Exploring the impact of epistemic uncertainty on a regional probabilistic seismic risk assessment model

Kalakonas, Petros; Silva, Vítor; Mouyiannou, Amaryllis; Rao, Anirudh

doi:10.1007/s11069-020-04201-7

Cited by 29 publications

(20 citation statements)

References 41 publications

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“…These results are in accordance with [21], who mentioned that the loss estimates become accurate and stable beyond a certain (fine) spatial resolution. They also proposed that a potential way to reduce this type of uncertainty is by improving the detail of information, concerning the location of the building inventory; however, this process can be time-and resource-demanding and, in many cases, it is simply impractical (e.g., for risk analysis at the national level).…”

Section: Harmonizing Vulnerability Indices From Different Exposure Databasessupporting

confidence: 92%

“…The practical implementation of aggregated exposure models unavoidably includes some form of spatial aggregation. Furthermore, the aggregation and relocation of buildings result in a misrepresentation of the distance between the assets and the seismic sources [21]. Thus, such building exposure models add uncertainty to the damage assessment.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity of Earthquake Damage Estimation to the Input Data (Soil Characterization Maps and Building Exposure): Case Study in the Luchon Valley, France

et al. 2021

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This article studies the effects of the soil data and exposure data of residential building inventories, as well as their spatial resolution, on seismic damage and loss estimates for a given earthquake scenario. Our aim is to investigate how beneficial it would be to acquire higher resolution inventories at the cost of additional effort and resources. Seismic damage computations are used to evaluate the relative influence of varying spatial resolution on a given damage model, where other parameters were held constant. We use soil characterization maps and building exposure inventories, provided at different scales from different sources: the European database, a national dataset at the municipality scale, and local field investigations. Soil characteristics are used to evaluate site effects and to assign amplification factors to the strong motion applied to the exposed areas. Exposure datasets are used to assign vulnerability indices to sets of buildings, from which a damage distribution is produced (based on the applied seismic intensity). The different spatial resolutions are benchmarked in a case-study area which is subject to moderate-to-average seismicity levels (Luchon valley in the Pyrénées, France). It was found that the proportion of heavily damaged buildings is underestimated when using the European soil map and the European building database, while the more refined databases (national/regional vs. local maps) result in similar estimates for moderate earthquake scenarios. Finally, we highlight the importance of pooling open access data from different sources, but caution the challenges of combining different datasets, especially depending on the type of application that is pursued (e.g., for risk mitigation or rapid response tools).

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Section: Harmonizing Vulnerability Indices From Different Exposure Databasessupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Sensitivity of Earthquake Damage Estimation to the Input Data (Soil Characterization Maps and Building Exposure): Case Study in the Luchon Valley, France

et al. 2021

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“…535 (Medina et al, 2019) would benefit future risk assessment studies for Lima. Another area that would benefit from future research is the differential selection of loss ratios with dependencies on the building classes, as for instance recently investigated by Kalakonas et al, (2020) for seismic risk applications. This might be also relevant for tsunami-induced losses that are strongly influenced by the presence and cost of non-structural building elements.…”

Section: Discussionmentioning

confidence: 99%

“…(1) To independently represent the building portfolio over a series of aggregation entities such as administrative units, or 100 equidimensional regular grids, and explore their individual contribution to the uncertainty in the losses imposed by certain hazard(s) to ultimately select a representative aggregation model. This option has been explored in Bal et al, (2010), Frolova et al, (2017, Senouci et al, (2018) and Kalakonas et al, (2020) for seismic vulnerability applications, and in Figueiredo and Martina, (2016) for flood vulnerability. These studies discuss the weakness of the physical vulnerability mapping at the individual building scale and over coarse aggregation areas, and highlight the importance of finding an optimal resolution for 105 building exposure modelling while minimizing the uncertainties in the losses estimates.…”

Section: Introductionmentioning

confidence: 99%

Variable-resolution building exposure modelling for earthquake and tsunami scenario-based risk assessment. An application case in Lima, Peru

Zapata¹,

Brinckmann²,

Harig³

et al. 2021

Preprint

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Abstract. We propose the use of variable resolution boundaries based on Central Voronoi Tessellations (CVT) 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 present intensity measures with contrasting footprints and spatial correlations such as in coastal environments. This proposal avoids the incorrect assumption that a single intensity value from hazards with low spatial correlation (e.g. tsunami) are considered as representative within large sized geocells 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 observe that for the portfolio located in the coastal area exposed to both perils in Lima, the ground-shaking dominates the losses for lower magnitudes whilst the tsunami does for the larger ones. For the latter, two sets of existing empirical flow-depth fragility models are used, finding large differences in the losses. This study arises awareness about the uncertainties in the selection of fragility models and aggregations entities for exposure modelling and loss mapping.

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“…This is partly because the associated complexity in the building classification would increase and lead to a more extensive set of classes in comparison with the available set of fragility functions Martins and Silva, 2020). Recently, further contributions about the epistemic uncertainties in regional exposure models have been explored in Kalakonas et al (2020). The authors observed negligible differences loss estimates when alternative exposure models were compared in a probabilistic risk assessment.…”

Section: Current State Of the Art In Building-exposure Modelling For mentioning

confidence: 99%

Epistemic uncertainty of probabilistic building exposure compositions in scenario-based earthquake loss models

Zapata¹,

Pittore²,

Cotton³

et al. 2021

Preprint

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In seismic risk assessment the sources of uncertainty associated to building exposure modelling have not received as much attention as other components related to hazard and vulnerability. We are introducing that the degree of knowledge of a building portfolio can be described within a Bayesian probabilistic approach to acknowledge the epistemic uncertainty of its composition (i.e. proportions per building class). We are investigating the impact of such uncertainty on earthquake loss models through a novel approach based on an exposure-oriented logic tree arrangement and scenario-based seismic risk simulations. We have found that the building class reconnaissance either from prior assumptions by desktop studies with aggregated data (top-down approach) or from building-by-building data collection (bottom-up approach), plays a fundamental role in statistical modelling of exposure. We successively show that the selection of the basic set of building classes is a major contribution to the uncertainties in the loss estimations given their dependence on specific fragility functions. If the set of fragility functions handle multiple spectral periods, cross-correlated ground motion fields are required for the vulnerability assessment which ultimately control the variability of the losses. When the exposure composition is poorly known due to, for instance, simplifications and assumptions over aggregated data, its absolute contribution to the loss uncertainties has been found to be larger than the one imposed by the spatially distributed cross-correlated ground motion fields. This work invites to redesign desktop exposure studies, while it also highlights the importance of a standardized iterative approach to exposure data collection and modelling.

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Exploring the impact of epistemic uncertainty on a regional probabilistic seismic risk assessment model

Cited by 29 publications

References 41 publications

Sensitivity of Earthquake Damage Estimation to the Input Data (Soil Characterization Maps and Building Exposure): Case Study in the Luchon Valley, France

Sensitivity of Earthquake Damage Estimation to the Input Data (Soil Characterization Maps and Building Exposure): Case Study in the Luchon Valley, France

Variable-resolution building exposure modelling for earthquake and tsunami scenario-based risk assessment. An application case in Lima, Peru

Epistemic uncertainty of probabilistic building exposure compositions in scenario-based earthquake loss models

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