Deterministic Rainfall Induced Landslide Approaches, Advantage and Limitation

Transcription

1 Deterministic Rainfall Induced Landslide Approaches, Advantage and Limitation Mehrdad Safaei Mountainous Terrain Development Research Center Department of Civil Engineering and Faculty of Engineering, University Putra Malaysia Serdang, Selangor, Malaysia Husaini Omar Mountainous Terrain Development Research Center Department of Civil Engineering and Faculty of Engineering, University Putra Malaysia Serdang, Selangor, Malaysia Bujang K Huat Mountainous Terrain Development Research Center Department of Civil Engineering and Faculty of Engineering, University Putra Malaysia Serdang, Selangor, Malaysia Bujan@eng.upm.edu.my Zenoddin B M Yousof Mountainous Terrain Development Research Center Department of Civil Engineering and Faculty of Engineering, University Putra Malaysia Serdang, Selangor, Malaysia zmy@eng.upm.edu.my Vahed Ghiasi Mountainous Terrain Development Research Center Department of Civil Engineering and Faculty of Engineering, University Putra Malaysia Serdang, Selangor, Malaysia Ghiasi_upm@yahoo.com ABSTRACT There are many approaches to assessing slope stability for landslide susceptibility and hazard mapping that mostly running in GIS platform by define a spatial extension with less error or more successes rate and availability of predictive landslide modeling. These models can be classified into four main methods: inventory, heuristic, statistic and deterministic. Recent studies have shown that the best approaches for landslide spatial prediction is the application of deterministic slope stability models, combined with steady state or transient models for hill slope hydrology. These may provide scenarios of potential instability under changing environmental and climatic conditions but are very data demanding at watershed scales. Several authors have developed GIS models by coupling a dynamic hydrological model that simulates the pore pressure over time with a slope stability model that quantifies the susceptibility as the critical pore pressure threshold. Therefore, those require with simplification of the landslides type and depth. The geotechnical model, which is deterministic or probabilistic, has been widely employed in civil engineering and engineering geology for slope stability analysis. A deterministic approach was traditionally considered sufficient for both homogenous and non-homogenous slopes. Calculating the safety factor requires geometrical data and information on the pore water pressure and ground water table. Base on literature review, there is some lack of a systematic comparison of different techniques in order to outline advantages and limitations of the methods to model the spatial distribution of landslides. We can use deterministic approaches for rainfall, and earthquake induced landslide or landslide run out modeling. KEYWORDS: Landslide, Rainfall, Deterministic, Susceptibility

2 Vol. 16 [2011], Bund. U 1620 INTRODUCTION Landslide or mass movement is a phenomenon of the denudation process, where by soil or rock is displaced along the slope by mainly gravitational forces, usually occurring on unstable slopes due to various reasons. The reasons can be either natural or man-made. Landslides occur in all geographic regions of the world in response to a wide variety of natural conditions and triggering processes that include storms, earthquakes, and human activity. It is very important to map the landslide potential of mountain area, to assure the safety of the people delineate the suitable land for development. In recent years the assessment of landslide hazard and risk has become a topic of major interest for both geoscientists and engineering professionals as well as for the community and the local administrations in many parts of the world (Aleotti and Chowdury, 1999). On average, landslides are responsible for 17% of all fatalities from natural hazards worldwide (Cred, 2006). In total, more than people were killed by landslides in the 20th Century. The majority of those fatalities occurred in mountainous areas within less-developed countries. In the first 25 weeks of 2003 alone there were nearly 2000 landslide fatalities in 139 large events, 95% of which occurred in less-developed countries. A more detailed study in Nepal recorded 55 fatalities in 2000, 185 fatalities in 2001 and 345 fatalities in 2002, reflecting a rising trend in landslide impacts worldwide, a trend that is exacerbated by climate change, population pressure and increased road construction. The economic costs are harder to quantify, but certainly exceed $10 billion per annum; with remote rural communities being particularly affected. Best estimates suggest that in the last 20 years, 120,000 people were killed and an economic loss of $200 billion was caused by landslides (Leroi, 1996). In order to mitigate losses caused by landslides, many landslide susceptibility mapping approaches have been developed and tested (Carrara, 1988; Van Westen et al., 1993; Aleotti et al., 1999; Guzzetti et al., 1999; Dai et al., 2002; Carrara and Pike, 2008; Alexander, 2008). These methods pose complex problems and difficulties for rate and reliability of spatial prediction. However, all the available methods for regional landslide susceptibility assessments have some uncertainties due to a lack of knowledge and variability for input and output parameters. Recent studies have shown that the best approaches for spatial prediction landslide is the application of deterministic slope stability models, combined with steady state or transient models for hill slope hydrology. These may provide scenarios of potential instability under varying environmental and climatic conditions (Van Beek, 2002), but are very data demanding over larger areas and require a substantial degree of simplification of the landslide types and depths (Van Westen et al, 2005). The motivation for this study is to develop slope stability analysis for deep-seated landslide by using ArcGIS spatial analyst. Landslide hazard maps are widely used in resource development planning and in planning linear projects such as roads, railways, pipelines and transmission lines. Landslide hazard is the probability of the occurrence of a potentially damaging landslide within a certain period within a given area (Varnes, 1984). The probability of a landslide event is the likelihood that a mass movement (or slope failure) will occur. It can be expressed in relative (qualitative) terms or probabilistic (quantitative) terms. Probability can refer to the probability of occurrence within a certain period, or to the probability caused by the uncertainty of geotechnical parameters or geotechnical models, or the frequency, intensity and duration of triggering agents (Chowdhury and Flentje, 2002). There are many approaches to assessing slope stability and landslide hazards (Sidle et al., 1985; Dietrich et al., 1986; Montgomery and Dietrich, 1988; Dietrich et al., 1992; Sidle, 1992; Dietrich et al., 1993; Montgomery and Dietrich, 1994; Wu and Sidle, 1995; Pack, 1995). The most widely used include (Montgomery and Dietrich, 1994): (A) field inspection using a checklist to identify sites susceptible to landslides; (B) projection of future patterns of instability from analysis of landslide

3 Vol. 16 [2011], Bund. U 1621 inventories; (C) multivariate analysis of characterizing observed sites of slope instability; (D) stability ranking based on criteria as slope, litho logy, land form, or geologic structure; and (E) failure probability analysis on slope stability models with stochastic hydrologic simulations. Each of approaches is valuable certain applications (Pack et al., 2001). In recent years, numerous Geographic Information System (GIS)-based applications have been developed to assess slope stability (Lee et al., 2004; Ohlmacher and Davis, 2003; Zhou et al., 2003; Dai and Lee, 2002; Atkinson and Massari, 1998). GIS slope stability models can be classified into four main methods: inventory, heuristic, statistic and deterministic. However, based on recent studies of GIS applications to model landslide susceptibility, it is established that models need to be improved to build the prediction maps rather more helpful and suitable for engineers, policy-makers, and developers choosing appropriate locations to carry out hazard mitigation (Xie and Xia, 2004) and also developing new models by defined spatial extension in ArcGIS platform with less error or successes rate and availability of predictive landslide modeling. The geotechnical model, which is deterministic or probabilistic, has been widely employed in civil engineering and engineering geology for slope stability analysis. A deterministic approach was traditionally considered sufficient for both homogenous and non-homogenous slopes. The index of stability is a well-known safety factor, based on an appropriate geotechnical model and on the physical mechanical parameters. Calculating the safety factor requires geometrical data, data on the shear strength parameters and information on pore water pressure. LANDSLIDE SUSCEPTIBILITY MAPPING Landslide Susceptibility is a quantitative or qualitative assessment of the classification, volume (or area), and spatial distribution of landslides, which exist or potentially may occur in an area. Susceptibility may also include a description of the velocity and intensity of the existing or potential land sliding. Although it is expected that land sliding will occur more frequently in the most prone areas, in the susceptibility analysis, time-frame is explicitly not taken into account. Landslide susceptibility includes landslides, which have their source in the area, or may have their source outside the area but may travel onto or regress into the area. Table1: Types of the landslides mapping and definition Types of the landslide's mapping Landslides inventory map Definition - Location and characteristics of existing landslides Landslides susceptibility map Location and characteristics of potencial landslides Which areas with a potential to experience landsliding in the future. Landslides hazard map Landslides risk map Estimated temporal frequency (annual probability) Intensity frequency relationships Estimated of the probability of the occurrence and the probability of the consequence

4 Vol. 16 [2011], Bund. U 1622 Table 2: Landslide zoning mapping scales and their application Scale Indicative Typical Area of Examples of Zoning application description range of scales zoning Landslide inventory and susceptibility to inform >10,000 square Small <1:100,000 policy makers and the general public kilometers medium large 1:100,000 to 1:25,000 1:25,000 to 1:5,000 detailed >1:5,000 Landslide inventory and susceptibility zoning for regional and local development; or very large scale engineering projects. Preliminary level hazard mapping for local areas Landslide inventory, susceptibility and hazard zoning for local areas. Preliminary level risk zoning for local areas. And advanced stages of planning for large engineering structures, roads and railways Intermediate and advanced level hazard and risk zoning for local and site specific areas and for the design phase of large engineering structures,roads and railways 1,000 to 10,0000 square kilometers 10 to 1,000 square kilometers Several hectares to 10 square kilometers Landslide susceptibility and hazard mapping have been the development during last few decades. Most of these mapping studies are qualitative in nature, although more recently there have been examples of quantifying the hazard by assigning an annual probability (frequency) to the potential landslides and quantifying the risks for existing development (Corominas, 2008). Landslide susceptibility methods can be classified into four main approaches: inventory, heuristic, statistic, and deterministic (Metternicht et al., 2005; Zhou et al., 2003; Dai et al., 2002; Clerici et al., 2002). Each type has advantages and limitations for regionalization of probability of land sliding. A review of recent publications indicates that mostly, the statistically-based susceptibility approaches have been used for landslide predictive modeling (Van Westen, 1993; Chung et al, 1995; Bonham-Carter, 1996; Davis and Keller, 1997; Binaghi et al., 1998; Atkinson and Massari, 1998; Chung and Fabbri, 1999; Chung and Fabbri, 1993, 1999; Dai et al., 2001; Lee et al., 2002,2004; Suzen and Doyuran, 2004; Van Westen et al., 2003; Ohlmacher and Davis, 2003; Dai and Lee, 2003). The statistical approach does not propose mechanisms that control slope failure and do not have a mechanical meaning, but rather it assumes that the prediction of future landslide areas can be assessed by measuring the combinations of variables that have led to landslide occurrence in the past (Lee et al., 2004; Zhou et al., 2003; Ohlmacher and Davis, Multivariate statistical analysis (such as logistic regression analysis) attempts to overcome the qualitative weaknesses of judgment based manual mapping by correlating terrain attributes with landslide occurrence to estimate the landslide propensity in locations with similar terrain (Carrera, 1983; Carrera et al., 1991; Carrera et al., 1995). This approach, though more quantitative than manual mapping, is empirical in nature and hence there are limitations to extrapolation beyond the study region (Dietrich et al., 2001). In deterministic analysis, the landslide hazard is determined using slope stability models, resulting in the calculation of factors of safety. Deterministic models provide the best quantitative information on landslide hazard that can be used directly in the design of engineering works, or in the quantification of risk. However, they require a large amount of detailed input data, derived from laboratory tests and field measurements, and can therefore, only are applied over small areas at large scales (Van Westen, 2004). Comparison of Four types of main approaches for landslide susceptibility mapping shows that there are meaningful differences between of landslide susceptibility methods (Long, 2008). In his study in the Western part of Thua Thien Hue province, Vietnam mentioned that among different types of landslide susceptibility approaches, the certainty factor is the best method because this method had most accurately for detection landslide prone areas (Long, 2008)

5 Vol. 16 [2011], Bund. U 1623 Figure 1: Classification of landslide susceptibility assessment approaches (Safaei et al; 2010) DETERMINISTIC APPROACHES Deterministic slope stability models need a soil thickness, soil strength, groundwater pressures, slope geometry,etc. as an input parameter or calculation average factor of safety and created the susceptibility relative hazard map based on factor f safety ranges as a main output. Deterministic models calculate slope instability in one, two or three dimensions (Terlien et al., 1995). Galang (2004) used one-dimensional infinite slope model (Bolt et al., 1975) to calculate instability at each pixel and also, Slope profiles are analyzed in two-dimensional models, and the entire landslide body is analyzed in three-dimensionadimension models; however, problems exist for both slope instability and hydrological models when models. Similarly, hydrological models can further more be one, two or three using three dimensions in the conventional two-dimensional GIS (Terlien et al., 1995). For two and three-dimensional instability models, the problem is often the complexity of the calculations whereas the hydrological models have problems converting three-dimensional output maps into forms usable in GIS calculations (Galang, 2004). Van Westen (1997) shown a schematic overview of the use of deterministic slope stability methods in a GIS is presented in Figure

6 Vol. 16 [2011], Bund. U 1624 Figure 2: Representation for application of GIS in deterministic slope stability Xie et al (2004) purposed a three-dimensional GIS-based deterministic modeling to calculating average safety factors regarding to different soil layers. Furthermore, Qui et al (2007) have been developed a new GIS-based 3D model by incorporating an infiltration model and taking account of

7 Vol. 16 [2011], Bund. U 1625 geo mechanical changes of soil strength during rainfall in the calculation. Of course, there are some difficulties for applicability of 3D deterministic models such as (1) the difficulties in creating continuously distributed data for topography, strata and groundwater through limited investigations; (2) processing a vast amount of complex information about the natural topography and geology; and (3) determining the location and the shape of an unknown failure surface (Qiu et al., 2007). The spatial distribution of the input data requires deterministic distributed model's maps. Savage et al. (2004) and Baum et al. (2005) have been mentioned that the variability of input data can be further used to calculate a probability of failure in combination with return periods of triggering factors. The main problem with these methods is the oversimplification of the geological and geotechnical model, and difficulties in predicting groundwater pore pressures and their relationship to rainfall and/or snow melted. The main output of this approach is the susceptibility map that predicted the landslide prone areas by using a stability index. Local models are usually much more accurate because they analyze the stability of a single slope by detailed hydrological, hydraulic and geotechnical information (capparelli et al., 2008). Terlien et al., 1995; Gritzner et al., 2001; Chen and Lee, 2003 developed a dynamic-hydrological model for calculation the pore water pressure over time together a slope stability analysis and Van Beek and Van Asch (2004) developed a model that couples a distributed hydrological model with a probabilistic assessment of slope stability using PCRaster program to predict landslide under land-use changes. Furthermore, the deterministic approaches for earthquake-induced landslide hazard analysis are based on the simplified Newmark's slope stability model, applied on a pixel by- pixel basis, which can be carried out completely within the current GIS computational environments (Miles and Ho, 1999; Luzi et al., 2000; Randall et al., 2000; Jibson et al., 2000). Refice and Capolongo (2002) have implemented a Monte Carlo simulation in combination with the N model (Van Westen, 2004). Deterministic landslide runs out modeling developed by Dymond et al., (1999) for run out shallow landslide to the stream network. Recently, the availability of digital elevation model (DEM) data has induced the development of approaches that obtain the advantage of GIS platform to quantify topography attributes related to sloping instability in watershed areas. Moreover, International Institute for Aerospace Survey and Earth Sciences (ITC) has developed a GIS called the Integrated Land and Water Information System (ILWIS) that have modules incorporated in the GIS for deterministic instability zonation (Van Westen, 1997). GIS based slope stability reviews for this research utilize the infinite slope stability model in ArcGIS Spatial Analyst. The input data for the models reviewed include DEM data as well as the field geotechnical data-slope angle, cohesion, internal friction angle, soil density and the ground water depth. The highlights of common GIS based slope stability models developed by Wu and Sidle, 1995; Montgomery and Dietrich, 1994; Shaw and Jonson, 1995; Pack et al., 1998) Different deterministic approaches Several researchers have been purposed different deterministic approaches based on an infinite slope stability model with rainfall infiltration models (Montgomery and Dietrich, 1994; Dietrich et al., 1995; Terlien et al., 1995, Dymond et al., 1999; Crosta and Dal Negro, 2003). Usually, a physically based model, including a coupled hydrological model (for soil moisture and pure water pressure under different simplification and assumption) and a slope stability model, along with an impact model, such as basin sediment yield. These models are used two main group hydrological simulation, Including:

8 Vol. 16 [2011], Bund. U 1626 A. Hydrological simplified distributed models: In this group, Hydrology is limited to a steady-state description of subsurface flows and these models are fundamentally unable to predict the timing of the occurrence of failure (temporal prediction) such as SHALSTAB model (Montgomery and Dietrich, 1994) and SINMAP model (Pack and Tarboton, 1998). B. hydrological dynamic models: Dynamic or Real time distributed model accounting for transient infiltration and soil moisture redistribution that these models integrate meteorological and earth observation data, e.g. CHASM (Wilkinson et al., 2002), TRIGRS (Baum et al., 2002, 2004), dslam /IDSSM (Wu and Sidle, 2004), GEOtop-FS (Simoni et al., 2008). In addition, the empirical methods can be used for assessing variables, which are hardly determined by deterministic methods and numerical simulating. Table 3: Review of most of deterministic approaches used at watershed scale by researchers. Deterministic approaches Description Researcher Date CHASM Combined Hydrology And Stability Model (Anderson and Lloyd, 1991; Anderson et al., 1996) 1991 LISA Level I Stability Analysis Hammond et al 1992 SHALSTAB Shallow Landsliding Stability Model Montgomery and Dietrich (1994,1998) 1994 SMORPH Slope MORPHology Shaw and Johnson (1995) 1995 dslam/idssm Distributed Shallow Landslide Model/Integrated Dynamic Slope Stability Shallow Landslide Model Wu and Sidle(1997) 1997 SINMAP Stability Index Mapping Pack et al.,(1998,2001) 1998 SHETRAN System Hydrology European TRANsport Ewen et al.,2000 Birkinshaw et al., TRIGRS (Iverson s model) The Transient Rainfall Infiltration and Gridbased Regional Slope-stability Iverson(2000) and extended by Baum et al. (2002) 2000, 2002 PROBSTAB PROBability of STABility PCRaster GIS package Van Beek (2002) 2002 TRIGRSunsaturated The Transient Rainfall Infiltration and Gridbased Regional Slope-stability Savage et al.,2004 PISA Probabilistic infinite slope analysis. Haneberg(2004) ,

9 Vol. 16 [2011], Bund. U 1627 GEOtop-FS combines the hydrological distributed model GEOtop (Bertoldi et al. 2006) and an infinite slope geotechnical model Simoni et al., SUSHI (Saturated Unsaturated Simulation for Hillslope Instability) model Capparelli et al., , 2007 As mentioned in table 3, the several deterministically based models can be used for predicting rainfall induced shallow landslides such as The Level I Stability Analysis (LISA; Hammond et al., 1992) Shallow landslide stability model (SHALSTAB; Montgomery and Dietrich., 1994 ) Stability Index Mapping (SINMAP; Pack et al., 1998), Transient Rainfall Infiltration and Grid based Regional Slope Stability (TRIGRS; Iverson., 2000 and Baum et al., 2002) Storage and Redistribution of Water in Agricultural and Re-vegetated Slopes coupled with Probability of Stability (STARWARS/PROBSTAB; van Beek, 2002) and currently GEOtop-FS (Simoni et al., 2008). Based on literature review some of the used deterministic approaches at watershed scale are briefly describe as follows: Shallow landslide stability model (SHALSTAB) The SHALSTAB model (Montgomery and Dietrich, 1994; Montgomery et al., 1998; Dietrich et al., 2001) is a physically based model, including the steady-state hydrologic model (O'Loughlin, 1986) for prediction shallow subsurface flow with an infinite slope stability equation using the Mohr- Coulomb failure criteria (Bolt et al., 1975), for the prediction of shallow slope instabilities based upon the minimum amount of steady-state rainfall required to trigger land sliding (or critical rainfall). SHALSTAB was developed for hilly terrain and may not be suitable for steeper slopes or mountainous terrain (Dietrich et al., 1998). This model calculates relative wetness based on steadystate saturated water flow parallel to the slope plane. = sin = [1] where, in the equation [1], W is relative wetness or relative saturation of soil. a is drainage area or contribution area, and b is width of outflow boundary or topographic contour length qc is effective precipitation; T is transmisivity of soil, θ is local slope

10 Vol. 16 [2011], Bund. U 1628 Figure 3: The parameters used in the program TOPOG (O Loughlin, 1986) to calculate relative wetness Also can write based on Darcy s law; where k is saturated hydraulic conductivity of soil,. = hcos sin [2] sin = cos sin [3] SHALSTAB also uses h/z parameter thus as a result from equation [2] and [3] we can write the equation [4]; W= = where h is the thickness of the saturated soil above the impermeable layer and z is the total thickness of the soil. If the SHALSTAB is solved one dimentional infinte slope stability equation under mohr-cloumb criteria in terms of the hydrologic ratio(q/t) and QC (the critical steady state neccesarry to trigger failure) to classified relative slope stability. for the case of coehsionless ( C/=0), can be experssed as: = tan 1 tan [5] [4] which can also be written as = tan sin 1 tan / = tan sin 1 tan / [6] [7] where qc is critical rainfall for shallow failure initiated, ρ is soil bulk density, ρ is water bulk density and φ is angle of internal friction of the soil mass at the failure plane,

11 Vol. 16 [2011], Bund. U 1629 Because q/t is always <1, log (qc/t) is used (Montgomery and Dietrich, 1994). A value of log ( ) calculates for each pixel on the DEM map of the study area. The model assumes soil parameters are constant and soil cohesion neglected. The model only determines the topographic influence on failure initiation with the minimum log ( ) class. Also, Montgomery et al 1998 used landslide frequency (slide/km2) in each critical rainfall category (QC) in terms of mm/day. A further simplification in SHALSTAB is to set the cohesion to zero. This approximation is clearly incorrect in most applications. While soil cohesion and root strength as an additional cohesive plays a major role in slope stability. Finally, the model defines the following stability classes under different conditions as shown in Table 4. Table 4: Stability Classes in SHALSTAB Model (Montgomery and Dietrich, 1994). Stability Field Unconditionally stable, saturated Condition tan tan ( 1 w s) ; Ab ( T R) sin θ φ ρ ρ θ Unconditionally stable unsaturated ( ) ( ) Unstable,saturated Unstable, unsaturated Stable, unsaturated Unconditionally unstable, saturated Unconditionally unstable, unsaturated tanθ tanφ 1 ρw ρs ; Ab T R sinθ A ρs tanθ Tsinθ 1 ; Ab ( T R) sinθ b ρ w tanφ R A ρs tanθ Tsinθ 1 ; Ab ( T R) sinθ b ρ w tanφ R A ρs tanθ Tsinθ 1 ; Ab ( T R) sinθ b ρ w tanφ R tanθ tan φ; Ab T R sinθ ( ) ( T R) tanθ tan φ; Ab sinθ DEM can calculate the ratio of q/t for each grid point on the map. Of course, course the ratio of q/t is a decimal value that the logarithm of q/t is usually applied for ease of application. As a sample, the Table 5 shows a comparison of q/t with others related parameters (based on Montgomery and Dietrich, 1994). Table 5: comparison of different form of hydraulic ratio and critical precipitation necessary to failure by high (T=65 m 2 /d) and low (T=17 m 2 /d) soil transmisivity and instability categories (Montgomery and Dietrich 1994; Montgomery et al., 1998). T/q (m) q/t (1/m) Log(q/T) (1/m) Precip for T=65 m 2 /d Precip for T=17 m 2 /d Log(q/T) interval Instability category *chronic slope to < to to to -2.2 Stability field unconditionally unstable, unsaturated unconditionally unstable, saturated stable, unsaturated unstable, unsaturated

12 Vol. 16 [2011], Bund. U to > -1.9 Slope< 21.9 **stable unstable, saturated unconditionally stable, unsaturated unconditionally stable, saturated Comparison of the relative frequency of landslide within each Qc category provides for direct testing of the assumption that lower QC implies higher failure frequency (Montgomery et al., 1998). Guimaraes et al (2004) mentioned that SHALSTAB model had been very good result in the western united states and also, in tropical areas but the model performance was sensitive to resolution of DEM and topography map scale. The high resolution DEM and 10,000 scale topography data cause increase model performance for landslide prediction. Figure 5: SHALSTAB stability field relationships and plots of contributing area per unit contour length (a/b) versus slope (tag) and mapped landslides scars point with each slope unites (Tennessee valley, Dietrich et al., 1993). Stability index mapping (SINMAP) model Using a similar approach with SHASTAB, Pack et al., (1998) developed The Stability Index Mapping (SINMAP) approach that combines the theory of a hydrologic model (Beven and Kirkby, 1979; O Loughlin, 1986) and the infinite slope stability model Factor of Safety (Hammond et al., 1992) to produce the stability index (SI). As shown in table 3.4 The main difference between these two models is that SHALSTAB assumes zero soil cohesion because of the spatial and temporal heterogeneity of soil cohesion (and therefore the difficulty in obtaining values) and because assuming a zero cohesion value results in the most conservative estimate of slope instability (Dietrich et al., 2001 ). SHALSTAB does not incorporate value range of parameters while SINMAP incorporates uncertain parameters through the use of uniform probability distributions and lower and upper bounds are set on uncertain parameters (Pack et al., 1998, 2001a, 2001b)

13 Vol. 16 [2011], Bund. U 1631 Table 6: The comparison of input parameters between two famous deterministic slope stability models (SHALSTAB and SINMAP). SHALSTAB SINMAP Φ [ ] ϕ min [ ] C [N/m 2 ] ϕ max [ ] ρ s [kn/m ] C min T [m 2 /h] C max q [m] T/R min [m] T/q [m] T/R max [m] Based on TOPOG (O Loughlin, 1986), two approaches are taken when the value of wetness is greater than one. SHASTAB set wetness to one and assume that the remaining water runs off as overland flow (Montgomery and Dietrich, 1994). SINMAP also constrains wetness to one, but its C code constrains in a different way. Wetness is coded between 0 and 1 based on the lower T/ /R (equal with T/q parameter in SHALSTAB) parameter in SINMAP. If wetness based on the lower T/R is greater than one, refer to wetness based on the upper T/ /R. When wetness based on the lower T/R is greater than one and wetness based on the upper T/R is less than one, it is called "threshold saturation zone" and wetness is coded as 2. If wetnesss based on the lower T/R is greater than one and wetness based on the upper T/R is greater than one, it is called "saturation zone" and coded as 3 (Tarboton, 1998). SINMAP calculates the Factor of Safety using the nfinite-slopee stability analyzes with regard to root and soil cohesion as given in following equation [8]: 2 Cr + Cs + cos θ ρsg ( Z z w ) + ( ρsg ρwg ) zw tanφ ρ gz sinθ cos θ s [8] where Cr and Cs are the root strength and soil cohesion, respectively, Z is the vertical soil thickness. Zw is the vertical thickness of the phreatic layer and others parameter already is defined. Figure 4 shows the parameters involved in the Factor of Safety in Equation [8]. Figure 6: Infinite-slope stability model based on the cartographic/ /hydrologic SINMAP uses a dimensionless Factor of Safety by using equation [9] that it was: C + cosθ [ 1 wr ]tan gφ FS = sinθ [9]

14 Vol. 16 [2011], Bund. U 1632 Where w is relative wetness (hw /H), C is a dimensionless cohesion (Cr + Cs /(Hρsg), and r is the water-soil density ratio (ρw/ρs) (Pack et al., 1998, 2001a, 2001b). In essence, Equations [8] and [9] are coupled in SINMAP to define the stability index (SI) that defined in equations [10]: R a C+ cosθ 1 r tanφ T sinθ SI = FS = sinθ [10] SINMAP keeps the soil density ratio as constant and allows uncertainty in the soil cohesion, internal friction angle, and T/R by using a uniform probability distribution on the specification of a lower and upper limit. The input of SINMAP is T/R ratios, but internally SINMAP calculates wetness based on R/T ratio. SINMAP analysis starts with what is called the worst case scenario. The worst case scenario is where the smallest cohesion and smallest internal friction angle exist in an area of high soil Saturation. Under this condition, if the dimensionless Factor of Safety becomes smaller than one, the area is always unstable and the SI is set to zero. Conversely, if the Factor of Safety is Larger than one, the area is unconditionally stable and the stability index is evaluated by using the best case scenario. The best case scenario is where the largest cohesion and largest internal friction angle exist in an area of low soil saturation. Under this condition, if the Dimensionless Factor of Safety becomes larger or equal to one, the area is always stable and the SI takes the Factor of Safety calculated with the best scenario parameters. Conversely, if SI is lower than one, SINMAP defines three regions of saturation by using the smallest and largest wetness values, and the SI is calculated by using conditional distribution functions to evaluate the geotechnical parameters. Model has been used to implement the criteria to Establish the SI classes by using uniform probability distribution: Cmin<C<Cmax, φ min < φ < φ max and R/Tmin < R/T < R/Tmax (Pack et al., 1998, 2001a, 2001b). Iverson s Transient Response Model Iverson (2000) developed another hydrological geotechnical model in order to predict shallow landslide in time and location that occurs when the soil is wet enough. This model calculated the relationship between pore pressure and unsteady rain rates based on a Richard s equation which is formulated by Lorenzo A. Richards in The equation represents the movement of water through unsaturated soils in permeable materials, and it depends on time, slope angle, failure depth and hydraulic diffusivity values. The model calculated the pore pressure response to unsteady (transient) rainfall infiltration on the different spatial and temporal scales in shallow soils for individual (short term) rainstorms. That already has been proposed for unsaturated shallow ground water flow that depends on time, slope angle, failure depth and hydraulic diffusivity values. The model calculated the pore pressure response to unsteady (transient) rainfall infiltration on the different spatial and temporal scales in shallow soils for individual (short term) rainstorms. The Richard s equation is known as an equation for unsaturated Darcian flow in response to infiltration at the ground surface and defined as:. ) = Ζ Κ ) Ζ sin [11]

15 Vol. 16 [2011], Bund. U 1633 where ψ, is pressure head,κ ψ), the pressure head dependent hydraulic conductivity, C ψ) is, θ is the volumetric water content and β, is the slope angle. Different techniques and methods have been used for solution of Richard s equation for unsaturated shallow groundwater flow. For first-time Iverson (2000) used the linearized solution and after that extended by Baum et al. (2002) for the impermeable bedrock at a finite soil depth (TRIGRS) to predict pore water pressure regime in unsaturated and saturated conditions. Iverson et al. (2000) and Baum et al. (2002) shown that the maximum pressure head cannot exceed that would result from having the water table at the ground surface than can be written: ψ z, t) zλcosβ where λ =cosβ [12] I, Is the long term (steady state) rainfall and K is the hydraulic conductivity in the z direction. The model assesses the effects of transient rainfall infiltration on the timing and locations of landslides by approximating the pore pressure of the water head in shallow soils for individual (short term) rainstorms by a simplified version of the Richards equation. Furthermore, the pore pressure is calculated for vertical flow not slope parallel flow as in SINMAP, in the unsaturated zone above the water table as well as in the saturated zone (water table). The model assumes that slopes are initially wet, and the catchment area (A) is much greater than the thickness (H) of the landslide (dimensionless length scale, H 1) and short-term rainstorms (t) is much smaller than the catchment area (A) A divided by the hydraulic diffusivity (Do )( assumed to equal k/soil moisture content )of the slope (t<<a/do) (Morrissey et al., 2001)., ) = 1 / ) + ) [13], < ) = 1 / ) + ) [14] where t is time, T is the rainfall duration, b = (cos2α) and α is slope steepness, d is the initial steady state water table depth, Z is vertical depth soil, I is the infiltration rate equal to rainfall rate, k is the hydraulic conductivity and R(t*) is the pressure head response function that can be expressed as: ) = 1 ) 1 [15] where is defined t* = t/ (Z2/4Do) is dimensionless time and also, dimensionless rainfall duration is expressed as T* = T/ (Z2/4Do). The change in water pressure during the rainstorm (t< T or t< 16 hr) can be sufficient to cause slope failure over the time period when the factor of safety becomes smaller than 1 that calculates the base on infinite slope stability method, and it defined as: FS = + + F c <1 = +, ) + [16] where α and are the slope and soil friction angle, c is soil cohesion, γ is depth average soil unit weight, γ is the unit weight of water. The safety factor of slope calculates as a function of depth and time based on changes in pore water pressure response during a rainstorm on an hourly and daily time scale. As compared with other models, the Soil properties as input parameters are shown in Table

16 Vol. 16 [2011], Bund. U 1634 Morrissey et al (2001) evaluated and compared the approaches of SINMAP, LISA, and Iverson's transient response model for slope stability analysis by applying each model to the landslide data from Madison County. They mentioned that Iverson s model would be preferred method of the three models mentioned above only Iverson's transient response model assessed stability conditions as a function of time and depth on a regional scale in areas prone to rainfall induced landslide. Table 7: the parameters required for factor of safety calculations for each model. (-) denotes parameters that are not included in the model and (*) denotes parameters used in the safety factor calculation. Transient Parameters SINMAP LISA/DLISA Response SHALSTAB Failure plane slope ( o ) * * * * Depth to failure plane (m) * * * * Internal angle of friction ( o ) * * * * Soil Cohesion (kpa) * * * * Root Strength (kpa) * * - - Soil Density (kg/m 3 ) * * * * Tree Surcharge (kg/m 3 ) - * - - Transmissivity (m 2 /s) * - - * Saturated Hydraulic Conductivity (m/s) - - * - Hydraulic diffusivity (m 2 /s) - - * - Depth to water table (m) or water table ratio( ) * * * * Rainfall rate (mm/hr) * - * * The Level I Stability Analysis (LISA/DLISA) The Level I Stability Analysis (LISA) developed by Hammond et al (1992). Deterministic Level I Stability Analysis (DLISA) and probabilistic Level I Stability Analysis (LISA) models and also Stability Index Mapping (SINMAP) have been developed, tested, and validated by US Forest Service researchers. Potential shallow landslides can be predicted with DLISA and LISA within planning areas in forestry watersheds. The model identifies the effect of the tree root strength and tree surcharge to the slope stability as an important parameter of forested hill slope areas. LISA uses the finite slope equation (Hammond et al 1992) to the calculation the factor of safety, because the model s simplicity allows for easy use in Mont Carlo's simulation. Because of uncertainty in model parameters, LISA uses different distribution functions such as uniform, normal and triangular distributions for input parameter as shown in Table

17 Vol. 16 [2011], Bund. U 1635 Table 8: the input distribution functions used by Hammond et al in their case study. Landform 1 landform 2 Distribution s Distribution s Soil depth T[1,4,7] T[3,4,5] Slope U[60,80] T[65,70,75] Tree surcharge U[5,15] U[5,15] Root cohesion U[20,140] T[50.0,70.0,120.0] Friction angle N[34,1] N[34.0,0.5] Soil cohesion N[50.15] N[50,10] Dry unit weight N[100,1] N[100,1] Moisture content N[20,0.5] N[20,0.5] Specific gravity U[ 0.4,1] T[0.5,0.7,0.9] Deterministic FoS Probability FoS histogram histogram LISA program enables the user to compute the probability of slope failure using up to 1,000 iterations of a Monte Carlo simulation by varying input values to the infinite slope equation Mont Carlo's simulation estimates the probability of failure rather than a single FS value. Therefore, this method uncertainty is useful tools for modeling an attribute that cannot be sampled or measured directly (Hammond et al 1992). Effects of uncertainties on probability of slope failure are most critical and important issues in deterministic landslide susceptibility. LISA software version 2.00 is created and developed by Forestry Sciences Laboratory under USDA (U. S. Department of Agriculture Forest Service) which is available as a free download in USDA s website (USDA, 2011). DLISA, enters values for the known, and receives a value or for the unknown parameter (USDA, 2011).For example, the C values and calculation list of as a unknown values which have a factor of safety equal 1.0. DLISA is useful program in performing sensitivity analyses and back analyses in input and output model parameters. Also, the back analyses can be used in order to find the critical input parameters such as groundwater height that gives a factor of safety of 1.00 bases on pre failure conditions of exiting landslides and for selecting ranges of values to use in LISA. Newmark's model Other slope stability models are based on distribution of seismically induced landslides. These models have been developed for predicting the effects of seismic shaking on the stability of slopes over large areas, or to explain the known distribution of seismically induced landslides (Jibson et al., 1998; Miles and Ho, 1999; Luzi and Pergalani, 2000; Jibson, 2001; Lin and Tung, 2004). One of the most reliable approaches is the Newmark method that designed for estimating the stability of individual slopes under seismic shaking (Wilson, 1993). The infinite slope stability method coupled with Newmark s analysis has been used to assess earthquake-induced landslide susceptibility on a regional scale in the process-driven models (Ho and Miles 1997; Jibson, 1993)

18 Vol. 16 [2011], Bund. U 1636 Newmark (1965) has been applied the cumulative displacement on an inclined plane using the critical acceleration as a result of earthquake loading for modeling the landslide as a rigid block (sliding block). Currently different regression equation used for calculation of Newmark s displacement as a function of Arias acceleration (Ia), acceleration ratio (ac/amax) and magnitude (M) (Jibson 2007). Β Figure 7: Sliding block model used for Newmark s analysis (Harp and Jibson, 1995) step 1 step 2 step3 step4 imput parameters α,h,β,ϲ',φ ' static safety factors F= critical accelaration a c =(F s - 1)g sin α Newmark displacement using earthquake parameters ( M, I a, a c /a Max ) Log D N = step 5 Landslide susceptibility mapping step 6 calibration model by exiting landslide map Figure 8: Flowchart of deterministic seismic slope stability approaches GEOtop-FS approach Simoni et al (2007) proposed a model for simulation of rainfall-induced shallow landslide triggering that called GEOtop-FS. The model combines the infinite slope stability analysis with a hydrological distributed model that called GEOtop (Bertoldi et al., 2006). GEO top is spatiallywater and distributed, finite-difference model, which performs water and energy budgets and solves energy budget equations for each voxel and for each time-step (Simoni, 2009). Therefore, GEOtop

19 Vol. 16 [2011], Bund. U 1637 able to simulate surface runoff and soil moisture redistribution, by solving the Richard s equation numerically in 3D physically based. GEO top simulates moisture content and pore pressure evolution resulting from infiltration and models subsurface saturated and unsaturated flows, surface runoff, channel flows, and turbulent fluxes across the soil-atmosphere interface. Qui et al (2007) introduce an approach Spatio- temporal estimation of shallow landslide's hazard triggered by rainfall using a threedimensional model. GEO top-fs model has been applied within a probabilistic framework in order to account for uncertainties of parameters, and Simoni et al (2006 ) have proven to be able to reproduce a real event that occurred in the eastern Italian Alps (Sauris catchment). In addition, Simoni et al in GEOtop-FS model and some of the researchers (Morrissey et al; Talebi et al) focused on the modeling of shallow slope instabilities base on the influence of Hillslope topography on hydrological conditions. All these researchers adopt the infinite slope approach to analyse slope stability, and couple this with hydrological models. Differences are related to the adopted hydrological models, and the specific objective of the analysis. For example, Talebi et al present an application of a steady-state analytical Hillslope model to study the role of topography on slope stability. The model combines simplified topographies that described by bivariate functions, a steady state hydrological model and the infinite slope stability analysis. By applying the model to nine simplified Hillslope topographies, the authors investigated the role of profile and planar curvatures on slope stability. They found that slope stability is lower for convex, convergent topographies. Physically based models to simulate rock fall Physically based models to simulate rock fall processes developed by Van Dijke and Van Westen (1990) and by Guzzetti et al. (2002), are based on a DTM and spatially distributed information on the location of the source areas of rock falls, and of the energy lost at impact. Points and where boulders are rolling, to simulate in three dimensions rock fall phenomena for areas ranging from a few thousands of square meters to several hundreds of square kilometres (Guzzetti et al., 2002, 2003). Results of the model include: (i) the extent and location of the areas potentially subject to rock falls, and (ii) estimates of the maximum velocity and of the maximum distance to the ground of the falling rocks. This information can be combined to obtain quantitative estimates of landslide hazards (Crosta and Agliardi, 2004; Guzzetti et al., 2004). LIMITATION AND ADVANTAGE OF DETERMINISTIC APPROACH Generally, each technique for prediction landslide has advantages and limitations that can be enhanced or reduced accuracy of approach. Base on literature review, there is some lack of study in exiting landslide susceptibility approaches that included: 1. Lack of a systematic comparison of different techniques in order to outline advantages and limitations of the methods to model the spatial distribution of landslides (Xie et al., 2006; Lee et al., 2004; Borga et al., 2002; Morrissey et al., 2001; Bishop, 1995) 2. Lack of the incorporation of vertical contrast of geotechnical properties in multiple soil layers and their analysis and representation in 3D (2006; Xie et al., 2006, 2004a, 2004b, 2003a, 2003b; 2004) 3. Lack of the integration of different approaches to assess and map slope instability in one system (Xie et al., 2006; Hammond et al., 2002; Montgomery and Dietrich, 1994; Pack et al., 1998; O Loughlin, 1986). 4. Few studies take into full account the topographic control through shallow subsurface water flow to predict potential landslide zones with sparse information (Dietrich et al., 2001; Pack

20 Vol. 16 [2011], Bund. U 1638 et al., 2001a, 2001b, 1998; Dietrich and Montgomery, 1998; Montgomery and Dietrich, 1994). 5. Lack of calculation slope stability for deep-seated and circular slip surface rainfall induced landslide's together ground water simulation at watershed scale. 6. Also, a little authors attention to suitable uncertainty model input and output parameters Deterministic. 7. Unfortunately, there has been an increasing tendency to implement an infinite slope model, which is well known as a one-dimensional model that represents the potential of slope failure along the slip surface parallel to the ground surface (Aleotti and Chowdhury, 1999; Van Westen and Terlien, 1996; Van Westen et al., 1997; Van Westen, 1998; Xie et al., 2001; Zhou et al., 2003). This model may be valid for shallow slope failure. 8. The deterministic landslide prediction models don t calculate in an absolute and accurate method the safety factor at watershed scale, because usually, the amount of input data for to assess the spatial distribution of parameters for estimation factor of safety in hill slope areas aren t enough. For this reason, the prediction of landslide can be done using a probabilistic approach that calculates the probability of failure instead of an absolute the safety factors. The probability of failure can be assessed in the landslide prone areas by using suitable distribution functions of parameters (van Asch et al., 1993). These models commonly have been used in site specific data, and therefore, they are able to generate more detailed spatial patterns on fine-scale gradations of instability than most statistical or weighted ranking hazard maps (Wang et al., 2005). Most of the deterministic approaches don t simulate the size or number of failure that might occur on a watershed scale and don t predict an accurate location of any failures, or the type of failure (although it should give more accurate results for translational failure modes. Furthermore, these approaches cannot be used to directly for assess the consequences of failure such as whether sediment will reach a stream, or the volume of sediment delivered unless model combines with run out model such have GEOtop approach. A significant limitation of deterministic models is the need for input data that are difficult to obtain over large areas (Terlien et al., 1995) such as an accurate spatial estimate of soil depth for preparation reliable slope stability analyzes. Most of the important limitations at a reasonable cost/benefit ratio are the poor quality of environment data together with the high spatial variability. To overcome these limitations, some of the researcher developed uncertainty methods and hydrological approaches. Sekhar et al., (2009) has been used a dynamic hydrological model coupled with a slope stability model in the Tikovil River basin (Kerala, India). The hydrological soil parameters for unsaturated conditions such as diffusivity, soil-moisture characteristic curves and also the distribution of soil depth and the initial water content are largely unknown (Crosta and Frattini, 2003; Mertens et al., 2005) and in addition as important factors are most difficult to measure.the models are unsuitable in predicting the development of complex landslides with a complex hydrological system (Van Asch et al., 1999). The advantage of the deterministic models is that they permit quantitative factors of safety to be calculated, whereas the main problem is the high degree of simplification that is usually necessary for the use of such models. Another problem that limits the applicability of the deterministic models is that data requirements for deterministic models can be prohibitive, and frequently it is impossible to acquire the input data necessary to use the models effectively (Wang et al., 2005). Deterministic approaches are GIS-based modeling. Therefore, GIS-based analyses of slope stability and landslide hazard for improving the predictive ability may be achieved by exploring more