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Since 1990, natural hazards have led to over 1.6 million fatalities globally, and economic losses are estimated at an average of around USD 260–310 billion per year. The scientific and policy communities recognise the need to reduce these risks. As a result, the last decade has seen a rapid development of global models for assessing risk from natural hazards at the global scale. In this paper, we review the scientific literature on natural hazard risk assessments at the global scale, and we specifically examine whether and how they have examined future projections of hazard, exposure, and/or vulnerability. In doing so, we examine similarities and differences between the approaches taken across the different hazards, and we identify potential ways in which different hazard communities can learn from each other. For example, there are a number of global risk studies focusing on hydrological, climatological, and meteorological hazards that have included future projections and disaster risk reduction measures (in the case of floods), whereas fewer exist in the peer-reviewed literature for global studies related to geological hazards. On the other hand, studies of earthquake and tsunami risk are now using stochastic modelling approaches to allow for a fully probabilistic assessment of risk, which could benefit the modelling of risk from other hazards. Finally, we discuss opportunities for learning from methods and approaches being developed and applied to assess natural hazard risks at more continental or regional scales. Through this paper, we hope to encourage further dialogue on knowledge sharing between disciplines and communities working on different hazards and risk and at different spatial scales.

In 2009, advancements in NWP and computing power inspired a vision to advance hazardous weather warnings from a warn-on-detection to a warn-on-forecast paradigm. This vision would require not only the prediction of individual thunderstorms and their attributes but the likelihood of their occurrence in time and space. During the last decade, the warn-on-forecast research team at the NOAA National Severe Storms Laboratory met this challenge through the research and development of 1) an ensemble of high-resolution convection-allowing models; 2) ensemble- and variational-based assimilation of weather radar, satellite, and conventional observations; and 3) unique postprocessing and verification techniques, culminating in the experimental Warn-on-Forecast System (WoFS). Since 2017, we have directly engaged users in the testing, evaluation, and visualization of this system to ensure that WoFS guidance is usable and useful to operational forecasters at NOAA national centers and local offices responsible for forecasting severe weather, tornadoes, and flash floods across the watch-to-warning continuum. Although an experimental WoFS is now a reality, we close by discussing many of the exciting opportunities remaining, including folding this system into the Unified Forecast System, transitioning WoFS into NWS operations, and pursuing next-decade science goals for further advancing storm-scale prediction.

Climate change is leading to more extreme weather hazards, forcing human populations to be displaced. We employ explainable machine learning techniques to model and understand internal displacement flows and patterns from observational data alone. For this purpose, a large, harmonized, global database of disaster-induced movements in the presence of floods, storms, and landslides during 2016–2021 is presented. We account for environmental, societal, and economic factors to predict the number of displaced persons per event in the affected regions. Here we show that displacements can be primarily attributed to the combination of poor household conditions and intense precipitation, as revealed through the interpretation of the trained models using both Shapley values and causality-based methods. We hence provide empirical evidence that differential or uneven vulnerability exists and provide a means for its quantification, which could help advance evidence-based mitigation and adaptation planning efforts. Ronco and colleagues analyze disaster-induced movements in the presence of floods, storms, and landslides during 2016–2021, providing empirical evidence that differential vulnerability exists and quantifying its extent. They achieve this by employing explainable machine learning techniques to model and understand internal displacement flows and patterns from observational data.

We describe and present a new model of global subduction zone geometries, called Slab1.0. An extension of previous efforts to constrain the two‐dimensional non‐planar geometry of subduction zones around the focus of large earthquakes, Slab1.0 describes the detailed, non‐planar, three‐dimensional geometry of approximately 85% of subduction zones worldwide. While the model focuses on the detailed form of each slab from their trenches through the seismogenic zone, where it combines data sets from active source and passive seismology, it also continues to the limits of their seismic extent in the upper‐mid mantle, providing a uniform approach to the definition of the entire seismically active slab geometry. Examples are shown for two well‐constrained global locations; models for many other regions are available and can be freely downloaded in several formats from our new Slab1.0 website, http://on.doi.gov/d9ARbS. We describe improvements in our two‐dimensional geometry constraint inversion, including the use of ‘average’ active source seismic data profiles in the shallow trench regions where data are otherwise lacking, derived from the interpolation between other active source seismic data along‐strike in the same subduction zone. We include several analyses of the uncertainty and robustness of our three‐dimensional interpolation methods. In addition, we use the filtered, subduction‐related earthquake data sets compiled to build Slab1.0 in a reassessment of previous analyses of the deep limit of the thrust interface seismogenic zone for all subduction zones included in our global model thus far, concluding that the width of these seismogenic zones is on average 30% larger than previous studies have suggested. Key Points Introduces a new set of detailed 3D global subduction zone models Focuses on the shallow seismogenic zone (unrepresented in previous models) Allows for improved finite‐fault, seismic and tsunami hazard calculations

By 31 December 2018, 39 537 quality-controlled reports of large hail had been submitted to the European Severe Weather Database (ESWD) by volunteers and ESSL. This dataset and the NatCatSERVICE Database of Munich RE jointly allowed us to study the hail hazard and its impacts across Europe over a period spanning multiple decades. We present a spatiotemporal climatology of the ESWD reports, diurnal and annual cycles of large hail, and indicate where and how they may be affected by reporting biases across Europe. We also discuss which hailstorms caused the most injuries and present the only case with hail fatalities in recent times. Additionally, we address our findings on the relation between hail size to the type of impacts that were reported. For instance, the probability of reported hail damage to roofs, windows, and vehicles strongly increases as hail size exceeds 5 cm, while damage to crops, trees, and greenhouses is typically reported with hailstone diameters of 2–3 cm. Injuries to humans are usually reported with hail 4 cm in diameter and larger, and number of injuries increases with increasing hail size. Using the NatCatSERVICE data, we studied economic losses associated with hailstorms occurring in central Europe and looked for long-term changes. The trend in hail losses and the annual number of hail loss days since 1990 to 2018 are compared to that of meteorological conditions favorable for large hail as identified by ESSL’s Additive Regression Convective Hazards model. Both hail loss days and favorable environments show an upward trend, in particular since 2000.

Many shallow landslides and debris flows are precipitation initiated. Therefore, regional landslide hazard assessment is often based on empirically derived precipitation intensity-duration (ID) thresholds and landslide inventories. Generally, two features of precipitation events are plotted and labeled with (shallow) landslide occurrence or non-occurrence. Hereafter, a separation line or zone is drawn, mostly in logarithmic space. The practical background of ID is that often only meteorological information is available when analyzing (non-)occurrence of shallow landslides and, at the same time, it could be that precipitation information is a good proxy for both meteorological trigger and hydrological cause. Although applied in many case studies, this approach suffers from many false positives as well as limited physical process understanding. Some first steps towards a more hydrologically based approach have been proposed in the past, but these efforts received limited follow-up. Therefore, the objective of our paper is to (a) critically analyze the concept of precipitation ID thresholds for shallow landslides and debris flows from a hydro-meteorological point of view and (b) propose a trigger–cause conceptual framework for lumped regional hydro-meteorological hazard assessment based on published examples and associated discussion. We discuss the ID thresholds in relation to return periods of precipitation, soil physics, and slope and catchment water balance. With this paper, we aim to contribute to the development of a stronger conceptual model for regional landslide hazard assessment based on physical process understanding and empirical data.

Increased wildfire events constitute a significant threat to life and property in the United States. Wildfire impact on severe storms and weather hazards is another pathway that threatens society, and our understanding of which is very limited. Here, we use unique modeling developments to explore the effects of wildfires in the western US (mainly California and Oregon) on precipitation and hail in the central US. We find that the western US wildfires notably increase the occurrences of heavy precipitation rates by 38% and significant severe hail (≥2 in.) by 34% in the central United States. Both heat and aerosols from wildfires play an important role. By enhancing surface high pressure and increasing westerly and southwesterly winds, wildfires in the western United States produce (1) stronger moisture and aerosol transport to the central United States and (2) larger wind shear and storm-relative helicity in the central United States. Both the meteorological environment more conducive to severe convective storms and increased aerosols contribute to the enhancements of heavy precipitation rates and large hail. Moreover, the local wildfires in the central US also enhance the severity of storms, but their impact is notably smaller than the impact of remote wildfires in California and Oregon because of the lessened severity of the local wildfires. As wildfires are projected to be more frequent and severe in a warmer climate, the influence of wildfires on severe weather in downwind regions may become increasingly important.

On Dec. 22, 2018, at approximately 20:55–57 local time, Anak Krakatau volcano, located in the Sunda Straits of Indonesia, experienced a major lateral collapse during a period of eruptive activity that began in June. The collapse discharged volcaniclastic material into the 250 m deep caldera southwest of the volcano, which generated a tsunami with runups of up to 13 m on the adjacent coasts of Sumatra and Java. The tsunami caused at least 437 fatalities, the greatest number from a volcanically-induced tsunami since the catastrophic explosive eruption of Krakatau in 1883 and the sector collapse of Ritter Island in 1888. For the first time in over 100 years, the 2018 Anak Krakatau event provides an opportunity to study a major volcanically-generated tsunami that caused widespread loss of life and significant damage. Here, we present numerical simulations of the tsunami, with state-of the-art numerical models, based on a combined landslide-source and bathymetric dataset. We constrain the geometry and magnitude of the landslide source through analyses of pre- and post-event satellite images and aerial photography, which demonstrate that the primary landslide scar bisected the Anak Krakatau volcano, cutting behind the central vent and removing 50% of its subaerial extent. Estimated submarine collapse geometries result in a primary landslide volume range of 0.22–0.30 km 3 , which is used to initialize a tsunami generation and propagation model with two different landslide rheologies (granular and fluid). Observations of a single tsunami, with no subsequent waves, are consistent with our interpretation of landslide failure in a rapid, single phase of movement rather than a more piecemeal process, generating a tsunami which reached nearby coastlines within ~30 minutes. Both modelled rheologies successfully reproduce observed tsunami characteristics from post-event field survey results, tide gauge records, and eyewitness reports, suggesting our estimated landslide volume range is appropriate. This event highlights the significant hazard posed by relatively small-scale lateral volcanic collapses, which can occur en-masse , without any precursory signals, and are an efficient and unpredictable tsunami source. Our successful simulations demonstrate that current numerical models can accurately forecast tsunami hazards from these events. In cases such as Anak Krakatau’s, the absence of precursory warning signals together with the short travel time following tsunami initiation present a major challenge for mitigating tsunami coastal impact.

Artificial-intelligence tools are transforming data-driven science — better ethical standards and more robust data curation are needed to fuel the boom and prevent a bust. Artificial-intelligence tools are transforming data-driven science — better ethical standards and more robust data curation are needed to fuel the boom and prevent a bust. Credit: Bing Guan/Bloomberg via Getty Components in the Expanse supercomputer at the San Diego Supercomputer Center at the University of California San Diego.

On January 15th, 2022, at approximately 4:47 pm local time (0347 UTC), several weeks of heightened activity at the Hunga volcano 65 km northwest of Tongatapu, culminated in an 11-h long violent eruption which generated a significant near-field tsunami. Although the Kingdom of Tonga lies astride a large and tsunamigenic subduction zone, it has relatively few records of significant tsunami. Assessment activities took place both remotely and locally. Between March and June 2022, a field team quantified tsunami runup and inundation on the main populated islands Tongatapu and Eua, along with several smaller islands to the north, including the Ha’apai Group. Peak tsunami heights were ~ 19 m in western Tongatapu, ~ 20 m on south-eastern Nomuka Iki island and ~ 20 m on southern Tofua, located ~ 65 km S and E and 90 km N from Hunga volcano, respectively. In western Tongatapu, the largest tsunami surge overtopped a 13–15 m-high ridge along the narrow Hihifo peninsula in several locations. Analysis of tide gauge records from Nukualofa (which lag western Tongatapu arrivals by ~ 18–20 min), suggest that initial tsunami surges were generated prior to the largest volcanic explosions at ~ 0415 UTC. Further waves were generated by ~ 0426 UTC explosions that were accompanied by air-pressure waves. Efforts to model this event are unable to reproduce the timing of the large tsunami wave that toppled a weather station and communication tower on a 13 m-high ridge on western Tongatapu after 0500 UTC. Smaller tsunami waves continued until ~ 0900, coincident with a second energetic phase of eruption, and noted by eyewitnesses on Tungua and Mango Islands. Despite an extreme level of destruction caused by this tsunami, the death toll was extraordinarily low (4 victims). Interviews with witnesses and analysis of videos posted on social media suggest that this can be attributed to the arrival of smaller ‘pre tsunami’ waves that prompted evacuations, heightened tsunami awareness due to tsunami activity and advisories on the day before, the absence of tourists and ongoing tsunami education efforts since the 2009 Niuatoputapu, Tonga tsunami. This event highlights an unexpectedly great hazard from volcanic tsunami worldwide, which in Tonga’s case overprints an already extreme level of tectonic tsunami hazard. Education and outreach efforts should continue to emphasize the ‘natural warning signs’ of strong ground shaking and unusual wave and current action, and the importance of self-evacuation from coastal areas of low-lying islands. The stories of survival from this event can be used as global best practice for personal survival strategies from future tsunami.