Progress in Disaster Science最新文献

{"title":"Community resilience to multi-hazard risks: Insights from Narayani Basin, Nepal","authors":"Prakriti Niraula ,&nbsp;Chandra Lal Pandey ,&nbsp;Raj Kumar Pariyar","doi":"10.1016/j.pdisas.2026.100523","DOIUrl":"10.1016/j.pdisas.2026.100523","url":null,"abstract":"<div><div>Nepal's diverse topography and changing climate expose communities to interconnected multi-hazard risks, including floods, landslides, storms, and earthquakes. This study examines the status and interlinkages of five resilience dimensions—social, economic, physical, institutional, and environmental—within the Narayani basin, one of the country's most hazard-prone regions. Employing a mixed-methods approach with household surveys, in-depth interviews and focus group discussions in Khairahani and Bharatpur municipalities, the study finds a disconnect between strong informal networks and limited formal systems. Social solidarity is robust, but formal social protection and inclusive decision-making are weak. Economic resilience is constrained by dependence on subsistence agriculture and daily wage labor, with limited access to financial services. Infrastructural resilience is uneven, with gaps in disaster-resilient infrastructure and early warning systems creating cascading vulnerabilities. Institutional capacity is limited by resource shortages and weak inter-agency coordination, while environmental resilience is threatened by climate change and fragile ecosystem services despite community-led initiatives. The findings highlight the interdependence of resilience dimensions, showing that deficits in one area amplify vulnerabilities in others. The study contributes a context-specific multi-hazard resilience framework for Nepal and provides actionable insights for integrated, multi-capital strategies to strengthen resilience in complex hazard environments.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"29 ","pages":"Article 100523"},"PeriodicalIF":3.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cyclone Ditwah: What next for Sri Lanka beyond the storm","authors":"Dilanthi Amaratunga ,&nbsp;Lahiru Kodituwakku","doi":"10.1016/j.pdisas.2026.100518","DOIUrl":"10.1016/j.pdisas.2026.100518","url":null,"abstract":"<div><div>Cyclone Ditwah inflicted catastrophic damage on Sri Lanka, claiming over 600 lives and displacing more than 200,000 people. Initial assessments indicate that nearly 10% of the population—approximately 2.3 million people—was affected by the disaster. Direct damages across the residential, infrastructure, and agricultural sectors are estimated at US$4.1 billion, representing roughly 4% of the nation's 2024 GDP. Furthermore, the health sector requires an estimated LKR 21,026.73 million to restore full operational capacity. While the immediate response was swift, the sheer scale of destruction exposed critical gaps in the national disaster preparedness and response framework. This paper analyses in detail the system performance in the aftermath of the cyclone to provide evidence-based recommendations for strengthening Sri Lanka's disaster management systems.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"29 ","pages":"Article 100518"},"PeriodicalIF":3.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic tsunami risk assessment based on supervised learning considering nonstationary sea-level rise and multiple source rupture: Application to Denpasar City of Bali","authors":"Willy Cahyadhiputra Gunawan ,&nbsp;Abdul Kadir Alhamid ,&nbsp;Mitsuyoshi Akiyama ,&nbsp;Muhamad Farhan Permana ,&nbsp;Mohammad Farid ,&nbsp;Alamsyah Kurniawan ,&nbsp;Mohammad Bagus Adityawan","doi":"10.1016/j.pdisas.2025.100505","DOIUrl":"10.1016/j.pdisas.2025.100505","url":null,"abstract":"<div><div>Tsunami is among most devastating natural disasters, posing major threats to coastal communities. In Indonesia, with its extensive megathrust zone, a key challenge is capturing the uncertainties of fault rupture location earthquake magnitudes. Sea-level rise further complicates this by increasing inundation depth and altering inundation patterns across rupture scenarios. This study aims to perform probabilistic tsunami risk assessments of economic loss based on supervised learning considering multiple source ruptures and sea-level rise effects. Historical earthquake and sea-level rise data are collected, and the fault rupture parameters are derived using scaling relationships. Tsunami propagation analyses are conducted based on several combinations of earthquake magnitude and sea-level rise. A Gaussian Process Regression surrogate model is employed to capture the relationship between inundation depth, magnitude, and sea-level rise. The tsunami hazard curve is estimated based on the magnitude and sea-level rise uncertainties using Monte Carlo simulation. Finally, tsunami risk is evaluated by convolving the tsunami hazard curve with the fragility curve of the exposed structures and their corresponding unit loss. The framework enables rapid risk assessment considering multiple source ruptures. Results indicate that the risk amplification due to sea-level rise is strongly influenced by rupture location and tsunami intensity.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"29 ","pages":"Article 100505"},"PeriodicalIF":3.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving disaster preparedness and recovery of Bangladesh riverbank erosion areas: Insights from household perceptions and practices","authors":"Rokshana Binta Samad ,&nbsp;Jouni Paavola ,&nbsp;James D. Ford ,&nbsp;Paula Novo","doi":"10.1016/j.pdisas.2026.100526","DOIUrl":"10.1016/j.pdisas.2026.100526","url":null,"abstract":"<div><div>Riverbank erosion significantly affects lives and livelihoods but is still neglected in disaster risk reduction (DRR) research and policy, which tend to focus on rapid-onset events. This research examines how household preparedness affects recovery outcomes in riverbank erosion-prone areas in Bangladesh, addressing an important gap in understanding anticipatory action to slow-onset disasters. Drawing from a mixed-methods approach consisting of household surveys (<em>n</em> = 280), focus group discussions, and key informant interviews in Harirampur and Sariakandi Upazilas, the research develops and utilises a multidimensional preparedness index. The index covers behavioural, socio-economic, and institutional aspects of preparedness, providing a new framework for assessing household preparedness in chronic hazard settings. The preparedness index shows that households in the low to somewhat prepared categories are among the most recently impacted. Past-impacted households in Harirampur are marginally better off than Sariakandi. Harirampur households' preparedness was linked with significantly lower asset loss. In both sites, insufficient financial resources and the absence of early warning systems were major barriers, and most households' recovery time exceeds two years. The findings show that local knowledge and informal coping strategies boost resilience, while the lack of early warnings and material support adversely affects preparedness. By linking preparedness to recovery and establishing its role in reducing asset losses, we add new empirical evidence and a practical tool that helps target households in need of support. The findings emphasize how context-specific preparedness should be integrated into national DRR frameworks and inclusive community-based approaches in erosion-prone areas.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"29 ","pages":"Article 100526"},"PeriodicalIF":3.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disaster relief strategies of the Buddhist Tzu Chi Charity Foundation: A case study of the 2024 Hualien Earthquake in Taiwan","authors":"Hiroka Sato ,&nbsp;Noriko Sudo ,&nbsp;Ted Hwang ,&nbsp;Jennifer Liu ,&nbsp;Suh-Ching Yang","doi":"10.1016/j.pdisas.2026.100517","DOIUrl":"10.1016/j.pdisas.2026.100517","url":null,"abstract":"<div><div>Following the 2024 Hualien Earthquake, the Buddhist Tzu Chi Charity Foundation (Tzu Chi) assumed a primary role in managing the disaster response. This study aimed to examine the organisational and contextual factors that enabled Tzu Chi to make such a significant contribution to disaster relief efforts. We conducted two semi-structured group interviews with Tzu Chi personnel: one with two officials from the Central Response Centre and another with three volunteers who supported on-site relief activities. Using thematic analysis, five main themes were extracted: disaster relief management; organisational capacity; collaboration across multiple levels of government; routine activities during non-disaster periods; and attitudes toward relief activities. Findings showed that Tzu Chi strategically coordinated volunteer deployment considering workload and psychological well-being. Its large pool of volunteers and abundant material resources enabled it to assume multiple roles during the emergency. Well-established public–private partnerships between Tzu Chi and the government also facilitated effective cooperation. Additionally, volunteer activities routinely conducted during non-disaster periods were effectively adapted to disaster relief operations. Finally, Tzu Chi volunteers placed strong emphasis on providing psychological support to disaster victims. These organisational characteristics and value-based practices offer valuable insights for countries that have experienced management challenges in past disaster responses.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"29 ","pages":"Article 100517"},"PeriodicalIF":3.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Warning cultures in practice: Shadow systems in local flood risk governance","authors":"Jeff Da Costa ,&nbsp;Hannah L. Cloke ,&nbsp;Jessica Neumann ,&nbsp;Nathan Salvidge","doi":"10.1016/j.pdisas.2025.100513","DOIUrl":"10.1016/j.pdisas.2025.100513","url":null,"abstract":"<div><div>Early warning reduces flood risk when forecasts are interpreted and converted into timely local action. In Luxembourg, a nationally centralised system, with no intermediate tier, places the next line of decision making immediately with municipalities. This paper examines how a structured shadow system emerges at the local scale to bridge gaps between national alerts and operational needs. Evidence is drawn from a focus group with municipal officials in a flood-affected community, including a flood-scenario exercise simulating an evolving rainfall event to examine decision-making under uncertainty. Thematic analysis shows that national flood alerts are generic, repetitive, and weakly linked to municipal thresholds for initiating preparedness measures. Ambiguous terminology, colour codes, and broad spatial and temporal framing limit their operational usefulness for local response. Frequent low-level alerts contribute to warning fatigue and erode trust. Officials construct meaning through institutional knowledge, lived experience, peer exchange, and heuristics. These locally embedded practices highlight the importance of scale, showing how municipal knowledge both localises and at times overrides national messages. The configuration strengthens local responsiveness but concentrates interpretive responsibility at municipal level without formal support, which can increase variability across jurisdictions. The analysis points to a need for impact-based, temporally precise, municipality-scale products with clear triggers and guidance co-developed with local officials and potentially residents, so that centrally issued forecasts can be converted into anticipatory action at the local level.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"29 ","pages":"Article 100513"},"PeriodicalIF":3.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Taiwanese settlements vulnerability and risk of earthquakes – Case study of indigenous areas","authors":"Petra Nepožitková","doi":"10.1016/j.pdisas.2026.100524","DOIUrl":"10.1016/j.pdisas.2026.100524","url":null,"abstract":"<div><div>Taiwan is among the most disaster-prone countries in the world, periodically affected by strong earthquakes that pose a risk to marginalised populations. Taiwanese indigenous peoples tend to be affected by structural inequalities, which influence the way they are affected by natural hazards, as well as post-disaster reconstruction, disaster preparedness, and recovery. Thus, there is a need to map the socioeconomic vulnerability of settlements located in Indigenous Areas, especially vulnerable populations such as the elderly, women, children, and indigenous peoples. Socioeconomic vulnerability is related to the local population's risks arising from the interaction between the social system and earthquake hazard. This article employs the Social Vulnerability Index (SVI) calculated from vulnerability indicators standardized using z-scores normalization to assess and map socioeconomic vulnerability in Indigenous Areas. The risk matrix combining SVI and hazard exposure identifies villages at the highest risk.</div><div>Geographic Information System (GIS) analysis reveals that villages in Indigenous Areas form a cluster of high SVI on the east coast and the southern part of the Central Mountains. The earthquake risk matrix shows that the Very High risk category covers a large part of the Indigenous Areas. Especially settlements located in areas surrounding the East Rift Valley and the east coast have both high SVI and risk, except for the major cities of Hualien and Taitung. Resulting risk maps offer an important insight that can be readily used by first responders, policy makers, and stakeholders to improve resilience and disaster management strategies for indigenous communities.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"29 ","pages":"Article 100524"},"PeriodicalIF":3.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rainfall-conditioned landslide susceptibility mapping using an FR–CART hybrid framework in southern Thailand","authors":"Thaksakorn Pornbunyanon ,&nbsp;Prasan Jitpat ,&nbsp;Piyapong Suwanno","doi":"10.1016/j.pdisas.2026.100530","DOIUrl":"10.1016/j.pdisas.2026.100530","url":null,"abstract":"<div><div>Rainfall-induced landslides in the steep tropical terrain of southern Thailand pose significant challenges to communities, infrastructure, and regional development. This study presents a rainfall-responsive landslide susceptibility framework that integrates static spatial conditioning factors with dynamic rainfall information. A Frequency Ratio (FR) model was first applied to generate a static Landslide Susceptibility Index (LSI) using 17 geospatial variables representing topographic, geological, hydrological, and anthropogenic characteristics. To incorporate the temporal influence of precipitation, a Classification and Regression Tree (CART) model was employed using short-term rainfall metrics, including daily, 3-day, 7-day, and 15-day cumulative rainfall, to derive rainfall-triggering thresholds that conditionally refine existing susceptibility classes rather than generate a new susceptibility index or estimate landslide hazard probability.</div><div>Model validation shows that the standalone FR model achieved an Area Under the Curve (AUC) of 0.74, while the integrated FR–CART framework improved performance to an AUC of 0.82. Among the rainfall indicators, the 15-day cumulative rainfall index exhibited the strongest association with landslide occurrences, emphasizing the role of antecedent moisture conditions. In this study, rainfall-conditioned susceptibility is explicitly defined as a rule-based upward adjustment of static LSI classes (e.g., from High to Very High) under real-time or forecasted rainfall scenarios. This conditioning process does not constitute the derivation of a new susceptibility index, nor does it imply probabilistic landslide hazard or risk assessment. The resulting rainfall-conditioned susceptibility outputs support susceptibility-informed interpretation within early-warning applications for monsoon-prone regions of southern Thailand.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"29 ","pages":"Article 100530"},"PeriodicalIF":3.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decision-making criteria for reverse triage in hospitalized patients following mass casualty incidents: A systematic literature review","authors":"Seyyed Ahmad Mohammadikia ,&nbsp;Fatemeh Falahati-Marvast ,&nbsp;Mahsa Khodayarian ,&nbsp;Ali Raee Ezzabadi ,&nbsp;Mohammad Reza Khajehaminian","doi":"10.1016/j.pdisas.2026.100516","DOIUrl":"10.1016/j.pdisas.2026.100516","url":null,"abstract":"<div><h3>Background and aim</h3><div>Mass casualty incidents pose substantial challenges to hospital surge capacity and resource management. Reverse triage has been recognized as a critical strategy for increasing bed availability through the early discharge of selected hospitalized patients. However, the absence of a consolidated, evidence-based, and operational framework of decision-making criteria has limited the systematic application of reverse triage in practice. This study aimed to systematically identify, synthesize, and categorize decision-making criteria for reverse triage of hospitalized patients following mass casualty incidents.</div></div><div><h3>Methods</h3><div>This systematic literature review was conducted in accordance with the PRISMA 2020 guidelines. A comprehensive search of PubMed, Scopus, and Web of Science databases was performed up to January 31, 2024. Studies addressing decision-making criteria for reverse triage in hospitalized patients were included without restrictions on study design or publication date. Two independent reviewers conducted study screening and data extraction. Study quality was assessed using the Mixed Methods Appraisal Tool (MMAT), and extracted criteria were synthesized through thematic analysis.</div></div><div><h3>Findings</h3><div>From 10,131 identified records, 19 studies met the inclusion criteria. A total of 374 initial criteria were extracted and synthesized into 34 final criteria. These criteria were organized into four main categories: (1) non-discharge criteria (12 criteria); (2) criteria for relatively stable patients requiring continued hospitalization (10 criteria); (3) social, environmental, or managerial barriers (9 criteria); and (4) supportive criteria for early discharge (3 criteria). In addition, the criteria were classified into four overarching domains: clinical, resource-related, individual, and other factors. Most included studies were assessed as high quality.</div></div><div><h3>Conclusion</h3><div>This review provides the most comprehensive synthesis of reverse triage decision-making criteria to date and contributes to the standardization of early discharge decision-making in disaster contexts. The identified criteria offer a structured, evidence-informed foundation for the development of hospital discharge protocols and clinical decision support systems, with potential applicability to both mass casualty incidents and routine hospital overcrowding. Future research should prioritize prospective clinical validation of these criteria and explore their implementation across diverse healthcare systems, particularly in resource-limited settings.</div><div>Systematic review registration: PROSPERO CRD420251007355.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"29 ","pages":"Article 100516"},"PeriodicalIF":3.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of open-source SAR-based flood datasets for flood extent mapping in emergency settings","authors":"Ira Karrel San Jose ,&nbsp;Sesa Wiguna ,&nbsp;Ryohei Kametaka ,&nbsp;Bruno Adriano ,&nbsp;Erick Mas ,&nbsp;Shunichi Koshimura","doi":"10.1016/j.pdisas.2025.100507","DOIUrl":"10.1016/j.pdisas.2025.100507","url":null,"abstract":"<div><div>The increasing frequency and severity of flood events, exacerbated by climate change, continue to threaten vulnerable communities worldwide, particularly in low-lying and coastal regions. Timely and accurate flood extent mapping is critical for supporting decision-making and resource allocation during disaster response operations. Advances in machine learning (ML) models, such as deep learning (DL), and the growing availability of Earth observation (EO) data have made automated inundation mapping more viable. However, the reliability of these models is highly dependent on the quality, diversity, and representativeness of the training datasets. Therefore, this study investigates several remote sensing flood datasets, both optical and synthetic aperture radar (SAR), to assess their applicability in emergency response settings. Based on the initial assessment and advantages of using SAR imagery in flood detection, the study evaluates five publicly available Sentinel-1 flood datasets using a DL segmentation model to determine their suitability for emergency response scenarios. Datasets with multi-temporal inputs and high-quality labels achieved superior F1 scores ranging from 0.844 to 0.959. To assess their ability to support emergency operations, the trained models were further tested on three independent flood disasters. While the datasets performed well on their test samples, their predictive accuracy declined substantially when applied to these new flood events, highlighting limitations in current dataset generalizability. The results emphasize that while segmentation models can achieve high accuracy in familiar sites, real-world applications require datasets that capture the heterogeneity of flood scenarios across different regions and conditions. An improved flood dataset design, incorporating multi-temporal and coherence data, a standardized preprocessing framework, and a broader flood geographic distribution, is significant in building reproducible and robust flood detection models.</div></div>","PeriodicalId":52341,"journal":{"name":"Progress in Disaster Science","volume":"29 ","pages":"Article 100507"},"PeriodicalIF":3.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}