Climate change, urbanization, and aging infrastructure contribute to flooding on roadways. This study evaluates the potential for flood reports collected from Waze – a community-based navigation app – to predict these events. Waze reports correlate primarily with low-lying depressions on roads. Therefore, we developed two data-driven models to determine whether roadways will flood. Analysis showed that in the city of Dallas, drainage area and imperviousness are the most significant contributors.

Compound events (CEs) result from a combination of several climate phenomena. In this study, we propose a new methodology to assess the time of emergence of CE probabilities and to quantify the contribution of marginal and dependence properties of climate phenomena to the overall CE probability changes. By applying our methodology to two case studies, we show the importance of considering changes in both marginal and dependence properties for future risk assessments related to CEs.

This study maps agriculture's vulnerability to drought in the European pre-Alpine regions of Thurgau (CH) and Podravska (SI). We combine region-specific knowledge with quantitative data mapping; experts of the study regions, far apart, identified a few common but more region-specific factors that we integrated in two vulnerability scenarios. We highlight the benefits of the participatory approach in improving the quantitative results and closing the gap between science and practitioners.

In this study, we developed a machine learning model to identify dominant controls of wildfire in Fennoscandia and produce monthly fire danger probability maps. The dominant control was shallow-soil water anomaly, followed by air temperature and deep soil water. The model proved skilful with a similar performance as the existing Canadian Forest Fire Weather Index (FWI). We highlight the benefit of using data-driven models jointly with other fire models to improve fire monitoring and prediction.

One of the most effective ways to describe the seismicity of a region is to map the b-value parameter of the Gutenberg-Richter law. This research proposes the study of the spatial cell-event distance distribution to define the smoothing kernel that controls the influence of the data. The results of this methodology depict tectonic stress changes before and after intense earthquakes happen, so it could enable operational earthquake forecasting (OEF) and tectonic source profiling.

In this work, we examine a set of observed extreme, non-earthquake-related and non-landslide-related wave runup events. Runup events with similar characteristics have previously been attributed to trapped waves, atmospheric disturbances, and abrupt breaking of long waves. However, we find that none of these mechanisms were likely at work in the observations we examined. We show that instead, these runup events were more likely due to energetic growth of bound infragravity waves.

This study proposes a quantitative method for the determination of warning water levels. The proposed method is a multidimensional scale, centered on the consideration of various factors that characterize various coastlines. The implications of our study are not only scientific, as we provide a method for water level determination that is rooted in the scientific method (and reproducible across various contexts beyond China), but they are also deeply practical.

This study aims to provide a practical and relatively fast solution for early-stage planning of marine infrastructure that must cross a faulted zone. Instead of investing huge efforts in finding whether each specific fault meets a pre-defined criterion of activeness, we map the subsurface and determine the levels of fault hazard based on the amount of displacement and the fault's plane size. This allows for choosing the least problematic infrastructure routes at an early planning stage.

On 14 July 2021, heavy rainfall fell over central Europe, causing considerable damage and human fatalities. We analyzed how accurate our estimates of rainfall and peak flow were for these flooding events in western Germany. We found that the rainfall estimates from radar measurements were improved by including polarimetric variables and their vertical gradients. Peak flow estimates were highly uncertain due to uncertainties in hydrological model parameters and rainfall measurements.

Researchers studying economic impacts of natural disasters increasingly use night light as a proxy for local economic activity, when socioeconomic data are unavailable. But often it is unclear what changes in light intensity represent in the context of disasters. We study this in detail for Hurricane Katrina and find a strong correlation with building damage and changes in population and employment. We conclude that night light data are useful to study local impacts of natural disasters.

In summer 2009 and 2014, rainfall events occurred in the Styrian Basin (Austria), triggering thousands of landslides. Landslide storylines help to show potential future changes under changing environmental conditions. The often neglected uncertainty quantification was the aim of this study. We found uncertainty arising from the landslide model to be of the same order as climate scenario uncertainty. Understanding the dimensions of uncertainty is crucial for allowing informed decision-making.

Multiple seismic anomalies were reported to be related to the 2015 Nepal earthquake. By sufficiently investigating both the space–time features and the physical models of the seismic anomalies, the coupling mechanisms of these anomalies in 3D space were revealed and an integrated framework to strictly root the sources of various anomalies was proposed. This study provides a practical solution for scrutinizing reliable seismic anomalies from diversified earthquake observations.

This paper proves the need to conduct an in-depth review of the existing loss modelling framework and makes it clear that only a transdisciplinary effort will be up to the challenge of building global loss models. These two factors are essential to capture the interactions and increasing complexity of the three risk drivers (exposure, hazard, and vulnerability), thus enabling insurers to anticipate and be equipped to face the far-ranging impacts of climate change and other natural events.

Emergency managers use event-based flood inundation maps (FIMs) to plan and coordinate flood emergency response. We perform a case study test of three different FIM frameworks to see if FIM differences lead to substantial differences in the location and magnitude of flood exposure and consequences. We find that the FIMs are very different spatially and that the spatial differences do produce differences in the location and magnitude of exposure and consequences.

We propose an approach exploiting PCA to derive hydrometeorological landslide-triggering thresholds using multi-layered soil moisture data from ERA5-Land reanalysis. Comparison of thresholds based on single- and multi-layered soil moisture information provides a means to identify the significance of multi-layered data for landslide triggering in a region. In Sicily, the proposed approach yields thresholds with a higher performance than traditional precipitation-based ones (TSS = 0.71 vs. 0.50).

For a slab avalanche to release, the snowpack must facilitate crack propagation over large distances. Field measurements on crack propagation at this scale are very scarce. We performed a series of experiments, up to 10 m long, over a period of 10 weeks. Beside the temporal evolution of the mechanical properties of the snowpack, we found that crack speeds were highest for tests resulting in full propagation. Based on these findings, an index for self-sustained crack propagation is proposed.

Human activities across China show a similar trend in response to rains. However, urban resilience varies significantly by region. The northwestern arid region and the central underdeveloped areas are very fragile, and even low-intensity rains can trigger significant human activity anomalies. By contrast, even high-intensity rains might not affect residents in the southeast.

Hazards from glaciers are becoming more likely as the climate warms, which poses a threat to communities living beneath them. We have developed a new camera system which can capture regular, high-quality 3D models to monitor small changes in glaciers which could be indicative of a future hazard. This system is far cheaper than more typical camera sensors yet produces very similar quality data. We suggest that deploying these cameras near glaciers could assist in warning communities of hazards.

On highly fractured rockwalls such as those found in northern Gaspésie, most rockfalls are triggered by weather conditions. This study highlights that in winter, rockfall frequency is 12 times higher during a superficial thaw than during a cold period in which temperature remains below 0 °C. In summer, rockfall frequency is 22 times higher during a heavy rainfall event than during a mainly dry period. This knowledge could be used to implement a risk management strategy.

This study aims to unravel large-fire behavior in northwest Europe, a temperate region with a projected increase in wildfire risk. We propose a new method to identify wildfire rate of spread from satellites because it is important to know periods of elevated fire risk for suppression methods and land management. Results indicate that there is a peak in the area burned and rate of spread in the months of March and April, and there are significant differences for forest-type land covers.

In this paper, we test two approaches for removing buildings and other above-ground objects from a state-of-the-art satellite photogrammetry topography product, ArcticDEM. Our best technique gives a 70 % reduction in vertical error, with an average difference of 1.02 m from a benchmark lidar for the city of Helsinki, Finland. When used in a simulation of rainfall-driven flooding, the bare-earth version of ArcticDEM yields a significant improvement in predicted inundation extent and water depth.