Landslide Hazard Potential Analysis Using Gis, Patalganga Valley, Garhwal, Western Himalayan Region...

8/8/2019 Landslide Hazard Potential Analysis Using Gis, Patalganga Valley, Garhwal, Western Himalayan Region of India[1] (1)

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LANDSLIDE HAZARD POTENTIAL ANALYSISUSING GIS, PATALGANGA VALLEY,GARHWAL, WESTERN HIMALAYAN

REGION OF INDIA

Dr.Kumar Kishor Scientist F, Geotechnical Engineering Division, Central Road Research

InstituteNew Delhi, IndiaRahul Devrani

Department of Geology,University of Delhi, [email protected]

Sudhir Mathur Scientist F, Geotechnical Engineering Division, Central Road ResearchInstitute

New Delhi, India

Abstract

Patalganga drainage basin, a mountainous valley, is located in North western Himalayanregion of Garhwal between longitude 79 0 29 15 E - 79 0 29 30 E and Latitude 30 0 29 25

N - 30 0 29 15 N. An important National Highway No. 58 goes across the valley and isthe only connecting route between this region and rest of the country. The valley as well

as the National Highway had suffered from bewildering variety of Landslides and other mass movements during an unprecedented rainfall of July 1970. Since 1970, the

population and the associated developmental activities in the valley got increased bymany folds without any disaster mitigation and management planning. The vulnerabilityof the valley to landslide disasters also increased relatively to the developmentalactivities. To study the current status as well as the future probability of landslidedevelopment and their consequences, a project was initiated in the year 2002. Under this

project Landslide Hazard Potential (LHP) analysis of the basin was carried out to preparea Landslide Susceptibility Potential Map (LSPM) for guiding future development withinthe basin. To do such analysis and prepare LSPM a variety of thematic information maps

pertaining to the different factors were generated in 1:12500 scale using latest satellite

data and extensive field investigations. All the maps with their analysis and outputs are prepared in GIS platform. In this paper a stepwise approach applied for landslide hazard potential analysis is described with a final output of Landslide Susceptibility PotentialMap of the area which will be helpful in guiding the development of the valley in future.

Keywords: Landslide Susceptibility Potential Map, Thematic Information Layers,Factors Rating, Probability Analysis
mailto:[email protected]:[email protected]


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1. IntroductionHazards related to landslides are major societal and environmental concern. These areresponsible for considerable socio economic losses than is generally recognized (Khire,2004) and because of their socio economic implications as well as the scientific interest,these can be considered as a problem of greater relevance (L. Cascini et al., 1992). The

study of landslides has drawn global attention mainly due to the increasing awareness of its socio-economic impacts and increasing pressure of urbanization on the mountainenvironment (Aleotti and Chowdhury, 1999). Landslides have represented 4.89% of thetotal natural disasters that occurred worldwide during the years 1990 - 2005 ( www.em-dat.net ). These are rapidly becoming the focus of major scientific research, engineeringstudy and practices, and landuse policy making throughout the world (Mihail Popescu,1996). The human activities and necessities accentuate slope failures, accelerate soilerosion and landslides (Valdiya, 1992).The development of human activity has demandedthe utilization of unstable slopes which often results in severe damage to theconstructions and residential areas (Hermosa et al., 1996).The increasing trend of landslide occurrences will continue in future with the increased unplanned urbanization

and development, continued deforestation and increased regional precipitation due tochanging climatic patterns (Schuster,1996) and Ercanoglu and Gokceoglu, 2004).

There is hardly any kilometer of the road left without landslides. It is estimated that everysquare kilometer in the fragile Himalaya shows up at least two landslide scars and onemore is added every 6 kilometer square (Bhandari, et al., 1984). Severity of the problem,as measured by the losses or the damages caused or by the engineering complexityinvolved, varies widely from location to location as well as time to time (Rao et al.,1996). For the last three decades frequency of landslides all over hilly areas of thecountry, particularly in and around developmental sites, has increased. It is thereforerequired to orient the studies in much larger perspective to identify the areas vulnerable to

landslide hazards, for sustainable and risk free development. In order to cope with theincreasing demand of modern industrialized India a large number of the developmental projects for communication, highways, dams, reservoirs etc has already started and are intheir different stages of development out of which some are being executed in and aroundstudy area. In view of this, landslide susceptibility analysis of the area was initiated toexplore the past, present position and future projection in respect of landslidedevelopment in the area.

2. Area of Interest and it strategic importancePatalganga valley, the study area, between latitudes 30 o 25 & 30 o 29 and correspondinglongitudes 79 o 28 & 79 o 35 in North western Himalayan region of Garhwal, was once

heavily damaged due to devastating landslides in 1970 (Fig. 1). The heavy rain followed by the cloudburst at the higher reaches of Patalganga generated a series of landslides alongthe two tributaries of Patalganga. A huge pile of debris discharged by the river Patalganga

blocked the main river Alaknanda and built a 20m high dam (Kishor Kumar et al., 2005).With the breaching of dam whole Belakuchi village (Fig.2) hat was located at less than akilometer away from the dam along with many other settlements in the downstream wasflushed within a few hours. The impact of this flashflood was so high that the entire
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Fig.1-Patalganga Valley

Srinagar city, located about 100 km downstream along Alaknanda, was heavily inundatedand was filled with silt and sand. Belakuchi (Fig. 2) that was once

used as a halting place for thousands of pilgrims and local people en-route to Badrinathshrine disappeared from the scene. A stretch of almost 13 km of the road between

Pipalkoti and Gulabkoti through Belakuchi (Fig.2) largely got washed away and theleftover portion was severely damaged and irreparable. To restore the road link onemergency basis, the existing footpath of 350m situated above the damaged alignmentwas converted into an alternate alignment without proper survey and mapping. This

activity made the entire stable stretch into highly landslide prone zone.


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Fig.2- Road through the Belakuchi

3.Methodology

The scope, scale and methodology of landslide hazard and risk assessment canvary significantly depending upon the aims and objectives (Chowdhury, 1999). LHZmapping is being carried out using qualitative or quantitative approaches. The qualitative

methods essentially depend on expert opinion in dividing an area into different zones of varying landslide susceptibility. The preliminary studies in the field of landslide zonationin India, comprising identification of vulnerable slopes in the Himalaya on the basis of

parameters like geology, hydrology and slopes were initiated by Oldham (1880),Middlemiss (1890). In a simple zonation, using the techniques of overlaying relief andgeological maps, Majumdar (1980) arrived at five, very general, landslide potential ratingsfor the northeastern region of India. In the Nilgiris, Southern India, Sheshagiri et al.(1982) carried out a five-category landslide zonation by assigning landslide susceptibilityvalues to different factors and landslide susceptibility index on the basis of percentage of landslides in each category. Bhandari (1994) described graded landslide hazard maps byoverlying various state of nature maps. The operative parameters used in the preparation

of Landslide Hazard Zonation maps in Northwest Himalaya on 1:50,000 scale includes (i)geology of the area, (ii) Morphometric features of slope segments and (iii) landslideincidences (Narula et al., 1996., Anbalgan., 1992., Gupta and Sharda., 1996., Sharda.,1994., Kishor., et al., 1996., Sharma., 1996). Gee (1992) suggested the term blind-weighting method for the qualitative/semi quantitative approach in which the expert givessubjective weights for the contributing factors and then integrate these to derive acumulative influence factor representing the degree of hazard. Pachauri and Pant (1992)demonstrated a weighted landslide hazard mapping procedure in the Aglar catchment of


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Fig.3- GCPs around Patalganga Landslide..

To evaluate the landslide susceptibility potential for the study area various causativefactors controlling the slope stability or influencing it have been identified and prioritizedsuch as lithology and structure, geomorphology, stream network, climate, vegetation,landuse land cover , existing landslides, slope angle and slope aspect. These factors have

been identified based on the knowledge of the past events and present conditions prevailing in the area in anticipation of future behavior of slope stability. For each of theidentified factors analytical maps were prepared using LISS III (Linear Imaging Self-scanning Sensor and Panchromatic (PAN) merged data, base map, extensive fieldmapping and other available information. All the maps have been generated in digital

platform in Arc-GIS 9.0 software. Each causative factor and their classes have beenassigned appropriate rating or weightage both in terms of alphabets (qualitative) andnumerals (Quantitative) as per their role in inducing slope instability (Kishor Kumar,1996). The final grading is provided on the basis of the sum of the total estimatedsignificance of each of the factors. Each of the factor maps were crossed according to the

priority assigned which has resulted into a combined map i.e. Landslide SusceptibilityPotential Map (LSPM) to evaluate the actual hazard situation in the area.The stepwise explanation of methodology followed in brief is given as under

1. Selection of the scale for mapping work: The working scale for a landslideSusceptibility potential Mapping (LSPM) should be determined by the requirementsof the user for whom the survey is executed. In most of the cases, in India, the scaleof LSPM is decided on the basis of availability of the base maps like topographic andgeological maps. In the present case, the maps were available only in the scale of 1:50,000. Such maps do not posses all the information required for preparing a


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purposeful LSP map, which can serve the purpose of guiding development in hillyareas. Therefore, the base map was prepared in the scale of 1:12,500.

2. The topographic map available in the scale of 1:50,000 was used as an importantreference map and Stereo pair data used for preparing a DEM (Digital ElevationModel) of the area. The DEM was then used for preparing a contour map

supplemented and corrected by the ground GCPs (Ground Control Points) collectedwith the help of a precision GPS (Global Positioning System). The contour map was prepared in the scale of 1:12500.

3. The factors (thematic information layers) to be studied were prioritized as per their role in inducing the slope instability in the area.

4. Individual factor (thematic) maps were prepared from remote sensing data, generatedcontour map and available topographic data.

5. Factual information about each theme was collected through extensive fieldobservations.

6. Final theme maps were prepared after correction of each of them based on the factualinformation collected.

7. Superimposition of each factor maps was done one by one through ArcGIS software.8. Out puts on interaction of each factors (their combinations) were derived.9. Rating system for each of the factors and their sub factors or classes were evolved.10. Final Landslide Susceptibility Potential map, as per the susceptibility criterias was

prepared.

Preparation of thematic data layers

A number of thematic data layers corresponding to the identified causative factorsnamely lithology and structure, geomorphology, stream network, landuse and landcover,

slope angle and aspect have been prepared. These causative factors have both individualand collective influence on slope instability and are considered responsible for theoccurrence of landslides in the area. Data pertaining to intrinsic (lithology,geomorphology, slope attributes, slope aspect) as well as extrinsic factors (landuse andlandcover) have been collected from the available resources as well as from the field.Data regarding rainfall and earthquake which are triggering extrinsic factors could not betaken into consideration because of the unavailability of past data in relation to landslideoccurrence. A thematic layer corresponding to landslide distribution has also been

prepared to establish correlation of existing landslides and the causative factors, whichwill be helpful for preparation and evaluation of the landslide susceptibility potential mapof the area.

IRS IC- LISS III (acquired in the month of December 2002) and ID-PAN (acquired inthe month of December 2002) data along with topographic map of Survey of India in 1:50,000 scale and the geological map of the area in 1:50,000 scale are the main sourcesused to generate these thematic data layers. Preliminary survey in the month of April-May 2003 followed by extensive field survey and mapping in 2003-2005 was conductedto collect data pertaining to existing landslide distribution, collection of GCPs using


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Fig. 5- Landform Classification Map of Patalganga basin

The area of morphological classes varies from 0.04 sq km of River terrace to maximum22.2 sq km of highly dissected hills and the area of landslides ranges from 0.017 sq kmagainst low dissected hills to 1.72054 in the class of Highly dissected hill followed by0.539035 in Intermountain valley fill, 0.200725 in River bed, 0.159209 in Moderatelydissected hill, 0.080589 in Snow fed zone and 0.026129 in River bed. Number of landslides against the morphological classes is as HDH- 175, Intermountain valley fill-140, LDH-8, MDH-51, River bed-44; River terrace- 7and Snow fed zone-2.

4.2. Drainage map

Since many of the mass wasting processes begin and continues with the help of either ground water or surface water, it becomes important to generate and analyze the streamnetwork and its parameters so as to understand their role in landslide occurrences in the

valley. Many of the landslides in the area occur due to drainages. Therefore a drainagemap of the area was prepared by digitizing Survey of India (SOI) topographic map in avector layer. Another drainage map was extracted from high-resolution remote sensingIRS-1C LISS- + PAN MERGED data. Both the maps were compared to obtain the


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Fig.6- Drainage map of Patalganga Basin

current stream network details (Fig.6). First order streams, on which the whole orderingsystem linked is required to be correctly defined. The ordering has been performed onthe basis of strahlers classification scheme (Strahler, 1964). A significant increase innumber of first order streams has been detected from the recent satellite data. On thetoposheet (of 1965) only 137 number of first order streams have been shown while thenumber detected from satellite data was 473. This increase could be because of tworeasons: first, not all the first order streams had been marked on the topographic map andsecondly there was some addition of the first order streams from 1965 to 2002.Drainages up to 5th order have been observed in the study area. 35m buffer zones oneither side of the drainages for all the drainage orders were created using the buffer wizard under the utility tools of Arc- GIS 9.0. When landslide distribution data layer wassuperimposed over the drainage buffers, it was observed that majority of landslideoccurred in the 1st, 2nd and 3rd order drainages. The buffer was created to understand therole of drainages in relation to landslide development, however, not used as a factor in

preparation of LSPM.

4.3. Lithological Map


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There was no large scale geological map available for the Patalganga basin. The regionalgeology map in 1:50,000 scale only displays approximate boundaries of the geologicalformations with broad geo-structural setup. The micro structures as well as the geologicalformations are missing out in the map and therefore may be inaccurate or misleading.The map of geology prepared in 1:12,500 scale gives scope to work out relationship

between the geological formation and the landslide development. Geological details canalso have important influences on the type and activity levels of slides (wills et al., 2004).Such geological map aims at meeting the requirements of exposing the geological factorsthat affect the stability of slopes. The map involves type of rock formation mapped in thefield, attitudes of bedding/foliation, presence of major structures such as faults or lineaments. The major part of the Patalganga drainage basin lying in south andsouthwestern side comprises of Lesser Himalayan metasedimentary rock sequencedominated by dolomitic limestones and slates with quartzites and metabasics. Thesequence is thrusted on the North and Northeastern side by Higher HimalayanCrystalline/metamorphics along the Main Central Thrust (MCT) (Auden 1935, 1937;Heim & Ganser 1939; Ganser 1964; Gaur et.al 1977; Valdiya 1980). The entire

geological sequence is exposed between the mouth of the Patalganga valley in the WSWto Kunwarikhal in ESE, within the Drainage Basin (Table 1 and Fig. 7). Detailedgeological traversing was done around the low lying areas in the valley and in allapproachable areas in the higher reaches. For rest of the areas the geology and structurewere interpolated in the light of satellite data interpretation. Lithology defines the typesof rock, their attitudes, their total exposure and overall existing conditions such asweathering erosion and landsliding. Seven types of rock were identified from in the areaviz Gulabkoti quartzite, Dolostone, Metabasics, Carbonaceous shales, Quartzite,Magnesite and Central Crystallines. Their stratigraphic and tectonic setup has already

been defined under the section of geology. Each of the rock are assigned weightagedepending on their type, structure, weathering, erosion and other characteristics: Thegrading has been assigned in such a way that, strong rock, less susceptible to landslide,comes first followed by moderate, weak and weakest, Table.2 Gulabkoti quartzite wasfound comparatively more fractured than other type of quartzites available in the area.Since fracturing is more, weathering and erosion are also proportionately developed. Outof 13.24 km 2 area of Gulabkoti quartzite, 1.04 km 2 is covered by 212 landslides(including small slumps and slips).


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Fig 8 - Major Geological Structures In PatalgangaBasin

Some landslides are large up to 0.3 km 2 while a number of others are small (0.001 km 2) tovery small (0.0002 km 2). Next to the Gulabkoti quartzite, Dolostone formation has 4.14km 2 area out of which 0.39 km 2 is covered by 94 landslides. Metabasics formation with

2.12 km2

area has 70 landslides covering 0.39 km2

area. Carbonaceous shales with 0.83km 2 area have 54 landslides covering 0.39 km 2 area. Quartzite, out of total 3.14 km 2 area,0.25 km 2 is of 29 landslides. If we compare the area vs. landslides the average size of thelandslides developed in central crystalline is the largest followed in decreasing order byquartzite, carbonaceous shale, metabasics, Gulabkoti quartzite, Dolostone and Magnesite.

4.4 Structure and Tectonics

The lesser Himalayan sequence is folded into a large domal anticline known as Pipalkotianticline, with a most conspicuous vertical joints/ fracture cleavage (some times having

steep dips in north or south) developed on the northern limb approaching the MCT zoneor MCT sole thrust. These steep dipping joints also appear on the air photos and satelliteimages as significant linear structures. Gulabkoti thrust is another major plane of dislocation in the area which broadly separates the underlying dominantly carbonatesequence from the overlying largely arenaceous facies. Two right lateral strike-slip faultsnamely the Patalganga fault, which follows the drainage course towards the basin mouthand Nauligwar fault, off setting obliquely to the Patalganga fault near the confluence of Patalganga (Semkura Nadi) and Neo-Ganeshganga, has been mapped. After identifyingand mapping these linear structures in the field, the map has been digitized in a vector layer. 250m buffer zones on either side of the fault/thrust have been created. Landslidedistribution data layer was superimposed over the buffer zones, it was observed that

majority of landslide occurred with in the distance of 250m from the fault/thrust zone(Fig.8).


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Landslide Inventory Map of Patalganga Basin

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Fig.9- Landslide Inventory Map

4.5. Landslide Inventory Map

This is an important factor map comprising of the information about the existinglandslides in the area. Such type of map not only provides information about the current

situation of landslide in the area but also provides opportunity to validate and correct thelandslide susceptibility potential map. The map can also be used for knowing recurrentlandslides and also old but quiescent landslides. Distribution of landslides in inhabitantareas, agriculture land, forest land, etc can also be seen on the map to have an instant ideaabout the gravity of landslide problem and its influence on socio-economic condition of the region. Using an inventory of existing landslides, the expert can assess the hazard of the area by identifying regions of similar geological and geomorphological conditions(John Mathew et al., 2007).

Since the terrain is difficult, it was not possible to map each and every landslide throughmanual field survey. The mapping was accomplished by the interpretation of satellite

images supported by field checks. The satellite images are used in the identification of landslide scars, demarcation of areas affected by landslides and areas susceptible tolandslides (Nagrajan et al., 1998, Soeters et al., 1992,). Identification of landslides onremote sensing images is based on the spectral characteristics, shape, contrast andmorphological expression (D.P.Kanungo et al. 2006). False color composites and Highresolution multispectral/PAN ortho-image have been used to interpret and maplandslides. In most of the cases landslides are recognized due to characteristic spectralresponse (mostly show up as bright pixels), typical oval/elongated shape and proximity tothe river bank/ridge or initiation point of first order stream. Many of the old slide zoneshave been extracted from their triangular/crescent pattern, fresh vegetation growth and

barrenness (Champati Ray 2004).

A total of 382 landslides of varying dimensions (200 m 2 to 390,000 m 2) have beenidentified from remote sensing images and field surveys. A majority of landslidesmapped are small having an areal extent ranging from 100 m 2 to 2800m 2 and are


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observed mostly in rocky slopes (rock slides and rockfall) while a number of other landslides having comparatively larger extent (100,000 m 2 to 390,000 m 2) have beenobserved in overburden slopes. Landslides thus identified have been digitized as

polygons. This layer has been converted to a rasterized landslide distribution map (Fig.8).

4.6. Landuse landcovers MapThe landuse and management of the areas is also one of the primary concerns becausethis is one factor which not only reflects the current use of the land, pattern and type of itsuse but also the importance of its use in relation to the living population and itsrelationship with the existing landslide development. The categories selected for landuselandcover (Fig.9 in the current case are based on the type of vegetation and agricultural

because these are only the current landuse management categories practiced in the area.It is generally noticed that almost all the roads in the region, including National highway58 are suffering from the recurrent landslide problems. These Landslides are reported assoon as they occur or recur. But the landslides which occur away from the highways

(close to the habitation) are rarely reported and are also not seriously considered for mitigation etc. For example, as a result of this negligence, landslides which developed inPatalganga valley during the heavy rain of 1970 gradually got enlarged in their size andfrequency and caused serious geo-environmental as well as socio-economic problems. Anentire village, a few other scattered houses and huge cultivation land (irretrievable) wasfound damaged. The Inhabitants still living there are seriously considering abandoningthe area. Most of the land in the valley is used for the cultivation of seasonal crops, fruit

bearing plants and forest. The categories of landuse selected for the current case aretherefore mainly based on type of vegetation and agricultural or grassland. Five classesof landuse have been selected as agriculture/grassland, alnus and pine, Barren/rocky land,Deodar/mixed forest and Rhododendron forests. Area of the each class is 14.46 sq km,

4.06 sq km, 4.63 sq km, and 6.37 sq km and 19.23 sq km respectively. Against each classthere are a number of landslides viz 95,23,82,15 and 59 respectively and area of landslides under each class as 0.47 sq km, 0.28 sq km, 0.29 sq km, 0.07 sq km and 0.39sq km respectively. However these numbers of landslides are misleading because they donot take into account the extent of each units exposure e.g. Agriculture and grasslandspread over 14.46 km 2 area where as barren slope covers only 4.63 km 2. When thelandslides that occurred in the agricultural and grassland areas are divided by the area of exposure, the number of landslides per unit area is 6.5. This number is a better indicator of the susceptibility of a type of landuse, agricultural and grassland (7), Alnus and Pine(6), Barren/rocky land (20), Deodar/mixed forest (2) and Rhododendron forests (3).


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4.7. Digital Elevation Model (DEM) and its derivativesDEM is a digital representation of ground surface topography or terrain. It is also widelyknown as a digital terrain model (DTM). DEM can be represented in raster (a grid of squires) or as a triangular irregular network. The use of DEM to observe topographicattributes, such as slope aspect and steepness allows researchers to comprehensivelyexamine the variables affecting landslide occurrence in a study area (Sawyer et al., 2004)DEM of the area was generated using the stereo pair (a pair of stereophotos) through

photogrametry techniques. The quality of the derived maps such as slope and aspectsdepends on the quality of the DEM representation of the earth surface. In many caseserrors in this representation are neither measured nor estimated (James et al., 1997). In

the present case, to get the accurate DEM, a real time differential GPS was used toacquire ground truth data. This ground truth data is compared with DEM generated fromstereo pair. DEM was used for determining attributes of terrain, such as slope andaspects.

Combination of the slope angles basically defines the form of the slope and itsrelationship with the lithology, structure, type of soil, drainage and the landslides.

Fig.10- Landuse andLandcover Map


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NW and flat as per the classification given in other studies (Sarkar and Kanungo, 2004;Saha et al., 2005).

5. Rating of the factors/themesAs discussed in the preceding sections, each factor/theme has been studied in relation to

landslide development in the area. This relationship has formed the basis to assignweightings/ratings to each of the factors and its classes as per their estimated contributionin inducing slope failures. To be simple and understandable the theme classes were listedin ascending order as per their contribution in inducing landslides and based on thenumber of inferred landslides in each class (Table 2). For example, if there are sevenclasses under a theme, the class with the minimum contribution comes first, on top of thelist and given 1(minimum rating) and the class with maximum contribution comes at the

bottom of the list i.e. at seventh place and 7 (maximum rating). The final rating is provided on the basis of the sum of the total estimated significance of each factor.The individual weighted theme class maps were laid over one another and arithmeticallyadded according to the following equation to generate a Landslide Susceptibility

Potential (LSP) map in GIS.

LSP = L + M + SL + AS + LU

Where L, M, SL, AS and LU are abbreviations for the weighted thematic layers for lithology morphology, slope, aspect and land use land respectively. A total of 489combinations of theme classes have been formed with weights ranging from 9 to 33.Comparing the combinations and their distribution with the distribution of the existinglandslides realistic rating ranges were evolved to categorize landslide susceptibility

potential as 9-14, 15-21, 22-27, 27-32 and 32 and above. Since all the above exerciseshave been done in GIS, simultaneous, updating and verification based on the actual field

inputs was also carried out. The boundaries of the theme classes were drawn at LSPvalues to obtain five susceptibility zones. The LSP map thus obtained is shown in Fig.12.The final LSP (Landslide Susceptibility Potential) grading on LSPM has the range from Ito V. Grading I & V indicate the area least and most vulnerable to landsliderespectively. LSPM was crossed with the existing landslide map of the area. The crossedmap has indicated that majority of existing landslides come under the most to moderatelysusceptible categories. This was followed by the probability analysis as indicated infollowing section.

5.1. Probability analysisThe LSPM was later verified by doing the probability analysis of the selected factors andtheir classes. For probability analysis, area of each factor and active landslides (existing)in each class of selected factors was calculated (Table 2). Probability value Pij = Nlsij / Nlsij was calculated for each class (i) and of each factor (j). The total probability wasanalyzed for all classes of selected factors using the equation by Gulakiyan G.A., kuntselV.V.,Postoev G.P. (1977)


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Wls= 1 k = 1

t

1 P k 1

i , j = 1

t

P ij 2

P k = P ij= Nls ij / N ij 3Where

Wls - Geotechnical potential of a landslide, P k - Total probability of all classes of factors

P ij - Probability for classes i of factor j, Nlsij - Area of landslide within the limit of a class Nlsi - Area of a class

Landslide potential values have been derived using above procedure range from 0.20 to0.90. This range was further divided into four sub ranges viz 0.20 -40, 0.41-0.60, 0.61-0.80, 0.81 and above. Using these values, landslide potential value map has been

obtained. On overlapping Landslide distribution map over the Landslide potential valuemap it was found that most of the landslides fall under the value category III followed IV,II and I. By comparing the total probability value of each factor indicated in Table 2 withthe landslide potential value, geomorphological factors indicate maximum probabilityvalue (90.0) and amongst its classes, river terrace, has the maximum value (0.75), whichis rightly so because most of the landslides in the area have occurred along the river course in the terrace formations having high degree of development including road,houses, agriculture etc.

Fig.13- Landslide Susceptibility


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Factors(j)

Class (i)Characteristic

s

ratings

Probability Analysis

Area of Probability

TotalProbability

Class

(Nlsi)

Landslide

(Nlsij)

Pij(Nlsij/N

lsi)

Pk

Lithology (L) CentralCrystalline

Magnesite/DolomiteQuartzite,CarbonaceousslatesMetavolcanics, ,Gulabkotiquartzite,Dolostone,

a(1)b(2)

c(3)d(4)e(5)f(6)g(7)

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3.343.143.112.7516.6

84.14

0.080.02

0.250.390.431.040.39

0.0070.005

0.0800.1250.1560.0620.094

0.529

LandformClassification (M)

River terraceLow dissectedhills

River bedSnow fed zoneIntermountainvalley fillModeratelydissected hillHighly dissectedhills

h (1)i(2)

j(3)

k (4)l (5)m(6)

n(7)

0.330.680.72

8.175.7211.3

322.1

7

0.300.020.20

0.080.540.161.07

0.7500.0290.278

0.0100.0940.0140.048

0.909

Slope Angle(SL)

51-600-1011-2041-50

21-3031-40

o(1)p(2)q(3)r(4)

s(5)t(6)

0.314.545.5810.8

010.7

818.5

9

0.040.280.490.53

0.761.03

0.1270.0620.0880.049

0.0710.055

0.452

Land useand landcover (LU)

Deodar/mixedforestalnus and pine,RhododendronforestsBarren/rockyland,agriculture/grassland,

u(1)v(2)w(3)x(4)y(5)

6.374.0619.234.6

314.4

6

0.080.290.400.300.47

0.0130.0710.0210.0650.033

0.203

Structure &Tectonics(T-Thrust, F-fault)

Buffer Distancefrom T/F1.0-1.50.5-1.00-0.5

z)1z1)2z2)3

Not taken for probability analysis

SlopeAspects(SA)

ESENNESSENNWWSWSSWENE

z 3)1z 4)2z 5)3z 6)4z 7)5z 8)6z 9)7

1.193.912.537.236.126.0712.1

0.040.130.300.220.310.280.37

0.0340.0330.1190.0300.0510.0460.030

0.384


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Table. 2 Probability analyses of selected factorsand their classes

6. Conclusions

In the present study, simple conventional based method of subjective weighting has beenapplied for Landslide Susceptibility Potential Mapping in Patalganga basin, part of NorthWestern Himalayan region of Garhwal. Probability analysis was carried out for verifyingthe LSP map. Although the method applied for such mapping was simple, result

produced proved accurate as may be attributed to the following reasons.

1) Landslide susceptibility Potential Map prepared indicates that most of the slopes inthe valley fall in High to moderate susceptibility potential category.2) A large part of the land in these categories is found under agricultural activities whichare indeed susceptible to sliding.

3) There are a few settlements including the partially damaged village Ganai indicatedin the LSP map under the high susceptible potential category which has been verified inthe field.4) A total of 386 landslides have been encountered in the valley out of which a few are

of moderate size as that of Patalganga landslide where as others are in between minor to moderate mostly indicated under High to moderate susceptibility potentialcategory.

5) During 1970, when a series of landslides occurred and devastation took place due tounprecedented rainfall, the topographical, geomorphological and geologicalconditions were different as the area was less exploited by human being, thevegetation was comparatively more resulting in minimum erosion, the rocks were less

exposed and weathering was least. In the present situation where a major part of thearea falls under the high and moderate vulnerable class, the slopes are more dissected,eroded and weathered with many existing landslides, if similar amount of

precipitation that occurred during July 1970 replicates, a bigger tragedy may strike asthe existing terrain conditions are extremely bad as compared to what had been 34years back.

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