Hazard Zone Map in Hemsedal, Norway

UNIVERSITY OF GOTHENBURG Department of Earth Sciences Geovetarcentrum/Earth Science Centre

Hazard zone map

in Hemsedal, Norway

A testbed for runout modelling

with RAMMS::ROCKFALL module

John Eliasson Axel Hellman

ISSN 1400-3821 B916 Bachelor of Science thesis Göteborg 2016

Mailing address Address Telephone Telefax Geovetarcentrum Geovetarcentrum Geovetarcentrum 031-786 19 56 031-786 19 86 Göteborg University S 405 30 Göteborg Guldhedsgatan 5A S-405 30 Göteborg SWEDEN Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Abstract

Hemsedal, Norway, is a prioritized area with regards to natural hazards such as rockfalls, which is the main focus of this thesis. Fieldwork and remote sensing were used to create a rapid mass movement deposit map. The spatial extension of rockfall deposits functioned as a testbed for modelling with RAMMS::ROCKFALL module. Rapid mass movement deposits are spatially distributed throughout the entire study site and several field observations suggest that Hemsedal is a potentially active area. The simulations of rockfalls correlate well with observations made in the field as well as the rapid mass movement deposit map. Thus validating the applicability of the modelling within this project. With 14C- datings of tree stems a return period of rockfalls could be approximated which was used in a (semi- )quantitative risk assessment together with the modelling results. However, the outputs of the modelling, and therefore the risk assessment, are exceedingly dependent on the quality of the input parameters. I conclude that more information is required in order to fulfill a risk assessment based on hazard probability based on the evaluation of return times and interviews.

Sammanfattning

Hemsedal, Norge, är ett prioritetsområde med hänsyn till geofaror, som till exempel stenras. Det är också på stenras som fokus i denna uppsatts ligger. En skredavsättningskarta har skapats med stöd i fältarbete och fjärranalys. Den spatiala utbredning av skredavsättningar fungerar som en plattform för skredmodellering med RAMMS::ROCKFALL. Avsättningarna är spatialt fördelade över hela studieområdet och flertalet fältobservationer antyder att Hemsedal som helhet kan vara ett aktivt skredområde. De simuleringar som genomförts korrelerar väl med de observationer som gjort samt med skredavsättningskartan, vilket validerar applicerbarheten av modellering i detta projekt. 14C- dateringar av trädgrenar från området ger en approximerad stenrasfrekvens som tillsammans med modellering resultat kan användas i en (semi-)kvantitativ riskanalys. Dock är modelleringsresultaten, och såldes riskanalysen, starkt beroende av högkvalitativa inparametrar. På grund av detta anser jag att mer data och information är en nödvändighet för att utföra en riskbedömning baserad på skredrisk som är grundad i en bedömning av skredfrekvens och intervjuer.

KEYWORDS: Rockfall, rapid mass movement deposits, RAMMS::ROCKFALL, natural hazards, hazard map, GIS

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Contents Abstract ...... 1 Sammanfattning ...... 1 1. Introduction ...... 4 1.1 Scope of the study ...... 4 1.2 Previous research ...... 4 1.2.1 Geohazard maps in Hemsedal, Norway ...... 4 1.2.2 Rockfalls and rockslides ...... 4 1.2.3 Rock Avalanche ...... 7 1.2.4 Debris flow ...... 8 1.2.5 Deep-seated gravitational slope deformation ...... 9 2. Study area ...... 10 2.1 Study site ...... 10 2.2 Geology ...... 11 2.3 Quaternary geology ...... 12 2.4 History of rockfalls and debris flows in Hemsedal ...... 14 3. Methods ...... 15 3.1 Field work ...... 15 3.2 Geographical Information Systems (GIS) and remote sensing ...... 15 3.2.1 Digital elevation model (DEM), Hillshade and terrain ...... 15 3.2.2 Slope map ...... 15 3.2.3 Hazard zone map ...... 16 3.2.4 Vegetation and satellite images ...... 17 3.3 14C-dating ...... 18 3.4 Modelling ...... 19 3.4.1 RAMMS::ROCKFALL ...... 19 4. Results ...... 27 4.1 Rapid mass movement deposit map ...... 27 4.1.1 Rockfall deposit ...... 30 4.1.2 Maximum extent: rockfall ...... 30 4.1.3 Rock avalanche deposit ...... 32 4.1.4 Rapid mass movement deposit (continuous and thin cover) ...... 33 4.1.5 Debrisflow deposit ...... 34 4.1.6 Debrisflow and fluvial erosion ...... 34 4.1.7 Ravines and fissures ...... 35 4.2 Slope map and release areas ...... 36 4.3 NDVI ...... 38

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

4.4 14C-dating ...... 39 4.5 RAMMS::ROCKFALL module ...... 40 5. Discussion ...... 52 5.1 Rockfall slope and release area map ...... 52 5.2 Boulder map ...... 52 5.3 Deposits and the rapid mass movement deposit map...... 53 5.4 Normalized difference vegetation index (NDVI) ...... 55 5.5 14C-dating ...... 56 5.6 Modelling ...... 56 5.6.1 Risk assessment ...... 58 6. Conclusions ...... 62 6.1 Future outlook ...... 63 7. Acknowledgements ...... 63 8. References ...... 64 9. Appendices ...... 68 9.1 Python-based script ...... 68 9.2 Quantitative risk assessment ...... 68 9.3 Combined tables ...... 70

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

1. Introduction 1.1 Scope of the study

Hemsedal, Norway, is a prioritized area in regards to natural hazard, e.g. rockfalls, which is the main focus of this thesis. This project is in collaboration with the Norwegian Water Resources and Energy Directorate (NVE) and can potentially be used as a basis for future risk assessments.

The thesis aims to test the possibility to identify and assess natural hazard areas based on geological field methods as well as remote sensing. A rapid mass movement deposit map is created, which aims to function as a platform and a testbed for runout modelling with RAMMS::ROCKFALL.

1.2 Previous research

1.2.1 Geohazard maps in Hemsedal, Norway

NVE got the assignment to handle and prevent geohazards from the Norwegian government in 2009 (NVE, 2011a). NVE has, in collaboration with the Geological Survey of Norway (NGU), created a list of prioritized areas in Norway. The assessment for this priority is based upon the relationship between geohazards and how it may affect its surroundings, specified by the security regulations and the construction legislation TEK10 (DiBK, 2015). Currently the only hazard maps concerning Hemsedal is based on GIS analysis, hence slope values indicative of potential risk areas.

1.2.2 Rockfalls and rockslides

Rockfall areas, or toppling areas, (Figure 1) are commonly found in sub-vertical mountain sides. They are gravitational mass movements which involves one or several blocks falling. This usually occurs at steeper slopes (>45°). However, most falls ensue in the range of 60° to 75° (Braathen, Blikra, Berg & Karlsen, 2004). They tend to happen as a fracture reaches the surface of the slope, meaning that a block located here is only kept in place by the frictional forces. The result is that when the driving forces become greater than the frictional forces, the block will no longer stay attached to the wall and therefor accelerate into free fall (Braathen et al., 2004).

The problems with rockfalls are that they can often occur in urban areas and can therefor get in direct contact with buildings. Furthermore they also pose a risk to roads and railways as they can obstruct the pathway or even destroy them. Hemsedal, Norway, is one of the prioritized areas in Hemsedal due to their problem with rockfalls and the like (NVE, 2011a; NVE, 2011b).

Rocksliding areas (Figure 1), are commonly found in less steep mountain sides (<45°). They are similar to rockfalls but they tend to slide along the mountain side rather than fall (Braathen et al., 2004). This causes the material of the gravitational movement to consist of smaller fractions, rock fragments, rather than a few large blocks. Due to the stress that is affecting the material when it is in, more or less, constant contact with the underlying material the larger blocks are grinded down (NVE, 2011b).

One of the most typical deposits formed through either rockfall or rockslide is a talus cone (Figure 2.1; Figure 3). Talus cones are formed due to the accumulation of debris. Typically the coarsest grain sizes are found near the talus base due to fall-sorting, forming a base fringe, while the smaller grains are closer to the top (Rapp, 1959). Furthermore, it is possible to discern different stratigraphy’s within a talus cone using ground penetrating radar (GPR). However, the ground topography is needed in order

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway to achieve this (Sass & Krautblatter, 2006; Erik Sturkell, personal communication, 19th of May 2016). Figure 3 is showing a talus cone present in Hemsedal. However, it does not present the classic cone shape.

Figure 1. a) Rockfall area. b) Rockslide area (Braathen et al., 2004).

Figure 2. Sketch of four types of debris accumulations. 1) Talus cone, 2) Alluvial cone, 3) Avalanche boulder tongue, 4) Rockslide tongue. (Rapp, 1959)

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 3. Image showing an outlined talus cone and an outlined talus at Imrestind, Hemsedal. (Norkart, 2016)

Another common deposit originating especially from rockfalls are boulders, individual boulders but also several boulders found in a group (Figure 4).

Figure 4. a) Boulder leaning against a tree, Hemsedal. Note cows for scale b) Group of boulders, Hemsedal. Photos by: Axel Hellman & John Eliasson

According to McCarrol, Shakesby and Matthews (2001) rockfall activity during the Little Ice Age (LIA) may have been up to 7 times higher than it is today, possibly due to the colder climate. Microclimatic factors such as freezing and thawing as well as permafrost are believed to enhance rockfall activity (Volkwein et al., 2011; Ravanel & Deline, 2011; Matzouka, 2007; Frayssines, 2005). A study conducted by Hanssen-Bauer et al. (2015) claims that permafrost in Norway generally occurs between temperatures of -3 and 0 °C and can penetrate up to 10 meters into a mountainside. Furthermore, permafrost has potentially decreased from covering 10% of the Norwegian land area to 6% (measuring periods 1961- 1990 and 1981-2010 respectively) (Gisnås, Etzelmüller, Farbrot, Schuler and Westermann, 2013). Additionally, based on RCP8.5 and RCP4.5 the temperature might increase (Førland, Engen-Skaugen, Benestad, Hanssen-Bauer & Tveito, 2004; Stahl et al., 2010) and therefore further decrease the amount of permafrost, which in turn can decrease rockfall activity.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

1.2.3 Rock Avalanche

A rock avalanche can be compared to a large scale rockfall. One of the main differences is that a rock avalanche consists of a much greater volume than a normal debris flow or rockfall. It also tends to move in a greater pace (NVE, 2011a; NVE, 2011b). This type of geohazard has the potential to be of incredibly high risk, as basically a large portion of a mountain side is collapsing. One of the main concerns regarding the rock avalanche is that they can trigger local tsunamis. This can happen if a fjord or lake is located in its trajectory (Hermanns et al., 2011; Hermanns et al., 2013). In Grøtø, an area in Hemsedal, one of these rock avalanches has occurred in more recent time (Figure 5 & Figure 6) (Furseth, 2006). However, it will not be of further part in this thesis as it is a problem being investigated by the Norwegian Geotechnical Institution (NGI).

Figure 5. Outlined image showing the rock avalanche at Grøtø, Hemsedal (Norkart, 2016).

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 6. Rock avalanche, Grøtø.

1.2.4 Debris flow

According to Tamotsu Takahashi (2014) a typical debris flow is a torrential flow of a mixture of water, mud and debris. Larger boulders can also be a part of a debris flow but are generally found further back from the flow front. Takashi (2014) further defines a debris flow as:

Debris flow is a flow of sediment and water mixture in a manner as if it was a flow of continuous fluid driven by gravity, and it attains large mobility from the enlarged void space saturated with water or slurry. (p. 9)

The movement of debris flows is divided into two types; one is gravity driven and moves en masse while the other is driven by fluid dynamic forces. However, the debris flows are classified into three types; stony-type, turbulent-muddy-type and viscous-type debris flow (Takahashi, 2014).

Debris flows usually occurs in slopes with inclinations of 25° to 45° and are often triggered by intense precipitation or constant precipitation over longer periods of time (NVE, 2011c). They commonly attain speeds of more than 10 m/s (Takahashi, 2014; Prochaska, Santi, Higgins and Cannon, 2008). Additionally a very common feature created by debris flows is levees which are formed at the edges of the flow. Generally debris flows tend to follow fairly incised channels (Nyberg, 1989).

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

1.2.5 Deep-seated gravitational slope deformation

Deep-seated gravitational slope deformation (DSGSD) is the slope movement with the size (of the material moving) comprising of the entire slope side. These large hillside movements show specific morpho-structural features such as double ridges, ridge top depressions and trenches (Figure 7) (Agliardi, Crosta & Zanchi, 2000). The movement that is taken place is very slow (few mm to cm annually) and the displacement is small in comparison with the slope size (Agliardi et al., 2000; Forcella, 1984; Nemčok, 1972). Furthermore DSGSD can have a basal sliding surface which can make it difficult to distinguish from a landslide (Agliardi et al., 2000). There can be several different causes for DSGSDs. The lack of lateral support is believed to be one of the main causes for DSGSD. This lack of lateral support can for example be from ice which was previously present; hence unloading occurs. The release of residual stresses linked to prior tectonic deformation which affected the rock can be the other main cause (Forcella, 1984). However, DSGSD is not of considerable importance to this thesis as it is the main topic of Jillerö (2016).

Figure 7. Morpho-structural features of a typical DSGSD (Agliardi et al., 2000).

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

2. Study area 2.1 Study site

Hemsedal is located in the south central parts of Norway (Figure 8), in proximity to the Sognefjord. It is an alpine environment situated at approximately 600 to 2000 m.a.s.l. There are roughly 2400 people living in Hemsedal during the entire year (SSB, 2016). However, Hemsedal is one of Scandinavia’s larger ski resorts which increase the seasonal population drastically.

The study site was chosen by NVE and was based on GIS-analysis in order to identify rapid mass movement hazard in the vicinity of habitations and was later modified in order to include the Skogshorn and Nibbi area.

Figure 8. A map showing Hemsedals location in Norway. As well as an outlined are which is the study site of this thesis, modified to include the Skogshorn and Nibbi area.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

2.2 Geology

The Proterozoic bedrock basement in Hemsedal is formed in the Gothian and Sveconorwegian orogeny, 1700-900 Ma (Ramberg, Bryhni, Nøttvedt & Rangnes, 2008). The Cambro-Silurian bedrock, on top of this bedrock basement, in Hemsedal consists mainly of sericitic- and chloritic slate as well as quartz rich phyllite and quartz slate (Figure 9). During the Silurian-Devonian time, the Scandian phase of the Caledonian orogeny started (Roberts, 2002; NGU, 2016). This thrusted meta-diabase, meta- gabbro as well as amphibolite on top of the Cambro-Silurian metasedimentary rocks, essentially creating an erosional cover. This means that the harder thrusted rocks prevents the underlying softer metasedimentary rocks from being eroded. However, where the Cambro-Silurian rocks are exposed it has been heavily eroded (Kristiansen & Sollid, 1996). During this thrusting event, certain parts of these rocks were exposed to a strain-regime necessary in order to form mylonite (Fossen & Dunlap, 1998). Furthermore, Kristiansen and Sollid (1996) also show that there are several areas with granites or granitoids, some of which have been metamorphosed into gneiss.

Figure 9. Bedrock map covering Hemsedal, Norway (NGU, 2016).

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

2.3 Quaternary geology

The last glacial period, Weichsel, started roughly 115 ka and reached the last glacial maximum (LGM) 20-18 ka ago. However, the ice retreated since then with only a brief stop and readvancement during the Younger Dryas (approximately 11 to 10 14C k years BP). Buskerud County started to become ice free at around 10.1 14C k years BP. Hemsedal became ice free somewhere between 9 and 8.5 14C k years BP (Figure 10) (Kristiansen & Sollid, 1996). This is reasonable when compared to the Fennoscandian ice sheet, which is part of the Eurasian ice-sheet, movement shown in Figure 11 (Hughes, Gyllencreutz, Lohne, Mangerud & Svendsen, 2015).

Figure 10. Map showing ice fronts at different times in Buskerud county. The approximate location of Hemsedal is marked in red, note that the ages are not calibrated (Kristiansen & Sollid, 1996).

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 11. TS = time-slice. DATED-1: compilation of dates and time-slice reconstruction of the build-up and retreat of the last Eurasian (British-Irish, Scandinavian, Svalbard-Barents-Kara Seas) Ice Sheets 11 -10 ka. As well as Hemsedals location (Modified after Hughes et al., 2015)

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

2.4 History of rockfalls and debris flows in Hemsedal

There is no database for rockfall events in Hemsedal. However, there is a database which is managing snow avalanches, debrisflows as well as rock avalanches (NVE, 2016). In Figure 12 there are three different types of incidents for two of the categories. ‘Debrisflow’ or ‘Snow avalanche’ signifies that a mass movement has occurred in this area, this applies for the rock avalanche as well. Subsequently, ‘debrisflow destruction‘ or ‘snow avalanche destruction’ indicates that there have been some sort of property damage, i.e. roads, buildings or similar. The last type indicates that there have been one or more deaths involved in the event. This database if partly based on the work done by Astor Furseth (2006).

Figure 12. Map describing the occurrence of different incidents in Hemsedal.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

3. Methods 3.1 Field work

The fieldwork was conducted together with Axel Hellman in Hemsedal during three periods; 16-18 October 2015, 4-8 January 2016 and 1-4 May 2016. During the first visit initial observations were made, and GPS-points as well as photos were taken and stored for the hazard map. Landforms and geomorphological features were mapped and four samples of wood were sampled for possible 14C- dating. The wood was found interbedded with the deposits in such a way that it may be of use as proxies for snow avalanche, rockfall or debris flow activity. The predefined study area was reviewed and the area around Skogshorn was further explored. During the visit in Skogshorn we sampled and studied some of the deposits related to snow avalanche activity as well as rockfall and got acquainted with the area.

On the second field trip the main objective was to examine more boulders and try to determine their origin. The majority of this field work during this visit was spent at Skogshorn, investigating boulders as well as snow depth. Most of the time at Skogshorn was regarding snow avalanches, e.g. examining deposits and studying snow accumulation.

The third trip was conducted between the 1st and 4th of May. The goal of this fieldwork was to re- examine areas in order to increase the accuracy of the hazard zone map. Furthermore areas which were only estimated by aerial orthoimages were examined in order to establish the accuracy and precision of the hazard zone map. 3.2 Geographical Information Systems (GIS) and remote sensing

Different sets of tools and functions available in ArcGIS were utilized when analyzing data, creating maps and performing GIS-operations. ArcGIS functioned as platform for analysis and interpretation of orthorectified images, and as a testbed for further analysis and RAMMS::ROCKFALL modelling software.

3.2.1 Digital elevation model (DEM), Hillshade and terrain

A 10x10 meter DEM raster was utilized for ArcGIS operations and analysis, which was obtained from kartverket.no. To aid the interpretation of terrain features a shaded relief raster, hillshade, was created based on the DEM. Furthermore, several slope profiles were constructed in order to better assess slope roughness and topography.

3.2.2 Slope map

The DEM was used as input data when creating slope maps in ArcGIS. This was done previous to reclassification and categorization of output values according the chosen intervals. The inclination intervals used for classification of the slope maps were chosen based on previous studies and articles as described in section 1.2.2 and 1.2.4 (Braathen et al., 2004; NVE, 2011a; NVE, 2011b). Braathen et al. (2004) organizes the rockfall release areas into three categories. A detailed slope map was created for rockfalls using these categories; 45°-60°, 60°-75° and 75°-90°. The reason for the categories is that, statistically, most of the rockfalls occur where the slope is between 60° and 75° and they usually cannot occur beneath 45° (Braathen et al., 2004). When creating the hazard zone map, the generalized interval 45°-90° was set as the release area for rockfalls and 25°-45° for debris flows.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

3.2.3 Hazard zone map

The hazard maps were created based on orthophotos from norgeibilder.no, quaternary maps, hillshade, the results from the GIS-analysis (e.g. the slope map) and auxiliary data gathered during fieldwork and literature studies. The maps were drawn based on geomorphological features and landforms using polygons and polylines in ArcGIS and a hillshade (with contour lines) was used as a basemap. Color scheme and symbology of the hazard map was chosen to accurately depict the geomorphology and to enhance the user's understanding of the area. The open-source vector graphics editor InkScape was used to visualize and outline features and landforms in aerial photos and 3D- enhanced photos.

The release areas were defined based on category specific slope criteria. Areas with slope values within the predetermined rapid mass movements categories, i.e. snow avalanches, rockfall and debris flow, were isolated and converted into polygons. The polygons were then used as a point of reference for the release areas and then modified and edited based on additional category specific criteria for triggering. Snow avalanches release areas were modified based on evidence of previous snow avalanches, vegetation, accumulation potential and the continuous size of potential trigger areas. The release area of rockfalls was mostly modified based on evidence of previous rockfalls such as rockfall deposits (e.g. talus cones). However, the degree of weathering as well as fractures in the headwall was taken into account when assessing these release areas.

There are several talus cones in Hemsedal which was used a basis for the hazard zone map. Furthermore, boulders found in the research area were also used to get a better understanding regarding the extent of the hazard zones. In order to classify the origin of a boulder (i.e. rockfall, erratic or unknown origin) the geometry was examined. Boulders with an angular shape it is more indicative of a rockfall, rather than it being a glacial erratic transported with the ice. Location was also weighed into the classification. As an example; if a boulder is angular but located in an area with no obvious mountains or steep hills from which it could originate, the boulder will be classified as a boulder with unknown origin.

Debrisflows were assessed based on geomorphological features. The small scale undulating appearance is indicative of such a deposit. Furthermore, the sedimentology of the deposit reflects that of the parent material, from small to large fractions. However, due to the saturated nature of the debris flow significant amounts of small fractions would be present, as well as noteworthy amounts of boulders which could have been transported with the flow.

GPS-points with corresponding data obtained during field work, e.g. erratic boulders and rockfall boulders, are represented on the maps as points. Field observations of rockfall, debris flow and snow avalanche deposits were used to further aid the representation on the map.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 13. Hillshade map depicting where GPS-points were taken.

The maximum extent and magnitude of the different geohazard were mainly based on observations made in the field and high resolution aerial images. Furthermore, historical data provided by NVE, and the local historian Astor Furseth (2006) was used to modify and evaluate these zones together with information from NGI (2011).

3.2.4 Vegetation and satellite images

A normalized difference vegetation index (NDVI) analysis was conducted to assess the amount of healthy vegetation in the study area. Chlorophyll in healthy green vegetation absorbs visible infrared light (VIS) and reflects near infrared light (NIR). Hence, the more leaves and chlorophyll a plant has the more light is affected. This result in a spectral signal which can be measured images. Values close to +1 are classified as healthy green vegetation and values close to zero define areas with no vegetation (Myneni & Hall, 1995). In doing so a Python-based script was utilized to facilitate the NDVI index calculations, available in the appendices, which was provided by J. Heyman (personal communication 10th of December 2015). A script is a piece of code which can, for instance, remove tedious manual work. In this case the script utilizes the tool float. It converts the integers of each cell in the indata raster into floating-point representations in order to execute calculations. The calculations were based on equation 1.

NIR−VIS 푁퐷푉퐼 = (1) NIR+VIS

Landsat 8 satellite images were used which has a resolution of 50 meters (USGS, 2016).

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

3.3 14C-dating

As mentioned in section 3.1, four pieces of wood was found at two different locations, Hulbak and Imrestind (Figure 14). The wood was found interbedded with the rapid mass movement deposits in such a way that it may be of use as proxies for snow avalanche, rockfall or debris flow activity and frequencies. Furthermore the samples taken were covered with mosses (Figure 15). In consultation with supervisor Anne Hormes, two of these pieces were later deemed appropriate for 14C-dating and sent to University of Uppsala for lab analysis. The samples are called HEMS-HUL-10/15 and HEMS-IM- 10/15 respectively.

Figure 14. The two locations where samples for 14C-dating were taken. Hulbak ( sample ID: HEMS-HUL-10/15) to the west and Imrestind (sample ID: HEMS-IM-10/15) to the east.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 15. Moss-covered samples interbedded with debris potentially deposited by rockfalls.

3.4 Modelling

Modelling can be a useful tool in order to come to a semi-quantitative or quantitative risk assessment. Furthermore, it can be used to make approximations regarding deposits and risk zones in areas which are unavailable.

3.4.1 RAMMS::ROCKFALL

Modeling of rockfall was executed in RAMMS: Rockfall module (Rapid Mass Movement System: Rockfall module), it models rockfall scenarios in 3D based on a contact-algorithm (WSL Institute for Snow and Avalanche Research SLF, 2016b). The modeling is predicated on a DEM and also takes into account values for terrain, vegetation, rock shapes and their material properties. However, RAMMS does not account for a height dependency in a forest structure. For instance, a large and old tree can have a drag force in the tree crown that might be negligible and thus the effective height of the tree in the calculation should be less than the actual tree height. Instead it assumes a homogeneous layer with mean drag properties (WSL Institute for Snow and Avalanche Research SLF, 2016a).

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Table 1. The different forest parameters applied in RAMMS:ROCKFALL

RAMMS:ROCKFALL Forest parameters Coverage [m2/ha] Forest drag [kg/s] Open Forest 20 250 Medium Forest 35 500 Dense Forest 50 750

In the simulations made for this thesis the coverage was estimated by examining high resolution orthoimages as well as interpretation of the NDVI. ‘Medium forest’ as well as ‘Dense forest’ was estimated for Imrestind (Figure 16), while only ‘Medium forest’ was estimated in Skogshorn (Figure 17).

Figure 16. Medium’ and ‘Dense forest’ polygons at the Imrestind location.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 17. Medium forest’ polygon in the Skogshorn area.

The RAMMS software computes runout trajectories as well as rock jump height, velocities, rotational velocities, total kinetic energy, and contact-impact forces. The software interface is partly GIS-based and compatible with standard GIS-files.

In order to simulate rockfalls several input parameters are required. By using ArcGIS polygons and points defining release zones, terrain properties and vegetation were created based on orthoimages, DEM, “Norge i 3D” and observations during fieldwork. These were imported into RAMMS and function as a base for the modelling. Rock shapes were created in RAMMS based on previous reports (NGI, 2011) and observations made during fieldwork. Simulations using area release was executed, utilizing different rock shapes and multiple releases. The properties and parameters belonging to each specific terrain category used in the simulations are shown in Table 2.

In accordance with the RAMMS::ROCKFALL manual v. 1.6 (WSL & SLF, 2016a) different terrain materials were set by assigning them to different polygons.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Table 2. Showing specific parameter values based on terrain category (WSL & SLF, 2016a).

Terrain Mu_min Mu_max Beta Kappa Epsilon Drag Extra soft 0,2 2 50 1 0 0,9 Soft 0,25 2 100 1,25 0 0,8 Medium soft 0,3 2 125 1,5 0 0,7 Medium 0,35 2 150 2 0 0,6 Medium hard 0,4 2 175 2,5 0 0,5 Hard 0,55 2 185 3 0 0,4 Extra hard 0,8 2 200 4 0 0,3 Snow 0,1 0,35 150 2 0 0,7

In Imrestind the overall terrain material was set to ‘Medium Hard’ as it fit the manual suggestion description; small penetration depths, rocky debris being present and shallow surface soil. In the area a larger talus cone is present which was assigned ’Hard’ (Figure 18) (rocks jump over ground, a mixture of large and small rocks without any noticeable vegetation; for instance a rock scree). The bedrock was assigned ‘Extra Hard’ (Figure 19) as it is made up of mainly meta-diabase and amphibolite (Figure 9). Two additional polygons were made in order to account for the variation of bedrock as the slope flattens out. These were both set to ‘Medium Hard’ (Figure 20 & Figure 21) but they differ in petrology to some extent (NGU, 2016).

Figure 18. Talus cone assigned ‘Hard’ material property at Imrestind.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 19. Area assigned as ‘Extra Hard’.

Figure 20. Sericitic-chloritic slate and mylonite given ‘Medium Hard’ material properties.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 21. Quartz-rich phyllite and quartz-slate given ‘Medium Hard’ material properties.

The release area (Figure 22) used for the simulation at Imrestind was estimated partly through field observation but mostly through slope maps created in ArcGIS. The release area is constricted to areas that have inclinations of >45° (Braathen et al., 2004).

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 22. Polygon representing the release area used in the simulation at Imrestind.

The simulations at Imrestind that will be discussed in more detail in this thesis used different number of rocks, as shown in Table 4 – Table 7. The reason for using multiple rocks of the same volume and the same dimensions is to create a more realistic rock size distribution. This distribution is based on observations made in field as well as, to some extent, the report by NGI (2011). Furthermore larger rocks were included in some simulations in order to investigate the potential effects of larger rock sizes, thus creating a more dynamic rockfall scenario.

At Skogshorn the overall terrain was set to ‘Extra Hard’ and one polygon was assigned ‘Hard’ material properties (Figure 23). Primarily because the area is mainly comprised of exposed bedrock (meta- diabase, amphibolite as well as meta-gabbroic rocks) with several talus cones in close proximity, furthermore there is very little vegetation which can influence the rockfall.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 23. Polygon representing talus cones with ‘Hard’ material properties at the Skogshorn location.

The simulation at Skogshorn which will be discussed in more detail in this thesis used a total number of three different rock shapes. These shapes were also estimated based on field observations. Why fewer rock shapes and a lower number of rocks were used in this simulation compared to the one at Imrestind is due to practical reasons. The release area at Skogshorn is significantly larger than that of Imrestind, meaning that each additional rock used in the simulation would dramatically increase the run time of the simulation. Table 8 presents the number, shape and volume of the rocks used in this simulation.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

4. Results 4.1 Rapid mass movement deposit map

The rapid mass movement deposit map covering Hemsedal (Figure 24 - 24) consists of numerous different types of deposits, or units, which will be presented below. They are related to slope susceptibility and instability coupled with gravitational processes.

As the main topic of this thesis is rockfalls, snow related deposits as well as deep-seated gravitational slope deformation will not be described. For further information on these see Hellman & Eliasson (2016) and Jillerö (2016).

The map units which will be described are

 Rockfall deposit  Maximum extent rockfall  Rock avalanche deposit  Rapid mass movement (continuous and thin cover)  Debrisflow deposit  Debrisflow and fluvial erosion  Ravines and fissures

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 24. Rapid mass movement deposit map, Hemsedal.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 25. Western segment of the rapid mass movement deposit map, Hemsedal.

Figure 26. Eastern segment of the rapid mass movement deposit map, Hemsedal.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

4.1.1 Rockfall deposit

Rockfall deposits can be found at several locations in and around Hemsedal. This unit is mainly based on observations of large amounts of boulders at the same location (Figure 3).

Furthermore, it is also dependent on the boulders showing characteristics of rockfall rather than, for example, ice transportation. However, glacially transported rocks above a prominent headwall can later accelerate into freefall, for instance if the mountainside would collapse or seismic activity occur. Although glacial erratics are generally neglected in this unit because it is exceedingly difficult to determine if they after glacial deposition have fallen down a steep hillside. Characteristics of rockfall include the geometry of the boulder (sub-angular or angular) and the boulder needs to be of local lithology (Figure 27). The slope of the area which is in connection to the rockfall deposit also has a big impact on the classification. For instance, at Imrestind the inclination is between 45° and 75° (Figure 33). This is within the span of inclinations prone to rockfalls (Braathen et al., 2004). Along with the presence of large amount of boulders this is interpreted as a rockfall deposit.

Figure 27. A) Angular boulders with local lithology. B) A talus cone with varying rock sizes at Hulbak.

4.1.2 Maximum extent: rockfall

This unit represents the maximum extent of rockfalls since the deglaciation of Hemsedal (approximately 9 000 BP) and does not reflect modern conditions. As in the rockfall unit, maximum extent is dependent on the presence of boulders which show characteristics of rockfall (Figure 28). Furthermore, these boulders need to have a clear source of origin, for instance a prominent headwall. The unit is established in connection to areas that are susceptible to rockfalls.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 28. Boulders with different origin which functioned as a base for the maximum extent unit.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

4.1.3 Rock avalanche deposit

In this unit large boulders are observed. Furthermore the sheer amount of material observed and the distance which it has travelled are distinguishing features for this unit (Figure 5). A clear release area was identified and a distinctive runout path was observed (Figure 29). Additionally, rock avalanches are often reported (Figure 12) and well documented, as it has been in Hemsedal.

Figure 29. Rock avalanche at Grøtø. Probable release area outlined in red and probable runout path outlined in yellow.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

4.1.4 Rapid mass movement deposit (continuous and thin cover)

Deposits categorized as continuous have a continuous spatial distribution with a clear and obvious demarcation. Furthermore the prevalence of the deposits can be related to a plausible release or source area. Deposits categorized as thin cover are mapped based on the same criteria but exhibit a discontinuous and thinner spatial distribution.

Rapid mass movement deposits are distributed over the entire study area (Figure 24) and consist of multiple types of deposits. This category is primarily utilized when the distinction between the mass movements which have affected the area is too vague. An example of this is in areas which are not solely or dominantly characterized by one type of process or deposit, e.g. debrisflow, rockfall or snow avalanche (Figure 30).

Figure 30. A) A large deposit (blue) with possible source areas (pink). B) Close-up version showing some of the larger blocks in this deposit. C) Close up on the rock size distribution.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

4.1.5 Debrisflow deposit

Like the rock avalanche at Grøtø there has been a large debrisflow reported in the area (Figure 12). Boulders were observed with a relatively large proportion of finer grains present as well. The area of the deposit has an undulating and irregular appearance indicating the influence of water (Figure 31).

Figure 31. The debrisflow deposit outlined in yellow. Note the undulating and irregular surface along with the larger rocks.

4.1.6 Debrisflow and fluvial erosion

This unit is present more dominantly in the east, in the Skogshorn area. However, it is still found throughout the entire area (Figure 24). In this unit incisions into the bedrock or sediments could be observed, often a distinct disturbance in vegetation as well. Less noticeable but still indicative of these are the reorganization of sediments. This can be caused by a saturated body of mud and debris. Usually these are found as extensions of ravines or fissures, as well as extensions of these funnel shaped snow avalanche accumulation zones (Figure 32).

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 32. A zoomed in version of the deposit map which is covering Skogshorn aimed to clearly illustrate the debrisflow and fluvial erosion unit.

4.1.7 Ravines and fissures

This unit is spatially distributed relatively evenly throughout the study site (Figure 24). There is often rapid mass movement activity in direct contact, or in close proximity to these. The ravines and fissures tend to have an N-S orientation trend, with NW-SE and NE-SW deviations.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

4.2 Slope map and release areas

The slope map (Figure 33) is categorized as described in section 1.2.2. The study area is predominantly characterized by 45°-60° slopes, however, many parts show slope values of 60°-75°. Hemsedal and Skogshorn are therefore highly exposed to potential rockfalls. The slope map can efficiently be used as a tool to interpret potential areas susceptible to rockfalls, making it a valid first step in geohazard risk assessments.

Figure 33. A slope map covering the study area with intervals specified in section 1.2.2.

The release area map (Figure 34) is divided into a red unit and a green unit. Both units are based on slope values and do not take other parameters into consideration, e.g. vegetation. The red zones are areas which theoretically could function as a release area for snow avalanches (Hellman & Eliasson, 2016). The green zones are areas steep enough to be susceptible to rockfalls. However, as specified in section 3.2.2, it is only based on inclinations above 45° and below 90°.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 34. The release map for both rockfalls and snow avalanches. The rockfall release area is defined as inclinations between 45°-90°. The snow avalanche release area is defined as inclinations between 30°-50°.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

4.3 NDVI

High NDVI values denote areas with healthy vegetation while low NDVI values denote areas with no vegetation. In the image below, high NDVI values are found at low relief areas while low values are present at high relief areas. Note that the yellow indicates non-significant amounts of vegetation and is concentrated to the north. However there are minor areas which have the same trend. Also, note that Grøtø can be observed (circumscribed in green) clearly, solely based on the NDVI values as well as Imrestind (circumscribed in black) to a certain degree.

Figure 35. NDVI map. High values represented as dark colors and low values represented as bright colors.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

4.4 14C-dating

The result from the 14C-dating of the branches from Hulbak and Imrestind was 151 ± 24 BP and 147.5 ± 0.4 pMC (percent modern carbon), respectively. This was measured and calculated with a δ13 C VPDB of -28.2‰ and -28.9‰ respectively. The sample from Imrestind (HEMS-IM-10/15) is modern (pMC). The calibrated radiocarbon age of the sample from Hulbak suggests an age of approximately 1670 AD to 1950 AD (Figure 36, Table 3)(Reimer et al., 2013).

Figure 36. Radiocarbon age in relation to the calibrated age. The radiocarbon age coincides with the calibration line multiple times, giving three possible calibrated ages (Reimer et al., 2013). The calibration was done in Calib v.7.1.

Table 3. Sample information regarding the sample from Hulbak.

Site Hulbak, Hemsedal Imrestind, Hemsedal Sample name HEMS-HUL-10/15 HEMS-IM-10/15 Longitude* 8°29'52.0"E 8°34'31.5"E Latitude* 60°53'00.4"N 60°51'51.2"N Altitude (m.a.s.l.) 771 711 Material Wooden branch Wooden branch Radiocarbon age (years BP) 151 ± 24 147 ± 0.4 pMC Cal. Age (AD) 1650 - 1950 ------

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

4.5 RAMMS::ROCKFALL module

The modelling was focused on two areas, Imrestind and Skogshorn, with the majority of time spent on Imrestind. For each location there were several fixed parameters, release area, forestation/vegetation and terrain properties. However, the number and shape of rocks did change between the simulations in order to see how different geometries and volumes would influence the outcome. Five simulations have been run in order to account for a number of different scenarios. Four of them concerning Imrestind and one with regards to Skogshorn.

The first sets of images are from a simulation done at Imrestind. The scenario consists of roughly 44.000 simulations and the number of blocks as well as volume is presented in Table 4. The total number of rocks used in this simulation is 52.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Table 4. The table is showing the distribution of equant and flat rocks at Imrestind, 44 000 simulations run.

Imrestind, simulation 1 Number of rocks Volume [m3] Equant (1.2) rock shape 0,1 4 0,3 4 0,5 0 0,8 4 1 3 2 0 3 4 5 4 7 0 10 1 20 1 50 7 70 5 100 2 Flat (1.2) rock shape 0,1 2 0,3 0 0,5 2 0,8 0 1 3 2 2 3 0 5 1 7 0 10 2 20 1 50 0 70 0 100 0 Total number of rocks 52

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 37. Kinetic rock energy output from scenario 1, Imrestind.

Figure 38. Number of deposited rocks output from scenario 1, Imrestind.

Note how the kinetic rock energy has a distinct decrease in energy (Figure 37). This trend coincides with the number of deposited rocks concentration (Figure 38).

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

The second scenario at Imrestind consisted of approximately 30 000 simulations and was simulated using 37 rocks. The volume and shape distribution of the 37 rocks are presented in Table 5. Compared to the larger scenarios this simulation utilizes no large blocks (>20m3) in its rock distribution.

Table 5. The table is showing the distribution of equant and flat rocks at Imrestind, 30 000 simulations run.

Imrestind, simulation 2 Number of rocks Volume [m3] Equant(1.2) rock shape 0,1 4 0,3 4 0,5 0 0,8 4 1 3 2 0 3 4 5 3 7 2 10 1 20 1 50 0 70 0 100 0 Flat (1.2) rock shape 0,1 2 0,3 0 0,5 2 0,8 0 1 3 2 2 3 0 5 1 7 0 10 1 20 0 50 0 70 0 100 0 Total number of rocks 37

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 39. Kinetic rock energy output from scenario 2, Imrestind.

Figure 40. Number of deposited rocks output from scenario 2, Imrestind.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

The same trend as in scenario 1 can be observed here. However, it is important to realize the difference in scale. The amount of kinetic energy (Figure 39), as well as the number of deposited rocks (Figure 40), are much lower here than in the first scenario. The highest values in scenario one is roughly 360 000 kJ (kinetic rock energy) compared to scenario two, which has a maximum value of approximately 91 000 kJ.

The third scenario at Imrestind consisted of approximately 16 500 simulations and was simulated using 22 rocks. The volume and shape distribution of the 22 rocks are presented in Table 6. This scenario utilizes some larger blocks (>20m3). However, the rock geometry distribution is more diverse, containing flat, equant and long rock shapes.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Table 6. The table shows the distribution of equant, flat and long rocks for scenario 3 at Imrestind, consisting of 16 500 simulations run.

Imrestind, simulation 3 Number of rocks Volume [m3] Equant(1.0) rock shape 0,56 1 1 1 Equant(1.2) rock shape 0,8 1 Equant(1.5) rock shape 1,1 1 2 1 Equant(2.0) rock shape 5 1 Flat (1.2) rock shape 0,5 1 3 1 12 1 30 1 50 1 Flat (1.5) rock shape 12 1 50 1 Flat (2.0) rock shape 3 1 100 2 Long (1.2) rock shape 2 1 12 1 50 1 Long (1.5) rock shape 1,3 1 7 1 50 1 Total number of rocks 22

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 41. Kinetic rock energy output from scenario 3, Imrestind.

Figure 42. Number of deposited rocks output from scenario 3, Imrestind.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Note how the number of deposited rocks (Figure 42) is significantly lower here than in the two previous scenarios, i.e. maximum value of 119 compared to 343 of scenario 1 and 240 of scenario 2. However, the kinetic energy is very high, ~397 000 kJ (Figure 41). Furthermore it consists of almost 30 000 less simulations than the first scenario.

The fourth scenario at Imrestind consisted of approximately 8 200 simulations and was simulated using 11 rocks. The volume and shape distribution of the 11 rocks are presented in Table 7. This scenario utilizes no blocks larger than 10m3. However, the rock geometry is less diverse compared to the 3rd scenario.

Table 7. The table shows the distribution of equant and flat rocks for scenario 4 at Imrestind, consisting of 8 200 simulations run.

Imrestind, simulation 4 Number of rocks Volume [m3] Equant (1.5) rock shape 0,1 1 0,5 1 0,8 1 1,1 1 10,0 1 3,0 1 7,0 1 Flat (1.5) rock shape 1,7 1 3,0 1 5,0 1 10,0 1 Total number of rocks 11

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 43. Kinetic rock energy output from scenario 4, Imrestind.

Figure 44. Number of deposited rocks output from scenario 4, Imrestind.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Both kinetic energy (Figure 43) and number of deposited rocks (Figure 44) are lower here in comparison with the other scenarios. The number of simulations run is significantly lower here than in the others.

The simulation of Skogshorn consisted of 14 628 simulations and was using 3 different rocks. The volume and shape distribution of the 3 rocks are presented in Table 8. This simulation is only considering rocks with a smaller volume (<2.0 m3). This is due to two reasons. i) There were a lot of smaller boulders observed in this area, ii) as mentioned before, additional rocks added increased the run time with a considerable amount of time due to the size of the release area.

Table 8. The table shows the distribution of equant rocks for scenario 5 at Skogshorn, consisting of roughly 14 600 simulations run.

Skogshorn, simulation 5 Number of rocks Volume [m3] Equant (1.2) rock shape 0,1 0 0,5 0 1,2 1 Equant 2.0 0,1 1 0,5 1 1,2 0 Total number of rocks 3

Figure 45. Kinetic rock energy output from scenario 5, Skogshorn.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 46. Number of deposited rocks output from scenario 5, Skogshorn.

Note that where areas with higher kinetic energy (Figure 45), thus areas with a considerable number of rocks (Figure 46) in the simulated scenario correspond well with the rapid mass movement map (Figure 24) as well as the boulder map (Figure 28).

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

5. Discussion

The discussion of this thesis will be divided into several parts. First the slope, release and boulder maps will be discussed. Followed by a discussion of the rapid mass movement deposit map. Subsequently the NDVI and 14C-dating will be discussed, as well as the modelling and risk assessments connected to the project.

The mapped deposits found in the rapid mass movement map are subjects of interpretation. Thus uncertainties will be related to these as there is a small bias, even if one tries to be objective.

5.1 Rockfall slope and release area map

This GIS generated slope and release area map (Figure 33 & Figure 34) depict all hillsides with inclinations above 45°, which constitutes the potential rockfall areas. However, these do not correlate accurately enough with reality as they do not take other parameters into consideration, e.g. vegetation, rock type, degree of weathering and existing fracture orientations. This is why fieldwork is a necessity. Without the proper geological knowledge these could be regarded as risk areas, especially the 60°-75° category which represents the statistically more active areas (Braathen et al., 2004). However, the slope map functions as an excellent tool in order to pinpoint areas likely susceptible to rockfalls, thus areas which should be further investigated. The release area map is based on the slope map but it does not differentiate between the different categories. Therefore it could be thought of as a more conservative and general version of the slope map. 5.2 Boulder map

This map (Figure 28), which functions as a foundation for the maximum extent of rockfalls unit, is based on observations in the field as well as remote sensing. Generally, boulders with evident rockfall characteristics are easily distinguished from other boulders. However, this is not always the case which is why the category “unknown origin” was established. During remote sensing analysis certain boulders were determined to have a rockfall origin although no field investigation had been conducted. This was the case when the boulders were located below prominent headwalls likely prone to this gravitational process, for instance at Skogshorn (Braathen et al., 2004). The boulders set as erratics have been investigated in the field and had a sub-rounded shape indicative of ice transportation and therefore carry no significance to the rapid mass movement deposit map.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

5.3 Deposits and the rapid mass movement deposit map

Rockfall deposits and rock avalanche

Evidence supporting the occurrence of continuous rockfall events is a prerequisite for the mapping of this unit. These are areas were talus cones or scree are present as these can indicate that there is a temporal continuity of rockfalls. In reality they are not necessarily spatially restricted to these areas but in order to be consistent certain limitations were needed. This is also the reason why the maximum extent of rockfalls is a unit, to account for the deposits which are less prominent.

One of the main concerns when mapping the rockfall deposits is whether or not the boulders at a specific location are from multiple events or one large event. In the latter case it might not be a rockfall but rather a rock avalanche. This would have a significant impact on not just the rapid mass movement deposit map, but also on the risk assessment. However, differentiation between the two is difficult as, per definition; the main difference is the total volume released during one event (NVE, 2011a). Furthermore, people have only been occupying the area for so long. The church in Hemsedal is believed to have been built between 1207 and 1224 (Munch, 1864). It is likely that historical notations of rapid mass movement were not taken before this. This means that all activity will not have been recorded. Which otherwise could have made the interpretations more accurate.

There are numerous ice-related preconditioning factors which could cause rockfalls and rock avalanches. Ballantyne (2002) describes glacial debuttressing as the removal of support of adjacent glacier ice during periods of downwastage and retreat with the consequent stress-release. This basically means that as the glacier retreats, the stabilizing force which acts on a slope is removed and an outward stress can occur. This can lead to a higher rockfall activity (McColl, 2012; Cossart, Braucher, Fort, Bourlès & Carcaillet, 2008; Evans & Clague 1994; Ghirotti, Martin & Genevois 2011). Furthermore glacial erosion can cause the oversteepening of rock walls, as the ice tends to enhance local relief. These headwalls are then more prone to gravitational processes such as toppling (Ambrosi & Crosta, 2011; MacGregor, Anderson, Anderson & Waddington, 2000; Abele, 1994; Radbruch-Hall, 1978). Cosmogenic nuclide dating can be an excellent method to give an age constraint on rockfalls deposits, therefore determine if the deposits cluster in the early Holocene.

As the Fennoscandian ice-sheet retreated from Hemsedal post-glacial rebound together with the oversteepened rock walls and glacial debuttressing, probably caused larger amounts of rockfalls and rock avalanches to occur. Since the time of deglaciation it is likely that the activity of the area has lowered incrementally, with a temporary increase during LIA (McCarrol et al., 2001), which might be supported by the finding of the tree sample in Hulbak (HEMS-HUL-15/10). Additionally, as stated earlier, climate fluctuations can greatly influence rock slope instabilities (McCarrol et al., 2001; Volkwein et al., 2011; Ravanel & Deline, 2011; Matzouka, 2007; Frayssines, 2005). For instance, a large contributing factor for expanding fractures which could lead to slope failures is permafrost (Volkwein et al., 2011). Due to a temperature variation, more permafrost has likely been present 8 000 years ago compared to today. With the same argument, it is likely that fewer rockfalls will occur in the future as less permafrost will affect the area (Gisnås et al., 2013; Førland et al., 2004; Stahl et al., 2010). However, there will probably be an increase of activity as the permafrost is melting with a subsequent decrease over time. But it is difficult to properly assess and validate the deposits with respect to their gravitational process as well as future prediction regarding microclimatic changes. Therefore further studies are required.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Maximum extent: rockfalls

This unit was created in order to account for the less prominent rockfall deposits, i.e. individual blocks. For the sake of clarity concerning the rapid mass movement map, it would be redundant to mark these deposits with the same color as the larger deposits. The maximum extent of rockfall is based on multiple components. Observations of blocks in the field which have a clear origin and rockfall characteristics, remote sensing, and interpolation between the two based on geological knowledge. It is important to realize that the unit does not express the maximum extent based on present day conditions, which means that this unit is not directly correlated to risk management or risk potential. In order to validate and properly utilize the maximum extent unit in a risk assessment capacity, geological knowledge is essential.

This is evident when considering the rock avalanche at Grøtø. Looking at the maximum extent unit (Figure 24), it reaches further than the actual rock avalanche deposit. This could be a reason for a higher risk classification. However, the maximum extent unit does not account for the fact that there is a massive deposit located here. This means that potential rockfalls which could fall here would most likely never reach any buildings or roads, since the existing deposit has a higher friction than the surrounding. The boulders from the avalanche are of considerable size and a potential future rockfall would get lodged in between. Furthermore, the spatial extension of the rock avalanche deposit creates a natural barrier for potential rockfalls.

Debrisflow

The debrisflow deposit is found in the western part of the study area (Figure 25). Looking at the rapid mass movement incident map (Figure 12), it occurred further to the north than our map shows. However, this observation is from 1860 and the exact location can therefore be discussed. According to historical notations, the debrisflow came from the hillside above the 1860 point entry (Furseth, 2006). I believe the debrisflow were channeled into the ravine to the southeast and at a later time deposited at the mouth of this ravine, which is in direct contact with the deposit. This is supported by the fact that there is barley any boulders or significant amount of smaller fractions to the northwest or north of the deposit. Even though there is potential for debrisflows more to the north, based on the inclination of the area, the ravine functions as a more natural run out path (NVE, 2011c). Furthermore, looking at the rapid mass movement incident map, there were one additional debrisflow event that took place during the same day. According to historical information provided by Astor Furseth (2006) there was heavy precipitation during this time which probably triggered these two events. This further strengthens the argument that the debrisflow was channeled through the ravine. The water, and the debrisflow, takes the easiest route. In this case a steeper ravine is the route with least resistance. Additionally, the ravine creates a path were saturation is easier to maintain and therefore the debrisflow can move both easier and further.

Rapid mass movement deposits

Rapid mass movement deposits have a large spatial distribution in Hemsedal. The unit can be fairly complex as it can possess signs indicative of several different gravitational processes. Or if the indications are less distinct and clear, equifinality might characterize the area. Hence differentiating between different processes which could have influenced the deposits is often complex. For instance, a small scree, is it caused solely by rockfalls or has it also been affected by snow? The genesis of a deposit, or several deposits in one area, can be difficult to determine accurately. One reason for this is the possibility that the fingerprint of one slope process is covering that of another. It is possible to discern this based on the likelihood of different gravitational slope processes, i.e. based on the

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway potential for different processes. By studying the inclinations, morphology, vegetation and similar parameters it is possible to make approximations for this. This can make the rapid mass movement unit a better representation of reality.

One of the greatest overall uncertainties regarding the rapid mass movement deposit map is the quality and resolution of both the DEM and the aerial images. The interpretations that have been made through GIS analysis are all based on the DEM and the aerial images.

Fissures and ravines

Fissures and ravines are spread across the entire study area (Figure 24). They are likely formed due to structural weaknesses in the bedrock, possibly related to the thrusting events of the Caledonian orogeny. Therefore these could be of potential interest when investigating areas prone to rockfalls and possibly even rock avalanches. A hillside can be within the inclination intervals defined as rockfall susceptible, but if the structural weakness of the rock itself is not creating preferable rockfall conditions, it is possible that the area is stable. Therefore investigations into the strain-regime of the area could entail vital information regarding its rockfall susceptibility.

5.4 Normalized difference vegetation index (NDVI)

The NDVI indicates areas with none or low vegetation to dense and healthy. This was used as an additional tool in order to identify the potential distance which a rock from a rockfall could travel. Furthermore the NDVI coupled with aerial image interpretation and field observations enhanced the modelling parameter “forest”. In general the NDVI is based on Landsat 8 images with a resolution of 50 meters which is an uncertainty that needs to be taken into account, and is why the NDVI cannot define the forest parameter without additional information.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

5.5 14C-dating

The 14C-dating from Imrestind resulted in a δ-carbon value which is indicative of a modern carbon age. Therefore this piece of wood could be less than 60 years old. This is because it is possible that nuclear bomb tests changed the 14C content in the atmosphere. Therefore a more precise age determination of the wood piece is not possible. It is possible that this piece of wood could even be less than 30-40 years old. This would make Imrestind a highly active area which would require further investigations, for instance using InSAR, as there are several residences at the base of the slope.

However, there are uncertainties related to the applicability of the 14C-dates. In order for the organic material to be useful as a proxy for rockfalls they need to be interbedded with the rockfall debris (Figure 15). Furthermore, it is imperative that they could not have been positioned in such a manner after the last rockfall event. Therefore substantial amount of time was spent investigating potential sources of error in regards to the position of the debris. The samples taken were covered by rockfall boulders and there were no trees in close proximity from which it could have fallen. Furthermore, mosses covered the entire piece of wood which suggest that they have been interbedded for a quite some time (Figure 15). These factors combined validate the samples taken as well as their authenticity as an age marker.

The age of the sample from Hulbak is not explicit due to the position on the calibration curve. It could be modern, from the Little Ice Age or anywhere in between. This could imply that it is an active area. However, due to the uncertainty of the calibrated age, i.e. the multiple correlations with the calibration line, it is difficult to approximate the activity of this area.

5.6 Modelling

The four scenarios covering Imrestind have the same conditions in terms of terrain and vegetation, what differs is the rock geometry and number of rocks.

The fixed parameters, i.e. terrain, vegetation and release area, are constant throughout the simulations. However, they are quite general. The terrain parameters are set based on several predetermined categories. Each category had a description, e.g. terrain categorized as ‘Hard’ is defined as:

Rocks jump over ground. Mixture of large and small rocks. Usually without any vegetation. (WSL, 2016a, p. 33)

This creates an uncertainty in the simulation. Although more parameters do not automatically make a simulation more accurate, specific categories limits our ability to describe our present conditions, i.e. there is no category between ‘Hard’ and ‘Medium hard’ which could be utilized to better describe the environment.

The vegetation which was mapped for the simulation at Imrestind (Figure 16) was based on the NDVI, fieldwork as well as the aerial images. Vegetation deemed less than ‘Open forest’ (Table 1) was therefore neglected due to the non-mitigating properties, i.e. no significant ‘forest drag’.

Comparing the different simulation outcomes for number of deposited rocks a clear trend can be observed. They all have a concentration of rocks approximately at the same location, although the total number of rocks differs widely. Furthermore they all tend to have similar spatial extent. This indicates that the deposited rocks follow a topographical divider. However, the varying amount of

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway rocks can probably be prescribed to the number of simulations run and the number of rocks used. Scenario 1 and 2 have the most simulations run and therefore have the highest amount of rocks, while scenarios 3 and 4 have the lowest. This trend seems to be impervious to rock size distribution or rock geometry. This accumulation of debris coincides well with the rapid mass movement deposit map created in this thesis, both at Imrestind and at Skogshorn.

With regards to kinetic energy there are significant differences between the simulations which are not solely due to the different number of simulations run. Scenario 1 and 3 are both high energy simulations compared to scenario 2 and 4. This could indicate that rock geometry has a large impact on the energy outcome. The only notable difference between scenario 2 and 3 is that scenario 2 has almost twice the amount of simulations run as well as a greater amount of flat-type rocks. Furthermore scenario 3 considers a smaller rock size and rock geometry distribution than scenario 2 which could have a large impact on the outcome. Scenario 2 considers no flat-type rocks larger than 20 m3 while scenario 3 takes five rocks larger than 20 m3 into account. This trend can also be observed in scenario 1 which has a higher number of rocks and geometries, thus creating a more dynamic scenario. However, 15 angular blocks larger than 20 m3 was used in this simulation but only 1 flat-type block. Therefore, simulations appear to be highly dependent on rock geometry, which in this case is based on approximations.

One of the largest differences between the simulations is the rock size and geometry distribution. It is likely that in scenario 3 the large flat-type blocks have a greater impact on the energy outcome due to distribution. The conclusion would then be that the rock size and geometry distribution, and therefore the influence of each rock, greatly affect the outcome.

Based on this it appears that simulations, especially in terms of kinetic energy, are speculative without further exploration of the area. These simulations show how different the model outcome can be dependent on the different parameters. Therefore simulations should be used with caution, geological knowledge as well as high resolution input data.

It is evident that simulations can potentially be used as an additional source of confirmation in conjunction with the deposit map. Mainly because the behavior of the deposited rocks does not change with geometry and rock size distribution.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

5.6.1 Risk assessment

There are two main types of risk assessments, qualitative and quantitative. A qualitative risk assessment relies on expert opinions which evaluates if a certain area is more hazardous than another, e.g. high risk, medium and low risk areas (Ko, Flentje & Chowdhury, 2004; Abella & Van Westen, 2008). This can be based on factors such as (Andrea Taurisano, personal communication, 3rd of May, 2016):

 Potential for activity (e.g. slope values)  Deposits indicative of gravitational processes (e.g. talus or scree)  Reports on previous events (e.g. Grøtø)

Whereas the quantitative risk assessment evaluates different parameters (e.g. the probability of the event and loss if this event were to occur) by assigning numeric values to them. Equations based on these parameters yields a numerical result which determines the risk of a specific area (Dai, Lee & Ngai, 2002; Abella & Van Westen, 2008). For instance, Hermanns et al. (2013b) has designed a score based risk assessment, describing the state of a slope, containing 9 criterions. Some of these criterions are weighted differently, meaning that certain aspects are more influential (e.g. displacement rates, see appendices). The total score derived from this risk assessment can be between 0 and 13, where a score of 13 would indicate a highly unstable slope.

Although risk assessment is not a part of this thesis it is possible to do a quantitative risk analysis based on simulations executed in this project. The quantitative parameters in this analysis are the energy output from the simulation in relation to the return period of events.

The 14C dating would theoretically give Imrestind a return period of 1-30 or 30-100 years. When combining this information with the output from scenario 1 and the Swizz hazard classification (Figure 47) (BWW, BRP & BUWAL, 1997; Volkwein et al., 2011), two different outcomes are attained:

 High risk, according to scenario 1 and 3 (A and B, Figure 49)  Medium risk, according to scenario 2 and 4 (C and D, Figure 49)

Figure 47. Risk classification scheme based on the intensity of the rockfall and the return period of events (BWW, BRP & BUWAL, 1997; Volkwein et al., 2011).

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Increasing the return period of Imrestind to 30-100 years could change this classification, depending on which part of the hillside is examined. It is likely that the difference in risk classification is due to the different rock geometry combinations as stated above. Furthermore, observations made in the field indicate that it is less likely that the amphibolite or meta-gabbro present above Imrestind would fracture into this flat-type geometry. They are more likely to fracture into large, angular pieces, as they are more rigid rock type than for example slates (Nesse, 2012).

This quantitative risk assessment is not necessarily more accurate than a qualitative one would be. Imrestind has deposits indicative of rockfalls but no historical events recorded. This would indicate a lower risk area but the simulations run indicates medium to high risk. Regarding the residential area, the risk category would be anywhere from high to low depending on which scenario is used. I believe that in this case, the potential uncertainties are greater in the quantitative risk assessment compared to the qualitative risk assessment. Mainly because of the approximations of geometries which is the most influential factor on the kinetic energy output. Additionally, in order to enhance the quantitative risk assessment different return periods for different rocks should be used. It is more likely that a 10 m3 rock has a 10yr return period than 1000 m3 rock. Therefore using longer return periods for larger rocks could yield a better result. For instance, in Figure 48 different return periods are assigned based on rock size and the distance which they have travelled; red = longer, orange = medium and green = shorter. In my opinion this would be a more accurate way of describing the risk of an area.

Figure 48. Different rocks which would get different return periods at Imrestind. Red = long return period, Orange = medium return period and Green = shorter return period (modified after Norkart, 2016)

In terms of perceived risk, it is possible that a quantitative risk assessment appears to be more accurate than a qualitative one. Thus potentially resulting in mishandling of information in a decision-making capacity by the population.

The study area provided by NVE is, as stated in section 2.1, based on GIS analysis. This means that assessments based on the relationship between population and the habituated areas susceptibility to

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway gravitational processes have been made. However, the study area is primarily situated north of the river Hemsila, where there are few historical records of incidents. Most of the observations are from the south side of the river, this is also where most of the fatal incidents have occurred (Figure 12). This is, however, reasonable to a certain extent. Due to the limited resources, risk analysis should be conducted where people, buildings and infrastructure can be affected. Economically, and time distribution wise, it makes less sense to investigate an area where people rarely are, in comparison to, e.g. residential areas.

With the assumption that the life expectancy of a house is 100 years and the return period of a rockfall is 1000 years, this means that there is a 10% risk that the building will be exposed to such an event during its lifetime. If the assumption that a person spends roughly half their time at their resident is made, the probability of a rock reaching a house with someone in it is 5%. This risk is deemed acceptable in most areas.

In comparison, with the same return period, a road or trail is much less exposed. One of the fundamental factors for this is that a house is stationary. A trail or road, however, is only occupied certain times at certain places. If there is a 10% chance that a boulder will reach the road or trail, the spatiotemporal risk of injury is incredibly low.

Therefore, doing risk analysis at areas with high rapid mass movement activity but with very low probability of human interaction can be thought of as a mismanagement of both time and resources. But at the same time it is difficult to exclude these areas as they are highly subjected to gravitational processes. This is a complex matter which requires further investigation.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Figure 49. ‘Kinetic rock energy’ output. A) Scenario 1, 45 000 simulations run B) Scenario 3, 16 500 simulations run C) Scenario 2, 30 000 simulations run D) Scenario 4, 8 500 simulations run.

Figure 50. ‘Number of deposited rocks’ output. A) Scenario 1, 45 000 simulations run B) Scenario 3, 16 500 simulations run C) Scenario 2, 30 000 simulations run D) Scenario 4, 8 500 simulations run.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

6. Conclusions

 It is possible to accurately identify and map rapid mass movement deposits based on field work and remote sensing. Hence, the rapid mass movement deposit map can function as a platform and as a testbed for RAMMS::Rockfall runout modelling.  The RAMMS::Rockfall simulations provide a foundation for future projects. It has been shown that the simulations yield distinct patterns, the position of accumulated debris, which can enhance the quality of both the rapid mass movement deposit maps and risk assessments (Figure 38, Figure 40,Figure 42 and Figure 44). The simulation of rockfalls correlates well with observations made in the field, thus establishing a link between simulations and observations.  Quantitative risk assessments are possible. However, as the outcome is highly dependent on the quality of the input parameters, more information is required in order to fulfill a risk assessment based on hazard probability based on the evaluation of return times and interviews. (Figure 37,Figure 39,Figure 41,Figure 43 and Table 4 -Table 7).

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

6.1 Future outlook

A future project could be to accurately measure rocks at Imrestind and determine their geometry. Thoroughly assessing the distribution of rock geometry may yield more accurate simulations. Furthermore, interviews with local residents are needed inputs to calculate the return factor as it is possible that rockfalls occur more regularly but are not reported as they are perceived as insignificant. The only rockfall event reported in Hemsedal is, as mentioned before, the rock avalanche at Grøtø in year 1650 which was an extreme event.

Furthermore 10Be cosmogenic radionuclide surface exposure dating of headwalls could provide the data necessary to determine whether a deposit is formed through one or several events (Ballantyne & Stone, 2004; Böhme, Oppikofer, Longva, Jaboyedoff, Hermanns & Derron, 2015). It is conceivable that an age disparity between the samples would suggest several events. Additionally, it could enhance our knowledge concerning the temporal distribution of rockfalls. Has the frequency of events been evenly distributed since deglaciation, or has there been a successive declination due to climatic fluctuations, postglacial uplift or seismic activity?

Another potential project could be to utilize a geophysical approach to investigate rapid mass movement chronology. If the base topography is known, GPR could be used in order to recognize a pronounced stratification in a rockfall deposit (Sass & Krautblatter, 2006; Erik Sturkell, personal communication, 19th of May 2016). An even stratigraphy could suggest one large event whereas a more discordant stratigraphy could indicate several events. Therefore this could potentially be used to differentiate between rockfalls and rock avalanches, thus enhancing rapid mass movement deposit maps as well as risk assessments.

7. Acknowledgements

I would especially like to thank Anne Hormes who has supervised this thesis. Her knowledge has been not only essential but invaluable. Through the entire project, both in fieldwork and during writing, she has been optimistic and supportive which has greatly increased the quality of this thesis. Additionally, I would like to thank Axel Hellman. His attitude, inputs and thoughts has had nothing but a positive effect during fieldwork as well as during the writing process. Thank you, Andrea Taurisano and Delia Kejo at NVE, for your thoughts and inputs regarding the rapid mass movement map as well as the modelling. Lastly, I would also like to mention Ingrid Jillerö, who assisted during the field work. Thank you.

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

8. References

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Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

9. Appendices

9.1 Python-based script import arcpy from arcpy import env from arcpy.sa import * env.workspace = "E:\Bachelor Thesis\Hemsedal\Sattelitbilder\NDVI_analysis"

NIR1 = Float("E:\Bachelor Thesis\Hemsedal\Sattelitbilder\NDVI_analysis/20130719_B5.TIF") R1 = Float("E:\Bachelor Thesis\Hemsedal\Sattelitbilder\NDVI_analysis/20130719_B4.TIF") NDVI1 = (NIR1 - R1)/(NIR1 + R1) NDVI1.save("E:\Bachelor Thesis\Hemsedal\Sattelitbilder\NDVI_analysis/NDVI20130719.img")

NIR2 = Float("E:\Bachelor Thesis\Hemsedal\Sattelitbilder\NDVI_analysis/20140713_B5.TIF") R2 = Float("E:\Bachelor Thesis\Hemsedal\Sattelitbilder\NDVI_analysis/20140713_B4.TIF") NDVI2 = (NIR2 - R2)/(NIR2 + R2) NDVI2.save("E:\Bachelor Thesis\Hemsedal\Sattelitbilder\NDVI_analysis/NDVI20140713.img")

NIR3 = Float("E:\Bachelor Thesis\Hemsedal\Sattelitbilder\NDVI_analysis/20150819_B5.TIF") R3 = Float("E:\Bachelor Thesis\Hemsedal\Sattelitbilder\NDVI_analysis/20150819_B4.TIF") NDVI3 = (NIR3 - R3)/(NIR3 + R3) NDVI3.save("E:\Bachelor Thesis\Hemsedal\Sattelitbilder\NDVI_analysis/NDVI20150819.img")

9.2 Quantitative risk assessment

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

9.3 Combined tables

Imrestind, simulation 1 Number of rocks Volume[m3] Equant (1.2) rock shape 0,1 4 0,3 4 0,5 0 0,8 4 1 3 2 0 3 4 5 4 7 0 10 1 20 1 50 7 70 5 100 2 Flat (1.2) rock shape 0,1 2 0,3 0 0,5 2 0,8 0 1 3 2 2 3 0 5 1 7 0 10 2 20 1 50 0 70 0 100 0 Total amount of rocks 52

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Imrestind, simulation 2 Number of rocks Volume[m3] Equant(1.2) rock shape 0,1 4 0,3 4 0,5 0 0,8 4 1 3 2 0 3 4 5 3 7 2 10 1 20 1 50 0 70 0 100 0 Flat (1.2) rock shape 0,1 2 0,3 0 0,5 2 0,8 0 1 3 2 2 3 0 5 1 7 0 10 1 20 0 50 0 70 0 100 0 Total amount of rocks 37

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Imrestind, simulation 3 Number of rocks Volume[m3] Equant(1.0) rock shape 0,56 1 1 1 Equant(1.2) rock shape 0,8 1 Equant(1.5) rock shape 1,1 1 2 1 Equant(2.0) rock shape 5 1 Flat (1.2) rock shape 0,5 1 3 1 12 1 30 1 50 1 Flat (1.5) rock shape 12 1 50 1 Flat (2.0) rock shape 3 1 100 2 Long (1.2) rock shape 2 1 12 1 50 1 Long (1.5) rock shape 1,3 1 7 1 50 1 Total amount of rocks 22

Bachelor Thesis, University of Gothenburg, 2016 John Eliasson Hazard zone map in Hemsedal, Norway

Imrestind, simulation 4 Number of rocks Volume[m3] Equant (1.5) rock shape 0,1 1 0,5 1 0,8 1 1,1 1 10,0 1 3,0 1 7,0 1 Flat (1.5) rock shape 1,7 1 3,0 1 5,0 1 10,0 1 Total amount of rocks 11

Skogshorn, simulation 5 Number of rocks Volume Equant (1.2) rock shape 0,1 0 0,5 0 1,2 1 Equant 2.0 0,1 1 0,5 1 1,2 0 Total amount of rocks 3