Submarine landslide catalogs (an example from the Mediterranean Sea): Ready for input into probabilistic tsunami hazard assessment?

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1 Submarine landslide catalogs (an example from the Mediterranean Sea): Ready for input into probabilistic tsunami hazard assessment? Roger Urgeles Institut de Ciències del Mar, CSIC, Barcelona, Catalonia, Spain

2 Aims Provide an overview of submarine slope failure at the scale of a large marine basin encompassing tectonically active and passive continental margins under variable sediment input 2/28 Put forward the pros and the cons of using submarine landslide catalogs for probabilistic submarine landslide tsunami hazard. Assess the information that I deem necessary for such approach and show how feasible is to get such information Mediterranean Sea Urgeles, R., Camerlenghi, A. (2013): Submarine landslides of the Mediterranean Sea: trigger mechanisms, dynamics and frequency magnitude distribution, Journal of Geophysical Research - Earth Surface, 118:

3 The Mediterranean Sea 3/28 Very densely-populated coastline: 160 million inhabitants sharing 46,000 km of coastline (3.5 inhabitants per m of coastline). World's leading holiday destination, receiving up 30% of global tourism and an average of 135 million visitors annually; this is predicted to increase to million tourists by year 2025 (European Environmental Agency - EEA). EEA web site Copyright EEA, Copenhagen. FRID-Arendal (2013) Population density and urban centres in the Mediterranean basin

4 The Mediterranean Sea 4/28 Rich record of historical tsunamis National Geophysical Data Center / World Data Center (NGDC/WDC) Historical Tsunami Database, Boulder, CO, USA. (Available at

5 The Mediterranean Sea 5/28 Some tsunamis related to submarine landslides (up to 15%) Small dimensions close proximity of tsunami sources and impact areas Scilla coastal landslide (Italy) 1908 Messina (Italy)?? 1956 Central Aegean Sea (Greece)?? 1979 Nice airport landslide (France) 1999 Değirmendere coastal landslide, (Turkey) 2002 Stromboli volcano landslide (Italy). National Geophysical Data Center / World Data Center (NGDC/WDC) Historical Tsunami Database, Boulder, CO, USA. (Available at

6 Approaches (Geist and ten Brink 2012) GOETECHNICAL APPROACH Using relationships between EQ and landslides (Grilli et al., 2009, MARGO; ten Brink et al., 2009, MARGO) pre-defined trigger mechanism EMPIRICAL APPROACH Relies on the dates of events identified in the geologic record. 6/28 Flowchart for the Monte Carlo Simulation (MCS) model for probabilistic approach for determining submarine landslide tsunami hazard (Grilli et al., 2009 MARGO) Example procedure tree for the inclusion of slide variability and uncertainty into a PTHA (Geist and Lynett, 2011,; TOS)

7 Approaches NanTroSEIZE Expedition 333 Site C % of sediment < 1Ma is MTD 6 MTDs over the last 1 Ma in ash bearing hemipelagites Recurrence of MTDs 100, ,000 y Recurrence of great subduction earthquakes in the Nankai Trough y Rhythm of turbidite deposition on slope is probably on the 1000 y timescale. No retrogression of slides near epicenter of Mw 8.8 Central Chilean on 27 February 2010 (Völker et al., 2011: Marine Geol.) 7/28 Expedition 333 scientists 2011: Strasser et al., 2012: ISSMMTC Kyoto

8 Empirical aproach 8/28 Advantages All trigger mechanisms can be accounted for Based on actual empirical landslide observations Records extend beyond historical times Drawbacks mapping entire continental margins and determining the age of submarine landslides is difficult and costly

9 What matters? 9/28 Where? How deep? How large? How fast? (landslide dynamics) How often?

10 What matters? 10/28 Landslide hazard (Guzzetti et al., 2006; NHESS): H L = P[A L a L ] in a time interval t A L is the area of a landslide greater or equal than a minimum size, a L. H L = P(A L ) P(N L ) P(S) expresses landslide hazard H L as the conditional probability of P(A L ) landslide size, P(N L ) landslide occurrence in an established period P(S) landslide spatial occurrence given the local environmental setting. For H T, H L needs to be propagated for Landslide runout Tsunami generation Tsunami propagation Tsunami runup

11 The landslide database 11/ MTDs and 924 failure scars sources that mapped submarine landslide deposit(s) or failure scars.

12 Where Noticeable correlation between known failure events and available high/resolution geophysical data. The catalogue of submarine slope failures is still largely uncomplete (specially for the North-African margin). Urgeles and Camerlenghi, 2013, JGR 12/28

13 13

14 Where? 14/28 Two clusters of know major submarine landslides aseismic margins (Ebro, Rhone, Nile). The most tectonically active area (The Hellenic Arc) has a relatively low density of known events. The deformation fronts of the Mediterranean and Calabrian ridges host few submarine landslides. Earthquakes USGS/NEIC (PDE) catalog Urgeles and Camerlenghi, 2013, JGR

15 Where? 15/28 Hazard models cannot predict when a landslide will occur based on where landslides have occurred in the past (Guzzetti, 2003) Approach Landslide scar (as opposed to MTD) density mapping Landslide susceptibility (Guzzetti et al., 2006; NHESS) Morphology variables Slope gradient Depth Rugosity Lithology strength Sediment thickness pore pressure Structure Seismology Presence of relict landslide scars

16 Seismology Submarine landslide susceptibility mapping Bathymetry 16/28 Sediment type Prev. landslides Slopes Sediment thickness Where?

17 How large? 17/28 Large-scale offshore slope failure Urgeles and Camerlenghi. 2013, JGR Saidmarreh landslide, Iran, ka Usoy rock slide, Tajikistan, 1911 Frank Slide, AL, 1903 Mount St. Helens rockslide, WA, 1980

18 How-large? 18/28 Urgeles and Camerlenghi, 2013, JGR Log-normal and power-law distributions can only be fit to a subset of the data Vuong s Test suggests that one model cannot be favored over the other Truncated power law fit may reflect: observational limitations geologic control Median landslide size 0.14 km3 The few largest failure volumes dominate the failure processes

19 How large? Landslide volume distribution by region Active margins have higher exponents explains perception that acive margins have smaller but more frequent landslides 19/28 Urgeles and Camerlenghi, 2013, JGR

20 How fast? 20/28 Angle of reach 0.8º ( m, 110 km) Consitency limits LI = τ y = Pa µ = Pa.s Subtracting base of the debris flow + blank the scar area + Spline smoothing Urgeles et al. (unpubl.)

21 How fast? 21/28 Bingham rheology ρ = 1700 kg/m 3 τ y = 1000 Pa µ = 400 Pa.s φ = 20º v f = 250 m/s Thickness (m) Only buoyancy forces added reduced gravity Urgeles et al. (unpubl.)

22 How fast? 22/28 Bingham rheology ρ = 1700 kg/m 3 τ y = Pa µ = 400 Pa.s φ = 20º v f = 250 m/s Unknowns Lubricating? Drag forces? Thickness (m) Buoyancy forces and mixing Urgeles et al. (unpubl.)

23 How fast? 23/28 Bingham rheology ρ = 1700 kg/m 3 τ y = Pa µ = 400 Pa.s φ = 20º v f = 250 m/s Velocity (m/s) Buoyancy forces and mixing Urgeles et al. (unpubl.)

24 How fast? The runout contains rheological information and therefore duration and velocity history of the failed masses. Runout can readily be obtained from the database. 24/28

25 How fast? 25/28 The ratio H/R of submarine landslides of the same volume exhibit an apparent friction coefficient some 10 times lower Relationship between typology and dynamics? Locat and Lee (2000); De Blasio (2011) Locat and Lee (2000); De Blasio (2011) Urgeles et al. (2013)

26 How often? Clear bias toward younger landslides in the database Most largest failures mobilized during lowstand periods Poisson process? Can we include landslides dated with an epoch? 26/28 n radioisotopic : 66 n epoch : 462

27 How often? Individual events weighted according to overlap between period and landslide age interval LR= N(V > v)/δt N(V>v): number of landslide events of volume (V) larger than volume (v) ΔT: time interval considered from present day LR -1 : recurrence rate 27/28 ~1 failure/ 100 yr

28 CONCLUSIONS: 28/28 Incomplete catalogue!! Mid- and large size landslides satisfy power law F-V statistics The trigger mechanism is implicit in the slope of the power-law At the basin scale large landslides dominate the sediment flux from slope instability Little is known for age of the failure events 70/696 ~ accurate age determinations. 483 reported with an epoch (difficult relation trigger - environmental factors) Large amount of Holocene events Bias Tectonically active margins numerous/frequent but small failures. Passive margins with large sediment supply larger slope failures; large overpressure necessary for failure; unfrequent process Translates in different slopes of the F/M relationship Landslide catalogs may provide adequate data for asessing landslide tsunami hazard Where spatial density, susceptibility mapping Volume Frequency/magnitude relationships Thickness A/V relationships Ages and recurrence rates tied to the magnitude; less constrained Dynamics Runout The Mediterranean Sea offers an opportunity to test such approach

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