Many areas of the world are prone to several natural hazards, and effective risk reduction is only possible if all relevant threats are considered and analyzed. However, in contrast to single-hazard analyses, the examination of multiple hazards poses a range of additional challenges due to the differing characteristics of processes. This refers to the assessment of the hazard level, as well as to the vulnerability toward distinct processes, and to the arising risk level. As comparability of the single-hazard results is strongly needed, an equivalent approach has to be chosen that allows to estimate the overall hazard and consequent risk level as well as to rank threats. In addition, the visualization of a range of natural hazards or risks is a challenging task since the high quantity of information has to be depicted in a way that allows for easy and clear interpretation. The aim of this contribution is to give an outline of the challenges each step of a multi-hazard (risk) analysis poses and to present current studies and approaches that face these difficulties.

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Quantifying flood hazard is an essential component of resilience planning, emergency response, and mitigation, including insurance. Traditionally undertaken at catchment and national scales, recently, efforts have intensified to estimate flood risk globally to better allow consistent and equitable decision making. Global flood hazard models are now a practical reality, thanks to improvements in numerical algorithms, global datasets, computing power, and coupled modelling frameworks. Outputs of these models are vital for consistent quantification of global flood risk and in projecting the impacts of climate change. However, the urgency of these tasks means that outputs are being used as soon as they are made available and before such methods have been adequately tested. To address this, we compare multi-probability flood hazard maps for Africa from six global models and show wide variation in their flood hazard, economic loss and exposed population estimates, which has serious implications for model credibility. While there is around 30%-40% agreement in flood extent, our results show that even at continental scales, there are significant differences in hazard magnitude and spatial pattern between models, notably in deltas, arid/semi-arid zones and wetlands. This study is an important step towards a better understanding of modelling global flood hazard, which is urgently required for both current risk and climate change projections.

Mountain hazards such as landslides, floods and avalanches pose a serious threat to human lives and development and can cause considerable damage to lifelines, critical infrastructure, agricultural lands, housing, public and private infrastructure and assets. The assessment of the vulnerability of the built environment to these hazards is a topic that is growing in importance due to climate change impacts. A proper understanding of vulnerability will lead to more effective risk assessment, emergency management and to the development of mitigation and preparedness activities all of which are designed to reduce the loss of life and economic costs. In this study, we are reviewing existing methods for vulnerability assessment related to mountain hazards. By analysing the existing approaches, we identify difficulties in their implementation (data availability, time consumption) and differences between them regarding their scale, the consideration of the hazardous phenomenon and its properties, the consideration of important vulnerability indicators and the use of technology such as GIS and remote sensing. Finally, based on these observations, we identify the future needs in the field of vulnerability assessment that include the user-friendliness of the method, the selection of all the relevant indicators, the transferability of the method, the inclusion of information concerning the hazard itself, the use of technology (GIS) and the provision of products such as vulnerability maps and the consideration of the temporal pattern of vulnerability.