the influence of reservoir filling on a preexisting bank...

Indian Journal of Geo Marine Sciences

Vol. 47 (02), February 2018, pp. 291-300

The influence of reservoir filling on a preexisting bank landslide stability

Danqing Song1,2

, Shouyun Liang1*

& Zhiqiang Wang1

1School of Civil Engineering and Mechanics, Lanzhou University, Lanzhou, 730000, China 2School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiaotong University, Shanghai, 200030, China

*[E-mail: [email protected]]

Received 15 January 2016 ; revised 29 March 2016

A new monitoring instrument (automatic GPS) was used to understand the relationship between the stability of landslide

and the hydrological triggering factors in the process of reservoir filling. These factors were drawn from a study that

reported on the relationship between surface movement and hydrological triggers of a typical reservoir landslide (Yanziping

landslide) in Jiudian Gorge Reservoir (JGR). A Fast Moving Zone (FMZ) can be spatially identified from the Main

Deformation Zone (MDZ), and the temporal evolution of the landslide consists of a progression in time with short periods of

Fast Movement (FM) and longer periods of slower movement. The results indicate that three FMs could be identified, which

are triggered by different factors. The continuous rapid water rise will definitely trigger FMs with the rainfall being the

secondary role and the longer duration of rapid water rise is not conducive to the stability of landslide in the phase of 70～100 m. Rapid drawdown of reservoir water level also triggers FMs from 100 to 130 m. Besides, there is a lag time of

approximately 5 days between FMs and rapid water rise. A most dangerous water level for the landslide movement also can

be identified approximately 80 m. The sliding mode was changed by reservoir water storage. Near-real-time monitoring by

GPS can provide more reliable and timely data, which is significantly important for disaster prevention and mitigation.

[Keywords: Landslide, Movement, Hydrological triggers, Monitoring, Jiudian Gorge Reservoir]

Introduction

Reservoir slope instability is a major geological

disaster induced by reservoir filling and generally

believed to be triggered by adverse changes of the

hydrodynamic condition in a slope1. Reservoir

landslides directly lead to economic losses, casualties

and dam break2. The Vajont landslide that occurred in

1963 in Italy was a reservoir landslide of approximate

240 million m3 of material killing more than 2,000

people due to reservoir filling3. Therefore, special

attention should be paid on the relationship between

the reservoir landslide stability and reservoir water

fluctuation in the process of reservoir filling4.

Stability and hydrological factors contributing

landslides were continuously reported in many papers.

Reservoir water levels fluctuation and intense rainfall

are the two main triggers of landslides in western

China5. In particular, the rise of water level is a main

triggering factor in the process of reservoir filling6.

The model test method7, the inversion analysis

method8, and detailed field investigation

9 were

usually used to study the formation mechanism of

landslide under the rise of water level. Numerical

simulation methods were also used to research on this

issue, such as the three-dimensional numerical

simulation model10

, FEM method and equilibrium

analysis11

, FLAC 3D12

and PLAXIS13

. Moreover, the

data of a monitoring system obtained from

inclinometers was used to interpret how the

hydrodynamic condition changes and relates to

landslide reactivation14, 15

. However, the former

INDIAN J. MAR. SCI., VOL. 47, NO. 02, FEBRUARY 2018

methods have a few disadvantages such as the

monitoring instruments were not timely, accurate and

easy operation, and the numerical simulation methods

need more accurate parameters, calculation and

boundary conditions. Moreover, the model tests can

not fully reflect internal changes of the actual

situation and lack detailed field investigation.

After the completion of the JGR, the water level

increased from 30 to 130 m with extensive reservoir

landslides triggered by the rise of water level.

Deformation from preexisting landslides carried on

occurring due to considerable rapid rise of reservoir

water level. Professional Monitoring by using global

positioning system (GPS) was used to detect the

landslides16

. Among these monitoring data, surface

displacements from GPS have been widely used to

identify patterns of movement and also understand the

relationship between movement and hydrological

triggers17

. In the past, the use of surface displacement

data is usually considered to be the simplest way to

observe the history of movement, analyse the

kinematics of the movement and assess the behavior

of a landslide, the response to the triggering

conditions or the efficiency of corrective measures.

The basic approach is to classify movement patterns

according to cumulative displacements and

velocities18

. To achieve more accurate, reliable, and

timely data compared to the other monitoring system,

near real-time monitoring by using automatic GPS has

been used around the world to forecast and detect

landslide activity19

. Near-real-time monitoring means

the observations are delayed slightly but still represent

the current field conditions timely20

. Therefore,

measurement of surface displacements through the

use of GPS has become the most important means of

tracking the behavior of landslides, and the automatic

GPS has become more reliable, cheaper, faster, and

easier to use compared to other monitoring

instruments.

Materials and Methods

A high-precision monitoring instrument (automatic

GPS) was used to analyze to identify spatial and

temporal patterns of surface displacement, and to link

the periods of movement to hydrological factors for

detailed understanding of the hydrological triggering

conditions in the process of reservoir filling.

Additionally, the sliding characteristics of Yanziping

landslide was identified as well, such as the change of

sliding mode. Fortunately, more than 3 years of near-

real-time monitoring data of the active Yanziping

landslide in JGR have been collected, which provide

the basis for further research.

Results and Discussion

Yanziping Landslide

The Yanziping landslide is a typical reservoir

landslide located in JGR, south of Gansu Province in

West China, on the right bank of the Tao River

approximately 960m from Jiudian Gorge Dam

[Figs.1(a and b)]. It extends weatwards from the head

of Yanziping and downslope to the toe area on the

bank of the Tao River, with an altitude of 2095-2360

m [Figs.1(c and d)]. The landslide is approximately

400 m wide in the north–south direction and 640 m in

length in the east–west direction with the entire

volume being approximately 8.94×106 m

3. The

monitoring results show that the south to middle part

of the landslide is the main deformation zone (MDZ),

with the area accounting for 80 percent of the entire

landslide. The rainy season is from July to September

in JGR with the average annual rainfall and daily

maximum precipitation being 588.2 mm and 61.25

mm respectively.

200 meters 150 metersc d

Fig.1—Location and overview of Yanziping landslide in

Jiudianxia Reservoir: (a) location of study area; (b) location of the

landslide; (c) Satellite land image; (d) overview photo

Fig.2—Schematic subsurface stratigraphy of the landslide

292

SONG et al.: MONITORING OF LANDSLIDE DEFORMATION

The materials found in Yanziping landslide area

are shown in Fig.2: the materials that is composed of

landslide originate from the collapse of high and steep

rock slope, loose debris composed of talus pluvial and

terrace materials. The sliding body is much looser and

30-73 m thick, which is made up of limestone stone,

gravel and loess soils. Gravel and clay interlayer own

zonality on the longitudinal axis. The upper layer is

mainly composed of boulders, the middle layer is a

mixture of stone and soil, and the lower layer is

mainly composed of loess soils containing stone,

gravel and sand gravel. The landslide leading edge

ownsⅠerosion accumulation terrace approximately

100 m wide. Yanziping landslide was reactivated in

June 9, 1999 owing to the rainfall with the volume of

the landslide approximately 5.8 × 106 m

3 and in June

5, 2008 due to reservoir filling of the water level from

approximately 30 to 70 m. The normal storage level

was approximately 130 m while the initial water level

was around 70 m, and the landslide triggered local

buckling many times during this period, repeatedly

damaging county road that traverses the middle of the

slide and continuously threatens the safety of lots of

infrastructure and people. It is particularly risky

during the overlapping time period of the water level

rise and the heavy rainy season, which usually occurs

between July and September.

103°50'1.49"103°50'17.80"

103°50'17.80"

34°5

5'1

.49"

34°5

4'5

6.8

5"

34°5

4'5

6.8

5"

34°5

5'1

.49"

103°50'1.49"

FMZ

MDZ

Landslide Boundary

Fig.3—Simplified geological map and monitoring network of the

Yanziping landslide: the plannar velocity fields of surface

movement in FMZ is more than 0.95mm/day

Yanziping Landslide Monitoring

The landslide monitoring was restored in October

2006, which was monitored by using the Polaris 9600

GPS. Fig.3 shows the field near-real-time continuous

GPS (ncGPS) monitoring network including nineteen

monitoring stations. Obviously, ten measurement

stations (G1-G3, G5, G6, G9, G13-G16) lied in the

MDZ. In addition, the measurement stations have

higher measurement accuracy with 10.0 mm ± 2.0

ppm in altimetry and 5 mm ± 1.0 ppm in planimetry,

and the collection frequency of the raw data can reach

1.0 Hz. Moreover, because the distribution of satellite

used by observation had a great influence on the GPS

positioning accuracy, the distribution of satellite and

the relationship between PDOP and time were

compiled as shown in Fig.4. The smaller PDOP value

represents the space geometry intensity factor of the

satellite distribution, and the smaller PDOP value

shows the more higher positioning accuracy.

Therefore, the best observation period is 7:00-13:00

based on the detailed observed data (Fig.4b). The

monitoring of hydrological triggering factors included

measuring water levels fluctuation.

Patterns of Movement

Fully understanding the patterns of landslide

movements is a crucial first step to studying the

relationships between deformation and hydrological

triggering factors21

. The ncGPS monitoring data in

process of reservoir filling are precious information

for the explaination of surface movement patterns

spatially and temporally for the landslide. The

reservoir filling process can be divided into three

phases that the first phase (0-70 m), the second phase

(70-100 m), and the third phase (100-130 m) (Fig.5).

0

1

2

3

4

5

6

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

PD

OP

va

lue

Time（h）

(b)

Best observation period

0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Th

e n

um

ber o

f sa

tell

ite

Time (h)

(a)

Fig.4—(a) The number of satellite distribution; (b) the

relationship between PDOP and time

293


Table 1—Summary of Displacements Recorded by Near-Real-Time GPS on the landslide

GPS

station

Cumulative displacement

(mm)

Average velocity

(mm/day)

Planar

azimuth

(°)

Monitoring period

Planar Vertical Planar Vertical Start End

G17 226.9 -128.0 0.20 -0.11 321.5 2006-12-1 2009-12-31

G18 201.7 -95.0 0.18 -0.09 46.2 2006-12-1 2009-12-31

G19 228.0 -136.0 0.21 -0.12 39.7 2006-12-1 2009-12-31

G1 1058.7 -875.3 0.96 -0.80 18.3 2006-12-1 2009-12-31

G7 888.5 -365.8 0.78 -0.33 22.1 2006-12-1 2009-12-31

G2 2854.9 -887.5 2.56 -0.81 23.0 2006-12-1 2009-12-31

G3 3760.3 -1911.0 3.42 -1.74 10.5 2006-12-1 2009-12-31

G5 3738.3 -1073.0 3.40 -0.98 16.7 2006-12-1 2009-12-31

G6 3941.8 -1350.0 3.58 -1.23 354.1 2006-12-1 2009-12-31

G9 3046.3 -998.0 2.77 -0.91 8.3 2006-12-1 2009-12-31

G13 612.3 -471.0 0.43 -0.43 14.6 2006-12-1 2009-12-31

G14 654.5 -452.5 0.61 -0.41 353.5 2006-12-1 2009-8-1

G15 647.3 -502.3 0.59 -0.47 7.5 2006-12-1 2009-7-21

G16 701.5 -528.0 0.64 -0.48 347.0 2006-12-1 2009-10-1

0

500

1000

1500

2000

2500

3000

3500

4000

Cu

mu

lati

ve p

lan

nar d

isp

lcaem

en

ts (

mm

)

G1 G5 G2

G3 G6 G9

FM2

FM3FM1

The first phase

The second phase

The third phase

(a)

-2000

-1800

-1600

-1400

-1200

-1000

-800

-600

-400

-200

0

Cu

mu

lati

ve p

lan

na

r d

isp

lca

em

en

ts (

mm

)

G1 G2 G3

G5 G6 G9

FM1

FM2 FM3

The first phase

The second phaseThe third phase

(b)

0

5

10

15

20

25

30

Pla

nn

ar d

isp

lacem

en

t velo

cit

ies

(mm

/day

)

G1 G5 G2 G3 G6 G9

FM1 FM2

FM3

The first phase

The second phase

The third phase

(c)

-0.5

0.5

1.5

2.5

3.5

4.5

5.5

Pla

nn

ar d

isp

lacem

en

t velo

cit

ies

(mm

/day

) G1 G2 G3G5 G6 G9

FM 2

FM 1

FM 3

The third phase

The second phase

The first phase

(d)

0

10

20

30

40

50

60

70

80

90

100

110

120

130

Rese

rv

oir

wa

ter l

ev

el

(m)

FM3

FM2

FM1

(e)

The first phase

The second phase

The third phase

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

Ra

te o

f w

ate

r r

ise (

m/d

ay

)

FM3FM2

FM1

The first phase

The second phase

The third phase

(f)

0

20

40

60

80

100

120

140

Ra

infa

ll (m

m)

(g)

FM3

FM2FM1

The first phase

The second phase

The third phase

Fig.5—Time series curves of the landslide between December 01,

2006 and December 31, 2009: (a) Cumulative planar

displacements; (b) Planar displacement velocities; (c) Cumulative

vertical displacements; (d) Vertical displacement velocities; (e)

Rate of water storage; (f) Reservoir water level

Spatial Patterns of Landslide Movements

Spatial visualization of movement patterns can be

provided by landslide surface displacement based on

differences in surface deformation. Due to the fact

294


that five GPS measurement stations (G4, G8, and

G10-12) were damaged by the landslide movement,

the monitoring data from other measurement stations

were reliable. Table 1 lists the summary of the

displacements of fourteen GPS measurements. The

results indicate that ten GPS stations (G1-G3, G5-6,

G9 and G13-16) in the MDZ had much larger

cumulative displacements than the others (G17-G19)

based on the average velocity at the same time.

However, G7 was taken control measures in 2009 as

it threatened an office building, therefore, it excluded

from MDZ. Additionally, there are obvious

differences in planar azimuth, only the deviation

angle value of planar azimuth (G17-19) was more

than 35°, which means that significant movement

only occurs in MDZ.

The average velocities and planar azimuths taken

from the fourteen GPS measurement stations are

shown in Fig.3. FMZ can be identified in Table.1,

including six measurement stations (G1–G3, G5, G6

and G9) which average planar and vertical velocities

were more than 0.96 and -0.81 mm/day respectively

between December 2006 and December 2009. In

addition, the velocity fields of the surface movement

were illustrated in Fig.3. The results suggested that

there is a FMZ within the MDZ which extends from

the top head (G2) through the part (G1, G3, G5 and

G6) to the middle area (G9). Obviously, the primary

slip direction was the southwest direction. More0ver,

the northwest and toe area of MDZ had less

deformation. Therefore, only the patterns of

movement in the FMZ are analyzed here. The middle

part began to occur fast movement firstly on the left

side of landslide with large deformation, and extended

to the right in MDZ gradually on the transverse

section.

0

100

200

300

400

500

600

700

800

900

1000

Cu

mu

lati

ve p

lan

na

r d

isp

lacem

en

ts(m

m) G1 G2 G3

G5 G6 G7

G8 G9

（a）

0

10

20

30

40

50

60

70

80

90

100

Cu

mu

lati

ve p

lan

nar d

isp

lacem

en

ts(m

m)

G4 G17

G18 G19

（b）

0

5

10

15

20

25

30

35

Cu

mu

lati

ve p

lan

nar d

isp

lacem

en

ts(m

m) G10 G11

G12 G13

G14 G15

G16

（c）

Fig.6—Time series curves of displacement between December 01,

2006 and December 31, 2007: (a) Cumulative planar

displacements of the middle part; (b) Cumulative planar

displacements of leading edge; (c) Cumulative planar

displacements of trailing edge

Figs.3 and 5 show that FMZ began to occurred

large displacement with the leading edge of landslide

occurred deformation subsequently, and the trailing

edge was pulled by MDZ on the longitudinal profile

with the sliding model being retrogressive landslide.

Therefore, the sliding feature had a typical

characteristics that showed blocked and partitioned

deformation. What’s more, the sliding model of the

landslide was changed by reservoir filling. Fig.5

shows that the middle part and trailing edge of

landslide have much larger cumulative displacement

before water storage, on the contrary, the Fig.6 shows

that the middle part and leading edge of landslide has

much larger deformation. Therefore, the sliding

model was thrust load caused landslide that showed in

before water storage while it was retrogressive

landslide after water storage.

Temporal Patterns of Landslide Movements

Time series curves of the cumulative displacements

taken from the fourteen GPS measurement stations

between December 2006 and December 2009 are

shown in Fig.5. Fig.5 shows that the most obvious

planar movement occurred in FMZ, which included

G1-3, G5-6 and G9, followed by G13-16 in MDZ,

and then the other three GPS stations (G17-19) with

minimum movement because it lies outside the MDZ.

In addition, Fig.5 shows that a typical stepwise

pattern of temporal evolution is identified from the

cumulative displacements in FMZ, including a overall

variation trend and two primary types of motion:

1. The deformation of the landslide in phase of

0~70 m was the smallest while it was the largest in

phase of 70~100 m, and the displacements tended to

be stable gradually within the phase of 100~130 m till

reservoir filling being accomplished; and 2. Longer

periods of slower movement, which last a longer time,

approximately twice as long as the FMs that usually

occurred in a relatively short time.

295

http://dict.baidu.com/s?wd=approximately


Three FMs can be identified in monitoring period,

including FM1 (July-September 2007), FM2 (July-

October 2008) and FM3 (June-August 2009), which

all occurred in period of rapid water level fluctuation

(Fig.5). In addition, FM2 have larger displacement

increment than FM3 while FM1 has the smallest

displacement increment. During FM2 and FM3

periods, the average planar and vertical velocities are

more than 3 and -1.5 mm/day respectively. The

maximum velocity reaches 30 mm/day in planimetry

and -18 mm/day in altimetry. In addition, slower

movement occurs at slow rates (approximately 0.5

mm/day) in both the horizontal and vertical

directions. Moreover, the movement of FM3 became

faster with the drawdown of water level from 115 to

100 m, which maximum plannar and vertical

velocities were approximately 14.0 and 4.5 mm/day

respectively and the maximum rate of drawdown was

0.3 m/day.

The deformation of the landslide showed a typical

temporal characteristic that creep damage for a long

time and local revival sliding characteristics for a

short period of time on the condition of hydrological

triggers. There is no doubt that ncGPS provides a

more detailed temporal evolution of the landslide

movements. As a whole, the stepwise pattern of the

temporal evolution in the cumulative displacement

suggests that the landslide moves at slow rates most

of the time, with FM2 period seeing a sudden increase

in velocity, though lasting only for a short time

between July and November 2008.

Landslide Movements and Hydrological Triggers

Fig.7 shows that the time series of the reservoir

water levels, rates of water fluctuation, rainfall, and

FMs between December 2006 and December 2009.

This figure indicates that: Two FM periods (FM1-

FM2) occurred at the early period of the reservoir

water level rise and lasted for several days when the

water level was rapid continuous rise with the

minimum rate of water rise being 0.25 m/day.

Moreover, if the reservoir water began to rise from a

new height to another level, there was much more

movement in the first and second phases (0-100 m).

Additionally, FM1 occurred after the water rise from

15 to 50 m; and FM2, from 70 to 100 m. There are

more obvious rapid rise of reservoir water level in the

period of FM2, which the rate of water rise was

similar to FM1 period and the late third phase

(2009.8.1-2009.12.31). However, the cumulative

displacements of FM2 being twice as large as FM1

though the water storage rate was similar, due to

continuous rapid water level rise in second phase and

another important reason that the longer duration of

continuous rapid water level rise in FM2 period than

FM1, which lasted for nearly 5 months and less than 3

months respectively.

However, the continuous rise of water level had

little influence on the movement of landslide in third

phase. The velocities of displacements of the

monitoring stations were much smaller with the

plannar and vertical velocity being approximately 2.0

and 0.5 mm/d respectively tending to stable gradually

from August 1, 2009 to December 25, 2009 although

the larger rate of water rise lasted for several days. In

other words, the movement tended to be stable in

third phase which indicated that the movement

affected by continuous water rise was different in

third phase (100-130 m) compared with the first and

second phase (0-100 m).

FM3 occurred in the period of reservoir drawdown

and the maximum plannar and vertical displacement

velocities were 13.5 and -4.5 mm/d respectively with

the maximum rate of drawdown being approximately

0.30 m/day, which indicated that if the water level fell

from a higher water level in third phase (100-130 m),

the landslide will be a greater deformation. It is

noticeable that the displacements velocities became

much larger with the rate of drawdown.

Three FM periods corresponded to the beginning

of the rainfall season, usually between June and

September of each year while the FM period ended

even before the end of the rainfall season, such as the

FM1 occurred from June, 2007 to Octomber 2007. On

the other hand, the movement of landslide will be

much faster if there was prolonged rainfall that lasted

for several weeks or even months with the fluctuation

of water level.

Moreover, if the amplitude of the rate of the

reservoir water rise became bigger for the second

stage, much more active landslide movements were

observed. The three FM periods are also closely

associated with the rainfall season (Fig.7), which

indicated that the FMs were affected by rainfall and

water level fluctuation together. The FM3 period

ended even before the end of the rainfall season with

the rapid reservoir drawdown, which means that FM3

was closely related to reservoir drawdown and

inclined to be affected by rapid reservoir drawdown

compared with rainfall while FM1 and FM2 were

more inclined to be affected by the rapid continuous

water level rise (Fig.7). Furthermore, the deformation

of FM1 is much smaller than that of FM2 although

the rate of water rise was similar, the deformation of

landslide shows a different variation trend with the

water level rise in different water storage phase, and

296


the duration of rapid continuous water rise is also

closely related to the movement of landslide.

0

10

20

30

40

50

60

70

80

90

100

110

120

130

Rese

rv

oir

wa

ter lev

el(m

)

FM3

FM2

FM1 (a)

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

Ra

te o

f w

ate

r r

ise (

m/d

ay

)

(b)

0

20

40

60

80

100

120

140

Ra

infa

ll (

mm

)

(c)

Fig.7—(a)The water fluctuation in Jiudian Gorge Reservoir; (b)

Rate of water fluctuation; (c) The rainfall

0

5

10

15

20

25

30

Pla

nn

ar

dis

pla

cem

ent

vel

oci

ties

(m

m/d

ay

) G1 G5 G2

G3 G6 G9

(d)

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

Ra

te o

f w

ate

r ri

se

(m/d

ay

)

(a)

lag time

lag time

lag time

0

10

20

30

40

50

60

70

80

90

100

110

120

130

Res

erv

oir

wa

ter

lev

el(m

)

FM3

FM2

FM1

(b)

0

500

1000

1500

2000

2500

3000

3500

4000

Cu

mu

lati

ve

pla

nn

ar

dis

plc

aem

ents

(m

m)

G1 G5 G2 G3 G6 G9

(c)

Period of continuous

rapid water rise


rapid water rise


rapid drawdown

Fig.8—Time series of water fluctuations: (a) Water fluctuation

rate; (b) Water level fluctuation; (c) Cumulative plannar

displacements; (d) Rate of cumulative plannar displacements.

The time series of the daily fluctuations of

reservoir water levels and cumulative displacements

in Fig.8 shows that: FM1 and FM2 occurred at the

early period of the reservoir water level phases, and

continuous reservoir water rise at a rapid rate always

preceded the two FM periods [FM1 and FM2, as

shown in Fig.7]. In other words, the FM period did

not immediately occur at the beginning of each

continuous rapid water rise with a large rate since

there is a time lag in the landslide movement

approximately 5 days. In addition, the velocities in

FM1 and FM2 showed strong positive correlation

with the rate of water rise, such as the period with the

fastest movement, FM2, occurred after the highest

rate of water level rise. The planar displacement in the

period of FM2 is the largest which lead to reservoir

bank landslides and collapse, as shown in Fig.9.

There was a fastest movement of landslide during

the period of 70~100 m with the displacement of

FMZ was 300~2000mm approximately half of all the

deformation in reservoir filling, which means that

landslide was the most unstable in this period. In

other words, the instability of landslide was primarily

triggered by rapid continuous water rise in the phase

of 70~100 m. What’s more, the maximum plannar and

vertical displacement velocities were 27mm/day and

5.5mm/day when the water level reached 80 m that

was conducive to triggering FM.

In summary, continuous rapid water level rise in

first and second stages (0-100 m) will trigger FMs

with lag time approximately 5 days with the period of

FM2 being the most dangerous while the continuous

water rise has less influence on the deformation in

third phase (100-130 m). The continuous reservoir

drawdown is the main triggering factor of FM in third

phase. Therefore, the water level rise has different

influence on the stability of landslide in different

phase with the duration of rapid continuous water rise

being closely related to the movement of landslide. At

the same time, prolonged and periodic heavy rainfalls

have some effects on the movement of landslide even

occurring new landslides (Fig.8), whether in slow or

FM periods, although the effects are not as relevant.

Crack

The reservoir bank collapse

Fig.9—The reservoir bank collapse

297


Mechanism of Landslide Movements

According to the patterns of the movements and

hydrological triggering factors, the deformation

mechanism of the landslide can be concluded as

follows. The slide mechanism of FM1 and FM2 is

mainly affected by mechanical effects of reservoir

bank slope, including dynamic water pressure effect,

hydrostatic water pressure effect, and floating force

effect. As water level rising, the flooding of the area

increases and the effective stress of sliding surfaces

and strength of sliding zone reduce, which lead to

bond force and friction coefficient between particles

decreasing and the sliding resistance of landslide

reducing. The pore water pressure appears with

underground water level rising in the landslide, and

the seepage field of leading edge has great changes,

which results in deformation of slope body occurring

and the cracking surface expands to the deep of

landslide until reaches potential shear plane. Then

shear stress concentration of shear plane will occur,

and the internal shear strength of rock mass is

reduced. Additionally, the sliding surface is immersed

by water, and virtual body pressure is greater than the

actual pressure in the sliding body above the sliding

surface while it produces float towing force in

submerged sliding body. The effective weight sliding

body immersed by water changes due to pore water

pressure. Therefore, the slope stability will be reduced

under the action of the former factors.

Rapid drawdown will weaken the stability of the

landslide, for example, the occurrence of FM3. The

speed of groundwater descent in landslide is smaller

the rate of reservoir water drawdown, which leads to

causing excess pore water pressure in sliding body.

Besides, dynamic water pressure in landslide will

increase as rapid drawdown with the landslide sliding

towards the reservoir. Then the unloading effect will

appear sliding body and water hammer effect also

occurs in the cracks, therefore, the landslide stability

will be reduced in the end.

Triggering Conditions of Landslide Movements

To quantitatively study the triggering conditions

for the initiation of FMs in Yanziping Landslide,

which are significantly important for early warning of

failure, a detailed analysis was used to observe the

rise of reservoir water levels during the three FMs

periods based on ncGPS monitoring with the results

are shown in Table 2.

Obviously, any continuous rapid rise will trigger

FMs in first and second phase (0-100 m). The Fig.10

shows that the time lag is approximately five days

between rapid rapid rise and the beginning of FM

periods, based on observations in FM1 and FM2.

However, the movement of landslide was mainly

triggered in second phase owing to the the

destabilizing effect was closely related to the rapid

water rise. Moreover, the continuous rapid drawdown

of reservoir water level in third phase (100-130 m)

definitely triggers FMs, as observed in FM3 period,

which indicated that much faster rates of reservoir

drawdown mean faster landslide movement. The

stability of landslides in JGR reduced to a minimum

with many a landslides being triggered when the

water level reached 80 m by the means of rapid

continuous water level rise in combination with

detailed field investigation.

Therefore, the most dangerous conditions when the

movement of Yanziping landslide is triggered when:

(1) rapid continuous rise of water level is from

approximately 70 to 100 m, and (2) the incremental of

continuous reservoir water rising rate is conducted at

a rate of more than 0.35 m/day before 100 m,

especially the faster movement of landslide occurred

nearly 80 m with the plannar and vertical

displacements velocities reaching up the maximum,

and (3) drawdown of reservoir water level is from 115

to 100 m. This has been observed to cause FMs of the

landslide of more than 3.5 mm/day. To avoid FMs of

the Yanziping landslide, rapid rise of the reservoir

water level in three phases at a slower rate (less than

0.30 m/day) may be effective in rising up movement.

Table 2—Relationship between Continuous Rapid Rising of Reservoir Water and Three FM Periods

Continuous rapid fluctuation of reservoir water FM periods

Time

(start-end)

Water level

(m)

Average–

maximum rate

Number

Time

(start-end)

Average–

maximum velocity

Lag time

(start-end)

May 20–August

3, 2007

12.3–49.5 -0.08–0.51

m/day

FM 1 May 25–August

8, 2008

≥ 3.5

mm/day

5 days

July 1–October

25, 2008

75.5–99.8 0.13–0.55

m/day

FM 2 July 6–July30,

2008

8.7-29.5

mm/day

5 days

June 5–Aug 21,

2009

115.5–100.2 -(0.07–0.18 )

m/day

FM 3 June 11–Aug 25,

2009 (unsure)

5.2–14.8

mm/day (unsure)

≤ 4 days

(unsure)

298


Field Observations and Triggering Conditions

Fig.10 shows the surface deformation features from

a field investigation of Yanziping landslide. Site 1 is

located on the trailing edge of the MDZ, which has a

large number of cracks, especially shear fracture. Site

2 is located on the west boundary of the MDZ.

Obvious cracks across the retaining wall within the

MDZ an be identified. Site 3 is located on the trailing

edge of the MDZ where cracking deformation is

regarded as the main deformation characteristics.

Sites 4 and 5, which are close to G5 and G9,

respectively, have more serious surface deformation

features that include shear moving and long

extensional tension cracks. Site 6 is located on the

east boundary of the MDZ that traverses a temporary

house and a road. There are straight extensional shear

cracks with relative block movements. Overall, Sites

3, 4 and 5, which lie in the FMZ, have more obvious

and destructive surface deformation features and are

very consistent with the spatial pattern of movement,

however, Sites 1, 2 and 6, which lie outside the FMZ,

have slower surface deformation feature (Fig.10).

Apparently, all FM1 and FM2 periods primarily occur

in the period of continuous rapid water rise, and the

period of continuous rapid rising always precedes

every FM. This provides verification of the triggering

conditions, because each FM is determined based on

the FM of the landslide. Additionally, Fig. 6 indicates

that a faster rate of water rise appeared in FM1 and

FM2, which leads to a larger movements. Therefore,

the use of near-real-time monitoring data to interpret

the movement patterns and hydrological triggers in

the JGR is very effective, and also has a guiding

significance in controlling reservoir landslide

movement.

Conclusions

According to analyzing near-real-time GPS

monitoring data of Yanziping landslide, spatial and

temporal patterns of surface movement and

hydrological triggers can be identified. Some

conclusion can be found as follow. The landslide

movement owns two characteristics including

temporal and spatial patterns. Typical stepwise

pattern of landslide movement can be definitely

identified, which consists of short periods of FMs and

longer periods of slower displacements. Spatial

pattern means that there is a FMZ within MDZ. The

FMZ which extends from the top head (G2) through

the part (G1, G3, G5 and G6) to the middle area (G9),

can be identified in MDZ, with the primary slip

direction of the MDZ being the southwest direction.

Fig.10—Field observations of spatial pattern of movement by

field surveying

Three FMs can be identified in process of reservoir

filling. For the hydrological triggers, continuous rapid

water rise, especially from 70 to 100m, will definitely

trigger FM periods (FM1 and FM2). Rapid drawdown

of reservoir water level triggers FMs from 100 to 130

m, which is also the main trigger of FM3.

Additionally, rainfalls have a slightly relevant effect

on movement, especially the period of water level rise

and rainfall coexisting at the same time.

The sliding mode of the landslide is changed by

the reservoir filling from thrust load caused landslide

to retrogressive landslide, which water storage is the

main triggering factor. There is a most dangerous

water level for the movement of landslide in second

water storage phase approximately 80 m, and a lag

time approximately 5 days between FMs and rapid

water rise. For the movement mechanism, the

influence of water rise is divided into three parts,

including the dynamic water pressure effect,

hydrostatic water pressure effect, and floating force

effect.

Validation from field investigation and analysis of

historical movement records have proved the research

method is very effective by using near-real-time

monitoring data to interpret the landslide movement

and hydrological triggering mechanisms.

Unquestionably, near-real-time monitoring with high

temporal resolution can provide more reliable,

accurate, and timely data, which is significantly

299


important in providing early warnings and taking

emergency measures when a reservoir landslide

reoccurs during the period of water storage, especially

many reactivated landslides can be found in the JGR,

and near-real-time monitoring is more effective than

other monitoring means for disaster prevention and

mitigation.

Acknowledgments

This work is financially supported by The national

key basic research and development program

(2014CB744701) in China. The authors warmly thank

Jiudian Gorge Company for its continuous support,

which have helped to provide the precious monitoring

data.

References 1. Deng, J.H., Tham, L.G., Dai, F.C., et al., Monitoring a

Preexisting Landslide during Reservoir Filling, Geotechnical

Special Publication, 130 (2005):1-10.

2. Pastor, M., Herreros, I., Merodo, J., et al., Modelling of fast

catastrophic landslides and impulse waves induced by them

in fjords, lakes and reservoirs, Engineering Geology, 109(s

1–2) (2009):124-134.

3. Zaniboni, F., Tinti, S., Numerical simulations of the 1963

vajont landslide, italy: application of 1d lagrangian

modelling, Natural Hazards, 70(1) (2014): 567-592.

4. Petley, D., Mantovani F, Bulmer MH, Zannoni A., The use of

surface monitoring data for the interpretation of landslide

movement patterns, Geomorphology, 66(1) (2005):133-147

5. Du, J., Yin, K., Lacasse, S., Displacement prediction in

Colluvial landslides, Three Gorges Reservoir, China,

Landslides, 10(2) (2013):203-218.

6. Paronuzzi, P., Rigo, E., Bolla, A., Influence of filling-

drawdown cycles of the Vajont reservoir on Mt.Toc slope

stability, Geomorphology, 191(5) (2013):75-93.

7. Zhou, Y.F., Tham, L.G., Yan, R., et al., The mechanism of

soil failures along cracks subjected to water infiltration,

Computers & Geotechnics, 55(2) (2014):330–341.

8. Manzoor, H., Timothy, D.S., Back-analysis of Preexisting

Landslides, Geo-Frontiers, 22(4) (2011) 3659-3668

9. Huang, H., Yi, W., Deng, Y., Dong, Z., Near-Real-time

Monitoring of Landslide Deformation and Inducing

Hydrologic Factors in Three Gorges Reservoir, Geo-Hubei

2014 International Conference on Sustainable Civil

Infrastructure 13(2) (2014):167-175.

10. Guo, D., Hamada, M., Qualitative and quantitative analysis

on landslide influential factors during Wenchuan earthquake:

A case study in Wenchuan County, Engineering Geology,

152(1) (2013):202-209.

11. Farzaneh, O., Askari, F., Three-Dimensional Analysis of

Nonhomogeneous Slopes. Journal of Geotechnical &

Geoenvironmental Engineering, 129(2) (2003):137-145.

12. Mao, W., Yan, E., Song, K., Zhang, T., Deformation of

reservior landslide during reservoir water fluctuation,

Applied Mechanics and Materials, 90 (2011):1334-1340.

13. Tang, X., Zheng, Y., Tang, H., et al., Numerical researches on

deformation characteristics and prediction of reservoir

landslides, Chinese Journal of Geotechnical Engineering,

35(5) (2013):940-947.

14. Pei, H., Cui, P., Yin, J., et al.,. Monitoring and warning of

landslides and debris flows using an optical fiber sensor

technology. Journal of Mountain Science, 8(5) (2011), 728-

738.

15. Yin, Y., Wang, H., Gao, Y., et al., Erratum to: Real-time

monitoring and early warning of landslides at relocated

Wushan Town, the Three Gorges Reservoir, China,

Landslides, 7(3) (2010):339-349.

16. Calcaterra, S., Cesi, C., Maio, C.D., et al., Surface

displacements of two landslides evaluated by GPS and

inclinometer systems: a case study in Southern Apennines,

Italy, Natural Hazards, 61(1) (2012):257-266.

17. Subramani, T., Kasiviswanathan, S.P., Nesna, C., et al.,

Demarcation of landslide vulnerable zones in and around

Achanakal, South India using remote sensing and GIS

techniques, Indian Journal of Geo-Marine Sciences, 46(2)

(2017):365-370.

18. Massey, C., Petley, D., Mcsaveney, M., Patterns of movement

in reactivated landslides, Engineering Geology, 159(12)

(2013):1–19.

19. Aleotti, P., A warning system for rainfall-induced shallow

failures, Engineering Geology, 73(s3–4) (2004):247–265.

20. Chen, C., Chen, T., Yu, F., et al., Rainfall duration and

debris-flow initiated studies for real-time monitoring,

Environmental Geology, 47(5) (2005):715-724.

21. Huang, H., Yi, W., Lu, S., et al., Use of monitoring data to

interpret active landslide movements and hydrological

triggers in three gorges reservoir. Journal of Performance of

Constructed Facilities, 30(1) (2016):C4014005.

300

the influence of reservoir filling on a preexisting bank...

Documents