Emergency preparedness tools for landslides

Transcription

1 Emergency preparedness tools for landslides A. Remaître, J.-P. Malet, S. Sterlacchinni, A. Pasuto Institut de Physique du Globe, Université de Strasbourg, Strasbourg, France CERG, Strasbourg, France

2 VARIOUS TYPES OF LANDSLIDES ROCK SOIL FLUID ROCKSLIDE La Clapière, France, 1996 LANDSLIDE Villerville, France, 1984 DEBRIS FLOW Wartschenbach, Autriche, 1997

3 LANDSLIDE MITIGATION FLOWCHART

4 LANDSLIDE MITIGATION FLOWCHART

5 Spatial and temporal modelling of hillslope dynamics 1. Remote-sensing techniques for landslide inventory and displacement monitoring (correlation of optical images, InSar, LiDar) 2. Landslide triggering and runout analysis (field scale identification of landslide mechanisms, threshold analysis, frequency/magnitude analysis) 3. Landslide susceptibility and hazard (geomorphology map, probabilistic spatial models) Simulated hazard map Prob. of slope failure for critical rainfall from model simulations Malet et al. (2007)

6 Spatial and temporal modelling of hillslope dynamics

7 Spatial and temporal modelling of hillslope dynamics

8 Prob. debris heights Spatial and temporal modelling of hillslope dynamics 1. Developing quantitative hazard and risk assessment procedures 2. Spatial analysis of vulnerability & risk (including risk perception) 3. Scenario modelling for impacts assessments (climate change) Monte-Carlo simulations prob. of debris heights Malet & Bégueria (2007) Debris heights (H<0.5; H<1.0; H<1.5; H>1.5) dh1 dh2 dh3 dh4 P dh 1 Null H2 H3 H3 P dh 2 H2 H3 H3 H4 P dh 3 H2 H4 H4 H4 Matrix-based approach 4 hazard classes: H1: Null; H2: Low H3: Moderate; H4: High HAZARD = f (debris heights; prob. of debris heights)

9 Spatial and temporal modelling of hillslope dynamics Probabilistitc and physical modeling of debris flows for the entire Barcelonnette basin (Kappes et al., 2011)

10 LANDSLIDE MITIGATION FLOWCHART

11 OMIV Observatoire des Instabilités de Versants French Landslide Observatory Objectives: long-term time series, standardized acquisition, space-time forecasting Instrumentation: multi-parametric observations (kinematics, seismology, hydrometeorology, geochemistry) Organisation: per sites and observation category Data: free access Users: scientists risk managers (RTM, BRGM, CEREMA, )

12 OMIV Monitored landslides Super Sauze La Valette La Clapière Villerville Avignonet Strasbourg

13 Displacement (cm) Objective of the OMIV Deformation Service E: < 1 cm E: < 5 cm 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? Tessina (IT) ? ? Slumgullion (CO, US) ? Time (day)

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

15 OMIV Landslide GNSS Network Instrumentation GPS reference Pont-Bourquin GPS moving Geodetic receivers: Trimble NetRS / NetR9 Acquisition: 24h / 30s; 1h / 1s June , UNIL Strasbourg, France

16 OMIV Landslide GNSS Network Data transfer

17 Example of results: baseline - Villerville Changes in baseline lengths: Moving GPS vlrh / vlrb at Villerville Period: Jan 10 Aug 10

18 Example of results: time series Time series daily cumulated displacements 2011: Sauze 2011: Avignonet 2013: La Clapière 2013: La Valette

19 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

20 Surface displacement forecast: La Valette Nov. 09 Mar. 13: lva1 Nov. 09 Mar. 13: lva1 - 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

21 LANDSLIDE MITIGATION FLOWCHART

22 Decision Support System (DSS) 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).

23 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

24 Decision Support System (DSS) Instructions of execution and people in charge of performing each procedural step Document to be issued for Code 2 - Critical state: moderate

25 Decision Support System (DSS) General information about People in charge involved

26 Decision Support System (DSS) General information about Entities involved (Control Room)

27 Decision Support System (DSS)

28 Decision Support System (DSS) 1. Risk Scenario; 2. Number of Elements at Risk Exposed; 3. Identification of Emergency Gathering Areas; 4. People Evacuation;

29 Decision Support System (DSS) 2. Number of affected people

30 Decision Support System (DSS) Communication systems: disaster managers are provided with Phone calls, SMS, , Skype, etc.

31 Decision Support System (DSS) Track logs (dot lines) system monitoring in real-time squads moving from different Civil Protection warehouses to the area of disaster.

32 LANDSLIDE MITIGATION FLOWCHART

33 Public Education

34 Public Education Exposition about mountain risks in the Barcelonnette basin (2014)

35 Public Education

36 Conclusions: + Assessing landslide hazard needs: - 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 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.

37 Thank you!