A Comparative Analysis of Terrain Stability Models for Predicting Shallow Landslides in Colluvial...

2007) 207–223www.elsevier.com/locate/geomorph

Geomorphology 87 (

A comparative analysis of terrain stability models for predictingshallow landslides in colluvial soils

C. Meisina ⁎, S. Scarabelli 1

Department of Earth Sciences, University of Pavia, Via Ferrata n°1, 27100 Pavia, Italy

Received 20 August 2004; received in revised form 9 January 2006; accepted 25 March 2006Available online 19 October 2006

Abstract

Most of the slopes of the hilly areas of the Apennines are composed of colluvial soils originating from the weathering of thebedrock and down slope transportation. Shallow slides affect this superficial cover, depend largely on the surface topography andare a recurrent problem. SINMAP and SHALSTAB are terrain stability models that combine steady state hydrology assumptionswith the infinite slope stability model to quantify shallow slope stability. They have a similar physical basis but they use differentindices to quantify instability. The purposes of this study are to test and compare the approaches of SINMAP and SHALSTABmodels for slope stability analysis and to compare the results of these analyses with the locations of the shallow landslides thatoccurred on November 2002 in an area of the Oltrepo Pavese (Northern Apennines). The territory of S. Giuletta, characterized byclayey–silty colluvial soils, represents the test site. The Digital Elevation Model was constructed from a 1:5000 scale contour mapand was used to estimate the slope of the terrain as well as the potential soil moisture conditions. In situ and laboratory testsprovided the basis for measuring values for soil hydraulic and geotechnical parameters (moisture content, soil suction, Atterberglimits, methylene blue dye adsorption, hydraulic conductivity). Soil thickness was extracted from a soil database. An inventory oflandslide from interpretation of aerial photographs and field surveys was used to document sites of instability (mostly soil slips)and to provide a test of model performance by comparing observed landslide locations with model predictions. The study discussesthe practical advantages and limitations of the two models in connection with the geological characteristics of the studied area,which could be representative of similar geological contexts in the Apennines.© 2006 Elsevier B.V. All rights reserved.

Keywords: Shallow landslide; Colluvial soils; SINMAP; SHALSTAB; Oltrepo Pavese

1. Introduction

Most of the slopes of the hilly areas of the Apenninesare covered by colluvial soils originating from theweathering of the bedrock and down slope transportation.Shallow translational landslides are the most commonly

⁎ Corresponding author. Tel.: +39 0382985831; fax: +39 0382985890.E-mail address: [email protected] (C. Meisina).

1 Tel.: +39 0382985831; fax: +39 0382985890.

0169-555X/$ - see front matter © 2006 Elsevier B.V. All rights reserved.doi:10.1016/j.geomorph.2006.03.039

observed failure mode involving colluvial slopes; theydepend largely on the surface topography and are arecurrent problem. The soil types involved are quitevaried, ranging from sand to clayey silt (Hutchinson,1988). The triggering factor is represented by heavyrainstormswith high rainfall intensities and high duration.Several authors have studied these phenomena in order todetermine the predisposing and triggering factors, toprevent future disasters (Van Asch et al., 1999; Antronicoand Gullà, 2000; Campus et al., 2001; D'Amato Avanziet al., 2003). Despite the modest volume involved,

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208 C. Meisina, S. Scarabelli / Geomorphology 87 (2007) 207–223

shallow landslides are very dangerous, due to the absenceof incipient movement evidence and their high velocity.

Numerical models are often used for the preliminaryidentification of slopes prone to shallow landslide occur-rence (Borga et al., 1998; Morrissey et al., 2001;Guimaraes et al., 2003). Several shallow landslide modelshave been developed on the basis of the infinite slopeequation. SHALSTAB (Montgomery and Dietrich, 1994)and SINMAP (Pack et al., 1998, 2001; Pack andTarboton, 2004) are two such models that combine steadystate hydrology assumptions with the infinite slopestability model to quantify slope stability. Inputs aredigital elevation data from which slope and drainage areaare evaluated as well as hydrologic and soil parameters.These are two models with similar physical basis but theyuse different indices to quantify instability. SINMAPquantifies terrain stability in terms of the probability thatthe infinite slope stability model factor of safety is greaterthan one over uniform probability distributions quantify-ing the uncertainty in model parameters. SHALSTABquantifies terrain instability in terms of the criticaleffective rainfall required to trigger pore pressure inducedinstability.

The aims of the study are:

• to apply and evaluate the different approaches ofSINMAPand SHALSTAB for shallow slope instabilityanalysis to a specific geological context (the OltrepoPavese), characterized by colluvial soils originatingfrom the weathering of the clayey bedrock and downslope transportation;

• to compare the results of these analyses;• to understand their respective potentials, with regard totheir applicability and data demand, in the Apenninescontext.

The territory of S. Giuletta represents the test site. It islocated in the Oltrepo Pavese (Northern Apennines, Italy)and it is characterized by clayey–silty colluvial soils onmarly bedrock. In this area, representative of similarsituations in Apennines, shallow landslides occurred onNovember 2002.

2. Weathered clay soils and shallow landslides

In most parts of the Italian Apennines the bedrock isrepresented by highly tectonized clays and by flyschconsisting of alternating layers of clay, clay shales withcalcareous arenaceous marls. Both represent structurallycomplex tectonized formations (Esu, 1977) having apredominant argillaceous component and they are verydegradable material, also in temperate climate. The

processes of chemical alteration and physical breakdownresult in the weathering of the bedrock and the pro-duction of thick sequences of weathered materials, gene-rally consisting in fine-grained soils (silt and clay),including diffused lapideous fragments of different size,with reduced physical and geotechnical properties. Thedegradation of the mechanical properties of the bedrockcan be referred to processes concerning with water con-tent changes, e.g. cycles of wetting–drying, softening(Picarelli, 2000), infiltration of different aqueous solu-tions (Calvello et al., 2005). Gravitational forces actingon these disaggregated materials cause them to movedown the slopes and accumulate in topographic depres-sions (colluvial deposits).

The most common slope instabilities in these colluvialdeposits are an initial shallow rotational or translationalslide followed by flowage of the disturbed mass (Turner,1996), which occur following periods of heavy rainfall.The infiltration of rain (saturation from above) and theformation and rise of a temporary perched water table, incontact with the less permeable bedrock, increase the porepressure and cause a reduction in shear strength of thecolluvial material. The failure surfacemay formwithin theweatheredmaterial, but generally corresponds to the pointof contact between the colluvial soils and the bedrock.

3. The slope stability models

The mathematical models developed by Montgomeryand Dietrich (1994), Pack et al. (1998) available forstudying shallow landslides, take into account the infiniteplane slope stability model coupled with a steady statetopographic hydrologic model. The term ‘steady state’ atthis point does not refer to any long term, e.g. annual,averages, but to a critical period (event) of wet weather thatis likely to trigger landslides. Both models are toolsdesigned as an ArcView extension. Important assumptionsunderlying the SINMAP (Stability INdex MAPping) andSHALSTAB (SHAllow Lansliding STABility Model)theory include that the subsurface hydrologic boundaryis parallel to the surface, and that soil thickness andhydraulic conductivity are uniform. Soil thickness isinterpreted perpendicular to the slope. Other hypothesesare a steady state shallow subsurface flow and the absenceof deep-drainage and flow in the substratum. Both modelsrequire very detailed field surveys, at 1:5000 or 1:2000scale and a minimum number of soil and hydraulicparameters determined with in situ and laboratory tests.

SINMAP outputs the stability index value (SI). Thestability index is defined as the probability that a loca-tion is stable assuming uniform distributions of theparameters over the uncertainty ranges. This value range

Fig. 1. Definition of specific catchment area.

209C. Meisina, S. Scarabelli / Geomorphology 87 (2007) 207–223

between 0 (most unstable) and 1 (stable). Where the mostconservative set of parameters (i.e. the set with the mostunfavorable combination of parameters for stability) inthemodel results in stability, the stability index is definedas the factor of safety at this location.

The factor of safety calculation (FS) in SINMAP isbased on the infinite slope form of the Mohr–Coulombfailure law as expressed by the ratio of stabilizing forces(shear strength) to destabilizing forces (shear stress) on afailure plane parallel to the ground surface.

FS ¼ Cr þ Cs þ cos2h½qsd gðD−DwÞ þ ðqsg−qwgÞDw�tan/Dqssinhcosh

ð1Þ

Where Cr = root cohesion [N/m2], Cs=soil cohesion [N/m2], θ=slope angle, ρs=wet soil density [kg/m3], ρw=the density of water [kg/m3], g=gravitational acceleration(9.81m/s2),D=the vertical soil depth [m],Dw=the verticalheight of the water table within the soil layer [m], andϕ theinternal friction angle of the soil [°]. The slope angle θ is thearc tangent of the slope, S, expressed as a decimal drop perunit horizontal distance. Soil thickness, h [m], and depthare related as follows

h ¼ Dcosh

With this change FS reduces to

FS ¼ C þ cosh½1−wr�tan/sinh

ð2Þ

Where, w=Dw/D=hw/h=relative wetness, C=(Cr+Cs) /(h ρs g)=combined cohesionmade dimensionless relativeto the perpendicular soil thickness, r=ρw/ρs=water tosoil density ratio.

Following TOPMODEL (Beven and Kirkby, 1979)SINMAP makes the following assumptions:

(1) Shallow lateral subsurface flow follows topo-graphic gradients. This implies that the contribut-ing area to flow at any point is given by the specificcatchment area (a) defined from the surfacetopography (Fig. 1);

(2) Lateral discharge q at each point is in equilibriumwith a steady state recharge R [m/h];

(3) The capacity for lateral flux at each point is Tsinθ,where T is the soil transmissivity [m2/h], i.e. hyd-raulic conductivity [m/h] times soil thickness, h [m].

Assumptions 1 and 2 together imply that lateraldischarge (depth integrated per unit contour length), is

q=Ra [m2/h]. With assumption 3 the relative wetnessis

w ¼ minRa

Tsinh; 1

� �ð3Þ

The relative wetness, which defines the relative depthof the perched water table within the soil layer, has anupper bound of 1 with any excess assumed to formoverland flow. The ratio R/T in Eq. (3) quantifies therelative wetness in terms of assumed steady state rechargerelative to the soil's capacity for lateral drainage of water.The ratioR/T, which SINMAP treats as a single parameter,therefore combines both climate and hydrogeologicalfactors. The quantity (T/R)sinθ [m] may be thought of asthe length of hillslope (planar, not convergent) required todevelop saturation in the critical wet period beingconsidered.

To define the stability index, the wetness index fromEq. (3) is incorporated into the dimensionless factor ofsafety, Eq. (2), which becomes

FS ¼ C þ cosh 1−min RaTsinh ; 1� �

r� �

tan/

sinhð4Þ

The variable a and θ are derived from the DigitalElevation Model topography whereas the values of C,tanϕ, r and R/T are user input. In SINMAP the cohesionis retained in the infinite slope stability model. A di-mensionless cohesion factor C is established, combiningcohesion due to soil and root properties and soil densityand thickness. The resulting factor gives a ratio of thecohesive strength of the soil relative to its weight.

SINMAP allows uncertainty of the variables throughthe specification of lower and upper bounds. Formallythese bounds define uniform probability distributions overwhich these quantities are assumed to vary at random.Wedenote R/T=x, tanϕ= t, and the uniform distributions withlower and upper bounds as C∼U(C1,C2), x∼U(x1,x2)and t∼U(t1,t2). The smallest C and t, (i.e. C1 and t1)


together with the largest x (i.e. x2) defines the worst case(most conservative) scenario under this assumed uncer-tainty (variability) in the parameters. Areas, where underthis worst case scenario FS is greater than 1, are in terms ofthis model unconditionally stable and SINMAP define

SI ¼ FSmin ¼C1þ cosh 1−min X2 a

sinh ; 1� �

r� �

t1

sinhð5Þ

For areas where the minimum factor of safety is lessthan 1, there is a possibility (probability) of failure. Thisis a spatial probability due to the uncertainty (spatialvariability) in C, tanϕ and T. This probability does havea temporal element in that R characterizes a wetness thatmay vary with time. Therefore the uncertainty in xcombines both spatial and temporal probabilities. Inthese regions (with FSminb1) SI=Prob(FSN1).

The best case scenario is when C=C2, x=x1, andt= t which leads to

SI ¼ FSmax ¼C2þ cosh 1−min X1 a

sinh ; 1� �

r� �

t2

sinhð6Þ

In the case that FSmaxb1, then SI=Prob(FSN1)=0.SINMAP differentiates between six different classes

of SI. Stable, moderately-stable and quasi-stable classeshave SIN1.5 and FSN1.0, and they represent regionsthat should not fail with the most conservativeparameters in the specified range (destabilizing factorsare required for instability, as local loading, roaddrainage, etc…). For lower threshold and upperthreshold classes the calculated FS is b1.0, yet theprobability of failure is less than and greater than 50%,respectively. These two classes define a lower and upperlimit for ground failure and have SI values 1.0–1.5 and0–1.0, respectively. The unconditionally unstable(defended) class means that the probability of failure,within the specified range of parameters, is greatest(assumed N90% probability).

SHALSTAB predicts the steady state rainfall necessaryfor slope failure throughout a study area. The output is logq/T that is the hydrological ratio (q=amount of waterinfiltration into the soil; T=water flow within the soil).

qT¼ qs

qw1−

tanhtan/

� �basinh ð7Þ

The drainage area a, the outflow boundary length band hillslope angle θ are defined by the numericalsurface used in the digital terrain model. Thus fourparameters may need to be assigned to apply this model:the soil bulk density ρs, the angle of internal friction ϕ,the soil transmissivity T, and the effective precipitation

q (rainfall minus evapotranspiration). Because thetransmissivity is always much larger than the effectiveprecipitation, the ratio of q/T is small, thus the logarithmof q/T is used.

A fundamental assumption of this model is that areascalculated to have the lowest q/T values (least amount ofprecipitation required for instability) should representthe least stable land that has the greatest potential forshallow landsliding. If tanθ equals or exceeds tanϕ,slope instability will occur even under dry conditionsaccording to the model. This category of instability iscalled “chronic”. If tanθb tanϕ (1−ρw /ρs), then slopeinstability is unlikely as the ground is not expected tofail even at saturation. Grid cells falling into thiscategory are ranked as “stable”.

4. Geological and climatological setting of the studyarea

The Oltrepo Pavese is characterized by a complexgeological and structural setting. The geology isdominated by sedimentary formations, ranging fromthe lower Cretaceous to the Pliocene, with a dominantclay component (Beatrizzotti et al., 1969; Braga et al.,1985). Silty and/or clayey deposits formed by weather-ing and down slope transportation cover the argillaceousbedrock units. The territory of S.Giuletta is located inthe northern part of Oltrepo Pavese (Northern Apen-nines) (Fig. 2). The study site covers an area of 5 km2

and lies within the municipalities of Santa Giuletta andRedavalle in the province of Pavia. In its upper part thearea is characterized by sandstones and conglomerates(M. Arzolo Sandstone) and cavernous limestones(Gessoso–Solfifera Formation), which act as hydro-geological reservoirs steadily imbibing the underlyingclayey and silty S.Agata Fossili Marls (Scagni andVercesi, 1987; Braghieri et al., 1997). These Mio-Pliocene units have a sub-horizontal layering and arehighly fractured, with different sets of subvertical faults.The Quaternary is represented by alluvial deposits of thePo River in the Northern part of the study area and bypedogenized colluvial soils, which cover the bedrockwith a thickness ranging from 0.5 to about 5–6 m intopographic hollows.

Elevation ranges from 80 m to 339 m above sea level(M. Zavo). The structural–geological context influencesthe morphology. The ridges bounding the area (Castello,M. Zavo) are made up mainly of sandstone. Slopevalues are between 0° (alluvial plain) and 40°. There area number of slopes that locally exceed 45° at the contactbetween the Gessoso–Solfifera Formation and the M.Arzolo Sandstone.

Fig. 2. Geological sketch of the study area (from Scagni and Vercesi, 1987, modified). 1: Alluvial deposits (Holocene–Pleistocene); 2: LugagnanoClay (lower-medium Pliocene); 3: M. Arzolo Sandstone (Upper Messinian); 4: Gessoso–Solfifera Formation (Messinian); 5: S. Agata Fossili Marls(Tortonian); 6: fault; 7: probable fault; 8:study area.


A shallow water table is present. The depth to thewater table varies between 0.5 and 3–4 m, the maxima(3–4 m) occur in November (Braghieri et al., 1997). Thearea has a mean annual rainfall of around 700 mm

(Voghera rain gauge). The general annual rainfall patternis characterized by a summer minimum in July and twomaxima, one in October–November and the other inMay (Rossetti and Ottone, 1979). A land use map was

Fig. 3. Landslide inventory with the location of shallow landslides.


established by photo-interpretation. Vineyard coverabout 83% of the S. Giuletta region.

5. The landslide inventory

A landslide inventory was completed in 1999 by aerialphotograph interpretation at 1:10,000 scale (Fig. 3). The

Fig. 4. A shallow landslide triggered by

slopes are subjected to widespread instability and verylarge landslides developed in the years 1976–78, due tohigh intensity rainfalls that mobilized some old dormantslides. These events caused also the collapse of manyhouses. Different types of slope instability are present.The deepest kinds of movement, with a depth down to40 m, concern the bedrock and are rotational slides, in the

heavy rainfall on November 2002.

Fig. 5. a) Daily and cumulative rainfalls measured for 60 day periodbefore the November 2002 event (Voghera rain gauge); b) comparisonbetween the average monthly rainfall of the Voghera rain gauge and therainfall of November 2002.


upper part of the slope, and translational slides in themiddle and lower slope (Cruden and Varnes, 1996). Atpresent they are dormant or stabilized. Multiple slidingsurfaces have been recognized (Braghieri et al., 1997).The landslides are situated in the S. Agata Fossili Marls,but their scars involved also the Gessoso–Solfifera

Fig. 6. 3D-block diagram showing some shallow lan

Formation. Remediation works consist of trench drainslocated downwards Castello.

Larger landslides mapped in Fig. 3 indicate areas wheremany small instability phenomena are currently present.Shallow landslides are the most commonly observedfailure mode involving colluvial soils. Shallowmovementswere already reported in 1955, particularly in the Pizzoloarea (Aquater–Snamprogetti, 1982). Their identificationand mapping is difficult due to their small size, and to theintensive farming activity. They developed above all in theupper portion of the slopes, underlain by S. Agata FossiliMarls and Gessoso–Solfifera Formation. Despite themodest volume involved, shallow landslides appear verydangerous to roads, buildings and vineyards (Fig. 4).

6. The November 2002 event

On November 2002 intense rainfall triggered someshallow landslides. During the rainstorm of 25–26–27November, roughly 140 mm of rain fell within a72-h period. During the 10 days prior to the storm,antecedent rainfalls were about 90 mm (Fig. 2),increasing the soil moisture. The total Novemberrainfall summed up to 249 mm, about three times theNovember average of 83 mm in normal years (Fig. 5)and was the highest since 1950. The rainfall during thisevent exceed the threshold for shallow landslidetriggering, according to the diagrams proposed byGovi et al. (1985) and Moser and Hohensinn (1982).

The greatest frequency of shallow landslides oc-curred near the village of Pizzolo in the eastern part ofthe area. The thicknesses of the superficial coverinvolved are all of the order of 1–1.5 m. The area ofeach phenomenon was from 500 to 800 m2 (Fig. 6),however it has been observed that critical pluviometric

dslides triggered by the November 2002 event.


conditions can produce contiguous single phenomenainvolving much larger portions of slope.

The field investigation consisted of a geologic–geomorphologic survey at a scale of 1:5000. Field studiesshowed that the majority of these landslides initiated onslopes from 12° to 25°, the landslide scars correspond tochange in slope angle. Many of the shallow landslideswere also placed within the scars of old landslides. Theland use was an important triggering factor, a greatnumber of instabilities occurred in the youngest vineyard.

7. Characterization of the colluvial soil

7.1. Pedological characteristics

The depth of a shallow landslide is generally not morethan 1–2 m. Therefore the pedological map provides auseful basis for the study of the characteristics of thesesoils. Information on the soils are available from the soilmaps at a scale of 1:10,000 covering the entire study area(ERSAL, 2001).

Following are the definitions of the soils (Fig. 7):

• MRI2 — Eutric Leptosol: silt and silt loam, up to0.8 m thick, derived from the strong weathering ofmarls with silty intercalations (S. Agata FossiliMarls), presence of surface marly stones, welldrained profile. The slope angle is of about 15° andthe soil is susceptible to erosion, especially sheet

Fig. 7. Soil map (ERSAL, 2001) (between bracke

erosion. Land use: vineyard (83%) and crop. Theregion represents the 52% of the study site, landslidescover the 70% of the area. Twenty-one shallowlandslides have been identified in the field.

• FGI — Calcaric Cambisol: clay loam and silt, up to1.20 m thick, derived from marls with intercalationsof sandstone and cavernous limestone (Gessoso–Solfifera Formations and M. Arzolo Sandstones),well drained. The slope angle is of about 7° and theslope presents slight erosion. Land use: vineyard(53%) and forest (29%). The region represents the20% of the study site, landslides cover the 20% of thearea. Twenty-two shallow instabilities occurred onNovember 2002 event.

• MRL1— Calcaric Cambisol: stony sandy loam; up to0.8 m thick; carbonate content increasing with depth(N20%), the parent material is composed by sandstoneand lenses of conglomerates. Well drained. The slopeangle is of about 15°, and it is characterized bymoderate erosion. Land use: vineyard (67%), forest(17%). The region represents the 15% of the study site,landslides cover the 38% of the area.

• CTU1 — Eutric Leptosol: the soil is uniform intexture (silt loam), has a thickness up to 1.50 m andhas almost uniform reddish brown color throughout.The parent material is sand and gravel belonging toold and strongly weathered alluvial deposits. Theslope angle ranges between 7 and 10°. Land use:vineyard (67%), crop (16%) and forest (13%).

ts the calibration regions used in SINMAP).

Fig. 8. Geotechnical profile of colluvium type A (borehole S3). w: natural water content, Wl: liquid limit; Wp: plastic limit; Sr: degree of saturation;γd: dry density.


• SNN1— Calcaric Cambisol: clay and silty clay, witha high carbonate content and with thickness up to1.50 m, the parent material is composed by clay,poorly drained. The soil cover alluvial terraces. Landuse: vineyard (32%), crop (43%).

• RSN1 — Calcaric Cambisol: silty clay with fewgravel, derived from alluvial silty gravel, poorlydrained, (average thickness of about 0.50 m). Thesoils cover the alluvial plane. Land use: vineyard(62%), crops (19%) and forest (16%).

Soils of CTU1, SNN1 and RSN1 type cover only the6.5% of the test site, and do not present shallowinstabilities. Urban areas cover the remaining 6.4% ofthe study area.

7.2. Geotechnical characteristics of the soil

At the end of 1980s the area, corresponding to theMRI2 soils, was studied by the Public Administration of

the Province of Pavia for the design of remediation worksconcerning deep landslides (Braghieri et al., 1997)(Fig. 3). Geotechnical investigation consisted of bore-holes and laboratory tests, including grain size analysis,Atterberg limits, measurement of water content, unitweight and degree of saturation (Figs. 8 and 9). In theseprevious studies direct shear tests, performed in the 100 to300 kPa stress range, allowed the measurement of shearstrength parameters in terms of effective stresses. For themeasure of the residual shear strength the Kanji methodwas adopted (Kanji, 1974).

In addition to these data the site of Pizzolo wasselected for geotechnical characterization of the collu-vial soils. Pizzolo, where shallow landslides had beenlocalised with the greatest frequency in the past, couldbe considered representative of the study area. The fieldinvestigation consisted of soil profile description, fieldpermeability tests and soil sampling. The values of thematric suction were determined during March 2003 (wetseason) by the filter paper technique (Chandler and

Fig. 9. Geotechnical profile of colluvium type B (borehole S2). w: natural water content, Wl: liquid limit; Wp: plastic limit; Sr: degree of saturation;γd: dry density.


Gutierrez, 1986; ASTM, 1994; Swarbrick, 1995). Thevalues range between 195 and 200 kPa. The soils aresaturated or quasi-saturated for most of the year. Thehydraulic conductivity of the saturated soils wasdetermined through field test (variable load well test).The results show that colluvial soils present a perme-ability ranging from 2.3/2.7 to 10−3 m/s.

For the geotechnical investigation laboratory tests wereconducted on samples taken up to a depth of 1–2 m fromground level in the landslide scar (Fig. 10). Laboratoryanalyses were carried out with the aim of identifying theparticle size classes, the Atterberg limits and themethyleneblue dye adsorption (Vb). Water content and dry densitywere also measured to determine the in situ state of thematerial. The tests were carried out following theAmerican Society for Testing and Materials Standards(ASTM, 1994). The methylene blue dye adsorption(“value of blue” Vb) was determined in accordance withthe French AFNOR standards (AFNOR, 1998). Thismethod is based on the adsorption of methylene blue dyeby clay minerals. Methylene blue dye in aqueous solutiondissociates into anion chloride and methylene blue cation.This cation displaces Na+, Ca2+, K+ and Mg2+cations

allowing the determination of the specific surfaces of theseminerals (Sa) (Magnan and Youssefian, 1989; Chiapponeand Scavia, 1999).

The geotechnical profiles (Figs. 8, 9) show a slightvariability of the geotechnical properties with depth. Thecolluvium is quite homogeneous especially on the top soil(thickness b2 m, Fig. 10). There is no evidence of amineralogical evolution down the profile. The Vb ofcolluvial soils varies between 4.4 and 5.5 g/100 g of soiland underlines the presence of a certain amount of swellingminerals (smectite and vermiculite), as confirmed byX-raydiffraction analyses (Piccio et al., 1990) and by themodified activity chart, that classify the swelling–shrinkage potential of these colluvial soils as high andvery high (Fig. 11).

More evident is the areal variation of the geotechnicalproperties. Silt with clay of high plasticity (CH) (Figs. 12and 13) characterizes colluvial soils in the lower part ofthe slope and in eastern part of the area (borehole S3, S4,S5 and trench pit T6) (colluvium of typeA) (Fig. 8). In theupper part of the area colluvial soils are represented byclayey silt with sand (boreholes S1 and S2) (Figs. 12, 13)being classified as CL and OL–MH in the USCS

Fig. 10. Geotechnical profile of the soils in the Pizzolo area (colluvium type A) (see Fig. 3). w: natural water content, Wl: liquid limit; Wp: plasticlimit; Sr: degree of saturation; γd: dry density, Vb: value of blue.


(colluvium of type B) (Fig. 9). This can be partiallyreferred to a substantial difference in the bedrock of the S.Agata Fossili Marls where most of the shallow landslidesoccur: from the top of this formation (borehole S1 and S2)down (boreholes S3, S4) the plasticity of the bedrockchanges from low to high (Braghieri et al., 1997).

Fig. 11. Modified activity chart (Williams and Donaldson, 1980).c: colluvium; s: bedrock.

Shallow landslides mainly involve a homogeneoussuperficial cover and they concentrate in correspondenceboth of the colluvium of type A (Pizzolo area) and oftype B.

Soil strength parameters (cohesion, residual andeffective friction angle) were measured in laboratory(direct shear tests and Kanji method) for regions 1a and1b. They were derived from back analysis for the otherregions. In colluvial soils, which are involved in shallowlandslides, the residual friction angles (ϕr) vary between11° and 25°, the lowest values, measured in laboratory,correspond to the colluvium of type B. Colluvial soils ofdepth till 2 m and not yet involved in shallow movementsshow an effective friction angle (ϕ′) ranging from valuesof 24°–27° (measured in laboratory on sample of the S3borehole) to 30°–35° (FGI, CTU1), while the effectivecohesion parameter (c′) is 18–20 kPa. The geotechnicalanalysis allows the subdivision of the MRL2 soil into twoportions, corresponding respectively to colluvial soils oftype A and B (Fig. 7).

8. Application of SINMAP and SHALSTAB

The input data set of SINMAP and SHALSTABconsists of topographic slope and parameters quantifyingmaterial properties and climate (primarily a hydrologic

Fig. 12. Grain size distribution envelope. c: colluvium; s: bedrock.


wetness parameter). A Digital Elevation Model (DEM)provides the topographic basis for SINMAP andSHALSTAB study. The accuracy of output is heavilyreliant on the accuracy of the DEM data input. Thereforethe DEM was constructed from a 1:5000 scale contourmap, with a resolution of the topographic data on theorder of 5 m (grid size of 5×5 m).

SINMAP requires the calibration regions, which aresub-samples of the total area of analyses in whichselected properties are assumed to be uniform enoughfor further analysis. Seven calibration regions werechosen on the basis of soil depth and soil texture derivedby the pedological map of the area, and on the basis ofthe geotechnical characterization of the colluvial soils(Fig. 7). Table 1 shows the parameters for the different

Fig. 13. Plastic

calibration regions. Uniform probability distributionswith upper and lower bounds of parameters account forparameter uncertainty (Pack et al., 1998). The cohesionand friction angles range from minimal values (residualconditions), corresponding to areas of old landslides, tothe maximal values measured in laboratory or calculatedthrough back analysis in areas not affected by shallowlandslides.

Generally, root systems contribute to soil strengthby providing an additional cohesion component. Nev-ertheless the vegetation in the area is mainly rep-resented by vineyards, with root systems that varywidely in time and space, in relationship also with theage of the vineyards. Due to the difficulty in esti-mating this parameter we take into account the most

ity chart.

Table 1Initial setting for the input parameters for SINMAP

Region ϕ′min [°] ϕ′max [°] Cmin Cmax T/Rmin [m] T/Rmax [m]

1a 18.0 24.0 0 0.05 3.083 6614.11b 18.0 27.0 0 0 50.00 6.000.02 25.0 35.00 0 0.097 134.40 36963 25.0 30.00 0 0 193.63 3227.324 25.0 35.00 0.054 0.060 7.06 635.595 21.0 24.00 0 0.034 4.87 481.236 19.4 32.00 0.20 0.231 1.79 172.78

Table 3Input parameters for SHALSTAB

Φ′ [°] C′ [N/m2] ρs [kN/m3] T [m2/h] q [m]

28 1044 19.50 8.93×10−2 7.93×10−5


conservative scenario and we excluded root strength inthe calculation.

The ratio steady state recharge R/T soil transmissivitycombines both climate and hydrogeological factors. Thetransmissivity T represents the water flow within the soiland was derived from the hydraulic conductivity(minimal and maximal) measured in the field. Theparameter R (steady state recharge rate) is difficult tomeasure; it is hard to evaluate the amount of infiltratedsubsurface water from the total rainfall measurement. Infact R is influenced by factors like rainfall intensity andduration. The recharge was assumed to be the effectiveprecipitation. It means rainfall minus evapotranspirationand bedrock infiltration. For each region the potentialevapotranspiration was calculated from the rainfall andtemperature with the Thornthwaite and Mather method(1957). The land use map allows determining the waterholding capacity necessary for the evapotranspirationcalculation. The amount of infiltration depends on slopeangle (Table 2) and we suppose that only ¼ of the waterinfiltrates in the substratum.

The other input is a point map with locations of thezone of initiation of the shallow landslides. This pro-cedure includes an interactive visual calibration, allowingadjusting parameters while directly referring to observedlandslides. As SINMAP's concept is based on fieldinformation for calibration, the models output dependsheavily on accurate positioning of known landslideinitiation zones. The landslide initiation points weretaken in field-survey at a 1:5000 scale map. A total of 44shallow landslide initiation points were identified. Thecalibration process was used to fine-tune the valuesentered in terms of upper and lower bounds. Regions 4

Table 2Amount of infiltrated subsurface water versus slope angle

Slope angle (°) Infiltration (%)

0–15 75%15–30 40%30–60 20%

and 5 were excluded from the analysis, as no landslidepoints exist to verify the calibration and the areas are verysmall.

For SHALSTAB the parameters are consideredconstant and uniformly spatially distributed all overthe study domain. They have been calculated asweighted average of the all parameters (Table 3). Thezone of initiation of shallow landslides (landslide scars)were used to test the model predictions.

9. Results and discussion

The stability index (SI) distribution in the study areais shown in Fig. 14. About the 53% of the study area isclassified with SI below one (upper threshold and lowerthreshold classes), which refer to conditions that arehighly unstable and thus critical. The areas most proneto instability are located on steep slopes on the contactbetween two different formations (Gessoso–SolfiferaFormation and M. Arzolo Sandstones). The area aroundthe village of Pizzolo (north-eastern sector of the studyarea) also represents an unstable situation. To assess theaccuracy of prediction the predicted instabilities werecompared to the shallow landslides initiation pointstriggered by the November 2002 event.

The SINMAP approach to instability modelling wasquite successful in describing slope failure in the studyarea, identifying 78% of the 44 observed failures duringNovember 2002 event (Fig. 15). The largest number oflandslides is found in the upper threshold stability class(47%) that represent the 25% of the entire area. Theareas classified as defended represents only the 1.8% ofthe study area.

The individual calibration regions contribute to theresult in different ways, which can reflect the differentmechanisms of instabilities. About 75% of the region 1aand 63% of the region 1b are classified as unstable. The85% (1a) and 75% (1b) of the landslides were predicted.The areas predicted to fail during the event of November2002 include areas with slope angle between 11° and25° and catchment areas b1000 m2. This is in goodagreement with the field surveys.

In region 2 the 77% of the landslides are predicted bySINMAP. The predicted areas are characterized by highslope angle (10° and 34°). This region is locatedin correspondence of the contact between different

Fig. 14. Stability Index Map (SINMAP).

Fig. 15. Prediction accuracy of SINMAP (balls = landslide density;bars = area of the stability classes).


lithologies: the marls (S. Agata Fossili Marls) and thelimestones and sandstones (M. Arzolo Sandstone andGessoso–Solfifera Formation). As observed in the field,the slope angle is the most important factor forinstability in this region.

For all the regions a certain number of landslides fallwithin the moderately stable, quasi-stable or stableindex classes. Landslides that had a SIN1.0 werechecked and were found to be concentrated in thewestern and southern part of the study area. In thewestern sector the discrepancy between measured andcalculated slopes may be attributed to other triggeringmechanisms as farming activities. Landslides notpredicted by SINMAP in the southern sector are locateddownward M. Zavo and have slopes less than 12°.

SINMAP needs high numeric and spatial consistencyin parameter measurement (friction angle, cohesion,hydraulic conductivity). The spatial variability of inputparameters was overcome by an accurate selection of thetesting locations which may be considered representa-tive of the regions, which are areas with homogeneoussoil characteristics. The parameter uncertainty was alsoavoided with the use of uniform probability distributionswith upper and lower bounds of parameters, represent-ing generally the values of friction angle and cohesion inshallow landsliding areas and in non-landsliding areas.The comparison of the results of the SINMAP analysiswith the landslide initiation points allows to calibrate the

model, to adjust the initial R/T parameters, which aredifficult to measure, and then to obtain an improvementof the model performance.

We excluded root strength from the calculation of C,therefore a sensitivity analysis of the role of theparameter C was carried out. Results from a series ofcalculations in which dimensionless cohesion variedand all other parameters remaining unchanged werefound to have a significant influence on the stabilitycalculation for region 1a and 1b. If C has a range of 0–0the 75% of the inventoried landslides are predicted tooccur. Under the range 0–0.5 about 60% of theinventoried landslides are considered unstable and allare within the lower threshold class.

Fig. 16. Log (q/T) distribution in the study area.

Table 4Results of SHALSTAB model

Log(q/T ) Area(%)

Landslides(n°)

Landslides(%)

Landslidedensity (#/km2)

Chronicinstability

2.09 17 38.64 125

b−3.1 2.84 5 11.36 27−3.1 to −2.8 4.31 6 13.64 21−2.8 to −2.5 6.62 9 20.45 21−2.5 to −2.2 5.03 2 4.55 6N−2.2 2.79 0 0.00 0Stable 76.32 5 11.36 1


Fig. 16 shows the slope instability map obtained withSHALSTAB. The lowest q/T values (least stable land)are located above all in the eastern sector of the area(village of Pizzolo). Near the contact between marl,limestones and sandstones, where the steepest slopes arepresent, the values of (q/T ) are also very low. This is inagreement with the results of SINMAP. The 76% of thearea is considered stable (Table 4).

Two means of examining model performance wereused. One method was to compare the distribution of q/Tvalues for mapped landslides with that created by abiased random distribution. Model success occurs if themajority of the landslide scars occur in grid cells withlow values of q/T. For areas mapped as “chronic” orthose falling into the category of b−3.1 (log (q/T )), theincidence of landsliding is high (Table 4). Thisdemonstrates that SHALSTAB is successful at identify-ing the most unstable areas of the landscape.

The other method of examining model performancewas to compare the drainage area per cell size (a/b) andslope values for each landslide with that computed forvarious q/T values in the model.

In Fig. 17 the drainage per cell size (a/b) and slopevalues, for each landslide, were compared with thosecomputed for various q/T. Two vertical lines separatethe fields of “unconditionally stable”, “potentiallyunstable” and “unconditionally unstable”. Most of thelandslides (39%) fall to the right of the “unconditionallyunstable” line, the slope is expected to be unstable even

when dry. Only 5 landslide scars (11%) are placed to theleft of the “unconditionally stable”. Each log (q/T ) curvealso defines a threshold: for a given q/T value, pointsplotted above curve are predicted to be unstable whereasbelow the curve the points would be stable (due toinsufficient effective precipitation). By comparing thesecurves with the landslide points, one can see the value oflog(q/T ) that places the majority of points above the line.Themajority of the points are placed above the −2.5 line,this is the critical value of q/T, which will produceunstable force balance when exceeded (log (q/T )=−3.05 during the event of November 2002).

10. Conclusions

The event of November 2002, which triggered manyshallow landslides in the S. Giuletta territory, can be

Fig. 17. Area-slope scatterplot.


considered as an extreme pluviometric event. The greatestfrequency of shallow landslides occurred near the villageof Pizzolo in the eastern part of the area. The superficialcovers involved are all of the order of 1–1.5 m andcorrespond to colluvial soils derived by the weathering ofthe bedrock and, above all, by the down slope transpor-tation due to deep landslides that characterize the area. Thesoil maps provide a useful tool for the identification of thedifferent types (in terms of thickness and texture) of soilsinvolved in shallow slides. Nevertheless the geotechnicalanalysis allows better distinction between the differenttypes of soils. On the basis of pedological and geotechnicalproperties seven types of colluvial soils were identified.They are represented by clayey silt and clayey silt withsandwith amedium hydraulic conductivity and a degree ofsaturation greater than 90% for most of the year. Importantfactors leading to shallow slope instabilities are representedby steep slopes on the contact between different lithologies(marls, sandstone and limestones) and by farming activity.

Both SINMAP and SHALSTAB identify the easternpart of S. Giuletta (Pizzolo) as themost unstable area. Thisis in agreement with the field surveys. The data required toimplement the models include soil and climate propertiesthat can be highly variable in both space and time.SINMAP does not require numerically precise input andaccepts ranges of values that represent this uncertainty.For SHALSTAB the parameters are constant anduniformly spatially distributed all over the study domain.The choice of an average valuewas very difficult for the S.Giuletta site, due to the different geotechnical propertiesand thicknesses of the colluvial soils.

SINMAPmodelling was donewith a parameter settingsimulating the extreme event of November 2002. The

multi-calibration regions allowed us to take into accountthe different types of colluvial soils and their geotechnicalproperties. Another advantage in SINMAP is thatimprovements of the model can be made for a smallarea, (e.g. the 1a and 1b regions needed more attentionduring calibration). Only few landslides are not predicted.The model seems promising; a high percentage oflandslides (75–85%) is captured. Nevertheless the sharesof the upper threshold class is very high for regions 1a and1b and sometimes the stability index map shows manyareas that did not fail during the November 2002 event.Therefore, the amount of unstable area is overestimated.The results of SHALSTAB seem to bemore realistic in thestudy area for non-extreme events. Using the number oflandslides associated with different log (q/T ) valuesthresholds value was assigned. The stability index (SI)and the ratio q/T should not be interpreted as numericallyprecise but in terms of relative hazard. In this context bothmodels can distinguish between areas of higher hazard(village of Pizzolo) and areas of lower hazard.

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