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Ecological Engineering 61P (2013) 658 668

Contents lists available at ScienceDirect

Ecological Engineering

jo u r n al hom epa ge : www.elsev ier .com/ locate /eco leng

odeling the contribution of trees to shallow landslide developmentn a steep, forested watershed

ongyeob Kima, Sangjun Ima,b,, Changwoo Leec, Choongshik Wooc

Department of Forest Sciences, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-921, Republic oforeaResearch Institute for Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-921, Republic of KoreaDivision of Forest Disaster Management, Department of Forest Conservation, Korea Forest Research Institute, 57 Hoigi-ro, Dongdaemun-gu, Seoul30-172, Republic of Korea

r t i c l e i n f o

rticle history:eceived 14 September 2012eceived in revised form 7 April 2013ccepted 6 May 2013vailable online 24 June 2013

eywords:hallow landslideRIGRSainfall interceptionoot reinforcement

a b s t r a c t

The objective of this study was to identify the contribution of trees to shallow landslide developmentin a steep forested watershed using a deterministic modeling approach. Rainfall interception, tree rootreinforcement, and tree surcharge were considered the main factors. A revised version of the Tran-sient Rainfall Infiltration and Grid-based Regional Slope-stability (TRIGRS) model was employed in theapproach. Hydrological modifications included adding the processes of rainfall interception using anapplication of the Rutter model. The revised infinite slope stability model was also used to consider treeroot reinforcement and tree surcharge. A comparative analysis was conducted with the results simu-lated by TRIGRS and the revised model to quantify the contribution of trees to landslide development.The Bonghwa site in South Korea, which was damaged by an extreme storm with 228 mm of rainfall onJuly 2425, 2008, was selected as the study site. Data related to the local topography, soil, and forestproperties were measured in the field for use in the model simulations, although some data were takenfrom the literature or assumed by the authors on the basis of the site characteristics. The results showedthe rainfall interception did not significantly affect the amount of rainfall reaching the soil surface, butit changed the temporal distribution of the rainfall intensity. Additionally, the rainfall interception wasfound to have little influence on infiltration from the simulation results of pore water pressure. The resultsof the simulated factor of safety indicated that root reinforcement and tree surcharge made significantcontributions to the enhancement of slope stability. The simulation results were compared to the results

from locations in which landslides occurred, indicating that the revised model estimated the landslidesusceptibility over the entire study site well, while TRIGRS appeared to overestimate the risk of shal-low landslides. In conclusion, trees appeared to make a significant mechanical contribution to shallowlandslide development during a severe storm event in steep, forested watersheds. Efforts to revise theexisting model improved its performance to assess the shallow landslide susceptibility of mountainouswatersheds despite some limitations of the current study.

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. Introduction

Landslides are caused by compositive interactions among natu-al internal factors and external factors (Carrara et al., 1999), except

or anthropogenic factors, e.g., forest road construction, clearcutarvesting, and land use changes. Internal factors are intrinsic envi-onmental properties of a specific region such as topographical,

Corresponding author at: Department of Forest Sciences, College of Agriculturend Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-21, Republic of Korea. Tel.: +82 2 880 4759; fax: +82 2 873 3560.

E-mail addresses: junie@snu.ac.kr, sangjunim@gmail.com (S. Im).

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925-8574/$ see front matter 2013 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.ecoleng.2013.05.003 2013 Elsevier B.V. All rights reserved.

eological, pedological, hydrological, and vegetational features,hile external factors are direct (or indirect) triggers that initi-tes landslides, such as earthquakes, rainfall, snowfall, and volcanicctivity. Most landslides are typically initiated by external factors,ut internal factors also significantly contribute to landslide initi-tion and development.Forest properties are one of the influential internal factors

hat cause landslides, especially rainfall-induced shallow land-lides. These properties are easier to monitor and manage than

ther internal factors. Some researchers have reported relation-hips between forest conditions and landslide occurrence. Itas reported that the landslide occurrence rate in harvestedreas was 3.5 times greater than that in unharvested areas over

dx.doi.org/10.1016/j.ecoleng.2013.05.003http://www.sciencedirect.com/science/journal/09258574http://www.elsevier.com/locate/ecolenghttp://crossmark.crossref.org/dialog/?doi=10.1016/j.ecoleng.2013.05.003&domain=pdfmailto:junie@snu.ac.krmailto:sangjunim@gmail.comdx.doi.org/10.1016/j.ecoleng.2013.05.003

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pproximately twenty years in Southeast Alaska (Swanston andarion, 1991). In addition, the frequency of landslides in loggedreas was found to be 9 times higher than that of unlogged forestreas around Vancouver Island, BC, Canada (Jakob, 2000).On an individual-tree-scale, the influences of a single tree on

lope stability, especially hydromechanical influences, were widelyeviewed by Greenway (1987), Gray and Sotir (1996), and Stokest al. (2008). Hydrologically, a tree intercepts rainfall with itsanopy and stem, and the tree reduces the amount of water in soilia transpiration. However, the tree can enhance water infiltrationy developing pores in the soil to increase the water content of theoil. Mechanically, a tree strengthens soil stability by its root whilets surcharge boosts soil shear stress to create an adverse influ-nce on slope stability. A significant amount of research quantifyinghe influence of a tree on slope stability with a focus of soil rein-orcement by tree roots using various methods has been conductede.g., Burroughs and Thomas, 1977; Wu et al., 1979; Buchanan andavigny, 1990; Norris and Greenwood, 2003; Pollen and Simon,005; Cazzuffi et al., 2006; Mickovski et al., 2007; Docker andubble, 2008).In South Korea, many shallow landslides occurred recently, and

any of them contributed to the initiation or transformation toebris flows resulting in vast damage in downstream areas. Char-cteristically, most landslide-damaged areas in South Korea weren steep, forested mountainous regions with shallow soil depthinternal factors) and were thought to be caused by heavy rainfallvents (external factors). Therefore, assessing of the contributionsf trees to landslides is important to evaluate the regional-scaledandslide susceptibility in South Korea. However, limited researche.g., Wu and Sidle, 1995; Bathurst et al., 2010) has been performedo quantitatively evaluate the regional landslide susceptibility inerms of the contribution of trees.

The main objective of the current study was to identify the con-ribution of trees to landslide development using a deterministicodeling approach. For a more detailed analysis, we concentratedn specific conditions, i.e., landslide type, landslide location, andiming of landslide. The current study focused on only rainfall-riggered shallow landslides on a steep, forested area during atorm event. Specific tree properties that affect shallow landslideevelopment during a storm event were selected, and a physi-ally based model that considers tree effects on a shallow landslideas constructed. Finally, the constructed model was applied to

andslide-damaged areas. Modeling processes were sufficientlyescribed in the following sections.

. Materials and methods

.1. Overview of modeling framework

The overall goal of the modeling was the comparative analy-is of the simulation results using an existing original model and

revised model that considered tree effects. Existing models forhallow landslide susceptibility were considered for revisions. Theelection criteria of existing models were (1) a physically basedodel, (2) a temporally dynamic hydrological model, and (3) aodel that did not consider tree effects. A physically based modelas required to quantify the process-based contributions of trees to

andslide development. Some existing physically based models forainfall-induced shallow landslide, e.g., SHASLSTAB (Montgomery

nd Dietrich, 1994), dSLAM (Wu and Sidle, 1995), SINMAP (Packt al., 1998), SHETRAN-landslide (Burton and Bathurst, 1998), andRIGRS (Baum et al., 2002), were evaluated with the criteria. TRIGRSas ultimately selected as a base model for the revisions.

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ring 61P (2013) 658 668 659

As tree effects for the revised model, the rainfall interception,oot reinforcement, and tree surcharge were determined in con-ideration of their significance and modeling capability. In terms ofhe hydrological influence of trees on landslides, the annual inter-eption loss is estimated to be one quarter or more of the annualotal rainfall, which is a significant amount (Dingman, 2002). Inddition, rainfall interception is still thought to work during a rain-all event. Thus, a model for rainfall interception was constructedy revising the selected existing model, TRIGRS. In contrast, rootptake was excluded for the revision work because its influenceas not significant during a rainfall event compared to the com-lexity of modeling its process. Meanwhile, root reinforcement andree surcharge were thought to be the most significant mechanicalactors, which were modeled as terms of slope stability model thatalculates the factor of safety (FS).

.1.1. TRIGRSTRIGRS (Transient Rainfall Infiltration and Grid-Based Regional

lope-Stability Model), developed by the U.S. Geological SurveyUSGS), is designed to model the timing and distribution of shal-ow rainfall-induced landslides (Baum et al., 2002). This physicallyased model calculates the changing pore water pressures duringainfall. TRIGRS consists of an infiltration model that has a gov-rning equation based on the linearized solution of the Richardsquation (Iverson, 2000; Baum et al., 2002) and an infinite equi-ibrium slope stability model. It is widely used to assess shallowandslide susceptibility in various regions (e.g., Baum et al., 2005;alciarini et al., 2006; Sorbino et al., 2010; Vieira et al., 2010). TRI-RS is thought to be suitable for assessing landslide susceptibilityn South Korea because the major type of landslide in this regions a shallow slip-type landslide that most often occurs during theummer season due to heavy rainfall. However, this model cannotonsider the tree effects with which the current study is concerned.hus, TRIGRS was selected as a base model in the current study.

.1.1.1. Hydrological model of TRIGRS. The hydrological modelf TRIGRS simulates two hydrologic processes: infiltration, andunoff. Conceptually, water can either infiltrate into the soil or runown to adjacent downslope cells in the TRIGRS simulation. Forach time step of one simulation, these processes occur instan-aneously from cell to cell, and rainfall eventually infiltrates intohe soil or exfiltrates from the spatial domain of the simulation atoundary cells during any given time step.On a cell simulated by TRIGRS, infiltration is determined by the

elationship of precipitation, runoff from upslope, and saturatedydraulic conductivity using the following Eq. (1):

={P + RuKs

P + Ru KsP + Ru > Ks

(1)

here I is infiltration at each cell, P is precipitation, Ru is runoff frompslope cells, and Ks is saturated hydraulic conductivity. Runoff todjacent downslope cell is calculated as the excess of infiltrationmmediately after determination of infiltration as follows,

d ={P + Ru I

0

P + Ru > IP + Ru = I

(2)

here Rd is runoff to downslope cells. In the process of runoff, theirection of the runoff depends on the elevation difference amongdjacent cells.

The infiltration model of TRIGRS has two forms depending on

ifferent basal boundary conditions: one for an infinitely deepasal boundary condition, and the other for an impermeable basaloundary condition at a finite depth. Considering the geological

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ondition of South Korea, the impermeable basal condition modelas applied in the current study. Eq. (3) represents the governingquation of the infiltration model with the impermeable boundaryondition where the first term represents the steady part, while theemaining terms represents the transient part. The pressure heads a function of depth is temporally calculated based on the ini-ial soil water conditions, and the additional infiltrated water. Thisnfiltration model can be applied to tension-saturated soil.

(Z, t) = (Z d) + 2Nn=1 InZKs H(t tn)[D1(t tn)]12

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ierf

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2[D1(t here is the pore water pressure as a head of water (m), Z isoil depth (m), t is time (s), d is the steady-state depth of the waterable measured in the vertical direction, = cos2 (IZLT/Ks), is thelope angle (), IZLT is the steady (initial) surface flux, Ks is the verti-al saturated hydraulic conductivity, N is the total number of timentervals, InZ is the surface flux of a given intensity for the nth timenterval, H(t tn) is the Heaviside step function, tn is the time at theth time interval in the rainfall infiltration sequence, D1 = D0/cos2,0 is the saturated hydraulic diffusivity, dLZ is the depth of thempermeable basal boundary measured in the Z-direction, and theunction ierfc is of the form

erfc() = 1

exp(2) erfc() (4)

here erfc() is the complementary error function.This infiltration model of TRIGRS is a one-dimensional infiltra-

ion model with the assumption of no lateral groundwater flown the soil. Therefore, TRIGRS is suitable for assessing the environ-ents where vertical gravitational flow is dominant, such as in thearly stage of a storm and/or on a steep hillslope. However, it is notuitable for assessing the environments where lateral groundwaterow is significant. Detailed descriptions of the hydrological modelf TRIGRS are provided in the TRIGRS manual (Baum et al., 2002).

.1.1.2. Slope stability model of TRIGRS. TRIGRS calculates FS asndex of slope stability on independent cells using a simple infi-ite slope stability model. In the analysis, FS is calculated as theatio of resisting stress to gravitational driving stress. A slope isredicted to be stable when FS 1, while a slope is predicted toe unstable (failed) when FS < 1. The equation of the slope stabilityodel follows:

S(Z, t) =c +

{sZ cos2 (Z, t)w

}tan

sZ sin cos (5)

here FS is the factor of safety, c is the soil effective cohesion (kPa),s is the soil unit weight (kN m3), is the slope angle (), is theore water pressure expressed as a head of water (m), w is theater unit weight (kN m3), and is the soil effective internal

riction angle ().

.1.2. Rainfall interception modelThe Rutter model (Rutter et al., 1971, 1975) was selected as a

ainfall interception model to estimate the amount of intercep-

ion loss and effective rainfall. The Rutter model is a conceptualodel that considers the tree canopy and stem as tanks with spe-ific water storage capacities. In the Rutter model, the rainfall inputs distributed to the canopy, trunk, and to free throughfall, and that

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ring 61P (2013) 658 668

m 1)dLZ (dLZ Z)

2[D1(t tn)]12

+ ierfc

(2m 1)dLZ + (dLZ Z)

2[D1(t tn)]12

LZ Z)

)]12

+ ierfc

(2m 1)dLZ + (dLZ Z)

2[D1(t tn+1)]12

(3)

hich is stored is then divided to throughfall, stemflow, and evap-ration. The canopy or stem is assumed to not generate any outputntil it has become fully saturated with input rainfall. In this study,he original Rutter model was revised to consider simplified calcu-ations and weather conditions during heavy storms that can causeandslides. No stemflow was assumed, meaning that the stem effectas not considered in the revised model, due to the stemflows

inor influence on the total interception loss during heavy rainfallvents. In addition, the canopys evaporation rate was assumed toave a maximum potential evaporation rate because this rate isetermined by the canopy saturation in the Rutter model, and theanopy would become fully saturated very rapidly during a heavytorm. The storage equation in the Rutter model, which is expressedn an exponential form, was also revised to focus on the drippingrocess during storms (Calder, 1977; Kim, 1993):

dC

dt= bC Q (6)

here C is the canopy water storage (mm), t is time (min), b ishe drip coefficient (min1), and Q is the rainfall entering into theanopy (mm min1).

.1.3. Revised infinite slope stability modelThe original slope stability model of TRIGRS is a simple infinite

lope stability model that does not include any term to account theffects of trees on slope stability. Thus, we introduced the infinitelope model including terms for tree root reinforcement and treeurcharge from Hammond et al. (1992). The equation is expresseduch as Eq. (7),

S = cr + c + {mt + (Z Zw) + (sat w)Zw} cos2 tan

{mt + (Z Zw) + satZw} sin cos (7)

here cr is the root reinforcement (kPa), mt is the tree surchargekPa), is the moist soil unit weight (kN m3), Zw is the saturatedoil depth (m), and sat is the saturated soil unit weight (kN m3).his equation assumes the steady groundwater flows by the termw, so Eq. (7) was modified such as Eq. (8) to integrate it with theransient infiltration model of TRIGRS.

S(Z, t) = cr + c + {mt cos2 + sZ cos2 (Z, t)w} tan

(mt + sZ) sin cos (8)

.1.4. The revised model-integration of TRIGRS and tree effectodelsFig. 1 shows the composition and data process flow of the

evised model. In the revised model, the infiltration model of TRI-RS was integrated with the rainfall interception model, and thelope stability model of TRIGRS was replaced with the revised slopetability model that considers root reinforcement and tree sur-

harge. The process of evaluating landslide susceptibility with theevised model consists of three steps: effective rainfall estimation,ore water pressure calculation, and FS calculation. First, the rain-all interception model estimates interception loss and effective

D. Kim et al. / Ecological Engineering 61P (2013) 658 668 661

RainfallData

InfiltrationModel

Slope StabilityModel

Factor of Saf ety(FS)

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IGR

SPore Water Pressure

TopographicalProperties

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RainfallData

Topog raphicalProperties

ForestProperti es

InterceptionModel

InfiltrationModel

Root Reinforc ement

Slope Stabil ityModel Tree

Surcharge

Factor of Saf ety(FS)

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: Revised Mod el(Tree E ffe ct)

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EffectiveRainfall

Pore Water Press ure

SoilProperties

(b) The revised TRIGRS

of TRI

rfTtt

p

Fig. 1. Conceptual diagram

ainfall, which is the net amount of rainfall that reaches soil sur-

ace, using data of hourly rainfall and forest properties as input.his model calculates the hourly effective rainfall and then inputshe effective rainfall data into the infiltration model. This infiltra-ion model calculates the corresponding changes in pore water

elwh

GRS and the revised model.

ressure and water table depth in the series using the estimated

ffective rainfall. Finally, the revised slope stability model calcu-ates FS based on these calculated pore water pressure values, alongith root reinforcement and tree surcharge, over every simulatedour.

662 D. Kim et al. / Ecological Enginee

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Fig. 2. Location, elevation, and soil depth of the Bonghwa site.

.2. Study site

A small watershed of approximately 14,000 m2 in Bonghwa,outh Korea, was selected as a study site. Referred to as theBonghwa site, this area was damaged by shallow landslides that

ere triggered by an extreme storm on July 2425, 2008. The stormaused six shallow landslides covering an average area of 233 m2

nd had an average depth of less than 1 m. The Bonghwa site isocated in the central-eastern part of the Korean peninsula (Fig. 2).

irrr

ring 61P (2013) 658 668

t has a temperate monsoonal climate with an annual mean tem-erature of 9.9 C and an annual mean precipitation of 1217.9 mmeasured over past 30 years (19812010). However, rainfall dur-

ng summer (June, July, and August) is approximately 60% of theotal precipitation. The Bonghwa site is situated on a steep val-ey with a maximum slope angle of 45 and an elevation rangingrom 593 to 722 m.a.s.l. The forest type on this land is an artifi-ially replanted forest, and the predominant species is 20-year-oldorean pine (Pinus koraiensis). The forest is well developed and fullytocked and has a high density of canopy. The soil type is sandyoam, meaning that it has a very good drainage rate. The bedrockn the Bonghwa site is composed of metamorphosed sedimentaryock and schist.

.3. Storm event July 2425, 2008

The storm event that caused landslides on the Bonghwa siteroduced a total of 228 mm of rainfall over 18 h between 20:00 onuly 24 and 13:00 on July 25, 2008. Antecedent rainfall of approxi-ately 100 mm fell on the Bonghwa site on July 19, 2008, five daysefore the main storm. The hourly average intensity of this rainfallas 12.7 mm h1, and the highest intensity was 38 mm h1. Fig. 3hows a hyetograph of the storm event in July 2008 at the Bonghwaite, which was produced from hourly rainfall data collected at andjacent Korea Meteorological Administration weather station.

.4. Data acquisition and model parameterization

The topographical data for the TRIGRS and the revised mod-ls were generated from 1:5000-scaled digital terrain maps. A

m 5 m-sized Digital Elevation Model (DEM), flow direction map,nd slope angle map for the Bonghwa site were constructed usingrcGIS 9.3 (ESRI, Inc.). In addition, a soil depth map was preparedsing spline interpolation with 109 point samples of the soil depthver the entire study site. These soil depths were measured usingn situ manual soil penetration tests, and measured values of soilepth ranged up to 2.7 m over the watershed area, with an averageepth of 0.9 m.The mechanical properties of the soil were estimated based on

he soil type, topography, and vegetational characteristics of theonghwa site to consider the uncertainty of the measured soilroperty data. Thus, the total unit weight, cohesion, and inter-al friction angle of the soil were set to 14.71 kN m3, 5.2 kPa,nd 34, respectively. Soil hydraulic conductivity was measuredo 4.52 105 m s1 using Guelph permeameter (Eijkelkamp, Inc.).

The parameters for the rainfall interception model were basedn digital forest stock maps and empirical data from the 26-year-ld pine (Pinus rigitaeda) forest (Kim, 1993). In particular, canopytorage (S), the fraction of free throughfall (p), and the drip coeffi-ient (b) were set to 1.49 mm, 0.268, and 0.04 min1, respectively.he potential evaporation rate (Ep) was assumed to be 0.3 mm h1

ver the entire simulated period.Lacking the baseline information on the distribution of trees

nd tree roots to measure root reinforcement prior to landslidesnitiation, estimate was therefore adopted from previous studies.n general, root reinforcement of pine trees ranges from 0.4 kPao 21.8 kPa depending on species, root density, soil propertiesnd assessment methods (Gray and Megahan, 1981; Waldron andakessian, 1981; Ziemer, 1981; Waldron et al., 1983; Gray, 1995;ampbell and Hawkins, 2003; Van Beek et al., 2005). Ziemer (1981)lso reported 3.021.0 kPa for lodgepole pine (Pinus contorta) using

n situ direct shear test which could reflect spatial variability ofoot reinforcement well. In this study, 3.0 kPa was adopted as theoot reinforcement to represent the typical tendency that rooteinforcement decreases with depth. It was supported by the

D. Kim et al. / Ecological Engineering 61P (2013) 658 668 663

g rain

ol

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3

3

surfro

Fig. 3. Hyetograph of the landslide-occurrin

bservation of the shallow rooting pattern of trees around theandslide scars of the Bonghwa site.

Tree surcharge was assumed to be 2.94 kPa due to the domi-ant tree species, forest age, and forest density. The trees on theonghwa site were very densely planted, which is why the treeurcharge was set to such a high value.

In terms of the initial setting of the TRIGRS simulations, theoilwater condition when the landslides began cannot be esti-ated exactly because no monitoring system was employed on the

ite when the landslide occurred. In the current study, the soil inhe Bonghwa site was assumed to be tension-saturated for appli-ation of the infiltration model of TRIGRS. Therefore, no detailednsaturated soilwater characteristics, e.g., soilwater character-stic curve, were required for the simulation. This assumptionppears to have little impact when assessing the contributions of

rees on landslide. In addition, the groundwater table was assumedo lie on the boundary between soil and bedrock because ground-ater commonly lies in very deep soil around the mountain tops ofouth Korea, and soil depth is shallow over the Bonghwa site. Using

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10

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20

25

30

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Rai

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ensi

ty (m

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T

Observed RainfallSimulated Effec tive Rainfall

July 24, 2008 July 25

Fig. 4. Temporal distribution of observed rainfall and simulatfall storm on July 24, 2008, in Bonghwa site.

revious research showing a comparison with the measured valuef saturated hydraulic conductivity (e.g., Godt et al., 2008; Liu andu, 2008), the initial infiltration rate was set to 4.52 109 m s1.

. Results

.1. Effective rainfall results

Fig. 4 shows hyetographs of the total observed rainfall and theimulated effective rainfall. The temporal distribution of the sim-lated effective rainfall was different from that of the observedainfall. The effective rainfall was smaller than the observed rain-all at each time step when the observed rainfall intensity waselatively high, while the effective rainfall was greater than thebserved rainfall when the latter was relatively low. However,

he total amount of intercepted rainfall was simulated to be only.5 mm over the entire period (Fig. 5). These results indicated thathe interception loss made up only a small portion of the totalbserved rainfall during an extreme rainfall event that caused

5 6 7 8 9 10 11 12 13

ime (h)

ed effective rainfall by the rainfall interception model.

664 D. Kim et al. / Ecological Engineering 61P (2013) 658 668

0

50

100

150

200

250

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Cum

ulat

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ount

(mm

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psed

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Fig. 5. Accumulated amount of observed rainfall and

andslides. However, rainfall interception could influence whenandslides are initiated through changes in the temporal distribu-ion of the rainfall.

.2. TRIGRS and the revised model simulation results

In Fig. 6, a series of pore water pressure simulated by TRIGRSnd the revised model at the depth of the lowest FS is shown overime. The results of both models show the same trend of negativeore water pressures (suction) increasing up to positive pressuresver time. However, there are no significant differences betweenhe two results. At the end of the simulation (elapsed time = 18 h),he average value of the pore water pressure of the study site wasalculated to be 0.6293 m using TRIGRS and 0.6267 m using theevised model. These results indicate that the tree rainfall intercep-ion had little influence on infiltration because other parametershat could affect the pore water pressure were consistent for theoth simulations.Fig. 7 shows a series of FS simulated by the TRIGRS and revised

odels over time. For both models, the results show that FS con-inuously decreased over the entire area during the simulated timeeriod. However, FS seemed to approach its minimum value only

h after the rainfall started. It was thought that the high rainfallntensity around that time period was enough to maximize theailure potentials in each cell, as shown in Fig. 3. In addition, Fig. 7raphically indicates that the revised model generated higher FSalues than did the TRIGRS model. Meanwhile, the revised modelaptured locations in which landslides occurred relatively well,hile TRIGRS evaluated too low slope stabilities, i.e., FSs over widereas. It is known that TRIGRS tends to overestimate landslide sus-eptibility in a region (Tsai and Chiang, 2012). Fig. 8 shows theumulative distributions of FS simulated by TRIGRS and the revisedodel at t = 0 and t = 18. In particular, from a total of 552 simulatedells the revised model simulated 66 cells (12.0%) which had FS val-es of less than 1.0 while TRIGRS simulated 146 cells (26.4%). Theverage simulated FS value was 2.061, and the minimum value was.7490 in the revised model. On the other hand, the average valueas 1.987, and the minimum value was 0.6176 in TRIGRS.

. Discussion and conclusionsIn terms of hydrological circulation, rainfall interception ishought to be a component of evaporation because rainfall that isemporarily captured by the canopy eventually evaporates or dripsrom the canopy in the form of throughfall or stemflow, and the

Tccn

Ti me (h)

ated effective rainfall by the canopy intercept model.

ifference between the total and effective rainfall is the quantityf the total rainfall that has evaporated in the end. Over a long-erm time span, evaporation can be a critical factor for reducing theisk of shallow landslide incidents. However, the influence of evap-ration on landslide development is thought to be minor duringxtreme storms with short durations because the weather condi-ions during such extreme storms are very humid. Therefore, thevaporation rate is low, and there is not enough time for inter-eption loss to reach a critical level that would affect landslideevelopment. In fact, more than half of the annual total inter-eption loss occurs after rainfall events finishes (Reid and Lewis,007). Therefore, the rainfall interception of 4.5 mm simulated byhe model in this study was a low but appropriate value, although

very high evaporation of 0.3 mm h1 was assumed for the rainfallnterception model. Meanwhile, the rainfall interception can affecthe temporal distribution of effective rainfall through delays duringanopy saturation, as shown in Fig. 4. Although the Rutter models a conceptual model that cannot perfectly simulate the naturalrocess of rainfall interception, the results it produces are reason-ble to a certain extent when considering the high water storageapacity of a tree canopy or stem.

In light of the mechanical effects of trees on landslide develop-ent, uniform values for the root reinforcement the tree surchargeere applied to the simulations assuming the study site had homo-eneous forest properties. These values were spatially averagedalues, but, in reality, tree surcharge acts as a point load that isot uniformly distributed, and root reinforcement decreases withepth. Because the mechanical effects can spatially vary depend-ng on the position of a single tree, their relevant values shoulde selected carefully. In the current study, the simulation resultshowed that the mechanical factors substantially affected slopetability, and the results simulated by the revised model capturedandslide-damaged areas well compared to the results given by TRI-RS despite the application of estimates, but not measurements.n general, parametric analysis is a good option to identify theechanical effects of trees in a regional scale lacking detailed infor-ation of distribution and growth of trees.In terms of the magnitude of the effects that controlling fac-

ors have on FS, the magnitude of pore water pressure is definitelyreater than that of tree effects, as shown Fig. 8. However, Fig. 8 alsohows trees contribute to slope stability positively until FS = 2.0.

his implies that aside from pore water pressure, trees also haveonsiderable influence on shallow landslide initiation, although theritical point that positive tree contribution is converted into theegative one depends on the relationship between a ratio of tree

D. Kim et al. / Ecological Engineering 61P (2013) 658 668 665

west

rt

B

Fig. 6. Results of pore water pressure at the depth of the looot reinforcement and surcharge, and other geographical proper-ies, especially slope angle.

Overall, the results of pore water pressure and FS in theonghwa site indicates that the effects of trees to shallow landslide

dtas

FS simulated by TRIGRS and the revised model with time.evelopment during a severe storm can be largely attributed toheir mechanical functions, but not their hydrological function. Themount of the rainfall interception was not substantial due to thehort duration time, and the effect of redistributed effective rainfall

666 D. Kim et al. / Ecological Engineering 61P (2013) 658 668

RIGRS

weii

Fig. 7. Results of FS simulated by Tas also not substantial due to the high rainfall intensity. How-ver, the hydrological effects of trees, e.g., evaporation by rainfallnterception and transpiration by root uptake, can be more signif-cant than the mechanical effects in places where its hydrological

ciet

and the revised model with time.haracteristics often fluctuate, during a long-term time span. Fornstance, Simon and Collison (2002) found that the hydrologicalffects of trees seasonally enhanced slope stability two-fold morehan did the mechanical effects in a streambank. However, it was

D. Kim et al. / Ecological Enginee

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40

60

80

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0 1 2 3 4 5 6 7 8

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ive

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a (%

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Factor of S afety (FS)

TRIGRS, t=0The revised, t=0TRIGRS, t=18The rev ise d, t=18

F

aaetca

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G

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G

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I

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ig. 8. Cumulative distribution of FS simulated by TRIGRS and the revised model.

lso found that the magnitude of the hydrological effects fluctu-ted temporally during the season, while that of the mechanicalffects remained essentially constant. Therefore, it is very impor-ant to understand that the contribution of trees to slope stabilityan be estimated in different ways, depending on the time scale ofnalysis.Moreover, the mechanical effects of trees on landslides appear

o vary depending on the method of evaluation. In the currenttudy, the revised slope stability model could have two potentialrawbacks if it will be used to assess landslide susceptibility inegional scale. First, the infinite slope stability model can apply tonly shallow planar landslides, but not for deep-seated landslides,r circularly failed landslides. Second, the infinite slope stabil-ty model cannot consider lateral root reinforcement although itpparently affects shallow landslide initiation. These limitationsan be attributed to the rigid characteristics of TRIGRS being a one-imensional and cell-based approach. Therefore, it is needed topecify conditions of target landslides to analyze.

In conclusion, trees have significant mechanical effects on shal-ow landslide development in steep, forested watersheds during aevere storm event. The revised model offers better assessmentor shallow landslide susceptibility of mountainous watersheds.lthough measured data for some simulation parameters wereacking and a quantitative analysis of tree effects was not com-letely performed, we tried to model and analyze the tree effectssing a physically based approach, in terms of the case of theandslide-occurred site. Further study to quantify the impacts ofrees on shallow landslides under various rainfall and topographi-al conditions was also regarded important to improve the model.

cknowledgements

This research was supported by the Korea Forest Research Insti-ute. The authors would like to thank the graduate students, Eunai Lee, Byungkyu Ahn, and Dixon T. Gevana in Forest Engineeringaboratory, Seoul National University, Republic of Korea for theirfforts in data collection.

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