Global Earthquake Model

Northeastern Patagonia is a recognized vulnerable region for wind erosion and desertification. Identifying dust emission sources in the coastal regions of Argentine Patagonia is crucial to understanding micronutrient transport to the Southern Atlantic Ocean. This study proposes an innovative integrated approach combining species distribution modelling and optical satellite images of the land surface to identify potential dust emission hotspots related to wind erosion. To test the approach, we selected a study area in an endorheic basin in the southwestern Buenos Aires province, Argentina. We identified the source of particulate matter emission using MODIS Aerosol Optical Depth (AOD-MAIAC) values above a threshold of 0.06, which was validated by fieldwork and served as the dependent variable in the model. As explanatory variables, we derived several satellite-based products such as the Aridity Index, and Soil Organic Carbon, as well as topographic indices like Vertical Distance to the Drainage Network and land cover. The applied MaxEnt model shows an acceptable performance with Receiver Operating Characteristic curve integrals of 0.75. For further validation, the trained model was transferred to a neighboring region. The most significant variables were Soil Organic Carbon, Vertical Distance to the Drainage Network, and Landcover. The results indicate that the most susceptible areas generating dust/salt plumes in the studied endorheic basin are the depressions and lowlands where fine sediments accumulate. The results yield valuable information for the proper management of Eastern Argentinian lowland areas and support general soil and water conservation practices and planning.

Abstract Reliable vulnerability functions are essential for earthquake risk assessment, yet those currently used in regional and large-scale contexts often rely on numerical models that oversimplify the complex behaviour of buildings. This can lead to biased predictions of structural performance and earthquake-induced losses. In the new Global Vulnerability Model supported by the Global Earthquake Model (GEM) Foundation, we present an improved structural vulnerability database for seismic risk assessment, featuring four key advances: 1) the number of building classes has been expanded from approximately 500 classes to over 1,400 to consider additional structural systems and seismic code levels; 2) an improved numerical modelling strategy that simulates the seismic response using efficient multi-degree-of-freedom stick-and-mass models; 3) an extensive dataset of ground motion records for response analysis covering diverse tectonic settings (e.g., active shallow crust, stable continental, and subduction zones), and 4) a new suite of vulnerability functions addressing specifically structural damage. This contribution describes the vulnerability modelling for structural components, which is used to compute damage and monetary losses within the scope of GEM’s Global Seismic Risk Model. The derived vulnerability functions in this study are available in an open-access repository for use in both probabilistic and scenario-based seismic risk assessments.

The acid‐catalyzed conversion of cannabidiol (CBD) to tetrahydrocannabinol and iso‐tetrahydrocannabinol derivatives is a well‐established synthetic strategy. However, since the reaction outcome is strongly dependent on the conditions, a careful investigation is always required to achieve the optimal chemoselectivity. Chemometrics recently emerged as an effective approach for improving synthetic methods, especially when multiple parameters are involved. The present paper aims to apply chemometrics tools to the optimization of the procedures for the preparation of Δ⁹‐THC, Δ⁸‐THC, Δ⁸‐iso‐THC, Δ⁴‐iso‐THC, and Δ⁴(⁸)‐iso‐THC. All the reactions have been performed at room temperature by tuning the initial concentration of CBD, the equivalents of the model Lewis acid considered (BF3·OEt2), the reaction time, and the nature of the media to achieve the desired products. The kinetics of the process, followed over the course of 1 to 48 hours, were analyzed by means of Principal Component Analysis (PCA) to initially simplify the multidimensional dataset and help identify the best media for Δ⁹‐THC and Δ⁸‐THC. In order to optimize the conditions needed for Δ⁴‐iso‐THC, Δ⁴(⁸)‐iso‐THC, and Δ⁸‐iso‐THC, multiple Design of Experiment (DoE) were employed, leading to the successful isolation of the target compounds. During the investigation, cannabinoid derivatives incorporating a portion of the reaction medium were also identified.

Abstract This study examines the potential risks to infant feeding practices following earthquakes in countries at high risk of fatality and/or infrastructure damage. Using secondary data from the Global Earthquake Model Foundation, we analyzed estimated average annual fatalities and average annual area damaged due to earthquakes to identify the top 20 countries globally with the highest value for each indicator. We then assessed these countries’ vulnerability to infant feeding disruptions by examining their exclusive breastfeeding rates. Countries with low exclusive breastfeeding rates face heightened challenges in safely feeding infants during natural disasters due to their heavy reliance on substantial resources needed for artificial feeding. Additionally, countries with high annual average fatalities require robust preparedness plans and support systems to ensure continued care and immediate nutrition for infants, breastfed and non-breastfed alike, in the event of caregiver loss. In contrast, countries with a high risk of significant infrastructure damage are particularly susceptible to food safety and access challenges even when caregivers remain with their infants. Out of 198 countries assessed, the findings identify 24 countries with either the highest average annual fatalities or the highest average annual area damaged values from earthquakes, and a cohort of 16 countries appears in both categories. Strengthening breastfeeding promotion, providing lactation support, and establishing emergency preparedness plans for infant feeding are critical for reducing mortality in the identified countries. Integrating the Operational Guidance for Infant Feeding in Emergencies (Operational Guidance) into national disaster response plans will improve preparedness, especially if it is available in the national languages of these countries (especially Mandarin, Farsi, and Urdu). We strongly encourage all identified countries to prioritize integrating infant feeding strategies into their earthquake preparedness plans to protect and promote the well-being of infants.

The Global EarthquakE ScEnarios (GEESE) algorithm retrieves from a seismic hazard input model the ruptures matching a set of criteria (e.g., magnitude, location, focal mechanism). We applied the GEESE algorithm to create a publicly available database (version 1.0) of finite rupture models for past earthquakes which can be used for scenario seismic hazard and risk analysis applications. To this end, we selected earthquakes with a moment magnitude larger than 7.0 and hypocentral depth less than 200 km in the ISC-GEM catalogue (version 10.0) and retrieved the best matching ruptures from the seismic hazard models in the GEM Mosaic. The GEESE algorithm also automatically computes a set of ground-motion fields using each matched rupture, which are also provided in the database. The ability of the GEESE algorithm to test whether a Mosaic model can generate a rupture sufficiently representative of a queried event is a useful means of evaluating the Mosaic model's seismic source characterisation (SSC). Sufficiently matching ruptures are retrieved from the Global Mosaic for 90 percent of the tested ISC-GEM events. The GEESE algorithm can also be used in post-event response analysis to rapidly obtain an initial finite rupture when only minimal event information is initially available. A demonstration of these capabilities of the GEESE algorithm is provided using the 2023 Morocco earthquake, the 1994 Northridge earthquake, and the 2023 Kahramanmaras earthquake.

We know more about earthquake processes, vulnerability modelling and characterization of the built environment than ever before. Yet, earthquake losses and casualties continue to increase, even in countries where modern seismic design regulations have been introduced decades ago. In this study we investigate the drivers of earthquake damage and losses using the global seismic hazard and risk model developed by the Global Earthquake Model (GEM) Foundation and its partners, as well as data from fatal earthquakes since 1950. We isolate specific parameters that can influence the severity of the ground shaking, the vulnerability of the building stock, and the spatial distribution of the population. These include the prevalence of soft soils, the average seismic hazard in each country, the likelihood of experiencing extreme ground shaking, the occurrence of earthquake-triggered hazards (i.e., liquefaction, landslides and tsunamis), the time of the event, the proximity of megacities to active faults, the percentage of specific types of construction, and some socio-economic factors. We compare these underlying parameters and the estimated or observed seismic risk between different countries and identify specific patterns that systematically exacerbate the overall impact. We observe that high economic losses are frequent in countries with well-established seismic regulations not only due to the high replacement/repair costs, but also due to the high prevalence of commercial and industrial facilities and complex infrastructure. On the other hand, high fatality risk is frequent in countries whose building stock is comprised of non-engineered buildings with heavy roofs and floors. Another relevant observation is that although ground shaking is overwhelmingly the main cause of damages and losses, under specific geological and demographic conditions, the impact of tsunamis, landslides and liquefaction phenomena can be devastating. Lessons drawn from these observations and patterns can be useful to understand how the impact of earthquakes can be better assessed, reduced, and managed.

This study introduces a unified framework for evaluating the physical impacts of earthquakes and volcanic eruptions on buildings, leveraging upon the existing capabilities of the OpenQuake engine for earthquake risk assessment and various existing packages for computing volcanic hazard footprints. We illustrate the capabilities of the new OpenQuake volcanic scenario module using two case studies: a VEI (Volcanic Explosivity Index) 3-4 eruption of Nevado del Ruiz volcano in Colombia and a VEI 6 eruption of Mount Pinatubo in the Philippines, employing various methods for simulating hazard footprints for tephra fall, lava flows, pyroclastic density currents, and lahars. The findings demonstrate the versatility of OpenQuake in managing diverse geohazards and its potential for further extension to other hazards, while this integration advances our ability to assess and manage disaster risk.

ABSTRACTThe estimation of road disruptions due to building debris in urban contexts requires the availability of exposure data at the building level, which is often not available. In this study, we explore how open global datasets at different scales can be integrated with machine learning algorithms to estimate road disruptions following seismic events, overcoming the need for detailed datasets. Using simulated impact data for the municipality of Lisbon, we train a Random Forest model to predict road disruptions due to building collapses. Then, we apply this model to another urban environment (the municipality of Amadora) to evaluate the performance of the model using input data unseen during the training process. Finally, we employ the surrogate model using information extracted from globally available datasets characterizing the built environment and the road network. The proposed approach allows identifying areas within urban centers where road disruptions are likely to occur, and where risk reduction measures should be prioritized to minimize the impact of destructive earthquakes.

Reliable seismic hazard modeling requires careful calibration to the datasets used to create the model. Additionally, assessing the performance of an existing model involves statistical comparisons to data not used in model construction. However, despite the benefits of such statistical evaluations, comparing a model with the observed data is inherently challenging due to the following reasons:1) A probabilistic seismic hazard model is essentially a structured collection of 105-108 unique potential earthquake ruptures and the occurrence rates and ground motion fields associated with each rupture. Seismological or geological observations usually include much smaller samples of earthquakes (in the order of 103-104) and active faults (in the order of 101-102), and the data are usually incomplete.2) To construct a model from such observations, various assumptions and processing are involved (e.g., Poisson time independence of modeled seismicity, declustering of seismic catalogs, and modeling earthquake occurrence based on fault slip rates).To facilitate model evaluations, we developed Hamlet (Hazard Model Evaluation and Testing), an application designed to process seismic hazard models and perform rigorous statistical comparisons between the model and observations efficiently and flexibly. Hamlet performs the M-, N-, S-, and L-tests recommended by the Center for the Study of Earthquake Predictability (CSEP) and other statistical comparisons (e.g., maximum earthquake magnitudes and seismic moments) by generating a large number of simulated earthquake catalogs of the same duration as an observed seismic catalog, mitigating many of the concerns about differences between the model and the observations. Hamlet can perform statistical comparisons both over the entire model domain and within equal-area hexagonal spatial cells of user-specified size, to better constrain the spatial differences in model performance. Moreover, Hamlet can subset a seismic source model by subregion, logic tree branch, and source type and perform the tests for the subset. This is particularly useful during model construction, as it allows the modeler to understand and adjust how individual components of the model perform.We have used Hamlet to analyze over 30 hazard models to gauge the applicability of different tests and evaluations to a variety of cases (e.g., seismic hazard models with different characteristics), and to test some widely used assumptions in hazard models to identify which work and which do not. Hamlet is a work in progress, and we are continually adding features and evaluations. Functionality in development or for consideration for the future includes matching the most similar rupture in the source model to each earthquake in an observed seismic catalog, and statistical evaluations against a seismic catalog with different durations for different magnitude ranges. We are also exploring testing of predicted ground motion values and loss (risk) for observed events.

Seismic risk describes the potential consequences of future earthquakes in terms of human and financial losses. As such, seismic risk models provide information that is crucial for earthquake mitigation and emergency response. For these models to be effective, their results must be accessible and comprehensible to a wide range of stakeholders including the general public. To achieve this, we applied a transdisciplinary approach to design and evaluate key outreach materials including seismic risk maps, scenario and rapid impact information. We conducted two representative online surveys with the general public (N1=580; N2=593), an online survey with students of European universities (N3=83), seven interviews with experts developing rapid impact assessments, and three workshops with about 150 representatives from cantonal authorities, first responders, and civil protection.Although the initial product designs were well received by the target groups, which we attribute to the close interdisciplinary collaboration during the design process, they have been significantly improved based on user feedback. This enhanced stakeholders’ understanding and the usability of the products. For example, the insights from the user testing led to a new preferred colour scheme and legend for the seismic risk map and in a new standard for displaying loss distributions in rapid impact assessments. In this talk, we will present the strategy elaborated for designing useful seismic risk information and provide insights to key findings from our accompanying research using the examples of the Swiss and European seismic risk model releases in 2022 and 2023.