As climate change escalates, the Berlin-Brandenburg region faces new challenges. Climate change-induced extreme events are expected to cause new conflicts to emerge and aggravate existing ones. To guide future research, we co-develop a list of key questions on climate and water challenges in the region. Our findings highlight the need for new research approaches. We expect this list to provide a roadmap for actionable knowledge production to address climate and water challenges in the region.

Advances in 3D modeling have improved understanding of geophysical flows like avalanches, but using these complex simulations in hazard mapping remains challenging. This study presents a tool that converts 3D simulation results into simplified 2D maps, making results more accessible for natural hazard applications. We applied this tool to model an ice avalanche and visualized the data in GIS, aiming to support practical hazard assessment and mitigation efforts.

On 17 August 2024, a thunderstorm in Vienna led to a record-breaking rainfall of 110 mm in two hours. An analysis of the exceptionally long hourly rain gauge time series (since 1941) estimates this event's return period at 662 years. The extremity of the event is further confirmed by neighbouring weather stations and rain-radar data.

Between September 17 and 20, 2024, the Lamone River basin in Northern Italy was hit by extreme precipitations. 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 Traversara village. 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.

The German federal state of Brandenburg is particularly prone to soil moisture droughts. To support the management of related risks, we introduce a novel soil moisture and drought monitoring network based on cosmic-ray neutron sensing technology. This initiative is driven by a collaboration of research institutions and federal state agencies, and it is the first of its kind in Germany to have started operation. In this brief communication, we outline the network design and share first results.

Communicating uncertainties is a crucial yet challenging aspect of spatial modelling – especially in applied research, where results inform decisions. In disaster risk reduction, susceptibility maps for natural hazards guide planning and risk assessment, yet their uncertainties are often overlooked. We present a new type of landslide susceptibility map that visualizes both susceptibility and associated uncertainty, alongside guidelines for creating such maps using free and open-source software.

Warming seas have been driving greater hailstorm risk in Europe over the past few decades. Modern climate models indicate anthropogenic aerosols caused the observed cooling of seas from about 1900 to the 1970s, while the recent rapid warming is mostly explained by rising greenhouse gases. Current trends in anthropogenic forcing are likely to persist, suggesting seas will continue warming, and hailstorm risk over Europe will continue rising over the next couple of decades, at least.

On April 2, 2024, a Mw 7.4 earthquake hit Taiwan’s eastern coast, causing extensive landslides and damage. We used automated methods combining Earth Observation (EO) data with Artificial Intelligence (AI) to quickly inventory the landslides. This approach identified 7,090 landslides over 75 km² within 3 hours of acquiring the EO imagery. The study highlights AI’s role in improving landslide detection and understanding earthquake-landslide interactions for better hazard mitigation.