Abstract
Tsunami damage, fragility and vulnerability functions are statistical models which provide an estimate of expected damage or losses due to tsunami. They allow for quantification of risk, and so are a vital component of catastrophe models used for human and financial loss estimation, and for land-use and emergency planning. This paper collates and reviews the currently available tsunami fragility functions in order to highlight the current limitations, outline significant advances in this field, make recommendations for model derivation, and propose key areas for further research. Existing functions are first presented, and then key issues are identified in the current literature for each of the model components: building damage data (the response variable of the statistical model), tsunami intensity data (the explanatory variable), and the statistical model which links the two. Finally, recommendations are made regarding areas for future research and current best practices in deriving tsunami fragility functions (section 6). The information presented in this paper may be used to assess the quality of current estimations (both based on the quality of the data, and the quality of the models and methods adopted), and to adopt best practice when developing new fragility functions.
Highlights
Tsunami are long propagating waves generated by large scale underwater displacements, or aerial impacts, which travel at high speeds across large bodies of water
Values of the dependent variable are constrained in [0, +∞], which is sensible when considering parameters such as flow depths,. This distribution appears to be skewed to the left, it can provide a better estimate for the smaller intensities, where typically the majority of the data lie. This has become a standard assumption, not well justified in the literature [e.g., “The capacity of the structure is generally assumed to be lognormally distributed” (Valencia et al, 2011); “(. . .) we develop the fragility functions for structural damage and casualties throughout the statistical analysis under the assumption that they can be represented by normal or lognormal distribution functions (. . .)” (Koshimura et al, 2009a,b)]
For data that are Missing Not at Random (MNAR), complete-case analysis would introduce bias and missing data cannot be estimated, and so the dataset must be supplemented with additional information to address this issue before fragility analysis can be conducted
Summary
Tsunami are long propagating waves generated by large scale underwater displacements (eg. earthquake, underwater explosions), or aerial impacts (eg. landslides), which travel at high speeds across large bodies of water. Following recent large tsunamis (e.g., Indian Ocean, 2004; Chile, 2010 and Japan, 2011) significant resources have been dedicated worldwide to improve tsunami hazard models (Suppasri et al, 2016). This has resulted in significant advances being made in the identification of tsunamigenic earthquake sources and their activity (Yamazaki and Cheung, 2011; Satake et al, 2013), and in the modeling of tsunami propagation and inundation both numerically (Synolakis et al, 2008) and experimentally (Rossetto et al, 2011; Goseberg et al, 2013; Foster et al, 2017). Less effort has been dedicated to the prediction of damage to the built environment from tsunami inundation and the accurate evaluation of tsunami risk
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