Landslide susceptibility zonation study using remote sensing and GIS technology in the Ken-Betwa...

ORIGINAL PAPER

Landslide susceptibility zonation study using remote sensingand GIS technology in the Ken-Betwa River Link area, India

Ram Avtar • C. K. Singh • Gulab Singh •

R. L. Verma • S. Mukherjee • H. Sawada

Received: 20 June 2010 / Accepted: 2 February 2011 / Published online: 15 March 2011

� Springer-Verlag 2011

Abstract The 231 km long Ken-Betwa River Linking

canal will transfer 1,020 hm3 of surplus water from the

Ken River to the deficit Betwa River basin. The landslide

susceptibility zonation map of the river link has been

assessed using remote sensing data in GIS. Various the-

matic maps such as geology, land use/land cover, linea-

ment, drainage, slope, aspect, normalized difference

vegetation index and soil type were generated from the

Landsat Thematic Mapper 5 satellite data of 2005, the

Survey of India topographic sheets, Shuttle Radar Topo-

graphic Mission Digital Elevation (SRTM-DEM) data and

other existing maps. Numerical rating schemes were used

for ranking the thematic layers. The results were supported

with the rainfall data, groundwater level data and a petro-

logical study of rock thin sections. In addition to providing

valuable information for project decision-makers, the

results will assist in slope management and land use

planning in the area.

Keywords Landslide susceptibility zonation (LSZ) �NDVI � Ken-Betwa River Link � GIS

Resume Le canal de liaison Ken-Betwa, de 231 km de

longueur, transferera 1020 hm3 par an d’eau excedentaire

du bassin de la riviere Ken vers le bassin de la riviere

Betwa. La carte de zonage de la sensibilite aux glissements

(LSZ) de la region du canal de liaison a ete etablie en

utilisant des donnees de teledetection traitees dans un

systeme SIG. De nombreuses cartes thematiques relatives a

la geologie, l’occupation des sols, les lineaments, le

drainage, les pentes, un indice de vegetation normalise

(NDVI) et les types de sols ont ete generees a partir

d’images Landsat (TM5) de 2005, de cartes de l’Institut

geographique national indien, de fichiers numeriques de

terrain (SRTM-DEM) et d’autres cartes preexistantes. Des

procedes de classement numerique ont ete mis en œuvre

pour organiser les couches thematiques. Les resultats ont

ete completes avec les donnees de pluviometrie, les nive-

aux d’eau souterraine et des etudes petrographiques de

lames minces. De plus, afin de produire une information

valable pour les decideurs, les resultats apporteront une

aide pour l’amenagement des pentes et la planification

territoriale de la region.

Motscles Zonage de la sensibilite aux glissements

(LSZ) � NDVI � Canal de liaison Ken-Betwa � SIG

Introduction

Numerous natural factors such as earthquake, heavy rain-

fall and river bank erosion by flood water, as well as

anthropogenic factors, including deforestation and slope

excavation, cause slope failure by decreasing the shear

R. Avtar (&) � H. Sawada

Institute of Industrial Sciences, The University of Tokyo, Ce509,

4-6-1 Komaba, Maguro-Ku, Tokyo 153-8505, Japan

e-mail: [email protected]

R. Avtar � C. K. Singh � S. Mukherjee

School of Environmental Sciences, Jawaharlal Nehru University,

New Delhi 110067, India

G. Singh

Graduate School of Science and Technology, Niigata University,

Niigata 9502181, Japan

R. L. Verma

Research Center for Advanced Science and Technology,

The University of Tokyo, Tokyo, Japan

123

Bull Eng Geol Environ (2011) 70:595–606

DOI 10.1007/s10064-011-0368-5

strength or increasing the shear stress of the soil mass

(Abramson et al. 1996). Landslides are a type of ‘‘mass

wasting’’, which denotes any down-slope movement of soil

and rocks under the direct influence of gravity. Landslide

susceptibility zonation (LSZ) is the study of the spatial

distribution of factors related to slope instability, in order

to determine landslide prone areas without considering

temporal implications. This approach is useful for the

regions where there is no historical record of landslide

events (Chacon et al. 2006). Landslide susceptibility

zonation mapping is important for land use planning, civil

engineering design, civil protection and risk reduction

programmes.

The objective of the Inter Linking of River (ILR) project

in India is to transfer water from ‘excess’ to ‘deficit’ basins.

The ILR is primarily intended to mitigate problems of

floods and droughts in different regions of the country. It is

also connected with the development of the energy,

transport industry, agriculture and human health sectors

(National Water Development Agency 2006).

The Ken-Betwa Link Project (KBLP) involves con-

necting the Ken and Betwa Rivers through the creation of

a dam, reservoir and canal to provide storage for excess

rainfall during the monsoon season in the upper Ken

basin and transfer the water for consumption and irriga-

tion purposes to the upper Betwa basin. KBLP is the first

river link project of the 30 projects proposed by the

Government of India National Water Development

Agency (NWDA), involving the states of Madhya

Pradesh (MP) and Uttar Pradesh (UP) in the Bundelkhand

region (NWDA 2007). The Ken-Betwa link project is a

diversion and storage scheme which will transfer about

1,020 hm3 of surplus water from the Ken River basin. It

involves a 73.80 m high, 1,468 m long earth dam across

the Ken River with a gross storage capacity of

2,775 hm3, a 231.45 km long linking canal and two

power houses with 3 9 20 MW and 2 9 6 MW capacity

(NWDA 2007). The works, including the construction of

roads, etc., will affect the stability of slope and cause

deforestation as well as creating other ecological imbal-

ances between the different ecosystems in the Panna tiger

reserve forest.

Remote sensing and GIS is useful for landslide sus-

ceptibility zonation mapping and can help identify the area

best suited for developmental activities (Pachauri and Pant

1992; Mukherjee 1999; Guzetti et al. 1999; Van Westen

2000; Dai et al. 2001; Kosaka 2000; Van Westen et al.

2003; Sarkar and Kanungo 2004; Saha 2005; Van Westen

et al. 2008; Gupta et al. 2008). The objective of this study

was to generate a landslide susceptibility map of the Ken-

Betwa River linking area to assist the project planners in

their decision-making regarding site suitability and hazard

mitigation.

Study area

Most of the 61,750-km2 study area is in Bundelkhand and

divided between the states of UP and MP; see (Fig. 1). The

area has a semi-arid type of climate, although this is highly

variable depending on the region and the time of year,

being prone to droughts in summer and disastrous floods

during monsoons (June–September) when over 90% of the

750–1,250 mm annual rainfall occurs, mainly in the

southeastern part of the study area.

The area has annual maximum and minimum tempera-

tures of 44.2�C and 6.7�C. May and June are generally the

hottest months and December and January are the coldest

months.

Fig. 1 Location map of the study area

596 R. Avtar et al.

123

Materials and methods

In the first step, the Survey of India (SOI) topographic sheets

and existing maps were converted into digital format and geo-

referenced into universal transverse mercator (UTM),

spheroid and datum WGS-1984. Landsat (TM5) data

acquired in 2005 was processed to generate a standard false

colour composite (FCC) from bands 4, 3, 2 coded in a red,

green and blue colour scheme. This FCC was used to generate

land use/land cover, lineament and NDVI maps. SRTM DEM

data with a resolution 90 m were used to generate elevation,

slope and aspect maps. Drainage, geology and soil maps were

generated from the SOI topographic sheets, Geological Sur-

vey of India maps and the National Bureau of Soil Survey and

Land Use Planning (NBSS & LUP, Nagpur) maps.

There are two basic approaches for landslide zonation

studies. The first is the qualitative map combination where

relative weighting values are assigned to the factors and

their classes on the basis of field knowledge and experi-

ence; the second uses statistics to compute the weighting

values based on the relationship of the factors with existing

landslides (Sarkar and Kanungo 2004). In this study, the

qualitative map combination method was used by devel-

oping a rating system, based on the relative importance of

the thematic maps.

Each thematic map was assigned a weight depending on

its influence on landslide hazard based on multiple criteria

decision-making techniques. GIS is an ideal tool to analyse

and solve multiple criteria problems (Belton and Stewart

2002). The relative score of each thematic unit in a theme

was calculated by multiplying the weight of the theme with

the rank of the thematic unit. The weight and rank of each

layer is given in Table 1.

The scored maps were overlaid using the spatial analyst

tool of ArcGIS 9.1 to generate a LSZ map. The resultant

map was classified into high, moderate, low and very low

susceptibility zones. The results were supported using

groundwater level maps, rainfall maps and petrological

study of rock thin sections from the area. The methodology

adopted in this study is shown as a flow chart in Fig. 2. The

following equation was used for the LSZ mapping using a

multi-criteria decision-making technique.

LSZ ¼ R Wt1ð Þ � Rt1ð Þþ Wt2ð Þ � Rt2ð Þþ W t3ð Þ½� Rt3ð Þþ � � � � � � � � � Wt8ð Þ � Rt8ð Þ�

where, W is the weight of the theme, R is the rank of the

theme and t1, t2, t3…….t8, etc. are the theme numbers.

Petrological analysis

Rock samples were collected from different sites in the

study area (Fig. 15). The composition of the rocks was

Table 1 Thematic maps, weight and ranking for landslide suscepti-

bility zonation study

Theme Weight Features Rank

Drainage density 8 Very high 9

High 7

Medium 5

Low 3

Very low 1

Lineament density 7 High 8

Medium 6

Low 4

Very low 2

Slope 6 [72� 9

57–72 7

39–57 5

14–39 3

\14� 1

Geology 5 Alluvial plain 5

Archaean granite 2

Bhander group 6

Bijawar group 4

Bundelkhand granite 1

Deccan traps 2

Kaimur group 6

Vindhyan super group 8

Land use/land cover 4 Dense forest 1

Land with scrub 7

Agricultural land 4

Degraded forest 3

Land without scrub 6

Fallow land 7

Waterlogged 1

Water body 1

Built up 1

Bare exposed rock 8

NDVI (Vegetation) 3 Dense 1

Moderate 3

Low 6

Barren 9

Soil cover 2 Clayed loam 7

Loamy 5

Loamy sand 3

Sandy loam 1

Aspect 1 Flat 1

North 2

Northeast 5

East 7

Southeast 8

South 9

Southwest 6

West 4

Northwest 3

Landslide susceptibility in the Ken-Betwa linkage area, India 597

123

examined using thin sections under the ML 9000 series

petrological microscope (Meiji, Japan). Thin section

petrography is particularly useful as in addition to the

characteristics of the rocks, their weathering potential and

hence landslide susceptibility can be determined.

Interpolation of groundwater level and rainfall data

Ground water level data from 124 observation sites

were obtained from the Central Ground Water Board

(CGWB) for the years 1999–2004 and annual rainfall

data at 19 rain gauge stations from the Indian Meteo-

rology Department (IMD). These data were converted

into shape files according to their latitude and longitude.

The observation points, originally expressed as geo-

graphic coordinates, were converted to UTM WGS-1984

for interpolation (Fig. 15). Finally, interpolations of the

groundwater level and rainfall data were undertaken

using spline interpolation.

Results and discussion

Geological study of the area

Geology plays an important role in landslide susceptibility

because different geological units have different suscepti-

bilities to active geomorphological processes (Anabalagan

1992; Pachauri et al. 1998; Dai et al. 2001; Lee and Talib

2005). The area contains three major groups of rocks: the

Bundelkhand complex ([2.6 billion years) the Bijawar

group (2.6–2.4 billion years), and the Vindhayan super

group (1.4–0.9 billion years); see Fig. 3 and Auden (1933).

a) The Bundelkhand Granite complex is dominant in the

study area. These granites are frequently associated

with pegmatites and quartz reefs (Ahmad, 1984).

Above the Bundelkhand gneissic complex, newer

alluvium, older alluvium, gravel, sand and clay of

recent origin are present; in some places they are also

termed as buried pediment plains depending upon their

mode of formation (Mukherjee, 1991).

Fig. 2 Flow chart depicting the methodology adopted for landslide susceptibility zonation map generation

Fig. 3 Geology of the study area

598 R. Avtar et al.

123

b) The Bijawar group in the area consists of a terruginous

sequence of basal carbonates and shales with green

schists or pillow basalts, chloritic shales, ferruginous

quartzites and banded iron formations.

c) The Vindhayan super group has been broadly divided

into four major groups based on lithological similar-

ities–the Semri (lower), Kaimur, Rewa and Bhander

(upper)–and into the Lower and Upper Vindhyans

based on the major unconformity. The upper Kaimur

group rocks of the Vindhayan super group are

represented by sandstones all over the Vindhayan

basin.

Rock weathering and mineral composition are important

for landslide hazard assessment (Van Westen et al. 2008).

Quartzite, protoquartzite, sandstone, granite, orthoquartzite

and greywacke rocks were identified using the petrological

microscope (Fig. 4a–f).

Quartzite is highly resistant to weathering, while granite

(feldspar 65–90%, quartz 10–60% and biotite 10–15%),

because of its coarse-grained texture and the differing

hardness of the minerals present, is less resistant. The

sandstone of the study area (quartz, silica or calcite) is

coarse-grained with a weak (calcareous/argillaceous)

bonding and hence again is less resistant to weathering.

Fig. 5 a Lineament interpretation b Rose diagram c Lineament length

density map (Avtar et al., 2010)

4 (A) Quartzite 4 (B) Protoquartzite

4 (C) Sandstone 4 (D) Granite

4 (E) Greywacke 4 (F) Orthoquartzite

Fig. 4 Petrological microscopic view of rocks thin section


123

The greywacke (quartz, feldspar and clay) is dark in colour

and rich in clay with fewer quartz sand grains and less

resistant to weathering (Wilson 2004). These analyses are

consistent with the LSZ map. It is found that rocks with a

high weathering rate represent moderate to highly suscep-

tible zones and low susceptibility zones take place in less

weathered rocks (Tobe and Chigira 2006).

The alluvial plain was ranked higher (5) compared with

the granite, Deccan (basalt) and Bijawar group as it has a

high clay fraction and hence is more susceptible to mud and

debris flows. The Kaimur, Bhander and Vindhyan groups

were also ranked higher, despite consisting of sandstone as

although the area has not experienced any major landslides,

mud and debris flows occur during the rainy season.

Lineament map of the study area

Lineaments are the manifestation of subsurface linear

features (Mukherjee 2008). As seen in Fig. 5a–c, they were

analysed using lineament density and rose diagrams (Zakir

et al. 1999). The rose diagram indicated that the most

dominant lineament direction was generally NNE-SSW

and NNW-SSE. The lineament density was high in the

southern and central parts of the study area, indicating that

these areas are more susceptible to landslide. Lineament-

length density (Ld) is the total length of all delineated

lineaments divided by the total area under consideration

(Greenbaun 1985):

Ld ¼Xi¼n

i¼1

Li=A � m�1� �

wherePi¼n

i¼1 Li= total length of all lineaments (m) and

A = area (m2).

Slope map of the study area

The general topography of the catchment area was studied

through a digital elevation model generated from the

SRTM data (Fig. 6). Most parts of the study area have

slope angles between 39� and 57� (Fig. 7). The alluvial

region in the north has lower slopes and is less prone to

landslides compared with the hilly area in the south

Fig. 6 Elevation map of the study area based on SRTM DEM data

Fig. 7 Slope map of the study area (Avtar et al., 2010)

Fig. 8 Aspect map of the study area

600 R. Avtar et al.

123

(Ohlmacher and Davis 2003). The slope angle correlates

well with rainfall and groundwater level (Jiao et al. 2005,

Iverson 2000, Budhu and Gobin 1996, Giannecchini et al.

2007). Heavy rainfall is a critical factor in triggering

landslides in Hong Kong, as it results in a rapid increase in

pore pressure in the vadose zone and groundwater flow in

the saturated area (Jiao et al. 2005). Richard and Mark

(1992) studied the effects of slope morphology, hydraulic

heterogeneity and material properties and found that where

a low-hydraulic conductivity layer parallel to the slope acts

as a cap on the flow system, outward-directed seepage

forces occur at the slope toe, leading to conditions con-

ducive to landsliding. Hodge and Freeze (1977) also

pointed out that gravity-induced groundwater flow in

slopes depends on both the topography and the distribution

of materials of different hydraulic conductivity within the

subsurface. Areas with high slope angles were therefore

ranked higher in the landslide susceptibility zonation in

Table 1.

Aspect map of the study area

Aspect identifies the steepest down-slope direction from

each cell to its neighbours and defines the direction of flow

(Fig. 8). The value of each cell in an aspect dataset indi-

cates the direction of the cell’s slope faces. The aspect map

was divided into nine classes: N, NE, E, SE, S, SW, W,

NW and flat. The areas where vegetation has degraded will

receive direct sunlight, creating drier soil conditions and

thus increasing the probability of landslide occurrences.

Aspect influences the amount of rainfall and moisture

content in soil and thereby influences the stability of the

slope (Rajkumar 2007).

Soil map of the study area

The climate, physiography and geological characteristics of

the soil play an important role in groundwater recharge

and run-off. The permeability of the soil is an important

Fig. 9 Soil map of the study area (Avtar et al., 2010)

Fig. 10 Drainage network map of the study area (Avtar et al., 2010)

Fig. 11 Drainage density map of the study area (Avtar et al., 2010)


123

parameter for landslide occurrence. As soil moisture

increases, pore water pressure rises, increasing the shear

stress and decreasing shear strength (Ray Ram and Jacobs

2007). The soil in the study area is divided into clayey

loam, sandy loam, loamy and loamy sand on the basis of

soil texture. Most of the Betwa basin is covered by loamy

sand while in the Ken basin, loamy sand predominates in

the upper part and sandy clay in the lower part (Fig. 9). The

higher the percentage of clay fraction in the soil, the lower

the permeability and the higher the probability of land-

slides (Table 1).

Drainage map of the area

The drainage map shows the major rivers (Yamuna, Ken,

Betwa, Dhasan, Jamni) and a large number of other streams

draining this region (Fig. 10). The drainage pattern in

the area is dendritic (pinnate type). Drainage density is the

ratio of the total length of the stream to the area of the

drainage basin (Lin and Oguchi 2004). The southern part of

the basin is associated with very high drainage density,

while about 40% of the area upstream of the Ganagu weir

has a medium drainage density and some parts of the

western and south-eastern catchment a low drainage den-

sity, due to the extent of vegetation and the permeable

nature of the soil (Fig. 11). Areas with a higher drainage

density have lower infiltration rates because of the fast

movement of the surface flow (Pachauri et al. 1998; Cevik

and Topal 2003). Landslide susceptibility is greater in areas

of high drainage density (Rajkumar 2007) and hence low

drainage density areas have been given a lower rank in

Table 1.

Land use/land cover map

The land use/land cover map was generated using the

unsupervised image classification procedure (Lillesand and

Keifer 2006) based on Landsat TM5 satellite data for 2005,

and the topographic maps. The study area was classified

into ten categories: water, dense forest, built-up, current

fallow/agricultural land, water logged, degraded forest,

land with scrub, bare exposed rock, fallow land, and land

without scrub (Fig. 12). The percentage of the different

types of land use/land cover is shown in Table 2. About

Fig. 12 Land use/land cover map of study area (Avtar et al., 2010)

Table 2 Percentage of various classes in the study area from Landsat

2005

Class Area (km2) % Different classes

Water body 1,333.38 2.58

Dense forest 18,634.05 36.11

Built-up 606.72 1.18

Current fallow/agriculture land 12,657.22 24.53

Waterlogged 219.91 0.43

Degraded forest 4,035.82 7.82

Land with scrub 3,929.60 7.62

Bare exposed rock 1,471.42 2.85

Fallow land 2,704.16 5.24

Land without scrub 6,011.10 11.65

Total 51,603.39 100

Fig. 13 NDVI map of the study area

602 R. Avtar et al.

123

35% of the area is covered by forest with high to moderate

vegetation density. Vegetative areas have a tendency to

reduce the impact of climatic factors such as rain. In

addition, deep-rooted vegetation anchors the soil, which is

an important factor in lowering landslide hazard (Styczen

and Morgan 1995; Greenway 1987). The area with dense

forest was therefore given a lower rank compared with

barren areas, which are more prone to landslides (Table 1).

Normalized difference vegetation index (NDVI) map

The normalized difference of the vegetation index (NDVI)

is a non-linear transformation of the visible (red) and near-

infrared bands of satellite information. NDVI is defined as

the difference between the visible (RED) and near-infrared

(NIR) bands over their sum, and is a measure of the amount

and condition of the vegetation (Bannari et al. 1995). It is

associated with vegetation canopy characteristics such as

biomass, leaf area index and percentage of vegetation

cover (Lillesand and Keifer 2006).

NDVI = NIR-REDð Þ= NIR + REDð Þ

For vegetation monitoring, the NDVI is obtained by

the combination of bands 3 (visible) and 4 (NIR) of the

Landsat TM5 data. The higher value of NDVI in the

southern region indicates the dense forest of the Panna tiger

reserve (Fig. 13).

Vegetation plays an important role in controlling soil

erosion and can help stabilize the slope by providing

mechanical strength to the subsoil (Singhal and Srivastava

2004). It provides a protective layer on the earth and

regulates the transfer of water from the atmosphere to the

surface, soil and underlying rocks (Greenway 1987). Areas

with high values of NDVI are therefore less prone to

landslide susceptibility and are given a lower ranking in

Table 1.

GIS integration of thematic layers

The various thematic layers for geology, land use, NDVI,

lineament, drainage density, soil, slope and aspect were

arranged in order of importance and assigned a weighting

number (from 1 to 8) on the basis of previous studies

on landslide hazard zonation (Pachauri and Pant 1992;

Mukherjee 1999; Saha 2005; Saha et al., 2002; Sarkar and

Kanungo, 2004). Similarly, each class within a layer was

given an ordinal ranking from 0 to 9 by using multi criteria

decision techniques in GIS, the highest rank being given to

the factors which are more important for landsliding.

Table 1 describes the weights and rank given to each data

layer and their classes, respectively.

The landslide susceptibility zonation map (Fig. 14) was

generated on the basis of the cumulative values assigned to

the different features of the thematic layers in GIS and was

classified into high (4.07% of the area), moderate (40.75%),

low (53.13%) and very low (2.05%) susceptibility.

Depth to water level

The LSZ map was checked by rainfall and groundwater

level data of the study area to support the study. In the

study area, as the precipitation is concentrated mainly

during the monsoon season, depth to groundwater isFig. 14 Landslide susceptibility zonation map of the study area

Fig. 15 Map showing location of monitoring wells


123

(a) May 1999 (b) August 1999 (c) November 1999 (d) Jan 2000

(e) May 2000 (f) August 2000 (g) November 2000 (h) Jan 2001

(i) May 2001 (j) August 2001 (k) November 2001 (l) Jan 2002

(m) May 2002 (n) August 2002 (o) November 2002 (p) Jan 2003

(q) May 2003 (r) August 2003 (s) November 2003 (t) Jan 2004

m

Fig. 16 Interpolation maps of water level from May 1999 to January 2004 (Avtar et al., 2010)

604 R. Avtar et al.

123

generally reduced during and immediately after the mon-

soons. As can be seen from Figs 15 and 16, it is generally

deeper in the north. The magnitude of seasonal fluctuation

in response to monsoon recharge is related to aquifer

porosity and storage. After recharge, the rise in ground-

water levels may be greater and sustained longer in aqui-

fers with low permeability than in aquifers with high

permeability (Sara 2003). Groundwater plays an important

role in slides, flows (debris flows, debris avalanches, earth

flows, mudflows and creep) and lateral spreads. Water is

often a primary controlling factor in landslide occurrence

and most landslides are preceded by saturation of the slope

due to heavy rainfall or periods of extended precipitation,

although the increase in groundwater level may be related

to earth dams, banks of lakes, reservoirs, canals and rivers

(United States Geological Survey 2004). Overlaying the

groundwater level maps on the LSZ map, the areas with

low groundwater level are generally in the low to moderate

hazard zones while high groundwater level areas largely

coincide with high hazard zones.

Rainfall maps

The rainfall maps (Fig. 17) showed that the rainfall in this

region varies both with the topography and temporally.

A high intensity of rainfall is always associated with an

increase in landslides (Au 1998). The annual rainfall in

1999 varied between 700 mm and 1,850 mm while in 2004

it was between 250 mm and 1,375 mm. There is generally

more rain in the SW, i.e. it increases from the Jhansi region

towards the Panna region, where the links start at the

Gangau weir. Changes in monsoon rainfall affect land-

sliding while a long, dry spell followed by a short and

heavy rain event can significantly affect the vegetation and

ground water level, making the area more vulnerable to

landslides (Ratnayake and Herath 2005).

Conclusions

Remote sensing and GIS technology is very useful for

resource management and project development as it facil-

itates the integration of numerous data to generate landslide

susceptibility zonation maps. The LSZ map produced in

this study divides the area into high, moderate, low and

very low susceptibility zones, which have been shown to

correlate well with rainfall and groundwater level. The

information generated from this study can be applied by

decision makers to mitigate slope instability during the

major construction works involved in the Ken-Betwa River

Link Project. In addition, the basic data will assist in slope

management and land-use planning as deforestation,

exposure of rocks, blasting for tunnels, construction of the

canal, etc. will affect the hydrological conditions of the

slopes and unless managed carefully, it may promote rapid

run off and erosion and hence increase the possibility of

landslides and debris flows.

Acknowledgments The Authors are very grateful to Council for

Scientific and Industrial Research (CSIR), New Delhi, for the Fel-

lowship. They are also indebted to the Indian Meteorological

Department (IMD), the National Water Development Authority

(NWDA) and the Central Ground Water Board (CGWB) for the

provision of invaluable information for the study. Last but not least,

the authors also would like to put on record the contribution of the

School of Environmental Science, JNU, New Delhi, in facilitating

data analysis in its laboratories and of the MEXT Japanese Govern-

ment Fellowship to pursue research in The University of Tokyo,

Japan.

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