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.

Electricity systems are prone to climate extremes. Our research combines four years of daily electricity outages data from 370 locations across India with temperature, wind, precipitation and flood inundation data to show how climate extremes affect power services, and how this differs across power systems. This information is key for countries that aim to meet universal access to energy in the coming decades, yet at the same time will experience more frequent and intense climate extremes.

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.

We construct an indicator-based framework, and assess urban resilience to typhoon in China’s contexts for the seven major river basins. Results verified the heterogeneity, and the resilience level in certain circumstance was not matched with city strength of economy. The analysis is helpful for government to enhance capability of resilience in specific dimensions, and provides a reference in probing urban resilience assessment confronting typhoon.

Understanding coastal flood protection is vital for assessing risks from natural disasters and climate change. However, current global data on coastal flood protection is limited and based on simplified assumptions, leading to potential uncertainties in risk estimates. As a step in this direction, we propose a comprehensive dataset, COASTPROS-EU, which compiles coastal flood protection standards in Europe.

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.  

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.