Landslide Hazard Potential Analysis Using Gis, Patalganga

European Journal of Scientific Research ISSN 1450-216X Vol.45 No.3 (2010), pp.346-366 © EuroJournals Publishing, Inc. 2010 http://www.eurojournals.com/ejsr.htm

Landslide Hazard Potential Analysis Using Gis, Patalganga

Valley, Garhwal, Western Himalayan Region of India

Kumar Kishor Scientist F, Geotechnical Engineering Division

Central Road Research Institute New Delhi, India

Rahul Devrani

Department of Geology, University of Delhi, India E-mail: [email protected]

Sudhir Mathur

Scientist F, Geotechnical Engineering Division Central Road Research Institute

New Delhi, India

Abstract

Patalganga drainage basin, a mountainous valley, is located in North western Himalayan region of Garhwal between longitude 790 29’ 15” E - 790 29’ 30” E and Latitude 300 29’ 25” N - 300 29’ 15” N. An important National Highway No. 58 goes across the valley and is the 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 by many folds without any disaster mitigation and management planning. The vulnerability of the valley to landslide disasters also increased relatively to the developmental activities. To study the current status as well as the future probability of landslide development 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 prepare a Landslide Susceptibility Potential Map (LSPM) for guiding future development within the 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 Potential Map 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

Landslide Hazard Potential Analysis Using Gis, Patalganga Valley, Garhwal, Western Himalayan Region of India 347

1. Introduction Hazards related to landslides are major societal and environmental concern. These are responsible 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 mountain environment (Aleotti and Chowdhury, 1999). Landslides have represented 4.89% of the total natural disasters that occurred worldwide during the years 1990 - 2005 (www.em-dat.net). These are rapidly becoming the focus of major scientific research, engineering study and practices, and landuse policy making throughout the world (Mihail Popescu, 1996). The human activities and necessities accentuate slope failures, accelerate soil erosion and landslides (Valdiya, 1992).The development of human activity has demanded the utilization of unstable slopes which often results in severe damage to the constructions 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 to changing climatic patterns (Schuster,1996) and Ercanoglu and Gokceoglu, 2004).

There is hardly any kilometer of the road left without landslides. It is estimated that every square kilometer in the fragile Himalaya shows up at least two landslide scars and one more 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 complexity involved, 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 the country, particularly in and around developmental sites, has increased. It is therefore required 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 the increasing demand of modern industrialized India a large number of the developmental projects for communication, highways, dams, reservoirs etc has already started and are in their different stages of development out of which some are being executed in and around study area. In view of this, landslide susceptibility analysis of the area was initiated to explore the past, present position and future projection in respect of landslide development in the area. 2. Area of Interest and it strategic importance Patalganga valley, the study area, between latitudes 30o 25’ & 30o 29’ and corresponding longitudes 79o 28’ & 79o 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 along the 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 a kilometer away from the dam along with many other settlements in the downstream was flushed within a few hours. The impact of this flashflood was so high that the entire Srinagar city, located about 100 km downstream along Alaknanda, was heavily inundated and 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 Badrinath shrine 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 the leftover portion was severely damaged and irreparable. To restore the road link on emergency basis, the existing footpath of 350m situated above the damaged alignment was converted into an alternate alignment without proper survey and mapping. This activity made the entire stable stretch into highly landslide prone zone.

348 Kumar Kishor, Rahul Devrani and Sudhir Mathur

Figure 1: Patalganga Valley

Figure 2: Road through the Belakuchi

3. Methodology The scope, scale and methodology of landslide hazard and risk assessment can vary significantly depending upon the aims and objectives (Chowdhury, 1999). LHZ mapping 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 zonation in 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 and geological maps, Majumdar (1980) arrived at five, very general, landslide potential ratings for the northeastern region of India. In the Nilgiris, Southern India, Sheshagiri et al. (1982) carried out a five-category landslide zonation by assigning landslide susceptibility values to different factors and landslide susceptibility index on the basis of percentage of landslides in each category. Bhandari (1994) described graded landslide hazard maps by overlying 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) landslide incidences (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 gives subjective weights for the contributing factors and then integrate these to derive a cumulative influence factor representing the degree of hazard. Pachauri and Pant (1992) demonstrated a weighted landslide hazard mapping procedure in the Aglar catchment of Himalaya and used parameter-weighting method for LHZ mapping in part of Bhagirathi valley of Garhwal Himalaya was used by Gupta et al., (1999) and Saha et al., (2002). In recent times, the availability of a wide range of remote sensing data along with data from other sources in digital form and their analysis using GIS, has made it possible to prepare different thematic layers corresponding to the causative factors that are responsible for the occurrence of landslides in a region (Kanungo et al., 2006; Gupta and Joshi, 1990; van Westen, 1994; Nagarajan et al., 1998; Gupta, 2003). The integration of these thematic layers with weights assigned according to their relative importance in a GIS environment leads to the generation of an LSZ map (Kanungo et al., 2006; Gupta et al., 1999; Saha et al., 2002; Sarkar and Kanungo, 2004; Saha et al., 2005).

Most of the landslide hazard maps in India referred above were prepared in the scale of 1:50,000. The working scale for most of the landslide hazard mapping programe was not determined according to the requirements of the users, instead merely as an academic exercise to broadly exhibit the potentiality of such maps for regional planning (Sikdar et al., 2002). The maps, created on scale like 1:50,000 and smaller do not posses all the information required for preparing a LHZ map, that serve the purpose of guiding development in hilly areas. However, if one uses more detailed map and a DGPS (Differential global positioning System), accurate mapping is possible (Lee et al 2004). In the present case a base map of the study area was prepared in 1:12,500 scale and was used for the preparation of rest of the thematic maps and landslide susceptibility potential map. The base map was prepared using stereo pair of IRS –satellite’s Panchromatic (PAN) and extensive field survey by a DGPS. The DEM (Digital Elevation Model) which is an excellent source to derive topographic attributes responsible for landslide activity was first generated from satellite data. To get a better accuracy, 53 GCP’s (Ground Control Points) (Fig.3) have been selected on the DEM for locating them on the ground with the help of DGPS. After locating the majority of points on the ground those were registered with the DEM and subsequently the contour map of the area was generated. The DEM was also used to derive the slope and aspect data layers Identification and mapping of a suitable set of instability factors bearing a relationship to slope failures requires a prior knowledge of the main causes of landslides (Guzzeti et al 1999).


Figure 3: GCPs around Patalganga Landslide

To evaluate the landslide susceptibility potential for the study area various causative factors controlling the slope stability or influencing it have been identified and prioritized such 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 the identified factors analytical maps were prepared using LISS III (Linear Imaging Self-scanning Sensor and Panchromatic (PAN) merged data, base map, extensive field mapping 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 been assigned appropriate rating or weightage both in terms of alphabets (qualitative) and numerals (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 estimated significance 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 Susceptibility Potential 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 landslide Susceptibility

potential Mapping (LSPM) should be determined by the requirements of the user for whom the survey is executed. In most of the cases, in India, the scale of LSPM is decided on the basis of availability of the base maps like topographic and geological 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 purposeful LSP map, which can serve the purpose of guiding development in hilly areas. 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 important reference map and Stereo pair data used for preparing a DEM (Digital Elevation Model) of the area. The DEM was then used for preparing a contour map supplemented and corrected by the


ground GCP’s (Ground Control Points) collected with 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, generated contour map and available topographic data.

5. Factual information about each theme was collected through extensive field observations. 6. Final theme maps were prepared after correction of each of them based on the factual

information 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 criteria’s was

prepared. 4. Preparation of Thematic Data Layers A number of thematic data layers corresponding to the identified causative factors namely lithology and structure, geomorphology, stream network, landuse and landcover, slope angle and aspect have been prepared. These causative factors have both individual and collective influence on slope instability and are considered responsible for the occurrence of landslides in the area. Data pertaining to intrinsic (lithology, geomorphology, slope attributes, slope aspect) as well as extrinsic factors (landuse and landcover) 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 be taken into consideration because of the unavailability of past data in relation to landslide occurrence. A thematic layer corresponding to landslide distribution has also been prepared to establish correlation of existing landslides and the causative factors, which will be helpful for preparation and evaluation of the landslide susceptibility potential map of the area.

IRS –IC- LISS III (acquired in the month of December 2002) and ID-PAN (acquired in the 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 sources used 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 conducted to collect data pertaining to existing landslide distribution, collection of GCP’s using DGPS for DEM validation, validation of thematic maps and also reliability checking of the landslide susceptibility potential map in the field (Fig.4)


4.1. Landform Classification Map of Patalganga basin

Figure 4: Base Map Patalganga Basin

Geomorphology is the out shaping of the landmass as an expression of the dynamic evolutionary processes. Broadly speaking the Patalganga basin has a few prominent landforms such as hills of high, medium and low relief, dissected by a number of streams; a snow covered zone in the eastern side around Kunwarikhal; thick Quaternary deposits lying all along the drainage courses also in the form of numerous conical talus structures; alluvial terraces alongside the rivers mostly around low altitudinal areas, deep stream channels, and significant precipitous hill slopes (Fig. 5) largely covered by dense to moderate forests of Alpine Texas. As discussed in previous sections landform is classified into 6 classes which are hills of high, medium and low, relief dissected by a number of streams; a snow covered zone, thick Quaternary deposits lying all along the drainage courses.


Figure 5: Landform Classification Map of Patalganga basin

The area of morphological classes varies from 0.04 sq km of River terrace to maximum 22.2 sq km of highly dissected hills and the area of landslides ranges from 0.017 sq km against low dissected hills to 1.72054 in the class of Highly dissected hill followed by 0.539035 in Intermountain valley fill, 0.200725 in River bed, 0.159209 in Moderately dissected 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 stream network 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 drainage map of the area was prepared by digitizing Survey of India (SOI) topographic map in a vector layer. Another drainage map was extracted from high-resolution remote sensing IRS-1C LISS-ΙΙΙ + PAN MERGED data. Both the maps were compared to obtain the current stream network details (Fig.6). First order streams, on which the whole ordering system linked is required to be correctly defined. The ordering has been performed on the basis of strahler’s classification scheme (Strahler, 1964). A significant increase in number of first order streams has been detected from the recent satellite data. On the toposheet (of 1965) only 137 number of first order streams have been shown while the number detected from satellite data was 473. This increase could be because of two reasons: first, not all the first order streams had been marked on the topographic map and secondly 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 on either 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 was superimposed over the drainage buffers, it was observed that majority of landslide occurred in the 1st, 2nd and 3rd order drainages. The buffer was


created to understand the role of drainages in relation to landslide development, however, not used as a factor in preparation of LSPM.

Figure 6: Drainage map of Patalganga Basin

4.3. Lithological Map

There was no large scale geological map available for the Patalganga basin. The regional geology map in 1:50,000 scale only displays approximate boundaries of the geological formations with broad geo-structural setup. The micro structures as well as the geological formations 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 can also 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 factors that affect the stability of slopes. The map involves type of rock formation mapped in the field, attitudes of bedding/foliation, presence of major structures such as faults or lineaments. The major part of the Patalganga drainage basin lying in south and southwestern side comprises of Lesser Himalayan metasedimentary rock sequence dominated by dolomitic limestones and slates with quartzites and metabasics. The sequence is thrusted on the North and Northeastern side by Higher Himalayan Crystalline/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 WSW to Kunwarikhal in ESE, within the Drainage Basin (Table 1 and Fig. 7). Detailed geological traversing was done around the low lying areas in the valley and in all approachable areas in the higher reaches. For rest of the areas the geology and structure were interpolated in the light of satellite data interpretation. Lithology defines the types of rock, their attitudes, their total exposure and overall existing conditions such as weathering erosion and landsliding. Seven types of rock were identified from in the area viz 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 weightage depending on their type, structure, weathering, erosion and other characteristics: The


grading 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 was found comparatively more fractured than other type of quartzites available in the area. Since fracturing is more, weathering and erosion are also proportionately developed. Out of 13.24 km2 area of Gulabkoti quartzite, 1.04 km2 is covered by 212 landslides (including small slumps and slips). Table 1: Tectono- stratigraphic sequence exposed in Patalganga Basin Kunwarikhal (ESE)

Physiographic/ Tectonic zone Rock type/ Formation Approx. thickness (m)

Higher Himalaya Higher Himalayan Crystallines

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+1000 (top not known)

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Gulabkoti Thrust Metavolcanics 50 Dolomitic limestones and slates 400 Carbonaceous slates 200

Lesser Himalaya Lesser Himalayan metasedimentary sequence

Quartzites +50 (base not exposed) Mouth of Patalganga River (WNW)

Figure 7: Lithological Map

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Lithology of Patalganaga Basin

Some landslides are large up to 0.3 km2 while a number of others are small (0.001 km2) to very small (0.0002 km2). Next to the Gulabkoti quartzite, Dolostone formation has 4.14 km2 area out of which 0.39 km2 is covered by 94 landslides. Metabasics formation with 2.12 km2 area has 70 landslides covering 0.39 km2 area. Carbonaceous shales with 0.83 km2 area have 54 landslides covering 0.39 km2 area. Quartzite, out of total 3.14 km2 area, 0.25 km2 is of 29 landslides. If we


compare the area vs. landslides the average size of the landslides developed in central crystalline is the largest followed in decreasing order by quartzite, 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 Pipalkoti anticline, 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 zone or MCT sole thrust. These steep dipping joints also appear on the air photos and satellite images as significant linear structures. Gulabkoti thrust is another major plane of dislocation in the area which broadly separates the underlying dominantly carbonate sequence from the overlying largely arenaceous facies. Two right lateral strike-slip faults namely the Patalganga fault, which follows the drainage course towards the basin mouth and Nauligwar fault, off setting obliquely to the Patalganga fault near the confluence of Patalganga (Semkura Nadi) and Neo-Ganeshganga, has been mapped. After identifying and 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. Landslide distribution 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).

Figure 8: Major Geological Structures In Patalganga Basin

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4.5. Landslide Inventory Map

This is an important factor map comprising of the information about the existing landslides 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 the landslide susceptibility potential map. The map can also be used for knowing recurrent landslides and also old but quiescent landslides. Distribution of landslides in inhabitant areas, agriculture land, forest land, etc can also be seen on the map to have an instant idea about 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 through manual 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 to landslides (Nagrajan et al., 1998, Soeters et al., 1992,). Identification of landslides on remote sensing images is based on the spectral characteristics, shape, contrast and morphological expression (D.P.Kanungo et al. 2006). False color composites and High resolution multispectral/PAN ortho-image have been used to interpret and map landslides. In most of the cases landslides are recognized due to characteristic spectral response (mostly show up as bright pixels), typical oval/elongated shape and proximity to the river bank/ridge or initiation point of first order stream. Many of the old slide zones have been extracted from their triangular/crescent pattern, fresh vegetation growth and barrenness (Champati Ray 2004).

Figure 9: Landslide Inventory Map

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A total of 382 landslides of varying dimensions (200 m2 to 390,000 m2) have been identified from remote sensing images and field surveys. A majority of landslides mapped are small having an areal extent ranging from 100 m2 to –2800m2 and are observed mostly in rocky slopes (rock slides and rockfall) while a number of other landslides having comparatively larger extent (100,000 m2 to 390,000 m2) have been observed 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 Map

The landuse and management of the areas is also one of the primary concerns because this is one factor which not only reflects the current use of the land, pattern and type of its use but also the importance of its use in relation to the living population and its relationship with the existing landslide development. The categories selected for landuse landcover (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 highway 58 are suffering from the recurrent landslide problems. These Landslides are reported as soon 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 in Patalganga valley during the heavy rain of 1970 gradually got enlarged in their size and frequency and caused serious geo-environmental as well as socio-economic problems. An entire village, a few other scattered houses and huge cultivation land (irretrievable) was found damaged. The Inhabitants still living there are seriously considering abandoning the 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 are therefore mainly based on type of vegetation and agricultural or grassland. Five classes of 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 class there 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 0.39 sq km respectively. However these numbers of landslides are misleading because they do not take into account the extent of each unit’s exposure e.g. Agriculture and grassland spread over 14.46 km2 area where as barren slope covers only 4.63 km2. When the landslides 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).

Figure 10: Landuse and Landcover Map

4.7. Digital Elevation Model (DEM) and its Derivatives

DEM is a digital representation of ground surface topography or terrain. It is also widely known 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 topographic attributes, such as slope aspect and steepness allows researchers to comprehensively examine 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


aspects depends on the quality of the DEM representation of the earth surface. In many cases errors 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 to acquire ground truth data. This ground truth data is compared with DEM generated from stereo pair. DEM was used for determining attributes of terrain, such as slope and aspects.

Combination of the slope angles basically defines the form of the slope and its relationship with the lithology, structure, type of soil, drainage and the landslides. Previous landslide mapping studies have reported the dominance topographic attributes, such as slope angle and aspects, on areas of instability (Aniya, 1985). Slope angle is one of the key factors in inducing slope instability. There are many studies where relationships between slope angle and landslides have been established (P.B.Saxena et al., 1982). The slope map derived from DEM consists of 6 classes with 10° interval as per the slope classification used in other studies (Kishor Kumar, et al., 1996; Anbalagan, 1992; Gupta et al., 1999; Dhakal et al., 2000).

Figure 11: Slope Category Map

Slope Categories0-1011-2021-3031-4041-5051-61

1 0 1 Kilometers

Slope Category Map

Aspect is defined as a direction along the maximum lope inclination and has an indirect influence on slope instability. There is a proved relationship between the slope aspects and development of landslides in hilly areas. Certain aspects of the slopes are considered vulnerable to landslides while some others are considered as safe depending upon the region. Slope aspect influences the type of vegetation, microclimate and various other properties of slope forming material. It was established by Perring (1959) that, vegetation varies systematically with region, slope and aspect. Measurements taken at Millington Pastures in the Yorkshire Wolds shown that the volumetric soil moisture on south-facing slopes was typically 10–20% lower than on north-facing slopes during the summers of 2001–03 (J. Bennie et al., 2006). Tanaka (2005) while studying the Amehata river basin in the Minobu mountain belt of Southern Japanese Alps, Honshu Island, Japan has noticed that the South-facing slopes of this area are clearly steeper than north-facing slopes. He further found that many small landslides occurred on the south-facing slopes, while landslides on the north-facing slopes were less frequent, but larger. The slope aspect map of Patalganga valley was derived as a new thematic layer. This theme was prepared to workout the relationship between the various aspects of the slope and the landslides in the valley. The aspect data layer derived here represents nine classes, namely, N, NE, E, SE, S, SW,W, NW and flat as per the classification given in other studies (Sarkar and Kanungo, 2004; Saha et al., 2005).


Figure 12: Slope Category Map

N

Slope Aspect Category Map

BaspectENEESENNENNWSSESSWWNWWSW

1 0 1 Kilometers

5. Rating of the Factors/Themes As 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 assign weightings/ratings to each of the factors and its classes as per their estimated contribution in inducing slope failures. To be simple and understandable the theme classes were listed in ascending order as per their contribution in inducing landslides and based on the number of inferred landslides in each class (Table 2). For example, if there are seven classes under a theme, the class with the minimum contribution comes first, on top of the list 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 arithmetically added 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 489 combinations of theme classes have been formed with weights ranging from 9 to 33. Comparing the combinations and their distribution with the distribution of the existing landslides 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 exercises have 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 LSP values 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 I to V. Grading “I” & “V” indicate the area least and most vulnerable to landslide respectively. LSPM was crossed with the existing landslide map of the area. The crossed map has indicated that majority of existing landslides come under the most to moderately susceptible categories. This was followed by the probability analysis as indicated in following section. 5.1. Probability Analysis

The LSPM was later verified by doing the probability analysis of the selected factors and their 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 was analyzed for all classes of selected factors using the equation by Gulakiyan G.A., kuntsel V.V.,Postoev G.P. (1977)

)1(11

−Π−== k

t

kPWls

(1)

1,∑==

ij

t

jik PP

(2) /= ijijij NNlsP (3)

Where W ls : Geotechnical potential of a landslide, Pk : Total probability of all classes of factors Pij : 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 to 0.90.

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 value map 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 with the landslide potential value, geomorphological factors indicate maximum probability value (90.0) and amongst its classes, river terrace, has the maximum value (0.75), which is 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.

Figure 13: Landslide Susceptibility Potential Map


Table 2: Probability analyses of selected factors and their classes

Probability Analysis

Area of Probability Total Probability Factors (j) Class (i) Characteristics ratings

Class (Nlsi)

Landslide (Nlsij)

Pij (Nlsij/Nlsi) Pk

Central Crystalline a(1) 11.32 0.08 0.007 Magnesite/Dolomite b(2) 3.34 0.02 0.005 Quartzite, c(3) 3.14 0.25 0.080 Carbonaceous slates d(4) 3.11 0.39 0.125 Metavolcanics, , e(5) 2.75 0.43 0.156 Gulabkoti quartzite, f(6) 16.68 1.04 0.062

Lithology (L)

Dolostone, g(7) 4.14 0.39 0.094

0.529

River terrace h (1) 0.33 0.30 0.750 Low dissected hills i(2) 0.68 0.02 0.029 River bed j(3) 0.72 0.20 0.278 Snow fed zone k (4) 8.17 0.08 0.010 Intermountain valley fill l (5) 5.72 0.54 0.094 Moderately dissected hill m (6) 11.33 0.16 0.014

Landform Classification (M)

Highly dissected hills n(7) 22.17 1.07 0.048

0.909

51-60 o(1) 0.31 0.04 0.127 0-10 p(2) 4.54 0.28 0.062 11-20 q(3) 5.58 0.49 0.088 41-50 r(4) 10.80 0.53 0.049 21-30 s(5) 10.78 0.76 0.071

Slope Angle(SL)

31-40 t(6) 18.59 1.03 0.055

0.452

Deodar/mixed forest u(1) 6.37 0.08 0.013 alnus and pine, v(2) 4.06 0.29 0.071 Rhododendron forests w(3) 19.23 0.40 0.021 Barren/rocky land, x(4) 4.63 0.30 0.065

Land use and land cover (LU)

agriculture/grassland, y(5) 14.46 0.47 0.033

0.203

Buffer Distance from T/F 1.0-1.5 z)1 0.5-1.0 z1)2

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

0-0.5 z2)3

Not taken for probability analysis

ESE z3)1 1.19 0.04 0.034 NNE z4)2 3.91 0.13 0.033 SSE z5)3 2.53 0.30 0.119 NNW z6)4 7.23 0.22 0.030 WSW z7)5 6.12 0.31 0.051 SSW z8)6 6.07 0.28 0.046 ENE z9)7 12.15 0.37 0.030

Slope Aspects (SA)

WNW z10)8 9.59 0.39 0.041

0.384

Rainfall Uniform for all classes z11)1 Not taken for probability analysis 6. Conclusions In the present study, simple conventional based method of subjective weighting has been applied for Landslide Susceptibility Potential Mapping in Patalganga basin, part of North Western Himalayan region of Garhwal. Probability analysis was carried out for verifying the 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 in the valley fall in High to moderate susceptibility potential category.

2) A large part of the land in these categories is found under agricultural activities which are indeed susceptible to sliding.


3) There are a few settlements including the partially damaged village “Ganai” indicated in the LSP map under the high susceptible potential category which has been verified in the 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 potential category.

5) During 1970, when a series of landslides occurred and devastation took place due to unprecedented rainfall, the topographical, geomorphological and geological conditions were different as the area was less exploited by human being, the vegetation 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 the area 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 as the existing terrain conditions are extremely bad as compared to what had been 34 years back.

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