Earthquake-induced Landslides Hazard zonation in Campania – application and implementation in ArcGIS SAFER Project - FINAL MEETING Elin Skurtveit & Amir.

Earthquake-induced Landslides

Hazard zonation in Campania – application and implementation

in ArcGIS

SAFER Project - FINAL MEETING

Elin Skurtveit & Amir M. Kaynia - NGI

Initial objectives1. Calibrate a landslide susceptibility model based on topographic and

lithological properties of slope and combine it with a deterministic model based on:• mechanical parameters of the soil• geometrical properties of the slope• characteristics of the earthquakes

Validate the model through documented case histories and database of historical landslides.

2. Establish a GIS-based methodology to couple landslide model to Shake maps in order to produce real-time slide maps.

3. In cooperation with AMRA and using the shake maps generated in WP2, apply the seismic landslide susceptibility model to a study region in Campania, Italy.

4. Establish a model for pore pressure generation/dissipation under cyclic loading of earthquake and develop computational tool for post-earthquake landslides.

Outline of presentation

1. Empirical Slope Deformation Analysis

2. Verification of models for landslide prediction

3. Implementation of landslide prediction model in ArcGIS

4. Application of ArcGIS model to test site in Campania, Italy

Empirical Slope Deformation Analysis

cossin

tancos)( 2

H

HncFS w

static

sin)1( gFSgka staticyy

Bray and Rathje (1998)

Empirical Slope Deformation Analysis

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k

k

Dk

u y

Empirical model of landslide:

1. Establish Factor of safety against failure (H = depth to sliding, C = soil’s shear strength)

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H

HncFS w

static

max955max10 477.387.1log

k

k

Dk

u y

2. Calculate yield acceleration of slope:

sin)1( gFSgka staticyy

3. Calculate slope’s displacement (D5-95 = significant duration, kmax = peak ground acceleration in g):

Verification of model for landslide prediction

Four Case histories of observed landslide or large slope displacements were used:1. Landslide movement during

Northridge Earthquake

2. Debris slump caused by Suusamyr Earthquake

3. Yokowatashi landslide during Niigata-Ken Chuetsu earthquake

4. Landslide movement during Coyote Lake Earthquake

Results of comparisons with case histories of landslides are encouraging and indicate a fairly good performance of the model.

Verification of model (cont.)

The model is also able to explain the upper bound of historical landslides, established by Keefer (1984), as function of distance and earthquake magnitude.

This model is being implemented in a GIS for mapping of landslide zones

Application of ArcGIS Model to test site in Campania, Italy

Study area – Campania

Destra Sele River Basin

Available data (provided by AMRA and INGV)

• Digital elevation model

• Geology

• Geotechnical parameters

• Shake Map Irpinia 1980 earthquake

Earthquake data

Peak Ground Acceleration map from the Irpinia 1980 earthquake

Provided by INGV

Geology of the Study area

Fiorillo & Wilson, 2004

Implementation of landslide prediction model in ArcGIS

The result of [Inlayer1] + [Inlayer2] / 2 results in an output grid displaying the mean value for every cell.

Raster Calculation and Map Algebra functions.Example:

Raster slope map calculated from Digital Elevation ModelCellsize: 40 m x 40 m

Implementation of landslide prediction model in ArcGIS

ArcGIS Raster calculation model for factor of safety and the yield acceleration of the soil

Input raster layers• Geology & geotechnical parameters• Geometry of slope

cossin

tancos)( 2

H

HncFS w

static

sin)1( gFSgka staticyy

Implementation of landslide prediction model in ArcGIS

Raster calculation model for soil displacement

Input raster layers• PGA• Soil strength / safety factor• Magnitude and distance

max955max10 477.387.1log

k

k

Dk

u y

Calculate slope’s displacement, u:

D5-95 = significant duration, kmax = peak ground acceleration in g

ArcGIS displacement map

Slope Displacement in cm

Sliding Displacement Evaluation – Comparison with the Extended Models

ReferenceSaygili and

Rathje (2008)

Bray and Travasarou

(2007)

Blake et. al. (2001)*

GM Parameters PGA,M PGA,PGV PGA PGA, D5-75

Min Displacement 0 cm 0 cm 0 cm 0 cm

Max Displacement 76 cm 78 cm 97cm 171 cm

0 - 5 cm 99.44% 99.44% 99.41% 99.37%

5 - 15 cm 0.31% 0.32% 0.34% 0.26%

15 - 30 cm 0.11% 0.10% 0.07% 0.12%

> 30 cm 0.14% 0.13% 0.18% 0.25%

Conclusions

• Verification of slope displacement model for landslide prediction using case histories.

• Application of landslide prediction model to test site in Italy.

• Results shows how a GIS-based method can be implemented for near real-time prediction and mapping of landslides.

• Results from various recent slope deformation models compare very well with the California Model used in this study.