Establishing a synergy between regional forecast and local warning for rain-induced landslides: Using a physically-based model

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Establishing a Synergy Between Regional Forecast and Local

Warning for Rain-Induced Landslides

Using a Physically-Based Model

Håkon Reinskau Knudsen

Thesis submitted for the degree of Master of Geoscience in Geohazards and Geomechanics

60 credits

Department of Geosciences

Faculty of Mathematics and Natural Sciences UNIVERSITY OF OSLO

December 2019

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Establishing a synergy between regional forecast and local warning for rain-induced

landslides using a physically-based model Using a physically-based model

Håkon Reinskau Knudsen

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Establishing a synergy between regional forecasts and local warning for rain-induced landslides: Using a physically-based model

Håkon Reinskau Knudsen http://www.duo.uio.no/

Trykk: Reprosentralen, Universitetet i Oslo

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Abstract

Rain-induced landslides as debris avalanches and debris flows is cause problems for

Norwegian infrastructure with over 30 damaging events recorded yearly. With the expected climatic setting for the future, the likelihood of these types of landslides will increase.

Because of the Norwegian topography, it’s impossible to secure the infrastructure with physical mitigation measures fully. Daily, the Norwegian landslide forecasting and warning system produce rain-induced landslide forecasts. The forecast covers vast regions of several counties or a group of municipalities, and local scale warnings are abundant.

In this thesis, the aim was to establish a synergy between regional forecasts and local warning by using a physically-based model. The model chosen was the Transient Rainfall Infiltration and Grid-Based Slope-Stability model (TRIGRS) developed by U.S. Geological Survey.

TRIGRS was tested on a slope-scale for two case studies located in western Norway by combining regional rainfall/snowmelt parameters given by the Norwegian landslide forecast and warning system and local parameters.

As specific aims, some of these local parameters were estimated, and an evaluation of the chosen method of estimation was conducted. These parameters were the spatially distributed soil thickness, and the saturated hydraulic conductivity. They were both estimated while during fieldwork.

The soil thickness method provided unsatisfactory results, where an overestimation in the study area was an issue. In terms of saturated hydraulic conductivity, the estimated result probably corresponded well with the specific type of soil observed in the study areas. As for slope stability analysis with TRIGRS resulted with promising results, but that further testing is needed. In this case, a procedure of how to move forward using TRIGRS was created as a part of the specific aims.

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Acknowledgments

I owe thanks to several persons who have helped me throughout this thesis.

At first, I want to save a big thank you to my supervisors Graziella Devoli and Robert McNabb, for their help and support to guide me in the right directions.

A big thanks to Zelalem Mengistu at NVE for introducing me to TRIGRS, and thanks to Emir Ahmet Oguz for a helpful discussion concerning TRIGRS.

I want to save a thank to my good friend Bjørn, and my father, for assisting me during fieldwork.

I want to thank all the family and friends for supporting me and cheering me up throughout the work on this thesis. Especially my girlfriend, who had to live with me during my darkest thesis-hours.

Håkon Reinskau Knudsen 22.12.2019

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Figure 1. Example of a Debris avalanche from Jølster, 2019. Photo from Jan Helge Aalbu. ...5 Figure 2. Example of a Debris flow from Gardsbekken, 2016. Photo from Multiconsult...5 Figure 3. Showing a detailed description of the transition between the initiation zone and transport zone. a: bedrock, b: stable soil, c: failed soil, d: propagating mass, e: material entrainment, d: boundary of an avalanche, g: propagating direction. I: karst, II: impact loading. Figure from Cascini, et al., (2013). ...7 Figure 4.The mohr-circle diagram which explains the relationship between stress tensors and the shear strength of engineering soils. c: cohesion, τ: shear strength, φ: internal friction angle, σ: normal stress, σ1 and σ3: major and minor principal stresses. ...9 Figure 5. Showing the role of the different water contents, and at which depths they are located. Z is the depth of soil profile, d is depth to the groundwater table, r is the residual water content, ands is the saturated water content. Figure from Baum, et al., (2008). ... 11 Figure 6. Showing the four main categories in an early warning system. (UN-ISDR, 2006) .. 13 Figure 7. Overview of regional LEWS's around the world; blue: the period of activity, red squares: dates of destructive landslide events. Figure from Piciullo, et al., (2018)... 15 Figure 8. Spatially distributed map of FS in the Armea valley basin, Italy 8^th of December 2008. Figure from Segoni, et al., (2009). ... 16 Figure 9. Overview of existing operational, prototype, and terminated local LEWS's since 1975. Figure from Pecoraro, et al., (2018). ... 17 Figure 10. Regional landslide warning issued 15.09.2019 visualized on Xgeo. Shades of blue are different intensities of rainfall, yellow and orange are the extent of the landslide warning.

... 18 Figure 11. The organization of the Norwegian rain-induced LEWS. Figure from Piciullo, et al., 2017. ... 19 Figure 12. The three thresholds produced by comparing modeled hydrometeorological parameters and the 206 landslides. (a) is the national threshold, (b) is the threshold fro Southern Norway, (c) is the threshold for Eastern Norway. Figure from Krøgli, et al., 2018. 20 Figure 13. Susceptibility maps, (a) first-order catchment, (b) site-specific modeled areas prone to debris avalanches and debris flows. ... 21 Figure 14. Groundwater conditions in hillside soils. du is the height from the surface down to capillary fringe, d is the height of unsaturated zone, Zmax is the soil thickness, δ is the slope angle. Figure from Baum, et al., (2008). ... 23 Figure 15. Location of the two case studies and their respective study areas. A is Bogelia, B is Gardsbekken. ... 28 Figure 16. Overview from Bogelia showing the study area and the extent of the landslide drawn with GIS... 29

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Figure 17. Showing the extent of the landslide from initiation to deposition marked in red with a possible initiation area marked in yellow. Picture from Hermanrud, (2017). ... 31 Figure 18. Showing part of a landslide scar from 12.05.2015, the steepness, and vegetation in Bogelia. Picture from Vegvesen, (2016). ... 32 Figure 19. Slope angle map of Bogelia with the landslide as reference. ... 32 Figure 20. Bedrock geology map of Bogelia. ... 33 Figure 21. Picture of what probably is the top of the initiation area (marked in red). The blue marker shows the small stream with its direction of flow. The picture is taken facing

approximately eastwards. Taken during fieldwork. ... 34 Figure 22. An overview of Gardsbekken showing the extent of the landslide and study area. 35 Figure 23. The extent of the landslide at Gardsbekken 2nd of June 2016 and the initiation area marked with a red ring. Modified picture from (Rese, 2016). ... 36 Figure 24. Slope angle map of Gardsbekken study area. ... 37 Figure 25. Detailed overview of the initiation area marked in red. The picture on the left is taken from a helicopter after the landslide and was extracted from Rese, (2016). Picture on the right was acquired during fieldwork and is with a westerly direction. ... 38 Figure 26. Showing the geomaterials in the initiation area. ... 39 Figure 27. Showing the area of extent for both case studies were gridded data will be

displayed. ... 40 Figure 28. Example of what the "Fill" tool does with a DTM. Figure from ESRI,( 2019). ... 41 Figure 29. DTM's produced in ArcGIS for both case studies. Bogelia on the left, Gardsbekken on the right. ... 42 Figure 30. A detailed slope angle map for both case studies. Bogelia on the left, Gardsbekken on the right. ... 43 Figure 31. How D8 method is working in terms of the blue raster cell centered in the middle.

... 43 Figure 32. Flow direction map for both case studies. Bogelia on the left, Gradsbekken on the right. ... 44 Figure 33. Measured points of soil thickness. Bogelia on the left, Gardsbekken on the right. 45 Figure 34. SoilFlow simulation from Bogelia. The red line represents the groundwater table were 0 and -1 are soil depth. Blue arrow represents the landslide day (21.05.2015) ... 46 Figure 35. SoilFlow simulation from Gardsbekken, the red line represents the groundwater table where 0, -1, and -2 are soil depth. Blue arrow represents the landslide day

(01.06.2016). ... 46 Figure 36. A screenshot of the grid-data from Xgeo showing the values in mm of "rain and snowmelt" in Bogelia area during the day of 21.05.2015. ... 47 Figure 37. Table 3 plotted as cumulative infiltration vs. time. ... 49 Figure 38. Table 4 plotted as cumulative infiltration vs. time. ... 51 Figure 39. Norway divided into rock provinces based on bedrock geology. Numbers 1-33 are the location with extracted till samples. Figure from Opsal, (2018) ... 52 Figure 40. The PVC cylinder used for the BEST- method. ... 58 Figure 41. Plastic bottles used to have a fixed volume of water poured into the cylinder. The red mark is the fixed volume of water... 59

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Gardsbekken on the right with the back scarp of the landslide marked in red... 60 Figure 44. A presented part of the initiation file for TRIGRS showing some of the input parameters, model set-up, etc ... 62 Figure 45. Soil thickness map of the modeled study area. ... 65 Figure 46. A detailed soil thickness map covering the initiation area and the start of Bogelia.

... 66 Figure 47. Soil thickness map from Gardsbekken presenting values in the study area. ... 67 Figure 48. A detailed map of the soil thickness in the initiation area from Gardsbekken. ... 68 Figure 49. A plot showing the ratio between cumulative infiltration and the square root of time plotted against the square root of time, from Bogelia. ... 69 Figure 50. A plot showing the ratio between cumulative infiltration and the square root of time plotted against the square root of time, from Gardsbekken. ... 70 Figure 51. Same plot as Figure 50, but using only the last five measurements from the

dataset. ... 70 Figure 52. The initial condition in terms of FS (left picture) and the FS at 15:00 (on the right). ... 73 Figure 53. In terms of FS, showing the initial conditions and the conditions after 36 hours. 74 Figure 54. Back analysis by using 80 % degree of saturation to acquire minimal FS at 18:00 ... 75 Figure 55. Back analysis using an 80 % degree of saturation and increased unit weight. ... 76 Figure 56. Back analysis using 80 % degree of saturation with a decreased cohesion. ... 77 Figure 57. The initial conditions with 90 % degree of saturation on the left. End of

simulations on the right. Showing the spatial development of FS for the initiation area at Gardsbekken. ... 79 Figure 58. The initial conditions with 10 % degree of saturation on the left with the end of simulations on the right. Showing the spatial development of FS for the initiation area at Gardsbekken. ... 80

List of tables

Table 1. Data from Bogelia showing the combination of rainfall and snowmelt with the converted values to m/s. ... 48 Table 2. Rain and snowmelt data for Gardsbekken acquired from Xgeo, using a mean from 3 hours data converted to m/s. ... 48 Table 3. Results from the infiltration test in Bogelia... 49 Table 4. Results from the infiltration test in Gardsbekken. ... 50 Table 5. Showing the average values for each rock province in terms of angle of friction, cohesion, and unit weight of soil. Table from Opsal, (2018) ... 53 Table 6. Geotechnical parameters acquired from Opsal, (2018) used in the slope stability analysis. ... 53 Table 7. Input parameters used in TRIGRS simulation. ... 62

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Table 8. Presents the result from the BEST-method in Bogelia concerning the saturated hydraulic conductivity, the slope from Figure 49, and the α -parameter. Ks and α were used

in TRIGRS. ... 69

Table 9. Presenting the result from the BEST-method in Gardsbekken concerning the saturated hydraulic conductivity, the slope from Figure 51, and the α -parameter. Ks and α were used in TRIGRS. ... 71

Table 10. Physical parameters used in TRIGRS for the case study in Bogelia. ... 72

Table 11. Gridded files used as input for the slope stability analysis in Bogelia. ... 72

Table 12. Rainfall/snowmelt data used in the slope stability analysis at Bogelia. ... 72

Table 13. Physical parameters used for slope stability analysis at Gardsbekken. ... 78

Table 14. Grided input files used for simulations at Gardsbekken. ... 78

Table 15. Hourly rainfall data used for simulations at Gardsbekken. ... 78

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1 Introduction

Each year debris flows and debris avalanches create problems for the Norwegian infrastructure with over 30 damaging events recorded yearly. These landslides have a substantial economic cost, and climate scenarios for the future predict higher temperatures with increased rainfall in both intensity and duration. These scenarios will increase the likelihood of these landslides to occur, and with the steep topography in Norway, it’s

impossible to fully secure infrastructure using only physical mitigation measures. This is why a reliable landslide forecasting system is vital in reducing life loss and economical cost along roads and railways.

There are many landslide forecasting and warning systems worldwide, and some operate at regional levels, other at a slope-scale (Piciullo, et al., 2018; Pecoraro, et al., 2018; Guzzetti, et al., 2019). Some are daily operational, while others are prototypes. Most of the time, even in the same country or region, regional and slope-scale landslide early warning systems are working separately, often managed by different organizations with scarce or no coordination or collaboration.

On a daily basis, the landslide forecasting and warning system at the Norwegian Water Resources and Energy Directorate (NVE) produces regional landslide forecasts by assessing hydrometeorological conditions (e.g., rainfall, snowmelt and water saturation of the soil) which may lead to landslide occurrence (Krøgli, et al., 2018). If a warning is issued, a

message is sent to receivers such as municipalities or road- and railway authorities, present in the territory of the warning.

The regional warning covers a large region, often over several counties or a group of several municipalities. The receivers should combine the regional warning with local expert

knowledge to define a local level of alert, and decide which actions should be taken.

However, the information at slope scale is not sufficient to form local decisions. Most of the time, there is an absence of instruments in slopes to monitor local conditions or a lack of knowledge of geological and local hydrometeorological conditions. There are a few local monitoring and warning systems in Norway. Still, most of them aim to detect and warn snow avalanches (e.g. Vegdirektoratet, 2014) or rock avalanches (e.g. Blikra, et al., 2016). Many of the highly susceptible sectors of roads and railways are yearly affected by landslides and

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don’t have any monitoring systems. Is a way to assist local authorities to make better decisions at local scale, by combining existing information and prognosis available for the regional forecast together with physically-based models? Can this relationship create a better synergy between regional forecast and local warnings?

In this thesis, regional data will be tested with a slope stability model on a slope scale. There are many slope stability models available today which models initial failures (Intrieri, et al., 2019). For this thesis, the physically-based model called Transient Rainfall Infiltration and Grid-based Regional Slope-Stability Model (TRIGRS) was used to model slope failure conditions. TRIGRS simulates pore pressure changes and the evolution of the factor of safety (FS) over a certain period for a given area. The model is applied to predict the initial failure of shallow rain-induced landslides, such as debris flow and debris avalanches. This model was chosen since its application has been tested in Norway as part of two projects, Klima 2050 (KLIMA 2050, 2016) (applied in Kvam area by Schiliro, et al., Unpublished) and in the spin- off project from Klima 2050: Klima digital (applied in Hegra-Meråker by SINTEF,

Unpublished). In this thesis, the model was tested on a slope scale in two areas (Bogelia and Gardsbekken) located in Western Norway. Bogelia was chosen because the area is frequently affected by the occurrence of landslides causing significant damage to both road and railway.

The event selected in this area was triggered by an extended period of rainfall and snowmelt.

The other area, Gardsbekken, is along with a sector of the road less frequent to landslides but was triggered by a short but intense period of rainfall lasting a couple of hours.

The general aim of this study was to create a synergy between regional forecast and local warning by testing a physically-based model and see its applicability, and how the model can be a supporting tool for local decisions after a regional warning is issued.

Specific aims were:

- Selection of case studies, data collection, and literature research

- Collection of relevant input parameters in TRIGRS from fieldwork and literature - Estimate the spatially distributed soil thickness in the selected areas by using Saulnier,

et al., (1997) method to prepare a soil thickness map

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3 - Estimate the saturated hydraulic conductivity by using Lassabatère, et al., (2006)

model

- Back analysis of two landslide events occurred in the selected areas

- Create a field procedure regarding the collection of input parameters for TRIGRS

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2 Theory

The main topics analyzed in this thesis are modeling of stability of rain-induced landslides, the factors controlling the stability, and how to connect regional warning systems with local warnings. As a natural consequence of that, this chapter will discuss rain-induced landslides briefly, in particular, debris avalanches and debris flows, slope stability, and landslide early warning systems (LEWS).

2.1 Debris Avalanches and debris Flows

In this document, the term rain-induced landslides are used to indicate all landslides triggered by the infiltration of water. This thesis will focus on debris avalanches and debris flows, also sometimes as flow-like landslides based on movement types down the slope. These landslide types are often triggered by small instabilities on a given slope (Hungr, et al., 2005). A general introduction of these two phenomena will be given herein focusing on their triggering mechanisms and their initial failure conditions.

A debris avalanche (Figure 1) is a rapid to extremely rapid shallow flow of partially or fully saturated debris on a steep slope, without confinement and not flowing in established channels (Hungr, et al., 2014).

A debris flow (Figure 2) is a “very rapid to the extremely rapid surging flow of saturated debris in steep channels with strong entrainment of material and water from the flow path”

(Hungr, et al., 2014).

The term “debris” refers to an engineering soil where the soil contains a significant proportion of coarse material and that 20-80 % of the fragments in “debris” have a grain size > 2 mm, and the rest is < 2 mm (Varnes, 1978), or as (Hungr, et al., 2001) stated that “debris” consists of more than 20 % gravel or coarser fragments. “Rapid” or “extremely rapid” refers to

velocities from 10^-1ms^-1 to velocities that exceed 5 ms^-1 (Hungr, et al., 2005), but a debris avalanche can reach velocities up to 20 ms^-1, while debris flows’ speed usually ranges from 0.5 ms^-1 to 20 ms^-1 (Hungr, et al., 2014).

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Figure 1. Example of a Debris avalanche from Jølster, 2019. Photo from Jan Helge Aalbu.

Figure 2. Example of a Debris flow from Gardsbekken, 2016. Photo from Multiconsult.

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Debris avalanches and debris flows divide into three main zones; initiation zone, transport zone, and deposition zone (Jakob & Hungr, 2005). The initiation zone is generally in areas with a slope angle between 20 and 45. Lower angles usually don’t have enough potential energy to initiate a landslide, while higher angles typically have an abundance of soil, or a too thin and discontinuous soil cover. In this zone, these landslides initiate due to slope failure in a headwall or a slope facing into channels (Jakob & Hungr, 2005). These failures can be small mass movements as rock topples, rock falls, deep-seated slides, wedge failures, or shallow debris slides. The initial volume of these small failures is a few tens of m³ and often

negligible (Hungr, et al., 2008). A note is that debris avalanches usually have a progressive failure, but can retrogress uphill due to failures further downslope (Hungr, et al., 2008). When these landslides enter the transport zone, the process of material entrainment starts, which increases the volume rapidly (Cascini, et al., 2013).

The transport zone is controlled by entrainment. Entrainment is known as the primary source for gaining mass and the destructive ability of debris avalanches and debris flows (Stiny, 1910 and Takahashi, 1978). It can cause a small initial volume to increase to several thousand m³. In open slopes, the entrainment creates debris avalanches characteristic triangular

shape(Figure 1). The triangular shape is an effect of steep open slopes and debris avalanche flow kinematics. As the avalanche initiates from a point area, it evolves progressively downslope increasing in velocity and volume, which increases the force, and then the avalanche widens. Since debris flows are constrained into channels and gullies, their

entrainment process is different. The entrainment is generally happening in the channel banks as a consequence of destabilization of bed deposits or erosion. This is also leading to a

secondary process, destabilization of side banks because of undercutting erosion in the channel (Jakob & Hungr, 2005).

The deposition zone appears where these landslides lose their energy as the slope flattens, and their confinement mass breaks up (Jakob & Hungr, 2005). The consequence is the deposition of landslide materials. If the landslides are repeatable, it’s normal to observe and colluvial fan, which determinates the deposition area.

A more detailed step-by-step description of the initiation and the transition to the transporting zone will now follow to clarify the processes. The failure and the formation stage can divide into five different zones (Figure 3). Cascini, et al.,(2013) split the zones in this matter; (1):

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discontinuities in soil deposits or rocks caused by natural features or human impact. These discontinuities are bedrock outcrops were there is a transition between soil deposits and exposed bedrock or anthropogenic cut slopes. (2): This zones involves the failure stage and is the impact zone from the previous masses (e.g. debris slide, rockfall, rock topple) in zone 1. If zone 1 is absent, zone 2 is the source area. In this zone, debris avalanches are usually initiated by infiltration of water from either karst springs or from the water-runoff at bedrock outcrops into deposited soils (This doesn’t relate to debris flow). (3): This zone corresponds to the formation stage. Either the avalanche will thrust downslope upon the stable material or entrainment of soil deposits will occur due to the propagating masses. (4): This zone exclusively corresponds to entrainment of soil deposits.

Figure 3. Showing a detailed description of the transition between the initiation zone and transport zone. a: bedrock, b:

stable soil, c: failed soil, d: propagating mass, e: material entrainment, d: boundary of an avalanche, g: propagating direction. I: karst, II: impact loading. Figure from Cascini, et al., (2013).

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Debris flow and debris avalanches are commonly triggered by rainfall. In Norway, these landslides generally occur as a consequence of hydro-meteorological conditions as prolonged rainfall/snowmelt/glacial melting. These conditions lead to a high degree of saturation in the soil, and surface and subsurface runoff (Meyer, et al., 2012). The consequence of these conditions is decreasing slope stability, which starts the process of initial failures. As a consequence, the focus in the next chapter will be on slope stability.

2.2 Slope stability

Since this thesis uses an infinite slope-stability model, a brief introduction to soil slope stability will be given in this chapter. The chapter will concern the critical parameters controlling the stability, and how the influence of water affect the stability

In general, slope stability is a term concerning how stable a slope is, and the factors controlling the stability. It’s well known in soil mechanics that three main parameters are controlling the stability or instability of soil slopes. These are the geometry of the soil, the strength of the soil, and pore-water pressure. Geometry depends on soil characteristics as the relationship between discontinuities as joints and shear zones, the relationship between soil and bedrock, and the stratigraphy of the soil profile.

The strength of the soil is a measure of how much stress the soil can withstand before it starts to deform and eventually break (Duncan, et al., 2014). The strength is generally controlled by strength parameters as cohesion and internal angle of friction. Cohesion is the force between interparticle stress in the soil, which keep the soil sticking together (Lu & William, 2013).

The angle of internal friction is a physical property governed by grain shape and sizes. It can be represented as the angle of response were loose dry grains will pile up in a rage of 28 to 34. The strength of the soil can be represented by the Mohr-Coloumb failure criteria (Equation 1).

𝜏 = 𝑐 + 𝜎 tan 𝜑 (1)

Where 𝜏 is the shear strength, c is the cohesion 𝜎 is the normal stress acting on a failure plane, φ is the angle of internal friction. The equation can be illustrated in a Mohr diagram (Figure 4).

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Figure 4.The mohr-circle diagram which explains the relationship between stress tensors and the shear strength of engineering soils. c: cohesion, τ: shear strength, φ: internal friction angle, σ: normal stress, σ1 and σ3: major and minor

principal stresses.

The Mohr diagram (Figure 4) is a good visualization tool to observe the relationship between the strength of the soil and how different stress states affect the soil. If the principal stresses (circle) exceeds the maximum strength of the soil (linear line), failure of the soil occurs.

Pore water pressure is maybe the most complicated of the three main parameters as it depends on many factors. These factors can initial conditions as groundwater and initial infiltration rate through the soil, or drainage, and runoff. Still, the main controlling factor of pore pressure changes is the infiltration of rainfall and or snowmelt (Noroozi & Hajiannia, 2015).

The effect of pore pressure on soil strength can be expressed through Mohr-Coloumb failure criteria in Equation 2.

𝜏 = 𝑐^′+ (𝜎 − 𝑝) tan 𝜑^′ (2)

Where pore pressure 𝑝 is added to Equation 1 as a negative factor since it’s referenced to pore air pressure (Gasmo, et al., 2000), 𝑐^′ is the effective cohesion, and 𝜑^′ is the effective internal angle of friction. Water is a fluid and has no shear stresses. As a consequence, the pore pressure pushes in every direction and acts as an incompressible medium (Verruijt, 2018).

This generates a reverse push on grains, reducing their interparticle stresses (Guiseppe, et al.,

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2016). As a consequence, the shear strength of the soil will decrease because of a reduction in effective stresses (Yeh & Tsai, 2018).

2.2.1 Hydrogeological factors controlling pore pressure

It’s been well documented that infiltration of water into slopes decreases stability, and is one of the main causes for slope failures (Gasmo, et al., 2000 & Santoso, et al., 2011). Transient infiltration of water into soils resting on a slope will induce a redistribution of soil moisture.

This redistribution cause changes in soil strength and effective stress due to pore pressure changes (Lu & Godt, 2013). Therefore, it’s essential to understand the hydrogeological factors which control the infiltration of water into soil slopes.

Mainly, it’s the hydraulic conductivity (K) that controls the infiltration (Santoso, et al., 2011).

In general, K is a measure of how well a soil profile can transmit water (Alyamani & Sen, 1993), and acts as a limit in terms of rainfall infiltration rate (Yeh & Tsai, 2018). K’s rate explains this limit. K is measured in L/T (often m/s), and the rainfall infiltration rate can’t exceed Ks’s rate. An example, if K of a soil is 1 mm/h, the soil profile can’t absorb more than 1mm/h rainfall rate. If the rainfall rate is higher, the excess of water will become surface runoff. This makes K accountable for how quick the soil will respond to rainfall and the increase in pore pressure. K highly depends on the volume distribution of pore water

(volumetric water content) in the soil mass. By increasing the volumetric water content, more pores will be continuous. This will increase the ability of the soil to conduct water and

therefore increase the K (Ng & Shi, 1998). K can also be distinguished between saturated and unsaturated. The saturated hydraulic conductivity Ks is the K when soil is fully saturated and was measured in this thesis to use as an input parameter in the slope stability model used.

The volumetric water content 𝜃 (Equation 3) is the volume of water per volume of soil. It’s a dimensionless ratio and a measure of how much water is stored in a given soil profile.

𝜃 =𝑉_𝑤

𝑉_𝑠 (3)

Where 𝑉_𝑤 is the volume of water, and 𝑉_𝑠 is the volume of soil.

The slope stability model in this thesis uses the saturated volumetric water content, and the residual water content to estimate the volumetric water content. The saturated water content

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capillary fringe. Residual water content represents the residual degree of saturation, which is the state in the soil where water is discontinuous. This state relates to the unsaturated zone in the soil (Salager, et al., 2010). These types of water contents can be visualized in Figure 5.

Figure 5. Showing the role of the different water contents, and at which depths they are located. Z is the depth of soil profile, d is depth to the groundwater table, r is the residual water content, ands is the saturated water content. Figure from Baum,

et al., (2008).

All these factors as the different hydraulic conductivity and water content, are a part of a dynamic system controlling the pore pressure in the soil profile. Were the conductivity in general controls the response of rainfall infiltration, and the water content controls the amount of water storage allowed in the soil profile.

2.3 LEWS – Landslide early warning system

Since the general goal of this thesis is to create a synergy between regional forecast and local warning, a description of landslide early warning system (LEWS) will be given. This chapter will include state of the art of LEWS on both regional and local scale and an introduction on how the LEWS in Norway works.

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An Early Warning System (EWS) is monitoring devices designed to avoid or minimize the impact of a threat on the elements at risk (Medina-Cetina & Nadim, 2007). The elements at risk could be humans, their houses or buildings, engineering structures, infrastructure, public services, economic activities, and environmental issues. An efficiently designed EWS can be a helpful tool to reduce the vulnerability and the consequences imposed by the threat

(Medina-Cetina & Nadim, 2007).

Operative Lanslide Early Warning Systems (LEWS) is complex and require careful planning.

A key to a successful LEWS is the ability to detect a landslide and issue a warning in a reasonable time for activating mitigation measures to protect the elements at risk. To acquire this, the LEWS need the ability to identify small but significant indicators that init iate a landslide (DiBiagio & Kjekstad, 2007) Then a LEWS includes many factors as types of landslide, risk scenarios, resources from authorities, etc. (Intrieri, et al., 2013).

LEWS can be divided into different scales, either local or regional (Thiebes & Glade, 2016).

The scale will affect the design of the LEWS and require different approaches (Segoni, et al., 2018). For regional LEWS’s, a more quantitative approach is applied, where statistical

methods are used to determine e.g., rainfall thresholds for rainfall-induced landslides (Segoni, et al., 2015). Since local LEWS’s often are site-specific, a more qualitative approach is

applied, where measurement of real-time landslide precursors are a key factor (Yongbo, et al., 2019). Four main categories (Figure 6) are needed to establish an effectively LEWS; risk knowledge, monitoring and warning service, dissemination and communication, and the response capability (UN-ISDR, 2006). A LEWS should ideally be based on hazard and risk assessments (SafeLand, 2013). This translates to risk knowledge and will be the focus of this chapter.

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Figure 6. Showing the four main categories in an early warning system. (UN-ISDR, 2006)

Risk knowledge needs as (Intrieri, et al., 2013) stated three phases; (1) determine needs and the vulnerabilities for the elements at risk, (2) identify the impediments for the elements at risk during an issued warning, (3) characterize the conditioning and triggering factors initiating a landslide which relates to the geological and meteorological environment.

Fundamentally risk knowledge is situated down to where and why a landslide occurs, how the elements at risk are exposed to the threat, and their ability to respond if a landslide initiate.

The design of a LEWS requires an understanding of the landslide area. Not only in the initiating areas which relate to geological and meteorological aspects, but also downstream where the elements at risk are exposed and can be negatively impacted (Stähli, et al., 2015).

Geological knowledge is fundamental for understanding the risk and is the most important criteria to understand in the design phase (Michoud, et al., 2013). It involves understanding geological and geomorphological features. By carrying out geological and geomorphological mapping will bring a better understanding of a landslide area (SafeLand, 2013). If the LEWS is designed for debris avalanche or debris flow, the geological and geomorphological

mapping will result in exclude or include areas prone to this type of landslide. The mapping may answer questions as: Is it soil or bedrock? Is it steep enough? Are there any

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geomorphological features that indicate a history of previous landslides?. This geological knowledge can then produce maps as landslide inventories and susceptibility maps, which are essential inputs for further establishments of the LEWS (SafeLand, 2013).

In addition, the knowledge of where and at which depth the landslides will initiate. Therefore, a good understanding of the soil properties needs to be obtained. These are soil properties as water content, hydraulic conductivity, the strength of the geomaterials, etc.

2.3.1 State of the art for regional LEWS

Over the last decades, a large number of regional LEWS’s have been implemented around the world, but mostly with empirical approaches involving rainfall thresholds (Thiebes & Glade, 2016). It’s a result of the cost-effectiveness by not constructing physical mitigation measures, it’s easy to apply where risk is distributed over large areas, and a result of increased

knowledge about correlations between rainfall and landslides together with better wheater forecasting (Piciullo, et al., 2018). The status of regional LEWS’s is presented in Figure 7.

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Figure 7. Overview of regional LEWS's around the world; blue: the period of activity, red squares: dates of destructive landslide events. Figure from Piciullo, et al., (2018)

Hong Kong in China was the first regional LEWS and has since 1977 operating a “Landslip Warning System” with the intention of alerting the public under heavy rainfall and increased landslide risk (Wong, et al., 2014). Since then, 23 other regional LEWS has been established and operating. The exception is the two systems in San Francisco and Seattle, which were terminated due to managing office and relocation. Most of the LEWS’s are dealing with multiple hazards as floods, hurricanes and typhoons, and snow avalanches (Piciullo, et al., 2018). In respect to landslides, most of them use the statistical rainfall threshold approach with intensity-duration (ID) curves. A study by Segoni, et al., (2009) used a slightly different approach. They used geotechnical properties, soil thickness, and rainfall intensities as input in a hydrological model and a slope stability model to create a real-time forecasting model for rainfall-induced shallow landslides. The output was a map of the factor of safety (FS) (Figure 8) spatially distributed over a basin in Italy.

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Figure 8. Spatially distributed map of FS in the Armea valley basin, Italy 8^th of December 2008. Figure from Segoni, et al., (2009).

2.3.2 State of the art for local LEWS

From 1977 to 2016, 29 registered (Figure 9) local LEWS’s have been established and operating, or in a prototype stage, and only two terminated (Pecoraro, et al., 2018). More common for local LEWS’s is to use on-site measurements on slope scales. The most common types of instruments are inclinometers, GPS, total station, and piezometers (Thiebes & Glade, 2016), and are mainly focused on displacement in terms of velocity and acceleration

measurements (Pecoraro, et al., 2018).

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Figure 9. Overview of existing operational, prototype, and terminated local LEWS's since 1975. Figure from Pecoraro, et al., (2018).

The first successful LEWS was established in China 1985. It was later terminated, but managed to alert a village and evacuate almost 1400 people from a landslide located on a riverbank.

2.3.3 LEWS in Norway

LEWS for rain-induced landslides in Norway was officially launched in 2013 after two years of testing. It’s a landslide warning service and is a cooperation between The Norwegian Meteorological Institute (MET), The Norwegian Public Road Administration (SVV), The Norwegian National Rail Administration (BaneNOR), and The Norwegian Water Resources

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and Energy Directorate (NVE). NVE has the primary responsibility for the warning service and has since 2009 had the national responsibility for the landslide risk and management (Jakobsen, et al., 2016). The warning service is a daily operative regional scale forecasting system and predict the occurrence of landslide types as debris avalanche, debris slide, debris flow and slush flow (Piciullo, et al., 2017). Each day a hazard assessment is produced, and a forecast is published on the landslide warning service site (www.varsom.no). The warning is also available at www.xgeo.no (Xgeo) (Figure 10) as a visual aid to see the extent of the warning.

Figure 10. Regional landslide warning issued 15.09.2019 visualized on Xgeo. Shades of blue are different intensities of rainfall, yellow and orange are the extent of the landslide warning.

The LEWS in Norway is based on hydrometeorological parameters, landslide inventories, and susceptibility maps (Piciullo, et al., 2017)(Figure 11). By comparing the relationship between the parameters and previous landslide events, a threshold can be made. The

hydrometeorological parameters are extracted from a model called HBV, which simulates the relative water supply and relative soil water content derived from rain or snowmelt in

Norway, distributed in grids of 1 km² (Krøgli, et al., 2018). The grid of 1 km² is also the boundaries of the study areas in this thesis since the goal is to test how well TRIGRS can be assisting the Norwegian LEWS in decision at a local scale.

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Figure 11. The organization of the Norwegian rain-induced LEWS. Figure from Piciullo, et al., 2017.

Thresholds

By combining the simulated hydrological parameters and 206 landslides, with an empirical decision tree, a landslide threshold was obtained (Figure 12) (Piciullo, et al., 2017). The first threshold was developed by investigating three major weather events, which caused many landslides in the categories of rapid shallow slides, debris avalanches, and debris flows (Krøgli, et al., 2018). These major events were; autumn 2000, an event with low precipitation intensity but lasting several months; autumn 2005, a typical high-intensity rainfall event caused by the remainings of a tropical cyclone; 2008, a spring situation with high temperatures and rapid snowmelt. The national threshold applies to all of Norway.

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Figure 12. The three thresholds produced by comparing modeled hydrometeorological parameters and the 206 landslides.

(a) is the national threshold, (b) is the threshold fro Southern Norway, (c) is the threshold for Eastern Norway. Figure from Krøgli, et al., 2018.

The thresholds for Southern and Eastern Norway divided Norway in hence of relief and climatic setting. In Eastern Norway, the threshold applies to areas with marine sediments and low-relief valleys (Boje, 2017). For Southern Norway, the threshold applies to the counties of Agder, parts of Rogaland and a small part of Telemark. The similarities for these counties is a low vulnerability in terms of rain-induced landslides (Boje, 2017). These two thresholds have been struggling with false alarms, and for this reason, the thresholds were modified by simply adjusting the yellow danger level until the false alarms ended (Krøgli, et al., 2018). The modification is shown in Figure 12 (b and c), where the yellow warning threshold for Southern and Eastern Norway is different compared to the national threshold (Figure 12 a).

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21 In the forecast, the thresholds are combined with susceptibility maps (Figure 13). Two

susceptibility maps are available: one that model susceptibility at first-order catchment for landslides in soil (Figure 13a), the other map model the susceptibility at a slope scale the specific source area, path, and runout for debris avalanches and debris flow (Krøgli, et al., 2018). Figure 13a is combined with the threshold to contribute as decision parameters in the landslide forecast.

Figure 13. Susceptibility maps, (a) first-order catchment, (b) site-specific modeled areas prone to debris avalanches and debris flows.

Decision making

By combining the thresholds, susceptibility maps, and observed data, the hazard level is in the end evaluated by landslides experts at NVE. The landslide experts use Xgeo as a visualization tool for wheater forecast, soil water content, simulated threshold predictions, etc. (Krøgli, et al., 2018). By using Xgeo together with forecasted thresholds, forecasted hydrometeorological parameters, daily real-time observations, and by using their landslide expertise, an assessment is produced and published on ”www.varsom.no.” and visualized in Xgeo (Krøgli, et al., 2018).

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2.4 Slope stability analysis

In this thesis, slope stability is determined using an infinite-slope stability model. This chapter will contain a description of the model used and its use of Factor of Safety (FS).

Analysis of slope stability refers to detailed modeling of slopes under known specific

boundary conditions (Margottini, et al., 2013). An infinite slope solution can be explained by thinking of a slope that is endless long were failure is assumed to be parallel to bedrock.

(Briaud, 2013). The infinite slope solution can also be referred to as an exact solution. When an infinite slope is characterized by the ratio of resisting shear strength of soils and the gravitationally induced shear stresses on a slope, its ratio is called the Factor of safety (FS).

The slope stability then depends on either a decrease in shear strength of the soil or an increase in shear stress (Duncan, et al., 2014). The FS can be expressed with Equation 4.

𝐹𝑆 = 𝑠

𝜏 (4)

Where s is the shear strength, and 𝜏 is the shear stress. When FS > 1 a slope is considered stable, when FS = 0 the slope are in equilibrium, and FS < 1 is considered as failure

conditions. The FS is simulated in the model used in this thesis by using pore pressure as a dynamic input. Therefore, a description of the model will follow.

2.4.1 TRIGRS – Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model

TRIGRS is a landslide prediction model made by the U.S. Geological Survey. It’s a program created with Fortran to simulate the changes in pore pressure and spatial variations in the factor of safety (FS) due to rainfall infiltration for a given area over a period of time. Pore pressure changes and the spatially distributed FS are provided by modeling the infiltration, runoff-routing of water, and subsurface flow based on various input as geotechnical-, hydrological- and meteorological parameters (Baum, et al., 2008). The program fits for rain- induced landslides and can be used for both saturated and unsaturated soils (Park, et al., 2013), and applies to areas prone to shallow rain-induced landslides (Baum, et al., 2008).

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23 TRIGRS uses different solutions and methods to deal with the hydrological input parameters.

The solutions are different infiltration models for both initial saturated/unsaturated conditions.

TRIGRS also uses runoff modeling based on the D8-method (chapter 4.1.3), and a slope stability model using an infinite slope stability analysis. In the following chapters, a brief description of the solutions and models will follow. Further details of the models are described in Baum, et al., (2008).

Infiltration Models

In terms of infiltration, TRIGRS divides hillsides into a saturated zone and an unsaturated zone together with a capillary fringe as a layer between the groundwater table and the

unsaturated zone. The design of how TRIGRS uses hillslope hydrology is shown in Figure 14.

Figure 14. Groundwater conditions in hillside soils. du is the height from the surface down to capillary fringe, d is the height of unsaturated zone, Zmax is the soil thickness, δ is the slope angle. Figure from Baum, et al., (2008).

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Infiltration Model for Initial Saturated Conditions

TRIGRS uses an infiltration model for saturated conditions based on Iverson, (2000) linearized solution of Richards, (1931) equation (Richards equation). Richards equation governs an irregular flow of groundwater under darcy’s law in response to rainfall infiltration (Iverson, 2000). Iverson , (2000) solutions govern calculations of groundwater pressure head at a certain depth and time where pressure head response only depending on normalized time.

In TRIGRS, Iverson, (2000) solution depends on two components; a steady component and a transient component. The steady component governs steady seepage into the soil and depends on the initial groundwater table and background infiltration rate. TRIGRS’s model result is sensitive to the initial steady seepage conditions, and by not knowing these conditions, TRIGRS is limited in terms of accurate modeling (Baum, et al., 2008).

Infiltration Model for Initial Unsaturated Conditions

In earlier versions of TRIGRS it wasn’t accurate to model unsaturated conditions. In TRIGRS 2.0 Baum, et al., (2008) added an infiltration model for unsaturated conditions to make

TRIGRS more applicable to a broader range of initial conditions by using a solution for unsaturated flow to estimate the infiltration at the ground surface. The purpose of the model is that parts of the water that infiltrates from the ground surface will be absorbed in the

unsaturated zone due to adhesive forces and surface tension, while the rest will accumulate on the bottom of the unsaturated zone on top of the initial groundwater table. (Baum, et al., 2008). Water that accumulates on the bottom of the unsaturated zone raises the initial groundwater. The water that is absorbed in the unsaturated zone propagates diffuse pressure waves downwards, which increases the pore pressure at depths (Baum, et al., 2008). The infiltration model uses parameters as residual water content θr, saturated water content θs, saturated hydraulic conductivity Ks, and α, which are determined as the relationship between gravitational forces and capillary forces.

Runoff Models

In TRIGRS the assumption for runoff are when the value of precipitation and runoff supplied to a cell are higher than the infiltration capacity (Baum, et al., 2008). The runoff model simulates the surplus of water on a cell, force it to flow to adjacent cells where it either infiltrates or flows further downslope using the D8 flow method. By adding a runoff model,

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25 TRIGRS is preventing loss of precipitation, which accumulates on cells that are saturated by water, and instead of flowing towards cells with a lower degree of saturation (Baum, et al., 2008).

The runoff model (Equation 5 and Equation 6) is simple and depending on the saturated hydraulic conductivity, Ks, which controls the infiltration capacity:

𝐼 = 𝑃 + 𝑅_𝑢, 𝑖𝑓 𝑃 + 𝑅_𝑢 ≤ 𝐾_𝑠 (5)

or

𝐼 = 𝐾_𝑠, 𝑖𝑓 𝑃 + 𝑅_𝑢 > 𝐾_𝑠 (6)

Where I is infiltration, P is precipitation, and 𝑅_𝑢 are runoff from upslope cells. Cells where 𝑃 + 𝑅_𝑢 > 𝐾_𝑠 are considered as runoff cells. Runoff is calculated using Equation 7 and Equation 8.

𝑅_𝑑 = 𝑃 + 𝑅_𝑢− 𝐾_𝑠, 𝑖𝑓 𝑃 + 𝑅 − 𝐾_𝑠 ≥ 0 (7)

or

𝑅_𝑑 = 0 , 𝑖𝑓𝑃 + 𝑅_𝑢− 𝐾_𝑠 < 0 (8)

Where 𝑅_𝑑 is considered runoff and will be diverted to adjacent downslope cells.

Slope Stability Model

As described earlier TRIGRS uses the calculation of the FS to analyze the slope stability.

TRIGRS uses and infinite slope stability method, which is based on the Coulomb failure criteria, where TRIGRS calculates the relationship between the resisting forces and the gravitational basal stress distributed on the slope (Baum, et al., 2008). The FS, 𝐹_𝑠 are calculated by using Equation 9.

𝐹_𝑠 (Z,t) =tan 𝜙^′

tan 𝛿 + 𝑐^′− 𝜓(𝑍, 𝑡)𝛾_𝑤tan 𝜙^′

𝛾_𝑠𝑍 sin 𝛿 cos 𝛿 (9)

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Where Z is at which depth FS are calculated, t is at which time FS are calculated, 𝜙′ are effective internal friction, 𝑐^′ are the effective cohesion, 𝜓 is the calculated pore pressure from the infiltration models at a certain depth and time, 𝛾_𝑤 is the unit weight of water, 𝛾_𝑠is the unit weight of soil. For calculating the changes in the FS over time, TRIGRS solely rely on the pore pressure. By increasing the pore pressure, the effective cohesion will decrease and further decreasing the FS. The pressure head is also the only transient component that depends on the precipitation and the soil's ability to store water. If the soil is fully saturated, the FS can’t change in TRIGRS unless changing the other parameters.

In Norway, the Norwegian Building Act has defined threshold concerning the FS when it comes to critical values. These values are regulated in terms of the type of construction In Norway, the critical values for FS ranges from 1.2 – 1.4, depending on the type of

construction (Direktoratet for Byggkvalitet, 2017).

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3 Case Studies

In this thesis, two case studies were studied. Bogelia in Vaksdal and Gardsbekken in

Øvstebødalen (Figure 15), both located in western Norway. Bogelia was selected because the area is frequently affected by the occurrence of landslides causing significant damage to both road and railway. The landslide was triggered by an extended period of rainfall and snowmelt.

The other area, Gardsbekken, is along a sector of the road less frequent to landslides, but was triggered by a short but intense period of rainfall lasting a couple of hours. In this chapter, I will briefly explain the characteristics of the area; location, description of landslide events, and geological setting. Since this thesis is concerning initiating processes, a focus will be the initiation zone, and the description of this zone will be based mostly on field observations.

The boundaries of the study areas are restricted to one Xgeo grid cell with a size of 1x1 km.

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Figure 15. Location of the two case studies and their respective study areas. A is Bogelia, B is Gardsbekken.

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3.1 Bogelia – Vaksdal

Bogelia is a steep slope belonging to the mountain Storeknuten, which is located in the western part of Norway around 20 km northeast from Bergen. It’s situated in the county of Vestlandet and belongs to the municipality of Vaksdal, which is also the name of the village adjacent to the slope.

Figure 16. Overview from Bogelia showing the study area and the extent of the landslide drawn with GIS.

Bogelia faces the main infrastructure lines between Bergen and Oslo. These lines are E16 (main road) and Bergensbanen (railway). E16 is a busy road, and in 2018 approximately 5700 vehicles were passing through Bogelia on a daily average (Statens Vegvesen, 2018).

Bergensbanen has one regional route Bergen-Oslo and a local route Bergen-Voss-Myrdal. In 2017 the total number of passengers on the regional route was approximately 1 150 000 yearly. The local route registers around 600 000 (VY, 2017). The road is an important

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transport route for users on a local, regional, and national scale. Closing of the road creates long detours, which are time and costs consuming (Statens vegvesen & Jernbaneverket, 2014). If the railway closes, there is no alternative way around, and often the railway and E16 are closed simultaneous.

The landslide (Figure 17) initiated 21.05.2015 after a prolonged period of rainfall and snowmelt. The landslide is described in two documents. A report from Vegvesen, (2016) suggested that the landslide was defined as a gliding snow avalanche because they observed fractures in the snowpack close to the initiation zone, and the consequence was entrainment of materials further downslope. A powerpoint presentation from Multiconsult, (Unpublished) suggested that a debris avalanche was initiated due to the loading of a small wet snow avalanche/slush flow. The landslide initiated at 560 m a.s.l. While the landslide propagated further downslope, it widened before entering established channels before it crossed both the railway and the main road. Most of the masses deposited on the main road and railway tracks.

A train was evacuated, and the infrastructure lines were closed for two weeks. From my own point of view, there is an agreement with the presentation from Multiconsult, (Unpublished).

If this was a gliding snow avalanche, it should be accumulated materials representing snow in the transport or deposition zone. I can agree on the fact that it started as a small gliding avalanche, but that it initated a debris avalanche.

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Figure 17. Showing the extent of the landslide from initiation to deposition marked in red with a possible initiation area marked in yellow. Picture from Hermanrud, (2017).

3.1.1 Terrain

Bogelia is a slope covered mostly by vegetation in the form of trees and bushes besides areas with previous landslide scars (Figure 18). From topographic maps, it’s possible to observe a couple of small streams, which probably are the main drainage channels from Storeknuten.

The top of the slope ranges from 550 m a.s.l to 600, while the end of slope reaches sea level.

The slope (Figure 19) has a steep drop in elevation from the top of around 300 m slope angles between 35 to 82. Further, the slope levels out to a slope angle between 0 and 34.

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Figure 18. Showing part of a landslide scar from 12.05.2015, the steepness, and vegetation in Bogelia. Picture from Vegvesen, (2016).

Figure 19. Slope angle map of Bogelia with the landslide as reference.

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33 3.1.2 Superficial deposits and bedrock

From the Geological Survey of Norway (NGU) superficial deposit map, the lower area of the slope is covered with colluvial deposits, while the steeper part of the slope is determined as bedrock. From my own observations in field and orthophotos, the bedrock is absent and should be replaced by a thin cover of glacial till or peat. In terms of bedrock (Figure 20), the slope consists mostly of anorthosite with some occurrence of amphibolite, quartzite, and mica shist.

Figure 20. Bedrock geology map of Bogelia.

3.1.3 Initiation zone

The initiation zone is situated at 560 m a.s.l. The zone itself wasn’t accessible, so a detailed description of the area was hard to obtain. A possible initiation area is marked in Figure 17, and a picture of what is believed to be the top of the landslide scar is displayed in Figure 21.

It’s this area which is the target for the slope stability analysis. From my judgment, the initiation area is believed to be around 1500 m². The area above the landslide was dominated by peat-like soil with a regular occasion of swamps. There is a small stream which flows directly into the initiation area (Figure 21). The field measurements estimated in the methods were sampled in this stream.

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Figure 21. Picture of what probably is the top of the initiation area (marked in red). The blue marker shows the small stream with its direction of flow. The picture is taken facing approximately eastwards. Taken during fieldwork.

3.2 Gardsbekken - Øvstabødalen

Gardsbekken (Figure 22) is a slightly southeast faced steep ravine located on the south-

western coast of Norway. The ravine drops into a valley called Øvstebødalen. Øvstebødalen is situated in the county of Rogaland, and most of the valley belongs to the municipality of Gjesdal. The ravine is facing the road Fylkesvei 45 (Fv 45). Fv 45, with a length of 69 km is the main road between Ålgård -Sirdal and has a daily average of 1100 vehicles passing through (Statens Vegvesen, 2018). The road is more prone to closure due to snow avalanches during wintertime than shallow rain-induced landslide based on the Norwegian landslide database and various news articles. By going 50 years back in time, there have not been any landslide/avalanche related incidents in Gardsbekken. An exception are a snow avalanche from the neighboring ravine eastwards, which happened during winter 2014 (NVE, 2019).

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Figure 22. An overview of Gardsbekken showing the extent of the landslide and study area.

The landslide (Figure 23) initiated around 16:30 on the 2^nd of June, 2016 due to a short period of intense rainfall, and classified by Rese, (2016) as a debris avalanche. It initiated 723 m.a.s.l at around 40⁰ as a debris slide with a size of just tens of m³ before entraining materials

downslope, transforming it to a debris avalanche (Rese, 2016). The debris avalanche entered a ravine and got channelized, which transformed the landslide into a debris flow. By entering the channel, a higher speed believed to be obtained (Rese, 2016), and in the end, deposited materials on Fv. 45 blocking around 100 meters of the road. No harm was done on humans or vehicles, but the road closed for one day.

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Figure 23. The extent of the landslide at Gardsbekken 2nd of June 2016 and the initiation area marked with a red ring.

Modified picture from (Rese, 2016).

3.2.1 Terrain

Gardsbekken is covered by a mix of bush and trees that covers colluvial deposit. The slope angle inside the study area (Figure 24) has a maximum angle of around 85⁰ and 0⁰ as a minimum. The average slope angle based on GIS-analysis is 27⁰. The top of the landslide is situated at 724 m a.s.l, and the river in the valley bottom is 300 m a.s.l. By looking at the terrain using GIS and my own experience from fieldwork, it seems as the ravine has not been prone to debris avalanche/debris flow in the recent past because of a lack of evidence from previous landslide scars or an alluvial fan formation in the valley bottom.

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Figure 24. Slope angle map of Gardsbekken study area.

3.2.2 Superficial deposits and bedrock

The bedrock in the area mainly consists of different types of gneiss with some bands of amphibolite. In terms of soil type, most of the area above the valley are either covered with glacial till or rockfall debris. The valley bottom is dominated by river deposits and some areas with glacial river deposits.

3.2.3 Initiation zone

From my observation during fieldwork, the initiation zone is presented in Figure 25. A more detailed overview of the initiation area is shown in Figure 25. It’s this area that is the main subject for TRIGRS simulations to observe how FS is changing with time. The area stretches for around 120 meters from top to bottom, and covers 1.250 m².

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Figure 25. Detailed overview of the initiation area marked in red. The picture on the left is taken from a helicopter after the landslide and was extracted from Rese, (2016). Picture on the right was acquired during fieldwork and is with a westerly

direction.

The initiation zone marks the beginning of a start ravine which follows the slope down.

Figure 25 (left) shows the main stream, which is Gardsbekken. Gardsbekken is a result of drainage from a small lake just a few tens of meters upstream. If this affected infiltration on the landslide day is not certain, but if the lake flooded over during the, more water was available for infiltration.

As stated earlier, the area in general is dominated by glacial till deposits and colluvial material. By looking closer in the initiation area during fieldwork it seems that the area consists of a mix of blocks and peat/hummus, which are shown in Figure 26. The rocks could either originate from glacial processes or small rockfalls from steeper areas above the

landslide scar.

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Figure 26. Showing the geomaterials in the initiation area.

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4 Data

In this chapter, the data used for slope stability measurements in TRIGRS will be presented and described. The input in TRIGRS varies as it contains gridded maps, hydrological parameters, and geotechnical parameters. This chapter will also present the raw data for the two methods used for the estimation of saturated hydraulic conductivity and soil thickness.

4.1 GIS

All of the grid-data used as input in TRIGRS was prepared in ArcMap. TRIGRS only read ASCII-files (.asc). Therefore all the grid-data was converted from raster to .ASC files. The data will be presented to the extent of the study area (Figure 27). This extent was the boundaries for gridded input parameters in TRIGRS. An exception is a more detailed slope angle map of the initiation area (Figure 27, green marker).

Figure 27. Showing the area of extent for both case studies were gridded data will be displayed.

Establishing a synergy between regional forecast and local warning for rain-induced landslides: Using a physically-based model

Establishing a Synergy Between Regional Forecast and Local

Warning for Rain-Induced Landslides

Using a Physically-Based Model

Håkon Reinskau Knudsen

Thesis submitted for the degree of Master of Geoscience in Geohazards and Geomechanics

60 credits

Department of Geosciences

Faculty of Mathematics and Natural Sciences UNIVERSITY OF OSLO

Establishing a synergy between regional forecast and local warning for rain-induced

landslides using a physically-based model Using a physically-based model

Håkon Reinskau Knudsen

Abstract

Acknowledgments

Table of Contents

List of figures

List of tables

1 Introduction

2 Theory

2.1 Debris Avalanches and debris Flows

2.2 Slope stability

2.3 LEWS – Landslide early warning system

2.4 Slope stability analysis

3 Case Studies

3.1 Bogelia – Vaksdal

3.2 Gardsbekken - Øvstabødalen

4 Data

4.1 GIS