Cees J. van Westen

Multi-Hazard Exposure and Vulnerability assessments rely on the systematic collection, integration, and analysis of spatial and temporal data describing assets and their susceptibility to hazard impacts. Exposure represents the location, distribution, and characteristics of elements at risk, such as populations, infrastructure, and ecosystems, within hazard-prone areas. Vulnerability encompasses the physical, social, economic, and environmental conditions that influence how these assets are affected by hazard events. Data for exposure and vulnerability can be derived from diverse sources, including remote sensing, field surveys, open geospatial databases, and modelling outputs. Increasingly, these datasets are processed through cloud-based and AI-assisted platforms that enable dynamic updating and interoperability. Establishing consistent data standards and multi-scale representations is essential for capturing changing conditions over time, driven by climate change, urbanisation, and disaster recovery. Addressing data gaps, institutional barriers, and methodological inconsistencies is crucial to strengthening exposure and vulnerability analysis, thereby enhancing the reliability and effectiveness of multi-hazard risk assessment and resilience planning.

Multi-Hazard Risk Assessment (MHRA) critically depends on the availability, quality, and integration of spatial and temporal data. Such data can be obtained from diverse sources, including field mapping, surveys, remote sensing, sensor networks, online platforms, modelling, and artificial intelligence. Increasingly, the processing and analysis of these large datasets are supported by cloud computing platforms that enable rapid computation and sharing. Spatial data for MHRA can be grouped into reference data (base datasets for integration), hazard data (characterising the intensity and extent of hazardous events such as floods, earthquakes, and wildfires), exposure data (elements at risk such as population, buildings, and infrastructure), and vulnerability data (factors influencing susceptibility to hazard impacts). Establishing a robust data ecosystem is essential to combining and managing these datasets effectively. Frameworks such as the United Nations’ Integrated Geospatial Information Framework (IGIF) provide guidance; however, significant challenges remain, including inconsistent data availability, institutional barriers to data sharing, and high acquisition costs. Addressing these challenges is crucial to strengthening MHRA and supporting disaster risk reduction and resilience-building efforts.

Multi-Hazard Risk Assessment (MHRA) integrates hazard, exposure, and vulnerability analyses to quantify potential disaster impacts and support risk-informed decision-making. It evaluates the probability and consequences of multiple, interacting hazards such as floods, earthquakes, hurricanes, and landslides, and their cascading effects on people, infrastructure, and ecosystems. MHRA approaches range from qualitative methods, including community-based assessments and indicator-based analyses, to quantitative and probabilistic models that estimate economic and human losses. Advances in data availability, modelling, and artificial intelligence have enhanced the precision of risk estimation, while frameworks such as CAPRA, HAZUS, RiskScape, and CLIMADA provide practical tools for implementation. The chapter also highlights the importance of regional collaboration, uncertainty analysis, and scenario-based forecasting for adaptive planning. Strengthening MHRA capacities, particularly in data integration and multi-scale modelling, is essential to improving disaster preparedness, financial resilience, and long-term climate adaptation strategies.

In this paper, we explore five core challenges that need to be addressed in order to move towards the Comprehensive Disaster and Climate Risk Management approach, which has been proposed within the newly emerging paradigm of a more holistic approach to managing the risks associated with climatic and/or non-climatic hazards and non-climatic risk drivers across varied time scales and levels. These five challenges relate to the following points. First, we have a lack of comprehensive and high quality data for observing how society is impacted by, prepares for, and responds to multi-risks. Second, as a result we have a limited understanding of why individuals, communities, and other decision-makers (fail to) prepare for complex multi-risks in the way they do, and how that affects their responses. Third, our capacity to simulate how current and future risk could be effectively reduced is hampered by the aforementioned lack and fragmentation of observations and understanding of multi-risk dynamics. Fourth, current governance structures often reinforce data and knowledge silos. Fifth, the high complexity and wicked nature of the multi-risk problem results in barriers in knowledge exchange at the science, policy and practice interface. In this paper, we elaborate on each of these challenges, discuss why they matter for comprehensive disaster risk management, and provide ideas of how to overcome these challenges. We end the paper with a brief outlook.

In this paper, we explore five core challenges that need to be addressed in order to move towards the Comprehensive Disaster and Climate Risk Management approach, which has been proposed within the newly emerging paradigm of a more holistic approach to managing the risks associated with climatic and/or non-climatic hazards and non-climatic risk drivers across varied time scales and levels. These five challenges relate to the following points. First, we have a lack of comprehensive and high quality data for observing how society is impacted by, prepares for, and responds to multi-risks. Second, as a result we have a limited understanding of why individuals, communities, and other decision-makers (fail to) prepare for complex multi-risks in the way they do, and how that affects their responses. Third, our capacity to simulate how current and future risk could be effectively reduced is hampered by the aforementioned lack and fragmentation of observations and understanding of multi-risk dynamics. Fourth, current governance structures often reinforce data and knowledge silos. Fifth, the high complexity and wicked nature of the multi-risk problem results in barriers in knowledge exchange at the science, policy and practice interface. In this paper, we elaborate on each of these challenges, discuss why they matter for comprehensive disaster risk management, and provide ideas of how to overcome these challenges. We end the paper with a brief outlook.

In this paper, we explore five core challenges that need to be addressed in order to move towards the Comprehensive Disaster and Climate Risk Management approach, which has been proposed within the newly emerging paradigm of a more holistic approach to managing the risks associated with climatic and/or non-climatic hazards and non-climatic risk drivers across varied time scales and levels. These five challenges relate to the following points. First, we have a lack of comprehensive and high quality data for observing how society is impacted by, prepares for, and responds to multi-risks. Second, as a result we have a limited understanding of why individuals, communities, and other decision-makers (fail to) prepare for complex multi-risks in the way they do, and how that affects their responses. Third, our capacity to simulate how current and future risk could be effectively reduced is hampered by the aforementioned lack and fragmentation of observations and understanding of multi-risk dynamics. Fourth, current governance structures often reinforce data and knowledge silos. Fifth, the high complexity and wicked nature of the multi-risk problem results in barriers in knowledge exchange at the science, policy and practice interface. In this paper, we elaborate on each of these challenges, discuss why they matter for comprehensive disaster risk management, and provide ideas of how to overcome these challenges. We end the paper with a brief outlook.

Multi-hazard events pose increasingly complex challenges as natural hazards interact in cascading and compounding ways that amplify risks beyond individual hazards. Understanding these interactions – from hazard processes to cascading effects across social, economic, governance, and infrastructure systems – is critical for effective disaster risk management. National and international frameworks increasingly recognise these risk dynamics, most notably the United Nations Sendai Framework for Disaster Risk Reduction 2015–2030. The Sendai Framework Mid-Term Review (MTR) in 2023, however, identified substantial implementation challenges across its four priorities; these challenges include gaps in risk data governance, fragmented multi-scale coordination, insufficient investment mechanisms, and limited coverage of multi-hazard early warning systems. With the Sendai Framework approaching its conclusion, there is a pressing need to address these current shortcomings. Responding to this need, the 3rd International Conference on Natural Hazards and Risks in a Changing World took place in Amsterdam, the Netherlands, on 12–13 June 2024, with the objective of: (1) assessing current progress in multi-hazard risk research and policy practice; and (2) identifying scientific priorities to support further implementation of Sendai Framework until 2030 and beyond. Here, we document the arc of the scientific discussions held at the conference, synthesise the main findings from sessions, and set forth expert knowledge on how state-of-the-art science can fill gaps outlined by the MTR by identifying four perspective themes: (1) assessments and tools for risk understanding and decision-making; (2) understanding and management of complex risk landscapes; (3) emerging technologies for risk and resilience; and (4) multi-level governance for coordinated risk management. Ultimately, we call for governance reform enabling multi-scale coordination, investment in knowledge brokers translating across systems and scales, and participatory technology deployment ensuring emerging tools are applied to reduce disaster risk management inequalities. This perspective emphasises that effective Disaster Risk Reduction requires both incremental technical improvements and fundamental shifts in governance, data sharing, and inclusive engagement to address systemic risks and implementation gaps.