The IPCC AR6 report outlined that global warming can grow the number of multi-hazards worldwide, with particular emphasis on coincident heatwaves and droughts, followed by wildfires; and floods and extreme sea level episodes leading to extensive costal floods (IPCC, 2023). To fully understand and increase preparedness against this kind of events the, a holistic multi-hazard and multi-sectoral perspective is needed (Russo et al., 2023; UNDRR, 2015). Coincident storm surges and extreme rainfall events present significant challenges for flood management as the interaction between both hazards can lead more severe scenarios: Storm surges result in temporary increase of sea level, while pluvial flooding overwhelms urban drainage systems due to excessive runoff. During storm surges, elevated sea levels can intrude into drainage systems of coastal cities through outfall pipes or block gravitational drainage. The backwater may reduce the network's capacity and potentially cause upstream flooding. This combination of factors can lead to more extensive flooding in low-lying coastal areas. However, there is limited knowledge about how to model this phenomenon.

A "one-way" coupling approach is proposed to assess this multi-hazard scenario. This method involves defining abnormal boundary conditions of model components. Outfall boundary conditions representing the extreme sea level retrieved from a hydrostatic storm surge model are used to simulate seawater intrusion into drainage network. Extreme high sea level boundary conditions are applied to account for the marine water overflow. The approach requires accurate topographic surveys of system outfalls and high-resolution digital terrain models, which can be challenging due to limited data availability. The final outputs are flood maps showing water depth and velocity in the affected areas.

Multi-hazard modelling of combined floods requires a previous joint probability assessment of occurrence of the single hazards involved.  Copula’s refer to a mathematical approach for the coupling/modelling the dependence between two or more random variables and have been used for this purpose as they allow to determine the complex dependency between random environmental variables. Therefore, they allow to evaluate the likelihood of coincident occurrence of multi-hazard events with specific return periods, and thus determine the intensity of the rainfall and the extreme sea level that would affect a region simultaneously. This information is essential to model scenarios of interest to understand the risk posed by these events and model the risk-reduction effect of different adaptation measures. Utilising Copulalib library in Python and inferring relationship between historic data variables based on their respective marginal distributions, synthetic data is generated.

Flood maps in liaison with sectoral impact assessment models allow to quantify the effect on a variety of risk receptors considering exposure information and vulnerability functions such as economic damage curves  or vulnerability curves. In addition, the holistic framework considered in ICARIA accounts for the cascading effects that the failure of one system can have on other interconnected services.

How to cite: de La Cruz Coronas, A., Russo, B., Evans, B., Chen, A., and Penny, J.: An approach to modeling interactions between extreme weather events during multi-hazard events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10321, https://doi.org/10.5194/egusphere-egu25-10321, 2025.

The impact of human-induced climate change on our living conditions is becoming increasingly evident, causing damage to infrastructure and posing a threat to human lives. The challenges urban environments face, home to approximately two-thirds of the global population, extend beyond rising temperatures to include altered precipitation patterns. Cities are particularly vulnerable due to their predominantly sealed surfaces, which exacerbate climate change effects such as increased heat and intensified rainfall events, in contrast to natural areas with different characteristics like albedo, heat capacity, and infiltration rates.

The KNOWING project focuses on two significant climate impacts: flooding (both fluvial and pluvial) and its effects on infrastructure and heat and its impact on public health. Granollers City serves as an urban case study for flood and heatwave analysis. Two models, PALM-4U [1] and ICM-Infoworks [2], are employed to evaluate potential adaptation measures for current and future climate change impacts.

PALM-4U, an urban climate model, is used to quantify the impact of greening initiatives on urban heat load. ICM-Infoworks assesses adaptation measures to mitigate pluvial and fluvial flooding. Both models rely on land use data, and the proposed changes to address heat (such as greening and unsealing) often coincide with those aimed at reducing flooding (like retention areas and unsealing).

The PALM-4U model considers interventions such as increased tree cover, new recreational parks, river renaturalization, and building-related measures like green roofs and retrofitting. These interventions, which lead to increased unsealing and improved infiltration, also help reduce flood risk and can be incorporated into the ICM-Infoworks model to quantify their impact on flooding. By evaluating the effectiveness of the same interventions using two different models and addressing two distinct climate risks (heat and flooding), this approach allows for a comprehensive assessment of climate change adaptation strategies.

Acknowledgements

KNOWING has received funding from the European Union’s Horizon Europe research and innovation program under grant agreement n° 101056841.

[1] Maronga, B., Banzhaf, S., Burmeister, C., Esch, T., Forkel, R., Fröhlich, D., Fuka, V., Gehrke, K. F., Geletič, J., Giersch, S., Gronemeier, T., Groß, G., Heldens, W., Hellsten, A., Hoffmann, F., Inagaki, A., Kadasch, E., Kanani-Sühring, F., Ketelsen, K., Khan, B. A., Knigge, C., Knoop, H., Krč, P., Kurppa, M., Maamari, H., Matzarakis, A., Mauder, M., Pallasch, M., Pavlik, D., Pfafferott, J., Resler, J., Rissmann, S., Russo, E., Salim, M., Schrempf, M., Schwenkel, J., Seckmeyer, G., Schubert, S., Sühring, M., von Tils, R., Vollmer, L., Ward, S., Witha, B., Wurps, H., Zeidler, J., and Raasch, S. (2020). Overview of the PALM model system 6.0, Geosci. Model Dev., 13, 1335–1372. https://doi.org/10.5194/gmd-13-1335-2020

[2] Mohd Sidek, Lariyah & Jaafar, Aminah Shakirah & Majid, Wan & Basri, Hidayah & Marufuzzaman, Mohammad & Fared, Muzad & Moon, Wei. (2021). High-Resolution Hydrological-Hydraulic Modeling of Urban Floods Using InfoWorks ICM. Sustainability. 13. 10259. 10.3390/su131810259.

How to cite: Soler, J., Martinez, M., Goler, R., Bügelmayer-Blaschek, M., Schneider, M., and Hochebner, A.: Urban climate challenges: An integrated approach to mitigating flood risks and heat stress., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4042, https://doi.org/10.5194/egusphere-egu25-4042, 2025.

Societies nowadays are increasingly reliant on electricity, underscoring the need for reliable energy production. In cold climates, ice accumulation can cause significant harm to structures such as power transmission lines, leading to power loss or, in the worst case, the collapse of wires or transmission towers. Thus, as climate change is expected to impact winter weather conditions in northern Europe, its effects on atmospheric icing occurrence over Fennoscandian region is a crucial area of study. We utilize an ice accretion model based on ISO 12494, driven by outputs from the high-resolution regional climate model HCLIM, to analyze in-cloud icing conditions over two twenty-year periods: mid-century (2040-2060) and end-of-century (2080-2100). The regional outputs are bounded by two global climate models (EC-EARTH and GFDL-CM3) under the RCP 8.5 emission scenario. We present the modelling results for in-cloud icing conditions over northern Europe compared to the control period (1985-2005).  The analysis is done over several altitudes, which allows consideration of the effect on transmission power lines in terms of corona losses, as well as on ice formation affecting wind power production.

This work is supported by EU HORIZON-RIA project n:o 101093939, RISKADAPT - Asset Level Modelling of Risks in the Face of Climate Induced Extreme Events and Adaptation.

How to cite: Rockas, O., Isolähteenmäki, P., Laine, M., Lindfors, A., Hämäläinen, K., and Laakso, A.: Future Atmospheric Icing Conditions for Energy Infrastructure over Fennoscandia Resolved with a High-Resolution Regional Climate Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5828, https://doi.org/10.5194/egusphere-egu25-5828, 2025.

Sea level rise (SLR), driven by climate change, poses a significant threat to coastal cultural heritage (CH) sites by exacerbating the intensity and frequency of extreme hydrodynamic events such as storm surges and wave impacts. These intensified processes can lead to accelerated erosion, structural instability, and increased vulnerability of CH sites. Over time, the cumulative effects of rising seas and amplified hydrodynamic forces may result in irreversible damage to these invaluable assets, threatening their historical, cultural, and economic significance. Despite growing awareness of these risks, a comprehensive understanding of the specific hydrodynamic effects associated with SLR on CH sites remains limited, creating a critical gap in developing effective mitigation strategies tailored to their preservation.

As part of the Horizon Europe project TRIQUETRA, this study investigates the effects of SLR on extreme hydrodynamic impacts imposed on coastal CH through advanced computational fluid dynamics (CFD) simulations. The Volume of Fluid (VOF) method is employed to model air-water interactions and track the evolution of waves and surges under varying sea level scenarios. Key hydrodynamic parameters, such as wave height, pressure distribution, and force intensity, are analyzed across multiple sections representative of the CH site with diverse cliff morphologies. Sensitivity analyses are conducted to ensure the robustness of the numerical framework and to explore the influence of different SLR scenarios on wave dynamics and their subsequent effects on coastal structures.

The results reveal that even moderate increases in sea level significantly amplify wave forces and pressure distributions on coastal structures, particularly under extreme weather conditions. The findings also demonstrate that specific morphological features, such as steep slopes or structural irregularities, affect the impact of hydrodynamic forces. This intensification poses a severe threat to the stability of CH sites, emphasizing the urgency of integrating SLR projections into comprehensive risk assessments and conservation planning to mitigate long-term impacts effectively. By advancing the understanding of SLR-induced hydrodynamic effects, this research provides a critical framework for assessing vulnerabilities and developing site-specific mitigation measures. The insights gained are essential for protecting coastal CH sites from the compounded effects of climate change.

Acknowledgments: This work is based on procedures and tasks implemented within the project “Toolbox for assessing and mitigating Climate Change risks and natural hazards threatening cultural heritage—TRIQUETRA”, which is a Project funded by the EU HE research and innovation program under GA No. 101094818.

How to cite: Istrati, D., Sobhani, R., Georgiadis, C., Chalkidou, S., Feliziani, F., Marmoni, G. M., and Martino, S.: Impact of sea level rise on the extreme hydrodynamic effects on coastal cultural heritage, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20041, https://doi.org/10.5194/egusphere-egu25-20041, 2025.

Ports are highly susceptible to compound climate events due to their coastal locations, which subject them to various interacting climate hazards. This study develops a novel multi-impact risk assessment framework that accounts for both the likelihood of simultaneous climate hazards (accounting for temperature, sea level, wind, precipitation and wave extremes) and their compounded effects on complex port infrastructure systems. Beyond evaluating potential physical damages to infrastructure and assets, the methodology also examines operational downtimes and yield losses triggered by these events, providing a comprehensive view of their cascading impacts.

Applied to the European port system, the framework underscores the critical role of compound effects in climate risk assessment. The findings reveal that these compound impacts can constitute up to 50% of annual repair costs and 20% of profit losses from downtime. Additionally, the synergistic interactions between hazards increase compound risks by 10%, emphasizing the non-linear nature of these threats. Spatial variability is also significant, with certain regions exhibiting clustered hazards and risks. Such insights are pivotal for guiding targeted and coherent strategies to reduce climate impacts at regional and supra-national levels.

By incorporating probabilities of joint hazards and their interactions, this approach pushes the boundaries of traditional coastal infrastructures’ risk assessment, offering more actionable insights for adaptation in coastal and port systems. Its application at the European scale demonstrates the importance of considering compound climate events in decision-making processes to improve resilience in critical infrastructure sectors.

How to cite: Fernandez-Perez, A., Verschuur, J., L. Lara, J., Losada, I. J., Pant, R., and Hall, J. W.: Compound climate risk analysis of European ports, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1417, https://doi.org/10.5194/egusphere-egu25-1417, 2025.

Two-dimensional hydrodynamic models are widely used for flood modeling; however, their computational complexity limits their application for real-time flood forecasting and iterative frameworks requiring a large number of model runs. To address this, previous research has focused on developing surrogate models using machine learning (ML) and deep learning (DL) techniques to predict flood depth. Despite advancements, many of these models lack spatial generalizability and are constrained to the specific locations where they were trained. This study compares the performance of four surrogate models developed using three traditional ML methods (Random Forest, XG-Boost, and Least-Squares Support Vector Machine), which do not inherently account for spatial relationships and a DL method (U-Net) to evaluate their generalizability to unseen locations for identical rainfall hyetograph. The dataset used for this study was generated using a calibrated HEC-RAS flood model for Montreal Island. To enhance model performance and capture relationship between spatial characteristics and flood depth, the modeling framework incorporates multiple explanatory variables: depth to water sinks, curvature, flow accumulation, slope, elevation difference between pixel and focal mean, roughness index, topographic position index, topographic wetness index, and surface elevation. Results demonstrate superior performance of the DL-based method compared to the traditional ML approaches considered, attributed to its capacity to capture the spatial correlation of flood depths between neighboring cells. The performance of the models over unseen locations show root mean squared error (RMSE, in m) and mean absolute error (MAE, in m) of 0.336 and 0.184 for RF, 0.341 and 0.181 for XG-Boost, 0.336 and 0.183 for LS-SVM, and 0.197 and 0.105 for U-Net models, respectively. These findings are consistent with previous studies that highlight the challenges of achieving spatial generalizability in surrogate models and show the competitive accuracy of the U-Net model. While the DL-based surrogate model exhibits limitations in accurately predicting high flood depths, which are critical for flood-induced damage assessment, these results underscore both the potential of DL-based surrogate models for efficient and spatially transferable flood modeling and the need for further research to improve predictions of extreme flood depths and extend the model’s generalizability to unseen hyetographs.

How to cite: Ziya, O., Sushama, L., and Almansour, H.: Evaluating the Spatial Generalizability of ML- and DL-Based Surrogate Models for Flood Depth Prediction, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14649, https://doi.org/10.5194/egusphere-egu25-14649, 2025.

The escalating risk of flooding attributed to climate change poses significant threats to infrastructure, particularly bridges, which are critical components of transportation networks. As severe weather events become more frequent, the damage to these structures has profound economic and social implications, impacting not only infrastructure maintenance costs but also community safety and mobility. Recent flood events have clearly shown the severe impact of extreme flooding on bridges and society. In September 2020, a major flood impacted Karditsa County in Greece causing over €30 million in direct losses due to damage to infrastructure and tens of bridges suffered substantial damage or complete failure. In July 2021 over one hundred bridges were damaged during the exceptional flood event in North Rhine-Westphalia and Rhineland-Palatinate in Germany. In September 2022, the Marche and Umbria regions in Italy were affected by an extreme flood and over 30 bridges were severely damaged. In August 2023, Slovenia also witnessed the most devastating floods ever recorded. In 2024, Europe experienced several floods caused by prolonged heavy rainfall, among which Storm Boris impacted numerous countries in Central and Eastern Europe.

Traditional assessments of resilience often focus narrowly on individual bridges, neglecting the interconnected nature of transportation networks. However, this approach overlooks how the failure of a single bridge can disrupt an entire network, amplifying the impact of natural disasters. To enhance overall system resilience, this work proposes a network-scale perspective analysis using Open Data and addressing the complexities and uncertainties associated with data gaps such as bridge characteristics, vulnerability data or accurate hazard intensity measures. The proposed approach helps to prioritise bridge structures that are particularly vulnerable to flooding and define a robust methodology for assessing network resilience concerning flood hazards.

The methodology is applied in a critical part of the road network of the Region of West Macedonia (Greece) using representative fragility functions and flood maps at regional scale. The results demonstrate the potential of Open Data as a valuable resource for conducting large-scale resilience analyses for critical infrastructure, enabling the identification of vulnerabilities and the prioritisation of interventions even in regions with limited access to proprietary or detailed data. This innovative approach not only aims to improve the understanding of network resilience in the face of climate change but also seeks to inform policymakers and stakeholders in making data-driven decisions for future infrastructure development and maintenance.

How to cite: Perugini, E., Argyroudis, S., Tubaldi, E., and Mitoulis, S.-A.: Regional Flood Assessment of Bridges Using Open Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17571, https://doi.org/10.5194/egusphere-egu25-17571, 2025.

European coastal regions host an extensive network of roads and railways that support economic activity and urban development. The European Union is working to complete its Trans-European Transport (TEN-T) core network by 2030, the extended core network by 2040, and the comprehensive network by 2050. A large share of this infrastructure development will happen in coastal areas. Global warming is expected to lead to large increases in coastal flood risk. For the European transport systems, this potential increase remains largely unknown. There is a clear need for better risk assessments to ensure sustainable infrastructure planning and management. Traditional risk assessment methods typically use gridded land use maps to quantify affected transport networks, treating them as raster data. This approach tends to overestimate risks. Additionally, uncertainties associated with damage functions and asset valuation further reduce confidence in risk quantification. Our study treats transport infrastructure as vector data and integrates type-specific damage functions and asset valuations for roads and railways to provide a fully probabilistic assessment of coastal flood risk to Europe’s roads and railways for global warming levels spanning 1.5°C to 4°C. Our findings show that, on average, approximately 1,500 km of European transport networks are exposed to coastal flooding annually under baseline (1980-2020) climate conditions, causing estimated damages of up to €730 million per year. Risks rise substantially with increasing global warming. If global warming reaches 1.5°C or 2°C above the pre-industrial levels by the end of 21^st century, the expected annual damage is projected to increase by ~55% compared to baseline. At 3°C of global warming, damages would rise by ~85%, and at 4°C, by ~100%, compared to baseline. The countries most affected across all considered warming levels in absolute numbers include the UK, Italy, Norway, France, and Denmark. Our results indicate that most European countries will need to allocate a greater share of their transport budgets to manage growing coastal flood risks with increasing global warming. Limiting global warming to the Paris Agreement’s targets offers significant financial benefits.

How to cite: Nawarat, K., Reyns, J., Vousdoukas, M., Mulholland, E., van Ginkel, K., Feyen, L., and Ranasinghe, R.: Assessing Coastal Flood Risks to European Critical Infrastructure under Different Global Warming Levels, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11131, https://doi.org/10.5194/egusphere-egu25-11131, 2025.

Floods are among the most destructive natural disasters, resulting in significant loss of life and property for millions of people around the world. Extreme flood events in the foothills of the Himalayan ranges and their forelands are closely linked to heavy monsoonal rainfall, steep slopes, and excessive surface runoff from the uphills. Floods hazards in Nepal have become increasingly devasting due to improper land use planning, unplanned settlement distribution, deforestation, land degradation in the upstream watershed, topography, geological setting and climate change. Nepal was hit by an unprecedented late monsoon rainfall, causing widespread landslides and flooding across the country in September 2024y, resulting in significant loss of life and property. This study investigated the use of Sentinel-1 Synthetic Aperture Radar (SAR) with Ground Range Detected (GRD) scenes  for rapid and robust flood detection during the September 2024 flood events in Kathmandu valley and the surrounding areas. The study area is of utmost interest as it comprises diverse geographical setting on the basis of topography and geological setting and these floods events have a significant impact on settlement, infrastructure and other environmental processes. In the study, a standard workflow was applied for the pre-processing of both the products. Based on the application of pre- and post-SAR imagery, this study estimated the extent of flood inundation, highlighting the major impacted area based on pre- and post-land cover map of the study area using machine learning (ML) algorithms and compare the changes with spectral indices. The change detection and Normalized Difference Flood Index (NDFI) was evaluated using threshhold value of temporal Sentinel-1 GRD data. High resolution Google Earth imagery was used for the accuracy assessment of pre flood environment; post flood site data was evaluated from field visit. Greater level of flood impacts were noted both within the Kathmandu valley (Kathmandu. Bhaktapur, Lalitpur district) and outside the valley Banepa, Dhulikhel, Panauti, Namobuddha, Roshi local area of Kaverepalanchok district; Sunkoshi, Golanjor , Phikkal local areas of Sindhuli district of the study area. The overall accuracy of flood inundation mapping was 95 % and the accuracy of land cover map was evaluated about 88 %. A detailed land use/ cover map of the study area was prepared to present the changes post-flood environment using Sentinel -2 Multi-spectral imagery. Further, Permanent water body (PWB) using Normalized Difference Water Index (NDWI) algorithm and Normalized Difference Vegetation Index (NDVI) were prepared for the evaluation of the post-flood impact area . Overall, the analysis inferred that watershed level flooding vulnerability results from natural factors like heavy rainfall and topography, which are further intensified by human activities such as infrastructure development, urbanization and poor land management.

How to cite: Rimal, B., Tiwary, A., and Rijal, S.: Application of Sentinel-1 and 2 Imagery for Rapid and Robust Flood Detection: A Case Study of Flood Event in Nepal., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3210, https://doi.org/10.5194/egusphere-egu25-3210, 2025.

Natural disasters, such as flooding, can cause significant social, environmental, and economic damage to communities. Transportation infrastructure plays a crucial role in flood response and recovery, but flooding can disrupt road functionality, leading to both direct and indirect negative impacts, including loss of access to essential services.

This paper presents a case study on the impact of flooding on transportation networks and the accessibility of critical amenities, such as health centers and fire stations, in Swords, Ireland. Using network analysis methods, including shortest path and criticality analysis, the study evaluates how flooding disrupts access from each small area (SA), defined as the lowest level of geography for statistical purposes, to these key services. Specifically, the analysis focuses on the accessibility of health centers and fire stations, assessing travel time indicators and road criticality to identify areas that become more vulnerable during flooding.

The study considers flood risk zones, including Flood Zone A (high risk of flooding with a greater than 1% chance of river flooding) and Flood Zone B (moderate risk of flooding with a 0.1% to 1% chance of river flooding). The methodology supports the development of a real-time decision support system, allowing decision-makers to explore different flood scenarios and identify vulnerable areas and populations. This approach can inform strategies for mitigating road network failures, such as temporarily relocating critical services and improving flood resilience. The results reveal varying impacts on road networks due to different environmental conditions, with significant losses in both road segments (edges) and access points (nodes), affecting critical service accessibility. In Flood Zone A, 6 critical locations were found to be inaccessible, while in Flood Zone B, this number increased to 15. The findings highlight the risk that many essential services in the area face during flooding. This research provides valuable insights for guiding infrastructure investments and hazard mitigation strategies to enhance community resilience and ensure equitable access to critical services during flood events.

How to cite: Sangeeta, S., Sarma, H. D., Teixeira, R., and Martinez-Pastor, B.: Assessment of Transportation System Disruption and Accessibility to Critical Infrastructure During Flooding: Swords, Ireland Case Study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10877, https://doi.org/10.5194/egusphere-egu25-10877, 2025.

ABSTRACT

Coastal urban areas, particularly those in the Mediterranean coast, face an increasing probability of compound flooding into both current and projected climate change conditions (Bevacqua et al, 2019). In the Costa del Sol Occidental region of southern Spain, multi-hazard flood events—encompassing pluvial, coastal, and fluvial hazards—interact to produce significant impacts on populations, economies, and ecosystems. Research (IPCC, 2023; Zscheischler et al., 2018) highlights that the combined effects of multiple hazards on human and economic assets often exceed the sum of their individual impacts. This interplay results in greater flood depths and wider extents than those caused by single hazards occurring independently.
Despite these challenges, there is a lack of understanding and comprehensive tools in the region that account for the interdependencies of these hazards, particularly the compounding effects of pluvial flooding combined with coastal hydrodynamics. The aim of this research is to fill this gap by developing a multi-hazard risk model that takes into account the  interplay among pluvial flooding, coastal inundation, and the influence of ephemeral rivers in the region.
This study is part of the EU-funded ClimEmpower project, which focuses on enhancing resilience in five Mediterranean regions that are highly vulnerable to climate risks. ClimEmpower aims to provide tools, datasets, and indicators to address climate risks, enabling stakeholders to make more informed decisions regarding climate adaptation strategies.
The case study focuses on the Costa del Sol, a region located in the province of Málaga (Andalusia) in southern Spain. It encompasses 11 municipalities covering a total area of approximately 800 km² and distributed along more than 100 km of coastline. The case of Costa del Sol will develop an integrated approach that combines 1D/2D sewer modeling (MIKE Urban) with coastal hydrodynamic simulations (MIKE Zero), addressing both pluvial and coastal flooding mechanisms under both present and future climate scenarios using a loosely-coupled approach. 
The research will also assess the probability of occurrence of compound flooding events and will update the IDF curves, which are crucial for designing urban drainage systems and planning flood mitigation measures. To achieve this, high-resolution pluviometric data (sub-hourly data) was requested to authorities such as Spanish Meteorological Agency (AEMET), the basin Authority and the Andalusian Environmental Information Network (REDIAM).
A key challenge in this study regards data collection. Sewer network data is often incomplete or unavailable due to the management of different water utilities across the 11 municipalities of the study area. To overcome these data gaps, the study will apply a gap-filling methodology developed under the EU-funded ICARIA project (Moumtzidou et al., 2024). Additionally, the project will develop a social media crowdsourcing methodology to collect information about events that will be used to calibrate the models.
This research is expected to provide local authorities with essential tools for flood risk management and climate adaptation, empowering them to design more resilient urban environments and flood management strategies to address increasing compound events.

AKNOWLEDGMENTS

This research is part of the ClimEmpower project, funded by the Horizon Europe program of the European Union under Grant Agreement No.101112728 (https://cordis.europa.eu/project/id/101112728/es).

How to cite: Molina López, P., Russo, B., and D'Alessandro, F.: Analysis of flood compound events in the Andalusian Costa del Sol. A ClimEmpower Case Study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15647, https://doi.org/10.5194/egusphere-egu25-15647, 2025.

The growing interconnectivity of critical infrastructure systems in urban areas has escalated cascading failure risks, where disruptions in one system propagate to others. Urban drainage networks, essential for pluvial flood risk reduction, can paradoxically be vulnerable due to their interconnected network structure. While existing studies focus on physical and geographical interdependencies, the role of ‘logical interdependencies’—rooted in the numerous nested institutional policies, contingency plans, and emergency response protocols— still remains unclear. This highlights the necessity of an integrated approach that combines complex network theory and automated text analysis tools to identify hidden vulnerabilities, or "blind-spots." Logical interdependencies within urban drainage networks play a crucial role during urban flooding, where institutional gaps or human errors may inadvertently align to amplify disaster risks. To address this issue, we hypothesize that: (1) logical interdependencies can influence failure propagation, but adaptive management of blind-spots can enhance resilience; and (2) text-mining tools can effectively identify and analyze these blind-spots through institutional analysis. By adopting a multidisciplinary approach that integrates network theory and institutional analysis, this research aims to uncover critical blind-spots in logical interdependencies. The findings will provide valuable insights for enhancing sustainable stormwater management and strengthening the flood resilience of interconnected urban water infrastructure systems against coupled disaster risks.

Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Ministry of Science and Technology (RS-2024-00356786) and Korea Environmental Industry & Technology Institute grant funded by the Ministry of Environment (RS-2023-00218973).

How to cite: Park, S., Kim, H. G., Jung, S., Yu, D. J., Shin, H. C., Kim, W., Li, S., Heo, E., and Park, J.: Enhancing Flood Resilience of Urban Drainage Networks by Identifying Blind-Spots in Logically Interconnected Systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14728, https://doi.org/10.5194/egusphere-egu25-14728, 2025.

Global coastal catchments are uniquely vulnerable to flooding due to the interplay of multiple flood drivers, including intense rainfall, storm surges, and tidal influences. These regions face particularly complex challenges because the nature and magnitude of flood risks vary significantly with seasonal changes. During the monsoon season, prolonged and heavy rainfall often leads to widespread inundation, whereas in the post-monsoon period, compounded effects of residual waterlogging, storm-tides, and episodic rainfall events create equally severe but distinctly different flood scenarios. This study, for the first time, develops an integrated framework to quantify and compare flood risks during these seasons, advancing flood management literature with a novel approach. A sophisticated 1D-2D coupled hydrodynamic flood model is employed to generate high-resolution flood hazard maps by simulating the compound interactions of rainfall and storm tides. Simultaneously, flood vulnerability is assessed at the finest administrative scale using a comprehensive suite of physical and socio-economic indicators. A Bivariate Risk Classifier framework is introduced to integrate hazard and vulnerability assessments, enabling nuanced spatial representation of risks through choropleth maps. Two novel indices are developed to enhance the understanding of multi-hazard flood risks: the Area Index, which highlights the spatial extent of risk, and the Multi-Hazard Risk Index, which captures the compound and marginal contributions of hazards and vulnerabilities. These indices provide critical insights into the varying nature and magnitude of flood risks during monsoon and post-monsoon periods. Our findings reveal a significantly higher proportion of villages falling into medium to very high hazard classes during the post-monsoon season, a critical insight that would remain obscured under conventional methodologies. Vulnerability assessments highlight that the majority of coastal villages exhibit severe vulnerability levels, driven largely by dense populations of illiterate and non-working residents. This research demonstrates that flood risks differ markedly between seasons, with varying degrees of impact on infrastructure and human systems. The integrated framework and incisive indices proposed herein offer actionable insights to support tailored, long-term flood management strategies aimed at mitigating risks and enhancing resilience in coastal floodplains.

How to cite: Thakur, D. A. and Mohanty, M. P.: How Divergent Are Flood Risks During Monsoon and Post-Monsoon Seasons? Revealing Contrasting Impacts over Coastal Multi-Hazard Catchments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-418, https://doi.org/10.5194/egusphere-egu25-418, 2025.

Monitoring, detecting, and responding to critical situations is becoming increasingly essential in light of the challenges that the built environment faces, such as heat-related stresses, floods, and droughts, further intensified by climate change. Enhancing the protective role of the built environment and improving the safety and quality of life for its occupants is crucial for the present and future. The MULTICLIMACT Horizon Europe project (GA 101123538) offers innovative solutions across three scales to address these challenges: building, urban, and territorial. Through the development of design practices, materials, technologies, and digital solutions, the project strengthens construction resilience, preparedness, and responsiveness to disruptive events, thereby improving safety and quality of life. Central to this objective is the development of an innovative platform for the prevention and damage estimation of extreme natural events across multiple scales—from individual buildings to entire regions—called the CIPCast Decision Support System. The current version of CIPCast, developed within MULTICLIMACT, integrates a wide range of data, including seismic events, weather forecasts, climate projections, Points of Interest, and Critical Infrastructure components. CIPCast analyses risk to vulnerable assets (e.g., buildings, substations, water towers) by applying established damage metrics. Additionally, it assesses the impact of restoration actions on interconnected systems, contributing to resilience assessment through social, economic, and operational indicators in real or simulated scenarios.

This study examines the use of some frameworks to assess potential damage to buildings and transportation infrastructure caused by heat-related stresses and floods, with a case study focusing on the Marche Region in Italy. For heat-related stresses, the focus is on railways and roads, critical components of transportation networks (Mulholland and Feyen 2021, doi: 10.1016/j.crm.2021.100365). Railways, susceptible to buckling under extreme heat, are assessed by combining maximum rail temperature maps with probability functions derived from the CWR-SAFE model (Kish and Samavedam 2013). This approach evaluates vulnerability based on temperature variations and track characteristics. Similarly, roads are analysed for asphalt softening using the Performance Grade (PG) metric, which defines the operational temperature range of asphalt. By integrating PG with exposure and maintenance factors, the study pinpoints areas prone to accelerated degradation, emphasising the importance of targeted maintenance. The study also examines damage caused by flooding, considering river, pluvial, and coastal floods. Damage estimation relies on probabilistic functions that correlate water depth with damage levels, as described by Huizinga et al (2017, doi: 10.2760/16510), and Karagiannis et al. (2019, doi: 10.2760/007069). These models have been applied to buildings, roads, and electric substations, enabling a comprehensive understanding of flood impacts. The frameworks adopted have been tailored, when possible, for real-time and medium-to-long-term applications, making them versatile tools for addressing both immediate risks and long-term planning needs. By delivering detailed predictions of physical damage and indirect socio-economic effects, CIPCast empowers decision-makers, such as Civil Protection agencies, to plan precise interventions, strengthen resilience to climate stress, and minimize service disruptions, ultimately enhancing safety, well-being, and quality of life for communities.

How to cite: Reder, A., Bonfiglio, A., Pugliese, A., Scalas, M., Di Pietro, A., Ormando, C., Stefani, A., Solari, C., Fuggini, C., Verga, A., Attanasio, C., and De Maio, F. V.: Enhancing Infrastructure Resilience and Risk Management through the CIPCast Decision Support System, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11887, https://doi.org/10.5194/egusphere-egu25-11887, 2025.

The EU-funded RISKADAPT project (GA: 101093939) aims at addressing the challenges posed by Climate Change (CC)-induced compound events. The project delivers a novel, integrated, modular, interoperable, customizable, and user-friendly platform, PRISKADAPT, developed in close collaboration with end-users. This platform supports systemic, risk-informed decision-making for adapting to climate events at the asset level, with a focus on structural systems. By integrating advanced datasets, models, and analytical tools, PRISKADAPT provides a solution for assessing risks, exploring adaptation measures, and enhancing infrastructure resilience in a changing climate. Central to PRISKADAPT is its robust Data Management System (DMS), which acts as a central repository and processing hub for critical datasets. It is engineered to handle data from external modules, and various other sources, ensuring a flexible and adaptable approach to data integration. This capability allows users to draw insights from diverse datasets, enabling more precise decision-making. The system also leverages algorithms and models to identify trends, risks, and optimize adaptation strategies, ensuring that the platform remains relevant and responsive to evolving climate challenges. Complementing the DMS is the intuitive User Interface (UI), designed to serve as the primary interaction point for stakeholders. The UI offers tailored visualizations, decision-support tools, and functionalities to accommodate diverse user roles and permissions. Administrators gain comprehensive control over system configurations, enabling efficient management of complex workflows and customization of the platform to specific needs. End-users, on the other hand, benefit from interactive modules that facilitate data exploration, structural assessments, and actionable insights, enhancing their ability to make informed decisions. Through PRISKADAPT, users can visualize assets’ administrative and structural details, including Building Information Management (BIM) models. The platform allows exploration of climatological and environmental data, assessment of material degradation, and comprehensive structural risk evaluations. Risk assessments incorporate various climate and environmental scenarios, including as-is conditions and potential adaptation measures (what-if scenarios). The platform’s outputs combine structural risk data with Life Cycle Assessment (LCA), Life Cycle Cost (LCC) analyses, and social impact evaluations, delivering a holistic total (technical and social) risk assessment to users. This integration ensures that adaptation strategies are not only effective but also economically and socially viable. The Model Information System (MIS) is another critical feature of PRISKADAPT, enabling users to evaluate and compare adaptation measures. By simulating the effectiveness, and impact of different strategies under various scenarios, the MIS helps stakeholders develop tailored adaptation plans that address specific vulnerabilities. Additionally, PRISKADAPT includes authoring tools for designing module interdependencies using functional flow block diagrams. These tools enable administrators to manage workflows, supporting dynamic and modular system configurations. Users can leverage PRISKADAPT in its entirety or integrate their own datasets and models for climate change forcing, structural analysis, lifecycle assessment, and cost evaluations. This flexibility supports the creation of new end-to-end analyses or the enhancement of existing workflows. By empowering users with precise, data-driven insights and a scalable architecture, RISKADAPT promotes sustainable and resilient infrastructure, paving the way for proactive planning and an adaptive future in the face of climate uncertainty.

How to cite: Camarinopoulos, S., Karali, T., Kourentzis, I., Osmani, S., Kontogeorgos, M., Frondistou, M., Becker, G., Bilionis, D., and Parasyris, A.: PRISKADAPT: An integrated platform for risk-informed climate adaptation of structural systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20289, https://doi.org/10.5194/egusphere-egu25-20289, 2025.

The world’s cities are growing rapidly, and by 2030, over 60% of the global population is expected to live in urban areas. As per a report by the Global Commission on Economy and Climate, Indian urban centers will home over 600 million of the country’s population by this time. Due to high concentration of people, the most adverse impacts of climate change-induced extreme events on infrastructures crucial to society and challenges of cascading and compound events will possible be in these areas, according to the World Bank. In this context, it is of the greatest urgency that a city is able to increase ‘actionable’ climate resilience strategies to avoid risks to the society due to climate change-induced extreme events in addressing the challenges of cascading and compound events.

Alluring on the theories of ‘actionable as development’ and in-depth examines of rolling development initiatives in the smart metropolitan city of India, this study explores the factors that promote or hamper ‘actionable’ resilient strategies for extreme events in the urban water cycle for hydroclimatic risks and vulnerabilities in urban systems of cascading and compound events on infrastructures crucial to society, such as health centers, transport infrastructure, sewage, storm water drainage and solid waste management.

The smart city of Patna (population 3 million) is one of the fastest growing cities in India. Based on the primary and secondary data, developmentally oriented project case studies that addresses the city’s most urgent extreme events risks in transportation, sewage, storm water drainage and solid waste management, it recommends a contingent ‘actionable’ resilient strategies approach as most-suited to such resource-constrained environments to the climatic risks in cascading and compound events. Such an approach has the ability to overcome essential local resource constraints, institutional limitations, while increasing the likelihood of adoption of ‘actionable’ resilient strategies oriented projects under the climate extremes in water cycle and risks to the society in addressing the challenge of climate change.

This research work identifies several factors-among them, developing collective partnerships to conduit technical deficits, taming local organizational structures to create internal resources, and constructing political consensus for climate action-as crucial for successful ‘actionable’ resilient strategies for climate change-induced extreme events in the urban water cycle and risks to the society.

Such contingent ‘actionable’ approaches may thereby deliver a blueprint for instant, realistic, and cost-effective feasible applications in similar smart cities in India and in comparable developing regions of the world. It recognizes the key fragile urban systems in the smart city, which are already, impacted by infrastructural, governance, economic, social, cultural and political issues and may be aggravated by climate change-induced extreme events.

This study concludes that the rudimentary measures, which are needed just to address city’s non-climatic risk concerns, are necessary as a stepping-stone to transformative pathways for addressing the uncertainties associated with climate change-induced extreme events for sustainable and resilient development of the resource constrained smart metropolitan city of India.

Keywords: Climate extremes, Crucial infrastructure, Urban water cycle, Hydroclimatic risks and vulnerabilities, Fragile urban systems and Actionable resilient strategies

How to cite: Mandal, S. K. and Rani, S.: Impact of Climate Change-Induced Extreme Events on Infrastructures Crucial to Society: Understanding Risk Assessment and ‘Actionable’ Resilient Strategies for the Resource Constrained Smart Metropolitan City of India , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-211, https://doi.org/10.5194/egusphere-egu25-211, 2025.

The Caribbean islands are extremely vulnerable to extreme storms and floods. Infrastructure systems, including energy, transport and water supply networks, are often disproportionately exposed and vulnerable to such extremes. Climate hazard impacts can be propagated through infrastructure networks far away from places where the extreme event hit. Post-disaster repairing and replacing of infrastructures can take months or even years, denying people of essential services and adding to financial burdens on governments. Caribbean countries have large stock of existing infrastructure, mostly not been designed to cope with the threat of climate change. New infrastructure is also needed in the Caribbean islands, to spur sustainable economic development. Most of the Caribbean islands are small, where space is limited, and hence investments made in hazard prone areas cannot be avoided. It is therefore essential that extreme climate change is factored into infrastructure planning right from the outset.

To address the above challenges systemic spatial risk assessment is needed to map locations of vulnerable infrastructure assets and quantify their socio-economic impacts. Such systemic risk assessment involves: (1) Assembling multi-hazard datasets under different climate scenarios – including return period maps and probabilistic event sets; (2) Creating spatial network flow models of interdependent energy and transport systems – that could help understand flow rerouting during disruptions; (3) Mapping infrastructure vulnerability hotspots to quantify direct damages from hazards; (4) Quantifying indirect economic losses through network disruptions; (5) Creating effective resilience interventions for risk reduction; (6) Optimisation of resilience intervention by comparing systemic resilience costs and benefits to help prioritise investments in long-term climate adaptation.

The proposed application of the problem is presented through a Jamaica Systemic Risk Assessment Tool (J-SRAT), which is a decision support platform for evaluation and prioritisation of policies and options to reduce climate risks and losses and enhance infrastructure resilience. The tool is being used to build capacity within the Government of Jamaica (GoJ) and other relevant public and private stakeholders for infrastructure risk analysis and adaptation decision making. We present the on-going advances made for Jamaica and its wider applications for the Caribbean Islands.

How to cite: Pant, R., Thomas, F., Russell, T., Campbell, J., Taylor, A., Samuels, R., and Hall, J.: Building Systemic Risk Assessment Tools for Climate Adaptation Assessment in the Caribbean – Case Study for Jamaica, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20176, https://doi.org/10.5194/egusphere-egu25-20176, 2025.

The EU-funded RISKADAPT project (GA: 101093939) introduces an innovative, modular, and user-friendly platform, PRISKADAPT, developed in collaboration with end-users to facilitate systemic, risk-informed decision-making for adapting to climate change (CC)-induced compound events. With a focus on structural systems, this study showcases results from one of RISKADAPT's four pilot initiatives, specifically targeting the energy transmission grid in a Nordic climate. Power grid infrastructure is a cornerstone of modern society, underpinning daily activities such as work, communication, transportation, and leisure. The uninterrupted distribution of electricity is essential, with power transmission lines, comprising conductors and steel towers, serving as the "highways" of electricity. Consequently, ensuring their high performance and resilience is of paramount importance. Experience of past events has shown that extreme weather events such as hurricanes, tornados or ice accretions (especially in combination with high winds) may cause failures, usually collapses, of power transmission towers leading to possible long power outages with significant socioeconomical impact. For this reason, evaluating the risk of power transmission infrastructure under adverse weather conditions is crucial. Moreover, climate change makes such risk evaluation more challenging due to the modification of extreme weather trends in terms of frequency and intensity. The aim of this study is to present a risk assessment framework of a steel power transmission tower used in a Nordic climate. More specifically, a 22.20 m high guyed portal frame transmission tower used by the Finnish power operator (Fingrid) is analyzed and its risk, expressed in terms of annual probability of failure, is evaluated under the combination of wind and ice accretion. Different versions of the tower are assessed such as: “as-built” tower using conventional steel, deteriorated versions assuming section loss due to aging (e.g., steel corrosion), restored cases of the deteriorated versions by using Fiber-Reinforced Polymer (FRP) plates, and finally rebuilding options of the tower using High Strength Steel (HSS). For all the above versions of the tower, the fragility, which is the probability of failure, under different combinations of wind speed and ice thickness is estimated and corresponding curves (fragility curves) are produced. Then, in order to estimate the risk of the tower, the fragility estimations will be combined with the hazard. The hazard refers to the probability of occurrence (or exceedance) of wind speed and ice thickness combinations that is provided by appropriate probability distributions (i.e., Generalized Extreme Value - GEV). It should be also noted that for specifying the hazard various climate models for past and future periods are used considering possible effects of the climate change. Finally, a comparison of the risk results of all tower types and climate-change scenarios considering also the associated financial costs and environmental impacts (e.g., CO₂ emissions) is made. All in all, the work presented herein constitutes a framework for evaluating the performance of steel transmission towers and possible adaptation options against climate change. Thus, it could be useful as a decision tool for stakeholders, such as power companies or grid operators in evaluating different options and determine their strategy for grid maintenance, uprates or upgrades.

How to cite: Bilionis, D., Karali, T., Camarinopoulos, A., and Karali, G.: Assessing the risk of a power transmission tower and its possible adaptation options to climate change in a Nordic Climate., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19818, https://doi.org/10.5194/egusphere-egu25-19818, 2025.