Emerging technologies such as artificial intelligence, digital twins, remote sensing, decision-support tools, and early warning systems are transforming systemic risk assessment and management. They offer new ways to understand multi-risk dynamics and enhance disaster risk management and climate adaptation strategies.
This session provides a platform to explore the latest technological advancements and innovations in systemic risk assessment across various sectors and regions. It will feature presentations and discussions highlighting the role of cutting-edge technologies in advancing systemic disaster risk management and climate adaptation planning.
We particularly encourage submissions of research, case studies, and practical applications that showcase how these technologies can provide valuable insights into the complexities of multi-risk dynamics, optimize decision-making, and enhance resilience-building efforts.
Potential research topics include, but are not limited to:
- Multi-Hazard Early Warning System and Impact-Based Multi-Hazard Forecasting, providing timely alerts for potential compounding hazards and risks.
- Decision-support tools, open source software and novel risk assessment methods co-developed with stakeholders to enhance the preparedness of first responders and decision-makers to multi-risk.
- Cutting-edge Artificial Intelligence and Machine Learning tools for multi-hazard, multi-sector risk and systemic risk management.
- Novel technologies for data collection and generation, including Large Language Models and remote sensing.
- Application of network science and digital twin technologies to model systems holistically, accounting for cascading and compounding dynamics.
- Innovative approaches in communications, knowledge-sharing and capacity building in multi-hazard risk assessment.
- Best practices enabling the transferability of the developed innovations to different territorial contexts and hazards (knowledge transfer).
Orals: Tue, 29 Apr | Room 1.31/32
Climate extremes, such as droughts, floods, and heatwaves, often trigger compound and cascading impacts due to interdependencies between coupled natural and social systems. Yet, our knowledge of these interactions remains limited mainly due to the lack of comprehensive impact data. Research typically considers only one isolated impact, system, socioeconomic sector, and/or hazard at a time, often ignoring dependencies between impacts as well as how they interact with response and adaptation measures.
Against this backdrop, the unprecedented abundance of digital texts and cutting-edge machine-learning tools has opened new research avenues for impact assessment research. In this talk, I will demonstrate how we can leverage natural language processing (NLP) and large language models on different text types to infer how climate extremes impact society. I will discuss the potential of unconventional data sources, such as meeting minutes, newspaper articles, and reports, to monitor the consequences of extreme events in near real-time.
How to cite: Madruga de Brito, M.: Climate impacts and where to find them: insights from text mining, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1930, https://doi.org/10.5194/egusphere-egu25-1930, 2025.
Our environment is characterized by a changing climate marked by rapidly increasing frequency and intensity of extreme weather leading to compound multi-hazard events. This evolving climate reality accentuates diverse needs across various sectors, as each grapples with unique vulnerabilities and adaptation requirements. Stakeholders, ranging from individuals, local communities to governmental bodies and private enterprises, need to take measures to mitigate these challenges.
These heterogeneous needs ask for tailored approaches to support disaster risk reduction, climate resilience and adaptive governance. However, significant barriers to access data and information products for an effective climate adaptation and increased preparedness exist. Despite the growing need for localized early warning and climate resilience (comp. UN initiative “Early Warnings for all”), the available data is often too generic and inaccessible to meet the specific needs of local stakeholders. This lack of actionable information hampers timely and informed decision-making, leaving communities and sectors ill-prepared for the impacts of extreme weather events. Furthermore, the limited interoperability of data, models, and information products exacerbates these challenges by creating inefficiencies and delays in decision processes. Addressing these issues is crucial for fostering adaptive capacity and enhancing preparedness at all levels.
The prototypical solutions developed in the European projects I-CISK and DIRECTED address the identified challenges by leveraging open-source, open-data, and open-science principles to enhance data accessibility, interoperability, and usability for local stakeholders. Central to this approach is a cloud-deployed research data infrastructure that produces tailored information products meeting diverse user needs across different climatic regimes and application scenarios. These products are co-developed in close collaboration with local stakeholders, ensuring alignment with specific information gaps and needs to improve preparedness and adaptive capacity.
The system builds upon open-source projects, including pygeoapi and React, and employs cloud-optimized data formats and storage to seamlessly integrate heterogeneous data sources. These range from continental-scale data (e.g., Copernicus) to local datasets, enabling a comprehensive understanding of spatial and temporal climate variability. A federated design, grounded in open standards such as the latest OGC APIs (e.g., Processing, Features, Connected Systems), ensures modularity, interoperability, and ease of customization for both research and operational spatial information infrastructures. This approach fosters scalability, credibility, and reusability, empowering stakeholders to use tailored solutions that address their sector-specific challenges.
One notable challenge during the development of the prototypical solutions is the complexity and effort required for a co-design approach, where diverse stakeholders collaborate to define requirements and identify information gaps, guiding the development of the solution. While this participatory method enhances relevance and user satisfaction, it demands significant time, resources, and coordination, particularly when balancing varying stakeholder priorities and expectations.
Another key challenge lies in ensuring the continuity and usability of the services and tools developed during the project. A strong focus on the development of sustainable business use-cases will solidify the adoption of the tailored solutions beyond the project’s lifecycle. This includes fostering stakeholder engagement, securing long-term funding, and adapting to evolving technological and climatic contexts.
This work has been funded by the European Union under Grant Agreement IDs 101037293 and 101073978.
How to cite: Gräler, B., Pontius, M., Schnell, J., Bagli, S., and Mazzoli, P.: Advancing Disaster Risk Management and Climate Adaptation: Modular, Scalable, and Open Standards-Based Spatial Data Infrastructure for Local Action, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17179, https://doi.org/10.5194/egusphere-egu25-17179, 2025.
Winter storms cause significant impacts to a range of sectors in the United Kingdom (UK) (Hanlon et al., 2021; Kendon et al. 2023). The nature of winter storms is that they are associated with multiple hazards (for example strong winds, rain, and storm surges) which will most often occur as compounding hazards: simultaneously, over large areas or one after another (Bloomfield et al., 2023, 2024; Kew et al., 2024; Zscheischler et al., 2020). The complexity of the hazards from winter storms makes them challenging to plan for by resilience specialists and critical infrastructure operators (Bloomfield et al., 2023; Zscheischler et al., 2020). Strong winds and gusts, alongside compounding impacts from rainfall, for example, are known to lead to societal disruption, such as to energy distribution lines (e.g., Gonçalves et al., 2024).
Some recent work has focused on understanding which aspects of a storm’s development lead to compound impacts over the UK (e.g. Manning et al., 2024) but no existing UK storm classification has focused on informing hazards and impacts potential. In this project we explore the feasibility of a new framework to better assess risk from the compounding hazards in winter storms to facilitate better preparation by the resilience community. The approach is based on techniques used by operational meteorologists to anticipate the potential outcomes of storms. From literature review and expert interviews, we believe that this is a novel approach within the resilience planning setting.
When a storm approaches the UK, meteorologists must quickly determine the likely impact on a wide range of sectors, determine worst-case scenarios and build a picture of the level of predictability. One approach they use is to assess a range of aspects of the impending storm related to its dynamical development (we refer to these as ‘storm development metrics’) and use these to rapidly validate the predicted hazards by the forecast models, as well as to identify potential high-risk outcomes. They often refer to previous storms with similar characteristics to infer possible scenarios. We investigate whether these ‘storm development metrics’ can be used to create a ‘typology’ of storms that can separate storms by the plausible hazard scenarios that could occur, and hence simplify the task of assessing risk from storms now and in the future.
Different machine learning clustering techniques are applied to the development metrics from a large set of historical named storms that affected the UK to explore the discrimination power in the hazard space of the resulting cluster sets. This furthers the project aim to convert the technical understanding of operational meteorologists into more digestible information for resilience specialists, building capacity to manage the threat of multi-hazard storms.
How to cite: Matthews, E., Gonzalez, P., Wallace, E., Ackerley, D., and Harley-Nyang, D.: Present-day risk from winter storms in the United Kingdom, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16996, https://doi.org/10.5194/egusphere-egu25-16996, 2025.
Climate change presents escalating challenges, impacting people, the environment, and the economy. Climate-related hazards are expected to intensify their effects in the future, making it essential to build disaster risk reduction capacity. To address these challenges, international financial institutions and governments worldwide require accurate, up-to-date, and comprehensive information to understand the spatiotemporal distribution of risk, identify risk hotspots, and support the preparation of adaptation strategies to enable the prioritization and effectiveness of adaptation investments. Within the ESA-funded GDA-Climate Resilience project, a comprehensive framework has been developed to assess spatially explicit relative risks under current and future climate scenarios on a global scale, serving as a basis for a decision support tool aimed at implementing climate risk services. This framework aims to provide decision-makers with critical insights into relative risk variations under future climate projections, considering key hazards such as droughts, warm spells, and floods and focusing on receptors such as agriculture and population. This innovative, pixel-level relative risk assessment approach utilizes open-source global datasets to evaluate future relative risk levels from multiple climate hazards. It encompasses three SSP-RCP scenarios—SSP1-2.6, SSP2-4.5, and SSP3-7.0—over the period from 2020 to 2099, with 20-year time steps, using 1995–2014 as the reference baseline. The current implementation examines drought-agriculture, warm spell-population and flood-population hazard/receptor combinations, adopting the IPCC’s framework for hazard, exposure, and vulnerability dimensions in defining risk. Risk estimation is provided both as a 0-to-1 index, enabling spatial and temporal global comparability, and as a risk variation index, informing decision-makers about areas where the risk is increasing most rapidly. Additionally, the framework explores multi-hazard risk, enabling the analysis of combined impacts from various climate hazards on different receptors, such as populations and agricultural systems. Our initial findings reveal a significant increase in relative risk for both droughts and warm spells over time compared to baseline levels. Several high-risk hotspots have been identified, as well as areas with shifting future risk profiles from individual hazards both globally and within individual countries, under specific time steps and climate scenarios. Further research may focus on the exploration of emerging risks arising from multi-hazard interactions. In general, this approach offers valuable insights to guide decision makers in fostering adaptation strategies and investments, and provides a foundational step for a future, spatially explicit and comprehensive global risk assessment platform.
How to cite: Albergo, E., Furlanetto, J., Nguyen, N. D., Masina, M., Sano, M., Carisi, M., Zabeo, A., and Critto, A.: A Framework for Mapping Global Climate Multi-Hazard Risks under Future Climate Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16560, https://doi.org/10.5194/egusphere-egu25-16560, 2025.
The increasing frequency and severity of extreme climate events necessitate robust multi-risk assessments. Traditional methods often fail to unravel complex hazard interactions and impacts. Artificial Intelligence provides a powerful tool for analysing environmental data, integrating diverse information sources, and modelling non-linear relationships, crucial for effective risk reduction strategies.
A stepwise AI-based framework to assess the risk posed by extreme climate events was developed for the Veneto region (North-East Italy). The main hazards considered are heatwaves, droughts, storm surges, extreme precipitation, extreme wind, landslide and wildfire. The first step involves identifying single hazard susceptibility maps, using statistical methods for atmospheric hazards and using supervised Machine Learning (XGBoost) for landslide and wildfire. In the second step, the single hazard susceptibility maps are integrated into a multi-hazard map, using a Random Forest model trained and validated on a multi-hazard event dataset in the historical timeframe. The multi hazard event dataset was created considering the spatial and temporal footprints of single hazards from climate data, utilizing statistical methods to detect extreme events, and applying unsupervised machine learning (DBSCAN) for clustering and counts the number of consecutive and compound multi-hazard events. Then, in the third step, the analysis is extended to multi-risk, integrating vulnerability and exposure indicators for multiple socio-economic variables (population, built environment, tourism and agriculture).
This comprehensive approach leverages advanced data-driven and AI techniques to enhance the understanding of the complex dynamics associated with multi-risk events. Applied within the Veneto case study of the Myriad-EU project, this framework has been tested for present and future scenarios considering RCP 4.5 and RCP 8.5, showing an increasing risk from hot and dry events in future multi-risk, especially for the tourism and agriculture sectors.
How to cite: Ferrario, D. M., Tiggeloven, T., Casagrande, S., Sanò, M., de Ruiter, M., Critto, A., and Torresan, S.: An AI approach for multi-risk assessment in the Veneto Region , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16640, https://doi.org/10.5194/egusphere-egu25-16640, 2025.
Bucharest can be considered Europe's most endangered capital to earthquakes. Intermediate-depth seismic events occurring in the Vrancea Area, with magnitudes greater than 7 MW, can significantly affect the city. In the 20th century, Bucharest experienced two major damaging earthquakes: in 1940 and 1977. But the occasioned lessons were not fully learned. The number of vulnerable buildings is nowadays considerable, as over 30% of them were built before 1963 (of which 22%, before 1941). New factors among which climate change, increased exposure, and road congestion, alongside perpetuated or augmented vulnerabilities of different types, can contribute to higher than ever losses. In this context, new ways of quantifying the impacts are necessary.
Within the PARATUS and MULTICARE EU Projects, we started a complex evaluation of multi-risk centered around multi-hazard scenarios relevant to Bucharest. Beside earthquakes, we also looked at the potential effects of a dam break and levee breach for the Morii Lake located in the north-western part of the city, upstream of the city center. To explain the intricate interplay among cascading hazards, exposure, and vulnerability, first of all we developed an Impact Chain for the present and future situation, integrating a wide range of feedback from relevant stakeholders (i.e., decision-makers, first and second responders, military, architects, academics, insurance companies, legal experts).
To support loss estimation and the evaluation of flood propagation, we developed a new exposure and vulnerability database at the building level. In our presentation, we will detail the data collection process involving satellite images (including from the KH-9 mission in the 70’s and 80’s), deep learning algorithms, open-source and census data, or evaluations of individual buildings conducted by engineers and architects. This database allowed us to run OpenQuake for a near-real time estimation of seismic losses, proving to be highly valuable for first-responders but also for developing scenario-driven preparedness plans. A fully 2D modelling approach was used for the flood hazard analysis aiming to determine the flooded areas and maximum water height, accounting for the resistance imposed to the water flow by buildings.
In order to highlight the importance of road network functionality given various seismic and flood scenarios, we also performed an evaluation of travel-time estimation, at the city level. For this, factored in incidents (such as road blockage due to debris, bridge collapse, or access restrictions caused by flooding) and their impacts on traffic at particular times. The analysis also reveals the importance of dedicated lanes for first response vehicles, currently delimited in Bucharest on tramway lines. Future research initiatives focus on 1) evaluating accessibility to hospitals, considering also their capacity of treating different types of patients and 2) proposing improvements in road network planning and hospital location, grounded on the obtained expected distribution of losses.
How to cite: Toma-Danila, D., Armas, I., Albulescu, A.-C., and Cozma, A.: Multi-risk estimation through Impact Chains, earthquake and flood loss simulations. Progress for Bucharest Case Study within the PARATUS and MULTICARE Projects., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16778, https://doi.org/10.5194/egusphere-egu25-16778, 2025.
We present a novel implementation of the impact-based drought monitoring and early warning component of the Africa Multi-Hazard Early Warning and Action System for Disaster Risk Reduction (AMHEWAS for DRR). AMHEWAS is a collaborative initiative led by the African Union Commission (AUC), in partnership with Regional Economic Communities, Member States, and with the technical and scientific expertise of UNDRR and CIMA Research Foundation. Its goal is to enhance Africa's resilience to natural hazards. The system adopts a comprehensive, multi-scale framework, integrating efforts at continental, regional, and national levels to strengthen early warning systems and advance disaster risk management strategies.
A key component of AMHEWAS is the Continental Watch (CW), an impact-based bulletin focused on rain, wind, flood, and drought hazards. The CW contains advisories on the levels of potential disaster impacts and is used by the African Union Commission to alert national authorities about potential threats. The CW consolidates data from automated monitoring and forecasting systems, providing decision-makers across Africa with timely, actionable information. This enables proactive interventions to reduce the potential impacts of disasters.
The drought bulletin, released to interested parties monthly, monitors drought hazard in near-real time at the continental scale. It uses openly available datasets to evaluate emerging drought conditions at different time scales, to ensure a diverse range of potential impacts is captured in the bulletin. For short-time drought evaluation (1-3 months), the Combined Drought Indicator (CDI) is used as hazard indicator, whereas for longer timescales, a 12-month Standardised Precipitation Index (SPI12) is used.
Hazard information from these indicators is paired with tailored layers for exposure and vulnerability to evaluate emerging potential drought impacts in various sectors. Results are presented monthly in the bulletin for the entire African continent and are aggregated at national and sub-national scales.
How to cite: Isabellon, M., Trotter, L., Cremonese, E., Alfieri, L., Mapelli, A., Rossi, L., Otieno, V., Nyambe Nyambe, H., Dube, N., Massabò, M., Yu, C., and Maurer, T.: Enhancing Drought Risk Monitoring for Disaster Risk Reduction: Innovations in the Africa Multi-Hazard Early Warning and Action System (AMHEWAS), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19063, https://doi.org/10.5194/egusphere-egu25-19063, 2025.
Societies face growing challenges driven by the compounded effect of climate change and interconnected hazards, alongside broader environmental issues. The Land-Sea Interface (LSI), where several ecosystems converge and interact, represents a complex environment with unique dynamics and interdependencies. These characteristics pose significant challenges for impact assessment and planning and require appropriate methodologies to navigate its complexities effectively.
The exploration and development of innovative tools for multi-hazard impact and adaptation planning have become essential for understanding and unraveling the interplay between multiple pressures while exploring future scenarios, anticipating uncertainties, and supporting informed and robust decisions.
In this setting, strategic foresight analysis has emerged as a proactive approach to address complex challenges, helping organizations to anticipate and prepare for future risks and opportunities. Its inherent interdisciplinarity and capacity to offer insights into complex dynamics make it useful to enhance building systemic resilience, and transdisciplinary collaboration, enabling the integration of diverse knowledge systems and stakeholder perspectives into adaptive planning and decision-making. Within this study, an AI-enhanced strategic foresight analysis, specifically tailored to the Valencia region's coastal wetlands, is proposed to respond to the critical need to understand the multidimensional dynamics of Land-Sea Interactions (LSI). The Valencia coastal case, highly prone to multiple hazards, is distinguished by complex interactions between anthropogenic and climate-driven pressures, which amplify their combined impacts on vital ecological.
The methodological approach integrates traditional foresight tools (i.e., horizon scanning, megatrend analysis, and scenario planning) with robust, innovative science-based approaches. Specifically, megatrend analysis was conducted using Copernicus climate data, and scenarios based on a Cumulative Impact Assessment (CIA) supported by Generative Artificial Intelligence (Generative-AI).AI tools were employed for data analysis and to generate impact weights that accurately reflect the effects of multiple hazards and anthropogenic pressures on different ecosystems. Local stakeholders and expert involvement played a central role in these tasks, ensuring that model development and application on the Valencia case were aligned with local priorities and challenges.
Looking at the key outcomes of the appraisal, the m
megatrend analysis revealed increasing trends in climate pressures, such as sea-level rise, storm surges, coastal erosion, and air temperature median scores over time. The most marked values were shown in the southern wetlands and on the Albufera coast, underpinning their experience to compounded pressures due to their proximity to the coast.
Finally, scenario analysis indicated a progressive intensification of cumulative impacts for different RCPs (RCP 4.5 and 8.5) and time horizons (2050 and 2100). In particular, in all scenarios, forests and seminatural areas consistently exhibited the highest cumulative impact scores due to their sensitivity to hazards, in particular sea level rise, storm surges, and air temperature changes.
However, coastal wetlands stood out as the most critical category in future scenarios, due to their exposition to the interplay of multiple marine-driven hazards.
This study facilitated the co-design of a novel CIA approach, providing insights into multi-hazard impacts and a solid foundation for further research while enhancing decision-making processes in spatial planning and equipping stakeholders with actionable insights enhancing resilience and long-term preparedness.
How to cite: Grassi, G., Zennaro, F., Furlan, E., and Critto, A.: AI-Enhanced Strategic Foresight Analysis for Multi-Hazard Land-Sea Interface Management in the Valencian Community , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19554, https://doi.org/10.5194/egusphere-egu25-19554, 2025.
Recent events in Europe, such as the devastating 2024 floods in Valencia (Spain) and Emilia Romagna (Italy), highlight the growing challenges posed by climate change and emphasize the urgent need for enhanced flood resilience within the EU. Developing effective flood-resilience strategies requires tailored approaches, deeply rooted in the local context. While Japan offers an inspiring example of managing seasonal flooding, its methods cannot be directly applied to the European context without adaptation.
In this presentation, I will introduce an ongoing Marie Curie Skłodowska Postdoctoral project, which focuses on the exchange of knowledge and best practices in community disaster preparedness and hazard mapping, with a particular emphasis on integrating lessons from Japan into the EU context while respecting local cultures.
The project begins with a focus on the value of local narratives and stories of past disasters, which provide crucial insight into how communities perceive current risks and motivate residents to take proactive steps in disaster preparedness. This research explores the role of local culture, including myths and legends, in shaping these perceptions. Using qualitative methods such as interviews, literature reviews, and surveys, the project aims to propose an educational framework centred on localised resilience and sustainability. Additionally, the project seeks to incorporate qualitative aspects into interactive hazard mapping, with an emphasis on identifying vulnerable social groups and improving evacuation strategies during flood-related emergencies.
How to cite: Petraroli, I.: Building Flood Resilience: Lessons from Japan for the EU, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9512, https://doi.org/10.5194/egusphere-egu25-9512, 2025.
As part of the MYRIAD-EU project (2021–2025), a suite of innovative multi-hazard and multi-risk tools has been developed to address both EU-scale and pilot-scale analyses, supporting proof-of-concept applications across multiple sectors. The associated open software package incorporates five key components designed to enhance risk assessment and decision-making for sectors such as Finance, Tourism, Food/Agriculture, Infrastructure, Energy, and Ecosystems.
The Exposure-at-Risk Calculator enables probabilistic, historic, and stochastic event overlaps using the large array of hazard sets created in EU projects including MYRIAD-EU by linking sectoral exposure footprints to calculate overlapping exposure-at-risk and their associated probabilities for specific thresholds. This tool leverages the open hazard and exposure datasets hosted on platforms such as Zenodo and the MYRIAD website.
Complementing this, the Multi-Hazard and Risk Scenario Calculator evaluates direct and indirect risks for current and future scenarios, integrating climate and socioeconomic data. Notable applications include overlapping earthquake and flood scenarios in the Danube region, enabling comprehensive risk assessments for complex hazard interactions.
A Multi-Vulnerability Curve Editor and Database allows users to view and contribute sectoral damage functions and dynamic vulnerability data. Examples include vulnerability dynamics for multi-hazard scenarios and heatwave mortality functions derived from MYRIAD-EU research. Additionally, the Multi-Risk Index QGIS Plugin supports quantitative and qualitative risk analyses at the NUTS3 level across Europe or other selected regions. The plugin’s adjustable weighting system facilitates the integration of multi-hazard scenarios into risk metrics and indicators such as for the tourism sector or looking at financial indirect losses after events.
The Canary Islands case study uses the full integrated approach of MYRIAD-EU, focusing on tourism-sector resilience under multi-hazard conditions, such as volcanic eruptions combined with drought and climate/socioeconomic changes out to 2050. The study combines quantitative results with semi-quantitative baseline resilience indices with qualitative resilience scorecards to provide actionable insights for tourism destination management.
Scheduled for release in 2025, the software is being developed collaboratively with MYRIAD-EU partners. A comprehensive user interface and documentation will guide users in selecting appropriate tools for specific locations and multi-sector, multi-risk problems, ensuring applicability across diverse scenarios and scales.
How to cite: Daniell, J., Schaefer, A., Claassen, J., Khazai, B., Blanz, B., Brand, J., Strelkovskii, N., Girard, T., Maier, A., Ferrario, D., Jaeger, W., Michalke, S., Mardell, C., Mysiak, J., de Polt, K., Stolte, T., De Ruiter, M., Padron-Fumero, N., and Ward, P.: The MYRIAD-EU Multi-Risk Software Suite: Integrating Multi-Hazard Solutions for Sectors across Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9769, https://doi.org/10.5194/egusphere-egu25-9769, 2025.
Effective flood risk management is often constrained by the lack of efficient dissemination of flood-related information to key stakeholders, including vulnerable communities. To address this critical gap, global disaster management organizations advocate for the establishment of robust information channels, aligning with the objectives of the United Nations Sustainable Development Goals (SDGs) 11 and 13, which emphasize building secure, resilient, and sustainable cities. To address this, this study introduces ‘MANAGE’, a web-based flood risk information system for a multi-hazard coastal catchment in India. MANAGE integrates high-resolution flood modeling, Multi-Criteria Decision-Making tools, and statistical techniques to provide a comprehensive repository of flood hazards, physical and socio-economic vulnerabilities, and bivariate flood risks at the finest administrative scale. Results reveal that flood risk is primarily influenced by physical vulnerability (69.86%), while combined indicators (51.6%) and socio-economic factors (39.66%) also play significant roles. The MANAGE platform is designed with a user-friendly architecture, offering enhanced accessibility through smartphone compatibility and multilingual support, ensuring seamless use by diverse end-user groups, including vulnerable populations. The study further proposes engineering measures and policy recommendations to enhance existing flood management strategies and build a roadmap for future resilience.
How to cite: Mohanty, M. and Thakur, D. A.: MANAGE: A Consolidated End-To-End Multi-Hazard Web-Based Flood Risk Information System for Coastal Regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-468, https://doi.org/10.5194/egusphere-egu25-468, 2025.
Climate change, driven by global warming, is increasingly intensifying the frequency and severity of coastal disasters, especially in densely populated coastal regions. Rising sea levels and elevated sea surface temperatures exacerbate the impacts of typhoons, storm surges, and wave overtopping, posing critical threats to coastal infrastructure and communities. An analysis of 35 years of tide gauge data from the Korea Hydrographic and Oceanographic Agency (KHOA) indicates an average sea level rise of 3.06 mm/year, with regional variations of 3.46 mm/year on the east coast and 2.74 mm/year on the south coast. This cumulative rise of approximately 10.7 cm has accelerated shoreline retreat and undermined coastal stability. Hypothetical scenarios based on Typhoon Maemi (2003) reveal that under current sea-level conditions, the storm’s destructive potential would be significantly amplified, particularly along Korea’s southern coast. Intensified typhoons and wave overtopping further jeopardize marine infrastructure and exacerbate sand loss from beaches, particularly in areas with increased artificial structures, such as breakwaters. To address these risks, we propose a state-of-the-art coastal disaster prevention simulation platform. This platform integrates digital twin technology, 3D GIS, and real-time meteorological and oceanographic data to model sea-level rise, typhoon trajectories, storm surges, and coastal erosion. It also provides digital twin based decision-support tools for early warnings, disaster preparedness, and adaptive response strategies. This study highlights the necessity of a coordinated, multi-agency approach involving Korean governmental bodies (e.g., MOF, MIS) and research institutions (e.g., KIOST and NDMI). By leveraging marine big data, this platform enhances coastal resilience and facilitates sustainable management practices in the face of a rapidly changing climate.
How to cite: Lim, H. S., Han, M., Park, J. H., Choi, Y., and Choi, H.: Development of coastal disaster prevention simulation platform beyond climate change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5405, https://doi.org/10.5194/egusphere-egu25-5405, 2025.
The global climate is undergoing an unprecedented rise in temperature, contributing to the increased frequency and intensity of extreme events worldwide. Coastal areas, recognized as critical hotspots of climate change, face amplified vulnerabilities due to their dense population, interconnected economic activities, and delicate ecosystems. These regions are particularly threatened by sea-level rise and more frequent extreme weather events, underscoring the urgent need for innovative and comprehensive strategies to enhance climate resilience and safeguard their future. Addressing these challenges requires a paradigm shift toward integrated, multi-hazard, and multi-risk approaches able to capture the intricate interplay of overlapping risks.
In this context, we present COAST-AId, a custom Large Language Model application designed to support multi-risk assessment and adaptation planning in the Veneto coastal region. COAST-AId facilitates the application of the climate risk assessment framework, outlined in the first European Climate Risk Assessment (EUCRA) report, focusing on its key components such as risk identification, analysis, and policy evaluation. By employing state-of-the-art prompt engineering techniques, COAST-AId has demonstrated its capability to generate relevant outputs for the case study, systematically evaluated using specific assessment metrics. This tool prioritizes risks for the Veneto coastal areas, providing essential insights to guide the development of Disaster Risk Management (DRM) pathways offering valuable support for refining the Veneto region Strategy for Climate Change Adaptation, with a particular emphasis on coastal challenges.
The development and application of COAST-AId were deeply integrated into the objectives of the MYRIAD-EU project, fostering the collaborative engagement with local stakeholders and experts. This participatory process played a central role in evaluating the tool’s performance, identifying critical vulnerabilities, and uncovering opportunities to enhance risk reduction and adaptation strategies. The findings highlight the transformative potential of AI-driven technologies in advancing the understanding of multi-risk dynamics, optimising decision-making processes, and enhancing resilience-building efforts in coastal areas.
How to cite: Dal Barco, M. K., Casartelli, V., Sano, M., Vascon, S., Torresan, S., and Critto, A.: COAST-AId: a custom large language model application supporting multi-hazard risk assessments in the Veneto region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13446, https://doi.org/10.5194/egusphere-egu25-13446, 2025.
The past decade has seen significant advancements in understanding multi-hazards and their associated risks, particularly in identifying interrelationships between different hazards. However, the effective management of multi-hazard risks and understanding of its challenges remains underexplored. This gap in understanding is partly due to the relative novelty of the topic and the scarcity of detailed case studies on past multi-hazard events. To address this gap, this work presents the first global database of past multi-hazard events, comprising 57 in-depth cases contributed by over 150 experts worldwide. The database includes compound, concurrent, and consecutive events spanning meteorological, hydrological, geological, environmental, and biological hazards. It provides detailed descriptions of the physical characteristics of the events, examines changes in exposure and vulnerability during multi-hazard scenarios, analyzes the synergies and trade-offs of implemented risk reduction measures, and identifies both bottlenecks and good practices in multi-hazard risk management based on past experiences. This presentation will synthesize key insights from the database and explore how it can be utilized by researchers, practitioners, and decision-makers to further integrate multi-hazard risk management into practice.
How to cite: Sakic Trogrlic, R., de Ruiter, M., de Angeli, S., Duncan, M., Gill, J., Kreibich, H., White, C., and Ward, P.: Unlocking Global Insights: Opportunities for Multi-Hazard Risk Management from a Unique Empirical Database, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17791, https://doi.org/10.5194/egusphere-egu25-17791, 2025.
Research on multi-hazard events and their impacts requires detailed information which is often limited or sparse across multiple portals. Heterogeneous data and metadata on the occurrence of individual hazards, their spatial and temporal proximity, their potential relationships as well as underlying information on exposure and vulnerabilities is needed to understand and address complex multi-hazard risks.
Funded by the European Space Agency, the Earth Observation for Multi-Hazard (EO4MULTIHA) project aims to collect and harmonize event information from existing repositories into an event database, providing data and on-the-fly analysis tools through a web portal.
The EO4MULTIHA database is a Relational Data Base Management System, based on open-source solutions, that collects events information regularly updated from providers such as EMDAT (the International Disaster Database of the Centre for Research on the Epidemiology of Disasters (CRED)), EFFIS (European Forest Fire Information System), and other national and regional databases. The initial focus is on three project study areas: (i) the Adige River Basin in Italy, (ii) the southern part of the United Kingdom, and (iii) Dominica Island in the Caribbean. Moreover, the portal has been set up allowing for potential future expansion to include other regions and their corresponding multi-hazard risk data.
The web portal enables users to query database content and provides an initial support for grouping single events into multi-hazard events. It offers event visualization on an interactive map, with the possibility to apply spatial and temporal filtering to refine the results. An automated pipeline ensures continuous data integration and updates, supporting ongoing and future multi-hazard risk research. The web portal also provides links to an extensive suite of data, including satellite imagery, climatological records, in-situ measurements, and relevant statistics needed to describe hazard, exposure, vulnerability, and impacts of multi-hazard events. Additionally, the portal integrates Geostories focusing on specific multi-hazard events, combining available data and information into explanatory reports of specific and complex multi-hazard events.
Overall, the EO4MULTIHA project empowers a deeper understanding and analysis of complex multi-hazard events by facilitating access to quantitative data for researchers, decision-makers, and citizens. It represents a significant step forward in understanding and managing multi-hazard risks, contributing with valuable resources to the scientific community and practitioners.
How to cite: Vianello, A., Niknahad, M., Ventura, B., Terzi, S., Renner, K., Jacob, A., and Pittore, M.: The Earth Observation for Multi-Hazard events database and portal, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11720, https://doi.org/10.5194/egusphere-egu25-11720, 2025.
Catalonia region is affected by extreme climate and weather events (e.g., floods, forest fires, heat waves, and drought). The Large-scale Demonstrator (LSD) Catalonia within the Horizon Europe RESIST project (2023-2027) proposes transfer solutions with the tools to enhance capacity building on natural risk awareness and proactive preparedness in emergency management.
The first two tools, a real-time Multi-Hazard Early Warning System and Impact-based Site-specific Warnings, have been implemented locally with available forecasts and observations from local sensors, risk maps, and information on critical points, producing impact-based forecasts with enhanced functionalities. The third one, the Citizen Participatory Toolkit, includes a set of public survey questionnaires and guidelines on community resilience for local and regional Civil Protection based on current social perspectives on climate change-related natural hazards, risk information, and inclusive risk communication.
This presentation will showcase examples of these tools implemented in the RESIST pilot cities.
How to cite: Park, S., Corral-Celma, C., Llort, X., Rodríguez-Giralt, I., Cifre-Sabater, M., and Berenguer, M.: Tools for emergency management and climate resilience in Catalonia (Spain), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16119, https://doi.org/10.5194/egusphere-egu25-16119, 2025.
Despite recent advances in modelling and forecasting natural hazards and how they impact communities, infrastructures and livelihoods, decision-makers still struggle to comprehensively understand local hazard impacts, thus failing to successfully plan integrated adaptation and impact mitigation strategies to reduce high economic, environmental and human losses. Climate change is expected to further increase the intensity of extreme events calling for systemic and interdisciplinary strategies for local mitigation and adaptation strategies. Moreover, in a progressively interconnected society, collective and unforeseen risks are likely to emerge because of dynamic change in exposure (e.g. due to population and urban growth), vulnerability (e.g. due to aging infrastructure) and decreases in coping capacity (e.g. due to aging populations). Within the Horizon Europe MEDiate project (Grant agreement ID: 101074075), scientists and stakeholders (disaster managers) representing local authorities are working closely to address these challenges, through the co-creation of a framework for multi-hazard disaster-resilience decision-support system (DSS), and implementation in operational environment in four European test beds (TBs) with different demography, cultural, geographical, and geopolitical conditions: TB1-City of Oslo (Norway), TB2-Metropolis of Nice Cote d’Azur (France), TB3-Essex County (UK), TB4-Múlaþing (Iceland). The conceptual DSS model uses multi-criteria decision-making approach to support the development of mitigation options and risk management plans. The conceptual DSS model has been integrated in a platform where multi-stakeholder groups can work together, define their local characteristics, preferences and priorities, and manage disaster risks considering multi-interacting hazards and cascading effects, and accounting for forecasted modifications in the hazard, exposure and vulnerability.
How to cite: Ibrahim, O. M., Revel, Y., Shepherd, L., Halldórsdóttir, T. K., Gjetrang, I. S., Thomin, C., Yousaf, Z., Palmer, C., Úlfarsson, A., Huang, C., Morga, M., Þorvaldsdóttir, S., and Meslem, A.: A conceptual multi-hazard and multi-risk decision-support system model: Stakeholders’ perspectives, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20660, https://doi.org/10.5194/egusphere-egu25-20660, 2025.
Posters on site: Wed, 30 Apr, 08:30–10:15 | Hall X3
The Korean government has revised the Coastal Management Act to conduct annual coastal disaster risk assessments. These assessments aim to identify the causes of coastal disasters and respond to them effectively. They provide scientific and quantitative risk information on vulnerable coastal areas, preparing for increased coastal disaster damage due to sea-level rise, typhoon intensification, and climate change.
In this study, the coastal disaster risk assessment framework was established by applying the IPCC AR6 (2023) framework. Indices and indicators suitable for the Korean coastal system were selected. To incorporate the concept of 'Response' emphasized in IPCC AR6, 'Reduction factors' were introduced to 'Vulnerability'. The Coastal Disaster Risk Index (CDRI) was evaluated by considering three factors: 'Hazards', 'Exposure', and 'Vulnerability'. Data for the evaluation were collected from statistical and basic data authorized by the Korean government. A high-resolution spatial grid of 100 meters was established, and evaluation results for each indicator and index were produced through statistical analysis. The results are displayed in five grades within the coastline (evaluation line) and coastal area (evaluation grid) after verification and validation. The 25 indicators and 31 basic data for coastal disaster risk assessment were updated with the latest data, and new databases were built for some indicators.
In conducting coastal disaster risk assessments, extreme analysis of external forces (Hazards such as rainfall, wind, surges, and waves) that cause disaster damage was performed to evaluate the possibility of disaster occurrence. The recurrence frequency was applied to the grade interval criteria, which were set based on the 50-year or 100-year recurrence frequency, referring to the design criteria of various external forces. To evaluate indicators with different units and sizes, a non-dimensionalization process called standardization (converting values between 0 and 1 using cumulative probability distribution) was performed. Weights for each indicator were applied to calculate the weighted average, and the 'Hazards', 'Exposure', and 'Vulnerability' indices, along with the CDRI, were calculated. In the CDRI, results of each indicator and index were displayed in five grades from 1 to 5. Grade 5, indicated as "High risk" (red), was the most dangerous, and grade 1, indicated as "Low risk" (green), was the safest.
As a result of the evaluation, Busan Metropolitan City and Jeju Island showed dangerous results with an average of grade 4. Since the evaluation results are a high-resolution spatial grid evaluation with a size of 100 meters, the distribution of risk grades by grid within each local government is well distinguished. The spatial distribution of coastal disaster risk areas is expressed more accurately than visualizing them as a "line" on the coastline. The results of the 'Exposure' and 'Vulnerability' evaluations showed a safe grade in grids with low population density or low density, such as residential complexes and industrial complexes. Therefore, the results of this study can provide realistic and detailed information to local government officials who need to make decisions on designating coastal disaster risk areas and establishing reduction measures.
How to cite: Kang, T.-S., Kim, M.-W., Lee, H.-Y., Jeong, K.-Y., and Seo, G.-H.: High-Resolution Coastal Disaster Risk Assessment: A Case Study of Korea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5597, https://doi.org/10.5194/egusphere-egu25-5597, 2025.
Pluvial floods, intensified by short-duration and high-intensity storms, are becoming increasingly frequent and severe due to climate change and urbanization. SaferPlaces addresses this with a digital twin platform that integrates high-resolution geospatial and climate data from sources such as Google Earth Engine (GEE), Open Street Map (OSM), Microsoft Planetary, Amazon, and Copernicus. These datasets are automatically integrated to construct detailed, multi-layered urban digital twins, enabling real-time flood hazard and risk modelling. As part of the SaferPlaces platform, Safer_RAIN, a fast-processing Hierarchical Filling-&-Spilling Algorithm (HFSA), combines spatially distributed rainfall input and infiltration simulation through a pixel-based Green-Ampt model (see https://saferplaces.co/) and enabling building-by-building flood risk modeling across large urban areas. Leveraging the platform’s cloud-based infrastructure, Safer_RAIN can efficiently run computationally intensive simulations at high resolution, delivering real-time results that support effective urban planning and climate resilience strategies. Comparisons with traditional 2D hydrodynamic models revealed limitations in Safer_RAIN, such as underestimation of flooded areas due to the lack of hydraulic backwater effect and single flow path constraints. We present significant enhancements of the SaferPlaces platform that were recently developed to address these challenges. These include: (1) incorporating a weir equation using a simplified kinematic approach to account for backwater effect, (2) introducing a travel-time distribution for water within watersheds and (3) implementing flow path flood extension using a Height Above Nearest Drainage (HAND) approach. These improvements are tested in a synthetic case study and applied to a set of real flooding events in urban areas of Pamplona, Spain. With its ability to run scalable simulations in real time and integrate diverse datasets, Safer_RAIN, as part of SaferPlaces' digital twin platform, offers a transformative solution for flood risk intelligence, empowering cities to build preparedness and enhance climate resilience.
Keyword: Digital twin, Hierarchical Filling-&-Spilling Algorithms, 2D hydrodynamic models, pluvial flooding
How to cite: Kyaw, K. K., Luzzi, V., Bagli, S., Mediero, L., and Castellarin, A.: Enhanced Kinematic Hierarchical Filling-&-Spilling Algorithms for Pluvial Flooding: Synthetic and Real-Case Applications with Comparative Analysis to Fully 2D models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15906, https://doi.org/10.5194/egusphere-egu25-15906, 2025.
Disaster events are often generated by a combination of factors, related to the occurrence of multiple hazards in space and time, leading to direct and indirect impacts in different sectors. Impact chains have proven to be a useful tool for understanding and visualising the sequence of impacts of such events. They are generally co-developed with stakeholders in workshops and may result in rather complex networks. Whereas these are very useful for understanding past events, their application to forward prediction, and the quantification of the various interactions and impacts, remains a major challenge.
The RiskChanges tool was developed to assess the impact of multi-hazards at a local level, especially for hazardous events that have a high spatial variation. The tool can be used to compare the current level of risk with those in future years, following climate change and urban growth scenarios. However, the impacts of individual hazards were still assessed individually and combined in the risk assessment phase.
Now we have further developed the tool, and have considered the possibility of multi-hazard exposure analysis, combining two or more hazardous events. This can be done using multi-hazard modelling (e.g. for landslides and flooding under extreme rainfall events), or by assessing the exposure to two hazards together. After this, simple impact chains can be built to determine how physical vulnerability and exposure are linked to assess the losses of consecutive, concurrent, and cascading events, for different types of elements at risk.
The tool offers a simple and useful tool to estimate the impacts of complex events and to improve preparedness planning and impact-based forecasting. An example of the application of the tool is presented for volcanic and hydrometeorological hazard interactions for the island of Saint Vincent in the Caribbean.
How to cite: van Westen, C., Ramadhani Prasetya, S., Hazarika, M., Das, D. K., and Mandal, A. K.: Modelling hazard interactions and cascading impacts; development of the RiskChanges tool, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16011, https://doi.org/10.5194/egusphere-egu25-16011, 2025.
Placing a few years between ourselves and the COVID-19 pandemic offers no shield or resilience against future pandemics and medical crises. Pushing healthcare facilities worldwide to their limits and disrupting the delivery of preventive and curative services. While it has provided scientists and stakeholders across various sectors with valuable insights, there are still reflections that remain to be fully understood. Despite a growing body of literature on pandemic lessons, a significant research gap emerges in understanding the unfolding of the COVID-19 pandemic as a long-lasting hazard through the lens of Disaster Risk Reduction.
This research work aims to investigate the impacts of the COVID-19 pandemic on the hospital network in the capital of Romania during 2020-2022, employing a multi-method approach that integrates the Impact Chain model and forensic analysis. Up to date, no forensic investigations have been conducted on COVID-19, which sets the present one at the forefront of pandemic-related research.
The Impact Chain integrates the COVID-19 pandemic as an epidemiological hazard, its various impacts, the contributing vulnerabilities, exposed elements, and adaptation options, as well as the connections established among them. The model was implemented following the guidelines formulated within the Paratus Project. It draws from scientific papers, official reports, statistical datasets, legislative documents, WHO official websites, and news reports, as well as from expert knowledge and the insights of medical personnel at the forefront of the fight against COVID-19 in Bucharest. The Impact Chain is used as a support tool for the forensic analysis that integrates elements from the Post-Event Review Capability (PERC) and the Detecting Disaster Root Causes (DDRC) frameworks.
This multi-method approach facilitated a detailed view of the pandemic impacts, the various types (i.e., financial, institutional, physical, and socio-cultural) vulnerabilities that contributed to them, and the mitigation measures implemented to address them. The impacts of the COVID-19 pandemic cover a broad spectrum, extending over both short and long periods, and are closely linked with the strain that the overwhelming number of COVID-19 patients placed on healthcare facilities. Most of the identified vulnerabilities stem from the ”chronic” underfunding of the medical system in Romania, as well as from the institutional vulnerability represented by the low performance of this system. In terms of mitigation efforts, the identified adaptation options tend to address impacts rather than vulnerabilities.
The systematic understanding of the key elements of pandemic risk provided by the structured model of Impact Chains, complemented by the detailed narrative put forward by the forensic analysis, offers a comprehensive and fresh understanding of the pandemic disaster and its effects on the medical facilities in Bucharest. Future research should extend the scope of the analysis beyond the hospital network, to include the local community.
How to cite: Savu, C., Atun, F., Albulescu, A.-C., and Armaș, I.: Tracking the pandemic footprint in Bucharest, Romania: A forensic and Impact Chain analysis from the PARATUS Project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16718, https://doi.org/10.5194/egusphere-egu25-16718, 2025.
Globally, the absence of a fully private insurance market for post-disaster recovery makes public sector involvement essential when insurance is either unaffordable or unavailable. Spain's public-sector-driven disaster risk response system offers a valuable case for analyzing post-disaster public compensation. The Gloria storm in January 2020, a significant multi-hazard event that activated all Spanish disaster compensation programs, serves as an ideal example to assess public intervention in disaster response. Hence, this study aims to assess the public sector’s role in mitigating financial losses during multi-hazard events, using Gloria storm as a case study. We analyze regional disaster losses through a three-step approach: compiling a recovery database, assessing key hazards, and examining the relationship between uninsured asset damages and hazard likelihood. Preliminary results show that the storm caused widespread damage, with recovery costs totaling 264 million Euros. In Spain, government interventions in disaster risk response are composed of fully public and public-private partnership (PPP) funds. Regarding the distribution of funding, the results reveal a distinction in asset coverage. Fully public funds are essential for restoring community services and infrastructure, especially in inland areas, focusing on sectors like water management, environment, culture, agriculture, and transportation. Public-private partnerships fund more privately-oriented assets, such as trade, industry, residential properties, vehicles, and office spaces, primarily in coastal and northern inland regions. Moreover, public funding prioritizes hazard type first, then impacted assets, while PPPs focus on directly funding affected assets without considering the hazard type. Although public compensation is not tailored to multi-hazard events, we identify municipalities affected by multiple hazards, showing more severe overall damage in areas with also a higher concentration of affected assets. Finally, the results prove that the hazard likelihood (return period) is not appropriate for understanding public compensation distribution, as no clear correlation with loss costs is found, suggesting that other factors should be considered. This study highlights the importance of assessing and understanding the distribution of public compensation, giving new insights in loss assessment of typically uninsured assets.
How to cite: Hürlimann, M., Pantaleoni Reluy, N., and Lantada Zarzosa, N.: What can we learn from public compensation efforts following a multi-hazard event? The 2020 Gloria storm in Catalonia., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18939, https://doi.org/10.5194/egusphere-egu25-18939, 2025.
The complexity and variability of slow-moving landslides require long-term, multi-parameter monitoring rather than short-term assessments to effectively evaluate and mitigate potential hazards. This forensic analysis, which is carried out as part of the PARATUS project, focuses on the Landslide Observatory Lower Austria, a unique setting encompassing three study sites: Hofermühle, Gresten, and Brandstatt. Rather than focusing on specific catastrophic events—which are rare for slow-moving landslides, such as the event at the Hofermühle in 2013—this study emphasizes process understanding and the cumulative impacts of slow events, which, over decades, can significantly shape landscapes and risks.
Landslides, the primary hazard under investigation, rank among the most critical natural hazards worldwide due to their ability to cause significant damage across sectors and landscapes. Triggered by various factors, including intense precipitation and human activities, landslides pose ongoing challenges in Lower Austria, where geological and climatic conditions exacerbate susceptibility. The region covers approximately 19,000 km² and serves as a prime setting for Landslide Observatory Lower Austria´s operations. The three observatories are comparable in terms of geological and climatic contexts, land use, and anthropogenic influences, such as drainage systems, yet differ in their spatial extent, landslide subsystems, and dynamics. This diversity enhances the forensic scope of the analysis, offering insights into distinct landslide behaviors and long-term trends.
The Landslide Observatory Lower Austria employs an array of monitoring methods, including automatic inclinometers, terrestrial laser scanning (TLS), UAV surveys, and piezometers, supplemented by meteorological data. These tools capture data at various spatial and temporal scales, from point-specific measurements to areal assessments, enabling the exploration of surface and subsurface movement dynamics. Slow-moving landslides present a unique opportunity for disaster prevention, as their gradual progression allows for detailed study and modeling.
Building resilience for future events requires a multifaceted approach that addresses both immediate needs and long-term strategies. In response to recent events, efforts have been made to improve the recognition of slow-moving processes and landslides as hazards, garnering more national and federal attention. However, challenges persist, particularly in mitigating physical vulnerabilities such as rebuilding infrastructure in high-risk areas and uncovering historical evidence like the locations of drainage pipes. To reduce future risks and increase resilience, ongoing initiatives focus on comprehensive assessments, proactive measures, and filling crucial knowledge gaps to ensure a more robust understanding of hazards and enhance preparedness for potential future incidents. By synthesizing data and insights from Hofermühle, Gresten, and Brandstatt, this analysis provides a robust foundation for improving landslide management in Lower Austria and beyond.
How to cite: Wenzel, T., Marr, P., and Glade, T.: Forensic analysis of three permanent landslide observatories in Lower Austria, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19017, https://doi.org/10.5194/egusphere-egu25-19017, 2025.
In the PARATUS project, we ran the forensic analysis for Hurricane Katrina, which occurred in 2005. This category-5 hurricane severely impacted the US Gulf Coast, particularly New Orleans. It contextualizes the disaster’s temporal, spatial, and operational dimensions, tracing the cascade of events and vulnerabilities that compounded the hurricane’s catastrophic effects. The existing studies identify Hurricane Katrina as an extreme event with a return period of 100-250 years.
The analysis reveals the multifaceted vulnerabilities underlying the disaster including physical infrastructure deficits, socio-economic inequalities, environmental degradation and governance failures. Pre-existing vulnerabilities such as poorly maintained leeves, wetland degradation and socio-economic disparities contributed to higher exposure and risk. The marginalized communities with limited access to resources and risk awareness were the worst hit, exemplifying systemic inequality. The infrastructure destruction caused by the disaster led to an immediate displacement of 1.2 million people and the loss of over 1400 lives. Short-term environmental consequences ranged from biodiversity loss to contamination 0pf air and water with hazardous materials. The hurricane disrupted critical services, livelihoods, and supply chains, causing economic losses that exceeded 100 billion USD. The disaster also triggered severe mental health issues among the survivors.
The federal and state agencies such as FEMA were mobilised during the recovery phase. Financial resources, advanced technologies and revised disaster frameworks were deployed shortly after the disaster. However, bureaucratic inefficiencies and inequitable resource distribution left the vulnerable population with minimal support. Restoration of critical infrastructures took over a year while rebuilding homes spanned several years. As resilience measures, the enhancement of the levee system advanced engineering practices in rebuilding, and improved risk communication were undertaken. The study concluded that despite the institutional measures, the low-income groups were disproportionately affected. Multi-dimensional vulnerabilities remain critical to reducing future disaster impacts. The forensic underscores the importance of integrated, equitable disaster risk management frameworks to enhance resilience against future extreme weather events, which are likely to be intensified by Climate Change.
Keywords: PARATUS project, Forensic Analysis, Hurricane Katrina, Multi-sectoral Impact, Disaster Risk Assessment
How to cite: da Silva Mano, A., Gosh, P., Bockarjova, M., Van den Bout, B., Romagnoli, F., and Atun, F.: Forensic analysis for Hurricane Katrina, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20464, https://doi.org/10.5194/egusphere-egu25-20464, 2025.
Combining knowledge and learning from past events is a preeminent way of advancing our knowledge to better forecast the potential impact of future multi-hazard risk events. While historical disaster data is indispensable, acknowledging the dynamic nature of economic, social, and environmental conditions, at the same time it challenges the prevailing notion that "the past is the key to the future." In the context of the PARATUS project, we developed a forensic approach based on three specific methodologies: Investigation of Disasters (FORIN), Post Event Review Capability (PERC), and Detecting Disaster Root Causes (DKKV). PARATUS approach applies a combination of these three forensic analyses to a set of learning case studies drawn from selected past disaster events to analyse and navigate the complexity of disaster impacts across diverse contexts.
In this poster, we will present the Forensic Analysis conducted for the Tohoku earthquake and Tsunami, emphasizing hazard characterization, cascading effects and the effectiveness of the early warning systems. The Tohoku earthquake and Tsunami that occurred on the 11th of March 2011 was one of the costliest disasters ever recorded with economic losses estimated at 235 billion USD. A 9.0 magnitude earthquake triggering a Tsunami with wave heights exceeding 40 meters impacted 400 kilometres of Japan’s coastline. The present study conducts a PARATUS forensic analysis of the Data obtained from scientific literature, institutional reports and expert evaluations that have been compiled to understand the nature of the disaster and its short and long-term effects on the physical, environmental, socio-cultural, economic and institutional dimensions. Besides that, the triggering effects of the disaster, such as tectonic subduction and the subsequent cascading hazards, such as tsunami and nuclear reactor failure in Fukushima Daiichi, have also been studied in detail.
A forensic analysis of Japan’s Earthquake Early Warning (EEW) system, the search and rescue operation, impacts, pre-disaster vulnerabilities and recovery (post-disaster) have been undertaken. This study highlights the role of the institutional response after the disaster. This study not only assesses the different aspects of the disaster in an all-encompassing way but also focuses on building back better by enhancing disaster resilience, capacity building and increased disaster preparedness by studying Japan’s disaster response and providing actionable recommendations. The analysis contributes to understanding systemic vulnerabilities and improving future disaster management strategies. Japan’s approach to strengthening community-level preparedness, improving warning systems for providing timely information to remote communities, and integrating sustainable recovery strategies for post-disaster recovery are among some of the key findings of the study.
How to cite: Atun, F., Ghosh, P., Cocuccioni, S., Romagnoli, F., and van Westen, C.: Learning from the past multi-hazard events. 2011 Tohoku Earthquake and Tsunami Forensic Analysis Application, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20510, https://doi.org/10.5194/egusphere-egu25-20510, 2025.
Posters virtual: Wed, 30 Apr, 14:00–15:45 | vPoster spot 3
EGU25-17470 | Posters virtual | VPS13
Earthquake Shaking Simulation Workflow for Urgent Computing Services: Challenges and AdvancesWed, 30 Apr, 14:00–15:45 (CEST) | vP3.10
Urgent Computing (UC) refers to the use of High-Performance Computing (HPC) and High-Performance Data Analytics (HPDA) and Artificial Intelligence (AI) modules during or immediately following emergencies. It typically integrates complex end-to-end workflows with scalable computing resources, where multiple model realizations are necessary to account for input and model uncertainties, all under strict time-to-solution constraints. Enabling urgent HPC in unpredictable events such as earthquakes can significantly enhance resilience and response efforts. The temporal horizon for UC usually spans from minutes to a few hours, providing decision-makers with rapid estimates of the potential outcomes of emergency scenarios. In particular, high-resolution synthetic ground motions for earthquakes can complement the tools used by seismological services for impact analysis. Here, the Urgent Computing Integrated Services for Earthquakes (UCIS4EQ) is proposed as an innovative UC seismic workflow designed to rapidly generate synthetic estimates of the consequences (such as synthetic time histories, shakemaps, PGA/PGV, among other proxies) of moderate to large earthquakes (M > 6). Over the last six years, UCIS4EQ has been developed from scratch and received contributions within the framework of three European projects (DT-GEO, eFlows4HPC, and ChEESE CoE). In this work, we demonstrate the technological maturity of UCIS4EQ and its operational readiness in collaboration with the Mexican Seismological Service (SSN). Furthermore, this work addresses the challenges we face to reach operational maturity addressing the specific requirements of a seismological service for an urgent computing framework providing reliable outcomes for decision making with global coverage.
How to cite: Monterrubio-Velasco, M., Boehm, C., Iglesias, A., Diez, G., Bhihe, C., Esquivel, L., Zamora, N., Tuinstra, K., and de la Puente, J.: Earthquake Shaking Simulation Workflow for Urgent Computing Services: Challenges and Advances, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17470, https://doi.org/10.5194/egusphere-egu25-17470, 2025.
EGU25-20476 | ECS | Posters virtual | VPS13
Chamoli Glacial Burst: Investigating the vulnerability of the Himalayan geology with the support of Forensic AnalysisWed, 30 Apr, 14:00–15:45 (CEST) | vP3.11
The Chamoli Glacial flood happened in the Indian state of Uttarakhand on the 7th of February 2021. This disaster was triggered by a rockslide-induced glacial burst near the Ronti peak. The event unleashed a massive debris flow that devastated the area’s critical infrastructure, including the Rishiganga and Tapovan Vishnugad hydropower projects. The event underscored the vulnerability of the fragile Himalayan geology, challenges in development, disaster preparedness and early warning systems.
PARATUS project's forensic approach is based on the combination of three specific forensic methodologies: Investigation of Disasters (FORIN), Post Event Review Capability (PERC), and Detecting Disaster Root Causes (DKKV). The forensic analysis investigates the disaster’s causes, multi-dimensional impacts and responses, highlighting the key vulnerabilities across physical, socio-cultural, economic and institutional dimensions. The study identifies poor infrastructure resilience, environmental degradation and limited emergency response capacity as major contributors to the severity of the disaster. Cascading effects such as sedimentation and artificial lake formation further exacerbated the risks. The immediate aftermath saw significant disruptions in transportation and communication networks, hindering rescue operations despite the swift deployment of ground and aerial relief to the affected population.
In the recovery phase, coordinated efforts under India’s National Disaster Management Plan facilitated relief and reconstruction. However, challenges associated with the long-term rehabilitation of the people affected by the disaster still persist. The governmental institutions are currently focusing on building resilience through slope stabilization, improved early warning systems and sustainable infrastructure development. Addressing systemic vulnerabilities, including governance gaps and socio-economic inequities remains a critical step toward mitigating future risks. This forensic analysis builds on existing scientific literature and institutional reports revealed by the Government of India to assess and emphasize the necessity of integrating multi-hazard approaches and localized strategies for disaster risk reduction in vulnerable mountainous regions like the central Himalayas.
How to cite: Ghosh, P., den Bout, B. V., Westen, C. V., and Atun, F.: Chamoli Glacial Burst: Investigating the vulnerability of the Himalayan geology with the support of Forensic Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20476, https://doi.org/10.5194/egusphere-egu25-20476, 2025.
