Traditional seismic risk assessments often require specialized expertise and extensive computational time, making probabilistic seismic risk evaluations less accessible to practitioners and decision-makers. To reduce the barriers related to applications of quantitative seismic risk analysis, this paper develops a Quick Loss Estimation Tool (QLET) designed for rapid seismic risk assessment of Canadian buildings. By approximating the upper tail of a seismic hazard curve using an extreme value distribution and by integrating it with building exposure-vulnerability models, the QLET enables efficient computation of seismic loss curves for individual sites. The tool generates seismic loss exceedance probability curves and financial risk metrics based on Monte Carlo simulations, offering customizable risk assessments for various building types. The QLET also incorporates regional site proxy models based on average shear-wave velocity in the uppermost 30 m to enhance site-specific hazard characterization, addressing key limitations of global site proxy models and enabling risk-based seismic microzonation. The QLET streamlines hazard, exposure, and vulnerability assessments into a user-friendly tool, facilitating regional-scale risk evaluations within practical timeframes, making it particularly applicable to emergency preparedness, urban planning, and insurance analysis.

In recent years, numerous satellite-based systems have been developed to monitor and study volcanic activity from space. This progress reflects the growing demand for accurate and timely monitoring to reduce volcanic risk. Observing volcanoes from a satellite perspective provides key advantages, enabling continuous data acquisition and near-real-time assessment of volcanic activity. Multispectral sensors operating across various regions of the electromagnetic spectrum can detect thermal anomalies associated with lava flows, pyroclastic flows, ash plumes, and volcanic gases. Traditional hotspot detection techniques based on fixed thresholds often miss subtle anomalies on a global scale. In contrast, advanced machine learning algorithms offer a data-driven alternative. We designed and implemented the V-STAR application (Volcanic Satellite Thermal Anomalies Recognition) on Google Earth Engine (GEE) to leverage cloud computing for processing large geospatial datasets in real time. It employs supervised machine learning, specifically Random Forests, to adapt to evolving volcanic conditions. This enhances the accuracy and responsiveness of volcanic monitoring, offering valuable insights into potential eruptive behavior. Here, we present V-STAR as a robust and accessible tool that integrates satellite data and advanced analytics. Through its intuitive interface, V-STAR provides a comprehensive visualization of key volcanic features. The resulting analyses reveal hidden patterns in thermal data, contributing to improved disaster risk reduction strategies associated with volcanic hazards.

The aim of the present research is to analyze community participation in disaster risk management due to tailings dam failures (DRM-TDF). Conceição do Mato Dentro, Minas Gerais State (Brazil) was used as case study. The aims of the study are to help developing more effective DRM-TDF strategies and to strengthen community participation in decision making, and in mapping and categorizing vulnerabilities (criticality and support capacity) by assessing current practices and prioritizing future strategies. Semi-structured questionnaires were applied to community leaders and open interviews were carried out with DRM experts for information collection purpose. The collected responses were categorized based on vulnerabilities by taking into account criticality (communities) and support capacity (public management and mining entrepreneurs). SWOT analysis identified “Weaknesses” (criticality) and “Threats” (support capacity), whereas Pareto analysis highlighted the most critical aspects. The results indicate that public policies and the Brazilian legal framework have made limited contributions toward achieving the Sendai Framework guidelines and the Sustainable Development Goals. A review of current practices is necessary to safeguard the rights of affected communities through their meaningful participation in decision-making processes.

Urban development and climate change are two main impacting factors in the thermal environment of cities. This study aims to analyze future changes in Mean Radiant Temperature (MRT), one of the main contributors to human thermal comfort and the concept of Urban Heat Island (UHI), considering climate change and urban development scenarios in the study area, Fredericton, New Brunswick, by 2050. The analysis utilizes the SOLWEIG (Solar and Longwave Environmental Irradiance Geometry) model from the Urban Multi-scale Environmental Predictor (UMEP) platform to calculate MRT values. By integrating these two impacting factors, this research provides insights into the potential future changes in MRT levels and the resulting thermal conditions and geohazards in the study area. The analysis enables the identification of areas susceptible to increased radiant heat exchange due to the proposed changes in land cover, urban morphology, and air temperature. Furthermore, this study contributes to a better understanding of the complex interactions between climate change, urbanization, and urban microclimates. By incorporating MRT assessments and prioritizing thermal comfort, cities can develop strategies to mitigate the negative effects of UHI and create sustainable and livable urban environments for future generations.

Rwanda, in eastern tropical Africa, is a small, densely populated country where climatic disasters are often the cause of considerable damage and deaths. Landslides are among the most frequent hazards, linked to the country’s peculiar configuration including high relief with steep slopes, humid tropical climate with heavy rainfall, intense deforestation over the past 60 years, and extensive use of the soil for agriculture. The Karongi region, in the west-central part of the country, was affected by an exceptional cluster of more than 700 landslides during a single night (6–7 May 2018) over an area of 100 km2. We analyse the causes of this spectacular event based on field geological and geomorphology investigation and CHIRPS and ERA5-Land climate data. We demonstrate that (1) the notably steep slopes favoured soil instability; (2) the layered soil and especially the gravelly, porous C horizon allowed water storage and served as a detachment level for the landslides; (3) relatively low intensity, almost continuous rainfall over the previous two months lead to soil water-logging; and (4) acoustic waves from thunder or mechanical shaking by strong wind destabilized the water-logged soil through thixotropy triggering the landslides. This analysis should serve as a guide for forecasting landslide-triggering conditions in Rwanda.

The effects of global warming, coupled with the continuing expansion of urbanization, have significantly increased vulnerability to urban flooding, widespread erosion risks, and related phenomena such as shallow landslides and mudflows. These challenges are particularly evident in both lowland and hill/foothill environments of urbanized regions. Improving resilience to urban flooding has emerged as a top priority at various levels of governance. This paper aims to perform an initial analysis with the goal of developing an early warning system to efficiently manage intense convective rainfall events in urban areas. To address this need, the paper emphasizes the importance of analyzing different hazard scenarios. This involves examining different hydro-meteorological conditions and exploring management alternatives, as a fundamental step in designing and evaluating interventions to improve urban flood resilience. The Turin Metropolitan Area (TMA), located in north-western Italy, represents a unique case due to its complex orography, with a mountainous sector in the west and a flat or hilly part in the east. During the warm season, this urban area is exposed to strong atmospheric convection, resulting in frequent hailstorms and high-intensity rainfall. These weather conditions pose a threat to urban infrastructure, such as drainage systems and road networks, and require effective management strategies to mitigate risks and losses. The TMA’s urban areas are monitored by polarimetric Doppler weather radars and a dense network of rain gauges. By examining various summer precipitation events leading to urban flooding between 2007 and 2021, this study assesses the practicability of deploying a weather-radar early-warning system. The focus is on identifying rainfall thresholds that distinguish urban flooding in lowland areas and runoff erosion phenomena in urbanized hills and foothills.

The use of unmanned aerial vehicles (UAVs) can significantly assist landslide detection and characterization in different geological contexts at a detailed scale. This study investigated the role of UAVs in detecting a first-failure landslide occurring in Calabria, South Italy, and involving weathered granitoid rocks. After the landslide event, which caused the interruption of State Road 107, a UAV flight was carried out to identify landslide boundaries and morphological features in areas where there are problems of safe access. The landslide was classified as flow-type, with a total length of 240 m, a maximum width of 70 m, and a maximum depth of about 6.5 m. The comparison of the DTMs generated from UAV data with previously available LIDAR data indicated significant topographic changes across the landslide area. A minimum negative value of −6.3 m suggested material removal at the landslide source area. An approximate value of −2 m in the transportation area signified bed erosion and displacement of material as the landslide moved downslope. A maximum positive value of 4.2 m was found in the deposition area. The landslide volume was estimated to be about 6000 m3. These findings demonstrated the effectiveness of UAVs for landslide detection, showing their potentiality as valuable tools in planning further studies for a detailed landslide characterization and for defining the most appropriate risk mitigation measures.

This study focuses on slope stability and geological hazard analyses at the Italian Paleolithic site of Grotta Paglicci. The site is characterized by a cave that contains rich archaeological and anthropological finds, spanning various Paleolithic periods, and includes faunal remains, lithic artifacts, human burials, ornaments, mobiliary art objects, and unique Paleolithic wall paintings. The study employs a multi-technique approach that includes topographic surveys carried out by the robotic total station and GNSS receivers, photogrammetric acquisitions with an unmanned aerial system, 3D SLAM-based LiDAR mapping, and an engineering geological survey. The collected data allowed for the creation of georeferenced 3D models that were utilized in rock slope stability analysis and modeling. The results of this comprehensive survey highlighted how the bedding and joint discontinuities influence rock stability in both the external and internal areas of the cave. The integrated use of SLAM-based LiDAR and photogrammetry has been proven to be an efficient and essential tool in the evaluation of the structural interactions between the external morphology and the cave, thus allowing the proposal of safety measures that will keep the site accessible for future activities.

A new set of observations has been compiled for tsunamis occurring in Greece and in the surrounding areas from 1900 to 2023. A variety of information sources has been collected and examined, including scientific and press reports, books, eyewitness accounts, pictorial and video material, and tide-gauge records. New material was also collected during our field surveys in the islands of Cephalonia, Karpathos, and Kos. Our investigation included 26 distinct events and revealed several tsunamis. The majority of them have remained unknown so far in the tsunami community. Our compilation also included little-known events for which further documentation has been provided. Among others, of particular importance is the collection of new information about the well-known tsunami associated with the 9 February 1948 large earthquake in Karpathos Island as well as the unknown so far series of local but powerful tsunamis generated during the seismic crisis of very strong earthquakes that destroyed the Ionian islands during August 1953. The new observational material collected is significant for the enrichment of existing tsunami catalogs with positive implications for better understanding the tsunami generation mechanisms and the assessment of tsunami hazards and risks.

A large number of existing reinforced concrete (RC) buildings have surpassed their anticipated service life and show signs of degradation due to aging; this degradation is a function of the construction practices adopted in the past as well as environmental conditions. This paper discusses seismic fragility and the risk assessment of RC structures, emphasizing the impact of corrosion due to concrete aging and the associated deterioration mechanisms. The literature on this topic is critically reviewed, and a methodology for studying the seismic fragility of deteriorated RC buildings is proposed. As a case study, a four-story RC building designed according to contemporary code provisions is examined. The investigation encompasses the derivation of fragility curves, considering critical parameters such as the corrosion rate, the initiation time, and the cover depth. The proposed approach enables the evaluation and quantification of the impact of corrosion mechanisms on the seismic performance of buildings.