Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/taylor-francis/use-of-geographic-information-system-for-the-vulnerability-assessment-mSfxThbqp9
References
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
- Publisher
- Taylor & Francis
- Copyright
- © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
- ISSN
- 2474-9508
- DOI
- 10.1080/24749508.2019.1608410
- Publisher site
- See Article on Publisher Site
Abstract
GEOLOGY, ECOLOGY, AND LANDSCAPES 2020, VOL. 4, NO. 3, 171–186 INWASCON https://doi.org/10.1080/24749508.2019.1608410 RESEARCH ARTICLE Use of geographic information system for the vulnerability assessment of landscape in upper Satluj basin of district Kinnaur, Himachal Pradesh, India Amit Jamwal, Nidhi Kanwar and Jagdish Chandra Kuniyal HP Unit, Govind Ballabh Pant National Institute of Himalayan Environment and Sustainable Development, Kullu, India ABSTRACT ARTICLE HISTORY Received 7 January 2019 The present study was conducted in the upper river Satluj basin of district Kinnaur, Himachal Accepted 13 April 2019 Pradesh, India. The vulnerability assessment was done on the basis of selected parameters like slope, slope profile, slope aspects, relative relief, curvature, soil texture, lithology, river KEYWORDS morphometric, precipitation, land use and land cover, mass movement, flood, geological Geographic information elements, earthquake occurrences, and anthropogenic activity (hydroelectric projects). Here, a system; vulnerability quantitative and qualitative approach was used to generate physical vulnerability assessment assessment index; hazards index and vulnerability-based maps. Vulnerability assessment of the landscape was done to and Kinnaur highlight the risks and sensitivity of the region due to prevailing hazards and anthropogenic activities. 1. Introduction forest. The slope profile basically influences the soil erosion and mass movement of the area (Tseng, Lin, The vulnerability is a multidimensional (i.e., physical, & Hsieh, 2015). In the Himalayan region, the sum- social, economic, environmental, institutional, and mital concavity or convexity of a slope is very com- human), dynamic (changes over time), scale depen- mon (Savinder, 2004). High relative relief indicates dent (from individuals to countries), and site specific the high vulnerability as a geo-hazards point of views. (each location might need its own approach) The high relative relief indicates the region as com- (Bankoff, 2003). Vulnerability assessment of land- plex and unstable topography (Gansser, 1964). High scape includes that how much system is vulnerable deviation of curvature influences the surface run-off as internally and externally due to the risk of climate and soil erosion. Land-use land cover also indicates change, hazards, and human intervention (Kanwar, the region’s ecological status and distribution of the Kuniyal, & Kumar, 2017). During the last two dec- resources and their balances in the region (Anderson ades, vulnerability assessment has become essential et al., 1976). Soil texture and lithology of the area mainly in the regions where developmental activities affect the human settlement, agriculture, vegetation, for energy or any other activities are continued. Yet, soil erosion, and mass movement of the area. All the Hyogo framework recognizes as a key activity to exogenes is processes are controlled by the particular develop a “system of indicators of disaster risk and climatic condition of that region. Indicators are vulnerability at national and subnational scales” dependent on each other and also influence each (UNEP, 1992). The term vulnerability is used other. But some time, human interference affects the diversely; therefore, scientists with various disciplines indicators and also changes the possible impacts of have an ongoing debate regarding its definition. these indicators. Vulnerability assessment generates the fruitful results In the Satluj basin, constructions of hydroelectric and provides a base for decision-making process projects have been increased after the 1990s in Satluj (Bogardi & Birkmann, 2004). Physical aspect also basin (Gupta & Sah, 2008a). The physical and social covered the basic parameters such as slope, slope environment of the Satluj valley has been adversely aspect, slope profile, curvature, land use, soil texture, affected by hydroelectric projects. Degradation of a lithology, and river morphometry (Birkmann, 2006). physical environment and natural resources of this A slope is a dominant and controlling factor for the high-altitude valley is due to haphazard development, incidences of mass movement. The slope aspect also which exploits the Agenda 21 (UNEP, 1992). The Satluj affects settlements, vegetation, agriculture, and geo- valley is also known for its multi-facet hazards, namely, hazards processes. The understanding of aspect is landslides, floo d, avalanches, and earthquakes (Gupta & important for forest management and planning Sah, 2008b). The vulnerability of landscape is high in because it affects the growth and productivity of this valley due to its vulnerable parameters such as steep CONTACT Amit Jamwal amit.uprofft.feb2009@gmail.com HP Unit, Govind Ballabh Pant National Institute of Himalayan Environment and Sustainable Development, GB Pant Institute Rd, Mohal, Kullu, Himachal Pradesh 175101, India © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 172 A. JAMWAL ET AL. slope, high relative relief, structural discontinuities, 3. Methodology lithology, and unhealthy land cover (Birkmann, 2006; It was a selective indicator’s assessment-based approach Papathoma, Kappes, & Keiler, 2011). in which we score every indicator on the basis of its characteristics and visible counted impact in terms of physical loss on selected indicators. The physical loss 2. Study area (affected area) was calculated in terms of landslides, The study area of the upper Sutlej basin in district floods, and construction activities, which was extracted Kinnaur, Himachal Pradesh, India, extended from 31° from the satellite images and validated through ground 19′ 04″N to 32° 05′ 59″N latitudes and 77° 82′ 01″Eto78° survey (Table 1). Total 13 indicators (hydropower project 83′ 05″E longitudes. Its altitude varies from 455 to types, slope, aspect, slope profile, relative relief, land use 6735 m. The total area of the district is 6401 km land cover, curvature, channel morphology, lithology, (Figure 1). Zanskar and Himalayas are high mountain landslide, flood, earthquakes, and soil texture) were ranges that enclose valleys of Sutlej, Spiti, Baspa, and selected; the selective indicators have high importance their tributaries. The slopes of the region are covered in study region. The presence of these indicators also with thick wood, orchards, fields, and picturesque ham- determined the vulnerability and risk of a landscape. lets. Temperate climate is common due to its high eleva- However, these indicators have heterogeneity, but their tion (455–6735 m), with long winters from October to evaluation decided the degree of vulnerability and risk. May, and short summers from June to September. The The vulnerability assessments were done in terms of low, lower part (Satluj and Baspa valley) of the district receives moderate, high, and very high. Finally, the low vulner- monsoon rain. The upper areas above the Reckong Peo ability is explained as a less affected area between 0 and of the valleys fall mainly in the rain shadow area and 0.05 km , investigation hydroelectric projects (no con- considered as arid region similar to the Tibet. Alpine struction activities), gentle slope, summital convexity, species such as juniper, pine, fir, cypress, and rhododen- high relative relief >4000 m (in-habitant area), low cur- dron can be found at the elevations between 3500 and vature value, flat slope aspect, low channel gradient, low 5000 m.Oak, chestnut,maple,birch,alder,magnolia, stream frequency, low bifurcation ratio, hard rock strata: apple, and apricot are temperate trees found at lower quartzite, grey shale, and hard rock. Moderate vulner- altitude. According to the 2011 census, Kinnaur district ability of region is explained as a affected area between 1 has a population of 84,298, roughly equal to the nation of and 5 km , moderate slope, slope profile of rectilinear Andorra and population density was only 13 per- section, high relative relief >4000 m, affected land use of son/km . barren land and waterbodies, moderate curvature value, Figure 1. Study area district Kinnaur, Upper Satluj Basin, Himachal Pradesh. GEOLOGY, ECOLOGY, AND LANDSCAPES 173 Table 1. Parameters selection, data sources, and analysis. Parameters Data sources Analysis Process 1. Hydropower Field observation; GPS, Google Earth First, the HEPs were categorized into four types on the basis of previous EIA study, then projects the location of hydroelectric projects were collected through field survey and the buffers of 1 km were created from the location of dam sites and affected area was identified in terms of landslide, flood, earthquake, and construction activities. The satellite imageries of Landsat ETM 30 m and Digital Glove were acquired. Validation was done through people interaction and field-based survey 2. Slope DEM 23 m; www.usgs.com The slope analysis of area was done on the basis of DEM at the resolution of 30 m, Young classification was used to reclassify the raster data of slope into six types. The slope map was generated and shape files of affected area were overlapped. After that, pixels were counted that how many pixels of different slope types got affected and counted pixel values were converted into km 3. Slope profile DEM 23 m; Google Earth Slope profile classification was done on the basis of Wood (1942) classification, after that, the slope profile was identified in the field. Terrain analysis and measurement were done in Google Earth. Then the raster file overplayed on the slope profile and pixels were counted. The loss area was counted on different identified segments of slope 4. Relative relief DEM 23 m; Habitation points Relative relief of study area was calculated through the DEM and classified into three categories after that the habitation, degraded area layers were overplayed. The affected area was measured in all three classes of relief after that the vulnerability analysis was done and relief-based vulnerability map was generated 5. LULC Landsat7 (ETM ) (resolution 30 m) Land-use land-cover study was classified into eight classes with the help of satellite imageries at the resolution of 23 m. The satellite imageries were geo-processed in Erdas Imagine 2018 and area were calculated in square kilometres. The raster layers were overlapped on land use land cover and affected area of the land use land covered was measured with the help of ArcGIS 10.3 6. Curvature DEM 23 m Slope curvature was calculated through ArcGIS; simply pixel values were classified into concavity and convexity and affected area pixels were counted on the concavity and convexity of surface curvature 7. Aspect DEM 2 3m Slope aspect was classified into eight classes on the basis of eight cardinal direction points. In ArcGIS, the surface analysis tool was used and slope aspect was generated. The affected area layers were overlapped and affected pixels were counted on every classified aspect 8. Channel DEM 2 3m Channel slope gradient identification was done in Global mapper 11, the channel slope morphology Topographic map gradient measured in degree 9. Morphometric Google Earth The streams of different order were digitized on the Google Earth image, after that, the DEM 23 m Horton morphometric-based analysis was done and stream frequency, stream density, Topographic map and bifurcation ratio were calculated. Flood vulnerability assessment was done on the 1:50,000 basis of different stream orders. The buffer of 30 m was taken from the different stream orders, and in this buffer, the total affected area was calculated. The flood incidences locations were also identified in field. The points were also overlapped on the stream orders area and the flood-affected area was calculated then flood vulnerable area was extracted 10. Lithology GSI map Lithology map was prepared on the basis of secondary Geological Survey India map of Topographic map Himalayan region at the scale of 1:250,000 and geological structures were identified 1:50,000 from the secondary map. The degraded area layer was overlapped and lithology types were also identified through GPS survey 11. Soil texture Soil map(NBSS) Soil texture map was prepared on the basis of prepublished map of NBSS. This map geo- Topographic map referenced and soil texture types digitized, soil sampling points were taken in the field 1:50,000 and then overplayed on the map and soil texture area was validated. After that, the affected areas layered were overplayed and affected area under different texture was calculated 12. Landslide Field observation, Google Earth, Landslide analysis was done on the basis of field survey and visual interpretation method. satellite images The landslide incidence points were collected in the field through GPS and affected area was digitized through Google Earth and with Landsat7 (ETM ) satellite image 13. Earthquakes Field observation, IRIS, Google Earth, The earthquakes studies were done on the basis of secondary data, the previous satellite images earthquake incidence points were collected. The geological element (Fault, MBF, and MCT) was identified through the lithological and geomorphological maps. The buffers of 1 km from the epicentre were taken for study and all degraded areas were measured. The people perception method was also taken to validate the data Compressive Overlay analysis was done by adding Finally all vulnerability maps were prepared and then all vector and raster data were vulnerability vector and raster layers superimposed on each other and vulnerable area identified in the study region. The vulnerability indexes were generated on the basis of calculated/observed value for each dependent parameters and independent parameters NBSS: National Bureau of Soil Science. less affected slope aspect of north, north east, east, north- 2000–4000 m, high degradation of land use such as west, moderate channel gradient, moderate stream fre- settlements, agricultural, and forestland. The high curva- quency, moderate drainage density, moderate frequency ture value, highly affected slope aspect of south east, high earthquake magnitude (5.0), hard rock strata: quartzite, bifurcation ratio, high stream frequency, weak lithology, basic volcanic, sandstone, and less affected soil texture of poor soil texture, and high earthquake magnitude >6 medium type. High vulnerability was explained as where were some responsible factors for high vulnerability. affected area varies 5–10 km , under construction type The very high vulnerability was explained on the basis hydroelectric projects, moderate curvature, steep slope, of some observed parameters: very highly affected/ basal convexity of slope profile, high relative relief of degraded area of <10 km , very steep slope to vertical 174 A. JAMWAL ET AL. slope, free face, R of 1244–2000 m, highly valuable land slope in terms of slump, slide, subsidence, fall, and use land cover such as settlements, agricultural land, crap. Flood impact was high on different segments of forest cover, high curvature value, south and southeast slope like gentle slope to moderate steep slope. The aspect of slope, high channel slope gradient, (S)high vulnerability of physical landscape is very high on stream frequency, drainage density (D ), high bifurcation slope segments of steep slope to vertical slope. Very ratio (R ), very weak lithology of limestone, siltstone, less affected area (6.24 km ) was recorded under the shale rocky/bad land, and soil of coarse texture. The gentle slope (00–50). Maximum settlement concen- geographic information system (GIS) and remote sen- trations were found on the gentle to moderate type of sing were the major tools, which were used to highlight slope. Tangling, Apka, Lippa, Asrang, Chakra, Spillo, the vulnerability through digital map and index. Finally, Poo, Khab, and Nako village areas had very steep all indicators’ impacts were recorded and observed dur- slope, which had poor soil texture, less vegetation ing fieldvisit andimpactmarkedasvalue “1” and no covered, high gully erosion, and dry climate condi- impact marked as “0.” The landscape vulnerability area is tion. People of the study region also revealed that the also explained on the basis of parameters characteristics. loss of vegetation due to anthropogenic activities Finally value means (x ) were taken and hazards vulner- (road and dam construction) was common. Slope ability and high value of indicators classes were identified stability was also affected due to the anthropogenic in terms of comprehensive vulnerability. All selected activities (road and dam construction) in the area of indicators were analysed and geo-processed in GIS envir- Karcham Wangtoo. Gentle slope to moderate slope onment. The overlay analysis was done and final physical was found in the region of Chaura, burang, and landscape map was prepared, which indicated a highly Wangtu. Above the region of Yashang Dhar, Punag vulnerable area (Figure 7)(Tables 1 and 2). Indicators Khas, Urni, Chooling, Merru Khas, Rurag, Kibla, analysis process and their data sources were well Karcham, and Rali have moderate steep to steep explained in Table 1. slope where forest cover is moderately sparse above the Rali Ranrang, Shongtong, Barang, Tangling, Kalpa, Pangi, Khadura, Akpa, Khab having very 4. Results and discussion steep slope with sparse vegetation. The highest vul- nerability was obtained by the moderate steep slope, 4.1. Vulnerability due to physical aspects high vulnerability on steep slope, while gentle having 4.1.1. Relative relief (R ) low vulnerability (Figure 2(b) and Table 2). Melton (1958) relative relief is used for the overall assessment of morphological characteristics of terrain 4.1.3. Slope profile and degree of dissection. The relative relief of study The distinctive segments such as profile are called area varies from R = 1244 to 6755 m. The dissection slope elements or slope segments (Savindra, 2004a). value varies between 0 to 1 and increasing value from These types of slopes profile (summital convexity, 0 to 1 indicates the high degree of dissection/erosion basal concavity, rectilinear section, and free face of of basin. Dissection index (DI = R /A ) has high R R slope profile) are commonly found in the Satluj valley value (0.64–0.87) which made cleared that basin has (Melton, 1958a). Free face slope profile cliff bare rock high vulnerability as a mass movement, soil erosion, vertical slope profile was commonly observed in the and flood (Savindra, 2004). The hazards incidences basin Reckong Peo to Khab. The concave segments of such as landslides, floods, earthquakes, avalanches, the slope profile were commonly affected by the mass and maximum human habitation were recorded movement. Talus accumulation was commonly under the elevation of 1244–2000 m. The maximum observed at this segment of the slope profile. The counted loss (56.17 km ) was recorded under the moderate-to-high vulnerability was found on all seg- relative relief of 1244–2000 m, which indicates high ments of the slope profile. Forest area losses were vulnerability and risk. The probability of landscape very commonly observed on the free face segments and human settlement loss was very high from 2000 of the slope. The loss of river morphology was found to 4000 m. The landscape vulnerability recorded low on basal concavity. Human property’s loss was found above 4000 m (Figure 2(a) and Table 2). on summital concavity, rectilinear, and basal concav- ity. The flood and soil erosion highly affected the 4.1.2. Slope basal concavity segments of the slope profile. High Slope is one of the most important parameters from and very high vulnerability was found under the basal stability consideration viewpoint (Lee, Choi, & Min, concavity (Table 2). 2004; Webb, Yin, & Harrison, 2011). The highest affected area (18.56 km ) of physical landscape was 4.1.4. Slope aspect recorded under the slope types of moderate steep The slope aspect category of north (901.88 km ) has slope (100–180) (Young, 1964). The landslide impact high area, followed by southwest (896.14 km ), east 2 2 was highly observed on moderate slope to vertical (889.68 km ), and northeast (889.29 km ). The lowest GEOLOGY, ECOLOGY, AND LANDSCAPES 175 Table 2. Vulnerability assessment on the physical landscape on the basis of selective indicators. Indicator natural processes as anthropogenic Affected Classification of area Hydroelectric Parameters parameters (km ) Landslide Flood Earthquakes projects Vulnerability Explanation of the parameter classes Empirical study 1. Hydropower Under construction 13.87 1 1 1 1 VH Under construction project has high-degree loss of physical landscape as Lata et al. (2017) and Gupta projects Commissioned 3.63 0 0 0 0 L comparison to other type of categories and Sah (2008a) Obtaining clearance 0.89 0 0 0 0 L Under investigation 0.81 0 0 0 0 L ° ° 2. Slope Gentle slope (0 –5 ) 3.02 0 1 0 0 L In mountainous region, the high degree of slope >30° has high Webb et al. (2011), Lee et al. ° ° Moderate slope (5 –10 ) 6.24 1 1 0 0 M vulnerability, which triggers the incidence of landslides, avalanches (2004), and Slosson & Krohn ° ° M. steep slope (10 –18 ) 18.56 1 0 0 1 H slope failure, and impact of earthquake is high on high degree of (1982) ° ° Steep slope (18 –30 ) 10.27 1 0 1 1 VH slope ° ° V. steep slope (30 –45 ) 9.71 1 0 1 1 VH ° ° Vertical slope (45 –90 ) 7.91 1 0 1 1 VH 3. Slope profile Summital convexity 8.16 1 0 1 0 L Landscape degradation is high on free face profile and basal convexity, Schoeneberger, Wysocki, and Rectilinear section 9.15 1 0 1 0 M gravity pulls snow, soil mass, rock mass into ground or lower layers Benham (2012) and Wood Free face 11.15 1 0 1 0 H (1942) Basal convexity 14.16 1 1 1 1 VH 4. Relative relief (R ) R 1244–2000 56.17 0 1 1 1 VH High degree of slope and higher relative relief will be vulnerable and is Hooijer, Klijn, Pedroli, and Van R R R 2000–4000 27.98 0 0 0 0 H adroit to physical loss Os (2004) >R 4000 10.9 0 0 0 0 L 5. LULC Settlements 0.31 1 1 1 0 H The loss of settlements, agriculture land, forestland, and barren land is Disse & Engel (2001), Hooijer et Agricultural land 1.89 1 1 0 1 VH high in region of high degree slope/high relative relief because the al. (2004), and Pinter, Van Forest cover 7.52 1 1 0 1 VH hazards (flood, landslide, avalanches, earthquakes, and der Ploeg, Schweigert, and Barren/Wasteland 38.57 1 1 0 0 H anthropogenic) impact intensity is always high. Land-use changes Hoefer (2006) Grass/Grazing 6.28 1 1 0 1 H have a major effect on large floods in large catchment areas of river Scrubland 5.97 1 1 0 1 L Waterbodies 0.96 1 1 0 0 L Snow and glacier 14.2 0 0 0 0 L 6. Curvature Concavity (97.81) 16.6 1 0 0 0 L High convexity triggers more physical losses in hilly to mountainous MacMillan, Pettapiece, Nolan, Convexity (−100.4) 59.1 1 1 1 1 VH region and Goddard (2000) 7. Slope aspect Flat 0.01 0 0 1 0 L A reasonable explanation of this pattern may be that slope aspect Dearman and Fookes (1974) North 2.11 0 0 0 1 L affects the density of shallow debris slides by limiting the Northeast 3.96 0 0 0 1 L development and thickness of drier slopes East 3.72 1 0 0 0 L Southeast 15.03 1 1 1 1 VH South 17.08 1 1 1 1 VH Southwest 10.37 1 0 0 1 VH West 10.26 1 0 1 1 H Northwest 3.71 1 0 0 0 L 8. Channel Channel slope gradients 12.11 0 1 0 1 M High channel slope gradient is directly proposal to soil erosion, high Gupta and Sah (2008b) and morphology 13.45 flood impacts and encourage hydro development Ligon et al. (1995) 9. Morphometry (S ) Stream frequency 4/0 14.64 1 1 0 1 H Medium drainage, density, frequency, and high bifurcation ratio indicate Horton (1945)) (D ) Drainage density4.2 high degree of physical loss and risk (R ) Bifurcation ratio 1.63 (Continued) 176 A. JAMWAL ET AL. Table 2. (Continued). Indicator natural processes as anthropogenic Affected Classification of area Hydroelectric Parameters parameters (km ) Landslide Flood Earthquakes projects Vulnerability Explanation of the parameter classes Empirical study 10. Lithology P regionally 3.98 0 0 0 0 L In the case of limestone, dolomite, slate terrain, the effects of t1 metamorphosed weathering can be very severe, due to a combination of physical Dearman & Fookes (1974), P greenish grey 18.84 1 0 0 1 VH alteration and limestone solution. The latter, in particular, may play a Kowalski (1991) and Zellmer t3e sandstone significant role in enlarging joints and fractures, and in favouring (1987) Y granite and granitoid 1.39 0 0 0 0 L detachment of rocks along the outer ridges. Palmström (1995) and Selmer- P boulder 0.12 1 1 0 0 L It may happen prior to some seismically induced rock falls Olsen (1971) g3o conglomerate, sandstone, shale, and clay OC limestone, siltstone, 7.36 1 0 0 1 H and shale P slate, phyllite, 0.36 1 0 0 0 L t23 quartzite, and grey shale P ortho quartzite, basic 0.39 1 0 0 1 L t2 volcanic, and limestone/ dolomite 11. Soil texture Coarse texture 18.75 0 0 0 0 VH Coarse texture soil types have high erosional capacity in cool Jenny (1941) and NRSA (1997) Fine texture 14.35 0 0 0 1 H temperature with humidity fine texture soil has high vulnerability Medium texture 11.97 1 0 0 1 L type has dry, extremely cold condition Rocky/Bad land 36.47 1 0 0 1 VH Total vulnerability mean 11.64 0.67 0.36 0.30 0.55 (X) Explanation: “0” indicates “not observed impacts,”“1” indicates “adverse impacts.” L: Low vulnerability; M: medium vulnerability; H: high vulnerability; VH: very high vulnerability; LULC: land use land cover. GEOLOGY, ECOLOGY, AND LANDSCAPES 177 Figure 2. Relative relief and slope-based vulnerability. area was recorded under the slope aspect of north- affected area under the mass movement/soil erosion/ west (820.44 km ). The slope aspect is affected by the avalanches, which indicates high vulnerability. The con- sunray angle and all geomorphic processes controlled vexity surface area had highly affected area of by solar energy, which is the main driving force 59.11 km . The risk factor of human settlements was (Dearman & Fookes, 1974). In general, the slope very high at the convexity segments of the slope aspect influenced the distribution and density of (Table 2). mass movement by controlling the concentration of soil moisture or orientation of tectonic fracture. In 4.1.6. Lithology northern hemisphere above 33 latitudes, the maxi- This was generalized and modified after preparing the mum sunny area was found under the south, south- geological map of the Himalaya at 1:1000,000 (Plate-1) west, and southeast aspects of a slope. The south by Geological Survey of India, Government of India 2 2 (17.08 km ), southeast (15.03 km ), and southwest (1989) (Melton, 1958). In Satluj basin of district (3.72 km ) slope aspects have the highest area under Kinnaur, the maximum area 1866.13 km (29%) was the landslide, soil erosion, barren and wasteland; covered under the geology types of Pt3e: greenish grey 2 2 southeast (15.03 km ) and south (17.08 km ) have sandstone, quartzite, grey and dark shale–sandstone, a very high vulnerability and risk; northeast, flat, north, band of limestone, and phosphorite. The affected area northwest have a low vulnerability (Table 2). (3.98 km ) was recorded under the lithology type of Pt1 (regionally metamorphosed katazonalmeta sediments), 4.1.5. Curvature while the lowest area (0.12 km )was observed under the The profile curvature affects the acceleration and decel- Pg3o (boulder conglomerate, sandstone, shale, clay, soft eration of flow across the surface. A negative value sandstone, and shale reddish and sandstone), occupying (−10.415) indicates that the surface was upwardly con- total 0.01 km under the Pt23 lithology categories (slate, vex at that cell, and flow will be decelerated. A positive phyllite,quartzite,greyshale,siltstone,limestone, gyp- profile (10.415) indicates that the surface was upwardly sum, metamorphosed in proximity of granite) which concave at that cell, and the flow will be accelerated have area 0.36 km and Pt3e had highest affected area (Rautelal & Lakhera, 2000). Talus accumulation was (18.84 km ) of greenish grey sandstone, highly very common in the concave segments of the slope. degraded under the process of landslides and ava- Concavity indicates the superiority of concavity and lanches. The Pt1, Pt23, Pt2, and Pg3 have a low vulner- rugged surface. The convexity of the slope has a highly ability (Kumar, Gupta, Jamir, & Chattoraj, 2018). The 178 A. JAMWAL ET AL. Figure 3. Vulnerability of soil texture and lithology. forest area of lithology type limestone, siltstone, shale 4.1.8. Morphometric was highly affected due to the landslide, flood, and soil The erosion work of the river depends on a channel erosion (Gupta, 2003)(Figure 3(a) and Table 2). gradient, a volume of water, velocity, water discharge, and river load (Horton, 1945). The high channel slope gradient has an adverse and supportive impact 4.1.7. Soil texture on the river morphology, soil erosion, and flood Coarse, medium fine, and very fine categories of soil (Figure 4). The channel sinuosity was tortuous, irre- texture were found in the study zones. The study region gular, and slope gradient was high. Exposed bedrock had maximum area under the very fine texture were commonly seen in the upper basin of Satluj. The (5135.4 km ) 81%, followed by fine texture stream frequency was moderate (S = 4). Horton 2 2 f (579.71 km ) 9%, medium texture (455.71 km )7%, (1945)defined drainage as the ratio of total length coarse texture (158.16 km )2%, androckyland had of all stream segments in a given drainage basin to (36.12 km )1%. Thevery fine texture area was highly the total area of that basin as follows: D = L /A , d k k vulnerable for the soil erosion and mass movements in where L indicates total length of all stream segments dry temperate (Jenny, 1941). The sandy soil had low of the basin and A indicates the total area of the infiltration of water, above the Karcham Wangtoo area basin. Drainage density was found moderate (D ); the which was barren having less vegetation cover. The soil bifurcation ratio (R ) relates to the branching pattern wassandyclayand sandyloamy.The waterretention of the drainage network which is defined as the ratio capacity was very low and soil erosion was high due to of a number of streams of a given order (N ) to the wind, where an area was well covered with the vegetation number of streams of the next order (N ). It is u+1 and water infiltration rate was very high (NRSA, 1997). expressed with the equation as R = N /N (Giusti b u u+1 But in the lower zone, the climate is humid; this region & Schneider, 1965; Horton, 1945). Bifurcation ratio has sandy loamy soil and good moist salty soil. The within the present region tends to decrease with highest vulnerable area 36.47 km was found under the increasing stream orders (Horton, 1945). A high rocky/badlandtypetopographyandvulnerability was value (1.79) of bifurcation ratio indicates that the recorded high. However, fine texture type soil had high region was very much dissected and very sensitive degraded area (14.35 km ) due to landslides and ava- to physical loss (Singh, 2004). The low values of lanches which had a very high vulnerability (Figure 3(b) bifurcation ratio indicate that the Satluj basin had and Table 2). GEOLOGY, ECOLOGY, AND LANDSCAPES 179 Figure 4. Channel slope gradient of river upper Satluj basin in district Kinnaur. very controlled bedrock strata. Trellised drainage pat- Along the Satluj valley area, Khani Dhar, Burang, tern was found because here stream pattern was well Bara Khamba, and Gharsu Nathpa Jhakri areas had adjusted to the regional slope and geological structure better cover of grassland and less recorded landslides. (folds, faults) (Smith & Pain, 2009). Many streams Because the share stress not reduces during the rain- were developed on both the flanks of the ridges. Few ing season and slope stability was maintained. But in parts of region have hard exposed bedrocks, which the region of Rarang, Wangtu, Tapri, Chagaon, control the development of the drainage network. Cholling, and Karcham area had less vegetation and High drainage density, high drainage frequency, and soil texture/rock strata was commonly exposed in this high bifurcation ratio indicate high vulnerability in area. During rainy season, the surface run-off was terms of physical landscape loss in the upper basin of very high and share stress reduces which triggered Satluj in district Kinnaur (Table 2). the landslides. It was made clear from the field survey that the area of Karcham Wangtoo, Powari, Tangling, Kalpa, Bokta, Pangi, Rarang Khas, Akpa, Rispa, Jangi, 5. Land use and land cover Spillo, and Nako had dry condition, very fine soil texture, and broken land topography. The soil com- Land-use land cover (LULC) is also considered to pactness was reduced during the snowfall and inci- be the major factor in influencing the mass move- dences of landslides increased. About 66.79 km area ment. Barren and sparsely vegetated areas are was damaged due to landslides and construction prone to weathering and slope instability activities, which include 1.89 km of agriculture (Anbalagan, 1992; Raghuvanshi, Ibrahim, & 2 2 land, 7.52 km forestland, 38.37 km wasteland, Ayalew, 2014; Raghuvanshi, Negassa, & Kala, 2 2 6.28 km grassland, 1.97 km scrubland, and 2015). The topo-sheet 2010 survey of India, at the 5.97 km under the waterbodies. The land degrada- scale of 1:50,000 data, were used to know the gen- tion was also high due to blasting: road widening and eral status of land use in the Satluj basin of district construction activities (hydropower projects). About Kinnaur (open series). The maximum area was 0.31 km land of human settlements was damaged. found under the categories of snow-cover High vulnerability was found under the area of agri- 2 2 2490 km (39%) and wasteland 2030.63 km culture, forest, and barren land. Low vulnerability (31%). However, waterbodies (5.3 km ), built up was found in the area of scrubland, waterbodies, 2 2 (1.54 km ), and agriculture (52.8 km ) land had and snow glacier areas (Table 2). very low area. Forest cover was only 484.63 km (8%) area and grassland cover was 1383.1 km (20.58%) of total area. The forest cover was only 5.1. Landslide 8%, according to the forest policy, the minimum 33% area must be covered under the forest for the The economic and loss of life due to landslides healthy landscape. The percentage of wasteland and were considerably increased in the last century, snow-covered land was very high, which stands and most of the landslides are due to global 70%. The 1.89 km of agriculture land was very climate change, such as El Niño and human activ- low about 1.2% which indicates the tuff terrain ities (Runqiu & Weile, 2011). There is also high and low possibility of agriculture. The built-up confidence that changes in heavy precipitation area covered only 0.31 km . The landscape has will affect landslides in some regions (IPCC, unhealthy land cover and which has high vulner- 2012). Precipitation pattern was different in the ability of physical loss. study region which was influenced by the 180 A. JAMWAL ET AL. topographic variance (434–6448 m). Isohyets Sah, Virdi, 1993). However, the earthquake aver- study made it clear that the annual isohyets var- age intensity in the upper basin varies from 5.0 to ied from 100 to 1400 mm (Singh & Jain, 2002). 5.5, which was sufficient to generate small land- Annual rainfall in this basin decreased from the slides. People also believed that landslides were lesser to the Greater Himalayan range (Singh & common at the base concavity and steep slope Kumar, 1997). Rotational landslide, transitional, of the basin. According to the Pacificnorth seis- rockfall, topple fall, and debris fall debris flow mic network, earthquakes of magnitude 4.0 and were commonly found in the noticed places of greater have been known to trigger landslides. Nathpa Jhakri, Shongtong, Spillo, Apka Khas. The vulnerabilities of the physical landscape and The landslides were due to snowfall which were human property were very high (Figure 5(a) and very common above the Reckong Peo to Khab. Table 2). Soil creep was common in an upper region because of unconsolidated types of very fine soil 5.2. Flood texture. Freeze and thaw weathering were very common in the upper areas of Kinnaur districts: Himalayan region due to the construction activities of Purbani, Ribba, Rarang, Khadura, Apka, Kwangi, dams and road-added sediments had adverse impact Spillo, Nasarg, Samdayan, Puh, Dublin, Chango, on the river system (Ligon, Dietrich, & Trush, 1995). and Nako. Slope excavation due to dam construc- In Satluj basin, the most disastrous floods were experi- tion and road construction was commonly enced in 1993, 1995, 1997, 2000, 2005, 2007, and 2013 observed in Karcham Wangtoo, Shongtong (Gupta & Sah, 2008b). The unusual high discharge was Reckong Peo, Pangi, Kasang, and Nathpa Jhakri. observed during the flash floods. The water level in a Total 1541 incidences of landslides were observed river had risen to about 15–20 m from normal level out of which 1396 are small (0–0.2 km ), 138 are and its discharge value was as high as 10–12 times medium (0.2–0.5 km ), and 7 fall under the cate- more than a normal discharge during the specified gory of a large landslide (>0.5 km ). The landslide period (Gupta & Sah, 2008b). The lowest area is occurrences were very high on the moderate steep recorded under the first-order stream which affects and steep slopes. The highest damaged area the lowest area about 9.77 km .The first-order (12.13 km ) was found under the small categories streams, nallas and khads, have not possible impacts of landslides (Gupta, Bartarya, Virdi, 2003;Gupta, on the risk elements and no direct indirect relation Figure 5. Landslide incidences and flood-based vulnerability. GEOLOGY, ECOLOGY, AND LANDSCAPES 181 with the hazards like earthquakes and avalanches. But 6. Vulnerability due to anthropogenic factor small rill formation and soil erosion were noticed in a 6.1. Hydroelectric projects field. Second- and third-order streams affect only 12.88 km area and flood impacts high during the In the Satluj basin, a hydroelectric project is one of rainy season. Nallas and khads were responsible for the main construction activities, which is responsible the small landslides and soil erosion. The loss of forest for landscape destruction. Slope instability was com- and agriculture land was observed during the field mon in and surrounding area of hydropower projects observation in the area of third (like Baspa, (CEIA, 2014). The topsoil, forestland, threatened fau- Shongtong, Karcham, and Bhaba) and fourth (Satluj nal species, wildlife sanctuary eco-sensitive zone, and river) large stream orders. The very high flood vulner- 228 villages were influenced under the hydroelectric able areas were found within the buffer of 100 m. The projects. Increase in landslide incidences, flash floods, high stream order had a highly vulnerable area under river morphological changes, and water quality dete- the physical loss because of high cross section and rioration and reduction in agricultural horticultural steep channel gradient (150). The vulnerability and production, forest degradation, land degradations, risk factor were very high at the mainstream and low inadequate compensation due to construction activ- at the first-stream orders (Figure 5(b) and Table 2). ities, damage to human health due to dust during construction activities, damage to houses due to blasting and tunnelling activities, and some adverse 5.3. Earthquake occurrences impacts were noticed within the project-affected area (Lata, Herojeet, & Dolma, 2017; Wang, Du, & Chen, However, the stronger earthquakes with greater mag- 2012). The four types of hydroelectric projects nitudes of MS7 might yield an even more pro- (Under construction, Obtaining clearance, nounced effect on the overall sediment flux (Hovius, Commissioned and Under investigation) were identi- Meunier, & Lin, 2011). Several studies have also ela- fied in the upper Satluj basin of the district Kinnaur. borated the link between earthquakes, landslides, and It was made clear that maximum affected areas of fluvial sediment transport in the seismically active landslides were found under the categories of under mountain belts (Dadson, Hovius, & Dade, 2003; construction type (13.87 km ). Commissioned type Meunier, Hovius, & Haines, 2008). The magnitude hydroelectrics were less affected. It was observed of earthquake incidences in the Kinnaur district mag- that under construction hydroelectric project has nitude varies from low to moderate in intensity level high vulnerability and risk. Due to these construction 4.0–6.1. The average magnitude of the basin in activities, the incidences of landslide and flood have Kinnaur district was 4.5. As per the mention guide- been increased. According to the people of the study line of the Mercalli intensity scale the magnitude of area, the loss of river morphology and forest earthquake explain that feel in intensity of IV-V, the (7.82 km ) had been observed (Figure 6(b) and tremors of earthquake can felt easily, some awakened, Table 2). Land degradation (75.75 km ) and loss of disturb window, door disturb, wall making cracking agriculture land (1.89 km ) were found under the sound, sensation like heavy truck strictly building, type of construction hydroelectric projects. High vul- unstable object overturned, Pendulum clock may nerability was found within the buffer of under con- stop. Earthquake magnitudes affect the physical struction hydroelectric projects. mass movement behaviour. The incidences of land- slides increased at the magnitude of 5.0. The active landslide is commonly observed in the Urni and 7. People perception on geophysical setting Shudarang Dhaku villages. People also explained and sustainable planning of landscape that earthquake impacts (cracks in a wall, landslides) were clearly seen in the study region. People also said The questionnaire survey was conducted in the study that geological setting of the area was affected with region to know the status of the physical landscape blasting and construction activities. Due to the earth- (Annexure 1). During this survey, the people percep- quakes, the small landslides were generated and prop- tion were taken on the different selected parameters. erty losses were seen in terms of agriculture land and Total 51 people told that hydropower were a major settlements. The problems of soil erosion and ava- contributor to the loss of physical landscape. 63 lanches were noticed due to its indirect causes. But respondents believed that high degree of the slope, huge loss of landscape was noticed during the time of high local relief are supporting factor for the land- landslides; these impacts were seen and observed by slides and soil erosion. 59 respondents were asserted people in the village Urni and Shudarang Dhaku. that the maximum area wasfall under the barren land. Sixty villages with population of 25,317 persons The forest cover was low. However, 33 respondents were found under the earthquake vulnerability also believed that the incidences of earthquakes were (Figure 6(a) and Table 2). high, but actual losses were not observed. During the 182 A. JAMWAL ET AL. Figure 6. Earthquake-based vulnerability and project-affected population vulnerability. interview of 61 respondents, it was found that the The impact intensity of hazards (landslide, flood, incidences of flood were very common and people anthropogenic activities, and earthquakes) was high. also told that River Satluj was known for its worst The LULC status plays a major effect on large floods floods in 1993, 1995, 1997, 2000, 2005, 2007, 2013, catchment areas of river because the ratio of barren and 2018. The channel morphology was also affected and forestland was very low. High convexity triggered with the construction activities of hydroelectric pro- more soil erosion in valley (14.16 km ). A slope jects; 57 people also told that there was not proper aspect affects the density of slides, southeast and dumping site and excavated material was directly south slope aspects received more physical losses dumped into the river, which had a adverse impact (32.11 km ). High channel slope gradient area has on the river morphology. Some old experienced high soil erosion. Medium drainage density, medium group of people (66) believed that siltstone, dolomite frequency, and high bifurcation ratio indicate high topography-based region had incidences of creep and degree of physical loss and high risk. In the case of subsidence. People also told that the soil of the region limestone, dolomite, and slate, terrain type lithology was easily eroded because of less vegetation where was highly affected by weathering. Coarse texture and soil texture was very fine, loss of soil was very com- fine texture soil types had high erosional capacity in mon, and dust problem was very common, which such cool temperate region. Overlay analysis was have a serious impact on the vegetation and horticul- done on the basis of different raster and vector data- ture. During the rainy season, the mudflow and rock set layers. The vulnerability area was identified on slides were very common from the region Tapri to every parameter such as slope, slope aspect, slope Reckong Peo (Figure 8). profile, soil texture, land use land cover, morpho- metric, lithology, geology, and hazards (earthquakes, flood, and landslide). To sum up all the parameters, 7.1. Comprehensive vulnerability finally the physical landscape vulnerability map was generated (Figure 7 and Table 2). Very high vulner- Physical landscape losses were highly recorded under ability areas (268.07 km ) of the Satluj basin basically the construction type hydroelectric projects fall under the main valley of Satluj. The maximum (13.87 km ). The high degree of slope >30 had high concentrations (91.1%) of rural settlements were vulnerability (17.62 km ). Landscape degradation was found in the main valley. The 346.45 km area was high on free face profile and basal convexity because found under the very high vulnerability. The high gravity pull was very high. The loss of settlements 2 2 vulnerability area was 268.07 km , moderate vulner- (0.31 km ), agriculture land (1.89 km ), forestland 2 2 2 2 able area was 922.83 km , and 4833.13 km falls (7.52 km ), and barren land (38.7 km ) was high. GEOLOGY, ECOLOGY, AND LANDSCAPES 183 Figure 7. (a) Lose unconsolidated soil texture near to Akpa village, (b) large landslide in Bara Khamba, (c) muck dumping at the river Satluj side of Karcham Wangtoo HEP, (d) drilling the hill at 100 mw Tidong Hydro., (e) Urni landslide, (f) large landslide at Rekong Peo, (g) questionnaire survey at village Kwangi, (h) houses affected by the landslide at village Nigulseri, (i) strategic environmental assessment meeting at deputy commissioner office Rekong Peo, Kinnaur, on November 2014, (j) construction site of Tidong project, (k) dumping of muck along the river Sainj by Parbati HEP, (l) house cracks in Yulla village because of the tunnel construction of Karcham Wangtoo HEP. Figure 8. Comprehensive vulnerability. under the low vulnerability. According to the vulner- found under the earthquake 0.30 and flood 0.36 ability index, the highest mean values were found (Table 2). Hydropower development was one of the under the hazards of landslides 0.67 and anthropo- major concerns in the region, which is responsible for genic activities had 0.55. The lowest impact value was huge physical landscape and human property losses. 184 A. JAMWAL ET AL. The vulnerability of 60 villages of population 25,317 Funding was found under the very high vulnerability area of This research work was part of institutional research in- 346.45 km . house project (Strategic Environmental Assessment of Hydroelectric Project), in Satluj Basin, Himachal Pradesh and related to PhD topic Vulnerability assessment of Satluj basin for sustainable development. During the research 8. Conclusion period, all funding was assisted by the G.B. Pant National In the whole study, one thing was found out that the Institute of Himalayan Environment and Sustainable devel- region has high vulnerability, because of its complex opment, Mohal, Kullu: 175101 [In house project no. 8]. geophysical setting. For future development, it is very necessary to keep in mind the complex geophysical setting. High degree of slope area must be avoided References from excessive construction activities. Slope profile of Anbalagan, R. (1992). Landslide hazard evaluation and free face is highly damaged due to blasting activities. zonation mapping in mountainous terrain. Engineering High relative relief, weak soil texture, and high morpho- Geology, 32(4), 269–277. metric parameters indicate its high vulnerability. The Anderson, J. R., Hardy, E. E., & Roach, J. T. (1976). A land use land cover classification System for use with remote unhealthy land use land cover is more prone to soil sensor data. US Geological Survey Professional Paper, erosion, floods, and landslides. The hydropower devel- 964, 28. opment should be in sustainable and control manner. Bankoff,G.(2003). Vulnerability as a measure of change in The maximum losses of hydropower project and social society. International Journal of Mass Emergencies and issues were counted in the main Satluj valley. For the Disasters, 21(2), 5–30. Birkmann, J. (2006). Indicators and criteria for measuring future study, it is very necessary and important that the vulnerability: Theoretical bases and requirements. In J. detailed study on landslides and slope stabilization Birkmann (Ed.), Measuring vulnerability to natural dis- should be undertaken by well-known research institute asters (pp. 55–77). Tokyo: United Nations University or concerned departments. The incidences of hazards Press. (landslides, flood, earthquakes, and anthropogenic) and Bogardi, J., & Birkmann, J. (2004). Vulnerability assess- their impact were highly observed in main Satluj valley. ment: The first step towards sustainable risk reduction. In D. Malzahn & T. Plapp (Eds.), Disaster and society The main valley patch of 346.45 km along river Satluj from hazard assessment to risk reduction (pp. 75–82). from Rampur to Khab received maximum losses. This Berlin: Logos Verlag. area has high indicator value, and 90% district popula- CEIA. (2014). Directorate of energy, government of tion resides and has high pressure of construction activ- Himachal Pradesh, cumulative environmental impact ities. It is made clear from the analysis that the excessive assessment of hydroelectric projects in Sutlej River basin, Himachal Pradesh,India. MaindraftReport: and haphazard developmental activities are increasing Volume 1. Retrieved from http://admis.hp.nic.in/doe/ the risk for local communities over this sensitive Citizen/openfile.aspx?id=93&etype=MNotice.pdf landscape. Dadson, S. J., Hovius, N. C., & Dade, W. B. (2003). Links between erosion, runoff variability and seismicity in the Taiwan orogeny. Nature, 426, 648–651. Acknowledgments Dearman, W. R., & Fookes, P. G. (1974). Engineering geological mapping for civil engineering practice in the Theauthors areheartilythankfultothe “Director, G.B. United Kingdom. Quarterly Journal of Engineering Pant National Institute of Himalayan Environment and Geology, 7, 223–256. Sustainable Development, Kosi-Katarmal, Almora, Disse, M., & Engel, H. (2001). Flood events in the Rhine Uttarakhand – 263 647, India” for providing facilities basin: Genesis, influences and mitigation. Natural at Himachal Regional Centre of the Institute. Thanks Hazards, 23, 271–290. are also due to different stakeholders (local communities, Gansser, A. (1964). The geology of the Himalayas. New project authorities, and local government) for their con- York: Wiley Interscience. stant support and cooperation during field study. We are Giusti, E. V., & Schneider, W. J. (1965).The distribution of also thankful to Dr. Kaser (Scientist C, G.B. Pant branches in river network. USGS Professional Paper, National Institute of Himalayan Environment and 422G,45–47. Sustainable Development Vivek Vihar, Itanagar – 791 Gupta, R. P. (2003). Remote sensing of geology (pp. 498– 113, Arunachal Pradesh, India) for providing construc- 524). Germany: Springer,Verlag publication. tive suggestion. Gupta, V., Bartarya, S. K., & Virdi, N. S. (2003). Landslide activity along the Satluj valley in the higher and lesser Himalaya of Himachal Pradesh. Proceedings of ISRS, Silver Jubilee Symposium, 994,80–86. Disclosure statement Gupta, V., & Sah, M. P. (2008a). Spatial variability of mass No potential conflict of interest was reported by the movements in the Satluj Valley, Himachal Pradesh dur- authors. ing 1990-2006. Journal of Mountain Science, 5(1), 38–51. GEOLOGY, ECOLOGY, AND LANDSCAPES 185 Gupta, V., Sah, M. P., & Virdi, N. S. (1993). Landslide induced landslides. Earth and Planetary Science Letters, hazard zonation in the upper Satluj Valley, district 275, 221–232. Kinnaur, Himachal Pradesh. Journal of Himalayan NRSA. (1997). Evaluation of IRS-1C data for mapping soil Geology, 4(1), 81–93. resources and degraded lands. Project report. Gupta, V., & Sah, P. M. (2008b). Impact of the Trans- Palmstrom, A. (1995). A rock mass characterization system Himalayan Landslide Lake Outburst Flood (LLOF) in for rock engineering purposes. Ph.D. thesis Univ. of the Satluj Catchment, Himachal Pradesh, India. Oslo, pp. 1–400. Natural Hazards, 45(3), 379–390. Papathoma, K., Kappes, M., & Keiler, M. (2011). Physical Hooijer, A., Klijn, F., Pedroli, G. B. M., & Van Os, A. G. vulnerability assessment for Alpine hazards: State of the (2004). Towards sustainable flood risk management in art and future needs. Natural Hazards, 58, 645–680. the Rhine and Meuse river basins: Synopsis of the find- Pinter, N., Van der Ploeg, R. R., Schweigert, P., & Hoefer, ings of IRMA-SPONGE. River Research and G. (2006). Floodmagnification on the RiverRhine. Applications, 20, 343–357. Hydrological Processes, 20, 147–164. Horton, R. E. (1945). Erosional development of streams Raghuvanshi, T. K., Ibrahim, J., & Ayalew, D. (2014). and their drainage basins- Hydro-physical approach to Slope stability susceptibility evaluation parameter quantitative morphology. Geological Society of American (SSEP) rating scheme – An approach for landslide Bulletin, 56, 275–370. hazard zonation. Journal of African Earth Sciences, Hovius, N., Meunier, P., & Lin, C. W. (2011). Prolonged 99,595–612. seismically induced erosion and the mass balance of a Raghuvanshi, T. K., Negassa, L., & Kala, M. P. (2015). GIS large earthquake. Earth and Planetary Science Letters, based grid overlay method versus modeling approach: A 304, 347–355. comparative study for landslide hazard zonation (LHZ) in IPCC. (2012). Managing the risks of extreme events and Meta Robi district of West Showa zone in Ethiopia. The disasters to advance climate change adaptation. A Egyptian Journal of Remote Sensing and Space Sciences, 18, Special Report of Working Groups I and II of the 235–250. doi:10.1016/j.jafrearsci.2014.05.004 Intergovernmental Panel on Climate Change [Field C. Rautelal, P., & Lakhera, R. C. (2000). Landslide risk analysis B., V., Barros, T.F., Stocker, D., Qin, D.J., Dokken, K.L., between Giri and Tons Rivers in Himachal Himalaya, Ebi, M.D., Mastrandrea, K.J., Mach, G.K., Plattner, S.K., India. International Journal of Applied Earth Allen, M., Tignor, P.M., &Midgley, (eds.) (pp. 582) Observationand Geo-Information, 2, 153–160. Cambridge, UK: Cambridge University Press. Runqiu, H., & Weile, L. (2011). Formationdistribution and Jenny, H. (1941). Factors of soil formation. New York: risk control of landslides in China. Journal of Rock McGraw-Hill. Mechanics and Geotechnical Engineering, 3(2), 97–116. Kanwar, N., Kuniyal, J. C., & Kumar, A. (2017). Savinder, S. (2004). Geomorphology (4th ed., pp. 381–382). Understanding climatic variability and forest vulnerabil- Allahabad: Kalyan Publication. ity due to hazards and anthropogenic activities: A study Schoeneberger, P. J., Wysocki, D. A., & Benham, E. C. from the Northwestern Himalaya. Journal of Himalayan (2012). Soil survey staff, field book for describing and Ecology Sustainable Development, 12,44–56. sampling soils.Version 3.0.U.S.Lincoln, NE: Kowalski, W. C. (1991). Engineering geological aspects of Department of Agriculture, Natural Resource different types of karst corrosion and fracture generation Conservation Service. in karst masses. Bulletin International Association Selmer-Olsen, R. (1971). Engineering geology. Part 1 (in Engineering Geology, 44,35–46. Norwegian) (pp.32).Trondheim,Norway:Tapirpublishers. Kumar, V., Gupta, V., Jamir, I., & Chattoraj, S. L. (2018). Singh, P., & Jain, S. K. (2002). Snow and glacier melt in the Evaluation of potential landslide damming: Case study Satluj River at Bhakra Dam in the Western Himalayan of Urni landslide, Kinnaur, Satluj Valley, India. region. Hydrological Sciences/Journal-des Sciences Geoscience Frontiers, 30, 1–15. Retrieved from Hydrologiques, 47(1), 93–104. doi:10.1016/j.gsf.2018.05.004 Singh, P., & Kumar, N. (1997). Effect of orography on Lata, R., Herojeet, R., & Dolma, K. (2017). Environmental precipitation in the Western Himalayan region. Journal and social impact assessment: A study of hydroelectric of Hydrology, 199, 183–206. power projects in Satluj Basin in District Kinnaur, Slosson, J. E., & Krohn, J. P. (1982).Southern California Himachal Pradesh, India. International Journal of Earth landslides of 1978 and 1980. Storms, Floods, and Debris Science and Engineering, 10(02), 270–280. Flows in Southern California and Arizona, 1978 and Lee, S., Choi, J., & Min, K. (2004). Probabilistic landslide 1980. Proceedings of a Symposium, National Research hazard mapping using GIS and remote sensing data at Council, and Environmental Quality Laboratory, Boun, Korea. International Journal of Remote Sensing, 25 California Institute of Technology, Pasadena, CA, 17– (11), 2037–2052. 18 September 1980. National Academy Press, Ligon, F. K., Dietrich, W. E., & Trush, W. J. (1995). Washington, pp. 291–319. Downstream ecological effects of dams: A geomorphic Smith, M. J., & Pain, C. (2009). Applications of remote perspective. Bioscience, 45, 183–192. sensing in geomorphology. Progress in Physical MacMillan, R., Pettapiece, W., Nolan, S., & Goddard, T. Geography, 33(4), 568–582. (2000). A generic procedure for automatically segment- Tseng, C. M., Lin, C. W., & Hsieh, W. D. (2015). Landslide ing landforms into landform elements using DEMs, susceptibility analysis by means of event-based multi- heuristic rules and fuzzy logic. Fuzzy Sets and Systems, temporal landslide inventories. Natural Hazards Earth 113,81–109. System Science Discussion, 3, 1137–1173. Melton, M. A. (1958a). Geometric properties of mature UNEP. (1992). Agenda 21 Technical report, United drainage basin system and their representation in an Nations environment program, Hyogo framework for E4 phase space. Journal of Geology, 66,35–56. action 2005-1015: Building the resilience of nations Meunier, P., Hovius, N., & Haines, A. J. (2008). and communities to disasters. In: World conference on Topographic site effects and the location of earthquake Disaster reduction, Kobe, Hyogo, Japan 186 A. JAMWAL ET AL. Wang, Q. G., Du, Y. H., & Chen, K. Q. (2012). The 18th (northwestern India) and its constraints on the forma- Biennial conference of international society for ecologi- tion mechanism of the Himalayan Orogen. Geosphere, 7, cal modeling environmental impact post-assessment of 1013–1061. dam and reservoir projects: A review appraisal center for Wood, A. (1942). The development of hillside slopes. environment and engineering, Ministry of Proceedings of the Geologists’ Association, 53, 128–138. Environmental Protection, Beijing, China. Proceeding Young, A. (1964). Deductive model of slope evolution. Environmental Sciences, 13, 1439–1443. Slope Commission Rep., 66(3), 45–66. Webb, A., Yin, A. G., & Harrison, A. (2011). Cenozoic Zellmer, J. T. (1987). The unexpected rockfall hazard. tectonic history of the Himachal Himalaya Bulletin Association Enging Geologists, 24(2), 281–283. Annexure 1. Villages under survey in the Satluj basin of district Kinnaur (H.P). Sr. No. Villages Name Altitude (m amsl) Longitude Latitude 1 Khab 2769 78.6448 31.8025 2 Morang 2536 78.4502 31.6012 3 Kutang 2460 78.4256 31.6393 4 Akpa 2454 78.3992 31.5858 5 Spello 2347 78.4437 31.6615 6 Chitkul 3429 78.432 31.3514 7 Rakchham 3133 78.3643 31.3801 8 Ribba 2761 78.357 31.5792 9 Pangi 2647 78.2789 31.5908 10 Sangla 2646 78.2654 31.4261 11 Barang 2467 78.2684 31.5059 12 Purvani 2390 78.3018 31.5891 13 Tapri 2385 78.097 31.516 14 Raang 2252 78.2442 31.5151 15 Rali 1917 78.2122 31.4955 16 Punag 1930 78.1031 31.5124 17 Chooling 1849 78.1388 31.523 18 Karcham 1802 78.1766 31.5017 19 Wangtu 1697 78.0159 31.5419
Journal
Geology Ecology and Landscapes – Taylor & Francis
Published: Jul 2, 2020
Keywords: Geographic information system; vulnerability assessment index; hazards and Kinnaur