1. Modelling of landslide release
Martin Mergili
Massimiliano Alvioli, Ivan Marchesini, Johannes P. Müller, Mauro Rossi
Introduction
Statistical
Physically-based
r.slope.stability
Instructions

2. Process understanding
Introduction
Statistical
Rule-based
Physically-based
Static
Stochastic
black
box
Deterministic
Dynamic
Process understanding
Introduction
Statistical
Physically-based
r.slope.stability
Instructions 2 2

3. High-mountain lakes in the Pamir
Rule-based & statistical models
High-mountain lakes in the Pamir
Introduction
Rule-based
Physically-based
r.slope.stability
Instructions 3 3

4. High-mountain lakes in the Pamir
Dam material
Contact to glacier
Catchment characteristics
Lake area
Introduction
Rule-based
Physically-based
r.slope.stability
Instructions 4 4

5. Lake outburst indicator score
Dam material
Lake outburst indicator score
Contact to glacier
Catchment characteristics
Lake area
r.glachaz
Rule-based lake outburst indicator scores
Introduction
Rule-based
Physically-based
r.slope.stability
Instructions 5 5

6. Landslide spatial probability
r.landslides.statistics
Statistically derived landslide spatial probability
Collazzone Area, central Italy
Data: CNR-IRPI Perugia
Introduction
Statistical
Physically-based
r.slope.stability
Instructions 6 6

7. Landslide spatial probability
Some people might say:
All this is nonsense, it has nothing to do with the processes going on!
We need physically-based models!
r.landslides.statistics
Statistically derived landslide spatial probability
Collazzone Area, central Italy
Data: CNR-IRPI Perugia
Introduction
Statistical
Physically-based
r.slope.stability
Instructions 7 7

8. Physically-based models
weight of water
buoyancy
weight of
moist soil θs γd dw φ
shear force c γw
normal force d
Limit equilibrium method
shear resistance S
friction term
cohesion term
seepage force
Δx = 1m
(Δy = 1m)
FoS > 1: slope is stable
γd ... specific weight of dry soil (N/m³)
γw ... specific weight of water (N/m³)
FoS < 1: slope is not stable
c' ... eff. cohesion soil + roots (N/m²)
φ' ... eff. angle of internal friction (°)
θs ... saturated water content (vol.-%)
Introduction
Statistical
Physically-based
r.slope.stability
Instructions 8 8

9. Infinite slope stability & slope water
Software packages are available coupling the infinite slope stability model with slope hydraulics
Rainfall events of defined duration and intensity are assumed, infiltration and the groundwater level are computed
SHALSTAB
SHALSTAB
SINMAP
SINMAP
TRIGRS
Introduction
Statistical
Physically-based
r.slope.stability
Instructions 9 9

10. Shallow & deep-seated landslides
Shallow landslide
Deep-seated landslide
Kirchdorf, Austria
Mont de la Saxe, Italy
INFINITE SLOPE STABILITY MODEL: L/D > 16 – 25
Introduction
Statistical
Physically-based
r.slope.stability
Instructions 10 10

11. Limits of limit equilibrium methods
Limit equilibrium methods only tell us about the state of stability of a slope
They do not support conclusions on movement rates
Tendency of a slope to fail
Rate of slope deformation
Val Pola, Italy
Mont de la Saxe, Italy
Introduction
Statistical
Physically-based
r.slope.stability
Instructions 11 11

12. Software of Rocscience
Model applications
Software tools used in geotechnical engineering do not always build on regular GIS rasters, but often on irregular rasters or vertical profiles
2D / 3D sliding surface (limit equilibrium) model for single slopes
SVSLOPE
Tools for various types of mass movement processes
Software of Rocscience
Discontinuum model for deformation of jointed rocks
UDEC
Introduction
Statistical
Physically-based
r.slope.stability
Instructions 12 12

13. The tool r.slope.stability
University of Vienna
BOKU, Vienna
University of Innsbruck
CNR-IRPI Perugia
„The open source GIS slope stability model“
Open source sliding surface (limit equilibrium) model suitable for the small catchment scale
Specific strategies for including parameter uncertainty and multi-core computing
Introduction
Statistical
Physically-based
r.slope.stability
Instructions 13 13

14. r.slope.stability: concept
Test of many randomly selected slip surfaces
Minimum FoS is determined for each pixel
Introduction
Statistical
Physically-based
r.slope.stability
Instructions 14 14

15. r.slope.stability: study area
Collazzone Area
Area: 90 km²
145 – 634 m asl.
Landslides of different types
Well-studied
Introduction
Statistical
Physically-based
r.slope.stability
Instructions 15 15

16. r.slope.stability: lithology and layering
Lower sands
Silt and clay 1
Silt and clay 2
Upper sands
Mixed deposits
Consolidated rock
Traces of 74 layers
Lithological class assigned to each layer
Introduction
Statistical
Physically-based
r.slope.stability
Instructions 16 16

17. r.slope.stability: parameterization
Textbook (Prinz & Strauss, 2011)
c' = x φ' + 17,321 + ec
R² = 0.395
Introduction
Statistical
Physically-based
r.slope.stability
Instructions 17 17

18. r.slope.stability: parameterizationφ‘ c‘
Sampling of c‘ and ϕ‘ according to probability density
Pf ~ fraction of parameter combinations with FS < 1
Strong statistical aspect of physical parameters
Introduction
Statistical
Physically-based
r.slope.stability
Instructions 18 18

19. r.slope.stability: failure probability
AUCROC = 0.66, comparable to the one yielded with the results of the simpler statistical method
Introduction
Statistical
Physically-based
r.slope.stability
Instructions 19 19

20. Instructions
Use the python script fos.py you have developed in one of the previous lessons as basis
Extend this script with the following components:
► Define the input parameters as command line arguments (library optparse)
► Derive the slope failure probability by repeating the FoS computation for several times with randomly varied input parameters (library random, for loop)
► Automatically validate and visualize the result, using the R scripts pfail.roc.r and pfail.map.r along with the library subprocess
Vajont, Italy
Introduction
Statistical
Physically-based
r.slope.stability
Instructions 20 20

21. Start with the following parameter values:
Instructions
Start with the following parameter values:
Symbol
Parameter name
Value S
Slope angle
30° d
depth of slip surface
1.0–3.0 m dw
saturated depth
2.0 m γd
specific weight of dry soil
16000 N/m³ γw
specific weight of water
9810 N/m³ c'
eff. cohesion soil + roots
0–6000 N/m² φ'
eff. angle of internal friction
20–35° θs
saturated water content
35% (0.35)
DTM: Castelnuovo test area (raster cn_elev)
Introduction
Statistical
Physically-based
r.slope.stability
Instructions 21 21

22. Thank You for your active participation!
Introduction
Statistical
Physically-based
r.slope.stability
Instructions 22 22