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Papers, articles and reports are released as part of GEM's advancing science & knowledge-sharing initiatives. Selected reports and other materials produced by the international consortia on global projects, working groups and regional collaborations can also be found below.
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Development of a global seismic risk model
The Development of a Global Seismic Risk Model was a mammoth undertaking that involved hundreds of people and for the first time presented a detailed view of seismic risk at the global scale. For some developing countries, this was the first time that a seismic risk map was produced, and the associated country profiles are being used by the local authorities.
GEM Strategic Plan and Roadmap to 2030
GEM was founded in 2009 with the purpose of improving the global knowledge of earthquake risk and contributing to the reduction of risk worldwide. In 13 years, GEM has become widely known for its global effort to improve the state of practice of earthquake hazard and risk assessment and for its contribution to improving the state of knowledge of earthquake risk.
New Statistical Perspectives on Bath's Law and Aftershock Productivity
The well-established Bath’s law states that the average magnitude difference between a mainshock and its strongest aftershock is roughly 1.2, independently of the size of the mainshock. The main challenge in calculating this value is the bias introduced by missing data points when the strongest aftershock is below the observed cut off magnitude. Ignoring missing values leads to a systematic error, because the data points removed are those with particularly large magnitude differences ∆M. The error is minimized, if we restrict the statistics to mainshocks at least two magnitude units above the cut-off, but then the sample size is strongly reduced. This work provides an innovative approach for modelling ∆M by adapting methods for time-to-event data, which often suffers from incomplete observation (censoring). In doing so, we adequately account for unobserved values and estimate a fully parametric distribution of the magnitude differences ∆M for M ą 6 mainshocks. Results show that magnitude differences are best modeled by the Gompertz distribution, and that larger ∆M are expected at increasing depths and higher heat flows. A simulation experiment suggests that ∆M is mainly driven by the number and the magnitude distribution of aftershocks. Therefore, in a second study, we modelled the variation of aftershock productivity in a stochastically declustered local catalog for New Zealand, using a generalized additive model approach. Results confirm that aftershock counts can be better modelled by a Negative Binomial than a Poisson distribution. Interestingly, there is indication that triggered earthquakes trigger themselves two to three times more aftershocks than comparable
A hybrid ML-physical modelling approach for efficient approximation of tsunami waves at the coast for probabilistic tsunami hazard assessment
This work investigates a novel approach combining numerical modelling and machine learning, aimed at developing an efficient procedure that can be used for large scale tsunami hazard and risk studies. Probabilistic tsunami hazard and risk assessment are vital tools to understand the risk of tsunami and mitigate its impact, guiding the risk reduction and transfer activities. Such large-scale probabilistic tsunami hazard and risk assessment require many numerically intensive simulations of the possible tsunami events, involving the tsunami phases of generation, wave propagation and inundation on the coast, which are not always feasible without large computational resources like HPCs. In order to undertake such regional PTHA for a larger proportion of the coast, we need to develop concepts and algorithms for reducing the number of events simulated and more rapidly approximate the simulation results needed. This case study for a coastal region of Japan utilizes a limited number of tsunami simulations from submarine earthquakes along the subduction interface to generate a wave propagation database at different depths, and fits these simulation results to a machine learning model to predict the water depth or velocity of the tsunami wave at the coast. Such a hybrid ML-physical model can be further coupled with an inundation scheme to compute the probabilistic tsunami hazard and risk for the onshore region.
Exploring benefit cost analysis to support earthquake risk mitigation in Central America
We performed benefit-cost analysis to identify optimum retrofitting interventions for the two most vulnerable building typologies in Central America, unreinforced masonry and adobe, considering the direct costs due to building damage and the indirect costs associated with the injured and fatalities. We reviewed worldwide retrofitting techniques, selected those that could be applied in the region for these building types, and derived vulnerability functions considering the impact of each retrofitting intervention in the strength, stiffness, and ductility of the structures. Probabilistic seismic risk analyses were performed considering the original configuration of each building class, as well as the retrofitted version. We calculated average annual losses to estimate the annual savings due to the different structural interventions, and benefit cost ratios were estimated based on the associated cost of each retrofitting technique. Based on the benefit-cost analyses, for a 50-year time horizon and a 4% discount rate, retrofitting these building classes could be economically viable along the western coast of Central America.
The adolescent years of seismic risk assessment
Vitor Silva reflects on the current position of seismic risk assessment compared to its hazard counterpart, and posits that this discipline is expected to become common practice in disaster risk management, providing decision makers with valuable information not just about the current threat, but also how the impact of future disasters is expected to evolve. The growth of seismic risk assessment into its adult years will allow a more efficient design and implementation of risk mitigation measures. ultimately contributing to its main and only goal: the reduction of the human and economic losses caused by earthquakes.
Exposure forecasting for seismic risk estimation: Application to Costa Rica
This study proposes a framework to forecast the spatial distribution of population and residential buildings for the assessment of future disaster risk. The approach accounts for the number, location, and characteristics of future assets considering sources of aleatory and epistemic uncertainty in several time-dependent variables. The value of the methodology is demonstrated at the urban scale using an earthquake scenario for the Great Metropolitan Area of Costa Rica. Hundreds of trajectories representing future urban growth were generated using geographically weighted regression and multiple-agent systems. These were converted into exposure models featuring the spatial correlation of urban expansion and the densification of the built environment. The forecasted earthquake losses indicate a mean increase in the absolute human and economic losses by 2030. However, the trajectory of relative risk is reducing, suggesting that the long-term enforcement of seismic regulations and urban planning are effectively lowering seismic risk in the case of Costa Rica.
Investment in Disaster Risk Management in Europe Makes Economic Sense
The physical, financial, and social impacts of disasters in Europe are growing and will continue to grow unless urgent actions are taken. In the European Union (EU), during the period from 1980 to 2020, natural disasters affected nearly 50 million people and caused on average an economic loss of roughly €12 billion per year (EEA, 2020). The impacts of flood, wildfire, and extreme heat are increasing rapidly, and climate damages could reach €170 billion per year according to conservative estimates for a 3 scenario unless urgent action is taken now (Szewczyk, et al., 2020). Earthquakes, while rare, have a devastating impact on the ageing buildings and infrastructure of Europe that were constructed prior to modern codes; in Bucharest, for example, nearly 90% of the population lives in multifamily buildings with pre-modern building codes3 (Simpson & Markhvida, 2020). Within the EU, the top-five countries with the highest annual average loss to earthquake are Cyprus, Greece, Romania, Bulgaria, and Croatia, and for floods the top-five countries are Romania, Slovenia, Latvia, Bulgaria, and Austria.4 However, disasters do not affect everyone equally: poor, elderly, very young, and marginalized populations are most affected and least able to recover. In Romania, Greece, Croatia, and Bulgaria, for example, the socio-economic resilience of the poor is on average less than 30% of the national average (World Bank, 2020). Moreover, the local and regional administrations in the poorer and more disadvantaged areas have the least capacity to design and implement resilience investments.
HAZARD INFORMATION PROFILES Supplement to : UNDRR-ISC Hazard Definition & Classification Review - Technical Report
The Sendai Framework for Disaster Risk Reduction 2015–2030 (‘the Sendai Framework’) was one of three landmark agreements adopted by the United Nations in 2015. The other two being the Sustainable Development Goals of Agenda 2030 and the Paris Agreement on Climate Change. The UNDRR/ISC Sendai Hazard Definition and Classification Review Technical Report supports all three by providing a common set of hazard definitions for monitoring and reviewing implementation which calls for “a data revolution, rigorous accountability mechanisms and renewed global partnerships”.
Regional based exposure models to account for local building typologies
The development of building inventory is a fundamental step for the evaluation of the seismic risk at territorial scale. Census data are usually employed for building inventory in large scale application and their use requires suitable rules to assign buildings typologies to vulnerability classes, that is an exposure model specifc for the considered vulnerability model. Several exposure models are developed proposing class assignment rules that are calibrated on building typological data available from post-earthquake survey data. However, this approach has the drawback of being based on data from specifc geographic areas that have been hit by damaging earthquakes. Indeed, the distribution of building typologies can vary greatly for diferent areas of a country and the difusion of one building’s typology rather than another one may depend on the availability of construction material in the area, the evolution of construction techniques and the codes in force at the time of construction. This paper aims to improve the exposure modelling at regional scale, investigating the variability of masonry building typologies distribution. It proposes a methodology to recalibrate the exposure models at regional scale and evaluates the infuence of the improved characterization of regional vulnerability on damage and risk assessment. The study shows that the analysis of local building typologies may strongly impact on the evaluation of the seismic risk at territorial scale.
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