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.

 

European windstorm damages varied by a factor three over recent multidecadal periods, and a better understanding of these changes could improve how this risk is managed. Here, we explored the impacts of anthropogenic aerosols (AA) using results from climate model experiments, and found AA boosted European wind losses by an average of 45 % in the late 20th century, though varying from zero to 100 % between the six models. Validation data suggest the signal may be at the higher end of this range.

We use several machine learning models to explore which factors best explain landslide release during an extreme rainfall event in eastern Norway. As landslides often occur in clusters, methods must be chosen carefully to account for any spatial effects. When considering this, we find that south-facing slopes, thicker soils and more water made landslides most likely. On forested slopes, landslides are most likely in deciduous rather than spruce or pine stands.

Post-fire debris flows or mudflows, can occur during intense rainfall following wildfires. Yet, it remains unclear how seasonal variation affects debris-flow initiation years after the fire. Using simulations of the 2020 Lake Fire in California, we show that debris-flow likelihood varies drastically between drier and wetter months, even four years after the fire. This study highlights the need to consider climatic and seasonal patterns in post-fire debris-flow assessment.

Extreme weather events are becoming more frequent and severe, creating a strong need for rapid and reliable information. We developed an open tool that automatically detects and tracks heatwaves, cold spells, heavy rain, and strong winds across Europe, both in real time and in past decades. By comparing current events with long historical records, the tool shows how unusual an event is and reveals clear increases in heatwaves, while other hazards show more mixed changes.

We investigated the Central Texas flash flood on July 2025 to understand why forecasts underestimated its severity. We analyzed operational streamflow forecasts and evaluate flood inundation maps against benchmark flood extent from High Water Mark. Results show that peak flooding was missed due to spatial error in rainfall and failures in monitoring stations, leading to an underestimation of impacts. We highlight the need for uncertainty aware flood predictions to support emergency response.

This study presents a quantitative method to capture how damage caused by concurrent and consecutive hazard events evolves, including how earlier damage and ongoing recovery affect later events. Applied to a sequence of hurricanes and earthquakes in Puerto Rico, it shows that ignoring residual damage significantly underestimates the impacts caused by consecutive events.  By accounting for recovery and interaction effects, the method supports more realistic damage assessment and risk planning.

Our research lays the groundwork for the next generation of disaster risk modelling by improving how building-level value and use are estimated across Germany. By testing multiple data sources and methods, we identify a transparent, adaptable approach that enhances forecasts of damage and recovery – helping to protect lives, property, and communities.

Earthquakes cause major losses worldwide, but their impacts differ across regions. Using global disaster records from 1980 to 2024, this study shows that while recorded disasters and economic losses increased, deaths declined relative to population size. Social and economic conditions are linked to disaster impacts, highlighting the importance of resilience planning.

The health burden of extreme heat necessitates the development of effective early warning systems. These systems depend critically on the underlying weather forecasts. We compare forecasts of temperature-related mortality based on data-driven weather forecasts with those based on physics-based weather forecasts for summer 2024 in Europe. We find that forecasts based on data-driven weather forecasts could represent a promising avenue for the development of heat-related health impact forecasts.

Deadwood supports biodiversity and contributes to the protective function of mountain forests, but under certain conditions it can itself become a hazard when sliding downslope. This study examines when and why sliding deadwood occurs and how far it can travel. Using a physics-based simulation model and forest data, we show that sliding is favoured by steep slopes, wet surfaces, and specific decay stages, helping identify where hazards can be reduced while preserving protective forest functions.

Natural systems are complex and uncertain, making probabilistic forecasts essential. Representing and communicating all uncertainties is challenging but vital for risk management. A multidisciplinary review identified common challenges across hazards and proposes a shared framework based on a clear hierarchy of uncertainties, complete forecasts, rigorous model evaluation, and effective communication to support decision-making.

This study develops the Accumulated-Event Risk Indicator to identify where major disaster events accumulated during 2013–2023 under country-level risk conditions. It identifies 14 principal hotspots, concentrated mainly in South and Southeast Asia and along the Himalayan belt. The study also presents the 3H Dataset, providing standardized high-resolution remote-sensing imagery for finer-scale disaster research.

This study examined new multi-layered tsunami defences for Kesennuma, Japan, to find designs that protect communities while improving coastal openness and liveability. Using computer simulations of tsunami with future sea level rise, we found that a transparent and socially integrated design achieved nearly the same protection as large concrete walls, showing that safety and human-centred design can coexist in coastal planning.

We studied how the Canterbury earthquakes changed household finances by linking national survey records with location and shaking data. Household income rose after the earthquakes, while total spending changed little overall. However, spending shifted toward insurance, transport, communication, housing, and repairs. Higher-income households adjusted across more areas, while relocated households faced higher housing costs. The findings show that rising income can coexist with uneven recovery.

This study synthesizes the multiscalar and nonlinear dynamics of drought impacts across Spain. It highlights the combined role of precipitation deficits and rising temperatures, showing the stronger performance of the standardized precipitation evapotranspiration index (SPEI) over the standardized precipitation index (SPI) under warming conditions. Agricultural impacts respond to intermediate drought durations, while hydrological systems show delayed long-term responses. Severe droughts amplify impacts nonlinearly, with strong spatial variability affecting crops, reservoirs and ecosystems.

This study looks at how lightning strikes happen over time and space, focusing on six storms in the UK during 2012 and 2015. By using real data, the research examines how often lightning strikes occur, how fast the storms move, and how far the strikes spread. The storms had different speeds (13–31 m s ^–1) and times between strikes (0.01 to 100 s), with strikes spreading up to 80 km. The study’s findings help create models to better characterise severe storms.

The scarcity of information on the Negros–Sulu megathrust impedes reliable seismic and tsunami hazard assessments. Here, we estimated 18 megathrust rupture scenarios based on available data and modeled tsunami propagations to identify highly exposed coastal areas surrounding the Sulu Sea. The generated rupture parameters, maximum wave heights, flow velocity, and minimum arrival times provide significant bases for regional-scale mitigation strategies and exploring tsunami deposits.

It is important to know how often an extreme precipitation amount can occur. This frequency can be derived via analysis of observed time series. With precipitation traditionally observed at single points (the rain gauges), the derived frequencies are not valid for e.g. catchments. Valid areal extreme values can be achieved by a subsequent reduction in space. This study analyses the impact of seasons, durations, frequencies and topography on reduction factors for extreme values (5 min to 1 day).

Mountain flash floods pose growing risks as climate and landscapes change. We reviewed 1,967 studies from 2000 to 2025 and found research concentrated in Europe and Asia. Rainfall and local conditions shape floods, while their impacts depend on where people live and their ability to respond. We propose combining warnings, planning, engineering and natural protection in a flexible approach to prevent new risks, reduce existing risks and manage those that remain.

This study explores how radar satellite images can rapidly assess cyclone damage in coastal Bangladesh, where clouds and limited access hinder traditional methods. By using satellite radar coherence to track surface changes across five major cyclones, it identifies impacts on land, vegetation, and infrastructure with high accuracy. The findings highlight a reliable tool for faster disaster response and improved planning in vulnerable regions.

Urban flooding can disrupt homes, transport,essential services, creating a need for timely and reliable mapping. This study develops a machine learning approach for mapping flood inundation in Mumbai using satellite observations, elevation, rainfall, and river-related data. Results show that combining these sources improves flood classification and helps identify areas with greater flood probability. Approach supports informed flood risk assessment and future decision-support applications.