We developed an automated workflow to detect rain-on-snow events, which cause flooding in the northeastern United States, in climate data. Analyzing the Susquehanna River basin, this technique identified known events affecting river flow. Comparing four gridded datasets revealed variations in event frequency and severity, driven by different snowmelt and runoff estimates. This highlights the need for accurate climate data in flood management and risk prediction for these compound extremes.

A framework combining a fire severity classification with a regression model to predict an indicator of fire severity derived from Landsat imagery (difference normalized burning ratio, dNBR) is proposed. The results show that the proposed predictive technique is capable of providing robust fire severity prediction information, which can be used for forecasting seasonal fire severity and, subsequently, impacts on biodiversity and ecosystems under projected future climate conditions.

The initiation of debris flows is significantly influenced by rainfall-induced hydrological processes. We propose a novel framework based on an integrated hydrological and hydrodynamic model and aimed at estimating intensity–duration (ID) rainfall thresholds responsible for triggering debris flows. In comparison to traditional statistical approaches, this physically based framework is particularly suitable for application in ungauged catchments where historical debris flow data are scarce.

Glide-snow avalanches release at the ground–snow interface, and their release process is poorly understood. To investigate the influence of spatial variability (snowpack and basal friction) on avalanche release, we developed a 3D, mechanical, threshold-based model that reproduces an observed release area distribution. A sensitivity analysis showed that the distribution was mostly influenced by the basal friction uniformity, while the variations in snowpack properties had little influence.

This study identifies a prehistoric earthquake around 4400 years ago near the city of Coyhaique (Aysén Region, Chilean Patagonia) and illustrates the potential seismic hazard in the region. We found deposits in lakes and a fjord that can be related to subaquatic and onshore landslides, all with a similar age, indicating that they were most likely caused by an earthquake. Through modeling we found that this was an earthquake of magnitude 6.3 to 7.0 on a fault near the city of Coyhaique.

This paper shows the first public and systematic dataset of flood episodes referring to the entire Pyrenees massif, at municipal scale, named PIRAGUA_flood. Of the 181 flood events (1981–2015) that produced 154 fatalities, 36 were transnational, with the eastern part of the massif most affected. Dominant weather types show a southern component flow, with a talweg on the Iberian Peninsula and a depression in the vicinity. A positive and significant trend was found in Nouvelle-Aquitaine.

European windstorms regularly cause damage to natural and human-made environments, leading to high socio-economic losses. For the first time, we compare estimates of these losses using a meteorological loss index (LI) and the insurance loss (catastrophe) model of Aon Impact Forecasting. We find that LI underestimates high-impact windstorms compared to the insurance model. Nonetheless, due to its simplicity, LI is an effective index, suitable for estimating impacts and ranking storm events.

We present a catalogue of tsunamis that occurred in the Vanuatu Arc. It has been built based on the analysis of existing catalogues, historical documents, and sea-level data from five coastal tide gauges. Since 1863, 100 tsunamis of local, regional, or far-field origins have been listed; 15 of them show maximum wave amplitudes and/or run-up heights of above 1 m, and 8 are between 0.3 and 1 m. Details are provided for particular events, including debated events or events with no known origin(s).

The pile–slab retaining wall, an innovative rockfall shield, is widely used in China's western mountains. However, its dynamic impact response and resistance remain unclear due to structural complexity. A comprehensive dynamic analysis of the structure, under various impacts, was done using the finite-element method. The maximum impact energy that the structure can withstand is 905 kJ, and various indexes were obtained.

Machine learning (ML) algorithms have increasingly received attention for modeling flood events. However, there are concerns about the transferability of these models (their capability in predicting out-of-sample and unseen events). Here, we show that ML models can be transferable for hindcasting maximum river flood depths across extreme events (four hurricanes) in a large coastal watershed (HUC6) when informed by the spatial distribution of pertinent features and underlying physical processes.

The Earthquake Risk Model of Switzerland (ERM-CH23), released in early 2023, is the culmination of a multidisciplinary effort aiming to achieve, for the first time, a comprehensive assessment of the potential consequences of earthquakes on the Swiss building stock and population. ERM-CH23 provides risk estimates for various impact metrics, ranging from economic loss as a result of damage to buildings and their contents to human losses, such as deaths, injuries, and displaced population.

Natural disturbances are projected to intensify in the future, threatening our forests and their functions such as wood production, protection against natural hazards, and carbon sequestration. By assessing risks to forests from wind and fire damage, alongside the vulnerability of carbon, it is possible to prioritize forest stands at high risk. In this study, we propose a novel methodological approach to support climate-smart forest management and inform better decision-making.

Accurate predictions of wave-driven flooding are essential to manage risk on low-lying, reef-lined coasts. Models to provide this information are, however, computationally expensive. We present and validate a modeling system that simulates flood drivers on diverse and complex reef-lined coasts as competently as a full-physics model but at a fraction of the computational cost to run. This development paves the way for application in large-scale early-warning systems and flood risk assessments.

We look at how compound flooding from the combination of river flooding and storm tides (storm surge and astronomical tide) may be changing over time due to climate change, with a case study of the Mekong River delta. We found that future compound flooding has the potential to flood the region more extensively and be longer lasting than compound floods today. This is useful to know because it means managers of deltas such as the Mekong can assess options for improving existing flood defences.

This work examines the use of interferometric synthetic-aperture radar (InSAR) alongside in situ borehole measurements to assess the stability of deep-seated landslides for the case study of El Forn (Andorra). Comparing InSAR with borehole data suggests a key trade-off between accuracy and precision for various InSAR resolutions. Spatial interpolation with InSAR informed how many remote observations are necessary to lower error in a remote sensing re-creation of ground motion over the landslide.

Flash floods belong to natural hazards that can be enhanced in frequency, intensity, and impact during recent climate change. This paper presents a complex analysis of spatiotemporal variability and human impacts (including material damage and fatalities) of flash floods in the Czech Republic for the 2001–2023 period. The analysis generally shows no statistically significant trends in the characteristics analyzed.