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Recent studies have been reporting more extreme, compounding impacts from multi-hazards than from single hazard events owing to complex interrelationships of hazard, exposure and vulnerability in a multi-hazard setting. However, our current understanding of multi-hazard impacts is primarily based on case studies of individual events. To complement this, we examine the disaster records of the global emergency events database EM-DAT for the period 2000–2018 for evidence of multi-hazard risk dynamics. We develop an algorithm to identify multi-hazard events which uses the information on associated hazards as well as spatiotemporal relationships between disaster records in EM-DAT. We then perform a statistical analysis to assess potential risk dynamics in reported impacts of selected hazard pair types. We identified that twice as many hazards are part of multi-hazard events when considering a spatial overlap of at least 25 % and a time lag of at most 1 year between disaster records in addition to the information of associated hazards. These multi-hazard events account for 78 % of the total damages, 83 % of the total people affected and 69 % of the total deaths in the reported disasters. The statistical comparison indicates that there are different patterns of how impacts compound depending on the impact metric as well as the hazard type. However, as a general trend, hazard pairs seem to have at least as or more impact than two isolated single hazards. To capture the patterns and to integrate them into risk analysis and decision making, we propose the development of generic archetypes of multi-hazard risk dynamics. Despite the well-known limitations of EM-DAT related to completeness of the records as well as reliability of the impact data, which prevents detailed analyses of the data, we found the database useful for exploring high-level patterns at the global scale. Nonetheless, the uncertainties and limitations encountered highlight that future research should be directed at improving and supporting the multi-hazard and impact information in EM-DAT. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are projected to increase in intensity globally, impacting human lives, infrastructure, and important agricultural activities, such as banana production. Banana production is already impacted by TCs in several parts of the world, leading to price volatility and impacted livelihoods of banana producers. While many potential impacts on banana production have already been quantified on a local scale, it remains unclear how bananas could be impacted by TCs across the globe under future climate conditions. To address this, we first looked at the documented impacts of cyclones on banana production from different places all around the world. Using spatially explicit data on banana producing regions and future TC occurrence and magnitude, we then identify the spatial distribution and extent of areas where TCs could impact banana production. Our results suggest that considerable portions of global banana production are at risk to be impacted by TCs under future climate conditions, and we show this for different return periods (RP). Globally, at the 100-year return period, 24.3 % of all bananas producing areas will be majorly or completely damaged under present climate conditions and 26.5 % under future climate conditions. When looking at production, we see that 30.1 % of global production is projected to be majorly or completely damaged at the 100-year RP. The regions predominantly affected by future TCs are Asia and the Caribbean, experiencing substantial disruption in banana production. Our results therefore indicate, that considerable efforts in climate change mitigation and adaptation need to be made in order to ensure the stability of global banana supply chains. [ABSTRACT FROM AUTHOR]

Rapid impact assessments immediately after disasters are crucial to enable rapid and effective mobilization of resources for response and recovery efforts. These assessments are often performed by analysing the three components of risk: hazard, exposure and vulnerability. Vulnerability curves are often constructed using historic insurance data or expert judgments, reducing their applicability for the characteristics of the specific hazard and building stock. Therefore, this paper outlines an approach to the creation of event-specific vulnerability curves, using Bayesian statistics (i.e., the zero-one inflated beta distribution) to update a pre-existing vulnerability curve (i.e., the prior) with observed impact data derived from social media. The approach is applied in a case study of Hurricane Dorian, which hit the Bahamas in September 2019. We analysed footage shot predominantly from unmanned aerial vehicles (UAVs) and other airborne vehicles posted on YouTube in the first 10 days after the disaster. Due to its Bayesian nature, the approach can be used regardless of the amount of data available as it balances the contribution of the prior and the observations. [ABSTRACT FROM AUTHOR]

In this study, we developed an enhanced approach for large-scale flood damage and risk assessments that uses characteristics of buildings and the built environment as object-based information to represent exposure and vulnerability to flooding. Most current large-scale assessments use an aggregated land-use category to represent the exposure, treating all exposed elements the same. For large areas where previously only coarse information existed such as in Africa, more detailed exposure data is becoming available. For our approach, a direct relation between the construction type and building material of the exposed elements is used to develop vulnerability curves. We further present a method to differentiate flood risk in urban and rural areas based on characteristics of the built environment. We applied the model to Ethiopia, and found that rural flood risk accounts for about 22 % of simulated damages; rural damages are generally neglected in the typical land-use-based damage models particularly at this scale. Our approach is particularly interesting for studies in areas where there is a large variation in construction types in the building stock, such as developing countries. It also enables comparison across different natural hazard types that also use material-based vulnerability, paving the way to the enhancement of multi-risk assessments. [ABSTRACT FROM AUTHOR]

A dormant volcano suddenly awakens and bring chaos to a fictitious quiet town. Because of the delayed evacuation and poor emergency planning, inhabitants are left to themselves to survive through the unexpected series of consecutive events the volcanic eruption will generate: an earthquake, an avalanche (both prior to the eruption), the volcanic eruption and floods (lahars). While this sensational plot was created for the blockbuster movie Dante's Peak (1997), recent examples show that these "black-swan" type of events do occur and as a result have the potential to cause devastating consequences. Last December, more than 400 people were killed in a volcano-triggered tsunami. While extremely rare, this tsunami event was actually the second one the country faced, after the earthquake-triggered tsunami causing around 2 200 casualties. The consequences of the Pinatubo eruption in 1991, one of the most important of the 20th century, were aggravated by the coincidence with typhoon Yunua, creating deadly lahars and displacing several communities. Yet, spatial and temporal interactions from consecutive events are often neglected in regional or large-scale multi-hazard studies. Notwithstanding the large modeling complexity of such an endeavor, one might argue that the probability of occurrence of such series of events is extremely low and thus superfluous. In this study, we build a statistical model and incrementally increase its complexity to take into account event duration and correlation within or between hazard types to investigate the likelihood of consecutive events. Starting with the assumption of a Poisson process, we later deviate from such an assumption to include changes in the inter-event time distribution and memory effects. For example, earthquakes can be followed by aftershock sequences, creating a series of dependent events. Similarly, we can expect a correlation between major flood events and typhoon occurrence, or tsunami generation and earthquake occurrence. We collect and analyze occurrences of four hazard types in the Philippines from publicly available global databases for typhoons, earthquakes and volcanic eruptions and floods between 1980 to 2015 to calculate the parameters needed to force the model. We can therefore quantify the number of consecutive events that can be expected for an area exposed to multiple hazard types with such statistical characteristics. We also identify key challenges in the statistical modelling of consecutive events. Finally, our findings highlight the need to consider consecutive events in order to improve the resilience of emergency response plans or infrastructure in areas exposed to multiple hazards. [ABSTRACT FROM AUTHOR]