This work introduces RIM2D, a hydrodynamic model for precise and rapid flood predictions, 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.

Forecasting rainfall over the next hour is an essential feature of early warning systems. Deep learning has emerged as a powerful alternative to conventional nowcasting technologies, but it still struggles to adequately predict impact-relevant heavy rainfall. We think that DL could do much better if the training tasks were defined more specifically, and that such a specification presents an opportunity to better align the output of nowcasting models with actual user requirements.  

Floods have caused significant damage in the past. To prepare for such events, we rely on historical data, but face issues due to rare rainfall events, lack of data, and climate change. Counterfactuals, or "what if" scenarios, simulate historical rainfall in different locations to estimate flood levels. Our new study refines this by deriving more plausible local scenarios, using the June 2024 Bavaria flood as a case study. This method could improve future flood preparation.

Extreme rainfall comprises a major hydro-hazard for New Zealand, and is commonly associated with atmospheric rivers – narrow plumes of very high atmospheric moisture transport. Here, we focus on improved forecasting of these events by testing a forecasting tool previously applied to similar situations in western Europe. However, our results for New Zealand suggest the performance of this forecasting tool may vary depending on geographic setting.

Our research reveals the power of high-resolution satellite SAR imagery for slope deformation monitoring. Using ICEYE data over the Brienz/Brinzauls instability, we measured surface velocity and mapped the landslide event with unprecedented precision. This underscores SAR's potential for timely hazard assessment in remote regions, aiding disaster mitigation efforts effectively.

In October 2023, one of the strongest storm surges on record hit the southwestern Baltic Sea coast, causing severe impacts in the German federal state of Schleswig-Holstein including dike failures. Recent studies on coastal flooding from the same region align well with the October 23 surge, with differences in peak water levels of less than 30 cm. Here we use this rare coincidence to assess current capabilities and limitations of coastal flood modeling and derive key areas for future research.