Probabilistic Landslide-Generated Tsunamis in the Indus Canyon, NW Indian Ocean, Using Statistical Emulation

Dimitra M. Salmanidou; Mohammad Heidarzadeh; Serge Guillas

doi:10.1007/s00024-019-02187-3

Probabilistic Landslide-Generated Tsunamis in the Indus Canyon, NW Indian Ocean, Using Statistical Emulation

Dimitra M. Salmanidou, Mohammad Heidarzadeh, Serge Guillas

Department of Architecture & Civil Engineering

Research output: Contribution to journal › Article › peer-review

22 Citations (SciVal)

Abstract

The Indus Canyon in the northwestern Indian Ocean has been reported to be the site of numerous submarine mass failures in the past. This study is the first to investigate potential tsunami hazards associated with such mass failures in this region. We employed statistical emulation, i.e. surrogate modelling, to efficiently quantify uncertainties associated with slump-generated tsunamis at the slopes of the canyon. We simulated 60 slump scenarios with thickness of 100–300 m, width of 6–10.5 km, travel distances of 500–2000 m and submergence depth of 250–450 m. These scenarios were then used to train the emulator and predict 500,000 trial scenarios in order to study probabilistically the tsunami hazard over the near field. Due to narrow–deep canyon walls and the shallow continental shelf in the adjacent regions (< 100 m water depth), the tsunami propagation has a unique pattern as an ellipse stretched in the NE–SW direction. The results show that the most likely tsunami amplitudes and velocities are approximately 0.2–1.0 m and 2.5–13 m/s, respectively, which can potentially impact vessels and maritime facilities. We demonstrate that the emulator-based approach is an important tool for probabilistic hazard analysis since it can generate thousands of tsunami scenarios in few seconds, compared to days of computations on High Performance Computing facilities for a single run of the dispersive tsunami solver that we use here.

Original language	English
Pages (from-to)	3099-3114
Number of pages	16
Journal	Pure and Applied Geophysics
Volume	176
Issue number	7
DOIs	https://doi.org/10.1007/s00024-019-02187-3
Publication status	Published - 1 Jul 2019

Bibliographical note

Funding Information:
This research was supported by the EPSRC (EP/P016774/1) network M2D (Models-to-Decisions): Decision making under uncertainty, and the EPSRC Impact Acceleration Account Grant (EP/R51163811). SG and MH acknowledge support from the NERC project (NE/P016367/1) “Tsunami risk for the Western Indian Ocean: steps toward the integration of science into policy and practice” under the Global Challenges Research Fund: Building Resilience programme. SG also acknowledges support from the Alan Turing Institute project “Uncertainty Quantification of multi-scale and multiphysics computer models: applications to hazard and climate models”. This work has been performed using resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service (http://www.hpc.cam.ac.uk) funded by EPSRC Tier-2 capital grant EP/P020259/1. Some of the figures are drafted using the open-source code GMT, (Wessel and Smith 1998). The bathymetry and topography data are derived from the Global Multi-Resolution Topography (GMRT) dataset (Ryan et al. 2009). The authors would like to thank Dr Devaraj Gopinathan for the fruitful discussions and Dr Toshitaka Baba for his advice and help with the numerical simulations.

Funding Information:
This research was supported by the EPSRC (EP/P016774/1) network M2D (Models-to-Decisions): Decision making under uncertainty, and the EPSRC Impact Acceleration Account Grant (EP/R51163811). SG and MH acknowledge support from the NERC project (NE/P016367/1) “Tsunami risk for the Western Indian Ocean: steps toward the integration of science into policy and practice” under the Global Challenges Research Fund: Building Resilience programme. SG also acknowledges support from the Alan Turing Institute project “Uncertainty Quantification of multi-scale and multiphysics computer models: applications to hazard and climate models”. This work has been performed using resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service (http://www.hpc.cam.ac.uk) funded by EPSRC Tier-2 capital grant EP/P020259/1. Some of the figures are drafted using the open-source code GMT, (Wessel and Smith ). The bathymetry and topography data are derived from the Global Multi-Resolution Topography (GMRT) dataset (Ryan et al. ). The authors would like to thank Dr Devaraj Gopinathan for the fruitful discussions and Dr Toshitaka Baba for his advice and help with the numerical simulations.

Publisher Copyright:
© 2019, The Author(s).

Keywords

Indian Ocean
Indus Canyon
landslide-generated tsunami
statistical emulation
uncertainty quantification

ASJC Scopus subject areas

Geophysics
Geochemistry and Petrology

Access to Document

10.1007/s00024-019-02187-3Licence: CC BY

Cite this

@article{b18c7813943a484dabd5fbd76ec79297,

title = "Probabilistic Landslide-Generated Tsunamis in the Indus Canyon, NW Indian Ocean, Using Statistical Emulation",

abstract = "The Indus Canyon in the northwestern Indian Ocean has been reported to be the site of numerous submarine mass failures in the past. This study is the first to investigate potential tsunami hazards associated with such mass failures in this region. We employed statistical emulation, i.e. surrogate modelling, to efficiently quantify uncertainties associated with slump-generated tsunamis at the slopes of the canyon. We simulated 60 slump scenarios with thickness of 100–300 m, width of 6–10.5 km, travel distances of 500–2000 m and submergence depth of 250–450 m. These scenarios were then used to train the emulator and predict 500,000 trial scenarios in order to study probabilistically the tsunami hazard over the near field. Due to narrow–deep canyon walls and the shallow continental shelf in the adjacent regions (< 100 m water depth), the tsunami propagation has a unique pattern as an ellipse stretched in the NE–SW direction. The results show that the most likely tsunami amplitudes and velocities are approximately 0.2–1.0 m and 2.5–13 m/s, respectively, which can potentially impact vessels and maritime facilities. We demonstrate that the emulator-based approach is an important tool for probabilistic hazard analysis since it can generate thousands of tsunami scenarios in few seconds, compared to days of computations on High Performance Computing facilities for a single run of the dispersive tsunami solver that we use here.",

keywords = "Indian Ocean, Indus Canyon, landslide-generated tsunami, statistical emulation, uncertainty quantification",

author = "Salmanidou, {Dimitra M.} and Mohammad Heidarzadeh and Serge Guillas",

note = "Funding Information: This research was supported by the EPSRC (EP/P016774/1) network M2D (Models-to-Decisions): Decision making under uncertainty, and the EPSRC Impact Acceleration Account Grant (EP/R51163811). SG and MH acknowledge support from the NERC project (NE/P016367/1) “Tsunami risk for the Western Indian Ocean: steps toward the integration of science into policy and practice” under the Global Challenges Research Fund: Building Resilience programme. SG also acknowledges support from the Alan Turing Institute project “Uncertainty Quantification of multi-scale and multiphysics computer models: applications to hazard and climate models”. This work has been performed using resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service (http://www.hpc.cam.ac.uk) funded by EPSRC Tier-2 capital grant EP/P020259/1. Some of the figures are drafted using the open-source code GMT, (Wessel and Smith 1998). The bathymetry and topography data are derived from the Global Multi-Resolution Topography (GMRT) dataset (Ryan et al. 2009). The authors would like to thank Dr Devaraj Gopinathan for the fruitful discussions and Dr Toshitaka Baba for his advice and help with the numerical simulations. Funding Information: This research was supported by the EPSRC (EP/P016774/1) network M2D (Models-to-Decisions): Decision making under uncertainty, and the EPSRC Impact Acceleration Account Grant (EP/R51163811). SG and MH acknowledge support from the NERC project (NE/P016367/1) “Tsunami risk for the Western Indian Ocean: steps toward the integration of science into policy and practice” under the Global Challenges Research Fund: Building Resilience programme. SG also acknowledges support from the Alan Turing Institute project “Uncertainty Quantification of multi-scale and multiphysics computer models: applications to hazard and climate models”. This work has been performed using resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service (http://www.hpc.cam.ac.uk) funded by EPSRC Tier-2 capital grant EP/P020259/1. Some of the figures are drafted using the open-source code GMT, (Wessel and Smith ). The bathymetry and topography data are derived from the Global Multi-Resolution Topography (GMRT) dataset (Ryan et al. ). The authors would like to thank Dr Devaraj Gopinathan for the fruitful discussions and Dr Toshitaka Baba for his advice and help with the numerical simulations. Publisher Copyright: {\textcopyright} 2019, The Author(s).",

year = "2019",

month = jul,

day = "1",

doi = "10.1007/s00024-019-02187-3",

language = "English",

volume = "176",

pages = "3099--3114",

journal = "Pure and Applied Geophysics",

issn = "0033-4553",

publisher = "Birkhauser Verlag Basel",

number = "7",

}

TY - JOUR

T1 - Probabilistic Landslide-Generated Tsunamis in the Indus Canyon, NW Indian Ocean, Using Statistical Emulation

AU - Salmanidou, Dimitra M.

AU - Heidarzadeh, Mohammad

AU - Guillas, Serge

N1 - Funding Information: This research was supported by the EPSRC (EP/P016774/1) network M2D (Models-to-Decisions): Decision making under uncertainty, and the EPSRC Impact Acceleration Account Grant (EP/R51163811). SG and MH acknowledge support from the NERC project (NE/P016367/1) “Tsunami risk for the Western Indian Ocean: steps toward the integration of science into policy and practice” under the Global Challenges Research Fund: Building Resilience programme. SG also acknowledges support from the Alan Turing Institute project “Uncertainty Quantification of multi-scale and multiphysics computer models: applications to hazard and climate models”. This work has been performed using resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service (http://www.hpc.cam.ac.uk) funded by EPSRC Tier-2 capital grant EP/P020259/1. Some of the figures are drafted using the open-source code GMT, (Wessel and Smith 1998). The bathymetry and topography data are derived from the Global Multi-Resolution Topography (GMRT) dataset (Ryan et al. 2009). The authors would like to thank Dr Devaraj Gopinathan for the fruitful discussions and Dr Toshitaka Baba for his advice and help with the numerical simulations. Funding Information: This research was supported by the EPSRC (EP/P016774/1) network M2D (Models-to-Decisions): Decision making under uncertainty, and the EPSRC Impact Acceleration Account Grant (EP/R51163811). SG and MH acknowledge support from the NERC project (NE/P016367/1) “Tsunami risk for the Western Indian Ocean: steps toward the integration of science into policy and practice” under the Global Challenges Research Fund: Building Resilience programme. SG also acknowledges support from the Alan Turing Institute project “Uncertainty Quantification of multi-scale and multiphysics computer models: applications to hazard and climate models”. This work has been performed using resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service (http://www.hpc.cam.ac.uk) funded by EPSRC Tier-2 capital grant EP/P020259/1. Some of the figures are drafted using the open-source code GMT, (Wessel and Smith ). The bathymetry and topography data are derived from the Global Multi-Resolution Topography (GMRT) dataset (Ryan et al. ). The authors would like to thank Dr Devaraj Gopinathan for the fruitful discussions and Dr Toshitaka Baba for his advice and help with the numerical simulations. Publisher Copyright: © 2019, The Author(s).

PY - 2019/7/1

Y1 - 2019/7/1

N2 - The Indus Canyon in the northwestern Indian Ocean has been reported to be the site of numerous submarine mass failures in the past. This study is the first to investigate potential tsunami hazards associated with such mass failures in this region. We employed statistical emulation, i.e. surrogate modelling, to efficiently quantify uncertainties associated with slump-generated tsunamis at the slopes of the canyon. We simulated 60 slump scenarios with thickness of 100–300 m, width of 6–10.5 km, travel distances of 500–2000 m and submergence depth of 250–450 m. These scenarios were then used to train the emulator and predict 500,000 trial scenarios in order to study probabilistically the tsunami hazard over the near field. Due to narrow–deep canyon walls and the shallow continental shelf in the adjacent regions (< 100 m water depth), the tsunami propagation has a unique pattern as an ellipse stretched in the NE–SW direction. The results show that the most likely tsunami amplitudes and velocities are approximately 0.2–1.0 m and 2.5–13 m/s, respectively, which can potentially impact vessels and maritime facilities. We demonstrate that the emulator-based approach is an important tool for probabilistic hazard analysis since it can generate thousands of tsunami scenarios in few seconds, compared to days of computations on High Performance Computing facilities for a single run of the dispersive tsunami solver that we use here.

AB - The Indus Canyon in the northwestern Indian Ocean has been reported to be the site of numerous submarine mass failures in the past. This study is the first to investigate potential tsunami hazards associated with such mass failures in this region. We employed statistical emulation, i.e. surrogate modelling, to efficiently quantify uncertainties associated with slump-generated tsunamis at the slopes of the canyon. We simulated 60 slump scenarios with thickness of 100–300 m, width of 6–10.5 km, travel distances of 500–2000 m and submergence depth of 250–450 m. These scenarios were then used to train the emulator and predict 500,000 trial scenarios in order to study probabilistically the tsunami hazard over the near field. Due to narrow–deep canyon walls and the shallow continental shelf in the adjacent regions (< 100 m water depth), the tsunami propagation has a unique pattern as an ellipse stretched in the NE–SW direction. The results show that the most likely tsunami amplitudes and velocities are approximately 0.2–1.0 m and 2.5–13 m/s, respectively, which can potentially impact vessels and maritime facilities. We demonstrate that the emulator-based approach is an important tool for probabilistic hazard analysis since it can generate thousands of tsunami scenarios in few seconds, compared to days of computations on High Performance Computing facilities for a single run of the dispersive tsunami solver that we use here.

KW - Indian Ocean

KW - Indus Canyon

KW - landslide-generated tsunami

KW - statistical emulation

KW - uncertainty quantification

UR - http://www.scopus.com/inward/record.url?scp=85064690755&partnerID=8YFLogxK

U2 - 10.1007/s00024-019-02187-3

DO - 10.1007/s00024-019-02187-3

M3 - Article

AN - SCOPUS:85064690755

SN - 0033-4553

VL - 176

SP - 3099

EP - 3114

JO - Pure and Applied Geophysics

JF - Pure and Applied Geophysics

IS - 7

ER -

Probabilistic Landslide-Generated Tsunamis in the Indus Canyon, NW Indian Ocean, Using Statistical Emulation

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this