Project results are expected to contribute to some or all of the following expected outcomes:

• Integrated single-hazard systems into multi-hazard next generation predictive models to assess cascading effects (e.g., heatwave, floods, droughts, landslides, heavy rain) and interactions across meteorological, geophysical, and technological hazards;
• Enhanced hazard forecasting and response through research on model integration and platform interoperability;
• Development of holistic risk and resilience metrics to support multi-hazard prevention strategies, encompassing main physical, economic and social effects;
• Improvement of analysis models considering evolving vulnerability state, due to cascading and cumulative effects, through numerical simulations and experimental tests, possibly also supported by AI applications;
• Collected reliable data (same granularity and format) and ways to share and analyse it. The interoperability of all kinds of systems and information sharing is crucial, based on the need-to-know principle;
• Improved knowledge/experience-sharing from past emergencies to cope with future emergencies, also strengthening trans-national knowledge and data exchange among EU countries as well as from early warning to early action;
• Improved disaster risk and resilience management due to single/multiple threats through a holistic, systemic and cross-cutting approach, also considering relevant changes such as changing climate, digital transformation, environmental and socio-economic conditions;
• Development of holistic Risk and Resilience Metrics to support multi-hazard prevention strategies, encompassing main physical, economic and social effects;
• Solutions shall include the analysis of the physical, social and governance systems and take into account the development of an EU comprehensive risks and threats assessment.

Advancing multi-hazard risk assessment and disaster resilience is a necessity. Building on the integrated all-hazard approach, the advancement will be achieved by integrating single-hazard models into next-generation predictive systems capable of analysing cascading and cumulative effects. A key focus should be on early and anticipatory action, improving the understanding of interactions between meteorological, geophysical, and technological hazards, including their compounding impacts on societies, economies, and critical infrastructure.

Proposals should aim to develop and validate integrated forecasting models, that enhance the prediction and management of multi-hazard scenarios, supports flexibility and extensibility, and coordination of responses by incorporating real-time data, AI-driven analytics, and remote sensing technologies. These models should facilitate improved hazard forecasting by addressing challenges in platform interoperability and data exchange, ensuring that diverse hazard monitoring systems at local, national, and global levels can effectively communicate and operate in synergy.

Efforts should also explore the interoperability of regional and national hazard warning systems, enhancing global forecasting capabilities for hazards such as landslides triggered by extreme weather events, or cumulative damage modelling for earthquakes and their aftershocks. Research should address gaps in loss estimation models by considering the cascading and long-term impacts of disasters on infrastructure, the built environment, supply chains, and diverse needs of communities. Furthermore, proposals should contribute to the development of advanced tools and methodologies to assess the combined effects of multiple hazards on critical infrastructure, ensuring that disaster risk management strategies account for interdependencies across sectors This should include scenario-based stress testing, digital twins for risk modelling, and AI-powered decision-support systems, taking into account existing biases, to enhance resilience planning for lifeline services such as energy, water, transport and telecommunications.

A holistic, systemic, and cross-cutting approach should be applied to disaster risk management, taking into consideration climate change trends, environmental degradation, and vulnerabilities such as gender, age, disabilities and others social factors. The topic should lead to the creation of comprehensive Risk and Resilience Metrics, integrating physical, economic, and social dimensions to support decision-makers in designing effective prevention and adaptation strategies. Considering building on and leveraging from existing systems, such as the Copernicus Emergency Management Service (CEMS) the Destination Earth Platform or Risk Data Hub, could be beneficial.

Projects should conduct a stakeholder or market analysis and a roadmap or plan for uptake of the developed methodologies, findings, and technologies to the industry, the research and innovation community, and/or the relevant authorities.

Projects may take into account the assets but also particular challenges faced by the European outermost regions and may include entities from these regions in the consortium’s composition.

Projects should contribute to strengthening risk governance at multiple levels by fostering collaboration between scientific communities, policymakers, emergency responders, and infrastructure operators. Efforts should be made in leveraging citizens-generated content in social media and decentralised digital platforms for citizen-driven early warning and situational awareness. Alignment with EU policies, international risk reduction frameworks, and best practices in resilience planning should be ensured, maximizing the applicability and impact of the developed solutions. The Preparedness Union Strategy is a key document in this regard and includes a key action on developing an EU comprehensive risks and threats assessment. Finding synergies with projects from operational grants, such as the Knowledge for Action in Prevention & Preparedness (KAPP)^[1], is recommended.

Where applicable, proposals should leverage the data and services available through European Research Infrastructures federated under the European Open Science Cloud, as well as data from relevant Data Spaces. Particular efforts should be made to ensure that the data produced in the context of this topic is FAIR (Findable, Accessible, Interoperable and Re-usable).