Deterministic approach for susceptibility assessment of shallow debris slide in the Darjeeling Himalayas, India

Catena 142 (2016) 36–46

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Deterministic approach for susceptibility assessment of shallow debris slide in the Darjeeling Himalayas, India Shraban Sarkar a,⁎, Archana K. Roy a,1, Priyankar Raha b a b

Department of Geography, Institute of Science, Banaras Hindu University, Varanasi 221005, India Department of Soil Science and Agricultural Chemistry, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, India

a r t i c l e

i n f o

Article history: Received 24 June 2015 Received in revised form 2 February 2016 Accepted 8 February 2016 Available online xxxx Keywords: Darjeeling Himalayas Debris slide Deterministic approach SHALSTAB model Critical rainfall ROC

a b s t r a c t High magnitude rainfall triggers numerous shallow debris slides in the Darjeeling Himalayas causing widespread damage to the environment, loss of life and property. Thin soil cover and steep topography make the region vulnerable to debris slides. The objective of the present study is to assess the susceptibility of the eastern part of Darjeeling Himalayas (covering about 330 km2) to shallow debris slides through the functional relationship of hillslope hydrology and mechanical properties of slope materials. Deterministic approach-based shallow landsliding stability (SHALSTAB) model following Mohr–Coulomb failure law was adopted to assess landslide susceptibility. Topographical parameters were derived from 8-m resolution Cartosat-1 digital elevation model (DEM) and mechanical properties of soil were obtained from an analysis of 15 soil samples. For slope stability assessment, the topographical and soil parameters were put into three different scenarios — (i) assuming the surface entirely free from vegetation (Model-1), (ii) involving the role of vegetation root cohesion (Model-2) and (iii) surcharge of vegetation, buildings and other structures along with root cohesion (Model-3). These predictive models were used to classify the area into various susceptibility classes with specific amounts of critical rainfall (Qc). The result shows that 28%, 9% and 10% of the study area come under unconditionally unstable class in the three models, respectively. About 22% land in Model-1 and 42% in each Model-2 and Model-3 come under unconditionally stable class. Protective capacity of roots against debris slide played a significant role in Model-2 and Model-3. Performance of models was validated by comparison of observed–predicted landslide areas and the area under the receiver operating characteristic (ROC) curve. It is found that the overall success rate of all the three models is relatively low (56.60% to 64.50%). Thus, it may be concluded that the SHALSTAB model in assessing landslide should either not be used at all at a regional level in the Himalayas or be used only with great caution along with additional field and lab data. © 2016 Elsevier B.V. All rights reserved.

1. Introduction According to the Centre for Research on the Epidemiology of Disasters (CRED), landslide ranked 7th in terms of number of deaths and is among the top ten disasters in India during 1900–2013 (OFDA/CRED, 2013). Nearly 0.49 million km2 area (i.e., 15% of the total land area) of India falls in the landslide prone zone, out of which, a major portion is spread over the Himalayan region (NDMA, 2009). Darjeeling Himalayas is one of the most critical zones for landslide occurrences (Basu and De, 2003). This part of the Himalayas receives heavy rainfall during the south-west monsoon from June to September, and consequently many shallow debris slides occur almost every year. In the recent past, the

⁎ Corresponding author. E-mail addresses: [email protected] (S. Sarkar), [email protected] (A.K. Roy), [email protected] (P. Raha). 1 Present address: Department of Migration and Urban Studies, International Institute for Population Sciences, Mumbai 400088, India.

http://dx.doi.org/10.1016/j.catena.2016.02.009 0341-8162/© 2016 Elsevier B.V. All rights reserved.

area has been severely affected by numerous landslides, triggered by the catastrophic rainfall events. Landsliding is a natural phenomenon and is difficult to control, but the loss of life and property can be minimised if it can be predicted in advance. In order to predict the landslide prone areas which are caused due to high magnitude rainfall events, it is important to identify the unstable slope segments with the help of landslide susceptibility maps. This study is an effort in this direction and tries to demarcate the unstable zones in accordance with the rainfall threshold. There are several methods to assess landslide susceptibility. Heuristic and probabilistic models are widely used, in which the statistical relationship of existing landslides with the geo-environmental factors are analysed (Carrara, 1983; Ruff and Czurda, 2008). However, in these models, the influence of physical and mechanical properties of slope materials on landslide is not taken into account. Deterministic models consider parameters like the physical and mechanical properties of slope materials and provide the best quantitative information on landslide susceptibility with a factor on forecasting of hazard in different land use scenarios (van Westen et al., 2006).

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In deterministic approach, functional relationship of physical properties of materials is established theoretically or experimentally in the form of infinite-slope or factor-of-safety equation to assess the stability of slope (Bathurst et al., 2010). The factor-of-safety is the ratio between the triggering factors of landslide and factors that prevent the landslide. Hence, to estimate slope stability, deterministic models consider rainfall intensity, infiltration capacity, hydraulic conductivity, dry and saturated unit weight of soil, unit weight of water, soil and root-induced cohesion, angle of internal friction of slope materials, soil depth, depth of groundwater table, pore water pressure, gravitational acceleration, external weight over the slope, seismic acceleration etc. (Godt et al., 2008; Jelínek and Wagner, 2007; van Westen and Terlien, 1996). In a situation, where the long-term rainfall data or landslide inventory maps are not available, deterministic models are used in predicting slope stability. The models are capable to explain slope stability in one, two or threedimensional ways in a static or dynamic mode (Bromhead, 1996; Dietrich et al., 2001; Jia et al., 2012; Montgomery et al., 1998; van Beek, 2002; van Westen and Terlien, 1996; Xie et al., 2004) and are used to investigate some particular slope failures with a specific triggering factor like rainfall or earthquake (Guzzetti et al., 2005). Of course, intensive site-specific field survey and reliable analysis of samples (in situ or laboratory) provide better results. Therefore, most of the deterministic models are recommended for local level or large scale investigation (van Westen et al., 2006). There are several types of deterministic models to measure the stability of the slope. These include shallow landsliding stability (SHALSTAB) model (Montgomery and Dietrich, 1994), stability index mapping (SINMAP) (Pack et al., 1999; Yilmaz and Keskin, 2009), SHETRAN (Bathurst et al., 2010; Bovolo and Bathurst, 2012; Ewen et al., 2000), transient rainfall infiltration and grid-based regional slope-stability (TRIGRS) model (Baum et al., 2005; Kuriakose, 2010; Vieira et al., 2010), distributed shallow landslide analysis model (dSLAM) (Dhakal and Sidle, 2003; Wu and Sidle, 1995) and triangulated irregular network real-time integrated basin simulator (tRIBS) model (Arnone et al., 2011). These models are based on infinite-slope condition, where stability for shallow landslides is measured by assuming a planar slip surface parallel to the topographic surface at a fixed depth. The framework of models and parameters are changed according to site property and purpose of the study. Studies conducted earlier in the Eastern Himalayas revealed that landslides are predominantly triggered by monsoonal rain and shallow medium magnitude earthquakes (Dubey et al., 2005; Ghosh et al., 2011, 2012; Soja and Starkel, 2007). Most of the shallow landslides in the region occur on steep slopes with thin soil cover, even after little amounts of rainfall. As there is a propensity of landslide in the study area during the monsoon (rainy season) and also as the present study intends to test various thresholds of rainfall that trigger the landslides, a deterministic approach is required. SHALSTAB model is one of such deterministic approaches which is simple and fast in analysis with good precision to predict slope stability at a regional level, as compared to other deterministic models (de Luiz Rosito Listo and Vieira, 2012; Meisina and Scarabelli, 2007). As a matter of fact, the SHALSTAB model has rarely been tested in the Himalayas at a regional level. Therefore, in the present study, a one-dimensional deterministic approach based on the SHALSTAB model is adopted to compute the critical steady-state rainfall as a measure of relative slope stability and also to test its efficiency at a regional level (about 330 km2 study area). 2. Study area The study was conducted in the eastern part of the river Tista in Darjeeling Himalayas (26°58′ N to 27°11′ N and 88°26′ E to 88°39′ E), covering an area of about 330 km2 (Fig. 1). The area under investigation is characterised by the rugged mountainous terrain of the Central and Lower Himalayas with altitudes ranging between 160 m and 2356 m above mean sea level. The climate is typically temperate (temperature

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ranges from 12 °C to 24 °C) with wet summer, characterised by monsoonal rain. This area has been classified as Cwb (subtropical highland climate) according to Köppen's climatic classification with the mean annual rainfall and relative humidity being 2754 mm and 76% respectively (Regional Sericulture Research Station, Kalimpong, 2010). The area has experienced several catastrophic rainfall events. In the Tista valley, during the period 1891–1965, rainfall intensities exceeded 250 mm in 24 h more than 40 times (Bhandari, 2006) which is enough to trigger slope failure. During the last few decades, specifically, in 1950, 1968, 1980, 1991, 1993, 1998, 2009 and 2011 extreme rainfall caused many landslide events in the entire Darjeeling Himalayas (Fig. 2). The study area comprises of highly metamorphosed rocks of Darjeeling Gneiss, thrust over the low-grade metasedimentary rocks of Daling Series along the Main Central Thrust (MCT) (Searle and Szulc, 2005). Brown hill soils – Inceptisols and Entisols – are the dominant soils of this region (Sarkar et al., 2005), where four textural classes such as clay (14%), clay loam (42%), sandy clay loam (37%) and sandy loam (7%) were found. Soil profiles are immature, comprising of coarse to fine gravels. Although dense forest covers a considerable extent of the study area, expansion of population growth and anthropogenic signatures are present almost in the entire hilly region here. Kalimpong, the only town in this region which is situated on a hilltop and is also the sub-divisional headquarter. It is well connected to the surrounding settlements by national highway (NH-31A), state highway (SH-12) and minor roads. In the study area, most of the landslides are shallow translational in nature containing debris and have a very small width in comparison to the length occurring mainly after intense rainfall. Shallow debris slides are very common in the upper reaches of gullies, along channels due to undercutting by streams and washout by overland flow. In the area under study, a total of 1262 debris slides (2.19 km2 in area) were reported up to the end of 2009, ranging from 41 to 29,857 m2 in size. These slides are mainly concentrated along the western steep side slopes of the Rilli Valley, which is the principal stream of the area. 3. Materials and methods 3.1. Soil sampling and analysis A total of 15 disturbed (1 kg each), as well as undisturbed core soil samples, were collected from the topsoil (15 cm depth) at each site in a plastic bag and sampling tube respectively (Table 1, Fig. 3a). The sampling sites were well distributed throughout the area representing each of the lithological units and the land use classes. After air drying and sifting through a 2 mm sieve, bulk density and water holding capacity were measured using pycnometer bottle and Keen–Raczkowski box respectively (as followed by Black, 1965). After a day of saturation in water, 32 mm diameter and 140 mm length core soil samples were put to the constant head test to determine the saturated hydraulic conductivity (Ksat). Ksat was calculated using Darcy's law and is expressed in terms of m/day. At equilibrium feeding of water on the core samples; if the discharge of water is Q, flowing out of the sample length L, and cross-sectional area A of sample, for a given hydraulic-head drop of Δ h, was measured at a particular time t,  Ksat ¼

 Q  L t  Δh  A

ð1Þ

Soil cohesion and angle of internal friction were measured using unconsolidated un-drained tri-axial shear test. Axial stress and strain were derived from 38 mm diameter and 76 mm length soil samples based on the applied loading pressures (0.50 kg/cm2, 1.00 kg/cm2 and 1.50 kg/cm2) and relative deformation on the samples. Stress was noted after 8 mm deformation on the sample in each of the applied

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S. Sarkar et al. / Catena 142 (2016) 36–46

Fig. 1. Location map of the study area: (a) India, (b) Darjeeling Himalayas and (c) Slope map showing existing shallow debris slides (polygons in black). Inset numbers (1, 2 & 3) in (c) indicate locations of photos 1, 2 & 3 (at the bottom), showing shallow translational debris slides occurred in thin soil cover (b1 m) and steep side slopes of hills.

load and subsequently, for each of the cases, the effective major (σO1′) and minor (σ3′) principle axes of normal and shear stress was calculated. Finally, based on these principal stresses, Mohr's circle was drawn to calculate the angle of internal friction and cohesion.

Fig. 2. The trend of annual rainfall with 5 years moving average of the Darjeeling Himalayas from 1901 to 2009 (data collected and compiled from http://www. indiawaterportal.org and Regional Sericulture Research Station, Kalimpong, 2010), vertical lines indicate fatal landslide events from 1950 to 2009.

3.2. Generation of thematic layers To analyse the functional relationship of hillslope hydrology and slope materials, and to assess landslide susceptibility in different scenarios; topographical, soil hydrological and mechanical properties along with the existing land use were mapped (Fig. 4). Topographical factors (especially slope being the dominant one) are important determinants of landslide occurrence (De Rose, 1996). Therefore, a high-resolution with good quality topographic data is necessary to understand the landslide processes better. Hence, digital elevation model (DEM) was prepared at 8 × 8-m pixel size from the 2.5-m resolution Cartosat-1 stereoscopic satellite image by the automatic imagematching technique using Leica Photogrammetric Suite 9.2 (® ERDAS, Inc.) (Table 1). The prepared DEM was tested for its accuracy, following the method proposed by Martha et al. (2010). The DEM was used to derive topographical and hydrological attributes in ArcGIS 9.2 (® Environmental Systems Research Institute, Redlands, CA, USA) (Table 1). The slopes range from 0°–78° in the area under investigation (Fig. 1). From the hill top, the slope increases gradually towards the rivers Tista in the west and Rilli in the east. The west facing slope towards Tista is steeper and complex, whereas the eastern slope towards Rilli is more or less uniform. The upslope contributing area which is one of the parameters used in deterministic approach has been derived from the DEM using a multiple-flow-direction algorithm (FD8).

S. Sarkar et al. / Catena 142 (2016) 36–46

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Table 1 Details of data used in this study. Data type

Source

Year

Spatial resolution

Parameters extracted/purpose

Satellite image

Resourcesat-1 LISS-IV Mx Cartosat-1 (Stereo) Google Inc.

2005 (Apr.) 2007 (Jan.) 2009 (Nov.) 2008 & 2010 2012 2012 2012

5.8 m 2.5 m

Landslide inventory DEM, slope, upslope contributing area, landslide inventory and land use Landslide inventory and land use Field check for verification of land use and landslide location Soil depth measurement Root parameters measurement and soil sampling Saturated density of soil, saturated hydraulic conductivity, soil cohesion and angle of internal friction

Google Earth image Field survey

Laboratory investigation

148 sites 15 sites 15 soil samples

To represent spatial variability of soil depth in the study area, 118 known data points where soil depths were measured during field survey (Fig. 3b), have been extended to a continuous surface through regression kriging to map it. Soil depth varies from 0 to 4.22 m, with a mean depth of around 1.67 m (Sarkar et al., 2013) (Fig. 4a). It is observed that the soil is thicker on the south facing slopes and near valleys in comparison to other areas. Soil physical, hydrological and mechanical properties (15 samples) were converted to an 8-m raster grids using an inverse distance weighted (IDW) interpolation technique with a second-order power (Fig. 4b-e). Root reinforcement is widely recognised as a significant beneficial effect of vegetation on slope stability (Bathurst et al., 2010; Genet et al., 2010; Greenwood, 2006; Kuriakose et al., 2009). Root reinforcement is determined by the interaction between mobilised root strength, root morphology and the shear strength at the soil–root interface. The tensile strength of root and the proportion of root cross-sectional area within a soil cross-sectional area are used to derive root-induced cohesion, which varies with soil depth, root diameter, vegetation species, density, age and health (Schmidt et al., 2001). In the present study, root diameter and root cross-sectional area per unit soil area has been measured at 15 selected sites (from where soil samples were collected) (Fig. 3a). While root tensile strength has not been tested directly in the present study, it was adopted or estimated from an empirical power law

equation (Root tensile strength [Tr] = αD-β, where D is root diameter, α and β are empirical constants depending on species) for similar vegetation species found in the Himalayas (Bischetti et al., 2005, 2009; De Baets et al., 2008; Genet et al., 2005, 2008; Kuriakose, 2010; Mao et al., 2012; Sidle, 1991; Stokes et al., 2009; Vergani et al., 2012). Finally, a generalised model of root-induced cohesion has been developed taking help from various sources together with field knowledge (Table 2). The surcharge of vegetation, buildings and other structures add additional weight on the slope, and this result into increased downslope force, but also increases the frictional resistance of slope materials. Based on earlier investigations conducted in similar environments (e.g. Kayastha, 2006) and data collected from the ground survey, surcharges were assigned to each land use class (Table 2). Land use map was prepared by qualitative image interpretation from orthorectified Cartosat-1 satellite image in conjunction with Google Earth (® Google Inc.) image (Table 1, Fig. 4f). The land use map shows more than 63% of the area as forest and 30% area as agricultural land with sparse settlements. An inventory map of shallow debris slides (Fig. 1) has been prepared for validation of predictions by on-screen digitization of landslide scars based on colour, tone, texture and shape from orthorectified Resourcesat-1 LISS-IV Mx and Cartosat-1 satellite image together with Google Earth (® Google Inc.) image (Table 1).

Fig. 3. (a) Locations (shown in square blocks) of soil samples collected and root diameter measured (n = 15). Inset numbers (1 & 2) in (a) indicate locations of photos 1 & 2 (at the bottom), showing roots in soil section. (b) Sites of soil depth measured (n = 148); 80% of samples (n = 118) were treated as training data to set-up the regression kriging model for the soil depth estimation and randomly selected remaining points were considered as testing data to validate the estimation.

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Fig. 4. Soil and land use parameters of the study area: (a) soil depth, (b) saturated density of soil, (c) saturated hydraulic conductivity (Ksat), (d) soil cohesion, (e) angle of internal friction (AIF) and (f) land use (DF: dense forest, MF: moderately dense forest, SF: sparse forest, AG: agriculture with sparse settlement, PL: plantation [cinchona], BL: barren land [non-rocky], BLR: barren land [rock outcrop/rocky], SL: settlement, BU: built-up area, OS: open space, SB: sand bar, R/W: river/water body).

3.3. Shallow landsliding stability model Shallow landsliding stability (SHALSTAB) model is based on the Mohr–Coulomb failure law, in the frame of infinite-slope form and a steady-state shallow subsurface flow; proposed by Montgomery and Dietrich (1994). The approach is based on the coupling of a hydrological model to a limit-equilibrium slope stability model to calculate the critical steady-state rainfall necessary to trigger slope instability at any point in a landscape (Dietrich et al., 2001; Guimarães et al., 2003; Montgomery and Dietrich, 1994; Montgomery et al., 1998, 2001). According to Mohr–Coulomb theory, landslide should occur when the shear stress within the soil mass exceeds the shear strength (τ) of the soil. τ is equal to the strength of resistance of material caused by cohesion (combination of soil and root cohesion) and angle of internal friction. τ ¼ C þ ðσ−μ Þ tan ϕ

ð2Þ

In Eq. (2), τ is the shear strength of the soil, C is soil and root cohesion, σ is normal stress, μ is hydrostatic stress (pore pressure), and ϕ is the angle of internal friction. By combining the hydrological model with the infinite-slope stability model, critical rainfall (Qc) in the steady-state rainfall condition can be predicted for slope instability as,    Tsinθ C0 ρS tan θ 1− þ Q c ¼  a tan ϕ ρw g z cos2 θ tanϕ ρw b

ð3aÞ

In Eq. (3a), a is the upslope contributing area (m2), b is the contour length across the flow path (m), T is the soil transmissivity (T [m2/day]= Ksat [m/day] × z [m]; Ksat is the saturated hydraulic conductivity), z is the soil depth (m), θ is the local slope (degree), ρs and ρw are saturated density of soil and density of water respectively (kg/ m3), g is the gravitational acceleration (m/s2), C' is the effective soil

Table 2 Root cohesion and surcharge value in different land use types. Land use

Range of root tensile strength (kPa)

Root cohesion (kPa)

Surcharge (kN/m2)

Dense old sal, teak and pine forest Dense and moderately dense young sal, teak and coniferous forest Sparse forest covered by old vegetation Plantation and young herbs Agricultural land, barren land and open space Built-up land and settlement

12.70–37.12 14.83–43.28 7.92–39.64 6.82–52.47 – –

3.0 4.0 2.0 2.8 0.0 0.0

0.7 0.7 0.3 0.1 0.0 4.7

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cohesion including root cohesion (C ' = Soil [Cs] + Root [Cr] cohesion) (Pa) and ϕ is the angle of internal friction (degree). In the case of non-cohesive soil where C ' = 0; the above equation can be rewritten as,    T sinθ ρs tan θ 1− Q c ¼  a ρw tan ϕ b

ð3bÞ

Based on the above equation the model has been built in ArcView GIS 3.2a (® Environmental Systems Research Institute, Redlands, CA, USA) environment for the cohesive and non-cohesive soil. In the resultant map, each pixel represents the critical rainfall (QC) as a measure of relative slope stability in a steady-state rainfall condition defining landslide susceptibility. The susceptibility map generated by the model makes seven susceptibility classes, which range from unconditionally unstable to unconditionally stable (Table 3). In the intermediate classes, stability increases with higher QC. When local topographic slope exceeds the soil shear strength even in dry condition (wetness = 0), the situation can be termed as unconditionally unstable. On the other hand, the slope would be unconditionally stable even in the presence of maximum wetness (wetness = 1), when soil shear strength is more than local slope. The model considers few assumptions (Borga et al., 1998) — (a) subsurface flow should be proportional to the recharge rate and upslope contributing area while recharge rate is spatially uniform, (b) groundwater table and subsurface flow would be parallel to the local slope, (c) local wetness does not exceed 1; therefore, Hortonian overland flow is ignored, and only subsurface flow is considered, (d) saturated hydraulic conductivity should be uniform with soil depth. 4. Results 4.1. Predictive susceptibility models To estimate the slope stability, three models have been generated by fitting different parameters (Table 4) to the SHALSTAB framework in a steady-state rainfall condition. The slope stability obtained by Model1, Model-2 and Model-3 for the study area is shown in Figs. 5, 6 and 7 respectively. Model-1 considered only topographical (slope and upslope contributing area) and soil parameters (soil depth, saturated soil density, saturated hydraulic conductivity, soil cohesion and angle of internal friction) for analysis. In Model-2, the impact of vegetation roots (root cohesion) has been considered for the prediction of slope failure along with the topographical and soil parameters. In Model-3, the surcharge of vegetation, buildings and other structures were added to root cohesion along with other parameters used in Model-1.

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Table 4 Symbol, unit and value of parameters used in the SHALSTAB model. Parameter

Symbol

Unit

Value range

Slope angle Soil depth Saturated density of soil Density of water Effective cohesion of soil (including root) Angle of internal friction Upslope contributing area Contour length Soil transmissivity Gravitational acceleration

θ z ρS ρw C′ ϕ a b T g

Degree m kg/m3 kg/m3 kPa Degree m2 m m2/day m/s2

0–78 0–4.22 1520–2100 1000 0–16.26 17–33 0–1.004e+7 8 0–29.61 9.81

Analysis obtained from Model-1 (Fig. 5a) shows that 28% (92 km2) and 22% (72.28 km2) of the area come under unconditionally unstable and unconditionally stable class, respectively. In 34% land of the study area, landslides may initiate after little amount of rainfall (0– 50 mm/day). Fig. 5a shows that few slope sections along the ridges and adjacent valleys, where slopes are gentle; landslides may occur when the rainfall is more than 400 mm/day. Stability, in this case, owes to the high cohesive nature and comparatively thicker deposition of the soil. The areas covered under steep slopes, below the crest lines and all the headwater areas of rivers, come under unconditionally unstable or highly sensitive towards landslide even within 50 mm/day rainfall. Further, it is revealed that the threshold given for mean slope of unconditionally unstable and unconditionally stable lands are 34° and 15°, respectively (Table 5). Thus predicted susceptibility classes obtained by Model-1 are strongly influenced by topography and soil depth while physical and mechanical properties of soil do not play a significant role in determining slope stability. Model-2 (Fig. 6a) shows a drastic change in prediction compared to Model-1. Nearly 9% (29.05 km2) and 42% (139.29 km2) of the area (against 28% and 22% of the area under the respective classes in Model-1) comes under unconditionally unstable and unconditionally stable class, respectively. While 26% (85.41 km2) of the area is considered as unstable at low QC (0–50 mm/day) in Model-2, it is seen to be about 34% in Model-1. The degree of stability increases over steep upper parts of the catchment of the river Rilli and also the downslope areas towards the river Tista. All unstable concave steep slopes below the crest lines of Model1 are classified into quasi-stable to stable land in Model-2. Assuming that the surfaces are totally vegetation free (where vegetation roots have no contribution to slope stability — Model-1), prediction pushes 41% of unconditionally unstable land into dense forest covered area throwing up a contradiction. And, in the presence of vegetation in Model-2 only 5% of the unconditionally unstable land is going into dense forest covered area (Table 5). Thus, Model-2 clearly explores

Table 3 Critical rainfall classes obtained by SHALSTAB model, according to the relation between upslope contributing area per unit contour length (a/b) (m) and slope (tan θ) (after de Luiz Rosito Listo and Vieira, 2012; Montgomery and Dietrich, 1994). Critical rainfall (mm/day)

Characteristics

Condition

Sig.

(1) Unconditionally stable

Unconditionally stable and saturated

Stable

(2) N400

Unconditionally stable and unsaturated

(3) 200–400

Stable and unsaturated

(4) 100–200

Unstable and unsaturated

(5) 50–100

Unstable and saturated

(6) 0–50 (7) Unconditionally unstable

Unconditionally unstable and unsaturated Unconditionally unstable and saturated

a=bN sin θðT=Q Þ or tanθ bð1−ρw =ρS Þ tanϕ a=bb sin θðT=Q Þ or tanθ bð1−ρw =ρS Þ tanϕ a=bb T=Q sin θ½ ρw =ρS ð1− tan θ= tan ϕÞ or a=bb sinθðT=QÞ or tanϕ N tanθ Nð1−ρw =ρS Þ tanϕ a=b≥T=Q sin θ½ ρw =ρS ð1− tanθ= tan ϕÞ or a=bb sin θðT=QÞ or tanϕ N tanθ Nð1−ρw =ρS Þ tanϕ a=bbT=Q sin θ½ρw =ρS ð1− tan θ= tan ϕÞ or a=bN sinθðT=Q Þ or tanϕN tan θN ð1−ρw =ρS Þ tan ϕ tan θ≥ tan ϕ or a/b b sin θ(T/Q) tan θ≥ tan ϕ or a/b N sin θ(T/Q)

Quasi-unstable

Unstable

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Fig. 5. (a) Critical rainfall for slope instability obtained from Model-1 parameters. (b) Graph showing area (in %) of landslide within each critical rainfall class.

the significance of vegetation root holding (in the form of root cohesion) on slope stability. Prediction through Model-3 shows a negligible change in the distribution of stability classes as compared to Model-2 (Fig. 7a). In Model-3, 10% (31.53 km2), 26% (85.21 km2) and 42% of areas (139.04 km2) fall under unconditionally unstable, unstable (Q C is 0–50 mm/day) and unconditionally stable classes, respectively.

4.2. Accuracy assessment of predictions Fig. 5b–7b provides the degree of reliability of predictions through a comparative assessment of the area (in %) predicted for a specific Q c class and the actual extent of the area of existing shallow debris slides (a total of 1262 scars) in the corresponding class. A large proportion of landslides within low Qc and high landslide density indicate the model reliability.

The number of existing landslides associated with each Q C varies from one sub-catchment to another with the change of model set-up. The observed–predicted comparison of landslides area reveals that Model-1 can partially explain the possible future landslide areas. However, a high density of landslides (in areal extent) occurred on stable land (Fig. 5b). The proportion of landslides that occurred within Q C class with rainfall b 50 mm/day was 54% and with rainfall b200 mm/day was 71% in both Model-2 and Model-3, whereas only 1% landslides occurred within N400 mm/day rainfall class (Figs. 6b & 7b). A significant number of landslides (293 scars) were noticed on the unconditionally stable area in both the predictions. From a comparative analysis, it is clear that the prediction at low QC (0–50 mm/day) in Model-1 faced the overestimation error. However, all the three models suffer from the lack of proper explanation and misclassification in the case of predicted stable land class. For further validation, the receiver operating characteristic (ROC) curve has also been used. ROC analysis shows that the performance of

Fig. 6. (a) Critical rainfall for slope instability obtained from Model-2 parameters. (b) Graph showing area (in %) of landslide within each critical rainfall class.

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Fig. 7. (a) Critical rainfall for slope instability obtained from Model-3 parameters. (b) Graph showing area (in %) of landslide within each critical rainfall class.

prediction improves after incorporating the effect of root tensile strength/cohesion (Model-2) and the impact of the surcharge of vegetation, buildings and other structures (Model-3). The areas under ROC curve obtained are 0.566, 0.637 and 0.645 for Model-1, Model-2 and Model-3, respectively (Fig. 8). 4.3. Chi-square (χ2) statistics The non-parametric test has examined the contribution of recognised determinants of shallow debris slides. The chi-square (χ2) statistics of Pearson's goodness-of-fit implies that χ2 is statistically significant at p b 0.05 for Model-1 and at p b 0.001 for the Model-2 and Model-3, against the proportional appearance of landslides in each Q c class if the model does not discriminate relative landslide hazard. Hence, χ2 denotes that model distinguishes areas with greater landslide hazard pointing towards the fact that the probability of slope failure is a function of topographic position, soil property and land use pattern.

5. Discussion 5.1. Assessment of susceptibility maps Many researchers have conducted studies to emphasise the topographic impact on shallow landslides in the light of uniform soil parameters (e.g. Montgomery et al., 1998). In the Himalayas, soil depth, physical and mechanical properties vary drastically (Sarkar et al., 2013). The present research tries to explain the real situation by considering soil properties as a spatial variable (Table 4) and highlights the effects of topography, soil and land use through three different scenarios/models. A strong correlation has been found between slope, soil thickness and vegetation root-induced cohesion in all prediction models. Most of the shallow landslides occurred on steep slopes, where the soil cover is very thin. Subsurface runoff follows the topographic gradients, and it accumulates below at the points of convergence (topographic

Table 5 The mean value of topographical, soil parameters and land use pattern in different predicted classes.

Model 1

Model 2

Model 3

Critical rainfall (mm/day)

Slope (Deg.)

S. soil den. (Mg/m3)

Cohesion (kPa)

AIF (Deg.)

Soil depth (m)

Land use (area in %) DF

MF

SF

AG

Un. unstable 0–50 50–100 100–200 200–400 N400 Un. stable Un. unstable 0–50 50–100 100–200 200–400 N400 Un. stable Un. unstable 0–50 50–100 100–200 200–400 N400 Un. stable

34 24 23 22 21 19 15 37 26 28 28 27 25 19 37 26 28 28 27 25 19

1.70 1.69 1.69 1.68 1.67 1.67 1.70 1.71 1.69 1.69 1.68 1.67 1.67 1.69 1.71 1.69 1.69 1.68 1.67 1.67 1.69

2.75 3.08 3.49 3.59 3.52 3.53 4.38 4.26 4.41 5.58 6.13 6.38 6.67 6.40 4.42 4.47 5.63 6.14 6.36 6.65 6.34

22 22 23 23 22 22 22 22 22 23 22 22 22 23 22 22 23 22 22 22 23

1.61 1.64 1.71 1.79 1.89 2.05 1.69 1.59 1.63 1.70 1.74 1.82 2.00 1.66 1.60 1.62 1.70 1.73 1.82 2.00 1.66

41 31 7 5 2 1 13 5 18 12 10 5 2 48 7 18 12 10 5 1 47

31 36 8 4 2 1 18 9 20 10 8 4 1 48 10 21 10 8 3 1 47

37 32 7 5 3 2 14 22 29 10 8 4 2 25 23 29 10 7 4 2 25

10 39 9 5 2 0 35 10 39 9 5 2 0 35 10 39 9 5 2 0 35

S. soil den.: saturated density of soil, AIF: angle of internal friction, DF: dense forest, MF: moderately dense forest, SF: sparse forest, AG: agriculture with sparse settlement.

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slope stability. A similar observation has been made by Keijsers et al. (2011) in Taiwan. However, apart from soil depth in some sections, predictions were not significantly sensitive towards soil properties as much as it is with topography. After considering topographic and soil properties on the SHALSTAB model in Rio de Janeiro, Guimarães et al. (2003) have reported that shallow landslide initiation has a strong bearing on topographic control rather than on tropical soils. 5.2. Evaluation of prediction performance

Fig. 8. ROC curve showing prediction performance in different models (AUC: area under curve, Std. E: standardised error).

hollows). These are probable locations of future landslides due to the presence of increasing pore water pressure (de Luiz Rosito Listo and Vieira, 2012; Montgomery and Dietrich, 1994; Talebi et al., 2008), which is positively associated with rainfall-infiltration rate (Muntohar and Liao, 2010). Many shallow debris slides along the steep side slopes of river valleys and on the topographic hollows observed in the field, point towards the fact as mentioned earlier. In Model-1, in the absence of root systems, most of the study area comes under unstable land (Fig. 5), indicating topographic dominance on the prediction. Normally, vegetation roots provide an additional cohesion in the soil and help to prevent materials from falling by its bonding capacity. Effect of root-induced cohesion on slope stability is clearly seen in Model-2 and Model-3 (Table 5). Some previous studies have also found a significant contribution of vegetation root system on slope stability (Mao et al., 2012; Meisina and Scarabelli, 2007; Montgomery et al., 1998; Tosi, 2007). On the contrary, in a few cases, it is noticed in the study area with forest cover having good many large trees with very stout old roots, overland flow develops turbulence at the base of the stems and buttresses, which may increase the probability of landslide. In all the three models, stream head areas were classified as unstable zones. In topographic hollows below these (unstable) zones, soils are comparatively thicker, and flow gets accumulated at points of convergence from where the flow takes a linear path in valleys. Therefore, slope failure is likely to be more in a large specific catchment area or where topographic wetness index (TWI) is high. Similar findings have also been reported by Borga et al. (2002) in the Eastern Italian Alps, Montgomery et al. (2000, 2009) in the Coast Range of U.S.A., and many others. Areas just below the steep slope or escarpment/scarps covered by low or non-cohesive thick, coarse debris with high permeability (higher Ksat) come under low Qc class. The saturated density of such soil is high and as cohesion of materials decreases in a normal soil profile with increasing soil depth and level of perched water (Hammond et al., 1992); after a certain amount of rainfall, the saturated materials move downslope along the impermeable planar slip surface (bedrock) in the form of blow-outs (Coe et al., 2004). Many blow-outs on thin debris along the steep side slope of river Rilli is a strong proof in favour of findings in the present study. Analogous observations are found in the work carried out by Lacerda (1997); Fernandes et al. (2004) and Dykes (2002) in tropical highlands of Brazil and Indonesia. Lacerda (2007) stated that saturated colluvium deposits in tropical areas have limited brittle behaviour. They present a strain hardening stress–strain response, which is favourable for progressive sliding. In the predictive models worked out in the present study, in some patches, a higher angle of internal friction and cohesion appeared to have given a higher resistance against shear stress thereby increasing

During the comparison of observed–predicted landslides, two types of errors in prediction were found. First, the absence of landslide scars in an unconditionally unstable or low Qc area, where landslides are predicted to occur. Second, there is the presence of landslide scars in unconditionally stable or high Qc area, where landslides are unlikely to occur. In all the predictive models, overestimation has been marked in unconditionally unstable or low Qc. However, a certain amount of landslides in stable or high Qc the models reflect poor discrimination. It is observed that first kind of error (absence of landslide scars in the unconditionally unstable area, where landslides are predicted to occur) may be the reason behind the missing out of small old landslide scars during landslide inventory mapping, because of difficulty in their identification. Borga et al. (1998) suggested further investigation to minimise the misclassification which might result in the second kind of error. As for such errors, Santini et al. (2009) have found out that prediction performance alters with adapted terrain analysis methods and Montrasio et al. (2011) have felt that model accuracy gets reduced in extensive areas because of variations in physical and mechanical properties of soil. Misclassification observed in the present prediction models could be due either to imperfect terrain analysis or lack of representation of slope sensitivity with a variation of soil properties. Validation of models through comparison of observed–predicted landslide areas and ROC curves indicate that the overall success rate of all the three models is relatively low. Although SHALSTAB is recommended for slope stability analysis at regional level, it may perform with better accuracy at small catchment scale (Fernandes et al., 2004; Meisina and Scarabelli, 2007), where uniformity was found in soil, especially the soil depth is uniform in the entire study area i.e., bedrock should be parallel to the surface slope. Though, this kind of simplicity is not possible in Himalayan terrain the prediction suffers from a lack of proper representation of geo-environmental conditions. In this respect, the predictive model is less satisfactory to specify the process at a regional level in the Himalayas. However, implementation of SHALSTAB in conjunction with intensive field work at a catchment scale is expected to give better results in the Himalayas. Like other physically based models, SHALSTAB is not free from limitations. The model was parameterised after taking many assumptions. The simplified assumption made about slope hydrology (steady-state slope parallel flow) in the present study, may be the reason for the relatively poor performance of SHALSTAB. Cervi et al. (2010) have made a similar observation in their study in the Northern Apennines. The most important drawback of the SHALSTAB model is that it does not consider lithology to assess slope stability. Therefore, the model fails to predict the lithology and structure controlled landslides. 6. Conclusions Shallow debris sliding is a common phenomenon after intense rainfall in mountainous areas. Hence, slope stability assessment for the shallow landslides is an essential input for hazard and risk estimation. From many deterministic approaches, the present study has adopted the SHALSTAB model due to its faster analytical capability and scientifically reliable result to compute seven rainfall threshold classes for initiation of debris slide even with regional variability in some factors/parameters. The model has been prepared for three different scenarios with varying soil and land use properties, incorporated in each of the predictions.

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After setting up the model through non-uniform soil properties, prediction shows 56.60% spatial variability of the likelihood of landslides in a vegetation-free scenario (Model-1), where a large proportion of land (62%) come under the unstable zone. On the contrary, the presence of vegetation increased the slope stability (35% unstable land) with a likelihood of 63.70% (Model-2) followed by 64.50% likelihood (Model-3) due to surcharge because of vegetation, buildings and other structures. Predictions indicate that the model sensitivity strongly depends on land use and slope rather than the soil properties, except for the soil depth. The contribution of vegetation roots on slope stability is clearly noticed in visual as well as analytical results obtained from all the three models. A comparison of observed–predicted landslide areas revealed that predictions suffered from overestimation error in a few cases. It is because of such overestimation errors, the performance, checked through ROC curve, is less satisfactory at a regional level over the Himalayas. It appears that this discrepancy could be due to the inadequate number of soil samples collected (15 samples for analysis of physical and mechanical properties and 118 for soil depth measurement in an area of about 330 km2) and other invalid assumptions as mentioned earlier. It has come to light through this study that variations in soil depth are significant determinants of model sensitivity over other soil parameters. From this, it becomes apparent that for satisfactory prediction of landslides in the Himalayas, care should be taken to collect a sufficient number of soil samples. Further, the prediction accuracies can be improved through inputs of in-depth analysis of root reinforcement and high-resolution topographic data. Acknowledgements The first author is grateful to UGC, New Delhi, India for providing the fellowship for carrying out the research work. The authors are thankful to Dr. Arun Prasad (Department of Civil Engineering, IIT-BHU, Varanasi) for providing geo-technical laboratory. They are also thankful to Prof. K. N. Prudhvi Raju (Department of Geography, BHU, Varanasi), Dr. Tapas R. Martha (NRSC, Hyderabad) and two anonymous reviewers for their constructive comments and suggestions to improve the manuscript. References Arnone, E., Noto, L.V., Lepore, C., Bras, R.L., 2011. Physically-based and distributed approach to analyze rainfall-triggered landslides at watershed scale. Geomorphology 133, 121–131. http://dx.doi.org/10.1016/j.geomorph.2011.03.019. Basu, S.R., De, S.K., 2003. Causes and consequences of landslides in the Darjeeling-Sikkim Himalaya, India. Geogr. Pol. 76, 37–52. Bathurst, J.C., Bovolo, C.I., Cisneros, F., 2010. Modelling the effect of forest cover on shallow landslides at the river basin scale. Ecol. Eng. 36, 317–327. http://dx.doi.org/10.1016/j. ecoleng.2009.05.001. Baum, R.L., Coe, J.A., Godt, J.W., Harp, E.L., Reid, M.E., Savage, W.Z., Schulz, W.H., Brien, D.L., Chleborad, A.F., McKenna, J.P., Michael, J.A., 2005. Regional landslide-hazard assessment for Seattle, Washington, USA. Landslides 2, 266–279. http://dx.doi.org/10. 1007/s10346-005-0023-y. Bhandari, R.K., 2006. The Indian landslide scenario, strategic issues and action points. First India Disaster Management Congress. November 29–30, New Delhi, pp. 1–18. Bischetti, G.B., Chiaradia, E.A., Epis, T., Morlotti, E., 2009. Root cohesion of forest species in the Italian Alps. Plant Soil 324, 71–89. http://dx.doi.org/10.1007/s11104-009-9941-0. Bischetti, G.B., Chiaradia, E.A., Simonato, T., Speziali, B., Vitali, B., Vullo, P., Zocco, A., 2005. Root strength and root area ratio of forest species in Lombardy (Northern Italy). Plant Soil 278, 11–22. http://dx.doi.org/10.1007/s11104-005-0605-4. Black, C.A., 1965. Methods of Soil Analysis, Part 1. American Society of Agronomy, Madison, WI. Borga, M., Dalla Fontana, G., Cazorzi, F., 2002. Analysis of topographic and climatic control on rainfall-triggered shallow landsliding using a quasi-dynamic wetness index. J. Hydrol. 268, 56–71. http://dx.doi.org/10.1016/S0022-1694(02)00118-X. Borga, M., Dalla Fontana, G., Da Ros, D., Marchi, L., 1998. Shallow landslide hazard assessment using a physically based model and digital elevation data. Environ. Geol. 35, 81–88. http://dx.doi.org/10.1007/s002540050295. Bovolo, C.I., Bathurst, J.C., 2012. Modelling catchment-scale shallow landslide occurrence and sediment yield as a function of rainfall return period. Hydrol. Process. 26, 579–596. http://dx.doi.org/10.1002/hyp.8158. Bromhead, E.N., 1996. Slope stability models. In: Dikau, R., Schrott, L., Dehn, M., Hennrich, K., Ibsen, M.-L., Rasemann, S. (Eds.), The Temporal Stability and Activity of Landslides in Europe with Respect to Climatic Change TESLEC. Final Report Part 1, Summary Report. European Community, CEC Environment Programme, pp. 87–97.

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