A high-resolution fixed-asset model can help improve the accuracy of earthquake loss assessment. We develop a grid-level fixed-asset model for China from 1951 to 2020. We first compile the provincial-level fixed asset from yearbook-related statistics. Then, this dataset is disaggregated into 1 km × 1 km grids by using multiple remote sensing data as the weight indicator. We find that the fixed-asset value increased rapidly after the 1980s and reached CNY 589.31 trillion in 2020.

Households play a crucial role in climate adaptation efforts. Yet, households require capacities to implement measures. We explore which capacities enable German households to adapt to flooding. Our results indicate that flood-related capacities such as risk perception, responsibility appraisal, and motivation are pivotal, whereas financial assets are secondary. Enhancing these specific capacities, e.g. through collaborations between households and municipalities, could promote local adaptation.

By combining limited tsunami simulations with machine learning, we developed a fast and efficient framework to predict tsunami impacts such as wave heights and inundation depths at different coastal sites. Testing our model with historical tsunami source scenarios helped assess its reliability and broad applicability. This work enables more efficient and comprehensive tsunami hazard modelling workflow, which is essential for tsunami risk evaluations and enhancing coastal disaster preparedness.

Protecting lives and livelihoods during volcanic eruptions is the key challenge in volcanology. Analysing social media usage during volcanic crises can help us better understand the impacts of volcanic eruptions and how warning messages are received and actioned, to eventually better protect those people and their livelihoods. Our work shows how social media data could be used in real time during a volcanic crisis to learn more about volcanic eruptions.

We present a classification method for extratropical cyclones and windstorms and show their impacts on Finland's electricity grid by analysing the 92 most damaging windstorms (2005–2018). The south-west- and north-west-arriving windstorms cause the most damage to the power grid. The most relevant parameters for damage are the wind gust speed and extent of wind gusts. Windstorms are more frequent and damaging in autumn and winter, but weaker wind speeds in summer also cause significant damage.

A persistent severe rainfall event occurred over North China in July 2023, which was regarded as one of the most extreme episodes globally during that year. The extreme rainfall was significantly underestimated by forecasters at that time. Flooding from this event affected 1.3 million people, causing severe human casualties and economic losses. We examined the convective initiation and subsequent persistent heavy rainfall based on simulations with the Weather Research and Forecasting model.

This work introduces RIM2D (Rapid Inundation Model 2D), a hydrodynamic model for precise and rapid flood predictions that is ideal for early warning systems. We demonstrate RIM2D's ability to deliver detailed and localized flood forecasts using the June 2023 flood in Braunschweig, Germany, as a case study. This research highlights the readiness of RIM2D and the required hardware for integration into operational flood warning and impact-based forecasting systems.

This study provides detailed insight into the thunderstorm characteristics associated with abrupt changes in the lightning activity of a thunderstorm – lightning jumps (LJs) and lightning dives (LDs) – using geostationary satellite observations. Thunderstorms exhibiting one or multiple LJs or LDs feature characteristics similar to severe thunderstorms. Storms with multiple LJs contain strong convective updrafts and are prone to produce high rain rates, large hail, or tornadoes.

Estimates of the risk posed by rare and catastrophic weather events are often derived from relatively short measurement records, which renders them highly uncertain. We investigate if (and by how much) this uncertainty can be reduced by making use of large datasets of simulated weather. More specifically, we focus on coastal flood hazard in the Netherlands and on the challenge of estimating the once in 10 million years coastal water level and wind stress as accurately as possible.