mouline island

From calculated runout-zones to hazard zonation
- Examples -
PD Dr. Thomas Glade
[email protected]
0
Lecture overview
ƒ Application of the infinite slope model: Example of
Bonn, Germany
ƒ Empirical and physically-based modeling: Examples
of Bíldudalur, Island
ƒ Empirical modeling of Rock fall and its application in
a GIS: Example of Bayern, Germany
ƒ 3-D trajectory analysis for mitigation of rock fall:
Example of La Désirade, French West Indies
ƒ Examples for numerical simulations
ƒ National scale analysis
1
1
Application of the Infinite Slope Model
c´+ z ∗ cos 2 β × (γ − m × γ w ) × tan φ´
FS = =
τ
γ × z × sin β × cos β
s
FS
s
τ
c´
z
zw
β
γ
m
γw
φ´
=
=
=
=
=
=
=
=
=
=
=
Factor of Safety (<1 unstable; ≥1 stable)
shear strength (resisting forces) [kN/m2]
shear stress (driving forces) [kN/m2]
effective cohesion [kN/m2]
depth of shear plane [m]
height of ground water table [m]
slope [°]
unit weight of soil [kN/m3]
relation z / zw (0 < m < 1) [-]
moist unit weight of soil [kN/m3]
effective friction angle [°]
2
Application of the Infinite Slope Model: Bonn
m=0
m = 0,5
m=1
N
A
B
C
1
Mouline-Richard & Glade, 2003
2
3
km
3
2
Application of the Infinite Slope Model: Bonn Region
m=0
m = 0,5
m=1
Mouline-Richard & Glade, 2003
4
Application of the Infinite Slope Model: Validation
100,00
active landslides
= 0,61 % of the
total area with a
slope angle > 7°
90,00
80,00
70,00
60,00
fos >= 1,8
fos >= 1,3 - < 1,8
50,00
fos >= 1 - < 1,3
fos < 1
40,00
30,00
20,00
10,00
0,00
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
Mouline-Richard & Glade, 2003
5
3
6
Mouline-Richard & Glade (2004)
Application of the Infinite Slope Model: Asumptions
ƒ
ƒ
ƒ
ƒ
ƒ
ƒ
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
7
4
Empirical and physically-based modeling: Examples
of Bíldudalur, Island
Bíldudalur,
Westfjords
Glade & Jensen, 2004
8
Aerial photography of Bíldudalur, view
to North
Iceland - Photopraphs
Bíldudalur,
Westfjords
9
(Photo: Matz Wibelund)
5
Schematic profile of west fjord slopes near settlements
Glade, 2005
10
Approach for debris flow modeling: Bíldudalur,
Island
Use of empirical and semi-empirical models
ƒ Dividing area into units of similar settings
ƒ Focus on correlation between rainstorm events, catchment
size and respective run-out distance
ƒ Empirical relationship between length of run-out and
slope angle
ƒ Ratio of horizontal and vertical distance & catchment size
ƒ Use of back-analysis to adapt models to the conditions in
the study area
ƒ Scenario modeling:
• Calculation of run-outs for different sized rain-storm events
11
6
Debris flow map: Bíldudalur, Island
UNIT V
UNIT IV
UNIT III
UNIT II
UNIT I
Glade & Jensen, 2004
12
Debris flow model
Volume
with VW
k =
P =
A =
Vw = k * P * A
=
Event magnitud of water [m3] in a particular period
Discharge-coefficient (0,85)
Rainfall P [m] in a particular period
Catchment [m2]
Transport distance
L = 1,2 Vwd0,19 * H0,78
with L = Transport distance [m]
Vwd = Debrid flow magnitude (70% sediment + 30% water) [m3]
H = Hight between lowest deposition and source area [m]
Rickenman, 1999
Scenario model is based on rainfall events (2yr / 10yr / 50yr)
13
Glade, 2005
7
Assumptions for empirical debris flow modeling:
Bíldudalur, Island
ƒ Coherent distribution of rainfall in catchment
ƒ Comparable surface structures
ƒ Minor water loss through infiltration, ground water
recharge, and evaporation
ƒ Minor delay between max. rainfall intensity and max.
discharge
ƒ Similar conditions of the catchment and the triggering
event – both in time and space
ƒ Unlimited sediment availability
14
Debris flows and
calculated run-outs
15
Glade & Jensen 2004
8
Approach for rock fall modeling: Bíldudalur, Island
Physically-based model
ƒ Dividing area into units of similar settings
ƒ Determination of characteristic profile in relation to rock
fall
ƒ Extrapolation of resulting values into respective unit
with consideration of local features
ƒ 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
16
Approach for rock fall modeling: Bíldudalur, Island
2 dim. Model (CRSP4.0)
Transport distance = f (rock size, shape, verticale profile, surface roughness)
Scenario modelling: Based on MC simulation for rock sizes (1.9t/10.7t/38.7t)
17
9
Zones of transport
distances for rock
falls
Glade & Jensen 2004
18
Assumptions for empirical debris flow modeling:
Bíldudalur, Island
ƒ Representativeness of slope profiles for total unit
ƒ Rocks do not break during movement (worst case
scenario)
ƒ Rock form is round and does not change during
movement
ƒ Characteristics of catchments and the triggering event
does not change neither in time nor in space
ƒ Unlimited sediment availability
19
10
Classification: From Run-out to HAZARD
Hazard Class
Rock Fall:
Rock weight [t]
Debris flow:
Triggering rainstorm event [mm / Ret.
Period]
< 1.9
68 / 2yr
Moderate
1.9 – 11.3
92 / 10yr
Low
11.3 – 38.7
117 / 50yr
>38.7
> 117 / 50yr
High
Very Low
20
21
Glade 2002
Glade (2002)
11
Empirical modeling of Rock fall and its application in
a GIS: Example of Bayern, Germany
ƒ Documentation and information system for mass
movements in Bavarian Alps: GEORISK
ƒ GIS-based system
ƒ Empirical approach: global angle model
ƒ Implementation in GIS-environment (ArcGIS)
Cazzaniga et al., 2005
22
Empirical modeling of Rock fall and its application in
a GIS: Example of Bayern, Germany
ƒ 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
23
12
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 line
and top of the detachment zone)
ƒ Results are compared to a process-based trajectory model
24
3-D modeling for rock fall map – general approach:
Bavaria, Germany (1/3)
1. Localisation of potential detachment zone/starting point
•
Use of DEM
25
13
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
26
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
Cazzaniga et al., 2005
•
•
27
14
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
28
3-D modeling for rock fall map – results: Bavaria,
Germany (1/3)
Calculated danger areas by using of viewshed
function
29
15
3-D modeling for rock fall map – results: Bavaria,
Germany (1/3)
30
Result of rock fall modeling applying global angle
model; orange areas are accumulation and
detachment areas
3-D modeling for rock fall map – results: Bavaria,
Germany (1/3)
31
Result of rock fall modeling applying process
based trajectory model; brown areas are
accumulation and detachment areas
16
3-D modeling for rock fall map – results: Bavaria,
Germany (1/3)
ƒ in 80% the models
produced the same
output
ƒ in 8% empirical
model more
pessimistic
ƒ in 12% trajectory
model more
pressimistic
Comparison of modeling outputs:
Red & green: coincidence
Orange: empirical model is more pessimistic
Yellow: trajectory model is more pessimistic
32
Example of La Désirade, French West Indies
ƒ 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
33
17
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
34
Hazard and multi-risk map
High Hazard
Final risk map
Medium to high hazard
Location of protecting
fences
35
18
Example of numerical simulations
© Fausto Guzzetti
36
CURRY VILLAGE
LEDGE TRAIL ROCK
FALL
Direkt
Rock fall (Source area Ledge Trail)
15.8.2001, 14.9.2001 und 25.9.2001
1
100%
100%
39
39
Model vs.
vs. Mapping
Mapping
Model
80%
80%
60%
60%
213
213
130
130
97
97
89
89
50
50
87
87
40%
40%
76
76
20%
20%
47
47
30
30
1-2
1-2
3-5
3-5
36
36
53
53
43
43
32
32
0%
0%
1-2
37
NUMBER OF BOULDERS
3-5
6-10 11-25 26-5051-100 >100
6-10
6-10
11-25
11-25 26-50
26-50 51-100
51-100 >100
>100
Number
Number of
of boulders
boulders
© Fausto Guzzetti
19
Presentation of a rock fall simulation
(Yosemite National Park)
38
National distribution of floods
and landslides in Italy
Guzzetti (2000)
39
20
Landslide susceptibility map -USA
Godt et al. (1999)
40
Example national Scale: Data availability
• DEM (25m resolution)
• Geology (1 : 1,250,000)
• Landslide distributions for two regions
(Bonn, Rheinhessen)
41
21
Methodology
Combination of slope angle & lithology
defines susceptibility class
• Review of existing classifications (coasts)
• Questionnaire asking for expert opinion
• Transferred presedence (Prinz 1997)
Susceptibility classes
Analysis on 25m – Results upscaled to 150m
42
Expert judgment
Region
Lithology / geological formation
Experts (Location)
Bavarian Alps
Calcatrous, dolomit, marls, sedimentary dep.
Prof. Bunza (München)
Lake Constanze
Lower sweet water molasse
PD Dr. Theilen-Willige (Stockach)
Bonn Region
Tertiary clays and sands / Devonian Series
Dr. Schmidt (Bonn)
MecklenburgVorpommern (coast)
Glacial and fluvioglacial deposits
Dr. Tiepolt & Dr. Gurwell (Rostock)
Göttingen
Muschelkalk Series
Dipl.-Geogr. Scholte (Osterode)
Rheinhessen
Oligocene marls, clays / Miocene Limestone
Dr. Jäger (Heidelberg)
Schleswig-Holstein
(Coast)
Glacial and fluvioglacial deposits
Dr. Schmidz & Dr. Ziegler (Flintbeck)
Schwäbische ~ /
Fränkische Alb
Muschelkalk / Keuper / Jurassic Series
Prof. Moser (Erlangen)
Thüringer Becken
Lower Muschelkalk / Upper Sandstone (Triassic)
Dr. Beyer & Prof. Schmidt (Halle)
43
22
Susceptibility classes
Class
Description
Very low
At human discretion no danger
Low
Building damage and directly affected people unlikely
Moderate
Building damage possible, people probably endangered
High
Building destruction likely, people endangered
44
Weightning Options
Lithology (n=219)
Oligocene Marls
Greywacke
Loess
.....
Slope angles [°]
Susceptibility
> 15
High
10-15
Moderate
5-10
Low
<5
Very low
> 60
High
10-60
Low
<10
Very Low
>33
High
22-33
Moderate
12-22
Low
.....
......
45
23
Shaded relief
of
Germany
46
1 : 2,750,000
National landslide
susceptibility map
Suscep. = f (Lithology; Slope angle)
47
1 : 2,750,000
Dikau & Glade (2003)
24
Potentially affected slopes
0.2
0.6
5.4
Very low
Low
Moderate
High
93.8
48
Percentage
Susceptibility within slope classes
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
88.2%
8.3%
2.4%
0.78%
0.23%
0.09%
% on total
area
High
Moderate
Low
Very low
0°-<10°
10°-<20° 20°-<30° 30°-<40° 40°-<50°
>=50°
Slope angle classes
49
25
Discussion 1/2
ƒ 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
ƒ National scale analysis require different
approaches and methods
ƒ Classified susceptibility classes
ƒ Results have not been statistically validated with
existing data
ƒ Further regions need to be surveyed
50
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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26
Change of disposition
• General disposition
Relief / Topography
Geology & material properties
Vegetation
• Variable disposition
• Triggering Event
Rainfall (Extreme, long prolonged
wet periods)
Snowmelt
Earthquakes
Anthropogenic interference
Climate fluctuations – seasons
Geotechnical properties
Material availability
Based on Zimmermann et al. 1999
52
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
54
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
ƒ 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.
ƒ Glade, T. 2005: Linking debris-flow hazard assessments with geomorphology.
Geomorphology 66, 189-213.
ƒ Glade, T. and Jensen, E.H. 2004: Landslide hazard assessments for Bolungarvík
and Vesturbyggð, NW-Iceland. Reykjavik: Icelandic Meteorological Office.
ƒ Leroi, E. 2005: Global rockfalls risk management process in ‘La Désirade‘ Island
(French West Indies). Landslides 2, 358-365
ƒ Mouline-Richard, V. and Glade, T. 2003: Regional slope stability analysis for the
Bonn region. Engineering Geology.
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