WITHDRAWN: Regional Slope Stability Modeling in Darjeeling-Sikkim Himalaya to Understand the Impact of Gravity Loading and Seismic Shaking

doi:10.21203/rs.3.rs-4828006/v1

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WITHDRAWN: Regional Slope Stability Modeling in Darjeeling-Sikkim Himalaya to Understand the Impact of Gravity Loading and Seismic Shaking

https://doi.org/10.21203/rs.3.rs-4828006/v1

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Plenty of progress has been made in the field of regional-scale slope stability mapping in response to the urgent need for effective ways to lessen the effects of both gravity- and earthquake-caused landslides. Darjeeling-Sikkim Himalaya has faced considerable slope instability issues attributed to a combination of factors such as rainfall, seismic shaking, complex lithological compositions and tectonic setting. In order to map the terrain stability in the region, Newmark displacement (D_n) model has been applied to estimate the Factor of Safety (FoS), Critical Acceleration (α_c) and Displacement (D_n). Utilizing a geospatial dataset comprising a high-resolution digital elevation model (DEM), shear strength data for soil and rock obtained from an in-depth in-situ measurement using rotary drilling for estimating physical and shear parameters such as, unit weight, cohesion and angle of friction, etc., and surface-consistent peak ground acceleration (pga-g) with a return period of 475 years, these datasets have been converted into a grid with a spatial resolution of 12.5×12.5m on Geographical Information System (GIS). The estimated of Factor of Safety (FoS), Critical Acceleration and co-seismic landslide displacement are calculated by integrating them into a dynamic model based on Newmark's Displacement. The calculated FoS ranges from 0.59–5.6, indicating that about 37% of the terrain becomes unstable as a result of rainfall and gravity-induced failure. The measured displacement (D_n) varies from 0-10cm, suggesting that sliding blocks become unstable due to seismic shaking as well. This investigation provides future disaster mitigation and management strategies to address perennial landslide devastation caused in this terrain.

Darjeeling-Sikkim Himalaya

Critical Acceleration

Newmark Displacement

Slope Stability

Factor of Safety

Landslides are frequently caused by seismic activity in mountainous terrain, posing significant secondary hazards. Seismic-triggered landslides cause significant damage to infrastructure, specifically transportation networks, lead to human suffering and result in huge monetary losses worldwide, particularly in India, where approximately 0.42 million km² of terrain is prone to such induced catastrophes (Nath et al., 2021). Precisely predicting the areas and circumstances in which earthquakes could cause landslides is essential for evaluating the potential risks of seismic activity in a certain region.

Researchers (Newmark, 1965; Jibson, 1987; Jisbon et al., 2000; Mankelow and Murphy, 1998; Caccavale et al., 2017; Zhuang et al., 2016; Robinson et al., 2016) have developed numerous methods to calculate the stability of slopes in seismic events. Not only do these models analyze individual slope stability (Jibson and Keefer, 1993), but they also integrate into broader assessments of landslide hazards. These assessments aim to anticipate the potential for landslides triggered by seismic activity (Wilson, 1993) or to delineate regions of instability, synthesising them into degree of hazard (Refice and Capolongo 2002; Rodrı´guez-Peces et al., 2014). Evaluating the efficacy of these methods is challenging due to their predictive nature regarding future hazard events.

Several researchers have applied Newmark's method to define seismic parameters (Jibson et al., 2007; Keefer, 2000; Caccavale et al., 2017). This approach involves assessing ground motion attributes such as, Peak Ground Acceleration (PGA) or other seismic parameters associated with earthquakes. These studies generate a probability function that charts Newmark's Displacement in relation to the number and types of slope failures associated with specific, typically large, historical earthquakes. In order to map a regional slope instability, hazard that earthquakes in Spain's Lorca basin can cause, Rodriguez-Peces et al. (2011) looked at different ways to use seismic scenario input (probabilistic, pseudo-probabilistic, and deterministic based on the seismic potential of certain seismogenic faults). Probabilistic scenarios, tailored to typical return periods (475, 975 and 2475 years) in seismology, offer a comprehensive perception of seismic hazards associated with a probability level but tend to yield only minor and isolated instances of slope instability. Consequently, Rodriguez-Peces et al. (2011, 2014) propose a deterministic scenario that links to less frequent yet higher-magnitude seismic events (M_w 6.7–6.8). They simulate this scenario by modeling the rupture of selected seismogenic structures, which can trigger significant slope instabilities. Jibson et al. (2007), for example, put together the calculated Newmark's displacements and landslide inventories caused by a reference earthquake. This creates a probability curve that connects predicted displacements with failure probabilities. Jibson et al. (2007) then utilize this function to construct a map that associates spatial distributions of slope failure probabilities with ground-shaking intensities. Similarly, Peng et al. (2009) develop their hazard map by aggregating and ranking index values from D_n map.

The study region encompasses the Darjeeling-Sikkim Himalaya, which is located on the foothills of the Eastern Himalaya inside the Teesta basin, ranging in altitude from 250m to 8003m and it surrounds Mount Kanchenjunga, as shown in Fig. 1(b). The topography consists of steep to moderate gradients and complex lithological formations and seismotectonic structures (Nath et al., 2021). Gansser (1964) identified distinct tectonostratigraphic domains, namely the Sub-Himalaya, Lesser Himalaya, Higher Himalaya, and Tethys Himalaya, separated by major fault zones such as the Main Boundary Thrust (MBT), Main Central Thrust (MCT), Teesta and Gangtok Lineaments, which trend NNW-SSE, WNW-ESE trending Goalpara Lineament, and SW-NE trending Kanchanjunga Lineament. The area comprises intra-thrusted rock slices of the Fold-Thrust Belt (FTB) of the Eastern Himalayas, where rocks ranging from Precambrian to Quaternary ages are juxtaposed along certain EW trending Tertiary periods. The Himalayan Frontal Thrust (HFT) separates the coarse to very coarse-grained clastics of the Siwalik Group from the nearby Quaternary sediments of the foredeep region further south. This lets the foredeep sediments face south along the foothills of the Himalayas. The Main Central Thrust (MCT) moves high-grade meta-sediments and granite gneisses from the Central Crystalline Gneissic Complex (CCGC) on top of the Daling Group's low-grade metamorphic rock in the northern part of the Higher Himalaya.

The Bureau of Indian Standards (BIS, 2002) classified Darjeeling-Sikkim Himalaya as Seismic Zone IV and V, indicating its high vulnerability to seismic shaking, which in turn leads to landslides. A great earthquake of M_w 8.1 struck Sikkim in January 1934, with its epicenter at the Nepal-Bihar border along the intraplate boundary, inducing ground motion equivalent to MMI intensity VII-VIII in the region. The 1988 Bihar-Nepal earthquake of M_w 6.8 occurred west of Sikkim, causing equivalent damage to MMI intensity VII-VIII, while the 2006 Sikkim earthquake of M_w 5.3 and 2011 Sikkim earthquake of M_w 6.9 also caused widespread damage in Darjeeling-Sikkim Himalaya. The region has a demographic growth of 7,42,593 inhabitants (Census, 2011) of which in Sikkim the average growth rate is 12.36% between 2001 and 2011, while Darjeeling had 9.47% growth between 2001 and 2011. The region is perennially landslide-prone, with a potential impact on socioeconomics and risk distribution in terms of loss and life.

Therefore, it has been felt imperative to take a fresh look at the spatial probability of static and dynamic slope instability obtained by the analysis of slope stability and Newmark Displacement with an ensemble of Remote Sensing-GIS and in-situ measurement implemented in Darjeeling-Sikkim Himalaya for future disaster mitigation and management from perennial landslide devastations.

2.1. Geotechnical and Satellite Data

To perform regional slope stability modeling, whose computational protocol is depicted in Fig. 3, there is a requirement of a Digital Elevation Model (DEM), Surface-consistent Probabilistic Seismic Hazard in terms of Peak Ground Accleration (PGA-g) and Geotechnical data. ALOS PALSAR DEM with a spatial resolution of 12.5×12.5m has been downloaded from Alaska Satellite Facility (source: https://search.asf.alaska.edu) and a Surface Consistent PGA map has been sourced from Nath et al (2016). On the other hand, geotechnical field investigation is one of the most promising technologies for surface near-surface and sub-surface exploration of landslide-prone areas to understand the lithological sequence and thickness of soil or rock along with its associated physical and shear properties, which further calculate the soil strength, unit weight, cohesion, angle of friction and other parameters of soil or rock. The representative dataset of the geotechnical field investigation of Kurseong and Kalimpong, presented in Table 1. Samples have been collected using a rotary drilling method, whose photographs are shown in Fig. 2.

Table 1

Representative geotechnical attributes used for slope stability analysis
Slide name	Site class	Unit Weight (KN/m³)	Cohesion (KPa)	Angle of friction (°)
Kurseong	B	23.21	175	40
6 Mile	C	17.45	30	30

2.2. Factor of Safety and Critical Acceleration

Landslide behaviour on natural slopes during seismic conditions has been studied using Newmark's permanent-displacement method. While this method describes slope response to seismic shaking, it may not reliably forecast landslide displacements in practical situations (Jibson et al., 2000). Newmark's approach models landslides as rigid friction blocks sliding on sloped surfaces, with a critical acceleration (\(\:{\alpha\:}_{c}\)) defines (Eq. 2 in Fig. 3) the threshold for sliding initiation (Jibson et al., 2000). The critical acceleration, which is dependent on the Factor of Safety (FoS) and the slope angle (α), is a measure of the slope's ability to withstand failure caused by seismic shaking (Rodríguez-Peces et al., 2014). Understanding a slope's dynamic stability during an earthquake necessitates considering its static stability. The initial step involves choosing a model for calculating the static Factor of Safety (FoS), which requires computing this factor for each grid cell expressed in Eq. 1.

2.3. Newmark Displacement

Another step of the methodology involved combining surface consistent probabilistic seismic hazard in terms of PGA with the critical acceleration derived from rigid-block method (Eq. 2 located in Fig. 3). An effective approach to amalgamate these results through a regression equation is proposed by Ambraseys and Menu (1988) to compute Newmark's Displacement (\(\:{D}_{n}\)) which is estimated using Eq. 3. Seismic hazard assessment often involves incorporating \(\:{D}_{n}\) computation through regression models that rely on fundamental earthquake parameters like magnitude and distance, as well as strong ground motion parameters such as Arias intensity, critical acceleration, critical acceleration ratio and PGA as discussed by Jibson (2007, 2011) and Rodríguez-Peces et al. (2014). This study utilized the regression equation for Newmark's Displacement in terms of critical acceleration ratio using Eq. 5, introduced by Jibson (2007) to improve upon the solution proposed by Ambraseys and Menu (1988).

3.1. Earthquake-induced Landslide Inventory

Landslides triggered by earthquakes represent one of the most devastating natural phenomena, particularly prevalent in mountainous regions characterized by hilly slopes. These occurrences are caused by seismic disturbance of saturated rock or soil during earthquakes. The Compilation of this inventory involved several methodologies, including digital interpretation of high-resolution multi-temporal satellite data, identification of topographic indicators of landslide formations from various sources such as, Nath et al. (2021), Rajendran et al. (2011), Martha et al. (2015), ground verification and digitization into GIS software. In this study, LISS III-IV, Landsat, Sentinel, Google Earth and ASTER and ALOS PALSAR DEM have been used for landslide inventory mapping. Landslide inventory on satellite imagery largely depends on the contrast of the spectral signature difference between the landslide and its surroundings. Landslides show high reflectance as they in general are scarce of any vegetation. A few of the landslides mapped have been confirmed in the field and the map has been modified accordingly. Figure 4(a) depicts an earthquake-induced landslide inventory map of Darjeeling-Sikkim Himalaya documents approximately 2553 landslides are distributed in the region; however, major concentrations are in the central and northern parts of the terrain. The predominant types of landslides in this area are rock and debris slides, as depicted in Figs. 4(b-d).

3.2. Topographic Gradient and Site Classification

Surface topography, which controls flow direction and soil moisture concentration, is an important factor that limits the density and spatial extent of landslides. Topographic Gradient or Slope Angle has been calculated from ALOS PALSAR DEM with a spatial resolution of 12.5m, which compares the elevations of adjacent cells and computes the slope angle. Despite potential errors, ALOS PALSAR DEM offers a high-precision model that eliminates significant errors and improves vertical accuracy. Slope angle of the terrain ranges from 0° to 87.82°, as depicted in Fig. 5(a). Higher values, ranging between 40 and 87.82°, are measured in the sub and higher Himalaya region.

In order to assign shear strength parameters to the slope units, a seismic site-class map has been formulated using the methodology developed by Wald and Allen (2007), which entails a terrain-centric V_s^eff model crafted through an automated classification methodology predicated on soil taxonomic parameters encompassing topographic gradient, local convexity and surface texture. The ALOS PALSAR Digital Elevation Model (DEM) is used for this purpose. The DEM undergoes conversion to an ASCII file format and is subsequently transposed into a grid file. A geotechnical site classification adhering to the NEHRP (National Earthquake Hazards Reduction Program) nomenclature is determined based on the effective shear-wave velocity for the soil or rock column, known as V_S^eff, and is illustrated in Table 2, predicated on the distribution of effective shear-wave velocity (V_s^eff), which is contingent upon the composition, grain size, lithology and topographic gradient. This classification divides the Darjeeling-Sikkim Himalaya region into distinct site classes: A (S-wave velocity, ≥ 1500m/s), B (760-1500m/s), C (360-760m/s), and D (180-360m/s) within the Darjeeling-Sikkim Himalayan region, as depicted in Fig. 5(b).

Table 2

NEHRP site classification scheme (BSSC, 2001); V_s^eff denotes effective shear-wave velocity for the soil or rock column
Site class	V_s^eff	Description
A	≥ 1500m/s	Hard rock
B	760 − 1500m/s	Rock site
C	360 − 760m/s	Soft rock, hard or very stiff soils or gravels
D	180 − 360m/s	Stiff soils
E	≤ 180m/s	More than 3m of soft clay defined as soil with plasticity index (PI) > 20, moisture content (w) ≥40 percent, and average undrained shear strength (Su) < 25kPa

3.3. Unit Weight and Angle of Friction

The unit weight, also referred as bulk density, is an essential parameter in slope stability. It quantifies the weight of soil or rock material per unit volume within a slope. It is essential in determining how gravitational forces are distributed within the mass of the slope. A higher unit weight signifies a larger mass per unit volume, resulting in more gravitational forces exerted on the slope and perhaps leading to instability. On the other hand, a decrease in the weight of a unit can lead to a decrease in gravitational forces and an increase in stability. However, other elements like cohesion and friction also play a role in determining how a slope behaves.

The angle of friction, characterizing the resistance of soil or rock materials to shear stresses. It represents the maximum angle at which a material can maintain stability against sliding along a plane surface without undergoing failure. Higher values of the angle of friction indicate greater resistance to shear deformation and, consequently, enhanced stability. Conversely, lower angles of friction signify reduced resistance to shear stresses, rendering slopes more prone to failure.

Based on the shear strength illustrated in Table 1, are assigned to each site-class. These strengths are clear indicators of the highest possible values within the range of probable strength fluctuations of a specific unit. They are necessary for sustaining stability in extremely steep slopes within that unit. Further examination will clarify that the overall amount of the assigned strength is less important compared to the differences in strength between units, which can be appropriately defined within a regional framework. Figure 6 (a) and (b) illustrates the assigned unit weight and angle of friction values for the site-class units in the region.

3.4. Cohesion and Surface Consistent Peak Ground Acceleration

Cohesion plays a pivotal role in slope stability, serving as a fundamental component alongside frictional resistance. Cohesion represents the intrinsic bonding forces within the soil matrix, contributing to its ability to resist shear stresses and maintain stability. In this study, cohesion map has been prepared through geotechnical shear strength data obtained from rotary drilling whose representative data has been presented in Table 1 and categorized into four classes viz. 20 < C ≤ 30, 30 < C ≤ 70, 70 < C ≤ 200 and 200 < C ≤ 300, as shown in Fig. 7(a). Higher cohesion values indicate greater soil strength and stability, while lower cohesion values signify increased susceptibility to failure.

Surface Consistent Peak Ground Acceleration (PGA) is an important contributing aspect in the initiation of co-seismic landslides. The seismic hazards level in the Darjeeling-Sikkim Himalaya is determined by the highest acceleration, which is used to assess the severity of ground-shaking. The PGA with 10% probability of exceedance in 50 years with a return period of 475 years, has been adopted from Adhikari and Nath (2016). The PGA values range from 0.260g to 0.860g, as shown in Fig. 7(b). Typically, the area around the epicenter experiences more intense vibration, which often leads to the occurrence of co-seismic landslides.

3.5. Factor of Safety and Critical Acceleration

Figure 8(a) exhibits the static Factor of Safety (FoS) map derived from the integration of slope angle and others geotechnical thematic layers according to Eq. 1. The unit weight (γ_w) of water, used 9.81kN/m³, adheres to the recommendations of Jibson et al. (2000). The geological composition of the terrain predominantly comprises phyllite, gneiss, mica schist and slope debris deposits. Consequently, for the sake of simplicity, dry conditions (m = 0) are exclusively considered based on Eq. 1, which adequately represents dry conditions. The thickness of the slab prone to failure (t) is specified as 5m, according to Geological Survey of India. The FoS map identifies areas with different levels of susceptibility to slope failures, as shown in Fig. 8(a). Thereupon, the LSI values are classified as: FoS ≤ 1 (Defended slope zone), 1.0 < FoS ≤ 1.5 (Upper threshold slope zone), 1.5 < FoS ≤ 2.0 (Lower threshold slope zone), 2.0 < FoS ≤ 2.5 (Quasi-stable slope zone), 2.5 < FoS ≤ 3.0 (Moderately stable zone) and FoS ≥ 3.0 (Moderately stable zone). Upon initial computation of the FoS map, approximately 5% of the total area exhibited static instability (FoS ≤ 1.0). FoS value of less than 1 indicates incipient failure, hence any slope with Factor of Safety greater than 1 is supposed to be stable. This instability primarily arises from reasons such as the existence of steep slopes, along with lithological units that have unfavorable geotechnical attributes. Specifically, areas encompassing Namchi, Darjeeling, Kalimpong, Gangtok, Mangan and Chungthang located under lower threshold slope zone.

Incorporating both the Factor of Safety (FoS) and slope maps via Eq. 2, generated critical acceleration (a_c) map, depicted in Fig. 8(b) and which has been classified as: a_c≤0.16, 0.16 < a_c≤0.32, 0.32 < a_c≤0.48, 0.48 < a_c≤0.64 and a_c>0.64. The critical acceleration defines insight into the seismic performance of structures by assessing the maximum acceleration level at which yielding or permanent deformation, begins to occur. It is determined that structures subjected to seismic forces experience yielding when the ground acceleration exceeds a certain threshold.

3.6. Newmark Displacement

Another step of this approach consisted in the integrating of probabilistic seismic hazard in terms of PGA with the critical acceleration derived from Newmark's rigid-block method. The estimation of Newmark's Displacement \(\:{D}_{n}\) for regional seismic hazard assessment is obtained by using regression models to calculate the \(\:{D}_{n}\), as proposed by Ambraseys and Menu (1988). The obtained \(\:{D}_{n}\) values range from 0 to > 10cm for 475 years return period as depicted in Fig. 9, show significant levels of earthquake-induced displacement in the central and northern region of the Darjeeling-Sikkim Himalaya, where historical earthquake epicenters are located. The resulting Newmark displacements provide critical information regarding the extent of structural movement and deformation, aiding engineers in assessing the structural integrity and seismic performance of buildings and infrastructure.

Through this analysis, we have employed an integrated methodology to this case study in order to assess the risk of land sliding in an otherwise unstable region infested with historical episodes of sliding and mass movement caused by earthquakes to ascertain the seismic input pertinent to the study region as expressed in terms of surface acceleration (PGA) with a return period of 475 years. This parameterization is imperative for attributing an annual probability to potential landslides induced by seismic activity.

This study exhibits the creation of seismic-induced landslide hazard map, precisely extracted at 1:25000 scale. These cartographic representations delineate the zoning and hierarchical categorization of the study region into sub-regions, on a pixel-level granularity, contingent upon the degree of susceptibility to landslides. This classification is predicated upon the calculated annual frequency of seismic events capable of precipitating landslide occurrences.

Present findings evince a congruence between the seismic-induced landslide hazard map presented for the Darjeeling-Sikkim Himalayan region and the mapped historical instances of seismic-triggered landslides within the study locale. Notably, the methodology proposed herein conscientiously integrates both spatial and temporal probabilistic considerations pertaining to landslide dynamics. As a result, it is proposed as a useful framework that supports quantitative hazard assessment and acts as a cornerstone for the preventative reduction of landslip dangers caused by earthquakes. The creation of strong emergency response plans, civil protection programmes and up-to-date land use planning.

Author Contribution

S.K.N. and A.S.G. took part in conceptualization, methodology, analysis, investigation, data acquisition, validation, and writing-original draft.

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No competing interests reported.

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WITHDRAWN: Regional Slope Stability Modeling in Darjeeling-Sikkim Himalaya to Understand the Impact of Gravity Loading and Seismic Shaking

Status:

Version 1

Editorial Note

Abstract

Figures

1. Introduction

2. Data and Methodology

2.1. Geotechnical and Satellite Data

2.2. Factor of Safety and Critical Acceleration

2.3. Newmark Displacement

3. Results and Discussion

3.1. Earthquake-induced Landslide Inventory

3.2. Topographic Gradient and Site Classification

3.3. Unit Weight and Angle of Friction

3.4. Cohesion and Surface Consistent Peak Ground Acceleration

3.5. Factor of Safety and Critical Acceleration

3.6. Newmark Displacement

4. Conclusions

Declarations

Author Contribution

References

Additional Declarations

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