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.

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.

The consolidated paper presents work carried out in the sphere of earthquake problems. On the base of theoretical and experimental studies, it is shown earthquake prediction possibility. There are discussed earthquake indicators and triggering exogenous factors in the example of the Caucasus region. Because the earthquake preparation process causes anomalous changes with complex characteristics in various geophysical fields, it is given scientifically proven suggestions for the classification of these fields as earthquake precursors, indicators, and triggering factors. It offers a short-term plan for future work in the earthquake prediction direction.