Athanasios N. Papadopoulos

Understanding seismic risk at both the national and sub-national level is essential for devising effective strategies and interventions aimed at its mitigation. The Earthquake Risk Model of Switzerland (ERM-CH23), released in early 2023, is the culmination of a multidisciplinary effort aiming to achieve for the first time a comprehensive assessment of the potential consequences of earthquakes on the Swiss building stock and population. Having been developed as a national model, ERM-CH23 relies on very high-resolution site-amplification and building exposure datasets, which distinguishes it from most regional models to date. Several loss types are evaluated, ranging from structural–nonstructural and content economic losses to human losses, such as deaths, injuries, and displaced population. In this paper, we offer a snapshot of ERM-CH23, summarize key details on the development of its components, highlight important results, and provide comparisons with other models.

Exposure models for regional seismic risk assessment often place assets at the centroids of administrative units for which data are available. At best, a top-down approach is followed, where such data are spatially disaggregated over a denser spatial grid, using proxy datasets such as the distribution of population or the density of night-time lights. The resolution of the spatial grid is either dictated by the resolution of the proxy dataset, or by constraints in computational resources. On the other hand, if a building-by-building database is available, it often needs to be aggregated and brought to a resolution that ensures acceptable calculation runtimes and memory demands. Several studies have now investigated the impact of exposure aggregation on loss estimates. Herein, unlike previous attempts, we can leverage upon an extensive building-by-building database for the Swiss territory, which we can use as ground truth. We firstly proceed to assess the aggregation-induced errors of standard risk metrics at different spatial scales. Then a new strategy for performing said aggregation is proposed, relying on a K-means clustering of site parameters and a reduction of the loss ratio uncertainty for aggregated assets. These interventions are designed with the objective of minimizing errors, while keeping the computational cost manageable.

Exposure models for regional seismic risk assessment often place assets at the centroids of administrative units for which data are available. At best, a top-down approach is followed, where such data are spatially disaggregated over a denser spatial grid, using proxy datasets such as the distribution of population or the density of night-time lights. The resolution of the spatial grid is either dictated by the resolution of the proxy dataset, or by constraints in computational resources. On the other hand, if a building-by-building database is available, it often needs to be aggregated and brought to a resolution that ensures acceptable calculation runtimes and memory demands. Several studies have now investigated the impact of exposure aggregation on loss estimates. Herein, unlike previous attempts, we can leverage upon an extensive building-by-building database for the Swiss territory, which we can use as ground truth. We firstly proceed to assess the aggregation-induced errors of standard risk metrics at different spatial scales. Then a new strategy for performing said aggregation is proposed, relying on a K-means clustering of site parameters and a reduction of the loss ratio uncertainty for aggregated assets. These interventions are designed with the objective of minimizing errors, while keeping the computational cost manageable.

Understanding seismic risk at both the national and sub-national levels is essential for devising effective strategies and interventions aimed at its mitigation. The National Earthquake Risk Model of Switzerland (ERM-CH23), released in early 2023, is the culmination of a multidisciplinary effort aiming to achieve, for the first time, a comprehensive assessment of the potential consequences of earthquakes on the Swiss building stock and population. Having been developed as a national model, ERM-CH23 relies on very high-resolution site-amplification and building exposure datasets, which distinguishes it from most regional models to-date. Several loss types are evaluated, ranging from structural/nonstructural and contents economic losses, to human losses, such as deaths, injuries and displaced population. In this paper, we offer a snapshot of ERM-CH23, summarize key details on the development of its components, highlight important results and provide comparisons with other models.

Understanding seismic risk at both the national and sub-national levels is essential for devising effective strategies and interventions aimed at its mitigation. The National Earthquake Risk Model of Switzerland (ERM-CH23), released in early 2023, is the culmination of a multidisciplinary effort aiming to achieve, for the first time, a comprehensive assessment of the potential consequences of earthquakes on the Swiss building stock and population. Having been developed as a national model, ERM-CH23 relies on very high-resolution site-amplification and building exposure datasets, which distinguishes it from most regional models to-date. Several loss types are evaluated, ranging from structural/nonstructural and contents economic losses, to human losses, such as deaths, injuries and displaced population. In this paper, we offer a snapshot of ERM-CH23, summarize key details on the development of its components, highlight important results and provide comparisons with other models.

Understanding seismic risk at both the national and sub-national levels is essential for devising effective strategies and interventions aimed at its mitigation. The National Earthquake Risk Model of Switzerland (ERM-CH23), released in early 2023, is the culmination of a multidisciplinary effort aiming to achieve, for the first time, a comprehensive assessment of the potential consequences of earthquakes on the Swiss building stock and population. Having been developed as a national model, ERM-CH23 relies on very high-resolution site-amplification and building exposure datasets, which distinguishes it from most regional models to-date. Several loss types are evaluated, ranging from structural/nonstructural and contents economic losses, to human losses, such as deaths, injuries and displaced population. In this paper, we offer a snapshot of ERM-CH23, summarize key details on the development of its components, highlight important results and provide comparisons with other models.

Understanding seismic risk at both the national and sub-national levels is essential for devising effective strategies and interventions aimed at its mitigation. The National Earthquake Risk Model of Switzerland (ERM-CH23), released in early 2023, is the culmination of a multidisciplinary effort aiming to achieve, for the first time, a comprehensive assessment of the potential consequences of earthquakes on the Swiss building stock and population. Having been developed as a national model, ERM-CH23 relies on very high-resolution site-amplification and building exposure datasets, which distinguishes it from most regional models to-date. Several loss types are evaluated, ranging from structural/nonstructural and contents economic losses, to human losses, such as deaths, injuries and displaced population. In this paper, we offer a snapshot of ERM-CH23, summarize key details on the development of its components, highlight important results and provide comparisons with other models.

Understanding seismic risk at both the national and sub-national levels is essential for devising effective strategies and interventions aimed at its mitigation. The National Earthquake Risk Model of Switzerland (ERM-CH23), released in early 2023, is the culmination of a multidisciplinary effort aiming to achieve, for the first time, a comprehensive assessment of the potential consequences of earthquakes on the Swiss building stock and population. Having been developed as a national model, ERM-CH23 relies on very high-resolution site-amplification and building exposure datasets, which distinguishes it from most regional models to-date. Several loss types are evaluated, ranging from structural/nonstructural and contents economic losses, to human losses, such as deaths, injuries and displaced population. In this paper, we offer a snapshot of ERM-CH23, summarize key details on the development of its components, highlight important results and provide comparisons with other models.

Earthquake early warning systems (EEWSs) aim to rapidly detect earthquakes and provide timely alerts, so that users can take protective actions prior to the onset of strong ground shaking. The promise and limitations of EEWSs have both been widely debated. On one hand, an operational EEWS could mitigate earthquake damage by triggering potentially cost‐ and life‐saving actions. These range from automated system responses such as slowing down trains to the actions of individuals that receive the alerts and take protective measures. On the other hand, the effectiveness of an EEWS is conditional on the ability to issue warnings that are sufficiently accurate and timely to facilitate an appropriate action. The refinement of earthquake early warning (EEW) algorithms and the installation of denser and faster seismic networks have improved performance; however, the benefit in risk reduction that an EEWS could achieve remains unquantified. In this study, we leverage upon regional event‐based probabilistic seismic risk assessment to devise a quantitative and fully customizable framework for evaluating the effectiveness of EEW in mitigating seismic risk. We demonstrate this framework using Switzerland as a testbed, for which we compute and contrast human loss exceedance curves with and without EEW.

Traditionally, probabilistic seismic hazard analysis (PSHA) considers only mainshock events and models their temporal occurrence through a homogeneous Poisson process. Thus, it disregards foreshocks and aftershocks, assuming they have a minor effect on PSHA. However, recent earthquake sequences, such as those in 2016–2017 in Central Italy and 2010–2011 in Christchurch, New Zealand, exposed the shortcomings of such a universally used but unconservative approach. Our efforts to quantify the bias in seismic hazard and risk estimates follow from these considerations. Herein, we investigate the epidemic-type aftershock sequence (ETAS) model’s ability to reproduce the statistical features of long-term historical seismicity in Italy in two different regions.In addition, we calculate and compare the seismic hazard at two sites in Central Italy using different approaches: (1) with seismicity clustering modeled using the ETAS model; (2) with only mainshocks modeled by means of the Poissonian approach; and (3) with seismicity clustering modeled via a combination of Poisson and modified Omori law. We consider two cases: (1) the “unconditional case,” which uses years of varying seismicity as initial conditions and, therefore, can be considered as a tool for predicting the long-term average hazard, and (2) “conditional case,” in which the hazard is estimated after a specific period, in our case higher than average seismicity. We scrutinize the different modeling assumptions during the process and investigate the effect of using different declustering methods in Poisson-based models. As expected, we find that using the mainshock-only seismicity models yields lower hazard estimates compared to those obtained with the Omori and ETAS model. In addition, we show that Omori and ETAS model predict similar results in the unconditional case, but the Omori model considerably underpredicts the hazard in the conditional case, for a site close to the sequences, when temporal variations in seismic hazard are accounted for.