Revision and improvement of the PTVA-3 model for assessing tsunami building vulnerability using “international expert judgment”: introducing the PTVA-4 model
- 255 Downloads
This work reviewed, assessed, enhanced and field-tested one of the most widely used index-based methods for assessing the vulnerability of buildings to tsunamis: the Papathoma Tsunami Vulnerability Assessment (PTVA) model. The review and assessment were undertaken through a participatory survey process engaging authors of scientific literature during 2005–2015 in the field of building vulnerability to tsunamis. Expert respondents updated the weights of the PTVA building vulnerability attributes based on their expertise and insights from the 2011 Tohoku Tsunami. The respondents were also free to suggest additional PTVA building attributes and to provide open comments on the model. We then analysed the outcomes of the questionnaire and we used them to generate a new improved version of the model, the PTVA-4, which we field-tested in the area of Botany Bay (Sydney), New South Wales. Using a cohort of over 2000 buildings and a tsunami scenario numerically simulated using state-of-the-art hydrodynamic modelling techniques, we applied the PTVA-4 model and compared the outcomes against its predecessor (i.e. the PTVA-3). Results showed the PTVA-4 model is significantly more accurate and more sensitive to variations in the tsunami demand parameter, the attributes of the exposed buildings and their surroundings. The PTVA-4 model is the first tool of its kind to integrate the judgment of specialised scientists worldwide. It constitutes a viable option to assess the vulnerability of buildings in areas where no tsunami vulnerability curves have been developed yet, or to consider the contribution to vulnerability given by a significantly wider range of building engineering and physical attributes. An ArcGIS toolbox that automatically calculates the relative vulnerability of buildings using the new PTVA-4 model is attached to this paper.
KeywordsTsunami vulnerability PTVA model Building vulnerability Fragility curves Catastrophe modelling
We thank all the experts who responded to the questionnaire and provided critical advice on how to improve the model. We thank the NSW Ministry for Police and Emergency Services and the Natural Disaster Resilience Scheme for funding the project.
FD. undertook the research work and wrote the manuscript. D.D.H. provided guidance and contributed identifying the questionnaire respondents. C.T. helped with the statistical analysis. S.S. contributed to the writing and provided advice. G.W. provided access to data and contact with local Councils. All the authors revised the manuscript.
- Aketa S, Yano K, Mizuno Y, Sato J, Terauchi K (1994) Reduction effect of port and fishing port facilities by tsunami damage. In: Proceedings of Coastal Engineering Conference, vol 41. Japan Society of Civil Engineers (JSCE), Tokyo, pp 1176–1180 (in Japanese)Google Scholar
- Albani AD, Cotis G (2007) Port Hacking: past and present, report prepared for the Council of Sutherland ShireGoogle Scholar
- Burbidge D, Mleczko R, Thomas C, Cumminis P, Nielsen O, Dhu T (2008) A probabilistic tsunami hazard assessment for Australia. Geoscience Australia Professional Opinion. No. 2008/04Google Scholar
- Dall’Osso F, Dominey-Howes D (2010a) ‘Reducing the loss’: using high-resolution vulnerability assessments to enhance tsunami risk reduction strategies. Aust J Emerg Manag 25(4):24–30Google Scholar
- Dall’Osso F, Dominey-Howes D (2010b) The emergency management implications of assessments of building vulnerability to tsunami. Aust J Emerg Manag 25:24–30Google Scholar
- IOC UNESCO (Intergovernmental Oceanographic Commission of UNESCO) (2011) Reducing and managing the risk of tsunamis, IOC Manuals and Guides, pp 57–74 (IOC/2011/MG/57Rev.2), Paris. http://unesdoc.unesco.org/images/0021/002147/214734e.pdf
- Jenks GF (1977) Optimal data classification for choropleth maps. Occasional Paper No. 2, Department of Geography, University of KansasGoogle Scholar
- Maqsood T, Senthilvasan M, Corby N, Wehner M, Edwards M (2013) Improved assessment of flood impact: an urban stormwater case study of a city of Sydney catchment. In: FMA ConferenceGoogle Scholar
- NGDC/WDS (National Geophysical Data Center/World Data Service) (2014) Global Historical Tsunami Database. National Geophysical Data Center, NOAA. doi: 10.7289/V5PN93H7
- NSW DECCW (New South Wales Department of Environment, Climate Change and Water) (2009) Derivation of the NSW Government’s sea level rise planning benchmarks. Technical Note, ISBN 978 1 74232 465 4Google Scholar
- Papathoma M (2003) Assessing tsunami vulnerability using GIS with special reference to Greece. Dissertation, Coventry UniversityGoogle Scholar
- Papathoma M, Dominey-Howes D, Zong Y, Smith D (2003) Assessing tsunami vulnerability, an example from Herakleio, Crete. Nat Hazards Earth Syst Sci 3:377–389. http://www.nat-hazards-earth-syst-sci.net/3/377/2003/
- Schultz MT, Gouldby BP, Simm JD, Wibowo JL (2010) Beyond the factor of safety: developing fragility curves to characterize system reliability (No. ERDC-SR-10-1). Engineer Research and Development Center Vicksburg MS Geotechnical and Structures LabGoogle Scholar
- Shuto N (1987). Changing of tsunami disasters. Tsunami Engineering Technical Report, 4, Tohoku University, Sendai, Japan, pp 1–41 (in Japanese)Google Scholar
- Suppasri A, Charvet I, Imai K, Imamura F (2013a) Fragility curves based on data from the 2011 Great East Japan tsunami in Ishinomaki city with discussion of parameters influencing building damage. Earthq Spectra. doi: 10.1193/053013EQS138
- Synolakis CE, Bernard EN, Titov VV, Kânoğlu U, González FI (2007) Standards, criteria, and procedures for NOAA evaluation of tsunami numerical models. NOAA Technical Memorandum OAR PMEL-135. NOAA/Pacific Marine Environmental Laboratory, SeattleGoogle Scholar
- Titov VV, Gonzalez FI (1997) Implementation and testing of the method of splitting tsunami (MOST) model. US Department of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, Pacific Marine Environmental Laboratory.Google Scholar