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

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

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

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

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

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

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

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

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 http://calm.geo.berkeley.edu/geomorph//shalstab/background.htm SHALSTAB SHALSTAB SINMAP SINMAP http://hydrology.usu.edu/sinmap/ TRIGRS http://pubs.usgs.gov/of/2008/1159/ Introduction Statistical Physically-based r.slope.stability Instructions 9 9

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

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

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 https://www.soilvision.com/products/svoffice5/svslope/ 2D / 3D sliding surface (limit equilibrium) model for single slopes SVSLOPE Tools for various types of mass movement processes Software of Rocscience https://www.rocscience.com/ Discontinuum model for deformation of jointed rocks http://www.itascacg.com/software/udec UDEC Introduction Statistical Physically-based r.slope.stability Instructions 12 12

The tool r.slope.stability University of Vienna BOKU, Vienna University of Innsbruck http://www.slopestability.org 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

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

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

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

r.slope.stability: parameterization Textbook (Prinz & Strauss, 2011) c' =-485.24x φ' + 17,321 + ec R² = 0.395 Introduction Statistical Physically-based r.slope.stability Instructions 17 17

r.slope.stability: parameterization φ‘ 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

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

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

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

Thank You for your active participation! martin.mergili@boku.ac.at http://www.mergili.at Introduction Statistical Physically-based r.slope.stability Instructions 22 22