Building on almost ten years of expertise and operational application of the Combined Drought Indicator (CDI) for the monitoring of agricultural droughts in Europe within the European Commission's European Drought Observatory (EDO), this paper proposes a revised version of the index. This paper shows that the proposed revised CDI reliably reproduces the evolution of major droughts, outperforming the current version of the indicator, especially for long-lasting events.

We proposed a new dynamic coupling model for flood simulation and prediction. The model can dynamically alter the coupling boundary position based on the characteristic theory to determine the non-inundation and inundation regions, taking into account both mass and momentum exchange. Then the model was validated by several classic numerical test cases as well as experiment data and implemented in a real study case. Results show its capability for flood simulation and risk assessments.

The evolutionary trend of deforming landslides and feasible treatments for huge reservoir landslides needs further study. A geomechanical model is presented to elucidate the deformation mechanism of reservoir landslides. The deformation process of Shuping landslide is well interpreted by the geomechanical model. A successful engineering treatment is applied in treating the Shuping landslide, providing references for treating other huge landslides in the Three Gorges Reservoir area.

We sampled 647 trees from 12 avalanche paths to investigate large snow avalanches over the past 400 years in the northern Rocky Mountains, USA. Sizable avalanches occur approximately every 3 years across the region. Our results emphasize the importance of sample size, scale, and spatial extent when reconstructing avalanche occurrence across a region. This work can be used for infrastructure planning and avalanche forecasting operations.

Global flood models are difficult to validate. They generally output theoretical flood events of a given probability rather than an observed event that they can be tested against. Here, we adapt a US-wide flood model to enable the rapid simulation of historical flood events in order to more robustly understand model biases. For 35 flood events, we highlight the challenges of model validation amidst observational data errors yet evidence the increasing skill of large-scale models.

In current marine seismic engineering research, the influence of overlying-seawater weight and soft soil on seabed ground motion is often ignored, which leads to unsafe seismic design. In this paper, four representative calculation models are constructed, and the finite-element method is used for numerical simulation analysis in order to evaluate the amplification effect of overlying seawater and the seafloor soft soil layer on ground motion.

Compound extremes correspond to events with multiple concurrent or consecutive drivers, leading to substantial impacts such as infrastructure failure. In many risk assessment and design applications, however, multihazard scenario events are ignored. In this paper, we present a general framework to investigate current and future climate compound-event flood impact on coastal critical infrastructures such as power grid substations.

Predicting the number of power outages caused by extratropical storms is a key challenge for power grid operators. We introduce a novel method to predict the storm severity for the power grid employing ERA5 reanalysis data combined with a forest inventory. The storms are first identified from the data and then classified using several machine-learning methods. While there is plenty of room to improve, the results are already usable, with support vector classifier providing the best performance.

We used interferometric radar coherence – a data quality indicator typically used to assess the reliability of radar interferometry data – to document the destabilization of the Mud Creek landslide in California, 5 months prior to its catastrophic failure. We calculated a time series of coherence on the slide relative to the surrounding hillslope and suggest that this easy-to-compute metric might be useful for assessing the stability of a hillslope.

Effective flood management requires information about the potential consequences of flooding. We show how openly accessible data from OpenStreetMap can support the estimation of flood damage for residential buildings. Working with methods of machine learning, the building geometry is used to predict flood damage in combination with information about inundation depth. Our approach makes it easier to transfer models to regions where no detailed data of flood impacts have been observed yet.

Due to the rarity of high-impact tropical cyclones (TCs), it is difficult to achieve a robust TC hazard assessment based on historical observations only. Here we present an approach to construct a TC event set that contains more than 10 000 years of TC events by using a computationally simple and efficient method. This event set has similar characteristics as the historical observations but includes a better representation of intense TCs. Thus, a robust TC hazard assessment can be achieved.

Severe convective storms (SCSs) and the related hail events constitute major atmospheric hazards in parts of Europe. In our study, we identified the regions of France, Germany, Belgium and Luxembourg that were most affected by hail over a 10 year period (2005 to 2014). A cell-tracking algorithm was computed on remote-sensing data to enable the reconstruction of several thousand SCS tracks. The location of hail hotspots will help us understand hail formation and improve hail forecasting.

Chronic flooding occurring at high tides under calm weather conditions is an early impact of sea-level rise. This hazard is a reason for concern on tropical islands, where coastal infrastructure is commonly located in low-lying areas. We focus here on the Guadeloupe archipelago, in the French Antilles, where chronic flood events have been reported for about 10 years. We show that the number of such events will increase drastically over the 21st century under continued growth of CO₂ emissions.

A typhoon always comes with heavy rainfall which leads to great loss. The aim of this study is to explore the reasonable use of Doppler radar data assimilation to correct the initial and lateral boundary conditions of the numerical weather prediction (NWP) systems for typhoon rainstorm forecasts at catchment scale. The results show that assimilating radial velocity at a time interval of 1 h can significantly improve the rainfall simulations and outperform the other assimilation modes.

Flood protection level (FPL) is vital for risk analysis and management but scarce in realty particularly for developing countries. This paper develops a policy-based FPL dataset for China and validates it using local FPL designs. The FPLs are much higher than that in a global database, suggesting Chinese flood risk could be lower with the policy-required FPLs. Moreover, the FPLs are lower for western China and vulnerable people, implying a spatial and social divergence of the FPLs.

Our research links upper atmospheric circulation patterns to a destructive and difficult-to-predict type of snow avalanche in the western United States. At each of our study sites, we find unique circulation patterns that tend to occur at the beginning of the winter season during years with major avalanche activity. We also find specific patterns that occur frequently in the days leading to major avalanche events. This work will enable practitioners to better anticipate these challenging events.

The validation of numerical models is a first unavoidable step before their use as predictive tools. This requirement is even more necessary when the developed models are going to be used for risk assessment in natural events where human lives are involved. The present work is the first step in this task for the Multilayer-HySEA model, a novel dispersive multilayer model of the HySEA suite developed at the University of Malaga, following the standards proposed by the NTHMP of the US.

Numerical models need to be validated prior to their use as predictive tools. This requirement becomes even more necessary when these models are going to be used for risk assessment in natural hazards where human lives are involved. The present work aims to benchmark the novel Multilayer-HySEA model for landslide-generated tsunamis produced by granular slides, in order to provide to the tsunami community with a robust, efficient, and reliable tool for landslide tsunami hazard assessment.

In southeast Texas, flood impacts are exacerbated by increases in impervious surfaces, human inaction, outdated FEMA-defined floodplains and modeling assumptions, and changing environmental conditions. The current flood maps are inadequate indicators of flood risk, especially in urban areas. This study proposes a novel method to model flood hazard and impact in urban areas. Specifically, we used novel flood risk modeling techniques to produce annualized flood hazard maps.

We propose a method that integrates fuzzy set theory and a weighted Bayesian model to evaluate the hazard probability of land subsidence based on Interferometric Synthetic Aperture Radar technology. The proposed model can represent the uncertainty and ambiguity in the evaluation process, and results can be compared to traditional qualitative methods.

Hurricanes are extreme storms that induce substantial biophysical changes on oceans. We investigated the effects induced by consecutive Hurricanes Dorian and Humberto over the western Sargasso Sea in 2019 using satellite remote sensing and modelled data. These hurricanes superimposed effects on the upper-ocean response because of the strong induced mixing and upwelling. The sea surface cooling and phytoplankton bloom induced by these hurricanes were higher compared to climatological records.