GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model

ENGEO-03875; No of Pages 16 Engineering Geology xxx (2014) xxx–xxx

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Engineering Geology journal homepage: www.elsevier.com/locate/enggeo

GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model Junghwan Kim a, Kwangwoo Lee a, Sangseom Jeong a,⁎, Gwangseob Kim b a b

Department of Civil Engineering, Yonsei University, Seoul 120-749, Republic of Korea Department of Civil Engineering, Kyungpook National University, Daegu 702-701, Republic of Korea

a r t i c l e

i n f o

Article history: Accepted 2 September 2014 Available online xxxx Keywords: Rainfall-induced landslide Prediction Physical model Infiltration Groundwater flow

a b s t r a c t This paper describes a physical-based model to predict rainfall-induced landsides. Special attention was given to the introduction of a GIS-based model, YS-Slope model (YonSei-Slope model), which can evaluate the susceptibility of landslides. The model has been improved by incorporating the combined effects of groundwater flow and rainfall infiltration into the raster model. The two dimensional Darcy's law was used to reflect the effect of groundwater flow in the soil slope, and the modified Green–Ampt model, which is a one-dimensional infiltration model, was selected to capture the effect of rainfall infiltration into the soil mass. These elements were integrated into the infinite slope-stability model for a rainfall-induced landslide. The proposed model was verified by comparing the results from existing models as well as an actual slope failure case that occurred in Korea. With further study, the YS-model could be adopted as a prediction method for rainfall-induced landslides. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Global climate change could trigger more landslides because of changes in the rainfall intensity, frequency, and rainfall depth (Borga et al., 2002; Liao et al., 2011; Kim et al. 2012). Landslides cause not only failures in slopes but also loss of lives as well as serious damage to infrastructure. Therefore, landslide susceptibility analysis and mapping are important issues for human lives, urban planning and infrastructure management (Liao et al., 2011; Kim et al. 2012). In Korea, landslides are a common natural hazard during the rainy season (June to September), which are characterized by a relatively shallow failure of surfaces that develop parallel to the original slope. A landslide could accompany a wide range of ground movement at one time. A global assessment of landslide should consider many factors, such as the soil, topography, vegetation and rainfall. Geographic information systems (GISs) are widely used in spatial landslide analyses because of the strong capability of GIS for spatially distributed data processing (Burrough, 1986; van Westen, 1993; Tarboton, 1997; Huabin et al., 2005; Simoni et al., 2008; Montrasio et al., 2011; Lepore et al., 2013). GIS is used to handle the spatial distribution of terrain parameters by modeling the correlation between terrain parameters within the distribution of landslides. This capability has facilitated many GIS-based analyses to assess landslides. Rainfall-induced landslides commonly occur when the wetting band is increased while soil suction is lost, and the effective vertical stress is reduced (Pack et al., 1998; Jeong et al., 2008, Lu and Godt, 2008). The ⁎ Corresponding author. Tel.: +82 2 2123 2807; fax: +82 2 2132 8378. E-mail address: [email protected] (S. Jeong).

unsaturated characteristics of soils affect landslide analysis (Kim et al., 2004; Jeong et al., 2009), and the quantity of infiltration in the hydrological model of rainfall infiltration-runoff must be accurately estimated. In the modeling of landslides, the types of slope failures, boundary conditions of rainfall infiltration and the initial condition of the groundwater table are the main factors to be analyzed to predict landslides. The soil–water content is spatially and temporally changed due to rainfall in complex mountain terrains. Therefore, a physically-based model for landslide analysis could simulate rainfall infiltrationgroundwater flow, which would significantly impact the prediction of rainfall-induced landslides. The methodology for the physically-based model consists of various parts, such as topographical, meteorological and geotechnical models. Data related to soil properties, rainfall and the digital elevation model (DEM) are required to predict the susceptibility of landslides (Liao et al. 2011; Kim et al. 2012). In recent years, some physically-based models have been developed to assess the landslide susceptibility using a range of topographic, geotechnical, and hydrologic parameters to compensate for deficiencies in the understanding of groundwater, including spatial and temporal distributions. These methods are characterized by different levels of complexity and high quality input data are required for predicting suitable results (Montgomery and Dietrich, 1994; Tarboton, 1997; Pack et al., 1998; Borga et al., 2002; Baum et al., 2008; Simoni et al., 2008; Montrasio et al., 2011; Lepore et al., 2013). Physically-based landslide analysis models generally consist of a methodology that combines the hydrological model for analyzing the pore-water pressure and the infinite slope-stability model for computing its safety factor. Different physically-based models have been proposed, including the Stability Index Mapping (SINMAP; Pack

http://dx.doi.org/10.1016/j.enggeo.2014.09.001 0013-7952/© 2014 Elsevier B.V. All rights reserved.

Please cite this article as: Kim, J., et al., GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model, Eng. Geol. (2014), http://dx.doi.org/10.1016/j.enggeo.2014.09.001

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J. Kim et al. / Engineering Geology xxx (2014) xxx–xxx

et al., 1998), Transient Rainfall Infiltration and Grid-based Regional Slope-stability Model (TRIGRS; Baum et al., 2008), Shallow Slope Stability Model (SHALSTAB; Montgomery and Dietrich, 1994), GEOtopFS model (Simoni et al., 2008), Shallow landslide Instability Prediction model (SLIP; Montrasio et al., 2011), and Triangulated Irregular Networks-based Real-time Integrated Basin Simulator and Vegetation Generator for Interactive Evolution (tRIBS-VEGGIE; Lepore et al., 2013). The constitution of these models is similar, which combines an infinite slope-stability model and a hydrologic model, although some use different hydrologic models and slope failure types. The failure criterion used to derive the FS equation, sometimes accounts for the unsaturation condition such as the SLIP model, or not such as the tRIBS-VEGGIE and GEOtop-FS models. SHALSTAB and SINMAP models do not take into account the infiltration over time and the timedependent groundwater flow. The TRIGRS model calculates the one-dimensional infiltration over time, but cannot consider the groundwater flow. Therefore, these models could not consider the spatial and temporal distributions of groundwater variations. The aim of this study was to develop a predictive model for landslide susceptibility, which is a function of the temporal distribution of groundwater. In the proposed model, the geotechnical model considers the shallow translational landslides controlled by groundwater flow as well as the wetting band. The hydrologic model considers the spatial and temporal variations of groundwater. In particular, the groundwater table will be elevated where the soil depth is shallow or the bedrock is gently sloping. Therefore, landslide susceptibility can be analyzed by using real-time precipitation data and calculated by using the change in groundwater over time. 2. Available landslide analysis models The susceptibility of rainfall-induced landslides can be predicted using many approaches, such as SINMAP, SHALSTAB, and TRIGRS. In this study, these available landslide analysis models were introduced and compared mainly focusing on the two main approaches, the (1) hydrological and (2) geotechnical models. The hydrologic and geotechnical models of the SINMAP and SHALSTAB models have many similarities. The SINMAP and SHALSTAB use hydrologic models in which the shallow lateral subsurface flow follows topographic gradients, which implies that the contributing area to flow at any point is given by the specific catchment area defined by the surface topography shown in Fig. 1 (Beven and Kirkby, 1979; Beven et al., 1995). These models use a

hydrological model that cannot consider the flow of groundwater but takes into account the discharge at equilibrium conditions (steadystate) (Montgomery and Dietrich, 1994; Pack et al., 1998). The depth of the groundwater table is defined as the wetness index: w¼

Dw Ra ðw ≤ 1Þ ¼ T  sinβ D

ð1Þ

where T is the soil transmissivity, the vertical integral of the hydraulic conductivity of soil, R is the steady-state recharge, a is the specific catchment area that indicates the upslope contributing area of the unit contour length, β is the slope of the bedrock, Dw is the vertical height of the water table within the soil layer, and D is the vertical soil depth. In contour-based steady-state hydrologic models, such as SINMAP and SHALSTAB, flow direction and flow accumulation data are used to estimate the spatial distribution of soil saturation levels. The original DEM and these derivative data sets are employed to process the drainage network, overland paths, watersheds and topographic contributing area (Mark, 1984; O'Callaghan and Mark, 1984; Jenson and Domingue, 1988; Martz and Jong, 1988; Montgomery and Dietrich, 1994). However, this process requires combining a raster analysis for the slope data of the original DEM and vector analysis for the drainage network and watershed. Therefore, this process is limited by distortions that arise from converting the data and at the interface between watersheds. The TRIGRS model computes the transient pore pressure changes to provide an analytical solution to the partial differential equations that represent one-dimensional vertical flow (Iverson, 2000; Hsu et al., 2002; Baum et al., 2008). The pore pressure for an impermeable basal boundary of bedrocks at a finite depth is given as    N X InZ Z 1=2 ϕðZ; t Þ ¼ ½Z−dβ þ 2 H ðt−t n Þ½D1 ðt−t n Þ K 2½D1 ðt−t n Þ1=2 " n¼1 Z " ## N XI    1=2 Z nZ H t−t nþ1 D1 t−t nþ1 −2    K 2 D1 t−t nþ1 1=2 n¼1 Z ð2Þ where ϕ(Z, t) is the groundwater pressure head at time t and at depth Z, which is positive in the downward direction. In Z = z/cos α, z is normal to the slope angle (α), d is the steady water table in the z-direction, for β = λ cos α, λ = cos α − [IZ/K]LT, KZ is the hydraulic conductivity, InZ is the surface flux for the nth time interval, LT stands for long term, H[t − tn] is the heavy-side step function, D1 = D0 cos2α, D0 is the saturated hydraulic diffusivity, and N is the total number of intervals. In these models, the infinite slope failure model is used to analyze the stability of the slope. The SINMAP and SHALSTAB consider a shallow translational landslide that is controlled by groundwater flow convergence while the TRIGRS model considers a slope failure below the wetting band which is temporarily saturated by the infiltration of rainfall. However, because the perched aquifer water table and groundwater table coexist, the spatial and temporal distributions of groundwater vary. It should be noted that the aforementioned models consider only one of them. 3. New approach: YS-Slope model 3.1. Geotechnical infinite slope model

Fig. 1. Flow directions and contributing areas in the GIS of the SINMAP.

Landslide analysis considers the infiltration by rainfall, which can be classified into three mechanisms: (1) a mechanism that considers the downward velocity of the wetting front, (2) a mechanism that considers the upward velocity of the groundwater level, and (3) a mechanism that considers both of these factors. In this study, the infinite slope model was used as a physically-based model for rainfall-induced landslides to use the aforementioned mechanisms for landslide analysis. In the

Please cite this article as: Kim, J., et al., GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model, Eng. Geol. (2014), http://dx.doi.org/10.1016/j.enggeo.2014.09.001

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Fig. 2. Infinite slippage plane for slope stability analysis, YS-Slope.

case of an infinite slope that is much shallower than its length, the most critical failure surface will be on a plane parallel to the slope. The infinite slope model was used for the physically-based approaches because of its effectiveness in landslide susceptibility analysis, and the adoption of GIS allowed the analysis, modeling and spatialization of landslide conditions using this simple geotechnical model for a broad area. The Mohr–Coulomb criteria for simulating the unsaturated soil strength that was adopted in the stability analysis were improved by considering the pore-water pressure and pore-air pressure (Pradel and Raad, 1993): 0

0

τ f ¼ c þ ðσ n −ua Þ  tanϕ þ ðua −uw Þ  tanϕ

b

b

0



S−Sr 100−Sr

 FS ¼

ð3Þ

where c′ is the effective cohesion, σn is the total normal stress on the failure plane, ua is the pore-air pressure, uw is the pore-water pressure, (ua-uw) is the matric suction, ϕ′ is the effective angle of the internal friction of soil, and ϕb is the angle indicating the increase in shear strength for an increase in the matric suction. The value of ϕb is deduced from the equation proposed by Vanapalli et al. (1996), which relates to the degree of saturation (S) and internal friction angle of the soil: tanϕ ¼ tanϕ þ

considering the uniform load from the vegetation and constant number of additional shear strengths from the roots of the vegetation. Using these previous studies, the present study improved the infinite slope model for the first or second case to interpret landslide susceptibility for the third mechanism mentioned above. As shown in Fig. 2, the safety factor for the infinite slope (FS) is calculated from the ratio of the resisting Coulomb friction and cohesion on a slip surface to the gravitationally induced downward slope driving stress:

ð4Þ

where Sr is the residual degree of saturation of the soil. For small changes in the matric suction, φ′ can be assumed equal to ϕb because these two values do not differ significantly. Hammond (Hammond et al., 1992) suggested the infinite slope model by

0 0 2 0 cs þ cr þ ðγt  Ds þ q0 þ ðγ sat −γ w Þ  Dw Þ  cos β  tanϕ ðγt  Ds þ γsat  Dw þ q0 Þ  sinβ  cosβ

ð5Þ

where c′s is the cohesion of the soil, c′r is the constant number of additional shear strengths from the roots of trees, q0 is the uniform load from trees, Dw is the depth of the wetting band (= Dwm + Dwn), Ds is the depth of the unsaturated soil (=Dmn), γt is the total unit weight of the soil, γsat is the saturated unit weight of the soil, γw is the unit weight of water, and β is the angle of the slope. Eq. (5) can calculate all cases of the aforementioned mechanisms. For the first case, the safety factor for the wetting band depth, Dw, is simply represented by Eq. (5). It can also be derived by combining two previous models (Hammond et al., 1992; Pradel and Raad, 1993):  FS ¼

 c0s þ c0r þ fðγ sat  Dw þ q0 Þ−ðγ w  Dw Þg cos2 β  tanϕ0 ðγsat  Dw þ q0 Þ  sinβ  cosβ

ð6Þ

where Dw is the depth of the wetting band (=Dwn) and γw is the unit weight of the pore water.

Fig. 3. Rainfall-infiltration rates of modified Green–Ampt Model. Modified from Mein and Larson (1973).

Please cite this article as: Kim, J., et al., GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model, Eng. Geol. (2014), http://dx.doi.org/10.1016/j.enggeo.2014.09.001

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Fig. 4. Fluid flow of an elementary volume of fluid in YS-Slope.

The pore-water pressure reduces the effective normal stress and the shear strength of the soil, which ultimately causes landslides. Thus, a thorough knowledge of the groundwater level is important to predict and prevent landslides. However, the groundwater level depends on the soil depth, rainfall intensity and hydraulic conductivity of the soil. Thus, the use of a constant or randomly selected value for a large area is not appropriate. To analyze the pore-water pressure, the present study used a hydrologic model that can evaluate the change in the pore-water pressure and be coupled to the infinite slope model.

analyze landslides. For the continuity of precipitation in the wet season, E and Sy will be neglected. St is regarded as the storage time to estimate the recharged groundwater level, it refers to the rainfall infiltration and water content deficit of the unsaturated soil. Because the terms E, Sy and St are neglected with the exception of time, QR refers only to P and IR which is the rainwater infiltrated into the ground. Thus, from hydrometeorological model for the runoff, it can be expressed as follows: Q R ¼ P−IR

ð8Þ

R ¼ IR

ð9Þ

3.2. Hydrologic model The recharge to the groundwater is determined by the hydrometeorological model, which is based on meteorological factors, such as the temperature and precipitation as follows (Sangrey et al., 1984): R ¼ P−Q R −E  St  Sy

ð7Þ

where R is the recharge, Q R is the runoff, E is the evapotranspiration, P is the precipitation, St is the storage, and Sy is the synthetic element. This paper describes a rainfall-induced change in the groundwater to

A

in which rainfall infiltration (IR) refers to the unsaturated soil characteristics and rainfall, and it is a time-dependent factor. In this study, the basic assumptions applied to the hydrologic model are that 1) the aquifer is homogeneous, 2) the recharge is non-uniform and varies with time, and 3) the groundwater flows only below the groundwater table. This hydrologic model was developed using the modified Green–Ampt model (Green and Ampt, 1911; Mein and Larson, 1973) while considering the behavior of the unsaturated soil

B D8 Method

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8

Fig. 5. Determination strategy of groundwater flow in YS-Slope. A: D8 method for a flow direction, B: an example (5 × 5 DEM and recharge depth in each cells), C: flow direction of the example, D: groundwater flow at time t.

Please cite this article as: Kim, J., et al., GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model, Eng. Geol. (2014), http://dx.doi.org/10.1016/j.enggeo.2014.09.001

J. Kim et al. / Engineering Geology xxx (2014) xxx–xxx

to estimate the rainfall infiltration (IR) and the recharge (R). The recharge (R), a quantity of rainfall infiltrated into the groundwater, was used as the input variable to calculate the flow of the groundwater. The flow of the groundwater can be calculated by combining the raster model (Soller et al., 1999; Smit, 2000) for GIS and Darcy's law. 3.2.1. Rainfall infiltration model The commonly used Green–Ampt model for one-dimensional rainfall infiltration was adopted in this study. Green and Ampt (1911) derived an infiltration model under ponded condition into deep homogeneous soil with a uniform initial moisture content. Modifications of the Green and Ampt model have attempted to transform it into a form suitable to model the infiltration. The model modified by Mein and Larson (1973) was also used in the proposed model. The proposed model assumed that the volumetric water content and deficit in the water content remain constant above or below the wetting front shown in Fig. 3. As shown, the two distinct cases of rainfall intensity are related to the rainfall infiltration and runoff: (1) in the first case, the rainfall intensity is less than the saturated conductivity; all of the rainfall infiltrates irrespective of the duration; (2) the second case considers a rainfall intensity that is greater than the saturated conductivity. In this case, all of the rainfall will continue to infiltrate until the rainfall intensity is less than the infiltration capacity. For a rainfall intensity greater than the infiltration capacity, the rainfall below the infiltration capacity curve infiltrates into the ground, and the remainder runs off. The ponding time (Eq. (10)) was considered to estimate the relationship between the rainfall infiltration and runoff. The rainfall infiltration (IR) is defined in Eq. (11), and it is estimated by the trial and error method with a variable of cumulative infiltration. The depth of the wetting front (Dwn) in the vadose zone is defined as the ratio of the rainfall infiltration (IR) and the deficit water content (Δθ) in Eq. (12): tp ¼

K s  ψ f  Δθ IR  ðK s −IR Þ

 ψ f  Δθ t IR ¼ I  t p þ ∫t w K s 1 þ p F

ð10Þ

ð11Þ

5

Table 1 The procedure to determine the recharge depth over time. Step

Procedure

1 2

Calculation of wetting band depth (①) using Δθ(θs − θi,0 or θs − θi,1) Changing ① to depth of unsaturated zone (②) using Δθ(θi,1 − θi,0) until ② is not exceeding soil depth Calculation of recharge depth using Δθ(θs − θi,1) when ② is larger than H Calculation of groundwater flow Repeat process of 1–4 in each cells

3 4 5

Dwn

 ψ f  Δθ t I  t p þ ∫t w K s 1 þ I p F ¼ R ¼ Δθ Δθ

ð12Þ

where I is the rainfall intensity, tp is the time for ponding, IR is defined in Eq. (10), tw is the rainfall duration, Ks is the saturated permeability, ψf is the head of the metric suction, F is the infiltration, and Δθ is the deficit water content. 3.2.2. Groundwater flow model In this study, Darcy's law and the raster model of GIS were combined to calculate the groundwater flow for the landside analysis. The groundwater flow was derived for a small elementary volume, for which the properties of the soil were assumed to be effectively constant. A mass balance was performed on the water flowing in and out of this small volume, and the flux was calculated in terms of the head using Darcy's law (Eq. (13)) which requires that the flow is slow (Fig. 4). Therefore, a form of the Laplace equation describes this steady-state flow of the groundwater (Eq. (14)): v ¼ ki ¼ k

dh ds

   ∂ ∂h ∂ ∂h ∂ ∂h kx þ ky þ kz ¼0 ∂x ∂x ∂y ∂y ∂z ∂z

ð13Þ

ð14Þ

where v is the seepage velocity, k is the permeability, i is the hydraulic gradient, s is the distance through the soil, and the subscript x (or y, z)

Fig. 6. Soil profile and hydrologic model concept in YS-Slope.

Please cite this article as: Kim, J., et al., GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model, Eng. Geol. (2014), http://dx.doi.org/10.1016/j.enggeo.2014.09.001

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Fig. 7. Flow chart of groundwater flow calculation in YS-Slope.

denotes the x (or y, z)-direction. The variable h is the groundwater potential, equal to the sum of the kinetic energy, pressure and potential energy heads as in (Eq. (15)): h¼

v2 p p þz ≈ þz þ ρg 2g ρg

ð15Þ

where ρ is the density of the fluid, g is the acceleration due to gravity, p is the pressure at the chosen point, and z is the elevation of the point above a reference plan.

The total volume of the flow leaving the element (Q x) in the x-direction for a unit time (Δt) can be expressed by assuming that the pore water pressure is constant over a small elementary volume of space shown in Fig. 4. This relationship is expressed by Eq. (16) as follows:   ∂vx ∂ ∂h dx dydzdt ¼ kx dydzdt ¼ kx  ix  dy  dz  dt ∂x ∂x ∂x ¼ kx  sinβ  dy  dz  dt ¼ kx  sinβ  s  Hw  dt

Qx ¼

ð16Þ

Fig. 8. Landslide inventories around Mt. Umyeonsan in 2011. A: location, B: test site 1 (Raemian watershed) and test site 2 (Dukwooam watershed). (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)

Please cite this article as: Kim, J., et al., GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model, Eng. Geol. (2014), http://dx.doi.org/10.1016/j.enggeo.2014.09.001

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Tensiometer

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Fig. 9. Location map, cross section and instrument of field measurement.

where sin β (= i = dh / ds) is the hydraulic gradient resulting from a difference in the groundwater potential across an element of the medium. To apply the raster model, the total volume of flow leaving

Fig. 10. Typical measurement results and rainfall data. A: T2 (−30, 60, 130), B: T5 (−20, 90, 140).

Fig. 11. Aerial photos in the test site. A: 2009, B: 2011.

Please cite this article as: Kim, J., et al., GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model, Eng. Geol. (2014), http://dx.doi.org/10.1016/j.enggeo.2014.09.001

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the element (Q x) in a cell can convert to the change in height (ΔH) from Eq. (14) and be expressed as follows: ΔH ¼

Qx 1 Q 1 k  sinβ  Hw ¼ x ¼ x  dt dxdy Δθ s2 Δθ s  Δθ

ð17Þ

where s is the distance between each cell (or size of a cell, = dx = dy), Hw is the height of groundwater in a cell (=dz), and Δθ is the volumetric water content deficit. The total volume of flow leaving the element and its flow direction were estimated using the raster model for GIS. The flow of groundwater was calculated using the slope and slope direction of the bedrock, which are calculated from a digital elevation model of GIS that is converted to a raster model of the matrix data structure with the elevation of each pixel stored in a matrix node. Based on the assumption that the kinetic energy is negligible, the total volume of flow leaving a cell can only be affected by the neighborhood cells. Therefore, a vector analysis that

includes the watershed and drainage network is not required, and the flow of groundwater can be calculated from a cell to a neighborhood by shifting the change of groundwater height. The process of calculating the change in the groundwater level and the flow direction of the groundwater is shown in Fig. 5. As shown in Fig. 5(A), the eight-flow method (D8 method; Jenson and Domingue, 1988) is used to build the flow direction data. In this method, the slopes and slope directions are assigned to a flow from each pixel to one of the neighbors, either adjacent or diagonally, in the direction with the steepest downward slope. It is encoded to correspond to the orientation of one of the eight cells that surround the cells expressed by 2n in which n is a number integer ranging from 0 to 7. Fig. 5(B)–(D) shows simple examples of groundwater flow in YS-Slope model. Fig. 5(B) shows average elevations using 5 × 5 DEM and initial values of recharge depth. Each cell is represented as an integer describing the elevation at the center of that cell, although, in general, these would be float values. As shown in Fig. 5(C), the flow directions are selected with the

Fig. 12. Digital elevation data (DEM) and direction of slope. A: test site 1, B: test site 2.

Please cite this article as: Kim, J., et al., GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model, Eng. Geol. (2014), http://dx.doi.org/10.1016/j.enggeo.2014.09.001

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test site 2

test site 1

9

test site 2

test site 1

test site 2

test site 1

Fig. 13. Weather radar images on July 27, 2011 of Seoul, Korea. Adopted from Korean Meteorological Administration.

arrow (and 2n) for each cell pointing to the cell of the steepest descent. A change in height (ΔH) due to groundwater flow in the left side of Fig. 5(D) is calculated by Eq. (17), which should flow to the neighborhood with the incorporation of flow direction (Fig. 5(C)). The right side of Fig. 5(D) shows the result of groundwater flow over time t. As an example, the cells marked A to C is a flow path from the cell marked A. The initial groundwater level of cell A is 5 shown in Fig. 5(B), and some groundwater from cell A should also flow to cell B shown in Fig. 5(C). Thus, for time t, the groundwater level of cell A is 3.0 (=5.0initial −2.0out) and 6.0 (5.0initial − 1.0out + 2.0in) for cell B. At cell C for time t, it is 19.1 (5.0initial − 0.0out +1.0in + 3.7in + 1.0in + 2.4in + 1.5in + 2.0in + 1.0in + 1.5in), and when including cell C with the yellow cells, it should make a sub-watershed. The soil layer is regarded as homogeneous and is ideally subdivided into three zones which are the wetting band zone, partially saturated zone, and fully saturated zone shown in Fig. 6. The vertical infiltration

A

(unit: km)

of water from the surface into the ground is modeled in the wetting band and partially saturated zones, and the storage time is also considered. The storage time is defined as the elapsed time at which rainwater infiltrates into the groundwater through the unsaturated zone. The horizontal movement of the groundwater is generated when the infiltrated water recharges the fully saturated zone. Additionally, the volumetric water content is assumed to remain constant above and below the groundwater table, as in the wetting above and below the front in the Green–Ampt model. In this hydrologic model, the following procedure was used to determine the recharge over time shown in Table 1. Fig. 7 shows the flow chart for the groundwater flow which considers the storage time in the YS-Slope. As stated by the rainfall infiltration (IR) in Fig. 7, the total infiltrated water was recharged into the groundwater using the hydrometeorological model and calculated by Eq. (11). The changes in each cell at time t are expressed as Hijk in

B

Seoul

Han River Seocho AWS Namhyeon AWS 0

2.0 2.0 Umyeonsan (Mt.)

10 km Fig. 14. Rainfall data in the test sites. A: locations of weather stations, B: daily and cumulative rainfall in the Seocho station.

Please cite this article as: Kim, J., et al., GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model, Eng. Geol. (2014), http://dx.doi.org/10.1016/j.enggeo.2014.09.001

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J. Kim et al. / Engineering Geology xxx (2014) xxx–xxx

Fig. 7 and calculated by considering the storage time elapsed to infiltrate from the ground surface to the groundwater table recharge and stored in unsaturated soil. The processes of calculating the change in groundwater level and the flow direction of groundwater are given in Fig. 4. The variations in the groundwater level in each cell over time are expressed as follows: ΔH ijk ¼

ks  sinβijH ijk  dt sΔθ

k ¼ t; initial H ijt −H ijðt−1Þ

ð18Þ



i ¼ 2; j ¼ 2; H 22t ¼ H 22t −ΔH 22t D ¼ 1; H23t ¼ H23t þ ΔH22t : D ¼ 4; H32t ¼ H32t þ ΔH22t : D ¼ 16; H21t ¼ H21t þ ΔH 22t : D ¼ 64; H12t ¼ H12t þ ΔH 22t :

D ¼ 2; H 33t ¼ H 33t þ ΔH 22t ð19Þ D ¼ 8; H 31t ¼ H31t þ ΔH 22t D ¼ 32; H 11t ¼ H 11t þ ΔH22t D ¼ 128; H13t ¼ H 13t þ ΔH22t

As mentioned above, the rainfall infiltration was completely recharged into the groundwater with the hydrometeorological model (Eq. (9)) and calculated with Eq. (11). Eq. (19) can be represented by Eq. (20) because of the recharge depth, which considers the storage time. Fig. 7 shows the flow chart for groundwater flow which considers the storage time in the YS-Slope. k ¼ t; initial Hijt ¼ Hijðt−1Þ þ IR;ijðt−t s Þ

 t s ¼ H−H ijt  K s H ijt ¼ H ijðt−1Þ −ΔHijt :

ð20Þ

4. Application of the proposed model The proposed model was applied to assess the susceptibility of the approach for the study site. Spatial topographic, geotechnical and hydrologic properties are needed to apply the landslide analysis method to the infinite slope model coupled with the hydrologic model. Specific properties include landslide occurrence, precipitation, topography, soil depth, vegetation, and soil parameters. To obtain these properties,

Table 2 Geotechnical and hydraulic properties of the soil used in this study. Parameters

Values

Description

Hydraulic conductivity, ks

8 × 10−4 cm/s (28.8 mm/h) 28.0–32.0 (30.0) % 0.20 830 mm

In-situ permeability test SWCC test SWCC test SWCC test

10.2–12.8 (11.0) kPa

Initial water contents, θi Water-content deficit, Δθ Wetting front suction head, ψf Soil cohesion, C ′s Soil friction angle, φ′

22.4–26.6 (26.5)°

Total unit weight of soil, γt Additional shear strength by roots of tree, C ′r Uniform load by tree, q0

17.0–18.5 (18.0) kN/m3 1.0 kPa

Direct shear test, borehole shear test Direct shear test, borehole shear test Laboratory test of density Suggested by Norris (2008)

0.253 kPa

Suggested by KFRI (2006)

( ) is the number of average.

field investigations and laboratory tests were performed, and the results were used as input parameters of the models. 4.1. Test site The test site Mt. Umyeonsan was located in downtown Seoul, Korea. Mt. Umyeonsan lies at 37°28′2″N latitude and 127°0′25″E longitude with a height of 312.6 m above sea level. It has a main ridge formed from the northeast to the southwest, including six valleys perpendicular to the main ridge. Most of the mountain consists of lacustrine biotite gneiss. Two typhoons, Typhoon Meari (June 22 to June 27) and Typhoon Muifa (July 28 to August 9) occurred in 2011, and the seasonal rain front remained in the middle of the Korean peninsula. They produced much more rainfall compared to the average rainfall and eventually led to landslides and debris flows around Umyeonsan. A large amount of data, such as longitude/latitude and topographical/geotechnical characteristics in places where landslides had occurred, was collected by field surveys using a portable GPS, a laser ranger and a clinometer. Based on the data from the field surveys, the coordinates regarding landslides were indicated on an aerial photograph of the area around Mt. Umyeonsan shown in Fig. 8. In this figure, the landslide locations are marked by colored dots, and the debris flow routes are marked by

Fig. 15. Locations of soil investigation. A: test site 1, B: test site 2.

Please cite this article as: Kim, J., et al., GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model, Eng. Geol. (2014), http://dx.doi.org/10.1016/j.enggeo.2014.09.001

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Fig. 16. Soil–water characteristic curves (SWCC) for the soil: experimental data and fitted curve.

Table 3 Curve-fitting parameters for the soil–water characteristic curve. Curve-fitting parameters

Value

α(1/kPa) N m=1−1/n Saturated volumetric water content (θs) Residual volumetric water content (θr)

11.3 1.35 0.25 0.5 0.18

yellow lines. Most of the debris flows resulted from landslides and flowed downward to the valley. They occurred simultaneously over the whole Umyeonsan area. A total of approximately 150 landslides were generated initially in forms of debris flows. Among these, two

test sites were selected for the landslide analysis in this study. The sites are the Raemian watershed (test site 1) and the Dukwooam watershed (test site 2) shown in Fig. 8. The surface areas of the two watersheds are 128,700 and 98,700 m2, respectively. In test sites 1 and 2, no 6 and no 4 landslides were generated with 685 and 49 m2, respectively.

4.2. Field measurement A matric suction that reflects rainfall infiltration into soils is very important to analyze rainfall-induced landslides. The volumetric water content can be estimated as a function of the matric suction of the soil with a laboratory test. Field measurements are commonly used in the dry season and the rainy season because of the preceding rainfall effect.

Fig. 17. Map of the soil layer depth. A: test site 1, B: test site 2.

Please cite this article as: Kim, J., et al., GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model, Eng. Geol. (2014), http://dx.doi.org/10.1016/j.enggeo.2014.09.001

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Therefore, the matric suction was measured at test site 1 from June 29 to July 19, 2012. A total of 18 tensiometers were installed at the site shown in Fig. 9. The matric suction was measured at a total of six locations, and representative results of the measurements, T1 and T5, are shown in Fig. 10. The initial matric suction measured on June 29, 2012 before the rainy season at the test site was approximately 75–85 kPa. On June 30, after the first rain of the rainy season, the matric suction decreased to 0–16 kPa at a shallow depth of 0.2 to 0.6 m. The matric suction between 0.6 m and 1.3 m decreased to 20 kPa over time. At a depth of 1.4 m, the matric suction was almost constant for rainfall in one day because the rainwater that had infiltrated into the ground surface did not reach this depth, and it was decreased after the 6th day. All tensiometers were maintained at 10–20 kPa before additional rainfall for the unsaturated characteristics of the soils. Therefore, an initial matric suction of 80 kPa for dry soils and 20 kPa for unsaturated soils was adopted in this study for the landslide analysis. 4.3. Model parameterization and spatial data sets 4.3.1. Digital elevation models Fig. 11 shows an aerial photograph of the test site before and after a landslide in 2011, and the debris flows and landslides can be identified by the trails in the two photos. The DEM data were computed from light detection and ranging (LiDAR). The 1-m grid cell spatial resolution for the landslide analysis in this study was provided by the LiDAR data in 2009, and the important parameters were derived from the LiDAR DEM, including the elevation, slope angle, and spatial location. The LiDAR data in 2011 were used to validate the incidences of landslides in 2011. The aspect of the slope data set was built by the 2009 LiDAR DEM shown in Fig. 12. For the two test sites, the faces of the northwest slope were the most dominant at 47.1% and 21.4%, which consisted of a valley formed by the northeast and north slopes. Therefore, test site 1 was formed by three sub-watersheds although test site 2 was formed by a single one.

Fig. 18. Results of analysis for rainfall infiltration-groundwater flow during 57 days. A: SINMAP, B: YS-Slope; day 34 (July 4), C: day 40 (July 10), D: day 57 (July 27).

4.3.2. Hydrologic data According to reports from the Korea Meteorological Administration (KMA), Seoul had 341 mm of rainfall over a 72-hour period from July

Fig. 19. Results of analysis for recharge and groundwater flow during day 57–day 70. A: depth of recharge, B: groundwater flow during day 57–day 70 (no rainfall).

Please cite this article as: Kim, J., et al., GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model, Eng. Geol. (2014), http://dx.doi.org/10.1016/j.enggeo.2014.09.001

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24 to 27, 2011. In addition, 307 mm of rainfall was observed within a 24-hour period ending at 9 AM on July 27, 2011. The spatial patterns of rainfall provided by the radar images during this period are shown in Fig. 13. In this study, the rainfall data obtained by Automatic Weather Stations (AWS) from the Seocho observatory through rain gauges near the landslide sites around the test site were used to analyze the landslides. As shown Fig. 14(A), both weather stations are located at a distance of 2 km from the highest peak of the Umyeonsan. The daily rainfall data from June 1 to July 27, 2011 were used for the landslide analysis shown in Fig. 14. 4.3.3. Geotechnical data A soil investigation was performed to obtain detailed information on the landslides in 2011. The soil investigation was carried out as shown in Fig. 15. The soil properties were estimated and are summarized in Table 2 (Gray, 1996; Clausnitzer et al., 1998; Smit, 2000; KFRI, 2006; Norris, 2008). The top layer of soil is the colluvial soil, and it is classified as SM according to the Unified Soil Classification System (USCS). The soil has been deposited by either weathering, transportation, or a variable combination of these processes, and it consists of parent rock fragments in a heterogeneous silty to sandy matrix. The plastic and liquid limits of the soil are in the range of 20.9 to 23.8% and 30.2 to 42.1%, respectively. The colluvial deposits contain fines of 28.8–55.7%. The soil–water characteristic curve (SWCC) was estimated with the GTCS pressure plate extractor test (Fredlund et al., 1978) and filter paper test shown in Fig. 16. The curve-fitting parameters for the soil– water characteristic curve are given in Table 3. The soil layer depth is one of the important parameters in verifying the cause of a disaster. To obtain the soil layer thickness at the test site, a Krigging method was performed using data from 14 boreholes, seismic prospecting, and a digital elevation model (DEM). The elevation of the surface before the 2011 landslide was calculated using the DEMs of the 2010 LiDAR. Fig. 17 shows a colored map of the soil layer depths in the study area. 4.4. Comparison of the results and discussion A series of GIS-based landslide analyses were performed to verify the present model by comparing with other physically-based models, such as the TRIGRS and SINMAP models. The validity of the proposed model was examined by comparing the results from the present approach with the landslide inventory. A 5 × 5 m gridded DEM converted from

Fig. 21. Erosion depth by analyzing LiDAR DEMs of 2009 and 2011.

the 2009 LiDAR data, soil data obtained from detailed investigations and field measurements, and precipitation gauged by the Korean Meteorological Administration (KMA) Automatic Weather Station (AWS) (Korean Metrological Administration, 2011) were used as the input data. Fig. 18 shows the results of the analysis for the rainfall infiltration-groundwater flow calculated by the proposed model (B)–(D) and SINMAP (A) in test site 1. The landslide occurred in 2011, and the rainfall in the rainy season began on June 1, and it began to fall frequently after June 22. The wetness index on the 34th day after the first rainfall was calculated as a dimensionless value, ranging from 0.0 to 0.5 across the region except in areas where the soil was shallow. The cumulative depth of the rainfall at the time was 540 mm. The wetness index was maximized on the 40th day, July 7, with a value of 0.9 in the area where the soil was shallow as shown in Fig. 18(B). On July 27, the day the landslide occurred, the groundwater table rose on

Fig. 20. Landslide case of the test site 1 in 2011 and results of landslide analysis using the models of A: YS-Slope, B: TRIGRS.

Please cite this article as: Kim, J., et al., GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model, Eng. Geol. (2014), http://dx.doi.org/10.1016/j.enggeo.2014.09.001

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the ground surface where the soil depth was shallow, and the values for the wetness index ranged from 0.2 to 0.4 in the watershed. Based on the results of the analysis by the SINMAP model, Fig. 18(A) shows that the model can clearly calculate the surface outflow. However, the time for the groundwater flow, rainfall duration, and recharge rate that consider the infiltration capacity and storage time were not considered. The amounts of water recharged into the groundwater at each matrix node were analyzed for a rainfall period of 57 days as shown in Fig. 19(A). The recharge rate at different locations depends on the soil depth, which indicates that this model can consider the storage time of the elapsed time to recharge and the surface runoff of the excess rainfall to infiltrate. The groundwater flow was analyzed for 13 days (day 57–day 70) after the last rainfall as shown in Fig. 19(B). The depth of the groundwater table changed between −0.8 m and 2.8 m, indicating that the hydrologic model, which considers groundwater flow for a slope, and the aspect of the bedrock could estimate the spatial and temporal changes in the groundwater table for the geotechnical model to evaluate the stability of the slope. Fig. 20(A) shows the results of the analysis using the proposed model, and the locations of the landslides investigated in the field are marked by black dots. This method analyzed the stability of a landslide considering both the downward velocity of the wetting front and the

upward velocity of the groundwater. All safety factors below the landslide dots were less than 0.8. Fig. 20(B) shows the results of the landslide analysis by the TRIGRS model, which considered the downward velocity of the wetting front depth. The wetting front depth was approximately 2.0 m. For the TRIGRS model, safety factors on the four dots were below 1.0, and larger discrepancies between the observed and predicted landslides were obtained. Nevertheless, the exact size or location of the landslides at this test site could not be determined because of the debris flows that occurred as a result of these landslides flowing down to the valley. Thus, deposition and erosion were computed from the LiDAR data of 2009 and 2011 as shown in Fig. 21. The landslides and debris flows eroded approximately 67% of the test site. The percentages of areas eroded below 1.0 m and 2.0 m were 53.3 and 15.3%, respectively. Approximately 2.8% of the area was deeply eroded, which covered the stream region in this watershed. The area of erosion from these results was compared with a map of safety factors obtained by using the proposed model (Fig. 22). The proposed model failed to predict landslides in approximately 49.7% of the test site shown in Fig. 22(A). Fig. 22(B) also shows the results of an analysis by TRIGRS. This analysis had an error rate of 59.5%, indicating that shallow landslides in the wetting band were not the only type

Fig. 22. Comparison between erosion depth from LiDARs and stability by using physically based models. A: YS-Slope, B: TRIGRS, C: YS-Slope (ignoring erosion depth below 1.0).

Please cite this article as: Kim, J., et al., GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model, Eng. Geol. (2014), http://dx.doi.org/10.1016/j.enggeo.2014.09.001

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Landslide source areas were collected by field surveys using a portable GPS, a laser ranger and a clinometer, which were represented by 685 and 49 m2 in test sites 1 and 2, respectively, and they were 0.53% of the total watershed area in test site 1 and 0.05% in test site 2. These results of the field survey were compared with the numerical results obtained from the YS-Slope and TRIGRS models (Table 4). 5. Conclusions

Fig. 23. The type of slope failure by YS-Slope model.

of slope failure at the test site. Despite an error rate deficit of 9.8% for the TRIGRS model, larger discrepancies between the observed and predicted landslides were obtained. Because the proposed model can consider various types of slope failures shown Fig. 23, it provides more accurate predictions of erosion. Fig. 22(C) shows the results of a comparison in which the reference of the erosion depth was limited to below 1.0 m, and this assumption was related to the effect of the debris flows. In this case, the model failed to predict landslides in 12% of the area, and approximately 88% of the area showed good agreement with the erosion area. Nevertheless, the YS-Slope approach to instability modeling successfully located all five landslide observations within the resultant unstable area. Fig. 24 shows the results of the analysis using the predictive models for test site 2. At this test site, the results of the analysis by both models reasonably agree with all of the landslide observations. Because this site has generally a characteristic shallow soil depth of 2.0–3.0 m, it is sufficiently shallow in relation to a wetting band depth of 2.0 m at the test site.

The primary objective of this study was to propose an improved physically-based analysis model for landslides. An important feature of the proposed model is that rainfall infiltration, recharge and groundwater flow over time are efficiently incorporated. A series of analytical studies were conducted. Comparisons with case histories demonstrated that the proposed model agrees well with the landslide inventory at the test site. Thus, the model could be practically used to assess rainfallinduced landslides. The following conclusions were drawn from the findings of this study. In the present study, the recharge was determined by considering the characteristics of the unsaturated soil in the calculation of the 1-D infiltration and the storage time related to the matric suction by field measurements. Furthermore, the recharge and characteristics of the flow through soils were considered in the 2-D flow of groundwater. By considering these characteristics, it can assess a rainfall-induced landslide depending on time by reflecting the changes in groundwater table and wetting band depth. This model agreed well with the landslide inventory and the TRIGRS model. At test site 2, the proposed model and TRIGRS model agree with all landslide observations. This result shows that the two prediction models can be properly used for a site with a shallow soil depth. Meanwhile, at test site 1, the proposed model produces considerably more reasonable results obtained by the TRIGRS model, which considers the downward velocity of the wetting front. Because shallow translational landslides are controlled by the groundwater flow and wetting band, the proposed model can consider both phenomena. A comparative study indicated that a prediction of landslide susceptibility is more reasonable when it considers various types of landslides, e.g., landslides were triggered by rainfall water infiltrating into ground and groundwater rising in ground. Because debris flows occurred at the test site, the area of erosion from the LiDAR data was compared with a map of the safety factors from the predictive models for an objective and quantitative assessment. The proposed model failed to predict a landslide in about 49.7% of the test site, and the TRIGRS model had an error rate of 59.5%. For

Fig. 24. Landslide case of the test sites 2 in 2011 and results of landslide analysis using the models of A: YS-Slope, B: TRIGRS.

Please cite this article as: Kim, J., et al., GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model, Eng. Geol. (2014), http://dx.doi.org/10.1016/j.enggeo.2014.09.001

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Table 4 Summary of landslide areas in the test sites. Test site 1

Area (m2) % of area Landslides % landslides

Test site 2

Survey

YS-Slope

TRIGRS

Survey

YS-Slope

TRIGRS

685 (27) 0.53 6 –

4672 (167) 3.63 6 100

1647 (66) 1.28 3 50

49 (4) 0.05 4 –

2013 (81) 2.04 4 100

1550 (62) 1.57 4 100

( ) is the number of unstable cell.

the TRIGRS model, there were larger discrepancies between the observed and predicted landslides with an error rate deficit of 9.8%. This finding shows that shallow landslides in the wetting band were not the only type of slope failure at test site 1, and the proposed model better predicts the erosion by considering various types of slope failures. The percentage of landslide areas that were not predicted decreased to 12% when neglecting erosion depths of less than 1.0 m. Acknowledgments This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (No. 2011-0030842). References Baum, R.L., Savage, W.Z., Godt, J.W., 2008. TRIGRS — A Fortran Program for Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis, Version 2.0. U. S. Geological Survey. Beven, K., Kirkby, M., 1979. Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant (A physically based, variable contributing area model of basin hydrology). Hydrol. Sci. J. 24, 43–69. Beven, K., Lamb, R., Quinn, P., Romanowicz, R., Freer, J., Singh, V., 1995. Topmodel. Computer Models of Watershed Hydrology. pp. 627–668. Borga, M., Dalla Fontana, G., Cazorzi, F., 2002. Analysis of topographic and climatic control on rainfall-triggered shallow landsliding using a quasi-dynamic wetness index. J. Hydrol. 268, 56–71. Burrough, P.A., 1986. Principles of Geographical Information Systems for Land Resources Assessment. Clausnitzer, V., Hopmans, J., Starr, J., 1998. Parameter uncertainty analysis of common infiltration models. Soil Sci. Soc. Am. J. 62, 1477–1487. Fredlund, D., Morgenstern, N., Widger, R., 1978. The shear strength of unsaturated soils. Can. Geotech. J. 15, 313–321. Gray, D.H., 1996. Biotechnical and Soil Bioengineering Slope Stabilization: A Practical Guide for Erosion Control. Wiley.com. Green, W.H., Ampt, G., 1911. Studies on soil physics, 1. The flow of air and water through soils. J. Agric. Sci. 4, 1–24. Hammond, C., Hall, D., Miller, S., Swetik, P., 1992. Level I Stability Analysis (LISA) Documentation for Version 2.0. US Department of Agriculture, Forest Service, Intermountain Research Station. Hsu, S.M., Ni, C.-F., Hung, P.-F., 2002. Assessment of three infiltration formulas based on model fitting on Richards equation. J. Hydrol. Eng. 7, 373–379. Huabin, W., Gangjun, L., Weiya, X., Gonghui, W., 2005. GIS-based landslide hazard assessment: an overview. Prog. Phys. Geogr. 29, 548–567.

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Please cite this article as: Kim, J., et al., GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model, Eng. Geol. (2014), http://dx.doi.org/10.1016/j.enggeo.2014.09.001