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Evaluation of tsunami inundation using synthetic aperture radar (SAR) data and numerical modeling

Author

Listed:
  • M. Iyyappan

    (Integrated Coastal and Marine Area Management (ICMAM) -Project Directorate, Ministry of Earth Sciences, Government of India
    Anna University)

  • Tune Usha

    (Integrated Coastal and Marine Area Management (ICMAM) -Project Directorate, Ministry of Earth Sciences, Government of India)

  • S. S. Ramakrishnan

    (Anna University)

  • K. Srinivasa Raju

    (Anna University)

  • G. Gopinath

    (Integrated Coastal and Marine Area Management (ICMAM) -Project Directorate, Ministry of Earth Sciences, Government of India
    Anna University)

  • S. Chenthamil Selvan

    (Integrated Coastal and Marine Area Management (ICMAM) -Project Directorate, Ministry of Earth Sciences, Government of India)

  • S. K. Dash

    (Integrated Coastal and Marine Area Management (ICMAM) -Project Directorate, Ministry of Earth Sciences, Government of India)

  • P. Mishra

    (Integrated Coastal and Marine Area Management (ICMAM) -Project Directorate, Ministry of Earth Sciences, Government of India)

Abstract

Little Andaman, the fourth largest island in the Andaman group of islands of India, was severely affected by the December 26, 2004, Indian Ocean tsunami generated by massive earthquake of moment magnitude 9.3 Mw which devastated the Andaman and Nicobar group of islands causing heavy damage to life and property. Due to hostile terrain conditions not much information was available on the extent of inundation and run-up along the island except for Hut Bay region. In order to study the vulnerability of the island to tsunami hazard, the inundation in the island due to the 2004 tsunami was studied using TUNAMI N2 numerical model and ENVISAT ASAR datasets. The extent of inundation derived from the SAR imagery was compared using the RTK-GPS field survey points collected in the Hut Bay regions immediately after the 2004 tsunami. The extent of inundation obtained from SAR images for the entire island was compared with inundation obtained from model. It was observed that the inundation obtained from the model matched well with inundation extent from SAR imagery for nearshore regions, while for low-lying areas and creeks large deviations were observed. In the absence of field datasets, the inundation derived from SAR imagery would be effective in providing ground data to validate the numerical models which can then be run for multiple scenarios for disaster mitigation and planning operation in areas that have hostile terrain conditions.

Suggested Citation

  • M. Iyyappan & Tune Usha & S. S. Ramakrishnan & K. Srinivasa Raju & G. Gopinath & S. Chenthamil Selvan & S. K. Dash & P. Mishra, 2018. "Evaluation of tsunami inundation using synthetic aperture radar (SAR) data and numerical modeling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(3), pages 1419-1432, July.
  • Handle: RePEc:spr:nathaz:v:92:y:2018:i:3:d:10.1007_s11069-018-3257-4
    DOI: 10.1007/s11069-018-3257-4
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    References listed on IDEAS

    as
    1. Tune Usha & M. Murthy & N. Reddy & Pravakar Mishra, 2012. "Tsunami vulnerability assessment in urban areas using numerical model and GIS," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 60(1), pages 135-147, January.
    2. G. Gopinath & F. Løvholt & G. Kaiser & C. Harbitz & K. Srinivasa Raju & M. Ramalingam & Bhoop Singh, 2014. "Impact of the 2004 Indian Ocean tsunami along the Tamil Nadu coastline: field survey review and numerical simulations," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 72(2), pages 743-769, June.
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    More about this item

    Keywords

    Little Andaman; India; SAR; Tsunami; TUNAMI N2;
    All these keywords.

    JEL classification:

    • N2 - Economic History - - Financial Markets and Institutions

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