This paper proposes a methodological framework designed for event-based evaluation in the context of an intense flash-flood event. The evaluation adopts the point of view of end users, with a focus on the anticipation of exceedances of discharge thresholds. With a study of rainfall forecasts, a discharge evaluation and a detailed look at the forecast hydrographs, the evaluation framework should help in drawing robust conclusions about the usefulness of new rainfall ensemble forecasts.

The strike-slip Carboneras fault is one of the largest sources in the Alboran Sea, with it being one of the faster faults in the eastern Betics. The dimensions and location of the Carboneras fault imply a high seismic and tsunami threat. In this work, we present tsunami simulations from sources generated with physics-based earthquake simulators. We show that the Carboneras fault has the capacity to generate locally damaging tsunamis with inter-event times between 2000 and 6000 years.

Rogue waves exceed twice the significant wave height. They occur more often than expected in the shallow waters off Norderney. When applying a nonlinear Fourier transform for the Korteweg–de Vries equation to wave data from Norderney, we found differences in the soliton spectra of time series with and without rogue waves. A strongly outstanding soliton in the spectrum indicated an enhanced probability for rogue waves. We could attribute spectral solitons to the measured rogue waves.

Debris flows often occur after wildfires. These debris flows move water, sediment, and wood. The wood can get stuck in channels, creating a dam that holds boulders, cobbles, sand, and muddy material. We investigated how the channel width and wood length influenced how much sediment is stored. We also used a series of equations to back calculate the debris flow speed using the breaking threshold of wood. These data will help improve models and provide insight into future field investigations.

This paper presents a new approach to assess avalanche risk on a large scale in mountainous regions. It combines a large-scale avalanche modeling method with a state-of-the-art probabilistic risk tool. Over 40 000 individual avalanches were simulated, and a building dataset with over 13 000 single buildings was investigated. With this new method, risk hotspots can be identified and surveyed. This enables current and future risk analysis to assist decision makers in risk reduction and adaptation.

This paper presents a new method for selecting streamflow drought hazard indicators for monitoring drought hazard for human water supply and river ecosystems in large-scale drought early warning systems. Indicators are classified by their inherent assumptions about the habituation of people and ecosystems to the streamflow regime and their level of drought characterization, namely drought magnitude (water deficit at a certain point in time) and severity (cumulated magnitude since drought onset).

The associations between household characteristics and hazard-related social vulnerability in Istanbul, Türkiye, were assessed using machine learning techniques. The results indicated that less educated households with no social security and job insecurity that live in squatter houses are at a higher risk of social vulnerability. We present the findings in an open-access R Shiny web application, which can serve as a guidance for identifying the target groups in the interest of risk mitigation.

Wave setup is a critical component of coastal flooding. Consequently, understanding and being able to predict wave setup is vital to protect coastal resources and the population living near the shore. Here, we applied machine learning to improve the accuracy of present predictors of wave setup. The results show that the new predictors outperform existing formulas demonstrating the capability of machine learning models to provide a physically sound description of wave setup.

Wind and storms are a major natural hazard and can cause severe economic damage and cost human lives. Hence, it is important to gauge the potential impact of using indices, which potentially enable us to estimate likely impacts of storms or other wind events. Here, we review basic aspects of wind and storm generation and provide an extensive overview of wind impacts and available indices. This is also important to better prepare for future climate change and corresponding changes to winds.

To investigate cumulative damage on extended building portfolios, we propose an alternative and modular method to probabilistically integrate sets of single-hazard vulnerability models that are being constantly developed by experts from various research fields to be used within a multi-risk context. We demonstrate its application by assessing the economic losses expected for the residential building stock of Lima, Peru, a megacity commonly exposed to consecutive earthquake and tsunami scenarios.

This work was carried out within the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) project and is focused on the first landslide susceptibility analysis at a regional scale for Central Asia. The most detailed available landslide inventories were implemented in a random forest model. The final aim was to provide a useful tool for reduction strategies to landslide scientists, practitioners, and administrators.

This study presents a framework for assessing compound flood risk using hydrodynamic, impact, and statistical modeling. A pilot in Mozambique shows the importance of accounting for compound events in risk assessments. We also show how the framework can be used to assess the effectiveness of different risk reduction measures. As the framework is based on global datasets and is largely automated, it can easily be applied in other areas for first-order assessments of compound flood risk.

The combined use of meteorological and ocean models enabled the analysis of extreme sea conditions driven by Medicane Ianos, which hit the western coast of Greece on 18 September 2020, flooding and damaging the coast. The large spread associated with the ensemble highlighted the high model uncertainty in simulating such an extreme weather event. The different simulations have been used for outlining hazard scenarios that represent a fundamental component of the coastal risk assessment.

A new methodology to calculate a probabilistic long-term tephra fallout hazard assessment in southern Italy from the Neapolitan volcanoes is provided. By means of thousands of numerical simulations we quantify the mean annual frequency with which the tephra load at the ground exceeds critical thresholds in 50 years. The output hazard maps account for changes in eruptive regimes of each volcano and are also comparable with those of other natural disasters in which more sources are integrated.

Numerical models are increasingly important for assessing urban flooding, yet reliable input data are oftentimes hard to obtain. Taking Ho Chi Minh City as an example, this paper explores the usability and reliability of open-access data to produce preliminary risk maps that provide first insights into potential flooding hotspots. As a key novelty, a normalized flood severity index is presented which combines flood depth and duration to enhance the interpretation of hydro-numerical results.

Like many other megacities in low-elevation coastal zones, Ho Chi Minh City in southern Vietnam suffers from the convoluting impact of changing environmental stressors and rapid urbanization. This study assesses quantitative hydro-numerical results against the background of the low-regret paradigm for (1) a large-scale flood protection scheme as currently constructed and (2) the widespread implementation of small-scale rainwater detention as envisioned in the Chinese Sponge City Program.

This paper presents a study on rockfall dynamics and hazard, examining the impact of the presence of trees along slope and block fragmentation. We compared rockfall simulations that explicitly model the presence of trees and fragmentation with a classical approach that accounts for these phenomena in model parameters (both the hazard and the kinetic energy change). We also used a non-parametric probabilistic rockfall hazard analysis method for hazard mapping.

We characterize meteorological (P), soil moisture (SM) and hydrological (Q) droughts and the propagation from one to the other for 318 catchments in the Horn of Africa. We find that propagation from P to SM is influenced by soil properties and vegetation, while propagation from P to Q is from catchment-scale hydrogeological properties (i.e. geology, slope). We provide precipitation accumulation periods at the subbasin level that can be used as a proxy in drought forecasting in dryland regions.