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Wildfire activity in Arctic and boreal regions is rapidly increasing, with severe consequences for climate and human health. Regional long-term variations in fire frequency and intensity characterize fire regimes. The spatial variability in Arctic-boreal fire regimes and their environmental and anthropogenic drivers, however, remain poorly understood. Here we present a fire tracking system to map the sub-daily evolution of all circumpolar Arctic-boreal fires between 2012 and 2023 using 375 m Visible Infrared Imaging Radiometer Suite active fire detections and the resulting dataset of the ignition time, location, size, duration, spread and intensity of individual fires. We use this dataset to classify the Arctic-boreal biomes into seven distinct pyroregions with unique climatic and geographic environments. We find that these pyroregions exhibit varying responses to environmental drivers, with boreal North America, eastern Siberia and northern tundra regions showing the highest sensitivity to climate and lightning density. In addition, anthropogenic factors play an important role in influencing fire number and size, interacting with other factors. Understanding the spatial variability of fire regimes and its interconnected drivers in the Arctic-boreal domain is important for improving future predictions of fire activity and identifying areas at risk for extreme events.

We investigate the divergence in risk preferences between individuals exposed to background risk and those who are not, using a field experiment conducted in Arequipa, Peru. Arequipa, a city located in close proximity to the El Misti volcano, is constantly threatened by lahars – mudflows originating from this volcano. Our study combines experimenta l assessments of risk tolerance and patience with a targeted survey designed to assess residents' awareness of their exposure to risks. Our primary finding reveals that residents living in hazard-prone areas exhibit higher levels of risk tolerance compared to their counterparts in safer zones. To address concerns about causality, we employ instrumental variable (IV) regressions and conduct stability analyses of coefficients. We subsequently discuss our findings in the context of the literature on preference adaptation. [ABSTRACT FROM AUTHOR]

People residing in the char regions of Assam are among the most vulnerable in the state, and particularly susceptible to multiple disasters such as floods, storms, and land erosion. Despite the existing vulnerabilities and widespread poverty, people continue to live in these areas. Our research aims to investigate why people continue to reside in these areas in such conditions and explore the feasibility of relocation as a possible solution to catastrophe risk mitigation, considering people's perceptions of living in these locations. This study uses a mixed method's approach to achieve its objectives, based on primary data, collected through a household survey and focus group discussions. A total of 394 char residents from four districts of Assam participated in data collection. The Contingent Valuation Method (CVM) has been applied to understand residents' living preferences. Additionally, we used regression models to determine the factors influencing char residents' preference for living in these vulnerable areas. Our findings reveal that while most people prefer to live in the char areas, the risks of multiple disasters are driving some to consider leaving, exacerbated by infrastructure bottlenecks. However, char dwellers do not view relocation as a desirable solution. Alternatively, improving infrastructure and reducing disaster risks could lead to higher-levels of development and satisfaction for char residents. [ABSTRACT FROM AUTHOR]

Hurricane Ian in 2022 was a destructive category 4 Atlantic hurricane striking the state of Florida, which caused hundreds of deaths and injuries, catastrophic property damage, and an economic loss of more than $112 billion. Before the landfall of Ian in Florida, the state government issued evacuation orders in high-risk zones to reduce casualties and injuries. However, there is limited data available to monitor the actual evacuation patterns and compliance with the evacuation orders at a large geographic scale. This study utilizes human mobility data (i.e. SafeGraph Weekly Pattern) to analyse the spatial patterns of evacuation during Hurricane Ian in 2022. The objectives of the study include three key aspects: 1) proposing an analytical workflow that utilizes human mobility data to detect mobility patterns in disasters and other emergency events; 2) identifying significant evacuation patterns, and 3) revealing the spatial variations in the compliance with evacuation orders in the affected areas. Using data science and spatial analysis techniques, this study detected notable changes in population movements, both within Florida and nationwide, which are potentially linked to the hurricane-induced population evacuation. The distance decay pattern of population flows from Florida demonstrates a propensity for individuals to relocate to nearby areas during the hurricane. Furthermore, the increase in population outflows from the impacted areas suggests the effectiveness of mandatory evacuation orders. A more pronounced increase in outflows from designated mandatory evacuation areas points to the public awareness of the evacuation zone designation. This study provides large-scale, fine-resolution analysis of evacuation behaviours in natural disasters which cannot be easily detected in traditional data sources. The analytical workflows provide actionable tools for government agencies and policymakers to evaluate the effectiveness of evacuation orders and improve evacuation plans in future disasters. [ABSTRACT FROM AUTHOR]

The current work presents a case study for the state-of-the-art multimodal risk assessment approach, which is especially appropriate for environmental wind-wave dynamic systems that are either directly physically observed or numerically modeled. High dimensionality of the wind-wave environmental system and cross-correlations between its primary dimensions or components make it quite challenging for existing reliability methods. The primary goal of this investigation has been the application of a novel multivariate hazard assessment methodology to a combined windspeed and correlated wave-height unfiltered/raw dataset, which was recorded in 2024 by in situ NOAA buoy located southeast offshore of Greenland. Existing hazard/risk assessment methods are mostly limited to univariate or at most bivariate dynamic systems. It is well known that the interaction of windspeeds and corresponding wave heights results in a multimodal, nonstationary, and nonlinear dynamic environmental system with cross-correlated components. Alleged global warming may represent additional factor/covariate, affecting ocean windspeeds and related wave heights dynamics. Accurate hazard/risk assessment of in situ environmental systems is necessary for naval, marine, and offshore structures that operate within particular offshore/ocean zones of interest, susceptible to nonstationary ocean weather conditions. Benchmarking of the novel spatiotemporal multivariate reliability approach, which may efficiently extract relevant information from the underlying in situ field dataset, has been the primary objective of the current work. The proposed multimodal hazard/risk evaluation methodology presented in this study may assist designers and engineers to effectively assess in situ environmental and structural risks for multimodal, nonstationary, nonlinear ocean-driven wind-wave-related environmental/structural systems. The key result of the presented case study lies within the demonstration of the methodological superiority, compared to a popular bivariate copula reliability approach. [ABSTRACT FROM AUTHOR]

Impacts of natural hazards are on an increase globally. To mitigate these impacts, sound disaster risk reduction strategies must rely on comprehensive inventory of natural hazards. However, to date, many regions worldwide still suffer from a dearth of information. The goal of our research is to assess how, with limited means and several methods, environments that are typically under-researched can be covered with accurate information on which further research aiming at understanding and mitigating disaster risk could be set out. We focus on the Kigezi highlands, an under-reported and populated tropical region of Uganda (Africa) known to be impacted by natural hazards. We conducted a multi-method multi-temporal inventory over the entire region using satellite image analysis, exploration of literature and archives, and field surveys. We also set up a network of 15 geo-observers to collect accurate space-time data on seven natural hazards. Our multi-method inventory generated a database comprising over 4000 natural hazard occurrences with frequent natural and anthropogenic interactions. Our results indicate that Kigezi highlands is a hotspot for natural hazards associated with extensive risks, whose numbers are underestimated. We therefore highlight the importance of working with a multiple-method approach in characterizing natural hazards and their interactions. [ABSTRACT FROM AUTHOR]

The physical structure of urban settlements has become increasingly vulnerable to hazards following the growing trends of natural hazards, including earthquakes. The concept of resilience has gained momentum to facilitate better planning and response to such hazards. This research seeks to develop a conceptual spatial framework considering different phases of disaster risk management to evaluate urban physical resilience. Twenty indicators that define urban structure are identified and included in an Interpretive Structural Modeling—Analytic Network Process (ISM-ANP) hybrid model for analysis. The model and the indicator weights are adjusted using statistical and optimization techniques. District 4 of Tehran has been selected as the study area, and the proposed evaluation framework is applied to several zones with different physical urban structures. According to the results, the most important indicators of urban structure are the Robustness of Buildings, Street Width, Building Density, and Aspect Ratio. Sensitivity analysis and scenario-making are performed to explore the desired state of urban physical resilience for each zone. The results of the case study indicate moderate levels of urban physical resilience. The study provides more clear and practical insights into the concept of resilience to help urban planners and decision-makers improve urban physical resilience. [ABSTRACT FROM AUTHOR]

The ionosphere is characterized by a large number of disturbances generated in response to a wide range of phenomena, including natural hazards, space weather and man‐made events. Identification of the origin of ionospheric disturbances (ID), especially in real or near‐real‐time (NRT), is an extremely difficult task, and it is one of the most interesting fundamental scientific questions. In this paper we present, for the first time, an approach for an automatic and NRT‐compatible detection and recognition of the source of ionospheric disturbances in time series of total electron content (TEC) measured by the Global Navigation Satellite Systems (GNSS) method. The main idea is (a) to analyze main characteristics (such as spatio‐temporal features and frequency content) of ID generated by known sources, and (b) in NRT, to rapidly examine ID's features, and, based on this information, recognize their source. Currently, our database contains TEC data series with response to earthquakes, volcanic eruptions, tornadoes, explosions, rocket launches, equatorial plasma bubbles and geomagnetic storms. Our developments are important for the future assessment of natural hazards from the ionosphere, and also for NRT Space Weather nowcast and applications. Also, our work presents important information about the physical properties of ID of different origins. Key Points: For the first time, we present a technique enabling identification of the source of ionospheric disturbances of different originsOur approach can be used for Near‐Real‐Time applications and Near‐Real‐Time discrimination of the source of ionospheric disturbanceA database with different Space Weather and Natural Hazard events is created and analyzed to study parameters of ionospheric disturbances [ABSTRACT FROM AUTHOR]

Increasing flood losses in the Gulf of Mexico related to development patterns and climate hazards pose serious threats to resilience and insurability. The purpose of this study is to understand how scale, social vulnerability, risk, and urban form relate to National Flood Insurance Program (NFIP) policy coverage and flood exposure. Our multilevel models identify that flooding is significantly clustered by region and counties, especially shoreline counties. Our measures of risk suggest that the Federal Emergency Management Agency (FEMA) special flood hazard area (SFHA) underestimates risk and exposure when compared with the Flood Factor and that there is some compensation in terms of insurance coverage, suggesting a pattern of adverse selection. Older housing stock appears both less insured and less exposed, raising questions of whether current growth patterns are increasing risk independent of environmental change. Our models suggest that census tracts with higher percentages of black residents are less insured and more exposed, and a similar pattern exists for rural areas. Our results highlight the need to seek common solutions across the Gulf of Mexico, concentrating on the most flood-exposed counties, and that specific resilience strategies may be necessary to protect areas with socially vulnerable populations, especially in rural areas. Underlying challenges exist due to the spatial relationship between exposure and social vulnerability and the potential for adverse selection in insurance markets due to different measures of risk. [ABSTRACT FROM AUTHOR]

The stability analysis of rainfall-induced slope failures considers a number of factors including the characteristics of the rainfall, vegetation, geometry of the slope, unsaturated soil characteristics, infiltration capacity, and saturation degree variations. Amongst all these factors, this study aims to investigate the effects of the antecedent rainfall and saturation conditions. A numerical modeling study was conducted using finite difference code software on a representative slope geometry with two different soil types. Two scenarios were followed: The first involved the application of three different rainfall intensities for varying initial saturation levels between 40% and 60%, representing the antecedent saturation conditions. The second scenario involved modeling successive rainfalls for a typical initial saturation degree of 50%. The impact of antecedent rainfall was assessed by determining the time required for failure during the application of a main extreme rainfall after a preceding rainfall of varying durations. Consequently, a zone of susceptible time for failure was suggested for use as a criterion in hazard management, allowing for the tracking of rainfall and its duration through the proposed chart for potential failures. Once the anticipated critical rainfall intensities have been determined through a meteorological analysis, a risk assessment for a specific slope can be conducted using the proposed practical procedure. Accordingly, a control mechanism may be established to detect the potential for a natural hazard. Furthermore, the proposed procedure was applied to a case study, whose modeling insights were in harmony with the real conditions of the slope failure. Thus, this demonstrated the significance of the antecedent conditions in modeling landslides triggered by rainfalls. [ABSTRACT FROM AUTHOR]

The Fraser River Delta (FRD) is a large sedimentary system and home to Metro Vancouver, situated within the unceded territories of several First Nations. This review provides an overview of the geological evolution of the FRD, connecting hydrodynamic processes with sedimentary deposits across its diverse environments, from the river to the delta slope. The study emphasizes the implications of sedimentation and delta evolution for natural hazards and coastal/delta management, pinpointing knowledge gaps. Comprising four main zones—river, delta plain, tidal flats, and delta slope—the FRD is subject to several natural hazards, including subsidence, flooding, earthquakes, liquefaction, and tsunamis. The delta plain, bordering the Fraser River's distributary channels, hosts tidal marshes and flats, including both active and abandoned areas. Active tidal flats like Roberts Bank and Sturgeon Bank receive sediment directly from the Fraser River, while abandoned tidal flats, like those at Boundary Bay and Mud Bay, no longer receive sediment. The tidal flats transition into the delta slope, characterized by sand in the south and mud in the north of the Main Channel. The FRD's susceptibility to hazards necessitates protective measures, with approximately 250 km of dykes shielding the delta plain from river floods and storm surges. Subsidence amplifies the impact of rising sea levels. Earthquakes in the region can induce tsunamis, submarine slope failures, and liquefaction of delta sediments, emphasizing the importance of incorporating sedimentation patterns and delta evolution into management strategies for sustainable urban development, habitat restoration, and coastal defence initiatives. [ABSTRACT FROM AUTHOR]

Natural hazards, such as flood, landslide, and erosion, are the reality of human life. spatial prediction of these hazards and their effectiveness factors are extremely important. The main goal of this study was to prepare multi-hazard probability mapping (flood, landslide, and gully erosion) of the Gorganrood Watershed. In addition, different machine learning models such as Random Forest (RF), Support Vector Machine (SVM), Boosted Regression Tree (BRT), and Multivariate Adaptive Regression Spilines (MARS) were applied. First, a flood, landslide, and gully erosion inventory map was produced using GPS in the field surveys and Google Earth. Factors affecting the hazards were identified, and GIS maps were prepared. The MARS model (AUC = 99.1%) provided the highest predictive performance for flood, landslide, and gully erosion hazards. However, for flood and landslide, the RF model exposed excellent and good performance, respectively. According to the variable importance analysis, drainage density (89.4%), digital elevation model (30.5%), and rainfall (41.7%) were consistently highly ranked variables for flood, landslide, and gully erosion, respectively. Multi-hazard maps can be a valuable tool for the conservation of natural resources and the environment, as well as for sustainable land use planning in multi-hazard-prone areas. [ABSTRACT FROM AUTHOR]

The article considers the impact made by natural hazards on the parameters of the staple food self-sufficiency for the Russian Federation. It is shown how the negative effect of such phenomena is gradually spreading and affects the components of "food sovereignty" and their links. A list for types and dynamic series of hydrometeorological hazards most frequently occurring on the territory of the Russian Federation is compiled for the time interval from 1991 to 2019. Correlation and regression analysis of the dependence observed for production indicators and the use of certain types of product groups (in the context of food balance sheet items) on the occurrence of specific natural hazard types was carried out. The results of the analytical study show that the integral effect of the consequences brought about by natural hazards is reflected in the matrix representing food balance sheets. [ABSTRACT FROM AUTHOR]

The Sundarbans is the world's largest contiguous mangrove forest with an area of about 10,000 square kilometers and shared between Bangladesh and India. This world-renowned mangrove forest, located on the lower Ganges floodplain and facing the Bay of Bengal, has long served as a crucial barrier, shielding southern coastal Bangladesh from cyclone hazards. However, the Sundarbans mangrove ecosystem is now increasingly threatened by climate-induced hazards, particularly tropical cyclones originating from the Indian Ocean. To assess the cyclone vulnerability of this unique ecosystem, using geospatial techniques, we analyzed the damage caused by past cyclones and the subsequent recovery across three salinity zones, i.e., Oligohaline, Mesohaline, and Polyhaline. Our study also examined the relationship between cyclone intensity with the extent of damage and forest recovery. The findings of our study indicate that the Polyhaline zone, the largest in terms of area and with the lowest elevation, suffered the most significant damage from cyclones in the Sundarbans region, likely due to its proximity to the most cyclone paths. A correlation analysis revealed that cyclone damage positively correlated with wind speed and negatively correlated with the distance of landfall from the center of the Sundarbans. With the expectation of more extreme weather events in the near future, the Sundarbans mangrove forest faces a potentially devastating outlook unless both natural protection processes and human interventions are undertaken to safeguard this critical ecosystem. [ABSTRACT FROM AUTHOR]

Cities are increasingly focused on building resilience. The literature on urban resilience governance emphasizes the importance of breaking down organizational silos and fostering collaboration between diverse actors. Yet few studies empirically examine whether existing resilience governance networks possess these theorized characteristics. This paper addresses this gap by focusing on flood resilience governance in four major U.S. cities and uses social network analysis to analyze the collaboration structures of organizations involved in resilience building efforts. Drawing on original survey data, results suggest that resilience governance is multi-level, with city, regional, state, and federal government organizations present in all city networks. The networks are also diverse, with nonprofit organizations, private firms, and educational or research institutions prominently featured. While these results are promising in terms of the potential for resilience to diversify participation in decision-making, we also find low levels of network density, meaning that the networks are generally sparsely connected. This suggests that the network is susceptible to fragmentation if organizations defect from the network. [ABSTRACT FROM AUTHOR]

Climate change has gained global recognition as one of the most urgent challenges in recent decades. Many indigenous people attempt to overcome great challenges in their lives by adhering to their cultural customs and traditional knowledge. This is especially true for those who live in isolated, hilly places and are dependent on natural resources. The aim of the paper is to critically investigate the cultural facets of indigenous people's adaptation to and resilience to climate change in the Chittagong Hill Tracts (CHT), namely in the Rangamati Sadar sub-districts of the Rangamati districts, in Bangladesh. This paper also examines how cultural components in indigenous community are changing and influencing the adaptation process in adverse situations. This study used a critical ethnography technique in conjunction with participant observation, in-depth interviews (N ​= ​55), and focus group discussions (N ​= ​5, 45 participants) during a one-year period (October 2019 to October 2020). According to the study's findings, climate change-related risks have significant adverse effects on indigenous people. However, they develop their strategies to adjust to the adverse environment, like building different kinds of houses, changing cultural customs, relying less on natural resources, and adhering to their indigenous knowledge. They also strengthen their prayers and ceremonial practices, which reinforce their communal ties and allow them to become more resilient against challenges in their community. The popular belief that Indigenous people are stupid, poor, and helpless while they wait for outside help is challenged by this study. It also refutes the discourse that indigenous strategies and knowledge are not undervalued and can be an effective instrument for the adaptation process and combating hazards.

Abstract People residing in the char regions of Assam are among the most vulnerable in the state, and particularly susceptible to multiple disasters such as floods, storms, and land erosion. Despite the existing vulnerabilities and widespread poverty, people continue to live in these areas. Our research aims to investigate why people continue to reside in these areas in such conditions and explore the feasibility of relocation as a possible solution to catastrophe risk mitigation, considering people's perceptions of living in these locations. This study uses a mixed method’s approach to achieve its objectives, based on primary data, collected through a household survey and focus group discussions. A total of 394 char residents from four districts of Assam participated in data collection. The Contingent Valuation Method (CVM) has been applied to understand residents' living preferences. Additionally, we used regression models to determine the factors influencing char residents' preference for living in these vulnerable areas. Our findings reveal that while most people prefer to live in the char areas, the risks of multiple disasters are driving some to consider leaving, exacerbated by infrastructure bottlenecks. However, char dwellers do not view relocation as a desirable solution. Alternatively, improving infrastructure and reducing disaster risks could lead to higher-levels of development and satisfaction for char residents.

In recent years, society become much more interested in nature disasters that cause damage. The consequences of these destructive processes of a meteorological, hydrological and geological nature sometimes remain for years until the affected areas are recultivated, restored and recovered. These natural processes work together, often following each other or triggering each other to create a massive system of destruction that causes a lot of damage and often leads to death and numerous casualties.Moreover, with the changes in the global climate of the Planet, leading to the more frequent manifestation of more destructive natural phenomena - cyclones, storms, huge amounts of precipitation - lead to intensify and manifest in larger scales and volumes geological phenomena such as landslides, mudflows, mud - stone torrents, rockfalls, collapses of different character of slopes, linear and planar erosion etc.This is extremely relevant for Bulgaria, the territory for which is no exception to the manifestation of catastrophic natural phenomena. The country is located in an active orogenic zone as part of the Alpo-Himalayan tectonic belt, characterised by a complex tectonic structure, variable and diverse geological formations and rapid topographical changes.The main focus in the study is to present an advanced, multi-faceted framework for natural disaster risk assessment, integrating biodiversity and archaeological sites. Moreover, the assessments of natural risks made so far do not include sufficient attention to biotopes and archaeological and historical sites.The presented methodology is applied to landslide hazards in a pre-selected area along the northern Bulgarian Black Sea coast. The main goal of this paper is to identify socio-environmental risks in the studied region, as well as to construct risk profiles and visually represent both qualitative and quantitative risk levels in the socio-ecological system and biodiversity.

Due to favorable natural conditions and human impact, the territory of North Macedonia is very susceptible to natural hazards. Steep hillslopes combined with soft rocks (schists on the mountains; sands and sandstones in depressions), erodible soils, semiarid continental climate, and sparse vegetation cover give a high potential for soil erosion and landslides. For this reason, this study presents a multi-hazard approach to geohazard modeling on the national extent in the example of North Macedonia. Utilizing Geographic Information Systems, relevant data about the entire research area were employed to analyze and assess soil erosion and susceptibility to landslides and identify areas prone to both hazards. Using the Gavrilović Erosion Potential Method (EPM), an average value of 0.36 was obtained for the erosion coefficient Z, indicating low to moderate susceptibility to erosion. However, a significant area of the country (9.6%) is susceptible to high and excess erosion rates. For the landslide susceptibility assessment (LSA), the Analytical hierarchy process approach is combined with the statistical method (frequency ratio), showing that 29.3% of the territory belongs to the zone of high and very high landslide susceptibility. Then, the accuracy assessment is performed for both procedures (EPM and LSA), showing acceptable reliability. By overlapping both models, a multi-hazard map is prepared, indicating that 22.3% of North Macedonia territory is highly susceptible to erosion and landslides. The primary objective of multi-hazard modeling is to identify and delineate hazardous areas, thereby aiding in activities to reduce the hazards and mitigate future damage. This becomes particularly significant when considering the impact of climate change, which is associated with increased landslide and erosion susceptibility. The approach based on a national level presented in this work can provide valuable information for regional planning and decision-making processes.

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications. This study addresses the importance of estimating the friction angle and presents the development of four soft computing models: YJ-FPA-MLPnet, YJ-CRO-MLPnet, YJ-ACOC-MLPnet, and YJ-CSA-MLPnet. First of all, the Yeo-Johnson (YJ) transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal variances. This technique is expected to improve the accuracy of friction angle prediction models. The friction angle prediction models then utilized multi-layer perceptron neural networks (MLPnet) and metaheuristic optimization algorithms to further enhance performance, including flower pollination algorithm (FPA), coral reefs optimization (CRO), ant colony optimization continuous (ACOC), and cuckoo search algorithm (CSA). The prediction models without the YJ technique, i.e. FPA-MLPnet, CRO-MLPnet, ACOC-MLPnet, and CSA-MLPnet, were then compared to those with the YJ technique, i.e. YJ-FPA-MLPnet, YJ-CRO-MLPnet, YJ-ACOC-MLPnet, and YJ-CSA-MLPnet. Among these, the YJ-CRO-MLPnet model demonstrated superior reliability, achieving an accuracy of up to 83% in predicting the friction angle of clay in practical engineering scenarios. This improvement is significant, as it represents an increase from 1.3% to approximately 20% compared to the models that did not utilize the YJ transformation technique.

Hurricane Ian in 2022 was a destructive category 4 Atlantic hurricane striking the state of Florida, which caused hundreds of deaths and injuries, catastrophic property damage, and an economic loss of more than $112 billion. Before the landfall of Ian in Florida, the state government issued evacuation orders in high-risk zones to reduce casualties and injuries. However, there is limited data available to monitor the actual evacuation patterns and compliance with the evacuation orders at a large geographic scale. This study utilizes human mobility data (i.e. SafeGraph Weekly Pattern) to analyse the spatial patterns of evacuation during Hurricane Ian in 2022. The objectives of the study include three key aspects: 1) proposing an analytical workflow that utilizes human mobility data to detect mobility patterns in disasters and other emergency events; 2) identifying significant evacuation patterns, and 3) revealing the spatial variations in the compliance with evacuation orders in the affected areas. Using data science and spatial analysis techniques, this study detected notable changes in population movements, both within Florida and nationwide, which are potentially linked to the hurricane-induced population evacuation. The distance decay pattern of population flows from Florida demonstrates a propensity for individuals to relocate to nearby areas during the hurricane. Furthermore, the increase in population outflows from the impacted areas suggests the effectiveness of mandatory evacuation orders. A more pronounced increase in outflows from designated mandatory evacuation areas points to the public awareness of the evacuation zone designation. This study provides large-scale, fine-resolution analysis of evacuation behaviours in natural disasters which cannot be easily detected in traditional data sources. The analytical workflows provide actionable tools for government agencies and policymakers to evaluate the effectiveness of evacuation orders and improve evacuation plans in future disasters.

Tropical cyclones (TC) pose a persistent natural hazard to Costa Rica. Exposure to natural hazards, such as mass movements and floods, is compounded by a growing urban population and inadequate land use planning. This study conducted a comprehensive analysis of the economic impacts of TC of Costa Rica from Hurricane Joan in 1988 to Hurricane Eta in 2020, assessing the impact by municipality and economic sector using baseline information of the Ministry of National Planning and Economic Policy. According to the study, road infrastructure (933.8 US million), agriculture (280.5 US million), river rehabilitation (153.96 US million), housing 98.26 (US million), and health (81.74 US million) were among the sectors most severely affected by TC over the past 30 years. The Pacific basin municipalities in Costa Rica were found to be the most vulnerable, primarily due to the indirect impacts of TC. The study's results offer useful information on the economic sectors and municipalities that are most exposed from TC in Costa Rica and provide a replicable methodology for other regions and countries facing similar tropical phenomena.

Abstract Background Archaeological sites are increasingly threatened by climate-related hazards. In response, heritage management authorities initiated projects to document damage and plan risk assessment measures. We present a project initiated after the heavy rainfall and subsequent flood event of July 2021, which involved extensive fieldwork to document the damage to archaeological sites in the Rhineland. We use this database to characterise and assess the damage and investigate site-specific and geospatial factors to identify potential predictive parameters for site damage. Results During fieldwork, we found that the flood damaged 19% of the 538 archaeological sites surveyed. The majority of damaged sites are relatively recent, dating from the medieval or modern periods, and are associated with the use of water power. Damage was mainly caused by erosion, floating debris and washout, e.g. mortar. In a case study, we tested the option of comparing pre- and post-disaster Airborne Laser Scanning elevation data to identify damages. It showed that not only the damage detected during fieldwork was found but also additional areas of loss. In general, however, and quantified based on the entire dataset, the ordnance survey Airborne Laser Scanning data were of limited use for monitoring flood-related damage and could not replace fieldwork. Our statistical analysis of possible risk factors, including both site characteristics and geospatial parameters, using Naïve Bayes Modelling and chi-squared tests, showed that no set of parameters could consistently predict the preservation or damage of archaeological sites across all catchments. In contrast, some external geospatial factors correlated with the occurrence of damage. Conclusions The study highlights both the strengths and limitations of the approaches used to assess and predict the damage to the archaeological heritage in the 2021 flood zones of the Rhineland. It also demonstrates the complexity of the data and spatial processes involved, which limits generalisation but can still inform decision-making for archaeological site management and on-site protection measures in flood-prone areas. With the prospect of more frequent heavy rainfall due to climate change, the specific needs of the archaeological heritage should be integrated into broader prevention and disaster management plans.

Soils are key natural resources for the Earth’s system; however, human impacts, especially, soil erosion are considered serious threats. Therefore, identifying and assessing effective factors to understand erosion hot spots at different scales is critical to developing effective land management plans and ensuring the sustainability of the territory. This study was conducted to determine and prepare an erosion risk map, but to prioritize the survey at different scales, such as sub-basin and watershed ones. To achieve this goal, geographic information system (GIS) and remote sensing data (RS) were used combining the analysis network process method (ANP) and ICONA model (Institute for the Conservation of Nature). As study case, we selected the degraded areas of the Gorganrood watershed located in the north of Iran. The study area was obtained for very low, low, medium, high, and very high-risk classifications of 14.0, 21.4, 17.9, 31.3, and 15.4%, respectively. Results from the ICONA model also indicated that 12.8, 28.8, 22.1, 27.9, 8.5, and 0.03% belong to very low, low, medium, high, very high, and without risk of erosion, respectively. According to the validation results, it was found that the accuracy of ANP and ICONA models are 0.83 and 0.80, respectively, which indicates the suitability of the models for preparing the erosion map of the region is appropriate and useful for designing land management plans. We conclude that both models can be used to develop the erosion map potential and to prioritize sub-basins if a complete database of geomorphological characteriscs and human activities are accurate previously defined.

Background: Archaeological sites are increasingly threatened by climate-related hazards. In response, heritage management authorities initiated projects to document damage and plan risk assessment measures. We present a project initiated after the heavy rainfall and subsequent flood event of July 2021, which involved extensive fieldwork to document the damage to archaeological sites in the Rhineland. We use this database to characterise and assess the damage and investigate site-specific and geospatial factors to identify potential predictive parameters for site damage. Results: During fieldwork, we found that the flood damaged 19% of the 538 archaeological sites surveyed. The majority of damaged sites are relatively recent, dating from the medieval or modern periods, and are associated with the use of water power. Damage was mainly caused by erosion, floating debris and washout, e.g. mortar. In a case study, we tested the option of comparing pre- and post-disaster Airborne Laser Scanning elevation data to identify damages. It showed that not only the damage detected during fieldwork was found but also additional areas of loss. In general, however, and quantified based on the entire dataset, the ordnance survey Airborne Laser Scanning data were of limited use for monitoring flood-related damage and could not replace fieldwork. Our statistical analysis of possible risk factors, including both site characteristics and geospatial parameters, using Naïve Bayes Modelling and chi-squared tests, showed that no set of parameters could consistently predict the preservation or damage of archaeological sites across all catchments. In contrast, some external geospatial factors correlated with the occurrence of damage. Conclusions: The study highlights both the strengths and limitations of the approaches used to assess and predict the damage to the archaeological heritage in the 2021 flood zones of the Rhineland. It also demonstrates the complexity of the data and spatial processes involved, which limits generalisation but can still inform decision-making for archaeological site management and on-site protection measures in flood-prone areas. With the prospect of more frequent heavy rainfall due to climate change, the specific needs of the archaeological heritage should be integrated into broader prevention and disaster management plans. [ABSTRACT FROM AUTHOR]

Enhancing disaster risk governance is crucial for improving the ability to cope with and adapt to the impacts of climate change. China experiences a diverse range of natural disasters distributed widely across the country. The evolving demographic, socio-economic, and geographic landscape requires heightened attention as it significantly shapes the extent and dynamics of regional vulnerability to natural hazards. This study aims to evaluate the spatial and temporal changes of social vulnerability in China based on data at the county level in the years of 2000, 2010 and 2020. The Social Vulnerability Index (SVI) was constructed by adopting nine demographic and socio-economic variables drawn from the variables in the US CDC SVI framework using Principal Component Analysis. Both economic development and demographic factors played important roles in determining the patterns of spatial distribution of SVI over the period. Result of spatial data analysis reveals distinctive and stable spatial heterogeneity of social vulnerability across counties. Furthermore, we applied the SVI with real-world impact data to validate the effectiveness and relevance of the constructed index. Using drought risk as an illustrative example, the research also assessed the applicability of the SVI in evaluating drought risks in China, considering hazard, exposure, and vulnerability. Public and private decision-makers can use the constructed SVI to identify hot spots and population subgroups that are most vulnerable to natural hazards to inform and facilitate adequate risk prevention and management at the national and local levels. [ABSTRACT FROM AUTHOR]

A significant number of people, either seasonally or permanently, migrate from the Thar Desert in Pakistan each year due to droughts caused by climate change. This study aims to investigate the determinants and consequences of these migration decisions, examine the effectiveness of migration as a climate adaptation strategy, and identify challenges in adapting to these changes. Data were gathered from 400 migrated households in the Mithi sub-district. A mixed-method approach was used, combining qualitative and quantitative methods. The findings revealed that threats to the standard of living, including lack of food and clean drinking water, unemployment, and limited educational and medical opportunities, were the primary reasons for permanent and temporary migration from ancestral locations. Migration significantly impacted the origin and destination regions, with positive or negative effects. Specifically, migrants identified various consequences for both the origin and destination communities, including population decline (63%), changes in age structure, increased demand for housing, economic fluctuations (73%), alterations in healthcare services, and increased psychological stress (77%). The study also revealed that individuals who migrated from the Thar Desert experienced improved conditions compared to their previous location, such as diversification of income sources, increased job stability, access to clean water and food, reduced health risks, and overall improvements in their living conditions. However, the destination communities faced significant challenges due to widespread resource depletion and environmental deterioration. Migrants encountered barriers to developing resilient livelihoods in destination areas, including lack of proper knowledge and information, institutional and government issues, environmental and technological challenges, and social and cultural issues. The study highlights the urgent need for comprehensive policies and sustainable solutions to address the root causes of migration and support the resilience of vulnerable populations. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TC) pose a persistent natural hazard to Costa Rica. Exposure to natural hazards, such as mass movements and floods, is compounded by a growing urban population and inadequate land use planning. This study conducted a comprehensive analysis of the economic impacts of TC of Costa Rica from Hurricane Joan in 1988 to Hurricane Eta in 2020, assessing the impact by municipality and economic sector using baseline information of the Ministry of National Planning and Economic Policy. According to the study, road infrastructure (933.8 US million), agriculture (280.5 US million), river rehabilitation (153.96 US million), housing 98.26 (US million), and health (81.74 US million) were among the sectors most severely affected by TC over the past 30 years. The Pacific basin municipalities in Costa Rica were found to be the most vulnerable, primarily due to the indirect impacts of TC. The study's results offer useful information on the economic sectors and municipalities that are most exposed from TC in Costa Rica and provide a replicable methodology for other regions and countries facing similar tropical phenomena. [ABSTRACT FROM AUTHOR]

Introduction Although a flood is an extreme and exceptional flow, every exceptional flow will not turn into a destructive flood, different factors must be changed in nature to cause destruction, damage, and casualties. In general, floods can be divided into four groups flash floods, river floods, urban floods, and coastal floods. Urban floods usually cause fewer casualties and mainly create damage caused by flooding, disruption of traffic, interruption in socio-economic activities, and problems of this kind. The damage caused by non-urban floods is often heavy and sometimes accompanied by high and catastrophic casualties. According to the Mediterranean climate, Iran is the seventh country in the world in terms of flooding. The flood-prone areas of the country are estimated to be around 91 MHA. In other words, 55% of the country's surface has contributed to the production of surface runoff, of which about 42 MHA have moderate to very high flood intensity. The review of sources shows the development of knowledge-based methods, statistical methods, and artificial intelligence algorithms in predicting flood-prone areas in urban and non-urban watersheds in different regions worldwide. However, a hybrid method of the Fuzzy, Delphi, and Analytic Hierarchy Processes (FDAHP) in urban flood susceptibility has not been used. Regarding the questions, what are the most important factors in urban flood occurrence? Is it possible to determine flood-prone areas in urban areas using the FDAHP hybrid model?, this study aims to identify the factors influencing the occurrence of floods and predict flood-prone areas in Sanandaj City. Materials and Methods In this study, which has a descriptive-analytical-comparative approach, to predict floods in Sanandaj City, the FDAHP was used. First, each of the conditioning factors (14 factors) was scored by flood experts and completed using the scores obtained from other stages of the FDAHP model. After collecting the opinions, the relative weights of the indicators were determined using the hybrid model, and finally, the flood susceptibility map of Sanandaj City was prepared using ArcGIS 10.5 software. The different stages of modeling with the FDAHP method are as follows: 1) experts' opinions, First, with the help of experts (technical and executive experts of the Kurdistan Province and Sanandaj Municipality's Natural Resources and Watershed Administration) the decision-making parameters according to their importance qualitatively or, if possible, quantitatively they rate. (Opinion scales are: very important with a nine score, importance with a seven score, average importance with a five score, low importance with a three score, and no importance with a one score). 2) Calculation of fuzzy numbers. After the preliminary stage, which includes a survey of experts in the form of a qualitative or quantitative questionnaire, fuzzy numbers were calculated based on the results of this survey. 3) Forming the matrix of fuzzy pairwise comparisons. 4) Calculating the fuzzy weight of the parameters. 5) De-fuzzification of model parameters. 6) Evaluation of the accuracy of the output of the spatial flood prediction model. Results and Discussion The findings showed that slopes of less than 10% (flat), areas with an elevation of fewer than 1400 m above sea level, slope aspect and curvature (flat), urban land use with high building density, rainfall of more than 369 mm and rock type Qt2 (Quaternary alluvium) have the highest susceptibility to floods compared to other types in Sanandaj City. Also, the results show that, on the one hand, increasing the distance from the residential areas, the distance from the roads, and the distance from the waterways decreases the susceptibility to urban flooding. On the other hand, with the increase in building density, the density of roads and the density of waterways in Sanandaj increases the susceptibility to flooding. These results are consistent with the other studies that concluded the residential areas with the largest area have the highest risk of vulnerability. Our findings indicated that the density of the waterway, the slope, and the distance from the waterway have the most influence on the occurrence of floods in Sanandaj City. Rainfall, road density, building density, distance from residential areas, distance from roads, flow accumulation, elevation, land use, lithology, and slope curvature are the next priorities in terms of importance in the occurrence of floods in Sanandaj City. Conclusion The flood prediction map showed that a large part of Sanandaj City including the City's northern, western, southern, and center, which is crowded, has more potential for urban flooding. For example, the old and dilapidated buildings of the City center are highly susceptible to floods, but the border areas around the City have less exposure to this phenomenon. Therefore, as the distance from the City center and the residential regions increases, the potential for flooding decreases. Based on the value of 80.56% of the area under the curve, the validation results indicated that 80.56% of the areas where urban flooding is visible have been correctly predicted. The FDAHP hybrid model had a high ability to estimate the areas prone to urban flooding and, therefore it can be tested and evaluated as a management tool to identify urban floods in other similar areas. Since our aim concerning the occurrence of floods is more on flood mitigation, according to the mentioned theoretical bases and the existing views in the field of flood prediction maps, it can be stated that the ruined canals and the network of waterways, unauthorized constructions, high density, and elevation of buildings, old and dilapidated buildings and the irregularity of the canals have aggravated the urban flooding. Overall, it can be said that obtaining an accurate and reasonable urban flood prediction map can help City managers and planners identify flood-prone areas to manage the urban flood crisis. [ABSTRACT FROM AUTHOR]

Geospatial technologies have revolutionised the field of hazard research as well as geography field-courses, providing powerful tools to analyse and visualise geospatial data for decision-making purposes. This paper presents a case study of Paper2GIS, an application for field data collection tested during an international field course in Seyðisfjörður, East Iceland. The field course was designed to expose students to the practical application of interdisciplinary geospatial technologies in hazard research. Students experienced geological/physical geography field mapping as well as data collection using Paper2GIS. The results demonstrate the effectiveness of Paper2GIS in facilitating hazard research with a participatory component, as well as the simplification of data collection during geography field courses. Paper2GIS proved to be an effective tool for enhancing collaboration, data visualisation, and decision-making. The findings provide valuable insights for researchers, practitioners, and educators looking to incorporate low-tech geospatial technologies into their work. [ABSTRACT FROM AUTHOR]

The influence of cartographic representation of landslides on susceptibility models is well-known but often neglected by academic studies, which rarely explore its practical implications. This study aims to address this knowledge gap by evaluating the impact of different landslide sampling strategies on statistical models of landslide susceptibility in Ouro Preto, a UNESCO World Heritage Site located in South-eastern Brazil. The study's main objective was to assess how different landslide sampling strategies affected statistical susceptibility models. It adopted an innovative methodological approach that categorized dependent variables between training and test subgroups, adopting both balanced and unbalanced divisions of dependent variables, and focusing on shallow and deep landslides. In addition, the study introduced a systematic and critical approach to cartographic representation, providing valuable insights for future research and practice on landslide susceptibility mapping. Eighteen models were produced using an inventory with 57 historical landslides mapped with an Unmanned Aerial Vehicle. The area and volume of these landslides were determined. Three divisions of dependent variables were adopted between training and test subgroups: one balanced division, with large and deep landslides in both subgroups, and two unbalanced divisions, with a predominance of large and deep landslides in the training subgroup or test subgroup. The construction of landslide susceptibility models employed the information value method, validated through success and prediction curves. The results show the significant influence of cartographic representation (point or polygon) on the quality of statistical models and the spatial distribution of susceptibility classes. The polygonal cartographic representation and balanced partition of dependent variables produced the best results. However, it is emphasized that this cartographic representation is not universally optimal in other contexts. The worst result was obtained using a point and random cartographic representation. Overall, findings indicate the need for more accurate landslide inventories and databases, possibly through standards and regulations. [ABSTRACT FROM AUTHOR]

With the burgeoning threats from natural hazards and extreme weather events driven by climate change in every section and sector of society, building resilience against disasters and risks posed by climate change has become an unescapable discourse. The study's main thrust is to establish the determinants of resilience building in Zimbabwe's rural communities. The study adopted the qualitative constructivist grounded theory approach in conjunction with participatory action research to gain insights from community members and traditional leaders. A total of forty-six participants from Chimanimani District, Zimbabwe participated in the study. The findings revealed that the availability of natural and human resources, collective efficacy, government and development agency support systems, indigenous knowledge systems, and livelihood diversification were crucial determinants of resilience building for rural communities in Zimbabwe. The identified determinants for resilience building signal that these determinants need to be harnessed to inform policy and practice interventions. Furthermore, the availability of the identified determinants implies that rural communities can minimise, and prevent losses, reduce exposure to natural hazards, disasters and climate change risks and manage the impacts of natural hazards whenever they emerge. The study recommends that these determinants be complemented by access to technology, resilient community infrastructures, robust social protection systems, and hazard risk assessment participatory processes by rural communities. In addition, effective emergency and health services, among other contingent measures essential to improve community resilience. [ABSTRACT FROM AUTHOR]

Floods are significant natural disasters that have wide-ranging social, economic, and environmental consequences on a global scale. They lead to land degradation and increased erosion, which in turn negatively impacts water quality. In the present study, the Söğütlü stream watershed in the Eastern Black Sea Region of Turkey was chosen as the study area to create a flood risk map using Analytical Hierarchy Process and Weighted Overlay tools in the ArcGIS. The raster maps, including soil type, rainfall, landuse, slope, aspect, elevation, and distance to the stream, were utilized as the foundation for the analysis. The flood risk map generated from this analysis reveals five risk zones that is very high, high, moderate, low, and very low-risk zones. Statistical analysis was performed to evaluate the obtained data. The study results indicate that low and moderate areas encompass 95.98% of the watershed, while high-risk and very high-risk areas account for 4.02% of the watershed in the flood risk map of the Söğütlü watershed. It was observed that areas in close proximity to rivers, low slope end elevation were more susceptible to flooding. This study is great importance as it allows for the development of preventive, preparatory, and post-disaster plans before flood events occur by providing a flood risk map. Moreover, the creation of a flood risk map at the watershed scale helps planners in undertaking soil and water conservation efforts during watershed planning. [ABSTRACT FROM AUTHOR]

A rapidly growing population across mountain regions is pressuring expansion onto steeper slopes, leading to increased exposure of people and their assets to slow‐moving landslides. These moving hillslopes can inflict damage to buildings and infrastructure, accelerate with urban alterations, and catastrophically fail with climatic and weather extremes. Yet, systematic estimates of slow‐moving landslide exposure and their drivers have been elusive. Here, we present a new global database of 7,764 large (A ≥ 0.1 km2) slow‐moving landslides across nine IPCC regions. Using high‐resolution human settlement footprint data, we identify 563 inhabited landslides. We estimate that 9% of reported slow‐moving landslides are inhabited, in a given basin, and have 12% of their areas occupied by human settlements, on average. We find the density of settlements on unstable slopes decreases in basins more affected by slow‐moving landslides, but varies across regions with greater flood exposure. Across most regions, urbanization can be a relevant driver of slow‐moving landslide exposure, while steepness and flood exposure have regionally varying influences. In East Asia, slow‐moving landslide exposure increases with urbanization, gentler slopes, and less flood exposure. Our findings quantify how disparate knowledge creates uncertainty that undermines an assessment of the drivers of slow‐moving landslide exposure in mountain regions, facing a future of rising risk, such as Central Asia, Northeast Africa, and the Tibetan Plateau. Plain Language Summary: Slow‐moving landslides can damage buildings and infrastructure, while potentially leading to thousands of fatalities with a sudden collapse. As populations expand in mountain regions, more communities settling into steeper terrain could be exposed to landslide‐prone areas. Yet, our estimates of populations exposed to landslides excludes slow‐moving landslides. We address this by identifying unstable slopes, inhabited by human settlements, from a new global database of 7,764 reported large slow‐moving landslides located in nine IPCC mountain‐risk regions. Across most regions, we find that landslide exposure increases with sprawling urbanized areas, though clearly not with steeper terrain. We show regional contrasts in how exposure to floods may drive people to settle on unstable slopes. East Asia stands out in how landslide exposure increases in more urbanized basins with gentler slopes and less flood exposure. Our results indicate that communities in mountain regions, facing increasing future landslide and flood risk, have the least certain insight on slow‐moving landslide exposure and their drivers. Key Points: We present a database of 7,764 reported large (A ≥ 0.1 km2) slow‐moving landslides in nine IPCC regions and find 563 are inhabitedWe learn more about landslide exposure from regional responses to flood exposure than from an abundance of slow‐moving landslidesUrbanization in basins can be a relevant driver of landslide exposure, while steepness and flood exposure have varying regional influences [ABSTRACT FROM AUTHOR]

Assam is one of the highly flood-affected states in India, mostly by river Brahmaputra and its territories. Millions of people become homeless, jobless and face a life threat, thousands lose their lives during floods. This article presents a flood monitoring and warning system using artificial neural networks (ANN) and embedded systems. Sensor nodes communicate with a cloud server to transmit real-time data, aiding in flood prediction. An ANN, trained on a decade of data from Sonitpur district in Assam, predicts flood probabilities. The ANN accurately mimics rainfall data with 97-98% accuracy by considering temperature, humidity, and rainfall. It correlates these factors to determine water levels and predict flood probabilities. The system monitors environmental conditions and flood likelihood based on past data, displaying real-time information on temperature, humidity, rainfall, and water levels. [ABSTRACT FROM AUTHOR]

Verbal accounts, supported by limited ground‐based and satellite images, reveal decreasing riparian woodland and a loss of large trees along the rivers of the Kruger National Park (KNP, South Africa) over the last century. A multi‐decadal analysis was conducted to identify trends in extent and possible drivers of riparian woodland change. Aerial and satellite imagery (1936–2018) were used to quantify changes in the extent of riparian woodland at 18 sites on 14 rivers in KNP. These changes were compared in a multivariate time‐series with river flow and local rainfall. Particular attention was paid to cumulative flow effects, as well as the frequency and magnitude of large infrequent disturbances such as droughts and floods. Riparian tree cover fluctuated over the time period, and the trajectory of change varied between sites. Most (11) sites experienced a decrease in overall riparian tree cover over ~80 years, with these declines being significant at six sites. Peak flow and maximum rainfall events were strongly associated with these decreases, indicating that flood events are potentially the biggest driver of tree loss from the system. Indeed, the mega‐flood event of 2000 and subsequent large floods have resulted in substantial declines in riparian woodland extent in recent decades. Alternatively, flow variability and cumulative rainfall significantly influenced woodland expansion in isolated cases. With global change models predicting more erratic rainfall and an increased likelihood of large infrequent disturbances, together with increasing demands to abstract more water, the long‐term future of these dynamic habitats and their associated biota here is uncertain. [ABSTRACT FROM AUTHOR]

The paper analyzes the concept of resilience in the context of the Cvjetno housing estate in Zagreb, Croatia, a recognized cultural and historical complex. While extensive literature exists on the estate's architectural and historical significance, limited research explores its resilience. This study aims to fill this gap by examining the Cvjetno estate's planned resilience to natural hazards and socioeconomic challenges. It investigates the estate's design, historical context, and physical characteristics through archival documents, historical records, and field research. The analysis focuses on the estate's planned resilience to natural disasters, particularly flooding from the nearby river Sava and seismic risks due to its location in a seismically active area. Based on the literature on resilient communities, the study draws on dimensions, characteristics, and qualities of resilience, and explores how the Cvjetno estate exemplifies these traits. Future research could explore and compare similar mid-20th century European settlements to enhance the understanding of urban resilience during the modern era. [ABSTRACT FROM AUTHOR]

Passive seismic monitoring (PSM) is emerging as a tool for detecting rockfall events and pre‐failure seismicity. In this paper, the potential of PSM for rockfall monitoring is assessed through a case study carried out in Gros‐Morne, Eastern Québec, in a region with prominent roadside cliffs, where more than 500 fallen rocks are found on the main regional road each year. The proposed method relies on using sensitive STA‐LTA windows to detect a very large number of seismic events and build a comprehensive catalog. In total, more than 70,000 seismic events were detected over one year. Gaussian mixtures are used to partition the data set. Based on visual inspection of the data, a main working hypothesis is that the seismic events can be clustered into three groups. After analyzing the spatio‐temporal distribution of the events in each group, we find that the events of one cluster can be associated with anthropogenic activity. The frequency of occurrence of the events of the different clusters and their link with meteorological data is also examined through a regression exercise, to assess the importance of the meteorological variables as explanatory variables. The results allow us to postulate on the physical origins of the signals in the different clusters, attributing them to rockfall activity and wind‐induced seismic noise. Plain Language Summary: This study explores the use of passive seismic monitoring (PSM) as a method to detect and track rockfall events, focusing on an area in Eastern Canada, where more than 500 rockfalls reach the main regional road each year. We utilized an automated seismic event detection algorithm to identify over 70,000 seismic events in a one‐year period. By analyzing these events with machine learning, we discovered patterns that allowed us to group the events into three categories. One of these groups is linked to human activity, while the other two are influenced by natural factors such as rainfall, strong temperature changes, and wind speed. We interpret the origins of the signals in the latter two groups as rock impacts during rockfalls for one, and strong winds hitting the rock cliff for the other. Our research shows that PSM, coupled with machine learning, is a powerful technique for understanding geomorphological dynamics at a small timescale. Key Points: We use feature engineering to automatically analyze seismic signals measured on a cliff with frequent rockfallsClustering proves useful to distinguish signals with different waveforms and frequency contentAnalysis of the relationships between meteorological variables with each cluster enables their physical interpretation, from geomorphological dynamics to anthropic activity [ABSTRACT FROM AUTHOR]

Many cities across the United States (U.S.) face threats from natural hazards, and as climate change continues to worsen, cities will face increased risks. Emergency management helps cities to mitigate these risks and adapt to shifting threats. Based on the Rockefeller City Resilience Framework and City Resilience Index Driver 3, Indicator 3.4, the scope of this research was to develop an emergency management maturity model and explore emergency management capacity for future disasters based on an assessment of five cities across the U.S. With the resulting data, a city's score would serve as an evaluation of the emergency response indicator (Indicator 3.4) in order to eventually enter the City Resilience Index. The cities selected were Buffalo, New York; Honolulu, Hawaii; Memphis, Tennessee; New Orleans, Louisiana; and Saint Paul, Minnesota, based on their hazard/risk factors. It should be noted that these scores were intended to compare resilience in a city over time and identify unique areas in which the cities could improve emergency management and were not meant to rank the cities against each other. An interesting find was the overlap between the counties and cities regarding policy data, creating a new avenue for research in local level emergency management. [ABSTRACT FROM AUTHOR]

During the rainy season in Brazil, landslides can have catastrophic consequences, including loss of life, damage to urban infrastructure and significant economic impacts. Now, more than eight million people in Brazil live in disaster-prone areas. The objective of this article is to present a chronological review of the principal policies and programmes at the national, state, and municipal levels, with an emphasis on their key actions aimed at mitigating the impact of disasters related to natural hazards, particularly landslides. Our analysis was based on official records of public policies from the national, state, and municipal governments. The results show that several initiatives were initially developed at the local and regional level. However, it was not until the late 1980s that a comprehensive organisation of civil protection at the national level emerged as a response measure. It is possible to highlight three phases (understanding, coexistence, and prevention) of institutional action focused on risk management in the face of disasters. Despite the growing knowledge of risk scenarios, disasters continue to occur in Brazil on an annual basis, revealing several obstacles to reducing their impact, particularly given the social and economic disparities between Brazilian regions and the applicability of public policies that must overcome the limitations of each municipality. [ABSTRACT FROM AUTHOR]

The global research community is concerned about the potential impact of Lake Outburst Floods (LOFs) worldwide due to glacier retreat and permafrost melting as a consequence of global climate change. These conditions have led to the formation of new potentially dangerous lakes. Extreme climatic conditions, high topography, and active tectonics highlight the vulnerability of the Andean region. Additionally, the region is exposed to increasing urbanization pressures in mountainous areas. This review presents an updated inventory of LOF events in the Argentinean Andes (31–51° S) as a complete catalog is currently lacking. The main characteristics and hydrodynamic parameters were compiled from the collected historical LOFs, while missing data were estimated in this study. These findings provide insight into the dynamics and outreach of this type of threatening phenomena in the Andean region. Possible triggering mechanisms were investigated to better understand the main causes of LOFs in the Andes. This understanding is crucial for the development of effective prevention and mitigation measures against regional lake-related hazards. [ABSTRACT FROM AUTHOR]

Geographically large climatic hazard events are occurring more frequently, and with this increase, more research emphasis is being placed on their impact. However, a metrology for selecting which communities to survey following an event is not frequently discussed and as a result does not effectively incorporate all relevant disciplines in disaster research. This article provides a method for selecting communities for inclusion in field studies not only based on anticipated damage but also community‐level social factors that show predictive power in long‐term analyses. Due to the perishable nature of disaster data, this social metric and the field study decision tool were developed with the intent of being as rapidly implementable as possible while still providing insight regarding long‐term post‐event community trends. The community‐level social metric was developed using a hindcasting technique for hazard events in the years 2011, 2012, and 2013. Thresholds for stability and decline were established for both the predicted and the actual community outcomes. Of the communities that the model predicted would recover, 73% recovered using the definition provided. Meanwhile 74% of the communities predicted to decline did indeed decline, again using the definition provided. To enhance operability of this social metric, a decision‐making tool for community selection was also formalized and implemented in a field study conducted following the December 2021 quad‐state tornado outbreak in the United States. The lessons learned from this first implementation were used to inform a refined version of the decision‐making tool that is presented herein. [ABSTRACT FROM AUTHOR]

Extreme heat poses severe health threats, as the increased numbers of hospitalizations and fatalities during heat waves show, though little is known about adaptive behavior toward heat. We conducted a household survey on individual perceptions of heat stress and individual heat protection in the summer and autumn of 2019. In total, 1417 people from three medium‐sized German cities participated via telephone or online. Based on the Protective Action Decision Model (PADM), which we adapted to heat stress, we analyzed links between risk perception, environmental and demographic factors, perceptions of stakeholders, different heat warning messages, as well as actual and intended adaptive behavior. Overall, the PADM constructs explained around 16% of the variance in protection motivation, 19% in protective response, and 23% in emotion‐focused coping. Context factors (i.e., temperature, risk communication, gender, age, and homeownership) were significant predictors of the addressed outcome variables as were psychological factors (i.e., perceived personal vulnerability, response efficacy, response costs, preparedness, and perceived external responsibility). We further explored the effect of different warning messages on situational knowledge and intended behavioral adaptation in an experimental setting. Results showed that respondents felt significantly better informed after receiving a warning with action recommendations and reported more intended specific behaviors. Our research gives insights into individual protective action decision‐making processes. Based on our findings, we recommend tailoring risk communication strategies and combining heat warnings with action recommendations whenever possible to increase understanding and individual adaptation. [ABSTRACT FROM AUTHOR]