This paper presents a method for characterizing regional landslide potential, which the authors suggest as an improved basis for landslide hazard forecasting during storms. It discusses the advantage of using considering the relative soil-saturation rather than a rainfall recurrence interval to understand landsliding triggered by rainfall, focusing on data from California.

The paper explains the factors which made the Ahr flood event of 2021 the deadliest one in Germany, causing more fatalities than all flood events of the previous 40 years together. It points out that 75% of the fatalities occurred outside of the established flood hazard zones. On the basis of individual fatality reports, factors additionally to early warning and evacuation deficits are identified. A particular result is that beyond danger zones like campsites and cellars the percentage of deaths on the ground floor of buildings (37 %) or outside on the street (18 %) were particularly high. Recommendations for improving emergency management of such unprecedented floods are given.

Forest fires have become a major problem in many regions of the world, including parts of Central Europe. The modelling study addresses the different factors for Forest Fire Susceptibility (FFS), making use of high spatial resolution of input data for the state of Brandenburg, Germany. An increasing susceptibility is found under rising greenhouse gas forcing scenarios when other changes are not taken into account. Extreme weather periods are of particular relevance in this respect. However, the importance of anthropogenic and vegetation parameters for modelling FFS on a regional level can outweigh the pure climatic effects. The paper also suggests some recommendations for forest management and environmental planning for a reduction of fire risk.

The manuscript offers significant contributions that justify its consideration as a highlight paper: It presents a novel, multifaceted methodology for identifying and compiling single hazard types and multi-hazard interrelationships in data-scarce urban settings, which is applied in the context of Kathmandu Valley, Nepal. The research provides a comprehensive database of 21 single hazard types and 83 multi-hazard interrelationships, contributing critical insights for disaster risk reduction (DRR) in “Global South” urban areas, with an emphasis on inclusive, people-centred approaches. By integrating stakeholder perspectives and multi-hazard scenario generation, the paper advances the development of dynamic risk scenarios, strengthening multi-hazard approaches for disaster preparedness and response.

The paper presents a methodology for spatial identification of regions exposed to multi-hazards at pan European level, thus offering a significant contribution to the knowledge gap of decision makers and stakeholders which are still lacking a place-specific information of the proneness of their region to multi-hazard events as opposed to single hazard events.

This study introduces a new approach to multi-hazard risk assessment, leveraging hypergraph theory to model interconnected risks posed by cascading natural hazards. Traditional single-hazard risk models fail to account for the complex interrelationships and compounding effects of multiple simultaneous or sequential hazards. Conceptualising and visualising risks within a hypergraph framework overcomes these limitations, enabling efficient simulation of multi-hazard interactions and their impacts on infrastructure. The authors apply their model to the 2015 Mw 7.8 Gorkha earthquake in Nepal as a case study, demonstrating its ability to simulate the primary and secondary effects of the earthquake on buildings and roads across the whole earthquake-affected area. The model predicts the overall pattern of earthquake-induced building damage and landslide impacts, albeit with a tendency towards over-prediction. Their findings underscore the potential of the hypergraph approach for multi-hazard risk assessment, offering advances in rapid computation and scenario exploration for cascading geo-hazards. This approach could provide valuable insights for disaster risk reduction and humanitarian contingency planning, where anticipation of large-scale trends is often more important than prediction of detailed impacts.