built in temperature sensors that can be used to make soil-temperature observations. 

Measurement Locations 

Obtaining meaningful or representative results from subsurface exploration and monitoring 

depends on placing boreholes and instruments in optimal locations (table 3-5). Mapping a 

landslide in enough detail to identify its main features (fig. 3-3) and distinguish shallow surficial 

movements from the main body of the landslide is the first step in defining those locations. 

Landslides tend to be thinner and generally move more slowly near the edges than in the central 

part of the main body. The critical parameters to be observed for an existing landslide include 

depth and shape of the basal failure surface, pore-water pressure, shear strength, and displacement 

or deformation. Similar observations need to be made for potential landslides, but finding the 

optimal locations for these observations is much more difficult because the boundaries of a 

potential landslide may be difficult or impossible to define. In either case, observations of the 

depth and shape of the basal slip surface should concentrate on the main body of the slide (or 

potential landslide), initially near the central axis that is parallel to the direction of downslope 

movement (table 3-5). The basal slip surface often intersects the ground surface at the main scarp 

and at the contact between the toe and undisturbed ground beneath the toe (fig. 3-3). For example, 

a line of boreholes like those in figure 3-14 makes it possible to construct a representative cross 

section of the landslide. In the case of more complicated landslide shapes (in plan view), or other 

complicating factors, a different arrangement of boreholes may be appropriate (Corsini and others, 

2006). 

Landslide investigations commonly proceed in stages. Information gathered during the 

early stages of exploration and instrumentation usually guide more detailed exploration and 

instrumentation in the later stages (Baum and Reid, 1995; Chelli and others, 2005; Read and others, 

2005). Once the depth of the basal slip surface has been determined by inclinometer or timedomain reflectometry observations, piezometers can be installed in adjacent boreholes as close as 

possible to the depth of the basal slip surface. If the budget allows, additional piezometers can be 

installed at lesser depths (higher levels) within the slide. Assuming adequate sample recovery, 

geotechnical testing (especially shear-strength measurements) should concentrate on materials from 

the depth of the observed slip surface. 

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Optimal locations of displacement measurements are usually in the same locations as depth 

and pore-pressure observations. For application to pipelines, observations should, at a minimum, 

be made at several points across the landslide along the pipeline alignment (table 3-5). Additional 

observations of displacement at points in the main body upslope and downslope of the alignment 

can help in recognizing waves of more rapid movement that are propagating either upslope or 

downslope toward the pipeline (Iverson, 1986). 

Costs and Reliability 

Available technology offers a wide range of possibilities for monitoring landslides 

depending on field conditions, expected style and potential consequences of movement, and the 

goals and budget of the monitoring program. Field data collection and monitoring, whether manual 

or automated, are labor intensive and therefore costly activities. Design of a monitoring program 

for an individual landslide or a group of landslides along a pipeline corridor requires substantial 

field work and analysis to determine the locations and types of measurements needed (Angeli and 

others, 2000). As a result of rapidly changing technologies, background research must be 

conducted to identify the most suitable components for a monitoring system. A common pitfall in 

this phase of landslide investigation is underestimating the true costs of instrumental monitoring 

programs. Estimating the cost of the initial investment in equipment and installation is relatively 

straightforward. However, in addition to the initial, one-time costs of background fieldwork and 

research, and equipment acquisition and installation, the costs of data collection, surveillance 

(monitoring system performance and data flow), repairs, maintenance, and data processing continue 

for the life of the project. Depending on the duration of the project, the continuing costs can 

approach or, in some cases, exceed the initial investment. 

Costs 

We estimated relative costs of various measurement and monitoring techniques applicable 

to landslides as an aid in comparing methods for obtaining similar kinds of measurements. These 

costs have been averaged over 5 years to balance acquisition costs against operating costs. 

Although the service life of instruments varies considerably, 5 years seems like a reasonable 

average based on our experience. To compute labor costs, we estimated amount of labor from 

USGS experience in installing, operating, or maintaining similar equipment, multiplied by an 

annual labor cost of $100,000. We did not include any costs for overnight travel associated with 

fieldwork. Tables 1 and 2 list techniques for campaign-style measurements and assume a low 

annual measurement frequency (one or two per year). Tables 3 and 4 list techniques for continuous 

measurement and assume high measurement frequencies (multiple measurements per day or per 

hour), with data analysis occurring four times per year. Continuous measurement techniques are 

automated and have much lower cost per data point than most campaign-style measurement 

techniques. Campaign-style measurements are labor-intensive and their cost rapidly escalates with 

increasing frequency of measurement. For example, measurements using tape or tape extensometer 

rise from low to intermediate cost as frequency increases to quarterly or monthly. 

Tables 3 and 4 compare the costs of operating individual instruments for various kinds of 

measurements. A more likely scenario is to operate a suite of instruments at a site. To provide a 

representative example, we computed the cost of a real-time monitoring system that includes a rain 

gage, three borehole tilt meters, and three piezometers (each in a separate borehole), data logger, 

solar power, radio telemetry, and a PC with software to receive and analyze data. As might be 

expected, the cost is in the "high" range ($22,500 annually for 5 years). Taken individually, 

however, the cost of each of the seven sensors drops into the "low" range and the quantity and 

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quality of data that can be collected by such a system far exceeds what can be done for the same 

cost using manual techniques. 

Reliability 

Reliability of monitoring results generally increases with the spatial distribution and 

density, and the frequency of measurements. Instrument accuracy and reliability aside, 

measurement of displacement, pore pressure, or any other quantity at a single point on a landslide 

has a high degree of uncertainty. Rates of movement, water levels, and other characteristics of 

landslides are known to be highly variable in space and time (Baum and Reid, 1995). 

Understanding the structure and geometry of a landslide helps ensure that measurements are made 

in areas that represent movement of the main parts of a landslide. Making frequent measurements 

at multiple locations is key to characterizing a landslide adequately to make any forecasts about 

future movements. Considerable thought and analysis of all available data must go into planning of 

a landslide instrumentation project (Mikkelsen, 1996; Angeli and others, 2000). 

A combination of technologies is generally needed to adequately monitor and characterize 

ground movements that potentially affect a pipeline or another linear facility. Most monitoring 

technologies offer adequate accuracy and precision (tables 1–4); their limiting factors tend to be 

high cost (for some techniques), mode of deployment, and their technical characteristics. For 

example, wire extensometers are capable of providing detailed continuous records of movement, 

but they provide no directional information and their range of motion is somewhat limited. 

Periodic surveys (total-station or GPS) are needed to provide a complete record of movement at an 

extensometer. Surveys to show the spatial distribution of movement are needed to detect changing 

patterns of movement and possible acceleration in locations where continuous monitoring is 

considered to be unnecessary or impractical. 

The need for redundant measurements is another factor that must be emphasized. Despite 

efforts to engineer sensors for harsh environmental conditions, instrumental measurements 

routinely fail, often at critical times. Lightening strikes, battery failure, vandalism, animals, and 

other adverse events can and do render monitoring equipment inoperable. Even when instruments 

are working correctly, measurement errors occur as a result of animals, weather, and other factors. 

A back-up system of measurements is needed to assure that measurements can continue during 

critical times and to maintain a continuous record. One advantage of real-time monitoring over 

continuous monitoring without telemetry is that instrument or measurement failure can be detected 

soon after it occurs. Another advantage is that the data can be integrated easily with a geographic 

information system (GIS) to facilitate interpretation and decision making (Hutchinson and others, 

2004). 

The choice between automated and campaign-style data collection depends on economics, 

safety, and remoteness of the landslide(s) as well as technical factors. In developing countries, 

where a pipeline crosses a populated area of slow landslides on gentle to moderate slopes, 

campaign-style monitoring using local labor and simple, inexpensive technology might prove 

effective and adequate. In areas where a pipeline crosses steep terrain and rapid movements are 

possible, automated monitoring may be the safest and most practical choice. However, in most 

areas, some combination of automated and campaign monitoring (either by site visit or remote 

sensing) will be needed to satisfy technical and budgetary requirements of the project. The 

demands of continuous or real-time data collection commonly preclude monitoring a large number 

of points, therefore campaign-style measurements and remote sensing techniques provide the 

spatial distribution of observations that cannot be observed by a few continuous monitoring stations 

alone (Coe, Ellis, and others, 2003). 

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Measurement priorities for landslides that affect or potentially affect pipelines are generally 

driven by the need to protect the pipeline from damage or rupture. Thus, displacement where the 

pipeline crosses the landslide boundaries (head scarp, toe, lateral shear zones, or internal shears) is 

generally the first priority. Next is displacement at points upslope and downslope of the pipeline to 

observe progressive or retrogressive movements that might soon affect the pipeline. At landslides 

where remedial measures are considered, measurements of landslide depth and pore-water pressure 

at the basal shear surface are needed as input for slope-stability analysis. Monitoring of pore-water 

pressure can also identify threshold pore pressure needed to induce movement and provide early 

warning of impending movement (Picarelli and Russo, 2004; Ellis and others, 2007). 

Areas for further research include use of strain gages or load cells along a pipeline where it 

crosses a landslide to provide early warning of distress before significant damage can occur. Use 

of horizontal coaxial cable encased in grout and buried in the trench alongside the pipeline might 

provide a means to monitor differential movements and locate them using time-domain 

reflectometry. TDR technology appears promising based on a similar application to a railway 

(Kane, 2007). The development of wireless networks based on low-cost sensors and wireless 

servers is an area of active research based on USGS contacts with universities (Sheth and others, 

2005; Kevin Moore, Colorado School of Mines, written commun., 2006). However, application of 

these technologies to landslide monitoring awaits advances to reduce power consumption to 

acceptable levels for remote applications and greatly improved locational accuracy of the wirelesssensor technology. 

Methods of Stability, Stress, and Deformation Analysis 

Mathematical analysis of landslides is used to understand their individual mechanisms and 

make predictions about their responses to natural or human-induced changes in their geometry, 

external loading, ground-water levels, and other factors. Simple empirical methods of analysis use 

observational data to make predictions about time to failure, travel distance for debris flows, or 

threshold pore-pressure levels or rainfall amounts to induce landslide movement. Limitequilibrium slope-stability analysis (LE) is very useful in determining the factors that affect 

stability of a landslide mass, hillside, or earthwork as well as planning and evaluating potential 

remedial works for landslides. LE methods have some important limitations because, as their name 

implies, LE methods attempt to solve only the equations of equilibrium. Analytical solutions have 

been obtained for a few boundary- and initial-value problems that describe important landslide 

processes in one or two dimensions; however, their value is primarily for understanding the process 

rather than application to particular landslides (Savage and Wasowski, 2006). Numerical methods 

of stress and deformation analysis solve the equations of motion (or equilibrium) and continuity for 

different material constitutive models to provide estimates of deformation as well as internal 

forces/stresses (Savage and others, 2003; Smith and Griffiths, 2004). Some recent methods are 

even capable of analyzing the dynamics of large deformations associated with movement of rock 

slides and debris avalanches (Crosta and others, 2003; Denlinger and Iverson, 2004). 

In the following paragraphs, we review the capabilities, data requirements, recent 

developments and trends, and pitfalls of the various types of mechanical analysis available for 

landslides. The discussion focuses on groups and classes of methods used in these analyses, rather 

than details of specific methods and software, which are constantly changing. Nevertheless, the 

capabilities and limitations of various methods are illustrated with a few specific examples from the 

literature. 

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Methods and Capabilities 

Empirical and Statistical Methods 

Prediction of Time to Failure 

In several case studies of creeping slopes that subsequently failed, post-failure empirical 

analysis of accelerating displacements has been relatively successful in predicting the observed 

time of failure. Small but measurable displacements commonly occur prior to initial failure of a 

slope or reactivation of landslide deposit. Saito (1965) observed an accelerating trend in these 

displacements and proposed using this trend in forecasting time to failure. Fukuzono (1990) found 

that plotting inverse velocity (v-1) versus time yields a straight line that can be used to predict the 

time of failure. Other workers have used Saito's observation or further developed the related theory 

(Varnes, 1982; Voight, 1988; Kilburn, and Petley, 2003). Recent work indicates that linearity 

probably is associated with crack growth and would be expected where brittle failure is the primary 

process occurring at depth. An asymptotic trend in the plot of inverse velocity versus time is 

expected where ductile failure or sliding on existing surfaces is occurring at depth (Kilburn and 

Petley, 2003). Using displacement observations at multiple points on the surface of the developing 

landslide or reactivation makes it possible to observe the spatial progression of the failure and 

greatly aids interpretation of the failure process (Petley, 2004). 

Movement Thresholds 

Landslide movement thresholds, based on either rainfall or pore pressure/water level, have 

been determined by comparison between measurements of rainfall or pore pressure and 

displacement. Such models are useful for making predictions and have modest data requirements, 

such as several years of displacement and rainfall observations. However, they are unable to 

predict changes in displacement by other factors, such as loading at the head of the landslide by 

debris flows or rock falls or erosion at the toe. Grivas and others (1996, 1998) and O'Neil and 

others (1996) developed empirical models for predicting movement of a slow landslide based on 

monthly rainfall. These authors also explored application of time-series analysis to the 

development of rainfall-displacement models. For the case studied, the models provided 

reasonably accurate predictions of long-term (10-year) cumulative displacement. Hong and others 

(2005) developed rainfall intensity and duration thresholds for movement of deep-seated rockslides. 

Dixon and Brook (2007) used predicted climate change with empirical precipitation thresholds for 

movement of the Mam Tor landslide in Derbyshire, UK, to analyze future reactivation potential and 

showed that damaging movements may become more frequent by the latter part of the 21st century. 

Rainfall thresholds have been developed for debris-flow initiation for many areas of the world as 

summarized by Wieczorek and Glade (2005). Pore-pressure thresholds have been developed for 

individual landslides, such as the Johnson Creek landslide on the Oregon coast (Ellis and others, 

2007). 

Travel Distance 

Several empirical and semianalytic approaches have been developed for predicting travel 

distance or potential travel distance of debris flows and rapidly moving landslides. For application 

to pipelines, these methods have application to identifying areas where above-ground pipeline 

facilities might be subject to damage or inundation by debris flows as well as identifying where 

debris flows might impact existing deep-seated landslides and increase their potential for 

reactivation. Hungr and others (2005) summarize most of the available methods, including their 

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advantages and limitations. The majority of these methods requires some type of observational 

data relating travel distance to one or more other parameters such as slope height, slope angle, or 

landslide volume. 

Limit-Equilibrium Analysis 

Despite considerable advances in numerical methods of stress and deformation analysis, 

review of recent literature reveals that LE methods continue to be used routinely in engineering 

investigations of landslides (Sharma, 2007). Research to improve the efficiency of LE methods 

also continues (Das, 2005; Zhu and others, 2005). LE methods require the analyst to make certain 

assumptions about the failure mechanism and provide no information about potential landslide 

deformation because these methods attempt to solve only the equations of equilibrium. 

Limit-equilibrium methods use the balance of forces acting on a landslide or potential 

landslide to compute what is called a factor of safety (also known as safety factor). In simplest 

terms, the factor of safety is the ratio of resisting forces that tend to keep the mass in stable 

equilibrium (shear strength of soil and/or rock, tree roots, retaining structures) to the driving forces 

(gravity and external loads) tending to cause the mass to slide downhill. This is based on a 

classical approach used in designing engineering structures which considers the relationship 

between the capacity C (strength or resisting force) of the element and the demand D (stress or 

disturbing force). The factor of safety of the structure can be simply defined as F = C/D and failure 

is assumed to occur when F is less than 1. Methods of slope-stability analysis assess the ratio of the 

shear strength to the shear stress required for equilibrium using soil cohesion, angle of internal 

friction, and normal stress on the basal slip surface, which depends on pore-water pressures, soil 

unit weight and landslide geometry (Morgenstern and Sangrey, 1978; Duncan, 1996). Many limitequilibrium methods subdivide the landslide into vertical slices (Morgenstern and Price, 1969; 

Duncan, 1996; Sharma, 2007), as shown in figure 3-15. 

The factor of safety is complex because landslides can begin in several different modes, 

including sliding, toppling, and a combination of sliding and toppling. An example of more 

complex slope-stability analysis using methods of slices within landslides is shown by Wu (1969, 

p. 248–250). Many factors contribute to slope-stability analysis, including distribution and 

physical properties of geologic materials, slope configuration, external loading conditions, groundwater infiltration and levels, types and depths of vegetation, and daily and seasonal precipitation 

affected by temperature. Different approaches to and methods of stability analysis are relevant to 

different types and depths of landslides, such as, shallow debris flows and deep rock slides, 

depending on their geometry, internal structure, mode of failure, and other factors. Dynamic loads 

may be treated as static loads (pseudo-static); equivalence between pseudo-static loading and 

gravity loading greatly simplify analysis (Shukha and others, 2005). 

Methods of static slope-stability investigations using available limit-equilibrium analyses, 

including an assessment of their accuracy were presented in Duncan (1996) and Chapter 9 of Blake 

and others (2002). The primary differences between methods are how interslice forces are treated 

and whether the method satisfies all equations of moment and force equilibrium. Methods that 

satisfy all equations of equilibrium are considered more accurate than those that satisfy only some 

of the equations (Duncan, 1996; Sharma, 2007). 

Performing slope-stability analysis using physical evaluation and depth measurements can 

be very difficult because of unknown information, as described by Duncan (1996). Errors in 

stability analysis arise more from choice of strength parameters, pore pressures and external 

loading than from the method of analysis used. Physically determining the detailed orientation of 

the geologic materials and variability of the strength and moisture at different depths of materials 

140
can affect the accuracy of slope-stability analysis. Fracturing, ground-water movement, and other 

changes at a landslide site make it difficult or impossible to determine pre-failure slope-stability 

conditions, even though landslide boundaries and depth can be determined precisely. Conversely, 

although the materials can be measured and ground water can be monitored before a landslide 

failure initiates, it generally is impossible to determine, in advance, the exact location, shape, and 

depth of the movement. 

Unless the shape of the slip surface is known in advance, LE methods must search for the 

so-called “critical” slip surface, which has the lowest factor of safety. Most search routines are 

limited to circular slip surfaces, but new methods based on optimization techniques of variational 

calculus (Baker, 2005), genetic algorithms (Das, 2005), and neural networks (Samui and Kumar, 

2006) as well as an approach using “stress acceptability criteria” (Sarma and Tan, 2006) have been 

devised to find the critical slip surface and overcome the limitations of previous methods. These 

new methods can search for circular and noncircular critical slip surfaces in homogeneous and nonhomogeneous materials, and their success is not limited by user assumptions about the shape or 

general location of the slip surface. However, these new methods have not yet been extensively 

applied; so their accuracy and reliability are not fully known. 

Three-Dimensional Methods 

Use of three-dimensional slope-stability analysis is still rare, but is becoming more common 

(Bromhead, 2004). Three-dimensional methods began to appear in the late 1970s and 1980s 

(Hovland, 1977; Chen and Chameau, 1982; Xing, 1987; Hungr and others, 1989; Casamichele and 

others, 2004). These methods involve subdividing the landslide or potential landslide into vertical 

columns and solving depth-averaged equations of equilibrium to obtain a factor of safety. Threedimensional methods differ from one another in the assumptions made about intercolumn forces 

and each is based on a corresponding 2-D method. Two-dimensional analyses continue to be the 

most widely used methods of slope-stability analysis, in part because they are widely considered to 

yield lower factors of safety than 3-D methods and in part because data and computational 

requirements of 3-D analysis are much greater than for corresponding 2-D analysis. However, 

recent work has shown that for certain landslides, 2-D factors of safety on the principal cross 

section are actually higher than 3-D factors of safety (Bromhead, 2004). 

Bromhead and others (2002) and Bromhead (2004) reviewed circumstances where threedimensional analysis is required; these include landslides of irregular shape, localized loading, 

weak zones, localized pore-water or pore-pressure concentration, and landslides with beddingcontrolled basal shear surfaces. In one recent application of 3-D methods, a digital-elevation model 

(DEM) is searched for the most critical slip surface centered at each grid cell to identify the areas 

most prone to rotational or other deep-seated failure (Brien and Reid, 2001; Brien and Reid, 2007). 

Bromhead (2004) described a numerically integrated “wide-column” approach that uses concepts 

from finite-element analysis to adapt methods of columns to landslides of general shape. 

Authors of different methods of 3-D slope-stability analysis have tested and verified their 

methods against corresponding 2-D methods and some simple 3-D cases (Chen and Chameau, 

1982; Hungr and others, 1989). The method of Hovland (1977) neglects intercolumn forces and 

computes lower factors of safety than other 3-D methods. Based on these analyses, 3-D methods 

appear to give accurate results; however, we are unaware of any rigorous evaluations of 3-D limitequilibrium methods. 

141
Ground-Water Flow Modeling 

Ground-water flow modeling is a broad field that continues to experience rapid 

advancements; in this section, we briefly review some recent applications of ground-water flow 

models to landslide analysis. Iverson (2000) analyzed the effects of transient rainfall infiltration on 

landslide initiation and movement. His analysis included an explanation of how rainfall infiltration 

affects movement rates of translational landslides. Malet and others (2005) used a depth-averaged 

three-dimensional model of ground-water flow to analyze long-term hydrologic effects and climate 

change on a large deep-seated landslide. Tacher and others (2005) used a three-dimensional 

ground-water model to analyze long-term ground-water levels in large deep-seated landslides 

considering effects of infiltration, heterogeneity lateral in flow, and artificial drainage. Their model 

demonstrated that a proposed drainage scheme could significantly reduce water pressures and the 

potential for future surges. 

Continuum Stress and Deformation Analysis 

Many landslides and hillsides can be modeled using a continuum approach. Numerical 

continuum methods currently available for slope-stability and deformation analysis make it 

possible to compute a full solution of the stress and deformation equations in two and three 

dimensions (Hungr and others, 2005; Griffiths and Marquez, in press). Some models are 

specifically designed for modeling stress and deformation of coherent landslides and can also 

compute a factor of safety just as computed by LE methods (Savage and others, 2003; Smith and 

Griffiths, 2004). In a recent development (Cala and others, 2004), the shear-strength-reduction 

technique used for computing the factor of safety has been extended to analyze several potential 

failure modes/slip surfaces. Besides computing factor of safety, some numerical codes are 

designed for modeling the movement of large, long-runout landslides, flows, and avalanches once 

initial failure has occurred (Crosta and others, 2003; Denlinger and Iverson, 2004; Hungr and 

others, 2005). Despite recent advances in software and methods, stress and deformation analyses 

of landslides still are not routine. They require more time and effort than limit-equilibrium 

analyses and require considerable expertise to achieve accurate and meaningful results. 

Numerical analyses currently available are based on finite-element, boundary-element, and 

finite-difference formulations and are capable of handling a wide variety of constitutive models. 

This includes the ability to introduce strain-softening properties for the various materials, without 

the need to pre-define or “average” strength reductions. Strain softening has been implemented in 

various 2-D and 3-D continuum codes. For example, Puebla and others (2006) modeled 

progressive/retrogressive strain softening and resulting moderate to large (1 m or more) 

deformations in good agreement with observed ground movements. Troncone (2005) used a strainsoftening constitutive model to analyze the progressive failure mechanism of the slope and 

obtained good agreement between the observed and predicted failure geometry and mechanism. 

Some codes offer the possibility of coupled analyses of ground-water flow and slope deformation 

(Hungr and others, 2005). For example, Konietzky and others (2004) modeled ground-water flow 

and slope stability in response to rainfall and changes in reservoir level. Most methods, including 

traditional finite-element analyses use Lagrangian (material or moving) coordinates and are able to 

model slope deformation so long as it does not drastically distort the mesh (Hungr and others, 

2005). Some recent efforts have used combined Lagrangian and Eulerian (fixed-in-space) 

coordinates to enable them to model large deformations without distorting the finite-element mesh 

(Crosta and others, 2003). Other recent advances include zero-thickness elements for representing 

joints (Hürlimann and others, 2004) and fracture-mechanics elements to represent growth of 

fractures or slip surfaces (Zi and Belytschko, 2003). 

142
One of the advantages of continuum methods over LE methods is that the model 

automatically or “naturally” determines the critical failure surface as part of the solution (Griffiths 

and Lane, 1999). Strain localization and development of a displacement or velocity discontinuity 

clearly delimits the depth of the failure zone (fig. 3-16). Numerical analysis of some complex 

landslides has predicted shear zones that are in close agreement with observed ones (Savage and 

others, 2000; Savage and others, 2003; Baron and others, 2005; Marcato and others, 2006; Chugh 

and others, 2007). Another advantage of these methods over the LE methods is that no 

assumptions need to be made about interslice or intercolumn forces to compute the factor of safety. 

Displacement Predictions 

Computation of displacements is of interest in application to pipelines, because expected 

displacement determines the potential for slope movement to damage a pipeline. Accuracy of 

displacement predictions depends on many factors, including material properties, geological 

details, and modeling details. Further, it is necessary to calibrate continuum models against 

reasonably well known landslide or ground-movement records prior to using them to make 

predictions about displacements under various pore-pressure or loading scenarios. Otherwise 

application of the models to conditions for which there is no or limited comparable existing 

landslide data is open to question. Model results are sensitive to variation in geotechnical 

parameters, ground-water conditions, and geometric factors (location and inclination of weak 

zones, material contacts, and so on). Natural soils and rock tend to be nonuniform and have more 

complex stress-deformation behavior than engineered materials (Duncan, 1996). Therefore, model 

construction, calibration, and analysis require considerable time and effort. Reliability of 

calculated movements for natural slopes is not as great as for engineered embankments (Duncan, 

1996). 

Recent work by various authors (cited below) using calibrated models has provided 

displacement estimates within a factor of two or three of observed displacements. Although 

displacements of some points in the models were well within ±10 percent of the observed values, 

and the overall patterns and directions of displacement agreed well, the least accurate points or 

rates differed by roughly a factor of two (-50 percent/+100 percent) or three (-67 percent/+200 

percent). Vulliet and Hutter (1988) obtained fair to good agreement between observed and 

predicted displacements using 3-D equations and viscous sliding laws to model the La Frasse 

landslide, Switzerland. Subsequently, Tacher and others (2005) used coupled ground-water flow 

and stress-deformation finite-element models to investigate the effects of the proposed drainage 

scheme on future landslide movement. Model results for a past episode of relatively rapid 

movement showed fair agreement between observed (2.6 m) and calculated (4.6 m) displacement. 

Konietzky and others (2004) used a Lagrangian finite-difference method to predict displacements 

within a factor of two of observed displacements for rain-induced slip events of a slope. Chugh and 

others (2007) obtained a similar level of accuracy in an analysis of a landfill failure. Amatruda and 

others (2004) used an indirect boundary-element method to compute mean-annual displacements of 

a large landslide. Deng and others (2007) obtained fair to good agreement between observed and 

computed displacement directions for a complex landslide. Corominas and others (2005) solved a 

one-dimensional version of the momentum equation using finite differences in a viscoplastic 

analysis and obtained fair to good agreement between model results and observations. Monitoring 

and modeling have contributed to greater understanding of how pore-pressure fluctuations, erosion, 

and stress relief drive the movement of slow active landslides (Picarelli and Russo, 2004). In 

addition to modeling deformation from static or quasi-static loading, continuum methods can also 

analyze stability during dynamic loadings such as earthquake shaking or sudden failure; however, 

temporal predictions are rarely attempted because of the complexity and detailed data requirements 

143
(Hungr and others, 2005). Various workers have been successful in modeling large displacements 

of debris avalanches and debris flows using either the equivalent-fluid approach in depth-averaged 

2-D (Hungr, 1995; Kwan and Sun, 2006) and 3-D models (Denlinger and Iverson, 2004; 

McDougall and Hungr, 2004; McDougall and others, 2006) or 2-D finite-element methods (Crosta 

and others, 2003). 

Discrete-Element Analysis 

Rather than treating a hillside as a continuum, the discrete- or distinct-element method 

subdivides it into independent, disconnected pieces or elements. This method is well suited to 

slopes where discontinuities, such as faults, joints, bedding planes, or foliation control the 

mechanism of instability. Thus, discrete-element analysis has obvious applications to modeling the 

stability of rock slopes, including failure as rock fall, rock slides, and debris avalanches, but it can 

also be applied to more coherent landslides (Hungr and others, 2005). A physical analogy for a 2-

D discrete-element model is a stack of rods (of any desired cross-sectional shape) with their long 

axes parallel to one another. In three dimensions, the discrete elements represent a pile of balls or 

blocks. The elements can be either rigid or deformable, but most methods allow the blocks to 

deform. Discrete-element models apply the laws of dynamics to each individual element and its 

interactions with its neighbors and the surroundings to compute the forces acting on and within the 

mass, as well as computing movement of the individual parts. 

Rock-fall-simulation models are one of the simplest examples of discrete-element analysis. 

For example, the extent of the areas potentially subject to rock-fall hazards in the Yosemite Valley 

were obtained using STONE (Guzzetti and others, 2002), a physically-based rock-fall-simulation 

computer program. The software computes 3-D rock-fall trajectories starting from a digitalelevation model (DEM), the location of rock-fall release points, and maps of the dynamic rollingfriction coefficient and of the coefficients of normal and tangential energy restitution. For the 

Yosemite Valley, a DEM with a ground resolution of 10×10 m was prepared using topographic 

contour lines from U.S. Geological Survey 1:24,000-scale maps (Guzzetti and others, 2003). For 

each DEM cell, the software calculates the number of rock falls passing through the cell, the 

maximum rock-fall velocity and the maximum flying height. However, the modeling software 

STONE is unable to consider the volume and mass of the falling boulder, the shape of the block, or 

the tendency of rock falls to split during successive impacts. 

Although generally used when more detailed topography or profiles, with slope breaks, are 

available, the Colorado Rockfall Simulation Program (CRSP) (Jones and others, 2000) and a 

probabilistic rock-fall program, based on the work of Stevens (1998), incorporate shape and mass 

(CRSP) or mass (Stevens, 1998) and provide data on both the velocity and impact energy of the 

rock fragment considered. The program by Stevens (1998) also provides estimates of rock-fall 

runout for the various sizes (masses) considered. 

In a survey of recent literature, Hungr and others (2005) found that distinct-element analysis 

has been used to investigate a wide variety of rock-slope failure mechanisms including planar 

sliding, complex deep-seated sliding, rotation, toppling, and buckling. Konietzky and others (2004) 

used particle modeling to investigate creeping rock-/debris-slide deposits on the slope of a reservoir 

and were able to decipher the failure mechanism and estimate the volume of material that would 

flow into the reservoir under worst-case conditions. Discrete-element models have also been used 

to analyze effects of rock fall on covered galleries (Shiu and others, 2006). Stead and others (2004) 

used a hybrid finite-/discrete-element method to model a complex rock-slope failure mechanism. 

Coupled hydromechanical, distinct-element modeling has been used to analyze the effects of 

drainage on landslide movement (Hungr and others, 2005). 

144
Data Requirements 

All types of landslide analysis have several basic data requirements in common. Table 3-6 

summarizes the data requirements for different methods of analysis. At a minimum, data 

requirements include geometry of the landslide or potential landslide mass (surface topography, 

stratigraphy, structure and discontinuities, and geometry of the basal slip surface), relevant strength 

properties, and subsurface water pressures. Stress and deformation analysis require some 

additional types of data beyond that required for LE methods (observed displacements, rates of 

movement, and elastic properties), but this addition is fairly modest. However, determining 

representative deformation moduli beyond the elastic range, necessary for continuum analyses of 

permanent ground movements and failures, is extremely difficult using normal laboratory or even 

field testing. The dimensionality and complexity of a slope or landslide have a great impact on the 

amount of data required to perform a satisfactory analysis regardless of the chosen method of 

analysis. 

Challenges, Dilemmas, and Reliability 

During the last decade, several advances have been made in numerical modeling techniques 

for landslides. These include some incremental improvements to application of limit-equilibrium 

analyses, major advances in continuum and discrete-element methods and increased use of 

probabilistic or reliability (Duncan, 2000) methods to define the degree of uncertainty in the results 

of numerical analyses of all types. These advances allow models to better represent and predict 

failure modes and mechanisms, slip-surface geometry, displacement, and interaction between 

subsurface water and hillslope materials (Hungr and others, 2005). 

Software for nearly all of the numerical modeling techniques described here (except new or 

experimental techniques) is available in ready-to-use commercial packages, but some is also 

available in the form of published source code (such as, Smith and Griffiths, 2004). The majority 

of the published analyses in recent years (including most of those cited in this report) have used 

commercial software packages to conduct sophisticated numerical analyses. Commercial packages 

generally offer technical support and software maintenance, but users are not able to view or 

modify the source code; thus the details of how the software handles certain situations or 

computations may not be well documented. Open-source software on the other hand is open to 

scrutiny and modification by the user, but generally little or no technical support is offered. 

A wide variety of methods is now available for modeling landslides and slope stability and 

the widespread availability of powerful personal computers and modeling software make modeling 

adaptable to diverse project requirements and relatively easy and accessible. However, this 

abundance of user-friendly modeling tools opens the possibility of modeling without having any 

real physical understanding of the processes and factors involved (Bromhead, 2004). Modeling 

efforts must be closely tied to field mapping and monitoring as well as to laboratory and in-place 

testing and measurements to ensure that meaningful results are obtained (Hungr and others, 2005). 

Modeling is a useful tool, but it must not be substituted for critical thinking and sound judgment. 

A series of questions may help define some of the challenges and limitations of modeling. 

• What are the objectives of modeling? 

• What tools and how much modeling effort are needed to achieve the objectives? 

• How much data of what quality is needed to produce the desired results? 

• How far can the problem be simplified and how does one recognize adequate conceptual models? 

• Are the modeling results consistent with field observations and measurements? 

145
The first question although obvious, is an important one, because modeling objectives must 

be clearly defined at the outset, and the objectives must determine the answers to the remaining 

questions. Modeling intended to make a prediction of landslide displacement over some given 

period of time requires different techniques, significantly more effort, and higher-quality data than 

modeling to determine a factor of safety. This translates into significantly higher costs, not only for 

the actual analysis, but also for the field investigation, monitoring, and material-properties testing. 

In many cases the quality of the data available is only adequate to model present conditions and to 

obtain some qualitative understanding of the failure mechanism and factors that influence stability. 

The question of simplification, although dependent on data quality, usually requires judgment and 

some experimentation to determine how much complexity must be included to enable the model to 

reproduce observed behavior of a landslide. Forward modeling to make predictions about potential 

movements or responses to various changes requires much additional high-quality data (Hungr and 

others, 2005). 

Cost 

The cost of modeling is another factor to be considered. The cost of field-data acquisition 

needed to support modeling is usually substantial; some of these costs have been described briefly 

in the section on monitoring and instrumentation. Costs specific to modeling include computer 

hardware and software and staff time needed to develop, parameterize, calibrate, and run the model. 

Using the cost scheme developed for monitoring, the cost of empirical modeling and 2-D LE 

modeling, is usually in the low to moderate range. As a model becomes more complicated, its cost 

increases by adding the third spatial dimension, time dependence, heterogeneity, or other special 

model features. As a result, the cost of using 2-D or 3-D, time-dependent continuum models to 

analyze displacements for complex landslides is usually in the high to very high cost range. 

Reliability 

The question of reliability in modeling landslides is challenging. Previous authors have 

shown that numerical modeling is capable of producing results that are in reasonable agreement 

with observation for factor of safety (Duncan, 1996; Bromhead, 2004; Chugh and others, 2007), 

slip -surface/shear-zone location and shape (Baron and others, 2005; Hungr and others, 2005; 

Chugh and others, 2007), and displacement amount (Crosta and others, 2003; Konietzky and 

others, 2004; Chugh and others, 2007). However, uncertainty remains in any model results, and the 

input data are usually sources of much greater uncertainty than the method of computation 

(Bromhead, 2004). 

Nadim and others (2005) discussed uncertainty in terms of a probabilistic approach. 

Among other notable findings, they report that in some cases a potential slip surface with a higher 

factor of safety may also have a higher probability of failure than a slip surface with a low factor of 

safety (fig. 3-17). This can result from greater uncertainty in pore pressure, strength parameters, 

and other factors. Most probabilistic analyses lack the necessary data to compute an actual annual 

probability of failure; rather they provide a measure of the uncertainty in the input data and the 

computed factor of safety or displacement. Computing an annual probability of landslide 

movement or slope failure requires information about landslide recurrence obtained either by 

historical (Coe, Michael, and others, 2004) or geochronological methods described previously. 

Probabilistic analyses require additional effort and some additional data (probability or frequency 

distribution of strength parameters, range of uncertainty on pore pressures, slip surface geometry, 

and so forth), but they complement conventional deterministic analyses (Nadim and others, 2005). 

146
Interpreting computed displacements in a probabilistic sense has special challenges. 

Several sources of error contribute to the differences between computed and observed 

displacements. First is measurement error in the observed displacements, which in most cases is 

relatively small. Other sources of error affect the computed displacements and include uncertainty 

in model parameters, uncertainty in the geometry of zones that have similar geotechnical properties 

and geologic structures, uncertainty in ground-water levels and pore pressures, and uncertainty or 

inaccuracy in the actual computational models. In cases like some reported in the literature where 

extreme values of the difference between observed and computed displacement exceed 100 percent, 

the difference has a skewed (non-normal) distribution. However, few data are available to define 

the expected form and parameters of the distribution, which may vary considerably depending on 

the modeling approach and quality of the input data. Computing displacement for different 

scenarios of changing pore pressure, erosion, or changes in external loading is usually necessary to 

define the range of probable slope responses to future events. The task of defining the most 

probable displacement during the design life of a pipeline becomes an exercise in combining model 

uncertainty with the computed slope responses of the different scenarios and their likelihoods. 

Conclusions 

A wide variety of techniques is available for investigating specific landslides and potential 

landslide areas to define the hazard they pose to pipelines. Many of these have advanced 

considerably during the last decade, making it possible to characterize and monitor landslides in 

considerable detail. Methods of numerical modeling have also greatly improved. However, 

predicting future ground displacements remains extremely challenging. Difficulties in adequately 

characterizing the subsurface and in foreseeing future changes in climate, weather, ground-water 

levels, and natural or man-made changes to the loading and configuration of existing slopes and 

landslide deposits remain the primary sources of uncertainty in such predictions. These 

uncertainties affect empirical and numerical techniques. A probabilistic approach that considers 

various scenarios and their likelihoods during the expected lifespan of a pipeline may be needed to 

determine the most probable displacement amounts for use in pipeline design. In addition to 

geotechnical investigation and analysis, geologic studies of landslide history are needed to define 

the annual probability of ground movement. 

Acknowledgments 

Partial funding for this report was provided by Pipeline Research Council International 

(PRCI) and the U.S. Department of Transportation through a Cooperative Research and 

Development Agreement (CRADA) with D.G. Honegger Consulting. William L. Ellis (USGS) 

made useful suggestions about computing and presenting cost data. D. Vaughan Griffiths 

(Colorado School of Mines) provided a stimulating discussion of numerical models. Mark E. Reid 

(USGS) shared insights about the challenges of predicting landslide movement. James P. 

McCalpin (GEO-HAZ Consulting, Inc.) provided an informative tour of his exploratory trenches at 

Snodgrass Mountain depicted in figure 3-5. Douglas G. Honegger (D.G. Honegger Consulting), 

Ray Boivin (Plateau Geotechnical, Ltd.), Moness Rizkalla (Visitless Integrity Assessment, Ltd.), 

and Richard Butler (Golder Associates, Ltd.) provided constructive reviews of an early draft of this 

report. Robert L. Schuster and William Z. Savage (both USGS, emeritus) provided constructive 

reviews of a revised version of this report. 

147
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Figure 3-1. Toe of the Anzar Road landslide, San Benito County, Califorina, that severed 

two 25-cm-diameter gas pipelines on April 22, 1998. The pipelines served approximately 

60,000 residential and business customers in Santa Cruz County. The white pipe is a 

temporary bypass pipe that was placed across the toe soon after the landslide occurred 

(photograph by Lynn Highland, USGS; Schuster and others, 1998). 

162
Velocity

Class

Description Velocity

(mm/s)

Typical

Velocity

7

6

5

3

2

4

1

Extremely 

Rapid

Very Rapid

Rapid

Moderate

Slow

Very Slow

Extremely

Slow

5 x 103

5 x 10-3

5 x 10-5

5 x 10-7

5 x 10-1

5 x 101

5 m/s

13 m/month

1.6 m/year

16 mm/year

1.8 m/hr

3 m/min

Figure 3-2. Landslide velocity scale (after Cruden and Varnes, 1996). 

163
Transverse cracks

Minor scarp

Head

Main scarp

Crown cracks

Crown

Surface of rupture

Main body

Toe of surface of rupture Foot

Surface of separation

Toe

Radial

cracks

Transverse ridges

Right flank

Original 

ground 

surface

Figure 3-3. Block diagram showing the main parts of a landslide (after Varnes, 1978; 

Highland, 2004). 

164
+

+

Explanation

21

15 Locations where a debris flow or rapid earth 

flow covered the tracks or otherwise 

affected the railroad

Locations where a slump or shallow earth 

slide covered the tracks or otherwise 

affected the railroad

Shallow earth slide or debris flow that 

occurred in 1996-97

Deep-seated landslide that occurred in 

1996-97

Landslide scarp--may predate 1996 

landslides

Pacific

Ocean

1 0 1 Kilometer

Contour Interval 25 feet

1000 0 1000 2000 3000 Feet

Seattle

Everett

WASHINGTON

Puget 

Sound

200

100

200

300

+

+

35

Woodway

Puget Sound

34

Figure 3-4. Examples of engineering geologic maps showing: (A) landslides from a major 

storm event near Seattle, Washington; red polygons represent shallow debris flows and 

earth slides, orange polygon represents a large deep-seated landslide (Baum and others, 

2000); 

165
N

Figure 3-4, Continued. (B) Engineering geology of a rock-fall-prone area (Coe and 

others, 2005, plate 1); Al, alluvium mixed with rock-fall debris; Ot, older talus, colluvium, 

soil; Yt, young talus; Stdd, steeply dipping limestone; Mdd, moderately dipping limestone 

and/or fold axial zone; Shdd, shallow dipping limestone; Dc, debris cone; Df, debris fan; 

Ml, modified land; Q50, measurement of rock-mass quality; ER 20, measurement of 

extreme rock-fall runout; 

166
Figure 3-4, Continued. (C) Major structures and features of a landslide (Baum and 

others, 1998). 

167
Figure 3-5. Use of trenches in landslide subsurface exploration: (A) Location of 

exploratory trenches in a landslide deposit on the southeast side of Snodgrass Mountain, 

near Crested Butte, Colorado; 

168
Figure 3-5, Continued. (B) Landslide basal shear zone exposed in lower trench. Red 

paint spots added to highlight contact between gray crushed-shale landslide debris and 

yellow-brown sandy clay of shear-zone material. 

169
Porous 

stones

Shear

 plane

Moveable 

top block

Fixed bottom block

Axial load Cylindrical soil sample

Porous stone

Confining 

pressure

Valve

Drainage 

line

Platen

Flexible 

membrane

FN

FS

A

B

Soil sample

Figure 3-6. Sketches of soil test apparatus (modified from Savage and Baum, 2005): (A) 

Cross-sectional sketch of a direct-shear apparatus. The shear box encloses the soil 

sample and may be rectangular or circular in plan view. (B). Cross-sectional sketch of a 

triaxial test apparatus. This apparatus is usually circular in plan view. 

170
0

20

40

60

80

100

120

140

0.6

0.65

0.7

0.75

0.8

0.85

0 5 10 15 20 25 30

Dense

Loose

Dense

Loose

AXIAL STRAIN, IN PERCENT 

DEVIATORIC STRESS,

σ1-σ3,

IN POUNDS PER SQUARE INCH

VOID RATIO

Figure 3-7. Stress-strain curves for drained triaxial tests on loose (contractive) and dense 

(dilatant) medium-fine sand (modified from Lambe and Whitman, 1969, p. 131). Solid 

lines, actual test data; dashed lines, extrapolations based on results of other tests. The 

dense soil reached its peak strength after small axial strain, but continued to dilate, as 

axial strain increased. The loose soil began contracting early in the test and reached its 

ultimate strength after a relatively large axial strain. 

171
A

B

C

NORMAL STRESS, IN KILOPASCALS

Mohr stress circles at failure

SHEAR STRESS, IN KILOPASCALS

Shear strength

Expansive clay,

Sandy clay,

Figure 3-8. The Mohr-Coulomb failure criterion and effective stress: (A) Sketch showing 

Mohr-Coulomb failure criterion. Here, ( ) 2 31 τ m = σ −σ is the maximum shear stress 

and ( ) 2 σ m σ += σ 31 is the mean stress, where σ 1 is the major (most compressive) 

principal stress and σ 3 is the minor (least compressive) principal stress in a triaxial test. 

The stresses τ andσ , are, respectively, the shear and normal stresses on planes 

undergoing shear failure, c is cohesion, and φ is the angle of internal friction of the soil. 

(B) Construction of a Mohr-Coulomb failure envelope for a soil. (C) Mohr-Coulomb 

strength parameters determined by direct-shear testing of two soils from the Alani-Paty 

landslide in Hawaii. Primes indicate that the strength parameters are determined under 

effective-stress conditions and the subscript r indicates that the strength parameters are 

residual values. 

172
D

p

Effective 

 stress

Shear strength

Total stress

σ

τ

Figure 3-8, Continued. (D). Function of effective stress in reducing the shear strength of 

soil (after Savage and Baum, 2005). 

173
Figure 3-9. Example images of landslide area obtained by laser scanning: (A) shaded 

relief and contour map, plan view, (B) oblique view, (C) difference plot showing changes in 

elevation that resulted from landslide movement (Collins and others, 2007). 

174
Figure 3-10. Map of landslide displacements obtained using aerial photogrammetry 

(modified from Baum and others, 1998). 

175
Figure 3-11. Plot of displacements since initial measurement (Nov. 28, 1989) versus 

depth obtained using a slope inclinometer: A-axis is the primary (roughly downslope) axis, 

and B-axis (dashed line) is the secondary axis, 90° from primary (modified from Baum and 

others, 1991). 

176
Approximate scale

10 m

N

Soil temperature 

and soil moisture

Rain gage

Tripod and 

datalogger

CET-1

Instrumentation station IS1

CET-2

anchor

anchor

Active

landslide

Non-moving

ground

cable

cable Active strike-slip fault along south flank of landslide

Air temperature

probe

Rain gage

Data logger

CET-1

CET-2

Active strike-slip fault 

(lateral shear zone)

Anchor for CET-1

A

B

C

Sonic sensor

for snow depth

Figure 3-12. Typical monitoring installation showing: (A) Base station with data collection 

hardware, (B) Wire extensometer (modified from Coe, Ellis, and others, 2003). 

177
-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

0

2

4

6

8

10

12

5 7 9 11 13 15 17 19 21 23 25

PRESSURE HEAD, IN METERS

15-MINUTE RAINFALL, IN MILLIMETERS

March 1991

3.35 m boring 24

3.44 m boring 22

Rainfall

Figure 3-13. Plot of rainfall measured at 15-minute intervals by a tipping-bucket rain gage 

and subsurface pore-pressure variation obtained using vibrating-wire piezometers (Baum 

and Reid, 1995). 

178
Figure 3-14. Map showing the location of boreholes and instrumentation in the Alani-Paty 

landslide, Honolulu, Hawaii (Baum and Reid, 1995). 

179
E

E +(d E /dx) δx

T

T+(d T/dx) δx

σnn (δx /cos(- θ))

σns (δx/cos (- θ))

D γt δx

δx

D

−θ

he

he + (dhe/dx ) δx

P

O

Ground surface, s(x)

Slip surface, b(x)

Typical slice

z

x

-δx (db/dx)

F z

F x

hc

Dγt δx(ax /g)

A

B

Figure 3-15. The method of slices used in limit-equilibrium slope-stability analysis: (A) 

Diagrammatic cross section through landslide, (B) Detail showing slice dimensions and 

the forces and stresses acting on a slice (Baum, 2000). 

180
Figure 3-16. Example of results from finite-element analysis (Savage and others, 2000): 

(A) deformed finite-element mesh showing computed displacements, (B) contour diagram 

comparing displacements to actual failure-surface profile; warm colors correspond to 

larger displacements. Geologic units are identified by the following symbols: Qvr, 

recessional outwash; Qva, advance outwash; Qtb, transitional beds; Qc, Lawton Clay; 

Qw, Whidbey Formation. 

181
0

0.5

1

1.5

2

2.5

3

3.5

4

0 0.5 1 1.5 2 2.5 3

PROBABILITY DENSITY

FACTOR OF SAFETY

Low mean and nominal factor of safety,

Low uncertainty,

Low failure probability

High mean and nominal factor of safety,

High uncertainty,

High failure probability

Probability of failure

(F<1), shaded areas

Figure 3-17. Probability chart for factor of safety, illustrating the relationship between 

data uncertainty, factor of safety, and likelihood of failure (after Nadim and others, 2005). 

The blue line represents a case where the computed factor of safety is low, but there is 

little uncertainty in the input data or failure mechanism so there is a low probability (blue 

shaded area) of failure. The red line represents a case where the computed factor of 

safety is high, but there is much uncertainty in the input data or failure mechanism, so 

there is a high probability (red plus blue shaded areas) of failure. 

182
Table 3-1. Comparison of equipment and resolution for repeat ground surveys to 

determine surface displacement. 

[Annual cost averaged over 5 years, including acquisition and maintenance, data collection and analysis assuming two 

measurement campaigns per year; low, less than $5,000; intermediate, $5,000–$15,000; high, $15,000–$25,000; very 

high, greater than $25,000] 

Technique Description Resolution Advantages and 

drawbacks 

Cost Reference 

Tape Measure distances 

between stakes 

arranged in grids, 

quadrilaterals, or 

other 

configurations to 

determine 

displacement. 

Sub-decimeter to 

centimeter 

resolution, 

depending on 

distance, groundsurface 

irregularities, and 

other factors. 

Simple, reliable, 

inexpensive 

technology 

Stakes subject to 

tilting or other 

disturbance, 

provides only 1-D 

displacement, 

manual readings 

contribute to 

increased frequency 

of errors. 

Low Baum and others, 

1989; Keaton and 

DeGraff, 1996 

Tape 

extensometer 

Measure distances 

between fixed 

points. 

Millimeter Simple, reliable 

technology 

Requires specialized 

monuments 

Low Mikkelsen, 1996 

Total station By means of 

standard surveying 

techniques, 

establish 3-D 

locations of points 

on the ground 

surface. 

Centimeter to 

sub-centimeter, 

depending on 

environmental 

conditions and 

skill of operator. 

Provides accurate 3-

D displacement of 

discrete points. 

Some units capable 

of measuring hardto-reach points 

without a prism. 

Intermediate Keaton and 

DeGraff, 1996 

Differential 

GPS 

Uses fixed GPS 

base station and 

roving receivers to 

acquire position 

data at selected 

points. 

Sub-centimeter 

horizontal, subdecimeter vertical 

if stations 

occupied at least 

20 minutes each. 

Rapidly provides 

accurate 

georeferenced 

positions of discrete 

points. 

Vertical position is 

less accurate than 

other methods. 

IntermediateHigh Keaton and 

DeGraff, 1996; 

Gili and others, 

2000; Coe, Ellis, 

and others, 2003; 

Brückl and others, 

2006 

Terrestrial 

Laser Scanner 

(Tripod 

LiDAR) 

Measures distance 

from instrument to 

millions of points 

on the ground 

surface, capable of 

producing detailed 

images or digital 

elevation models 

of the surface. 

Sub-decimeter to 

millimeter, 

depending on setup, field and 

atmospheric 

conditions, 

instrument, and 

other factors. 

Can monitor 

displacement of 

discrete areas on a 

slope face, rather 

than a few single 

points. 

Equipment and 

software are very 

expensive and postprocessing is 

complex. 

Very high Rowlands and 

others, 2003; 

Jones, 2006; 

Collins and others, 

2007; Bawden, 

oral commun., 

2007 

183
Table 3-2. Aerial and satellite remote sensing methods for landslide displacement. 

[Annual cost averaged over 5 years, including acquisition and maintenance, data collection and analysis assuming one 

measurement campaign per year; low, less than $5,000; intermediate, $5,000–$15,000; high, $15,000–$25,000; very 

high, greater than $25,000] 

Technique Description & Resolution Advantages and 

drawbacks 

Cost Reference 

High-resolution 

aerial 

photogrammetry 

Uses an analytical plotter or 

digital photogrammetry to 

measure displacement of photoidentifiable points on time-lapse, 

stereoscopic, aerial photography. 

Resolution depends on quality 

and scale of photography and 

ground control. Sub-centimeter 

accuracy has been reported from 

1:2000-scale photography. 

Capable of providing 

accurate 3-D 

displacement. 

Requires groundcontrol survey and 

permanent photoidentifiable targets for 

most-accurate results. 

Intermediate Fraser and 

Gruendig, 1985; 

Baum and others, 

1998; Brückl and 

others, 2006 

INSAR Uses synoptic, time-lapse 

satellite radar images to measure 

landslide displacement; 

applicable only to slow 

movement and small 

deformations; millimeter 

accuracy. 

Applicable over large 

areas. 

Provides only 1-D 

displacement, which is 

either vertical or a 

combination of vertical 

and horizontal, 

depending on the angle 

of incidence of radar 

beam. 

Intermediate Van Westen, 

2004; Colesanti 

and Wasowski, 

2006 

184
Table 3-3. Techniques and sensors for continuous measurement of landslide 

displacement and deformation. 

[Annual cost averaged over 5 years, including acquisition, installation, and maintenance; continuous measurement 

assuming data collection from data loggers and analysis four times per year; low, less than $5,000; intermediate, 

$5,000–$15,000; high, $15,000–$25,000; very high, greater than $25,000. Acquisition cost includes purchase price of 

sensor, data logger, cable, and accessories] 

Observation Technique 

or sensor 

Description, Comments 

and resolution 

Advantages and 

drawbacks 

Cost Reference

Surface or 

subsurface 

displacement 

Extensometer Can be installed across a 

landslide boundary or in a 

borehole. Resolution 

increases as measurement 

range decreases. Borehole 

extensometers tend to 

measure smaller 

displacement than surface 

displacement. 1-D, 

centimeter to submillimeter accuracy. 

Provides highly 

detailed time series 

record of landslide 

movement. 

Cable subject to 

disturbance by wind 

and animals. 

Intermediate Corominas, 

and others, 

2000; 

Ellis and 

others, 

2007 

Surface 

displacement 

Theodolites Motorized, computercontrolled theodolites or 

total stations observe 

position of monitoring 

points. 3-D, sub-centimeter 

accuracy. 

Accurate, non-contact 

measurement of 

selected points. 

Requires shelter and 

stable base for longterm measurements. 

High Angeli and 

others, 

2000 

Surface 

displacement 

Laser and 

ultrasonic 

sensors 

Laser or ultrasonic beam 

aimed at target across 

landslide boundary. Return 

time determines distance, 

1-D, sub-decimeter to 

centimeter accuracy, 

depending on distance from 

target. 

Non-contact 

measurement 

Limited by short 

range of sensors 

(about 15 m) and 

potential for landslide 

movement to cause 

misalignment between 

sensor and target. 

Intermediate W.L. Ellis, 

USGS, 

2007, oral 

commun. 

Surface 

displacement 

and 

deformation 

Terrestrial 

radar 

interfereometry 

A portable synthetic 

aperture radar scans 

landslide surface at 

frequent intervals. 

Interferometric techniques 

reveal surface changes. 

Reported accuracy ranges 

from millimeters to 

centimeters. 

Interferogram reveals 

spatial distribution of 

displacement. 

Measured 

displacements are 1-

D; apparatus not yet 

commercially 

available. 

High Tarchi and 

others 

2003; 

Duranthon, 

2004; 

185
Observation Technique 

or sensor 

Description, Comments 

and resolution 

Advantages and 

drawbacks 

Cost Reference

Surface 

displacement 

Wireless 

transceiver 

Wireless networks. Other 

applications using wireless 

sensor networks are under 

development, 3-D, meter to 

sub-meter accuracy. 

Non-contact 

measurement 

Information available 

so far indicates that 

the spatial resolution 

of these networks is 

not yet adequate for 

landslide monitoring. 

High Kevin 

Moore, 

Colorado 

School of 

Mines, oral 

commun., 

2006; 

Sheth, and 

others, 

2005 

Displacement LVDT The Linear Variable 

Differential Transformer 

(LVDT) consists of a 

mobile armature and outer 

transformer windings. 

LVDTs are capable of high 

precision (2-5 x 10-4 mm) 

measurements over a small 

range of displacement (±5-

±75 mm). Application is 

mainly to rock slopes 

where small displacements 

can be expected. 3-D 

displacements are possible 

by using three mutually 

perpendicular LVDTs. 

Provides highly 

accurate 

measurements across 

cracks. 

Limited to use in 

areas of small 

displacement. 

Intermediate Greif and 

others, 

2004 

Subsurface 

soil strain 

Soil strain 

meter 

Measures soil strain over a 

distance of 2-5 m, if ground 

is cracked, strainmeter is 

installed perpendicular to 

cracks. 1-D, displacement 

range of a few centimeters, 

capable of detecting unit 

strains of 0.0001. 

Useful in detecting 

distributed strain in 

the soil prior to 

cracking or other 

visible evidence of 

deformation. 

Intermediate Mikkelsen, 

1996; 

Husaini and 

Ratnasamy, 

2001 

186
Observation Technique 

or sensor 

Description, Comments 

and resolution 

Advantages and 

drawbacks 

Cost Reference

Subsurface 

strain or 

displacement 

Coaxial cable 

using timedomain 

reflectometry 

A length of coaxial cable, 

grouted into a borehole, 

serves as the system’s 

sensor. Electronic pulses 

are sent down the cable; 

reflected pulses are related 

to deformation of the cable 

or to pre-established 

reference points (crimps). 

Areas of offset in the 

resulting trace depict zones 

of extension or shear along 

the cable. 

Crimps, at measured 

intervals along the cable, 

partially reflect the 

transmitted signal and 

provide a more accurate 

scale for correlation of 

deformational zones to 

depth. Crimps appear as 

small negative polarity 

events along the trace of 

the waveform. Events that 

offset the waveform 

indicate deformational 

zones; the polarity of the 

offset indicates whether a 

zone is experiencing tensile 

or shear deformation. 

1-D, decimeter to subdecimeter accuracy for 

depth of deformation. 

An alternative to 

inclinometers that 

allows for remote or 

continuous monitoring 

to establish landslide 

depth. 

Intermediate Dowding, 

and others, 

1989; Kane 

and Beck, 

1994; 

Campbell 

Scientific, 

Inc., 2007 

Surface 

displacement 

Real-time GPS Measurements on 

approximately 30-minute 

cycle reveal displacement 

at point(s) on landslide 

relative to base station, subcentimeter resolution, 

location of measurement 

stations can be anywhere 

on the landslide surface. 3-

D, sub-centimeter accuracy. 

Capable of making 

displacement 

measurements 

anywhere on the 

surface of a landslide. 

Can be deployed in 

remote locations. 

Requires highbandwidth 

communications. 

Commercial 

applications tend to be 

high to very high cost. 

Intermediatehigh LaHusen 

and Reid, 

2000 

187
Observation Technique 

or sensor 

Description, Comments 

and resolution 

Advantages and 

drawbacks 

Cost Reference

Tilt of ground 

surface or 

borehole 

inclination 

Borehole 

tiltmeters 

Repeated or continuous 

measurements of tilt reveal 

surface or shallow 

subsurface deformation. 1-

D or 2-D, 5 nanoradian to 1 

microradian resolution. 

Sensitive to very 

small changes. 

Interpretation 

sometimes difficult 

without aid of 

displacement 

measurements. 

Intermediate Mikkelsen, 

1996 

Landslide 

movement at 

discrete 

depths 

Borehole 

inclinometers 

Internally grooved plastic 

or metal pipe grouted into 

vertical borehole. Repeat 

measurements of biaxial 

borehole inclination at 

fixed depth increments 

reveal depth, amount, and 

plan-view directions of 

displacement. Millimeter 

resolution of displacement 

if correctly installed and 

carefully measured. 2-D 

displacement, millimeter 

accuracy. 

Capable of providing 

detailed time series 

record of deformation 

at various depths. 

Instrument destroyed 

or becomes 

ineffective after 

displacement exceeds 

a few decimeters. 

Intermediate Mikkelsen, 

1996 

Surface or 

subsurface 

strain or 

displacement 

Optical timedomain 

reflectometry 

An optical fiber is used as a 

sensor. The optical timedomain reflectometer 

(OTDR) measures 

variations in intensity of 

light reflections. 

Experimental device used 

in laboratory, but Baek and 

others (2004) claim it could 

be adapted to field use. 1-

D displacement 

Not commercially 

available 

Intermediate Baek and 

others, 

2004 

188
Table 3-4. Subsurface water measurement techniques. 

[Annual cost averaged over 5 years, including acquisition, installation, and maintenance, continuous measurement 

assuming data collection from data loggers and analysis four times per year; low, less than $5,000; intermediate, 

$5,000–$15,000; high, $15,000–$25,000; very high, greater than $25,000. Acquisition cost includes purchase price of 

sensor, data logger, cable, and accessories. For observation well, open-tube piezometer, and direct-burial piezometer, 

acquisition cost includes $5,000 for drilling and well construction.] 

Measurement Technique or 

sensor 

Description and 

comments 

Advantages and 

drawbacks 

Cost Reference 

Water-table 

depth 

Water-level 

indicator or 

pressure 

transducer in 

observation 

well 

Screened or slotted 

casing for entire depth 

of well allows 

measurement of depth of 

water table. 

Measurements can be 

taken manually or 

automatically. 

Damaged 

instrument can be 

easily removed and 

replaced. 

Oversimplifies 

pore-pressure 

distribution unless 

used in 

combination with 

piezometers. 

Intermediate Mikkelsen, 

1996 

Pore pressure Pressure 

transducer in 

open-tube 

piezometer 

Usually constructed 

from PVC pipe 

terminated with a porous 

tip. The piezometer tip 

is installed at the desired 

depth in the borehole 

and covered with coarse 

backfill. Layers of 

bentonite or grout above 

and below the 

piezometer tip prevent 

the flow of water 

between different 

horizons intersected by 

the borehole. Response 

time increases with 

decreasing diameter. 

Damaged 

instrument can be 

easily removed and 

replaced. Pore 

pressures can be 

observed at depths 

relevant to 

landslide 

movement. 

Response time may 

be too slow to 

accurately indicate 

pore pressures 

associated with 

rainfall and 

snowmelt events. 

Intermediate Lambe and 

Whitman, 

1969; 

Mikkelsen, 

1996 

Pore pressure 

and matric 

suction below 

depth range of 

tensiometers 

Pressure 

transducer 

(direct burial 

piezometer) 

Intallation in cementbentonite grout or use of 

high-air-entry porous tip 

allows measurement of 

matric suction of soils 

subject to seasonal 

drying. 

Response time 

faster than in opentube piezometer. 

Replacement of 

damaged 

instrument requires 

new boring. 

Intermediate Mikkelsen, 

1996, 2002 

Matric suction Tensiometer Depth range is surface to 

about 2 m. Useful for 

monitoring rainfall 

infiltration. 

Conventional 

tensiometers require 

regular maintenance of 

fluid level; newer 

designs require only 

annual maintenance. 

Rapid accurate 

measurements of 

pore pressure above 

water table. 

Subject to damage 

by freezing. 

Intermediate Hillel, 1982; 

Baum and 

Reid, 1995; 

Baum and 

others, 2005 

189
Measurement Technique or 

sensor 

Description and 

comments 

Advantages and 

drawbacks 

Cost Reference 

Matric suction Thermal sensor Experimental design, 

uses moisture-induced 

variation in thermal 

properties of porous 

ceramic tip to estimate 

soil-matric suction. 

Does not require a 

water reservoir like 

tensiometers. 

Provides fast and 

accurate 

measurements. 

Not commercially 

available. 

Intermediate Beneveli and 

others, 2004; 

Tan and 

others, 2007 

Soil volumetric 

water content 

Time-domain 

reflectometry 

Depth range is surface to 

about 2 m for profilers; 

certain probe designs 

can be buried to depths 

of several meters in 

boreholes. Relies on 

changes in the soil 

dielectric constant to 

observe water content. 

Capable of fast 

accurate 

measurements of 

soil water content. 

For accurate 

measurements, 

must be calibrated 

to site soils. 

Intermediate Campbell 

Scientfic, 

2007 

Soil volumetric 

water content 

Soil moisture 

profilers and 

probes 

Depth range is surface to 

about 2 m for profilers; 

certain probe designs 

can be buried to depths 

of several meters in 

boreholes. For accurate 

measurements, must be 

calibrated to site soils; 

relies on soil 

capacitance to observe 

water content. 

Provides accurate 

measurements of 

soil-water content 

for as many as eight 

different depths in a 

vertical profile. 

For accurate 

measurements, 

must be calibrated 

to site soils. 

Intermediate Baum and 

others, 2005 

190
Table 3-5. Preferred locations for landslide exploration and instrumentation. 

Observation or 

measurement 

Primary location Secondary locations Comments 

Landslide depth Central part of main body Upslope and downslope of 

central area first along main 

axis and then off the axis 

At least three points between the 

head and the toe to control cross 

section 

Pore pressure Central part of main body Upslope and downslope of 

central area first along main 

axis and then off main axis 

Install piezometers as close as 

possible to basal slip surface. 

Additional piezometers above or 

below the basal slip surface help 

determine ground-water flow 

directions. 

Shear strength Basal slip surface Other weak layers 

discovered during 

subsurface exploration 

See earlier section on soil testing and 

Wu (1996) for guidance on selecting 

appropriate test procedures 

depending on soil type. Wyllie and 

Norrish (1996) describe procedures 

for determining appropriate rock 

strength parameters. 

Displacement Main body along pipeline 

alignment 

Upslope and downslope of 

alignment 

A line of points adjacent to the 

pipeline will show how displacement 

varies across the landslide. 

Additional points may be needed to 

detect differential movement on 

internal structures such as lateral 

shear zones. 

191
Table 3-6. Data types used in various numerical and analytical methods of landslide 

analysis. 

[LE, limit-equilibrium methods; FE/FD, finite-element and finite-difference methods; DE, discrete-element methods; 

A, data used to perform analysis; V, data used to validate analysis results; N, not used by these methods] 

Data type Source and purpose LE FE/FD DE

Surface topography 

and landslide 

features 

Detailed site survey (profiles or map) to define free surface 

of landslide as well as any surcharge loads, detailed 

geologic mapping define plan-view shape of landslide and 

any shear zones or other features that subdivide the 

landslide into mechanically distinct parts. 

A A A 

Site stratigraphy Geologic mapping and subsurface exploration (drilling, 

trenching) define internal geometry of hillside or landslide, 

with attention to details that affect variation in material 

properties. 

A A A 

Discontinuities and 

structure 

Geologic mapping and subsurface exploration define 

internal geometry of hillside or landslide, with attention to 

details that affect bulk material properties and identify 

potential surfaces of shear or separation. 

A A A 

Slip-surface 

geometry 

Subsurface exploration and inclinometer or TDR monitoring 

define the basal boundary of the landslide for accurate 

computation of the factor of safety by limit-equilibrium 

methods or validation of predicted basal shear zone for 

other methods. 

A V V 

Shear-strength 

parameters 

Subsurface sampling and in-place or laboratory testing of 

landslide materials determines internal shearing resistance 

as well as shearing resistance along the basal slip surface. 

A A A 

Stress-strain 

properties 

Subsurface sampling and in-place or laboratory testing of 

landslide materials to determine stress-strain properties 

allow numerical models to predict landslide 

movement/deformation in response to changes in pore 

pressure, external loading, or other factors. 

N A A 

Displacements and 

rates 

Time-series monitoring data of movement and pore pressure 

allow definition of minimum pore pressures needed to 

initiate or sustain movement. Spatial distribution of 

displacement amounts or rates provides check on model 

predictions. 

A/V V V 

Subsurface water 

pressures 

Pore pressure at the basal slip surface and within the 

landslide as determined by monitoring (possibly 

supplemented by numerical modeling of ground-water flow) 

allows computation of the effective stress, which in turn 

affects strength and deformation of landslide materials. 

A A A 

192