Tsunamis: can engineering research mitigate the risk?

Transcription

1 Tsunamis: can engineering research mitigate the risk? Giorgio Bellotti 2011 Junior Enrico Marchi Lecture

2 The Sumatra tsunami December ,594 deaths 125,000 injured 45,752 missing 1.69 million displaced

3 The Sumatra tsunami December Readiness level: zero No ability of recognising warning signs

4 The Sumatra tsunami December No tsunami warning system Sri Lanka (35,000 deaths) and the east coast of India (18,000 deaths) were hit hours later Thailand (8,000 deaths) was hit 2 hours later

5 Tsunamis in Italy 80 events (200 b.c 2000 a.c.)

6 Tsunami generation and propagation Earthquakes Subaerial and submerged landslides Tsunami wave length (offshore): km Wave period: 100 s 30 min Wave celerity (offshore) km/h Wave height (offshore): m Wave height (inshore): 1 20 m

7 The tsunami at Stromboli December 30th A landslide of Mm3: run up of about 10 m along the coast of the village. Sea level gauges Stromboli village Aerial view from West Ginostra village 5 km Sciara del Fuoco Ginostra village

8 The tsunami at Stromboli Civil Protection needed scientific cooperation to set up a warning system, inundation maps for managing future emergencies, to prepare the population: guidance on how read warning signs. Previous researches carried out for the National Dam Office (coordinators: prof. A. Noli & prof. P. De Girolamo)

9 The tsunami at Stromboli

10 The tsunami at Stromboli No formulae for simple run up prediction in such specific conditions No benchmarks for the validation of numerical models Research projects funded by Civil Protection and PRIN (coordinator: prof. P. De Girolamo)

11 The island physical model New data for Study of the generation/propagation hydraulics Numerical/analytical models validation Study the feasibility of an early warning system

12 The island physical model The conical island and the landslide model are similar to Stromboli, if scaled down 1:1000 using the Froude law 16S 24S 22S 7S 15S 11S 20S 12S Circular undisturbed shoreline 600m 0.8m 800m

13 The island physical model Experiments carried out at the Coastal Engineering Laboratory (LIC) of Politecnico di Bari, Italy, in collaboration with LIAM

14 The island physical model 80 cm Run up gauges 40 cm Landslide thickness=5 cm At the prototype scale the volume of the landslide is of about 8 Mm3 48 surface + run up gauges Surface level gauges

15 The generation process Submerged landslide depression superelevation Cross sections Subaerial landslide superelevation coast coast sea Plan views sea

16 Run up time series (subaerial landslide) 2 Ru 1 (cm) Ru 4 (cm) Ru 5 (cm) t(s) The second wave gives an inundation larger than the first wave

17 Run up time series (subaerial landslide) 2 Ru 1 (cm) Ru 6 (cm) Ru 7 (cm) t(s) First wave: small crest and large trough > receding waters Second wave: large inundation

18 Run up time series (subaerial landslide) 2 Ru 1 (cm) Ru 9 (cm) Ru 1 1 (cm) t(s) Receding waters The maximum inundation is given by the 3rd wave

19 Output of the research part 1 Tsunamis are (partially) trapped by the bathymetry Edge waves Despite shallow waters conditions frequency dispersive effects take place The first crest becomes smaller as the waves propagate around the island

20 Output of the research part 1 For subaerial landslides, only very close to the landslides the first wave gives the largest inundation In the far field the first wave has a small crest and a large trough (receding waters), then the second wave gives the largest inundation In the very far field the maximum inundation is given by the 3rd and then by the 4th waves Di Risio M., De Girolamo P., Bellotti G., Panizzo A., Aristodemo F., Molfetta M., A.F. Petrillo (2009). Landslide generated tsunamis runup at the coast of a conical island: new physical model experiments. Journal of Geophysical Research Oceans, 114, C Di Risio M., Bellotti G., Panizzo A., P. De Girolamo (2009). Three dimensional experiments on landslide generated waves at a sloping coast. Coastal Engineering, vol. 56, pp

21 Output of the research part 2 Validation/calibration of numerical models

22 Output of the research part 2 Validation/calibration of numerical models 3D model VOF High accuracy good for run up high computational costs Precomputed scenarios preparation of inundation maps

23 Output of the research part 2 Full 3d Navier Stokes solver: careful modelling of the run up 2 2 numeric experimental Run up (cm) Run up (cm) Run up (cm) t (s) Run up (cm) Montagna F., Bellotti G., Di Risio M. (2011). 3D numerical modeling of landslide generated tsunamis around a conical island. Natural Hazards, in press. 1 0 t (s)

24 Output of the research part 2 Validation/calibration of numerical models 3D model VOF High accuracy good for run up high computational costs Precomputed scenarios preparation of inundation maps Depth integrated model linear MSE with full frequency dispersion Reasonable accuracy good for the far field low computational costs Computations during emergencies Support to the warning system

25 Output of the research part 2 Depth integrated model: focus on the far field Frequency dispersion effects dominate the propagation Hyperbolic ok for narrow banded spectra seas Elliptic in the freq domain ok for broad banded spectra seas New method for considering the tsunami generation Perfect reproduction of frequency dispersion of small tsunamis in the far field Bellotti G., Cecioni C., P. De Girolamo (2008). Simulation of small amplitude frequencydispersive transient waves by means of the mild slope equation. Coastal Engineering, vol. 55 (6), pp

26 Output of the research part 2 Depth integrated model: focus on the far field experimental numeric Cecioni C., G. Bellotti (2010). Modeling tsunamis generated by submerged landslides using depth integrated equations. Applied Ocean Research, 32, pp Cecioni C., G. Bellotti (2010). Inclusion of landslide tsunamis generation into a depth integrated wave propagation model. Natural Hazards and Earth System Sciences, vol. 10, pp

27 Output of the research part 3 Numerical model to support in real time a tsunami early warning system Precomputed landslide scenarios to produce a database of results Using (partial) measurements of water level the model results are used to predict the waves at some reference gauge An inversion technique is applied (the model equations are linear and solved in the frequency domain)

28 Output of the research part 3 Inversion gauge Control gauge Inversion gauge Control gauge Blu: the real time measurements Red: the real time prediction Black: experimental data Unpublished preliminary results

29 Output of the research part 4 Evaluate the feasibility of a Tsunami early warning system for a small island Distance from the landslide (1000 times Fr scale) Arrival time (laboratory scale) Arrival time (1000 times Fr scale) Distance from the landslide (laboratory scale) Bellotti G., M. Di Risio, and P. De Girolamo (2009). Feasibility of Tsunami Early Warning Systems for small volcanic islands. Natural Hazards and Earth System Sciences, vol. 9, pp

30 Conclusions: Has this research mitigated the risk? Better understanding of the hydraulic processes: guidance to correctly recognize the warning signs of a tsunami attack Benchmark for models validation/calibration: more reliable preparation of inundation maps (parametric studies with validated models), development of new models Feasibility of warning systems: the results allow to test and optimize the system

31 Work in progress neutrino Weak interaction Hadronic shower Pressure wave e.m.shower byology, particle physics and tsunamis

32 Tsunami Early Warning Systems for large scale tsunamis Systems based on seismic measurements Tsunami measurements are essential to increase the reliability of the system

33 Location of DART stations Deep ocean Assessment and Reporting of Tsunamis but in small seas (Mediterranean Sea), waiting for measurements of the tsunami waves implies a large reduction of the time for spreading the alert

34 Can we use acoustic waves instead of surface waves? Simplified earthquake The movement of the bottom generates pressure waves and surface waves (tsunamis) 3 km 30 km 70 km

35 t = 1 s P (kpa) Surface waves (not to scale) t = 10 s p Acoustic waves t = 30 s p Tsunami front t = 50 s 30 km 70 km Acoustic wave front p Unpublished preliminary results

36 Tsunami early warning systems based on acoustic waves Acoustic waves travel much faster than tsunamis Measurements in deep water Real time transmission to coastal stations Very expensive measurement networks

37 The Catania test site infrastructure LNS-INFN Catania Installation at 2100 m depth Test Site North INGV Internet Radio Link 20 km LNS Test Site Laboratory at the port of Catania Test Site South NEMO JB

38 Acoustic neutrino detection neutrino Weak interaction Hadronic shower Pressure wave ν e e.m.shower INGV

39 Sperm whales (capodogli) detection and tracking N. Nosengo, G. Pavan and G. Riccobene, Nature 462 (2009), 560 Measurement of Inter-Pulse-Interval permits to determine the size of the sperm whale

40 New FIRB (2008) research project: Design, construction and operation of the Submarine Multidisciplinary Observatory Giorgio Riccobene, INFN (national coordinator) Giorgio Bellotti, University of Roma Tre Francesco Simeone, University of Rome Sapienza

41 Work in progress: comments Multidisciplinarity: the road to build/use expensive measurement networks and research infrastructures Cooperation with researchers of other areas: not obvious

42 Conclusions Part 1 of the presentation: Research on landslide tsunami: new experiments and numerical modelling. Part 2 of the presentation: Work in progress use acoustic waves for tsunami early warning systems. Research carried out thanks to the funding of several national institutions (Registro Italiano Dighe, Protezione Civile, PRIN, FIRB) Research carried out in Italian hydraulic laboratories (Universities of L Aquila and Bari)

43 Acknowledgement part 1 The Organizing Comittee (special thanks to prof. Andrea Rinaldo and prof. Piero Ruol) Prof. Mario Calabrese Prof. Alberto Noli Prof. Leopoldo Franco Prof. Paolo De Girolamo

44 Acknowledgement part 2 Prof. Antonio Felice Petrillo, prof. Leonardo Damiani, Matteo Molfetta (Polytechnic of Bari) Mario Nardi, Lucio Matergia (technicians of L Aquila University) Claudia Cecioni, Francesca Montagna and Alessandro Romano (PhD students at UR3) Marcello Di Risio (researcher at the University of L Aquila)

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