One of the pressing challenges of our time is bridging the gap between climate science and decision-making to effectively manage risks from climate change, with weather and climate extremes being central to climate-related risk. Traditional climate science predominantly employs probabilistic approaches, generating large model ensembles to explore likely ranges of future conditions. While valuable, this approach often neglects low-likelihood, high-impact events that pose profound risks to society [1].
Strengthening the connection between climate science and decision-making is increasingly critical, particularly as the frequency and severity of extreme weather events rise. Integrated risk assessment and management require a holistic approach encompassing robust knowledge of potential impacts, hazard identification, risk monitoring, early warning and effective communication. While uncertainties in climate projections and predictions are unavoidable, they should not result in decision paralysis. Instead, the focus should be on interdisciplinary collaboration and enhancing links between climate science and decision-making through a better and more decision-relevant understanding of climate impacts [2].
This talk will address recent approaches, highlighting the importance of bridging disciplines and incorporating user-needs to address the complex challenges posed by climate risks. For instance, event-based storylines considering high-impact events, integrating system vulnerability and exposure to better assess risk will be discussed. When co-developed by climate scientists and stakeholders, storylines informed by physical climate and impact modeling provide actionable insights tailored to specific contexts.
References
[1] Sillmann J, Shepherd TG, van den Hurk B, Hazeleger W, Martius O, Zscheischler J, 2021: Event-based storylines to address climate risk, Earth’s Future, 9, doi: 10.1029/2020EF001783.
[2] Sillmann J, Raupach TH, Findell KL, Donat M, Alves LM, Alexander L, Borchert L, Borges de Amorim P, Buontempo C, Fischer EM, Franzke CL, Guan B, Haasnoot M, Hawkins E, Jacob D, Mahon R, Maraun D, Morrison MA, Poschlod B, Ruane AC, Shampa, Stephenson T, van der Wel N, Wang Z, Zhang X and Županić J, 2024: Climate extremes and risks: links between climate science and decision-making. Front. Clim. 6:1499765. doi: 10.3389/fclim.2024.1499765.
How to cite: Sillmann, J.: Climate Extremes and Risk: Connecting Climate Science and Decision-Making via Interdisciplinary Approaches Focusing on Climate Impacts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9881, https://doi.org/10.5194/egusphere-egu25-9881, 2025.
Projections of macroeconomic damage from future climate change tend to suggest mild to moderate impacts. This leads to welfare-optimal climate policies in Integrated Assessment Models (IAMs) that recommend very slow emissions reductions over the coming decades, in sharp contrast with the ambitions of the Paris Agreement. These econometric models assume that weather impacting a single country is all that affects the economy of that country. We examine whether the addition of global weather conditions in the empirical modelling of economic growth affects the projections of the impact of climate change on global GDP. In effect, we explore whether the interconnectedness of the global economy makes individual countries vulnerable to weather changes that impact other countries. Using three influential econometric models we add global weather to the regressions. We find that this leads to significant worsening of the projections of macroeconomic damage for given future emissions scenarios. Damage to world GDP in 2100 under SSP5-8.5, averaged across both econometric models and climate models increases from ~11% under models without global weather to ~40% if global weather is included. Further, we demonstrate that when the damage function used in IAMs is estimated from empirical models augmented with global weather conditions, they reduce the welfare-optimal amount of climate change from ~2.7C to ~1.7C which is consistent with the Paris Agreement targets. Our results highlight the need for econometric modelling and climate science’s understanding of extreme events to be integrated much more consistently to ensure the costs of climate change are not underestimated.
How to cite: Neal, T., Newell, B., and Pitman, A.: Reconsidering the macroeconomic damages of severe warming, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3401, https://doi.org/10.5194/egusphere-egu25-3401, 2025.
Evidence shows an ongoing increase in the frequency and severity of global heatwaves, raising concerns about the future impacts of climate change and the associated socio-economic costs. Here, we develop a disaster footprint analytical framework by integrating climate models, epidemiological and hybrid input-output, and computable general equilibrium global trade models to estimate the mid-century socioeconomic impacts of heat stress. We consider health costs related to heat exposure, the value of heat-induced labor productivity loss, and indirect losses due to economic disruptions cascading through supply chains. We find that the global heatwave days would increase by 104% in 2060 compared to 2022 under SSP585, and the global average annual number of heat-induced deaths would increase to around 1.12 million (0.85 ~ 1.39 million). For economic impacts, we show that the global annual incremental loss increases exponentially from 0.03±0.01 (SSP245) ~ 0.05±0.03 (SSP585) percentage points during 2030 – 2040 to 0.05±0.01 ~ 0.15±0.04 percentage points during 2050 – 2060. By 2060, the expected global economic losses reach a total of 0.6% ~ 4.6% with losses attributed to health loss (37%~45%), labor productivity loss (18%~37%), and indirect loss (12%~43%) under different SSPs. Small and medium-sized developing countries in Southeast Asia and Africa suffer the most from heat risks as well as regional supply chain disruptions.
How to cite: Zhu, S., Sun, Y., Wang, D., and Guan, D.: Health and Economic Costs of Future Extreme Heat Risk, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1449, https://doi.org/10.5194/egusphere-egu25-1449, 2025.
This work investigates how the damages in cross-sectoral climate change risk hotspots can be assessed drawing on methodologies developed and applied to eight case studies conducted within the EU CROSSEU project. Hotspot analysis represents several key challenges in the assessment of the impacts of climate hazards and the focus here is on extreme climate events rather than on the impacts of gradual climate change. Hotspots are defined as areas where climate events are likely to generate high potential damages. The hotspot identification methodology provides a framework for identification of context specific vulnerabilities due to a combination of factors, including the magnitude of extreme climate events (physical aspects), the presence of critical infrastructure and vulnerable populations (socio-economic aspects), and the sector specific vulnerabilities as well as interconnectedness of different sectors (cross-sectoral aspects). The identification of hotspots is based on a combination of quantitative and qualitative data, including climate projections, socio-economic data, and stakeholder consultations.
The hotspot methodological framework is applied to a range of case study sectors and geographical settings. The case studies cover heat waves in Czech Republic and United Kingdom; drought in regions of Germany, Czech Republic, Poland, and Romania; floods in Denmark, Germany, and Italy; and snow avalanches in the Alps and Carpathian Mountains. While three other case studies addressed climate change impacts and spillover effects in the Lower Danube region and across Europe- particularly on renewable energy infrastructure and agriculture.
In terms of physical vulnerabilities, the case studies demonstrate that Prague and Southern Moravia in the Czech Republic, and London in the UK, are hotspots for heat-related mortality and morbidity, and specific social and structural vulnerabilities in these areas are related to high population densities, aging populations, and the urban heat island effect. Several regions in Germany, Czech Republic, Poland, and Romania are identified as hotspots for drought. The economic vulnerability of these regions is primarily due to the reliance of agriculture on rainfed water sources. Coastal cities in Southern Denmark and Northern Germany are vulnerable to storm surges, impacting thousands of residents by disrupting daily life, socioeconomic activities, restricting movement and even necessitating temporary relocation. The mountainous areas of the Trentino Alto Adige region in Italy are hotspots for debris flows and flash floods, and are vulnerable due to their low-lying coastal areas, high population densities, and critical infrastructure. The Italian Alps and the Făgăraș Mountains in the Romanian Carpathians are hotspots for snow avalanches with potential high economic losses for tourism. The Lower Danube region is a hotspot for both droughts and floods, posing significant risks to a unique biodiversity ecosystem, as well as to agriculture, energy infrastructure, and human settlements.
This hotspot analysis in the CROSSEU project provides key comparative risk assessment measures, contributing to the establishment of effective adaptation strategies in the EU and also at regional levels.
This research received funds from the project “Cross-sectoral Framework for Socio-Economic Resilience to Climate Change and Extreme Events in Europe (CROSSEU)” funded by the European Union Horizon Europe Programme, under Grant agreement n° 101081377.
How to cite: Some, S., Halsnæs, K., Cheval, S., Micu, D., Skougaard Kaspersen, P., Adamescu, M., Arhire, G., Borga, M., Calzadilla, A., Charousset, S., Dessens, O., Falcescu, V., Franceschinis, C., Giucă, R., Igescu, D., Jenkins, K., Vasilakos, N., Nielsen, K., Nertan, A., and Oueslati, B. and the et al.: Cross-Sectoral Climate Change Risk Hotspots in Europe: Insights from CROSSEU Case Studies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15952, https://doi.org/10.5194/egusphere-egu25-15952, 2025.
Hail is a main contributor to weather-related damages to buildings, cars, and agriculture in Switzerland, demanding actionable information on hail risks and forecasts across sectors. The research project scClim addresses this demand by establishing a seamless model chain from observing, modelling and forecasting hail events to the quantification of hail impacts, including simulations to compare hail occurrence in current and future climate.
Within the project, we study several types of impact-based hail forecasts and warnings for Switzerland, addressing the interests of different stakeholder groups. We employ ensemble weather forecasts by the Swiss Meteorological Office combined with (a) impact-informed vulnerability thresholds to produce local hail warnings, and (b) information about exposed assets and their calibrated vulnerability to produce aggregated hail impact forecasts. While the impact-based forecasts would have to be thoroughly validated before operational use, the forecast products highlight how varying demands of different stakeholder groups shape the forecast product and the provided information.
How to cite: Gebhart, V., Schmid, T., and Bresch, D. N.: Impact-Based Hail Forecasts for Switzerland in the scClim Project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4445, https://doi.org/10.5194/egusphere-egu25-4445, 2025.
Climate change is increasing the frequency and intensity of extreme events and hazards, posing serious risks to societies and ecosystems worldwide. These phenomena do not only threaten economic systems but also broader dimensions of human well-being, including inequality, health, and education. Despite a growing recognition of these risks, the global mechanisms linking climate extremes to human development remain poorly understood. Furthermore, besides GDP, explicit estimation of future climate change damages and extremes on socio-economic projections remain limited.
In this study, we focus on the impacts of climate extremes on human development, analyzing their effects on three main components of the Human Development Index (HDI): life expectancy, expected years of schooling, and gross national income per capita. Using a dataset covering 1,773 sub-national regions over three decades from 1990 to 2020, we employ high-resolution climate data to examine immediate and lagged socio-economic responses to extreme events and hazards, particularly rainfall extremes, heatwaves, and droughts. By exploiting fixed effects panel modeling, our approach accounts for the simultaneous inclusion of multiple extremes in damage functions and evaluates the integration of an adaptation proxy to capture regional differences in vulnerability.
Finally, we apply the derived impact functions to Shared Socioeconomic Pathways (SSP) scenarios, providing projections of climate-driven damages on HDI across different development and climatic narratives, capturing the key climatic and social uncertainties.
How to cite: Mastropietro, M., Spinoni, J., and Tavoni, M.: Past and Projected Climate Extremes Impacts on Human Development , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5881, https://doi.org/10.5194/egusphere-egu25-5881, 2025.
Extreme precipitation events often lead to local or downstream flooding and can have devastating impacts from infrastructure damage to loss of life. As climate change progresses, these events have become increasingly frequent and intense, posing significant challenges to societies. While warning systems for fluvial flooding such as the Global Flood Awareness System (GloFAS) exist, localised downpours over impermeable urban areas remain difficult to predict. Even in countries with advanced early warning systems, precipitation intensity is often underestimated, leading to misrepresentations of potential impacts. Further, many localised flood events driven by very intense precipitation are not predicted at all.
Here, we present a novel warning index that predicts the likelihood of extreme precipitation and targets localised urban and pluvial flooding, thereby addressing a gap in existing warning systems. The presented warning index is based on a novel set of return period forecasts for extreme precipitation, that enable a correction of model biases. The index then estimates a risk through the mapping of likelihood and potential impacts, incorporating a fuzzy neighborhood approach that accounts for displacement errors in the prediction of extreme events as a function of lead time. Through this risk approach, the warning index aims to capture extreme but less probable events to improve the warnings’ reliability. Here, we present results for the 30 activations from the Copernicus Emergency Management System Rapid Mapping (CEMS RM) in 2024. The index shows reliable and actionable warnings for localised flooding events, offering significant advancements in risk management and preparedness for extreme precipitation impacts.
This work was developed in the context of the Horizon Europe CENTAUR project https://centaur-horizon.eu/
How to cite: Keune, J., Di Giuseppe, F., Wetterhall, F., and Barnard, C.: A return period-based early warning index for extreme precipitation , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8483, https://doi.org/10.5194/egusphere-egu25-8483, 2025.
In emergency management, the gap between scientific risk knowledge and operational decision-making remains a persistent challenge for early warning systems. While vast amounts of data—ranging from risk assessments to historical event records—are available, they are often underutilized due to the complexity and fragmentation of information sources. This research proposes an innovative approach to bridge this gap by integrating Retrieval-Augmented Generation (RAG) with domain-specific knowledge graphs to enhance situational awareness and decision support in emergency operations centers.
The proposed solution focuses on developing a graph-based RAG pipeline that interacts with an external repository of risk data on Italy, specifically tailored for emergency response personnel, including civil protection agencies and the Italian Red Cross. The repository incorporates emergency plans, historical events, risk assessments, civil protection guidelines and legislation, and real-time updates from external sources such as news and media. By structuring the data through a knowledge graph aligned with established risk frameworks (e.g., RISK INFORM), the system enables precise, explainable, and contextual information retrieval.
Key features of the tool include an explainability module for transparency, a PDF parser for document integration, and a web interface that allows users to interact with the system through natural language queries. For example, an analyst responding to severe floods in Northern Italy could query the system for demographic data, flood risk hotspots, and critical infrastructure at risk, receiving actionable insights grounded in both historical and live data.
The project demonstrates how AI-driven approaches, when combined with structured domain knowledge, can make early warning systems more effective by improving accessibility, scalability, and interoperability across sectors. The use of knowledge graphs ensures data explainability and traceability, addressing key challenges in emergency management, such as trust in AI outputs and timely decision-making. The platform, currently under development, aims to serve as a proof-of-concept for future applications in multi-hazard early warning systems.
This research contributes to the evolving field of AI-enhanced early warning systems, offering a novel, trans-disciplinary methodology that combines data science, emergency management, and humanitarian operations to improve anticipatory action and disaster preparedness.
How to cite: Bove, J.-B., Rudari, R., Trasforini, E., D'Andrea, M., Massucchielli, L., and Gioia, A.: Bridging Risk Knowledge and Operational Outcomes through Retrieval-Augmented Generation and Knowledge Graphs for Early Warning Systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16239, https://doi.org/10.5194/egusphere-egu25-16239, 2025.
Wildfires are an emerging peril in traditional natural hazard risk assessment. Increasingly extreme fire behavior, unprecedented mega-fires and rising economic damages are commonly attributed to a combination of climatic shifts, expansion in areas where human development meets natural landscapes (wildland-urban interface), and an accumulation of fuel.
Remote sensing products provide the most comprehensive data source for the global assessment of wildfires and their impacts. However, scientists and practitioners in Disaster Risk Reduction are faced with several fire products from different satellite missions, whose differences, advantages and limitations can be difficult to assess and understand, especially for users outside the remote sensing domain. At best, this issue complicates the process of identifying the most appropriate dataset, making it a challenging and time-consuming endeavor; at worst, it can result in inaccurate results.
We address these issues by offering a concise overview of remote sensing fire products and clarifying terms that are interpreted differently across scientific communities, with a focus on their application in risk assessment. Our analysis centers on products representing burned area and active fire locations. While burned area products leverage several satellite overpasses and reflect the area affected by large fires best, active fire location products provide the fire radiative power, a measure of the fire intensity, which is an important metric linked to impacts.
We present a historic wildfire hazard set, which combines burned area data and fire radiative power recorded by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite product for the years 2002–2023. We join this hazard set with exposure datasets (representing physical assets and population) and damage records to calibrate socio-economic vulnerabilities to wildfires. This forms the basis for estimating wildfire impacts and risks, necessary for prioritising adaptation options and the pricing of insurance.
How to cite: Steinmann, C. B., Koh, J., Kropf, C. M., Bresch, D. N., and Hantson, S.: Data requirements for assessing global socio-economic wildfire impacts and risks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8464, https://doi.org/10.5194/egusphere-egu25-8464, 2025.
The past few years have seen increasingly frequent and intense floods, culminating in 2024 with a year characterized by widespread and devastating inundations worldwide. In Europe despite major advancements in flood forecasting and flood protection measures undertaken in the past, in2024 heavy rainfall events resulted in severe flood impacts and massive socio-economic losses, claiming over three hundred lives. Extreme precipitation, breaking observed records, is expected to have an increased likelihood under global warming all the more we should question and scrutinize our knowledge about the frequency and severity of floods. Reliable estimates of these are challenging even for the current climate conditions, and disaster gaps can lead us to an underestimation of the risks. The use of the UNSEEN method (Thompson et al. 2017) has been proved to be very valuable in estimating both, unprecedented but plausible extreme floods and droughts.
Here we present a simple method to expand the UNSEEN method to develop storylines under various global warning levels. We selected precipitation scenarios with different spatial patterns for estimated return periods between 100 and 1000 years from pooled re-forecasts from ECMWF (ENSext and SEAS5), providing 8400 years of plausible weather sequences. The selected climate scenarios are perturbed by increasing the precipitation intensity according to the Clausius-Clapeyron relation for five different global warming levels, and used to run coupled hydrologic-hydraulic simulations. The results show that record-breaking, high-impact river floods are possible under the current atmospheric conditions, and climate change substantially aggravates flood impacts, as the relative increase in peak discharge can be significantly larger than the increase in precipitation, leading to a disproportionally high flood impact increase. The development of storylines of extreme flood events with a high spatial and temporal resolution are a valuable tool to explore, describe, and communicate extreme events and their dynamics. Such instruments are key for developing an informed vision and comprehensive protective measures in terms of flood risk management and emergency response.
References
Thompson, V., Dunstone, N. J., Scaife, A.A., Smith, D. M., Slingo, J. M., Brown, S. and Belcher, S.E.: High risk of unprecedented UK rainfall in the current climate. Nat Commun 8, 107, https://doi.org/10.1038/s41467-017-00275-3, 2017.
How to cite: Kauzlaric, M., Munz, L., Mosimann, M., Martius, O., and Zischg, A. P.: A simple approach for developing storylines of flood impacts under various global warming levels, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8731, https://doi.org/10.5194/egusphere-egu25-8731, 2025.
Assessing climate risk due to climate change for the present and future periods has been the focus of both academic and applied research in recent years, reflecting its critical importance. In this study, we evaluated climate risks for the Marmara Region in northwestern Türkiye by integrating high-resolution climate projections with socio-economic data, aiming to inform and support regional climate policies.
To achieve this, we generated climate projections at a 0.025° x 0.025° resolution using the convection-permitting COSMO-CLM model, driven by EC-Earth3-Veg from CMIP6. These projections cover both the reference period (1995–2014) and a future period (2050–2059) under the SSP3-7.0 scenario) for a broader western part of Türkiye. The Marmara Region was selected as a focal area due to its vital economic significance, its diverse and densely populated urban centers, and its extensive agricultural areas. This approach allows for a comprehensive assessment of climate impacts on a region with critical socio-economic importance, providing actionable guidance to inform policy development and adaptation strategies.
We conducted a comprehensive climate risk assessment by integrating hazard data with components of sensitivity, vulnerability, and adaptive capacity components, which were derived from reliable socio-economic datasets provided by institutions such as the Turkish Statistical Institute and the Turkish State Meteorological Service. For the weighting phase, we employed multiple methodologies, including the Analytic Hierarchy Process (AHP), Principal Component Analysis (PCA), and variance-based distribution methods, to investigate their respective contributions to the final risk evaluation.
Preliminary findings reveal city-level climate risks for both the present and future periods, offering critical insights for key vulnerabilities and areas of concern. These results provide essential guidance for regional policymakers, enabling the identification of specific risk hotspots and developing targeted strategies that address the region-specific challenges. These results serve as a foundation for developing targeted strategies to mitigate climate risks, strengthening resilience, and enhance adaptation capacity in the Marmara Region.
How to cite: Moral, A. C., Yürük Sonuç, C., and Ünal, Y.: Climate Risk Analysis for Marmara Region, Türkiye, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8933, https://doi.org/10.5194/egusphere-egu25-8933, 2025.
In order to advance new theoretical and practical integration of Earth and Social Sciences to address the climate crisis and its impacts on society, the World Climate Research Programme has created the My Climate Risk (MCR) Lighthouse activity. The goal of MCR is to develop and mainstream a bottom-up approach to climate risk. To progress in this path, MCR has assembled regional centres (‘Hubs’) from institutions/researchers with knowledge in the field of climate risk and that allow this approach to be taken to local and regional scales. These Hubs comprise a variety of forms and modes of operation depending on the local interests and needs. In March 2022, the MCR CONICET Argentina Regional Hub was created (https://sites.google.com/view/mcrhubconicet). Through MCR CONICET Argentina Regional Hub its members learn, participate and motivate a scientific-technical and social perspective to promote adaptation and face climate extremes in Argentina employing the co-production of knowledge, storylines and multiple lines of evidence.
This work aims to share initiatives and projects from the Global South that are rooted in inter- and transdisciplinary dialogue and the inclusion of actors and institutions of the region, to address climate risk research. The case studies presented here address Argentina's need to improve hydrometeorological services availability, accessibility and interpretation. The first case study presents the coproduction cycle that led to a subseasonal novel local prediction product in northeastern Argentina, co-produced between climatologists, anthropologists and family farming actors within the framework of the CLIMAX project. The successful experience of this development highlights the importance of involving local communities in the development of climate information products that can be socially appropriated. A case of use of climate storylines as a tool for improving decision making is presented. Physical Climate Storylines was put in dialogue with Socio-anthropological Narrative Analysis around a drought event in Southeastern South America. Finally, the strategy of multiple lines of evidence is used, showing results of the “A River All Waters” project, which integrated transversal lines of work to address the impact of climate change on the Chubut River in Argentine Patagonia. This project shows a reduction in precipitation and an increase in temperature since 1960, which caused a decrease in river flows. These three case studies showed the need to explore novel methodologies that favour a bottom-up approach to regional and local climate risk.
How to cite: Cappelletti, L. M., Cánneva, J., Díaz, L., Fossa Riglos, M. F., Gulizia, C., Hernández, V., Incicco, C., Hurtado de Mendoza, M. S., Mindlin, J., Pansa, D., Pessacg, N., Prudente, C., Rivera, J. A., Robledo, F., Rosales, D. A., Ruscica, R. C., Sörensson, A. A., and Testani, N.: A bottom-up approach to climate risk from the Global South: the case of the CONICET Argentina My Climate Risk Hub, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12539, https://doi.org/10.5194/egusphere-egu25-12539, 2025.
An early warning system Stromynazeleznici.cz (trees on railway tracks) has been developed to assist the national rail infrastructure administrator (Správa železnic, SZ) in managing the hazard of tree falls. A forecast of the tree-fall hazard on a 3-hour basis for the following three days is provided. The model incorporates data from weather forecasts (Aladin model) and a tree-fall susceptibility layer which delimits the locations where falling trees are capable of crossing railway tracks.
The tree-fall susceptibility layer is prepared from the raster of a normalized digital surface model. One-meter cells contain information about the absolute height of the surface above the relief model. All non-vegetated areas (all types of buildings, tall objects, bridges, masts, etc.) and areas with low vegetation that do not pose a hazard are filtered out. Impact zone buffers are defined for the remaining vegetation areas according to the actual height of the vegetation. The final output is a proportion of the length of railway lines per unit section which are threatened by falling vegetation.
Stromynazeleznici.cz contains tree fall evidence for recording, presenting, and exporting incidents. The forecast is based on a regression model programmed in R (server solution Project R). A multivariate logistic regression was chosen as the most suitable approach to construct the model according to cross-validation results and practical requirements. The following characteristics were selected as explanatory variables in the logistic regression: maximum daily wind gust, soil saturation index, snow index, the occurrence of thunderstorms, the season, the range of altitudes in the vicinity of the rail track, the median height of trees along the railway tracks, and the length of the rail track section with trees along the rail track.
The hazard level of tree falls is calculated for the "hectolines" (i.e., 100-meter segments) of the railway track. These are then aggregated into three levels of administrative units defined by SZ. The hazard level is calculated for three-hour intervals, covering a 45-hour forecast period – resulting in 15 time slots for each hectoline (the rail network in Czechia consists of 94,759 hectolines). The forecast is updated four times a day as new meteorological data become available.
The data is stored in a database and presented in the form of graphs, tables, and an interactive map. Hazard information can be found on the map: the tree-fall hazard level is represented by a five-level colour scale for individual administrative units. When zooming in, the risk is shown in relation to the hectolines. A timeline is located at the bottom of the screen, allowing users to switch between different time slots or aggregated time windows. Clicking on an administrative unit or hectoline will display the forecast and details for the selected element. The map also offers additional thematic layers — fallen trees, a layer showing vegetation susceptibility to falling onto the railway track, a tree health layer (derived from the Sentinel-2 data), and a forest tree species layer.
How to cite: Bíl, M., Nezval, V., Andrášik, R., Kubeček, J., Cícha, V., and Lepka, Z.: Early Warning System for Tree-Fall Hazards on Railways: An Example of a System Developed for the Czech Railway Infrastructure Administrator, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9793, https://doi.org/10.5194/egusphere-egu25-9793, 2025.
Low- and middle-income countries are often vulnerable to extreme weather events and simultaneously have limited access to insurance markets, leaving damages largely uncovered. To address the issue, the World Bank advocates for a risk-layering approach. Part of this approach is the issuance of catastrophe bonds (CAT bonds), especially for high-risk layers and when capital requirements are substantial. However, access to the CAT bond market remains limited. Market entry of potential issuers, but also investors, is hindered by the extensive, and most often, expensive technical knowledge needed. Additionally, high investor premium demands pose a great challenge for low- and middle-income countries, a situation likely to worsen with climate change intensification.
We propose three interdependent solutions to enhance countries and investors access to the CAT bond market. First, we develop an open-source and -access CAT bond tool and implement it within the CLIMADA environment, an open-source global risk assessment platform. The tool allows for the design and evaluation of either potential or already existing CAT bonds and was tested in a tropical cyclone insurance case study in Samoa. Second, we propose a multi-country CAT bond design, which pools risk across nations. We apply this design to a case study of tropical cyclone risk in Small Island States (SIDS). We find that such pooling allows to decrease both capital requirements and premiums by up to 27% and 17%, respectively, while still offering competitive returns to investors. Third, we introduce a financial scheme addressing premium support, capital supply, and greenhouse gas reduction incentives. We apply the scheme in a case study to the SIDS utilizing the previously developed CAT bond tool and the presented pooling approach. These solutions together aim to expand access to risk transfer for vulnerable countries, offering a more
sustainable and affordable pathway to disaster resilience.
How to cite: Bergmüller, K. O., Håkansson, V. W., Juhel, S., Bresch, D. N., and Kropf, C. M.: Closing the Insurance Gap – Enhancing Access to the CAT Bond Market, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17829, https://doi.org/10.5194/egusphere-egu25-17829, 2025.
The "Hamburg Pluvial Flood Risk Map" aims to improve our understanding of the drivers, dynamics and interactions of climate-induced (disaster) risks in Hamburg. Following the risk framework of the IPCC, we calculate a risk index based on hazard, exposure and (social) vulnerability. In this sense, we combine data from the previously published Social Vulnerability Index (von Szombathely et al., 2023) with novel meter-scale hydraulic simulations of urban flooding provided by the heavy rain hazard map of the city of Hamburg (BKG/FHH 2023). We have enhanced the modeling of social vulnerability by applying the TOPSIS method and the Shannon Entropy procedure. and propose a high-resolution exposure modeling designed for urban flooding, with different exposure layers threatening health and restricting mobility and accessibility. We show that fundamentally new spatial patterns emerge for pluvial flood risk in Hamburg, which differ from familiar socio-economic urban structures and at the same time differ clearly from a pure representation of the hazard. Presented through high-resolution spatial maps, this analysis aids in identifying adaptation needs and prioritizing policy measures for climate change adaptation.
References:
BKG/FHH 2023. Eine Starkregen-Gefahrenkarte für Deutschland. https://www.business-geomatics.com/2023/02/02/eine-starkregen-gefahrenkarte-fuer-deutschland/
von Szombathely M., Hanf F. S., Janka B., Meier L., Ossenbrügge J., Pohl T. 2023. An Index-Based Approach to Assess Social Vulnerability for Hamburg, Germany: International journal of disaster risk science. 14, 5, p. 782-794 13 p. DOI: 10.1007/s13753-023-00517-7
How to cite: von Szombathely, M., Behrens, J., Hanf, F. S., Lennartz, M., Poschlod, B., Vogelbacher, A., and Sillmann, J.: Hamburg Pluvial Flood Risk Map, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13534, https://doi.org/10.5194/egusphere-egu25-13534, 2025.
Myanmar is highly vulnerable to climatic changes in extreme weather such as increased precipitation and extreme temperatures. During the dry season of the last two years, Myanmar has already suffered such events. High-resolution climate model simulations are urgently needed to understand the complexity of future impacts of extreme weather and climate change in Myanmar. While global climate model simulations cover the region with a horizontal resolution of around 100 km, most regional climate models available over this region have a resolution of up to 25 km. This is still not enough to accurately assess the vulnerability to climate change for such a diverse country with complex topography, and thus, new high-resolution simulations are needed to understand the effect of climate change on regional to local scales.
We are conducting dynamically downscaled climate simulations across a large part of Myanmar using the Weather Research and Forecasting (WRF) model. Downscaled climate data are generated for five simulation periods: one for the present (1981-2010) and two for each of two future periods (2031-2060 and 2071-2100), under both the intermediate-emission shared socioeconomic pathway (SSP2-4.5) and the very high-emission pathway (SSP5-8.5). The simulations are performed at a high spatial (5 km) and temporal resolution. Through these simulations, we can achieve more realistic precipitation patterns and detailed information on local precipitation and temperature extremes, considering also the daily cycle.
We will present our preliminary findings from the downscaled modelling of weather extremes and information about heat stress and drought indices. This will provide insight into potential impacts on food security and fragility to climate change in general, both of great implications for local society and economy.
How to cite: Messmer, M., González-Rojí, S. J., Mo Aung, N. C., Hunt, G., and Leonard, S.: Assessment of Climate Fragility in Myanmar based on high-resolution regional climate model simulations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18200, https://doi.org/10.5194/egusphere-egu25-18200, 2025.
The private sector and industry are increasingly accessing physical climate data to a) identify and disclose climate risk as required by regulations and b) seek to estimate and limit both present and future economic impacts of physical risk on their business.
Projections of physical climate risk and associated changes in estimates in losses are typically provided to private sector stakeholders in isolation from the wider social and systemic picture. Whilst individual private sector stakeholders can take some measures to minimise impacts from climate hazards, a collective approach in conjunction with local communities and the public sector may be more effective, in terms of both risk reduction, and upfront cost.
We provide physical climate storylines and narratives as a complement to the typical physical risk data provided to private stakeholders. We work with many private stakeholders and are seeking interdisciplinary discussion and collaboration with a view to exploring and quantifying the cost- benefit of individual stakeholder action versus collective action.
How to cite: Sagoo, N. and Leach, N.: Exploring how physical climate storylines and narratives impact private stakeholder behaviour, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20300, https://doi.org/10.5194/egusphere-egu25-20300, 2025.
Floods, as one of the most devastating natural disasters, have far-reaching impacts on property, safety, and mental health. This study employs Structural Equation Modeling (SEM) to explore the pathways linking flood experiences to psychological distress, focusing on mediating factors such as property loss, recovery efforts, and socioeconomic conditions. Drawing on data from the 2021 floods in Germany, the analysis provides insights into how direct and indirect factors interact to shape mental health outcomes.
Flood experience is conceptualized as direct exposure to flood hazards, including water depth, flow velocity, and contamination. These factors collectively capture the intensity and severity of the flood event. Key findings reveal that flood experience significantly predicts property loss (Estimate = 0.254, p < 0.001) and direct impacts such as self-injury, family injury, and uncertainty about the safety or whereabouts of family members or close friends during flood, which, in turn, exacerbate psychological effects. These direct impacts, alongside property loss, drive psychological impacts, measured through post-traumatic stress disorder (PTSD) screening and ongoing mental health effects, including persistent thoughts about the event and whether it continues to affect individuals' daily lives (Estimate = 2.227, p = 0.006). Socioeconomic factors, such as income and property ownership, influence recovery efforts, which mitigate psychological distress (Estimate = 0.294, p < 0.001). While recovery efforts mitigate distress (Estimate = 0.294, p < 0.001), property loss remains a substantial stressor. The total indirect effect of flood experience on psychological burden (Estimate = 0.304, p = 0.002) underscores the cumulative impact of material loss, immediate threats, and recovery challenges.
The model achieves strong fit indices (χ²/df = 2.15, RMSEA = 0.048, CFI = 0.925), validating its conceptual framework. These findings emphasize the critical role of flood experience in shaping mental health outcomes and the need for holistic disaster response strategies that address immediate impacts and foster long-term psychological recovery. By emphasizing both direct and cascading effects, this study informs policies aimed at enhancing resilience and mental health support in flood-prone areas.
How to cite: Pham, T. T. T. and Sairam, N.: Understanding the Psychological Impacts of Flooding: A Structural Equation Modeling Approach from the 2021 German Floods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-830, https://doi.org/10.5194/egusphere-egu25-830, 2025.
Climate action (SDG 13) and reducing inequalities (SDG 10) are central goals of sustainable development. However, the distribution of climate risks and carbon emissions across regions is uneven, and this disparity poses significant challenges in global climate change governance. To address this issue, this study defines the concept of climate risk and introduces the "Mismatch Responsibility Index" to quantify the imbalance between carbon emissions (carbon footprint) and climate risk burdens. The study further examines the socio-economic and technological factors that drive this imbalance. The key findings include: (1) Climate risks and carbon footprints exhibit significant spatial and temporal variability, with the gap between cities expanding over time; (2) In China, more than half of the prefecture-level cities experience a significant mismatch in climate responsibility, with underdeveloped regions facing disproportionately high climate risks; (3) The main factors contributing to this mismatch are energy consumption patterns, population size, and the level of technological innovation. Further policy analysis indicates that local government policies, the promotion of regional green energy transitions, and technological innovation are essential to narrowing the gap in responsibility distribution. (4) Using simulations of different policy scenarios, the study proposes several recommendations, including strengthening local government climate policies, supporting green energy transitions, promoting technological innovation, and reallocating international climate finance. These measures are expected to reduce regional disparities in climate responsibility and contribute to more equitable climate governance.
How to cite: Li, Y., Liu, X., Hasi, E., Ji, R., Zhang, S., and Hao, Y.: The Climate Risk and Regional Carbon Emission Responsibility in China from the Perspective of "Mismatch Responsibility": Temporal-Spatial Variability and Driving Factors Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-383, https://doi.org/10.5194/egusphere-egu25-383, 2025.
The aging agricultural labor force presents significant challenges to farm productivity and sustainability, particularly when compounded by climate change. This issue is especially critical in countries like Thailand, where the agricultural workforce is aging rapidly. Notably, 2.84 million older adults are engaged in the agricultural sector, representing 59.2% of all older adults participating in the economy. A Swiss Re Institute (2021) study ranked Thailand as the fifth most vulnerable country to GDP impacts from climate change. Furthermore, projections indicate that climate change could inflict cumulative damages on Thailand's agricultural sector, totaling 0.61–2.85 trillion baht from 2011 to 2045, averaging 17.9–83.8 billion baht annually. Without adequate preparation, Thailand risks significant economic downturns driven by agricultural productivity and production losses.
Effective local governance is one of the most crucial determinants of coping with the crisis above. This study used a mixed-methods approach, combining quantitative and qualitative research. The quantitative research included a survey of 2,500 older farmers in 2024 from three provinces in Thailand: Chiang Rai in the northern region, Buriram in the Northeastern region, and Uthai Thani in the central part, where extreme drought exists. The qualitative approach involved focus groups with older farmers and in-depth interviews with policymakers and older farmers.
The findings revealed that flexible and adaptable local governance is among the most critical factors contributing to the resilience of older farmers. Drought management for older farmers in Thailand requires coordination among multiple agencies with distinct roles, emphasizing the need for integration to ensure effective communication. Agencies must collaborate to share information and coordinate efforts to disseminate accurate, comprehensive, and timely information. Examples include broadcasting weather forecasts from the Meteorological Department, coordinating cloud-seeding operations, and providing water and resource management guidance through the Irrigation Department. More importantly, its communication strategies must specifically target older farmers. Various communication channels should be utilized, particularly platforms that older farmers can readily access, such as community radio, village loudspeakers, and local media. Additionally, digital platforms and social media can be leveraged to inform younger family members, who can relay the information to older adults. To ensure accessibility, communication materials should be simple, straightforward, and audience-specific. This includes tailored communication, such as using local dialects or translating complex information into user-friendly formats, as older farmers may have difficulty understanding formal language or technical terms. This effectively supports their resilience in drought. After implementing drought management measures, the government must assess the effectiveness of its communication strategies. This evaluation should determine whether the information was delivered efficiently to older farmers and identify gaps or barriers in the communication process. Based on these insights, necessary adjustments should be made to enhance future communication efforts, ensuring they are tailored to this demographic's needs, preferences, and limitations.
Keywords: Adaptive Governance, Older Farmers, Risk Reduction, Resiliency, Drought
How to cite: Swangsilp, S.: Local Governance for Enhancing Resilience to Climatic Challenges Among Older Farmers: A Case Study from an Extreme Drought-Prone Area in Thailand, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5150, https://doi.org/10.5194/egusphere-egu25-5150, 2025.
The "Early Warning for All" initiative, launched at COP27, aims to ensure global coverage by early warning systems (EWS) for hazardous weather, water, or climate events by 2027. This study evaluates the effectiveness of EWS in Northeast China focusing on six meteorological disasters: heavy rain, cold waves, high winds, high temperatures, hail, and frost over the past five years.
We analyze the evolution in the timeliness and content of early warnings, correlating these with the integration of new technologies. Our findings reveal significant variations in EWS performance across different disaster types and geographical areas. For instance, while some systems provide warnings with substantial lead times for events like heavy rain and cold waves, others, particularly for hail and frost, show less temporal advance or accuracy.
This research highlights the disparity between scientific advancements in EWS and their practical application, underscoring the need for improved communication and decision-making processes within the warning system framework. We discuss the reasons for these imbalances, such as technological adoption rates, regional infrastructure variances, and policy implementation challenges.
Our study suggests that while technological capabilities have advanced, the translation into operational EWS effectiveness remains uneven. We propose several strategies to bridge the gap between scientific potential and operational reality, aiming to enhance the third pillar of EWS—effective communication and decision-making. These insights are crucial for refining EWS to better protect communities from natural hazards, contributing to the global aim of universal early warning coverage by 2027.
Keywords: Early Warning Systems, Meteorological Disasters, Northeast China, Timeliness, Accuracy, Communication Strategies, Decision-Making, Climate Resilience
How to cite: li, C.: Enhancing Timeliness of EWS with new technology: A case study in Northeast China , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7739, https://doi.org/10.5194/egusphere-egu25-7739, 2025.
Hailstorms have shown rising severity and frequency in recent years, posing a growing threat to crops and presenting significant challenges for the agricultural and insurance sectors in the face of climate change. As part of an interdisciplinary project (scCLIM, Seamless coupling of kilometer-resolution weather predictions and climate simulations with hail impact assessments for multiple sectors), this study focuses on assessing the impact of future hail occurrence on wheat across Europe.
We utilize results from high-resolution climate simulations with a grid spacing of 2.2 km, which were conducted using the COSMO regional climate model for both current and future climate. The future climate simulation, targeting a 3°C global warming scenario, was performed using the pseudo-global warming approach. Hail activity was simulated using the hail growth model HAILCAST, which was embedded within COSMO. A model of wheat phenology was used to estimate the wheat harvest dates based on COSMO outputs, enabling an assessment of the present and future exposure of wheat to hail. By integrating high-resolution climate simulations with a crop phenology model, this approach bridges the gap between agricultural production and climate risks associated with extreme events.
In this contribution, we examine the temporal and spatial alignment between hail events and crop development, with a particular focus on assessing the sensitivity of future risk of hail damage to wheat with respect to the interplay between changes in hail occurrence and earlier harvest dates. The results reveal regional variations in hail impacts on wheat across Europe, offering valuable insights into crop management, climate change adaptation strategies, and risk assessment within the insurance sector.
How to cite: Cui, R., Portmann, R., Thurnherr, I., and Calanca, P.: Assessing the impact of hail on wheat production in Europe under climate change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8356, https://doi.org/10.5194/egusphere-egu25-8356, 2025.
Society has become more dependent on reliable electricity infrastructure to function normally. The ascending trend of blackouts in recent years suggests that today’s power system is becoming increasingly vulnerable to severe weather and puts an accent on an emerging issue that deals with power system resilience. Resilience, in this context, refers to the system's capacity to limit the extent, severity, and duration of service disruptions following extreme events.
To better understand and improve power system resilience, this study presents a comprehensive analysis of outage statistics in Sweden from 2007 to 2021, utilizing data from Energiföretagen Sverige. The findings reveal that approximately 26% of all outages are attributable to weather-related events, affecting nearly one-third of customers and contributing significantly to customer outage durations. These disruptions directly undermine the reliability and resilience of the power grid.
This research examines the correlation between specific weather phenomena—such as storms, heavy snowfall, and high winds—and the frequency and severity of power outages. The analysis identifies a strong connection between severe weather patterns and prolonged outages, particularly in rural and forested regions where overhead power lines are more vulnerable. By analyzing spatial and temporal patterns, this study identifies vulnerable areas within Sweden's power infrastructure and emphasizes the need for targeted resilience strategies. Proposed measures include enhanced vegetation management, infrastructure reinforcement, and the adoption of advanced grid technologies to mitigate the impacts of extreme weather events. These insights contribute to developing a more robust and reliable electricity system, better equipped to withstand future climate challenges.
How to cite: Duvnjak Zarkovic, S. and Messori, G.: Impact of Climate Extremes on Power Systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8932, https://doi.org/10.5194/egusphere-egu25-8932, 2025.
The increase in the number of heat days caused by climate change leads to intensified thermal heat stress for the human population, especially for vulnerable groups such as the elderly, children, and people with chronic illnesses. As climate change progresses, the demand for healthcare services will rise sharply in the coming years, considering that the number of heat days (Tmax > 30 °C) and tropical nights (Tmin > 20 °C) has already doubled or tripled in recent decades in Austria.
A transdisciplinary team of health, climate, and complexity scientists is needed to comprehensively investigate the effects and risks of climate change, with the focus on heat, on the health system. In a first step, the correlations between meteorological conditions (temperature, humidity, etc.) and health outcomes are analysed. To assess the effect of heatwaves on hospital admissions and deaths, data of daily maximum temperature, deaths, and hospital admissions per care region in Austria for the months June-September of the period 2007-2019 are used. In the detailed analyses, various definitions of heat waves, latency periods, and other factors are examined.
The investigated correlations between prevailing climate conditions and their effects on health are used to investigate future climate scenarios with respect to their conditions. Thus, projections can be made about imminent risks for people and consequently healthcare organisations. For this purpose, the different impacts of heat stress on staff, clients and management assessed with the participating healthcare organizations of the research project. Climate impact chains are developed and applied to ensure a systemic understanding of the risk, exposure and vulnerabilities. Derived adaptation measures are subsequently identified at an institutional level. In addition, areas are identified in which the institutions have no influence and need support, for example through urban planning (e.g. greening and unsealing of outdoor areas not owned by the institutions, shaded path to an existing cooling center).
The results of the correlation analysis show significantly higher risk ratios for deaths in hospitals and for hospital admissions during heatwaves. This applies both to the population as a whole and to elderly people (>= 75 years). However, the increased burden is not only noticeable for clients, but also for healthcare staff, as analysed with the healthcare organisations within climate impact chains. The results indicate that there are some fields of action in which the institutions can take measures, such as regular training on the topic of heat, adapted uniforms, or adapting work processes and medication during heatwaves.
However, there are also areas in which healthcare organisations are dependent on the support and implementation of measures at city/regional level. For example, Nature-based solutions (Nbs) such as large-scale greening and unsealing are measures to reduce heat stress in the long term, thus reducing the strain on people – positively impacting health conditions. Furthermore, outdoor retreats are created in this way, reducing the burden of poor living standards.
How to cite: Bügelmayer-Blaschek, M., Ledebur, K., Hochebner, A., Schneider, M., and Klimek, P.: Healthcare organizations under heat stress: Risk assessment and solutions in Austria , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9152, https://doi.org/10.5194/egusphere-egu25-9152, 2025.
As the frequency and intensity of extreme weather events continue to increase due to climate change, risk mitigation has become a critical aspect of climate change adaptation in cities. The impacts of extreme weather events in cities are extremely diverse. Consequently, integrative, systems-based approaches have been praised given their capacity to structure holistic risk assessments, account for both qualitative and quantitative data, and for accounting for the interactions between system components. Given the diversity and complexity of urban systems, interdisciplinary knowledge integration is critical in order to account for varied perspectives related to the impacts of extreme weather events on urban systems. Despite advances made to integrate different strands of knowledge through systems-based approaches, few methods exist to contextualize, analyse and evaluate its diversity. Assessing knowledge diversity exposes varying ways in which stakeholders identify and problematize the impacts of extreme weather events uncovering knowledge gaps and dominant knowledge framings that might hinder risk governance processes. This study presents a novel methodology that integrates mental models and the social-ecological-technological systems (SETS) framework to assess and compare individual stakeholder perceptions of urban systems under the lens of an extreme weather event. By classifying system components and interactions into social, ecological, and technological domains, mental models enable the visualization of knowledge diversity, as well as the identification of potential gaps and silos in stakeholder understanding. The methodology is applied to New York City as a case study, engaging 20 stakeholders from diverse disciplines and sectors involved in mitigating the impacts of extreme precipitation. Findings reveal significant variability in how stakeholders emphasize SET domains and interactions. This methodology offers a transferable framework for assessing knowledge diversity in urban climate adaptation, emphasizing the importance of reflecting on stakeholder perspectives to identify gaps and synergies. By supporting more holistic and inclusive co-production processes, this approach provides a theoretical and empirical foundation for advanced modelling efforts that are capable of addressing the multifaceted challenges posed by climate change in urban environments.
How to cite: Herreros Cantis, P., Khromova, S., Olazabal, M., McPhearson, T., Langemeyer, J., and Neumann, M.: Knowledge Diversity for Climate Change Adaptation: A Social-Ecological-Technological Systems (SETS) Approach to Mental Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11120, https://doi.org/10.5194/egusphere-egu25-11120, 2025.
Crop models currently have a limited capacity to accurately simulate the impacts of extreme climate events (ECEs), and there is considerable uncertainty across different models. Consequently, the assessment of food security risks from future climate extremes based on existing frameworks is less reliable. To address this issue at global scale, we are developing an advanced hybrid model that integrates process-based crop models with information on the occurrence of extreme climate events and a deep learning framework. Specifically, our model uses outputs from multiple crop models provided by the third round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP 3a) as the initial input. The second input will consist of the daily occurrence of four types of extreme events: two related to temperature (heatwaves and coldwaves) and two related to precipitation (droughts and pluvials). We employ a Long Short-Term Memory (LSTM) network with an attention mechanism designed to dynamically capture the varying impacts of ECEs at different crop growth stages. The results are expected to offer a more precise simulation and deeper understanding of how ECEs affect food security. This study highlights the potential of AI-hybrid modeling to enhance the accuracy of crop impact assessments under climate change.
How to cite: Shan, B., Qian, H., Guan, X., and De Michele, C.: Enhancing crop yield simulations under extreme climate events using a hybrid model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12074, https://doi.org/10.5194/egusphere-egu25-12074, 2025.
Vegetables are full of nutrients that are difficult to obtain from meat or grains, such as vitamins, minerals, and dietary fiber, but they are vulnerable to abiotic stress, making it difficult to obtain consistent yields. Climate extreme events have caused a decline in vegetable production, often leading to elevated vegetable prices. Here, we investigate how climatic factors influence vegetable price changes in China, focusing on colder months when extreme weather impacts are more pronounced. We found three major patterns in vegetable consumer price index (VCPI) data, including data from 31 provinces in China from 2003 to 2023. The first empirical orthogonal function (EOF) mode shows that vegetable prices in all provinces vary together, and this is linked with temperature variations in China. The second EOF mode has a north-south dipole spatial pattern, and it is linked to low-temperature events in southern China, which are closely linked to Arctic warming during colder months and central Pacific La Niña occurrences, especially in December. In addition to temperature, precipitation also affects vegetable prices, with cold rain and snow contributing to VCPI increases resulting from the third EOF mode. Also, the third mode, showing an east-west dipole pattern, is associated with eastern Pacific El Niño occurrences during January and February. Major VCPI patterns and relevant climate factors will facilitate the prediction of vegetable prices on a seasonal time scale and can be used as scientific evidence to prepare for a surge in vegetable prices by combining with seasonal climate forecasts. As China accounts for half of the world’s vegetable production and fluctuations in its vegetable prices can profoundly affect global food security, our findings would be useful to support stable vegetable production, ensure food security, and minimize economic losses globally.
How to cite: Qiu, Y. and Kim, J.: Impact of climatic variability on vegetable price in China during colder months, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14763, https://doi.org/10.5194/egusphere-egu25-14763, 2025.
