Natural Hazards最新文献

Trust is recognised as a critical element of disaster risk reduction; it shapes how people perceive risks, mobilise resources, and respond to hazards. Here, trust is defined as an individual's confidence in the ability and/ or intention of a system to act in their best interest. Importantly, many studies do not distinguish between ability- and intention-trust or acknowledge that such a distinction exists. Here, we present a systematic review of 100 articles and book chapters published between 2000 and 2025 which reveals that trust can both strengthen and weaken resilience to hazard events, depending on geographical factors, cultural context, hazard type, and the parties in whom trust is placed (e.g., government, community, scientific institutions, or personal beliefs). From the 100 studies, we identified 209 relationships between trust and individuals' resilience to natural hazards. We find that in the majority of case studies, trust is associated with increased resilience (58%), compared with trust being associated with decreased resilience (33%), or no change to resilience (10%). Our findings highlight the need for clarity when defining or theorising trust, and recognise the dynamic and context-dependent nature of trust when seeking to improve resilience to support effective disaster risk reduction.

Supplementary information: The online version contains supplementary material available at 10.1007/s11069-025-07816-w.

In this paper, we analyze the impact of natural hazards on internal migration in the United States (US). The analysis uses Internal Revenue Service (IRS) tax records and analyzes different versions of a gravity model to assess the impact of economic damage caused by natural hazards on migration flows between counties. We find that natural hazards in the origin county are associated to larger outflows, with hurricanes exhibiting the most substantial impact on migration, followed by floodings and severe storms. These results emphasize the need for migration research that distinguishes between various types of hazards. Furthermore, we extend the gravity model to investigate whether people tend to relocate to relatively nearby areas in response to natural hazards, an established phenomenon in existing literature. For floods and severe storms, the analysis demonstrates that migration flows to nearby counties are comparatively larger than to distant counties. These findings may be concerning, as it implies that individuals could still be at a high risk of experiencing future disasters. Additionally, we provide several robustness checks to investigate to what extent our results are driven by extreme events in the dataset.

Supplementary information: The online version contains supplementary material available at 10.1007/s11069-024-06987-2.

A quarry landfill slope is commonly partially or entirely filled with quarry waste. On the surface, a substantial amount of rough stone waste accumulates. This study specifically investigated the hazards posed by individual rockfalls and cluster rockfalls induced by landslides in such slopes, using an engineering slope as an illustrative example. The discontinuous deformation and displacement analysis method was employed to analyze the individual and cluster rockfall motion characteristics, as well as the dynamic response of protection structures. The results indicate that: (1) The impact of individual falling rocks on structures results in deformation and damage that far surpasses that caused by a flat plane impact. Interestingly, the stress generated upon rockfall contact with the structure is not initially at its maximum; it gradually increases to a peak as deformation occurs. When the structure is damaged or rebounds, the impact stress significantly diminishes. For wedge-shaped falling rocks impacting the upper part of the structure, bending tilting failure tends to occur. Conversely, irregular blocks with larger volumes impacting the lower part of the structure often lead to direct toppling failure; (2) Clusters falling rocks impede the movement of the sliding body. As the front and rear sliding bodies fracture along the middle, the rear sliding body tilts. Consequently, accumulated blocks are struck by the sliding body, initiating oblique throwing movements. There is a high likelihood of these rocks crossing protective structures; (3) The protection rate of the protective structure against single block stone impact stands at 86.7%. However, when subjected to the impact of a group of rockfalls, the protective structure completely fails. Overall, although the current protective measures are relatively cost-effective, the extremely high probability of casualties makes them unacceptable.

Recent earthquake disasters have highlighted an urgent need for continuous advancements in approaches to reducing seismic risk. Decision-making on such strategies should consider subsurface geophysical information (e.g., seismic site response), given its direct link to seismic hazard. This may be particularly important in regions where the poorest in society often reside in areas with softer soils that lead to higher ground-motion amplifications. In this context, we propose a framework to support decision-making on earthquake risk policies, which explicitly integrates information on the geophysics of an urban system as well as its physical and social environment. The framework is based on the Tomorrow's Cities Decision Support Environment, which was designed to support urban planning with a focus on pro-poor disaster risk reduction in countries of the Global South. It is further underpinned by a cost-benefit analysis, which facilitates the assessment of potential policies in terms of both their ability to reduce earthquake risk as well as their value for (often limited) money. We illustrate the framework using a well-established virtual urban testbed based on Global South cities, which reveals that geophysics-informed policy making can successfully lead to pro-poor earthquake risk reduction.

Hydrodynamic models can provide accurate information on the consequences of a dike breach, but their long computation times hinder the analysis of uncertainties and scenarios during a time-sensitive emergency situation. Conceptual models use simplified rules and relations, and allow for much faster computation while preserving reasonable accuracy. In this study, we develop a conceptual model with breach growth that estimates the dike breach outflow for varying river discharge events and for varying dike breach locations along the Rhine's bifurcations in the Netherlands and Germany. The results show that the model is able to provide a good estimate of the breach outflow, regardless of river discharge waves shape and peak discharge. The model achieves an approximate error of 10 to 15% compared to an operational hydrodynamic model of the study area. Its computation speed allows the analysis of thousands of scenarios per minute, enabling decision makers to probabilistically analyse breach outflow hydrographs at sampled critical water levels for an incoming extreme river discharge wave. We conclude that this conceptual model can provide realistic first estimates of breach outflow for large-scale dike breaches, while requiring little input data and computational time.

Natural hazards such as tropical cyclones (TCs) cause widespread destruction. Historical impact data provides a resource for understanding TC impacts and associated societal vulnerabilities which is essential for building resilience. However, characteristics of impact data such as resolution and coverage can influence its utility for disaster risk reduction (DRR) applications. With this in mind, we present a province-level impact dataset for TCs in the Philippines between 2010 and 2020 for deaths, affected population, housing damage and economic loss curated for DRR applications. Specifically, we evaluate the effect of the dataset's spatial resolution and its coverage of hazard intensities, impact magnitudes and impact types and discuss the implications for DRR applications. Considering the utility of impact data within the context of DRR is crucial, and a dataset with comprehensive coverage of impact and hazard magnitudes and appropriate spatial resolution is pivotal for DRR applications. The research presents a guide for others using this dataset and data more generally in DRR applications.

Supplementary information: The online version contains supplementary material available at 10.1007/s11069-025-07394-x.

This study examines the application of WildfireGPT for wildfire forecasting, focusing on its limitations in quantitative predicting Fire Radiative Power (FRP) spread and comparing its performance with a specialized predictive model based on TabNet. While WildfireGPT is widely accessible and convenient for wildfire-related discussions, it lacks the specialized training, real-time data integration, and algorithmic precision required for reliable wildfire forecasting. To highlight these shortcomings, we conducted an experiment using real-world NASA Fire Radiative Power (FRP) datasets. Our TabNet-based model, trained on variables such as Vapor Pressure Deficit (VPD), temperature (T), pressure (P), and Fire Weather Index (FWI), demonstrated high correlation, with low Mean Absolute Error (MAE) and Mean Squared Error (MSE) in forecasting FRP values. In contrast, RAG (retrieval-augmented generation) and LLM (large language model)-based chatbots like WildfireGPT have unreliable performance on quantitative FRP forecasting with the same input data as prompts. The findings underscore the potential risks of over-reliance on general-purpose AI tools like WildfireGPT for quantitative modeling tasks in wildfire management. This study advocates for informed usage of AI tools, emphasizing the necessity of domain-specific models for accurate and actionable wildfire forecasting.

Due to changing climates and rising sea levels, low-lying coastal regions, such as the Netherlands, face increased risks of flooding driven by extreme sea levels. Thus, understanding extreme sea level events and their underlying dynamics is crucial for effective coastal management. This study developed and applied a novel classification framework to investigate historical storm surge events along the Dutch coast and improve the understanding of regional storm surge dynamics. Using 16 sea level records, storm surges were identified with the Peak Over Threshold (POT) method, using the 70th (POT70) and 99th (POT99) percentiles as thresholds. POT70 captured a more comprehensive storm surge activity, including multiple peaks and successive surges that are critical for coastal management. In contrast, POT99 captured surge peaks but missed significant pre- and post-storm surge activities. The POT70-derived surges were classified into 56 event types using clustering methods based on surge values across the whole event time series, and event duration. Event types were then characterised by temporal patterns, peak magnitude, duration, probability of occurrence, yearly frequency, and cumulative surge intensity. Key findings revealed frequent two-peak storm surges and significant variations in storm surge intensity along the coast, with stronger events occurring in northern regions. The results highlight the complexity of storm surge patterns, indicating that while simplified hydrograph models are useful, they may not always capture the full range of surge pattern variations. This novel classification framework offers a more detailed approach to evaluating surge patterns and can be applied to other coastal regions as well.

Supplementary information: The online version contains supplementary material available at 10.1007/s11069-025-07351-8.