Abstract
Abstract. We propose the use of variable resolution boundaries based on central Voronoi tessellations (CVTs) to spatially aggregate building exposure models for risk assessment to various natural hazards. Such a framework is especially beneficial when the spatial distribution of the considered hazards presents intensity measures with contrasting footprints and spatial correlations, such as in coastal environments. This work avoids the incorrect assumption that a single intensity value from hazards with low spatial correlation (e.g. tsunami) can be considered to be representative within large-sized geo-cells for physical vulnerability assessment, without, at the same time, increasing the complexity of the overall model. We present decoupled earthquake and tsunami scenario-based risk estimates for the residential building stock of Lima (Peru). We observe that earthquake loss models for far-field subduction sources are practically insensitive to the exposure resolution. Conversely, tsunami loss models and associated uncertainties depend on the spatial correlations of the hazard intensities as well as on the resolution of the exposure models. We note that for the portfolio located in the coastal area exposed to both perils in Lima, the ground shaking dominates the losses for lower-magnitude earthquakes, whilst tsunamis cause the most damage for larger-magnitude events. For the latter, two sets of existing empirical flow depth fragility models are used, resulting in large differences in the calculated losses. This study, therefore, raises awareness about the uncertainties associated with the selection of fragility models and spatial aggregation entities for exposure modelling and loss mapping.
Highlights
The spatial distribution of damage and/or losses expected to be incurred by an extensive building portfolio from a natural hazard event can be quantified and mapped once a physical vulnerability analysis is performed
Epistemic uncertainties underlying the two steps discussed above are explored by a condition tree with these hierarchical levels: 1. selection of a suitable scheme to describe the building inventory in the study area for risk assessment; 2. weight arrangement values
We have presented a workflow to find an adequate resolution of the exposure model where it really matters, i.e. in areas where buildings are densely distributed and/or hazard intensities vary over short distances
Summary
The spatial distribution of damage and/or losses expected to be incurred by an extensive building portfolio from a natural hazard event can be quantified and mapped once a physical vulnerability analysis is performed. Despite building exposure models for flood and earthquake vulnerabilities being able to be aggregated at moderate resolutions (e.g. 4 × 4 km grid in Dabbeek and Silva, 2019), similar thematic uncertainties can evolve due to the profound differences between both spatially correlated hazard intensities and when the calculated losses are mapped over regional administrative units (Dabbeek et al, 2020). These studies discuss the weakness of physical vulnerability mapping at the individual building scale and over coarse aggregation areas and highlight the importance of finding an optimal resolution for building exposure modelling while minimising the uncertainties in the loss estimates These attempts did not explicitly address the spatial correlation or attenuation of the hazard intensity onto the predefined aggregation areas and focused on the vulnerability towards individual hazards rather than on multi-hazard risk applications. The uncertainties arising from steps 3 and 5 are explored through the formulation of a condition tree
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