Impact of exposure spatial resolution on seismic loss estimates in regional portfolios

Dabbeek, Jamal; Crowley, Helen; Silva, Vítor; Weatherill, Graeme; Paul, Nicole; Nievas, Cecilia I.

doi:10.1007/s10518-021-01194-x

Cited by 33 publications

(28 citation statements)

References 25 publications

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“…These authors observed negligible differences in the loss estimates when alternative exposure compositions were compared within a sensitivity analysis for probabilistic risk assessment. Notably, recent studies have highlighted the importance of the statistical nature of the exposure models by forecasting its dynamic spatiotemporal evolution (Rivera et al 2020;Calderón and Silva 2021) as well as counting with efficient techniques for their spatial aggregation (Dabbeek et al 2021).…”

Section: Top-down Approach: Building Class From the Analysis Of Aggregated Datamentioning

confidence: 99%

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

Zapata

Pittore

Cotton

et al. 2022

Bull Earthquake Eng

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In seismic risk assessment, the sources of uncertainty associated with building exposure modelling have not received as much attention as other components related to hazard and vulnerability. Conventional practices such as assuming absolute portfolio compositions (i.e., proportions per building class) from expert-based assumptions over aggregated data crudely disregard the contribution of uncertainty of the exposure upon earthquake loss models. In this work, we introduce the concept that the degree of knowledge of a building stock can be described within a Bayesian probabilistic approach that integrates both expert-based prior distributions and data collection on individual buildings. We investigate the impact of the epistemic uncertainty in the portfolio composition on scenario-based earthquake loss models through an exposure-oriented logic tree arrangement based on synthetic building portfolios. For illustrative purposes, we consider the residential building stock of Valparaíso (Chile) subjected to seismic ground-shaking from one subduction earthquake. We have found that 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 the statistical modelling of exposure. To model the vulnerability of such a heterogeneous building stock, we require that their associated set of structural fragility functions handle multiple spectral periods. Thereby, we also discuss the relevance and specific uncertainty upon generating either uncorrelated or spatially cross-correlated ground motion fields within this framework. We successively show how various epistemic uncertainties embedded within these probabilistic exposure models are differently propagated throughout the computed direct financial losses. This work calls for further efforts to redesign desktop exposure studies, while also highlighting the importance of exposure data collection with standardized and iterative approaches.

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Section: Top-down Approach: Building Class From the Analysis Of Aggregated Datamentioning

confidence: 99%

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

Zapata

Pittore

Cotton

et al. 2022

Bull Earthquake Eng

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“…While the focus herein has been placed on a damage estimation due to the occurrence of a scenario earthquake, which allows for the quantification of potential losses due to one specific event and, consequently, the planning of post-earthquake emergency response, the use of a building-by-building exposure model is not limited to such a calculation and can be used for larger scale regional probabilistic risk analyses as well. Apart from the question on the availability of data for larger regions, the challenge in that case lies in the identification of a resolution with which to carry out the analyses that is appropriate for the available computational resources while providing sufficiently accurate results (e.g., Dabbeek et al 2021 ), and/or the development of strategies to spatially segment the calculations in a useful and meaningful way. Some critical issues in this regard are the size of the target region and the complexity of the hazard model to be used, particularly in terms of number of end branches of the source/ground motion model logic tree.…”

Section: Discussionmentioning

confidence: 99%

Calculating earthquake damage building by building: the case of the city of Cologne, Germany

Nievas

Pilz

Prehn

et al. 2022

Bull Earthquake Eng

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The creation of building exposure models for seismic risk assessment is frequently challenging due to the lack of availability of detailed information on building structures. Different strategies have been developed in recent years to overcome this, including the use of census data, remote sensing imagery and volunteered graphic information (VGI). This paper presents the development of a building-by-building exposure model based exclusively on openly available datasets, including both VGI and census statistics, which are defined at different levels of spatial resolution and for different moments in time. The initial model stemming purely from building-level data is enriched with statistics aggregated at the neighbourhood and city level by means of a Monte Carlo simulation that enables the generation of full realisations of damage estimates when using the exposure model in the context of an earthquake scenario calculation. Though applicable to any other region of interest where analogous datasets are available, the workflow and approach followed are explained by focusing on the case of the German city of Cologne, for which a scenario earthquake is defined and the potential damage is calculated. The resulting exposure model and damage estimates are presented, and it is shown that the latter are broadly consistent with damage data from the 1978 Albstadt earthquake, notwithstanding the differences in the scenario. Through this real-world application we demonstrate the potential of VGI and open data to be used for exposure modelling for natural risk assessment, when combined with suitable knowledge on building fragility and accounting for the inherent uncertainties.

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“…It turns out that the modelled and recorded residential building floor areas are highly compatible for many counties and prefectures. To further check the applicability of the modelled results in seismic risk assessment, an empirical seismic loss estimation is performed based on the intensity map of the 2008 Wenchuan M s 8.0 earthquake, the empirical loss function in Daniell (2014), and our modelled replacement value of residential buildings in Sichuan Province. By reducing the difference in unit construction price used in this paper and other studies, our estimated loss range is consistent with the loss derived from damage reports based on field investigation.…”

Section: Discussionmentioning

confidence: 99%

“…According to studies on assessing the resolution of exposure data required for different types of natural hazards (e.g. Chen et al, 2004;Thieken et al, 2006;Bal et al, 2010;Figueiredo and Martina, 2016;Röthlisberger et al, 2018;Dabbeek et al, 2021), the 1 km × 1 km residential building stock model developed in this paper is sufficient for seismic risk assessment. However, limitations in our model are inevitable due to the assumptions and approximations em-ployed in the modelling process.…”

Section: Limitations In the Model And Directions For Future Improvementmentioning

confidence: 99%

Residential building stock modelling for mainland China targeted for seismic risk assessment

Xin

Daniell

Tsang

et al. 2021

Nat. Hazards Earth Syst. Sci.

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Abstract. To enhance the estimation accuracy of economic loss and casualty in seismic risk assessment, a high-resolution building exposure model is necessary. Previous studies in developing global and regional building exposure models usually use coarse administrative-level (e.g. country or sub-country level) census data as model inputs, which cannot fully reflect the spatial heterogeneity of buildings in large countries like China. To develop a high-resolution residential building stock model for mainland China, this paper uses finer urbanity-level population and building-related statistics extracted from the records in the tabulation of the 2010 population census of the People's Republic of China (hereafter abbreviated as the “2010 census”). In the 2010 census records, for each province, the building-related statistics are categorized into three urbanity levels (urban, township, and rural). To disaggregate these statistics into high-resolution grid level, we need to determine the urbanity attributes of grids within each province. For this purpose, the geo-coded population density profile (with 1 km × 1 km resolution) developed in the 2015 Global Human Settlement Layer (GSHL) project is selected. Then for each province, the grids are assigned with urban, township, or rural attributes according to the population density in the 2015 GHSL profile. Next, the urbanity-level building-related statistics can be disaggregated into grids, and the 2015 GHSL population in each grid is used as the disaggregation weight. Based on the four structure types (steel and reinforced concrete, mixed, brick and wood, other) and five storey classes (1, 2–3, 4–6, 7–9, ≥10) of residential buildings classified in the 2010 census records, we reclassify the residential buildings into 17 building subtypes attached with both structure type and storey class and estimate their unit construction prices. Finally, we develop a geo-coded 1 km × 1 km resolution residential building exposure model for 31 provinces of mainland China. In each 1 km × 1 km grid, the floor areas of the 17 residential building subtypes and their replacement values are estimated. The model performance is evaluated to be satisfactory, and its practicability in seismic risk assessment is also confirmed. Limitations of the proposed model and directions for future improvement are discussed. The whole modelling process presented in this paper is fully reproducible, and all the modelled results are publicly accessible.

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Impact of exposure spatial resolution on seismic loss estimates in regional portfolios

Cited by 33 publications

References 25 publications

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

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

Calculating earthquake damage building by building: the case of the city of Cologne, Germany

Residential building stock modelling for mainland China targeted for seismic risk assessment

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