The current research investigated spatio-temporal evolution of wet–dry events collectively, their characteristics, and their transition (wet to dry and dry to wet) across the Upper Jhelum Basin using the standardized precipitation evapotranspiration (SPEI) at a monthly timescale. The results provide significant knowledge to identify and locate most vulnerable geographical hotspots of extreme events, providing the basis for more effective risk reduction and climate change adaptation plans.

Extreme fire episodes in Indonesia emit large amounts of greenhouse gasses and have negative effects on human health in the region. In this study we show that such burning events can be predicted several months in advance in large parts of Indonesia using existing seasonal climate forecasts and forest cover change datasets. A reliable early fire warning system would enable local agencies to prepare and mitigate the worst of the effects.

The aim of our study was to question the validity of the assumed direct linkage between drivers of drought and its impacts on water and food securities, mainly found in the frameworks of drought early warning systems (DEWSs). We analysed more than 5000 scientific studies leading us to the conclusion that the local context can contribute to drought drivers resulting in these drought impacts. Our research aims to increase the relevance and utility of the information provided by DEWSs.

Extratropical cyclones are one of the major causes of coastal floods in Europe and the world. Understanding the development process and the flooding of storm Xynthia, together with the damages that occurred during the storm, can help to forecast future losses due to other similar storms. In the present paper, an analysis of shallow water variables (flood depth, velocity, etc.) or coastal variables (significant wave height, energy flux, etc.) is done in order to develop damage curves.

A well-constrained rainfall-runoff model forced by radar-derived precipitation is used to define rainfall intensity-duration (ID) thresholds for flash floods. The rainfall ID doubles in 5 years after a severe wildfire in a watershed in southern California, USA. Rainfall ID performs stably well for intense pulses of rainfall over durations of 30-60 minutes that cover at least 15%-25% of the watershed. This finding could help issuing flash flood warnings based on radar-derived precipitation.

We propose a workflow to model the response of an alluvial fan located in the Atacama Desert during an extreme storm event. For this alluvial fan, five different deposits were identified and associated with different debris flow surges. Using a commercial software program, our workflow concatenates these surges into one model. This study depicts the significance of the mechanical classification of debris flows to reproduce how an alluvial fan controls the tributary–river junction connectivity.

As land use influences hydrological processes (e.g., forests have a high water retention and infiltration capacity), it also impacts floods downstream in the river system. This paper demonstrates an approach quantifying the impact of land use changes on economic flood damages: damages in an initial situation are quantified and compared to damages of simulated floods associated with a land use change scenario. This approach can be used as an explorative tool in sustainable flood risk management.

This study is devoted to comparing the effectiveness of three different filters for noise reduction of instruments. It was observed that the Savitzky–Golay and Gaussian-weighted moving average filters outperform the simple moving average. Application of these two filters in real-time landslide monitoring leads to timely detection of acceleration moment and better preservation of information regarding displacement and velocity.

Characterizing extreme wave environments caused by tropical cyclones in the Caribbean Sea near Guadeloupe is difficult because cyclones rarely pass near the location of interest. STM-E (space-time maxima and exposure) model utilizes wave data during cyclones on a spatial neighbourhood. Long-duration wave data generated from a database of synthetic tropical cyclones are used to evaluate the performance of STM-E. Results indicate STM-E provides estimates with small bias and realistic uncertainty.

More frequent rockfalls in high alpine environments due to climate change are a growing threat to mountaineers. This hazard is particularly important on the classic route up Mont Blanc. Our results show that rockfalls are most frequent during snowmelt periods and the warmest hours of the day, and that mountaineers do not adapt to the local rockfall hazard when planning their ascent. Disseminating the knowledge acquired from our study caused management measures to be implemented for the route.

Local communities in northern Malawi have well-developed knowledge of the conditions leading to flash floods, spatially and temporally. Scientific analysis of catchment geomorphology and global reanalysis datasets corroborates this local knowledge, underlining the potential of these large-scale scientific datasets. Combining local knowledge with contemporary scientific datasets provides a common understanding of flash flood events, contributing to a more people-centred warning to flash floods.

Earthquakes can trigger thousands of landslides, causing severe and widespread damage. Efforts to understand what controls these landslides rely heavily on costly and time-consuming manual mapping from satellite imagery. We developed a new method that automatically detects landslides triggered by earthquakes using thousands of free satellite images. We found that in the majority of cases, it was as skilful at identifying the locations of landslides as the manual maps that we tested it against.

During the last 20 years extreme wildfires have challenged firefighting capabilities. Several fire danger indices are routinely used by firefighting services but are not suited to forecast convective extreme wildfire behaviour at the global scale. This article proposes a new fire danger index for deep moist convection, the extreme-fire behaviour index (EFBI), based on the analysis of the vertical profiles of the atmosphere above wildfires to use along with traditional fire danger indices.

Mass movement hazards in the form of gullies and landslides pose significant risks in urbanizing areas yet are poorly documented. This paper presents observations and modeling of mass movement events over a 5-year period in Tijuana, Mexico. Three major events were observed, and all were linked to water resources infrastructure failures (WRIFs), namely leaks and breaks in water supply pipes. Modeling shows that WRIF-based erosion was also a non-negligible contributor to the total sediment budget.

There is no consensus about how to assess the quality of binary (yes or no) rare and severe event forecasts, i.e. forecasts involving natural hazards like tornadoes or avalanches. We offer a comprehensive overview of the challenges we face when making such an assessment and provide a critical review of existing solutions. We argue against all but one existing solution to assess the quality of such forecasts and present practical consequences to improve forecasting services.

In the past, wildfires in the UK have occurred mainly in spring, with occasional events during hot, dry summers. Climate models predict a large future increase in hazardous fire weather conditions in summer. Wildfire can be considered an emergent risk for the UK, as past events have not had widespread major impacts, but this could change. The large increase in risk between the 2 °C and 4 °C levels of global warming highlights the importance of global efforts to keep warming below 2 °C.

We evaluate the usefulness of different data sources and variables to the short-term prediction (nowcasting) of severe thunderstorms using machine learning. Machine-learning models are trained with data from weather radars, satellite images, lightning detection and weather forecasts and with terrain elevation data. We analyze the benefits provided by each of the data sources to predicting hazards (heavy precipitation, lightning and hail) caused by the thunderstorms.

This work studies the benefit of using more reliable local climate scenarios to analyse hydrological impacts. It has been applied in the Cenajo basin (south-eastern Spain), where we showed that the best approximations of the historical meteorology also provide the best approximations of the hydrology. The two selected climate models predict worrying changes in precipitation, temperature, streamflows and meteorological and hydrological droughts for the period 2071–2100 under the RCP8.5.

Volcanic eruptions can produce tsunamis through multiple mechanisms. We present validation cases for a numerical method used in simulating waves caused by submarine explosions: a laboratory flume experiment and waves generated by explosions at field scale. We then demonstrate the use of the scheme for simulating analogous volcanic eruptions, illustrating the resulting wavefield. We show that this scheme models such dispersive sources more proficiently than standard tsunami models.

Fracturing and fragmentation of rock blocks are important and universal phenomena during the movement of rock avalanches (large and long-run-out rockslide-debris avalanches). The movement of a fragmenting rock block is simulated by the discrete element method, aiming to quantify the fracturing and fragmentation effect of the block in propagation. The fracturing and fragmentation processes and their influences on energy transformation in the system are described in detail.

Natural disasters are causing high losses worldwide. To adequately deal with this loss potential, a reinsurer has to quantitatively assess the individual risks of natural catastrophes and how these risks are changing over time with respect to climate change. From a reinsurance perspective, the most pressing scientific challenges related to natural hazards are addressed, and broad changes are suggested that should be achieved by the scientific community to address these hazards in the future.

The coastal area is a major floodplain in compound flood events in coastal cities, primarily due to storm tide, with the inundation severity positively correlated with the height of the storm tide. Simply accumulating every single-driven flood hazard (rainstorm inundation and storm tide flooding) to define the compound flood hazard may cause underestimation. The assessment of tropical cyclone compound flood risk can provide vital insight for research on coastal flooding prevention.

For various applications, it is crucial to have profound knowledge of the frequency, severity, and risk of extreme flood events. Such events are characterized by very long return periods which observations can not cover. We use a large ensemble of regional climate model simulations as input for a hydrological model. Precipitation data were post-processed to reduce systematic errors. The representation of precipitation and discharge is improved, and estimates of long return periods become robust.