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Tropical cyclones (TCs) are identified and tracked in six recent reanalysis datasets and compared with those from the IBTrACS best-track archive. Results indicate that nearly every cyclone present in IBTrACS over the period 1979–2012 can be found in all six reanalyses using a tracking and matching approach. However, TC intensities are significantly underrepresented in the reanalyses compared to the observations. Applying a typical objective TC identification scheme, it is found that the largest uncertainties in TC identification occur for the weaker storms; this is exacerbated by uncertainties in the observations for weak storms and lack of consistency in operational procedures. For example, certain types of storms, such as tropical depressions, subtropical cyclones, and monsoon depressions, are not included in the best-track data for all reporting agencies. There are definite improvements in how well TCs are represented in more recent, higher-resolution reanalyses; in particular MERRA-2 is comparable with the NCEP-CFSR and JRA-55 reanalyses, which perform significantly better than the older MERRA reanalysis.

Realistic environmental problems drive the growth of pro-environment behavior research, among which the most important progress is about the theoretical innovation and development of pro-environmental behavior. Thus, the main purpose of this paper was to review the literature and help researchers to understand the theoretical progress of pro-environmental behavior. This study systematically analyzed 1806 papers published in SCI-EXPANDED and SSCI databases. It presented the research overview of pro-environmental behavior in terms of status of literature publication, research hotspots and topics. On this basis, this paper further focused on key theoretical papers and summarized three paths of theoretical progress for pro-environmental behavior: theoretical development, theoretical exploration and theoretical integration. Along the theoretical development path, studies mainly apply theories of psychology, sociology and economics to analyze and explain the formation and consequences of pro-environmental behavior. In terms of theoretical exploration, existing studies propose and develop value-belief-norm theory, behavioral theories related to contexts and pro-environmental behavior decision models. Theoretical integration is the direction of future research, such as the combination of rationality and sensibility, and the combination of external and internal causes. Therefore, this paper summarized the theoretical progress of pro-environmental behavior and proposed future research directions, which contribute to its theoretical development.

Germany, Belgium and the Netherlands were hit by extreme precipitation and flooding in July 2021. This brief communication provides an overview of the impacts to large-scale critical infrastructure systems and how recovery has progressed. The results show that Germany and Belgium were particularly affected, with many infrastructure assets severely damaged or completely destroyed. Impacts range from completely destroyed bridges and sewage systems, to severely damaged schools and hospitals. We find that (large-scale) risk assessments, often focused on larger (river) flood events, do not find these local, but severe, impacts due to critical infrastructure failures. This may be the result of limited availability of validation material. As such, this brief communication not only will help to better understand how critical infrastructure can be affected by flooding, but also can be used as validation material for future flood risk assessments.

The ability to map floods from satellites has been known for over 40 years. Early images of floods were rather difficult to obtain, and flood mapping from satellites was thus rather opportunistic and limited to only a few case studies. However, over the last decade, with a proliferation of open-access EO data, there has been much progress in the development of Earth Observation products and services tailored to various end-user needs, as well as its integration with flood modeling and prediction efforts. This article provides an overview of the use of satellite remote sensing of floods and outlines recent advances in its application for flood mapping, monitoring and its integration with flood models. Strengths and limitations are discussed throughput, and the article concludes by looking at new developments.

Catastrophic loss data are known to be heavy-tailed. Practitioners then need models that are able to capture both tail and modal parts of claim data. To this purpose, a new parametric family of loss distributions is proposed as a gamma mixture of the generalized log-Moyal distribution from Bhati and Ravi (2018), termed the generalized log-Moyal gamma (GLMGA) distribution. While the GLMGA distribution is a special case of the GB2 distribution, we show that this simpler model is effective in regression modeling of large and modal loss data. Regression modeling and applications to risk measurement are illustrated using a detailed analysis of a Chinese earthquake loss data set, comparing with the results of competing models from the literature. To this end, we discuss the probabilistic characteristics of the GLMGA and statistical estimation of the parameters through maximum likelihood. Further illustrations of the applicability of the new class of distributions are provided with the fire claim data set reported in Cummins et al. (1990) and a Norwegian fire losses data set discussed recently in Bhati and Ravi (2018).

In this article, we outline a methodological framework for studying the inter-organizational aspects of paradoxes and specify this in relation to grand challenges. Grand challenges are large-scale, complex, enduring problems that affect large populations, have a strong social component and appear intractable. Our methodological insights draw from our study of the insurance protection gap, a grand challenge that arises when economic losses from large-scale disaster significantly exceed the insured loss, leading to economic and social hardship for the affected communities. We provide insights into collecting data to uncover the paradoxical elements inherent in grand challenges and then propose three analytical techniques for studying inter-organizational paradoxes: zooming in and out, tracking problematization and tracking boundaries and boundary organizations. These techniques can be used to identify and follow how contradictions and interdependences emerge and dynamically persist within inter-organizational interactions and how these shape and are shaped by the unfolding dynamics of the grand challenge. Our techniques and associated research design help advance paradox theorizing by moving it to the inter-organizational and systemic level. This article also illustrates paradox as a powerful lens through which to further our understanding of grand challenges.

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. Key Points The number of countries suffering from consecutive disasters is increasing, and their impacts can be distinctly different from single disasters An overview is provided of the state‐of‐the‐art in the understanding of consecutive disasters as discussed in the literature As current scientific models and policy settings do not allow to properly assess the risk of consecutive disasters and their impacts, we identify a roadmap for the future

This paper presents an automatic algorithm for mapping floods. Its main characteristic is that it can detect not only inundated bare soils, but also floodwater in urban areas. The synthetic aperture radar (SAR) observations of the flood that hit the city of Houston (Texas) following the landfall of Hurricane Harvey in 2017 are used to apply and validate the algorithm. The latter consists of a two-step approach that first uses the SAR data to identify buildings and then takes advantage of the Interferometric SAR coherence feature to detect the presence of floodwater in urbanized areas. The preliminary detection of buildings is a pre-requisite for focusing the analysis on the most risk-prone areas. Data provided by the Sentinel-1 mission acquired in both Strip Map and Interferometric Wide Swath modes were used, with a geometric resolution of 5 m and 20 m, respectively. Furthermore, the coherence-based algorithm takes full advantage of the Sentinel-1 mission’s six-day repeat cycle, thereby providing an unprecedented possibility to develop an automatic, high-frequency algorithm for detecting floodwater in urban areas. The results for the Houston case study have been qualitatively evaluated through very-high-resolution optical images acquired almost simultaneously with SAR, crowdsourcing points derived by photointerpretation from Digital Globe and Federal Emergency Management Agency’s (FEMA) inundation model over the area. For the first time the comparison with independent data shows that the proposed approach can map flooded urban areas with high accuracy using SAR data from the Sentinel-1 satellite mission.

Hodographs are valuable sources of pattern recognition in severe convective storm forecasting. Certain shapes are known to discriminate between single cell, multicell, and supercell storm organization. Various derived quantities such as storm-relative helicity (SRH) have been found to predict tornado potential and intensity. Over the years, collective research has established a conceptual model for tornadic hodographs (large and “looping,” with high SRH). However, considerably less attention has been given to constructing a similar conceptual model for hodographs of severe hail. This study explores how hodograph shape may differentiate between the environments of severe hail and tornadoes. While supercells are routinely assumed to carry the potential to produce all hazards, this is not always the case, and we explore why. The Storm Prediction Center (SPC) storm mode dataset is used to assess the environments of 8958 tornadoes and 7256 severe hail reports, produced by right- and left-moving supercells. Composite hodographs and indices to quantify wind shear are assessed for each hazard, and clear differences are found between the kinematic environments of hail-producing and tornadic supercells. The sensitivity of the hodograph to common thermodynamic variables was also examined, with buoyancy and moisture found to influence the shape associated with the hazards. The results suggest that differentiating between tornadic and hail-producing storms may be possible using properties of the hodograph alone. While anticipating hail size does not appear possible using only the hodograph, anticipating tornado intensity appears readily so. When coupled with buoyancy profiles, the hodograph may assist in differentiating between both hail size and tornado intensity.