In recent years, the western Baltic Sea has experienced severe storm surges. By analysing the individual contributions and the total water level, these events can be put into a climate perspective. It was found that individual contributions were not exceptional in all events, and no clear trend can be identified. Often the combination of the individual contributions leads to the extreme events of recent years. This points to the importance of analysing composite events.

This study evaluates 12 sets of drought policy and planning guidelines for their alignment with the four priority areas of the SENDAI framework. The guidelines do not align very well with the contemporary disaster risk reduction agenda. The study highlights strengths, weaknesses, opportunities, and threats and provides useful insights to develop the next generation of drought guidelines that are better aligned with contemporary science–policy–practice agendas.

Tree fall events have an impact on forests and transport systems. Our study explored tree fall in relation to wind and other weather conditions. We used tree fall data along railway lines and ERA5 and radar meteorological data to build a logistic regression model. We found that high and prolonged wind speeds, wet conditions, and high air density increase tree fall risk. These factors might change in the changing climate, which in return will change risks for trees, forests and transport.

Fragile geologic features test long-term seismic hazard models. We modeled a free-standing 42 m high rock pillar in Israel’s Negev Desert using a validated finite-element model based on aerial lidar, in situ rock data, and measured vibrations. Dynamic analysis shows an M 7 quake on the Dead Sea Transform (45 km) will not break it, but an M 6 quake on the Sinai–Negev Shear Zone (6–20 km) probably will. With a fragility age of 11.4 kyr, the pillar challenges the SNSZ’s (Sinai–Negev Shear Zone) ability to produce M 6 events.

Our study investigates the initiation of snow slab avalanches. Combining experimental data with numerical simulations, we show that on gentle slopes, cracks form and propagate due to compressive fractures within a weak layer. On steeper slopes, crack velocity can increase dramatically after approximately 5 m due to a fracture mode transition from compression to shear. Understanding these dynamics provides a crucial missing piece in the puzzle of dry-snow slab avalanche formation.

The authors performed verification on the relationships between extreme monsoon rainfall over Japan and Pacific sea surface temperature variability in the “database for Policy Decision-making for Future climate changes” (d4PDF). Observations showed widespread weak relationships between hourly extremes and the warming mode but reversed relationships between daily extremes and the decadal variability mode. Biases in d4PDF could be explained by the monsoon's slower movement over Japan in the model.

Offshore wind turbines are sensitive to tropical cyclones (TCs). Wind data from super typhoons Mangkhut and Saola, impacting south China, are vital for design and operation. Despite Saola's higher intensity, it caused less damage. Both had concentric eyewall structures, but Saola completed an eyewall replacement before landfall, becoming more compact. Mangkhut decayed but affected a wider area. Their wind characteristics provide insights for turbine maintenance and operation.

This study addresses the challenge of accurately predicting floods in regions with limited terrain data. By utilising a deep learning model, we developed a method that improves the resolution of digital elevation data by fusing low-resolution elevation data with high-resolution satellite imagery. This approach not only substantially enhances flood prediction accuracy, but also holds potential for broader applications in simulating natural hazards that require terrain information.

Global flood models are key to the mitigation of coastal flooding impacts, yet they still have limitations when providing actionable insights locally. We present a multiscale framework that couples dynamic water level and flood models and bridges the fully global and local modelling approaches. We apply it to three historical storms. Our findings reveal that the importance of model refinements varies based on the study area characteristics and the storm’s nature.

On 11 July 2024, Stromboli erupted violently, generating a volcanic ash column and pyroclastic flows along the Sciara del Fuoco. The event was preceded by intense explosions, lava flows and crater rim collapses. A multiparameter analysis using seismo-acoustic and drone data was conducted to reconstruct the eruption's key features. This was done to estimate material loss and to document summit morphology changes, aiming to improve the understanding of eruption processes and to enhance forecasting and risk management.

The study investigates how the intensity of previous windstorm events and the time between two events affect the vulnerability of residential buildings in Germany. By analyzing 23 years of data, it was found that higher intensity of previous events generally reduces vulnerability in subsequent storms, while shorter intervals between events increase vulnerability. The results emphasize the approach of considering vulnerability in risk assessments as temporally dynamic.

Our research explores how social media can help understand public responses to floods, focusing on the 2021 western European flood. We found that discussions varied by location and flood impact: in-disaster concerns were more common in severely affected upstream areas, while post-disaster topics dominated downstream. Findings show the potential of social media for improving disaster coordination along cross-border rivers in time-sensitive situations.

On 2 April 2024, a M_w 7.4 earthquake hit Taiwan's eastern coast, causing extensive landslides and damage. We used automated methods combining Earth observation (EO) data with AI to quickly inventory the landslides. This approach identified 7090 landslides over 75 km² within 3 h of acquiring the EO imagery. The study highlights AI's role in improving landslide detection efforts in disaster response.

This paper introduces two new, simple, physics-informed hazard forecast models of rip current and shore-break waves, which are the two primary natural hazards beachgoers are exposed to in the surf zone. These models, which depend on a limited number of free parameters, accurately predict rip-current and shore-break wave hazard levels, including their modulation by tide elevation and incident wave conditions, opening new perspectives for forecasting multiple surf-zone hazards on sandy beaches.

This study assesses RAMMS::EXTENDED's predictive power in estimating avalanche runout distances critical for mountain road safety. Leveraging meteorological data and sensitivity analyses, it offers meaningful predictions, aiding near real-time hazard assessments and future model refinement for improved decision-making.

We demonstrate an approach for the development of inventories from internet sources to geolocalize geohazard incidents. We created a tool that autonomously gets news, processes it using natural language processing, and then builds inventories. Consequently, we present spatiotemporal inventories for geohazards, resulting in a total of 13 940 incidents between 1997 and 2023 in Türkiye. Our alternative and easy-to-implement development inventory method aids geohazard management and resilience.

Many households in Vietnam depend on revenue from micro-businesses (shop houses). However, losses caused by regular flooding are not modelled. Business turnover, building age, and water depth were found to be the main drivers of flood losses of micro-businesses. We built and validated probabilistic models (non-parametric Bayesian networks) that estimate flood losses of micro-businesses. The results help with flood risk management and adaption decision making for micro-businesses.

Fast flood assessments are important for providing effective help during emergencies and planning for future floods. This study presents a new automated way to quickly measure flood depth. Using satellite images and digital elevation models, this method makes it easier to get real-time flood information. We applied this new method to several flood-prone areas in India and found that it provides more accurate results than existing flood measurement tools.

Between 17 and 20 September 2024, the Lamone River basin in northern Italy was hit by extreme precipitation. This study adopts the hydrological model Rhyme and the hydrodynamic model PARFLOOD to simulate the hydrological processes in the watershed and the levee-breach-induced inundation affecting the village of Traversara. The close match between the resulting flooded areas and the observed ones shows the capability of these numerical models to support the preparedness for at-risk populations.

This work deepens the understanding of how perceived heat stress, human vulnerability (e.g. age, income) and adaptive capacities (e.g. green, shaded spaces) are coupled with urban structures. The results show that perceived heat stress decreases with distance from the urban center, however, human vulnerability and adaptive capacities depend more strongly on inner variations and differences between urban structures. Planning policies and adaptation strategies should account for these differences.