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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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Mouline-Richard & Glade, 2003
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°
Mouline-Richard & Glade, 2003
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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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Empirical modeling of Rock fall and its application in
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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Empirical modeling of Rock fall and its application in
a GIS: Example of Bayern, Germany
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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Empirical modeling of Rock fall and its application in
a GIS: Example of Bayern, Germany
• 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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3-D modeling for rock fall map – general approach:
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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3-D modeling for rock fall map – general approach:
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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3-D modeling for rock fall map – results: Bavaria,
Germany (1/3)
Calculated danger areas by using of viewshed
function
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3-D modeling for rock fall map – results: Bavaria,
Germany (1/3)
Result of rock fall modeling applying global angle
model; orange areas are accumulation and
detachment areas
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3-D modeling for rock fall map – results: Bavaria,
Germany (1/3)
Result of rock fall modeling applying process
based trajectory model; brown areas are
accumulation and detachment areas
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3-D modeling for rock fall map – results: Bavaria,
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
43
76
213 13097
8987
50
39
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
43
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.
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