This interdisciplinary session invites contributions from a wide range of disciplines and sectors involved with the full spectrum of EWS development and implementation, including but not limited to natural hazards science, atmospheric and hydrologic research, social sciences, and disaster management practice. We encourage submissions addressing the following key themes and sharing of lessons from successes and failures:
● Early warning and anticipatory action: Frameworks and multi-stakeholder implementation in translating early warnings/EWS into effective disaster response and preparedness mechanisms;
● Impact-based approaches: methodologies and approaches for design and implementation of impact-based EWS;
● Technological innovations: advances in AI, machine learning, Earth observation, IoT and other cutting-edge technologies in components of EWS;
● Risk communication and community engagement: strategies that integrate behavioral and psychological insights, building trust, and ensure effective warning communication and dissemination, particularly at the community level;
● Data integration and system interoperability: approaches to integrate diverse data sources that address challenges in cross-agency data sharing and platform integration.
Orals: Thu, 7 May, 08:30–10:15 | Room 2.17
Operational extreme weather forecasts and early warnings are generally limited to timescales of up to around 10 days and to predicting single events, such as flooding or a heatwave. However, experimental ‘extended-range’ weather predictions that extend up to 46 days have been developed over the last decade by the world’s leading meteorological centres. A key motivation of exploring this prediction timescale is to bridge the gap between timescales, incorporate the latest ‘multi-hazard’ approaches, and improve early warnings and anticipatory actions. Currently, however, the extended-range prediction and the multi-hazard research and operational communities are largely disconnected. To date, there has been no coordinated effort to build a network that connects these disciplines and communities towards the development of operational systems. However, it is essential that these communities come together to explore windows of opportunity and instigate a step-change in the way forecasts are designed, produced and used. To address this challenge, here we present the ANTICIPATE COST Action (CA24144) that has created the first pan-European network focused on extended-range multi-hazard predictions and warnings. Over the next 4 years, ANTICIPATE will bring together existing but largely disconnected disciplines, operational practitioners and stakeholders (including extreme weather forecasting, extended-range prediction and climate dynamics, disaster risk reduction, multi-hazards, and communications) to drive forward advancements in the science, training, communication and application that will support next generation of effective early warnings that enable preparedness and action across hazards and forecasting lead times. In this talk, we will explore upcoming events and activities, and share how ANTICIPATE will provide vital leadership in multi-hazard predictions and warnings, address gaps and challenges, and help educate the next generation of forecasters and communicators for societal benefit. Further details about the ANTICIPATE COST Action are available here: https://www.cost.eu/actions/CA24144/.
How to cite: White, C., Rivoire, P., Walpole, O., Ramos, A., Wegmann, M., Russo, A., Pechlivanidis, I., Bloomfield, H., Larsen, M., Robbins, J., Arosio, M., Šakić Trogrlić, R., de Ruiter, M., De Angeli, S., O'Loughlin, F., Domeisen, D., Caltabiano, N., Ribeiro, A., and Hronček, S.: ANTICIPATE COST Action: extended-range multi-hazard predictions and early warnings, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4949, https://doi.org/10.5194/egusphere-egu26-4949, 2026.
Sustainable water and land management strategies to address land subsidence in Dutch built-up areas, as outlined in the backcasting approach of the Living on Soft Soil (NWA-LOSS) research programme, require robust projections of future subsidence and associated risks in the built-up area, such as the structural-damage risk of shallow foundation buildings, under different intervention water and land management strategies. While InSAR data, such as the ortho vertical displacement of ground surface from the European Ground Motion Service (EGMS), provides excellent records of recent vertical ground-surface deformation with millimeter accuracy, it is not a standalone tool that can be used to forecast future subsidence under varying future conditions. To bridge this gap, we introduce EGMS+, a machine learning framework that integrates the EGMS data with a Random Forest (RF) regressor to project future subsidence under various future conditions. The Random Forest algorithm was employed to learn the complex, non-linear relationships between EGMS mean annual velocity rates and a suite of relevant spatial predictors. These predictors consist of the mean lowest groundwater level, percentage of built-up area, percentage of old buildings, ground-surface elevation, Holocene soft-soil thickness, and percentage of grass cover within each 100 m grid cell across the built-up areas of Gouda and Krimpenerwaard municipalities in the Netherlands. The model achieved high predictive accuracy (R² = 0.73, Out-of-Bag score OOB = 0.73, Mean Absolute Error MAE = 0.095 mm/year) on five years of data (2019–2023). For the structural-damage risk assessment, we use a fragility curve developed by the NWA-LOSS team at TU Delft, which defines the probability of slight structural damage as a function of 5 mm crack width. This curve was used to compute building-specific structural-damage probabilities by integrating differential settlement with the short-side dimension of each building unit in the study area. Essentially, the EGMS+ framework enables future scenario projection by simulating how changes in these predictors affect future subsidence. This capability can be demonstrated by projecting future subsidence and associated risks, such as the structural-damage risk of shallow foundation buildings under several NWA-LOSS targeted future states, such as those involving raised water tables and intervention targeting shallow-foundation building units. This EGMS+ framework provides quantitative estimates of the effectiveness of various mitigation strategies, offering a powerful, dynamic decision-support and spatial planning tool that can evaluate and prioritize sustainable pathways of addressing land subsidence in the Dutch built-up areas.
How to cite: Hammad, M. and Stouthamer, E.: EGMS+: A Machine Learning Framework for Projecting Future Land Subsidence and the Associated Structural-Damage Risk of Shallow-Foundation Buildings: A case study of Gouda and Krimpenerwaard municipalities, the Netherlands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18341, https://doi.org/10.5194/egusphere-egu26-18341, 2026.
Climate change brings emerging complex risks, subtle and weak, starting to manifest in some regions around the world, followed by the recurrence of preventable tragedies across regions. For instance, in Macau in 2017, people drowned in flooded underground car parks as they tried to save their vehicles. Tragically, similar preventable tragedies have since recurred in South Korea (2022) and Spain (2024). Before these incidents, local disaster risk reduction strategies in Macau, South Korea, and Spain did not cover specific guidelines addressing the resilience of underground spaces to extreme weather. Although local governments eventually enhance their regulations, such action is typically a reactive measure, triggered only by catastrophe rather than proactive foresight.
The primary obstacle to foresight is the challenge of identifying ‘unknown unknowns’—rare, variable-severity emerging risks. Our study directly addresses this critical gap in the early warning chain by demonstrating a systematic methodology that leverages cross-regional knowledge of analogous events to identify ‘unknown unknowns’ for regions without prior experience, thereby transforming them into foreseeable risks and enabling proactive preparation and strengthening response capabilities.
This study utilizes Natural Language Processing to analyze 7.7 million news articles across four dimensions—public awareness, priority of human needs, level of severity, and scope of influence—identifying 639 emerging climate threats, subsequently refined by an expert intervention to pinpoint the most critical tail-end risks. The findings uncover a wide spectrum of lesser-known emerging risks across diverse sectors, such as health, food, infrastructure, finance, transportation, and wildlife-related threats. An example of the findings is a paradox, first identified in peer-reviewed research and subsequently reported by the media. This paradox reveals that mercury in fish is increasing even as oceanic mercury declines, a phenomenon driven by warmer seawater that compels fish to migrate to cooler regions, which in turn elevates their energy consumption and accelerates bioaccumulation.
Ultimately, this research provides a practical decision-support tool for a range of stakeholders. By translating ‘unknown unknowns’ into actionable insights, our methodology enables a paradigm shift from reactive post-disaster response to proactive risk management. Specifically, these identified risks can be used to inform targeted risk communication strategies and establish triggers for anticipatory action. This provides a crucial component for the UN’s ‘Early Warnings for All’ (EW4All) initiative, enabling communities and disaster managers to prepare for emerging complex risks before they manifest as localized crises.
How to cite: Zhang, Y., Yarime, M., Lau, A. K. H., Chan, J. W. M., Fung, J. C. H., Shun, C. M., and Chan, K.: A systematic approach to identify 'unknown unknowns' for impact-based early warning systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1454, https://doi.org/10.5194/egusphere-egu26-1454, 2026.
In June 2017, a large landslide in Umiammakku Nunaat (Karrat), West Greenland caused a huge tsunami wave of about 90 m height on the opposite fjord slope and reached the village of Nuugaatsiaq 32 km far away. The tsunami caused severe property damage and the death of four people in the village. After the tsunami, the two settlements Nuugaatsiaq and Illorsuit were evacuated due to the still high risk of another potential tsunamigenic landslide in the fjord. To this day the two settlements are still under evacuation but none of the villages have been permanently relocated so far.
This disaster highlighted the urgent need for natural hazard monitoring systems in this region. In 2021, Geoprevent and the local responsible authorities made a feasibility study and installed the first ever natural hazard monitoring system in Greenland. This monitoring system in Umiammakku Nunaat runs year-around. Two deformation cameras were installed at the counter slope with a view on three regions of interest. An additional camera was installed next to one of these unstable slopes. These deformation cameras take multiple high-resolution images per day. Cross-correlation based algorithms are then used to identify differences between these images and estimate the deformation of these areas.
A deformation analysis can be compared to a timelapse with which one can see slow processes that a human eye cannot see. The local experts use the information provided by these monitoring systems for a continuous risk assessment. The continuous monitoring helps to evaluate the sitiation constantly and supports authorities in their decision making related to the evacuation of certain settlements.
From a technical point of view, Greenland presents quite some challenges to maintain a monitoring station under these harsh conditions. Remoteness, cold temperatures, heavy winds and polar night are only a few of them. In order to have enough power during polar night, the system is running on a methanol-based fuel cell solution with the option of solar charging during the sunny months. Moreover, communication with the station and the data transmission are satellite-based, so that the station can be controlled remotely.
How to cite: Wetter, J., Carrel, M., Stitelmann, O., St. Pierre, T., Von Wartburg, J., Mätzler, E., and Petersen, J.: Year-Around Monitoring of Slope Instabilities in Umiammakku Nunaat (Karrat), West Greenland with Deformation Analysis , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6834, https://doi.org/10.5194/egusphere-egu26-6834, 2026.
All nuclear power plant operators have a duty to inform the international community in case of potential damages or incidents at their plants with transboundary effects. This duty is a paramount, such that neighboring nations can take the appropriate actions to mitigate the effects of the potential nuclear fallout. History unfortunately shows that this duty may be neglected. This leads to a need for independent verification of nuclear power plant health. Remote sensing technologies present a promising avenue to achieve indications of nuclear power plant distress. New advances within Machine Learning methodologies for Remote Sensing presents an ability to automatically monitor nuclear power plants for changes or damages, which could raise concern. The goal is to achieve persistent, automatic, and global monitoring of nuclear power plants, for nuclear fallout early warning.
This study uncovers how new and existing remote sensing methodologies can be leveraged to detect changes and damages at nuclear power plants. This study includes existing and repurposed fire detection, structural change detection, and flood detection Machine Learning methodologies. Combined with new research on measuring steam generation from cooling towers, and temperature changes in cooling water reservoirs. This study is based on a large body of data from nuclear power plants from optical and SAR remote sensing payloads. The study also leverages existing, and open-source data from various natural disasters which are transferrable to the nuclear power plant monitoring task.
How to cite: Bostani Nezhad, K., Pedersen, H. B., and Sørensen, K.: Remote Sensing for Persistent Monitoring of Nuclear Power Plants, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20828, https://doi.org/10.5194/egusphere-egu26-20828, 2026.
More than a billion people live within 150 km of an active volcano, facing a variety of hazards such as ash fall, lava flows, and toxic gases that threaten lives, infrastructure, and livelihoods. Co-Existence with a volcano requires informed, prepared, and resilient societies capable of rebuilding.
Volcanic crises impose severe decision-making challenges and enormous economic costs, often borne by governments and individuals. Yet, the insurance gap for eruptions remains close to 100%. Alternative risk transfer solutions, that include parametric insurance structures and specifically catastrophe bonds, are innovative financial tools that can alleviate the financial impact of natural disasters. Unlike traditional insurance, parametric structures provide immediate payouts when predefined hazard severity thresholds are exceeded, enabling faster response and recovery. These thresholds and payout formulas are based on catastrophe models. While the structure is active, so-called “calculation agents” monitor the insured peril and calculate payouts for ongoing events.
Mitiga Solutions has pioneered methodologies for parametric coverage of both explosive and effusive eruptions, building on the world’s first volcano catastrophe bond (2021-2024) for the Danish Red Cross. Our approach uses “modelled-loss triggers”, meaning payouts are based on near-real-time impact calculations. Impact-based forecasting not only supports insurers but also provides actionable intelligence for emergency managers and other decision-makers during a volcanic eruption.
Inspired by this concept, the UNICORN project (EU Horizon Europe Programme grant agreement No 101180172) is developing a disaster management tool for lava flows at Etna volcano. Leveraging information from the volcano observatory, the tool will deliver concise, impact-focused reports with simulated lava inundation paths, updated satellite imagery, and modelled impact. By combining observatory data with impact-based forecasting, this tool aims to turn scientific insights into actionable strategies for both emergency response and financial resilience.
How to cite: Strehlow, K., Ghabrous Larrea, C., De Beni, E., Ganci, G., Cannavò, F., and Baladima, F.: Impact-based forecasting for volcanic eruptions: science driving financial preparedness , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20318, https://doi.org/10.5194/egusphere-egu26-20318, 2026.
For early warning systems (EWS) to translate into timely, protective early actions, particularly in the contexts marked by deep social, linguistic, and structural inequalities, effective risk communication and community engagement (RCCE) are essential. In Nepal, the Resilience, Adaptation and Inclusion in Nepal (RAIN) programme demonstrates a holistic, community-led approach for strengthening RCCE by embedding behavioural and psychological insights, fostering trust, and creating inclusive communication pathways that target the most vulnerable groups. RAIN, which is designed to support transformative impact for resilience, adaptation, and inclusion through a community-led approach, places community organisations, local governments, and at-risk populations such as landless communities, persons with disabilities, ethnic minorities, women, and girls at the centre of the early warning and early action system. This abstract examines how RAIN put RCCE into practice to improve the accessibility, credibility, and behavioural effectiveness of warnings across multiple hazards.
RAIN addresses a core challenge in Nepal’s EWS landscape, i.e., existing alerts are highly technical, often inaccessible to non-native Nepali speakers, and do not convey clear, actionable behaviour. Realizing that people act on warnings only when they trust the source, understand the message, and see its relevance, the programme has redesigned communication flows to be community-centred, multi-lingual, and multi-modal. Behavioural insights ranging from simplifying messages and making actions concrete to tailoring messages to literacy levels and reinforcing social norms through trusted local actors to shape how communities receive and interpret alerts. Community-based organisations (CBOs) and committees become active co-designers and disseminators of warnings, leveraging their embedded trust to increase credibility, reduce uncertainty, and motivate action.
To overcome structural and psychological barriers such as low-risk perception, fatalism, gender norms restricting mobility, and limited trust in government systems, RAIN strengthens risk communication channels. These include Interactive Voice Response (IVR) systems, door-to-door dissemination, mobilisation of community volunteers, sign language videos, and accessible formats for people with disabilities to support different communication needs. The programme also integrates locally relevant languages and culturally grounded communication approaches, acknowledging that linguistic relevance and cultural resonance are crucial for behavioural uptake. By incorporating Organisations of Persons with Disabilities (OPDs) and diverse CBO networks, RAIN enhances inclusive communication, adaptive behaviour, and equitable access to life-saving information.
At the system level, RCCE is institutionalised through collaboration with the Department of Hydrology and Meteorology (DHM), the National Disaster Risk Reduction and Management Authority (NDRRMA), provincial governments, and local governments, ensuring standardised message templates, impact-based forecasting, and a strengthened communication flow that connects scientific information to community understanding from information producers to at-risk communities. The programme’s localisation model will build trust over time by enabling communities not only to receive warnings but also to shape how warnings are generated, translated, disseminated, and acted upon.
Overall, RAIN offers a scalable model for RCCE that demonstrates how deeply rooted behavioural insights, trusted community actors, inclusive communication technologies, and systemic coordination can together ensure that early warnings effectively reach and are acted upon by the people who need them most.
How to cite: Khadka, P., Neupane, S., Pradhanang, A., and Manandhar, V.: From Warnings to Early Action: Community-Led Risk Communication and Engagement in Multi-Hazard Early Warning Systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1971, https://doi.org/10.5194/egusphere-egu26-1971, 2026.
The flood disaster in western Germany in July 2021 revealed substantial shortcomings in the communication and understanding of official warnings and risk information, which contributed to severe impacts (DKKV, 2024; Thieken et al., 2023). In response, the German federal and state governments initiated the development of a Natural Hazards Portal to provide a central, authoritative access point for harmonized information on natural hazards. The Deutscher Wetterdienst (DWD), Germany’s National Meteorological Service, was commissioned to design and implement the portal.
The Natural Hazards Portal integrates official warnings with preparedness, impact-related information, and behavioural guidance across multiple natural hazards. Its objectives are to improve the visibility and comprehension of warnings, strengthen individual and societal preparedness, and contribute to long-term resilience. To this end, the portal combines event-driven warning information with contextualized data on local hazard exposure, impact-oriented indicators, and recommended actions before, during, and after hazardous events. All content is designed to be clear, accessible, and comprehensible for diverse user groups.
This presentation presents the conceptual framework, current implementation status, and future development of the portal. We discuss key challenges related to the integration and standardization of heterogeneous data sources from multiple authorities, as well as the role of the portal within an ecosystem of specialized, hazard-specific platforms. Particular emphasis is placed on the transition from hazard-centered to impact-based warning and risk communication and on the portal’s potential to support anticipatory action and informed decision-making.
The Natural Hazards Portal represents a joint, holistic response by German authorities to increasing natural hazard risks under climate change. By providing localized, impact-relevant information and official warnings through a single, central access point, the portal aims to strengthen preparedness and resilience without replacing existing specialized warning services.
References
DKKV (2024). Governance und Kommunikation im Krisenfall des Hochwasserereignisses im Juli 2021, DKKV-Schriftenreihe Nr. 63, Januar 2024, Bonn. https://dkkv.org/wp-content/uploads/2024/01/HoWas2021_DKKV_Schriftenreihe_63.pdf.
Thieken, A. H., Bubeck, P., Heidenreich, J., von Keyserlingk, L., Dillenardt, J., & Otto, A. (2023). Performance of the flood warning system in Germany in July 2021 – insights from affected residents. Natural Hazards and Earth System Sciences, 23, 973–995 https://doi.org/10.5194/nhess-23-973-2023.
How to cite: Erhardt, B., Brendel, C., Hafer, M., Haller, M., Hüser, I., Koziar, C., Lengfeld, K., Rode, D. K., Rauthe-Schöch, A., Reetz, B., Riede, H., and Walawender, E.: Developing the Natural Hazards Portal for Germany – a central access point for warnings, preparedness and resilience, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11232, https://doi.org/10.5194/egusphere-egu26-11232, 2026.
Early Warning Systems (EWS) are widely recognized as a cornerstone of disaster risk reduction; yet, their effectiveness depends not only on scientific accuracy but also on how warnings are translated into a collective understanding and timely action at the community level. In many hazard-prone contexts, early warnings fail not because data is unavailable, but because communication infrastructures do not align with local languages, temporalities, and practices of attention. This paper presents a situated design research project in development in Maroantsetra, Madagascar, which reframes EWS as a socio-spatial and relational infrastructure.
The project is being developed through a collaboration between EPFL–ALICE Lab, the NGO Medair, local communities in Ambinanitelo, Ankofa, and Anjanazana, and national disaster management institutions (CPGU). It explores the co-design of a Sensitive Risk Warning Infrastructure (SRWI) that integrates institutional early-warning protocols with vernacular communication systems and environmental indicators, including town criers, drums, conch shells, and animal behaviour. Rather than replacing scientific alerts, the approach focuses on translation, rehearsal, and trust-building as key conditions for effective anticipatory action.
Methodologically, the ongoing research combines walk-along interviews, participatory mapping, role-play rehearsals, and low-tech prototyping to identify breakdowns in the warning chain and to co-design hybrid warning practices. Preliminary findings indicate that warning communication is inherently spatial and material, unfolding through proximity, sound, visibility, and shared places such as schools and community halls. By foregrounding these dimensions, the SRWI aims to advance a community-centred and impact-oriented approach to EWS, enhancing comprehension, ownership, and timely response.
The paper contributes to ongoing discussions on community engagement, last-mile communication, and anticipatory action by presenting design as an interface between scientific warning systems and situated action. Developed in parallel with the Architectures of Emergency research and an Atlas of Inhabiting Emergency, it connects multiple geographies of risk and positions design as a form of spatial inquiry that supports infrastructures of care, enabling communities to sense, interpret, and rehearse risk collectively.
How to cite: Mompean Botias, E.: Co-Designing a Sensitive Early Warning Infrastructure in Maroantsetra, Madagascar. , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9637, https://doi.org/10.5194/egusphere-egu26-9637, 2026.
Effective Early Warning Systems (EWS) does not only rely on natural hazard forecasting but also on how actors are prepared, coordinated, and respond in the different stages of the warning chain. We here present a scenario‑based methodology designed to assess emergency response at different responsibility levels before, during, and after multi-hazard events.
The method involves systematic information gathering during facilitated scenario exercises, followed by synthesis of findings to improve action cards and emergency plans. Study area is rather remote regions of Norway, and the approach effectively consolidated existing knowledge, initiated cross‑level dialogue, and revealed clear gaps in preparedness, coordination, and resource allocation.
The workshop objectives were to evaluate current response protocols, identify training needs among responders and communities, and propose interactive, scenario‑based training approaches. A three‑phase scenario—covering the warning, action, and recovery stages—was developed using local knowledge and recent events to ensure realism and relevance.
Key findings include: (i) inconsistencies in responsibility distribution and inter‑agency coordination, (ii) missing competencies and resource limitations, and (iii) the need for clearer communication pathways throughout the evolving event. Even though there is a strong and knowledgeable commitment from participants, improvement areas were identified.
Conducted in the aftermath of Extreme Weather Hans (2023) and Amy (2025), the work demonstrates the value of scenario‑based evaluation as an integral component of EWS development. This contribution forms part of the Norwegian research project Beredt! – Scalable Services & Risk-Based Governance for Climate-Driven Natural Hazards in Norway and highlights the importance of continuous training and assessment to enhance disaster preparedness and resilience.
How to cite: Hegdahl, T. J., Gilbert, G., Devoli, G., Müller, K., Syndes, A. K., and Sydnes, M.: A Scenario‑Based Framework for Evaluating Emergency Response and Communication Throughout Multi-Hazard Events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16980, https://doi.org/10.5194/egusphere-egu26-16980, 2026.
Posters on site: Thu, 7 May, 10:45–12:30 | Hall X3
Natural geological, environmental, and anthropogenic-induced hazard identification and associated impacts are of key concern for the survival of all species on Earth. Each hazard is related and linked to one another through geology. For purposes of this paper, a hazard refers to a natural geologic, environmental, or anthropogenic-induced event. Risk is a measure of the magnitude of an event and the frequency of occurrence. Risk can be applied by evaluating the probability of a negative outcome or impact from a geologic, environmental, or anthropogenic-induced hazard source. Sensitivity is a measure of how resilient a target population or ecological sector is to the hazard. The combination of these factors can be expressed as an equation (Equation 1), where the result is potential Impact Severity
Geologic/Environmental/Anthropogenic-induced Hazard X Magnitude and Risk of Occurrence X Sensitivity = Impact Severity Equation 1
Understanding the geological, hydrological, and ecological environment is the first step in assessing risk and is represented by the general term Impact Severity. The second step is evaluating aspects of human behavior that affect the environment either through negative or positive outcomes. The third variable is evaluating the effectiveness of risk reduction measures. An equation is created by combining these fundamental concepts through which a Sustainability Index is the output (see Equation 2 below). The Sustainability Index represents a measure of sustainability for any particular location with a higher value representing increased risk for potential harm to human health or the environment and therefore, less sustainable.
Impact Severity x Behavioral Aspects x 1/ Risk Reduction Measure = Sustainability Index Equation 2
To evaluate the potential effectiveness of the Sustainability Index, it has undergone 18 years of testing at as many at 67 manufacturing locations in 12 different countries of the world. Primary risk inputs involved numerous geologic hazards and vulnerability, climate change using NOAA CMIP5 models, and contaminant risk factors using toxicity, persistence and mobility variables for air, water and soil. Over the 18-year period, improvements in Risk Reduction Measures have been realized by an average of 80% resulting in a significant reduction in overall risk. The most significant challenge during the 18-year implementation and evaluation period was changing cultural attitudes and behaviors. This highlights the difficult actions that must be addressed to change cultural attitudes and behaviors toward Earth.
How to cite: Rogers, D.: Empirical Evaluation of a Geologic, Environmental, and Anthropogenic Risk Reduction Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2217, https://doi.org/10.5194/egusphere-egu26-2217, 2026.
Monitoring slope stability in mountainous regions is often constrained by limited power supply and communication capacity. Under such conditions, low-power wireless transmission technologies, such as LoRa and NB-IoT, become indispensable for ensuring reliable data delivery in long-term monitoring systems. Real-time image monitoring of slope deformation, combined with automated image recognition and early-warning mechanisms, has emerged as a rapidly advancing approach in geotechnical hazard mitigation. These technologies enable continuous observation of slope variability and provide timely alerts that can significantly reduce the risk of catastrophic slope failures. However, the enormous volume of image data generated by continuous monitoring poses substantial challenges for transmission efficiency, data storage, and timely analysis. To address these issues, edge computing is increasingly employed at the monitoring site. By processing data locally, edge devices can filter and preserve only critical events before transmitting them to central servers for further recognition and decision-making. This strategy not only accelerates the early-warning process but also reduces false alarms, thereby enhancing the reliability of hazard detection. Furthermore, integrating edge computing with low-power wireless transmission creates a synergistic framework that balances energy efficiency, communication constraints, and analytical accuracy. Such integration is particularly valuable in remote or resource-limited environments where conventional high-bandwidth communication is impractical. The proposed approach highlights the importance of combining advanced sensing technologies with intelligent data management to achieve robust slope monitoring systems. Ultimately, this framework contributes to improving disaster preparedness, reducing misjudgment in early-warning systems, and supporting sustainable infrastructure development in mountainous regions.
How to cite: Kuo, C.: The critical role of edge computing and energy-efficient wireless transmission in real-time image-based recognition of slope deformation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-188, https://doi.org/10.5194/egusphere-egu26-188, 2026.
Anticipating and defining food crises remain primary challenges for humanitarian and governmental actors [1]. Traditional frameworks rely on predefined risk thresholds for different levels of food intake, but they neglect sudden-onset or "flash" events that abruptly alter the status quo [2]. This research proposes a data-driven methodology to identify and characterize these events, framing them as critical transitions in food security. By leveraging high-frequency, district-level data of food intake, we examine the evolution of food consumption across highly vulnerable countries and compare these findings with qualitative assessments from domain experts.
Building on previous research, this work evaluates the efficacy of multiple quantitative methods, ranging from time series analysis (variance, autocorrelations), unsupervised statistical change-point detection [3], dynamical systems theory [4], and deep learning [5], for defining food crises directly from raw data streams. To validate this framework, we first present results from synthetic experiments designed to simulate the noisy, daily measurements typical of this setting. Then, we assess the capacity of these methods to discern system-wide changes to real-world events. These experiments showcase the feasibility of objectively distinguishing between noise and genuine system transitions.
This study highlights the necessity of moving beyond static metrics toward a multi-method detection framework. We aim to provide humanitarian actors with a data-driven trigger for intervention, ensuring that flash deteriorations are no longer obscured by the limitations of static indicators and noisy measurements. Ultimately, this unified approach contributes to the development of more effective early warning systems and supports evidence-based decision-making for global food security.
References:
[1] P. Foini, M. Tizzoni, G. Martini, D. Paolotti, and E. Omodei, ‘On the forecastability of food insecurity’, Sci Rep, 2023, doi: 10.1038/s41598-023-29700-y.
[2] Herteux et al., ‘Forecasting trends in food security with real time data’, Commun Earth Environ, 2024, doi: 10.1038/s43247-024-01698-9.
[3] Wu, D., Gundimeda, S., Mou, S., Quinn, C. ‘Unsupervised Change Point Detection in Multivariate Time Series’, AISTATS 2024, PMLR, https://proceedings.mlr.press/v238/wu24g.html
[4] Zoeter, Onno, and Tom Heskes, ‘Change point problems in linear dynamical systems’, JMLR, 2005, https://www.jmlr.org/papers/volume6/zoeter05a/zoeter05a.pdf
[5] T. De Ryck, M. De Vos and A. Bertrand, ‘Change Point Detection in Time Series Data Using Autoencoders With a Time-Invariant Representation,’ IEEE Tran Signal Process, 2021, doi: 10.1109/TSP.2021.3087031
How to cite: Frazzetto, P., Gavrilov, A., Cerdà-Bautista, J., Piovani, D., and Camps-Valls, G.: Comparative Approaches for Detecting Critical Transitions in Food Crises, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20227, https://doi.org/10.5194/egusphere-egu26-20227, 2026.
Millions of people worldwide are exposed to hazards associated with volcanic activity. Currently, in México, around dozens of volcanoes pose different levels of risk to the surrounding population. Various monitoring methods have been employed at the highest-risk volcanoes, most of which rely on seismological and geodetic surveillance. However, the complexity of volcanic activity requires additional methods, among them the follow-up of the chemistry of volcanic products, such as gases and tephra, as well as their secondary effects, mainly their interactions with water bodies in or near volcanic edifices. To that aim, joint efforts have been developed for more than 30 years between the Geophysics Institute of the National Autonomous University of Mexico and the National Center for Disaster Prevention. These methods have included the sampling and chemical analysis of water from springs, wells, and lakes from Popocatépetl, Ceboruco, Nevado de Toluca, Pico de Orizaba, San Martín Tuxtla, El Chichón, and Tacaná volcanoes, and tephra leachates from Popocatépetl volcano, followed by the interpretation of their analyses in terms of their implications in the context of volcanic risk. Important changes have been observed in the chemistry of the 7 springs around Popocatépetl volcano sampled since 1995, such as the finding of boron above its detection limit in one of them before the emplacement of the first lava dome in March 1996, and the increase of dissolved CO2 and boron in all of them about 5 months before the fast growth of the largest dome recorded in the current period of activity in December 2000, that was followed by its destruction by intense explosions in January 2001. This episode, along with other signals of unrest, was a primary factor in the decision of the Civil Protection Authorities to evacuate over 40,000 inhabitants from the area around the volcanic edifice. The chemistry of Popocatépetl ash leachates has also shown changes related to fluctuations in volcanic activity, mainly an increase in the Cl/F ratio and changes in the SO4, Cl, and F relations associated with phreatic and magmatic eruptions. The chemistry of springs at Ceboruco, San Martín Tuxtla, and Pico de Orizaba has been stable for a decade, while the crater lake waters of Nevado de Toluca and El Chichón have shown important differences reflecting the quiet state of the former and the influence of an active geothermal volcanic system in the latter. Recent changes at El Chichón have also prompted the authorities to take preventive actions involving the population to enhance their awareness and resilience to the hazard posed by that volcano.
How to cite: Armienta, M. A., Gómez-Vázquez, Á., De la Cruz-Reyna, S., Cruz, O., Aguayo, A., and Neri, O.: Reducing volcanic risk through joint efforts of academia and key decision-makers, with the geochemical monitoring of volcanic activity , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7958, https://doi.org/10.5194/egusphere-egu26-7958, 2026.
Catalonia is one of the regional pilots within the Horizon Europe RESIST project (2023–2027), aiming to improve regional and local preparedness for extreme risks such as floods, forest fires, and extreme heat.
In the pilot cities of Terrassa, Blanes, and Alcanar, two digital technologies have been deployed: a real-time Multi-Hazard Early Warning System (EWS) and Site-specific Impact-based Warnings. These systems utilize meteorological observations and model forecasts alongside local sensor data and risk mapping to provide municipalities with actionable insights. These decision-making support tools help local authorities and emergency managers move beyond reactive crisis management to more effective and targeted resource allocation. Complementing these technical solutions is a Citizen Participatory Toolkit, designed to integrate the lived experiences of local residents and vulnerable populations into risk communication strategies.
The presentation showcases ongoing demonstrations and lessons learned across the pilot sites in building local climate resilience by integrating technology developments with social participation. This approach enables Civil Protection, first responders, and the public to move toward a more proactive, inclusive, and better-prepared emergency management, while fostering community self-protection.
How to cite: Park, S., Corral-Celma, C., Llort, X., Rodríguez-Giralt, I., Cifre-Sabater, M., and Berenguer, M.: Strengthening Local Climate Resilience: The RESIST Local EWS and Social Participatory Solutions in Catalonia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20564, https://doi.org/10.5194/egusphere-egu26-20564, 2026.
Repeated false alarms for adverse weather events can undermine public trust, potentially leading to a reluctance in taking appropriate actions, such as evacuation. Therefore, understanding the mechanisms by which false alarms influence perceptions and actions is essential for building socially effective early warning systems (EWS). We aimed to examine perceptions, emotions, and actions regarding false flood warnings in Japan. Specifically, we investigated (1) people’s definition of hits; (2) emotional responses toward false alarms; (3) the effect of the false alarm ratio (FAR) on the perceived FAR (pFAR) and the heterogeneity of this effect according to participants’ definition of hits; and (4) the effect of pFAR on evacuation action and the heterogeneity of this effect according to the emotional responses toward false alarms. We used municipality-level FAR data newly derived for this study and questionnaire data collected from residents of the Kyushu Region (n = 997), which is recognized as a flood-prone area in Japan. The results showed that participants tended to consider a warning as a hit when the river water reached a hazardous water level or when an overflow or levee breach occurred. Furthermore, when a false alarm occurred, negative emotions such as sadness and anger tended to decrease, whereas positive emotions such as being pleased and at ease tended to increase. We found a non-significant relationship between FAR and pFAR, which was maintained regardless of the participants’ hit definitions. However, we found that pFAR had a significantly negative effect on the probability of evacuation, and this negative effect was weaker among those who experienced positive emotions toward false alarms. These findings suggest that effective EWS require not only improvements in scientific warning accuracy but also risk communication strategies that consider emotional responses to false alarms (e.g., encouraging the public to view false alarms as opportunities for evacuation drills).
How to cite: Watanabe, M., Kotani, H., Yagi, R., Sawada, Y., and Kawabata, T.: Emotional responses and perceptions of false alarms in flood warnings and their impact on evacuation action in the Kyushu Region, Japan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8511, https://doi.org/10.5194/egusphere-egu26-8511, 2026.
Effective Early Warning Systems are essential for reducing disaster risk, particularly as climate change increases the frequency of extreme events. The July 2021 floods were Luxembourg’s most financially costly disaster to date. Although strong early signals were available and forecast products were accessible, these were not consistently translated into timely warnings or coordinated protective measures. While response actions were taken during the event, they occurred too late or at insufficient scale to prevent major impacts. We use a value chain approach to examine how forecast information, institutional responsibilities, and communication processes interacted during the event. Using a structured database questionnaire alongside hydrometeorological data, official documentation, and public communications, the analysis identifies points where early signals did not lead to anticipatory action. The findings show that warning performance was shaped less by technical limitations than by procedural thresholds, institutional fragmentation, and timing mismatches across the chain. A new conceptual model, the Waterdrop Model, is introduced to show how forecast signals can be filtered or delayed within systems not designed to process uncertainty collectively. The results demonstrate that forecasting capacity alone is insufficient. Effective early warning depends on integrated procedures, shared interpretation, and governance arrangements that support timely response under uncertainty.
How to cite: Da Costa, J., Ebert, E., Hoffmann, D., Cloke, H. L., and Neumann, J.: Signals without action: A value chain analysis of Luxembourg’s2021 flood disaster, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12698, https://doi.org/10.5194/egusphere-egu26-12698, 2026.
Mount Rainier, a heavily glaciated stratovolcano in Washington State [USA], has a documented history of producing major lahars. The potential for future high-magnitude flows threatens approximately 90,000 downstream residents and has prompted one of the nation’s most extensive volcanic monitoring systems, including a specialized lahar detection network. Because portions of Rainier’s west flank are composed of hydrothermally altered, unstable rock, the region is especially vulnerable to “no-notice” lahars triggered by sudden, non-eruptive slope failure. In response, schools in at-risk zones have conducted lahar evacuation drills – now a legal requirement – for over two decades, demonstrating that on-foot evacuation is the most effective strategy for student and staff safety. Despite these efforts, many parents report an intention to retrieve their children from school during an emergency lahar evacuation, contradicting official guidance. Such actions could obstruct evacuation routes, delay emergency response, and increase personal risk, especially in areas where modeled lahar arrival times are under one hour. Parent decision-making thus presents a critical, yet understudied, variable in evacuation planning and is considered integral to the success of city-wide evacuations.
Here we present the ongoing work from focus groups held with local parents to explore motivations behind their intentions. Topics of discussion within the focus groups include parents’ general understanding of lahar hazards, their intended actions, their confidence in school evacuation plans, and underlying factors in their decision-making. These insights can support more effective communication and preparedness strategies by emergency managers and school officials, while also contributing to broader discussions about protective action decision-making in rapid-onset hazards beyond volcanic settings.
How to cite: Ghent, J., Weiss-Racine, H., Christie, J., Errett, N., Bostrom, A., and Crowell, B.: When Every Second Counts: Parental Decision-Making in Mt Rainier’s Lahar Inundation Zone, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18120, https://doi.org/10.5194/egusphere-egu26-18120, 2026.
