Periodic Reporting for period 1 - MEDEWSA (Mediterranean and pan-European forecast and Early Warning System against natural hazards)
Okres sprawozdawczy: 2023-11-01 do 2025-04-30
The European, Mediterranean and African region is increasingly vulnerable to the devastating consequences of weather and climate-induced extreme events including floods, wildfires, landslides, droughts, heatwaves and coastal hazards becoming more frequent, intense, and complex. Human-induced climate change has led to widespread losses and damages (L&D) across society, nature, and the economy, with vulnerable populations disproportionately affected. Over the past 50 years almost 2000 disaster events were recorded in Europe alone, causing more than 170,000 deaths and over USD 600 billion in economic losses. Africa experienced almost 2000 disasters in the same period, resulting in approximately 800000 deaths and significant increases in financial damage, particularly in regions where early warning capacities remain limited. The increasingly severe impacts of extremes and compound events are not solely due to anthropogenic climate change but also to insufficient integration of disaster risk management into development policies and a lack of investment in multi hazard early warning systems (MHEWS).
To address these critical gaps, the MedEWSa aims to develop an innovative, inclusive, and fully integrated MHEWS across pilot regions in Europe (Barcelona (Spain), Sweden, Venice (Italy), Kosice (Slovakia), Tbilisi (Georgia), the Mediterranean (Venice (Italy) and northern Egyptian coast), and Africa (Ethiopian National Parks). The project seeks to overcome the fragmentation in existing EWS and strengthen decision-making capabilities by delivering a modular, interoperable Decision Support and Dissemination System (DSDS). This platform integrates real-time and forecast-based data to support timely, informed actions by first responders, civil protection agencies, policymakers, and vulnerable communities.
Through the co-development of advanced AI-based forecasting models and impact assessment tools, MedEWSa enhances the accuracy, timeliness, and relevance of early warnings tailored to local hazard user needs. These tools are being deployed and validated in the eight pilot regions mentioned above, spanning different geographical, climatic, and socio-economic contexts, and ensuring adaptability and transferability. Forecast-based financial mechanisms, including parametric insurance and risk transfer tools, embedded within the DSDS strengthen financial preparedness and resilience in crisis response.
MedEWSa places particular emphasis on societal inclusion and participatory planning, integrating social science insights to ensure communication strategies, messaging formats, and alert systems are accessible and effective for all, including marginalized and at-risk populations. The project aligns global and regional priorities such as the Sendai Framework for Disaster Risk Reduction, the UN Sustainable Development Goals, the UN Secretary-General’s "Early Warnings for All" initiative and the ITU Global Initiative Resilience to natural hazards through AI solutions
MedEWSa aims to reduce disaster-related losses, strengthen adaptation, and empower communities through early and actionable information. Its “lighthouse activity” further supports the scalability, dissemination, and uptake of results by external stakeholders. By linking innovation, technology, governance, and inclusion, MedEWSa delivers a powerful framework for building climate resilience in some of the world’s most disaster-prone regions.
To address these critical gaps, the MedEWSa aims to develop an innovative, inclusive, and fully integrated MHEWS across pilot regions in Europe (Barcelona (Spain), Sweden, Venice (Italy), Kosice (Slovakia), Tbilisi (Georgia), the Mediterranean (Venice (Italy) and northern Egyptian coast), and Africa (Ethiopian National Parks). The project seeks to overcome the fragmentation in existing EWS and strengthen decision-making capabilities by delivering a modular, interoperable Decision Support and Dissemination System (DSDS). This platform integrates real-time and forecast-based data to support timely, informed actions by first responders, civil protection agencies, policymakers, and vulnerable communities.
Through the co-development of advanced AI-based forecasting models and impact assessment tools, MedEWSa enhances the accuracy, timeliness, and relevance of early warnings tailored to local hazard user needs. These tools are being deployed and validated in the eight pilot regions mentioned above, spanning different geographical, climatic, and socio-economic contexts, and ensuring adaptability and transferability. Forecast-based financial mechanisms, including parametric insurance and risk transfer tools, embedded within the DSDS strengthen financial preparedness and resilience in crisis response.
MedEWSa places particular emphasis on societal inclusion and participatory planning, integrating social science insights to ensure communication strategies, messaging formats, and alert systems are accessible and effective for all, including marginalized and at-risk populations. The project aligns global and regional priorities such as the Sendai Framework for Disaster Risk Reduction, the UN Sustainable Development Goals, the UN Secretary-General’s "Early Warnings for All" initiative and the ITU Global Initiative Resilience to natural hazards through AI solutions
MedEWSa aims to reduce disaster-related losses, strengthen adaptation, and empower communities through early and actionable information. Its “lighthouse activity” further supports the scalability, dissemination, and uptake of results by external stakeholders. By linking innovation, technology, governance, and inclusion, MedEWSa delivers a powerful framework for building climate resilience in some of the world’s most disaster-prone regions.
During the first reporting period, MedEWSa has made substantial scientific and technical progress towards developing an integrated, impact-based MHEWS for the EuMeA region. A key milestone was the design and initial deployment of the DSDS, a modular and interoperable platform that consolidates real-time and forecast data from meteorological, hydrological, and hazard-specific sources. Built with most recent technologies the DSDS provides tailored decision-support dashboards for first responders, policymakers, and civil protection agencies.
Advanced AI and hybrid machine learning models have been developed to enhance forecasting accuracy and interpretability. Scientific contributions also include explainable AI techniques and impact-based forecasting linked to vulnerability indicators. Tools are developedfor wildfire risk, sea-level changes and storm surges, landslides and flood nowcasting, and sub-seasonal drought and heatwave prediction, many of which have been integrated into the DSDS for operational use in the pilot regions.
An initial version of the MedEWSa Risk Transfer Platform has been provided. It includes core architecture, early hazard-financial model integration, and interfaces for parametric insurance and EU Solidarity Fund applications. Work is ongoing to define forecast-triggered payout thresholds in collaboration with stakeholders.
Complementing the technical work, analysis of DSS/EWS across EuMeA has been done and revealed key usability features, together with a review of global and regional EWS, with stakeholders’ support and input. Critical capability gaps and opportunities have been identified for enhanced user-oriented early warning services, while a socio-economic vulnerability assessment informed the design of tools and interfaces. Co-creation activities with end users have ensured region-specific adaptation and user-centered innovation, laying the groundwork for a validated, scalable MHEWS until the end of the project.
Advanced AI and hybrid machine learning models have been developed to enhance forecasting accuracy and interpretability. Scientific contributions also include explainable AI techniques and impact-based forecasting linked to vulnerability indicators. Tools are developedfor wildfire risk, sea-level changes and storm surges, landslides and flood nowcasting, and sub-seasonal drought and heatwave prediction, many of which have been integrated into the DSDS for operational use in the pilot regions.
An initial version of the MedEWSa Risk Transfer Platform has been provided. It includes core architecture, early hazard-financial model integration, and interfaces for parametric insurance and EU Solidarity Fund applications. Work is ongoing to define forecast-triggered payout thresholds in collaboration with stakeholders.
Complementing the technical work, analysis of DSS/EWS across EuMeA has been done and revealed key usability features, together with a review of global and regional EWS, with stakeholders’ support and input. Critical capability gaps and opportunities have been identified for enhanced user-oriented early warning services, while a socio-economic vulnerability assessment informed the design of tools and interfaces. Co-creation activities with end users have ensured region-specific adaptation and user-centered innovation, laying the groundwork for a validated, scalable MHEWS until the end of the project.
MedEWSa is developing an integrated, AI-enhanced MHEWS and risk transfer system with the potential to transform weather and climate resilience across Europe, the Mediterranean, and Africa. MedEWSa goes beyond the state of the art through the first operational version of the DSDS platform, AI-based forecasting tools, and the initial version of the risk transfer platform, to support faster, scientific robust and data-driven emergency response as well as financial preparedness. To ensure uptake and long-term impact, further actions are needed: real-world demonstrations, stakeholder training, enhanced access to markets and finance, support for IP exploitation, and alignment with regulatory frameworks. Continued cooperation with lighthouse stakeholders, international organisations, and policy bodies will be critical. At project end, MedEWSa will deliver validated, user-centric tools and services, ready for scale-up and deployment across high-risk regions.