ACTA GEOGRAPHICA GEOGRAFSKI ZBORNIK SLOVENICA 2020 60 1 ACTA GEOGRAPHICA SLOVENICA GEOGRAFSKI ZBORNIK 60-1 • 2020 Contents Mojca POKLAR Comparison of the sonar recording method and the aerial photography methodfor mapping seagrass meadows 7 Vanja PAVLUKOVIĆ, Uglješa STANKOV, Daniela ARSENOVIĆ Social impacts of music festivals: A comparative study of Sziget (Hungary) and Exit (Serbia) 21 Péter János KISS, Csaba TÖLGYESI, Imola BÓNI, László ERDŐS, András VOJTKÓ,István Elek MAÁK, Zoltán BÁTORI The effects of intensive logging on the capacity of karst dolines to provide potential microrefugia for cool-adapted plants 37 Radu SĂGEATĂ Commercial services and urban space reconversion in Romania (1990–2017) 49 Kristina IVANČIČ, Jernej JEŽ, Blaž MILANIČ, Špela KUMELJ, Andrej ŠMUC Application of a mass movement susceptibility model in the heterogeneous Miocene clastic successions of the Slovenj Gradec Basin, northeast Slovenia 1 Andrej GOSAR Measurements of tectonic micro-displacements within the Idrija fault zone in the Učjavalley (W Slovenia) 79 Piotr RAŹNIAK, Sławomir DOROCKI, Anna WINIARCZYK-RAŹNIAK Economic resilienceofthe command andcontrolfunctionof citiesin Centraland EasternEurope 95 Mateja FERK, Rok CIGLIČ, Blaž KOMAC, Dénes LÓCZY Management of small retention ponds and their impact on flood hazard prevention in the Slovenske Gorice Hills 107 Gregor KOVAČIČ Sediment production in flysch badlands: A case study from Slovenian Istria 127 Vesna LUKIĆ, Aleksandar TOMAŠEVIĆ Immigrant integration regimes in Europe: Incorporating the Western Balkan countries 143 Mitja DURNIK Community development: LocalImmigrationPartnershipsin Canadaand implications forSlovenia 155 ISSN 1581-6613 9 771581 661010 APPLICATION OF A MASS MOVEMENT SUSCEPTIBILITY MODEL IN THE HETEROGENEOUS MIOCENE CLASTIC SUCCESSIONS OF THE SLOVENJ GRADECBASIN,NORTHEASTSLOVENIA Kristina Ivančič, Jernej Jež, Blaž Milanič, Špela Kumelj, Andrej Šmuc The hilly landscape between Slovenj Gradec and Velenje consists of soft sedimentary rock, which is susceptible to landslide formation. DOI: https://doi.org/10.3986/AGS.7040 UDC: 551.435.62:528.94(497.413) COBISS: 1.01 Kristina Ivančič1, Jernej Jež1, Blaž Milanič1, Špela Kumelj1, Andrej Šmuc2 Application of a mass movement susceptibility model in the heterogeneous Miocene clastic successions of the Slovenj Gradec Basin, northeast Slovenia ABSTRACT: In Slovenia, mass movements are not only a threat to the population, but also a major envi­ronmentalandsocialsciencechallenge.Lithologicallyheterogeneousareashavebeenfoundtobeproblematic, and the Miocene Slovenj Gradec basin (in northeast Slovenia) is one such area. For this area, we devel­opedlandslideandrockfallsusceptibilitymapsbasedondetailedgeologicalresearchcombinedwithstatistical modeling schemes. Crucial factors include lithological composition, land use, geological structural ele­ments, slope curvature, aspect and inclination, and bed dipping. The approach taken in the development of mass movement susceptibility maps presented here is transferable to other areas defined by heteroge­neouslithology.Suchmapscouldproveusefulspatialplanning,forestry,environmentalprotection,landscape architecture, and other fields. KEY WORDS: Miocene, landslides, rockfalls, mass movement process modeling, heterogeneous lithology, Slovenia Model nevarnosti za pobočne procese na primeru heterogenih miocenskih zaporedij klastičnih kamnin v slovenjgraški kotlini v severovzhodni Sloveniji POVZETEK: V Sloveniji pobočni procesi ogrožajo prebivalstvo, hkrati pa so tudi velik okoljski in druž­boslovni izziv. S tega vidika so se za problematična izkazala litološko heterogena območja, med katerimi jetudimiocenskaslovenjgraškakotlinavseverovzhodniSloveniji.Zatoobmočjesoavtorjinapodlagipodrob­nihgeološkihraziskavvkombinacijisshemamistatističnegamodeliranjaoblikovalizemljevidenevarnosti za zemeljske plazove in skalne podore. Glavni dejavniki, ki vplivajo na oblikovanje tovrstnih procesov, so litološkasestava,rabatal,geološkestrukturneprvine,ukrivljenostpobočja,ekspozicija,nakloninusmer­jenost kamninskih plasti. Predstavljeni pristop k oblikovanju zemljevidov nevarnosti za pobočne procese jeprenosljivnadrugalitološkoheterogenaobmočja.Tovrstnizemljevidibibililahkouporabnizapodročja, kot so prostorsko načrtovanje, gozdarstvo, okoljevarstvo in krajinska arhitektura. KLJUČNE BESEDE: miocen, zemeljski plazovi, skalni podori, modeliranje pobočnih procesov, litološka heterogenost, Slovenija The paper was submitted for publication on October 9th, 2018. Uredništvo je prejelo prispevek 9. oktobra 2018. 1 Geological Survey of Slovenia, Ljubljana, Slovenia kristina.ivancic@geo-zs.si, jernej.jez@geo-zs.si, blaz.milanic@geo-zs.si, spela.kumelj@geo-zs.si 2 University of Ljubljana, Faculty of Natural Sciences and Engineering, Department of Geology, Ljubljana, Slovenia andrej.smuc@geo.ntf.uni-lj.si 1 Introduction MassmovementsareaverycommongeologicalphenomenoninSlovenia(Mikoš,BrillyandRibičič2004; Zornand Komac2008;Mikoš and Majes 2010; Zorn, Komacand Kumelj2012). Theiroccurrenceis related to diverse geological and tectonic structures, relief, and land use. They have caused considerable damage and occasionally threatened or even claimed human lives (Mikoš 2000/2001). In recent decades, geolog­icalphenomenaincludinglandslides,debrisflows,androckfallshavebeenintensivelyinvestigatedinSlovenia (Mikošetal.2006;Mikoš,FazarincandMajes2007;ZornandKomac2002,2007,2008,2011;JemecAuflič et al. 2017). Preventive measures have been identified as an important basis for avoiding damage and loss caused by mass movements and helping solve socioeconomic challenges. Among these measures, land­slide susceptibility maps are a useful tool for minimizing potential hazard (Komac and Jež 2018) in terms of appropriate spatial planning based on the results of the investigations. Many susceptibility models for landslide zonation (Carrara et al. 1991, 1995; Guzzetti et al. 1999, 2006; Zorn and Komac 2007; Rossi et al.2010;Petschkoetal.2014)androckfallsusceptibilityzonation(Pannatieretal.2009;Shirzadietal.2012; Böhme, Derron and Jaboyedoff 2014) have been used around the world. The methodology used in our research was developed as part of research projects at the Geological Survey of Slovenia (Bavec, Budkovič and Komac 2005; Komac and Jež 2018). It was developed using a linear model of weighted spatial factors testedbyaunivariatechi-square(.2)statisticalmethod,whichhasalreadybeenutilizedbyseveralauthors (Stančič and Veljanovski 1998, 2000a, 2000b; Veljanovski 1999; Komac 2005b). Initially, the methodolo­gy was used at smaller scales at the national level (Komac and Ribičič 2006), and later it was also applied tolargerscales;thatis,atthemunicipalandlocallevels.Inordertoverifythetransferabilityofthemethod­ology to other environments (also outside of Slovenia), it was tested on the example of the Municipality of Zvornik (Republika Srpska, Bosnia and Herzegovina), where it proved successful (Kumelj et al. 2014). The methodwaspreviouslyusedforlandslideandrockfallsusceptibilityzonationinthewiderstudyareaatascale of1:25,000.Itturnedoutthatapproximately70%oftheareaofthemunicipalitiesofSlovenjGradecandVelenje are exposed toa medium, high, andvery high occurrence of mass movement (Bavec et al. 2012a; 2012b). Inpreparingthegeologicalmodelinginputdata,themostproblematicareaswerefoundtobeareaswith lithologicallyheterogeneoussequences.Insuchareas,rockswithdifferentgeomechanicalpropertiesalter­natefrequentlywithinveryshortdistances.Suchrocks includeMioceneandPermian–Carboniferousclastic sedimentarysuccessions,whicharemostcommonlyfoundincentralandnortheasternSlovenia(Figure1A). According to the Landslide Susceptibility Map of Slovenia, these are the most exposed hazardous land­slide areas (Figure 1B). The problem of common occurrences of landslides in Neogene rocks in the wider Pannonian Basin was also addressed by Tošić et al. (2014). This paper describes a possible approach in geologically diverse areas where detailed geological map­pinganddetailedgeologicalprofileloggingconstitutethemainanalyticaltools.Theyprovidemoreprecise geological input data required for the model. The main purpose is to improve maps so that they are pre­cise for large scales (e.g., 1:5,000), on which the susceptibility zonation fora specific location can be seen. The area between Podgorje (in the Municipality of Slovenj Gradec) and Gaberke was appropriate for our research because it is lithologically and morphologically heterogeneous with different land-use charac­teristics. The approach can be transferred to other areas with similar rock successions. 2 Study area The investigated area is located between Podgorje (in the Municipality of Slovenj Gradec) and Gaberke innortheasternSlovenia,anditcovers11km2(Figures1A,2).TheareastudiedispartoftheAlpinemacrore­gion and corresponds to the Eastern Karawanks, the Velenje and Konjice Hills, and the Strojna, Kozjak, andPohorjemountains(Perko1998).Paleogeographically,theinvestigatedareaispartoftheSlovenjGradec Basin and consists of alternating Miocene clastic sedimentary rocks. The terrain in the study area is morphologically diverse. Only 3.6% of the study area has a slope less than 5°. In areas with steeper slopes, landslides could occur. A slope steeper than 38° is found in 17.3% of the area and rockfalls can occur (Komac 2005a). Rarely, conglomerate and sandstone beds can even form overhanging walls. The highest point of the area reaches 825m, and the lowest is at 425m. The morpho­logical diversity of the area is conditioned by the geological diversity there and, consequently, a branched A B Scale: 1:2,000,000 Content and map by: Kristina Ivančič Source: Buser 2009 (A); Komac 2005 (B) © 2018, Geological Survey of Slovenia 0 12.5 25 50 km Miocene rocks Carboniferous and Permian rocks Study area Landslide susceptibility map [A - Distribution of a landslide susceptibility rate area for Miocene, Carboniferous and Permian rocks] None [A: 10.9%] Very low [A: 2.0%] Low [A: 7.7%] Moderate [A: 11.7%] High [A: 41.5%] Very high [A: 26.2%] Figure 1: A) Areas with Miocene and Permian–Carboniferous clastic rocks, B) landslide susceptibility map of Slovenia (Komac and Ribičič 2006). 64 Figure 2: Topographic map with the location of the investigated area (red). river system of the torrent type. The main valleys and ridges formed in a northwest–southeast direction and are partly conditioned by the tectonic structure present there. The investigated area is faulted and folded. Faults and fault zones are more frequent in the southern partofthearea.TheybelongtothePeriadriaticfaultzone,whichseparatestonalitefromclasticsedimentary rocks. Smaller-scale faults are present in other parts of the study area. In the entire investigated area, the rocks are fractured. The most prominent fractures are in the southern part of the area, occurring mainly in conglomerate (Figure 3A) and sandstone, and they are subordinate in siltstone. In the area there are also two synclines and an anticline with a northwest–southeast axis orientation. The folding has resulted in variable dip of the beds, which ranges from 310–30/20–60 to 190–225/30–50. 3 Methods Landslide and rockfall susceptibility models were developed for the area between Podgorje and Gaberke. The study is based on two primary types of data collection and processing schemes: a) geological map­ping and sedimentological analyses, and b) the preparation of input data and statistical modelling. 3.1 Geological methods Detailedgeologicalmappingofrockoutcropswasperformedatascaleof1:5,000inordertoobtainadetailed lithological map with geomechanical properties. In addition, tectonically fractured rocks, the location of faults and fault zones, and bed dip were evidenced, and a special focus was placed on existing landslides. Seven lithological sections present in different parts of the Slovenj Gradec Basin succession were record­ed at a scale of 1:100 in order to precisely determine the type and properties of rocks (Figure 4A). Thelithologicalunitswerereclassifiedintosixsusceptibilitycategoriesaccordingtolandslideandrock­fallsusceptibility(Bavec,BudkovičandKomac2005;KomacandJež2018),whereCategory1showsareas withnooccurrence(areaunder5°)andCategory6showsareasthatareverypronetomassmovementevent occurrence. 3.2 Input data and statistical modeling Twoexistingconceptualmodels(amethodologyforestimationofgeohazardinducedbymassmovements; Komac 2003a, 2005a; Bavec, Budkovič and Komac 2005) were used to determine landslide and rockfall susceptibilityrates.ThemethodisexplainedinKomac(2003a,2003b,2005a)andKomacandRibičič(2006). It was developed using a linear model of weighted spatial factors. Univariate statistical methods (the chi-square method) were used to test the influences of individual spatial factors on landsliding, and multivariate statistical methods were used to test the importance of individual factors in landslide occur­rence. Komac (2005a) developed 3,142 models for landslide susceptibility and 7,674 for rockfall susceptibility using the Monte Carlo method. For the landslide model, lithological data are combined lin­early with the synchronicity of the bed dips and the slope aspect, applying a weighting ratio of 0.8 for the lithologicalcompositionand0.2forthedipofthebeds.Inaddition,land-usefactors,distancesfromstruc­turalelementsandfaultzones,slopeaspect,andcurvaturewereallincluded.Preconditionspatial-temporal factors for landslide occurrence are lithology 0.3, slope inclination 0.25, landcover type 0.25, slope cur­vature 0.1, distance to structural elements 0.05, and slope aspect 0.05 (Figure 5A). For the rockfall model, lithologicalcompositionandfaultzoneparameterswereweightedasfollows:0.5forslopeinclination,0.35 synchronicity of dip of beds, and 0.15 for slope aspect weight (Figure 5B). In general, the methodology of both models at a scale of 1:25,000 is based on four consecutive phas­es (Bavec, Budkovič and Komac 2005; Komac 2005a; Komac and Jež 2018): 1) synthesis of the existing cartographic archival geological data and verification fieldwork examinations in the phase map of geo­logicalhazardsduetoslopemassmovements;2)theproductionofaprobabilitystatisticalmodelofgeological Figure 4: A) Lithological map of the investigated area with structural elements (faults). The red circle marks the location of the Gaberke section; B) the Gaberke section, where frequent alternation of different rock types with different geomechanical properties can be observed. p Figure 5: A) Input parameters for the landslide susceptibility model, B) input parameters for the rockfall susceptibility model. hazards due to slope mass movements; 3) production of maps of geological hazards due to processes of slope mass movement based on the synthesis of Phase Map 1 and Probability Model 2 by exclusion of the mostproblematicareas,and4)detailedmappingofmoreproblematicareasatascaleof1:5,000or1:10,000 and production of a detailed geological hazards map due to slope mass movements for these areas. In this paper, the modeling is based exclusively on new geological data, and therefore Phase 1 related to archival geological data is not included. The methodology starts with the process of Phase 4. Data on slope inclination, aspect, and curvature were derived from the digital relief model produced onthebasisofLiDARdataataspatialresolutionof1m(MOP2018).Areasunder5°forlandslidesandareas under 38° for rockfall are considered areas where the probability of landslide or rockfall is essentially zero, and they were therefore not included in the model (cf. Komac 2005a). The values of the calculated models were classified into six landslide and rockfall susceptibility cate­goriesandwerearrangedaccordingtochangesinthedistributionoffrequencyoccurrence(NaturalBreaks– Jenks; natural boundary method). Class breaks are identified that best group similar values and that max-imizethedifferencesbetweenclasses.Thefeaturesaredividedintoclasseswhoseboundariesaresetwhere there are relatively large differences in the data values (Esri 2006). All landslides in the area studied from the national database (Komac and Hribernik 2015) were ver­ified in the field, and new landslides identified during fieldwork were added to the database. This served as a basis for model validation. If the input parameters were identified as incorrect in the field (e.g., lithol­ogy or land use), we modified them accordingly in the model and ran a new one. 4 Results 4.1 Lithological map The investigated area is composed of tonalite and alternation of conglomerate, sandstone, siltstone,marl­stone,andclaystone(Figure4A).Tonaliteisnotpartoftheclasticsedimentarysuccession,anditistherefore not involved in our research. Conglomerate and sandstone predominate and were found throughout the entire investigated area. They frequently occur together, meaning that layers of sandstone alternate with conglomerate layers(Figure3B). Ingeneral,morecoarse-grainedlithologiespredominate inthe northern part of the investigated area, whereas fine-grained intercalations and packages are more common in its southern part. 4.2 Detailed description of lithological units Basedondetailedfieldwork(Ivančičetal.2018),alllithologicalunitsweredefinedandtheirgeomechanical properties were evaluated. The frequent alternation of different lithological units could be well observed in the Gaberke section, where conglomerates, sandstone, and silty marlstone occur (Figure 4B). Fine- to coarse-grained conglomerate is grain-supported, thin- to thick-bedded, or massive in places. The conglomerate is very well to poorly lithified, and sometimes fractured. It is present throughout the entire investigated area, and it is common in hilltops and ridges. Fine-tocoarse-grainedsandstoneiswelltopoorlylithified.Thegrainsarebondedwithcarbonateand quartz cement, with a carbonate matrix. Sandstone was found throughout the investigated area, mostly in combination with conglomerate layers. Siltstone occurs locally throughout the entire mapped area. It is laminated and bedded, and contains plant remains (Figure 3C). Although silty layers are usually present only in thin layersbetween sandstone and conglomerate beds, locally successions can be up to 32m thick. Marlstoneandsiltymarlstonearefrequentlyfoundonthesouthernpartofthemap,occurringincom­bination with conglomerate and sandstone. Marlstone is laminated and usually shaly, which contributes to its rapid weathering and poor geomechanical characteristics. Claystoneismostlyfoundinthenorthernpartoftheinvestigatedarea,occurringasthinlayersincom­bination with conglomerate and sandstone. Retained water on the surface suggests impermeability of the unit (Figure 3D). 4.3 Model The values of the final model (between 0 and 1) were classified into six landslide or rockfall susceptibili­ty classes. The method determines the boundaries between groups of data that exhibit relatively large differences between pairs of adjacent values. The classes are not equally distributed (Figures 8 and 9). 4.3.1 Landslide susceptibility model The landslide susceptibility model indicatesthat19.9% of the investigated area exhibits a very high prob-abilityoflandslideoccurrence.Amoderatelyhighprobabilityisexhibitedby22.7%ofthearea,and44.3% has a relatively low to a very low probability of landslide occurrence. The greatest landslide hazard areas are Plešivec (Figure 6) and Vodriž (Figure 7). The areas coincide with the real state of nature. The very highlandslidesusceptibilityclassthatcharacterizesthePlešivecareacoincideswithalreadyidentifiedland­slidehazardareas.Theserelativelyvastunstableareasaremainlylimitedtomeadowsandpastures,whereas inforeststheyarenotcommon.Generallyspeaking,thosemoresusceptibleareascoincidewithfine-grained sedimentary rocks (Figure 4A), such as siltstone, marlstone, and claystone. These locationsare presented in Figure 8. 4.3.2 Rockfall susceptibility model The rockfall susceptibility model indicates that 22.1% of the investigated area belongs to the class of very high susceptibility, and 28.6% of the area to moderate susceptibility. Locations where rockfall occurrence has been documented are rare. The highest possibility for their occurrence is on the northwest part, the central part, and the southwest part of the investigated area (Figure 9). The area is mostly composed of a conglomerate, subordinate sandstone. It is bound to vertical walls in places. The result presented by the modelcoincideswiththerealsituationinnature.ThelocationsofthesusceptibleareasaremarkedinFigure9. 5 Discussion With this approach, the mass movement susceptibility models of the rockfall and landslides provedto be very precise and gave a very good approximation of the natural state. This was proved by verification. The areas of highest landslides and rockfall susceptibility coincide with the actual state of nature even in the study area between Podogrje and Gaberke, where the lithology frequently alternates. Very high and high landslidesusceptibilityinthestudyareaturnsouttobeonthemeadowsandpastureswithsiltstone,marl-stone, or claystone as bedrock. Landslides from the national database and new landslides are located in the high and very high susceptibility areas. Moreover, new landslides were determined during the verifi­cation process. They are located in areas of high and very high probability for landslide occurrence. The model did not specify the correct landslide susceptibility zonation in the case of locally changed land use or in the case of newly changed land use that is not included in the modeling process. One specific land­slide occurs in the area of deforestation (Figure 7C). In this case, the bedrock is composed of alternating sandstone and conglomerate. Rockfalls in the study area are subjected to the lithology of conglomerate with rare sandstone layers. Manydifferentmodelswereusedinthepast(Carraraetal.1991,1995;Guzzettietal.1999,2006;Zorn and Komac 2004, 2007; Rossi et al. 2010; Reichenbach et al. 2018) to produce mass movement suscepti­bility zonation. Such models usually do not precisely define susceptibility to slope mass movements in lithological heterogeneous areas. The problem of heterogeneous lithology has already been discussed Figure 6: Examples of larger labile areas in the Plešivec region. A) The area below the Koližnik farm, B) the area near the Grah farmhouse, C) the area at the Grabnar farm. The locations of areas 6A to 6C are also indicated in Figure 8. p p. 71 Figure 7: Examples of individual landslides in the investigated area. A) Landslide at the Petelanšek farm, B) cleanup of the landslide along the road toward the Pelc farm, C) newly formed landslide on the freshly deforested section along Vodriž Creek, with marked main scarp. The locations of areas 7A to 7C are also indicated in Figure 8. p p. 72 Figure8:Landslidesusceptibilitymapofthe investigatedarea.Points6A,6B,6C,7A,7B,and7Cmarkthelocationsofthephotos in Figures6and7. p p.73 Figure 9: Rockfall susceptibility map of the investigated area. p p. 74 73 74 (Lee et al. 2008; Zorn and Komac 2009; Blahut, Van Westen and Sterlacchini 2010; Petschko et al. 2014). Thegreatestdifferenceinourmodelisinitsinputfactors,whicharemorepreciselydefinedforslope(cur­vature,inclination,andaspect)andstrata(dippingandaspect).Moreover,detailedmappingimprovesthe input data of lithology and tectonic elements, and therefore the final landslide and rockfall susceptibility zonationismoredetailedandusableatlargerscales(e.g.,1:5,000).Themodelprovidesaccuracywithrespect to the worst-scale input parameter, but nevertheless more accurate data (such as LiDAR) show charac­teristic features that must be taken into account when interpreting the results. Geological maps at smaller scales (e.g., 1:100,000 or 1:25,000) usually do not sufficiently separate or differentiate lithologically het­erogeneousunits.Consequently,suchmapsareoflimiteduseformassmovementsusceptibilitymodeling. The quality of the final predictions of the formation of slope mass movements primarily depends on the quality of the input geological data, and therefore it is necessary to combine the existing methodolo­gy of the modeling (Komac, Kumelj and Krajnik 2012; Bavec et al. 2012a, 2012b) with classic techniques of geological research (e.g., geological mapping and detailed recording of lithological sections). Only in this way can we obtain quality data, which, in combination with the geomorphological parameters of the terrain, make a significant contribution to the production of a useful final model of susceptibility to slope mass movements. Todaypubliclyavailablereliefdata(i.e.,LiDARdatasusceptibility)aresignificantlymoreaccuratethan that used by existing lithological maps, and this therefore contributes to more detailed geological field­work. In addition, current land-use data are also very important in the modeling process. Statistical data indicate that shallow landslides frequently occur in areas used as meadows, pastures, orchards, and vine-yards(Komac2005a).Certainfactorsrelatedtochangesinlanduse,suchasdeforestationandthecultivation ofpasturesandorchards,playamajorroleinthedestabilizationoflabileareas(Fidejetal.2018).Consequently, agricultural areas located in hazardous areas are often affected. Examples of such phenomena were also observed and documented in the investigated area. Determining the weighting ratio of the impact of the individual land use class (forest, vineyard, etc.) proves a particular challenge within the basic input fac­tor: land use. In addition to activities related to agriculture and forestry, the data in the model are indispensable in allspatialplanningprocessesintheenvironment.Regionalandlocalspatialplanningandlanduseshould beadjustedaccordingtosuchmodels.Itisworthmentioningthatthemodelsindicatesourceareasofpoten­tial mass movements, not their transport paths or deposition areas. As a rule, landslide deposition areas aregenerallynotfarfromthelandslidesource,withtheexceptionofcasesinwhichlargequantitiesofwater are present, and the material can be converted into a mass (debris) flow. In the case of rockfalls, the mate­rial (blocks) may be deposited far from the source site (Zorn 2002). 6 Conclusion Existing geological and pedological maps are not sufficiently accurate to produce quality detailed mod-els,makingitnecessarytoincludeprecisefieldmappingandotherbasicgeologicalresearchintheprocess. This is absolutely suggested in lithologically heterogeneous areas. A good example of this is thestudy area between Podgorje and Gaberke, where more than 40% of the area exhibits a very high, high, or moder­ate probability of landslide occurrence. Alongside lithology, land use as model input factor (e.g., meadows and pastures, and deforestation) wasfoundtobeveryimportantinthemodelingprocess.Inaddition,changesinlandusemayplayamajor role in the destabilization of labile areas. The susceptibility models presented here are sufficiently accurate and have been verified by checking known slope movement events in the investigated area. The approach used is transferable and compara­ble at all susceptibility levels, and it can be used in lithologically heterogeneous areas for large-scale maps (1:5,000). Theresultscanbeusedforspatialplanningintheenvironment.Byconsideringsuchmodels,themost hazard-prone areas can be avoided or can be dealt with in a geotechnically professional manner. ACKNOWLEDGEMENT:ThisstudywasfundedbytheSlovenianResearchAgency(ARRS)intheframe­workoftheYoungResearchersprogram,theGroundwatersandGeochemistryresearchprogram(P1-0020), andaspartofresearchcorefundingno.P1-0011(regionalgeology),whichiscarriedoutattheGeologicalSurveyofSlovenia.WewouldliketothankMladenŠtumergarforpreparationofsamplesforpetrographic analysis. The authors are also grateful to Dragomir Skaberne and Matevž Novak for their generous help and support. 7 References Bavec,M.,Budkovič,T.,Komac,M.2005:Geohazard–geološkopogojenanevarnostzaradiprocesovpobočnega premikanja. Primer občine Bovec. Geologija 48-2. 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