The COVID-19 pandemic highlighted the urgent need to strengthen public health systems.In response,the United Nations Disaster Risk Reduction(UNDRR) Public Health System Resilience Scorecard(Scorecard) was applied in workshops across multiple countries.The aim of our research was to explore the workshop findings to develop priority strategies for strengthening public health system resilience.We conducted a workshop from 14 to 16 March 2023,at the UNDRR Global Education and Training Institute in Incheon,Republic of Korea.A sequential modified Delphi method was utilized to develop a set of prioritized resilience strategies.These were drawn from 70 strategies identified from 13 distinct workshops in eight countries.After two surveys,23 strategies were finalized.Ten received ratings of "High" or Very High" from89% of participants.These related to the inclusion of public health risks in emergency plans,integrating multidisciplinary teams into public health,enabling local transport mechanisms,and improving the ability to manage an influx of patients.The Scorecard provides an adaptable framework to identify and prioritize strategies for strengthening public health system resilience.By leveraging this methodology,our study demonstrated how resilience strategies could inform disaster risk reduction funding,policies,and actions.

Earth observation(EO) technologies,such as very high-resolution optical satellite data available from Maxar,can enhance economic consequence modeling of disasters by capturing the fine-grained and real-time behavioral responses of businesses and the public.We investigated this unique approach to economic consequence modeling to determine whether crowd-sourced interpretations of EO data can be used to illuminate key economic behavioral responses that could be used for computable general equilibrium modeling of supply chain repercussions and resilience effects.We applied our methodology to the COVID-19 pandemic experience in Los Angeles County,California as a case study.We also proposed a dynamic adjustment approach to account for the changing character of EO through longer-term disasters in the economic modeling context.We found that despite limitations,EO data can increase sectoral and temporal resolution,which leads to significant differences from other data sources in terms of direct and total impact results.The findings from this analytical approach have important implications for economic consequence modeling of disasters,as well as providing useful information to policymakers and emergency managers,whose goal is to reduce disaster costs and to improve economic resilience.

Solving complex post-disaster reconstruction challenges requires the altruistic involvement of heterogeneous stakeholder groups.However,small,more organized groups,such as government parastatals,private developers,and contractors often exploit large,unorganized groups,such as affected communities,leaving them more vulnerable to future disasters.Based on data collected from a case study in Pakistan,this study proposed a framework to assess,anticipate,and mitigate the exploitation of vulnerable stakeholders in post-disaster reconstruction projects.The framework draws on influential management theories and utilizes reciprocal relationships between stakeholder attributes(power,legitimacy,and urgency),participation,and exploitation.The study also argued for non-binary treatment of stakeholder attributes.The framework will allow practitioners to address issues around the exploitation of stakeholder interests in future post-disaster reconstruction projects.

Flooding is a global threat,necessitating a comprehensive management approach.Due to the complexity of managing flood hazards and risks,researchers have advocated for holistic,comprehensive,and integrated approaches.This study,employing a systems thinking perspective,assessed global flood risk management research trends,gaps,and opportunities using132 published documents in BibTeX format.A systematic review of downloaded documents from the Scopus and Web of Science databases revealed slow progress of approximately 11.61% annual growth in applying systems thinking and its concomitant approaches to understanding global flood risk management over the past two decades compared to other fields like water resource management and business management systems.A significant gap exists in the application of systems thinking methodologies to flood risk management research between developed and developing countries,particularly in Africa,highlighting the urgency of reoriented research and policy efforts.The application gaps of the study methodology are linked to challenges outlined in existing literature,such as issues related to technical expertise and resource constraints.This study advocates a shift from linear to holistic approaches in flood risk management,aligned with the Sendai Framework for Disaster Risk Reduction 2015-2023 and the Sustainable Development Goals.Collaboration among researchers,institutions,and countries is essential to address this global challenge effectively.

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.

Disaster information content is an objective mapping of disaster situations,social response,and public opinions.Social response to emergency is an important mechanism for implementing and guaranteeing emergency management of major natural hazard-related disasters.Understanding how disaster information content affects social response to emergencies is helpful for managing risk communication and efficient disaster response.Based on the 2008 freezing-rain and snowstorm disasters in southern China,this study used Python to extract 7,857 case-related media reports and applied natural language processing for text analysis.It used three typical cases to identify and analyze disaster media report content and the relationship between these reports and the social response to the emergency.Eight categories of disaster response—such as prewarning and forecasting,announcements by the authorities,and social mobilization—appeared in the disaster information in the media,along with disaster impact information,that is,real-time disaster status.Disaster response information and an appropriate amount of disaster impact information played important roles in prewarning,disaster relief,public opinion guidance,and social stability maintenance and can serve important functions in communicating with all stakeholders of emergency management,assisting or influencing emergency departments or individuals in decision making,and eliminating "information islands." Empathy caused the general public to become "disaster responders" through receiving information.Rumors and an excess of negative information may have a perverse amplification effect on public opinion and increase the unpredictability of the disaster situation and the risk of social crisis.

Vulnerability assessment is essential for understanding and launching effective flood risk reduction strategies.This study aimed to examine the vulnerability of flood-prone rural communities in southern Punjab,Pakistan to external shocks.The concept of vulnerability encompasses a range of dimensions,including physical,social,institutional,environmental,economic,and attitudinal.Using a composite index method,indices were developed for each dimension and combined to create a multidimensional measure of vulnerability.A sample of 365 communities was selected using the Yamane sampling technique,and data were collected through a questionnaire containing 65 indicators across all dimensions.Descriptive statistics and ANOVA tests were used to analyze the data.The results show that communities near the Chenab River had higher attitudinal and institutional vulnerability compared to other communities.High attitudinal vulnerabilities were as sociated with poorly perceived flood risks and low preparedness measures,whereas institutional vulnerabilities were driven by conventional flood protection strategies,lack of institutional trust,and lack of flood risk awareness.This research provides insights into the various components of vulnerability in flood-prone rural communities in Pakistan and demonstrates a useful methodology that can be applied to other disasters at different spatial scales.

In recent years,the notion of resilience has been developed and applied in many technical areas,becoming exceptionally pertinent to disaster risk science.During a disaster situation,accurate sensing information is the key to efficient recovery efforts.In general,resilience aims to minimize the impact of disruptions to systems through the fast recovery of critical functionality,but resilient design may require redundancy and could increase costs.In this article,we describe a method based on binary linear programming for sensor network design balancing efficiency with resilience.The application of the developed framework is demonstrated for the case of interior building surveillance utilizing infrared sensors in both twoand three-dimensional spaces.The method provides optimal sensor placement,taking into account critical functionality and a desired level of resilience and considering sensor type and availability.The problem formulation,resilience requirements,and application of the optimization algorithm are described in detail.Analysis of sensor locations with and without resilience requirements shows that resilient configuration requires redundancy in number of sensors and their intelligent placement.Both tasks are successfully solved by the described method,which can be applied to strengthen the resilience of sensor networks by design.The proposed methodology is suitable for large-scale optimization problems with many sensors and extensive coverage areas.

This study presents a novel method for optimizing parameters in urban flood models,aiming to address the tedious and complex issues associated with parameter optimization.First,a coupled one-dimensional pipe network runoff model and a two-dimensional surface runoff model were integrated to construct an interpretable urban flood model.Next,a principle for dividing urban hydrological response units was introduced,incorporating surface attribute features.The K-means algorithm was used to explore the clustering patterns of the uncertain parameters in the model,and an artificial neural network(ANN)was employed to identify the sensitive parameters.Finally,a genetic algorithm(GA) was used to calibrate the parameter thresholds of the sub-catchment units in different urban land-use zones within the flood model.The results demonstrate that the parameter optimization method based on K-means-ANN-GA achieved an average Nash-Sutcliffe efficiency coefficient(NSE) of 0.81.Compared to the ANN-GA and K-means-deep neural networks(DNN) methods,the proposed method better characterizes the runoff generation and flow processes.This study demonstrates the significant potential of combining machine learning techniques with physical knowledge in parameter optimization research for flood models.

Facing the escalating effects of climate change,it is critical to improve the prediction and understanding of the hurricane evacuation decisions made by households in order to enhance emergency management.Current studies in this area often have relied on psychology-driven linear models,which frequently exhibited limitations in practice.The present study proposed a novel interpretable machine learning approach to predict household-level evacuation decisions by leveraging easily accessible demographic and resource-related predictors,compared to existing models that mainly rely on psychological factors.An enhanced logistic regression model(that is,an interpretable machine learning approach) was developed for accurate predictions by automatically accounting for nonlinearities and interactions(that is,univariate and bivariate threshold effects).Specifically,nonlinearity and interaction detection were enabled by low-depth decision trees,which offer transparent model structure and robustness.A survey dataset collected in the aftermath of Hurricanes Katrina and Rita,two of the most intense tropical storms of the last two decades,was employed to test the new methodology.The findings show that,when predicting the households' evacuation decisions,the enhanced logistic regression model outperformed previous linear models in terms of both model fit and predictive capability.This outcome suggests that our proposed methodology could provide a new tool and framework for emergency management authorities to improve the prediction of evacuation traffic demands in a timely and accurate manner.

A reliable economic risk map is critical for effective debris-flow mitigation.However,the uncertainties surrounding future scenarios in debris-flow frequency and magnitude restrict its application.To estimate the economic risks caused by future debris flows,a machine learning-based method was proposed to generate an economic risk map by multiplying a debris-flow hazard map and an economic vulnerability map.We selected the Gyirong Zangbo Basin as the study area because frequent severe debris flows impact the area every year.The debris-flow hazard map was developed through the multiplication of the annual probability of spatial impact,temporal probability,and annual susceptibility.We employed a hybrid machine learning model—certainty factor-genetic algorithm-support vector classification—to calculate susceptibilities.Simultaneously,a Poisson model was applied for temporal probabilities,while the determination of annual probability of spatial impact relied on statistical results.Additionally,four major elements at risk were selected for the generation of an economic loss map:roads,vegetation-covered land,residential buildings,and farmland.The economic loss of elements at risk was calculated based on physical vulnerabilities and their economic values.Therefore,we proposed a physical vulnerability matrix for residential buildings,factoring in impact pressure on buildings and their horizontal distance and vertical distance to debrisflow channels.In this context,an ensemble model(XGBoost) was used to predict debris-flow volumes to calculate impact pressures on buildings.The results show that residential buildings occupy 76.7% of the total economic risk,while roadcovered areas contribute approximately 6.85%.Vegetation-covered land and farmland collectively represent 16.45% of the entire risk.These findings can provide a scientific support for the effective mitigation of future debris flows.

This study achieved the construction of earthquake disaster scenarios based on physics-based methods—from fault dynamic rupture to seismic wave propagation—and then population and economic loss estimations.The physics-based dynamic rupture and strong ground motion simulations can fully consider the three-dimensional complexity of physical parameters such as fault geometry,stress field,rock properties,and terrain.Quantitative analysis of multiple seismic disaster scenarios along the Qujiang Fault in western Yunnan Province in southwestern China based on different nucleation locations was achieved.The results indicate that the northwestern segment of the Qujiang Fault is expected to experience significantly higher levels of damage compared to the southeastern segment.Additionally,there are significant variations in human losses,even though the economic losses are similar across different scenarios.Dali Bai Autonomous Prefecture,Chuxiong Yi Autonomous Prefecture,Yuxi City,Honghe Hani and Yi Autonomous Prefecture,and Wenshan Zhuang and Miao Autonomous Prefecture were identified as at medium to high seismic risks,with Yuxi and Honghe being particularly vulnerable.Implementing targeted earthquake prevention measures in Yuxi and Honghe will significantly mitigate the potential risks posed by the Qujiang Fault.Notably,although the fault is within Yuxi,Honghe is likely to suffer the most severe damage.These findings emphasize the importance of considering rupture directivity and its influence on ground motion distribution when assessing seismic risk.

In this rapid review, we critically scrutinize the disaster management infrastructure in Saudi Arabia, illuminating pivotal issues of interoperability, global cooperation, established procedures, community readiness, and the integration of cuttingedge technologies. Our exploration uncovers a significant convergence with international benchmarks, while pinpointing areas primed for enhancement. We recognize that continual commitments to infrastructural progression and technology adoption are indispensable. Moreover, we underscore the value of robust community involvement and cross-border collaborations as key factors in bolstering disaster response capabilities. Importantly, we spotlight the transformative influence of emerging technologies, such as artificial intelligence and the Internet of Things, in elevating the effectiveness of disaster management strategies. Our review champions in all-encompassing approach to disaster management, which entails harnessing innovative technologies, nurturing resilient communities, and promoting comprehensive disaster management strategies, encapsulating planning, preparedness, response, and recovery. As a result of our analysis, we provide actionable recommendations to advance Saudi Arabia's disaster management framework. Our insights are timely and crucial, considering the escalating global focus on disaster response in the face of increasing disaster and humanitarian events.

Numerous scholars and researchers have long advocated for citizen engagement in post-disaster recovery and reconstruction initiatives, although unique opportunities and challenges in effectively implementing citizen engagement still exist. It has been 12 years since the Great East Japan Earthquake, where the government called for a citizen-centered recovery and reconstruction process, and reconstruction in most areas in the Tohoku region has almost been concluded. Using qualitative data acquired through interviews with the residents, field observations during the World Bosai Walk, and questionnaire and archival research, this study aimed to discuss the overall reconstruction of Unosumai in Iwate Prefecture, giving the residents' perspective on the benefits and challenges they faced in participating in recovery planning and reconstruction and how the community has been able to strengthen their participation in disaster reduction initiatives since the earthquake and tsunami. This discussion is crucial as it would effectively offer lessons on engaging residents in post-disaster recovery and reconstruction after mega-disasters.

The estimated seismic hazard based on the delineated seismic source model is used as the basis to assign the seismic design loads in Canadian structural design codes. An alternative for the estimation is based on a spatially smoothed source model.However, a quantification of differences in the Canadian seismic hazard maps(CanSHMs) obtained based on the delineated seismic source model and spatially smoothed model is unavailable. The quantification is valuable to identify epistemic uncertainty in the estimated seismic hazard and the degree of uncertainty in the CanSHMs. In the present study, we developed seismic source models using spatial smoothing and historical earthquake catalogue. We quantified the differences in the estimated Canadian seismic hazard by considering the delineated source model and spatially smoothed source models.For the development of the spatially smoothed seismic source models, we considered spatial kernel smoothing techniques with or without adaptive bandwidth. The results indicate that the use of the delineated seismic source model could lead to under or over-estimation of the seismic hazard as compared to those estimated based on spatially smoothed seismic source models. This suggests that an epistemic uncertainty caused by the seismic source models should be considered to map the seismic hazard.

In mountainous rural settlements facing the threat of geohazards, local adaptation is a self-organizing process influenced by individual and group behaviors. Encouraging a wide range of local populations to embrace geohazard adaptation strategies emerges as a potent means of mitigating disaster risks. The purpose of this study was to investigate whether neighbors influence individuals' adaptation decisions, as well as to unravel the mechanisms through which neighborhood effects exert their influence. We employed a spatial Durbin model and a series of robustness checks to confirm the results. The dataset used in this research came from a face-to-face survey involving 516 respondents residing in 32 rural settlements highly susceptible to geohazards. Our empirical results reveal that neighborhood effects are an important determinant of adaptation to geohazards. That is, a farmer's adaptation decision is influenced by the adaptation choices of his/her neighbors. Furthermore, when neighbors adopt adaptation strategies, the focal individuals may also want to adapt, both because they learn from their neighbors' choices(social learning) and because they tend to abide by the majority's choice(social norms). Incorporating neighborhood effects into geohazard adaptation studies offers a new perspective for promoting disaster risk reduction decision making.

Hurricane Ike, which struck the United States in September 2008, was the ninth most expensive hurricane in terms of damages. It caused nearly USD 30 billion in damage after making landfall on the Bolivar Peninsula, Texas. We used the Delft3dFM/SWAN hydrodynamic and spectral wave model to simulate the storm surge inundation around Galveston Bay during Hurricane Ike. Damage curves were established through the relationship between eight hydrodynamic parameters(water depth, flow velocity, unit discharge, flow momentum flux, significant wave height, wave energy flux, total water depth(flow depth plus wave height), and total(flow plus wave) force) simulated by the model and National Flood Insurance Program(NFIP) insurance damage data. The NFIP insurance database contains a large amount of building damage data, building stories, and elevation, as well as other information from the Ike event. We found that the damage curves are sensitive to the model grid resolution, building elevation, and the number of stories. We also found that the resulting damage functions are steeper than those developed for residential structures in many other locations.

Rapid building damage assessment following an earthquake is important for humanitarian relief and disaster emergency responses. In February 2023, two magnitude-7.8 earthquakes struck Turkey in quick succession, impacting over 30 major cities across nearly 300 km. A quick and comprehensive understanding of the distribution of building damage is essential for e fficiently deploying rescue forces during critical rescue periods. This article presents the training of a two-stage convolutional neural network called BDANet that integrated image features captured before and after the disaster to evaluate the extent of building damage in Islahiye. Based on high-resolution remote sensing data from WorldView2, BDANet used predisaster imagery to extract building outlines; the image features before and after the disaster were then combined to conduct building damage assessment. We optimized these results to improve the accuracy of building edges and analyzed the damage to each building, and used population distribution information to estimate the population count and urgency of rescue at different disaster levels. The results indicate that the building area in the Islahiye region was 156.92 ha, with an affected area of 26.60 ha. Severely damaged buildings accounted for 15.67% of the total building area in the affected areas. WorldPop population distribution data indicated approximately 253, 297, and 1,246 people in the collapsed, severely damaged, and lightly damaged areas, respectively. Accuracy verification showed that the BDANet model exhibited good performance in handling high-resolution images and can be used to directly assess building damage and provide rapid information for rescue operations in future disasters using model weights.

Landslides cause huge human and economic losses globally. Detecting landslide precursors is crucial for disaster prevention.The small baseline subset interferometric synthetic-aperture radar(SBAS-InSAR) has been a popular method for detecting landslide precursors. However, non-monotonic displacements in SBAS-InSAR results are pervasive, making it challenging to single out true landslide signals. By exploiting time series displacements derived by SBAS-InSAR, we proposed a method to identify moving landslides. The method calculates two indices(global/local change index) to rank monotonicity of the time series from the derived displacements. Using two thresholds of the proposed indices, more than 96% of background noises in displacement results can be removed. We also found that landslides on the east and west slopes are easier to detect than other slope aspects for the Sentinel-1 images. By repressing background noises, this method can serve as a convenient tool to detect landslide precursors in mountainous areas.

Interorganizational collaboration networks have become an important tool for disaster management. However, research on how different organizations can effectively collaborate throughout the entire disaster management process in centralized states such as China is scarce. This study begins to fill this lacuna by investigating interorganizational collaboration in different phases of disaster management and analyzing changes in the structure of the networks constructed during the preparedness and response phases of the 2020 flood disaster in Hubei Province, China. Building on the complex adaptive systems(CAS)theory, we argue that interorganizational collaboration changes dynamically according to its tasks and requirements. In the preparedness phase, interorganizational collaborations primarily follow established plans and choose horizontal selforganized collaboration mechanisms. However, when the urgent information and resource requirements increase in the response phase, many organizations choose vertical mandatory collaboration mechanisms. We found that organizations at the central and provincial levels in China were well positioned to coordinate information and resources and strengthen the interorganizational collaboration and communication that is crucial in disaster management. These findings contribute to the study of interorganizational collaboration networks in disaster management.

Major disasters such as wildfire, tornado, hurricane, tropical storm, and flooding cause disruptions in infrastructure systems such as power and water supply, wastewater management, telecommunication, and transportation facilities. Disruptions in electricity infrastructure have negative impacts on sectors throughout a region, including education, medical services,financial services, and recreation. In this study, we introduced a novel approach to investigate the factors that can be associated with longer restoration time of power service after a hurricane. Considering restoration time as the dependent variable and using a comprehensive set of county-level data, we estimated a generalized accelerated failure time(GAFT) model that accounts for spatial dependence among observations for time to event data. The model fit improved by 12% after considering the effects of spatial correlation in time to event data. Using the GAFT model and Hurricane Irma's impact on Florida as a case study, we examined:(1) differences in electric power outages and restoration rates among different types of power companies—investor-owned power companies, rural and municipal cooperatives;(2) the relationship between the duration of power outage and power system variables; and(3) the relationship between the duration of power outage and socioeconomic attributes. The findings of this study indicate that counties with a higher percentage of customers served by investor-owned electric companies and lower median household income faced power outage for a longer time. This study identified the key factors to predict restoration time of hurricane-induced power outages, allowing disaster management agencies to adopt strategies required for restoration process.

With the acceleration of global climate change and urbanization, disaster chains are always connected to artificial systems like critical infrastructure. The complexity and uncertainty of the disaster chain development process and the severity of the consequences have brought great challenges to emergency decision makers. The Bayesian network(BN) was applied in this study to reason about disaster chain scenarios to support the choice of appropriate response strategies. To capture the interacting relationships among different factors, a scenario representation model of disaster chains was developed, followed by the determination of the BN structure. In deriving the conditional probability tables of the BN model, we found that, due to the lack of data and the significant uncertainty of disaster chains, parameter learning methodologies based on data or expert knowledge alone are insufficient. By integrating both sample data and expert knowledge with the maximum entropy principle, we proposed a parameter estimation algorithm under expert prior knowledge(PEUK). Taking the rainstorm disaster chain as an example, we demonstrated the superiority of the PEUK-built BN model over the traditional maximum a posterior(MAP) algorithm and the direct expert opinion elicitation method. The results also demonstrate the potential of our BN scenario reasoning paradigm to assist real-world disaster decisions.

Enterprises play a vital role in emergency management, but few studies have considered the strategy choices behind such participation or the collaborative relationship with the government. This study contended that enterprises have at least three strategies regarding emergency management: non-participation, short-term participation, and long-term participation. We constructed a two-stage evolutionary game model to explore the behavioral evolution rules and evolutionary stability strategies of the government and enterprises, and employed numerical simulation to analyze how various factors influence the strategy selection of the government and enterprises. The results show that if and only if the utility value of participation is greater than 0, an enterprise will participate in emergency management. The evolutionary game then enters the second stage, during which system stability is affected by a synergistic relationship between participation cost, reputation benefit, and government subsidies, and by an incremental relationship between emergency management benefit, government subsidies, and emergency training cost. This study provides a new theoretical perspective for research on collaborative emergency management, and the results provide important references for promoting the performance of collaborative emergency management.

On 6 February 2023, two 7.8 magnitude earthquakes consecutively hit south-central Türkiye, causing great concern from all governments, the United Nations, academia, and all sectors of society. Analyses indicate that there is also a high possibility of strong earthquakes with a magnitude of 7.8 or above occurring in the western region of China in the coming years. China is a country that is highly susceptible to catastrophic disasters such as earthquakes, floods, and other natural calamities, which can cause significant damages to both human life and property, as well as widespread impacts on the society. Currently, China's capacity for disaster prevention and control is still limited. In order to effectively reduce the impact of catastrophic disasters, ensure the safety of people's lives and property to the greatest extent possible, maintain social stability in high-risk areas, and ensure high-quality and sustainable regional development, it is urgent to improve the seismic resistance level of houses and critical infrastructure in high earthquake risk zones and increase the earthquake-resistant design level of houses in high-risk fault areas with frequent seismic activities; significantly enhance the ability to defend against extreme weather and ocean disasters in economically developed areas along the southeastern coast, as well as the level of fortification in response to extreme meteorological and hydrological disasters of coastal towns/cities and key infrastructure; vigorously enhance the emergency response capacity and disaster risk prevention level in western and ethnic minority regions; comprehensively improve the defense level of residential areas and major infrastructure in high geological hazard risk zones with flash floods, landslides, and mudslides; systematically promote national disaster prevention and mitigation education; and greatly enhance the societal disaster risk reduction ability, including catastrophic insurance.

The concepts of business continuity management,operational resilience,and organizational resilience each refer to actions that businesses and organizations can take in anticipating and responding to disruptions.However,the existing definitions and usages are difficult to differentiate due to overlapping objectives,implementation processes,and outcomes.This article examines definitions and approaches for these three concepts and suggest a framework to operationalize methods and tools relevant to each.These definitions emphasize three dyads:risk versus resilience;organizational processes versus assets;and normal operating conditions versus crisis conditions.Using these dyads to differentiate the concepts of business continuity management,operational resilience,and organizational resilience can support planners in clarifying objectives and identifying which approach will be most beneficial as businesses or organizations plan for and encounter disruptions.This article evaluates these concepts by examining illustrative examples of disruptions and responses.

Disaster forensic approaches aim to identify the causes of disasters to support disaster risk management.However,few studies have conducted a systematic literature review of scientific articles that labeled themselves as a forensic approach to disasters.This article provides a qualitative analysis of these forensic studies,focusing on five main issues:(1) the methodologies applied;(2) the forensic approaches used in the disaster risk management phases;(3) the hazards addressed;(4) if the methodologies involve social participation,and using what types of participation;and(5) if there are references to urban planning in the scientific studies analyzed.Our results showed a predominance of the Forensic Investigations of Disasters(FORIN)and Post-Event Review Capability(PERC) methodologies used in isolation or combination.There is a need for methodologies that engage people in participatory FORIN,fostering the co-production of knowledge and action research approaches.

Since the proposal of the pioneering "resilience triangle" paradigm,various time-series performance-based metrics have been devised for resilience quantification.The numerous choices diversify the toolbox for measuring this compound system concept;however,this multiplicity causes intractable questions for applications,including "Do these metrics measure the same resilience?" and "Which one to pick under what circumstance?" In this study,we attempted to address these two fundamental issues using a comprehensive comparative investigation.Through a quantitative-qualitative combined approach,12 popular performance-based resilience metrics are compared using empirical data from China's aviation system under the disturbance of COVID-19.Quantitative results indicate that only 12 of the 66 metric pairs are strongly positively correlated and with no significant differences in quantification outcomes;qualitative results indicate that the majority of the metrics are based on different definition interpretations,basic components,and expression forms,and thus essentially measure different resilience.The advantages and disadvantages of each metric are comparatively discussed,and a "how to choose" guideline for metric users is proposed.This study is an introspective investigation of resilience quantification studies,aiming to offer a new perspective to scrutinize those benchmarking metrics.

Extreme precipitation-induced landslide events are projected to increase under climate change,which poses a serious threat to human lives and property.In this study,a global-sc ale landslide risk assessment model was established using global landslide data,by considering landslide hazard,exposure,and vulnerability.The global climate model data were then employed to drive the established global landslide risk model to explore the spatial and temporal variations in future landslide risk across the globe as a result of extreme precipitation changes.The results show that compared to the 30-year period from1971 to 2000,the average annual frequency of landslides triggered by extreme precipitation is projected to increase by 7%and 10%,respectively,in the future 30-year periods of 2031-2060 and 2066-2095.The global average annual casualty risk of landslides is projected to increase from about 3240 to 7670 and 8380,respectively(with growth rates of 140% and 160%),during the 2031-2060 and 2066-2095 periods under the SSP2-4.5 scenario.The top 10 countries with the highest casualty risk of landslides are China,Afghanistan,India,the Philippines,Indonesia,Rwanda,Turkey,Nepal,Guatemala,and Brazil,60% of which are located in Asia.The frequency and intensity of extreme precipitation will increase under climate change,which will lead to an increase in casualties from landslides in mountainous areas globally,and this risk should be taken seriously.The present study was an attempt to investigate and quantify the impact of global landslide casualty risk under climate change,which still has uncertainty in terms of outcomes,and there remains a need for further understanding in the future of the propagation of uncertainty between the factors that affect the risk.

Vulnerability assessment and mapping play a crucial role in disaster risk reduction and planning for adaptation to a future earthquake.Turkey is one of the most at-risk countries for earthquake disasters worldwide.Therefore,it is imperative to develop effective earthquake vulnerability assessment and mapping at practically relevant scales.In this study,a holistic earthquake vulnerability index that addresses the multidimensional nature of earthquake vulnerability was constructed.With the aim of representing the vulnerability as a continuum across space,buildings were set as the smallest unit of analysis.The study area is in Izmit City of Turkey,with the exposed human and structural elements falling inside the most hazardous zone of seismicity.The index was represented by the building vulnerability,socioeconomic vulnerability,and vulnerability of the built environment.To minimize the subjectivity and uncertainty that the vulnerability indices based on expert knowledge are suffering from,an extension of the catastrophe progression method for the objective weighing of indicators was proposed.Earthquake vulnerability index and components were mapped,a local spatial autocorrelation metric was employed where the hotspot maps demarcated the earthquake vulnerability,and the study quantitatively revealed an estimate of people at risk.With its objectivity and straightforward implementation,the method can aid decision support for disaster risk reduction and emergency management.

In this study,we set out to develop a new social vulnerability index(SVI).In doing so,we suggest some conceptual improvements that can be made to existing methodical approaches to assessing social vulnerability.To make the entanglement of socio-spatial inequalities visible,we are conducting a small-scale study on heterogeneous urban development in the city of Hamburg,Germany.This kind of high-resolution analysis was not previously available,but is increasingly requested by political decision makers.We can thus show hot spots of social vulnerability(SV) in Hamburg,considering the effects of social welfare,education,and age.In doing so,we defined S V as a contextual concept that follows the recent shift in discourse in line with the Intergovernmental Panel on Climate Change's(IPCC) concepts of risk and vulnerability.Our SVI consists of two subcomponents:sensitivity and coping capacity.Populated areas of Hamburg were identified using satellite information and merged with the social data units of the city.Areas with high SVI are distributed over the entire city,notably in the district of Harburg and the Reiherstieg quarter in Wilhelmsburg near the Elbe,as well as in the densely populated inner city areas of Eimsbüttel and St.Pauli.As a map at a detailed scale,our SVI can be a useful tool to identify areas where the population is most vulnerable to climate-related hazards.We conclude that an enhanced understanding of urban social vulnerability is a prerequisite for urban risk management and urban resilience planning.