k aec

on

2007 Elsevier B.V. All rights reserved.

for Natural Disaster Reduction (IDNDR, 1999), the 1997). These remedial measures reflect greater hazardawareness by both the general public and decisionmakers, in part due to improved information but also

Available online at www.sciencedirect.com

Geomorphology 94 (2008)Keywords: Landslide-risk mapping; Vulnerability; Hazard; Losses; Financial valuation

1. Introduction

Concern about the consequences of geomorphichazards has been growing for the last couple of decades,from international to local levels. Among manyexamples of this concern are the International Decade

increasing presence of natural hazards in EuropeanResearch programmes (European Commission, 1999,2000), national analyses of the impact of natural hazardsand local programmes for geomorphic hazard assess-ment and reduction (Tams et al., 1986; Ayala-Carcedoet al., 1987; del Val et al., 1996; Daz de Tern et al.,Abstract

A quantitative procedure for mapping landslide risk is developed from considerations of hazard, vulnerability and valuation ofexposed elements. The approach based on former work by the authors, is applied in the Bajo Deba area (northern Spain) where adetailed study of landslide occurrence and damage in the recent past (last 50 years) was carried out. Analyses and mapping areimplemented in a Geographic Information System (GIS).

The method is based on a susceptibility model developed previously from statistical relationships between past landslides andterrain parameters related to instability. Extrapolations based on past landslide behaviour were used to calculate failure frequencyfor the next 50 years. A detailed inventory of direct damage due to landslides during the study period was carried out and the mainelements at risk in the area identified and mapped. Past direct (monetary) losses per type of element were estimated and expressedas an average specific loss for events of a given magnitude (corresponding to a specified scenario). Vulnerability was assessed bycomparing losses with the actual value of the elements affected and expressed as a fraction of that value (01).

From hazard, vulnerability and monetary value, risk was computed for each element considered. Direct risk maps (/pixel/year)were obtained and indirect losses from the disruption of economic activities due to landslides assessed. The final result is a risk mapand table combining all losses per pixel for a 50-year period. Total monetary value at risk for the Bajo Deba area in the next50 years is about 2.4106 Euros.DCITIMAC, Universidad de Cantabria, Santander, Spain

Received 14 April 2005; received in revised form 5 October 2005; accepted 13 October 2006Available online 23 June 2007Quantitative landslide rison the basis of r

Juan Remondo , Jaime B Corresponding author. Tel.: +34 942201509; fax: +34 942201402.E-mail address: remondoj@unican.es (J. Remondo).

0169-555X/$ - see front matter 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.geomorph.2006.10.041ssessment and mappingent occurrences

achea, Antonio Cendrero

496507www.elsevier.com/locate/geomorphsignificant increase in the number of disasters andresulting damage during the second half of the last

orphoJ. Remondo et al. / Geomcentury (EM-DAT, 2005). That increase can be observedfor specific geomorphic processes or local areas, as isthe case for landslides (Evans, 1997; Remondo et al.,2005b).

An analysis of data on world population growth,energy consumption, gross domestic product (GDP),number and extent of disaster events (Munich Re, 2001;Organisation for Economic Co-operation and Develop-ment-OECD, 2001) shows that efficiency of the socio-economic system, expressed as unit GDP output perperson or per energy unit consumed, has increasedsignificantly. However, our relation to natural disastersseems to be less efficient, as illustrated by the number ofevents and the total damage, which is roughly triple theincrease in world GDP. This increasing trend in thefrequency of events is also observed at the local level(Remondo et al., 2005b).

Fig. 1. Location of the Bajo Deba study area, m497logy 94 (2008) 496507These data show a clear need to improve ourunderstanding of geomorphic processes in general andslope instability in particular, to reduce future damage.Many efforts by geomorphologists in the last threedecades have developed slope hazard maps, with theexplicit or underlying aim of improving the managementof slope instability (Brabb et al., 1972; Carrara et al.,1978; Leroi, 1996; Pike et al., 2003). However,geomorphologic analyses have normally stopped at thehazard assessment stage. Fewer attempts have gonefurther, to the qualitative and/or quantitative assessmentof risk (Cendrero et al., 1987; Bernknopf et al., 1988;Carrara et al., 1991; Chacn et al., 1994; Meja-Navarroet al., 1994; Leone et al., 1996; Glade, 2003). Thiscontrast probably reflects the difficulties of obtaining dataon past losses and, even more so, assessing future losses,especially if not only direct damages are considered, but

ain lines of communication and buildings.

also indirect costs due to the disruption of economicactivity. If the ultimate aim of geomorphic hazard and riskassessment is a more efficient management of hazardousprocesses, procedures to forecast future losses must bedeveloped. Quantitative risk assessments will make itpossible to identify the areas where greater losses are to beexpected and, therefore, where mitigation efforts shouldbe first directed in order to achieve the best benefit/costratios.

This paper continues the development of a GIS(geographic information system)-based procedure toassess landslide risk quantitatively from quantitativehazard maps and considering both direct damage andindirect losses. The Bajo Deba study area in Guipzcoaprovince, northern Spain (Fig. 1), is intensely affected byslope movements, particularly shallow translational slidesand flows, triggered both directly by intense rainfall andindirectly by human activities (Remondo et al., 2005b).Remondo et al. (2003a,b) describe instability processesand characteristics of the area in detail. The analysispresented here has been carried out in a 140 km2 area,using a pixel size of 11 m.

The methodological approach is shown schematical-ly in Fig. 2. It is based on analysis of the temporal andspatial distribution of landslides and correspondingdamages during a period of nearly 50 years. Assuminguniformitarian behaviour of slope processes, thisdetailed analysis of the recent past enables forecasts tobe made for a similar period in the future.

2. Hazard modelling

We modelled landslide hazard in a prior statisticalanalysis of landslide behaviour during the second half oflast century (Remondo et al., 2003a,b). The procedurestarted with the development and evaluation of aprobabilistic susceptibility model, which subsequentlywas transformed into a hazard model. Susceptibilityanalysis was carried out through correlation betweenpast shallow translational landslides (affecting on theregolith) and 17 spatial parameters related to instability:terrain geometry, land cover and use, geology, geomor-phology, regolith type, thickness and hydrologic condi-tions. Analyses of the statistical relations were carried

498 J. Remondo et al. / Geomorphology 94 (2008) 496507Fig. 2. Flow diagram outlining the approach used to model landslide risk in this study.

orphoJ. Remondo et al. / Geomout by favourability functions (Chung and Fabbri, 1993,2005). Susceptibility models thus obtained reflectrelative spatial probability of landslide occurrence.Detailed descriptions and discussions of the method ofanalysis and testing are provided in Remondo et al.,(2003a,b, 2005b). Evaluations have been carried outcomparing susceptibility maps based on past landslideswith failures that occurred in several, later periods. Theprediction capability of the model was thus established.The best model obtained (more accurate prediction) wasthe one using the likelihood ratio function and terrainheight, slope gradient, aspect, lithology, and vegetationas conditioning variables.

Transformation of landslide susceptibility into hazardmaps requires, apart from an assessment of frequency, theconsideration of run-out distance and landslide magni-tude. The single-magnitude scenario considered for thisparticular analysis corresponds to the average of pastoccurrences (Remondo et al., 2005a): average run-outdistance 30meters, involving 200m3 ofmaterial and veryto extremely rapid (several m/min) velocity (Varnes,1978).

Future frequency, in terms of number of landslides tooccur in a given period, must be considered in order to

Fig. 3. Shallow landslide hazard model. Future landslide frequency considrepresented using 11 m pixels.499logy 94 (2008) 496507estimate temporal probability. We make the straightfor-ward and rather conservative assumption that the samenumber of events will occur in the future as occurredduring a similar period in the past. Landslide frequencyhas shown a clear increase during the period analysed(Remondo et al., 2005b), and thus it would also bereasonable to consider alternative scenarios fitting thatrising trend, but only a business as usual scenario isconsidered here. Fig. 3 shows a landslide hazard modelexpressing the probability of occurrence of futurelandslides according to that scenario. Similar, butquantitatively different models have been presented byRemondo et al. (2005a) who provide a description ofthe procedure used. This type of hazard maps can bedirectly integrated with vulnerability maps to producerisk maps.

3. Vulnerability assessment

We assessed vulnerability from a detailed analysis ofpast damage from mass movements. An inventory ofdirect losses during nearly 50 years was carried out. Theinventory is based on field surveys and consultationswith both local inhabitants and public and private

ered similar as past frequency. Probability for the next 50 years is

institutions, including municipal services, transport andfinance departments, insurance and construction com-panies, etc. Because the data are not complete, extra-polations and theoretical assumptions were needed forthe assessment. This is particularly significant for in-direct losses. While past direct losses can often beobtained from records, indirect losses must be mainlyestimated. However, public expenditure for road-damage repairs as well as other indicators can be usedto test the estimated losses.

Landslide effects were analysed by considering boththe characteristics of the element (or service) at risk andthe magnitude of the process. Accordingly, differentexposed elements were identified and mapped andentered into a digital database comprising infrastructure,land use, buildings, people, and socio-economic indi-cators. Because landslide effects for a given type ofelement depend also on process magnitude, differentspecific loss scenarios expressing potential losses in agiven element due to an event of a certain type andmagnitude can be drawn up. Past direct losses

Direct vulnerability can be assessed by comparingthe value of damage with the actual value of the elementat risk, as shown in Fig. 4, where

V Loss of the element due to a landslide of a given type and magnitudeValue of the element

Vulnerability values could theoretically be greaterthan 1, since repair could cost more than construction ofa new structure. However, the maximum value consid-ered in this analysis is 1 (total loss). Values of past losseswere corrected for inflation, an assumption which canalso be used for predicted future losses. Vulnerabilityvalues thus obtained express the degree of potentialmonetary loss (fraction of value). These values wererepresented on vulnerability maps which show thedistribution and magnitude of potential future damage.According to the type and magnitude of the landslidesanalysed here (shallow slides affecting regolith),vulnerability values are in general small, as mostelements affected are not damaged structurally. Becauseno deaths have been recorded in the study area as a result

500 J. Remondo et al. / Geomorphology 94 (2008) 496507(monetary) can thus be estimated and expressed as theaverage specific loss per type of element for a landslideevent of a given type and magnitude, conditioned to theoccurrence of that particular landslide. Different valuesrepresenting those estimates for different elements weredeveloped for each magnitude scenario, but here onlyone, business as usual, scenario is presented as anexample.Fig. 4. Flow chart representing the conceptualof mass movements either during the period analysed orhistorically, vulnerability of people has been discarded.

3.1. Vulnerability of transport infrastructure

The different types of transport infrastructure in thestudy area, totalling 684 km in length, include: motor-way, national, regional and local roads, and railwayprocedure for vulnerability assessment.

tracks; other lifelines were not considered. All such reflect destruction of crops and trees as well as costs of

Table 1Average construction cost, average losses, and vulnerability to shallow landslides

Type of infrastructure Construction cost (/m) Losses (/m) Vulnerability

Railway track 120 101.0 0.84Local road 100 92.5 0.93Regional road 690 89.5 0.13National road 1400 85.0 0.06Motorway 6000 72.5 0.01

501J. Remondo et al. / Geomorphology 94 (2008) 496507structures were represented on a polygon map (accord-ing to the resolution of the analysis, infrastructure areconsidered as areal features) connected to an attributetable, in a Ilwis 3.1 and ArcGis 9.0 environment,including several items needed for subsequent vulner-ability assessment (name of road, class of road, area andperimeter, average daily traffic intensity, and so on).Losses were calculated on the basis of past damagerecords and taking into account budgets for road andrailway track repairs. Values of new structures wereobtained from construction companies and the Depart-ment of Public Works of the provincial government(Diputacin Foral de Guipzcoa). Vulnerability fordifferent types of transport infrastructure (Table 1)was directly calculated on a per meter basis [V=Losses(/m) /Value (/m)].

3.2. Vulnerability of land resources

From the forest inventory map of the BasqueGovernment a land use map was created by reclassify-ing former units into those shown in Table 2. Amonetary value (/m2) was assigned to units thusobtained, by means of data provided by the TreasuryDepartment of Diputacin Foral de Guipzcoa. Vulner-ability was estimated as the ratio of losses per unit areadue to landsliding to the current market values per unitarea, as presented in Table 2 and Fig. 5. The lossesTable 2Average land values, average losses and vulnerability to shallow landslides

Land use class Market value (/m2)

Built-up area Water Rock Grasslands 1Pastureland 0.6Scrubland 0.1Hawthorn land 0.1Coniferous, reforested 0.71Cultivation, fruit trees 0.8land rehabilitation.

3.3. Vulnerability of buildings

A polygon map showing all buildings in the area(homes, industries, commercial and public buildings),was also connected to an attribute table including type ofbuilding, function, area, cadastral value, and so on.During the second half of the last century only a few ofnearly 3000 buildings in the area were slightly affectedby the type of movements analysed here. Economicvalues for individual buildings were estimated fromcadastral values provided by Treasury Department of theProvincial Government (Diputacin Foral de Guipz-coa, DFG), corrected through comparison with marketvalues provided by state agents. Building lossesregistered during the period analysed were used toestimate an average loss per type of building, given themagnitude scenario modelled. Vulnerability was com-puted as the ratio of monetary loss due to a landslide tovalue of the building (Table 3). Building vulnerability islow because landslides in the area are shallow and rarelyaffect the structure.

4. Direct risk modelling

Once maps of hazard, vulnerability and value werecreated for different elements at risk, theywere combinedLosses (/m2) Vulnerability

0.3 0.300.1 0.170.01 0.100.01 0.100.23 0.320.48 0.60

in a direct risk map using the expression proposed byVarnes (1984):

R Hd Ed Vwhere R = Risk (/50 year); H = Hazard (01/50 year);E = Element value (), and V = Vulnerability (01);.Risk values thus obtained are equivalent to specificlosses multiplied directly by the hazard.

Mapping is carried out separately for each type ofelement (specific risk) and then combined into a map oftotal risk by adding all maps of specific risk. A completeview of the risk for a given element should integratemultiple scenarios of hazards and vulnerability values forthe different types of mass movements and theirmagnitude, but in the example presented here onlyshallow landslides of a given magnitude have beenmodelled. Risk maps represented show, for each 1 m2

pixel, theoretical monetary losses due to shallowlandslides in the next 50 years, providing future activityof the process is similar to that in the past. Fig. 6 shows anexample of specific risk for one of the elements analysed.Fig. 7 is a direct risk map obtained by combining the threespecific risk maps. All such maps are expressed in mone-tary terms (/pixel) for a period of 50 years. The studyarea contains 140106 pixels of 11 m of resolution;

therefore, each element at risk is represented in themap byhundreds or thousands of pixels.

5. Indirect risk modelling

When a hazardous event takes place, damage tomaterial elements has an indirect effect by disruptingsocio-economic activities. In the example analysed here,indirect effects mainly originate in damage to infra-structure and may affect a wide area, even far from thestudy area.

The concept of indirect damage (resulting formindirect disruption of non-material elements) is fuzzyand difficult to assess. Our approach is to estimatepotential losses indirectly due to the occurrence ofdamaging phenomena of a given magnitude that affecteconomic activity. In order to assess indirect losses, twoaspects were considered:

Scenario of hazard that defines the type andmagnitude of landslide events. This scenario controlspotential direct and indirect effects.

Socio-economic analysis of the study area todetermine the most relevant elements and activities

502 J. Remondo et al. / Geomorphology 94 (2008) 496507Fig. 5. Vulnerability map for land resources in the study area.

and how they could be disrupted by a potentiallydamaging phenomenon.

The type of movement considered here, a shallowlandslide (see hazard, above) is small and not verydamaging (see vulnerability, above). As average land-

indirect effect is therefore the temporal loss of use of theaffected element (for instance a road blocked by a massof debris). On the other hand, Bajo Deba study area is agrowing industrial area, as well as a communicationroute between northern Spain and the rest of Europe andbetween the important economic centres of Bilbao andSan Sebastian. Therefore, transport lifelines, mainlymotorways, could induce higher indirect effects in casethey became blocked. Indirect effects due to damage toother elements (buildings and land) are so small thatwere disregarded in this study.

Given the hazard scenario and the different types oftransport infrastructure, the period of time they couldbe blocked as a result of the occurrence of a landslideevent was estimated on the basis of experience duringpast events (through consultations with the TransportDepartment of the DFG). Average times of completeshutdown are: 2 hours for motorway, half a day fornational and regional roads, one day in the case oflocal roads, and one day and a half for railway lines.The main indirect effect of interrupted lines ofcommunication is on economic productivity (loss ofworking activity), and is directly related to the numberof people affected. Data on vehicle traffic (type of

Table 3Average values, losses, and vulnerability of example buildings in thestudy area to shallow landslides

Building identifier a Market value(/pixel)

Losses(/pixel)

Vulnerability

1 140.4 2.8 0.02012 8595.8 8.4 0.00103 1301.1 1.2 0.00094 11109.1 9.2 0.00085 290.8 4.8 0.01656 451.5 0.5 0.00127 334.2 0.2 0.00068 123.2 1.3 0.0109 a There are about 3000 buildings in the area, each one with its own

cadastral value. Damages to buildings are small, do not affect thestructure and do not depend on the size/type of building.

503J. Remondo et al. / Geomorphology 94 (2008) 496507slide thickness is small, the structural effects on theelements at risk are generally also small. The mainFig. 6. Risk map for land resources (specific risk). Values are expressed in mothe top left.vehicles, intensity, average number of passengers) foreach road sector was obtained from Department ofnetary terms per pixel for a 50-year period. A detailed view is shown at

nts at

504 J. Remondo et al. / GeomorphoTransport of the DFG. Only the population fraction

Fig. 7. Direct risk map (/50 years), including all elemecorresponding to active workers was considered, asthese people are directly involved in economicactivities. Indirect losses were calculated using theexpressions below.

For motorways:

Losses due to loss of working time

affected workers Potential No: of events in sector work cost=h time transport blocked h=pixels in the sector

The above expression is applied to motorways wheretraffic is only stopped for a couple of hours, producing atraffic jam.

Roads:When roads are affected by a landslide they remain

blocked for a longer time and vehicles take alternativeroutes (longer distance and more time spent). Of course,work time is also lost.

Losses due to distance increase

additional length of the alternative road Potential No: of events in sector

No: of vehicles Cost of additional length= pixels in the sectorPlus

risk (see text). A detailed view is shown at the top left.

logy 94 (2008) 496507Losses due to loss of working time

affected workers Potential No: of events in sector work cost=h time transport blockedh=pixels in the sector

Railways:

Losses in railways

f affected workers Potential No: of events in sector work cost =htime delayed h=pixels in the sectorg cost of alternative transportation by bus

Data necessary to calculate the different types of losseswere gathered from the statistical services of the regional(Eustat) and national (INE) governments and fromtopological attributes within the spatial database. Thepotential number of events for the period modelled wasderived from the length of each road or railway sector andthe analysis of past landslide activity.

Delays in the transportation of goods were notconsidered. This indirect effect as well as other vaguerconsequences (i.e: disruption to non working people)were not attempted; the assessment of large areas wouldbe very complex and uncertain, but could be estimatedby means of an in-depth socio-economic analysis.

Nevertheless, preliminary estimates suggest that thoseeffects would be minor, perhaps by an order ofmagnitude, compared to the former.

Indirect losses calculated for each sector of transportinfrastructure were represented as a risk map expressingthe expected indirect losses per pixel and unit of time, asshown in Fig. 8.

6. Total risk

Total landslide risk is the sum of the different directand indirect risks described above:

RT RD RI Rs i Rs b Rs l RIwhere,

RT is total risk, RD is direct risk, RI is indirect risk,Rs_i is specific risk for transport infrastructure, Rs_b isspecific risk for buildings, and Rs_l is specific risk forland.

Direct and indirect risk maps differ in meanings.Direct risk maps show damages to structures actuallylocated in each pixel, whereas indirect losses generated

total risk pixels, whether this risk is direct or indirect.Mitigation efforts devoted to those areas would yield thebest benefit/cost ratios.

The final risk figures for the Bajo Deba study area areshown in Table 4. Indirect risk represents one-third ofthe total. However, risk per pixel varies between 0 and2.9 for direct damage and between 0 and 60.5 forindirect losses, suggesting that the latter can be muchmore important in certain locations.

7. Conclusions

Quantitative models of landslide susceptibility, trans-formed into hazard models by scenarios derived fromthe analysis of past behaviour of hillslopes, provide asound foundation for risk assessment. The mosthazardous areas can be identified, and the magnitudeand probability of future events forecasted with rea-sonable accuracy.

Vulnerability of different material elements (infra-structure, lands, and buildings) can be estimated bycomparing past damage and the cost of the elements.

505J. Remondo et al. / Geomorphology 94 (2008) 496507in a pixel do not affect the pixel itself, but a much widerand undefined area. The map pixel in this case should beconsidered as a source of losses. Nevertheless, becauserisk is expressed in monetary terms, both maps can besummed to obtain the total risk map. Obviously, priorityfor prevention measures should be given to maximumFig. 8. Indirect risk map showing riskValues for direct damage (risk) can thus be obtained. Theassessment of indirect damage is less precise, but theproposed method provides one way to incorporate theminto the analysis.

Expressing the different risks as potential monetarylosses per pixel and unit time enables integrated riskmapssources in /50 years (see text).

506 J. Remondo et al. / Geomorphology 94 (2008) 496507to be obtained by summing the constituent maps. Theeconomic values represented in risk maps should not beconsidered exact predictions of future losses but ratherpotential, approximate losses, assuming that futurelandslide processes will resemble those of the past.

Successful spatial modelling of landslide risk de-pends critically on detailed analysis and quantificationof geomorphic processes. The GIS-based proceduredescribed enables analyses using 11 m pixels, as wellas elaboration ofmore precise hazardmodels that could bederived using more accurate elevation models or im-proved, up-to-dated information on infrastructure andsocio-economic activity.

The method and tools presented here facilitate theidentification of areas where efforts at hazard mitigationwill be most cost-effective.

Acknowledgements

Financial support from European Commission (proj-ect ALARM, Contract No. EVG1-CT-2001-00038. Theauthors thank the Diputacin Foral de Guipzcoa(Gipuzkoako Foru Aldundia) and its personnel for theirhelp during data gathering.

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Quantitative landslide risk assessment and mapping on the basis of recent occurrencesIntroductionHazard modellingVulnerability assessmentVulnerability of transport infrastructureVulnerability of land resourcesVulnerability of buildings

Direct risk modellingIndirect risk modellingTotal riskConclusionsAcknowledgementsReferences