Download - Emergency preparedness tools for landslides...1. Remote-sensing techniques for landslide inventory and displacement monitoring (correlation of optical images, InSar, LiDar) 3. Landslide

Civil protection in diverse societies... June 12-13 2014, Strasbourg, France

A. Remaître, J.-P. Malet, S. Sterlacchinni, A. Pasuto

Institut de Physique du Globe, Université de Strasbourg, Strasbourg, France

CERG, Strasbourg, France

Emergency preparedness tools for landslides

http://omiv.unistra.fr


VARIOUS TYPES OF LANDSLIDES…

Wartschenbach, Autriche, 1997

DEBRIS FLOW LANDSLIDE

Villerville, France, 1984

ROCKSLIDE

La Clapière, France, 1996

ROCK FLUID SOIL


LANDSLIDE MITIGATION FLOWCHART


Spatial and temporal modelling of hillslope dynamics

2. Landslide triggering and runout analysis (field scale identification of landslide mechanisms, threshold analysis, frequency/magnitude analysis)

1. Remote-sensing techniques for landslide inventory and displacement monitoring (correlation of optical images, InSar, LiDar)

3. Landslide susceptibility and hazard (geomorphology map, probabilistic spatial models)

Prob. of slope failure for critical rainfall from model simulations

‘Simulated’ hazard map

Malet et al. (2007)



2. Spatial analysis of vulnerability & risk (including risk perception)

3. Scenario modelling for impacts assessments (climate change)

1. Developing quantitative hazard and risk assessment procedures

• Monte-Carlo simulations prob. of debris heights

HAZARD = f (debris heights; prob. of debris heights)

• Matrix-based approach

• 4 hazard classes: H1: Null; H2: Low H3: Moderate; H4: High

dh1 dh2 dh3 dh4

Pdh1 Null H2 H3 H3

Pdh2 H2 H3 H3 H4

Pdh3 H2 H4 H4 H4

Debris heights (H<0.5; H<1.0; H<1.5; H>1.5)

Pro

b.

deb

ris

hei

ghts

Malet & Bégueria (2007)



Probabilistitc and physical modeling of debris flows for the entire Barcelonnette basin (Kappes et al., 2011)


Objectives: long-term time series, standardized acquisition, space-time forecasting Instrumentation: multi-parametric observations (kinematics, seismology, hydro-meteorology, geochemistry) Organisation: per sites and observation category Data: free access

OMIV – Observatoire des Instabilités de Versants French Landslide Observatory

Users:

scientists

risk managers

(RTM, BRGM, CEREMA, …)


La Clapière Super Sauze

Avignonet

La Valette

Villerville

OMIV – Monitored landslides

Strasbourg


Objective of the OMIV Deformation Service 1. Time series of surface displacement very accurate in absolute 3D coordinates (GNSS) 2. Maps of surface deformation (optical imagery, radar imagery, LiDAR) 3. Displacement forecast? Pre-alert / Alert thresholds?

70

1

70

4 70

3

70

5

70

2

0 1 2 3 4 5 6 7 8

0.0

1.0

2.0

3.0

4.0

5.0

6.0

701 702 703 704 705

Dis

pla

cem

ent (c

m)

Time (day)

?

?

? ?

E: < 5 cm

E: < 1 cm

Slumgullion (CO, US)

Tessina (IT)


- point-based vs. image-based -permanent and campaign surveys - in-situ and remote monitoring Focus of this talk: service for GNSS data

OMIV approach: combination of techniques


GPS ‘reference’

GPS ‘moving’

Instrumentation

Geodetic receivers: Trimble NetRS / NetR9

Acquisition: 24h / 30s; 1h / 1s

Pont-Bourquin

OMIV Landslide GNSS Network

©UNIL


Data transfer

OMIV Landslide GNSS Network


Example of results: baseline - Villerville

Changes in baseline lengths:

Moving GPS vlrh / vlrb at Villerville

Period: Jan’10 – Aug’10


2011: Sauze 2011: Avignonet

2013: La Clapière 2013: La Valette

Time series – daily cumulated displacements

Example of results: time series


FLAME: Forecasting Landslide Accelerations induced by Meteorological Events

• To predict changes in displacement rates according to changes in

hydro-meteorology

• Three approaches: – Statistical model: Impulse Response model IR Output signal S simulated with a convolution product of

an input signal E by a transfer function G (Impulse Response)

– Mechanical model: 1D viscoplastic model MA 1D infinite viscous model (Herrera et al., 2009) assuming a pre-existing slip

surface and a rigid body on top.

– Statistical-Mechanical model (IR & MA): IRMA IR model for rainfall –> GWT

MA model for GWT -> Displacement


Surface displacement forecast: La Valette

- The model reproduces the observed data with very good accuracy

- MA model (rain -> GWL) provides the best predicted displacements

- No large acceleration / fluidization event up to now

Nov. 09–Mar. 13: lva1 Nov. 09–Mar. 13: lva1


Example of the SISTEMATI PROJECT

The DSS provides Decision Makers with:

- the list of actions to put in practice

- the instruction of executions

- the people in charge to perform each action

- the resources available,

- documents to be issued

(according to national, regional and local laws).

Decision Support System (DSS)


Code 0 – Critical state: null

Code 1 - Critical state: normal

Code 2 - Critical state:

moderate

Code 3 - Critical state: high

Code 4 - Critical state:

emergency



Document to be issued for

Code 2 - Critical state: moderate

Instructions of execution and people in charge

of performing each procedural step



General information about

People in charge involved




General information about

Entities involved

(Control Room)


1. Risk Scenario; 2. Number of Elements at Risk Exposed; 3.

Identification of Emergency Gathering Areas; 4. People Evacuation;



2. Number of affected people



Communication systems: disaster

managers are provided with Phone

calls, SMS, e-mail, Skype, etc.



Track logs (dot lines) system

monitoring in real-time squads

moving from different Civil

Protection warehouses to the

area of disaster.



Public Education


Public Education

Exposition about mountain risks in the Barcelonnette basin (2014)


Public Education


Conclusions:

- Long-time series and historical events database;

- Accurrate and frequent observations of triggering factors (climate, tectonic,etc.);

- The use of different kind of models at different time and spatial case using strong hazard scenarios.

+ Assessing landslide hazard needs:

+ Assessing landslide risk needs:

- Complete and evolutive database of stakes ;

- Emergy warning systems based on Observatories (OMIV);

- A Decision Support System which can link all the stakeholders and the scientific community in charge of hazard evaluation;

- To educate the population threatened by natural hazards like landslides.


Thank you !