This study introduces an Accumulated-Event Risk Indicator (ARI) to identify global hotspots of recent major-event accumulation using 2013–2023 records from the Emergency Events Database (EM-DAT). For each 5° × 5° latitude–longitude grid cell, ARI sums the country-level World Risk Index (WRI) values associated with events meeting a study-specific threshold of at least 50 reported fatalities, thereby combining recent major-event recurrence with contextual risk conditions. The baseline analysis identifies 153 grid cells with non-zero ARI values and 14 principal hotspots, located mainly in South and Southeast Asia and along the Himalayan belt. Diagnostic comparisons with a grid-level WRI-only reference, unweighted major-event count, and summed reported fatalities show that ARI produces a distinct upper-priority pattern. A temporally separated comparison indicates that major events recorded in Asia during 2024–2025 are concentrated in high-ARI sets at rates above the defined equal-grid benchmark. Sensitivity analyses further show that the principal hotspot geography is largely retained under the tested changes to the fatality threshold, grid resolution, and spatial allocation. The study also presents the 3H Dataset, an ARI-guided compilation of standardized high-resolution remote-sensing imagery assembled from open-data resources and supplementary acquisitions for the 14 hotspot cells. The dataset serves as a downstream observational resource for finer-scale analyses in data-scarce priority regions.
Uncertainties in natural systems are pervasive, varied, and unavoidable due to inherent open system complexity and limited knowledge. Therefore, the evolution of a natural system cannot be predicted deterministically and probabilistic forecasts are commonly used to account for these uncertainties. As the Voltaire-inspired title suggests, representing and quantifying all uncertainties in hazard and risk forecasting is challenging yet essential for an effective risk management cycle and for a meaningful scientific evaluation of forecasting models. Although this paper focuses on hazard forecasting, we argue that the discussion and treatment of uncertainty apply equally to vulnerability and, therefore, to risk assessment. The most important challenges are reflected in the current absence of a common hierarchy of uncertainties, of a shared quantitative procedure to include all uncertainties in a forecast, and of effective communication and decision-making protocols, across different hazards and risks. Deepening the understanding of these distinct challenges has been the main goal of a dedicated task force of scientists from different disciplines, experts in communication, and decision-makers in the framework of a large Italian project on multirisk under NextGenEU funds – the RETURN project (https://www.fondazionereturn.it/en/, last access: 5 October 2026) – which includes eighteen Italian universities and research centers, the Italian Civil Protection Department, Italian State Railways, Assicurazioni Generali, other profit entities, and one Italian River Basin Authority. Within this initiative, we examined several examples of natural hazard forecasting and projections, from the perspectives of experts in various fields and/or users of these forecasts. The task force found that different hazards share key features and challenges regarding uncertainty definition, understanding, quantification and communication, which may be embedded in a common framework. Such a framework introduces the concept of a “complete forecast”, defined as a forecast that explicitly represents and keeps distinct all recognized uncertainties. This distinction is essential for the proper evaluation of forecasting models. This work categorizes the common key scientific and communication challenges, propose potential solutions, and intend to stimulate a deeper reflection on these issues.
Deadwood is an integral component of mountain forests, supporting biodiversity and contributing to protection against gravitational hazards. However, under specific conditions, deadwood may itself become a hazard when mobilised and transported downslope. Although sliding logs have been repeatedly observed in steep forests, a quantitative framework to assess their hazard potential has so far been lacking. We present a physics-based model to simulate the motion and runout of sliding deadwood logs in complex terrain. The model extends an existing rockfall simulation framework based on nonsmooth rigid-body dynamics with hard contact laws and Coulomb friction, explicitly representing deadwood log geometries and interactions with terrain, standing trees, and protective structures. Model calibration and evaluation are performed using two recent Swiss case studies in which deadwood logs up to 35 m in length travelled several hundred metres in a single rapid descent and impacted infrastructure. Simulations indicate that sliding deadwood hazard is favoured by very steep slopes > 35°, wet surface conditions, and a narrow decay-stage window characterised by the loss of bark and branches to reduce sliding friction while still retaining sufficient structural strength. Sliding trajectories are strongly controlled by micro-topography, with preferential paths along gullies, while standing trees limit downslope propagation but increase lateral spread through repeated deflections. The proposed model highlights the importance of adaptive forest management in mountain forests and provides a quantitative basis for optimising the balance between the protective and hazardous roles of deadwood.
Heat has emerged as a major public health concern. Over 62 000 heat-related deaths were estimated to have occurred during the European summer of 2024, exemplifying the pressing need to develop effective early warning systems. Such systems depend critically on the quality of the underlying forecasts, and recent work has focused on developing impact-based forecasts for heat-related mortality, which provide impact-oriented information. To date, heat-related mortality forecasts have been based on the output of numerical weather prediction models, or physics-based forecasts. The field of weather forecasting is undergoing a rapid transformation with the advent of skillful data-driven forecasts. This case study compares European temperature-related mortality forecasts for summer 2024 based on physics-based weather forecasts with those based on data-driven weather forecasts. Our results highlight that both the physics-based and data-driven forecasts systematically underestimate temperature-related mortality, more pronouncedly so in the latter. Both types of forecasts appear sensitive to forecast errors at hot temperatures, due to the non-linear relationship between temperature and mortality. Nevertheless, temperature-related mortality forecasts based on data-driven weather forecasts appear to be a promising alternative to traditional physics-based weather forecasts, and targeted improvement of the representation of hot temperatures through bias correction or adjustment of the loss function to give greater weighting to hot temperatures could be beneficial for temperature-related mortality forecasting. We suggest the application of this approach to both data-driven and physics-based forecast ensembles as an important next step in the continued development of informative, impact-oriented forecasts.
Verified, harmonised, open-access, object-level exposure data are essential for next-generation risk assessments, risk management, and impact-based forecasting. However, this object-level information is often proprietary, protected by regulation, poorly documented, and fragmented because data on building usage, structural type, or replacement costs are not readily available or not compiled in one dataset. To address this gap, we present an evaluation of exposure model workflows employing various disaggregation approaches and source data from cadastre-derived, crowd-sourced, national accounts, and fit-for-purpose datasets. Using information collected from one flood-affected region in Germany and a weighted scoring model, we evaluate the ability of each workflow to assign a building's economic sector and asset value against our hand-labelled benchmark dataset. Ultimately, we find an exposure model workflow disaggregating national accounts onto cadastre-derived building footprints slightly outperforms the other workflows owing mainly to its transparency and adaptability. However, we conclude that all but the land-use-derived workflow are defensible for object-level exposure modelling – when validated. While these findings are limited to one specific region, the workflows developed here provide the basis for broader evaluation across regions with different building stocks, economic structures, and data quality. Workflows like these enable the transparent, reproducible, and maintainable multi-sector object-level exposure modelling necessary for the next generation of risk analysis and impact forecasting.
Understanding how earthquake disaster impacts vary across space and time is essential for global disaster risk assessment and reduction planning. Using earthquake disaster records from the Emergency Events Database (EM-DAT) during 1980–2024, this study investigates temporal trends, multi-scale spatial patterns, centroid shifts, and factors associated with cross-national differences in earthquake fatalities. Temporal analyses reveal a divergence between recorded earthquake disaster frequency and impact consequences. While the number of recorded earthquake disaster events, affected population, and cumulative economic losses increased overall, mortality-related indicators declined, particularly after the early 2000s. Spatial analyses demonstrate substantial heterogeneity among continents, countries, and tectonic plates. Asia accounts for a large proportion of global earthquake disaster events and associated impacts, whereas the magnitude of impacts differs considerably among countries. At the global scale, the unweighted centroid of recorded earthquake disaster events shifted eastward across the three periods (1980–1994, 1995–2009, and 2010–2024), with consecutive inter-period displacements of 1872.4 and 770.4 km, respectively. Weighted centroids based on fatalities, affected population, and economic losses showed different spatial trajectories, indicating distinct spatial organizations of impact dimensions. The Geographical Detector results indicate that earthquake magnitude showed the highest explanatory power for spatial differences in fatalities (q = 0.17), and interactions among factors generally enhanced spatial associations. These findings provide a global assessment of earthquake disaster impacts by integrating temporal evolution, spatial redistribution, and associated factors, offering comparative evidence for regional risk identification, exposure management, and disaster preparedness planning.
Space and time play a crucial role in multi-hazard impact assessment. When two or more natural hazards occur simultaneously or in sequence at the same location, the physical integrity of assets and infrastructures can be compromised, and the resulting damage can be greater than that generated by individual hazards occurring in isolation. Despite widespread conceptual recognition of these interactions, the literature lacks quantitative, standardised methods for the systematic analysis of multi-hazard impacts. This study presents a quantitative methodology for evaluating multi-hazard physical damage to the built environment, translating qualitative impact dynamics into a transparent and reproducible analytical framework, implemented as modular Python code. The approach covers both concurrent and consecutive hazards by modelling: (i) the increased damage resulting from the combined impact of two or more concurrent hazards that overlap in space and time, and (ii) the effects of cumulative damage on asset vulnerability and the recovery dynamics in the case of consecutive hazards that overlap in space. Using the Python implementation, a model behaviour analysis is conducted to systematically explore how variations in inter-event time intervals, vulnerability interactions, and recovery trajectories affect cumulative physical damage. The methodology is applied to a real multi-hazard sequence in Puerto Rico, including the concurrent wind and flood impacts of Hurricane Maria and the consecutive seismic impacts of the 2019–2020 earthquake sequence. The reconstruction of past damage dynamics highlights that ignoring residual hurricane damage would significantly underestimate the subsequent earthquake losses, and that damage accumulation is path-dependent, strongly influenced by event timing and recovery processes. By providing a generalised, transparent, and reproducible quantitative structure, this study offers a tool for forensic analysis of past multi-hazard events, systematic exploration of damage drivers, and scenario-based assessment of alternative hazard and recovery conditions, supporting both post-disaster learning and planning-oriented applications.
The increasing severity of wildfires in Western North America is widely hypothesized to lead to an increase in the likelihood of post-fire debris flows (PFDF), specifically those triggered by high-intensity rain. PFDF likelihoods are highest in the first year and tend to decrease over time. However, it is not well understood how seasonal variation affects PFDF initiation in the years following a fire. Here, we investigated how different physical parameters influence seasonal PFDF likelihood, informed by four years of field and satellite observations from the 2020 Lake Fire in Southern California. We found that unsaturated hydraulic conductivity was an order of magnitude greater during the dry season than during the wet season, significantly reducing the PFDF likelihood. Our simulations show that vegetation cover has less of an impact on PFDF likelihood than hydraulic conductivity or grain size. This study helps clarify the relative importance of hydraulic conductivity, grain size, and vegetation on PFDF as these parameters vary seasonally and evolve over four years after the fire. Our results suggest that seasonal variation plays a major role in determining PFDF likelihood and therefore, climatic and seasonal patterns need to be considered in future field and modeling studies.
Extreme weather hazards are increasing and stakeholders need rapid, transparent information during unfolding events. We present RHITA (Real-time Hazard Identification and Tracking Algorithm), an open-source framework and web tool for near real-time detection and tracking of weather-related hazards over Europe. RHITA identifies grid cells exceeding local quantile thresholds, groups them into spatial clusters, and links clusters through time to reconstruct three-dimensional events in longitude, latitude, and time. For each event, RHITA provides intensity, extent and duration metrics and estimates rarity through return periods derived from a long historical record. RHITA is operated with ECMWF open forecasts for daily monitoring and ERA5 reanalysis for a consistent historical archive from 1950 to 2024. We target four hazards: heatwaves, cold spells, heavy precipitation and strong winds. Key spatial and temporal parameters are optimized against EM-DAT disaster records (2000 to 2023). Applying RHITA to ERA5 yields a European climatology of hazard events and reveals robust increases in heatwave frequency, intensity and affected area, a decline in cold spell frequency, and more heterogeneous signals for heavy precipitation and strong winds at the continental scale. RHITA provides open access data and an interactive interface to support rapid hazard characterization, event contextualization and downstream risk analysis.
The flash flooding across Central Texas on 4 July 2025, caused more than 130 fatalities and property losses exceeding USD 20 billion. The objective of this study is to evaluate the performance of the NOAA operational flood forecasting pipeline, including Quantitative Precipitation Forecasts, National Water Model short-range streamflow forecasts, and Office of Water Prediction flood inundation mapping during this catastrophic event, and to characterize how forecast skill and impact-based predictions varied with forecast lead time at gauged and ungauged locations. Using the Operational National Water Model short-range streamflow forecast product, we generated 306 forecasted flood inundation maps between 3 and 4 July 2025. For evaluation, we constructed an inundation extent benchmark derived from USGS high water marks. Both impact based and skill-based assessments are presented.
A recently developed set of historical storm reconstructions were extensively validated by insurance loss data and revealed how European windstorm damages were three times higher in the 1980s and '90s compared to a few decades before and since. A better understanding of these slower fluctuations could improve how this costly risk is managed. Here, we explore the impacts of anthropogenic aerosols (AA) on European property damage using results from DAMIP (Detection and Attribution Model Intercomparison Project) climate model experiments. Multimodel mean DAMIP results indicate AA boosted European wind losses by 45 % in the late 20th century relative to preindustrial times, with the signal varying from zero to 100 % between the six models. A validation of these modelling results using independent data from previous climate studies suggested the signal is more likely to be at the higher end of this range. While this evidence suggests AA forcing contributed significantly to recent multidecadal changes in European windstorm losses, there remains significant uncertainties in model responses and the observational data used in their validation. Further research would benefit those exposed to this hazard.
The exploration of shallow landslide susceptibility is often impaired by biased landslide inventories, and by over-optimistic performance metrics linked to inadequate models. Here, we use a systematically mapped event inventory of 571 shallow landslides triggered in southern Norway and apply 32 gradient boosted decision tree models to rigorously test the effects of (1) a nested vs. simple cross-validation strategy, (2) spatial vs. non-spatial models, (3) four different cross-validation sampling strategies which were applied on (4) full vs. forest-only datasets. Model evaluation shows that models with random cross-validation provided the highest test performance metrics but did not account for autocorrelation and were likely over-optimistic. The spatial models with spatial cross-validation reduced autocorrelation in the model residuals at the expense of predictive power. Although no model emerged as optimal, findings across models suggest that important explanatory factors like elevation, aspect and bedrock weatherability serve as soil indicators, illustrating a need for improved datasets for soil thickness and heterogeneity. In the forest-only models, tree type was consistently an important predictor, with higher landslide susceptibility in deciduous forest, illustrating the potential of forest variables and forest-specific threshold values in shallow landslide susceptibility mapping.
Facing water scarcity, the Berlin-Brandenburg region explored underground water storage to capture excess surface water during wet periods and store it in deep aquifers for use during droughts. We identified extensive areas suitable for storage and found substantial downstream water availability under ecological flow constraints. The approach could help offset local water demand and provide a cost-effective, transferable strategy for strengthening water security in other water-stressed regions.
 
Managed Aquifer Recharge (MAR), particularly Aquifer Storage, Transfer and Recovery (ASTR), can enhance groundwater resilience in confined multi-layer aquifers under drought stress. We develop an integrated and scalable framework to assess ASTR feasibility by combining (i) meteorological and groundwater drought analysis using the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Groundwater Index (SGI), (ii) GIS-based multi-criteria decision analysis (MCDA) for recharge site suitability, and (iii) dynamic assessment of surface-water availability using ecological flow thresholds. Applied to the water-stressed Berlin-Brandenburg region, one of Germany's driest areas, where water supply relies heavily on induced bank filtration and faces emerging deficits. Results show that groundwater levels respond to climatic variability, supporting climate- informed ASTR planning. The MCDA identified 62.8 % of the area (2154 km2) as viable for ASTR. Flow-threshold analysis at 27 gauges showed that specific high-potential downstream sites could provide mean annual recharge volumes of 1.6–4.3 Mm3, offsetting 6 %–79 % of local extractions at those locations, although these volumes decreased substantially (40 %–100 %) during severe drought years (e.g., 2018), highlighting the importance of capturing wet-period surpluses for reliable ASTR operation. At the catchment scale, total mean annual available recharge is 18.2–23.0 Mm3. Literature-based cost estimates (EUR 0.30–0.60 m−3) are substantially lower than regional drinking-water production costs (EUR 1.80 m−3), suggesting potential for substantial cost savings relative to conventional water supply, though precise economic benefits require site-specific validation.
Compound weather events involving both strong wind gusts and intense snowfall can have significant impacts on critical infrastructure and public safety. This study analyses the frequency, duration, and intensity of such events in Finland using ERA5 reanalysis data for 1960–2024. Extreme wind gust and snowfall conditions were identified using spatially varying 95th and 98th percentile thresholds. Events with both snowfall and wind gust exceeding these thresholds simultaneously were classified as compound events (SWG). SWGs were most frequent along Finland's south facing coastal regions and eastern Finland. Increasing the threshold from 95th to 98th reduced the number of SWGs but emphasized the dominance of coastal areas and highlighted more severe cases. Approximately half of the events were short-lived (<5 h) and involved limited accumulation of snow water equivalent (<5 mm). However, a few prolonged SWGs were identified, characterized by wind gusts > 20 m s−1, hourly snowfall rates > 2.5 mm h−1, and total snowfall > 20 mm. Although rare, these extreme SWGs can create substantial operational challenges for energy production and other critical infrastructures. The results underscore the importance of incorporating compound-event analysis into hazard assessments and preparedness strategies for regions exposed to severe winter weather.
After a primary seismic event, subsequent aftershocks frequently induce progressive damage to slopes. Evaluating the response of slopes to mainshock–aftershock sequences (MAS) from a probabilistic perspective is crucial for disaster prevention and mitigation. Current research primarily focuses on single mainshock events, and commonly adopts peak ground acceleration (PGA), with limited consideration of the cumulative aftershock effects. This study proposes a PDEM-based reliability framework for soil slopes subjected to MAS. First, the random input field of the MAS is first constructed by integrating theoretical models with real data. Then, considering the peak, cumulative, and spectral characteristics of the MAS, correlation analysis is conducted to identify cumulative absolute velocity (CAV) as the controlling parameter for the soil slope response among the 21 candidate parameters. Finally, based on the probability density evolution method (PDEM), a reliability assessment framework for soil slope behavior under MAS is constructed. Compared with existing methods, the proposed approach more effectively incorporates the effects of aftershocks and enables more accurate reliability assessment of slope permanent displacement under MAS. This study provides a probabilistic framework and methodological approaches for assessing slope stability under MAS.
In this paper, we explore five core challenges that need to be addressed in order to move towards the Comprehensive Disaster and Climate Risk Management approach, which has been proposed within the newly emerging paradigm of a more holistic approach to managing the risks associated with climatic and/or non-climatic hazards and non-climatic risk drivers across varied time scales and levels. These five challenges relate to the following points. First, we have a lack of comprehensive and high quality data for observing how society is impacted by, prepares for, and responds to multi-risks. Second, as a result we have a limited understanding of why individuals, communities, and other decision-makers (fail to) prepare for complex multi-risks in the way they do, and how that affects their responses. Third, our capacity to simulate how current and future risk could be effectively reduced is hampered by the aforementioned lack and fragmentation of observations and understanding of multi-risk dynamics. Fourth, current governance structures often reinforce data and knowledge silos. Fifth, the high complexity and wicked nature of the multi-risk problem results in barriers in knowledge exchange at the science, policy and practice interface. In this paper, we elaborate on each of these challenges, discuss why they matter for comprehensive disaster risk management, and provide ideas of how to overcome these challenges. We end the paper with a brief outlook.
Macroseismic intensity data are a fundamental source of information for characterising historical earthquakes. This study presents a methodology to constrain the sources of large earthquakes through the analysis and modelling of macroseismic intensity data. The proposed workflow consists of three main stages: (1) the identification and removal of outlier intensity data points; (2) the revision of macroseismic earthquake parameters (epicentral location and magnitude); and (3) the construction of three-dimensional (3D) seismogenic sources, simulation of ground shaking including site effects, and a subsequent residual analysis. The key novelty lies in the detection of intensity outliers through residual analysis between observed and predicted macroseismic intensities using an intensity prediction equation (IPE). The application of this workflow to a set of case studies from the parametric catalogue of Italian earthquakes demonstrates that the implemented approach allows the identification of seismogenic sources for large to moderate magnitude events, which are consistent with macroseismic data, geological structures and ground motion data, when available. The results of this study can have direct implications for seismic hazard assessment and shaking scenario modelling. The proposed workflow can be systematically applied to reconstruct the seismogenic sources of the strongest Italian historical earthquakes (Mw ≥ 5.5).
We present a dynamic probabilistic flood risk model that addresses key challenges in the implementation of integrated flood risk management. These include the need for holistic, large-scale risk assessments that adopt a system-based perspective, and a decision-making framework based on benefit-cost analysis. The proposed model allows for the explicit representation and dynamic coupling of the flood process components, including downstream flood wave propagation and possible dike failures, in a computationally efficient and data-sparse manner. This enables the consideration of aleatory and epistemic uncertainties in a 2-level Monte Carlo framework. By separating these uncertainties, the model supports robust risk assessments and facilitates an uncertainty-aware evaluation of the benefit of mitigation measures. The model is applied to the Bavarian Danube, demonstrating its ability to estimate the flood risk-reduction potential of mitigation measures.
The accurate classification of infrasound signals is a cornerstone of global geophysical monitoring, essential for both natural hazard early warning systems (e.g., volcanic eruptions, debris flows, and earthquakes) and the verification of the Comprehensive Nuclear-Test-Ban Treaty. However, the development of reliable automated systems is hindered by the inherent scarcity of representative event data, particularly for rare extreme events, as well as the presence of complex, non-stationary background interferences. To maximize the diagnostic value of limited geophysical datasets, this paper proposes Infra-Net, a novel parallel decision-making network driven by multi-view feature learning and a confidence-based fusion mechanism. We introduce a logarithmic wavelet scattering transform to produce robust, mathematically grounded feature representations. Unlike conventional methods that process scattering matrices holistically, our approach treats individual columns as independent feature vectors, providing multiple localized perspectives of the same acoustic event. Architecturally, Infra-Net utilizes a dual-branch structure to simultaneously capture multi-scale spatial features and discriminative temporal patterns. These parallel evaluations are synthesized through a custom confidence-based fusion module, which employs weighted averaging and an inner-product mechanism to ensure a stable and comprehensive final classification. Tested on both public infrasound datasets and real-world Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO)-measured data, Infra-Net achieved accuracies of 100 % and 82.07 %, respectively. These results demonstrate that Infra-Net offers a highly robust soft computing solution for enhancing Earth system monitoring and the reliable identification of natural hazards amidst anthropogenic noise.
This study presents a novel method to map the areas affected by ballistic fallout generated by major explosions and paroxysms at Stromboli as well as quantitative analyses of these areas. The mapping method is based on a simplified description of the affected areas by a circular proximal area and up to three circular sectors with variable radius and width, and uncertainty based on expert judgement. The dataset of maps includes a total of 67 events over ≈ 150 years, based on an extensive review of historical, observational, and monitoring data. Our findings highlight that: (1) 12 %–14 % of major explosions can exceed 1000 m, and 29 % of paroxysms extend over 2000 m of distance; (2) directional analysis of ballistic dispersal shows a predominant direction towards the East half-plane (87 %) for major explosions and towards the North half-plane (64 %) for paroxysms; (3) the average affected area was 6.9 × 104 m2× 105 m2≈ 90° for both categories. Notably, major explosions and paroxysms show a continuous distribution of maximum ballistic distance and area affected, suggesting the absence of a net separation between these two categories in terms of these products dispersal. Results highlight the limited influence of uncertainty in reconstructing the dispersal areas and stress the importance of ad hoc continuous observation of ballistic dispersal. By quantifying distances, directions, and areas affected by ballistic fallout, we provide the necessary data, together with their uncertainty, to produce probabilistic maps of ballistic hazard presented in the companion study.