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Quantitative assessment of landslide hazard along transportation lines using
historical records
Article in Landslides · September 2011
DOI: 10.1007/s10346-011-0252-1
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Original Paper
Landslides (2011) 8:279–291
DOI 10.1007/s10346-011-0252-1
Received: 25 March 2010
Accepted: 9 January 2011
Published online: 27 January 2011
© Springer-Verlag 2011
Pankaj Jaiswal I Cees van
Westen
I Victor
Jetten
J. van
Westen
I Victor
Jetten
Quantitative assessment of landslide hazard
along transportation lines using historical records
Abstract In this paper, a quantitative landslide hazard model is
presented for transportation lines, with an example for a road and
railroad alignment, in parts of Nilgiri hills in southern India. The
data required for the hazard assessment were obtained from
historical records available for a 21-year period from 1987 to 2007.
A total of 901 landslides from cut slopes along the railroad and
road alignment were included in the inventory. The landslides
were grouped into three magnitude classes based on the landslide
type, volume, scar depth, and run-out distance. To calculate
landslide hazard, we estimated the total number of individual
landslides per kilometer of the (rail) road for different return
periods, based on the relationship between past landslides
(recorded in our database) and triggering events. These were
multiplied by the probability that the landslides belong to a given
magnitude class. This gives the hazard for a given return period
expressed as the number of landslides of a given magnitude class
per kilometer of (rail) road. The relationship between the total
number of landslides and the return period was established using
a Gumbel distribution model, and the probability of landslide
magnitude was obtained from frequency–volume statistics. The
results of the analysis indicate that the total number of landslides,
from 1- to 50-year return period, varies from 56 to 197 along the
railroad and from 14 to 82 along the road. In total, 18 hazard
scenarios were generated using the three magnitude classes and
six return periods (1, 3, 5, 15, 25, and 50years). The hazard
scenarios derived from the model form the basis for future direct
and indirect landslide risk analysis along the transportation lines.
The model was validated with landslides that occurred in the year
2009.
Keywords Landslide inventory . Historical record . Magnitude
class . Quantitative hazard assessment . Transportation line .
Nilgiri
Introduction
Transportation lines such as roads and railroads in mountainous
terrain are often prone to landslides which may occur on cut or
natural slopes. Any damage to these facilities due to landslides
can result in consequences, which can be direct or indirect.
Along transportation lines, landslides may occur frequently
from untreated cut slopes (Dai and Lee 2001). Landslides
initiating from cut slopes are generally small in size but pose
considerable risk to life and property. To quantify risk along a
road or a railroad, we require the estimation of the frequency of
landslides and the degree of loss to specific elements at risk
resulting from the specified landslide magnitude (AGS, Australian
Geomechanics Society and Sub-committee on Landslide Risk
Management 2000; Fell et al. 2005, 2008; van Westen et al. 2006).
Researchers have used different statistical models to estimate
frequency of landslides, such as those based on the magnitude–
frequency relationship of landslides (Hungr et al. 1999), exceedance probability of rainfall threshold (Jaiswal and van Westen
2009), and exceedance probability of landslides based on Poisson
or binomial probability models (e.g., Coe et al. 2000; Guzzetti et
al. 2002, 2005). The models based on rainfall threshold provide an
estimate of the probability of occurrence of one or more rainfall
events that can trigger landslides, whereas those based on the
occurrence of past landslides provide exceedance probability of
one or more landslides that can occur in an area. Both thresholdand landslide-based models are ultimately used to obtain the
probability of occurrence of one or more landslides in a specified
time period. For transportation lines, specific information on the
expected number of landslides and their annual probability of
occurrence (or return period) are important for estimating direct
and indirect risk. This information is used to estimate the
probability of a landslide hitting a moving vehicle or a commuter
(AGS, Australian Geomechanics Society and Sub-committee on
Landslide Risk Management 2000; Wilson et al. 2005) and to
calculate the blockage time by estimating the total volume of
debris on the transportation line.
An estimate of the frequency of a specific number of
landslides per unit area can be made if a relationship can be
established between the number of landslides triggered by an
event and the probability of occurrence of this event. To establish
such a statistical relationship, we require a continuous record of
landslide inventory. Some technical offices, such as road and
railroad maintenance departments, or geotechnical offices, produce inventories containing continuous records of landslide
occurrences within a region or along transportation lines. Most
often they refer to landslides from cut slopes and fills along
transportation lines (Fell et al. 1996). The main advantage of such
records is that they are prepared shortly after the occurrence of a
landslide-triggering event thereby recording most of the landslides
in an area, but the major limitation remains the availability and
accessibility of such data. The unavailability of a continuous record
of landslides is one of the major drawbacks in the quantitative
assessment of landslide hazard (van Westen et al. 2006).
In this, we assessed hazard from landslides originating from cut
slopes along a road and a railroad in the Nilgiri hills of Tamil Nadu,
India. We used the historical data from the railroad maintenance
unit and geotechnical office to obtain a substantially complete
landslide inventory on cut slopes. The landslide hazard descriptor
for roads and railroads, as recommended by Joint Technical
Committee on landslides and Engineered Slopes, JTC-1 guidelines
(Fell et al. 2008), is expressed as the number of landslides of a given
magnitude (area or volume) per annum per kilometer of cut slopes.
We used this descriptor in our analysis and have tried to quantify the
hazard based on historical information.
The study area
The study was carried out along a transportation corridor in
southern India. Figure 1 shows the location of the 17-km-long
railroad and 24-km-long national highway road (NH-67) connecting Mettupalayam and Coonoor in the Nilgiri hills, in the
Landslides 8 & (2011)
279
Original Paper
Fig. 1 Location of the road and the railroad alignment in the Nilgiri hills of southern India. Black circles are the location of landslides on cut slopes. The numbers
indicate the kilometer marks along the railroad and the road
western Tamil Nadu region of southern India. The railroad was
constructed in the late nineteenth century and became operational in 1899.
Geologically, the transportation lines expose gneisses belonging to the Charnockite Group of Archaean age (Seshagiri and
Badrinarayanan 1982), overlain by weathered soil, exposed along
cut slopes of the road, railroad, and landslide scarps. The gneisses are
light bluish gray in color and medium to coarse grained and show
only crude foliation on fresh surfaces. However, when weathered
these rocks show well-developed foliation planes. The regional strike
of the foliation is ranging from ENE–WSW to E–W direction with
moderate to steep dips. The sub-tropical climate and intense physical
and chemical weathering have resulted in a thick yellowish to
reddish brown soil. Borehole data from a location south of Katteri
reveals that the maximum thickness of the weathering profile is
about 32 m (Seshagiri and Badrinarayanan 1982). Along the road and
the railroad, the weathering soil thickness varies from less than a
meter to 20 m, as observed in cut slopes.
The area has two distinct landforms, namely moderately
dissected plateau remnants to the west of Marapallam and highly
dissected denudational slopes to the east of Marapallam (Seshagiri
and Badrinarayanan 1982). The elevation difference of the transportation lines, from Kallar farm to Coonoor, is about 1,300 m. The
area forms part of the Coonoor river basin and is characterized by a
sub-dendritic drainage pattern.
Both transportation lines pass mostly through reserved forest
and tea plantations. To the west of Marapallam, the road and the
railroad go through gentle slopes covered by tea plantations, whereas
to the east of Marapallam, they are passing through steep slopes
containing patches of forest.
280
Landslides 8 & (2011)
Generation of the landslide inventory along transportation lines
The landslide inventory along the road and the railroad was
prepared by interpreting historical records available with different
organizations in the Nilgiri area, which include a railroad
maintenance register, a summary table of landslides along the
railroad, and several technical reports.
Data sources and types of information
The main continuous record of landslides along the railroad was
available from the railroad maintenance register (locally called
“railway slip register”). Since 1992, data were present in a paper
form recorded and maintained by the Southern Railway office at
Coonoor. The railway slip register is updated soon after the
occurrence of a landslide-triggering event and is used for
tendering contracts for railroad clearance. It contains data on
the spatial distribution of landslide debris on the railroad. The
type of landslide data compiled from the register is shown in
Table 1. The location of landslides is referenced with respect to the
nearest telephone posts, which are used as permanent markers
along the railroad by the railroad authorities. The average
distance between two telephone posts is 50 m. The landslide
description contains the type of material (e.g., earth mixed with
boulder), the total volume of debris on the railroad, and the date
of occurrence.
From 1987 to 1991, landslide data were only available as a
“landslide summary table” maintained by the Southern Railway
office, which contains the spatial distribution of landslide debris
on the railroad in different months. Landslides prior to 1987 were
not available for the study area. The type of information on
landslides available in the “landslide summary table” is shown in
Table 1 Types of information on landslides compiled from the historical records (railway slip register, railroad landslide table, and technical reports) and inventory
obtained after field check
Railway slip register
Railroad landslide table
Technical reports
Inventory after field check
Date
Kilometer mark
Location of slide
Identification number
Between stations
Telephone post
Data source
Location of slide
Kilometer mark
Month
Latitude of slide
Type of slide
Telephone Post
Longitude of slide
Date of occurrence
Volume of earth
Type of slide
Length of slide (m)
Volume of boulders
Fresh (date)
Width of slide (m)
Damage detail
Reactivated (date)
Scar depth (m)
Restoration date
Slope
Area (m2)
Remarks, if any
Soil type
Volume (m3)
Rainfall in mm
Run-out distance (m)
Length of slide (m)
Land cover/land use
Width of slide (m)
Regolith thickness (m)
Height of slide (m)
Probable cause
Depth of slide (m)
Remarks (including damages, etc.)
Crown height (m)
Probable cause
Remarks (including damages, overburden
thickness, etc.)
Table 1. The summary table does not contain information on the
date of occurrence of landslides, type of material involved, and
total volume of debris on the railroad. It only provides the
location of landslides with respect to the nearest telephone post as
well the month and year of landslide occurrence.
Apart from the above-mentioned sources, landslide information was also collected from published and unpublished technical
documents of landslide investigations. Most of the reports
contained detailed geotechnical investigation of major landslides
in the Nilgiri area. Some reports contain inventories of landslides
along the road (NH-67). The oldest inventory record was from the
year 1987. All reports provide some basic information on
landslides but the type of information varies depending on the
purpose of investigation and the time and resources that were
available to carry out the investigation. Table 1 gives the type of
landslide data compiled from the technical reports.
Method used for inventory mapping
After selecting the data sources, all relevant data pertaining to
landslides on cut slopes was compiled in a spreadsheet, which
formed the basis for field mapping where all the data related to
each specific location were checked and updated. All landslide
sites compiled from the historic archives were visited in order to
identify the landslide scars based on the description provided in
the historical records. The data on landslide volume were used to
infer the size of landslides. The morphological signatures left by
landslides on slopes, such as scarp faces and concave slopes, were
used to identify the shape of landslides. Some of the landslide
scars and deposition areas were not clearly discernable due to the
removal of debris and remedial works. To overcome this, five
criteria were adopted for the identification of landslides: (1)
changes in the slope morphology characterized by slope concavity
or surface irregularities, (2) presence of scarp faces with contrast
in vegetation cover with the surrounding areas, (3) presence of
retaining walls or other slope stabilization structures, (4) presence
of removed debris on the down slope part, and (5) information
from local people.
After identifying the location of landslides, they were mapped
on a 1:10,000 scale topographic map. The initiation (source) and
depositional area of landslides were separately marked. The
morphological parameters (landslide scar length, width, and
depth) were plotted after carefully measuring them in the field
using a meter tape. Additional data such as type of landslide, runout distance, present land use, thickness of weathering profile,
probable cause, and damage details were also added to the
inventory. The mapped landslides were digitized as polygons or
points and entered in a geo-database of Arc GIS. Table 1 shows the
type of landslide information obtained after the field check.
Data were compiled from the historical records covering a 21year period from 1987 to 2007. Out of the 901 landslides, 565
landslides (63%) were obtained from railway slip registers (from
1992 to 2007), 220 (24%) from the railroad landslide table (from
1987 to 1991), and 116 (13%) from technical reports (from 1987 to
2007). Through field mapping, it was possible to identify and
map 67% of the compiled landslides of the railway slip register
and technical reports. Some of the smaller landslides with a
volume less than 20 m3 were not identifiable in the field due to
possible reactivations which have obliterated the earlier morphology. The location and volume of these small landslides were
therefore taken directly from the original source data. Since they
Landslides 8 & (2011)
281
Original Paper
were small and located along the road or the railroad, it was
presumed that most of the released material from these landslides
was accumulated on the road and the railroad. Therefore, the
measured volume from the maintenance records was considered a
good representation of the size of these small landslides. The
location of the mapped landslides on cut slopes, indicated as
points, along the railroad and the road is shown in Fig. 1. The
number of recorded landslides along the road is relatively less
than along the railroad. The relative lack of data along the road
might be due to the fact that smaller landslides are not reported
as they do not cause damage to the road itself.
Landslide characteristics
The landslides are shallow translational debris slides and mostly
triggered by retreating monsoon rainfall during the period from
October to December, with a maximum (54%) during the month
of November. Figure 2 shows the annual distribution of recorded
landslides. Landslides occur annually (except in 1995) with an
average rate of 43 landslides per year. In 1995, the rainfall was less
than the required threshold value for landslide initiation, and
hence, no landslide occurred (Jaiswal and van Westen 2009).
Major landslide activities were observed during 1987, 1992, 2001,
and 2006. Landslides occur both as a first time or reactivated
failures. Reactivations were common on cut slopes that provide
more surface area for slope failures to retrograde.
Examples of the type of landslides that are common along the
road (Fig. 3a) and the railroad (Fig. 3b) are shown in Fig. 3. On cut
slopes, these landslides initiate as a small slope failure due to
excavation and develop into landslides of larger dimension with
passage of time. The slided material mostly consists of weathering
soil mixed with debris of weathered gneissic rock. Failures are
common during rains due to the buildup of pore water pressures
on the contact of the weathering soil and weathered rock exposed
in cut slopes (Iverson 2000). Even though the majority of the
slides are small, they are capable of causing extensive damage to
the infrastructure properties and the temporal blockage of the
(rail) road thus resulting in direct and indirect losses.
Table 2 summarizes the main characteristics of landslides on
cut slopes along the railroad and the road. The inventory from the
railroad landslide table is not added in the statistics shown in
Table 2 because of the unavailability of data on volume.
Landslides are generally small in size (median volume <200 m3)
but occur with a high frequency. In some sections of the railroad,
the density of slope failures exceeds 50 landslides per kilometer
and for the road ten landslides per kilometer.
Fig. 2 Annual distribution of recorded landslides from 1987 to 2007
282
Landslides 8 & (2011)
From historical records, we have differentiated 110 landslide
events. The term “landslide events” is used here for days when
one or more landslides were triggered by rainfall. Out of 110
landslide events, the actual date was only known for 86 events.
For 24 events, the month of occurrence was available (taken from
the railroad landslide table). It is assumed that in the latter case,
only one event had occurred each month. Table 3 provides the
main characteristics of all landslide events that have affected the
area in the 21 years from 1987 to 2007. The number of landslides
per event varies from one to 148, and the number of events per
year varies from one to 11.
Since the railroad maintenance record is prepared specifically
for tendering contracts of debris clearance and is updated soon
after each landslide-triggering event, we are certain that it
contains practically all landslides that occurred along the railroad.
The presence of data on a substantial fraction of small landslides
(median volume ~20 m3, see Table 2) and the availability of a
record of 110 events in 21 years time (average ~5.2 events per
year), including events that triggered even one landslide, clearly
indicate that the landslide inventory along the railroad is
substantially complete at least for the time period from 1987 to
2007.
For the road, on the contrary, the technical reports provided
information on only seven events. From the description given in
some of the technical reports, it is evident that few events that
have resulted in small landslides along the road are not recorded
as they did not cause damage to the road. However, for two events
(i.e., 14 December 1987 and 14 November 2006), a complete
landslide investigation was carried out so that a substantially
complete inventory was prepared.
Method for landslide hazard assessment
The presence of a fairly complete landslide inventory allowed to
estimate the probability of occurrence of landslides (hazard)
along cut slopes by analyzing the total number of landslides
expected per kilometer for different return periods and multiplying this by the probability that the landslides belong to a given
magnitude class.
For the hazard calculation, we assumed that the probability
of landslide occurrence can be calculated directly from the
complete landslide inventory by analyzing the number of
expected landslides per kilometer of the (rail) road for different
triggering events. We have used the Gumbel method for
frequency–magnitude analysis in which the magnitude is represented as the number of landslides per kilometer. The volume of
expected landslides was analyzed separately using the volume–
frequency analysis. Given the limitations in the available data, we
assumed that the volume estimation is independent of the
number of landslides triggered by the event.
Probability of landslide volume
It is recommended in the JTC-1 guidelines (Fell et al. 2008) that
landslide hazard should also include information on the landslide
magnitude. Hungr (1997) argued that in literature, no unique
measure of landslide magnitude is available and proposed to
use landslide damage (destruction) as a measure of landslide
magnitude. Damage caused by a landslide largely depends on
the velocity and impact pressure of the mass, but these
parameters are extremely difficult to obtain and to integrate
Fig. 3 Examples of landslides on cut
slopes along the road (a) and the
railroad (b)
in the hazard zoning. Therefore, researchers have used only
landslide area or volume as a proxy for landslide magnitude (e.
g., Guzzetti et al. 2005). In this study, the availability of data
on volume allows using it as a proxy for landslide magnitude.
Velocity was not considered, as most of the landslides in the
area are fast moving debris slides. For the probability
calculation, we grouped all landslides on cut slopes into three
magnitude classes (i.e., M-I, M-II, and M-III) based on
landslide type, volume, and other characteristics such as scar
depth, run-out distance, and depth of accumulated debris
(Table 4).
In order to estimate the probability of landslide volume,
we first studied the probability density distribution of all
landslides for which data on volume were available. The
probability density p(VL) was computed using the equation
given by Malamud et al. (2004) as:
pðVL Þ ¼
1 dNL
NLT dVL
ð1Þ
where δNL is the number of landslides with volumes between VL
and VL +δNL and NLT is the total number of landslides.
Figure 4 shows the probability density distribution computed
using Eq. 1. The observed probability distribution shows a distinct
“rollover effect” for landslide volumes less than 10 m3. The
rollover may be due to the fact that not all of the very small-scale
landslides, particularly along the road, were reported.
In many studies, the relationship of landslide size and
frequency shows a similar distribution pattern as observed in
Table 2 Main characteristics of the landslide inventory along the railroad and the
road
Attributes
Total number of landslides
Railroad
565
116
Total volume of landslides
3
m
52,500
41,600
Volume of smallest landslide
m3
2
2
3
3600
5250
3
90
360
Volume of largest landslide
Average volume of landslides
nr
Road
m
m
Median volume of landslides
3
m
20
160
Standard deviation of
landslide volume
m3
270
700
Average landslide density
nr/km
33
4
Fig. 4, with a power law for landslides of large sizes and a linear
curve for those whose size is small (e.g., Stark and Hovius 2001;
Guthrie and Evans 2004; Malamud et al. 2004; Brardinoni and
Church 2004; Catani et al. 2005). The studies concluded that the
rollover or flattening of the curve is either a real effect reflecting
slope stability processes (Guthrie and Evans 2004) or it could also
be due to the incompleteness of the inventory (Malamud et al.
2004; Brardinoni and Church 2004; Catani et al. 2005) as smaller
landslide deposits are either removed or are not discernible. Some
authors have fitted various functional relationships to the rollover
(Stark and Hovius 2001; Malamud et al. 2004), while few explain
the distribution with two power law trends having different slope
values (Brardinoni and Church 2004). Brardinoni and Church
(2004) have also shown an increase in the frequency of small
landslides when the photo-interpreted inventory was integrated
by intensive field-based inventory.
In this study, the analysis of the landslide inventory suggests
that the number of landslides and the range of volume vary
according to landslide events. To investigate the frequency–
volume distribution further, we selected landslide inventories
from the railway slip register only because of the availability of
continuous records of landslides, and we analyzed the probability
density distribution for each year separately. Ideally, if a sufficient
number of landslides are available for every landslide event, then
it is better to consider landslide event inventories for such
analysis. However, in this case, the number of landslides triggered
by individual landslide events is not sufficient for plotting the
probability density for each event individually as some events
contain only a few landslides. We therefore considered the total
landslides per year in the analysis. We computed probability
density distribution curves for each year separately using Eq. 1.
Thus, for the 15-year data (1992 to 2007), we obtained 15
curves. Based on the analysis of the obtained curves, three
distinct distribution patterns of the probability density were
observed (Fig. 5): type I pattern, where the probability density
distribution of volumes shows a power law distribution for the
whole data range (e.g., 1993, 1994, 1997, 1998, 1999, 2001, 2002,
2003, and 2007); type II pattern, where the distribution exhibits
distinct rollover for volumes less than 30 m3 (e.g., 1992, 2004,
and 2005); and type III pattern, where distribution shows a
flattening of the curve for volume less than 100 m3 (e.g., 1996,
2000, and 2006).
The three distinct patterns shown in Fig. 5 indicate that
the probability distributions are not the same in all years
though all landslides were triggered by rainfall events and
Landslides 8 & (2011)
283
Original Paper
Table 3 Main characteristics of the multi-temporal landslide inventory
Event year
Date of landslide event (date/month (number of
landslides))
Total landslide (nr)
1987a
Oct (23), Nov (19); Dec (31); 14/12 (43)b
1988a
Mean (m3)
116c
50b
1,050b
238b
Sept (1); Nov (2); Dec (1)
4
–
–
–
a
Feb (1); Mar (1); Jul (1); Sep (3); Oct (1); Nov (22); Dec (2)
31
–
–
–
a
1990
Jan (5); Feb (1); Mar (3); Oct (29); Nov (10)
48
–
–
–
1991a
Jan (6); Apr (1); May (3); July (1); Oct (2); Nov (46)
59
–
–
–
1992
14/11 (7); 15/11 (27); 16/11 (17); 20/11 (3); 21/11 (14)
68
6
900
77
1993
10/11 (6); 11/11 (23)
29
10
5,250
324
1994
11/11 (5)
05
2
3,600
883
1995
No event
–
–
–
–
1996
17/12 (40)
40
2
2,000
120
1997
3/7 (1); 11/7 (1); 16/10 (1); 10/11 (1); 27/11 (16)
20
6
1,944
133
1998
8 /1 (1); 18/8 (1); 27/9 (1); 9/10 (2); 13/10 (1); 9/11 (2);
2/12 (16); 11/12 (5); 12/12 (1)
30
3
240
54
1999
8/2 (1); 14/4 (1); 16/8 (1); 2/9 (1); 8/10 (1); 16/10 (23);
6/11 (1); 23/11 (2); 24/11 (1); 29/11 (2)
34
2
900
53
2000
25/9 (1); 14/11 (1); 15/11 (2); 22/11 (1); 24/11 (19); 1/12
(2); 31/12 (1)
27
2
600
49
2001
1/1 (1); 31/7 (1); 25/10 (1); 26/10 (3); 27/10 (22); 16/11
(4); 17/11 (25); 23/11 (3); 24/11 (1); 25/12 (13); 27/12 (7)
81
2
1,200
53
2002
5/5 (1); 8/10 (19); 2/11 (2); 5/11 (12)
34
4
270
36
2003
20/3 (1); 30/4 (1); 13/5 (1); 29/5 (1); 19/8 (1); 10/11 (1)
06
2
77
17
2004
5/5 (21); 17/10 (1); 20/10 (3); 22/10 (4); 31/10 (14); 2/11
(3); 7/11 (2); 9/11 (6); 13/11 (2)
56
5
200
27
2005
7/10 (4); 22/10 (1); 7/11 (2); 13/11 (3); 25/11 (6)
16
2
100
22
2006
17/10 (4); 18/10 (9); 21/10 (2); 22/10 (5); 2/11 (8); 4/11
(2); 13/11 (1); 14/11 (148)
179
3
5,000
286
2007
8/4 (7); 27/10 (11)
18
2
420
66
Total
901
1989
a
Landslide volume
Max (m3)
Min (m3)
Landslides data taken from the railroad landslide table that only contains information on the number and month of occurrence of landslide (month (nr))
b
Data based on technical report pertaining landslide inventory along the road
c
Out of 116 landslides 78 slides are taken from the railroad landslide table for which volume is not known
Table 4 Landslide volume classes for landslides on cut slopes and the corresponding probabilities based on the analysis of the frequency distributions, separated in two
groups (less than 100 slides per year or more than 100 per year)
Size
class
V, range
(m3)
Sd (m)
RD (m)
M-I
<102
<1
<10
M-II
102–103
<2
M-III
>103
2–8
Ad (m)
P, range (average)
<100 slides/
≥100 slides/
year
year
Potential consequences
1
0.85
0.39
Minor or no damage to road, rail or house;
one can escape unhurt
10–50
<2
0.13
0.53
Damage rail and non-RCC structures; minor or
major injuries; society accept risk
>50
<5
0.02
0.08
Complete damage to rail and buildings; injuries
major or even death in some cases; society
lives and tolerates risk
V landslide source volume, Sd depth of scar, RD run-out distance, Ad depth of accumulated debris, P probability of occurrence based on number of landslides per annum
284
Landslides 8 & (2011)
volumes less than 100 m3 varies from 0.5 to 1 (average=0.85).
For volumes ranging from 100 to 1,000 m3, the probabilities
varied from 0.01 to 0.33 (average 0.13) and for volumes larger
than 1,000 m3 from 0 to 0.16 (average=0.02). For years with
more than 100 landslides, the following probability values were
used: 0.39 for <100 m3, 0.53 for 100 to 1,000 m3, and 0.08 for
>1,000 m3.
Fig. 4 Probability density distribution of landslide volume. The black line is the
fitted power trend to the linear portion of the curve (power scaling exponent is −1.8)
occur on cut slopes along the railroad. Differences are also
observed in the maximum volume of landslides. The maximum
volume in most years lies between 100 and 1,000 m3, except in
2003 and 2005 where it is less than 100 m3. These variations
could be due to changes in the local terrain conditions and the
amount of rainfall, as observed also by Dai and Lee (2001) in a
case study of Hong Kong. The authors showed a dependency
of landslide volume and the intensity of rainfall and indicated
that the number of landslides varies according to the rainfall
intensity. In this study, it is observed that in a year if all
landslides are triggered by a low intensity rainfall, then the
total number of landslides is expected to be few and of small
volume range in comparison to a very high intensity rainfall.
Also the rainfall distribution may vary significantly during one
event, as was also evidenced in a major event that occurred in
2009.
The actual frequency distribution of landslide volumes on
cut slopes thus remains unclear, and it is difficult to design
specific frequency distributions of landslide volume for different triggering events. Due to the large variability in the
frequency of landslide volumes, it is evident that a single
probability density distribution is not enough to explain the
probability of landslide volumes for all triggering events.
Therefore in this study, we calculated the frequency percentage
of landslide volumes in different years, and the average value
was taken for the hazard analysis. We decided to use two sets
of probabilities for years with a threshold of 100 landslides
because the event inventories indicate that if rainfall triggers
less than 100 landslides in a year, then the majority (>55%) has
a volume less than 100 m3. These are the events that occur
more frequently, have lower intensity, and trigger more small
landslides. But for events resulting in more than 100 landslides
(e.g., 14 November 2006), the inventory constitutes of landslides with a volume greater than 100 m3. Such events occur
less frequently, but due to the high rainfall amounts, they can
trigger more landslides with large volumes.
The frequency analysis of landslide volumes in different
years allowed us to conclude that for years with less than 100
landslides, the probability of occurrence of landslides with
Estimation of landslide frequency and return period
The number of landslides varies significantly in different
sections of the railroad and the road. For example, during
the period from 1987 to 2007, a total of 785 landslides have
occurred along the railroad of which the lowest number was
recorded at km 26 (14 landslides) and the highest at km 12 (101
landslides). The number of landslides per year per kilometer
varies from 1 to 25 along the railroad. The maximum number
of landslides in a year was recorded in 2006 (25 landslides at
km 11). The inventory suggests that a frequency of landslides
of less than three landslides per year per kilometer is common
in many sections, but extreme events (e.g., >10 landslides per
year per kilometer) occur less frequently. Thus, it is possible to
use the number of landslides per kilometer per year as an
indication of the magnitude of the triggering events and
calculate the frequency of occurrence based on the complete
landslide inventory.
It is possible to relate the magnitude of extreme events to
their frequency of occurrence through the use of probability
distributions, such as the Gumbel extreme value distribution
(Gumbel 1958). The Gumbel function is frequently used in
hydrological applications to model extreme events. In landslide
studies, it was used to model return periods of landslide events
through the analysis of extreme precipitation and extreme
ground water changes (e.g., Miller 1988; Odorico and Fagherazzi 2003; Frattini et al. 2009). In a study focusing on the
hazard along linear infrastructures such as roads or railroads,
the information on the expected number of landslides that can
occur per unit length in a year and their probability of
occurrence are essential for estimating the risk. Furthermore,
traffic disruption time and expected indirect loss can be
Fig. 5 Three types of pattern observed in the probability density distribution of
landslide volume for different years from 1992 to 2007
Landslides 8 & (2011)
285
Original Paper
modeled if information on the number of landslides per unit
length is available. The cumulative probability distribution of
the Gumbel extreme model can be applied in a landslide study
to model the probability of occurrence of the number of
landslides (NL) equal to or less than some value n. The model
can be expressed as:
PðNL nÞ ¼ ee
ðaþnÞ=c
ð2Þ
where α and c are two parameters of the Gumbel distribution. By
the method of moments, the parameters are evaluated as (e.g.,
Chow et al. 1988):
a ¼ gc ð3Þ
pffiffiffi
6
c¼
p
ð4Þ
where γ=0.57721 is a Euler’s constant, μ is the mean, and σ is
the standard deviation. For a specified time interval in a year,
Eq. 2 can be rewritten for the value (NL) equal to or greater than
some value n as:
PðNL nÞ ¼
1
ðaþnÞ=c
¼ 1 ee
T
ð5Þ
Two methods are commonly used for fitting distributions to
the Gumbel model and using them for frequency analysis: the
plotting position method and the frequency factor method. The
former is a straightforward plotting technique to obtain the
distribution function by use of certain “plotting position”
formulas (Chow et al. 1988). The technique is to arrange the data
in increasing or decreasing order of magnitude and to assign
order number R to the ranked values. The Weibull formula is
commonly used to obtain the plotting position, which for P(NL ≥n)
can be expressed as:
P¼
R
mþ1
ð6Þ
where R is the rank and m is the total number of observations.
When R is ranked from lowest to highest, P is an estimate of P
(NL ≤n); when the rank is from highest to lowest, P is P(NL ≥n).
Equation 6 can be plotted on a probability paper to represent the
cumulative probability distribution. The graph is designed in such
a way that it directly gives the return period and the corresponding magnitude of an event. The probability paper tends to
linearize the distribution so that the plotted data can be easily
analyzed for extrapolation or comparison purposes. When using
the graphical method, it is recommended not to extrapolate the
data more than two times the period of record; otherwise, the
uncertainty in prediction will be high (Viessman et al. 1989).
To obtain the probability or return period of a certain
number of landslides per unit length (kilometer) along the
transportation corridor exceeding a given value, the yearly total
number of landslides from cut slopes was selected for each
kilometer section for the period 1987 to 2007. The total number of
observations (m) is 21 years. The values were ranked from low to
286
Landslides 8 & (2011)
high, such that the lowest rank (1) was assigned to the lowest data
value and the highest rank (21) to the highest data value. Using
the plotting position method, the data were plotted on a
probability paper and a curve was fitted to the plotted points.
An example of the result of the Gumbel plot for km 14 of the
railroad is shown in Fig. 6. In km 14, the maximum number of
landslides triggered in a year was recorded in 2001 (11), and years
with no landslide were 1988, 1995, and 1997. The fitted straight line
in the figure can be extrapolated to make an estimate of the
number of landslides in different return periods. As an example,
for km 14, the figure gives nine landslides for a 15-year return
period and 12 landslides for a 50-year return period.
Along the railroad, the Gumbel analysis was carried out for
each of the 17-km sections producing 17 Gumbel plots. Along the
road, data on the occurrence of landslides were not available for
every kilometer, and therefore, the Gumbel analysis was performed on two larger sections: a section with a length of 10 km (SI, from km 390 to 400) and a section of 14 km length (S-II, from
km 400 to 414). The two sections were selected on the basis of the
difference in geomorphological setting and the density of landslide scars, which is the percentage of length of the road covered
by landslide scars. In section S-I, the average density is 14 km−1,
which is about three times higher than in section S-II. The average
landslide density for the entire road is about 9 km−1. The location
of landslides and kilometer marks along the railroad and the road
are shown in Fig. 1. For each section of the railroad and the road,
the expected number of landslides with T1, T3, T5, T15, T25, and T50
year return periods was estimated.
Figure 7 shows results of the Gumbel analysis for each
kilometer length along the railroad represented as a smooth line
graph. Results for the two sections along the road are given in
Table 5. Results indicate that one or more landslides can occur on
average once in three or more years. A 4-km stretch of the
railroad (from km 10 to 13) is relatively more prone to landslides,
as is the S-I section (from km 390 to 400) along the road. Total 56,
84, 140, 164, and 197 landslides are expected to occur along the
entire railroad, and about 14, 28, 55, 66, and 82 landslides are
expected along the entire road in T3, T5, T15, T25, and T50 year
return period, respectively.
The results indicate that the maximum number of landslides
per kilometer is expected toward the east of Burliyar. This section
of the transportation lines also has the maximum probability of
experiencing rainfall events that can trigger one or more landslides (Jaiswal and van Westen 2009).
Landslide hazard assessment
The specific hazard, expressed as the probability of a given
number of landslides with a particular volume to occur along a
specific section of the road or railroad, was obtained by multiplying the probability of having a certain number of landslides
per unit length resulting from the Gumbel analysis, with the
results of the volume–frequency analysis explained earlier. In
total, 18 specific hazard scenarios were generated using combinations of the three volume classes (M-I, M-II, and M-III) and six
return periods (T1, T3, T5, T15, T25, and T50 year).
The results of three hazard scenarios, related to the three
landslide volume classes defined in Table 4, for T50 year return
period are given in Table 6 (for the railroad) and Table 7 (for the
road). The hazard categories H-I, H-II, and H-III show the
Fig. 6 Gumbel plot for km 14 of the
railroad used to establish relationship
between total number of landslides
and return period
number of landslides of volume classes M-I, M-II, and M-III,
respectively, that can occur per kilometer of cut slopes in a 50year return period. The analyses show that on average once in
50 years (annual probability of 0.02), the entire railroad will be
affected by 76.7, 104.3, and 16 landslides and the road by 32, 43.4,
and 6.6 landslides of H-I, H-II, and H-III hazard, respectively.
Validation of landslide hazard model
Validation of results of a predictive model is absolutely essential in
order to make the model applicable for practical purposes (Chung
and Fabbri 2003). Validation should be performed using landslide
data that are independent from those used in the developing the
model. The most suitable validation dataset is the one that includes
landslides that occurred after the period considered in the modeling.
In our case, we were able to use the landslide inventory from 2009 for
the validation of the hazard model.
Fig. 7 Total number of landslides in different return periods (T1 to T50 years)
along the railroad obtained using a Gumbel distribution
Between 8 and 10 November 2009, an intense rainfall event
triggered 136 landslides along the road and the railroad within the
study area. The 3-day cumulative rainfall was recorded as 202 mm at
Kallar Farm, 387 mm at Burliyar, 560 mm at Hillgrove, 865 mm at
Katteri farm, and 937 mm at Coonoor. The amount of rainfall shows
a dramatic decrease from West (Coonoor) to East (Kallar Farm) of
the study area. A landslide inventory was prepared from the railway
slip register and landslide technical reports. Field mapping was
carried out in February 2010 to spatially map the recorded landslides.
Figure 8 shows the spatial distribution of landslides (which are all
shallow debris slides) on cut slopes (represented as points), and
Table 8 gives the statistics of these landslides. Out of the total
136 slides, 71 occurred on cut slopes along the railroad and 65
along the road. About 80 are reactivated old landslides and 56
occurred for the first time. Prior to November 2009, seven
more landslides have occurred along the railroad, which makes
a total of 143 landslides in 2009.
The availability of data on landslides of the 2009 event
provided an opportunity to compare the probability of landslide volume used in the hazard modeling. For cut slopes, the
probability of occurrence of the landslides in 2009 was found
to be 0.61 for <100 m3, 0.35 for 100 to 1,000 m3, and 0.04 for
>1,000 m3, which is different from the values used in the
hazard model on cut slopes for years with more than 100
landslides (0.39 for <100 m3, 0.53 for 100 to 1,000 m3, and 0.08
for >1,000 m3). The occurrence of a higher proportion of small
landslides (<100 m3) in this event is probably due to the fact
that most landslides occurred between Marapallam and Katteri,
which has rather gentle slopes covered by tea plantation. The
difference in values is mainly due to the decreasing trend in
rainfall from west to east, whereas in the eastern sections of
the road and railroad, larger volume landslides are expected to
occur based on the 21-year record. The percentage of landslides
Landslides 8 & (2011)
287
Original Paper
Table 5 Number of landslides in different return periods (T1 to T50) along the road
Road section
Number of landslides per kilometer
T3
T1
T15
T25
T50
S-I (total length 10 km)
0
1.01
2.04
4.08
4.93
6.12
S-II (total length 14 km)
0
0.29
0.53
1.00
1.20
1.48
of larger volume would have been more if the same amount of
rainfall that triggered landslides between the Marapallam and
Katteri section would have affected the area east of Burliyar
where slopes are steep and cut slopes are high.
The validation of a hazard model for different temporal
scenarios is difficult because for the single year of 2009, it is
not possible to have information on landslide events with
different return periods. Using the landslide event of 2009, we
attempted to validate the results of the Gumbel analysis (the
number of landslides per kilometer of cut slopes in different
return periods). Figure 9 compares the results of the Gumbel
analysis (Fig. 7) with the occurrence of landslides per kilometer
along the railroad in 2009. The figure indicates that for the
sections of the railroad of km 17, 19, and 20, the number of
landslides that occurred in 2009 corresponds to a return
period of 25 or more years. The amount of rainfall that
triggered landslides in this section of the railroad (i.e., 865 mm
around Katteri farm) has never occurred in the period
Table 6 Landslide hazard along the railway line in T50 year return period
Kilometer mark
Number of landslides per kilometer
H-I
H-II
H-III
10
8.2
11.2
1.7
11
10.1
13.7
2.1
12
8.9
12.2
1.8
13
7.1
9.7
1.5
14
4.7
6.5
1.0
15
4.8
6.5
1.0
16
4.1
5.6
0.9
17
3.8
5.2
0.8
18
3.3
4.5
0.7
19
2.7
3.7
0.6
20
2.6
3.5
0.5
21
2.8
3.7
0.6
22
3.4
4.6
0.7
23
2.7
3.7
0.6
24
2.8
3.7
0.6
25
2.5
3.4
0.5
26
2.2
2.9
0.4
76.7
104.3
Total
16
H-I, H-II, and H-III are the hazard related to landslide of M-I, M-II, and M-III, indicated in
Table 4, respectively
288
T5
Landslides 8 & (2011)
considered in the modeling (i.e., 1987 to 2007) but was known
to have occurred in 1979 resulting in floods and triggering
many landslides around the Coonoor area (Seshagiri and
Badrinarayanan 1982).
Figure 9 also shows that in most of the other railroad
sections (between km 10 and 17 and in the sections from
km 20 to 26), the number of landslides that occurred in 2009
corresponds to a much lower return period (5 years or less;
Fig. 9). Although the sections from km 10 to 17 are more prone
to landslides as indicated in Fig. 7, the amount of rainfall that
triggered landslides in these sections in 2009 was comparatively less than those recorded around km 19. In fact the
rainfall decreased sharply from Coonoor toward the east. The
amount of rainfall recorded in Burliyar on 10 November 2009
was 192 mm. Rainfall events with this amount have actually
occurred three times in the past 21-year period (1987 to 2007),
which corresponds to a recurrence interval of 7 years.
A total 78 landslides occurred along the entire railroad in
2009, which is comparable to the result of the Gumbel analysis
for a 5-year return period (i.e., 84 landslides). It is reasonable
to expect here that if the entire area would have received the
amount of rainfall that triggered landslides around Katteri
farm (corresponding to a landslide event with a 25-year return
period), then the total number of landslides would have been
much higher (around 160) as predicted by the Gumbel analysis
for a scenario with a 25-year return period. In fact, only a
small section actually received a very high rainfall in 2009 (i.e.,
around Katteri farm), and since in most sections the rainfall
was less therefore, as expected, the total number of landslides
was also less along the entire railroad.
Along the road, the occurrence of 65 landslides is
comparable with the model output for a 25-year return period
(66 landslides). The occurrence of more landslides between
km 390 and 414 (S-II) triggered by extremely high rainfall
corresponds to an event similar to the one from 1979.
Discussion
Given the completeness of the historical landslide information
especially along the railroad for the past 21 years, we were able to
apply a landslide hazard assessment method that is based only on
the historical information, without including statistical analysis of
possible contributing factors or carrying out physically based
modeling. Such conventional hazard assessment methods have
proven to be difficult to predict spatiotemporal occurrence of
landslides with different magnitudes. Most of the landslide hazard
models presented in the literature are actually susceptibility
models providing an estimate of “where” landslides are expected,
rather than giving also temporal and size probability (Brabb 1984;
Guzzetti et al. 2005). The proposed method allowed us to
determine quantitative landslide hazard along a road and a
Table 7 Landslide hazard along the road in T50 year return period
Road section
Table 8 Statistics of landslides triggered between 8 and 10 November 2009
Number of landslides per kilometer
H-I
H-II
H-III
S-I (total length 10 km)
2.39
3.24
0.49
S-II (total length 14 km)
0.58
0.78
0.12
H-I, H-II, and H-III are the hazard related to landslide of M-I, M-II, and M-III, respectively
railroad estimated directly from the frequency of past slope
failures. In fact, landslide hazard assessment approaches using
frequency of past landslides not necessarily require prior landslide susceptibility analysis if a complete inventory is available
(Corominas and Moya 2008). In such cases, the probability of
occurrence of landslides in a given area can be estimated directly
from the past landslides, especially when we are interested to
know the hazard over particular sections of the (rail) road and
not for every location individually.
According to the definitions given by Varnes (1984), the
landslide hazard should provide information on the probability of
occurrence of landslides and their magnitudes in a given area.
Along a transportation line such as a road or railroad, this
definition of hazard is more appropriate if it includes information
on the probability of occurrence of a certain number of landslides
of a given size over a given unit length (e.g., per kilometer
section). The number of landslides is required if direct risk to
road, vehicles, commuters, and the indirect risk due to traffic
disruption is to be analyzed. The number of landslides per unit
length is an important input required to estimate the probability
of a landslide hitting a vehicle (AGS, Australian Geomechanics
Society and Sub-committee on Landslide Risk Management 2000;
Wilson et al. 2005), and if it is multiplied by the probability that
the landslide will be of a given volume, then the total volume of
landslide deposits expected and the time required to remove the
debris can be estimated.
All the information on landslides and rainfall were obtained
from historical records, which were fairly well maintained in this
part of India. The availability of continuous landslide records
along the railroad allowed to make a substantially complete
landslide inventory, at least for the available time period. Never-
Attributes
Total landslides on cut slopes
nr
136
m
3
2
m
3
2,500
Average volume of landslides
m
3
170
Median volume of landslides
m3
50
3
320
Volume of smallest landslide
Volume of largest landslide
Standard deviation of landslide volume
m
theless, the use of information from historical records is
complicated and prone to errors. Errors may arise from many
sources, such as the difficulty in translating maintenance records
into actual landslide events as different sources record landslides
with different perspectives, which may not be directly related to
landslide studies. Other sources of error are related to the
difficulty in recognizing past landslides in the field and the
method used in plotting landslides onto topographic base map.
In historical records, if the description of a landslide contains
all the necessary information such as landslide occurrence date,
type, location, and size and accompanied by photographs or
maps, then the identification of the landslide is easy and mapping
is subjected to very little error. Some technical records contained
detailed description of the landslides along with illustrations. For
a small landslide where all its material is accumulated on the
railroad, the recorded volume is a good indication of the landslide
size, and therefore, identification of the landslide and its size is
subjected to minimum error. Where data on volumes are
incomplete then other indications were used with a higher
uncertainty. In case of the unavailability of data on volume (e.g.,
from the railroad landslide table), the record was only used for
obtaining the number of landslides per section of the (rail) road
and not for spatially mapping of landslide scars.
In landslide modeling, the Gumbel model has been used to
analyze the frequency of extreme events in spite of certain
limitations and assumptions, of landslides being stochastic
processes and random distributed independent events, as dis-
Fig. 8 Location of landslides (black circles) along the road and the railroad triggered between 8 and 10 November 2009
Landslides 8 & (2011)
289
Original Paper
The results of the hazard assessment are also prone to
uncertainty due to the non-cyclical nature of landslide events.
Slopes that are subjected to landslides once might behave
differently under similar rainfall conditions. Also human intervention along the road in the form of the construction of
retaining walls and drainage works will have an influence. The
rainfall event with a 50-year return period (i.e., as characterized
by the number of landslides per kilometer caused by this event)
may cause less landslides when it happens later again.
Fig. 9 Comparison of model output of the Gumbel analysis and landslides that
occurred in 2009 for different sections of the railroad
cussed by Miller (1988) and Odorico and Fagherazzi (2003). These
assumptions should be considered when interpreting and using
the results of the probability model. In this study, the use of
Gumbel model was found suitable for estimating the frequency of
slope failures that are small in size and occur repeatedly from cut
slopes at a number of locations. The Gumbel model was applied
to the available time series data and predicts the expected
frequency of occurrence of extreme events, also with return
periods that are larger than the time period used for the input
data. It is important to realize that these extensions are only as
valid as the data used and uncertainty could be high if
extrapolation is done more than twice the length of the available
time series. The Gumbel analysis is sensitive to outliers, and if the
inventory contains recorded events related to much larger return
periods, one should be careful with analyzing the results. In this
study, we estimated probabilities only up to a 50-year return
period, which is slightly more than twice the 21-year period for
which data were available. The records also indicate that landslides occur frequently in the area, and therefore, it was prudent
here to consider scenarios based on the lower return period, i.e., 1,
3, and 5 years.
The result of the Gumbel analysis is also prone to uncertainty. The degree of uncertainty can be low if the inventory
contains records for all landslides such as those available for the
railroad. The availability of a substantially complete inventory
along the railroad provided a better assessment of the frequency
of slope failures. Along the road, we were not able to perform a
Gumbel analysis for every kilometer section because of the
incompleteness of the landslide records. However, the results of
the analysis along the road can be acceptable because the
inventory at least contained the large and damaging landslides.
The validation with the data from 2009 illustrated that the
high spatial variability of the rainfall along the (rail) road can
induce uncertainty in the result of the hazard analysis. High
rainfall amounts occurring on certain sections of the road and the
railroad may trigger more landslides locally, similar to the one
observed in 2009. The result is then that the same triggering
events will be related to different return periods, due to the spatial
variation of triggering rainfall. However, over a longer period of
time, rainfall amounts do not vary significantly in the study area.
The average rate of occurrence of extreme events might be
increased due to climate change; however, this has not been
proven yet for the area based on historical rainfall records.
290
Landslides 8 & (2011)
Conclusions
In our earlier work, we estimated the temporal probability of
occurrence of one or more rainfall events that can trigger
landslides along the transportation lines (Jaiswal and van
Westen 2009), based on rainfall thresholds for the same area.
However, such an analysis does not provide information on
the frequency of landslides in different sections of the road
and the railroad. The method presented here does allow us to
make an estimation of the expected number of landslides per
unit length and return period.
The proposed hazard model can also forecast landslides
directly for the entire route instead of per kilometer section.
However, we recommend carrying out hazard assessment
individually for different sections since rainfall amounts can
vary locally to the extent that the same rainfall event can
represent a 30-year event in the west and only a 7-year event
in the east, as observed in 2009.
The inclusion of the proposed landslide volume classes in
the hazard assessment will help in assessing landslide risk
along the road and the railroad. Ideally, landslide magnitude
should be quantified based on absolute values of landslide
velocity, intensity, peak discharge, etc. but such parameters are
very site specific and vary with local condition such as channel
geometry, terrain roughness, and land use and are, therefore,
difficult to obtain and integrate in the hazard analysis. Due to
this limitation and the complexity of landslide phenomena, the
proposed magnitude classification is considered the best
solution for this study.
This study aimed to quantify landslide hazard along a
transportation corridor where landslides are small in size but
occur frequently. Hazard assessment including small-sized
landslides is important if these occur along roads and
railroads. One could argue that small slides are actually local
slips which may not be of much significance. This could be
true if such slides are on natural slopes, but along a road or a
railroad, even small slips can be disastrous such as resulting in
a road accident or train derailment. Small landslides, in fact,
constitute the largest part of landslide inventories, particularly
if inventories contain cut slope failures as well. For example,
out of the 2,811 landslides reported in the Geotechnical
Engineering Office database of Hong Kong for the period from
1992 to 1997, about 90% were of volume less than 50 m3 (Dai
and Lee 2001).
Temporal prediction of few small-sized slides in a given
area is difficult, but in the Gumbel extreme value method, it is
possible to estimate the probability of occurrence and return
period of the larger (extreme) events based on past records.
This information will help to manage landslide risk more
rationally.
The study of landslide volume–frequency distribution showed
large variation both in the number of landslides triggered in a year as
well as the range of volumes. It was observed that these changes are
related to the amount of rainfall, and therefore, the estimation of the
probability of landslide size based on the frequency percentage
related to different magnitudes of rainfall events can be a workable
solution.
The hazard model performed reasonably well in forecasting
landslides, as evidenced from the validation result. However, we
need to validate the model with landslide events of different return
periods, and for this, we have to test it for events that will happen in
the near future to improve the model.
Acknowledgments
We acknowledge the help of Southern Railway, Geo-technical Cell
and Tea Estates of Coonoor, Tamil Nadu, India for the relevant
data and support. The research was carried out under the United
Nations University—ITC School on Disaster Geo-Information
Management (www.itc.nl/unu/dgim).
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P. Jaiswal ())
Geological Survey of India,
Bandlaguda, Hyderabad, Andhra Pradesh, India
e-mail: jaiswal@itc.nl
P. Jaiswal : C. J. van Westen : V. Jetten
ITC,
University of Twente,
Hengelosestraat 99, 7500 AA, Enschede, The Netherlands
Landslides 8 & (2011)
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