In recent decades, a striking number of countries have suffered from consecutive disasters: events whose impacts overlap both spatially and temporally, while recovery is still under way. The risk of consecutive disasters will increase due to growing exposure, the interconnectedness of human society, and the increased frequency and intensity of nontectonic hazard. This paper provides an overview of the different types of consecutive disasters, their causes, and impacts. The impacts can be distinctly different from disasters occurring in isolation (both spatially and temporally) from other disasters, noting that full isolation never occurs. We use existing empirical disaster databases to show the global probabilistic occurrence for selected hazard types. Current state‐of‐the art risk assessment models and their outputs do not allow for a thorough representation and analysis of consecutive disasters. This is mainly due to the many challenges that are introduced by addressing and combining hazards of different nature, and accounting for their interactions and dynamics. Disaster risk management needs to be more holistic and codesigned between researchers, policy makers, first responders, and companies.

Automated classification of building damage in remote sensing images enables the rapid and spatially extensive assessment of the impact of natural hazards, thus speeding up emergency response efforts. Convolutional neural networks (CNNs) can reach good performance on such a task in experimental settings. How CNNs perform when applied under operational emergency conditions, with unseen data and time constraints, is not well studied. This study focuses on the applicability of a CNN-based model in such scenarios. We performed experiments on 13 disasters that differ in natural hazard type, geographical location, and image parameters. The types of natural hazards were hurricanes, tornadoes, floods, tsunamis, and volcanic eruptions, which struck across North America, Central America, and Asia. We used 175,289 buildings from the xBD dataset, which contains human-annotated multiclass damage labels on high-resolution satellite imagery with red, green, and blue (RGB) bands. First, our experiments showed that the performance in terms of area under the curve does not correlate with the type of natural hazard, geographical region, and satellite parameters such as the off-nadir angle. Second, while performance differed highly between occurrences of disasters, our model still reached a high level of performance without using any labeled data of the test disaster during training. This provides the first evidence that such a model can be effectively applied under operational conditions, where labeled damage data of the disaster cannot be available timely and thus model (re-)training is not an option.

People possess a creative set of strategies based on their local knowledge (LK) that allow them to stay in flood-prone areas. Stakeholders involved with local level flood risk management (FRM) often overlook and underutilise this LK. There is thus an increasing need for its identification, documentation and assessment. Based on qualitative research, this paper critically explores the notion of LK in Malawi. Data was collected through 15 focus group discussions, 36 interviews and field observation, and analysed using thematic analysis. Findings indicate that local communities have a complex knowledge system that cuts across different stages of the FRM cycle and forms a component of community resilience. LK is not homogenous within a community, and is highly dependent on the social and political contexts. Access to LK is not equally available to everyone, conditioned by the access to resources and underlying causes of vulnerability that are outside communities’ influence. There are also limits to LK; it is impacted by exogenous processes (e.g., environmental degradation, climate change) that are changing the nature of flooding at local levels, rendering LK, which is based on historical observations, less relevant. It is dynamic and informally triangulated with scientific knowledge brought about by development partners. This paper offers valuable insights for FRM stakeholders as to how to consider LK in their approaches.

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The 1/f noise in three types of aluminum lines has been investigated in the temperature range 140-510 K. The types are one long single crystal, a chain of short single crystals ͑''bamboo''͒, and a polycrystal. In the lines of the first two types the 1/f noise power is significantly lower than in the polycrystalline specimens. The temperature dependence of the noise power in the polycrystalline lines shows a plateau between 370 and 415 K, corresponding to activation energies 0.9-1.0 eV. Both types of monocrystalline lines have equal noise power with a peak around 340 K, corresponding to an activation energy of about 0.8 eV. In the polycrystalline lines the dominant contribution to 1/f noise appears to be the thermally activated motion of atoms in grain boundaries. The measurements on the monocrystalline lines reveal the existence of at least one further contribution to 1/f noise in metals, presumably associated with the thermally activated diffusion of atoms along dislocations.