04 run out zones field suveys modelling examples

8/23/2019 04 Run Out Zones Field Suveys Modelling Examples

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From calculated runout-zones to hazard zonation

- Examples -

PD Dr. Thomas [email protected]

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Lecture overview

Application of the infinite slope model: Example of

Bonn, Germany

Empirical and physically-based modeling: Examples

of Bíldudalur, Island

Empirical modeling of Rock fall and its application in

a GIS: Example of Bayern, Germany

3-D trajectory analysis for mitigation of rock fall:

Example of La Désirade, French West Indies

Examples for numerical simulations

National scale analysis



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β β γ

φ γ γ β

τ cossin

´tan)(cos´ 2

×××

××−×∗+==

z

m zcsFS w

FS = Factor of Safety (<1 unstable; ≥1 stable)s = shear strength (resisting forces) [kN/m2]

τ = shear stress (driving forces) [kN/m2]c´ = effective cohesion [kN/m2]

z = depth of shear plane [m]

zw = height of ground water table [m]

β = slope [°]

γ = unit weight of soil [kN/m3]

m = relation z / zw (0 < m < 1) [-]γ w = moist unit weight of soil [kN/m3]

φ ́ = effective friction angle [°]

Application of the Infinite Slope Model

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Application of the Infinite Slope Model: Bonn

Mouline-Richard & Glade, 2003

A B C

m = 0 m = 0,5 m = 1

N

1 2 3km



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Application of the Infinite Slope Model: Bonn Region

m = 0

m = 0,5

m = 1

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Application of the Infinite Slope Model: Validation

0,00

10,00

20,00

30,00

40,00

50,00

60,00

70,00

80,00

90,00

100,00

m =0 m =0,1 m =0,2 m =0,3 m =0,4 m =0,5 m =0,6 m =0,7 m =0,8 m =0,9 m =1

fos >=1,8fos >=1,3 - <1,8

fos >=1 - <1,3

fos <1

active landslides

=0,61 % of the

total area with a

slope angle >7°




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6Mouline-Richard & Glade(20 04)

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Application of the Infinite Slope Model: Asumptions

Unlimited slope

Within each pixel similar structure

Constant depth of shear plane

Geotechnical conditions do not change

Hydrological changes are not included Vegetation is not considered

=> Shallow translational landslides



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Empirical and physically-based modeling: Examples

of Bíldudalur, Island

Bíldudalur,

Westfjords

Glade & Jensen, 2004

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Iceland - Photopraphs

Aerial photography of Bíldudalur, view

to North

Bíldudalur,

Westfjords

(Photo: Matz Wibelund)



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Schematic profile of west fjord slopes near settlements

Glade, 2005

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Approach for debris flow modeling: Bíldudalur,

Island Use of empirical and semi-empirical models

Dividing area into units of similar settings

Focus on correlation between rainstorm events, catchment

size and respective run-out distance

Empirical relationship between length of run-out and slope angle

Ratio of horizontal and vertical distance & catchment size

Use of back-analysis to adapt models to the conditions in

the study area

Scenario modeling:

• Calculation of run-outs for different sized rain-storm events



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UNIT IV

UNIT I

UNIT II

UNIT III

UNIT V

Debris flow map: Bíldudalur, Island

Glade & Jensen, 2004

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Debris flow model

Scenario model is based on rainfall events (2yr / 10yr / 50yr)

with V W = Event magnitud of water [m3] in a particular period

k = Discharge-coefficient (0,85)

P = Rainfall P [m] in a particular period

A = Catchment [m2]

Volume V w = k * P * A

Glade, 2005

Transport distance L = 1,2 V wd

0,19 * H 0,78

with L = Transport distance [m]

V wd = Debrid flow magnitude (70% sediment + 30% water) [m3]

H = Hight between lowest deposition and source area [m]

Rickenman, 1999



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Assumptions for empirical debris flow modeling:

Bíldudalur, Island Coherent distribution of rainfall in catchment

Comparable surface structures

Minor water loss through infiltration, ground water

recharge, and evaporation

Minor delay between max. rainfall intensity and max.

discharge

Similar conditions of the catchment and the triggering

event – both in time and space

Unlimited sediment availability

15Glade& Jensen 2004

Debris flows and

calculated run-outs



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Approach for rock fall modeling: Bíldudalur, Island

Physically-based model Dividing area into units of similar settings

Determination of characteristic profile in relation to rock

fall

Extrapolation of resulting values into respective unit

with consideration of local features

Use of Colorado Rock fall Simulation Program (CRSP)

• 2-D rock fall model

• Input variables:• Surface roughness

• Tangential coefficient of frictional resistance

• Normal coefficient of restitution

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Approach for rock fall modeling: Bíldudalur, Island

Scenario modelling: Based on MC simulation for rock sizes (1.9t/10.7t/38.7t)

2 dim. Model (CRSP4.0)

Transport distance = f (rock size, shape, verticale profile, surface roughness)



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Glade& Jensen 2004

Zones of transport

distances for rock falls

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Assumptions for empirical debris flow modeling:

Bíldudalur, Island

Representativeness of slope profiles for total unit

Rocks do not break during movement (worst case

scenario)

Rock form is round and does not change during

movement Characteristics of catchments and the triggering event

does not change neither in time nor in space

Unlimited sediment availability



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Classification: From Run-out to HAZARD

> 117 / 50yr >38.7Very Low

117 / 50yr 11.3 – 38.7Low

92 / 10yr 1.9 – 11.3Moderate

68 / 2yr < 1.9High

Debris flow:

Triggering rainstorm event [mm / Ret.

Period]

Rock Fall:

Rock weight [t]

Hazard Class

Glade 2002

21Glade (2002)



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a GIS: Example of Bayern, Germany Documentation and information system for mass

movements in Bavarian Alps: GEORISK

GIS-based system

Empirical approach: global angle model

Implementation in GIS-environment (ArcGIS)

Cazzaniga et al., 2005

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Maximum run-out zone is determined by:

• Minimum global angles between the horizontal line and

the line connecting the farthest blocks and different points

within the detachment area or the top of the talus



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• Use of two angles

• Shadow angle (angle between horizontal line & top of talus)

• Geometrical slope angle (angle between the horizontal lineand top of the detachment zone)

Results are compared to a process-based trajectory model

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3-D modeling for rock fall map – general approach:

Bavaria, Germany (1/3)

1. Localisation of potential detachment zone/starting point

• Use of DEM



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Bavaria, Germany (2/3)2. Data preparation

• Generation of necessary attributes for “viewshed-function”

• Checking the angles between every point and starting points

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Bavaria, Germany (3/3)3. Modeling

• Viewshed-function: Starting points of rock falls

• Limiting horizontal (lateral spread) and vertical angles (run

out)

• Checking for errors

C a z z a n i g a e t a l . ,

2 0 0 5



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3-D modeling for rock fall map – results: Bavaria,

Germany (1/3)

Test area: red areas are potential starting points

of rock falls, extracted from GEORISK

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Germany (1/3)

Calculated danger areas by using of viewshed

function



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Germany (1/3)

Result of rock fall modeling applying global angle

model; orange areas are accumulation and

detachment areas

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Germany (1/3)

Result of rock fall modeling applying process

based trajectory model; brown areas are

accumulation and detachment areas



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Germany (1/3)

Comparison of modeling outputs:

Red & green: coincidence

Orange: empirical model is more pessimistic

Yellow: trajectory model is more pessimistic

in 80% the models

produced the same

output

in 8% empirical

model more

pessimistic

in 12% trajectory

model more

pressimistic

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Example of La Désirade, French West Indies

Rock fall risk management project

• Application of 3-D trajectory rock fall model

• DEM

• Starting points

• Rebound conditions

• Hazard and multi-risk map

• Determination of solutions & risk prevention plan

Leroi, 2005



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Application of 3-D trajectory rock fall model

a Main window of 3-D

trajectory model

b Computed 3-D trajectories

c Trajectories with impact

on existing buildings

d Design of protecting

fences and inclusion into

DEM

e Trajectories after included fences

f Location of protecting

fences across the island

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Hazard and multi-risk map

High Hazard

Medium to high hazard

Location of protecting

fences

Final risk map



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© Fausto Guzzetti

Example of numerical simulations

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Direkt Rock fall (Source area Ledge Trail)15.8.2001, 14.9.2001 und 25.9.2001

CURRY VILLAGE

LEDGE TRAIL ROCK

FALL

1-2 3-5 6-10 11-2526-5051-100>100NUMBER OF BOULDERS

1

47 3036

5332

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76

213 13097

8987

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0%

20%

40%

60%

80%

100%

1-2 3-5 6-10 11-25 26-50 51-100 >100

Number of boulders

M o d e l v s .

M a p p i n g

47 3036

5332

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76

213 13097

8987

50

39

0%

20%

40%

60%

80%

100%

1 -2 3-5 6-10 1 1-25 2 6-50 5 1-100 >1 00

Number of boulders

M o d e l v s .

M a p p i n g

© Fausto Guzzetti



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Presentation of a rock fall simulation

(Yosemite National Park)

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National distribution of floods

and landslides in Italy

Guzzetti (2000)



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Godt et al. (1999)

Landslide susceptibility map -USA

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Example national Scale: Data availability

• DEM (25m resolution)

• Geology (1 : 1,250,000)• Landslide distributions for two regions

(Bonn, Rheinhessen)



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Methodology

Combination of slope angle & lithology

defines susceptibility class

• Review of existing classifications (coasts)

• Questionnaire asking for expert opinion

• Transferred presedence (Prinz 1997)

Susceptibility classesAnalysis on 25m – Results upscaled to 150m

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Expert judgment

Dipl.-Geogr. Scholte (Osterode)Muschelkalk SeriesGöttingen

Dr. Jäger (Heidelberg)Oligocene marls, clays / Miocene LimestoneRheinhessen

Dr. Schmidz & Dr. Ziegler (Flintbeck)Glacial and fluvioglacial depositsSchleswig-Holstein(Coast)

Prof. Moser (Erlangen)Muschelkalk / Keuper / Jurassic SeriesSchwäbische ~ /Fränkische Alb

Dr. Beyer & Prof. Schmidt (Halle)Lower Muschelkalk / Upper Sandstone (Triassic)Thüringer Becken

Dr. Schmidt (Bonn)Tertiary clays and sands / Devonian SeriesBonn Region

Glacial and fluvioglacial deposits

Lower sweet water molasse

Calcatrous, dolomit, marls, sedimentary dep.

Lithology / geological formation

Mecklenburg-

Vorpommern (coast)

Lake Constanze

Bavarian Alps

Region

Dr. Tiepolt & Dr. Gurwell (Rostock)

PD Dr. Theilen-Willige (Stockach)

Prof. Bunza (München)

Experts (Location)



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Susceptibility classes

Building destruction likely, people endangered High

Building damage possible, people probably endangered Moderate

Building damage and directly affected people unlikelyLow

At human discretion no danger Very low

DescriptionClass

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Weightning Options

Moderate22-33

Low12-22

Very low<5

Low10-60

Very Low<10

High>33Loess

................

High> 60Greywacke

Low5-10

Moderate10-15

High> 15Oligocene Marls

SusceptibilitySlope angles [°] Lithology (n=219)



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Shaded relief

of

Germany

1 : 2,750,000

471 : 2,750,000

National landslide

susceptibility map

Dikau& Glade(2003)

Suscep. = f ( Lithology; Slope angle)



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Potentially affected slopes

93.8

5.4

0.6

0.2

Very low

Low

Moderate

High

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Susceptibility within slope classes

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0°-<10° 10°-<20° 20°-<30° 30°-<40° 40°-<50° >=50°

Slope angle classes

P e r c e n

t a g e

High

Moderate

Low

Very low

88.2% 8.3% 2.4% 0.78% 0.23% 0.09%% on total

area



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Discussion 1/2

High susceptible regions include:• Alpine regions

• Steep cruestas

• Deeply dissected valleys in the low mountain ranges

(e.g. Mittelrhein; Mosel)

• Coasts along North and East Sea

• Failures along natural river banks

National scale analysis require different

approaches and methods

Classified susceptibility classes

Results have not been statistically validated with

existing data

Further regions need to be surveyed

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Summary

• Rock fall analysis for a slope profil

• Debris flow modelling based on trigger

• Estimated event magnitude & frequency =>Hazard

• Calculated run-out is based on field evidence

• Risk Analysis is performed on single objectes

• Social impacts of detailed results is crucial

• Scenarios can be analysed



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Change of disposition

Based on Zimmermann et al.1999

• General dispositionRelief / Topography

Geology & material properties

Vegetation

• Variable dispositionClimate fluctuations – seasons

Geotechnical properties

Material availability

• Triggering EventRainfall (Extreme, long prolonged

wet periods)

Snowmelt

Earthquakes

Anthropogenic interference

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Advantages of spatial modelling

• Abstraction to key-issues

• Subjectivity by model development and choice

• Objectivity: Repetition of similar analysis gives

identical results

• Unambiguous rules - Concepts and structures

- Uniformity based on objective criteria

- Transparency is inherent

- Transferability is possible

• Potential for scenarios



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Disadvantages of spatial modelling

• Reduction to single parameter indispensable

• Commonly statistical relation (if - when)

• Danger: Essential, important process-determining

parameter will not be considered

• Quality has to be ensured

• Assumptions have to be reflected for interpretations

• Transferability has to be critically questioned

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Scientific challenges

• Development of process-specific methods

• Scale dependent choice of methods is important

• Spatial models have to be improved, or further developed

• Validation of results is essential for the judgement

of the quality

• Scenarios of events



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References

Cazzaniga, C., Sciesa, E., Thüring, M. and Zonta, M.F. (eds.) 2005: Mitigation of

hydrogeological risk in alpine catchments – “CatchRisk”. Final report of the

Program INTERREG II B – Alpine Space. pp. 189.

Glade, T. 2005: Linking debris-flow hazard assessments with geomorphology.

Geomorphology 66, 189-213.

Glade, T. and Jensen, E.H. 2004: Landslide hazard assessments for Bolungarvík

and Vesturbyggð, NW-Iceland. Reykjavik: Icelandic Meteorological Office.

Leroi, E. 2005: Global rockfalls risk management process in ‘La Désirade‘ Island

(French West Indies). Landslides 2, 358-365

Mouline-Richard, V. and Glade, T. 2003: Regional slope stability analysis for the

Bonn region. Engineering Geology.

04 run out zones field suveys modelling examples

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