Extreme disaster events, particularly floods, impact individuals, households, and communities differently depending on a range of factors. Some residents are more vulnerable than others to floods, hence urgent action is needed to improve preparedness of vulnerable groups to reduce potential impacts of flooding. To achieve this, it is important to assess household flood preparedness, and identify the influence of household-level attributes on flood preparedness. Using a large-scale community survey, this study investigated how key household attributes such as evacuation assistance, Internet access, and key motivational factors and sources of motivation such as personal and social networks influence flood preparedness among vulnerable households. Households were categorized into three levels of vulnerability (high, medium, and low) by combining household economic capacity, as measured by household monthly expenditure, and the presence of vulnerable family members. Six key findings emerged: (1) Highly vulnerable households showed higher resilience to floods, and flood preparedness levels are independent of household vulnerability levels; (2) Self-reported household flood preparedness is positively influenced by learning from past disaster experience; (3) Financial and time commitments, and a sense of urgency for household-level flood preparedness are key intrinsic motivational factors that influence flood preparedness; (4) Access to reliable Internet can be used as a proxy to predict the degree of household flood preparedness; (5) Higher levels of awareness and knowledge of flood preparedness were reported despite low levels of community consultation; and (6) Self-motivation is the key source of motivation for flood preparedness. The study findings will support key institutional stakeholders such as local governments to devise strategies to strengthen the flood resilience of vulnerable households and communities.

Road networks are a critical infrastructure system for the sustainable functioning of cities. However, they are frequently disrupted by urban flooding, leading to increased travel times and hindering emergency responses. This study proposed a novel dynamic flood-response simulation framework for urban transportation to evaluate the impacts of rainstorms and flooding on traffic systems, focusing on coupling the Integrated Hydrology and Hydrodynamics Urban Flood Model (IHUM) and the Simulation of Urban MObility (SUMO) model. The results obtained from Xiaoguwei Island, Guangzhou City, indicate that a 2-h rainstorm of a 2-year return period can affect traffic for over 4.5 h. During a 100-year return period rainstorm, average travel speed declines by 54%, while the emergency response time, for example, for police services, increases from 4.83 to 14.52 min. These findings highlight the significant impacts of flooding on urban traffic networks, assisting local authorities and stakeholders to proactively identify vulnerable network segments and prioritize targeted interventions for enhancing transportation system resilience to floods.

This study critically interrogates dominant models of post-disaster recovery by combining an interdisciplinary review of critical scholarship with grounded empirical analysis from Brazil. It focuses on landless and unhoused populations, as well as residents of informal settlements, to explore how disaster recovery frameworks, rather than reducing vulnerability, often reproduce spatial inequality and deepen exclusion. The research draws on a multi-site, multi-temporal mixed-methods study conducted in collaboration with the Movimento dos Atingidos por Barragens (MAB), analyzing Brazil's most severe recent climate-related disasters: landslides and floods in Petrópolis (2011, 2022, 2024) and São Sebastião (2023). Fieldwork involved participant observation, over 200 semistructured interviews with affected residents and officials, and 110 completed questionnaires. Findings reveal that Brazil's disaster governance framework embeds exclusionary dynamics, privileging legally recognized property owners while marginalizing those without formal tenure. Recovery programs often simplify complex social realities through rigid eligibility criteria, thereby silencing diverse lived experiences. As a result, recovery often becomes a prolonged, secondary disaster for the most vulnerable. The article argues that prevailing recovery models, anchored in technocratic management and depoliticized resilience discourse, fail to address the structural roots of marginalization. By centering the role of grassroots movements such as MAB, this article highlights how collective action can expose recovery injustices and foster more inclusive, participatory, and transformative approaches to disaster governance.

Previous studies have found that the higher the level of quality of life (QoL) or well-being is, the more desirable behavior people may take, including disaster preparedness actions. However, other variables like socio-demographics and risk perception might have varying effects on both of them. Therefore, comparisons between socio-demographic groups might be of help to disentangle this complex mechanism, but no studies have investigated their relationships. This study revisited surveys in Taiwan, China and Kinki, Japan and took a simple QoL measurement as an indicator of well-being. Multi-group analysis provided the analysis of the relationships between QoL, trust in government, disaster experience (EX), disaster risk perception (RP), and preparedness behavior (PB) for each dataset and for each grouping, here, by gender and by marital status. As a result, among others, the effect of QoL on PB was only significant in the female group in Taiwan, while RP and EX differently affected PB in different gender groups in Kinki. Moreover, no significant differences were found between the marital status groups in the associations between QoL, RP, and PB in Taiwan. These results imply the importance of group-specific approaches in intervention measures. Considering and addressing the limitations of this study, such as a cross-sectional design and the omission of potential variables, future studies may further explore the associations in different groups or with new variables such as self-efficacy.

Rural settlements have experienced a noticeable increase in extreme weather events and associated disasters in recent years. Pakistan is consistently ranked as one of the most affected regions globally, and the catastrophic floods of 2022 further underscored its vulnerability to floods, causing unprecedented human, economic, and environmental losses. This study conducted a multidimensional vulnerability assessment of flood-prone rural areas in Dera Ismail Khan, Pakistan, using a composite index approach informed by principal component analysis (PCA). Principal component analysis was employed to assign statistically robust weights to selected indicators, ensuring an objective aggregation of vulnerability components. A questionnaire with a mix of closed-ended and open-ended items was used to collect data through a household survey. The findings revealed that a substantial proportion (40%) of respondents experienced high multidimensional vulnerability, while approximately 30% exhibited moderate vulnerability. Factors such as age distribution, household income, infrastructure quality, and risk perception significantly contributed to overall vulnerability. This study developed a scalable and replicable model for assessing rural flood vulnerability, offering practical insights for policymakers, planners, and disaster management authorities.

Identifying the optimal intensity measures (IMs) of ground motion is a critical step in the seismic fragility analysis of slopes using probabilistic seismic demand models. In this study, we investigated various IMs of earthquake ground motions, considering both vertical and horizontal components that can trigger landslides, through numerical simulations. A set of 19 IMs was examined for three heights of slopes. The optimal IMs were assessed using the maximum permanent displacement as an engineering demand parameter. The findings reveal that acceleration-related parameters specifically sustained maximum acceleration (SMA) and root-mean-square of acceleration, and velocity-related parameters namely sustained maximum velocity (SMV) and peak ground velocity, are the most effective IMs for slopes subjected to both vertical and horizontal ground motions. For slopes subjected exclusively to horizontal ground motion, SMA is recommended as the optimal IM for lower-height slopes, while SMV is more suitable for the taller slope. In contrast, for models subjected to combined horizontal and vertical ground motions, SMA is consistently identified as the optimal IM for slope across all heights. Notably, the study concludes that peak ground acceleration, a commonly used parameter in seismic analysis, is unsuitable for the considered slopes.

Building structure characteristics are essential for seismic risk assessment and management. However, accurately classifying building structural types at the city scale remains challenging due to limited data availability, spatial heterogeneity, and the difficulty in capturing contextual dependencies. In this study, a novel graph neural network (GNN)-based framework, BSPGNN (Building Structural Type Prediction using Graph Neural Networks), was proposed, integrating spatial relationships and building geometric features to improve structural type classification. A Delaunay triangulation (DT) graph was constructed from building footprint centroids to represent spatial proximity, and node features included footprint area, height, and construction year. Experiments using a real-world building dataset from Tianjin, China demonstrated that BSPGNN significantly outperformed traditional machine learning models, such as random forest (RF) and support vector machine (SVM), particularly in capturing spatially coherent patterns. The proposed model achieved a classification accuracy of 90.25% and 85.51% on the training set and validation set respectively, showing robust performance under missing data conditions. The results highlight the potential of spatial graph-based models in advancing building structural type classification for seismic risk assessment and management.

Deep learning models are widely used for urban flood prediction, but current research lacks a clear explanation of how indicator weight changes affect model accuracy. This study incorporated the attention mechanism between the convolutional and fully connected layers of the convolutional neural network (CNN) to enable the model to focus on critical flood-inducing factors, and employed the particle swarm optimization (PSO) to optimize the key hyperparameters (for example, the number of filters and learning rate). Furthermore, we employed Shapley additive explanation (SHAP) to analyze how flood-inducing indicator weight changes affect prediction accuracy. The model was tested on Haidian Island, China. The Nash-Sutcliffe efficiency (NSE) coefficient of the CNN model is 0.9287. After incorporating the attention mechanism into the CNN and optimizing the hyperparameters using PSO, the NSE is improved to 0.9503. The model demonstrates higher accuracy in predicting larger inundations, with the NSE for the 100-year return-period flood reaching 0.9535, compared to 0.8341 for the 5-year return period. Interpretability analysis shows that elevation is the most important flood-inducing factor, accounting for 44% of the total importance, followed by tidal levels, which account for 33%. The attention mechanism increases the weights of important flood-inducing factors (for example, elevation, tide level); after hyperparameter optimization, the model achieves more comprehensive learning, increasing the weights of the rainfall indicators that are neglected by the unoptimized model, and these weight changes improve the accuracy of the model. The research revealed the impacts of different flood-inducing factors on flooding and the influence of indicator weight changes on model accuracy.

The public's attribution of responsibility during a crisis is a central process in crisis communication, often explained by the situational crisis communication theory (SCCT). However, SCCT was developed in a pre-social media era, and its applicability in the new ecosystem of algorithm-driven, short-video platforms remains a critical theoretical gap. This study investigated how the core mechanisms of public responsibility attribution are reconfigured in the unique context of China's leading short-video platform, Douyin. Analyzing 185,148 comments following the tragic Yingcai School fire, our large language model (LLM)-based analysis answered two questions: (1) How are public attributions of responsibility structured in this emotionally charged, algorithmic environment? and (2) How do offline socioeconomic factors shape these digital crisis discourses? Our findings reveal two distinct attribution pathways, namely an anger-accountability track and a sadness-reflection track and demonstrate that critical discourse is systematically linked to regional development. This research provides a crucial empirical validation of SCCT for the short-video era and offers a data-driven guide for context-aware public administration.

In 2022, the United Nations launched the “Early Warnings for All” (EW4All) initiative, which aims to ensure that early warning systems are available to all inhabitants of the world by 2027. It is a response to the fact that climate-related disasters are increasing and many countries still do not have effective systems in place. For EW4All, the World Meteorological Organization (WMO) has selected 30 countries whose meteorological and hydrological services will be supported to strengthen their monitoring capacities for recurrent climate-related hazards in their country. This work aims to assess the impact of disasters in the period from January 2000 to November 2024 for the 30 EW4All countries using a composite scale representing a combination of different impact indicators. The unique feature of this work is that the different societal impacts are summarized in a single index. In this way, a comparative assessment of impacts between countries can be made, which can serve as a basis for action beyond that undertaken by the WMO and purely academic interest. This is a tool that can help decision makers to implement risk management measures. For this study, we selected the Emergency Events Database (EM-DAT) from the available global datasets because it is the only dataset with a time series long enough to fulfill the statistical criteria of this study and also uses a common disaster recording protocol for all countries.

This study examined public support for disaster-related measures (risk awareness, memory transmission, prevention facilities, and information networks) in the long-term recovery phase of a disaster-affected municipality. Focusing on Rikuzentakata City, which was severely impacted by the 2011 Great East Japan Earthquake and Tsunami, a stated-preference survey was conducted and analyzed using discrete choice models. Three research questions were addressed: RQ1 on the main effects of disaster-related measures, RQ2 on the interaction effects among these measures, and RQ3 on the interaction effects between disaster-related measures and economic-livelihood measures (ELMs). Stand-alone disaster-related measures and their within-domain combinations reduced public support, indicating dilution effects. By contrast, support increased when they were integrated with complementary ELMs. The policy implications are threefold. First, disaster-related measures should be implemented with caution in the medium- to long-term recovery process, as pursuing them alone may generate dilution effects. Second, integrated policy packages that combine disaster-related measures with complementary ELMs should be prioritized. Third, shrinking-city municipalities can better overcome fiscal and human resource constraints and enhance sustainability by leveraging complementarities across policy domains. Overall, the findings provide quantitative evidence that disaster-related policy must be reconceptualized as part of a broader policy portfolio. This insight has broader relevance for hazard-prone regions worldwide and offers international implications for long-term disaster governance in line with the Sendai Framework, the Sustainable Development Goals, and the Paris Agreement.

Climate change has intensified extreme rainfall events, challenging progress toward SDG 11's urban resilience targets. Current assessment methods often neglect dynamic recovery processes and regional precipitation disparities. We propose a three-phase framework combining interpretable machine learning (ML) and factorial experiments, using the Prep_shock index that integrates standardized rainfall intensity, capital exposure, and historical probability, to evaluate the dynamic resilience of 220+ Chinese cities from 2019 to 2022. Key findings reveal that: (1) The Prep_shock index effectively eliminates north-south precipitation biases, identifying Shandong coastal cities and Yangtze River Delta city clusters (36.2%) as high-resilience areas, in contrast to Henan Province. COVID-19 exacerbated systemic risks in megacities, undermining their capital protection capacities. (2) Spatial diagnostics classify 75.6% of the cities into Quadrant III (the balanced resilience category), with recovery times decreasing from the west to the east. Super-large cities like Zhengzhou (2021) exhibited critical recovery deficiencies (Quadrant IV). (3) Interpretable ML models (XGBoost/EBM) identify redundancy as the dominant resilience driver—robustness governs baseline resilience, while recovery relies on emergency support (for example, hospital beds density and fiscal inputs) and redundant infrastructure (for example, road network density). (4) Factorial experiments reveal optimization trade-offs: simultaneous enhancement of rapidity and redundancy diminishes their individual benefits, necessitating context-specific prioritization. The study advances dynamic resilience assessment methods and proposes quadrant-specific strategies for tailored urban adaptation.

The intensification of extreme rainfall events, driven by climate change, has exacerbated hydrological hazards in Brazil, affecting millions of people. This study investigated how previous disaster experience influences individuals' self-preparedness and key elements of community resilience, following the model by Norris et al. (2008), which includes information, social capital, community competence, and economic resources. A total of 1064 responses to a structured questionnaire survey was analyzed using the χ² test to identify statistical associations between behavioral variables and disaster experience. The results show that individuals with prior disaster experience report higher levels of perceived individual preparedness, concern about the impacts of heavy rainfall, and greater sensitivity to climate change. However, direct experience did not translate into greater confidence in institutional measures or stronger perceptions of community preparedness, revealing weaknesses in information flows and social cohesion. All interpretations are presented strictly in relation to the sample and do not imply population-level generalization. The study reinforces the relevance of experiential learning in shaping risk perception and preparedness, while highlighting contextual constraints associated with Brazil's socio-spatial inequalities and uneven governance capacities. These findings underscore the need for policies that strengthen institutional credibility, participatory risk communication, and community-level preparedness structures. The findings contribute to advancing risk management strategies in vulnerable contexts.

<正>1 Introduction The Sendai Framework for Disaster Risk Reduction 2015–2030 shifts the focus from managing disasters to reducing risks.Such a shift requires a better understanding of risk in all its dimensions of environment,hazards,exposure,and vulnerability;a disaster risk governance that ensures

Relocation is not typically considered the best planning option for post-disaster reconstruction and rehabilitation,but it may be necessary if the site has suffered severe damage or is at imminent risk.There is a growing recognition that strong community participation is necessary in the post-disaster relocation decision-making process since relocation can have detrimental effects on a community's livelihood,cultural system,and way of life,among others.However,the realization of this still needs to be improved.As of yet,few studies have examined a comprehensive account of meaningful community engagement in post-disaster relocation and reconstruction,particularly in developing countries.This study investigated what factors influenced local communities' participation in post-disaster relocation and reconstruction works after the 2017Cyclone Dineo flood disaster in the Tsholotsho District of Zimbabwe.Qualitative research methods such as face-to-face interviews,observations,and focus groups were used to collect qualitative data from a purposive sample of 25 community members and 6 stakeholders.This empirical investigation showed that despite the fact that the relocation project was conceived as a community-centered project,there was no meaningful community engagement,due to the absence of a participatory framework or planning guidelines for stakeholder engagement,as well as the lack of political willingness among government officials.The study concluded that the lack of community involvement led to local communities abandoning the reconstruction sites because relocation projects failed to accommodate the cultural beliefs,place attachments,and livelihood concerns of local communities.This study suggested that it is imperative to enhance the awareness of government officials and other stakeholders about the importance of community participation for the effective implementation of post-disaster relocation works.Meaningful community participation can also provide avenues for incorporating local needs and concerns,cultural beliefs,and alternative and sustainable livelihood restoration,which are essential for effective reconstruction after disasters.This research aimed to enrich the academic discourse by providing valuable insights into the intricacies of postdisaster recovery initiatives in the country.

The importance of workplace safety in the ready-made garment(RMG) industry in Bangladesh came to the forefront after a series of disastrous events in recent years. In order to reduce the loss of lives and to ensure sustainable development, an in-depth understanding of the determining factors governing structural vulnerability in the RMG industry is needed. This research explores the key factors influencing the vulnerability of factory buildings under both vertical and earthquake loads. For this purpose,an ordered probit model was applied to 3746 RMG factory buildings to determine the key factors that influenced their vertical load vulnerability. A smaller subset of the original sample, 478 buildings, was examined by the same modeling method in greater detail to assess the key factors that influenced their earthquake load vulnerability. This research reveals that column capacity, structural system,and construction materials are the most influential factors for both types of vulnerabilities. Among other factors, soil liquefaction and irregular internal frame affect earthquake load vulnerability significantly. These findings are expected to enable factory owners and designers to better weigh the appropriate vulnerability factors in order to make informed decision that increase workplace safety. Theresearch findings will also help the designated authorities to conduct successful inspections of factory buildings and take actions that reduce vulnerability to both vertical and earthquake loads.

Inefficient and ineffective fire management practices are common to most urban areas of developing countries.Nigerian cities are typical examples of high vulnerability and low preparedness level for fire disaster.This study examined the institutional framework for fire disaster risk reduction(FDRR) and explored the roles of key actors in fire disaster preparedness in Ibadan,a large traditional city in Nigeria.The study was anchored on the concept of urban governance.A case study research design was adopted using primary and secondary data.Primary data were obtained through field observation aided by a structured checklist and key informant interview.Interviews were conducted on key officials of the major organs for FDRR-Oyo State Fire Service(OSFS) and Oyo State Emergency Management Agency(OYSEMA).The study identified a disjointed and fragmented approach to fire management.Matters relating to fire risk reduction and disaster recovery were domiciled under the OYSEMA,while emergency response to fire disasters was the prerogative of the OSFS.The results show that only five out of 11 local government areas had public fire stations;only three fire stations had an on-site water supply;three fire stations lacked firefighting vehicles;and distribution of fire stations and facilities was uneven.Two fire stations responded to 80% of all fire cases in 12 years.The study concluded that the institutional structure and resources for fire risk reduction was more empowered to respond to fire disaster,rather than facilitating preparedness capacity to reduce disaster risk.

In many low-income countries, migrant remittances are essential in sustaining people's livelihoods and become even more important during and after disasters.Researchers, policymakers, and practitioners increasingly emphasize the need to better support this people-based mechanism, so disaster risk can be reduced. This suggests the importance of understanding migrants' perspectives on the remittance channels used and the challenges and opportunities of supporting remittances. However, such information is largely missing. Drawing on interviews and a focus group discussion carried out with Pacific Island migrants living in New Zealand, the article identifies the capacity of migrants to utilize different remittance channels and resources to assist those affected in their country of origin. Challenges faced include high transfer fees, lack of information and support from external stakeholders, and limited resources to effectively send both individual and collective remittances. The article concludes that there is a serious need to involve a large array of stakeholders in finding ways to better support remittances for disaster risk management, including migrants, government agencies, the private sector, nongovernmental organizations, and those receiving remittances.

One of the most serious resource and health issues in coastal communities of Bangladesh is the scarcity of safe drinking water, triggered by the combined effects of salinity, arsenic, and drought. This article explores community perception of vulnerabilities in daily life, livelihood, and environment, and investigates how communities and institutions cope with or adapt to drinking water scarcity. This study outlines community expectations for support from government and nongovernment organizations to overcome this problem. The findings reveal that nearly all respondents from the drinking water scarcity area perceive that salinity is the primary reason for the lack of safe drinking water compared to arsenic and drought hazards. Despite a number of socioeconomic factors and a geographical location that aggravates the coastal communities' vulnerability, these communities have established their own adaptation mechanism to cope with this crisis.Government and nongovernment organizations have also supported community efforts to cope with the problem. By emphasizing both community adaptation methods and efforts of institutions, this article illustrates an integrated community-based approach, which would be effective for reducing drinking water scarcity in the southwestern coastal region of the country.

To estimate human loss in an earthquake-prone area, it is necessary to analyze the role played by the spatiotemporal distribution of the area's resident population. In order to evaluate earthquake impact, this article focuses on the spatiotemporal distribution of population and five scenario earthquakes that form the basis for loss estimation in the city of Sylhet, Bangladesh. Four temporal contexts(weekday, weekly holiday, the 30 days of Ramadan, and strike days) expand the more typical daytime and nighttime settings in which to examine hazard risk. The population distribution for every 2 hour interval in a day is developed for each type of day. A relationship between the occupancy classes and average space(persons per 100 m~2)is used to distribute people in each building regardless of building locations. A total daytime and nighttime population is obtained for each building and the estimated nighttime population is used to model the population for four temporal scenarios in a year based on different factors and weights. The resulting data are employed to estimate population loss for each of the temporal and earthquake scenarios. This study used building-specific human vulnerability curves developed by the Central American Probabilistic Risk Assessment(CAPRA) to obtain possible loss of life estimates. The results reveal that there is a high positive correlation between the spatiotemporal distribution of population and the potential number of casualties.

This article summarizes and analyzes the Children & Youth Forum and youth participation in the process during and leading up to the Third UN World Conference on Disaster Risk Reduction(WCDRR) in2015. An organizing committee consisting of international students and young professionals brought together around200 young professionals and students from around the globe to exchange ideas and knowledge on reducing disaster risk, building resilient communities, and advocating for the inclusion of youth priorities within the Sendai Framework for Disaster Risk Reduction 2015–2030(SFDRR). The knowledge exchange during the Forum was structured around a Toolbox for Resilience that connected to the SFDRR section on Priorities for Action. This article presents the outcomes of these young people's participation in the disaster risk reduction capacity building eventsand policy-making, as well as the follow-up actions envisioned by the young participants of the Forum. The voices of the younger generation were heard in the SFDRR and young people are ready to expand their actions for the framework's effective implementation. Young people call on technical experts, donors, NGOs, agencies, governments, and academia to partner with them on this journey to create a more resilient tomorrow together.

To prepare a landslide susceptibility map of Shivkhola watershed,one of the landslide prone parts of Darjeeling Himalaya,remote sensing and GIS tools were used to integrate 10 landslide triggering parameters:lithology,slope angle,slope aspect,slope curvature,drainage density,upslope contributing area(UCA),lineament,settlement density,road contributing area(RCA),and land use and land cover(LULC).The Analytical Hierarchy Process(AHP) was applied to derive factor weights using MATLAB with reasonable consistency ratio(CR).The frequency ratio(FR) model was used to derive class frequency ratio or class weights that indicate the relative importance of individual classes for each factor.The weighted linear combination(WLC) method was used to determine the landslide susceptibility index value(LSIV) on a GIS platform,by incorporating both factor weights and class weights.The Shiv-khola watershed is classified into five landslide susceptibility zones.The overall classification accuracy is 99.22 and Kappa Statistics is 0.894.

The eastern Mediterranean region is an active tectonic setting that includes the Dead Sea Transform Fault, which forms the boundary between the African and the Arabian Plates and crosses Lebanon from south to north, striking in a restraining bend around 25–30°NE. The major structural feature in Lebanon is the Yammouneh Fault, which reaches to Syria and southern Turkey in a north–south direction. Measurements of radon gas concentration and exhalation rates in two locations along the southern segment of the Yammouneh Fault in south Lebanon were performed. Two profiles in the El-Khiam basin and Blat pull-apart basin and perpendicular to the Yammouneh Fault trace were analyzed. An approximate fault width 25–30 m wide was determined in the El-Khiam study area. Temporal increase of radon concentration was measured and correlated with stress/strain tectonic activity and stress drops along the studied fault segment boundary.Anomalous variable radon concentrations were detected during one of the measurements where an earthquake occurred in the region of Tiberias Lake in northern Palestine along the Yammouneh Fault in the study area. Measurements of radon concentration along a station's profile in Blat village did not show any radon anomalous variation due to the discontinuity along the fault(pull-apart), and possible absence of stress and energy accumulation along the Yammouneh Fault line in that location.

To investigate why people live in areas at high risk of floods, a qualitative case study was carried out in the areas around Akuressa, in southwest Sri Lanka. Data collection consisted mainly of semistructured interviews with local residents and government officials. The purpose was to study why people live in areas at high risk of floods, by looking beyond the purely physical aspects of living with hazards and exploring the underlying social factors. Four main factors were identified: an overall good living situation; a sense of place; difficulties relocating; and being well-adapted to the situation. The analysis also examined whether government officials shared the views of local residents. The findings highlighted both areas of consensus and discrepancies related to risk awareness, and the efficiency of risk reduction measures that had been implemented by the government. The case study identified and explored underlying social factors, such as risk normalization, risk trade-offs, and push-and-pull processes, which seem to influence the decision to live in a high-risk area.

The third UN World Congress on Disaster Risk Reduction, held in Sendai, Japan in March 2015, agreed on a new framework to guide disaster risk reduction policy and practice for the next 15 years. The Sendai Framework for Disaster Risk Reduction 2015–2030(SFDRR) leaves important implementation issues unspecified and potentially creates both problems and opportunities for complex,multilevel governance systems in coping with hazards and disastrous events. Early warning systems(EWS), if built into the mainstream of planning for development and disaster relief and recovery, could present a significant opportunity to realize many SFDRR goals. We explore the complexities of using hydrometeorological EWS to prepare for drought and flood disasters in the densely populated communities of Pakistan's Indus River Basin in contrast to the African Sahel's less densely settled grasslands. Multilevel governance systems are often dominated by a topdown, technocentric, centralized management bias and have great difficulty responding to the needs of peripheral and vulnerable populations. People-centered, bottom-up approaches that incorporate disaggregated communities with local knowledge into a balanced, multilevel disaster risk management and governance structure have adramatically better chance of realizing the SFDRR goals for disaster risk reduction.

This article addresses three large earthquake disasters in Iran: Tabas in 1978, Rudbar in 1990, and Bam in 2003. Lessons and "Lessons Learned" from these three earthquake disasters were investigated together with their contributions over time towards earthquake disaster risk reduction in Iran. Many lessons from 1978 Tabas, 1990 Rudbar, and 2003 Bam did not become "Lessons Learned" and they were identified again within the dramatic context of other earthquake disasters in various places of Iran. Both lessons and "Lessons Learned" from Tabas, Rudbar, Bam,and other earthquake disasters in Iran require a sustainable long-term framework—an earthquake culture.

Tropical cyclones(TCs) may cause severe impacts on the activities of coastal fishing vessels. Due to the unavailability or unacceptability of detailed Automatic Identification System(AIS) data that are capable of differentiating fishing activity from navigation, as well as the lack of detailed models and observation data of TC winds, few studies have provided quantitative and reliable assessment of the impacts of TCs on fishing activities. In this study,we modeled snapshots for the TC winds of 52 TCs over the Northwest Pacific(NWP) basin from 2013 to 2018, as well as daily fishing hours and daily hours of presence(hereafter “vessel hours”) of fishing vessels. Based on these data, the spatiotemporal pattern of fishing vessel activity over off-shore China was first analyzed and mapped. Then, a TC wind hazard index and absolute and relative impact indices were proposed to assess the impact of the 52 TCs on fishing and vessel hours. Their relationship was then fitted with the cumulative distribution function(CDF) of the log-normal distribution. The results show that in the 2013-2018 period,the most active fishing areas were located in the South China Sea. In each instance, an increase was first observed in the initial several years; then a decrease followed in the yearly total fishing hours per vessel in the remaining years. The relative impact index was significantly correlated to the TC wind hazard index proposed in this study. Based on the quantitative relationship between the specified TC hazard index and the impact indices, it is possible to implement a pre-cyclone rapid loss assessment due to TC avoidance in the future.

The presence of high indoor radon concentrations, C_x, is a major concern of the public worldwide.Measurements of indoor radon in South Lebanon have been achieved using CR-39 detectors. Cxvalues ranged between 30 and 122 Bq m~(-3) in one of the major towns in the area and are compared with measured atmospheric air Radon.Moderate seasonal variation of Cxindoors has been observed, although 90 % of the obtained indoor Cxmeasurements are below 100 Bq m~(-3). Some homes produced Cxreadings above the permissible limit. Results showed that the ventilation effect played a major factor in reducing radon concentration levels indoors. Health assessment has been considered. Annual effective doses of radon varied from 1.01 ± 0.10 to 8.65 ± 0.50 m Sv a~(-1) in six homes in one of the four cases studied. These dosage values are above the permitted limit of 2.5 m Sv a~(-1). Several types of granite contained in the studied homes were identified and their contributions to radon levels indoors were calculated.

Recent seismic events show that urban areas are increasingly vulnerable to seismic damage,which leads to unprecedented levels of risk.Cities are complex systems and as such their analysis requires a good understanding of the interactions between space and the socioeconomic variables characteristic of the inhabitants of urban space.There is a clear need to develop and test detailed models that describe the behavior of these interactions under seismic impact.This article develops an overall vulnerability index to seismic hazard based on a spatial approach applied to Bucharest,Romania,the most earthquake-prone capital in the European Union.The methodology relies on:(1) spatial post-processed socioeconomic data from the2011 Romanian census through multicriteria analysis;and(2) analytical methods(the Improved Displacement Coefficient Method and custom-defined vulnerability functions)for estimating damage patterns,incorporated in a GIS environment.We computed vulnerability indices for the128 census tracts of the city.Model sensitivity assessment tested the robustness of spatially identified patterns of building vulnerability in the face of uncertainty in model inputs.The results show that useful seismic vulnerability indices can be obtained through interdisciplinaryapproaches that enhance less detailed datasets,which leads lead to better targeted mitigation efforts.

The Millennium Development Goals(MDGs)with their holistic perspective of development are focused on different issues of vulnerability.This article highlights the situation of women in disasters and the challenges in achieving the MDGs with special reference to India.It is accepted that there is no disaster without human engagement and that issues of differential impact on genders is an essential consideration for recovery.The international guidelines on disaster management and intervention have a considerable focus on gender equality,balance,mainstreaming,and sensitive programing,yet the situation is quite grim.India still lacks separate policy guidelines on gender aspects in disaster.In the twenty-first century,India has witnessed a series of disasters in different parts of the country.The author's personal experiences of working in intervention programs of these disasters showed that gender vulnerability depends on various factors like the intensity of the disaster impact,local sociocultural perspectives,effective disaster intervention strategies,the specific focus on issues of women in training of personnel,and gender-sensitive disaster intervention programs in the community.In the context of the MDGs,while development has become a priority concern to end age-old inequalities in society,the added challenge of disasters needs considerable focus on gender inequalities to achieve the goal of gender equity.

Industrial accidents have shown that many people can be affected,such as in Seveso,Italy,in 1976.Industrial accidents in nuclear power plants have also led to fatalities and evacuations.To better guide preparedness against and mitigation of industrial accidents,an assessment is necessary to evaluate hazard exposure and the type of potentially vulnerable social groups that need to be taken into account.This study conducted a spatial assessment of three types of industrial facilities in Germany:facilities registered under the Seveso Directive,chemical parks,and nuclear power plants.The method consisted of a spatial assessment using a Geographic Information System of exposure around hazardous sites registered under the S eveso Directive in Germany and of census data to analyze social vulnerability.Hazards analyzed included industrial accidents and earthquakes.The results revealed that most industrial sites are in urban areas and that population density,the numbers of foreigners,and smaller housing unit sizes are higher in close proximity to these sites.The buffer zones analyzed in circles between 1 and 40 km show a decreasing vulnerability with more distance.This can guide emergency management planners and other stakeholders to better prepare for major accidents and better devise disaster risk reduction strategies specifically for different social groups.

As in many other settings in developing countries, discussions on urban flooding in South Africa tend to focus on informal settlements. There is less attention to poor but formal housing areas, based on the largely untested assumption that the formalization of housing addresses risk. This is at odds with an extensive literature from the housing and developmental sectors that highlights weaknesses in the location and construction of low-income housing, particularly state-subsidized housing. Drawing on research in 10 poor, flood-prone settlements in Cape Town,South Africa, this article explores whether providing housing addresses risk. The results show that flooding remains a challenge in subsidized housing areas and that risk is linked strongly to the buildings themselves. Poorly designed and constructed dwellings perpetuate risk in lowincome areas. While divorced conceptually and practically,disaster risk and housing issues are critically linked, and housing concerns must be factored into discussions on flooding in Cape Town and comparable settings elsewhere.

In-depth interviews with local Haitian volunteers trained in a psychological disaster recovery program called Health Support Team(HST) provide insight into the psychosocial outcomes resulting from their engagement with the program. Qualitative interviews were conducted with four male Haitian participants who had survived the January 2010 Haiti earthquake and had worked as HST volunteers for at least 6 months. Interviews were analyzed using narrative inquiry analysis, which allows individuals to discover and disclose a deeper meaning in their experience and enables the researchers to access more detailed data. Previous research supports the claim that volunteerism provides many important psychological benefits, and the results of the present study suggest that among survivors of large-scale disasters, volunteerism is beneficial as a means of increasing psychological resilience and facilitating personal recovery. Results and themes of our analysis included a reported increase in both hope and purpose for the respondents. Findings suggest that volunteerism on the part of members of the surviving community following large-scale disaster increases resilience among the volunteers and further contributes to their recovery.

Floods affect more people globally than any other type of natural hazard. Great potential exists for new technologies to support flood disaster risk reduction. In addition to existing expert-based data collection and analysis, direct input from communities and citizens across the globe may also be used to monitor, validate, and reduce flood risk. New technologies have already been proven to effectively aid in humanitarian response and recovery. However, while ex-ante technologies are increasingly utilized to collect information on exposure, efforts directed towards assessing and monitoring hazards and vulnerability remain limited. Hazard model validation and social vulnerability assessment deserve particular attention. New technologies offer great potential for engaging people and facilitating the coproduction of knowledge.

A number of indices have been developed for measuring vulnerability to disasters, but little attention has been paid to recovery indices. Post-disaster periods are usually divided into four phases. The terms established by the United Nations Development Programme for post-disaster phases—relief, early recovery, recovery, and development—are used in this article. This research examines the hypothesis that the boundaries between post-disaster recovery phases are fuzzy and should be defined by the progress achieved in the recovery process, rather than by the amount of time elapsed since the event. The methodology employed involved four steps: fieldwork, mapping,identification of indicators, and assessment. The case study area was the city of L'Aquila in the Abruzzo region of central Italy, which was struck by an earthquake in April2009. For each phase of the recovery process in L'Aquila a score was calculated based on the progress observed in2016, 7 years after the earthquake. The highest score went to the early recovery phase(14 points), followed by the recovery phase(13 points), the development phase(12points), and the relief phase(4 points). The results demonstrate the possibility of defining post-disaster recovery phases in an affected area based on measuring achievements through indicators rather than defining recovery phases in terms of elapsed time after a disaster.

This study analyzes data on households' risk perception and protective behavior following a natural disaster triggered technological accident(Natech accident)that occurred at an industrial park in Sendai during the Great East Japan Earthquake and Tsunami, on 11 March2011. The results indicate that some households carried out multiple evacuations and that households' risk perceptions changed throughout the Natech accident evacuation process. Risk perception differed according to household location and demographic characteristics. We also found differences in the protective measures adopted for households in different locations. Specifically, those living closer to the Natech accident tended to evacuate immediately,whereas those living further away tended to shelter in place. Wind direction is discussed as a factor that influences households' risk perception and evacuation response to a Natech accident. The findings of this study advance knowledge of household behavior in response to a Natech accident and can assist emergency managers in developing strategies for better management of evacuation processes.

In this study, loss estimation models were developed for reasonably accurate assessment of economic and human losses from seismic events in the Mediterranean region, based on damage assessment at an urban scale.Data were compiled from existing worldwide databases,and completed with earthquake information from regional studies. Economic data were converted to a single common currency unit(2015 USD value) and the wealth of the areas affected by 65 earthquakes of the region from 1900 to 2015 was assessed. Reduced-form models were used to determine economic and human losses, with earthquake magnitude and intensity as hazard-related variables, and gross domestic product of the affected area and the affected population as exposure-related variables. Damage to buildings was also used as a hazard-related variable to predict economic and human losses. Finally, site-specific regression models were proposed for economic and human losses due to earthquakes in the Mediterranean region, and more specifically, in Algeria. We show that by introducing the damage variable into the models, prediction error can be reduced, and that accuracy of loss model estimation is site dependent and requires regional data on earthquake losses to improve. A case study for Constantine, Algeria shows the improvements needed for increased accuracy.

The 2014 Jure landslide in Sindhupalchok District, Nepal, caused significant loss and damage to ecosystems and livelihoods in the area. In the direct aftermath of the landslide, several disaster loss assessments were conducted, with the aim of counting casualties,injured people, and damaged houses and infrastructure.Although useful and necessary in their own right, such rapid assessments do not reveal the true extent and significance of the losses and damages that people in disaster areas face, including their monetary value. We address this gap, based on a comprehensive household survey(N = 234), as well as other, qualitative research tools. Our results highlight the importance of differentiating between loss and damage in absolute monetary terms as opposed to losses relative to annual income. We find a stark contrast between the high absolute losses incurred by nonpoor households, and the high relative losses—up to 14 times their annual income—experienced by poor households.These results have important implications for policy that addresses loss and damage, not only of landslides but also of other disasters. Loss and damage assessments need to take the livelihood characteristics of affected households into account to identify and support those most in need of compensation and relief.

Like other subjects, disaster risk science has developed its own vocabulary with glossaries. Some keywords, such as resilience, have an extensive literature on definitions, meanings, and interpretations. Other terms have been less explored. This article investigates core disaster risk science vocabulary that has not received extensive attention in terms of examining the meanings,interpretations, and connotations based on key United Nations glossaries. The terms covered are hazard, vulnerability, disaster risk, and the linked concepts of disaster risk reduction and disaster risk management. Following a presentation and analysis of the glossary-based definitions,discussion draws out understandings of disasters and disaster risk science, which the glossaries do not fully provide in depth, especially vulnerability and disasters as processes. Application of the results leads to considering the possibility of a focus on risk rather than disaster risk while simplifying vocabulary by moving away from disaster risk reduction and disaster risk management.

Italy is significantly affected by ever-present flood and landslide risks and has experienced many disasters. Local social awareness and engagement, however,differ and need to be increased by decision makers and citizens through improvements in risk preparedness. With this aim, the #italiasicura web platform was developed by Fondazione Politecnico di Milano and released in 2015 to show country to local level hazard maps and risk reduction projects in Italy. Any stage of the user experience with the platform can be shared via social media. Using this tool, an awareness-oriented web analytics process was structured to develop a set of indicators for the increase of knowledge linked to flood and landslide hazards. In so doing, it is possible to measure community disaster awareness actions and competence in the area of hazard knowledge. This article presents the results obtained by using data from the platform.