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

Abstract

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.

Validation of physics-based ground shaking scenarios for empirical fragility studies: the case of the 2009 L’Aquila earthquake

“…The availability of building-by-building data allows for a punctual definition of both seismic input and damage, avoiding the uncertainty driven by aggregated models (e.g. Nievas et al 2022). The PBS scenario in terms of PGA is combined with the fragility model resulting from the hybrid strategy (PBS + ShakeMap).…”

Towards a Sensitivity Analysis in Seismic Risk with Probabilistic Building Exposure Models: An Application in Valparaíso, Chile Using Ancillary Open-Source Data and Parametric Ground Motions

“…Although this type of volunteer-acquired data lacks standardised formats and completeness [40,41], the data offer valuable information on the spatial location of certain attributes without necessarily performing time-consuming in situ data collection. They have proved useful for acquiring socio-economic indicators [42]), assessing the physical vulnerability of local buildings to earthquakes (e.g., [43][44][45]) and floods (e.g., [46,47]), and in global exposure initiatives [48,49].…”

Benefits of global earth observation missions for disaggregation of exposure data and earthquake loss modeling: evidence from Santiago de Chile

“…However, such time-demanding and cost-intensive approaches are decreasingly able to cope with the high spatiotemporal dynamics of built environments. Simultaneously, the information provided on a coarser spatial level, such as spatially aggregated census data (e.g., Corbane et al 2017;Santa Maria et al 2017;Yepes-Estrada et al 2017), hampers the consideration of small-scale hazard effects in a downstream risk model (Gomez Zapata et al 2021;Nievas et al 2022) and frequently shows a high level of uncertainty (Pittore et al 2017). Moreover, recent empirical works underlined that the accuracy of damage estimates is very sensitive regarding the exposure component (Gomez-Zapata et al 2022a, b), whereby the coarsest aggregation levels were found to be the most inaccurate (Senouci 2018;Dabbeek et al 2021).…”