Physics and Chemistry of the Earth 49 (2012) 52ndash63

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Physics and Chemistry of the Earth

journal homepage wwwelsevier comlocate pce

Assessment and mapping of debris-flow risk in a small catchment in easternSicily through integrated numerical simulations and GIS

Giuseppe T Aronica auArr Giovanni Biondi a Giuseppina Brigandigrave a Ernesto Cascone a Stefania Lanza bGiovanni Randazzo b

a Dipartimento di Ingegneria Civile Universitagrave di Messina Contrada di Dio Villaggio S Agata 98166 Messina Italyb Dipartimento di Scienze della Terra Universitagrave di Messina Via F Stagno drsquoAlcontres 31 98166 Messina Italy

a r t i c l e i n f o

Article historyAvailable online 25 April 2012

KeywordsDebris-flow propagationFloodSHALSTABRisk mappingGISSicily

1474-7065$ - see front matter 2012 Elsevier Ltd Ahttpdxdoiorg101016jpce201204002

uArr Corresponding author Tel +39 090 3977164 faxE-mail address garonicaunimeit (GT Aronica)

a b s t r a c t

This paper describes the application of a methodology for the evaluation of debris-flow risk in alluvial fansby incorporating numerical simulations with Geographical Information Systems to identify potentialdebris-flow hazard areas The methodology was applied to a small catchment located in the north-easternpart of Sicily Italy where an extreme debris flow event occurred in October 2007 The adopted approachintegrates a slope stability model that identifies the areas of potential shallow landslides under differentmeteorological conditions using a two-dimensional finite-element model based on the De Saint Venantequation for the debris-flow propagation The mechanical properties of the debris were defined using bothlaboratory and in situ test results The risk classification of the area under study was derived using totalhydrodynamic force per unit width ( impact pressure) as an indicator for event intensity Based on thesimulation results a potential risk zone was identified and mapped

2012 Elsevier Ltd All rights reserved

1 Introduction

Throughout the past few decades anthropic disturbances haveoften resulted in an increased risk of debris flow in mountain areasFast kinematics flows like debris flow often originate upon hillslopes as a result of storms with remarkable intensity andor rainvolume Such movements of mass influence both the slope fromwhere they originate and the natural hydrographic network theyinteract with modifying their morphologic character and oftenaffecting densely inhabited areas when they are far from the sourcearea It is therefore necessary to attain an exhaustive understandingof such phenomena to develop specific systems for the diagnosis ofgeomorphological instabilities which may be used to analyse theinfluence of the anthropic effects on their triggering to reducehuman losses and damages to properties

Extreme meteorological events may induce instabilities in stablenatural slopes and accelerate existing movements in unstableslopes or reactivate quiescent landslides for natural slopes consist-ing in a soil mantle overlying a bedrock formation heavy rain-storms may saturate the top layer of soils and may detach all or apart of the material from the slope inducing a debris flow Gener-ally the increase in pore pressure at the soilndashbedrock interface orat the discontinuity surface determined by the wetting front duringheavy rainfall events is recognised as one of the major factors

ll rights reserved

+39 090 3977480

responsible for shallow instabilities including debris flows (Sidleand Swanston 1982 Megahan 1983)

Understanding the mechanism that controls the failure thepost-failure and the propagation stage of these extremely rapidlandslides represents a crucial step in the process of mitigatingthe risks related to these phenomena

For a given area the local geomorphology the geotechnicalproperties of the involved soils the long-term drainage patternand the hydrological processes related to the meteorological condi-tions are recognised as the factors that are most involved in thesusceptibility of that area to shallow landslides For these phenom-ena the amount of antecedent precipitation may represent acrucial triggering factor (Wieczorek 1987 Glade et al 2000)

There are two very common approaches reported in the scien-tific literature that are used for the estimation of the relationbetween landslide occurrence and rainfall measurements (Brunettiet al 2010)

The first approach adopts process-based models which includespatially distributed and physically based numerical models (Mont-gomery and Dietrich 1994 Wilson and Wieczorek 1995 Wu andSidle 1995 Iverson 2000 Crosta and Frattini 2003) This approachrelies upon the understanding of the physical laws controlling slopeinstabilities and attempts to extend spatially simplified geotechni-cal stability models based on the limit equilibrium approach (eginfinite-slope scheme wedge-slope scheme) In this frameworkthe distribution of pore water pressure in potentially unstable soilsis assumed or is estimated using rainfall-infiltration models

Fig 1 Block diagram of the methodology

GT Aronica et al Physics and Chemistry of the Earth 49 (2012) 52ndash63 53

The second approach uses hydrological models based on thedefinition of hydrological thresholds that represent rainfall soilmoisture or any hydrological conditions that when reached orexceeded are likely to trigger landslides (Reichenbach et al1998) Several applications of this second approach can be foundin the literature (eg Aleotti 2004 Wieczorek and Glade 2005)

Among the approaches used to assess and to zone shallow land-slide hazards on the catchment scale those related to spatiallydistributed and physically based numerical models are preferredto those derived using historical observations (Rosso et al 2006)Thus the first of the two approaches previously described has beenadopted in this paper and integrated process-based modellingused to analyse debris-flow risk has been implemented

In process-based models the equations describing the slopestability conditions and the equations related to the hydrologicalmodel of the slope are combined and solved simultaneously Basedon the spatial distribution of geomorphological geotechnical andhydraulic data these equations allow for the detection of the trig-gering factors of shallow landslides The infinite-slope scheme isfrequently assumed as the reference scheme and the subsurfaceflow in the slope is evaluated for a steady state or dynamic condi-tions (eg Wu and Sidle 1995 Montgomery and Dietrich 19942004 Biondi et al 2000 2002 2011) Generally the increase inpore pressure which produces the shallow instability is assumedto be a consequence of the increase in the thickness of a saturatedlayer above a potential or actual sliding plane

This kind of analysis has typically been carried out locally on theplot scale due to the difficulties of collecting and managing a greatamount of heterogeneous data To apply these methods to wideareas the use of advanced instruments able to manage and elabo-rate a large amount of spatial data is necessary and GIS softwarerepresents the ideal working environment Obviously the spatialdistribution of geomorphological and geotechnical data representsthe minimum dataset required by the analysis

For flood-risk assessment a number of approaches and modelsare available to be used at different scales (Apel et al 2009 2006Buumlchele et al 2006 Archetti and Lamberti 2003 Iovine et al2003) For vulnerability assessment it is common practice to relateit to the flow depth h and many empirical vulnerability functionsare available in the literature (USACE 2000 2003) However theapplication of vulnerability functions depending on flow depthdoes not seem fully appropriate for debris flow because the flowvelocities may be very high and the hydrodynamic forces may besignificantly greater than the hydrostatic forces Vulnerabilityfunctions were proposed by Zanchetta et al (2004) they analysedthe damages to buildings during the catastrophic debris flow thatoccurred in Sarno (Italy) in 1998 and proposed proper vulnerabilityfunctions Faella and Nigro (2001ab) developed a vulnerabilityequation depending upon debris-flow velocities Gentile et al(2008) performed a risk analysis by combining hazard and suscep-tibility maps to obtain a debris-flow risk map In this work theauthors used the SHALSTAB model (Montgomery and Dietrich1994) to calculate the total debris volumes and the volumetricconcentrations of the event associated with the three return peri-ods whereas the propagation of the debris flows was carried outby means of the FLO-2D model (2006) The methodology to mapdebris-flow hazard used in this work is based on the definition ofhazard and a risk matrix and was first applied in northern Venezu-ela by Garcia et al (2003)

To quantify debris-flow hazard Calvo and Savi (2009) presenteda Monte Carlo procedure in which the input variables of mathe-matical models simulating the triggering propagation and stop-page of debris flows are randomly selected This allowed theresearchers to estimate the probability density function of the out-put variables characterising the destructive power of debris flow ateach point of the alluvial fan

In this study an integrated modelling approach based on ageotechnical stability model that identifies the areas potentiallysubjected to shallow landslides and a two-dimensional debris-flowrouting model that identifies flooded areas is presented The resultsfrom the models ie hazard and risk maps can be reported using aGIS interface A flow chart of the proposed methodology is shown inFig 1 This methodology was tested in the Mastroguglielmo catch-ment located in the north-eastern part of Sicily Italy where a cata-strophic debris flow affected the alluvial fan on October 25 2007 Anaccurate post-event field survey permitted the collection of geomor-phological geotechnical hydrological and hydraulic data

2 Methodology

21 Debris-flow triggering

All of the calculations performed for the area potentially sub-jected to shallow landslides were carried out using the approachintroduced by Dietrich et al (1992 1993) This approach was up-graded by the same Authors (Montgomery and Dietrich 1994Dietrich et al 1995) and was implemented in the freeware com-puter program SHALSTAB SHALSTAB Tools is a suite of softwareroutines that has been implemented as an extension of theArcView geographic information system (GIS) program by Environ-mental Systems Research Institute Inc (ESRI) SHALSTAB incorpo-rates a coupled geotechnicalndashhydrological model that allows forthe identification of areas where possible shallow landslides mayoccur under different meteorological conditions The physicallybased model implemented in the computer code SHALSTAB doesnotaccount for transient movements of water in soils and thus as-sumes an unrealistic steady-state flow in the course of a rainstormHowever several studies have shown that the use of the coupledgeotechnical and hydrological model implemented in SHALSTABallows for a reliable description of the spatial variability of shallowlandslides governed mainly by topography (Dietrich et al 2001)specifically the model uses the limit-equilibrium approach todetect potentially unstable areas The geotechnical model imple-mented in SHALSTAB consists of a slope-stability model based onthe infinite-slope scheme This scheme assumes that the potentialsliding surface is parallel to the surface of the slope and that thedepth d of the sliding soil mass is small compared to the overalllength of the potentially unstable soil mass On the basis of fieldevidence both of these hypotheses seem to hold for most coversoils detected in the study area Moreover the use of the infinite-slope scheme is generally justified by the relative shallow sliding

54 GT Aronica et al Physics and Chemistry of the Earth 49 (2012) 52ndash63

surfaces and by the moderate thickness of the soil masses incomparison with the extension of the flow length triggered byheavy rainfalls

However the infinite-slope scheme reduces the stability calcu-lations to a simplified closed-form equation which can easily beapplied to large areas using the spatial analyst typically incorpo-rated into GIS platforms Hence using the infinite-slope schemethe static safety factor Fs can be computed as follows

Fs frac14c0

c B sin b cos bthorn tan u0

tan b 1 cw

c hB

eth1THORN

where c (kNm3) c0 (kPa) and 0 are the unit weight the cohesionand the angle of shear strength of the soil respectively B (m) isthe depth of the sliding surface b (mm) is the terrain slope h (m)represents the water level above the potential sliding surface andcw (981 kNm3) is the unit weight of water

The limit-equilibrium condition corresponds to Fs = 1 and can berewritten in the following form

hBfrac14 c

cw 1 tan b

tan u0

thorn c0

cw B cos2 b tan u0eth2THORN

The flow-tube-based hydrological model incorporated intoSHALSTAB assumes that the steady-state flow of water in the uppersoil layer runs parallel to the potential sliding surface The modelestimates the relative soil saturation based on the analysis ofupslope contributing areas slope and soil transmissivity T (mday)

At saturation and under steady-state conditions assuming thatno overland flow no significant deep drainage and no significantflow in the bedrock occur the following relationship between theeffective precipitation q (mday) and T can be written

hBfrac14 q

T ab sin b

eth3THORN

where b (m) is the outflow boundary length and a (m2m) the drain-age area of each grid cell of the GIS model

The coupled hydrologic-slope-stability model implemented inSHALSTAB is obtained combining Eqs (2) and (3) and is describedby the following relationship for the hydrologic ratio qT at thelimit-equilibrium condition

qTfrac14 b

a sin b c0

cw B cos2 b tan u0thorn c

cw 1 tan b

tan u0

eth4THORN

Conceptually higher values of the hydrologic ratio qT indicatethat the rate of precipitation input is greater than the soilrsquos abilityto transmit the water downslope causing soil saturation whichincreases the pore water pressure reduces the shearing resistanceof cover soils and may trigger shallow instabilities

Even if the effective precipitation q and the transmissivity T areassumed to be uniform in the study area the values of the qT ratiocomputed across the DTM grid cells will vary because Eq (4) esti-mates the value of this ratio at the limit-equilibrium conditionwhich is largely controlled by topography (b) and soil shear-strength parameters (c0) A high value of the ratio qT implies thatheavy rain andor impermeable soils are required to induce insta-bility Thus for risk-assessment analyses the interest is mainlyfocused on those areas characterised by the lower values of thehydrologic ratio where shallow instabilities could occur evenduring moderate more frequent rainfall events The potential deb-ris-flow volumes under different pluviometric conditions can beestimated by the evaluation of the extension of unstable areasand thicknesses of sediment layers

To model the debris-flow propagation solid-discharge hydro-graphs are required as input to the routing model For our applica-tion solid-discharge hydrographs were calculated by comparingthe liquid volumes derived from rainfallndashrunoff transformation

with the SHALSTAB results Generally not all of the dischargesproduce solid transport but with the simple application of theSchoklitschrsquos relationship (Armanini 2005) it was easy to verifythat due to the very high slope the hydrograph tails which donot produce solid transport are very limited Thus the soliddischarges can be simply obtained by multiplying the liquid dis-charges by the ratio between the solid volumes and liquid volumes(De Wrachien and Mambretti 2011)

Liquid-discharge hydrographs have been obtained using alumped rainfallndashrunoff model based on a Kinematic InstantaneousUnit Hydrograph that includes the Soil Conservation ServicendashCurveNumber (SCSndashCN) module (USDA 1986) for calculating net rainfallFor the net rainfall calculation it is not possible to use the SCSndashCNin its classical form because it is a cumulative model and conse-quently is not able to consider the temporal variation of the rain-fall Hence to overcome this limitation for the rainfallndashrunofftransformation the SCS dynamic approach has been used (Chowet al 1988) This approach was chosen because of its simplicityand particularly because of the small number of parameters thatneed to be estimated

22 Debris flow propagation

To simulate the propagation of the debris flow on the alluvial fana hyperbolic single-phase fluid model in two-dimensional form hasbeen used This model originally written for the propagation ofwater flooding (Aronica et al 1998) is based on the De Saint Venant(DSV) equations and is capable of simulating the two-dimensionalflow of a single-phase fluid (OrsquoBrien et al 1993 Laigle and Coussot1997) by considering instead of the classical Chegravezy formula a differ-ent set of equations for modelling friction terms In the two-dimen-sional case the De Saint Venant equations when convective inertialterms are neglected can be written in the following form

Htthorn ethuhTHORN

xthorn ethvhTHORN

yfrac14 0

ethuhTHORNtthorn gh

Hxthorn ghJx frac14 0

ethvhTHORNtthorn gh

Hythorn ghJy frac14 0

eth5THORN

where H(txy) is the free surface elevation u and v are the horizon-tal and transverse (x and y) components of flow velocity h is thedepth of debris flow and Jx and Jy are the friction terms along thex and y directions To model friction terms the Takahashi (1991)equations were adopted according to the dilatant fluid hypothesisdeveloped by Bagnold (1954) The friction terms were computedas the sum of two terms related to the shear stresses ie turbulentand dispersive (Brufau et al 2000 Naef et al 2006)

Jx frac14uffiffiffiffiffiffiffiffiffiffiu2thornv2p

eth 25d

1khTHORN2 1

aB sin frac12cthorneth1cTHORN qqs

ghthorn n2u

ffiffiffiffiffiffiffiffiffiffiu2thornv2ph4=3

Jy frac14vffiffiffiffiffiffiffiffiffiffiu2thornv2p

eth 25d

1khTHORN2 1

aB sin cthorneth1cTHORN qqsfrac12 gh

thorn n2vffiffiffiffiffiffiffiffiffiffiu2thornv2ph4=3

eth6THORN

where d is the mean diameter of the sediment particles is the inter-nal friction angle k is the linear concentration qs is the solid-phasedensity and n is the Manning roughness in sm13 Moreover the lin-ear concentration k depends on the granulometry of the solids asfollows

k frac14 cs

c

1=3 1

1

eth7THORN

where c is the depth-averaged concentration and cs is the maximumpacking concentration of the solid material in the bed

The model equations were solved with a finite-element tech-nique with triangular elements The free surface elevation was as-

Table 1Hazard classes

Intensity Probability

T = 50 years T = 100 years T = 300 years

h lt 01 m H2 H1 H1Ptot lt 20 kPA H3 H2 H2

20 kPa ltP

tot lt 35 kPa H4 H3 H2Ptot gt 35 kPa H4 H4 H3

Table 2Risk classes according Flood Management Plan for Sicily

Hazard Exposure

E1 E2 E3 E4

H1 Low Low Medium MediumH2 Low Medium High HighH3 Medium Medium High Very highH4 Medium High Very high Very high

GT Aronica et al Physics and Chemistry of the Earth 49 (2012) 52ndash63 55

sumed to be continuous and piece-wise linear inside each elementwhere the unit discharges uh and vh along the x and y directionswere assumed to be constant The proposed version of the finite-element approach avoids a simplified description of the geometri-cal peculiarities of the model domain unlike the finite-differencemethod (FLO-2D Userrsquos Manual 2006) In fact the triangular ele-ments are able to reproduce the detailed complex topography ofthe built-up areas (ie blocks streets etc) exactly as they appearwithin the floodable area Blocks and other obstacles were treatedas internal islands within the triangular mesh covering the entireflow domain The overall structure including levees and any ter-rain unevenness can be easily modelled following the approachproposed by Aronica et al (1998) who to take into account sharpvariations of the ground elevation proposed to split the originaldomain into several sub domains connected by vertical discontinu-ities The spatial and temporal variation of debris flow dischargewas included as a source term (upstream boundary condition)Dry-bed conditions were assigned in the computational domainas initial conditions

23 Debris-flow risk and hazard assessment

The term risk has different meanings it is understood in differentways by different people and often used with a lack of coherenceThus an unambiguous clear and consistent definition of risk isessential (see eg (De Bruijn et al 2007)) In the scientific commu-nity risk is widely defined as the expected number of lives lost per-sons injured damage to property or disruption of economic activitydue to a particular natural phenomenon In mathematical terms thisdefinition can be translated as the product of hazard vulnerabilityand exposure (European Union 2007 ISDR 1999 USACE 1996)

R frac14 H V E eth8THORN

Here hazard (H) represents the physical and statistical aspectsof flooding It depends on many variables such as return period ex-tent and depth of inundation flow velocity duration of floodingproduct of water depth by flow velocity and hydrodynamic forces(Gentile et al 2008 Santi et al 2011 see also references in Merzet al 2007) Vulnerability (V) means the degree of loss to a givenelement or set of element at risk resulting from the occurrence ofa flooding event of a given intensity It is expressed on a scale from0 (no damage) to 1 (total loss) Exposure (E) or the value of theelement at risk represents the real damage to human lives prop-erties and assets Where there are no people or values there isno risk even if hazard may be very high conversely in a poorlyprepared inhabited area a moderate event may cause a devastat-ing catastrophe In this case risk is high even if hazard is low Inother words the term lsquolsquoriskrsquorsquo captures both the probability of theflooding event (return time) and flooding-related losses Followingthis definition hazard and vulnerability can be treated separatelyinitially but must be combined for the final risk analysis

The methodology used in this work to delineate debris-flowhazard is based on the above definitions and the Flood Manage-ment Plan for Sicily (Regione Sicilia 2004) which refers to fourdistinct hazard classes (namely H1 H2 H3 H4) for three differentreturn periods (50 100 and 300 years) Those indexes use thedepth of flooding (h) as an indicator to evaluate the intensity of aflood because this is considered the flood characteristic that hasthe greatest influence on flood-induced damage In the current sit-uation considering event intensity depending only on flow depthdoes not seem fully appropriate for debris flow because the flowvelocities may be very high and can have dramatic effects on what-ever they impact Therefore here the total hydrodynamic force perunit width (impact pressure) was considered as a better indicatorfor event intensity This force can be expressed in the followingform

Rtot frac14 qmethu2 thorn v2THORN thorn 12qmgh eth9THORN

where qm = cqs + (1 c)q is the density of the solidndashliquid mixtureTable 1 shows the hazard classes combining intensity I and proba-bility P

The above classification considers intensity thresholds based onflow depth and impact pressure (i) flow depths of 01 m arerelated to minimum flooding conditions (FEMA 2002) (ii) an im-pact pressure of 20 kPa corresponds to weak structural damagesand (iii) an impact pressure of 35 kPa corresponds to severe struc-tural damages (Zanchetta et al 2004 Calvo and Savi 2009)

Regarding exposure (E) in Eq (8) the Flood Management Planfor Sicily refers to four distinct classes (namely E1 E2 E3 E4)according to a qualitative estimation of people buildings struc-tures etc under risk For instance E1 refers to scattered housescemeteries recreational and sports facilities and small agriculturalindustries E4 refers to cities and essential buildings such as hospi-tals schools and churches

In a GIS environment a final risk map can be obtained by com-bining the exposure and the hazard maps according to some rela-tion between hazard and the elements at risk and the unit value ofthe vulnerability Table 2 shows the risk qualitatively classified(Regione Sicilia 2004) in four classes (Low Medium High VeryHigh)

3 The case study

The proposed methodology was applied to map the debris-flowrisk in the Mastroguglielmo coastal fan where the small village ofAligrave Terme is located (Fig 2) The study area lies on the north-east-ern part of Sicily south of the city of Messina The area of the catch-ment is approximately 13 km2 rising to approximately 580 masl and the main river channel is approximately 26 km long(Table 3)

The catchment is characterised by two water courses flowingNW to SE and joining at the base of the mountain chain to crossthe town in a unique course that is largely artificially confinedThe shape of the catchment and the development of the hydro-graphical network with a few tributaries forming a linear patternand steep hillslopes along the watershed is a system with low evo-lution typical of a recently lifted area

The topography of the area ranges from flat areas near thecoastal zone to very steep slopes (gt70) in the NO zone of the wa-tershed as shown in Figs 3 and 4 where a high-resolution digital

Table 3Morphological characteristics of the watershed

Area (km2) 13Max altitude (m asl) 579Main channel length (m) 2600Average channel slope (mm) 022

Fig 3 Digital elevation model (2 m resolution)

56 GT Aronica et al Physics and Chemistry of the Earth 49 (2012) 52ndash63

elevation model (DEM) and the map of the terrain slope are plot-ted respectively

The geology of the area (Fig 5a) is characterised by bedrock out-cropping from more than 80 of the area mainly formed by meta-sedimentary terrain belonging to the Peloritani Belt (PB) thatrepresents the westernmost part of the Calabria-Peloritani Arc(CPA) Only subordinately alluvial deposits and Pleistocenic con-glomerates are present The PB exhibits a complex structure charac-terised by an Alpine continental crust stack of tectono-stratigraphicunits with Africa-ward vergence involving both Variscan or oldercrystalline rocks and MesozoicndashCenozoic deposits (Amodio-Morelliet al 1976 Bonardi et al 1976 1996 2001 Messina et al 1996)Phyllites and metarenites develop a soil cover especially at med-iumlow elevations asl as the result of weathering the thicknessof the colluvium is in the range of 10ndash30 m

The lithology of the area was obtained through field surveys All ofthe lithological formations identified in the study area were groupedinto several homogeneous units The available data were reclassifiedusing a single criterion based on the physical and mechanical prop-erties of each lithological unit The lithology map was then realisedin the adopted GIS platform and is shown in Fig 5b

Because the slope of the main river channel and of the hillslopes issteep short concentration times are to be expected with a conse-quent fast hydrological response of the catchment Moreoverseveral slopes suffered local or global instability processes in thepast and the stability conditions of the shallowest portions of mostof rock slopes are in some cases unsatisfactory due to the poorgeotechnical properties of the cover soils The area exhibits a typicalMediterranean climate with rainfall events (mainly convective)characterised by short durations and high intensities during thewet season (OctoberndashApril) and almost no rainfall during the dryseason (MayndashSeptember) As a consequence this area representsan example of a high-risk-damage scenario with respect to boththe particular meteorological conditions and geological-morpholog-ical conditions Regarding land use the catchment is predominantlyrural with woods and sparse shrubs in the upper mountainousregion whereas the areas near the mouth of the river are highlyurbanised

2555000 2555500 2556000 2556500 2557000

2555000 2555500 2556000 2556500 2557000

4206000

4206500

4207000

4207500

4208000

Fig 2 Study area (Aligrave te

4 Application of the proposed methodology and results

A geotechnical characterisation of the study area based exclu-sively on extensive laboratory and in situ tests is practically andeconomically impracticable on the catchment scale Thus in thephase devoted to the implementation of data sources attentionwas mainly focussed on the geometrical and geotechnical parame-ters directly involved in the stability analyses In particular thoseparameters that are directly involved in the analyses on which this

2557500

2557500

4206000

4206500

4207000

4207500

4208000

rme village right)

Fig 4 Map of terrain slope (in degrees)Fig 6 Map of soil cover thickness in the Matroguglielmo catchment

GT Aronica et al Physics and Chemistry of the Earth 49 (2012) 52ndash63 57

paper focuses among all of the collected geotechnical data includethe thickness of the cover soils the unit weight and the strengthparameters of cover soils and of rock masses

Using all of the data available from field sampling stored in theGIS platform reliable and conservative values of geotechnicalparameters were estimated for each grid cell using a judgment-based approach The procedure utilised for this purpose consistedof two stages In the first stage for each lithological formation inthe catchment the average values of the geotechnical parameterswere computed using all the collected data These average valueswere attributed to the corresponding grid cell When data wereunavailable reliable estimates of geotechnical parameters wereevaluated in two ways If available the results of in situ and labo-ratory tests were assumed to be representative of an entire geolog-ical unit regardless of the location of the test sites otherwise dataavailable in the literature (Maugeri and Motta 2011) for similarsoilsrocks deposits located near the catchment were used

In the second stage attention was focused on the evaluation ofthe thickness d of the cover soils and on the comparison betweenthe obtained distributions of strength parameters c0 and 0 and theresults of back analyses carried out for some slopes of the catchmentAmong the parameters involved in the stability calculations thethickness B of the cover soils is one of the most complex to estimateIn fact the depth of cover soils is spatially variable and depends onmany factors including morphology climate and the mechanicalproperties of both cover soils and underlying bedrock Generally

Fig 5 Geological map (a) and lithological map

to obtain this information a large number of measurements shouldbe taken at different sites where the soil cover depth is assessableand the spatial distribution should be derived based on thosemeasurements

Here following an empirical approach proposed by Del Monacoet al (2003) the map of the cover soil thickness B was obtainedwith respect to the slope angle b of the terrain The empirical lawgiving the thickness as a function of the slope angle is the following

ln B frac14 C1 b C2 eth10THORN

where C1 and C2 are two numerical constants equal to 005 and70 respectively and the slope angle b is expressed in degrees(Fig 4) The values of the two constants were obtained from a linearregression of the soil thickness and slope angle at a few points of thecatchments where in situ measurements were carried out

After determining the spatial distribution of the terrain slope(Fig 5) the map of the thickness B of the cover soils was derivedusing Eq (10) directly within the GIS framework (Fig 6)

With respect to the distribution of the strength parametersobtained in the first stage adjustments were made using theresults of the back analyses Fig 7a and b shows the maps of theeffective cohesion c0 and shear angle 0 obtained after the geotech-nical analysis

Thus the static stability condition of each grid cell was analysedby computing the stability factor Fs from Eq (1) Map algebra imple-mentation of this equation allows this calculation putting togetherthe maps of the terrain slope angle b (Fig 5) of the thickness d

and (b) for the Matroguglielmo catchment

Fig 7 Map of effective cohesion (a) and shear angle and (b) distribution

Fig 8 Map of rainfall threshold for stability conditions

Fig 9 Depth-duration Frequency curves

Table 4Debris flow volumes of sediments estimated for different return times

Return time (years) Rainfall (mm) Total debris volumes (m3)

50 1356 4192000100 1568 4665920300 1939 5484480

58 GT Aronica et al Physics and Chemistry of the Earth 49 (2012) 52ndash63

(Fig 6) and of the soil shear strength parameters c0 and 0 (Fig 7)Only grid cells steeper than 5 were considered in the analysesUsing the computed values of Fs unstable grid cells (Fs lt 1) were de-tected using a map query function incorporated into the spatial ana-lyst For those grid cells the strength parameters were slightlymodified using the constraint that the developed geotechnical mod-el must be stable (Fs P 1) with the only exception being the docu-mented unstable area Specifically strength parameters weregradually increased with respect to the selected average values (first

Fig 10 Map of stability conditions for each return time

Fig 11 Time area curve

GT Aronica et al Physics and Chemistry of the Earth 49 (2012) 52ndash63 59

stage) until the condition Fs P 1 was obtained A small number ofslopes remained statically unstable even when using high but rea-sonable values of the strength parameters those slopes require fur-ther investigation and were not considered in the analysis presentedin this paper

Depending on the values of the depth ratio hd and of the hydro-logic ratio qT and on the initial stability condition of each grid cellof the GIS model three different behaviours of the correspondingslope were evaluated according to the stability fields introducedby Montgomery and Dietrich (1994) unconditionally stableunstable and unconditionally unstable The threshold values ofthe effective precipitation q that causes instabilities could thenbe evaluated according to Eq (4) In the analyses presented herein

Fig 12 Design storm

the value T = 65 m2day was adopted and the obtained map for thethreshold values of the rainfall is shown in Fig 8

The areas potentially subjected to shallow instabilities can bedetected by comparing the threshold values of the precipitationwith rainfall scenarios depending on the return time A statisticalanalysis of the maximum rainfall depths for durations of 1 3 612 and 24 h recorded at the Aligrave Terme rain gauge station allowedfor the calculation of the Depth-Duration Frequency curves forthe three different return periods (50 100 and 300 years)(Fig 9) these results in turn permitted the estimation of the rain-fall quantiles for an event duration of 6 h (Table 4) to be used forthe stability analysis A Generalised Extreme Value (GEV) distribu-tion was used and its parameters were estimated using the meth-od of L-moments (Aronica et al 2008)

Potentially unstable areas are those areas where the value of theratio between rainfall thresholds and estimated quantiles is lessthan unity Fig 10andashc shows the locations of potentially unstableareas for each return time which were recognised using this pro-cedure The total debris-flow volumes were estimated using thosemaps and the map of soil cover thickness (Fig 6)

Using these values the corresponding debris volumes for eachreturn time are reported in Table 4

Furthermore a rainfallndashrunoff transformation was applied todefine the debris-flow discharge hydrographs to be used as exter-nal inflows at the apex of the alluvial fan Given the lack of any his-torical discharge data because no flowgauge is present in theMastroguglielmo catchment the parameters of the rainfallndashrunoffmodel were derived based on the available information and on pre-vious studies (Aronica et al 2008) Land-use maps from the Corineproject and soil-type maps were available a lumped value ofCN = 76 for medium saturated conditions (AMC = II) was derivedand a corresponding value of CN = 88 (AMC = III) was used forthe simulations The kinematic IUH was calculated using the

hyetographs

Fig 11 Time area curve

GT Aronica et al Physics and Chemistry of the Earth 49 (2012) 52ndash63 59

stage) until the condition Fs P 1 was obtained A small number ofslopes remained statically unstable even when using high but rea-sonable values of the strength parameters those slopes require fur-ther investigation and were not considered in the analysis presentedin this paper

Depending on the values of the depth ratio hd and of the hydro-logic ratio qT and on the initial stability condition of each grid cellof the GIS model three different behaviours of the correspondingslope were evaluated according to the stability fields introducedby Montgomery and Dietrich (1994) unconditionally stableunstable and unconditionally unstable The threshold values ofthe effective precipitation q that causes instabilities could thenbe evaluated according to Eq (4) In the analyses presented herein

Fig 12 Design storm

the value T = 65 m2day was adopted and the obtained map for thethreshold values of the rainfall is shown in Fig 8

The areas potentially subjected to shallow instabilities can bedetected by comparing the threshold values of the precipitationwith rainfall scenarios depending on the return time A statisticalanalysis of the maximum rainfall depths for durations of 1 3 612 and 24 h recorded at the Aligrave Terme rain gauge station allowedfor the calculation of the Depth-Duration Frequency curves forthe three different return periods (50 100 and 300 years)(Fig 9) these results in turn permitted the estimation of the rain-fall quantiles for an event duration of 6 h (Table 4) to be used forthe stability analysis A Generalised Extreme Value (GEV) distribu-tion was used and its parameters were estimated using the meth-od of L-moments (Aronica et al 2008)

Potentially unstable areas are those areas where the value of theratio between rainfall thresholds and estimated quantiles is lessthan unity Fig 10andashc shows the locations of potentially unstableareas for each return time which were recognised using this pro-cedure The total debris-flow volumes were estimated using thosemaps and the map of soil cover thickness (Fig 6)

Using these values the corresponding debris volumes for eachreturn time are reported in Table 4

Furthermore a rainfallndashrunoff transformation was applied todefine the debris-flow discharge hydrographs to be used as exter-nal inflows at the apex of the alluvial fan Given the lack of any his-torical discharge data because no flowgauge is present in theMastroguglielmo catchment the parameters of the rainfallndashrunoffmodel were derived based on the available information and on pre-vious studies (Aronica et al 2008) Land-use maps from the Corineproject and soil-type maps were available a lumped value ofCN = 76 for medium saturated conditions (AMC = II) was derivedand a corresponding value of CN = 88 (AMC = III) was used forthe simulations The kinematic IUH was calculated using the

hyetographs

Fig 13 Debris flow hydrographs

60 GT Aronica et al Physics and Chemistry of the Earth 49 (2012) 52ndash63

time-area curve derived for the catchment from the digital eleva-tion model and a concentration time of 30 min estimated usingthe Kirpich formula (Fig 11) To define the temporal patterns of

7

8 6

50

288

469

319169

394

793

429

349

155

235

9263

944 K17

MENA

VIA

Fig 14 Layout of the co

the design hyetograph the idea of mass curves as defined by Chow(Chow et al 1988) was used In particular the mass curve for the50 percentile of historical mass curves was considered becausethis choice has been recognised to return a single-peak-centredhyetograph (Fig 12) The statistical analysis performed to derivethis mass curve used 10-min rainfall data that comes from theFiumedinisi raingauge station using the dataset covering the timeperiod between October 1 2004 and December 31 2008 thesedata were obtained from the data service section of the SIAS (Ser-vizio Informativo Agrometeorologico Siciliano wwwsiasregi-onesiciliait) The selected gauge located in a catchment close tothe Mastroguglielmo catchment is the only one in the area withavailable sub-hourly rainfall data Following Huff (1967) onlyevents lasting shorter than 12 h were considered in this analysis

The application of the lumped rainfallndashrunoff model describedabove generates the liquid-discharge hydrographs The debris-flowhydrographs (Fig 13) were derived simply by rescaling the liquiddischarges (Takahashi 1991) to debris-flow discharges using theratio between debris-flow volumes and liquid volumes (Table 4)

The definition of the finite-element mesh boundary (Fig 14)was based on the morphology of the study area to cover the allu-

493

134

59

137

128

87

K24

ALIrsquo

MARINA

14

66140

5256

mputation domain

Fig 15 Hazard map

Fig 16 Risk map

GT Aronica et al Physics and Chemistry of the Earth 49 (2012) 52ndash63 61

vial fan to leave the blocks and the single houses out of the domainand to take in account internal barriers and hydraulic discontinu-ities The total domain area is approximately 026 km2 and was dis-cretised into 21318 triangular elements The geometric features (xy z coordinates) of 12429 nodes were derived from the Digital Ele-vation Map (DEM) with 2-m resolution (Fig 3) Regarding theparameters of the Eq (6) expressing the friction terms the follow-ing values have been considered d = 25 cm = 36 c = 045cs = 065 qs = 2650 kgm3 aB = 0045 and n = 005 m13s The val-ues of the mean diameter of the sediment particles d of the inter-

nal friction angle and the density of the solid phase qs wereassigned according to the results of a field survey after the 2007event whereas the Manning roughness n was estimated by Aronicaet al (2008)

For each synthetic debris-flow hydrograph the maximum flowvelocities water depths and impact pressures were computed ateach node and element of the computational domain to derivethe event intensity By combining this intensity and the probability(return period) the hazard map based on the classification shownin Table 1 was obtained (Fig 15)

62 GT Aronica et al Physics and Chemistry of the Earth 49 (2012) 52ndash63

Furthermore by combining the hazard and the exposure mapsthe final risk map was obtained (Fig 16) For simplicity the expo-sure map is not reported here because the entire area is classifiedas a single class (E4) according to the Flood Management Plan forSicily

5 Discussion and conclusions

In this study an integrated methodology for the analysis of deb-ris-flow risk was proposed and applied to the Mastroguglielmocatchment located in the north-eastern part of Sicily Italy Givenits particular meteorological and geologicalndashmorphological condi-tions this area is an example of a high-risk-damage scenario asevidenced by the extreme event that occurred on 25 October 2007

The methodology presented herein integrates a stability modelthat identifies the areas of potential shallow landslides under dif-ferent meteorological conditions with a two-dimensional debris-flow routing model that allows for debris-flow hazard evaluationand mapping

The stability model used relies upon slope-stability analysis andattempts to extend spatially simplified geotechnical stability mod-els based on a limit-equilibrium approach Due to the difficulties ofcollecting and managing a large amount of data this type of anal-ysis has typically been carried out locally on small areas To applythese methods to large areas GIS software was adopted because itrepresents the ideal working environment Thus all of the calcula-tions were carried out using the approach implemented in the free-ware computer program SHALSTAB The geotechnical modeladopted in the study was calibrated on some reference slopes forwhich accurate back-analyses were performed Using the spatialanalyst of the adopted GIS platform the threshold values of theeffective precipitation q that causes instabilities were evaluatedand mapped Then for each of the pluviometric condition consid-ered in the analyses the areas potentially subjected to shallowinstabilities were detected by comparing the estimated and thethreshold values of the effective precipitation

The proposed hydrodynamic model used for the debris-flowpropagation although originally developed for modelling liquid-flow patterns in floodplains under complex topography conditionswas demonstrated to be well suited after some modifications fordebris-flow modelling The finite-element approach demonstratedits capability of describing the complex geometries of the urbanenvironments as the distributed nature of the 2D code allows itto be efficiently linked with the stability slope model In fact theflooded areas identified by the 2D propagation model are consis-tent with those surveyed after the October 2007 event

The model is also capable of providing those debris-flow charac-teristics such as depths flow velocities and hydrodynamic forcesthat are necessary to conduct a spatially detailed flood-risk analy-sis in urban areas

Currently flood-plain management requires new approaches toaddress non-structural measures and to take into account environ-mental and social requirements The availability of robust ap-proaches such as those presented in this paper that support theplanning of flood mitigation strategies is becoming more and morenecessary in mapping debris-flow hazard and risk in alluvial-proneareas

References

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Apel H Thieken AH Merz B Bloschl G 2006 A probabilistic modelling systemfor assessing flood risks Nat Hazards 38 79ndash100

Apel H Aronica GT Kreibich H Thieken AH 2009 Flood risk analysesmdashhowdetailed do we need to be Nat Hazards 49 79ndash98

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Armanini A 2005 Principi di Idraulica Fluviale Ed BIOS CosenzaAronica GT Brigandigrave G Marletta C Manfregrave B 2008 Hydrological and hydraulic

analysis of the flash flood event on 25 October 2007 in North-Eastern part ofSicily Italy in Proceedings of FLOODrisk 2008 Oxford UK pp 1609ndash1615

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Biondi G Cascone E Maugeri M 2002 Flow and deformation failure of sandyslopes Soil Dyn Earthq Eng 22 1103ndash1114

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Bonardi G Giunta G Liguori V Perrone V Russo M Zuppetta A 1976 Schemageologico dei Monti Peloritani Boll Soc Geol It 95 49ndash74

Bonardi G Giunta G Messina A Perrone V Russo M 1996 The Calabria-Peloritani Arc and its correlation with Northern Africa and Southern Europe ndashField Trip Guidebook 6th Field Meeting IGCP Project No 276 Newsletter 6 pp1ndash80

Bonardi G Cavazza W Perrone V Rossi S 2001 Calabria-Peloritani terrane andNorthern Ionian Sea In Vai GB Martini IP (Eds) Anatomy of an Orogen TheApennines and Adjacent Mediterranean Basins Kluwer Academic PublishersDordrecht The Netherlands pp 287ndash306

Buumlchele B Kreibich H Kron A Thieken A Ihringer J Oberle P Merz BNestmann F 2006 Flood-risk mapping contributions towards an enhancedassessment of extreme events and associated risks Nat Hazards Earth Syst Sci6 485ndash503

Brufau P Garciacutea-Navarro P Ghilardi P Natale L Savi F 2000 1D mathematicalmodelling of debris flow J Hydraul Res 38 (6) 435ndash446

Brunetti MT Peruccacci S Rossi M Luciani S Valigi D Guzzetti F 2010Rainfall thresholds for the possible occurrence of landslides in Italy NatHazards Earth Syst Sci 10 447ndash458

Calvo B Savi F 2009 A real-world application of Monte Carlo procedure for debrisflow risk assesment Comput Geosci 35 967ndash977

Chow VT Maidment DR Mays LW 1988 Applied Hydrology McGraw-HillInternational Editions New York

Crosta GB Frattini P 2003 Distributed modelling of shallow landslides triggeredby intense rainfall Nat Hazards Earth Syst Sci 3 81ndash93

De Bruijn KM Green C Johnson C McFadden L 2007 Evolving concepts inflood risk management searching for a common language Adv NatTechnologic Hazards Res 25 61ndash75

Del Monaco G Leoni G Margottini C Puglisi C Spizzichino D 2003 Large scaledebris-flow hazard assessment a geotechnical approach and GIS modellingNat Hazards Earth Syst Sci 3 443ndash455

De Wrachien D Mambretti S 2011 Assessment of debris flow magnitude in smallcatchments of the Lombardy alps the Val Gola case study Agric Sci 2 (1) 9ndash15

Dietrich WE Reiss R Hsu ML Montgomery DR 1995 A process-based modelfor colluvial soil depth and shallow landsliding using digital elevation dataHydrol Proc 9 383ndash400

Dietrich WE Wilson CJ Montgomery DR McKean J 1993 Analysis of erosionthresholds channel networks and landscape morphology using a digital terrainmodel J Geol 101 259ndash278

Dietrich WE Wilson CJ Montgomery DR McKean J Bauer R 1992Channelization thresholds and land surface morphology Geology 20 675ndash679

Dietrich WE Bellugi D Real de Asua R 2001 Validation of the shallow landslidemodel SHALSTAB for forest management in Wigmosta MS Burges SJ (Eds)Land Use and Watersheds Human Influence on Hydrology and Geomorphologyin Urban and Forest Areas American Geophysical Union Water Science andApplication Washington DC 2 195ndash227

European Union Directive 200760EC 2007 BrusselsFaella C Nigro E 2001a Effetti delle colate rapide sulle costruzioni Parte prima

descrizione del danno in Forum per il Rischio Idrogeologico in Campania ndashFenomeni di Colata Rapida di Fango nel Maggio lsquo98 Napoli Italy pp102ndash112

Faella C Nigro E 2001b Effetti delle colate rapide sulle costruzioni in ParteSeconda Valutazione della Velocitagrave Forum per il Rischio Idrogeologico inCampania ndash Fenomeni di Colata Rapida di Fango nel Maggio lsquo98 Napoli Italypp 113ndash125

FEMA (Federal Emergency Management Agency) 2002 Guidelines andSpecifications for Flood Hazard Mapping Partners FEMA Publications Floodstudies and mapping vol 1

FLO-2D Userrsquos Manual 2006 Version 200610 Nutrioso AZ USA pp 94Gentile F Bisantino T Trisorio Liuzzi G 2008 Debris-flow risk analysis in south

Gargano watersheds (Southern-Italy) Nat Hazards 44 1ndash17Garcia R Lopez JL Noya M Bello ME Bello MT Gonzalez N Paredes G

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GT Aronica et al Physics and Chemistry of the Earth 49 (2012) 52ndash63 63

Glade T Crozier T Smith P 2000 Applying probability determination to refinelandslide triggering rainfall thresholds using an empirical lsquolsquoantecedent dailyrainfall modelrsquorsquo Pure Appl Geophys 157 1059ndash1079

Huff FA 1967 Time distribution of rainfall in heavy storms Water Resour Res 29227ndash238

Iovine G Di Gregorio S Lupino V 2003 Debris-flow susceptibility assessmentthrough cellular automata modeling an example from 15ndash16 December 1999disaster at Cervinara and San Martino Valle Caudina (Campania southern Italy)Nat Hazards Earth Syst Sci 3 457ndash468

International Strategy for Disaster Reduction (ISDR) 1999 International Decade forNatural Disaster Reduction Successor Arrangements Report of the Secretary-General lthttpwwwunisdrorggt

Iverson RM 2000 Landslide triggering by rain infiltration Water Resour Res 36(7) 1897ndash1910

Laigle D Coussot P 1997 Numerical modelling of mudflows J Hydraul Eng 123617ndash623

Maugeri M Motta E 2011 Effects of heavy rainfalls on slope behaviour theOctober 1 2009 Disaster of Messina (Italy) In Susumu L (Ed) Geotechnicsand Earthquake Geotechnics Towards Global Sustainability Springer Science +Business Media BV Dordrecht pp 169ndash190

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Merz B Thieken AH Gocht M 2007 Flood risk mapping at the local scaleconcepts and challenges In Begum S Stive MJF Hall JW (Eds) Flood RiskManagement in Europe Innovation in Policy and Practice Springer Dordrechtpp 231ndash251

Messina A Russo S Stagno F 1996 The crystalline basements of the Calabrian-Peloritani Arc in 6th Field Meeting IGCP Project No 276 Newsletter 6 pp 93ndash144

Montgomery DR Dietrich WE 1994 A physically based model for thetopographic control of shallow landsliding Water Resour Res 30 (4) 1153ndash1171

Naef D Rickenmann D Rutschmann P McArdell BW 2006 Comparison of flowresistance relations for debris flows using a one-dimensional finite elementsimulation model Nat Hazards Earth Syst Sci 6 155ndash165

OrsquoBrien JS Julien PJ Fullerton WT 1993 Two-dimensional water flood andmudflow simulation J Hydraul Eng 119 (2) 244ndash261

Regione Sicilia 2004 Piano Stralcio di bacino per lrsquoAssetto Idrogeologico dellaRegione Siciliana ndash Relazione generale lthttp885321452 paigt

Reichenbach P Cardinali M De Vita P Guzzetti F 1998 Regional hydrologicalthresholds for landslides and floods in the Tiber River Basin (central Italy)Environ Geol 35 (2ndash3) 146ndash159

Rosso R Rulli MC Vannucchi G 2006 A physically based model for thehydrologic control on shallow landsliding Water Resour Res 42 1153ndash1171

Santi PM Hewitt K VanDine DF Barillas Cruz E 2011 Debris-flow impactvulnerability and response Nat Hazards 56 (1) 371ndash402

Sidle RC Swanston DN 1982 Analysis of a small debris slide in costal AlaskaCanadian Geotech J 38 (1) 995ndash1024

Takahashi T 1991 Debris Flow IAHR Monograph Series Balkema PublishersRotterdam

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USACE (US Army Corps of Engineers) 2000 Generic Depth-Damage RelationshipsEconomic Guidance Memorandum Report EGM 01-03

USACE (US Army Corps of Engineers) 2003 Generic Depth-Damage Relationshipsfor Residential Structures with Basements Economic Guidance MemorandumReport EGM 04-01

USACE (US Army Corps of Engineers) 1996 Risk-Based Analysis for Flood DamageReduction Studies Engineering and Design Washington DC Report EM 1110-2-1619

Wieczorek GF 1987 Effects of rainfall intensity and duration on debris flows inthe Central Santa Cruz Mountains California In Costa JE Wieczorek GF(Eds) Debris FlowAvalanches Process Recognition and Mitigation BoulderColo pp 93ndash104

Wieczorek G F Glade T 2005 Climatic factors influencing occurrence of debrisflows in Jakob M Hungr O (Eds) Debris Flow Hazards and RelatedPhenomena Springer Berlin Heidelberg pp 325ndash362

Wilson RC Wieczorek GF 1995 Rainfall thresholds for the initiation of debrisflow at La Honda California Environ Eng Geosci 1 (1) 11ndash27

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Zanchetta G Sulpizio R Pareschi MT Leoni FM Santacroce R 2004Characteristics of May 5ndash6 1998 volcaniclastic debris flows in the Sarno area(Campania southern Italy) relationships to structural damage and hazardzonation J Volcanol Geotherm Res 133 377ndash393