Introduction - Semi-quantitative Approaches for Landslide Assessment and Prediction - page 43

soil. Topographic Index Model is applied after Beven and Kirkby ( 1979 )to
understand the soil saturation of the slope. Geo-technical parameters were estimated
through Tri-axial compression test for determining the safety factor values. The
study reveals that weak lithological composition with cumulative rain for few days
makes the slope more vulnerable and causes debris slide in the Shivkhola Water-
shed of Darjiling Himalaya.

1.11 Slope Stability Models

Among the various natural hazards, slope failure is the most widespread and dam-
aging hazard (De Smedt 2005 ). A sudden failure of the slope is caused by sliding,
rolling, falling or slumping. The constant pull of gravity makes all the hill slopes and
mountain cliffs susceptible to slope failure. When failure occurs, material is trans-
ported down slope until a stable slope condition is reestablished. The high suscep-
tibility to failure in the Darjiling Himalayan terrain is due to a complex geological
structure and an interaction among various processes acting upon the steep mountain
escarpment slopes. The interaction among the processes leading to hill slope evo-
lution was studied by Ahnert ( 1987 ), Kirkby ( 1980 ), Poesen (1985), and Deploey
(1982). The stability of mountain slope is generally viewed in relative terms. It is
apparent, that in analysis of slope, stability is not totally a descriptive term. Perhaps,
an appropriate term would be functional stability, which necessarily relates to a
speci

c need and speci c governing criteria (Cernica 1995). Soeters and Westen

( 1996 ) recommended in nite slope stability analysis but due to complication in

establishing vertical depth of failure plane in 3D mechanism Monte Carlo Method, a
simpli

ed approach was considered by them reducing 3D depth to 2D equivalent
depth based slope stability model. Again it (2D MODEL) was converted to equiv-
alent translational depth of 1D slope stability model by Bhattarai et al. ( 2005 ).
Hollingworth and Kovacs (1981), Burton and Bathrust (1994), Bradinoni and
Church ( 2004 ), Young ( 1963 ), Montgomery and Dietrich ( 1994 ), Van Westen and
Terlien (1996), Burton and Bathurst ( 1998 ), Pack et al. (2001), Borga et al. (2002)
and Saha et al. ( 2002 ) introduced numerous models in connection to the slope
stability, shallow and deep seated landslides. The stability equation is applied for a
mass of loose, friable cohesion less debris after Jumikis (1967), Melnikov and
Chesnokov ( 1969 ). The most widely used landslide inventory techniques include
(Montgomery and Dietrich 1994 ): (i) field investigation using a check list to

identify landslide susceptibility sites; (ii) projection of future pattern of instability
from the landslide inventories; (iii) multivariate analysis of factors; (iv) stability
ranking based on criteria such as slope, lithology, landform; and (v) failure prob-
ability analysis based on slope stability models with stochastic hydrologic simu-
lations. Recently, the availability of GIS data has provided a lot of advantages to
quantify topographic attributes related to slope instability and landsliding. Mont-
gomery and Dietrich ( 1994 ) propounded a contour based steady state hydrologic
model with the in nite slope stability (simpli ed for cohesionless soils) to de ne