Semi-quantitative Approaches for Landslide Assessment and Prediction

Landslides are simply defined as the movement of mass of dislodged rock, debris or earth materials down a slope including a broad range of motions whereby falling, sliding and flowing under the influence of gravity.

The stability of mountain slope depends upon physical and chemical properties of the soil. In the present work geomorphic properties such as slope angle, slope aspect, slope curvature, lithological composition, and lineament as well as behaviour of slope materials such as texture, cohesion (c), friction angle (\(\varphi\)), water holding capacity, porosity, weight soil density and density of soil water were assessed and found out the relationship with landslip in the Shivkhola Watershed. Slope angle, slope aspect and slope curvature were derived from DEM on GIS platform. Lineament map was prepared using Satellite Image LISS III (2010). Soil samples were collected from 50 different locations and their laboratory test were being carried out to assess cohesion, friction angle, water holding capacity, pore space, and wet soil density. The spatial distribution of the mentioned characteristics of slope forming materials is done using ARC GIS Software. To estimate evolutionary stage through which the Shivkhola Watershed is passing, hypsometric analysis was made. Integration between landslides inventory map and derived thematic geomorphic maps was done to assess the spatial distribution of landslide potentiality.

Quantitative geomorphology provides a systematic approach to the analysis of a complex landscape of any size. The stability of the mountain slope depends upon the prevalence of various hydrologic variables. In the present work, the excess and deficit moisture period in a year and its role in slope instability were assessed studying rainfall and evapotranspiration. Study envisages that July and August are the most consistent rainfall months of the year where the values of co-efficient of variation are very low. The distribution of drainage and its evolution has been studied to determine the drainage concentration over the slope surface and their role in slope steepening and instability. To assume the slope saturation of materials saturation, stream confluence points/junction points were studied for individual sub-watersheds. The length of drainage per unit area and upslope contributing area were analyzed spatially in connection to the landslide potentiality. The existence of moderate drainage density may invite havoc slope failure on convex slope segment. Greater the upslope contributing area, maximum is the slope saturation and slope instability in the Shivkhola watershed. Some important drainage basin parameters such as basin shape, form factor, circularity ratio, elongation ratio, compactness factor, and elipticity index of the sub-watersheds were considered to develop the priority scale on slope instability. The sub-basin I and IV are more efficient in drainage and are more erosion and landslide prone followed by sub-watershed V, II, III and VI.

The dynamic nature of a landscape results from the interaction of surface run-off with rocks and soil being guided by geo-hydrologic variables. The estimation of surface run-off and its better understanding reveals a clear idea about the degree and amount of surface erosion and slope vulnerability over the space. In this chapter Soil Conservation Service (SCS) Run-off Curve Number (CN) model proposed by United State Department of Agriculture (USDA 1972) is used to determine the surface run-off from six individual sub-watersheds for predicting the periodical spatial distribution of slope instability and soil erosion. The determined Curve Number (CN) under antecedent moisture condition-III (AMC-III) for sub-watershed I, II, III, IV, V and VI are 85.02, 73.52, 87.36, 87.76, 85.57 and 89.85 respectively. Sub-watershed I contributes maximum run-off from a rainfall of 90.5 mm (4,52,359.4 m³) which is followed by VI, III, IV, V and II. Landslide Potentiality Index Value (LPIV) is derived for each watershed which reveals that Sub-watershed I and VI is the significant landslide prone unit of the study area. Finally, considering both run-off and LPIV an instability scale has been made which reveals that Sub-watershed VI, I and III have to be paid more attention for a proper management of land, water and soil during the months of July, August and September. All the necessary constructions, plantation and related preparedness through raising awareness and making task forces during pre-monsoon dry period are of utmost importance for managing landslip and soil erosion at Shivkhola Watershed.

The present study established the link between critical rainfall (c_r), critical slope angle (c_s), critical height (c_h) and landslide. The critical rainfall was estimated incorporating geo-technical parameters such as angle of internal friction (\(\varphi\)), slope angle (⊖), upslope contributing area (UCA), transmissivity (T), wet soil density (p_s), and density of water (p_w). Cohesion (c), angle of internal friction (\(\varphi\)), unit weight of the materials (γ), and slope angle (⊖) were taken into account to estimate critical slope height. The thickness of total soil (h), thickness of saturated soil (z), wet soil density (Ps), density of water (Pw), friction angle (\(\varphi\)) and slope steepness (⊖) were considered to derive critical slope angle. Study attempted to calculate critical rain to slope failure and its return period. The temporal probability of the landslide events were estimated applying Binomial and Poisson Probability Distribution Model based on past landslide occurrences. The probability model suggests that occurrences of major landslides with more than 90 % certainty could be expected in every 7.5 years.

The present study deals with the assessment of geo-technical parameters i.e. surface inclination (⊝), soil depth (z), cohesion (c), angle of internal friction (φ), soil saturation index (m), soil density (γ_s) and density of water (γ_w) and to construct 1D (one dimensional) Slope stability model for preparing the slope instability map under dry, semi-saturated and saturated condition of the landslide prone small hilly Shivkhola Watershed of Darjeeling Himalaya. To determine the spatial distribution of slope instability in the watershed, safety factor value for 50 different locations were being estimated and with the help of GIS tools. The probability or the chances of landslide phenomena in each class of slope instability maps were extracted by means of frequency ratio (FR) which shows that the probability/chances of landslide events could be expected as very high in the high to very high landslide susceptibility area and vice versa in all three conditions. The analysis of slope instability under three conditions also suggested that there was an aerial expansion of very high landslide susceptibility in saturated condition in comparison to dry and semi-saturated condition. This aerial expansion was the outcome of complete saturation and reduction of shearing strength of the slope materials above the failure plane surface. Finally, an accuracy assessment was made by ground truth verification of the existing landslide locations where the classification accuracy for dry, semi-saturated and saturated conditions was 93.86, 94.58 and 85.44 % respectively.

To prepare landslide susceptibility map of the Shivkhola watershed, one of the landslide prone part of Darjiling Himalaya, RS and GIS tools were being used to integrate 10 landslide triggering parameters like lithology, slope angle, slope aspect, slope curvature, drainage density, lineament, upslope contributing area (UCA), road contributing area (RCA) settlement density, and land use and land cover (LULC). Analytical Hierarchy Process (AHP) was applied to quantify all the factors by estimating factors weight on MATLAB Software with reasonable consistency ratio (CR). Frequency ratio model (FR) was used to derive class frequency ratio or class weight incorporating both pixels with and without landslides and to determine the relative importance of individual classes. All the required data layers were prepared in consultation with SOI Topo-sheet (78B/5), LIIS-III Satellite Image (2010) by using Erdas Imagine 8.5, PCI Geomatica, and ARC GIS Software. The weighted linear combination (WLC) method was followed to combine factors weight and class weight and to determine the landslide susceptibility coefficient value (LSCV or ‘M’) on GIS platform. Greater the value of ‘M’, higher is the susceptibility of landslide. The Shivkhola watershed was classified into five landslide susceptibility zones by averaging window lengths of 3, 5, 7, and 9 and taking into account the landslide threshold boundaries value of 7.05, 9.29, 11.5, and 13.8. The overall classification accuracy rate is 92.22 % and overall Kappa statistics is 0.894. The elements like weighted LULC map, RCA (road contributing area) map and settlement density map were developed and their weighted linear combination was performed to prepare landslide risk exposure map. Then by integrating landslide susceptibility map and landslide risk exposure map landslide hazard risk co-efficient values were derived and a classification was incorporated on ARC GIS Platform to prepare landslide hazard risk map of the Shivkhola watershed. To evaluate the validity of the landslide hazard risk map, probability/chance of landslide hazard risk event has been estimated by means of frequency ratio (FR) between landslide hazard risk area (%) and number of risk events (%) for each landslide hazard risk class. Finally, an accuracy assessment was made through a comparative study between true GPS derived data and a set of randomly selected pixels points from the classified image corresponding to the true data from 50 locations on ERDAS Imagine (8.5) which depicts that the classification accuracy of the landslide hazard risk map was 92.89 with overall Kappa statistics of 0.8929.

The fundamental impetus of any kind of natural hazard and risk management is an awareness of threat, a notion of responsibility and a brief that human action might reduce the risk. Various components such as susceptibility analysis, hazard and risk identification, consequence analysis, hazard analysis, and risk evaluation are included in the landslide management framework. In the present study of the Shivkhola Watershed for slope stabilization, some mitigation measures have been proposed on the basis of community wisdom assessed through perception study on people living in four landslide prone villages such as Paglajhora, Tindharia, Gayabari and Giddapahar. The value of experience depicts that Catch-water drain along the junction between road and the hill slope, jhora training, retaining wall, catchment water drainage, introduction of vegetation etc. may bring stability of slope. Besides, author’s intensive field investigation suggested that the construction and maintenance of buildings, introduction of landslide warning system, and improvement of soil strength would be taken into account as landslide mitigation measures. Here, some control works and restraint works have been taken into account to reduce landslide hazard and risk.