Despite transdisciplinary approaches being increasingly explored to study droughts and their impacts, their depth and breadth are yet to be fully exploited. By integrating insights from different research fields, we present five key dimensions to deepen and broaden the knowledge co-creation process for drought impact studies. Emphasizing social dynamics and power imbalances, we support hydrologists in developing more integrated, power-sensitive, inclusive, situated, and reflexive studies.

Compound events, where multiple weather or climate hazards occur together, pose significant risks to both society and the environment. These events, like simultaneous wind and rain, can have more severe impacts than single hazards. Our review of compound event research from 2012–2022 reveals a rise in studies, especially on events that occur concurrently, hot and dry events, and compounding flooding. The review also highlights opportunities for research in the coming years.

This paper investigates the relationship between changes in weather forecasts and predictability, which has so far been considered weak. By studying how weather scenarios persist over successive forecasts, it appears that conclusions can be drawn about forecasts' reliability.

Strong thunderstorms have been studied mainly over flat terrain in the past. However, they are particularly frequent near European mountain ranges, so observations of such storms are needed. This article gives an overview of our existing knowledge on this topic and presents plans for a large European field campaign with the goals to fill the knowledge gaps, validate tools for thunderstorm warnings, and improve numerical weather prediction near mountains.

Our research presents a new method for determining warning levels for any hazard. Using risk matrices, our framework addresses issues found in other approaches. Illustrative examples demonstrate how the approach works. A powerful method for evaluating warning accuracy is given, allowing for a cycle of continuous improvement in warning services. This research is relevant to a broad audience, from those who develop forecast systems to practitioners who issue or communicate warnings.

Local thresholds for landslide forecasting, combining hydrologic predisposing factors and rainfall features, are developed from a physically based model of a slope. To extend their application to a wide area, uncertainty due to the spatial variability of geomorphological and hydrologic variables is introduced. The obtained hydrometeorological thresholds, integrating root-zone soil moisture and aquifer water level with rainfall depth, outperform thresholds based on rain intensity and duration.

We analysed debris flows in Sichuan, China, using satellite data and simulations to assess check dam efficacy. Our study found that while check dams can mitigate smaller flows, they may increase exposure to extreme events, with up to 40 % of structures in some areas affected. Urban development and reliance on check dams can create a false sense of security, increasing exposure during large debris flows and highlighting the need for risk management and infrastructure planning in hazard-prone areas.

This study proposes a response-bias-tolerant methodology for estimating the production capacity loss rate (PCLR), which addresses response bias in extreme flood scenarios and considers the distribution characteristics of PCLR under different damage states. The core value of this study is to provide a competing and promising input in economic modeling, such as input–output and computable general equilibrium models.

Cities near volcanoes expose populations to hazards. We ranked 1106 cities by population exposed to volcanoes within < 100 km, nearest distance, and number of nearby volcanoes. Bandung ranks highest, with ~8 M exposed within < 30 km of 12 volcanoes. Jakarta leads populations exposed within <100 km (~38 M). Central America has the highest proportion of city exposure, with San Salvador near 23 volcanoes. We provide a global city exposure perspective, identifying areas for localized mitigation.

Multiple hazards, occurring simultaneously or consecutively, can have more extreme impacts than single hazards. We examined the disaster records in the global emergency events database EM-DAT to better understand this phenomenon. We developed a method to identify such multi-hazards and analysed their reported impacts using statistics. Multi-hazards have accounted for a disproportionate number of the impacts, but there appear to be different archetypal patterns in which the impacts compound.

We used a fiber optic cable along a road in western Norway to study snow avalanches signals. Our study shows that avalanches create distinct signals in the 20–50 Hz frequency range, with larger ones having weak early warning signals. However, road traffic interference complicates automatic detection. This research highlights the potential of using existing infrastructure for avalanche monitoring. Further improvements are needed for automated detection.

Brandenburg is among the driest federal states in Germany. The low groundwater recharge (GWR) is fundamental to both water supply and the support of natural ecosystems. In this study, we show that the decline of observed discharge and groundwater tables since 1980 can be explained by climate change in combination with an increasing leaf area index. Still, simulated GWR rates remain highly uncertain due to the uncertainty in precipitation trends.

The study assesses the practical predictability of an extreme rainfall event on 8 to 12 December 2018 over central Vietnam using quantitative precipitation forecasts (QPFs) from a time-lagged cloud-resolving ensemble system. To do this, 29 time-lagged (8 d in the forecast range) high-resolution (2.5 km) members were run every 6 h from 3 to 10 December 2018. Results reveal that the cloud-resolving model predicted the rainfall fields in the short range (less than 3 d) for 10 December (the rainiest day).

Pyroclastic deposits are the only records left by pyroclastic flows at Vesuvius, and deposits from past eruptions are the only way to get information about the expected range of impact parameters. It is necessary to investigate the deposits first and then define a general model of the current that links deposit characteristics to flow dynamics, finally reconstructing the impact parameters that better represent flow intensity in terms of damaging potential. This is the way the paper is organized.

Ho Chi Minh City (HCMC) faces severe flood risks from climatic and socio-economic changes, requiring effective adaptation solutions. Flood loss estimation is crucial, but advanced probabilistic models accounting for key drivers and uncertainty are lacking. This study presents a probabilistic flood loss model with a feature selection paradigm for HCMC’s residential sector. Experiments using new survey data from flood-affected households demonstrate the model's superior performance.

Tropical cyclone (TC) risk models have been used by the insurance industry to quantify occurrence and severity risk since the early 1990s. To date, these models have mostly been built from backward-looking statistics and portray risk under a static view of the climate. Here we introduce a novel approach, based on machine learning, that allows sampling of climate variability when assessing TC risk globally. This is of particular importance when computing forward-looking views of TC risk.

Using loss data, we assess when and how single and multiple types of meteorological extremes (river floods and heavy rainfall events, windstorms and convective gusts, and hail) are related. We find that the combination of several types of hazards clusters robustly on a seasonal scale, whereas only some single hazard types occur in clusters. This can be associated with higher losses compared to isolated events. We argue for the relevance of jointly considering multiple types of hazards.

This paper reviews existing wildfire propagation models and a comparison of different grid types including random grids to simulate wildfires. This paper finds that grids can simulate wildfires more efficiently than continuous models while still retaining a reasonable level of similarity. It also shows that an irregular grid called the flammable resolution grid tends to retain greater similarity to continuous models than regular grids at the cost of slightly longer computational times.

What do we regret about our disaster preparedness? This paper explores the regrets of 438 citizens who were affected by flooding in Germany in 2021. It shows that regret can primarily be associated with inaction (instead of actions), which contrasts with psychological studies from fields other than disaster science. The findings of this study suggest that the no-regret approach could be a suitable framework for moving towards longer-term disaster preparedness to reduce future regrets.