landslide hazard assessment of the 2008 wenchuan...

Landslide hazard assessment of the 2008Wenchuan earthquake: a case study in Beichuanarea

Chuan Tang, Jing Zhu, and Xin Qi

Abstract: The Wenchuan earthquake (magnitude Ms = 8.0) of 12 May 2008 triggered widespread and large-scale land-slides over an area of about 50 000 km2. A study was undertaken to determine the primary factors associated with seismiclandslide occurrence. An index-based approach used to assess earthquake-triggered landslide hazard in the central part ofthe Wenchuan earthquake area affected is described. Slope gradient, relief amplitude, lithology, bedding–slope relations,fault proximity, stream proximity, and antecedent rainfall are recognized as factors that may have had an important influ-ence on landslide occurrence. The assessment of the influence of each of these factors is presented through use of a seriesof maps showing areas of low, moderate, high, and very high landslide hazard. Areas identified as having ‘‘very high andhigh landslide hazard’’ were located along the earthquake-source fault and along both banks of the Jian River. The role ofrainfall is very significant for future landslide occurrence in the earthquake area. The results of this study will assist deci-sion makers in the selection of safe sites during the reconstruction process. The maps can also be used for landslide riskmanagement in the study area.

Key words: landslides, hazard mapping, index-based method, geographical information system (GIS), Wenchuan earth-quake, Beichuan area.

Resume : Le seisme du 12 mai 2008 a Wenchuan (magnitude Ms = 8,0) a provoque d’importants glissements de terrainsur une superficie d’environ 50 000 km2. Une etude a ete entreprise afin de determiner les facteurs primaires associes auxglissements de terrain causes par les seismes. Cet article decrit une approche basee sur des indices utilisee pour evaluer ledanger de glissements de terrain engendres par les seismes dans la partie centrale de la zone du tremblement de terre deWenchuan. Des facteurs qui sont identifies comme ayant une influence importante sur les glissements de terrain sont : legradient de pente, l’amplitude du relief, la lithologie, les relations entre le sol et la pente, la proximite d’une faille, laproximite d’un ruisseau et les precipitations anterieures. L’evaluation de l’influence de chacun de des facteurs est presenteea l’aide d’une serie de cartes montrant les zones de danger de glissement de terrain a un niveau bas, modere, eleve et treseleve. Les zones identifiees comme ayant un niveau eleve et tres eleve de danger de glissement de terrain sont situees lelong de la faille qui est la source des seismes et le long des deux cotes de la riviere Jian. Le role des precipitations esttres important pour les evenements futurs de glissement de terrain dans la zone du seisme. Les resultats de cette etudevont permettre d’aider les decideurs dans la selection de sites securitaires lors de la reconstruction. Les cartes peuventaussi etre utilisees pour la gestion des risques de glissement de terrain dans la zone etudiee.

Mots-cles : glissements de terrain, cartographie des risques, methode basee sur des indices, systeme d’information geogra-phique (SIG), seisme de Wenchuan, zone de Beichuan.

[Traduit par la Redaction]

Introduction

At 2:28 p.m. on 12 May 2008, an earthquake with a sur-face wave magnitude (Ms) of 8.0, and epicenter location at318N and 103.48E, occurred near Yingxiu town in Wen-chuan County. The earthquake source was located at a depthof 12 km. The entire Sichuan province in southwesternChina was severely affected. The earthquake was the mostdestructive in China’s recent history and led to 69 197 fatal-

ities, 18 341 persons missing, 374 176 persons injured, 6.5million houses destroyed, and 5 million persons left home-less. The earthquake triggered more than 15 000 landslidesof various types in steep mountainous terrain covering anarea of about 50 000 km2. The landslides caused more than20 000 fatalities (i.e., one-quarter of the total of fatalities andpersons missing on account of the earthquake were relatedto landslides; Yin et al. 2009).

The direct economic damage was estimated to be US$170billion, while the long-term impact is likely considerablyhigher. Thousands of strong aftershocks occurred for severalmonths after the earthquake. The aftershocks extended fromthe major earthquake source in a northeastern directionalong the Longmen Shan fault zone into the Gansu andShanxi provinces for a length of more than 300 km. Thelargest aftershock had a magnitude (Ms) of 6.4 and occurredin the Qingchuan area (Dong et al. 2008).

Received 27 April 2009. Accepted 21 July 2010. Published onthe NRC Research Press Web site at cgj.nrc.ca on 17 December2010.

C. Tang,1 J. Zhu, and X. Qi. State Key Laboratory of Geo-Hazard Prevention, Chengdu University of Technology,Chengdu 610059, China.

1Corresponding author (e-mail: [email protected]).

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Can. Geotech. J. 48: 128–145 (2011) doi:10.1139/T10-059 Published by NRC Research Press

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The earthquake-triggered landslides also produced exten-sive damage to housing settlements and irrigation channels.In addition, highways and bridges were blocked and (or) de-stroyed, and the city of Wenchuan and many other townsbecame isolated. These occurrences greatly frustrated therescue and relief efforts. Landslide dams generated 34 largebarrier lakes, threatening the residents who lived down-stream of these dams.

After the Wenchuan earthquake, research on landslide dis-tribution and characteristics was carried out by several au-thors. Wang et al. (2009) presented preliminary investigationresults of some large landslides triggered by the earthquakeand discussed the influences of seismic, topographic, geo-logic, and hydrogeologic conditions for seismic landslide oc-currence. Yin et al. (2009) analyzed the earthquake-inducedlandslide distribution and the mechanism of some typicallandslides, and evaluated the potential hazards caused bysome of the landslide dams. Huang and Li (2009) mapped11 308 landslides in 16 seriously damaged counties using10 m resolution ALOS imagery and aerial photography asso-ciated with a field investigation and noted that most signifi-cant landslides were located on the hanging-wall side of themain fault, although some occurred in deeply incised rivergorges further away from the main rupture zone. Sato andHarp (2009) carried out a preliminary study on landslides in-terpretation by using pre- and post-earthquake FORMOSAT-2imagery and Google Earth. The 257 large landslides wereidentified and their locations were plotted in the Beichuanarea. Ouimet (2009) used satellite images and aerial pho-tography taken in the months following the earthquake tomap the extent and density of landsliding associated withthe main earthquake and aftershocks and noted that thereis a good correlation between the magnitude and distribu-tion of ground shaking experienced during the earthquakeand the mapped landslide density. Chigira et al. (2010)also used ALOS imageries to interpret the distribution ofseismic landslides in the affected areas and showed thatlandslides were concentrated on the hanging wall of theLongmen Shan fault zone and in the valley of the Min-jiang River. Cui et al. (2009) identified 257 landslidedams in the earthquake-hit region and carried out a pre-liminary risk evaluation of some key landslide-dammedlakes. Xu et al. (2009a) presented a statistical analysis ofthe distribution, classification, characteristics, and hazardevaluation of 32 main landslide dams induced by the earth-quake.

An emergency field investigation and remote sensing im-agery interpretation study was conducted by the Sichuan De-partment of Land and Resources (SDLR) and the State KeyLaboratory of Geohazard Prevention (SKLGP) for the Bei-chuan County immediately after the earthquake. The datawere collected with the objective of producing a set of land-slide hazard maps for the entire Wenchuan earthquake af-fected area. The hazard maps were developed at the requestof the national government of China and the local author-ities. A pilot study area was selected in the area of BeichuanCounty. The results were then applied to the entire Wen-chuan earthquake area. The intent was that the hazard mapswould help future decision makers in the selection of safesites for housing of refugees and in the site planning process

for future reconstruction. The maps will also be used as abasis for landslide risk management in the study area.

Description of the study areaThe Wenchuan earthquake occurred on the easternmost

margin of the Tibetan Plateau, along a series of predomi-nantly north–northeast striking thrust faults that lie at thebase of the Longmen Shan Mountains on the northwesternedge of the Sichuan Basin (Fig. 1; Burchfiel et al. 1995).The Longmen Shan thrust belt comprises three main faults,the Yingxiu–Beichuan fault, Guanxian–Anxian fault andMao–Wen fault, of which the Yingxiu–Beichuan fault is in-ferred to be the main structure that generated the 2008earthquake (Li et al. 2008). The Wenchuan earthquake gen-erated a surface rupture extending for ~250 km along theYingxui–Beichuan fault system and for ~72 km on the Pen-gguan fault (Xu et al. 2009b). The field investigations im-mediately after the Wenchuan earthquake showed that theearthquake ruptured two northwest dipping imbricate thrustfaults along the Longmen Shan fault zone at the easternmargin of the Tibetan Plateau, and the maximum verticaland horizontal displacements are, respectively, 6.2 and4.9 m in the Beichuan area (Ouimet 2009). Moreover, itssurface rupture length is the longest for inland reverse fault-ing events ever reported among the coseismic surface rup-ture zones (Xu et al. 2009b). Therefore, this earthquake hasled to many landslides in this region. In the ground motionrecordings, there are over 560 components with peakground acceleration (PGA) values over 10 gal (1 gal =1 cm/s2), the largest being 957.7 gal, which is a very highlevel of ground shaking (Li et al. 2008). The maximum ac-celeration occurs on the mountain ridges, leading to land-slides that move debris into the valleys. Figure 1 alsoshows the distribution pattern of coseismic landslides in thewhole Wenchuan earthquake area, as described by Huangand Li (2009).

The pilot study area is selected in the central part of thearea affected by the Wenchuan earthquake in the BeichuanCounty in the province of Sichuan. The area is 160 kmnorth of Chengdu, between the eastern longitudes of103844’ and 104844’ and the northern latitudes of 31841’and 32814’ (see Fig. 2). The area covers 2865 km2 and hasapproximately 161 000 inhabitants. The Yingxiu–Beichuanfault cuts through this area. Cambrian sandstones and argil-laceous limestones, Silurian slates and phyllites, and Devon-ian and Carboniferous limestones are exposed in the studyarea. Loose Quaternary deposits are widely distributed inthe form of terraces and alluvial fans.

All bedrock are deeply fractured and highly weathered,and the soils developed on the bedrock are moderatelyweathered. Soils are developed predominantly over sand-stones, slates, and phyllites. Joints are also predominant incompetent lithologies, which, combined with active faultsand bedding, produce many potential failure surfaces on therock slopes. The geological structure in the study area showsa northeast–southwest orientation. The strike of the rockstrata shows a similar orientation. The Yinxiu–Beichuanfault, which triggered the Wenchuan earthquake, is locatedto the southeast of the study area. The fault has the form ofa northwest dipping thrust fault with dip angles of 608–708.

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Fig. 1. Map showing seismically triggered landslides and the peak ground acceleration (PGA) distribution in the Wenchuan earthquakeaffected area. The epicenter of the Wenchuan main shock is located in the Yingxiu area. The location of the study area is also indicated inthis map.

Fig. 2. Simplified geological map and coseismic landslide inventory map of the study area, Beichuan County, of the Wenchuan earthquakearea.

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The Cambrian sandstones form the hanging wall (i.e., up-thrown side) of the fault, while the footwall consists of Si-lurian and Devonian limestones (Fig. 2).

The Beichuan County is situated in the transitional moun-tainous belt between the Sichuan Basin and the WesternSichuan Plateau. The northwestern part of the area is charac-terized by high mountains with elevations of 1200–3100 m.The central part of the area is characterized by a mediumrelief landscape with rugged mountains with elevations be-tween 800 and 1800 m with deeply incised valleys. Thesoutheastern part of the area has low and medium reliefmountains with elevations between 700 and 1400 m. Thehighest mountain in the study area is Mount Chaqi, with anelevation of 4769 m, and is located in the northwestern partof the study area. The lowest point is the Jian River valleyin the southeastern corner of the area, with an elevation of540 m. The main river in the study area is the Jian River,which originates in the northwestern mountainous area. TheJian River flows towards the southeastern corner and finallyinto the Pei River. The Jian River is 47.9 km long and has adrainage area of 455 km2. The annual average discharge ofthe Jian River is 102 m3/s. The annual sediment dischargeprior to the earthquake is 4� 106 – 5� 106 t.

The study area is situated in the subtropical, humid mon-soon climate zone, with an annual average temperature of15.6 8C (Table 1). The study area belongs to the heavy rain-fall area of Lutou Mountain. The annual average precipita-tion is 1400 mm, with the highest recorded annualprecipitation of 2340 mm in 1967. The rainfall is largelyconcentrated in the period from June to September (Table 1).On average, 83% of the annual precipitation falls during thisperiod, while 90% of the precipitation fell in this period in1967. Figure 3 shows the annual precipitation in BeichuanCounty from 1971 to 2007. The graph shows a considerableirregularity in the annual precipitation. The Beichuan areashows a general decreasing precipitation trend from thesoutheast to the northwest.

Event-based landslide inventoryLandslide inventory mapping is an essential first step in

the analysis of landslide development in the study area. Thelandslide inventory maps provide information for the assess-ment of the influence of different terrain instability factors.The following procedure was applied to obtain a landslideinventory map of the study area.

(1) Field work — A landslide inventory map was developedat a 1:50 000 scale on the basis of intensive field workfrom 18 May 2008 to 30 June 2008 after the main earth-quake shock. Field observations were made in areas withthe highest density of landslides. Information was col-lected on the slope instability mechanism and the in-stability factors involved in each slopefailure. Anotherpurpose of the field work was to examine some potentiallandslides that are still precariously positioned on theslopes, which could not be identified by aerial photo-graphs.

(2) Interpretation of satellite images and aerial photo-graphs — In this study, the locations of the event-based in-dividual landslides were detected from the post-earthquakesatellite images and aerial photographs. The pre-earthquake

images included IRS-P5 images taken on 28 January2007. Post-earthquake images consisted of ALOS AV-NIR-2 (10 m resolution) and aerial photographs (0.5 m re-solution) data. The initial evaluation of ALOS data forlandslide interpretation indicated that simple standard falsecolour composites (SFCC) of AVNIR-2 image band 4(NIR) in red, band 3 (Red) in green, and band 2 (Green)in blue are the most effective and efficient imagery to ac-curately and reliably locate these landslides for regionalmapping. The reason is that landslides appear in lightcyan patches, which are very distinctive in a mainly redbackground on SFCC images. In this case, most coseismiclandslides were indentified using this imagery. The aerialphotographs taken on 18 May 2008 were enlarged to a1:5000 scale and provided supplementary interpretation oflandslide location points. The debris flows and new land-slides that were triggered by subsequent heavy rains on 24September 2008 were identified using SPOT 5 multispec-tral imagery taken on 14 October 2008.

(3) Digitizing of the landslide map — The resulting digitallandslide inventory map documented the landslide loca-tion points.

During the post-earthquake field investigations and the in-terpretation of images of the pilot study area (i.e.,2865 km2), a total of 1754 earthquake-triggered landslides(with a surface area greater than 1600 m2 or four pixels onan ALOS image) were identified (Fig. 2). The highest den-sity of landslides occurred within a zone that stretches alongthe Yingxiu–Beichuan fault and on both sides of the rivers.In some locations, multiple landslides appear to blend to-gether, forming landslide complexes, indicating that asmuch as 20%–40% of the hillslopes in these areas havebeen stripped by landsliding. Shallow landslides are domi-nant, but large deep-seated landslides were also developedin the study area; some of these larger landslides are stillhanging on the steep slopes after a partial displacement,while some other large landslides are separated completelyfrom their source areas, to form blockages in the rivers andtheir tributaries. The Wenchuan earthquake-triggered land-slides inventory in the pilot study area is shown in Fig. 2.

The general characteristics of the landslides in the pilotstudy area were similar to the landslides observed after largeearthquakes in mountainous regions in other parts of theworld (Lin et al. 2004; Sato et al. 2007; Owen et al. 2008).Observations in the field and the interpretations from satel-lite imagery and aerial photography indicate that the mostcommon types of landslides were the falls (including rockfalls, debris falls), slides (rock slides, debris slides, and earthslides), and flows (debris flows) according to the classifica-tion of Cruden and Varnes (1996). The dominant types ofcoseismic landslides are rock falls and rock slides, whereasearth slides were much less frequent. Most of the landslidesin the pilot study area occurred in highly fractured Silurianslates and phyllites, Cambrian sandstones, and Devonianlimestones, with the largest numbers of landslides occurringin areas dominated by slates and sandstones near the faulttraces.

Most landslides in the inventory have volumes between1000 and 10 000 m3. There are several thousand smallerlandslides that were not recorded. Eighteen large landslidesexceeded a volume of 1 million m3. Although the large

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deep-seated landslides triggered by the earthquake were lessnumerous than the shallow landslides, the large landslidescontributed significantly to the total volume of landslide ma-terial in the study area. The large landslides were muchmore destructive than the small landslides. Some slump fail-ures were developed on reactivated deep-seated landslides.

Most slope failures were confined to steep slopes associ-ated with ridges and along rivers. The reactivated slope fail-ures are characterized by numerous newly opened cracksand fissures that were formed along the main scarps. Threeof the largest landslides (the Tangjiashan, Wangjiayang, andNew Benchuan Middle School landslides) were truly cata-strophic. These landslides were responsible for most of thelives lost by landslides in Beichuan County (Figs. 4 and 5).

Tangjiashan landslideThe Tangjiashan landslide occurred 5.5 km upstream from

Beichuan city, along the Jian River. The landslide occurredon a dip slope of shale and slate and travelled about 600 mdownslope to the valley floor, forming a dam with a heightof 82–141 m across the valley of the Jian River, the largestlandslide dam in the affected area. The impounded lakereached a length of more than 20 km. The volume of thesliding materials was about 20.5 million m3, and the land-slide dam was 610 m in length across the river, and 800 min length along the river. The dam consists mainly of quater-nary deposits, colluvial soil, and broken rocks. Figure 4shows the landslide dam, barrier lake, and the failure surfaceassociated with the Tangjiashan landslide.

Wangjiayan landslideThe Wangjiayan landslide was a preexisting landslide

complex that was reactivated by the earthquake on the steepwestern valley slope in Beichuan city. This catastrophiclandslide destroyed more than 100 residential buildings and

claimed the lives of about 1600 people. The sliding massconsisted of about 7 million m3 of metamorphic rocks. Themain scarp was about 180 m high. Witnesses of the land-slide reported that they heard a loud noise about 10 minafter the main shock, and saw a heavy dust cloud producedby the landslide. This indicated that the movement of theslide was very rapid and that nearly all the landslide mate-rial came down shortly after the main shock. Air blasts re-sulting from the rapid failure and movement of thelandslide were observed in the ruined zone.

New Beichuan Middle School landslideThe New Beichuan Middle School landslide was mobi-

lized on the steep eastern valley side in Beichuan city. Thelandslide buried 906 people at the toe of the landslide. Thelandslide deposit is 610 m long, 380 m wide, and about 20–25 m thick. The volume of the landslide material is about 5million m3. The landslide probably began as a block slideand disintegrated into a debris avalanche that ran down themountain to form a cone-shaped deposit consisting of a cha-otic mixture of massive blocks of various sizes. Parts of aplanar basal sliding surface is still visible, dipping about508 downslope towards the city.

The Wenchuan earthquake has resulted in large numbersof earthquake-triggered landslide bodies that are now stillpresent on the slopes in the area. As well, a large numberof slopes have been observed that have been weakened anddestabilized by the earthquake but did not fail. For that rea-son, some extreme rainfall events after the earthquake haveinitiated excessive landslides and debris flows. This has cre-ated a secondary hazard category, with rainfall as trigger.The influence of rainfall intensity was investigated as partof the pilot study on the new landslides and debris flowsthat occurred during and after the 24 September 2008 rain-storm, an event with a 20 year return period.

Table 1. Average temperature and precipitation in the Beichuan County area from 1971 to 2007.

Jan. Feb. Mar. Apr. May June July Aug. Sep. Oct. Nov. Dec. YearTemperature (8C) 5.3 7.0 11.3 12.9 20.4 21.6 24.4 24.4 20.2 16.0 11.3 6.8 15.6Precipitation (mm) 5.9 11.4 22.8 52.6 97.3 135.2 370.7 350.3 206.7 64.4 18.6 4.1 1399

Fig. 3. Mean annual precipitation in Beichuan County from 1971 to 2007.



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The rainfall event triggered 72 debris flows and 1401 newlandslides with areas greater than 400 m2, based on the in-terpretation of SPOT5 imagery. Those debris flows and newlandslides resulted in the death of 42 persons, serious dam-age of roads, and damage in the relocation areas for theearthquake-struck people (Tang et al. 2009). Figure 6 showsSPOT5 imagery with a 2.5 m resolution that is used for theinterpretation of rainfall-induced debris flow and the newlandslide distribution in the eastern part of the study area.

Methodology

General considerationsVarnes (1984) proposed the most widely adopted defini-

tion for landslide hazard as ‘‘the probability of occurrenceof a potentially damaging phenomenon (landslide) within agiven area and in a given period of time’’. Normally, a land-slide hazard assessment consists of two major aspects, e.g.,the spatial probability of landslide occurrence, which can be

Fig. 4. Aerial photograph taken on 18 May 2008, indicating the location of three major catastrophic landslides and barrier lakes in thesurrounding of the Beichuan city area.

Fig. 5. Photo shows Wangjiayang and New Benchuan Middle School landslides and debris flow inundated area induced by heavy rainfallafter the Wenchuan earthquake.

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conducted by zoning areas with different levels of hazardand temporal probability that is related to the magnitude ofthe return period of the triggering event and the occurrenceof landslides (Varnes 1984). In the literature, useful reviewsof landslide hazard assessment and mapping can be found(i.e., Brabb 1984; Varnes 1984; Van Westen 1993; Aleottiand Chowdhury 1999; Dai et al. 2002; Hadmoko et al.2010).

The assessment of landslide susceptibility and hazard at aregional scale has been attempted in the last few decades us-ing direct (geomorphological) and indirect (qualitative andquantitative) methods (Hansen 1984; Hutchinson 1995; So-eters and Van Westen 1996; Aleotti and Chowdhury 1999;Carrara et al. 1999). Both approaches are based on the prin-ciple that future landslides are likely to occur under thesame conditions that led to past slope instability.

Within a quantitative method, mechanical or statisticalmodels for slope failure are used to predict landslides (e.g.,Carrara 1983; Donati and Turrini 2002; Zhou et al. 2003).This approach is difficult to apply in a large area, becausemechanical parameters cannot be extrapolated on a regionalscale (Aleotti and Chowdhury 1999). In the qualitative (heu-ristic or index) method, actual landslides are evaluated withcharacteristics of geomorphology or geology (e.g., Stevenson1977; Anbalagan and Singh 1996; Ayalew et al. 2004).

This method is strongly dependent on the experience ofthe surveyors, but it is the only practicable approach forlandslides caused by different mechanisms (Ruff andCzurda 2008). The choice of the method to be applied islargely depending on both the desired accuracy of the out-come and the nature of the problem and should be compat-ible with the quality and quantity of the available data.Generally, for a large area where the quality and quantityof available data are too limited for quantitative analysis,a qualitative risk assessment may be more applicable;while for site-specific slopes that are amenable to conven-tional limit equilibrium analysis, a detailed quantitative riskassessment should be carried out (Hadmoko et al. 2010).

The method to determine the landslide hazard in thisstudy is qualitative, and includes heuristic or index-based(weighting of different thematic layers) approaches. It isvery flexible and permits a complete inclusion of expertknowledge. Heuristic or index approaches are suitable forqualitative and semiquantitative hazard assessment and canprovide reliable maps over larger areas with limited costs,provided they are carried out by (teams of) experts (VanWesten et al. 2006). In many countries, qualitative hazardassessment procedures based on index approaches havebeen implemented, for example, in New Zealand (Glasseyet al. 2003), Australia (Michael-Leiba et al. 2003), Austria

Fig. 6. SPOT5 imagery taken on 14 October 2008 shows debris flow initiation areas, drainage paths, and alluvial fans in the eastern studyarea.



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(Ruff and Czurda 2008), France (Flageollet 1989), USA(Wachal and Hudak 2000), and Indonesia (Hadmoko et al.2010).

It is a challenge for landslide hazard assessment to addthe temporal dimension to the susceptibility maps at a re-gional scale to produce real hazard maps (Van Westen et al.2006). However, it is still difficult to include the temporalprobability of landslide events in hazard maps owing to sev-eral factors: (i) absence of multi-temporal data of landslideevents in the hazardous area; (ii) heterogeneity of the sub-surface conditions; (iii) scarcity of input data; and (iv) ab-sence or insufficient length of historical records of thetriggering events (Terlien 1996; Van Westen et al. 2006;Kouli et al. 2010). Consequently, most of the published haz-ard maps have only presented the spatial aspect of landslidehazard and do not provide an estimate of ‘‘when’’ landslidesare likely to occur (Hadmoko et al. 2010). Because of theseproblems, a qualitative method representing only the‘‘spatial extension’’ of landslide hazard is applied in thestudy area.

As a supplementary consideration for the temporal dimen-sion of a seismic landslide susceptibility analysis, one trig-gering factor was also studied; namely, the rainfall. Afterthe Wenchuan earthquake, an abundance of loose landslidedebris was left on the slopes. This debris serves as sourcematerial for rainfall-induced debris flows or shallow land-slides. In addition, numerous extension cracks were causedon the hill slopes by the earthquake, and these can also leadto landslide activity during subsequent heavy rains. There-fore, extreme rainfall events with a 20 year return periodare considered to be a dynamic variable for landslide hazardassessment. An analysis of the relationship between the an-tecedent rainfall and the rainfall-induced debris flows aswell as the landslide frequency was performed. This rela-tionship allows to give an estimation on how often land-slides may occur in the study area. The proposed methodthat uses numerical ratings for various influencing factorsmakes it easily applicable in the other 40 counties of theWenchuan earthquake area.

Data sourcesMany environmental factors, such as geology, geomor-

phology, hydrology, and land use, have the potential to af-fect landsliding. A long list can be found in Soeters andVan Westen (1996). Only a few of them, however, can becost effectively acquired over large areas and be used inslope stability assessment on a regional scale. Others arefound to be relevant only in specific areas (Clerici et al.2002). The method of hazard assessment that we used re-quires us to consider a limited number of factors. On the ba-sis of our field experience and the data available, the sevenfactors that are more or less unanimously considered to havethe closest bearing on the slope failures in a seismic area areas follows: slope gradient, relief amplitude, lithology, bed-ding–slope relations, fault proximity, stream proximity, andrainfall. All factors were subdivided into 4–8 classes as partof the susceptibility and hazard analysis. The basic informa-tion used for this study included a 20 m� 20 m grid digitalelevation model (DEM), 1:50 000 geologic maps, and se-lected 1:5000 aerial photographs. The geological maps wereprovided by the Chinese Geological Survey (CGS). The aer-

ial photographs were taken by the Ministry of Land and Re-sources (MLR) after the Wenchuan earthquake event. Thefollowing ‘‘weights of evidence’’ factor maps were gener-ated from the available basic information:

� Map of bedrock geology and structure — The bedrockmap shows the main geological units in the area, whichwere digitized from the existing geological map at a scaleof 1:50 000. The structural geological map was preparedfrom the existing geological map information and addi-tional fieldwork. The earthquake-source faults were lo-cated from the geological map at a 1:50 000 scale, whichwas buffered at three intervals of 1000 m.

� Slope gradient map — The slope gradient map was gen-erated from the available ‘‘Digital Elevation Model’’(DEM). The DEM was prepared by interpolation betweendigitized contour lines from 15 1:50 000 topographic mapsheets, with a contour interval of 5 m. The slope gradientwas classified into four classes (i.e., <208, 208–308, 308–458,and >458) for the geographical information system(GIS) based analysis.

� Relief amplitude map — The relief amplitude map wasalso generated from the 20 m� 20 m DEM. It is essentialto get the most sensitive analyzing windows with an opti-mal size, which cannot only reflect the surface relief, butalso accurately expresses the relief amplitude related withthe specific landslide (Yin et al. 2010). Thus, differentrectangular cell sizes of 0.4, 0.8, 1.2, 1.5, and 1.8 kmwere selected. The relief amplitude in a rectangular sizeof 1.2 km reflects the characteristics of the landform inthe study area in the best way. The relief amplitude inthe study area ranges from 0 to 1490 m.

� Distance from streams map — The ‘‘distance fromstreams’’ map was developed by applying buffers (i.e.,<500, 500–1000, 1000–1500, and beyond 1500 m) to thestream network. The distance from streams informationwas classified into four categories. The hypothesis to bestudied was the relationship between landslide frequencyand the distance to undercutting streams.

� Rainfall distribution map — The rainfall distribution mapwas prepared from the rainfall database compiled by theSichuan Meteorological Station of China (SMS). Beforethe Wenchuan earthquake, there were only two meteoro-logical stations in the study area; namely, the Beichuanstation and the Leigu station. These stations were locatedclose to one another, but there were also seven other me-teorological stations around the study area. The hourlyrainfall data were used to produce rainfall intensity mapsfor the study area, and this provided a detailed ground-based record of rainfall. The selected rainfall data periodwas from May to September 2008. From 23 to 24 Sep-tember 2008, the Beichuan region experienced intenserainfall, which triggered regional debris flows and shal-low landslides.A geographical information system database containing

the different data layers was compiled. All maps were storedin raster format with a pixel size of 20 m� 20 m.

Conditional and triggering factorsThe occurrence of landslides triggered by earthquakes is a

function of direct and indirect natural and human factors.These factors include lithology, geologic structure, tectonics,

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geomorphology, topography, slope geometry, precipitation,terrain roughness land cover, and land use (Parise and Jibson2000; Garcıa-Rodrıguez et al. 2008; Kamp et al. 2008). Asimplified analytical approach to determine landslide sus-ceptibility requires the selection of a limited number of keyhazard factors. These factors are selected in accordance withsubjective expert opinion and depend on prior knowledge(Akgun and Bulut 2007; Conoscenti et al. 2008; Kamp etal. 2008) and on field observations, which contribute to anunderstanding of the earthquake-triggered slope failuremechanisms. Based on our field observations and on theavailable data, the following six conditional factors for land-slide susceptibility zonation were selected in this study:slope gradient, relief amplitude, lithology, bedding–slope re-lations, fault proximity, and stream proximity. The trigger-ing factors for landslide occurrence can be both heavyrainfall and earthquakes (Van Westen et al. 2006; Garcıa-Rodrıguez et al. 2008). Rainfall is considered to be themain triggering factor contributing to future slope failures.The rainfall distribution data available for the study areawere used for further hazard assessment.

All factors that may influence the landslide hazard wereentered into a GIS database and if necessary converted fromvector to raster maps. To analyze the influence of each fac-tor class with regard to landslide occurrence, a ‘‘suscepti-bility index’’ (SI) was calculated by comparing the numbersof landslides occurring in the area occupied by each factorclass. This index is similar to the landslide susceptibility in-dex (LSI) used by Sarkar et al. (1995). The index representsthe relative hazard of a landslide occurrence as attributed byeach factor class.

Slope gradientThe slope gradient exerts a significant influence on land-

slide hazard: all other factors being equal, steeper slopes aremore susceptible to failure than flatter slopes (Parise andJibson 2000). The generation of a digital representation ofthe slope gradient and the surface elevation forms an impor-tant step in the analysis (Garcıa-Rodrıguez et al. 2008;Kamp et al. 2008). We analyzed the influence of slope gra-dient on landslide hazard very simply by calculating slopeclasses and numbers of landslides in 108–158 bands of slopegradient. The study showed that the maximum frequency oflandslides occurred on slopes steeper than 308, while half ofthe landslides occurred when the slope gradients rangedfrom 308 to 458. Landslides occur clearly less frequently ongentle (08–208) terrain. The correlation of landslide occur-rence with slope gradient also shows the highest susceptibil-ity index (i.e., SI = 0.755) for slope angles greater 458.Figure 7 and Table 2 illustrate the relationship between sus-ceptibility index and the density of coseismic landslides.

Relief amplitudeThe relief amplitude is another very important factor de-

termining the seismic landslide occurrence (Sarkar et al.1995; Anbalagan and Singh 1996; Yin et al. 2010). The re-lief amplitude was obtained in differently sized moving win-dows using the Focalrange module of ArcGIS in the digitalelevation model. The relief amplitude in the study arearanges from 0 to 1492 m. The lowest value comes from theriver beds, and the maximum of about 1492 m is restricted

to the northwestern part of the area (Fig. 8). According tothe classification of relief amplitude in China (Tu and Liu1991), five classes with elevation intervals of <200, 200–400, 400–600, 600–800, and >800 m were used in the studyarea. Table 2 and Fig. 9 show that the number of landslideshas a normal relationship with the relief amplitude. For a re-lief amplitude of less than 200 m and larger than 800 m,there are 14 landslides (0.8% of the total number of land-slides) and 83 landslides (4.7%), respectively. Most of thelandslides occurred at the relief amplitude of 200–800 m,amounting to about 94.5% of the total landslides. The studyshowed that the maximum frequency of landslides occurredon relief amplitude of 400–600 m with the highest suscepti-bility index (i.e., SI = 0.827).

LithologyThe lithology is considered to be another important factor

in essentially all studies dealing with landslide susceptibilityassessment and hazard (Maharaj 1993; Lin et al. 2006; Mah-davifar et al. 2006; Kamp et al. 2008; and Kouli et al.2010). Use of rock mass rating or slope mass rating contain-ing a lithological factor for landslide hazard assessment isconsidered reasonable, but could not be applied in this re-search because of lack of sufficient data from the field andlaboratory. To determine the relative influence of bedrocklithology on landslide occurrences in larger areas, the sus-ceptibility index of each rock type for landslide occurrenceis calculated by comparing the density of landslides withinthe area occupied by each rock type. This method has beenwidely used for regional landslide susceptibility and hazardmapping (Aleotti and Chowdhury 1999; Carrara et al. 1999;Parise and Jibson 2000; Ardizzone et al. 2002; Kamp et al.2008; Kouli et al. 2010). The present study shows that mostof the landslides in the pilot study area occurred in highlyfractured slate, limestone, and sandstone, with the largestnumbers (40%) of landslides occurring in areas dominatedby slate. However, the susceptibility index for the sand-stone–shale and limestone materials was relatively high(Table 2).

Bedding–slope relationsDifferent landsliding densities are the result of different

angular relationships between bedding attitude, slope aspect,and slope angle. To reflect the impact of bedding–slope re-lations to the landslide occurrence, we applied a method de-scribed in detail by Clerici et al. (2002) and Fourniadis et al.(2007). If the bedding direction is opposed to the slope as-pect or the dip angle is less then 108, a stable condition canbe presumed. If the bedding dip direction coincides with theslope aspect, two relationships between the apparent dip an-gle and the slope angle can exist: when the apparent dip an-gle is less than the slope angle, the slope is in the worststability condition; when the apparent dip angle is greaterthan the slope angle, the slope has a relatively stable condi-tion. With any other relationship than the above-mentionedsituations, the condition is considered as stable. To achievethe expression of the above relationships, various GIS func-tions are applied to combine and define the angular relation-ship between bedding attitude, slope aspect, and slope angle.

The spatial orientation of bedding is characterized by thedip direction (in reference to geographic North) and the dip



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Fig. 7. Distribution of slope gradient in the study area.

Table 2. Relationship of coseismic landslide frequency to each factor and rating within the study area.

Factora CategoriesArea(km2)

Number oflandslides

Susceptibilityindex (SI) Rating

Slope gradient (X1) <20 386 186 0.482 120–30 766 414 0.540 230–45 1329 864 0.650 3>45 384 290 0.755 4

Relief amplitude (m) (X2) <200 97 14 0.144 1200–400 340 202 0.594 3400–600 1006 832 0.827 4600–800 1096 623 0.568 3>800 326 83 0.255 2

Lithology (X3) Argillite 314 28 0.089 1Phyllite 55 43 0.782 3Slate 1548 701 0.453 2Sandstone 549 329 0.599 2Limestone 379 613 1.617 4Sandstone–shale 20 40 2.000 4

Bedding–slope relations (X4) Bedding–aspect: opposite 972 458 0.471 1Bedding–aspect: normal 1004 566 0.564 2Bedding–aspect: same (slope < bedding) 779 581 0.746 3Bedding–aspect: same (slope > bedding) 110 149 1.355 4

Fault proximity (m) (X5) 0–1000 102 377 3.696 41000–2000 97 242 2.495 32000–3000 95 177 1.863 2>3000 2571 958 0.373 1

Stream proximity (m) (X6) <500 167 324 1.940 4500~1000 181 229 1.265 31000~1500 176 158 0.898 2>1500 2341 1043 0.446 1

aXj, rating for each factor class.

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angle (in reference to the horizontal). Slope aspects and an-gles were derived from the 1:50 000 DEM. Dip directionsand angles were taken from the 1:50 000 geological map,and then interpolated surfaces of dip directions and angleswere produced using ordinary kriging (Meentemeyer andMoody 2000). To determine the relationship between bed-ding dip angle and slope angle, the apparent dip angle canbe calculated from

tanf0 ¼ tanf� cosb

where f0 is the apparent dip angle, f is the measured bed-ding dip angle, and b is the angle between the slope direc-tion and the dip direction.

The datasets of the bedding direction and the slope aspectwere overlaid firstly, and their relationship was expressed in

the form of raster files to express three situations of oppo-site, normal, and same. Then, the bedding direction dippingin the same direction as the slope aspect was divided intotwo situations where (i) the apparent dip angle was lessthan the slope angle and (ii) the apparent dip angle wasgreater than the slope angle (Fig. 10). The correlation oflandslide occurrence with bedding–slope relations shows thehighest susceptibility index (i.e., SI = 1.355) for thebedding–aspect: same (slope > bedding), while bedding–aspect:opposite shows the lowest (i.e., SI = 0.471) (Table 2).

Fault proximityThe frequency of occurrence of coseismic landslides gen-

erally decreases as the distance from the earthquake-sourcefault increases (Sarkar et al. 1995; Gokceoglu and Aksoy1996; Pachauri et al. 1998). Other studies (Leroi 1996; Ein-stein 1997; Donati and Turrini 2002; Van Westen et al.2006) have used the distance to the faults to classify thehazard zones. Two earthquake fault systems are plotted onthe geological map at a scale of 1:50 000. These faults werebuffered at four distance intervals of 1000 m. The studyshowed that the susceptibility index of landslides increaseswith shorter distances from the fault lines (e.g., 3.696 in the1000 m zone, 2.495 in the 1000–2000 m zone, 1.863 in the2000–3000 m zone, and 0.373 beyond the 3000 m zone;Table 2). The bedrock is tectonically disturbed and thus lessstable in the zones closest to the faults.

Stream proximityNear river channels, slope failures are occurring more

often. This increased slope instability hazard appears to berelated to high ground water levels in the fragmented land-slide materials and colluvium present in the slope toes just

Fig. 8. Map of relief amplitude in the study area.

Fig. 9. Relationship between relief amplitude and number of land-slides.



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above the river banks. Therefore, the seismic landslide haz-ard level is closely related to the distance from the mainstreams. In the present study, the buffer zones for streamlines were set to one of four classes; namely, <500, 500–1000, 1000–1500, and >1500 m. The study showed that thesusceptibility index for landsliding increased with decreasingdistances from the streams (e.g., 1.940 in the <500 m zone,1.265 in the 500–1000 m zone, 0.898 in the 1000–1500 mzone, and –0.446 beyond the 1500 m zone; Table 2).

RainfallLandslides can be triggered both by heavy rains and by

earthquakes (Cruden and Varnes 1996; Carrara et al. 1999;Van Westen et al. 2006). In our study of post-earthquakehazards, only rainfall trigger was considered in the analysisof the new inventory of debris flows and landslides recordedafter the 2008 Wenchuan earthquake. This new inventorymap was combined in the GIS with the antecedent rainfallcontour map to produce a hazard map for rainfall-triggeredlandslides.

Weighting landslide susceptibilityThe Analytical Hierarchy Process (AHP) method is a

well-known multi-attribute weighting method for decisionmaking. It has been widely used to weigh landslide suscept-ibility and hazard (Yoshimatsu and Abe 2006; Kamp et al.2008; Prabu and Ramakrishnan 2009). Pairwise comparisonsare used in this decision-making process to form a reciprocalmatrix by transforming qualitative data to crisp ratios. Thereciprocal matrix is then solved by a weight finding methodto determine the criteria importance and alternative perform-ance. The AHP method is relatively precise and easy to use

(Saaty 1980). It compares all factors and assigns valuesagreed upon by experts. Then, a matrix is formed for multi-factor comparison and judgment. The AHP applies a one-level weighting system developed by a panel of experts, inthis case based on our experience obtained during the fieldwork.

The results of the pairwise comparison matrix and the re-sulting weighting factors are shown in Table 3. The weight-ing results for slope gradient, relief amplitude, lithology;bedding–slope relations, fault proximity, and stream proxim-ity are 0.15, 0.08, 0.08, 0.15, 0.27, and 0.27, respectively.The total of these weight values is 1.00. The results supportthe field observations and show that fault proximity andstream proximity are the most influential controlling factorsin earthquake-triggered landslide hazards in the Wenchuanearthquake region. Relief amplitude, fault proximity, andstream proximity were recognized as significant with regardto slope instability processes.

Assessment procedureA spatial modeling approach in a GIS with classical over-

lay operations was used to produce a landslide susceptibilitymap that represented the major landslide susceptibility fac-tors for earthquake-triggered landslides. This susceptibilitymap was combined with the rainfall indicator hazard map toproduce a hazard map for post-earthquake rainfall-triggeredlandsliding.

As a first step, each factor class was reclassified to gener-ate a separate susceptibility map for each of the four factorclasses. The separate susceptibility index (SI) calculation re-sults are shown in Table 2. The separate susceptibility index

Fig. 10. Map shows the bedding–slope relations in the study area. Slopes where bedding is opposite to slope aspect are relatively stable,while those with same-direction bedding and aspect, and slope angle greater than bedding angle, are toward greater instability.

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values had to be standardized and were converted to a valueof 1, 2, 3, or 4 to represent rating levels of low, moderate,high, and very high landslide susceptibility as shown inTable 2.

The second step was calculating the cumulative landslidesusceptibility value. The landslide susceptibility index (LSI)was calculated by the summation of the ratings of all sixfactors multiplied by the weight of each of the factors. TheLSI represents the relative susceptibility of a landslide oc-currence. Therefore, the higher the index, the more suscepti-ble the area is to landslide. The calculations are performedas shown in the following equation and the results areshown in Table 4:

LSI ¼X

ajXj

where LSI is the cumulative landslide susceptibility index, ajis the weight of each factor, and Xj is the rating of each fac-tor class.

After multiplying the corresponding factor weights withthe class ratings of the slope gradient (X1), relief amplitude(X2), lithology (X3), bedding–slope relations (X4), fault prox-imity (X5), and stream proximity (X6), the cumulative land-slide susceptibility index, LSI, was calculated by addition asfollows:

LSI ¼ 0:15X1 þ 0:08X2 þ 0:08X3

þ 0:15X4 þ 0:27X5 þ 0:27X6

The cumulative value represents the relative propensity ofthe terrain to landsliding in each pixel. It must be reclassi-fied into one of four intervals to define the cumulative sus-ceptibility level as very high, high, moderate, and low. Asusceptibility level classification based on the maximumand minimum total scores of all factors ratings was used inthis study (Wachal and Hudak 2000; Hadmoko et al. 2010).The accumulated LSI values were classified into four sus-ceptibility classes (i.e., low with a range from 6.00 to 10.5,moderate with a range from 10.6 to 15.0, moderate with arange from 15.1 to 19.5, and very high with a range from19.6 to 24.0).

The third step involves an analysis of the relationship be-tween the antecedent rainfall and the rainfall-induced debrisflows as well as new landslides. The hazard map that is usedas a rainfall indicator with a given return period can be pro-duced by an analysis of subsequent rainfall-induced debrisflow and landslide frequencies in each class of antecedentrainfall contours. The final hazard map is constructed byoverlaying the rainfall indicator hazard map with the cumu-

lative landslide susceptibility map. At last, the created land-slide hazard in the study area was reclassified into fourclasses of hazard; namely, low, moderate, high, and veryhigh.

The GIS processing module applied in this study used apixel size of 20 m� 20 m for each digital map layer. Thefinal mapping procedure was carried out in the ArcGIS spa-tial analysis module.

Results

Susceptibility maps for earthquake-triggered landslidesThe analysis of this study resulted in a very high susceptibility

area of 347 km2 (i.e., 12.1% of the total pilot study area), a highsusceptibility level area of 613 km2 (i.e., 21.4%), a moderatesusceptibility area of 953 km2 (i.e., 33.3%), and a low suscepti-bility area of 952 km2 (i.e., 33.2%) (Table 4 and Fig. 11).

The area in the northwestern part of the pilot study areahad a low to moderate landslide susceptibility. The south-eastern area can be classified as an area with very high orhigh landslide susceptibility. Very high and high landslidesusceptibility values are found in most areas that are in closeproximity to rivers and the earthquake-source faults.

From landslide susceptibility to hazard assessmentThe presented analysis has resulted in seismic landslide

susceptibility zonation maps that indicate the spatial likeli-hood of the occurrence of landslides triggered by earth-quakes. The zonation is based on the influence of a numberof conditional factors. However, to use these maps for land-slide hazard assessment, information on temporal probabilityshould also be included. To make an approximation of the‘‘time dimension’’ of the triggering factor, the rainfall eventwith a 20 year return period is considered to be an extrinsicfactor for landslide hazard assessment. A rainfall-induceddebris flow and new landslide inventory map was preparedfrom interpretation of SPOT imagery and was combinedwith the contour map for 2 days of antecedent rainfall, usingthe GIS platform to produce a hazard map for rainfall-trig-gered landslides. The correlation between the antecedentrainfall amount and the debris flow drainage area as well asthe new landslides inventory map shows that 72% of thenew debris flow drainage area and 88.5% of new landslideswere located on the zone with an antecedent rainfall of 240–280 mm. The results also showed that 18.5% of total debrisflow drainage areas and 9.7% of new landslides were in the200–240 mm antecedent rainfall zone and that the debrisflow drainage area and new landslides were much less de-veloped in 180–200 mm zone (Table 5 and Fig. 12).

Table 3. Comparative matrix of factors impacting landslide susceptibility.

Attributea LithologyReliefamplitude

Slopegradient

Bedding–sloperelations

Faultproximity

Streamproximity

Factorweights

Slope gradient (X1) 2 2 1 1 1/2 1/2 0.15Relief amplitude (X2) 1 1 1/2 1/2 1/3 1/3 0.08Lithology (X3) 1 1 1/2 1/2 1/3 1/3 0.08Bedding–slope relations (X4) 2 2 1 1 1/2 1/2 0.15Fault proximity (X5) 3 3 2 2 1 1 0.27Stream proximity (X6) 3 3 2 2 1 1 0.27

aXj, rating for each factor class.



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These data clearly demonstrate that there is a relationshipbetween antecedent rainfall and the occurrence of new land-slides as well as debris flows in the Beichuan area. The areacould be divided into four zones corresponding to very high,high, moderate, and low level rainfalls, representing antece-

dent rainfall amounts of 260–280, 240–260, 200–240, and<200 mm, respectively (Fig. 12).

The final hazard map was produced by overlaying thepixel map with accumulated LSI values, with the pixel mapshowing the rainfall indicator hazard. The study area was

Table 4. Results of landslide susceptibility and hazard assessment for the study area of the 2008 Wenchuan earthquake.

LSI classArea(km2)

Area(%)

Number ofcoseismiclandslides Hazard class

Area(km2)

Area(%)

Number ofcoseismiclandslides

Number ofrainfall-inducedlandslides

Very high 347 12.1 938 Very high 669 23.3 1271 923High 613 21.4 492 High 730 25.5 324 174Moderate 953 33.3 250 Moderate 531 18.6 93 260Low 952 33.2 74 Low 935 32.6 66 44

Fig. 11. Landslide susceptibility map obtained by applying the index-based approach.

Table 5. Correlation of antecedent rainfall contour with debris flow drainage area as well as subsequent rainfall-induced landslides and rating.

Rainfall-induced debris flows Rainfall-induced landslides

Antecedentrainfall (mm)

Area(km2)

Drainagearea (km2) Hazard index

Number oflandslides Hazard index Rating

<180 195 0.0 0.000 0 0.000 1180–200 524 49.0 0.094 25 0.048 2200–220 388 38.3 0.099 35 0.090 2220–240 411 57.0 0.139 101 0.246 3240–260 569 91.1 0.160 224 0.394 3260–280 778 280.0 0.360 1016 1.306 4

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Fig. 12. Antecedent rainfall contour map and the location of new landslides and debris flows induced by the extreme intensity rainfall on24 September 2008 following the Wenchuan earthquake.

Fig. 13. Resultant landslide hazard map of the 2008 Wenchuan earthquake affected Beichuan area.



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divided into zones of very high, high, moderate, and low ha-zard level (Fig. 13); 48.8% of the study area shows a veryhigh or high hazard for future slope instabilities, while theremaining 51.2% of the area has a low or moderate hazardfor future slope instabilities (Table 4).

There are three parts of the pilot study area where there isa very high or high landslide hazard. The very high and highlandslide hazard areas are situated in the southern part of thestudy area along the earthquake-source fault as well as alongboth banks of the Jian River and along the Beichao River,flowing from the north-central part of the area southwardsinto the Jian River. The area with antecedent rainfall contourof 260–280 mm is also classified as a very high landslidehazard area. Each of these areas has different combinationsof factors that lead to a relatively high landslide hazard.

ConclusionsIn an emergency field investigation and interpretation of

remote sensing images in the area of Beichuan County afterthe Wenchuan earthquake, 1754 landslides with area greaterthan 1600 m2 were identified within a area of 2865 km2. Thedominant types of coseismic landslides are rock falls androck slides, whereas earth slides were much less frequent.Some of these larger landslides are hanging on the steepslopes after their partial displacement, while some otherlarge landslides are separated completely from their sourceareas, to form blockages in the rivers and their tributaries.Most of the landslides in the pilot study area occurred inhighly fractured Silurian slates and phyllites, Cambrian sand-stones, and Devonian limestones, with the largest numbers oflandslides occurring in areas dominated by slates and lime-stones near the fault traces. Reactivated slope failures arecharacterized by numerous newly opened cracks and fissuresthat were formed along the main scarps and lateral scarps.

This study applied an index-based method for landslidesusceptibility and hazard mapping to the Wenchuan earth-quake area, which provided a rapid response based on an in-ventory of earthquake-triggered landslides followed by ananalysis. Slope gradient, relief amplitude, lithology, bed-ding–slope relations, fault proximity, stream proximity, andantecedent rainfall are recognized as factors that may havehad an important influence on landslides occurrence. The al-location of factor weighting values was assisted by the ana-lytical hierarchy process, which permits a quantitativeevaluation of each factor based on analyst expertise.

The method is suitable for semiquantitative hazard assess-ment and can provide reliable maps over larger areas andwith limited costs. However, because of the subjectivity onranking and weighting operation, which may change, de-pending on the knowledge of the expert, the index methoddoesn’t provide reproducible results. On the other hand, theindex method is simpler than statistical methods, and it maygive similar results to quantitative methods if there is an ex-pert(s) who knows the study region well. The final landslidehazard map was prepared after the combination of suscepti-bility values for the coseismic landslides and hazard valuesfor the subsequent rainfall-induced landslides and debrisflows. The assessment of hazard factors used in our studysuggests that the roles of rainfall are very significant for fu-ture landslide processes in the earthquake area. Stream prox-

imity and fault proximity are shown to be event-controllingfactors for coseismic landslide occurrence in the study area.This landslide hazard map appears to be a satisfactory pre-dictor of future potential landslide activity. Therefore, themap can be a useful tool for planners involved with recon-struction site selection and for infrastructure planning proc-esses. The maps may also be used as a basis for landsliderisk management in the study area.

AcknowledgementsThis work was supported by 973 Program (No.

2008CB425801), the National Foundation for Natural Scienceof China (No. 40772206), and Research Fund of the State KeyLaboratory of Geo-Hazard Prevention (SKLGP2009Z004).The authors wish to express their sincere thanks to Prof. NiekRengers for the comments and suggestions on earlier versionsof the manuscript. We also would like to thank the editor andthe two anonymous reviewers for their constructive commentsand useful suggestions, which significantly improved the qual-ity of this paper.

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