In many urban areas, people face high exposure to hazards. Resettling them to safer locations becomes a major strategy, not least because of climate change. This paper dives into the success factors of government-led resettlement in Manila and finds surprising results which challenge the usual narrative and fuel the conversation on resettlement as an adaptation strategy. Contrary to expectations, the location – whether urban or rural – of the new home is less important than safety from floods.

Desert dust poses a hazard to aircraft via degradation of engine components. This has financial implications for the aviation industry and results in increased fuel burn with climate impacts. Here we quantify dust ingestion by aircraft engines at airports worldwide. We find Dubai and Delhi in summer are among the dustiest airports, where substantial engine degradation would occur after 1000 flights. Dust ingestion can be reduced by changing take-off times and the altitude of holding patterns.

Reliance on infrastructure creates vulnerabilities to disruptions caused by natural hazards. To assess the impacts of natural hazards on the performance of infrastructure, we present a framework for quantifying resilience and develop a model of recovery based upon an application of project scheduling under resource constraints. The resilience framework and recovery model were applied in a case study to assess the resilience of building infrastructure to flooding hazards in Accra, Ghana.

The purpose of this study is to estimate the spatial distribution of the tsunami hazard in the South China Sea from the Manila subduction zone. The plate motion data are used to invert the degree of locking on the fault plane. The degree of locking is used to estimate the maximum possible magnitude of earthquakes and describe the slip distribution. A spatial distribution map of the 1000-year return period tsunami wave height in the South China Sea was obtained by tsunami hazard assessment.

We present an improved Multigrid Dynamical Bidirectional Coupled hydrologic–hydrodynamic Model (IM-DBCM) with two major improvements: (1) automated non-uniform mesh generation based on the D-infinity algorithm was implemented to identify flood-prone areas where high-resolution inundation conditions are needed, and (2) ghost cells and bilinear interpolation were implemented to improve numerical accuracy in interpolating variables between the coarse and fine grids. The improved model was reliable.

To improve the challenging representation of hazardous hailstorms, a proxy for hail frequency based on satellite detections, convective parameters from high-resolution reanalysis, and crowd-sourced reports is tested and presented. Hail likelihood peaks in mid-summer at 15:00 UTC over northern Italy and shows improved agreement with observations compared to previous estimates. By separating ambient signatures based on hail severity, enhanced appropriateness for large-hail occurrence is found.

Fire can dramatically increase the risk of debris flows to downstream communities with little warning, but hazard assessments have not traditionally included estimates of inundation. We unify models developed by the scientific community to create probabilistic estimates of inundation area in response to rainfall at forecast lead times (≥ 24 h) needed for decision-making. This work takes an initial step toward a near-real-time postfire debris-flow inundation hazard assessment product.

About 820 km² of agricultural land was inundated in central Greece due to Storm Daniel. A detailed analysis revealed that the crop most affected by the flooding was cotton; the inundated area of more than 282 km² comprised ~ 30 % of the total area planted with cotton in central Greece. In terms of livestock, we estimate that more than 14 000 ornithoids and 21 500 sheep and goats were affected. Consequences for agriculture and animal husbandry in Greece are expected to be severe.

The models used to estimate the probability of exceeding a level of earthquake damage are essential to the reduction of disasters. These models consist of components that may be tested individually; however testing these types of models as a whole is challenging. Here, we use observations of damage caused by the 2019 Le Teil earthquake and estimations from other models to test components of seismic risk models.

Coastal areas are at risk of flooding from rising sea levels and extreme weather events. This study applies a new approach to estimating the likelihood of coastal flooding around the world. The method uses data from observations and computer models to create a detailed map of where these coastal floods might occur. The approach can predict flooding in areas for which there are few or no data available. The results can be used to help prepare for and prevent this type of flooding.

Micro-businesses, often overlooked in adaptation research, show surprising willingness to contribute to collective adaptation despite limited finances and local support. Based on a study in Ho Chi Minh City in Vietnam, approximately 70 % are ready for awareness campaigns, and 39 % would provide financial support if costs were shared. These findings underscore the need for increased involvement of micro-businesses in local adaptation plans to enhance collective adaptive capacity.

This study presents the results of a climatological investigation of extreme precipitation events (EPEs) in Ukraine for the period 1979–2019. During all seasons EPEs are associated with pronounced upper-level potential vorticity (PV) anomalies. In addition, we find distinct seasonal and regional differences in moisture sources. Several extreme precipitation cases demonstrate the importance of these processes, complemented by a detailed synoptic analysis.

We coupled earth system, hydrology, and hydrodynamic models to generate plausible and physically consistent ensembles of hurricane events and their associated water levels from the open coast to tidal rivers of Delaware Bay and River. Our results show that the hurricane landfall locations and the estuarine wind can significantly amplify the extreme surge in a shallow and converging system, especially when the wind direction aligns with the surge propagation direction.

Monitoring the long-term trends in sea surface warming is crucial for informed decision-making and adaptation. This study offers a comprehensive examination of prevalent trend extraction methods. We identify the least-squares regression as suitable for general tasks yet highlight the need to address seasonal signal-induced bias, i.e., the phase–distance imbalance. Our developed method, evaluated using simulated and real data, is unbiased and better than the conventional SST anomaly method. 

We analyse the meteo-hydrological features of the 27 and 28 August 2023 event that occurred in Genoa. Rainfall observations were made using rain gauge networks based on either official networks or citizen science networks. The merged analysis stresses the spatial variability in the precipitation, which cannot be captured by the current spatial density of authoritative stations. Results show that at minimal distances the variations in cumulated rainfall over a sub-hourly duration are significant.

EUCLID's lightning data unveil distinctive ground strike point (GSP) patterns in Europe. Over seas, GSPs per flash surpass inland, reaching a minimum in the Alps. Mountainous areas like the Alps and Pyrenees have the closest GSP separation, highlighting terrain elevation's impact. The daily peak current correlates with average GSPs per flash. These findings could significantly influence lightning protection measures, urging a focus on GSP density rather than flash density for risk assessment.

A glacial lake outburst flood (GLOF) is a natural hazard in which water from a glacier-fed lake is swiftly discharged, causing serious harm to life, infrastructure, and communities. We used numerical models to predict the potential consequences of a GLOF originating from the Thorthomi glacial lake in Bhutan. We found that if a GLOF occurs, the lake could release massive flood water within 4 h, posing a considerable risk. Study findings help to mitigate the impacts of future GLOFs.

The study presents an open-source model to determine the occurrence of hail damage to field crops and grapevines after hailstorms in Switzerland based on radar, agricultural land use data, and insurance damage reports. The model performs best at 8 km resolution for field crops and 1 km for grapevine and in the main production areas. Highlighting performance trade-offs and the relevance of user needs, the study is a first step towards the assessment of risk and damage for crops in Switzerland.

Inland flooding is threatening coastal lowlands. If rainfall and storm surges coincide, the risk of inland flooding increases. We examine how such compound events are influenced by climate change. Data analysis and model-based scenario analysis show that climate change induces an increasing frequency and intensity of compounding precipitation and storm tide events along the North Sea coast. Overload of inland drainage systems will also increase if no timely adaptation measures are taken.

Modelling floods at the street level for large countries like Canada and the United States is difficult and very costly. However, many applications do not necessarily require that level of detail. As a result, we present a flood modelling framework built with artificial intelligence for socioeconomic studies like trend and scenarios analyses. We find for example that an increase of 10 % in average precipitation yields an increase in displaced population of 18 % in Canada and 14 % in the US.

As part of the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) programme, funded by the European Union in collaboration with the World Bank and GFDRR, a regionally consistent probabilistic multi-hazard and multi-asset risk assessment has been developed. This paper describes the preparation of the input datasets (earthquake catalogue and active-fault database) required for the implementation of the probabilistic seismic hazard model.

The Revised Infinite Slope Model (RISM) is proposed using the equal differential unit method and correcting the deficiency of the safety factor increasing with the slope increasing when the slope is larger than 40°, as calculated using the Taylor slope infinite model. The intensity–duration (I–D) prediction curve of the rainfall-induced shallow loess landslides with different slopes was constructed and can be used in forecasting regional shallow loess landslides.