The influence of different DEMs on the prediction of landslides is also evaluated. Comparison of FORM and Random Field results showing the influence of the spatial correlation length Θ (log normally distributed tan φ') (Griffiths et al., 2011).
Introduction
- General
- Rainfall-Induced Slope Instability
- Variability of soil propeties
- GIS application in Landslide research
- Motivation of the Study
- Organisation of the Report
Based on two factors, vis. the slope of the site and its land use, Phukon et al. 2012) identified the potential landslide risk zones around Guwahati city using GIS application. The summary of the test results highlighting the variety of various soil properties is provided.
Literature Review
Introduction
Definition
Classification
Instead of the main types of separate movement processes, one complex class may exist that contains two or more different movement processes that work together with the downslope movement of the landslide mass. A second widely recognized classification of landslides is based on movement velocity, as suggested by Cruden and Varnes (1996), which ranges from 'extremely fast' to.
Causal Factors
Triggering Factors
Landslide study approach
Slope–Stability Analysis
- Rainfall – Induced Slope Instability
- Probabilistic Methods
For the location studied, the trend of the safety factor indicates that the critical point for failure occurs approximately 18 hours after the onset of the storm (Figure 2.9). This emphasizes the key role that soil permeability plays.
Regional Scale Landslide Analysis
- Landslide Susceptibility
- Landslide Hazard
- GIS for Landslide Analysis
- Physically–based Models for Landslide Analysis
- Indian Standard Code Provisions
The depth of the basal boundary is considered as an exponential relation of the slope angle α (Figure 2.31(b)). The input parameters of the models were chosen based on the available dataset and indirect analyses.
Landslide studies in the Indian context
2005) used the Information Value (InfoVal) and Landslide Nominal Susceptibility Factor (LNSF) methods based on bivariate statistical analysis for landslide susceptibility zoning (LSZ) mapping in a part of the Himalayas. The time histories for the tilt angles of the sensors in the X and Y directions are depicted in Figure 2.52.
Literature pertaining to Study Area
Das and Saikia (2010) reported that the hilly areas of northeastern India are mostly covered by residual soils. From field studies of the hillslopes around Guwahati, Das and Saikia (2010) concluded that they are mostly of two types.
Critical appraisal
A comprehensive LHZ is based on proper identification of the conditions and processes that promote instability. There are uncertainties regarding the estimation of geotechnical parameters due to limitations on the extent and quality of investigation and testing.
Objectives and Scope of the Study
Develop the Landslide Hazard Zone map of Guwahati city by presenting the obtained results of TRIGRS simulation within a GIS framework. Address the uncertainty issues in landslide hazard analysis, developing failure probability maps for the region.
Novelty of the Present Study
Most of the available studies considered a constant rainfall intensity over a specified duration, together with the changes in the initial simulation conditions to account for antecedent conditions. Thus, in the present study, the conventional methodology is extended to consider the uncertainty and variability of the soil parameters to determine the probable landslide hazard scenario of Guwahati city, India.
Summary
Therefore, overall, this dissertation study is successful in blending geotechnical and geological perspectives in a physically based model, TRIGRS, operating in a GIS platform, to present deterministic landslide hazard maps and failure probability maps for the city of Guwahati, India. In this diploma work, the analysis of the risk of landslides on a local and regional scale is successfully explained.
Study Area & Methodology
Introduction
History of Landslide occurence
Mostly, the landslides are initiated as sudden translational movements, while the rainwater infiltration leads to the triggered failure by reducing soil suction. The core rocks embedded in the soil matrix may be exposed due to the slide itself or due to and washout of the soil from the slope, resulting in an aggravated debris flow that contributes to the disaster.
ASDMA report of the June, 2012 event – RVS Points
The findings were published in the form of an official report – “Rapid Visual Screening (RVS) of Potential Landslide Areas of Guwahati” (Goswami, 2013). The areas identified in the report were revisited on site and 347 locations were considered for this study.
Geomorphology
Zones of well-drained areas with thick soil formation as well as zones of moderate, incomplete and poorly drained areas with exposed rock layers (Das and Saikia due to mantle stripping leading to the formation of etch forms and inselbergs are prominent from the geomorphology of the area. the topography of these hills is of the most winding nature with repeatedly undulating terrain.
Climate
Rainfall
An estimated landslide causing rainfall intensity threshold of 4.3 mm/hour (100 mm/day) for a duration of 48 hours can be derived from Figure 3.8.
Geology
Depending on the degree of weathering, the soil may retain much of the fabric or structural features of the parent rock. From field surveys of the hill slopes around Guwahati, Das and Saikia report two types of commonly found residual soils.
Topographical Data – Digital Elevaton Models
The hillslopes within the city of Guwahati consist of geological stratification, characterizing progressive stages of residual weathering, which can be categorized as basal rocks, decomposed granitic rocks and bedrock, saprolitic and lateritic residual waste.
Methodology
The result is then combined to form the landslide hazard map of the study area. Probabilistic slope stability is performed the physically based model to generate the probability of failure map, taking into account the variability of the soil parameters.
Softwares and Codes
- GeoStudio
- FLAC
- Geographic Information System
Landslide recurrence of the study area is determined in response to applied rainfall of intensity-duration representative of the return periods. QGIS offers a wide range of easy-to-use tools for efficient editing such as slicing, extracting and merging of geospatial data.
Physically-based models
- SHALSTAB
- SINMAP
- TRIGRS
The factor of safety (FoS) can be calculated for each grid cell and the results presented in the form of a FoS map. The uncertainty regions of the input parameters generate a probability distribution of slope stability (in terms of the evaluated FoS) between threshold limits.
Summary
For SI < 0.5, the site is classified as unstable and stabilizing efforts are required; while for SI values ranging from 0.5–1.0 and FoSmin values ranging from 1.0–1.25 the sites are classified as quasi-stable to moderately stable and slope destabilization factors are required for landslides. TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-stability) (Baum et al., 2002; Savage et al., 2004; Baum et al., 2008) is a FORTRAN code developed with the aim of evaluating the transient pore pressure response to rainfall infiltration, and thereby simulate the temporal and spatial distribution of shallow rainfall-induced landslides, expressed as a decrease in safety factor values.
Material Characterization of Hillslope Soils
Introduction
Geotechnical Soil Properties
- Soil type and Classification
- Hydraulic Conductivity
- Soil – Water Retention Characteristics
- In–Situ Infiltration Characteristics
- Shear Strength
The EC-5 sensor measures volumetric water content via the dielectric constant of the soil using capacitance technology. The EC-5 probe outputs a voltage proportional to the dielectric permittivity, and thus the water content of the soil.
Results and Summary
Local Slope Stability Analysis
Introduction
Rainfall Induced Slope Instability Analysis
- Transient Seepage Analysis
- Slope Stability Analysis
- Validation of the numerical analysis
- Description of the numerical model
- Change in Factor of Safety with Rainfall Infiltration
However, changes in the state of stress and the density or porosity of the soil affect the changes in volumetric water content. A relatively deeper critical surface is observed in the case of the slope consisting of Red Silty Clay (RS), but the factor of safety is still sufficient to prevent failure of the slope, which can be attributed to the cohesive component and SWCC of the Red Silty Clay (RS).
Probabilistic Slope Stability Analysis
- Slope Stability Analysis in FLAC 2D
- Random Variable and its Distribution
- Spatial Variability – Random Field
- Covariance – Correlation
- Local Averaging within a Zone
- Cross – Correlation
- Spatial distribution of C-φ
- Probability of Failure (PoF)
- Effect of Correlation Length on PoF
This assumption makes it possible to express the spatial variability as a sum of two quantities vis., the Deterministic trend and the Residual variability over that trend (Baecher and Christian, 2003; Fenton and Griffiths, 2008). For all the cases, increasing the correlation length showed an increase in the probability of failure of the system.
Summary
It has been observed that the formation of larger zones of near-value parameters occurs with increasing width for the anisotropic random field, thereby increasing the probability of failure. The present study shows that a slope with a given factor of safety may have different probability of failure depending on the spatial correlation structure, and thus will be associated with different levels of risk.
Conclusions
TRIGRS Model for Guwahati City
Introduction
Surface Hydrology
- Runoff
- Topographic Indexing and Flow Routing
Thus, the TopoIndex score defines the continuous path of the runoff flow from the upstream cell to the downstream cell and the ratios by which the runoff of one cell will be distributed to neighboring downstream cells. By comparing the weights with the elevation values, we can see the distribution pattern of the runoff.
Infiltration model and Pore Pressure estimation
The constant seepage component depends on the initial depth to the groundwater table and a constant infiltration rate. The hydraulic conductivity is therefore constant and independent of the pressure head, and is equal to the saturated hydraulic conductivity within the capillary edge and below the groundwater table.
Slope–Stability Model
This model compares the destabilizing stresses and the recovering strength of the material on an infinite plane parallel to the soil surface (Figure 6.3). The Mohr-Coulomb failure criterion (equation 6.23) gives the shear strength of the material as a function of the soil cohesion and frictional resistance due to the effective normal stress on the failure surface.
Input – Parameters for Guwahati Hillslopes
- Digital Elevation Model (DEM)
- Slope Map
- Depth of Basal Boundary and Initial Ground Water Level
The topographic input parameter vis., the slope map (Figure 6.5) and the aspect map, required as input for the analysis, are derived from the DEM. From the available literature, it is observed that the slope angle is the main factor affecting the thickness of the residual soil formation, and, therefore, it is more appropriate to assume a simple exponential relationship between the soil thickness and the slope angle ( Delmonaco et al., 2003; Salciarini et al., 2006; Tan et al., 2008).
TRIGRS – SEEP/W comparative analysis
- Sub–Surface Flow Parameters
- Shear Strength Parameters
However, detailed data on the groundwater table in the hills of the study area do not exist. In the TRIGRS analysis, the initial water table is therefore considered to be the same as the depth of the basal rock.
Evaluation and Validation of TRIGRS output
In the same way, the FoS maps of the previous days of the precipitation event are also obtained. Once the RVS points are overlaid on the FoS map, the ROC of the TRIGRS becomes FoS–.
Limitations
Summary
Landslide Analysis of Guwahati City using TRIGRS
- Introduction
- Rainfall Events
- TRIGRS – Simulations
- Effect of Antecedent Conditions
- Discussion
- Effect of the Digital Elevation Model
- ALOS – 3D
- CartoDEM
- ASTER – GDEM
- SRTM DEM
- TRIGRS – simulation and results
- Evaluation of the simulation result
- Discussions
- Remarks
- Landslide Recurrence - Hazard
- Rainfall Intensity–Duration–Frequency
- Landslide Hazard Map
- Remarks
- Effect of the Rainfall Pattern
- Discussion
- Summary
It can be seen from figure 3.8 that the landslide occurrences are not always related to the wettest years in terms of the annual cumulative precipitation. It can be observed that TRIGRS was able to predict the location of landslides with the location of the observed landslide.
Probability of Failure (PoF) Map of Guwahati City using TRIGRS
Introduction
Distribution of the Random Parameters
Probability of Failure Map
Discussion
Summary
Concluding Remarks
Introduction
Summary and Contributions
Practical Applications of the Present Study
Conclusions
Limitations and Future Scope
