This study introduces a multivariate demand model for Engineering Demand Parameters (EDPs) in Performance Based Seismic Design (PBSD), utilizing Gaussian copulas to characterize the dependence structure of the demand vector. The effectiveness of this approach is assessed by comparing EDPs generated using Gaussian copulas against those assumed under a joint lognormal distribution. This validation study is further carried forward to values of economic loss for the four special steel moment frames obtained via the three sets of EDPs. The Performance Assessment Calculation Tool (PACT) developed by the Federal Emergency Management Agency (FEMA) P-58 (2015) is used for loss estimation. Results indicate that using copulas to represent the dependence structure of EDPs better captures the characteristics of the population of EDPs rather than assuming a joint lognormal distribution. Distributions of economic loss generated using copulas match the loss generated from the true observations of EDPs better than loss generated assuming a joint lognormal distribution. The sample size of the selected and scaled ground motions required for the generation of realizations of building response via nonlinear dynamic analysis is also investigated, which proves to yield more accurate values of response but, at the expense of using a larger number of initial observations.

The Campi Flegrei area (CF) is a highly interesting place for scientific research due to a “dual” value concerning Earth and Planetary Science. On the one hand, from a natural hazard assessment point of view, CF is one of the most dangerous volcanoes on Earth in terms of its proximity to a densely populated urban area (the Neapolitan district is home to about three million people living between CF, Vesuvius and Ischia). On the other hand, from a Planetary Science point of view, the Solfatara crater high-temperature geothermal environment is comparable to terrestrial analogue of Early rocky bodies inside the Solar System; and therefore, represents a prime astrobiology target. Due to the scientific value this site represents, in the view of a comprehensive characterization, we show the preliminary results of the exploitation of PRISMA data for the mineralogical characterization of the acidic products of the Solfatara crater and for the fumarolic gases detection from space. Two datasets by PRISMA were analyzed: one before and one after the main earthquake in the last 40 years, occurred on 27th September 2023 at the Solfatara site. Our preliminary results show some variations in the carbon dioxide emissions from the fumarolic field.

A dam is a hydraulic structure made of natural materials, such as earth, or artificial ones, such as concrete, whose main function is to block a watercourse to create an artificial basin with multiple purposes, irrigation, energy, flow regulation, and protection. These structures allow for the storage of large quantities of water which, in the case of a collapse, can have devastating effects on human lives and territory. Therefore, the regulations prescribe severe safety checks and provide operational guidelines for civil protection activities and emergency plans. Through some case studies in Calabria, a region of Southern Italy, the paper analyzes Italian regulations concerning scenarios where it is necessary to safely empty the reservoir behind the dam following an earthquake and allow the consequent civil protection activities and emergency plans to be defined. Also, this paper describes the coupled hydrological and hydrodynamic modeling carried out using HEC-HMS and HEC-RAS, respectively, to define three thresholds for each dam according to Italian regulations. These thresholds are the maximum flow rate for emptying the dams that are contained in the hydraulic pertinence areas downstream of the dams, the attention flow rate for the discharge of the dam beyond which hydraulic criticalities occur, and the incremental thresholds that identify scenarios with greater hydraulic criticalities.

Abstract

 Understanding the dynamics of pre-earthquake seismicity is crucial for advancing earthquake forecast and risk assessment. In this paper, we embark on a mathematical exploration of pre-earthquake seismic activity, aiming to elucidate the underlying patterns and mechanisms leading up to major seismic events. Leveraging probabilistic modeling techniques, we analyze historical seismic data to identify precursory signals and assess their predictive value. Our investigation encompasses the study of foreshock activity, preceding earthquakes, shedding light on the temporal and spatial characteristics of seismic activity prior to the main shock events. Through mathematical modeling and simulation, we aim to unveil the complex interplay of factors contributing to pre-earthquake seismicity, with implications for enhancing earthquake forecasting capabilities and disaster preparedness efforts. This research contributes to the ongoing endeavor to unravel the mysteries of earthquake occurrence, ultimately striving towards a more resilient and proactive approach to seismic risk management. This study introduces a novel mathematical framework for analyzing pre-earthquake seismic activity, leveraging a 15-day foreshock window and machine learning techniques to predict seismic events. The approach addresses gaps in existing methodologies by incorporating comprehensive feature engineering and a robust random forest classification model. Additionally, we draw upon insights from prior studies, such as Kumazawa et al. (2020) and Luo et al. (2023), which emphasize the significance of spatial-temporal dynamics and natural orthogonal expansion methods in identifying seismic precursors. By integrating interdisciplinary methodologies and advanced machine learning models, this study bridges critical gaps in real-time predictive capabilities, offering a tailored approach for region-specific seismic forecasting.

Wildfires pose a growing global danger for ecosystems and human activities. The degraded ecosystem functions of burnt sites, include, among others, shifts in hydrological processes, land cover, vegetation, and soil erosion, that make them more vulnerable to flood and extreme sediment transport risks. Several post-fire erosion and flood protection treatments (PFPs) have been developed to avoid and mitigate such consequences and risks. The Mediterranean region faces severe climate change challenges that are projected to escalate the wildfire and post-fire flood risks. However, there is limited research on the dynamics of post-fire flood risks and their mitigation through the design of the appropriate PFPs. This paper aims to cover this gap by simulating a real post-fire flash-flood event in Central Greece, and design the PFPs for this case study, considering their suitability and costs. An integrated framework was used to represent the flood under the baseline scenario: the storm conditions that caused the flood were simulated using the atmospheric model WRF-ARW; the burn extent, severity, and the flood extent were retrieved through remote sensing analyses; and a HEC-RAS hydraulic-hydrodynamic model was developed to simulate the flood event, applying the rain-on-grid technique. Several PFPs were assessed, and certain channel- and barrier-based PFPs were selected as the most suitable for the study area. The recommended PFPs were spatially represented within a geographic information system (GIS). Moreover, we present a detailed analysis of their expected costs. This study provides an interdisciplinary and transferable framework for understanding and enhancing the flood resilience of burnt sites.

Drought identification is crucial for different environmental and ecological considerations. This study observed the spatial and temporal variation of drought based on satellite-derived vegetation condition index (VCI) on different time scales i.e. Winter, Pre-monsoon, and Annual compared with remotely sensed Land Surface Temperature (LST) and precipitation. MODIS NDVI product, LST, and meteorological station data for rainfall were used. VCI was used to classify drought. The Pearson correlation between VCI with LST and precipitation was conducted. The results show that severe drought was detected in 2001 in winter and pre-monsoon season and also in 2006 during pre-monsoon season. No severe drought was detected on the annual time scale but normal droughts were found in several years. The VCI trends have increased at the rate of 1.29 yr-1, 1.52 yr-1 and 1.72 yr-1 for annual, winter, and pre-monsoon respectively. Conversely, the LST decreased at the rate of 0.007 yr-1, 0.044 yr-1 and 0.028 yr-1 during annual, winter, and pre-monsoon. These increased VCI and decreased LST indicates decreases in drought trends on a yearly scale. The positively increased Anomaly of VCI (AVCI) indicates a better vegetation growth under moist soil condition. The VCI was also linked with precipitation showing positive correlation on annual and pre-monsoon time scales but a negative correlation with the winter season. The correlation between LST and VCI was negative in all time scales and more significant during pre-monsoon and winter season. This study helps to understand vegetation-based drought and its relation with temperature and precipitation in the Koshi basin in Nepal.

Historic arch bridges, a common feature of Turkish infrastructure, represent a significant aspect of the country’s cultural heritage. To ensure their continued existence and preservation, it is essential to conduct a detailed examination of their structural features and behaviours. This study aimed to investigate the performance of the historic Yeşiltepe Bridge under earthquake conditions. To achieve this, the bridge was modelled using the SAP2000 finite element software, enabling a deeper understanding of its structure and the prediction of its behaviour during an earthquake. In order to ascertain the dynamic behaviour of the historical bridge, modal analysis and nonlinear time history analysis were conducted. The results of the modal analysis yielded period values, mass participation rates and mode shapes for the bridge. The time history analysis yielded displacement, base shear force and stress values for the historical structure, which were subsequently presented in graphical form. The data obtained from the study enabled the identification of the critical regions of the structure exhibiting the highest stress concentration values.

Forest-fire is a pressing global problem that has far-reaching effects on human life and the environment, with climate change exacerbating their frequency and intensity. There is an urgent need for advanced predictive systems to mitigate these impacts. To address this issue, this study introduces a forest-fire prediction framework integrating wireless sensor networks (WSNs), data analysis, and machine learning. Sensor nodes deployed in a forest area collected real-time meteorological data, which was transmitted using LoRaWAN technology. Data mining techniques prepared the data for analysis using the SVM algorithm, revealing relationships between meteorological parameters and wildfire risk. The SVM model demonstrated an accuracy of 86% in classifying forest-fire risk levels based on temperature, humidity, wind speed, and rainfall data. The integrated framework of WSNs and the SVM algorithm provides a high-accuracy model for forest-fire risk prediction. The model is compared to the Canadian Forest Fire Hazard Rating System to validate its accuracy, demonstrating strong agreement with historical records and reports. The model's practical implications include efficient management, early detection, and prevention strategies. However, the model's limitations suggest avenues for future research, we should consider broader geographic applications and using advanced machine-learning methods to enhance the model's predictive capabilities.

On January 1, 2024, at 16:10, a magnitude 7.6 earthquake struck the Noto region of Ishikawa Prefecture. The maximum seismic intensity of 7 was observed in Shika Town of Noto region. About 10 minutes after the earthquake, a major tsunami warning, tsunami warning, and tsunami advisory were issued. The Japan Meteorological Agency designated the earthquake as the “2024 Noto Peninsula Earthquake”. The Shika Nuclear Power Station of Hokuriku Electric Power Co. is in the town. This paper reviews the damage to the Shika Nuclear Power Plants over the past month from the viewpoint of industrial accidents (NATECH) caused by natural hazards and the response to such accidents. The power plant had originally been subject to safety measures based on the "New Regulation Standards" after the Fukushima Daiichi Nuclear Accident since 2011, but this time the station was hit by a tremor that exceeded expectations, and although it escaped external spills, there were reports of leaks of radioactive spent fuel storage pool water, oil leaks from transformers, tsunami, and damage to power transmission lines. Discussions held by the Nuclear Regulatory Authority also included problems with monitoring posts. In addition, many of the evacuation roads were closed at this time, and of the 11 national and prefectural roads designated as Shika Nuclear Power Station’s evacuation routes in the event of a nuclear accident, the majority, seven, were closed due to collapses or cracks. Furthermore, the repeated intensity and frequency of the earthquakes made it difficult to evacuate indoors and take protective measures against radiation, even at residences and designated evacuation centers. Although this series of disasters did not develop into a severe nuclear disaster that resulted in radiation leakage, the most important and necessary information, actions to utilize the wisdom of that are now being questioned once again for the protection of the lives and property of the people and the global environment.

Glacier retreat, a major impact of climate change that continues to occur in many parts of the world, continues to increase the risk of glacial lake outburst floods (GLOFs) in northern Pakistan. The rapid melting of glaciers in the mountains of Northern Pakistan, including the Hindu Kush, the Himalayas and the Karakoram, the rapid melting of glacier has led to the formation of 3044 glacial lakes, with 33 identified as particularly vulnerable to GLOFs. This study uses remote sensing and geographic information systems (GIS) methods for mapping and representing GLOFs. Based on the observational data of lake area, volume, and depth, empirical equations are developed through statistical methods. Only two lakes, Chitral-GL2 and Swat-G31, are classified as lakes with high potential for GLOF. Through modeling techniques using HEC-RAS and HEC-GeoRAS spatial hydrological models integrated with GIS remote sensing, the spatial extent and depth of inundations under different lake volumes are assessed. The analysis reveals that a total area of 20.56 km² is susceptible to submersion by GLOFs, with Chitral-GL2 flooding area of 14.80 km² and Swat-GL31 5.79 km². Different land types are impacted by critical water depths, with built-up and agricultural lands 2.7 km² totally, and barren lands 8.93 km² under different flood depths ranging from less than 5 m to over 15 m.