基于自然灾害风险评价框架的省级地质灾害风险区划方法探讨——以吉林省为例

地质灾害风险区划是地质灾害风险管理与防治的有效手段之一,对于科学防治地质灾害具有重要意义。基于自然灾害风险理论,从致灾因子危险性,承灾体暴露性和脆弱性以及防灾减灾能力(恢复力)等入手,选取评价指标,构建省级地质灾害风险评价模型,对全省地质灾害进行风险区划。该模型在吉林省地质灾害风险区划中应用表明,评价结果合理,与野外调查情况吻合,可以为规划和地质灾害防治等工作提供依据。     

Touristic disaster: Spectacle and recovery in Post-Katrina New Orleans

This paper develops the concept touristic disaster as a heuristic device to examine the conflictual and contradictory aspects of showcasing disaster-devastated neighborhoods as tourist attractions. Touristic disaster refers to the application of tourism modes of staging, visualization, and discourse to reenchant the money making deterrents (stigma) of “destruction” and “ruin” and re-signify disaster to indicate “recovery” and “rebirth.” This paper uses empirical examples from New Orleans to examine the transition from “disaster tourism” to “recovery tourism” in tourism framings of post-Katrina rebuilding. The concept of touristic disaster views disaster-devastated neighborhoods as sites and arenas of contestation in which opposing groups and interests battle to control representations of urban space. The paper illustrates the motivations, processes, and paradoxical impacts of the commodification and global representation of “disaster” and “recovery” and provides insights into the ways in which people can use spectacle to contest marginalization.     

An efficient framework for ensemble of natural disaster simulations as a service

Calculations of risk from natural disasters may require ensembles of hundreds of thousands of simulations to accurately quantify the complex relationships between the outcome of a disaster and its contributing factors. Such large ensembles cannot typically be run on a single computer due to the limited computational resources available. Cloud Computing offers an attractive alternative, with an almost unlimited capacity for computation, storage, and network bandwidth. However, there are no clear mechanisms that define how to implement these complex natural disaster ensembles on the Cloud with minimal time and resources. As such, this paper proposes a system framework with two phases of cost optimization to run the ensembles as a service over Cloud. The cost is minimized through efficient distribution of the simulations among the cost-efficient instances and intelligent choice of the instances based on pricing models. We validate the proposed framework using real Cloud environment with real wildfire ensemble scenarios under different user requirements. The experimental results give an edge to the proposed system over the bag-of-task type execution on the Clouds with less cost and better flexibility.     

Participatory action research: working beyond disaster toward prevention

Drawing on qualitative analyses of post-disaster effects on vulnerable rural communities in Pakistan, this paper puts forward a participatory action research stance as a next step or approach toward prevention. This approach stands in contrast to the current post-disaster relief model commonly practiced by many non-government organizations (NGOs). The participatory action research perspective was used to examine the qualitative evidence and provide a framework to effectively work with rural vulnerable communities. Qualitative analyses documented community wisdom and ideas toward prevention which, when potentially partnered with resources offered by NGOs, could dramatically reduce their vulnerability to natural disasters.     

Resilience: a capacity and a myth: findings from an in-depth case study in disaster management research

Hurricane Andrew, which made landfall on August 24, 1992, was one of the most destructive hurricanes in American history, causing atypically high levels of psychological and physical health impairment among the resident population and especially among vulnerable groups. This article investigates whether maternal exposure to Hurricane Andrew during pregnancy increased the risk of dystocia (or dysfunctional labor) and infant delivery by cesarean section, the standard medical response to abnormal labor progression. We analyze 297,996 birth events in Miami-Dade and Broward counties in Florida from 1992 to 1993 using propensity score methodology with stratification and nearest-neighbor matching algorithms. Results show that hurricane-exposed pregnant women were significantly more likely to experience stress-induced abnormal labor and cesarean delivery outcomes as compared to statistically matched comparison groups. The conclusion details the policy implications of our results, with particular attention to the importance of maternal prenatal care in the aftermath of disasters.     

The tourism disaster vulnerability framework: an application to tourism in small island destinations

Islands are known to be vulnerable to natural hazards, resulting in substantial risks for their tourism industries. To facilitate the systematic analysis of the underlying vulnerability drivers, a tourism disaster vulnerability framework was developed. The conceptual model then guided qualitative empirical research in three regions: the Caribbean, the South Pacific, and the Indian Ocean. The results from 73 interviews highlight common, as well as idiosyncratic, factors that shape the islands’ hazardscapes and vulnerabilities. Key vulnerabilities included social, economic, political, and environmental dimensions. Probably, the most critical vulnerability driver is the lack of private sector investment in disaster risk reduction. This is interrelated with deficient planning processes, on-going demand for coastal products, lack of political will, and poor environmental conditions. Notwithstanding many barriers, some businesses and organisations engage proactively in addressing disaster risk. The paper’s empirical evidence supports the validity of the framework, and suggestions for further research are made.     

The use of digital technologies for landslide disaster risk research and disaster risk management: progress and prospects

Landslide is a serious natural disaster next only to earthquake and flood, which will cause a great threat to people’s lives and property safety. The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective , difficult to quantify, and no pertinence. As a new research method for landslide susceptibility assessment, machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models. Taking Western Henan for example, the study selected 16 landslide influencing factors such as topography, geological environment, hydrological conditions, and human activities, and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination (RFE) method. Five machine learning methods [Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)] were used to construct the spatial distribution model of landslide susceptibility. The models were evaluated by the receiver operating characteristic curve and statistical index. After analysis and comparison, the XGBoost model (AUC 0.8759) performed the best and was suitable for dealing with regression problems. The model had a high adaptability to landslide data. According to the landslide susceptibility map of the five models, the overall distribution can be observed. The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest, the Xiaoshan Mountain range in the west, and the Yellow River Basin in the north. These areas have large terrain fluctuations, complicated geological structural environments and frequent human engineering activities. The extremely high and highly prone areas were 12043.3 km² and 3087.45 km², accounting for 47.61% and 12.20% of the total area of the study area, respectively. Our study reflects the distribution of landslide susceptibility in western Henan Province, which provides a scientific basis for regional disaster warning, prediction, and resource protection. The study has important practical significance for subsequent landslide disaster management.     

旱灾风险定量评估总体框架及其关键技术

作为旱灾风险管理的核心内容和关键环节,旱灾风险评估逐渐成为旱灾研究的热点问题。从科学界定旱灾风险概念入手,辨析了干旱风险与旱灾风险之间的关系,并从机理上对旱灾风险形成机制进行了剖析和阐述;首次提出了旱灾风险定量评估总体框架,即通过建立干旱频率~潜在损失~抗旱能力之间的定量关系实现对旱灾风险的定量评估,并探讨了该评估框架所涉及的干旱频率分析技术、灾损评估技术、抗旱能力评估技术和旱灾风险表征技术等关键技术及其难点。     

Towards an integrated management framework for emerging disaster risks in Japan

An integrated framework for disaster risk management is presented to cope with the risk of low-probability high-consequence (LPHC) disasters in urban communities. Since the 2000 Tokai flood in Japan, there has been a shift in the management strategy from disaster prevention with a presumed zero risk to disaster reduction with an acceptable risk. The framework consists of: (i) integration of a different categories of risk reduction options in terms of structural and nonstructural measures, regulation and market-oriented measures, (ii) strengthening of the capacity of local communities to make their own management choices for LPHC-type disaster risks, and (iii) promoting the participation of stakeholders throughout the entire cycle of risk management. The interdisciplinary framework is discussed with reference to lessons learned from two recent major flood disasters (the 2000 Tokai flood and the 2004 Niigata flood). To implement the goals of the integrated framework, a participatory platform for disaster risk communication called “Pafrics” has been developed. Preliminary results of the pilot study of participation and risk communication supported by Pafrics are presented.     

Landslide catastrophes and disaster risk reduction: A GIS framework for landslide prevention and management

A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning. Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors. Recent advancements in high-resolution satellite imagery, coupled with the rapid development of artificial intelligence, particularly data-driven deep learning algorithms (DL) such as convolutional neural networks (CNN), have provided rich feature indicators for landslide mapping, overcoming previous limitations. In this review paper, 77 representative DL-based landslide detection methods applied in various environments over the past seven years were examined. We analyze the structures of different DL networks, discuss on five main application scenarios, and assess both the advancements and limitations of DL in geological hazard analysis. The results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence, with U-Net-based structures gaining prominence due to their flexibility in feature extraction and generalization. Finally, we explored the hindrances of DL in landslide hazard research based on the above research content. Challenges such as black-box operations and sample dependence persist, warranting further theoretical research and future application of DL in landslide detection.     

The link between social capital and disaster recovery: evidence from coastal communities in Bangladesh

Natural Hazards - The purpose of this research is to explore the role social capital played in disaster coping and the recovery process among the southwest coastal villages of Bangladesh....     

Social vulnerability and participation in disaster recovery decisions: public housing in Galveston after Hurricane Ike

Natural Hazards - In September 2008, Hurricane Ike caused massive damages to Galveston Island’s residential structures including four public housing developments. These developments were...     

Constructing a positive circuit of debt among survivors: an action research study of disaster volunteerism in Japan

Natural Hazards - Following a disaster, survivors sometimes reject volunteer support because it would make them feel indebted. In this study, we collaborated with survivors of the 2016 Kumamoto...     

A conceptual framework for integrated management of disasters recovery projects

Natural Hazards - Following the increasing rate of disasters’ impacts on societies, more attention is being paid to recovery projects after these disasters. However, from the perspective of...     

The cost of social vulnerability: an integrative conceptual framework and model for assessing financial risks in natural disaster management

Natural Hazards - Groups that are unable to prepare for disasters, or to recover from damage on their own, have a high dependency on government services, which inevitably leads to more government...     

寒冷地区输水渠道冻胀破坏链式分析及减灾研究

寒冷地区输水渠道在运行过程中,由于持续负温的影响,地温逐步下降,引起渠基土体水分分布发生变化导致土体冻胀,致使输水渠道结构破坏,这种作用影响所表现出来的链式效应是冻胀破坏作用的重要特征。为了探明寒冷地区输水渠道冻胀破坏机理,分析渠道的冻胀破坏因素,引入链式破坏理论进行分析。从系统理论出发,分析冻胀破坏系统的链式关系结构,建立冻胀破坏链式效应关系模型。以寒冷地区渠道衬砌结构冻胀破坏为例,分析经过一个冻融周期,渠基土体颗粒、土体成分组成、气温、地温变化规律和土壤水分迁移规律对渠道衬砌结构冻胀破坏的影响。分析渠道衬砌结构冻胀破坏链式机理,是在外部环境气温、水分等条件输入下,地温分布受土体性质及土体水分的影响,土体水分迁移受地温和土体性质的作用,土体产生冻胀是对地温和水分分布作用的响应,以此为依据提出寒区渠道冻胀破坏断链减灾方法。