Search Results (1,769)

As wildfires intensify globally due to climate change, accurate wildfire danger forecasting systems have become essential for effective disaster management and early warning. Fuel Moisture Content (FMC), defined as the ratio of water mass to dry fuel mass, plays a critical role in determining ignition probability and fire spread dynamics. This study conducts a comparative analysis of five major national wildfire danger rating systems: the National Fire Danger Rating System (NFDRS, USA), Canadian Forest Fire Danger Rating System (CFFDRS), European Forest Fire Information System (EFFIS), Australian Fire Danger Rating System (AFDRS), and the Korean Forest Fire Danger Rating System (KFDRS). Using a multi-criteria comparative framework, the systems were evaluated based on fuel classification structure, input variables, modeling approach, and spatiotemporal prediction resolution. The results reveal substantial disparities in spatial resolution (100 m to district-level), temporal resolution (hourly vs. daily), and fuel moisture modeling approaches (physics-based, index-based, and hybrid systems). Specifically, NFDRS and AFDRS provide high-frequency forecasting with hourly temporal resolution, operating at spatial resolutions of 1 km and 100 m, respectively, and incorporating dynamic fuel moisture modeling. In contrast, CFFDRS and KFDRS primarily rely on daily index-based predictions. Furthermore, while many global systems increasingly leverage remote sensing and machine learning for real-time FMC estimation, South Korea’s KFDRS remains predominantly empirical and weather-driven. The analysis identifies critical limitations in the KFDRS, including coarse spatial resolution (district-level), limited integration of Live Fuel Moisture Content (LFMC) modeling, and the lack of AI-augmented hybrid approaches. Accordingly, this study proposes a phased three-stage policy roadmap (2026–2035), emphasizing sensor-network expansion, AI–physics fusion modeling, and high-resolution (10 m) FMC mapping to enhance forecasting accuracy in complex terrains. These findings provide strategic insights for improving wildfire risk management and supporting the transition from reactive response to predictive wildfire forecasting under increasing climate variability. Full article

Urban pluvial flooding presents growing challenges for disaster risk management, yet most susceptibility studies rely on watershed-based frameworks that inadequately capture the localized dynamics of urban systems. This study proposes a city-scale flood susceptibility modeling framework for Cincinnati, Ohio. Cincinnati was chosen because it is a city with a documented history of severe urban flooding, including a once-in-a-century storm in 2016. Multi-source historical flood data were compiled from NOAA storm event records and crowdsourced reports to enhance spatial coverage. Four machine learning algorithms (Random Forest, Support Vector Machine, XGBoost, and Logistic Regression) were implemented to identify the most effective approach for urban pluvial flood prediction. Random Forest (RF) and Support Vector Machine (SVM) achieved the highest accuracy (0.84) and demonstrated strong discriminatory power. RF was selected as the optimal model because it had a higher AUC (90%) and the lowest RMSE (0.35). To assess generalizability, the RF model was validated on updated land use data and flood records from a 2020 storm event. It demonstrated robust performance (accuracy = 0.89, RMSE = 0.36, precision = 0.75, recall = 1, and AUC = 0.95), despite urban development changes. This study’s novelty lies in combining multi-source flood records with a grid-based machine learning framework and rigorously validating model robustness under evolving urban conditions. The results advance urban pluvial flood susceptibility modeling and offer actionable guidance for evidence-based flood risk management worldwide. Full article

Malawi lies within the southern segment of the East African Rift System and is exposed to infrequent but potentially damaging earthquakes. While recent advances in fault mapping, seismic monitoring, and hazard modelling have substantially improved scientific understanding of earthquake hazard in the Malawi Rift Zone, the practical reduction in seismic risk remains limited. This Perspective paper argues that earthquake resilience in Malawi is constrained less by scientific uncertainty than by challenges in integrating existing hazard knowledge into governance, planning, and preparedness. Drawing exclusively on published geological, geophysical, engineering, and policy literature, the paper synthesises evidence on seismic hazard, historical earthquake impacts, institutional preparedness, and barriers to the operational use of scientific risk assessments. An integrated, multi-pillar framework is proposed to support improved coordination between science, governance, infrastructure practice, and community preparedness. The framework is conceptual in nature and is intended to inform policy dialogue, prioritisation, and future empirical research rather than to provide a validated operational model. While grounded in the Malawian context, the insights presented are relevant to other low-income, rift-hosted regions facing similar challenges in translating earthquake science into effective disaster risk reduction. Full article

We developed two distinct automatic packet reporting system (APRS) payload designs to establish a reliable backup communication system for disaster risk reduction and management. The payloads are designed to perform a significant key operation, primarily APRS digital repeating (DP), enabling continuous communication access even in areas where conventional ground-based infrastructure is damaged by natural disasters through the relay of APRS packets to extend communication coverage. A detailed framework is designed using the standard amateur packet radio (AX.25 protocol). It specifies the structure of APRS data frames and packets, which are used to transmit alerts, emergency status updates, and text messages. This structure ensures that important information is transmitted reliably and effectively during an emergency. The designs for the APRS payloads share a common overall operating system architecture but differ in their very high frequency transceiver modules used for the amateur radio (Radiometrix BiM1H very high frequency (VHF) Narrowband Transceiver and Dorji DRA818V VHF Band Voice Transceiver Module). Full article

The alpine grassland ecosystem in the Source Region of the Yellow River (SRYR) faces the dual pressures of ecological protection and economic development. Its ecological fragility and climate sensitivity make local animal husbandry susceptible to meteorological disasters. To overcome adverse selection and moral hazard in traditional animal husbandry insurance, this study integrates 963 field sampling observation data, over 400 valid herdsmen survey data, and long-term environmental time series variables. A random forest model (R² = 0.59, RMSE = 65.84 g/m², superior to the artificial neural network in this paper) was used to estimate grass yield. Hodrick–Prescott (HP) filtering was used to separate meteorological yield per unit area and derive yield loss rate. A joint distribution model of meteorological indicators and loss rate was constructed using a Copula function to capture tail-dependent structures, providing a basis for determining trigger thresholds and actuarial pricing of pure insurance premiums. The study reveals the transmission mechanism of climate disasters to feeding costs and designs regional drought and snow disaster index insurance. The compensation standard is based on meteorological indicators falling below the trigger threshold and a yield reduction rate greater than 5%. Using 10,000 Monte Carlo simulations, the drought premium rates for zones I-IV are determined to be 2.03–6.03%, and the snow premium rates to be 2.25–5.42%, corresponding to a premium of RMB 5.21–9.61 per mu for drought and RMB 5.78–8.64 per mu for snow. This design reduces basis risk through zoning and composite triggering, providing a scientific tool for climate risk management in alpine grasslands. Full article

Flood events represent a major natural threat, and identifying the key factors contributing to flood occurrence has gained considerable attention in 2010 and 2022 in the Swat River, Pakistan. In this study, Google Earth Engine was utilized to extract flood-related indices for the Mohmand Dam catchment, Pakistan. Different types of datasets were used to calculate fourteen influencing parameters. These indices were processed and normalized in ArcMap 10.8 and Python to enhance their visual and analytical representation. Two multi-criteria analyses with AHP, FAHP, and five machine learning models, including logistic regression, K-nearest neighbors, random forest, support vector machine, and multi-layer perception, were applied to determine the relative importance of each parameter and produce a flood susceptibility map. The results indicate that rainfall, LULC, and soil texture are the most influential factors, each contributing 11.11% to flood susceptibility. The random forest approach demonstrated stronger predictive performance than the AHP and FAHP techniques. The flood susceptibility map reveals that approximately 31.67% (4320.40 km²) of the study area falls under high flood risk. This methodology provides valuable support for planners, policymakers, hydrologists, and disaster management authorities in developing effective flood mitigation, watershed management, and resilience strategies. Full article

In response to worsening environmental challenges, ensuring the continuity of energy services during extreme weather events has become increasingly urgent. A proactive and coordinated approach is therefore required, encouraging cooperation among stakeholders to share knowledge, provide training, and adopt common strategies. Such an approach is intended to mitigate both direct and indirect impacts of power outages on territorial systems, while enhancing their ability to manage and promptly recover from disruptions, thereby reinforcing the protection and resilience of the energy sector infrastructures. Based on the experience gained with the Lombardia Region (Northern Italy), operational recommendations are proposed to strengthen territorial resilience and reduce power network vulnerabilities to weather-related power outages. These recommendations are elaborated in accordance with the current European and national regulatory frameworks on the topic and account for emerging exposure and vulnerability factors in Lombardia by explicitly addressing differences between mountain and plain areas. They provide local authorities with coordinated planning tools to manage blackout risks across all disaster phases, supporting risk prevention and preparedness, facilitating emergency management, and enabling the rapid restoration of normal conditions in territories potentially exposed and vulnerable to electrical blackouts. Full article

The increasing frequency of disasters caused by landslides, mainly due to climate change leading to more intense extreme events, requires reliable predictive models for risk mitigation. Italy, in particular, is a country at high risk of landslides, but the lack of an updated catalogue of landslide activation dates poses a significant challenge for defining reliable activation thresholds. This study develops a methodology for mapping landslide susceptibility based on events in a pilot area of central Italy, integrating a database of landslides with known activation dates with predisposing and triggering parameters. Two statistical techniques were compared to assess their predictive performance in discriminating landslide from non-landslide conditions during extreme precipitation events. A comparison between binary logistic regression (BLR) and decision trees (QUEST) revealed the clear superiority of the BLR model, which achieved excellent predictive accuracy (AUC = 0.913). The model identified clay-rich lithology, gentle slopes (0–16°) and maximum daily precipitation as the most significant controlling factors. This result led to the generation of three derivative products: a susceptibility map, a hazard map for an extreme precipitation scenario with a 100-year return period, and a spatially distributed map of activation thresholds. This threshold map quantifies the intensity of precipitation required to exceed a critical probability of landslide initiation (p > 0.7) at any point in the territory. The susceptibility map highlights critical areas within the study area, while the hazard map also includes the return period of the event. The threshold map is a direct and operational tool for early warning systems, transforming a statistical model into a guide for real-time risk management. The study area serves as a pilot area that could allow this methodology to be replicated. With the integration of real-time meteorological data, it could function as a real-time warning system. The proposed framework therefore provides a directly actionable tool for civil protection agencies, land-use planning authorities, and emergency managers, enabling location-specific rainfall alert thresholds to be issued rather than a single regional value, with the potential to reduce both false alarms and missed warnings. Full article

Climate variability has adversely affected the agricultural production of smallholder farmers in developing countries like Ethiopia. This study aims to examine the overall vulnerability of smallholder farmers to climate variability in different agro-ecological zones of Legambo district, north central, Ethiopia. The research used quantitative and qualitative data collection methods through cross-sectional survey data, focus group discussions, and key informant interviews from 347 randomly selected smallholder farmers. Then, 48 sub-component indicators categorized into twelve major components and then into three contributing factors of vulnerability (exposure, adaptive capacity, and sensitivity) were used to assess the livelihood vulnerability index (LVI) and LVI-based on the Inter-governmental Panel on Climate Change (IPCC) approach method in three agro-ecological-zones for vulnerability analysis. LVI and LVI-IPCC results showed that cold highland agro-ecology was the most vulnerable, and midland agro-ecology was the least vulnerable to climate variability effects. These findings can guide policymakers in designing adaptive strategies to enhance the resilience of smallholder farmers to climate variability. Full article

Coastal cities exposed to extreme wind events are facing increasing challenges in emergency management under climate change. Accurate and high-resolution wind environment information over complex urban terrain is essential for disaster risk assessment and evidence-based emergency planning; however, such information is often unavailable in conventional management practices. This study proposes an integrated UAV–CFD framework to support urban wind risk assessment by combining multi-source geospatial data and high-resolution numerical simulation. A refined urban terrain model with a spatial resolution of 0.5 m was constructed through the fusion of Google Earth data and UAV oblique photogrammetry, and subsequently coupled with a computational fluid dynamics (CFD) model to analyze the urban wind environment. Field measurements obtained from a 50 m wind observation tower were used to validate the simulation results. The results reveal significant wind speed amplification caused by complex terrain and building configurations, with a maximum amplification factor of 1.95 due to the canyon effect. The relative errors between simulated and measured wind speeds and turbulence intensity were generally within 15%, demonstrating the reliability of the proposed framework. By providing high-resolution and spatially explicit wind risk information, this study offers practical decision-support for emergency management, urban planning, and resilience-oriented disaster mitigation in coastal cities. Full article

Currently, a standardized process reference model specifically tailored for the business continuity management system (BCMS) is absent. Moreover, BCMS processes have not been a primary focus of ongoing research endeavors. This paper aims to fill this research gap by presenting findings from a process mapping study concerning BCMS processes within the most prominent and widely acknowledged standards for business continuity management, alongside insights gleaned from expert interviews. The authors propose a collection of BCMS processes that should comprise a BCMS process reference model intended for implementation at a maturity level tailored to individual organizational needs. It aims to strengthen the resilience of organizations to cyber threats and to optimize the processes for effective management within the disaster management cycle. The study identifies and maps the necessary processes required to build a comprehensive BCMS model. These processes include, among others, risk assessment, business impact analysis, the development of BC strategies and solutions, the creation of BC plans and procedures, incident and emergency management, and periodic reviews and exercises. The relevance of these processes was validated through expert interviews, making a clear distinction between core, management, and support processes. Full article

Climate change and rapid urbanization are intensifying flood risks in China, particularly in regions with complex terrain and dense populations. Traditional risk assessment methods often lack the flexibility to handle uncertainties in multi-dimensional risk systems. This study proposes a probabilistic flood risk assessment framework integrating Monte Carlo simulation with a composite indicator system from the perspective of disaster system theory. Taking Hunan Province as a case study, we constructed a hierarchical indicator system encompassing environmental susceptibility, hazard intensity, exposure vulnerability, and mitigation capacity. The analytic hierarchy process (AHP) and coefficient of variation (CV) methods were combined for indicator weighting, and Monte Carlo simulation was employed to quantify uncertainties and classify risk levels. Results reveal significant spatial heterogeneity in flood risk across the province, with high-risk areas concentrated in regions exhibiting intense rainfall, dense river networks, and insufficient mitigation infrastructure. The study provides a transferable, data-driven approach for spatially explicit flood risk zoning, offering evidence-based insights for land-use planning, resilient infrastructure development, and sustainable flood governance. This research contributes to the integration of probabilistic modeling into land system science, supporting disaster risk reduction and climate adaptation strategies aligned with SDG 11. This study also provides policy-relevant insights for regional flood governance by supporting risk-informed land-use planning, targeted infrastructure investment, and adaptive flood management strategies, thereby contributing to more resilient and sustainable land system development under increasing climate uncertainty. Full article

Frequent flash floods threaten human well-being, hydropower infrastructure, and ecosystems. However, the long-term evolution of flash flood patterns over recent decades remains insufficiently understood, particularly in data-scarce high-altitude regions. Using multi-source remote sensing data integrated with historical disaster records and field investigations, this study examined the spatiotemporal evolution and driving factors of flash floods across the Qinghai–Tibet Plateau (QTP). The results indicate that flash floods have increased exponentially, which may be influenced by disaster management policies, with peaks in July–August and frequent occurrences from April to September. The seasonal trajectory of the center of gravity of flash floods from April to September exhibited a clear directional pattern. Regions with the highest disaster density were concentrated in the headwaters of five major rivers, including the Yarlung Zangbo, Jinsha, Nu, Lancang, and Yellow Rivers. Shapley Additive Explanation (SHAP) and Random Forest analyses reveal that soil moisture, anthropogenic intensity, and seasonal runoff variability are the dominant driving factors. With ongoing socioeconomic development, intensified human activities have become a key contributor to the increasing frequency of flash floods. These findings highlight the value of remote sensing-based assessments for flash flood monitoring and early warning and provide scientific support for risk mitigation, loss reduction, and the advancement of water-related targets under the United Nations’ Sustainable Development Goals. Full article

Global climate change and increasingly complex geological conditions have led to more frequent landslides in the red-bed sedimentary layers of western Sichuan, China, posing severe threats to human safety and hindering progress toward regional Sustainable Development Goals (SDGs), particularly those related to disaster risk reduction and ecological protection. To address this challenge and advance sustainable disaster management, this study proposes a lightweight hybrid model, termed Transformer–Attention–LSTM, which integrates the global attention mechanism of Transformers with the local time-series modeling capabilities of Long Short-Term Memory networks. Focusing on the Kuyaogou landslide, the model achieves an optimal balance between parameter scale, sequence length, and prediction accuracy. The mean Coefficient of Determination (R²) values for the test samples in the X, Y, and Z directions reached 0.948, representing enhancements of 9.9%, 4.2%, and 2.3%, respectively, compared to the suboptimal Attention–LSTM model. Concurrently, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were reduced to 9.23 mm and 7.17 mm, respectively. Based on these displacement predictions, the landslide evolution stage was determined by calculating the tangent angle, indicating that the Kuyaogou landslide will remain in a stable creep phase over the ensuing ten-day period with low overall risk of rapid movement, though localized instability requires continued monitoring. This research provides a ‘small, fast, and accurate’ paradigm for red-bed landslide displacement prediction, offering scientific support for disaster prevention and emergency decision-making. The framework demonstrates potential for broader application in monitoring other geological hazards, thereby contributing to the implementation of sustainable development strategies in geohazard-prone regions. Full article

Disaster resilience has become a key focus of risk reduction efforts, but measuring it remains complex due to differences in hazards, development paths, and data systems. This study modifies the Baseline Resilience Indicators for Communities (BRIC) approach, based on the Disaster Resilience of Place (DROP) framework, to evaluate community resilience in Serbia and highlight regional differences. An initial list of 186 indicators was created from international BRIC studies and resilience research, then tailored to Serbian conditions through contextual review and data checks. Indicators were normalized using min–max scaling (0–1), and indicators with negative orientation were inverted to ensure that higher values indicate greater resilience. Scores for each dimension were calculated as equally weighted averages across six areas: social, economic, social capital, institutional, infrastructural, and environmental. The overall BRIC index was derived as the average of these dimension scores. Z-scores facilitated the classification of resilience levels and the comparison between regions. The results show clear regional disparities: in the complete model, Belgrade has the highest resilience (BRIC = 0.557), while Southern and Eastern Serbia have the lowest (BRIC = 0.414). Patterns across dimensions show that Belgrade excels in social and economic capacity but lags in environmental indicators; Vojvodina has the strongest institutional and infrastructural capacity; and Šumadija and Western Serbia perform best in environmental indicators. Correlation analysis revealed multicollinearity, leading to the removal of 14 redundant indicators and the refinement to a set of 57. After this reduction, regional rankings change, with Vojvodina (BRIC = 0.530) and Šumadija and Western Serbia (BRIC = 0.522) emerging as higher-resilience regions, while Southern and Eastern Serbia remain the least resilient (BRIC = 0.456). The adapted BRIC-DROP model offers a clear, locally relevant tool for mapping resilience and guiding targeted policies in Serbia, enabling region-specific efforts to address structural resilience gaps. Full article